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sympy__sympy-14280@6d2a066
sympy/sympy
Python
14,280
kinematic_equation returns zero if speeds are zero
earlier kinematic equations returned a non-zero vector if the speeds were zero. Now if the speeds are zero kinematic_equation returns a zero vector. fixes #12600
2018-02-19T14:02:15Z
kinematic_equations should return a zero vector if the speeds are all zero This is currently the behavior: ``` moorepants@garuda:sympy(master)$ ipython --pdb Python 3.5.3 |Continuum Analytics, Inc.| (default, Mar 6 2017, 11:58:13) Type 'copyright', 'credits' or 'license' for more information IPython 6.0.0 -- An enhanced Interactive Python. Type '?' for help. In [1]: import sympy as sm In [2]: import sympy.physics.mechanics as me In [3]: me.kinematic_equations((0, 0, 0), (sm.pi, sm.pi / 2, 0), 'body', '132') Out[3]: [nan, 0, nan] ``` This expected output should be: ``` [0, 0, 0] ```
Hello Sir, I am very much interested in contributing, but am new to open source. Where can I find this particular function kinematic_equation(), I opened sympy/physics/mechanics but could not find the function in the files. Can you please help me with this? I read that the code is present in sympy/core but could not find this It is defined in sympy/physics/vector/functions.py. q1d - (w1 * c3 + w3 * s3)/ c2 This is what is returned c2 is a function dealing with cos(q2) q2 being Pi/2 makes c2 to become 0 x/ 0 is infinite which is getting to NaN My line of thought was that if the speeds are all zero it supersedes any result due to the definition of coordinates that may result in a singularity. From a physical, instead of mathematical, perspective I think that makes sense. @moorepants : Yes! But getting that mathematically will force us to equate infinity with 0. Binding a special case will look like a history text book rather than a mathematical equation. Lets see If I can do anything The function `kinematic_equations` is not some mathematical construct in SymPy. It's purpose is to provide the correct angular velocity given a variety of rotation types. If you tell the function that angular velocity is zero, then it should return the same, regardless of any mathematical rules. Here is a little more context: ``` moorepants@garuda:mae297(master)$ ipython Python 3.5.3 |Continuum Analytics, Inc.| (default, Mar 6 2017, 11:58:13) Type 'copyright', 'credits' or 'license' for more information IPython 6.0.0 -- An enhanced Interactive Python. Type '?' for help. In [1]: import sympy as sm In [2]: import sympy.physics.mechanics as me In [3]: N = me.ReferenceFrame('N') In [4]: A = N.orientnew('A', 'body', (sm.pi, sm.pi / 2, 0), '132') In [5]: A.dcm(N) Out[5]: Matrix([ [ 0, -1, 0], [-1, 0, 0], [ 0, 0, -1]]) In [6]: A.ang_vel_in(N) Out[6]: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /home/moorepants/miniconda3/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj) 664 type_pprinters=self.type_printers, 665 deferred_pprinters=self.deferred_printers) --> 666 printer.pretty(obj) 667 printer.flush() 668 return stream.getvalue() /home/moorepants/miniconda3/lib/python3.5/site-packages/IPython/lib/pretty.py in pretty(self, obj) 379 if callable(meth): 380 return meth(obj, self, cycle) --> 381 return _default_pprint(obj, self, cycle) 382 finally: 383 self.end_group() /home/moorepants/miniconda3/lib/python3.5/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle) 499 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs: 500 # A user-provided repr. Find newlines and replace them with p.break_() --> 501 _repr_pprint(obj, p, cycle) 502 return 503 p.begin_group(1, '<') /home/moorepants/miniconda3/lib/python3.5/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle) 697 """A pprint that just redirects to the normal repr function.""" 698 # Find newlines and replace them with p.break_() --> 699 output = repr(obj) 700 for idx,output_line in enumerate(output.splitlines()): 701 if idx: /home/moorepants/miniconda3/lib/python3.5/site-packages/sympy/physics/vector/vector.py in __str__(self, printer) 351 for j in 0, 1, 2: 352 # if the coef of the basis vector is 1, we skip the 1 --> 353 if ar[i][0][j] == 1: 354 ol.append(' + ' + ar[i][1].str_vecs[j]) 355 # if the coef of the basis vector is -1, we skip the 1 TypeError: 'NaN' object does not support indexing ``` The automatic calculation of angular velocity relies on `kinematic_equations`. This should simply return a zero vector for any non-time varying orientation. The error could be fixed in the `.orient` method or maybe it is appropriate for `kinematic_equations` to be aware of this. Is this issue open? Is this a closed issue or an open issue ? Please confirm . I am really confused as it is shown as open in list and closed when we look at it with different issue numbers. It is an open issue. @smichr worked on it here: https://github.com/sympy/sympy/pull/12698 but I didn't think his solution was correct. These equations have to be solved explicitly for the derivative terms and rewriting the equations to avoid zeros on the denominator only postpones this issue. I tried on this issue to fix. Someone please review it .
[ { "body": "This is currently the behavior:\r\n\r\n```\r\nmoorepants@garuda:sympy(master)$ ipython --pdb\r\nPython 3.5.3 |Continuum Analytics, Inc.| (default, Mar 6 2017, 11:58:13) \r\nType 'copyright', 'credits' or 'license' for more information\r\nIPython 6.0.0 -- An enhanced Interactive Python. Type '?' for help.\r\n\r\nIn [1]: import sympy as sm\r\n\r\nIn [2]: import sympy.physics.mechanics as me\r\n\r\nIn [3]: me.kinematic_equations((0, 0, 0), (sm.pi, sm.pi / 2, 0), 'body', '132')\r\nOut[3]: [nan, 0, nan]\r\n```\r\n\r\nThis expected output should be:\r\n\r\n```\r\n[0, 0, 0]\r\n```", "number": 12600, "title": "kinematic_equations should return a zero vector if the speeds are all zero" } ]
947cc2f76ec2ce335de77b66d66b78610601c82f
{ "head_commit": "6d2a06600516d5c1e74b8feefdd76070ad6854fe", "head_commit_message": "kinematic_equation returns zero if speeds are zero\n\nearlier kinematic equations returned a non-zero vector if the speeds were zero. Now if the speeds are zero kinematic_equation returns a zero vector.", "patch_to_review": "diff --git a/sympy/physics/vector/functions.py b/sympy/physics/vector/functions.py\nindex 814d2fc99724..6d3c105460b2 100644\n--- a/sympy/physics/vector/functions.py\n+++ b/sympy/physics/vector/functions.py\n@@ -280,6 +280,8 @@ def kinematic_equations(speeds, coords, rot_type, rot_order=''):\n w1, w2, w3 = speeds\n s1, s2, s3 = [sin(q1), sin(q2), sin(q3)]\n c1, c2, c3 = [cos(q1), cos(q2), cos(q3)]\n+ if w1==w2==w3==0:\n+ return [0,0,0]\n if rot_type.lower() == 'body':\n if rot_order == '123':\n return [q1d - (w1 * c3 - w2 * s3) / c2, q2d - w1 * s3 - w2 *\n" }
[ { "diff_hunk": "@@ -280,6 +280,8 @@ def kinematic_equations(speeds, coords, rot_type, rot_order=''):\n w1, w2, w3 = speeds", "line": null, "original_line": 280, "original_start_line": null, "path": "sympy/physics/vector/functions.py", "start_line": null, "text": "@user1:\nCould you move `w1, w2...` and the test `if w1 == w2...` to appear before `q1, q2, ...`?\r\n\r\nSurround `==` with a single space.\r\n\r\n`return [S.Zero]*3` since it's usually good form to return SymPy objects instead of Python objects.\n\n@author:\nsure.\r\nHow to edit the current PR?" } ]
3e4b1adab7413b2959c9098a4809ba09368a1a3e
diff --git a/sympy/physics/vector/functions.py b/sympy/physics/vector/functions.py index 814d2fc99724..d7f87dff153a 100644 --- a/sympy/physics/vector/functions.py +++ b/sympy/physics/vector/functions.py @@ -275,9 +275,11 @@ def kinematic_equations(speeds, coords, rot_type, rot_order=''): if len(coords) != 3: raise ValueError('Need 3 coordinates for body or space') # Actual hard-coded kinematic differential equations + w1, w2, w3 = speeds + if w1 == w2 == w3 == 0: + return [S.Zero]*3 q1, q2, q3 = coords q1d, q2d, q3d = [diff(i, dynamicsymbols._t) for i in coords] - w1, w2, w3 = speeds s1, s2, s3 = [sin(q1), sin(q2), sin(q3)] c1, c2, c3 = [cos(q1), cos(q2), cos(q3)] if rot_type.lower() == 'body':
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
sympy__sympy-14253@529f319
sympy/sympy
Python
14,253
solveset modifications for use of Piecewise
<!-- Your title above should be a short description of what was changed. Do not include the issue number in the title. --> #### References to other Issues or PRs <!-- If this pull request fixes an issue, write "Fixes #NNNN" in that exact format, e.g. "Fixes #1234". See https://github.com/blog/1506-closing-issues-via-pull-requests .--> fixes #14223 fixes #10158 fixes #14238 closes #13913 as an alternative do not address #10532 which is a WIP at #10751 #### Brief description of what is fixed or changed Rather than modifying sovleset to recognize functions which can be written in terms of Piecewise, the `f` passed to `solveset` is now rewritten unconditionally in terms of Piecewise. In addition: - Piecewise needed modification to pay attention to the given domain when computing the sets in which the expressions are valid - solving of Relational (Eq and otherwise) was moved to `_solveset` since solution of Piecewise which uses `_solveset` can entail solving for expressions that are Relational (Eq and otherwise) - the solution of 0 is excluded from `_invert` if it is not in the domain #### Other comments There is an asymmetry in how `solveset_real` and `invert_real` behave that I suspect exists because unlike `solveset`, the name `invert` was already taken by `sympy.polys.polytools.invert`. Whereas `solveset_real` doesn't accept a domain, `invert_real` does and simply passes it along to `_invert` so if one uses a domain other than S.Reals it doesn't matter if `invert_complex` (alias for `_invert`) or `invert_real` is called with that domain. - [ ] check that the solution of Abs(Max(Abs(x-1),2-x))-3 is {-1, 4} and guard all Abs from replacements or perhaps only do so if the folded expression's symbol-dependent part is Abs. Maybe this for guarding Abs: ``` mask = [Tuple(*i) for i in zip(ordered(f.atoms(Abs)), numbered_symbols('a', cls=Dummy)))] for i in range(len(mask)): for j in range(i + 1, len(mask)): mask[j] = mask[j].replace(*mask[i]) for o, n in mask: f = f.replace(o, n) f = f.rewrite(Piecewise) # everything that's not in Abs for o, n in reversed(mask): # everything *in* abs f.replace(n, o.args[0].func(o.args[0].rewrite(Piecewise))) f = piecewise_fold(f) ```
2018-02-17T18:13:17Z
solve(x*Max(x, 15) - 10, x) doesn't work `solve(x*Max(x, 15) - 10, x)` gives NotImplementedError. `solveset` can't solve it either. I think to solve equations like this, one should replace the Max with each argument and solve independently, then backtrack and remove any solutions where the Max argument is not actually the maximum. For instance, in this case, one would solve `x**2 - 10` and `x*15 - 10`. The first one gives `[sqrt(10), -sqrt(10)]`, but both of those solutions are invalid because they aren't the maximum in `Max(x, 15)`. The second gives `2/3`, which is the correct solution. solveset needs to clip solution with constraining set When a Piecewise is being solved by solveset, a check is not made that the solution is within the conditions under which the expression is constrained. This leads to spurious solutions as shown below. The solution should be clipped by the constraining set: ```diff diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py index 1ce7e20..0595502 100644 --- a/sympy/solvers/solveset.py +++ b/sympy/solvers/solveset.py @@ -797,7 +797,7 @@ def _solveset(f, symbol, domain, _check=False): in_set = in_set.as_set() if in_set.is_Interval: dom -= in_set - solns = solver(expr, symbol, in_set) + solns = solver(expr, symbol, in_set) & in_set result += solns else: lhs, rhs_s = inverter(f, 0, symbol) ``` current behavior ```python >>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x,S.Reals) {-1, 0, 1} ``` after fix ```python >>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x,S.Reals) {-1, 1} ``` On review of the situation, there should be no need for clipping the result since the domain passed to `solver` was already identified as `in_set`. Perhaps the following is better: ```diff diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py index 1ce7e20..ca3f6ad 100644 --- a/sympy/solvers/solveset.py +++ b/sympy/solvers/solveset.py @@ -848,13 +848,20 @@ def _solveset(f, symbol, domain, _check=False): # -- leave it alone return result - # whittle away all but the symbol-containing core - # to use this for testing - fx = orig_f.as_independent(symbol, as_Add=True)[1] - fx = fx.as_independent(symbol, as_Add=False)[1] + ### XXX improve this. result & domain should just work + # but consider the current state where an ImageSet doesn't + # know how to intersect with Complexes: + # >>> solveset(exp(x) - 1, x) + # Intersection(ImageSet(Lambda(_n, 2*_n*I*pi), S.Integers), Complexes(S.Reals x S.Reals, False)) + if isinstance(result, (Interval, FiniteSet)): + result = result & domain if isinstance(result, FiniteSet): # check the result for invalid solutions + # whittle away all but the symbol-containing core + # to use this for testing + fx = orig_f.as_independent(symbol, as_Add=True)[1] + fx = fx.as_independent(symbol, as_Add=False)[1] result = FiniteSet(*[s for s in result if isinstance(s, RootOf) or domain_check(fx, symbol, s)]) ``` recursion error when doing subs with Piecewise Ignore the semantics...I've tried to pare it down to a minimal working example. ``` >>> var('r') >>> eq=Abs(r + Abs(r + 1)) >>> a=Abs(r + 1) >>> eq.subs(a, Piecewise((0, r > 0), (-r+1, True))) Abs(r + Piecewise((0, r > 0), (-r + 1, True))) >>> var('r',real=True) >>> p = Piecewise((0, r > 0), (-r+1, True)) >>> eq = Abs(1+abs(r+1)) >>> a = Abs(r + 1) >>> eq.subs(a, p) Piecewise((0, r > 0), (-r + 1, True)) + 1 >>> eq=Abs(r + Abs(r + 1)) >>> eq.subs(a, p) ... RecursionError: maximum recursion depth exceeded while calling a Python object ```
Perhaps rewriting as a Piecewise and resubmitting to `solve` rather than handling the solution in-place would be a way to go? The `piecewise_fold`would then take care of multiple Max/Min in the expression `x*Max(x, 15) - Max(x, 10)` -> `Piecewise ((x**2, x>15), (15, True)) - Piecewise ((x, x>10), (10, True))` for which sympy says the solution is 15. Yep, this seems to work ``` In [4]: solve(x*Piecewise((x, x >=15), (15, x < 15)) - 10, x) Out[4]: [2/3] ``` So I guess the algorithm I hinted at is already implemented for Piecewise. You don't need to fold to solve multiple Piecewise, by the way, just check every combination of Piecewise args. It looks like Max.rewrite(Piecewise) isn't implemented. By the way, this is from http://stackoverflow.com/q/33803806/161801. > You don't need to fold to solve multiple Piecewise, by the way, just check every combination of Piecewise args That is now what `piecewise_fold` does. I will take this up. @smichr I tried this with the modifications in PR #13913 and it gives the correct result. ``` >>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x , S.Reals) {-1, 1} ``` @Yathartha22 , how about adding the diff as a PR to @ishanaj 's #13913? @ishanaj , how about the following? ``` solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x , Interval(0,2)) ``` The 2nd diff that I showed above should really be applied. It would be a good exercise of collaboration if @Yathartha22 made that PR to yours. I will leave another note on your PR regarding your work on Min/Max and Piecewise rewriting. another change in behaviour i think: before ``` >>> solveset(Piecewise((-x,x<0),(4,True)),x ,S.Reals) {0} ``` after ``` >>> solveset(Piecewise((-x,x<0),(4,True)),x ,S.Reals) EmptySet() ``` > how about adding the diff as a PR to @ishanaj 's #13913? That will be a good idea, because this issue seems in accordance with the PR. ``` >>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x , Interval(0,2)) {-1, 1} #as per PR #13913 ``` >It would be a good exercise of collaboration if @Yathartha22 made that PR to yours. Yeah, this seems cool!!! That result should not include that -1...I think that is the part the @Yathartha22 could work out. ``` >>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x , Interval(0,2)) {-1, 1} #as per PR #13913 ``` Uggh. Forget the diff and just see why this fails: ``` >>> solveset(x,x,Interval(2,3)) {0} ``` > just see why this fails: This is due to [this](https://github.com/sympy/sympy/blob/master/sympy/solvers/solveset.py#L108). The condition evaluates to `True`, since `x1==f_x`. Either we fix it here or have another check before returning the solution in `_solveset` Alternate to `x_1==f_x`, we should use `(x1 == f_x and f_x != x)`.
[ { "body": "`solve(x*Max(x, 15) - 10, x)` gives NotImplementedError. `solveset` can't solve it either. \n\nI think to solve equations like this, one should replace the Max with each argument and solve independently, then backtrack and remove any solutions where the Max argument is not actually the maximum. For instance, in this case, one would solve `x**2 - 10` and `x*15 - 10`. The first one gives `[sqrt(10), -sqrt(10)]`, but both of those solutions are invalid because they aren't the maximum in `Max(x, 15)`. The second gives `2/3`, which is the correct solution. \n", "number": 10158, "title": "solve(x*Max(x, 15) - 10, x) doesn't work" }, { "body": "When a Piecewise is being solved by solveset, a check is not made that the solution is within the conditions under which the expression is constrained. This leads to spurious solutions as shown below. The solution should be clipped by the constraining set:\r\n\r\n```diff\r\ndiff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py\r\nindex 1ce7e20..0595502 100644\r\n--- a/sympy/solvers/solveset.py\r\n+++ b/sympy/solvers/solveset.py\r\n@@ -797,7 +797,7 @@ def _solveset(f, symbol, domain, _check=False):\r\n in_set = in_set.as_set()\r\n if in_set.is_Interval:\r\n dom -= in_set\r\n- solns = solver(expr, symbol, in_set)\r\n+ solns = solver(expr, symbol, in_set) & in_set\r\n result += solns\r\n else:\r\n lhs, rhs_s = inverter(f, 0, symbol)\r\n```\r\n\r\ncurrent behavior\r\n\r\n```python\r\n>>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x,S.Reals)\r\n{-1, 0, 1}\r\n```\r\nafter fix\r\n```python\r\n>>> solveset((Abs(x+Min(x,2))-2).rewrite(Piecewise),x,S.Reals)\r\n{-1, 1}\r\n```\r\n\r\nOn review of the situation, there should be no need for clipping the result since the domain passed to `solver` was already identified as `in_set`. Perhaps the following is better:\r\n\r\n```diff\r\ndiff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py\r\nindex 1ce7e20..ca3f6ad 100644\r\n--- a/sympy/solvers/solveset.py\r\n+++ b/sympy/solvers/solveset.py\r\n@@ -848,13 +848,20 @@ def _solveset(f, symbol, domain, _check=False):\r\n # -- leave it alone\r\n return result\r\n\r\n- # whittle away all but the symbol-containing core\r\n- # to use this for testing\r\n- fx = orig_f.as_independent(symbol, as_Add=True)[1]\r\n- fx = fx.as_independent(symbol, as_Add=False)[1]\r\n+ ### XXX improve this. result & domain should just work\r\n+ # but consider the current state where an ImageSet doesn't\r\n+ # know how to intersect with Complexes:\r\n+ # >>> solveset(exp(x) - 1, x)\r\n+ # Intersection(ImageSet(Lambda(_n, 2*_n*I*pi), S.Integers), Complexes(S.Reals x S.Reals, False))\r\n+ if isinstance(result, (Interval, FiniteSet)):\r\n+ result = result & domain\r\n\r\n if isinstance(result, FiniteSet):\r\n # check the result for invalid solutions\r\n+ # whittle away all but the symbol-containing core\r\n+ # to use this for testing\r\n+ fx = orig_f.as_independent(symbol, as_Add=True)[1]\r\n+ fx = fx.as_independent(symbol, as_Add=False)[1]\r\n result = FiniteSet(*[s for s in result\r\n if isinstance(s, RootOf)\r\n or domain_check(fx, symbol, s)])\r\n```", "number": 14223, "title": "solveset needs to clip solution with constraining set" }, { "body": "Ignore the semantics...I've tried to pare it down to a minimal working example.\r\n```\r\n>>> var('r')\r\n>>> eq=Abs(r + Abs(r + 1))\r\n>>> a=Abs(r + 1)\r\n>>> eq.subs(a, Piecewise((0, r > 0), (-r+1, True)))\r\nAbs(r + Piecewise((0, r > 0), (-r + 1, True)))\r\n\r\n>>> var('r',real=True)\r\n>>> p = Piecewise((0, r > 0), (-r+1, True))\r\n>>> eq = Abs(1+abs(r+1))\r\n>>> a = Abs(r + 1)\r\n>>> eq.subs(a, p)\r\nPiecewise((0, r > 0), (-r + 1, True)) + 1\r\n\r\n>>> eq=Abs(r + Abs(r + 1))\r\n>>> eq.subs(a, p)\r\n...\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```", "number": 14238, "title": "recursion error when doing subs with Piecewise" } ]
fb536869fb7aa28b2695ad7a3b70949926b291c4
{ "head_commit": "529f319418230403080fe3e3f45c02e811e54760", "head_commit_message": "additional test for non-Expr SymPy object", "patch_to_review": "diff --git a/sympy/functions/elementary/complexes.py b/sympy/functions/elementary/complexes.py\nindex f3cc98a232f1..77bc6da274f0 100644\n--- a/sympy/functions/elementary/complexes.py\n+++ b/sympy/functions/elementary/complexes.py\n@@ -499,7 +499,7 @@ def eval(cls, arg):\n return arg2\n # reject result if all new conjugates are just wrappers around\n # an expression that was already in the arg\n- conj = arg.conjugate()\n+ conj = signsimp(arg.conjugate(), evaluate=False)\n new_conj = conj.atoms(conjugate) - arg.atoms(conjugate)\n if new_conj and all(arg.has(i.args[0]) for i in new_conj):\n return\ndiff --git a/sympy/functions/elementary/piecewise.py b/sympy/functions/elementary/piecewise.py\nindex 7cb3cec8262e..19dd5190a128 100644\n--- a/sympy/functions/elementary/piecewise.py\n+++ b/sympy/functions/elementary/piecewise.py\n@@ -857,27 +857,32 @@ def __eval_cond(cls, cond):\n except TypeError:\n pass\n \n- def as_expr_set_pairs(self):\n+ def as_expr_set_pairs(self, domain=S.Reals):\n \"\"\"Return tuples for each argument of self that give\n- the expression and the interval in which it is valid.\n+ the expression and the interval in which it is valid\n+ which is contained within the given domain.\n If a condition cannot be converted to a set, an error\n will be raised. The variable of the conditions is\n assumed to be real; sets of real values are returned.\n \n Examples\n ========\n- >>> from sympy import Piecewise\n+ >>> from sympy import Piecewise, Interval\n >>> from sympy.abc import x\n- >>> Piecewise(\n+ >>> p = Piecewise(\n ... (1, x < 2),\n ... (2,(x > 0) & (x < 4)),\n- ... (3, True)).as_expr_set_pairs()\n+ ... (3, True))\n+ >>> p.as_expr_set_pairs()\n [(1, Interval.open(-oo, 2)),\n (2, Interval.Ropen(2, 4)),\n (3, Interval(4, oo))]\n+ >>> p.as_expr_set_pairs(Interval(0, 3))\n+ [(1, Interval.Ropen(0, 2)),\n+ (2, Interval(2, 3)), (3, EmptySet())]\n \"\"\"\n exp_sets = []\n- U = S.Reals\n+ U = domain\n for expr, cond in self.args:\n cond_int = U.intersect(cond.as_set())\n U = U - cond_int\ndiff --git a/sympy/functions/elementary/tests/test_complexes.py b/sympy/functions/elementary/tests/test_complexes.py\nindex 60127d5b5e57..866846d1d81a 100644\n--- a/sympy/functions/elementary/tests/test_complexes.py\n+++ b/sympy/functions/elementary/tests/test_complexes.py\n@@ -877,3 +877,9 @@ def test_issue_14216():\n A = MatrixSymbol(\"A\", 2, 2)\n assert unpolarify(A[0, 0]) == A[0, 0]\n assert unpolarify(A[0, 0]*A[1, 0]) == A[0, 0]*A[1, 0]\n+\n+\n+def test_issue_14238():\n+ # doesn't cause recursion error\n+ r = Symbol('r', real=True)\n+ assert Abs(r + Piecewise((0, r > 0), (1 - r, True)))\ndiff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py\nindex 1ce7e20f5c4f..dec2bbd8f348 100644\n--- a/sympy/solvers/solveset.py\n+++ b/sympy/solvers/solveset.py\n@@ -11,19 +11,22 @@\n \n from sympy.core.sympify import sympify\n from sympy.core import S, Pow, Dummy, pi, Expr, Wild, Mul, Equality\n+from sympy.core.containers import Tuple\n+from sympy.core.facts import InconsistentAssumptions\n from sympy.core.numbers import I, Number, Rational, oo\n-from sympy.core.function import (Lambda, expand_complex, AppliedUndef)\n+from sympy.core.function import (Lambda, expand_complex, AppliedUndef, Function)\n from sympy.core.relational import Eq\n from sympy.core.symbol import Symbol\n from sympy.simplify.simplify import simplify, fraction, trigsimp\n from sympy.functions import (log, Abs, tan, cot, sin, cos, sec, csc, exp,\n acos, asin, acsc, asec, arg,\n- piecewise_fold)\n+ piecewise_fold, Piecewise)\n from sympy.functions.elementary.trigonometric import (TrigonometricFunction,\n HyperbolicFunction)\n-from sympy.functions.elementary.miscellaneous import real_root\n+from sympy.functions.elementary.miscellaneous import real_root, Application\n from sympy.sets import (FiniteSet, EmptySet, imageset, Interval, Intersection,\n Union, ConditionSet, ImageSet, Complement)\n+from sympy.sets.sets import Set\n from sympy.matrices import Matrix\n from sympy.polys import (roots, Poly, degree, together, PolynomialError,\n RootOf)\n@@ -32,12 +35,54 @@\n from sympy.solvers.polysys import solve_poly_system\n from sympy.solvers.inequalities import solve_univariate_inequality\n from sympy.utilities import filldedent\n+from sympy.utilities.iterables import numbered_symbols\n from sympy.calculus.util import periodicity, continuous_domain\n from sympy.core.compatibility import ordered, default_sort_key, is_sequence\n \n from types import GeneratorType\n \n \n+def _masked(f, *atoms):\n+ \"\"\"Return ``f``, with all objects given by ``atoms`` replaced with\n+ Dummy symbols, ``d``, and the list of replacements, ``(d, e)``,\n+ where ``e`` is an object of type given by ``atoms`` in which\n+ any other instances of atoms have been recursively replaced with\n+ Dummy symbols, too. The tuples are ordered so that if they are\n+ applied in sequence, the orgin ``f`` will be restored.\n+\n+ Examples\n+ ========\n+\n+ >>> from sympy import cos\n+ >>> from sympy.abc import x\n+ >>> from sympy.solvers.solveset import _masked\n+\n+ >>> f = cos(cos(x) + 1)\n+ >>> f, reps = _masked(cos(1 + cos(x)), cos)\n+ >>> f\n+ _a1\n+ >>> reps\n+ [(_a1, cos(_a0 + 1)), (_a0, cos(x))]\n+ >>> for d, e in reps:\n+ ... f = f.xreplace({d: e})\n+ >>> f\n+ cos(cos(x) + 1)\n+ \"\"\"\n+ sym = numbered_symbols('a', cls=Dummy)\n+ mask = []\n+ for a in ordered(f.atoms(*atoms)):\n+ for i in mask:\n+ a = a.replace(*i)\n+ mask.append((a, next(sym)))\n+ mask = list(reversed(mask))\n+ for i, (o, n) in enumerate(mask):\n+ f = f.replace(o, n)\n+ mask[i] = (n, o)\n+ if mask and f == mask[0][1]:\n+ f = mask[0][0]\n+ return f, mask\n+\n+\n def _invert(f_x, y, x, domain=S.Complexes):\n r\"\"\"\n Reduce the complex valued equation ``f(x) = y`` to a set of equations\n@@ -105,7 +150,7 @@ def _invert(f_x, y, x, domain=S.Complexes):\n else:\n x1, s = _invert_complex(f_x, FiniteSet(y), x)\n \n- if not isinstance(s, FiniteSet) or x1 == f_x or x1 != x:\n+ if not isinstance(s, FiniteSet) or x1 != x:\n return x1, s\n \n return x1, s.intersection(domain)\n@@ -757,15 +802,9 @@ def _solveset(f, symbol, domain, _check=False):\n S.NegativeInfinity]):\n f = a/m + h # XXX condition `m != 0` should be added to soln\n \n- f = piecewise_fold(f)\n-\n # assign the solvers to use\n solver = lambda f, x, domain=domain: _solveset(f, x, domain)\n- if domain.is_subset(S.Reals):\n- inverter_func = invert_real\n- else:\n- inverter_func = invert_complex\n- inverter = lambda f, rhs, symbol: inverter_func(f, rhs, symbol, domain)\n+ inverter = lambda f, rhs, symbol: _invert(f, rhs, symbol, domain)\n \n result = EmptySet()\n \n@@ -789,16 +828,29 @@ def _solveset(f, symbol, domain, _check=False):\n a = f.args[0]\n result = solveset_real(a > 0, symbol)\n elif f.is_Piecewise:\n- dom = domain\n result = EmptySet()\n- expr_set_pairs = f.as_expr_set_pairs()\n+ # the conditions can only be solved on the real domain\n+ expr_set_pairs = f.as_expr_set_pairs(domain & S.Reals)\n for (expr, in_set) in expr_set_pairs:\n if in_set.is_Relational:\n in_set = in_set.as_set()\n- if in_set.is_Interval:\n- dom -= in_set\n solns = solver(expr, symbol, in_set)\n result += solns\n+ elif isinstance(f, Eq):\n+ from sympy.core import Add\n+ result = solver(Add(f.lhs, - f.rhs, evaluate=False), symbol, domain)\n+ elif f.is_Relational:\n+ if not domain.is_subset(S.Reals):\n+ raise NotImplementedError(filldedent('''\n+ Inequalities in the complex domain are\n+ not supported. Try the real domain by\n+ setting domain=S.Reals'''))\n+ try:\n+ result = solve_univariate_inequality(\n+ f, symbol, domain=domain, relational=False)\n+ except NotImplementedError:\n+ result = ConditionSet(symbol, f, domain)\n+ return result\n else:\n lhs, rhs_s = inverter(f, 0, symbol)\n if lhs == symbol:\n@@ -961,6 +1013,7 @@ def solveset(f, symbol=None, domain=S.Complexes):\n \n \"\"\"\n f = sympify(f)\n+ symbol = sympify(symbol)\n \n if f is S.true:\n return domain\n@@ -969,10 +1022,26 @@ def solveset(f, symbol=None, domain=S.Complexes):\n return S.EmptySet\n \n if not isinstance(f, (Expr, Number)):\n- raise ValueError(\"%s is not a valid SymPy expression\" % (f))\n+ raise ValueError(\"%s is not a valid SymPy expression\" % f)\n+\n+ if not isinstance(symbol, Expr) and symbol is not None:\n+ raise ValueError(\"%s is not a valid SymPy symbol\" % symbol)\n+\n+ if not isinstance(domain, Set):\n+ raise ValueError(\"%s is not a valid domain\" %(domain))\n \n free_symbols = f.free_symbols\n \n+ if symbol is None and not free_symbols:\n+ b = Eq(f, 0)\n+ if b is S.true:\n+ return domain\n+ elif b is S.false:\n+ return S.EmptySet\n+ else:\n+ raise NotImplementedError(filldedent('''\n+ relationship between value and 0 is unknown: %s''' % b))\n+\n if symbol is None:\n if len(free_symbols) == 1:\n symbol = free_symbols.pop()\n@@ -985,31 +1054,29 @@ def solveset(f, symbol=None, domain=S.Complexes):\n # the xreplace will be needed if a ConditionSet is returned\n return solveset(f[0], s[0], domain).xreplace(swap)\n \n- elif not free_symbols:\n- b = Eq(f, 0)\n- if b is S.true:\n- return domain\n- elif b is S.false:\n- return S.EmptySet\n- else:\n- raise NotImplementedError(filldedent('''\n- relationship between value and 0 is unknown: %s''' % b))\n-\n- if isinstance(f, Eq):\n- from sympy.core import Add\n- f = Add(f.lhs, - f.rhs, evaluate=False)\n- elif f.is_Relational:\n- if not domain.is_subset(S.Reals):\n- raise NotImplementedError(filldedent('''\n- Inequalities in the complex domain are\n- not supported. Try the real domain by\n- setting domain=S.Reals'''))\n- try:\n- result = solve_univariate_inequality(\n- f, symbol, domain=domain, relational=False)\n- except NotImplementedError:\n- result = ConditionSet(symbol, f, domain)\n- return result\n+ if domain.is_subset(S.Reals):\n+ if not symbol.is_real:\n+ assumptions = symbol.assumptions0\n+ assumptions['real'] = True\n+ try:\n+ r = Dummy('r', **assumptions)\n+ return solveset(f.xreplace({symbol: r}), r, domain\n+ ).xreplace({r: symbol})\n+ except InconsistentAssumptions:\n+ pass\n+ # Abs has its own handling method which avoids the\n+ # rewriting property that the first piece of abs(x)\n+ # is for x >= 0 and the 2nd piece for x < 0 -- solutions\n+ # can look better if the 2nd condition is x <= 0. Since\n+ # the solution is a set, duplication of results is not\n+ # an issue, e.g. {y, -y} when y is 0 will be {0}\n+ f, mask = _masked(f, Abs)\n+ f = f.rewrite(Piecewise) # everything that's not an Abs\n+ for d, e in mask:\n+ # everything *in* an Abs\n+ e = e.func(e.args[0].rewrite(Piecewise))\n+ f = f.xreplace({d: e})\n+ f = piecewise_fold(f)\n \n return _solveset(f, symbol, domain, _check=True)\n \ndiff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py\nindex 03eb18c3c5e5..607cb85b09ee 100644\n--- a/sympy/solvers/tests/test_solveset.py\n+++ b/sympy/solvers/tests/test_solveset.py\n@@ -9,7 +9,7 @@\n from sympy.functions.elementary.exponential import (LambertW, exp, log)\n from sympy.functions.elementary.hyperbolic import (HyperbolicFunction,\n atanh, sinh, tanh)\n-from sympy.functions.elementary.miscellaneous import sqrt\n+from sympy.functions.elementary.miscellaneous import sqrt, Min, Max\n from sympy.functions.elementary.piecewise import Piecewise\n from sympy.functions.elementary.trigonometric import (\n TrigonometricFunction, acos, acot, acsc, asec, asin, atan, atan2,\n@@ -56,6 +56,9 @@ def test_invert_real():\n def ireal(x, s=S.Reals):\n return Intersection(s, x)\n \n+ # issue 14223\n+ assert invert_real(x, 0, x, Interval(1, 2)) == (x, S.EmptySet)\n+\n minus_n = Intersection(Interval(-oo, 0), FiniteSet(-n))\n plus_n = Intersection(Interval(0, oo), FiniteSet(n))\n assert solveset(abs(x) - n, x, S.Reals) == Union(minus_n, plus_n)\n@@ -189,10 +192,12 @@ def test_domain_check():\n assert domain_check(x, x, oo) is False\n assert domain_check(0, x, oo) is False\n \n+\n def test_issue_11536():\n assert solveset(0**x - 100, x, S.Reals) == S.EmptySet\n assert solveset(0**x - 1, x, S.Reals) == FiniteSet(0)\n \n+\n def test_is_function_class_equation():\n from sympy.abc import x, a\n assert _is_function_class_equation(TrigonometricFunction,\n@@ -263,6 +268,10 @@ def test_garbage_input():\n raises(ValueError, lambda: solveset_complex([x], x))\n assert solveset_complex(x, pi) == S.EmptySet\n \n+ raises(ValueError, lambda: solveset((x, y), x))\n+ raises(ValueError, lambda: solveset(x + 1, S.Reals))\n+ raises(ValueError, lambda: solveset(x + 1, x, 2))\n+\n \n def test_solve_mul():\n assert solveset_real((a*x + b)*(exp(x) - 3), x) == \\\n@@ -278,7 +287,9 @@ def test_solve_invert():\n assert solveset_real(3**(x + 2), x) == FiniteSet()\n assert solveset_real(3**(2 - x), x) == FiniteSet()\n \n- assert solveset_real(y - b*exp(a/x), x) == Intersection(S.Reals, FiniteSet(a/log(y/b)))\n+ assert solveset_real(y - b*exp(a/x), x) == Intersection(\n+ S.Reals, FiniteSet(a/log(y/b)))\n+\n # issue 4504\n assert solveset_real(2**x - 10, x) == FiniteSet(log(10)/log(2))\n \n@@ -652,7 +663,7 @@ def test_atan2():\n assert solveset_real(atan2(x, 2) - pi/3, x) == FiniteSet(2*sqrt(3))\n \n \n-def test_piecewise():\n+def test_piecewise_solveset():\n eq = Piecewise((x - 2, Gt(x, 2)), (2 - x, True)) - 3\n assert set(solveset_real(eq, x)) == set(FiniteSet(-1, 5))\n \n@@ -793,10 +804,11 @@ def test_solve_trig():\n 9*sqrt(57))**(S(1)/3))/(3*(67 + 9*sqrt(57))**(S(1)/6))) +\n 2*pi), S.Integers))\n \n- assert solveset_real(2*tan(x)*sin(x) + 1, x) == \\\n- Union(ImageSet(Lambda(n, 2*n*pi + 2*atan(sqrt(4\n- + sqrt(17)))), S.Integers), ImageSet(Lambda(n, 2*n*pi\n- - 2*atan(sqrt(4 + sqrt(17))) + 2*pi), S.Integers))\n+ assert solveset_real(2*tan(x)*sin(x) + 1, x) == Union(\n+ ImageSet(Lambda(n, 2*n*pi + atan(sqrt(2)*sqrt(-1 + sqrt(17))/\n+ (-sqrt(17) + 1)) + pi), S.Integers),\n+ ImageSet(Lambda(n, 2*n*pi - atan(sqrt(2)*sqrt(-1 + sqrt(17))/\n+ (-sqrt(17) + 1)) + pi), S.Integers))\n \n assert solveset_real(cos(2*x)*cos(4*x) - 1, x) == \\\n ImageSet(Lambda(n, n*pi), S.Integers)\n@@ -958,17 +970,17 @@ def test_conditionset():\n assert solveset(Eq(sin(x)**2 + cos(x)**2, 1), x, domain=S.Reals) == \\\n ConditionSet(x, True, S.Reals)\n \n- assert solveset(Eq(x**2 + x*sin(x), 1), x, domain=S.Reals) == \\\n- ConditionSet(x, Eq(x*(x + sin(x)) - 1, 0), S.Reals)\n+ assert solveset(Eq(x**2 + x*sin(x), 1), x, domain=S.Reals\n+ ) == ConditionSet(x, Eq(x*(x + sin(x)) - 1, 0), S.Reals)\n \n- assert solveset(Eq(sin(Abs(x)), x), x, domain=S.Reals) == \\\n- ConditionSet(x, Eq(-x + sin(Abs(x)), 0), Interval(-oo, oo))\n+ assert solveset(Eq(-I*(exp(I*x) - exp(-I*x))/2, 1), x\n+ ) == imageset(Lambda(n, 2*n*pi + pi/2), S.Integers)\n \n- assert solveset(Eq(-I*(exp(I*x) - exp(-I*x))/2, 1), x) == \\\n- imageset(Lambda(n, 2*n*pi + pi/2), S.Integers)\n+ assert solveset(x + sin(x) > 1, x, domain=S.Reals\n+ ) == ConditionSet(x, x + sin(x) > 1, S.Reals)\n \n- assert solveset(x + sin(x) > 1, x, domain=S.Reals) == \\\n- ConditionSet(x, x + sin(x) > 1, S.Reals)\n+ assert solveset(Eq(sin(Abs(x)), x), x, domain=S.Reals\n+ ) == ConditionSet(x, Eq(-x + sin(Abs(x)), 0), S.Reals)\n \n \n @XFAIL\n@@ -988,9 +1000,8 @@ def test_solveset_domain():\n def test_improve_coverage():\n from sympy.solvers.solveset import _has_rational_power\n x = Symbol('x')\n- y = exp(x+1/x**2)\n- solution = solveset(y**2+y, x, S.Reals)\n- unsolved_object = ConditionSet(x, Eq((exp((x**3 + 1)/x**2) + 1)*exp((x**3 + 1)/x**2), 0), S.Reals)\n+ solution = solveset(exp(x) + sin(x), x, S.Reals)\n+ unsolved_object = ConditionSet(x, Eq(exp(x) + sin(x), 0), S.Reals)\n assert solution == unsolved_object\n \n assert _has_rational_power(sin(x)*exp(x) + 1, x) == (False, S.One)\n@@ -1676,6 +1687,7 @@ def test__is_finite_with_finite_vars():\n Dummy(), Dummy(real=True), Dummy(complex=True)))\n assert f(1/Dummy(real=False)) is True # b/c it's finite but not 0\n \n+\n def test_issue_13550():\n assert solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) == FiniteSet(-3)\n \n@@ -1683,3 +1695,35 @@ def test_issue_13550():\n def test_issue_13849():\n t = symbols('t')\n assert nonlinsolve((t*(sqrt(5) + sqrt(2)) - sqrt(2), t), t) == EmptySet()\n+\n+\n+def test_issue_14223():\n+ x = Symbol('x')\n+ assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x,\n+ S.Reals) == FiniteSet(-1, 1)\n+ assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x,\n+ Interval(0, 2)) == FiniteSet(1)\n+\n+\n+def test_issue_10158():\n+ x = Symbol('x')\n+ dom = S.Reals\n+ assert solveset(x*Max(x, 15) - 10, x, dom) == FiniteSet(2/S(3))\n+ assert solveset(x*Min(x, 15) - 10, x, dom) == FiniteSet(-sqrt(10), sqrt(10))\n+ assert solveset(Max(Abs(x - 3) - 1, x + 2) - 3, x, dom) == FiniteSet(-1, 1)\n+ assert solveset(Abs(x - 1) - Abs(y), x, dom) == FiniteSet(-Abs(y) + 1, Abs(y) + 1)\n+ assert solveset(Abs(x + 4*Abs(x + 1)), x, dom) == FiniteSet(-4/S(3), -4/S(5))\n+ dom = S.Complexes\n+ assert solveset(x*Max(x, 15) - 10, x, dom) == FiniteSet(2/S(3))\n+ assert solveset(x*Min(x, 15) - 10, x, dom) == FiniteSet(\n+ -sqrt(10), sqrt(10))\n+ # the following gets rewritten to\n+ # Piecewise((x - 1, x + 2 >= Abs(x - 3) - 1), (Abs(x - 3) - 4, True))\n+ # and the Abs in the relational is solved as though x were real so\n+ # should there be a check in Piecewise for Abs in conditions when the\n+ # domain is complex?\n+ assert solveset(Max(Abs(x - 3) - 1, x + 2) - 3, x, dom) == FiniteSet(-1, 1)\n+ raises(ValueError, lambda: solveset(Abs(x - 1) - Abs(y), x, dom))\n+ raises(ValueError, lambda: solveset(Abs(x + 4*Abs(x + 1)), x, dom))\n+ assert solveset(2*Abs(x + Abs(x + Max(3, x))) - 2, x, S.Reals\n+ ) == FiniteSet(-1, -2)\n" }
[ { "diff_hunk": "@@ -1676,10 +1687,43 @@ def test__is_finite_with_finite_vars():\n Dummy(), Dummy(real=True), Dummy(complex=True)))\n assert f(1/Dummy(real=False)) is True # b/c it's finite but not 0\n \n+\n def test_issue_13550():\n assert solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) == FiniteSet(-3)\n \n \n def test_issue_13849():\n t = symbols('t')\n assert nonlinsolve((t*(sqrt(5) + sqrt(2)) - sqrt(2), t), t) == EmptySet()\n+\n+\n+def test_issue_14223():\n+ x = Symbol('x')\n+ assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x,\n+ S.Reals) == FiniteSet(-1, 1)\n+ assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x,\n+ Interval(0, 2)) == FiniteSet(1)\n+\n+\n+def test_issue_10158():\n+ x = Symbol('x')\n+ dom = S.Reals\n+ assert solveset(x*Max(x, 15) - 10, x, dom) == FiniteSet(2/S(3))\n+ assert solveset(x*Min(x, 15) - 10, x, dom) == FiniteSet(-sqrt(10), sqrt(10))\n+ assert solveset(Max(Abs(x - 3) - 1, x + 2) - 3, x, dom) == FiniteSet(-1, 1)\n+ assert solveset(Abs(x - 1) - Abs(y), x, dom) == FiniteSet(-Abs(y) + 1, Abs(y) + 1)\n+ assert solveset(Abs(x + 4*Abs(x + 1)), x, dom) == FiniteSet(-4/S(3), -4/S(5))\n+ dom = S.Complexes\n+ assert solveset(x*Max(x, 15) - 10, x, dom) == FiniteSet(2/S(3))\n+ assert solveset(x*Min(x, 15) - 10, x, dom) == FiniteSet(\n+ -sqrt(10), sqrt(10))\n+ # the following gets rewritten to", "line": null, "original_line": 1720, "original_start_line": null, "path": "sympy/solvers/tests/test_solveset.py", "start_line": null, "text": "@author:\n@user1 , do you have any ideas about this situation? If the domain is complex then a bare Abs raises an error about not being inverted in the real domain. But if it appears in the condition of a Piecewise it is solved as though its arg were real.\r\n\r\nAlso `Max(x, y)` is rewritten assuming that the args are real even though it is not defined for complex values. I'm not sure if that is consistent with `Abs(x)` *not* being rewritten unless x is real (but Abs *is* defined for complex values).\n\n@user1:\n> do you have any ideas about this situation?\r\n\r\nWhat I figured out @author is that this equation gets converted to `Piecewise((x - 1, x + 2 >= Abs(x - 3) - 1), (Abs(x - 3) - 4, True))`, which further divides into `[(x - 1, Interval(0, oo)), (Abs(x - 3) - 4, Interval.open(-oo, 0))]`, now since both these `expr` are in the real domain, for the case of `Abs` _invert_real is called giving the result as {-1}. There is no `ValueError` because `_invert_real` solves it, while in cases for bare Abs in complex domain, there is no implementation in `_invert_complex` therfore they will always go to `_solve_abs` and raise error.\n\n@user1:\n> Also Max(x, y) is rewritten assuming that the args are real even though it is not defined for complex values. I'm not sure if that is consistent with Abs(x) not being rewritten unless x is real (but Abs is defined for complex values).\r\n\r\nYes, max or min is rewritten irrespective the args are real or complex [see here](https://github.com/sympy/sympy/blob/master/sympy/functions/elementary/miscellaneous.py#L750), but in case of `Abs` it checks for args to be real, then only rewrites. [see here](https://github.com/sympy/sympy/blob/master/sympy/functions/elementary/complexes.py#L586)\r\n\r\nTwo things need to be done:\r\n\r\n- Restrict rewriting of Min/Max only to real, (can be done in this PR itself)\r\n\r\n- Also, Abs should be made to be able to rewrite in complex domain. Atleast solveset should solve Abs in complex domain (should be solved in another PR)\r\n\r\nIf you agree with the above I would like to make the changes\n\n@author:\nIt could be just as simple as only solving Piecewise if the conditions don't involve relationals when the variable is not real:\r\n```\r\n>>> solveset(x<1,x)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"C:\\Users\\leslie\\sympy\\sympy\\solvers\\solveset.py\", line 1006, in solveset\r\n\r\n setting domain=S.Reals'''))\r\nNotImplementedError:\r\nInequalities in the complex domain are not supported. Try the real\r\ndomain by setting domain=S.Reals\r\n>>> solveset(Eq(x,1),x)\r\n{1}\r\n>>> solveset(Eq(x,I),x)\r\n{I}\r\n```\n\n@user1:\n> It could be just as simple as only solving Piecewise if the conditions don't involve relationals when the variable is not real:\r\n\r\nRather than raising error for Piecewise let `_solve_abs` handle it. Committed in your repo.\n\n@author:\n> If you agree with the above I would like to make the changes\r\n\r\nOk, I will raise an error if an attempt to solve a Piecewise with non-Equality conditions in the complex domain is made. Then this can be committed and your changes can be a separate PR. How does that sound?\n\n@user1:\nSounds great!" } ]
f0f38841b208d574ad0f27c3cb61837635bed42e
diff --git a/sympy/functions/elementary/complexes.py b/sympy/functions/elementary/complexes.py index f3cc98a232f1..77bc6da274f0 100644 --- a/sympy/functions/elementary/complexes.py +++ b/sympy/functions/elementary/complexes.py @@ -499,7 +499,7 @@ def eval(cls, arg): return arg2 # reject result if all new conjugates are just wrappers around # an expression that was already in the arg - conj = arg.conjugate() + conj = signsimp(arg.conjugate(), evaluate=False) new_conj = conj.atoms(conjugate) - arg.atoms(conjugate) if new_conj and all(arg.has(i.args[0]) for i in new_conj): return diff --git a/sympy/functions/elementary/piecewise.py b/sympy/functions/elementary/piecewise.py index 7cb3cec8262e..adb6db521ae6 100644 --- a/sympy/functions/elementary/piecewise.py +++ b/sympy/functions/elementary/piecewise.py @@ -3,7 +3,8 @@ from sympy.core import Basic, S, Function, diff, Tuple, Dummy, Number from sympy.core.basic import as_Basic from sympy.core.sympify import SympifyError -from sympy.core.relational import Equality, Relational, _canonical +from sympy.core.relational import (Equality, Unequality, Relational, + _canonical) from sympy.functions.elementary.miscellaneous import Max, Min from sympy.logic.boolalg import (And, Boolean, distribute_and_over_or, true, false, Not, Or, ITE, simplify_logic) @@ -857,28 +858,42 @@ def __eval_cond(cls, cond): except TypeError: pass - def as_expr_set_pairs(self): + def as_expr_set_pairs(self, domain=S.Reals): """Return tuples for each argument of self that give - the expression and the interval in which it is valid. + the expression and the interval in which it is valid + which is contained within the given domain. If a condition cannot be converted to a set, an error will be raised. The variable of the conditions is assumed to be real; sets of real values are returned. Examples ======== - >>> from sympy import Piecewise + + >>> from sympy import Piecewise, Interval >>> from sympy.abc import x - >>> Piecewise( + >>> p = Piecewise( ... (1, x < 2), ... (2,(x > 0) & (x < 4)), - ... (3, True)).as_expr_set_pairs() + ... (3, True)) + >>> p.as_expr_set_pairs() [(1, Interval.open(-oo, 2)), (2, Interval.Ropen(2, 4)), (3, Interval(4, oo))] + >>> p.as_expr_set_pairs(Interval(0, 3)) + [(1, Interval.Ropen(0, 2)), + (2, Interval(2, 3)), (3, EmptySet())] """ exp_sets = [] - U = S.Reals + U = domain + complex = not domain.is_subset(S.Reals) for expr, cond in self.args: + if complex: + for i in cond.atoms(Relational): + if not isinstance(i, (Equality, Unequality)): + raise ValueError(filldedent(''' + Inequalities in the complex domain are + not supported. Try the real domain by + setting domain=S.Reals''')) cond_int = U.intersect(cond.as_set()) U = U - cond_int exp_sets.append((expr, cond_int)) diff --git a/sympy/functions/elementary/tests/test_complexes.py b/sympy/functions/elementary/tests/test_complexes.py index 60127d5b5e57..866846d1d81a 100644 --- a/sympy/functions/elementary/tests/test_complexes.py +++ b/sympy/functions/elementary/tests/test_complexes.py @@ -877,3 +877,9 @@ def test_issue_14216(): A = MatrixSymbol("A", 2, 2) assert unpolarify(A[0, 0]) == A[0, 0] assert unpolarify(A[0, 0]*A[1, 0]) == A[0, 0]*A[1, 0] + + +def test_issue_14238(): + # doesn't cause recursion error + r = Symbol('r', real=True) + assert Abs(r + Piecewise((0, r > 0), (1 - r, True))) diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py index 1ce7e20f5c4f..fc89050605ed 100644 --- a/sympy/solvers/solveset.py +++ b/sympy/solvers/solveset.py @@ -11,19 +11,22 @@ from sympy.core.sympify import sympify from sympy.core import S, Pow, Dummy, pi, Expr, Wild, Mul, Equality +from sympy.core.containers import Tuple +from sympy.core.facts import InconsistentAssumptions from sympy.core.numbers import I, Number, Rational, oo -from sympy.core.function import (Lambda, expand_complex, AppliedUndef) +from sympy.core.function import (Lambda, expand_complex, AppliedUndef, Function) from sympy.core.relational import Eq from sympy.core.symbol import Symbol from sympy.simplify.simplify import simplify, fraction, trigsimp from sympy.functions import (log, Abs, tan, cot, sin, cos, sec, csc, exp, acos, asin, acsc, asec, arg, - piecewise_fold) + piecewise_fold, Piecewise) from sympy.functions.elementary.trigonometric import (TrigonometricFunction, HyperbolicFunction) -from sympy.functions.elementary.miscellaneous import real_root +from sympy.functions.elementary.miscellaneous import real_root, Application from sympy.sets import (FiniteSet, EmptySet, imageset, Interval, Intersection, Union, ConditionSet, ImageSet, Complement) +from sympy.sets.sets import Set from sympy.matrices import Matrix from sympy.polys import (roots, Poly, degree, together, PolynomialError, RootOf) @@ -32,12 +35,54 @@ from sympy.solvers.polysys import solve_poly_system from sympy.solvers.inequalities import solve_univariate_inequality from sympy.utilities import filldedent +from sympy.utilities.iterables import numbered_symbols from sympy.calculus.util import periodicity, continuous_domain from sympy.core.compatibility import ordered, default_sort_key, is_sequence from types import GeneratorType +def _masked(f, *atoms): + """Return ``f``, with all objects given by ``atoms`` replaced with + Dummy symbols, ``d``, and the list of replacements, ``(d, e)``, + where ``e`` is an object of type given by ``atoms`` in which + any other instances of atoms have been recursively replaced with + Dummy symbols, too. The tuples are ordered so that if they are + applied in sequence, the orgin ``f`` will be restored. + + Examples + ======== + + >>> from sympy import cos + >>> from sympy.abc import x + >>> from sympy.solvers.solveset import _masked + + >>> f = cos(cos(x) + 1) + >>> f, reps = _masked(cos(1 + cos(x)), cos) + >>> f + _a1 + >>> reps + [(_a1, cos(_a0 + 1)), (_a0, cos(x))] + >>> for d, e in reps: + ... f = f.xreplace({d: e}) + >>> f + cos(cos(x) + 1) + """ + sym = numbered_symbols('a', cls=Dummy) + mask = [] + for a in ordered(f.atoms(*atoms)): + for i in mask: + a = a.replace(*i) + mask.append((a, next(sym))) + mask = list(reversed(mask)) + for i, (o, n) in enumerate(mask): + f = f.replace(o, n) + mask[i] = (n, o) + if mask and f == mask[0][1]: + f = mask[0][0] + return f, mask + + def _invert(f_x, y, x, domain=S.Complexes): r""" Reduce the complex valued equation ``f(x) = y`` to a set of equations @@ -105,7 +150,7 @@ def _invert(f_x, y, x, domain=S.Complexes): else: x1, s = _invert_complex(f_x, FiniteSet(y), x) - if not isinstance(s, FiniteSet) or x1 == f_x or x1 != x: + if not isinstance(s, FiniteSet) or x1 != x: return x1, s return x1, s.intersection(domain) @@ -757,15 +802,9 @@ def _solveset(f, symbol, domain, _check=False): S.NegativeInfinity]): f = a/m + h # XXX condition `m != 0` should be added to soln - f = piecewise_fold(f) - # assign the solvers to use solver = lambda f, x, domain=domain: _solveset(f, x, domain) - if domain.is_subset(S.Reals): - inverter_func = invert_real - else: - inverter_func = invert_complex - inverter = lambda f, rhs, symbol: inverter_func(f, rhs, symbol, domain) + inverter = lambda f, rhs, symbol: _invert(f, rhs, symbol, domain) result = EmptySet() @@ -789,16 +828,28 @@ def _solveset(f, symbol, domain, _check=False): a = f.args[0] result = solveset_real(a > 0, symbol) elif f.is_Piecewise: - dom = domain result = EmptySet() - expr_set_pairs = f.as_expr_set_pairs() + expr_set_pairs = f.as_expr_set_pairs(domain) for (expr, in_set) in expr_set_pairs: if in_set.is_Relational: in_set = in_set.as_set() - if in_set.is_Interval: - dom -= in_set solns = solver(expr, symbol, in_set) result += solns + elif isinstance(f, Eq): + from sympy.core import Add + result = solver(Add(f.lhs, - f.rhs, evaluate=False), symbol, domain) + elif f.is_Relational: + if not domain.is_subset(S.Reals): + raise NotImplementedError(filldedent(''' + Inequalities in the complex domain are + not supported. Try the real domain by + setting domain=S.Reals''')) + try: + result = solve_univariate_inequality( + f, symbol, domain=domain, relational=False) + except NotImplementedError: + result = ConditionSet(symbol, f, domain) + return result else: lhs, rhs_s = inverter(f, 0, symbol) if lhs == symbol: @@ -961,6 +1012,7 @@ def solveset(f, symbol=None, domain=S.Complexes): """ f = sympify(f) + symbol = sympify(symbol) if f is S.true: return domain @@ -969,10 +1021,26 @@ def solveset(f, symbol=None, domain=S.Complexes): return S.EmptySet if not isinstance(f, (Expr, Number)): - raise ValueError("%s is not a valid SymPy expression" % (f)) + raise ValueError("%s is not a valid SymPy expression" % f) + + if not isinstance(symbol, Expr) and symbol is not None: + raise ValueError("%s is not a valid SymPy symbol" % symbol) + + if not isinstance(domain, Set): + raise ValueError("%s is not a valid domain" %(domain)) free_symbols = f.free_symbols + if symbol is None and not free_symbols: + b = Eq(f, 0) + if b is S.true: + return domain + elif b is S.false: + return S.EmptySet + else: + raise NotImplementedError(filldedent(''' + relationship between value and 0 is unknown: %s''' % b)) + if symbol is None: if len(free_symbols) == 1: symbol = free_symbols.pop() @@ -985,31 +1053,29 @@ def solveset(f, symbol=None, domain=S.Complexes): # the xreplace will be needed if a ConditionSet is returned return solveset(f[0], s[0], domain).xreplace(swap) - elif not free_symbols: - b = Eq(f, 0) - if b is S.true: - return domain - elif b is S.false: - return S.EmptySet - else: - raise NotImplementedError(filldedent(''' - relationship between value and 0 is unknown: %s''' % b)) - - if isinstance(f, Eq): - from sympy.core import Add - f = Add(f.lhs, - f.rhs, evaluate=False) - elif f.is_Relational: - if not domain.is_subset(S.Reals): - raise NotImplementedError(filldedent(''' - Inequalities in the complex domain are - not supported. Try the real domain by - setting domain=S.Reals''')) - try: - result = solve_univariate_inequality( - f, symbol, domain=domain, relational=False) - except NotImplementedError: - result = ConditionSet(symbol, f, domain) - return result + if domain.is_subset(S.Reals): + if not symbol.is_real: + assumptions = symbol.assumptions0 + assumptions['real'] = True + try: + r = Dummy('r', **assumptions) + return solveset(f.xreplace({symbol: r}), r, domain + ).xreplace({r: symbol}) + except InconsistentAssumptions: + pass + # Abs has its own handling method which avoids the + # rewriting property that the first piece of abs(x) + # is for x >= 0 and the 2nd piece for x < 0 -- solutions + # can look better if the 2nd condition is x <= 0. Since + # the solution is a set, duplication of results is not + # an issue, e.g. {y, -y} when y is 0 will be {0} + f, mask = _masked(f, Abs) + f = f.rewrite(Piecewise) # everything that's not an Abs + for d, e in mask: + # everything *in* an Abs + e = e.func(e.args[0].rewrite(Piecewise)) + f = f.xreplace({d: e}) + f = piecewise_fold(f) return _solveset(f, symbol, domain, _check=True) diff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py index 03eb18c3c5e5..00c82d6c932c 100644 --- a/sympy/solvers/tests/test_solveset.py +++ b/sympy/solvers/tests/test_solveset.py @@ -9,7 +9,7 @@ from sympy.functions.elementary.exponential import (LambertW, exp, log) from sympy.functions.elementary.hyperbolic import (HyperbolicFunction, atanh, sinh, tanh) -from sympy.functions.elementary.miscellaneous import sqrt +from sympy.functions.elementary.miscellaneous import sqrt, Min, Max from sympy.functions.elementary.piecewise import Piecewise from sympy.functions.elementary.trigonometric import ( TrigonometricFunction, acos, acot, acsc, asec, asin, atan, atan2, @@ -56,6 +56,9 @@ def test_invert_real(): def ireal(x, s=S.Reals): return Intersection(s, x) + # issue 14223 + assert invert_real(x, 0, x, Interval(1, 2)) == (x, S.EmptySet) + minus_n = Intersection(Interval(-oo, 0), FiniteSet(-n)) plus_n = Intersection(Interval(0, oo), FiniteSet(n)) assert solveset(abs(x) - n, x, S.Reals) == Union(minus_n, plus_n) @@ -189,10 +192,12 @@ def test_domain_check(): assert domain_check(x, x, oo) is False assert domain_check(0, x, oo) is False + def test_issue_11536(): assert solveset(0**x - 100, x, S.Reals) == S.EmptySet assert solveset(0**x - 1, x, S.Reals) == FiniteSet(0) + def test_is_function_class_equation(): from sympy.abc import x, a assert _is_function_class_equation(TrigonometricFunction, @@ -263,6 +268,10 @@ def test_garbage_input(): raises(ValueError, lambda: solveset_complex([x], x)) assert solveset_complex(x, pi) == S.EmptySet + raises(ValueError, lambda: solveset((x, y), x)) + raises(ValueError, lambda: solveset(x + 1, S.Reals)) + raises(ValueError, lambda: solveset(x + 1, x, 2)) + def test_solve_mul(): assert solveset_real((a*x + b)*(exp(x) - 3), x) == \ @@ -278,7 +287,9 @@ def test_solve_invert(): assert solveset_real(3**(x + 2), x) == FiniteSet() assert solveset_real(3**(2 - x), x) == FiniteSet() - assert solveset_real(y - b*exp(a/x), x) == Intersection(S.Reals, FiniteSet(a/log(y/b))) + assert solveset_real(y - b*exp(a/x), x) == Intersection( + S.Reals, FiniteSet(a/log(y/b))) + # issue 4504 assert solveset_real(2**x - 10, x) == FiniteSet(log(10)/log(2)) @@ -652,7 +663,7 @@ def test_atan2(): assert solveset_real(atan2(x, 2) - pi/3, x) == FiniteSet(2*sqrt(3)) -def test_piecewise(): +def test_piecewise_solveset(): eq = Piecewise((x - 2, Gt(x, 2)), (2 - x, True)) - 3 assert set(solveset_real(eq, x)) == set(FiniteSet(-1, 5)) @@ -665,8 +676,11 @@ def test_piecewise(): f = Piecewise(((x - 2)**2, x >= 0), (0, True)) assert solveset(f, x, domain=S.Reals) == Union(FiniteSet(2), Interval(-oo, 0, True, True)) - assert solveset(Piecewise((x + 1, x > 0), (I, True)) - I, x) == \ - Interval(-oo, 0) + assert solveset( + Piecewise((x + 1, x > 0), (I, True)) - I, x, S.Reals + ) == Interval(-oo, 0) + + assert solveset(Piecewise((x - 1, Ne(x, I)), (x, True)), x) == FiniteSet(1) def test_solveset_complex_polynomial(): @@ -793,10 +807,11 @@ def test_solve_trig(): 9*sqrt(57))**(S(1)/3))/(3*(67 + 9*sqrt(57))**(S(1)/6))) + 2*pi), S.Integers)) - assert solveset_real(2*tan(x)*sin(x) + 1, x) == \ - Union(ImageSet(Lambda(n, 2*n*pi + 2*atan(sqrt(4 - + sqrt(17)))), S.Integers), ImageSet(Lambda(n, 2*n*pi - - 2*atan(sqrt(4 + sqrt(17))) + 2*pi), S.Integers)) + assert solveset_real(2*tan(x)*sin(x) + 1, x) == Union( + ImageSet(Lambda(n, 2*n*pi + atan(sqrt(2)*sqrt(-1 + sqrt(17))/ + (-sqrt(17) + 1)) + pi), S.Integers), + ImageSet(Lambda(n, 2*n*pi - atan(sqrt(2)*sqrt(-1 + sqrt(17))/ + (-sqrt(17) + 1)) + pi), S.Integers)) assert solveset_real(cos(2*x)*cos(4*x) - 1, x) == \ ImageSet(Lambda(n, n*pi), S.Integers) @@ -958,17 +973,17 @@ def test_conditionset(): assert solveset(Eq(sin(x)**2 + cos(x)**2, 1), x, domain=S.Reals) == \ ConditionSet(x, True, S.Reals) - assert solveset(Eq(x**2 + x*sin(x), 1), x, domain=S.Reals) == \ - ConditionSet(x, Eq(x*(x + sin(x)) - 1, 0), S.Reals) + assert solveset(Eq(x**2 + x*sin(x), 1), x, domain=S.Reals + ) == ConditionSet(x, Eq(x*(x + sin(x)) - 1, 0), S.Reals) - assert solveset(Eq(sin(Abs(x)), x), x, domain=S.Reals) == \ - ConditionSet(x, Eq(-x + sin(Abs(x)), 0), Interval(-oo, oo)) + assert solveset(Eq(-I*(exp(I*x) - exp(-I*x))/2, 1), x + ) == imageset(Lambda(n, 2*n*pi + pi/2), S.Integers) - assert solveset(Eq(-I*(exp(I*x) - exp(-I*x))/2, 1), x) == \ - imageset(Lambda(n, 2*n*pi + pi/2), S.Integers) + assert solveset(x + sin(x) > 1, x, domain=S.Reals + ) == ConditionSet(x, x + sin(x) > 1, S.Reals) - assert solveset(x + sin(x) > 1, x, domain=S.Reals) == \ - ConditionSet(x, x + sin(x) > 1, S.Reals) + assert solveset(Eq(sin(Abs(x)), x), x, domain=S.Reals + ) == ConditionSet(x, Eq(-x + sin(Abs(x)), 0), S.Reals) @XFAIL @@ -988,9 +1003,8 @@ def test_solveset_domain(): def test_improve_coverage(): from sympy.solvers.solveset import _has_rational_power x = Symbol('x') - y = exp(x+1/x**2) - solution = solveset(y**2+y, x, S.Reals) - unsolved_object = ConditionSet(x, Eq((exp((x**3 + 1)/x**2) + 1)*exp((x**3 + 1)/x**2), 0), S.Reals) + solution = solveset(exp(x) + sin(x), x, S.Reals) + unsolved_object = ConditionSet(x, Eq(exp(x) + sin(x), 0), S.Reals) assert solution == unsolved_object assert _has_rational_power(sin(x)*exp(x) + 1, x) == (False, S.One) @@ -1676,6 +1690,7 @@ def test__is_finite_with_finite_vars(): Dummy(), Dummy(real=True), Dummy(complex=True))) assert f(1/Dummy(real=False)) is True # b/c it's finite but not 0 + def test_issue_13550(): assert solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) == FiniteSet(-3) @@ -1683,3 +1698,30 @@ def test_issue_13550(): def test_issue_13849(): t = symbols('t') assert nonlinsolve((t*(sqrt(5) + sqrt(2)) - sqrt(2), t), t) == EmptySet() + + +def test_issue_14223(): + x = Symbol('x') + assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x, + S.Reals) == FiniteSet(-1, 1) + assert solveset((Abs(x + Min(x, 2)) - 2).rewrite(Piecewise), x, + Interval(0, 2)) == FiniteSet(1) + + +def test_issue_10158(): + x = Symbol('x') + dom = S.Reals + assert solveset(x*Max(x, 15) - 10, x, dom) == FiniteSet(2/S(3)) + assert solveset(x*Min(x, 15) - 10, x, dom) == FiniteSet(-sqrt(10), sqrt(10)) + assert solveset(Max(Abs(x - 3) - 1, x + 2) - 3, x, dom) == FiniteSet(-1, 1) + assert solveset(Abs(x - 1) - Abs(y), x, dom) == FiniteSet(-Abs(y) + 1, Abs(y) + 1) + assert solveset(Abs(x + 4*Abs(x + 1)), x, dom) == FiniteSet(-4/S(3), -4/S(5)) + assert solveset(2*Abs(x + Abs(x + Max(3, x))) - 2, x, S.Reals + ) == FiniteSet(-1, -2) + dom = S.Complexes + raises(ValueError, lambda: solveset(x*Max(x, 15) - 10, x, dom)) + raises(ValueError, lambda: solveset(x*Min(x, 15) - 10, x, dom)) + raises(ValueError, lambda: + solveset(Max(Abs(x - 3) - 1, x + 2) - 3, x, dom)) + raises(ValueError, lambda: solveset(Abs(x - 1) - Abs(y), x, dom)) + raises(ValueError, lambda: solveset(Abs(x + 4*Abs(x + 1)), x, dom))
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
streamlit__streamlit-9337@693bb43
streamlit/streamlit
Python
9,337
Remove `st.logo` if stale
## Describe your changes Currently, a stale logo persists between re-runs for a SPA, MPA v1, & MPA v2. This PR attaches the `scriptRunId` to the `st.logo` call so that a stale logo is cleaned up on script finish (when `clearStaleNodes` is called). ## GitHub Issue Link (if applicable) Closes #9336 ## Testing Plan - Unit/Integration Tests: Added โœ… - Manual Testing: โœ…
2024-08-27T01:08:18Z
Stale `st.logo` not removed ### Summary After the MPAv2 refactor, a stale logo is not removed for single-page apps, MPA v1, & MPA v2 ### Steps To Reproduce 1. Create an app with `st.logo` in the script 2. Run the script -> logo appears as expected 3. Comment out the `st.logo` call 4. Re-run script 5. Notice the logo is still displayed ### Is this a regression? - [x] Yes, this used to work in a previous version.
**If this issue affects you, please react with a ๐Ÿ‘ (thumbs up emoji) to the initial post.** Your feedback helps us prioritize which bugs to investigate and address first. ![Visits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fstreamlit%2Fstreamlit%2Fissues%2F9336&title=visits&edge_flat=false)
[ { "body": "### Summary\r\n\r\nAfter the MPAv2 refactor, a stale logo is not removed for single-page apps, MPA v1, & MPA v2\r\n\r\n### Steps To Reproduce\r\n\r\n1. Create an app with `st.logo` in the script\r\n2. Run the script -> logo appears as expected\r\n3. Comment out the `st.logo` call\r\n4. Re-run script\r\n5. Notice the logo is still displayed\r\n\r\n### Is this a regression?\r\n- [x] Yes, this used to work in a previous version.", "number": 9336, "title": "Stale `st.logo` not removed" } ]
5ca2a9aa1fc7830bab933244aad185b7be3359da
{ "head_commit": "693bb430b3d2930fec597e77e222ebf1c610c645", "head_commit_message": "Update comments", "patch_to_review": "diff --git a/frontend/app/src/App.tsx b/frontend/app/src/App.tsx\nindex 1cde884a75c4..ac53a8c0c015 100644\n--- a/frontend/app/src/App.tsx\n+++ b/frontend/app/src/App.tsx\n@@ -739,9 +739,17 @@ export class App extends PureComponent<Props, State> {\n }\n \n handleLogo = (logo: Logo, metadata: ForwardMsgMetadata): void => {\n+ // Pass the current page & run ID for cleanup\n+ const { scriptRunId } = this.state\n+ const { activeScriptHash } = metadata\n+\n this.setState(\n {\n- elements: this.pendingElementsBuffer.appRootWithLogo(logo, metadata),\n+ elements: this.pendingElementsBuffer.appRootWithLogo(\n+ logo,\n+ activeScriptHash,\n+ scriptRunId\n+ ),\n },\n () => {\n this.pendingElementsBuffer = this.state.elements\ndiff --git a/frontend/lib/src/AppNode.ts b/frontend/lib/src/AppNode.ts\nindex 506c631686b4..515988e1625d 100644\n--- a/frontend/lib/src/AppNode.ts\n+++ b/frontend/lib/src/AppNode.ts\n@@ -50,6 +50,9 @@ interface AppLogo {\n logo: Logo\n // Associated scriptHash that created the logo\n activeScriptHash: string\n+\n+ // Associated scriptRunId that created the logo\n+ scriptRunId: string\n }\n \n /**\n@@ -676,11 +679,15 @@ export class AppRoot {\n return this.appLogo?.logo ?? null\n }\n \n- public appRootWithLogo(logo: Logo, metadata: ForwardMsgMetadata): AppRoot {\n- const { activeScriptHash } = metadata\n+ public appRootWithLogo(\n+ logo: Logo,\n+ activeScriptHash: string,\n+ scriptRunId: string\n+ ): AppRoot {\n return new AppRoot(this.mainScriptHash, this.root, {\n logo,\n activeScriptHash,\n+ scriptRunId,\n })\n }\n \n@@ -791,6 +798,9 @@ export class AppRoot {\n this.bottom.clearStaleNodes(currentScriptRunId, fragmentIdsThisRun) ||\n new BlockNode(this.mainScriptHash)\n \n+ const appLogo =\n+ this.appLogo?.scriptRunId === currentScriptRunId ? this.appLogo : null\n+\n return new AppRoot(\n this.mainScriptHash,\n new BlockNode(\n@@ -799,7 +809,7 @@ export class AppRoot {\n new BlockProto({ allowEmpty: true }),\n currentScriptRunId\n ),\n- this.appLogo\n+ appLogo\n )\n }\n \n" }
[ { "diff_hunk": "@@ -739,9 +739,17 @@ export class App extends PureComponent<Props, State> {\n }\n \n handleLogo = (logo: Logo, metadata: ForwardMsgMetadata): void => {\n+ // Pass the current page & run ID for cleanup\n+ const { scriptRunId } = this.state\n+ const { activeScriptHash } = metadata\n+\n this.setState(\n {\n- elements: this.pendingElementsBuffer.appRootWithLogo(logo, metadata),\n+ elements: this.pendingElementsBuffer.appRootWithLogo(\n+ logo,\n+ activeScriptHash,\n+ scriptRunId", "line": null, "original_line": 751, "original_start_line": null, "path": "frontend/app/src/App.tsx", "start_line": null, "text": "@user1:\nNot strongly held opinion, but it seems like `activeScriptHash` and `scriptRunId` should be in an object representing the `metadata` for the logo. I think it will just be cleaner. I think it goes well with the function title (appRootWithLogo takes in a logo and associated metadata) and future-proofs as we add more metadata associated with the logo.\n\n@author:\nThink thats fair - made an update let me know if it makes sense to you or not!" } ]
d2a3e81051d0bcda6e1cb30ab3c5d95d710f9e5b
diff --git a/frontend/app/src/App.test.tsx b/frontend/app/src/App.test.tsx index dc61df1df051..cdf372b23c1a 100644 --- a/frontend/app/src/App.test.tsx +++ b/frontend/app/src/App.test.tsx @@ -1663,6 +1663,85 @@ describe("App", () => { }) }) + describe("Logo handling", () => { + it("adds logo on receipt of logo ForwardMsg", () => { + renderApp(getProps()) + + sendForwardMessage( + "logo", + { + image: + "https://global.discourse-cdn.com/business7/uploads/streamlit/original/2X/8/8cb5b6c0e1fe4e4ebfd30b769204c0d30c332fec.png", + }, + { + activeScriptHash: "page_script_hash", + } + ) + + expect(screen.getByTestId("stLogo")).toBeInTheDocument() + }) + + it("will remove logo if activeScriptHash does not match", async () => { + renderApp(getProps()) + + sendForwardMessage( + "logo", + { + image: + "https://global.discourse-cdn.com/business7/uploads/streamlit/original/2X/8/8cb5b6c0e1fe4e4ebfd30b769204c0d30c332fec.png", + }, + { + activeScriptHash: "page_script_hash", + } + ) + + expect(screen.getByTestId("stLogo")).toBeInTheDocument() + + // Trigger a new session with a different pageScriptHash + sendForwardMessage("newSession", { + ...NEW_SESSION_JSON, + pageScriptHash: "different_page_script_hash", + }) + + // Since the logo was added with a different activeScriptHash, it should be removed + await waitFor(() => { + expect(screen.queryByTestId("stLogo")).not.toBeInTheDocument() + }) + }) + + it("will remove logo if scriptRunId does not match", async () => { + renderApp(getProps()) + + sendForwardMessage( + "logo", + { + image: + "https://global.discourse-cdn.com/business7/uploads/streamlit/original/2X/8/8cb5b6c0e1fe4e4ebfd30b769204c0d30c332fec.png", + }, + { + activeScriptHash: "page_script_hash", + } + ) + + expect(screen.getByTestId("stLogo")).toBeInTheDocument() + + // Trigger a new scriptRunId via new session + sendForwardMessage("newSession", NEW_SESSION_JSON) + + // Trigger cleanup in script finished handler + sendForwardMessage( + "scriptFinished", + ForwardMsg.ScriptFinishedStatus.FINISHED_SUCCESSFULLY + ) + + // Since no logo is sent in this script run, logo must not be present in the script anymore + // Stale logo should be removed + await waitFor(() => { + expect(screen.queryByTestId("stLogo")).not.toBeInTheDocument() + }) + }) + }) + // * handlePageNotFound has branching error messages depending on pageName describe("App.handlePageNotFound", () => { it("includes the missing page name in error modal message if available", () => { diff --git a/frontend/app/src/App.tsx b/frontend/app/src/App.tsx index 1cde884a75c4..5d27cc1202a0 100644 --- a/frontend/app/src/App.tsx +++ b/frontend/app/src/App.tsx @@ -739,9 +739,18 @@ export class App extends PureComponent<Props, State> { } handleLogo = (logo: Logo, metadata: ForwardMsgMetadata): void => { + // Pass the current page & run ID for cleanup + const logoMetadata = { + activeScriptHash: metadata.activeScriptHash, + scriptRunId: this.state.scriptRunId, + } + this.setState( { - elements: this.pendingElementsBuffer.appRootWithLogo(logo, metadata), + elements: this.pendingElementsBuffer.appRootWithLogo( + logo, + logoMetadata + ), }, () => { this.pendingElementsBuffer = this.state.elements diff --git a/frontend/lib/src/AppNode.test.ts b/frontend/lib/src/AppNode.test.ts index c4ff513ca682..4ab03c8fdf40 100644 --- a/frontend/lib/src/AppNode.test.ts +++ b/frontend/lib/src/AppNode.test.ts @@ -26,6 +26,7 @@ import { Element, ForwardMsgMetadata, IArrowVegaLiteChart, + Logo as LogoProto, } from "./proto" import { AppNode, AppRoot, BlockNode, ElementNode } from "./AppNode" import { IndexTypeName } from "./dataframes/Quiver" @@ -1151,6 +1152,21 @@ describe("AppRoot.clearStaleNodes", () => { expect(newRoot.getElements().size).toBe(1) }) + it("clears a stale logo", () => { + const logo = LogoProto.create({ + image: + "https://global.discourse-cdn.com/business7/uploads/streamlit/original/2X/8/8cb5b6c0e1fe4e4ebfd30b769204c0d30c332fec.png", + }) + const newRoot = ROOT.appRootWithLogo(logo, { + activeScriptHash: "hash", + scriptRunId: "script_run_id", + }) + expect(newRoot.logo).not.toBeNull() + + const newNewRoot = newRoot.clearStaleNodes("new_script_run_id", []) + expect(newNewRoot.logo).toBeNull() + }) + it("handles currentFragmentId correctly", () => { const tabContainerProto = makeProto(DeltaProto, { addBlock: { tabContainer: {}, allowEmpty: false }, diff --git a/frontend/lib/src/AppNode.ts b/frontend/lib/src/AppNode.ts index 506c631686b4..454b7a814895 100644 --- a/frontend/lib/src/AppNode.ts +++ b/frontend/lib/src/AppNode.ts @@ -46,10 +46,16 @@ import { Quiver } from "./dataframes/Quiver" import { ensureError } from "./util/ErrorHandling" const NO_SCRIPT_RUN_ID = "NO_SCRIPT_RUN_ID" -interface AppLogo { - logo: Logo + +interface LogoMetadata { // Associated scriptHash that created the logo activeScriptHash: string + + // Associated scriptRunId that created the logo + scriptRunId: string +} +interface AppLogo extends LogoMetadata { + logo: Logo } /** @@ -676,11 +682,10 @@ export class AppRoot { return this.appLogo?.logo ?? null } - public appRootWithLogo(logo: Logo, metadata: ForwardMsgMetadata): AppRoot { - const { activeScriptHash } = metadata + public appRootWithLogo(logo: Logo, metadata: LogoMetadata): AppRoot { return new AppRoot(this.mainScriptHash, this.root, { logo, - activeScriptHash, + ...metadata, }) } @@ -791,6 +796,9 @@ export class AppRoot { this.bottom.clearStaleNodes(currentScriptRunId, fragmentIdsThisRun) || new BlockNode(this.mainScriptHash) + const appLogo = + this.appLogo?.scriptRunId === currentScriptRunId ? this.appLogo : null + return new AppRoot( this.mainScriptHash, new BlockNode( @@ -799,7 +807,7 @@ export class AppRoot { new BlockProto({ allowEmpty: true }), currentScriptRunId ), - this.appLogo + appLogo ) }
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-14207@cdb2dee
sympy/sympy
Python
14,207
_print_Mul : parenthesize non-evaluated Pow with `exp=-1`
Fixes #14160 The issue occurs only for non-evaluated `Pow` with exponent `-1`. `_print_Mul` prints such terms as it is (like `Pow(Mul(a,a,evaluate=False), -1, evaluate=False)` gets printed to `a*a` in the denominator) #### Brief description of what is fixed or changed I just added a list `pow_paran` which takes account of all such vulnerable `pow` and paranthesize them explicitly. `isinstance(item.base, Mul)` has been included because `Add` (such as `y+1`) wont face this issue(there precedence matches negative integer with a value of `40`). I was not able to find explicit tests for `Mul`, so I added them inside `test_python.py`. Output in this branch : ``` >>> Mul(-2, u, Pow(Mul(a,a,evaluate=False), -1, evaluate=False), evaluate=False) -2*u/(a*a) >>> Mul(-2, u, Pow(Mul(a,b,a,evaluate=False), -1, evaluate=False), evaluate=False) -2*u/(a*a*b) ``` Identical changes are added to `CodePrinter`, `JuliaCodePrinter` and `OctaveCodePrinter`
2018-02-14T17:34:16Z
Necessary parenthesis in printing of some multiplications Reproducer: ``` from sympy import * a = Symbol('a') u = Symbol('u') a2inv = Pow(Mul(a,a,evaluate=False), -1, evaluate=False) d = Mul(-2, u, a2inv, evaluate=False) print("This should be -2*u/(a*a)") print(d) ``` Output: ``` This should be -2*u/(a*a) -2*u/a*a ``` The evaluate=False's are necessary because this is being used in a code-generation context, and the desired code is ``float lhs = -2*u/(a*a)`` not ``float lhs = -2*u*pow(a,-2)`` (which promotes the operations to double precision). Python 3.6 Sympy Version: latest master (sympy-1.1.1-2784-g98d5dd9) but present before that. Also occurs (importantly, in this case) in the C and Python code generation printers (which duplicate a lot of the logic in print_Mul, so may possibly need duplicated fixes).
[ { "body": "Reproducer:\r\n\r\n```\r\nfrom sympy import *\r\n\r\na = Symbol('a')\r\nu = Symbol('u')\r\n\r\na2inv = Pow(Mul(a,a,evaluate=False), -1, evaluate=False)\r\nd = Mul(-2, u, a2inv, evaluate=False)\r\n\r\nprint(\"This should be -2*u/(a*a)\")\r\nprint(d)\r\n```\r\n\r\nOutput:\r\n\r\n```\r\nThis should be -2*u/(a*a)\r\n-2*u/a*a\r\n```\r\n\r\nThe evaluate=False's are necessary because this is being used in a code-generation context, and the desired code is ``float lhs = -2*u/(a*a)`` not ``float lhs = -2*u*pow(a,-2)`` (which promotes the operations to double precision).\r\n\r\nPython 3.6\r\nSympy Version: latest master (sympy-1.1.1-2784-g98d5dd9) but present before that.\r\nAlso occurs (importantly, in this case) in the C and Python code generation printers (which duplicate a lot of the logic in print_Mul, so may possibly need duplicated fixes).\r\n\r\n", "number": 14160, "title": "Necessary parenthesis in printing of some multiplications" } ]
9ebcce18bd0ea331b55b8538d2c45e56a3821715
{ "head_commit": "cdb2dee4e1e0d3ed20faf97f7919b1dfcadf19c4", "head_commit_message": "parenthesize non-evaluated Pow with exp=-1", "patch_to_review": "diff --git a/sympy/printing/str.py b/sympy/printing/str.py\nindex 0baefe0f7fc2..16899ec94224 100644\n--- a/sympy/printing/str.py\n+++ b/sympy/printing/str.py\n@@ -270,6 +270,8 @@ def _print_Mul(self, expr):\n a = [] # items in the numerator\n b = [] # items that are in the denominator (if any)\n \n+ pow_paran=[] # Will collect all pow with more than one base element and exp = -1\n+\n if self.order not in ('old', 'none'):\n args = expr.as_ordered_factors()\n else:\n@@ -282,6 +284,8 @@ def _print_Mul(self, expr):\n if item.exp != -1:\n b.append(Pow(item.base, -item.exp, evaluate=False))\n else:\n+ if len(item.args[0].args) != 1 and isinstance(item.base, Mul): # To avoid situations like #14160\n+ pow_paran.append(item)\n b.append(Pow(item.base, -item.exp))\n elif item.is_Rational and item is not S.Infinity:\n if item.p != 1:\n@@ -296,6 +300,11 @@ def _print_Mul(self, expr):\n a_str = [self.parenthesize(x, prec, strict=False) for x in a]\n b_str = [self.parenthesize(x, prec, strict=False) for x in b]\n \n+ # To parenthesize Pow with exp = -1 and having more than one Symbol\n+ for item in pow_paran:\n+ if item.base in b:\n+ b_str[b.index(item.base)] = \"(%s)\" % b_str[b.index(item.base)]\n+\n if len(b) == 0:\n return sign + '*'.join(a_str)\n elif len(b) == 1:\ndiff --git a/sympy/printing/tests/test_python.py b/sympy/printing/tests/test_python.py\nindex b059fbdeddaa..327e463d7733 100644\n--- a/sympy/printing/tests/test_python.py\n+++ b/sympy/printing/tests/test_python.py\n@@ -43,6 +43,9 @@ def test_python_basic():\n \"y = Symbol('y')\\nx = Symbol('x')\\ne = 1 - 3*y/(2*x)\"]\n \n # Multiplication\n+ from sympy import Mul, Pow\n+ assert python(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False)\n+ , evaluate=False)) == \"x = Symbol('x')\\ny = Symbol('y')\\ne = -2*x/(y*y)\"\n assert python(x/y) == \"x = Symbol('x')\\ny = Symbol('y')\\ne = x/y\"\n assert python(-x/y) == \"x = Symbol('x')\\ny = Symbol('y')\\ne = -x/y\"\n assert python((x + 2)/y) in [\n" }
[ { "diff_hunk": "@@ -43,6 +43,9 @@ def test_python_basic():\n \"y = Symbol('y')\\nx = Symbol('x')\\ne = 1 - 3*y/(2*x)\"]\n \n # Multiplication\n+ from sympy import Mul, Pow\n+ assert python(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False)", "line": null, "original_line": 47, "original_start_line": null, "path": "sympy/printing/tests/test_python.py", "start_line": null, "text": "@user1:\nWhy did you add a test to the Python printer? The fix is in the str printer, so the test should go in the str printer tests." }, { "diff_hunk": "@@ -270,6 +270,8 @@ def _print_Mul(self, expr):\n a = [] # items in the numerator\n b = [] # items that are in the denominator (if any)\n \n+ pow_paran=[] # Will collect all pow with more than one base element and exp = -1", "line": null, "original_line": 273, "original_start_line": null, "path": "sympy/printing/str.py", "start_line": null, "text": "@user1:\nShould this be called pow_paren (with an e)?" } ]
6a720b4005878f0855f770a1ae3af38c9f0bc28b
diff --git a/sympy/printing/codeprinter.py b/sympy/printing/codeprinter.py index 89677d1be66f..1c28e168ae82 100644 --- a/sympy/printing/codeprinter.py +++ b/sympy/printing/codeprinter.py @@ -420,6 +420,8 @@ def _print_Mul(self, expr): a = [] # items in the numerator b = [] # items that are in the denominator (if any) + pow_paren = [] # Will collect all pow with more than one base element and exp = -1 + if self.order not in ('old', 'none'): args = expr.as_ordered_factors() else: @@ -432,6 +434,8 @@ def _print_Mul(self, expr): if item.exp != -1: b.append(Pow(item.base, -item.exp, evaluate=False)) else: + if len(item.args[0].args) != 1 and isinstance(item.base, Mul): # To avoid situations like #14160 + pow_paren.append(item) b.append(Pow(item.base, -item.exp)) else: a.append(item) @@ -441,6 +445,11 @@ def _print_Mul(self, expr): a_str = [self.parenthesize(x, prec) for x in a] b_str = [self.parenthesize(x, prec) for x in b] + # To parenthesize Pow with exp = -1 and having more than one Symbol + for item in pow_paren: + if item.base in b: + b_str[b.index(item.base)] = "(%s)" % b_str[b.index(item.base)] + if len(b) == 0: return sign + '*'.join(a_str) elif len(b) == 1: diff --git a/sympy/printing/julia.py b/sympy/printing/julia.py index f973b5f96b80..c1e28061dc1b 100644 --- a/sympy/printing/julia.py +++ b/sympy/printing/julia.py @@ -134,6 +134,8 @@ def _print_Mul(self, expr): a = [] # items in the numerator b = [] # items that are in the denominator (if any) + pow_paren = [] # Will collect all pow with more than one base element and exp = -1 + if self.order not in ('old', 'none'): args = expr.as_ordered_factors() else: @@ -147,6 +149,8 @@ def _print_Mul(self, expr): if item.exp != -1: b.append(Pow(item.base, -item.exp, evaluate=False)) else: + if len(item.args[0].args) != 1 and isinstance(item.base, Mul): # To avoid situations like #14160 + pow_paren.append(item) b.append(Pow(item.base, -item.exp)) elif item.is_Rational and item is not S.Infinity: if item.p != 1: @@ -161,6 +165,11 @@ def _print_Mul(self, expr): a_str = [self.parenthesize(x, prec) for x in a] b_str = [self.parenthesize(x, prec) for x in b] + # To parenthesize Pow with exp = -1 and having more than one Symbol + for item in pow_paren: + if item.base in b: + b_str[b.index(item.base)] = "(%s)" % b_str[b.index(item.base)] + # from here it differs from str.py to deal with "*" and ".*" def multjoin(a, a_str): # here we probably are assuming the constants will come first diff --git a/sympy/printing/octave.py b/sympy/printing/octave.py index 91ba9e4fdfe6..b395d8c98e98 100644 --- a/sympy/printing/octave.py +++ b/sympy/printing/octave.py @@ -146,6 +146,8 @@ def _print_Mul(self, expr): a = [] # items in the numerator b = [] # items that are in the denominator (if any) + pow_paren = [] # Will collect all pow with more than one base element and exp = -1 + if self.order not in ('old', 'none'): args = expr.as_ordered_factors() else: @@ -159,6 +161,8 @@ def _print_Mul(self, expr): if item.exp != -1: b.append(Pow(item.base, -item.exp, evaluate=False)) else: + if len(item.args[0].args) != 1 and isinstance(item.base, Mul): # To avoid situations like #14160 + pow_paren.append(item) b.append(Pow(item.base, -item.exp)) elif item.is_Rational and item is not S.Infinity: if item.p != 1: @@ -173,6 +177,11 @@ def _print_Mul(self, expr): a_str = [self.parenthesize(x, prec) for x in a] b_str = [self.parenthesize(x, prec) for x in b] + # To parenthesize Pow with exp = -1 and having more than one Symbol + for item in pow_paren: + if item.base in b: + b_str[b.index(item.base)] = "(%s)" % b_str[b.index(item.base)] + # from here it differs from str.py to deal with "*" and ".*" def multjoin(a, a_str): # here we probably are assuming the constants will come first diff --git a/sympy/printing/str.py b/sympy/printing/str.py index 0baefe0f7fc2..ff41af68b556 100644 --- a/sympy/printing/str.py +++ b/sympy/printing/str.py @@ -270,6 +270,8 @@ def _print_Mul(self, expr): a = [] # items in the numerator b = [] # items that are in the denominator (if any) + pow_paren = [] # Will collect all pow with more than one base element and exp = -1 + if self.order not in ('old', 'none'): args = expr.as_ordered_factors() else: @@ -282,6 +284,8 @@ def _print_Mul(self, expr): if item.exp != -1: b.append(Pow(item.base, -item.exp, evaluate=False)) else: + if len(item.args[0].args) != 1 and isinstance(item.base, Mul): # To avoid situations like #14160 + pow_paren.append(item) b.append(Pow(item.base, -item.exp)) elif item.is_Rational and item is not S.Infinity: if item.p != 1: @@ -296,6 +300,11 @@ def _print_Mul(self, expr): a_str = [self.parenthesize(x, prec, strict=False) for x in a] b_str = [self.parenthesize(x, prec, strict=False) for x in b] + # To parenthesize Pow with exp = -1 and having more than one Symbol + for item in pow_paren: + if item.base in b: + b_str[b.index(item.base)] = "(%s)" % b_str[b.index(item.base)] + if len(b) == 0: return sign + '*'.join(a_str) elif len(b) == 1: diff --git a/sympy/printing/tests/test_ccode.py b/sympy/printing/tests/test_ccode.py index a473f800fbd5..adb62fccb27e 100644 --- a/sympy/printing/tests/test_ccode.py +++ b/sympy/printing/tests/test_ccode.py @@ -1,6 +1,6 @@ import warnings -from sympy.core import (S, pi, oo, symbols, Rational, Integer, Float, Mod, - GoldenRatio, EulerGamma, Catalan, Lambda, Dummy, Eq, nan) +from sympy.core import (S, pi, oo, symbols, Rational, Integer, Float, Mod, GoldenRatio, + EulerGamma, Catalan, Lambda, Dummy, Eq, nan, Mul, Pow) from sympy.functions import (Abs, acos, acosh, asin, asinh, atan, atanh, atan2, ceiling, cos, cosh, erf, erfc, exp, floor, gamma, log, loggamma, Max, Min, Piecewise, @@ -58,6 +58,9 @@ def test_ccode_Pow(): # Related to gh-11353 assert ccode(2**x, user_functions={'Pow': _cond_cfunc2}) == 'exp2(x)' assert ccode(x**2, user_functions={'Pow': _cond_cfunc2}) == 'pow(x, 2)' + # For issue 14160 + assert ccode(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False), + evaluate=False)) == '-2*x/(y*y)' def test_ccode_Max(): diff --git a/sympy/printing/tests/test_julia.py b/sympy/printing/tests/test_julia.py index 80c1ddfe14a9..54f3fcedff52 100644 --- a/sympy/printing/tests/test_julia.py +++ b/sympy/printing/tests/test_julia.py @@ -1,6 +1,6 @@ from sympy.core import (S, pi, oo, symbols, Function, Rational, Integer, Tuple, Symbol) -from sympy.core import EulerGamma, GoldenRatio, Catalan, Lambda +from sympy.core import EulerGamma, GoldenRatio, Catalan, Lambda, Mul, Pow from sympy.functions import Piecewise, sqrt, ceiling, exp, sin, cos from sympy.utilities.pytest import raises from sympy.utilities.lambdify import implemented_function @@ -44,6 +44,9 @@ def test_Pow(): g = implemented_function('g', Lambda(x, 2*x)) assert julia_code(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \ "(3.5*2*x).^(-x + y.^x)./(x.^2 + y)" + # For issue 14160 + assert julia_code(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False), + evaluate=False)) == '-2*x./(y.*y)' def test_basic_ops(): diff --git a/sympy/printing/tests/test_octave.py b/sympy/printing/tests/test_octave.py index a7d9f81c7b11..cff6c9365f1b 100644 --- a/sympy/printing/tests/test_octave.py +++ b/sympy/printing/tests/test_octave.py @@ -1,6 +1,6 @@ from sympy.core import (S, pi, oo, symbols, Function, Rational, Integer, Tuple, Symbol) -from sympy.core import EulerGamma, GoldenRatio, Catalan, Lambda +from sympy.core import EulerGamma, GoldenRatio, Catalan, Lambda, Mul, Pow from sympy.functions import (Piecewise, sqrt, ceiling, exp, sin, cos, LambertW, sinc, Max, Min, arg, im, re) from sympy.utilities.pytest import raises @@ -53,6 +53,9 @@ def test_Pow(): g = implemented_function('g', Lambda(x, 2*x)) assert mcode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \ "(3.5*2*x).^(-x + y.^x)./(x.^2 + y)" + # For issue 14160 + assert mcode(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False), + evaluate=False)) == '-2*x./(y.*y)' def test_basic_ops(): diff --git a/sympy/printing/tests/test_str.py b/sympy/printing/tests/test_str.py index 884c09d9413e..360b72d91df5 100644 --- a/sympy/printing/tests/test_str.py +++ b/sympy/printing/tests/test_str.py @@ -7,7 +7,7 @@ WildFunction, zeta, zoo, Dummy, Dict, Tuple, FiniteSet, factor, subfactorial, true, false, Equivalent, Xor, Complement, SymmetricDifference, AccumBounds, UnevaluatedExpr, Eq, Ne, Quaternion) -from sympy.core import Expr +from sympy.core import Expr, Mul from sympy.physics.units import second, joule from sympy.polys import Poly, rootof, RootSum, groebner, ring, field, ZZ, QQ, lex, grlex from sympy.geometry import Point, Circle @@ -214,6 +214,10 @@ def test_Mul(): assert str(-2*x/3) == '-2*x/3' assert str(-1.0*x) == '-1.0*x' assert str(1.0*x) == '1.0*x' + # For issue 14160 + assert str(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False), + evaluate=False)) == '-2*x/(y*y)' + class CustomClass1(Expr): is_commutative = True
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-14158@bd51bc3
sympy/sympy
Python
14,158
Implemented Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent()
Fixes #14112 . Fixes #14152 . This PR also introduces a new convergence test -- the **Ratio Test**. Actually, when I followed normalhuman's comment on the issue, I solved the particular issue, but I had another problem - ``` >>> from sympy import * >>> n = symbols('n') >>> a = Sum(factorial(n)/5**n, (n, 1, oo)) >>> a.is_convergent() NotImplementedError : The algorithm to find the Sum convergence of 5**(-_n)*factorial(_n))**(1/_n) is not yet implemented. ``` While looking through the file, I then came to realize that the algorithm of **Ratio Test** hadn't been implemented, which can solve this problem easily. So, the code snippet of the Ratio_test algorithm has been implemented, and the issue fixed. Test case has also been added.
2018-02-11T11:06:46Z
Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent() not implemented ``` >>> Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent()` Traceback (most recent call last): File "<ipython-input-23-75eab3fe4b66>", line 1, in <module> Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent() File ".\sympy\concrete\summations.py", line 557, in is_absolutely_convergent return Sum(abs(self.function), self.limits).is_convergent() File ".\sympy\concrete\summations.py", line 428, in is_convergent order = O(sequence_term, (sym, S.Infinity)) File ".\sympy\series\order.py", line 217, in __new__ expr = expr.as_leading_term(*args) File ".\sympy\core\expr.py", line 2864, in as_leading_term obj = self._eval_as_leading_term(x) File ".\sympy\core\mul.py", line 1625, in _eval_as_leading_term return self.func(*[t.as_leading_term(x) for t in self.args]) File ".\sympy\core\mul.py", line 1625, in <listcomp> return self.func(*[t.as_leading_term(x) for t in self.args]) File ".\sympy\core\expr.py", line 2864, in as_leading_term obj = self._eval_as_leading_term(x) File ".\sympy\core\function.py", line 741, in _eval_as_leading_term '%s has no _eval_as_leading_term routine' % self.func) NotImplementedError: Abs has no _eval_as_leading_term routine ``` ValueError in Sum((-1)**(2*n)/n, (n, 1, oo)).is_convergent() ``` var('n') Sum((-1)**(2*n)/n, (n, 1, oo)).is_convergent() ``` results in ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/lvk/sympy/sympy/concrete/summations.py", line 489, in is_convergent maxima = solveset(sequence_term.diff(sym), sym, interval) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 947, in solveset return _solveset(f, symbol, domain, _check=True) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 763, in _solveset result += _solve_as_rational(equation, symbol, domain) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 409, in _solve_as_rational valid_solns = _solveset(g, symbol, domain) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 717, in _solveset result = Union(*[solver(m, symbol) for m in f.args]) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 717, in <listcomp> result = Union(*[solver(m, symbol) for m in f.args]) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 696, in <lambda> solver = lambda f, x, domain=domain: _solveset(f, x, domain) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 736, in _solveset lhs, rhs_s = inverter(f, 0, symbol) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 701, in <lambda> inverter = lambda f, rhs, symbol: inverter_func(f, rhs, symbol, domain) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 122, in invert_real return _invert(f_x, y, x, domain) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 104, in _invert x1, s = _invert_real(f_x, FiniteSet(y), x) File "/home/lvk/sympy/sympy/solvers/solveset.py", line 201, in _invert_real s, b = integer_log(rhs, base) File "/home/lvk/sympy/sympy/core/power.py", line 130, in integer_log raise ValueError('y cannot take value as 0') ValueError: y cannot take value as 0 ```
@asmeurer @normalhuman I would like to fix this issue, can you guide me how to proceed! The method `is_absolutely_convergent` returns `Sum(abs(self.function), self.limits).is_convergent()` (incidentally, that should be Abs). The problem is this: ``` >>> a = (-1)**n / sqrt(n) >>> Abs(a) exp(-pi*im(n))*Abs(1/sqrt(n)) ``` Even though we are talking about n being an index of a series, it is not treated as an integer (hence real) number. The logical place to enforce assumptions on the index is [here](https://github.com/sympy/sympy/blob/master/sympy/concrete/summations.py#L403), where the series is already reduced to positive-index form. Something like this: ``` sym_ = Dummy(sym.name, integer=True, positive=True) sequence_term = sequence_term.xreplace({sym: sym_}) sym = sym_ ``` should be enough. Then in the rest of computation goes as before. In the example given here, after that xreplace the terms will become `1/sqrt(n)` which is known to be divergent by the p-series test. > (incidentally, that should be Abs) Documentation of `Abs` says: ``` If you pass a SymPy expression to the built-in abs(), it will pass it automatically to Abs(). ``` So I don't think it is necessary to do so. Since `self.function` is going to be a SymPy object. It's not necessary. But using SymPy functions in SymPy expressions is a natural thing to do, and it saves the round-trip to `abs` and back. Yes, that can be done. If you do so, then do it in other places in `is_convergent` method as well. Just one more thing, I want to comment. With your suggested code-snippet, I guess the output error (in case it occurs), gives sequence term with dummy expression, like this: ``` In [4]: Sum(sin(n)/n, (n, 1, oo)).is_convergent() --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) <ipython-input-4-a000bd8a6a22> in <module>() ----> 1 Sum(sin(n)/n, (n, 1, oo)).is_convergent() NotImplementedError: The algorithm to find the Sum convergence of sin(_n)/_n is not yet implemented ``` Instead of simply `sin(n) / n` should be printed. I know it wouldn't be hard to do that, but I wanted to leave the comment here, in case I don't get to the PR when someone submits :) I'm confident I could find a large number of cases where is_convergent() breaks down is_convergent() seems to always break, I'm not sure what the problem is
[ { "body": "```\r\n>>> Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent()`\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-23-75eab3fe4b66>\", line 1, in <module>\r\n Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent()\r\n\r\n File \".\\sympy\\concrete\\summations.py\", line 557, in is_absolutely_convergent\r\n return Sum(abs(self.function), self.limits).is_convergent()\r\n\r\n File \".\\sympy\\concrete\\summations.py\", line 428, in is_convergent\r\n order = O(sequence_term, (sym, S.Infinity))\r\n\r\n File \".\\sympy\\series\\order.py\", line 217, in __new__\r\n expr = expr.as_leading_term(*args)\r\n\r\n File \".\\sympy\\core\\expr.py\", line 2864, in as_leading_term\r\n obj = self._eval_as_leading_term(x)\r\n\r\n File \".\\sympy\\core\\mul.py\", line 1625, in _eval_as_leading_term\r\n return self.func(*[t.as_leading_term(x) for t in self.args])\r\n\r\n File \".\\sympy\\core\\mul.py\", line 1625, in <listcomp>\r\n return self.func(*[t.as_leading_term(x) for t in self.args])\r\n\r\n File \".\\sympy\\core\\expr.py\", line 2864, in as_leading_term\r\n obj = self._eval_as_leading_term(x)\r\n\r\n File \".\\sympy\\core\\function.py\", line 741, in _eval_as_leading_term\r\n '%s has no _eval_as_leading_term routine' % self.func)\r\n\r\nNotImplementedError: Abs has no _eval_as_leading_term routine\r\n```", "number": 14112, "title": "Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent() not implemented" }, { "body": "```\r\nvar('n')\r\nSum((-1)**(2*n)/n, (n, 1, oo)).is_convergent()\r\n```\r\nresults in\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/lvk/sympy/sympy/concrete/summations.py\", line 489, in is_convergent\r\n maxima = solveset(sequence_term.diff(sym), sym, interval)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 947, in solveset\r\n return _solveset(f, symbol, domain, _check=True)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 763, in _solveset\r\n result += _solve_as_rational(equation, symbol, domain)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 409, in _solve_as_rational\r\n valid_solns = _solveset(g, symbol, domain)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 717, in _solveset\r\n result = Union(*[solver(m, symbol) for m in f.args])\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 717, in <listcomp>\r\n result = Union(*[solver(m, symbol) for m in f.args])\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 696, in <lambda>\r\n solver = lambda f, x, domain=domain: _solveset(f, x, domain)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 736, in _solveset\r\n lhs, rhs_s = inverter(f, 0, symbol)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 701, in <lambda>\r\n inverter = lambda f, rhs, symbol: inverter_func(f, rhs, symbol, domain)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 122, in invert_real\r\n return _invert(f_x, y, x, domain)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 104, in _invert\r\n x1, s = _invert_real(f_x, FiniteSet(y), x)\r\n File \"/home/lvk/sympy/sympy/solvers/solveset.py\", line 201, in _invert_real\r\n s, b = integer_log(rhs, base)\r\n File \"/home/lvk/sympy/sympy/core/power.py\", line 130, in integer_log\r\n raise ValueError('y cannot take value as 0')\r\nValueError: y cannot take value as 0\r\n```", "number": 14152, "title": "ValueError in Sum((-1)**(2*n)/n, (n, 1, oo)).is_convergent()" } ]
98d5dd9b743116b0b967db57d53782e61f63453d
{ "head_commit": "bd51bc3b1a8a83e05b445156221f853a3c586fcc", "head_commit_message": "Added ratio test", "patch_to_review": "diff --git a/sympy/concrete/summations.py b/sympy/concrete/summations.py\nindex 2d6361e0a7c5..52f8825ec1d0 100644\n--- a/sympy/concrete/summations.py\n+++ b/sympy/concrete/summations.py\n@@ -401,6 +401,10 @@ def is_convergent(self):\n lower_limit = -upper_limit\n upper_limit = S.Infinity\n \n+ sym_ = Dummy(sym.name, integer=True, positive=True)\n+ sequence_term = sequence_term.xreplace({sym: sym_})\n+ sym = sym_\n+\n interval = Interval(lower_limit, upper_limit)\n \n # Piecewise function handle\n@@ -429,6 +433,15 @@ def is_convergent(self):\n \n order = O(sequence_term, (sym, S.Infinity))\n \n+ ### ----------- ratio test ---------------- ###\n+ next_sequence_term = sequence_term.xreplace({sym: sym + 1})\n+ ratio = simplify(next_sequence_term/sequence_term)\n+ lim_ratio = limit(ratio, sym, upper_limit)\n+ if (lim_ratio) > 1:\n+ return S.false\n+ if (lim_ratio) < 1:\n+ return S.true\n+\n ### --------- p-series test (1/n**p) ---------- ###\n p1_series_test = order.expr.match(sym**p)\n if p1_series_test is not None:\n@@ -526,6 +539,8 @@ def _dirichlet_test(g_n):\n if dirich2 is not None:\n return dirich2\n \n+ _sym = self.limits[0][0]\n+ sequence_term = sequence_term.xreplace({sym: _sym})\n raise NotImplementedError(\"The algorithm to find the Sum convergence of %s \"\n \"is not yet implemented\" % (sequence_term))\n \ndiff --git a/sympy/concrete/tests/test_sums_products.py b/sympy/concrete/tests/test_sums_products.py\nindex 21534f0f1c93..4f663f4d7862 100644\n--- a/sympy/concrete/tests/test_sums_products.py\n+++ b/sympy/concrete/tests/test_sums_products.py\n@@ -936,7 +936,6 @@ def test_is_convergent():\n \n # root test --\n assert Sum((-12)**n/n, (n, 1, oo)).is_convergent() is S.false\n- assert Sum(2**n/factorial(n), (n, 1, oo)).is_convergent() is S.true\n \n # integral test --\n \n@@ -1010,3 +1009,7 @@ def test_issue_10156():\n e = 2*y*Sum(2*cx*x**2, (x, 1, 9))\n assert e.factor() == \\\n 8*y**3*Sum(x, (x, 1, 3))*Sum(x**2, (x, 1, 9))\n+\n+\n+def test_issue_14112():\n+ assert Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent() is S.false\n" }
[ { "diff_hunk": "@@ -429,6 +433,15 @@ def is_convergent(self):\n \n order = O(sequence_term, (sym, S.Infinity))\n \n+ ### ----------- ratio test ---------------- ###\n+ next_sequence_term = sequence_term.xreplace({sym: sym + 1})\n+ ratio = simplify(next_sequence_term/sequence_term)", "line": null, "original_line": 438, "original_start_line": null, "path": "sympy/concrete/summations.py", "start_line": null, "text": "@user1:\nsimplify should be avoided in favor of more specific operations, as [its own docstring](http://docs.sympy.org/latest/modules/simplify/simplify.html#sympy.simplify.simplify.simplify) says. Here you want to cancel powers of n (that's done by `powsimp`) and the factorials (with `combsimp`). So, use `combsimp(powsimp(next_sequence_term/sequence_term))`" }, { "diff_hunk": "@@ -429,6 +433,15 @@ def is_convergent(self):\n \n order = O(sequence_term, (sym, S.Infinity))\n \n+ ### ----------- ratio test ---------------- ###\n+ next_sequence_term = sequence_term.xreplace({sym: sym + 1})\n+ ratio = simplify(next_sequence_term/sequence_term)\n+ lim_ratio = limit(ratio, sym, upper_limit)\n+ if (lim_ratio) > 1:\n+ return S.false\n+ if (lim_ratio) < 1:", "line": null, "original_line": 442, "original_start_line": null, "path": "sympy/concrete/summations.py", "start_line": null, "text": "@user1:\nIs this test for series with positive terms only? Consider something like `sequence_term = (-1)**n`.\n\n@author:\n`sequence_term = (-1)**n` will be caught by the 'Divergence test` code snippet.\r\nAnd yes, this is intended for positive terms only.\n\n@user1:\nThere should probably be a comment explaining the assumptions for this test for future maintenance. \n\n@author:\nI''ll add a comment about the test.\r\nAre there any problems in the other changes?\n\n@user2:\nIf the ratio is -2, the series diverges but the test will declare it convergent. The limit should be taken of `Abs(next_sequence_term/sequence_term)`\n\n@user2:\nFor a concrete example, here is a wrong result we get because of incorrect ratio test implementation.\r\n```\r\n>>> Sum((-2)**n + (-3)**n, (n, 1, oo)).is_convergent()\r\nTrue\r\n```" } ]
b03869f1be2e725c7e6a992f94de824b575992f1
diff --git a/sympy/concrete/summations.py b/sympy/concrete/summations.py index 2d6361e0a7c5..e4ca7ba44a5b 100644 --- a/sympy/concrete/summations.py +++ b/sympy/concrete/summations.py @@ -16,6 +16,8 @@ from sympy.series.limits import limit from sympy.series.order import O from sympy.sets.sets import FiniteSet +from sympy.simplify.combsimp import combsimp +from sympy.simplify.powsimp import powsimp from sympy.solvers import solve from sympy.solvers.solveset import solveset from sympy.core.compatibility import range @@ -401,6 +403,10 @@ def is_convergent(self): lower_limit = -upper_limit upper_limit = S.Infinity + sym_ = Dummy(sym.name, integer=True, positive=True) + sequence_term = sequence_term.xreplace({sym: sym_}) + sym = sym_ + interval = Interval(lower_limit, upper_limit) # Piecewise function handle @@ -429,6 +435,15 @@ def is_convergent(self): order = O(sequence_term, (sym, S.Infinity)) + ### ----------- ratio test ---------------- ### + next_sequence_term = sequence_term.xreplace({sym: sym + 1}) + ratio = combsimp(powsimp(next_sequence_term/sequence_term)) + lim_ratio = limit(ratio, sym, upper_limit) + if abs(lim_ratio) > 1: + return S.false + if abs(lim_ratio) < 1: + return S.true + ### --------- p-series test (1/n**p) ---------- ### p1_series_test = order.expr.match(sym**p) if p1_series_test is not None: @@ -526,6 +541,8 @@ def _dirichlet_test(g_n): if dirich2 is not None: return dirich2 + _sym = self.limits[0][0] + sequence_term = sequence_term.xreplace({sym: _sym}) raise NotImplementedError("The algorithm to find the Sum convergence of %s " "is not yet implemented" % (sequence_term)) diff --git a/sympy/concrete/tests/test_sums_products.py b/sympy/concrete/tests/test_sums_products.py index 21534f0f1c93..af13f7c4b72a 100644 --- a/sympy/concrete/tests/test_sums_products.py +++ b/sympy/concrete/tests/test_sums_products.py @@ -936,7 +936,6 @@ def test_is_convergent(): # root test -- assert Sum((-12)**n/n, (n, 1, oo)).is_convergent() is S.false - assert Sum(2**n/factorial(n), (n, 1, oo)).is_convergent() is S.true # integral test -- @@ -1010,3 +1009,9 @@ def test_issue_10156(): e = 2*y*Sum(2*cx*x**2, (x, 1, 9)) assert e.factor() == \ 8*y**3*Sum(x, (x, 1, 3))*Sum(x**2, (x, 1, 9)) + + +def test_issue_14112(): + assert Sum((-1)**n/sqrt(n), (n, 1, oo)).is_absolutely_convergent() is S.false + assert Sum((-1)**(2*n)/n, (n, 1, oo)).is_convergent() is S.false + assert Sum((-2)**n + (-3)**n, (n, 1, oo)).is_convergent() is S.false
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
sympy__sympy-13988@425744c
sympy/sympy
Python
13,988
Symbolic Riemann sums for definite integrals
#### References to other Issues or PRs Fixes #12707 #### Brief description of what is fixed or changed Adds a Boolean flag `evaluate` to `as_sum` method, True by default for backward compatibility. Also allows a symbolic number of subintervals to be used, which is both natural and more interesting for a symbolic library: for some simple functions the sum can be evaluated with a general n, showing that it converges to the integral (and at different rates for different rules). Concerning the values at endpoints: by the definition of a Riemann sum, direct evaluation `f(a)` or `f(b)` is performed, not a limit as x->a or x->b. For example, ``` Integral(sign(x), (x, 0, 1)).as_sum(1, 'trapezoid') ``` should be (f(0)+f(1))/2 = 1/2, not 1 as it was returned previously. #### Other comments A link to Wikipedia is updated, as the previously referenced article was merged. Previously, passing oo as the number of subintervals or "qwerty" as the method would raise NotImplementedError. Now it raises ValueError, as is appropriate for absurd/invalid inputs.
2018-01-22T07:22:00Z
Integral.as_sum() should output a Sum() object Currently, Integral.as_sum() outputs an evaluated summation instead of an unevaluated expression: ``` In [1]: import sympy as sm In [2]: t, t0, tf = sm.symbols('t, t0, tf') In [3]: x = sm.Function('x')(t) In [4]: y = sm.Function('y')(t) In [5]: J = sm.Integral((x - y)**2, (t, t0, tf)) In [6]: J.as_sum(20, 'trapezoid') Out[6]: (-t0/20 + tf/20)*((x(t0/20 + 19*tf/20) - y(t0/20 + 19*tf/20))**2 + (x(t0/10 + 9*tf/10) - y(t0/10 + 9*tf/10))**2 + (x(3*t0/20 + 17*tf/20) - y(3*t0/20 + 17*tf/20))**2 + (x(t0/5 + 4*tf/5) - y(t0/5 + 4*tf/5))**2 + (x(t0/4 + 3*tf/4) - y(t0/4 + 3*tf/4))**2 + (x(3*t0/10 + 7*tf/10) - y(3*t0/10 + 7*tf/10))**2 + (x(7*t0/20 + 13*tf/20) - y(7*t0/20 + 13*tf/20))**2 + (x(2*t0/5 + 3*tf/5) - y(2*t0/5 + 3*tf/5))**2 + (x(9*t0/20 + 11*tf/20) - y(9*t0/20 + 11*tf/20))**2 + (x(t0/2 + tf/2) - y(t0/2 + tf/2))**2 + (x(11*t0/20 + 9*tf/20) - y(11*t0/20 + 9*tf/20))**2 + (x(3*t0/5 + 2*tf/5) - y(3*t0/5 + 2*tf/5))**2 + (x(13*t0/20 + 7*tf/20) - y(13*t0/20 + 7*tf/20))**2 + (x(7*t0/10 + 3*tf/10) - y(7*t0/10 + 3*tf/10))**2 + (x(3*t0/4 + tf/4) - y(3*t0/4 + tf/4))**2 + (x(4*t0/5 + tf/5) - y(4*t0/5 + tf/5))**2 + (x(17*t0/20 + 3*tf/20) - y(17*t0/20 + 3*tf/20))**2 + (x(9*t0/10 + tf/10) - y(9*t0/10 + tf/10))**2 + (x(19*t0/20 + tf/20) - y(19*t0/20 + tf/20))**2 + x(t0)**2/2 - x(t0)*y(t0) + x(tf)**2/2 - x(tf)*y(tf) + y(t0)**2/2 + y(tf)**2/2) ``` For large n this takes a long time to compute. It seems like this should output an unevaluated sum and if the user wants to expand the sum they'd call `.doit()` on the result. It may not be worth deprecating this behavior, but maybe we need to have a `as_unevaluated_sum()` method.
Hi @moorepants ! I want to work on this issue . Go for it! @moorepants Any kind of help will be appreciated.I'm new to this organization. Start here: https://github.com/sympy/sympy/wiki/introduction-to-contributing Read the materials, setup your dev environment, and do the tutorial. @moorepants I've read the materials. Coming back to the issue tell me about the changes need to be done and point me to the files to be worked with. Here is the associated code: https://github.com/sympy/sympy/blob/master/sympy/integrals/integrals.py#L993 I suggest seeing if the reviewers would accept a deprecation so that we can change the behavior of `as_sum` but if not you'll need to create a new method. It would also be worth determining if the `Sum` object has duplicate code for the expansions in it's `doit()` method.
[ { "body": "Currently, Integral.as_sum() outputs an evaluated summation instead of an unevaluated expression:\r\n\r\n```\r\nIn [1]: import sympy as sm\r\n\r\nIn [2]: t, t0, tf = sm.symbols('t, t0, tf')\r\n\r\nIn [3]: x = sm.Function('x')(t)\r\n\r\nIn [4]: y = sm.Function('y')(t)\r\n\r\nIn [5]: J = sm.Integral((x - y)**2, (t, t0, tf))\r\n\r\nIn [6]: J.as_sum(20, 'trapezoid')\r\nOut[6]: (-t0/20 + tf/20)*((x(t0/20 + 19*tf/20) - y(t0/20 + 19*tf/20))**2 + (x(t0/10 + 9*tf/10) - y(t0/10 + 9*tf/10))**2 + (x(3*t0/20 + 17*tf/20) - y(3*t0/20 + 17*tf/20))**2 + (x(t0/5 + 4*tf/5) - y(t0/5 + 4*tf/5))**2 + (x(t0/4 + 3*tf/4) - y(t0/4 + 3*tf/4))**2 + (x(3*t0/10 + 7*tf/10) - y(3*t0/10 + 7*tf/10))**2 + (x(7*t0/20 + 13*tf/20) - y(7*t0/20 + 13*tf/20))**2 + (x(2*t0/5 + 3*tf/5) - y(2*t0/5 + 3*tf/5))**2 + (x(9*t0/20 + 11*tf/20) - y(9*t0/20 + 11*tf/20))**2 + (x(t0/2 + tf/2) - y(t0/2 + tf/2))**2 + (x(11*t0/20 + 9*tf/20) - y(11*t0/20 + 9*tf/20))**2 + (x(3*t0/5 + 2*tf/5) - y(3*t0/5 + 2*tf/5))**2 + (x(13*t0/20 + 7*tf/20) - y(13*t0/20 + 7*tf/20))**2 + (x(7*t0/10 + 3*tf/10) - y(7*t0/10 + 3*tf/10))**2 + (x(3*t0/4 + tf/4) - y(3*t0/4 + tf/4))**2 + (x(4*t0/5 + tf/5) - y(4*t0/5 + tf/5))**2 + (x(17*t0/20 + 3*tf/20) - y(17*t0/20 + 3*tf/20))**2 + (x(9*t0/10 + tf/10) - y(9*t0/10 + tf/10))**2 + (x(19*t0/20 + tf/20) - y(19*t0/20 + tf/20))**2 + x(t0)**2/2 - x(t0)*y(t0) + x(tf)**2/2 - x(tf)*y(tf) + y(t0)**2/2 + y(tf)**2/2)\r\n```\r\n\r\nFor large n this takes a long time to compute. It seems like this should output an unevaluated sum and if the user wants to expand the sum they'd call `.doit()` on the result. It may not be worth deprecating this behavior, but maybe we need to have a `as_unevaluated_sum()` method.", "number": 12707, "title": "Integral.as_sum() should output a Sum() object" } ]
219d0b02f3402e158ebd809c8e91420208870467
{ "head_commit": "425744ced4d1ac0a8e7e2662c176c592d0eb8081", "head_commit_message": "Symbolic Riemann sums for definite integrals\n\nFixes #12707\n\nAdds a Boolean flag `evaluate` to `as_sum` method, True by default for\nbackward compatibility.\n\nAlso allows a symbolic number of subintervals to be used, which is both\nnatural and more interesting for a symbolic library: for some simple\nfunctions the sum can be evaluated with a general n, showing that it\nconverges to the integral (and at different rates for different rules).\n\nConcerning the values at endpoints: by the definition of a Riemann sum,\ndirect evaluation `f(a)` or `f(b)` is performed, not a limit as x->a or\nx->b. For example,\n```\nIntegral(sign(x), (x, 0, 1)).as_sum(1, 'trapezoid')\n```\nshould be (f(0)+f(1))/2 = 1/2, not 1 as it was returned previously.\n\nA link to Wikipedia is updated, as the previously referenced article was\nmerged.", "patch_to_review": "diff --git a/sympy/integrals/integrals.py b/sympy/integrals/integrals.py\nindex ea787ea94b80..3e8742828aa6 100644\n--- a/sympy/integrals/integrals.py\n+++ b/sympy/integrals/integrals.py\n@@ -1047,23 +1047,29 @@ def _eval_as_leading_term(self, x):\n break\n return integrate(leading_term, *self.args[1:])\n \n- def as_sum(self, n, method=\"midpoint\"):\n+ def as_sum(self, n, method=\"midpoint\", evaluate=True):\n \"\"\"\n- Approximates the definite integral by a sum.\n+ Approximates a definite integral by a sum.\n \n- method ... one of: left, right, midpoint, trapezoid\n+ Arguments\n+ ---------\n+ n\n+ The number of subintervals to use.\n+ method\n+ One of: 'left', 'right', 'midpoint', 'trapezoid'\n+ evaluate\n+ If False, returns an unevaluated Sum expression. The default\n+ is True, evaluate the sum.\n \n- These are all basically the rectangle method [1], the only difference\n- is where the function value is taken in each interval to define the\n- rectangle.\n+ These methods of approximate integration are described in [1].\n \n- [1] http://en.wikipedia.org/wiki/Rectangle_method\n+ [1] https://en.wikipedia.org/wiki/Riemann_sum#Methods\n \n Examples\n ========\n \n >>> from sympy import sin, sqrt\n- >>> from sympy.abc import x\n+ >>> from sympy.abc import x, n\n >>> from sympy.integrals import Integral\n >>> e = Integral(sin(x), (x, 3, 7))\n >>> e\n@@ -1098,9 +1104,8 @@ def as_sum(self, n, method=\"midpoint\"):\n >>> (e.as_sum(2, 'left') + e.as_sum(2, 'right'))/2 == _\n True\n \n- All but the trapexoid method may be used when dealing with a function\n- with a discontinuity. Here, the discontinuity at x = 0 can be avoided\n- by using the midpoint or right-hand method:\n+ Here, the discontinuity at x = 0 can be avoided by using the\n+ midpoint or right-hand method:\n \n >>> e = Integral(1/sqrt(x), (x, 0, 1))\n >>> e.as_sum(5).n(4)\n@@ -1111,12 +1116,24 @@ def as_sum(self, n, method=\"midpoint\"):\n 2.000\n \n The left- or trapezoid method will encounter the discontinuity and\n- return oo:\n+ return infinity:\n \n >>> e.as_sum(5, 'left')\n- oo\n- >>> e.as_sum(5, 'trapezoid')\n- oo\n+ zoo\n+\n+ The number of intervals can be left unspecified.\n+ >>> e = Integral(x**2, (x, 0, 2))\n+ >>> e.as_sum(n, 'right').expand()\n+ 8/3 + 4/n + 4/(3*n**2)\n+\n+ This shows that the midpoint rule is more accurate, as its error\n+ term decays as the square of n:\n+ >>> e.as_sum(n, 'midpoint').expand()\n+ 8/3 - 2/(3*n**2)\n+\n+ A symbolic sum is returned with evaluate=False:\n+ >>> e.as_sum(n, 'midpoint', evaluate=False)\n+ 2*Sum((2*_k/n - 1/n)**2, (_k, 1, n))/n\n \n See Also\n ========\n@@ -1124,48 +1141,38 @@ def as_sum(self, n, method=\"midpoint\"):\n Integral.doit : Perform the integration using any hints\n \"\"\"\n \n+ from sympy.concrete.summations import Sum\n limits = self.limits\n if len(limits) > 1:\n raise NotImplementedError(\n \"Multidimensional midpoint rule not implemented yet\")\n else:\n limit = limits[0]\n- if len(limit) != 3:\n- raise ValueError(\"Expecting a definite integral.\")\n- if n <= 0:\n- raise ValueError(\"n must be > 0\")\n- if n == oo:\n- raise NotImplementedError(\"Infinite summation not yet implemented\")\n- sym, lower_limit, upper_limit = limit\n- dx = (upper_limit - lower_limit)/n\n-\n- if method == 'trapezoid':\n- l = self.function.limit(sym, lower_limit)\n- r = self.function.limit(sym, upper_limit, \"-\")\n- result = (l + r)/2\n- for i in range(1, n):\n- x = lower_limit + i*dx\n- result += self.function.subs(sym, x)\n- return result*dx\n- elif method not in ('left', 'right', 'midpoint'):\n- raise NotImplementedError(\"Unknown method %s\" % method)\n-\n- result = 0\n- for i in range(n):\n- if method == \"midpoint\":\n- xi = lower_limit + i*dx + dx/2\n- elif method == \"left\":\n- xi = lower_limit + i*dx\n- if i == 0:\n- result = self.function.limit(sym, lower_limit)\n- continue\n- elif method == \"right\":\n- xi = lower_limit + i*dx + dx\n- if i == n:\n- result += self.function.limit(sym, upper_limit, \"-\")\n- continue\n- result += self.function.subs(sym, xi)\n- return result*dx\n+ if (len(limit) != 3 or limit[1].is_finite is False or\n+ limit[2].is_finite is False):\n+ raise ValueError(\"Expecting a definite integral over \"\n+ \"a finite interval.\")\n+ n = sympify(n)\n+ if (n.is_positive is False or n.is_integer is False or\n+ n.is_finite is False):\n+ raise ValueError(\"n must be a positive integer, got %s\" % n)\n+ x, a, b = limit\n+ dx = (b - a)/n\n+ k = Dummy('k', integer=True, positive=True)\n+ f = self.function\n+\n+ if method == \"left\":\n+ result = dx*Sum(f.subs(x, a + (k-1)*dx), (k, 1, n))\n+ elif method == \"right\":\n+ result = dx*Sum(f.subs(x, a + k*dx), (k, 1, n))\n+ elif method == \"midpoint\":\n+ result = dx*Sum(f.subs(x, a + k*dx - dx/2), (k, 1, n))\n+ elif method == \"trapezoid\":\n+ result = dx*((f.subs(x, a) + f.subs(x, b))/2 +\n+ Sum(f.subs(x, a + k*dx), (k, 1, n - 1)))\n+ else:\n+ raise ValueError(\"Unknown method %s\" % method)\n+ return result.doit() if evaluate else result\n \n def _sage_(self):\n import sage.all as sage\ndiff --git a/sympy/integrals/tests/test_integrals.py b/sympy/integrals/tests/test_integrals.py\nindex bdab321a8caa..b13bea5ab5cd 100644\n--- a/sympy/integrals/tests/test_integrals.py\n+++ b/sympy/integrals/tests/test_integrals.py\n@@ -683,10 +683,13 @@ def test_as_sum_midpoint1():\n \n def test_as_sum_midpoint2():\n e = Integral((x + y)**2, (x, 0, 1))\n+ n = Symbol('n', positive=True, integer=True)\n assert e.as_sum(1, method=\"midpoint\").expand() == S(1)/4 + y + y**2\n assert e.as_sum(2, method=\"midpoint\").expand() == S(5)/16 + y + y**2\n assert e.as_sum(3, method=\"midpoint\").expand() == S(35)/108 + y + y**2\n assert e.as_sum(4, method=\"midpoint\").expand() == S(21)/64 + y + y**2\n+ assert e.as_sum(n, method=\"midpoint\").expand() == \\\n+ y**2 + y + 1/3 - 1/(12*n**2)\n \n \n def test_as_sum_left():\n@@ -695,7 +698,9 @@ def test_as_sum_left():\n assert e.as_sum(2, method=\"left\").expand() == S(1)/8 + y/2 + y**2\n assert e.as_sum(3, method=\"left\").expand() == S(5)/27 + 2*y/3 + y**2\n assert e.as_sum(4, method=\"left\").expand() == S(7)/32 + 3*y/4 + y**2\n-\n+ assert e.as_sum(n, method=\"left\").expand() == \\\n+ y**2 + y + S(1)/3 - y/n - 1/(2*n) + 1/(6*n**2)\n+ assert e.as_sum(10, method=\"left\", evaluate=False).has(Sum)\n \n def test_as_sum_right():\n e = Integral((x + y)**2, (x, 0, 1))\n@@ -703,15 +708,27 @@ def test_as_sum_right():\n assert e.as_sum(2, method=\"right\").expand() == S(5)/8 + 3*y/2 + y**2\n assert e.as_sum(3, method=\"right\").expand() == S(14)/27 + 4*y/3 + y**2\n assert e.as_sum(4, method=\"right\").expand() == S(15)/32 + 5*y/4 + y**2\n+ assert e.as_sum(n, method=\"right\").expand() == \\\n+ y**2 + y + S(1)/3 + y/n + 1/(2*n) + 1/(6*n**2)\n+\n \n+def test_as_sum_trapezoid():\n+ e = Integral((x + y)**2, (x, 0, 1))\n+ assert e.as_sum(1, method=\"trapezoid\").expand() == y**2 + y + S(1)/2\n+ assert e.as_sum(2, method=\"trapezoid\").expand() == y**2 + y + S(3)/8\n+ assert e.as_sum(3, method=\"trapezoid\").expand() == y**2 + y + S(19)/54\n+ assert e.as_sum(4, method=\"trapezoid\").expand() == y**2 + y + S(11)/32\n+ assert e.as_sum(n, method=\"trapezoid\").expand() == \\\n+ y**2 + y + S(1)/3 + 1/(6*n**2)\n+ assert Integral(sign(x), (x, 0, 1)).as_sum(1, 'trapezoid') == S(1)/2\n \n def test_as_sum_raises():\n e = Integral((x + y)**2, (x, 0, 1))\n raises(ValueError, lambda: e.as_sum(-1))\n raises(ValueError, lambda: e.as_sum(0))\n raises(ValueError, lambda: Integral(x).as_sum(3))\n- raises(NotImplementedError, lambda: e.as_sum(oo))\n- raises(NotImplementedError, lambda: e.as_sum(3, method='xxxx2'))\n+ raises(ValueError, lambda: e.as_sum(oo))\n+ raises(ValueError, lambda: e.as_sum(3, method='xxxx2'))\n \n \n def test_nested_doit():\n" }
[ { "diff_hunk": "@@ -1047,23 +1047,29 @@ def _eval_as_leading_term(self, x):\n break\n return integrate(leading_term, *self.args[1:])\n \n- def as_sum(self, n, method=\"midpoint\"):\n+ def as_sum(self, n, method=\"midpoint\", evaluate=True):", "line": null, "original_line": 1050, "original_start_line": null, "path": "sympy/integrals/integrals.py", "start_line": null, "text": "@user1:\nHow about using `Symbol('n', integer=True, positive=True)` as default `n` if `n` is not given?\n\n@author:\nYes, it makes sense. But I guess it should be a dummy if a symbol isn't provided, to avoid clashing with any `n` in user's context." } ]
13a358fb8fa57bd7d1a51b770a22e87134f4debd
diff --git a/sympy/integrals/integrals.py b/sympy/integrals/integrals.py index ea787ea94b80..00a59fc11efc 100644 --- a/sympy/integrals/integrals.py +++ b/sympy/integrals/integrals.py @@ -1047,23 +1047,29 @@ def _eval_as_leading_term(self, x): break return integrate(leading_term, *self.args[1:]) - def as_sum(self, n, method="midpoint"): + def as_sum(self, n=None, method="midpoint", evaluate=True): """ - Approximates the definite integral by a sum. + Approximates a definite integral by a sum. - method ... one of: left, right, midpoint, trapezoid + Arguments + --------- + n + The number of subintervals to use, optional. + method + One of: 'left', 'right', 'midpoint', 'trapezoid'. + evaluate + If False, returns an unevaluated Sum expression. The default + is True, evaluate the sum. - These are all basically the rectangle method [1], the only difference - is where the function value is taken in each interval to define the - rectangle. + These methods of approximate integration are described in [1]. - [1] http://en.wikipedia.org/wiki/Rectangle_method + [1] https://en.wikipedia.org/wiki/Riemann_sum#Methods Examples ======== >>> from sympy import sin, sqrt - >>> from sympy.abc import x + >>> from sympy.abc import x, n >>> from sympy.integrals import Integral >>> e = Integral(sin(x), (x, 3, 7)) >>> e @@ -1098,9 +1104,8 @@ def as_sum(self, n, method="midpoint"): >>> (e.as_sum(2, 'left') + e.as_sum(2, 'right'))/2 == _ True - All but the trapexoid method may be used when dealing with a function - with a discontinuity. Here, the discontinuity at x = 0 can be avoided - by using the midpoint or right-hand method: + Here, the discontinuity at x = 0 can be avoided by using the + midpoint or right-hand method: >>> e = Integral(1/sqrt(x), (x, 0, 1)) >>> e.as_sum(5).n(4) @@ -1111,12 +1116,25 @@ def as_sum(self, n, method="midpoint"): 2.000 The left- or trapezoid method will encounter the discontinuity and - return oo: + return infinity: >>> e.as_sum(5, 'left') - oo - >>> e.as_sum(5, 'trapezoid') - oo + zoo + + The number of intervals can be symbolic. If omitted, a dummy symbol + will be used for it. + >>> e = Integral(x**2, (x, 0, 2)) + >>> e.as_sum(n, 'right').expand() + 8/3 + 4/n + 4/(3*n**2) + + This shows that the midpoint rule is more accurate, as its error + term decays as the square of n: + >>> e.as_sum(method='midpoint').expand() + 8/3 - 2/(3*_n**2) + + A symbolic sum is returned with evaluate=False: + >>> e.as_sum(n, 'midpoint', evaluate=False) + 2*Sum((2*_k/n - 1/n)**2, (_k, 1, n))/n See Also ======== @@ -1124,48 +1142,41 @@ def as_sum(self, n, method="midpoint"): Integral.doit : Perform the integration using any hints """ + from sympy.concrete.summations import Sum limits = self.limits if len(limits) > 1: raise NotImplementedError( "Multidimensional midpoint rule not implemented yet") else: limit = limits[0] - if len(limit) != 3: - raise ValueError("Expecting a definite integral.") - if n <= 0: - raise ValueError("n must be > 0") - if n == oo: - raise NotImplementedError("Infinite summation not yet implemented") - sym, lower_limit, upper_limit = limit - dx = (upper_limit - lower_limit)/n - - if method == 'trapezoid': - l = self.function.limit(sym, lower_limit) - r = self.function.limit(sym, upper_limit, "-") - result = (l + r)/2 - for i in range(1, n): - x = lower_limit + i*dx - result += self.function.subs(sym, x) - return result*dx - elif method not in ('left', 'right', 'midpoint'): - raise NotImplementedError("Unknown method %s" % method) - - result = 0 - for i in range(n): - if method == "midpoint": - xi = lower_limit + i*dx + dx/2 - elif method == "left": - xi = lower_limit + i*dx - if i == 0: - result = self.function.limit(sym, lower_limit) - continue - elif method == "right": - xi = lower_limit + i*dx + dx - if i == n: - result += self.function.limit(sym, upper_limit, "-") - continue - result += self.function.subs(sym, xi) - return result*dx + if (len(limit) != 3 or limit[1].is_finite is False or + limit[2].is_finite is False): + raise ValueError("Expecting a definite integral over " + "a finite interval.") + if n is None: + n = Dummy('n', integer=True, positive=True) + else: + n = sympify(n) + if (n.is_positive is False or n.is_integer is False or + n.is_finite is False): + raise ValueError("n must be a positive integer, got %s" % n) + x, a, b = limit + dx = (b - a)/n + k = Dummy('k', integer=True, positive=True) + f = self.function + + if method == "left": + result = dx*Sum(f.subs(x, a + (k-1)*dx), (k, 1, n)) + elif method == "right": + result = dx*Sum(f.subs(x, a + k*dx), (k, 1, n)) + elif method == "midpoint": + result = dx*Sum(f.subs(x, a + k*dx - dx/2), (k, 1, n)) + elif method == "trapezoid": + result = dx*((f.subs(x, a) + f.subs(x, b))/2 + + Sum(f.subs(x, a + k*dx), (k, 1, n - 1))) + else: + raise ValueError("Unknown method %s" % method) + return result.doit() if evaluate else result def _sage_(self): import sage.all as sage diff --git a/sympy/integrals/tests/test_integrals.py b/sympy/integrals/tests/test_integrals.py index bdab321a8caa..b13bea5ab5cd 100644 --- a/sympy/integrals/tests/test_integrals.py +++ b/sympy/integrals/tests/test_integrals.py @@ -683,10 +683,13 @@ def test_as_sum_midpoint1(): def test_as_sum_midpoint2(): e = Integral((x + y)**2, (x, 0, 1)) + n = Symbol('n', positive=True, integer=True) assert e.as_sum(1, method="midpoint").expand() == S(1)/4 + y + y**2 assert e.as_sum(2, method="midpoint").expand() == S(5)/16 + y + y**2 assert e.as_sum(3, method="midpoint").expand() == S(35)/108 + y + y**2 assert e.as_sum(4, method="midpoint").expand() == S(21)/64 + y + y**2 + assert e.as_sum(n, method="midpoint").expand() == \ + y**2 + y + 1/3 - 1/(12*n**2) def test_as_sum_left(): @@ -695,7 +698,9 @@ def test_as_sum_left(): assert e.as_sum(2, method="left").expand() == S(1)/8 + y/2 + y**2 assert e.as_sum(3, method="left").expand() == S(5)/27 + 2*y/3 + y**2 assert e.as_sum(4, method="left").expand() == S(7)/32 + 3*y/4 + y**2 - + assert e.as_sum(n, method="left").expand() == \ + y**2 + y + S(1)/3 - y/n - 1/(2*n) + 1/(6*n**2) + assert e.as_sum(10, method="left", evaluate=False).has(Sum) def test_as_sum_right(): e = Integral((x + y)**2, (x, 0, 1)) @@ -703,15 +708,27 @@ def test_as_sum_right(): assert e.as_sum(2, method="right").expand() == S(5)/8 + 3*y/2 + y**2 assert e.as_sum(3, method="right").expand() == S(14)/27 + 4*y/3 + y**2 assert e.as_sum(4, method="right").expand() == S(15)/32 + 5*y/4 + y**2 + assert e.as_sum(n, method="right").expand() == \ + y**2 + y + S(1)/3 + y/n + 1/(2*n) + 1/(6*n**2) + +def test_as_sum_trapezoid(): + e = Integral((x + y)**2, (x, 0, 1)) + assert e.as_sum(1, method="trapezoid").expand() == y**2 + y + S(1)/2 + assert e.as_sum(2, method="trapezoid").expand() == y**2 + y + S(3)/8 + assert e.as_sum(3, method="trapezoid").expand() == y**2 + y + S(19)/54 + assert e.as_sum(4, method="trapezoid").expand() == y**2 + y + S(11)/32 + assert e.as_sum(n, method="trapezoid").expand() == \ + y**2 + y + S(1)/3 + 1/(6*n**2) + assert Integral(sign(x), (x, 0, 1)).as_sum(1, 'trapezoid') == S(1)/2 def test_as_sum_raises(): e = Integral((x + y)**2, (x, 0, 1)) raises(ValueError, lambda: e.as_sum(-1)) raises(ValueError, lambda: e.as_sum(0)) raises(ValueError, lambda: Integral(x).as_sum(3)) - raises(NotImplementedError, lambda: e.as_sum(oo)) - raises(NotImplementedError, lambda: e.as_sum(3, method='xxxx2')) + raises(ValueError, lambda: e.as_sum(oo)) + raises(ValueError, lambda: e.as_sum(3, method='xxxx2')) def test_nested_doit():
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
sympy__sympy-13974@3cfa337
sympy/sympy
Python
13,974
Adding support for simplifying powers of tensorproducts
This commit adds support for `tensor_product_simp()` to also handle `Pow` expressions with `TensorProduct`s. There is a new function `tensor_product_simp_Pow()` that only evaluates powers whose base is a `TensorProduct`, and `tensor_product_simp_Mul()` has been changed to call `tensor_product_simp_Pow()` when appropriate. Together with the use of `expand(tensorproduct=True)` and `expand_power_base()`, very general expressions can now be evaluated using `tensor_product_simp()`. Fixes #13779 . Also resolves one of the "TODO" items present in a comment in `tensorproduct.py` (comment has been changed), fixes errors being raised inconsistently, and is an alternative solution to the PR #13784 .
2018-01-20T18:25:59Z
Evaluating powers of `TensorProduct` Powers of tensor product expressions are not possible to evaluate with either `expand(tensorproduct=True)` method nor the `tensor_product_simp`function. This is an example session showing the issue ``` In [1]: from sympy import * from sympy.physics.quantum import TensorProduct as tp from sympy.physics.quantum import tensor_product_simp as tps from sympy.physics.paulialgebra import Pauli a = Symbol('a', commutative=False) In [2]: t1 = tp(1,1)*tp(1,1) t1 Out[2]: 1x1**2 In [3]: tps(t1) Out[3]: 1x1**2 In [4]: t1.expand(tensorproduct=True) Out[4]: 1x1**2 In [5]: tps(tp(1,1)*tp(1,a)).subs(a, 1) Out[5]: 1x1 In [6]: t2 = tp(1,Pauli(3))*tp(1,Pauli(3)) t2 Out[6]: 1xsigma3**2 In [7]: tps(t2) Out[7]: 1xsigma3**2 In [8]: t2.expand(tensorproduct=True) Out[8]: 1xsigma3**2 In [9]: tps(tp(1,Pauli(3))*tp(1,a)).subs(a, Pauli(3)) Out[9]: 1x1 ``` where `[5]` and `[9]` shows expected result for `t1` and `t2` respectively.
I would like to work on it. Can you please guide me as to how I should proceed? @ArighnaIITG The file would be `sympy/physics/quantum/tensorproduct.py`, in which you can see that `tensor_product_simp` acts differently depending on the argument. Just as there is a `tensor_product_simp_Mul` when the argument is `Mul`, I would write a `tensor_product_simp_Pow` when the argument is `Pow` (replacing the line that is already there for `Pow`). This function should simply take the exponent that is over the tensor product and apply that to each argument in the tensor product. That is, in some form of pseudo-expression: ``` In []: tps(tp(a, b, c, ...)**n) Out[]: tp(a**n, b**n, c**n, ...) ```
[ { "body": "Powers of tensor product expressions are not possible to evaluate with either `expand(tensorproduct=True)` method nor the `tensor_product_simp`function.\r\n\r\nThis is an example session showing the issue\r\n```\r\nIn [1]: from sympy import *\r\n from sympy.physics.quantum import TensorProduct as tp\r\n from sympy.physics.quantum import tensor_product_simp as tps\r\n from sympy.physics.paulialgebra import Pauli\r\n a = Symbol('a', commutative=False)\r\n\r\nIn [2]: t1 = tp(1,1)*tp(1,1)\r\n t1\r\nOut[2]: 1x1**2\r\n\r\nIn [3]: tps(t1)\r\nOut[3]: 1x1**2\r\n\r\nIn [4]: t1.expand(tensorproduct=True)\r\nOut[4]: 1x1**2\r\n\r\nIn [5]: tps(tp(1,1)*tp(1,a)).subs(a, 1)\r\nOut[5]: 1x1\r\n\r\nIn [6]: t2 = tp(1,Pauli(3))*tp(1,Pauli(3))\r\n t2\r\nOut[6]: 1xsigma3**2\r\n\r\nIn [7]: tps(t2)\r\nOut[7]: 1xsigma3**2\r\n\r\nIn [8]: t2.expand(tensorproduct=True)\r\nOut[8]: 1xsigma3**2\r\n\r\nIn [9]: tps(tp(1,Pauli(3))*tp(1,a)).subs(a, Pauli(3))\r\nOut[9]: 1x1\r\n```\r\nwhere `[5]` and `[9]` shows expected result for `t1` and `t2` respectively.", "number": 13779, "title": "Evaluating powers of `TensorProduct`" } ]
84c125972ad535b2dfb245f8d311d347b45e5b8a
{ "head_commit": "3cfa3377eec97cdfbf55b8dd70a7472576ef28d6", "head_commit_message": "Tests for `tensor_product_simp()` acting on expressions with powers\n\nNon-trivial tests for expressions mixing powers and tensorproducts", "patch_to_review": "diff --git a/sympy/physics/quantum/tensorproduct.py b/sympy/physics/quantum/tensorproduct.py\nindex 9dd10d219e43..b861d9a12ca1 100644\n--- a/sympy/physics/quantum/tensorproduct.py\n+++ b/sympy/physics/quantum/tensorproduct.py\n@@ -18,6 +18,8 @@\n matrix_tensor_product\n )\n \n+from sympy import srepr\n+\n __all__ = [\n 'TensorProduct',\n 'tensor_product_simp'\n@@ -309,19 +311,27 @@ def tensor_product_simp_Mul(e):\n (A*C)x(B*D)\n \n \"\"\"\n- # TODO: This won't work with Muls that have other composites of\n- # TensorProducts, like an Add, Pow, Commutator, etc.\n+ # TODO: This don't work with Muls that have other composites of\n+ # TensorProducts, like an Add, Commutator, etc.\n # TODO: This only works for the equivalent of single Qbit gates.\n if not isinstance(e, Mul):\n return e\n c_part, nc_part = e.args_cnc()\n n_nc = len(nc_part)\n- if n_nc == 0 or n_nc == 1:\n+ if n_nc == 0:\n+ return e\n+ elif n_nc == 1:\n+ if isinstance(nc_part[0], Pow):\n+ return Mul(*c_part) * tensor_product_simp_Pow(nc_part[0])\n return e\n elif e.has(TensorProduct):\n current = nc_part[0]\n if not isinstance(current, TensorProduct):\n- raise TypeError('TensorProduct expected, got: %r' % current)\n+ if isinstance(current, Pow):\n+ if isinstance(current.base, TensorProduct):\n+ current = tensor_product_simp_Pow(current)\n+ else:\n+ raise TypeError('TensorProduct expected, got: %r' % current)\n n_terms = len(current.args)\n new_args = list(current.args)\n for next in nc_part[1:]:\n@@ -335,15 +345,32 @@ def tensor_product_simp_Mul(e):\n for i in range(len(new_args)):\n new_args[i] = new_args[i] * next.args[i]\n else:\n- # this won't quite work as we don't want next in the\n- # TensorProduct\n- for i in range(len(new_args)):\n- new_args[i] = new_args[i] * next\n+ if isinstance(next, Pow):\n+ if isinstance(next.base, TensorProduct):\n+ new_tp = tensor_product_simp_Pow(next)\n+ for i in range(len(new_args)):\n+ new_args[i] = new_args[i] * new_tp.args[i]\n+ else:\n+ raise TypeError('TensorProduct expected, got: %r' % next)\n+ else:\n+ raise TypeError('TensorProduct expected, got: %r' % next)\n current = next\n return Mul(*c_part) * TensorProduct(*new_args)\n+ elif e.has(Pow):\n+ new_args = [ tensor_product_simp_Pow(nc) for nc in nc_part ]\n+ return tensor_product_simp_Mul(Mul(*c_part) * TensorProduct(*new_args))\n else:\n return e\n \n+def tensor_product_simp_Pow(e):\n+ \"\"\"Evaluates ``Pow`` expressions whose base is ``TensorProduct``\"\"\"\n+ if not isinstance(e, Pow):\n+ return e\n+\n+ if isinstance(e.base, TensorProduct):\n+ return TensorProduct(*[ b**e.exp for b in e.base.args])\n+ else:\n+ return e\n \n def tensor_product_simp(e, **hints):\n \"\"\"Try to simplify and combine TensorProducts.\n@@ -382,7 +409,10 @@ def tensor_product_simp(e, **hints):\n if isinstance(e, Add):\n return Add(*[tensor_product_simp(arg) for arg in e.args])\n elif isinstance(e, Pow):\n- return tensor_product_simp(e.base) ** e.exp\n+ if isinstance(e.base, TensorProduct):\n+ return tensor_product_simp_Pow(e)\n+ else:\n+ return tensor_product_simp(e.base) ** e.exp\n elif isinstance(e, Mul):\n return tensor_product_simp_Mul(e)\n elif isinstance(e, Commutator):\ndiff --git a/sympy/physics/quantum/tests/test_tensorproduct.py b/sympy/physics/quantum/tests/test_tensorproduct.py\nindex 9f65844c85d6..c8955a08f164 100644\n--- a/sympy/physics/quantum/tests/test_tensorproduct.py\n+++ b/sympy/physics/quantum/tests/test_tensorproduct.py\n@@ -10,7 +10,7 @@\n from sympy.physics.quantum.density import Density\n from sympy.core.trace import Tr\n \n-A, B, C = symbols('A,B,C', commutative=False)\n+A, B, C, D = symbols('A,B,C,D', commutative=False)\n x = symbols('x')\n \n mat1 = Matrix([[1, 2*I], [1 + I, 3]])\n@@ -47,6 +47,11 @@ def test_tensor_product_commutator():\n \n def test_tensor_product_simp():\n assert tensor_product_simp(TP(A, B)*TP(B, C)) == TP(A*B, B*C)\n+ # tests for Pow-expressions\n+ assert tensor_product_simp(TP(A, B)**x) == TP(A**x, B**x)\n+ assert tensor_product_simp(x*TP(A, B)**2) == x*TP(A**2,B**2)\n+ assert tensor_product_simp(x*(TP(A, B)**2)*TP(C,D)) == x*TP(A**2*C,B**2*D)\n+ assert tensor_product_simp(TP(A,B)-TP(C,D)**x) == TP(A,B)-TP(C**x,D**x)\n \n \n def test_issue_5923():\n" }
[ { "diff_hunk": "@@ -309,19 +311,27 @@ def tensor_product_simp_Mul(e):\n (A*C)x(B*D)\n \n \"\"\"\n- # TODO: This won't work with Muls that have other composites of\n- # TensorProducts, like an Add, Pow, Commutator, etc.\n+ # TODO: This don't work with Muls that have other composites of", "line": null, "original_line": 314, "original_start_line": null, "path": "sympy/physics/quantum/tensorproduct.py", "start_line": null, "text": "@user1:\nI don't think that this change (won't -> don't) should be done.\n\n@author:\nThat was a mistake... sorry about that, should have read the diffs more carefully before committing. Will change." }, { "diff_hunk": "@@ -18,6 +18,8 @@\n matrix_tensor_product\n )\n \n+from sympy import srepr", "line": null, "original_line": 21, "original_start_line": null, "path": "sympy/physics/quantum/tensorproduct.py", "start_line": null, "text": "@user1:\nThis seems to be not used.\n\n@author:\nAh, sorry. Needed it for debugging. Will remove." } ]
c2360561e9fe2119791149f5b097bd860e0ab4cc
diff --git a/sympy/physics/quantum/tensorproduct.py b/sympy/physics/quantum/tensorproduct.py index 9dd10d219e43..3ffda734c2ec 100644 --- a/sympy/physics/quantum/tensorproduct.py +++ b/sympy/physics/quantum/tensorproduct.py @@ -18,6 +18,7 @@ matrix_tensor_product ) + __all__ = [ 'TensorProduct', 'tensor_product_simp' @@ -310,18 +311,26 @@ def tensor_product_simp_Mul(e): """ # TODO: This won't work with Muls that have other composites of - # TensorProducts, like an Add, Pow, Commutator, etc. + # TensorProducts, like an Add, Commutator, etc. # TODO: This only works for the equivalent of single Qbit gates. if not isinstance(e, Mul): return e c_part, nc_part = e.args_cnc() n_nc = len(nc_part) - if n_nc == 0 or n_nc == 1: + if n_nc == 0: + return e + elif n_nc == 1: + if isinstance(nc_part[0], Pow): + return Mul(*c_part) * tensor_product_simp_Pow(nc_part[0]) return e elif e.has(TensorProduct): current = nc_part[0] if not isinstance(current, TensorProduct): - raise TypeError('TensorProduct expected, got: %r' % current) + if isinstance(current, Pow): + if isinstance(current.base, TensorProduct): + current = tensor_product_simp_Pow(current) + else: + raise TypeError('TensorProduct expected, got: %r' % current) n_terms = len(current.args) new_args = list(current.args) for next in nc_part[1:]: @@ -335,15 +344,32 @@ def tensor_product_simp_Mul(e): for i in range(len(new_args)): new_args[i] = new_args[i] * next.args[i] else: - # this won't quite work as we don't want next in the - # TensorProduct - for i in range(len(new_args)): - new_args[i] = new_args[i] * next + if isinstance(next, Pow): + if isinstance(next.base, TensorProduct): + new_tp = tensor_product_simp_Pow(next) + for i in range(len(new_args)): + new_args[i] = new_args[i] * new_tp.args[i] + else: + raise TypeError('TensorProduct expected, got: %r' % next) + else: + raise TypeError('TensorProduct expected, got: %r' % next) current = next return Mul(*c_part) * TensorProduct(*new_args) + elif e.has(Pow): + new_args = [ tensor_product_simp_Pow(nc) for nc in nc_part ] + return tensor_product_simp_Mul(Mul(*c_part) * TensorProduct(*new_args)) else: return e +def tensor_product_simp_Pow(e): + """Evaluates ``Pow`` expressions whose base is ``TensorProduct``""" + if not isinstance(e, Pow): + return e + + if isinstance(e.base, TensorProduct): + return TensorProduct(*[ b**e.exp for b in e.base.args]) + else: + return e def tensor_product_simp(e, **hints): """Try to simplify and combine TensorProducts. @@ -382,7 +408,10 @@ def tensor_product_simp(e, **hints): if isinstance(e, Add): return Add(*[tensor_product_simp(arg) for arg in e.args]) elif isinstance(e, Pow): - return tensor_product_simp(e.base) ** e.exp + if isinstance(e.base, TensorProduct): + return tensor_product_simp_Pow(e) + else: + return tensor_product_simp(e.base) ** e.exp elif isinstance(e, Mul): return tensor_product_simp_Mul(e) elif isinstance(e, Commutator): diff --git a/sympy/physics/quantum/tests/test_tensorproduct.py b/sympy/physics/quantum/tests/test_tensorproduct.py index 9f65844c85d6..c8955a08f164 100644 --- a/sympy/physics/quantum/tests/test_tensorproduct.py +++ b/sympy/physics/quantum/tests/test_tensorproduct.py @@ -10,7 +10,7 @@ from sympy.physics.quantum.density import Density from sympy.core.trace import Tr -A, B, C = symbols('A,B,C', commutative=False) +A, B, C, D = symbols('A,B,C,D', commutative=False) x = symbols('x') mat1 = Matrix([[1, 2*I], [1 + I, 3]]) @@ -47,6 +47,11 @@ def test_tensor_product_commutator(): def test_tensor_product_simp(): assert tensor_product_simp(TP(A, B)*TP(B, C)) == TP(A*B, B*C) + # tests for Pow-expressions + assert tensor_product_simp(TP(A, B)**x) == TP(A**x, B**x) + assert tensor_product_simp(x*TP(A, B)**2) == x*TP(A**2,B**2) + assert tensor_product_simp(x*(TP(A, B)**2)*TP(C,D)) == x*TP(A**2*C,B**2*D) + assert tensor_product_simp(TP(A,B)-TP(C,D)**x) == TP(A,B)-TP(C**x,D**x) def test_issue_5923():
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
sympy__sympy-13970@87b5fbd
sympy/sympy
Python
13,970
Convert more Pow expressions to field elements
#### References to other Issues or PRs Fixes #13947 #### Brief description of what is fixed or changed The `FracField._rebuild_expr` method did not try hard enough to express a given Pow in terms of field generators: it just expected the exponent to be integer, and the base be in the field. This missed, for example, that `Z(2**pi)` contains `2**(-pi)`. Now the Pow and ExpBase instances are treated more carefully, by comparing their bases to the bases of Pow or ExpBase among generators, and seeking an integer ratio of exponents.
2018-01-20T07:30:15Z
integrate(a**t /(a**t+1), t) sucess but a**(t-s)/(a**t+1) fail No matter whether `conds='none'`. ``` python import sympy as sm print(sm.__version__) # 1.1.1 from sympy import * a, t, s = symbols('a t s') f = a**t/(a**t+1) integrate(f, t) # >> log(a**t + 1)/log(a) g = a**(t-s)/(a**t+1) integrate(g, t) # hoping : a**(-s) log(a**t + 1)/log(a) # but failed: # CoercionFailed: can't convert exp(-s*log(a)) of type exp to ZZ(log(a),exp(s*log(a))) ``` And the error stack is ``` --------------------------------------------------------------------------- CoercionFailed Traceback (most recent call last) <ipython-input-35-ff749dece9c9> in <module>() ----> 1 integrate(g, t) /usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in integrate(*args, **kwargs) 1293 if isinstance(integral, Integral): 1294 return integral.doit(deep=False, meijerg=meijerg, conds=conds, -> 1295 risch=risch, manual=manual) 1296 else: 1297 return integral /usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in doit(self, **hints) 484 function, xab[0], 485 meijerg=meijerg1, risch=risch, manual=manual, --> 486 conds=conds) 487 if antideriv is None and meijerg1 is True: 488 ret = try_meijerg(function, xab) /usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in _eval_integral(self, f, x, meijerg, risch, manual, conds) 778 if risch is not False: 779 try: --> 780 result, i = risch_integrate(f, x, separate_integral=True, conds=conds) 781 except NotImplementedError: 782 pass /usr/lib/python3.5/site-packages/sympy/integrals/risch.py in risch_integrate(f, x, extension, handle_first, separate_integral, rewrite_complex, conds) 1727 fa, fd = fa.cancel(fd, include=True) 1728 if case == 'exp': -> 1729 ans, i, b = integrate_hyperexponential(fa, fd, DE, conds=conds) 1730 elif case == 'primitive': 1731 ans, i, b = integrate_primitive(fa, fd, DE) /usr/lib/python3.5/site-packages/sympy/integrals/risch.py in integrate_hyperexponential(a, d, DE, z, conds) 1457 1458 g1, h, r = hermite_reduce(a, d, DE) -> 1459 g2, b = residue_reduce(h[0], h[1], DE, z=z) 1460 if not b: 1461 i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) - /usr/lib/python3.5/site-packages/sympy/integrals/risch.py in residue_reduce(a, d, DE, z, invert) 1215 z = z or Dummy('z') 1216 a, d = a.cancel(d, include=True) -> 1217 a, d = a.to_field().mul_ground(1/d.LC()), d.to_field().mul_ground(1/d.LC()) 1218 kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level] 1219 /usr/lib/python3.5/site-packages/sympy/polys/polytools.py in mul_ground(f, coeff) 1204 """ 1205 if hasattr(f.rep, 'mul_ground'): -> 1206 result = f.rep.mul_ground(coeff) 1207 else: # pragma: no cover 1208 raise OperationNotSupported(f, 'mul_ground') /usr/lib/python3.5/site-packages/sympy/polys/polyclasses.py in mul_ground(f, c) 405 def mul_ground(f, c): 406 """Multiply ``f`` by a an element of the ground domain. """ --> 407 return f.per(dmp_mul_ground(f.rep, f.dom.convert(c), f.lev, f.dom)) 408 409 def quo_ground(f, c): /usr/lib/python3.5/site-packages/sympy/polys/domains/domain.py in convert(self, element, base) 156 pass 157 --> 158 raise CoercionFailed("can't convert %s of type %s to %s" % (element, type(element), self)) 159 160 def of_type(self, element): CoercionFailed: can't convert exp(-s*log(a)) of type exp to ZZ(log(a),exp(s*log(a))) ```
I don't know much about `integral` module so I read `integral.py` file and saw we can try to integrate with different methods, namely: `Full Risch algorithm`, `The Meijer G-Function algorithm` and `The "manual integration" algorithm`. While i was getting error with first two, with `manual integration`i got : `>>> integrate(g, t ,manual = 1)` `Piecewise((zoo*Piecewise((zoo*a**t, Eq(log(a), 0)), (log(a**t*log(a) + log(a))/log(a), True)), Eq(a**s, 0)), (a**(-s)*log(a**t + 1)/log(a), True))` Which is even better result than expected I think (because `a = 0` is in the domain so returning integral at that point is not that bad), though I am confused why there is `zoo` instead of `oo` (maybe because `oo โŠ‚ zoo`). So, **if I am correct**, issue reduces to why can't the the other methods cant evaluate the integral and if they cant then why not pass it to another algo ? The error here is a bug. Usually integrate does pass on to the different algorithms until it finds one that works, but if there is an exception that isn't expected, it results in a crash. So the issue here should be to fix the bug. @asmeurer +1. So should it be solved by one best algorithm or just print maximum solved equation from the all possible algorithms as @jashan498 stated. Because I think that most precious and reliable algorithms are had been used in the module already. If these algorithms can't solve it more then it should return as much they can solve. I would like to work on this so can you guide me up? I have the knowledge of some of the integral algorithms that are looking in `integrals.py` BTW `a**(t+s)/(a**t+1)` works so I guess may something about zero detection wrong ? Anyway thank you fix the bug. @asmeurer I would like to fix this bug. Can you please guide me? Looks like the polys code isn't recognizing that `exp(-s*log(a))` is `1/exp(s*log(a))`. A simpler case ```pytb >>> ZZ.frac_field(exp(x))(exp(-x)) Traceback (most recent call last): File "./sympy/polys/fields.py", line 221, in _rebuild return domain.convert(expr) File "./sympy/polys/domains/domain.py", line 145, in convert return self.from_sympy(element) File "./sympy/polys/domains/gmpyintegerring.py", line 39, in from_sympy raise CoercionFailed("expected an integer, got %s" % a) sympy.polys.polyerrors.CoercionFailed: expected an integer, got exp(-x) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "./sympy/polys/fields.py", line 234, in from_expr frac = self._rebuild_expr(expr, mapping) File "./sympy/polys/fields.py", line 228, in _rebuild_expr return _rebuild(sympify(expr)) File "./sympy/polys/fields.py", line 224, in _rebuild return domain.get_field().convert(expr) File "./sympy/polys/domains/domain.py", line 145, in convert return self.from_sympy(element) File "./sympy/polys/domains/gmpyrationalfield.py", line 45, in from_sympy raise CoercionFailed("expected `Rational` object, got %s" % a) sympy.polys.polyerrors.CoercionFailed: expected `Rational` object, got exp(-x) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "./sympy/polys/domains/domain.py", line 84, in __call__ return self.new(*args) File "./sympy/polys/domains/fractionfield.py", line 40, in new return self.field.field_new(element) File "./sympy/polys/fields.py", line 199, in field_new return self.from_expr(element) File "./sympy/polys/fields.py", line 236, in from_expr raise ValueError("expected an expression convertible to a rational function in %s, got %s" % (self, expr)) ValueError: expected an expression convertible to a rational function in Rational function field in exp(x) over ZZ with lex order, got exp(-x) ```
[ { "body": "No matter whether `conds='none'`.\r\n\r\n``` python\r\nimport sympy as sm\r\nprint(sm.__version__)\r\n# 1.1.1\r\nfrom sympy import *\r\na, t, s = symbols('a t s')\r\nf = a**t/(a**t+1)\r\nintegrate(f, t)\r\n# >> log(a**t + 1)/log(a)\r\ng = a**(t-s)/(a**t+1) \r\nintegrate(g, t)\r\n# hoping : a**(-s) log(a**t + 1)/log(a)\r\n# but failed:\r\n# CoercionFailed: can't convert exp(-s*log(a)) of type exp to ZZ(log(a),exp(s*log(a)))\r\n```\r\n\r\nAnd the error stack is\r\n```\r\n---------------------------------------------------------------------------\r\nCoercionFailed Traceback (most recent call last)\r\n<ipython-input-35-ff749dece9c9> in <module>()\r\n----> 1 integrate(g, t)\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in integrate(*args, **kwargs)\r\n 1293 if isinstance(integral, Integral):\r\n 1294 return integral.doit(deep=False, meijerg=meijerg, conds=conds,\r\n-> 1295 risch=risch, manual=manual)\r\n 1296 else:\r\n 1297 return integral\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in doit(self, **hints)\r\n 484 function, xab[0],\r\n 485 meijerg=meijerg1, risch=risch, manual=manual,\r\n--> 486 conds=conds)\r\n 487 if antideriv is None and meijerg1 is True:\r\n 488 ret = try_meijerg(function, xab)\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/integrals.py in _eval_integral(self, f, x, meijerg, risch, manual, conds)\r\n 778 if risch is not False:\r\n 779 try:\r\n--> 780 result, i = risch_integrate(f, x, separate_integral=True, conds=conds)\r\n 781 except NotImplementedError:\r\n 782 pass\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/risch.py in risch_integrate(f, x, extension, handle_first, separate_integral, rewrite_complex, conds)\r\n 1727 fa, fd = fa.cancel(fd, include=True)\r\n 1728 if case == 'exp':\r\n-> 1729 ans, i, b = integrate_hyperexponential(fa, fd, DE, conds=conds)\r\n 1730 elif case == 'primitive':\r\n 1731 ans, i, b = integrate_primitive(fa, fd, DE)\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/risch.py in integrate_hyperexponential(a, d, DE, z, conds)\r\n 1457 \r\n 1458 g1, h, r = hermite_reduce(a, d, DE)\r\n-> 1459 g2, b = residue_reduce(h[0], h[1], DE, z=z)\r\n 1460 if not b:\r\n 1461 i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) -\r\n\r\n/usr/lib/python3.5/site-packages/sympy/integrals/risch.py in residue_reduce(a, d, DE, z, invert)\r\n 1215 z = z or Dummy('z')\r\n 1216 a, d = a.cancel(d, include=True)\r\n-> 1217 a, d = a.to_field().mul_ground(1/d.LC()), d.to_field().mul_ground(1/d.LC())\r\n 1218 kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level]\r\n 1219 \r\n\r\n/usr/lib/python3.5/site-packages/sympy/polys/polytools.py in mul_ground(f, coeff)\r\n 1204 \"\"\"\r\n 1205 if hasattr(f.rep, 'mul_ground'):\r\n-> 1206 result = f.rep.mul_ground(coeff)\r\n 1207 else: # pragma: no cover\r\n 1208 raise OperationNotSupported(f, 'mul_ground')\r\n\r\n/usr/lib/python3.5/site-packages/sympy/polys/polyclasses.py in mul_ground(f, c)\r\n 405 def mul_ground(f, c):\r\n 406 \"\"\"Multiply ``f`` by a an element of the ground domain. \"\"\"\r\n--> 407 return f.per(dmp_mul_ground(f.rep, f.dom.convert(c), f.lev, f.dom))\r\n 408 \r\n 409 def quo_ground(f, c):\r\n\r\n/usr/lib/python3.5/site-packages/sympy/polys/domains/domain.py in convert(self, element, base)\r\n 156 pass\r\n 157 \r\n--> 158 raise CoercionFailed(\"can't convert %s of type %s to %s\" % (element, type(element), self))\r\n 159 \r\n 160 def of_type(self, element):\r\n\r\nCoercionFailed: can't convert exp(-s*log(a)) of type exp to ZZ(log(a),exp(s*log(a)))\r\n```", "number": 13947, "title": "integrate(a**t /(a**t+1), t) sucess but a**(t-s)/(a**t+1) fail " } ]
84c125972ad535b2dfb245f8d311d347b45e5b8a
{ "head_commit": "87b5fbdf2383c035921ed86a91fd348a1674cfec", "head_commit_message": "Convert more Pow expressions to field elements\n\nFixes #13947\n\nThe `FracField._rebuild_expr` method did not try hard enough to express\na given Pow in terms of field generators: it just expected the exponent\nto be integer, and the base be in the field. This missed, for example,\nthat `Z(2**pi)` contains `2**(-pi)`. Now the Pow and ExpBase instances\nare treated more carefully, by comparing their bases to the bases of\nPow or ExpBase among generators, and seeking an integer ratio of exponents.", "patch_to_review": "diff --git a/sympy/integrals/tests/test_risch.py b/sympy/integrals/tests/test_risch.py\nindex 821338146a76..f8b9d29c06a5 100644\n--- a/sympy/integrals/tests/test_risch.py\n+++ b/sympy/integrals/tests/test_risch.py\n@@ -353,6 +353,13 @@ def test_integrate_hyperexponential_returns_piecewise():\n # TODO: Add a test where two different parts of the extension use a\n # Piecewise, like y**x + z**x.\n \n+def test_issue_13947():\n+ a, t, s = symbols('a t s')\n+ assert risch_integrate(2**(-pi)/(2**t + 1), t) == \\\n+ 2**(-pi)*t - 2**(-pi)*log(2**t + 1)/log(2)\n+ assert risch_integrate(a**(t - s)/(a**t + 1), t) == \\\n+ exp(-s*log(a))*log(a**t + 1)/log(a)\n+\n def test_integrate_primitive():\n DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)],\n 'Tfuncs': [log]})\ndiff --git a/sympy/polys/fields.py b/sympy/polys/fields.py\nindex 1917501d57ee..8927724ad738 100644\n--- a/sympy/polys/fields.py\n+++ b/sympy/polys/fields.py\n@@ -6,8 +6,10 @@\n \n from sympy.core.compatibility import is_sequence, reduce, string_types\n from sympy.core.expr import Expr\n+from sympy.core.mod import Mod\n from sympy.core.symbol import Symbol\n from sympy.core.sympify import CantSympify, sympify\n+from sympy.functions.elementary.exponential import ExpBase\n from sympy.polys.rings import PolyElement\n from sympy.polys.orderings import lex\n from sympy.polys.polyerrors import CoercionFailed\n@@ -204,6 +206,8 @@ def field_new(self, element):\n \n def _rebuild_expr(self, expr, mapping):\n domain = self.domain\n+ powers = tuple((gen, *gen.as_base_exp()) for gen in mapping.keys()\n+ if gen.is_Pow or isinstance(gen, ExpBase))\n \n def _rebuild(expr):\n generator = mapping.get(expr)\n@@ -214,16 +218,24 @@ def _rebuild(expr):\n return reduce(add, list(map(_rebuild, expr.args)))\n elif expr.is_Mul:\n return reduce(mul, list(map(_rebuild, expr.args)))\n- elif expr.is_Pow and expr.exp.is_Integer:\n- return _rebuild(expr.base)**int(expr.exp)\n- else:\n- try:\n- return domain.convert(expr)\n- except CoercionFailed:\n- if not domain.is_Field and domain.has_assoc_Field:\n- return domain.get_field().convert(expr)\n- else:\n- raise\n+ elif expr.is_Pow or isinstance(expr, ExpBase):\n+ b, e = expr.as_base_exp()\n+ # look for bg**eg whose integer power may be b**e\n+ choices = tuple((gen, bg, eg) for gen, bg, eg in powers\n+ if bg == b and Mod(e, eg) == 0)\n+ if len(choices) > 0:\n+ gen, bg, eg = choices[0]\n+ return mapping.get(gen)**(e/eg)\n+ elif e.is_Integer:\n+ return _rebuild(expr.base)**int(expr.exp)\n+\n+ try:\n+ return domain.convert(expr)\n+ except CoercionFailed:\n+ if not domain.is_Field and domain.has_assoc_Field:\n+ return domain.get_field().convert(expr)\n+ else:\n+ raise\n \n return _rebuild(sympify(expr))\n \ndiff --git a/sympy/polys/tests/test_fields.py b/sympy/polys/tests/test_fields.py\nindex 92a5b5659270..eaf849b04177 100644\n--- a/sympy/polys/tests/test_fields.py\n+++ b/sympy/polys/tests/test_fields.py\n@@ -1,6 +1,6 @@\n \"\"\"Test sparse rational functions. \"\"\"\n \n-from sympy.polys.fields import field, sfield, FracField\n+from sympy.polys.fields import field, sfield, FracField, FracElement\n from sympy.polys.rings import ring\n from sympy.polys.domains import ZZ, QQ\n from sympy.polys.orderings import lex\n@@ -129,6 +129,12 @@ def test_FracElement_from_expr():\n raises(ValueError, lambda: F.from_expr(2**x))\n raises(ValueError, lambda: F.from_expr(7*x + sqrt(2)))\n \n+ assert isinstance(ZZ[2**x].get_field().convert(2**(-x)),\n+ FracElement)\n+ assert isinstance(ZZ[x**2].get_field().convert(x**(-6)),\n+ FracElement)\n+\n+\n def test_FracElement__lt_le_gt_ge__():\n F, x, y = field(\"x,y\", ZZ)\n \n" }
[ { "diff_hunk": "@@ -214,16 +218,24 @@ def _rebuild(expr):\n return reduce(add, list(map(_rebuild, expr.args)))\n elif expr.is_Mul:\n return reduce(mul, list(map(_rebuild, expr.args)))\n- elif expr.is_Pow and expr.exp.is_Integer:\n- return _rebuild(expr.base)**int(expr.exp)\n- else:\n- try:\n- return domain.convert(expr)\n- except CoercionFailed:\n- if not domain.is_Field and domain.has_assoc_Field:\n- return domain.get_field().convert(expr)\n- else:\n- raise\n+ elif expr.is_Pow or isinstance(expr, ExpBase):\n+ b, e = expr.as_base_exp()\n+ # look for bg**eg whose integer power may be b**e\n+ choices = tuple((gen, bg, eg) for gen, bg, eg in powers", "line": null, "original_line": 224, "original_start_line": null, "path": "sympy/polys/fields.py", "start_line": null, "text": "@user1:\nThis could have `for gen, (bg, eg) in powers` if `*` is omitted from `*gen.as_base_exp()` above." } ]
95a39c0a3fd5e8af9fbd2f312cd6149e53b3f6f7
diff --git a/sympy/integrals/tests/test_risch.py b/sympy/integrals/tests/test_risch.py index 821338146a76..f8b9d29c06a5 100644 --- a/sympy/integrals/tests/test_risch.py +++ b/sympy/integrals/tests/test_risch.py @@ -353,6 +353,13 @@ def test_integrate_hyperexponential_returns_piecewise(): # TODO: Add a test where two different parts of the extension use a # Piecewise, like y**x + z**x. +def test_issue_13947(): + a, t, s = symbols('a t s') + assert risch_integrate(2**(-pi)/(2**t + 1), t) == \ + 2**(-pi)*t - 2**(-pi)*log(2**t + 1)/log(2) + assert risch_integrate(a**(t - s)/(a**t + 1), t) == \ + exp(-s*log(a))*log(a**t + 1)/log(a) + def test_integrate_primitive(): DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)], 'Tfuncs': [log]}) diff --git a/sympy/polys/fields.py b/sympy/polys/fields.py index 1917501d57ee..9f9fdb49ff12 100644 --- a/sympy/polys/fields.py +++ b/sympy/polys/fields.py @@ -6,8 +6,10 @@ from sympy.core.compatibility import is_sequence, reduce, string_types from sympy.core.expr import Expr +from sympy.core.mod import Mod from sympy.core.symbol import Symbol from sympy.core.sympify import CantSympify, sympify +from sympy.functions.elementary.exponential import ExpBase from sympy.polys.rings import PolyElement from sympy.polys.orderings import lex from sympy.polys.polyerrors import CoercionFailed @@ -204,6 +206,8 @@ def field_new(self, element): def _rebuild_expr(self, expr, mapping): domain = self.domain + powers = tuple((gen, gen.as_base_exp()) for gen in mapping.keys() + if gen.is_Pow or isinstance(gen, ExpBase)) def _rebuild(expr): generator = mapping.get(expr) @@ -214,16 +218,24 @@ def _rebuild(expr): return reduce(add, list(map(_rebuild, expr.args))) elif expr.is_Mul: return reduce(mul, list(map(_rebuild, expr.args))) - elif expr.is_Pow and expr.exp.is_Integer: - return _rebuild(expr.base)**int(expr.exp) - else: - try: - return domain.convert(expr) - except CoercionFailed: - if not domain.is_Field and domain.has_assoc_Field: - return domain.get_field().convert(expr) - else: - raise + elif expr.is_Pow or isinstance(expr, ExpBase): + b, e = expr.as_base_exp() + # look for bg**eg whose integer power may be b**e + choices = tuple((gen, bg, eg) for gen, (bg, eg) in powers + if bg == b and Mod(e, eg) == 0) + if choices: + gen, bg, eg = choices[0] + return mapping.get(gen)**(e/eg) + elif e.is_Integer: + return _rebuild(expr.base)**int(expr.exp) + + try: + return domain.convert(expr) + except CoercionFailed: + if not domain.is_Field and domain.has_assoc_Field: + return domain.get_field().convert(expr) + else: + raise return _rebuild(sympify(expr)) diff --git a/sympy/polys/tests/test_fields.py b/sympy/polys/tests/test_fields.py index 92a5b5659270..eaf849b04177 100644 --- a/sympy/polys/tests/test_fields.py +++ b/sympy/polys/tests/test_fields.py @@ -1,6 +1,6 @@ """Test sparse rational functions. """ -from sympy.polys.fields import field, sfield, FracField +from sympy.polys.fields import field, sfield, FracField, FracElement from sympy.polys.rings import ring from sympy.polys.domains import ZZ, QQ from sympy.polys.orderings import lex @@ -129,6 +129,12 @@ def test_FracElement_from_expr(): raises(ValueError, lambda: F.from_expr(2**x)) raises(ValueError, lambda: F.from_expr(7*x + sqrt(2))) + assert isinstance(ZZ[2**x].get_field().convert(2**(-x)), + FracElement) + assert isinstance(ZZ[x**2].get_field().convert(x**(-6)), + FracElement) + + def test_FracElement__lt_le_gt_ge__(): F, x, y = field("x,y", ZZ)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13915@9f4b5b1
sympy/sympy
Python
13,915
Expression should be NaN when Mul "cancels out infinity"
#### References to other Issues or PRs Fixes #13904 #### Brief description of what is fixed or changed Mul combines powers with identical base, adding their exponents. When this results in zero as exponent, the term is simply dropped, which is wrong to do when the base is infinite. There is already a simple check that suffices for oo/oo (returning NaN) but it does not recognize (x+oo)/(x+oo) as a meaningless expression. A more careful check is added: canceling a term where the base is an Add or Mul with an infinite argument results in NaN. #### Other comments The full analysis of base expression seems impractical for such a basic method as Mul, which needs to be very fast. Many expressions containing oo are in fact finite, like `Pow(2, -oo)` or `periodic_argument(I, oo)`. The check for Add and Mul should be enough for common cases like substitution in a rational function that presently results in an incorrect value: ``` var('a b') r = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b)) r.subs(b, a) ``` The above returns 1, contrary to any interpretation of the formula (the limit as b->a would be -1).
2018-01-13T20:41:07Z
Issue with a substitution that leads to an undefined expression ``` Python 3.6.4 |Anaconda custom (64-bit)| (default, Dec 21 2017, 15:39:08) Type 'copyright', 'credits' or 'license' for more information IPython 6.2.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: from sympy import * In [2]: a,b = symbols('a,b') In [3]: r = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b)) In [4]: r.subs(b,a) Out[4]: 1 In [6]: import sympy In [7]: sympy.__version__ Out[7]: '1.1.1' ``` If b is substituted by a, r is undefined. It is possible to calculate the limit `r.limit(b,a) # -1` But whenever a subexpression of r is undefined, r itself is undefined.
In this regard, don't you think that `r.simplify()` is wrong? It returns `-a/b` which is not correct if b=a. `simplify` works for the generic case. SymPy would be hard to use if getting a+b from `simplify((a**2-b**2)/(a-b))` required an explicit declaration that a is not equal to b. (Besides, there is currently no way to express that declaration to `simplify`, anyway). This is part of reason we avoid `simplify` in code: it can change the outcome in edge cases. The fundamental issue here is: for what kind of expression `expr` do we want expr/expr to return 1? Current behavior: zoo / zoo # nan (zoo + 3) / (zoo + 3) # nan (zoo + a) / (zoo + a) # 1 (zoo + a) / (a - zoo) # 1 because -zoo is zoo (zoo is complex infinity) The rules for combining an expression with its inverse in Mul appear to be too lax. There is a check of the form `if something is S.ComplexInfinity`... which returns nan in the first two cases, but this condition is not met by `zoo + a`. But using something like `numerator.is_finite` would not work either, because most of the time, we don't know if a symbolic expression is finite. E.g., `(a+b).is_finite` is None, unknown, unless the symbols were explicitly declared to be finite. My best idea so far is to have three cases for expr/expr: 1. expr is infinite or 0: return nan 2. Otherwise, if expr contains infinities (how to check this efficiently? Mul needs to be really fast), return expr/expr without combining 3. Otherwise, return 1 "But using something like numerator.is_finite would not work either" I had thought of something like denom.is_zero. If in expr_1/expr_2 the denominator is zero, the fraction is undefined. The only way to get a value from this is to use limits. At least i would think so. My first idea was that sympy first simplifies and then substitutes. But then, the result should be -1. (zoo+a)/(a-zoo) # 1 explains what happens, but i had expected, that zoo/expr leads to nan and expr/zoo leads to nan as well. I agree, that Mul needs to be really fast, but this is about subst. But i confess, i don't know much about symbolic math. zoo/3 is zoo, and 4/zoo is 0. I think it's convenient, and not controversial, to have these. Substitution is not to blame: it replaces b by a as requested, evaluating 1/(a-a) as zoo. This is how `r` becomes `(1/(2*a) + zoo) / (1/(2*a) - zoo)`. So far nothing wrong has happened. The problem is that (because of -zoo being same as zoo) both parts are identified as the same and then the `_gather` helper of Mul method combines the powers 1 and -1 into power 0. And anything to power 0 returns 1 in SymPy, hence the result. I think we should prevent combining powers when base contains Infinity or ComplexInfinity. For example, (x+zoo) / (x+zoo)**2 returning 1 / (x+zoo) isn't right either. I dont really understand what happens. How can i get the result zoo? In my example `r.subs(b,a)` returns ` 1`, but `r.subs(b,-a)` returns `(zoo + 1/(2*a))/(zoo - 1/(2*a))` So how is zoo defined? Is it `(1/z).limit(z,0)`? I get `oo` as result, but how is this related to `zoo`? As far as i know, `zoo` is ComplexInfinity. By playing around, i just found another confusing result: `(zoo+z)/(zoo-z)` returns `(z + zoo)/(-z + zoo)`, but `(z + zoo)/(z-zoo)` returns 1 I just found, `1/S.Zero` returns `zoo`, as well as `(1/S.Zero)**2`. To me, that would mean i should not divide by `zoo`. There are three infinities: positive infinity oo, negative infinity -oo, and complex infinity zoo. Here is the difference: - If z is a positive number that tends to zero, then 1/z tends to oo - if z is a negative number than tends to zero, then 1/z tends to -oo - If z is a complex number that tends to zero, then 1/z tends to zoo The complex infinity zoo does not have a determined sign, so -zoo is taken to be the same as zoo. So when you put `(z + zoo)/(z-zoo)` two things happen: first, z-zoo returns z+zoo (you can check this directly) and second, the two identical expressions are cancelled, leaving 1. However, in (zoo+z)/(zoo-z) the terms are not identical, so they do not cancel. I am considering a solution that returns NaN when Mul cancels an expression with infinity of any kind. So for example (z+zoo)/(z+zoo) and (z-oo)/(z-oo) both return NaN. However, it changes the behavior in a couple of tests, so I have to investigate whether the tests are being wrong about infinities, or something else is. Ok. I think i got it. Thank you for your patient explanation. Maybe one last question. Should `z + zoo` result in `zoo`? I think that would be natural.
[ { "body": "```\r\nPython 3.6.4 |Anaconda custom (64-bit)| (default, Dec 21 2017, 15:39:08) \r\nType 'copyright', 'credits' or 'license' for more information\r\nIPython 6.2.1 -- An enhanced Interactive Python. Type '?' for help.\r\n\r\nIn [1]: from sympy import *\r\n\r\nIn [2]: a,b = symbols('a,b')\r\n\r\nIn [3]: r = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b))\r\n\r\nIn [4]: r.subs(b,a)\r\nOut[4]: 1\r\n\r\nIn [6]: import sympy\r\n\r\nIn [7]: sympy.__version__\r\nOut[7]: '1.1.1'\r\n```\r\n\r\nIf b is substituted by a, r is undefined. It is possible to calculate the limit\r\n`r.limit(b,a) # -1`\r\n\r\nBut whenever a subexpression of r is undefined, r itself is undefined.", "number": 13904, "title": "Issue with a substitution that leads to an undefined expression" } ]
5c1644ff85e15752f9f8721bc142bfbf975e7805
{ "head_commit": "9f4b5b14d60b7c5a0d967f039cb225c7bcb5696b", "head_commit_message": "Expression should be NaN when Mul \"cancels out infinity\"\n\nFixes #13904\n\nMul combines powers with identical base, adding their exponents.\nWhen this results in exponent of 0, the term is simply dropped, which\nis wrong when the base is infinite. There is already a simple check\nthat suffices for oo/oo but does not recognize (x+oo)/(x+oo) as a\nmeaningless expression. A more careful check is added: canceling a\nterm where the base is an Add or Mul with an infinite argument results\nin NaN.\n\nThe full analysis of base expression seems impractical for such a basic\nmethod as Mul, which needs to be very fast. Many expressions containing oo\nare in fact finite, like `Pow(2, -oo)` or `periodic_argument(I, oo)`.\nThe check for Add and Mul should be enough for common cases like substitution\nin a rational function that presently results in an incorrect value:\n```\nvar('a b')\nr = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b))\nr.subs(b, a) # returns 1, contrary to any interpretation of the formula\n```", "patch_to_review": "diff --git a/sympy/core/mul.py b/sympy/core/mul.py\nindex 4f01eeedbdf5..1de8cde76864 100644\n--- a/sympy/core/mul.py\n+++ b/sympy/core/mul.py\n@@ -423,6 +423,11 @@ def _gather(c_powers):\n changed = False\n for b, e in c_powers:\n if e.is_zero:\n+ # canceling out infinities yields NaN\n+ if (b.is_Add or b.is_Mul) and any(infty in b.args \\\n+ for infty in (S.ComplexInfinity, S.Infinity,\n+ S.NegativeInfinity)):\n+ return [S.NaN], [], None\n continue\n if e is S.One:\n if b.is_Number:\ndiff --git a/sympy/core/tests/test_arit.py b/sympy/core/tests/test_arit.py\nindex 0eef31360493..9cc84a725e0e 100644\n--- a/sympy/core/tests/test_arit.py\n+++ b/sympy/core/tests/test_arit.py\n@@ -1,7 +1,7 @@\n from __future__ import division\n \n from sympy import (Basic, Symbol, sin, cos, exp, sqrt, Rational, Float, re, pi,\n- sympify, Add, Mul, Pow, Mod, I, log, S, Max, symbols, oo, Integer,\n+ sympify, Add, Mul, Pow, Mod, I, log, S, Max, symbols, oo, zoo, Integer,\n sign, im, nan, Dummy, factorial, comp, refine\n )\n from sympy.core.compatibility import long, range\n@@ -1937,6 +1937,14 @@ def test_Mul_with_zero_infinite():\n assert e.is_positive is None\n assert e.is_hermitian is None\n \n+def test_Mul_does_not_cancel_infinities():\n+ a, b = symbols('a b')\n+ assert ((zoo + 3*a)/(3*a + zoo)) is nan\n+ assert ((b - oo)/(b - oo)) is nan\n+ # issue 13904\n+ expr = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b))\n+ assert expr.subs(b, a) is nan\n+\n def test_issue_8247_8354():\n from sympy import tan\n z = sqrt(1 + sqrt(3)) + sqrt(3 + 3*sqrt(3)) - sqrt(10 + 6*sqrt(3))\n" }
[ { "diff_hunk": "@@ -423,6 +423,11 @@ def _gather(c_powers):\n changed = False\n for b, e in c_powers:\n if e.is_zero:\n+ # canceling out infinities yields NaN\n+ if (b.is_Add or b.is_Mul) and any(infty in b.args \\", "line": null, "original_line": 427, "original_start_line": null, "path": "sympy/core/mul.py", "start_line": null, "text": "@user1:\nCould `b` itself be one of the infinities, not an Add or Mul?\r\n\r\nBackslash is not needed for continuation here. (PEP 8: Long lines can be broken over multiple lines by wrapping expressions in parentheses. These should be used in preference to using a backslash for line continuation.)\n\n@author:\nIf an infinity is divided by infinity, or multiplied by 0, this code is not reached: the checks returning NaN in those cases are in `__mul__` and `__div__` methods of the Infinity classes, implemented in numbers.py ([Example](https://github.com/sympy/sympy/blob/master/sympy/core/numbers.py#L2753)).\r\n\r\nI will get rid of the backslash" } ]
5ff00e9352ae999bc7607beffd974c1e97256c9a
diff --git a/sympy/core/mul.py b/sympy/core/mul.py index 4f01eeedbdf5..29e6085ce387 100644 --- a/sympy/core/mul.py +++ b/sympy/core/mul.py @@ -423,6 +423,11 @@ def _gather(c_powers): changed = False for b, e in c_powers: if e.is_zero: + # canceling out infinities yields NaN + if (b.is_Add or b.is_Mul) and any(infty in b.args + for infty in (S.ComplexInfinity, S.Infinity, + S.NegativeInfinity)): + return [S.NaN], [], None continue if e is S.One: if b.is_Number: diff --git a/sympy/core/tests/test_arit.py b/sympy/core/tests/test_arit.py index 0eef31360493..9cc84a725e0e 100644 --- a/sympy/core/tests/test_arit.py +++ b/sympy/core/tests/test_arit.py @@ -1,7 +1,7 @@ from __future__ import division from sympy import (Basic, Symbol, sin, cos, exp, sqrt, Rational, Float, re, pi, - sympify, Add, Mul, Pow, Mod, I, log, S, Max, symbols, oo, Integer, + sympify, Add, Mul, Pow, Mod, I, log, S, Max, symbols, oo, zoo, Integer, sign, im, nan, Dummy, factorial, comp, refine ) from sympy.core.compatibility import long, range @@ -1937,6 +1937,14 @@ def test_Mul_with_zero_infinite(): assert e.is_positive is None assert e.is_hermitian is None +def test_Mul_does_not_cancel_infinities(): + a, b = symbols('a b') + assert ((zoo + 3*a)/(3*a + zoo)) is nan + assert ((b - oo)/(b - oo)) is nan + # issue 13904 + expr = (1/(a+b) + 1/(a-b))/(1/(a+b) - 1/(a-b)) + assert expr.subs(b, a) is nan + def test_issue_8247_8354(): from sympy import tan z = sqrt(1 + sqrt(3)) + sqrt(3 + 3*sqrt(3)) - sqrt(10 + 6*sqrt(3))
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
streamlit__streamlit-9205@ecf7134
streamlit/streamlit
Python
9,205
Extend dataframe-handling support for collection-like types
## Describe your changes Extends the dataframe-handling support for a variety of (Python-native) collection-like types: - dict.keys() - dict.values() - dict.items() - collections.OrderedDict - collections.defaultdict - collections.Counter - collections.deque - collections.ChainMap - Frozenset - NamedTupel - dataclasses - frozenset - range - TypedDict - (String) Enum - Generator functions - array.array - MappingProxyType instances - UserDict instances - Pyarrow Array - Pandas Array This PR also contains a big refactoring of all the dataframe-handling-related unit tests by centralizing all tests to a single list of test cases that contain many data formats. In addition, this PR also contains a couple of smaller refactorings to better support all these types across all commands. ## GitHub Issue Link (if applicable) - Closes https://github.com/streamlit/streamlit/issues/2737 ## Testing Plan - Updated unit tests --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2024-08-02T15:48:31Z
Support defaultdict as data source for built-in charts ### Problem Charts that accept regular dicts, such as st.bar_chart, throw an error when given a defaultdict, which I would expect to work and just be treated as a normal one. ### Solution **MVP:** Allow the use of a defaultdict by treating it as a dict, ignoring any unset keys. **Possible additions:** Could potentially try to do something to show missing keys as having the default value, if the present keys form a dense, mostly contiguous set. ### Additional context Discovered this problem during my interview --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
[ { "body": "### Problem\r\nCharts that accept regular dicts, such as st.bar_chart, throw an error when given a defaultdict, which I would expect to work and just be treated as a normal one.\r\n\r\n### Solution\r\n\r\n**MVP:** \r\nAllow the use of a defaultdict by treating it as a dict, ignoring any unset keys.\r\n\r\n**Possible additions:**\r\nCould potentially try to do something to show missing keys as having the default value, if the present keys form a dense, mostly contiguous set.\r\n\r\n\r\n### Additional context\r\nDiscovered this problem during my interview\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**", "number": 2737, "title": "Support defaultdict as data source for built-in charts" } ]
8301afe06cfc52cf514f74c7993ad49c6258c791
{ "head_commit": "ecf71342eb0e38609fe8113a5e9d47375268b9f0", "head_commit_message": "Add test for custom dict check", "patch_to_review": "diff --git a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[chromium].png b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[chromium].png\nindex f137b417b521..b6228ac197bf 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[chromium].png and b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[chromium].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[firefox].png b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[firefox].png\nindex ea86118ec2ac..6a2b0a28e583 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[firefox].png and b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[firefox].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[webkit].png b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[webkit].png\nindex 5fb213317126..6e77d431d062 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[webkit].png and b/e2e_playwright/__snapshots__/linux/st_data_editor_input_data_test/st_data_editor-input_data_21[webkit].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[chromium].png b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[chromium].png\nindex aeef84c1d718..67c2ff535aa4 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[chromium].png and b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[chromium].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[firefox].png b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[firefox].png\nindex ddd9c642343a..62083d2941d6 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[firefox].png and b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[firefox].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[webkit].png b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[webkit].png\nindex 7156102c8994..7b1e3426b2c4 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[webkit].png and b/e2e_playwright/__snapshots__/linux/st_dataframe_input_data_test/st_dataframe-input_data_21[webkit].png differ\ndiff --git a/lib/streamlit/dataframe_util.py b/lib/streamlit/dataframe_util.py\nindex ffcf6a286352..dc638e7f8faf 100644\n--- a/lib/streamlit/dataframe_util.py\n+++ b/lib/streamlit/dataframe_util.py\n@@ -17,8 +17,14 @@\n from __future__ import annotations\n \n import contextlib\n+import dataclasses\n+import inspect\n import math\n+import re\n+from collections import ChainMap, UserDict, deque\n+from collections.abc import ItemsView, KeysView, ValuesView\n from enum import Enum, EnumMeta, auto\n+from types import MappingProxyType\n from typing import (\n TYPE_CHECKING,\n Any,\n@@ -34,9 +40,14 @@\n \n from typing_extensions import TypeAlias, TypeGuard\n \n-import streamlit as st\n from streamlit import config, errors, logger, string_util\n-from streamlit.type_util import is_type\n+from streamlit.type_util import (\n+ has_callable_attr,\n+ is_custom_dict,\n+ is_dataclass_instance,\n+ is_namedtuple,\n+ is_type,\n+)\n \n if TYPE_CHECKING:\n import numpy as np\n@@ -51,6 +62,7 @@\n # Maximum number of rows to request from an unevaluated (out-of-core) dataframe\n _MAX_UNEVALUATED_DF_ROWS = 10000\n \n+_PANDAS_DATA_OBJECT_TYPE_RE: Final = re.compile(r\"^pandas.*$\")\n _PANDAS_STYLER_TYPE_STR: Final = \"pandas.io.formats.style.Styler\"\n _SNOWPARK_DF_TYPE_STR: Final = \"snowflake.snowpark.dataframe.DataFrame\"\n _SNOWPARK_DF_ROW_TYPE_STR: Final = \"snowflake.snowpark.row.Row\"\n@@ -61,7 +73,6 @@\n _SNOWPANDAS_DF_TYPE_STR: Final = \"snowflake.snowpark.modin.pandas.dataframe.DataFrame\"\n _SNOWPANDAS_SERIES_TYPE_STR: Final = \"snowflake.snowpark.modin.pandas.series.Series\"\n \n-\n V_co = TypeVar(\n \"V_co\",\n covariant=True, # https://peps.python.org/pep-0484/#covariance-and-contravariance\n@@ -111,9 +122,11 @@ class DataFormat(Enum):\n PANDAS_DATAFRAME = auto() # pd.DataFrame\n PANDAS_SERIES = auto() # pd.Series\n PANDAS_INDEX = auto() # pd.Index\n+ PANDAS_ARRAY = auto() # pd.array\n NUMPY_LIST = auto() # np.array[Scalar]\n NUMPY_MATRIX = auto() # np.array[List[Scalar]]\n PYARROW_TABLE = auto() # pyarrow.Table\n+ PYARROW_ARRAY = auto() # pyarrow.Array\n SNOWPARK_OBJECT = auto() # Snowpark DataFrame, Table, List[Row]\n PYSPARK_OBJECT = auto() # pyspark.DataFrame\n MODIN_OBJECT = auto() # Modin DataFrame, Series\n@@ -136,9 +149,9 @@ def is_dataframe_like(obj: object) -> bool:\n This does not include basic collection types like list, dict, tuple, etc.\n \"\"\"\n \n- if obj is None or isinstance(\n- obj, (list, tuple, set, dict, str, bytes, int, float, bool)\n- ):\n+ # We exclude list and dict here since there are some cases where a list or dict is\n+ # considered a dataframe-like object.\n+ if obj is None or isinstance(obj, (tuple, set, str, bytes, int, float, bool)):\n # Basic types are not considered dataframe-like, so we can\n # return False early to avoid unnecessary checks.\n return False\n@@ -148,13 +161,16 @@ def is_dataframe_like(obj: object) -> bool:\n DataFormat.PANDAS_SERIES,\n DataFormat.PANDAS_INDEX,\n DataFormat.PANDAS_STYLER,\n+ DataFormat.PANDAS_ARRAY,\n DataFormat.NUMPY_LIST,\n DataFormat.NUMPY_MATRIX,\n DataFormat.PYARROW_TABLE,\n+ DataFormat.PYARROW_ARRAY,\n DataFormat.SNOWPARK_OBJECT,\n DataFormat.PYSPARK_OBJECT,\n DataFormat.MODIN_OBJECT,\n DataFormat.SNOWPANDAS_OBJECT,\n+ DataFormat.COLUMN_SERIES_MAPPING,\n ]\n \n \n@@ -166,6 +182,7 @@ def is_unevaluated_data_object(obj: object) -> bool:\n - PySpark DataFrame\n - Modin DataFrame / Series\n - Snowpandas DataFrame / Series\n+ - Generator functions\n \n Unevaluated means that the data is not yet in the local memory.\n Unevaluated data objects are treated differently from other data objects by only\n@@ -176,9 +193,15 @@ def is_unevaluated_data_object(obj: object) -> bool:\n or is_pyspark_data_object(obj)\n or is_snowpandas_data_object(obj)\n or is_modin_data_object(obj)\n+ or inspect.isgeneratorfunction(obj)\n )\n \n \n+def is_pandas_data_object(obj: object) -> bool:\n+ \"\"\"True if obj is a Pandas object (e.g. DataFrame, Series, Index, Styler, ...).\"\"\"\n+ return is_type(obj, _PANDAS_DATA_OBJECT_TYPE_RE)\n+\n+\n def is_snowpark_data_object(obj: object) -> bool:\n \"\"\"True if obj is a Snowpark DataFrame or Table.\"\"\"\n return is_type(obj, _SNOWPARK_TABLE_TYPE_STR) or is_type(obj, _SNOWPARK_DF_TYPE_STR)\n@@ -186,13 +209,12 @@ def is_snowpark_data_object(obj: object) -> bool:\n \n def is_snowpark_row_list(obj: object) -> bool:\n \"\"\"True if obj is a list of snowflake.snowpark.row.Row.\"\"\"\n- if not isinstance(obj, list):\n- return False\n- if len(obj) < 1:\n- return False\n- if not hasattr(obj[0], \"__class__\"):\n- return False\n- return is_type(obj[0], _SNOWPARK_DF_ROW_TYPE_STR)\n+ return (\n+ isinstance(obj, list)\n+ and len(obj) > 0\n+ and is_type(obj[0], _SNOWPARK_DF_ROW_TYPE_STR)\n+ and has_callable_attr(obj[0], \"as_dict\")\n+ )\n \n \n def is_pyspark_data_object(obj: object) -> bool:\n@@ -221,6 +243,78 @@ def is_pandas_styler(obj: object) -> TypeGuard[Styler]:\n return is_type(obj, _PANDAS_STYLER_TYPE_STR)\n \n \n+def _is_list_of_scalars(data: Iterable[Any]) -> bool:\n+ \"\"\"Check if the list only contains scalar values.\"\"\"\n+ from pandas.api.types import infer_dtype\n+\n+ # Overview on all value that are interpreted as scalar:\n+ # https://pandas.pydata.org/docs/reference/api/pandas.api.types.is_scalar.html\n+ return infer_dtype(data, skipna=True) not in [\"mixed\", \"unknown-array\"]\n+\n+\n+def _iterable_to_list(\n+ iterable: Iterable[Any], max_iterations: int | None = None\n+) -> list[Any]:\n+ \"\"\"Convert an iterable to a list.\n+\n+ Parameters\n+ ----------\n+ iterable : Iterable\n+ The iterable to convert to a list.\n+\n+ max_iterations : int or None\n+ The maximum number of iterations to perform. If None, all iterations are performed.\n+\n+ Returns\n+ -------\n+ list\n+ The converted list.\n+ \"\"\"\n+ if max_iterations is None:\n+ return list(iterable)\n+\n+ result = []\n+ for i, item in enumerate(iterable):\n+ if i >= max_iterations:\n+ break\n+ result.append(item)\n+ return result\n+\n+\n+def _fix_column_naming(data_df: DataFrame) -> DataFrame:\n+ \"\"\"Rename the first column to \"value\" if it is not named\n+ and if there is only one column in the dataframe.\n+\n+ The default name of the first column is 0 if it is not named\n+ which is not very descriptive.\n+ \"\"\"\n+\n+ if len(data_df.columns) == 1 and data_df.columns[0] == 0:\n+ # Pandas automatically names the first column with 0 if it is not named.\n+ # We rename it to \"value\" to make it more descriptive if there is only\n+ # one column in the dataframe.\n+ data_df.rename(columns={0: \"value\"}, inplace=True)\n+ return data_df\n+\n+\n+def _dict_to_pandas_df(data: dict[Any, Any]) -> DataFrame:\n+ \"\"\"Convert a key-value dict to a Pandas DataFrame.\n+\n+ Parameters\n+ ----------\n+ data : dict\n+ The dict to convert to a Pandas DataFrame.\n+\n+ Returns\n+ -------\n+ pandas.DataFrame\n+ The converted Pandas DataFrame.\n+ \"\"\"\n+ import pandas as pd\n+\n+ return _fix_column_naming(pd.DataFrame.from_dict(data, orient=\"index\"))\n+\n+\n def convert_anything_to_pandas_df(\n data: Any,\n max_unevaluated_rows: int = _MAX_UNEVALUATED_DF_ROWS,\n@@ -246,81 +340,123 @@ def convert_anything_to_pandas_df(\n pandas.DataFrame\n \n \"\"\"\n+ import array\n+\n import numpy as np\n import pandas as pd\n \n if isinstance(data, pd.DataFrame):\n return data.copy() if ensure_copy else cast(pd.DataFrame, data)\n \n- if isinstance(data, (pd.Series, pd.Index)):\n+ if isinstance(data, (pd.Series, pd.Index, pd.api.extensions.ExtensionArray)):\n return pd.DataFrame(data)\n \n if is_pandas_styler(data):\n return cast(pd.DataFrame, data.data.copy() if ensure_copy else data.data)\n \n if isinstance(data, np.ndarray):\n- return pd.DataFrame([]) if len(data.shape) == 0 else pd.DataFrame(data)\n+ return (\n+ pd.DataFrame([])\n+ if len(data.shape) == 0\n+ else _fix_column_naming(pd.DataFrame(data))\n+ )\n \n if is_modin_data_object(data):\n data = data.head(max_unevaluated_rows)._to_pandas()\n \n- if isinstance(data, pd.Series):\n+ if isinstance(data, (pd.Series, pd.Index)):\n data = data.to_frame()\n \n if data.shape[0] == max_unevaluated_rows:\n- st.caption(\n+ _show_data_information(\n f\"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} \"\n- \"rows. Call `_to_pandas()` on the dataframe to show more.\"\n+ \"rows. Call `_to_pandas()` on the data object to show more.\"\n )\n return cast(pd.DataFrame, data)\n \n if is_pyspark_data_object(data):\n data = data.limit(max_unevaluated_rows).toPandas()\n if data.shape[0] == max_unevaluated_rows:\n- st.caption(\n+ _show_data_information(\n+ f\"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} \"\n+ \"rows. Call `toPandas()` on the data object to show more.\"\n+ )\n+ return cast(pd.DataFrame, data)\n+\n+ if is_snowpandas_data_object(data):\n+ data = data[:max_unevaluated_rows].to_pandas()\n+\n+ if isinstance(data, (pd.Series, pd.Index)):\n+ data = data.to_frame()\n+\n+ if data.shape[0] == max_unevaluated_rows:\n+ _show_data_information(\n f\"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} \"\n- \"rows. Call `toPandas()` on the dataframe to show more.\"\n+ \"rows. Call `to_pandas()` on the data object to show more.\"\n )\n return cast(pd.DataFrame, data)\n \n if is_snowpark_data_object(data):\n data = data.limit(max_unevaluated_rows).to_pandas()\n if data.shape[0] == max_unevaluated_rows:\n- st.caption(\n+ _show_data_information(\n f\"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} \"\n- \"rows. Call `to_pandas()` on the dataframe to show more.\"\n+ \"rows. Call `to_pandas()` on the data object to show more.\"\n )\n return cast(pd.DataFrame, data)\n \n- if is_snowpandas_data_object(data):\n- data = data.head(max_unevaluated_rows).to_pandas()\n+ if is_snowpark_row_list(data):\n+ return pd.DataFrame([row.as_dict() for row in data])\n \n- if isinstance(data, pd.Series):\n- data = data.to_frame()\n+ if has_callable_attr(data, \"to_pandas\"):\n+ return pd.DataFrame(data.to_pandas())\n+\n+ # Support for generator functions\n+ if inspect.isgeneratorfunction(data):\n+ data = _fix_column_naming(\n+ pd.DataFrame(_iterable_to_list(data(), max_iterations=max_unevaluated_rows))\n+ )\n \n if data.shape[0] == max_unevaluated_rows:\n- st.caption(\n+ _show_data_information(\n f\"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} \"\n- \"rows. Call `to_pandas()` on the dataframe to show more.\"\n+ \"rows. Convert the data to a list to show more.\"\n )\n- return cast(pd.DataFrame, data)\n+ return data\n \n- # This is inefficient when data is a pyarrow.Table as it will be converted\n- # back to Arrow when marshalled to protobuf, but area/bar/line charts need\n- # DataFrame magic to generate the correct output.\n- if hasattr(data, \"to_pandas\"):\n- return pd.DataFrame(data.to_pandas())\n+ if isinstance(data, EnumMeta):\n+ # Support for enum classes\n+ return _fix_column_naming(pd.DataFrame([c.value for c in data])) # type: ignore\n+\n+ # Support for some list like objects\n+ if isinstance(data, (deque, map, array.ArrayType)):\n+ return _fix_column_naming(pd.DataFrame(list(data)))\n+\n+ # Support for Streamlit's custom dict-like objects\n+ if is_custom_dict(data):\n+ return _dict_to_pandas_df(data.to_dict())\n+\n+ # Support for named tuples\n+ if is_namedtuple(data):\n+ return _dict_to_pandas_df(data._asdict())\n+\n+ # Support for dataclass instances\n+ if is_dataclass_instance(data):\n+ return _dict_to_pandas_df(dataclasses.asdict(data))\n+\n+ # Support for dict-like objects\n+ if isinstance(data, (ChainMap, MappingProxyType, UserDict)):\n+ return _dict_to_pandas_df(dict(data))\n \n # Try to convert to pandas.DataFrame. This will raise an error is df is not\n # compatible with the pandas.DataFrame constructor.\n try:\n- return pd.DataFrame(data)\n-\n+ return _fix_column_naming(pd.DataFrame(data))\n except ValueError as ex:\n if isinstance(data, dict):\n with contextlib.suppress(ValueError):\n # Try to use index orient as back-up to support key-value dicts\n- return pd.DataFrame.from_dict(data, orient=\"index\")\n+ return _dict_to_pandas_df(data)\n raise errors.StreamlitAPIException(\n f\"\"\"\n Unable to convert object of type `{type(data)}` to `pandas.DataFrame`.\n@@ -419,6 +555,14 @@ def convert_arrow_bytes_to_pandas_df(source: bytes) -> DataFrame:\n return reader.read_pandas()\n \n \n+def _show_data_information(msg: str) -> None:\n+ \"\"\"Show a message to the user with important information\n+ about the processed dataset.\"\"\"\n+ from streamlit.delta_generator import main_dg\n+\n+ main_dg.caption(msg)\n+\n+\n def convert_anything_to_arrow_bytes(\n data: Any,\n max_unevaluated_rows: int = _MAX_UNEVALUATED_DF_ROWS,\n@@ -449,8 +593,16 @@ def convert_anything_to_arrow_bytes(\n if isinstance(data, pa.Table):\n return convert_arrow_table_to_arrow_bytes(data)\n \n+ if is_pandas_data_object(data):\n+ # All pandas data objects should be handled via our pandas\n+ # conversion logic. We are already calling it here\n+ # to ensure that its not handled via the interchange\n+ # protocol support below.\n+ df = convert_anything_to_pandas_df(data, max_unevaluated_rows)\n+ return convert_pandas_df_to_arrow_bytes(df)\n+\n # Fallback: try to convert to pandas DataFrame\n- # and then to Arrow bytes\n+ # and then to Arrow bytes.\n df = convert_anything_to_pandas_df(data, max_unevaluated_rows)\n return convert_pandas_df_to_arrow_bytes(df)\n \n@@ -475,7 +627,9 @@ def convert_anything_to_sequence(obj: OptionSequence[V_co]) -> Sequence[V_co]:\n if obj is None:\n return [] # type: ignore\n \n- if isinstance(obj, (str, list, tuple, set, range, EnumMeta)):\n+ if isinstance(\n+ obj, (str, list, tuple, set, range, EnumMeta, deque, map)\n+ ) and not is_snowpark_row_list(obj):\n # This also ensures that the sequence is copied to prevent\n # potential mutations to the original object.\n return list(obj)\n@@ -569,8 +723,7 @@ def _maybe_truncate_table(\n # we just display the exact numbers.\n displayed_rows = str(table.num_rows)\n total_rows = str(table.num_rows + truncated_rows)\n-\n- st.caption(\n+ _show_data_information(\n f\"โš ๏ธ Showing {displayed_rows} out of {total_rows} \"\n \"rows due to data size limitations.\"\n )\n@@ -579,7 +732,8 @@ def _maybe_truncate_table(\n \n \n def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool:\n- \"\"\"Return True if the column type is known to cause issues during Arrow conversion.\"\"\"\n+ \"\"\"Return True if the column type is known to cause issues during\n+ Arrow conversion.\"\"\"\n from pandas.api.types import infer_dtype, is_dict_like, is_list_like\n \n if column.dtype.kind in [\n@@ -610,7 +764,8 @@ def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool:\n ]:\n return True\n elif inferred_type == \"mixed\":\n- # This includes most of the more complex/custom types (objects, dicts, lists, ...)\n+ # This includes most of the more complex/custom types (objects, dicts,\n+ # lists, ...)\n if len(column) == 0 or not hasattr(column, \"iloc\"):\n # The column seems to be invalid, so we assume it is incompatible.\n # But this would most likely never happen since empty columns\n@@ -622,7 +777,8 @@ def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool:\n \n if (\n not is_list_like(first_value)\n- # dicts are list-like, but have issues in Arrow JS (see comments in Quiver.ts)\n+ # dicts are list-like, but have issues in Arrow JS (see comments in\n+ # Quiver.ts)\n or is_dict_like(first_value)\n # Frozensets are list-like, but are not compatible with pyarrow.\n or isinstance(first_value, frozenset)\n@@ -684,15 +840,6 @@ def fix_arrow_incompatible_column_types(\n return df_copy if df_copy is not None else df\n \n \n-def _is_list_of_scalars(data: Iterable[Any]) -> bool:\n- \"\"\"Check if the list only contains scalar values.\"\"\"\n- from pandas.api.types import infer_dtype\n-\n- # Overview on all value that are interpreted as scalar:\n- # https://pandas.pydata.org/docs/reference/api/pandas.api.types.is_scalar.html\n- return infer_dtype(data, skipna=True) not in [\"mixed\", \"unknown-array\"]\n-\n-\n def determine_data_format(input_data: Any) -> DataFormat:\n \"\"\"Determine the data format of the input data.\n \n@@ -706,6 +853,8 @@ def determine_data_format(input_data: Any) -> DataFormat:\n DataFormat\n The data format of the input data.\n \"\"\"\n+ import array\n+\n import numpy as np\n import pandas as pd\n import pyarrow as pa\n@@ -722,26 +871,43 @@ def determine_data_format(input_data: Any) -> DataFormat:\n return DataFormat.NUMPY_MATRIX\n elif isinstance(input_data, pa.Table):\n return DataFormat.PYARROW_TABLE\n+ elif isinstance(input_data, pa.Array):\n+ return DataFormat.PYARROW_ARRAY\n elif isinstance(input_data, pd.Series):\n return DataFormat.PANDAS_SERIES\n elif isinstance(input_data, pd.Index):\n return DataFormat.PANDAS_INDEX\n elif is_pandas_styler(input_data):\n return DataFormat.PANDAS_STYLER\n- elif is_snowpark_data_object(input_data):\n- return DataFormat.SNOWPARK_OBJECT\n+ elif isinstance(input_data, pd.api.extensions.ExtensionArray):\n+ return DataFormat.PANDAS_ARRAY\n elif is_modin_data_object(input_data):\n return DataFormat.MODIN_OBJECT\n elif is_snowpandas_data_object(input_data):\n return DataFormat.SNOWPANDAS_OBJECT\n elif is_pyspark_data_object(input_data):\n return DataFormat.PYSPARK_OBJECT\n- elif isinstance(input_data, (list, tuple, set)):\n+ elif is_snowpark_data_object(input_data) or is_snowpark_row_list(input_data):\n+ return DataFormat.SNOWPARK_OBJECT\n+ elif isinstance(\n+ input_data, (range, EnumMeta, KeysView, ValuesView, deque, map, array.ArrayType)\n+ ):\n+ return DataFormat.LIST_OF_VALUES\n+ elif (\n+ isinstance(input_data, (ChainMap, MappingProxyType, UserDict))\n+ or is_dataclass_instance(input_data)\n+ or is_namedtuple(input_data)\n+ or is_custom_dict(input_data)\n+ ):\n+ return DataFormat.KEY_VALUE_DICT\n+ elif isinstance(input_data, (ItemsView, enumerate)):\n+ return DataFormat.LIST_OF_ROWS\n+ elif isinstance(input_data, (list, tuple, set, frozenset)):\n if _is_list_of_scalars(input_data):\n # -> one-dimensional data structure\n if isinstance(input_data, tuple):\n return DataFormat.TUPLE_OF_VALUES\n- if isinstance(input_data, set):\n+ if isinstance(input_data, (set, frozenset)):\n return DataFormat.SET_OF_VALUES\n return DataFormat.LIST_OF_VALUES\n else:\n@@ -751,23 +917,23 @@ def determine_data_format(input_data: Any) -> DataFormat:\n first_element = next(iter(input_data))\n if isinstance(first_element, dict):\n return DataFormat.LIST_OF_RECORDS\n- if isinstance(first_element, (list, tuple, set)):\n+ if isinstance(first_element, (list, tuple, set, frozenset)):\n return DataFormat.LIST_OF_ROWS\n elif isinstance(input_data, dict):\n if not input_data:\n return DataFormat.KEY_VALUE_DICT\n if len(input_data) > 0:\n first_value = next(iter(input_data.values()))\n+ # In the future, we could potentially also support tight & split formats\n if isinstance(first_value, dict):\n return DataFormat.COLUMN_INDEX_MAPPING\n if isinstance(first_value, (list, tuple)):\n return DataFormat.COLUMN_VALUE_MAPPING\n if isinstance(first_value, pd.Series):\n return DataFormat.COLUMN_SERIES_MAPPING\n- # In the future, we could potentially also support the tight & split formats here\n- if _is_list_of_scalars(input_data.values()):\n- # Only use the key-value dict format if the values are only scalar values\n- return DataFormat.KEY_VALUE_DICT\n+ # Use key-value dict as fallback. However, if the values of the dict\n+ # contains mixed types, it will become non-editable in the frontend.\n+ return DataFormat.KEY_VALUE_DICT\n return DataFormat.UNKNOWN\n \n \n@@ -783,12 +949,30 @@ def _unify_missing_values(df: DataFrame) -> DataFrame:\n return df.fillna(np.nan).replace([np.nan], [None])\n \n \n+def _pandas_df_to_series(df: DataFrame) -> Series[Any]:\n+ \"\"\"Convert a Pandas DataFrame to a Pandas Series by selecting the first column.\n+\n+ Raises\n+ ------\n+ ValueError\n+ If the DataFrame has more than one column.\n+ \"\"\"\n+ # Select first column in dataframe and create a new series based on the values\n+ if len(df.columns) != 1:\n+ raise ValueError(\n+ \"DataFrame is expected to have a single column but \"\n+ f\"has {len(df.columns)}.\"\n+ )\n+ return df[df.columns[0]]\n+\n+\n def convert_pandas_df_to_data_format(\n df: DataFrame, data_format: DataFormat\n ) -> (\n DataFrame\n | Series[Any]\n | pa.Table\n+ | pa.Array\n | np.ndarray[Any, np.dtype[Any]]\n | tuple[Any]\n | list[Any]\n@@ -818,6 +1002,7 @@ def convert_pandas_df_to_data_format(\n DataFormat.PYSPARK_OBJECT,\n DataFormat.PANDAS_INDEX,\n DataFormat.PANDAS_STYLER,\n+ DataFormat.PANDAS_ARRAY,\n DataFormat.MODIN_OBJECT,\n DataFormat.SNOWPANDAS_OBJECT,\n ]:\n@@ -838,13 +1023,12 @@ def convert_pandas_df_to_data_format(\n import pyarrow as pa\n \n return pa.Table.from_pandas(df)\n+ elif data_format == DataFormat.PYARROW_ARRAY:\n+ import pyarrow as pa\n+\n+ return pa.Array.from_pandas(_pandas_df_to_series(df))\n elif data_format == DataFormat.PANDAS_SERIES:\n- # Select first column in dataframe and create a new series based on the values\n- if len(df.columns) != 1:\n- raise ValueError(\n- f\"DataFrame is expected to have a single column but has {len(df.columns)}.\"\n- )\n- return df[df.columns[0]]\n+ return _pandas_df_to_series(df)\n elif data_format == DataFormat.LIST_OF_RECORDS:\n return _unify_missing_values(df).to_dict(orient=\"records\")\n elif data_format == DataFormat.LIST_OF_ROWS:\n@@ -868,7 +1052,8 @@ def convert_pandas_df_to_data_format(\n return_list = df[df.columns[0]].tolist()\n elif len(df.columns) >= 1:\n raise ValueError(\n- f\"DataFrame is expected to have a single column but has {len(df.columns)}.\"\n+ \"DataFrame is expected to have a single column but \"\n+ f\"has {len(df.columns)}.\"\n )\n if data_format == DataFormat.TUPLE_OF_VALUES:\n return tuple(return_list)\ndiff --git a/lib/streamlit/delta_generator.py b/lib/streamlit/delta_generator.py\nindex 063b5c385997..e05fa0a0c0e6 100644\n--- a/lib/streamlit/delta_generator.py\n+++ b/lib/streamlit/delta_generator.py\n@@ -708,7 +708,8 @@ def _prep_data_for_add_rows(\n ) -> tuple[Data, AddRowsMetadata | None]:\n if not add_rows_metadata:\n if dataframe_util.is_pandas_styler(data):\n- # When calling add_rows on st.table or st.dataframe we want styles to pass through.\n+ # When calling add_rows on st.table or st.dataframe we want styles to\n+ # pass through.\n return data, None\n return dataframe_util.convert_anything_to_pandas_df(data), None\n \ndiff --git a/lib/streamlit/elements/arrow.py b/lib/streamlit/elements/arrow.py\nindex ef978847530a..d5f5b8a96a42 100644\n--- a/lib/streamlit/elements/arrow.py\n+++ b/lib/streamlit/elements/arrow.py\n@@ -513,12 +513,7 @@ def dataframe(\n data_df = dataframe_util.convert_anything_to_pandas_df(\n data, ensure_copy=False\n )\n- apply_data_specific_configs(\n- column_config_mapping,\n- data_df,\n- data_format,\n- check_arrow_compatibility=False,\n- )\n+ apply_data_specific_configs(column_config_mapping, data_format)\n # Serialize the data to bytes:\n proto.data = dataframe_util.convert_pandas_df_to_arrow_bytes(data_df)\n \n@@ -599,8 +594,10 @@ def table(self, data: Data = None) -> DeltaGenerator:\n \n \"\"\"\n \n- # Check if data is uncollected, and collect it but with 100 rows max, instead of 10k rows, which is done in all other cases.\n- # Avoid this and use 100 rows in st.table, because large tables render slowly, take too much screen space, and can crush the app.\n+ # Check if data is uncollected, and collect it but with 100 rows max, instead of\n+ # 10k rows, which is done in all other cases.\n+ # We usse 100 rows in st.table, because large tables render slowly,\n+ # take too much screen space, and can crush the app.\n if dataframe_util.is_unevaluated_data_object(data):\n data = dataframe_util.convert_anything_to_pandas_df(\n data, max_unevaluated_rows=100\ndiff --git a/lib/streamlit/elements/json.py b/lib/streamlit/elements/json.py\nindex 79b2aec908d9..cbcd9bf33053 100644\n--- a/lib/streamlit/elements/json.py\n+++ b/lib/streamlit/elements/json.py\n@@ -15,13 +15,13 @@\n from __future__ import annotations\n \n import json\n+import types\n+from collections import ChainMap, UserDict\n from typing import TYPE_CHECKING, Any, cast\n \n from streamlit.proto.Json_pb2 import Json as JsonProto\n-from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders\n from streamlit.runtime.metrics_util import gather_metrics\n-from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy\n-from streamlit.user_info import UserInfoProxy\n+from streamlit.type_util import is_custom_dict, is_namedtuple\n \n if TYPE_CHECKING:\n from streamlit.delta_generator import DeltaGenerator\n@@ -79,18 +79,18 @@ def json(\n \"\"\"\n import streamlit as st\n \n- if isinstance(\n- body,\n- (\n- SessionStateProxy,\n- UserInfoProxy,\n- QueryParamsProxy,\n- StreamlitHeaders,\n- StreamlitCookies,\n- ),\n- ):\n+ if is_custom_dict(body):\n body = body.to_dict()\n \n+ if is_namedtuple(body):\n+ body = body._asdict()\n+\n+ if isinstance(body, (map, enumerate)):\n+ body = list(body)\n+\n+ if isinstance(body, (ChainMap, types.MappingProxyType, UserDict)):\n+ body = dict(body)\n+\n if not isinstance(body, str):\n try:\n # Serialize body to string and try to interpret sets as lists\ndiff --git a/lib/streamlit/elements/lib/column_config_utils.py b/lib/streamlit/elements/lib/column_config_utils.py\nindex 8e39d3f0f4a8..4c27c47241c7 100644\n--- a/lib/streamlit/elements/lib/column_config_utils.py\n+++ b/lib/streamlit/elements/lib/column_config_utils.py\n@@ -21,7 +21,7 @@\n \n from typing_extensions import TypeAlias\n \n-from streamlit.dataframe_util import DataFormat, is_colum_type_arrow_incompatible\n+from streamlit.dataframe_util import DataFormat\n from streamlit.elements.lib.column_types import ColumnConfig, ColumnType\n from streamlit.elements.lib.dicttools import remove_none_values\n from streamlit.errors import StreamlitAPIException\n@@ -466,9 +466,7 @@ def update_column_config(\n \n def apply_data_specific_configs(\n columns_config: ColumnConfigMapping,\n- data_df: DataFrame,\n data_format: DataFormat,\n- check_arrow_compatibility: bool = False,\n ) -> None:\n \"\"\"Apply data specific configurations to the provided dataframe.\n \n@@ -480,24 +478,9 @@ def apply_data_specific_configs(\n columns_config : ColumnConfigMapping\n A mapping of column names/ids to column configurations.\n \n- data_df : pd.DataFrame\n- The dataframe to apply the configurations to.\n-\n data_format : DataFormat\n The format of the data.\n-\n- check_arrow_compatibility : bool\n- Whether to check if the data is compatible with arrow.\n \"\"\"\n- import pandas as pd\n-\n- # Deactivate editing for columns that are not compatible with arrow\n- if check_arrow_compatibility:\n- for column_name, column_data in data_df.items():\n- if is_colum_type_arrow_incompatible(column_data):\n- update_column_config(columns_config, column_name, {\"disabled\": True})\n- # Convert incompatible type to string\n- data_df[column_name] = column_data.astype(\"string\")\n \n # Pandas adds a range index as default to all datastructures\n # but for most of the non-pandas data objects it is unnecessary\n@@ -514,23 +497,6 @@ def apply_data_specific_configs(\n ]:\n update_column_config(columns_config, INDEX_IDENTIFIER, {\"hidden\": True})\n \n- # Rename the first column to \"value\" for some of the data formats\n- if data_format in [\n- DataFormat.SET_OF_VALUES,\n- DataFormat.TUPLE_OF_VALUES,\n- DataFormat.LIST_OF_VALUES,\n- DataFormat.NUMPY_LIST,\n- DataFormat.KEY_VALUE_DICT,\n- ]:\n- # Pandas automatically names the first column \"0\"\n- # We rename it to \"value\" in selected cases to make it more descriptive\n- data_df.rename(columns={0: \"value\"}, inplace=True)\n-\n- if not isinstance(data_df.index, pd.RangeIndex):\n- # If the index is not a range index, we will configure it as required\n- # since the user is required to provide a (unique) value for editing.\n- update_column_config(columns_config, INDEX_IDENTIFIER, {\"required\": True})\n-\n \n def _convert_column_config_to_json(column_config_mapping: ColumnConfigMapping) -> str:\n try:\ndiff --git a/lib/streamlit/elements/widgets/data_editor.py b/lib/streamlit/elements/widgets/data_editor.py\nindex d9241790b80c..e81996158b54 100644\n--- a/lib/streamlit/elements/widgets/data_editor.py\n+++ b/lib/streamlit/elements/widgets/data_editor.py\n@@ -817,15 +817,31 @@ def data_editor(\n \n # Convert the user provided column config into the frontend compatible format:\n column_config_mapping = process_config_mapping(column_config)\n- apply_data_specific_configs(\n- column_config_mapping, data_df, data_format, check_arrow_compatibility=True\n- )\n+\n+ # Deactivate editing for columns that are not compatible with arrow\n+ for column_name, column_data in data_df.items():\n+ if dataframe_util.is_colum_type_arrow_incompatible(column_data):\n+ update_column_config(\n+ column_config_mapping, column_name, {\"disabled\": True}\n+ )\n+ # Convert incompatible type to string\n+ data_df[column_name] = column_data.astype(\"string\")\n+\n+ apply_data_specific_configs(column_config_mapping, data_format)\n \n # Fix the column headers to work correctly for data editing:\n _fix_column_headers(data_df)\n- # Temporary workaround: We hide range indices if num_rows is dynamic.\n- # since the current way of handling this index during editing is a bit confusing.\n- if isinstance(data_df.index, pd.RangeIndex) and num_rows == \"dynamic\":\n+\n+ if not isinstance(data_df.index, pd.RangeIndex):\n+ # If the index is not a range index, we will configure it as required\n+ # since the user is required to provide a (unique) value for editing.\n+ update_column_config(\n+ column_config_mapping, INDEX_IDENTIFIER, {\"required\": True}\n+ )\n+ elif num_rows == \"dynamic\":\n+ # Temporary workaround: We hide range indices if num_rows is dynamic.\n+ # since the current way of handling this index during editing is a bit\n+ # confusing.\n update_column_config(\n column_config_mapping, INDEX_IDENTIFIER, {\"hidden\": True}\n )\ndiff --git a/lib/streamlit/elements/write.py b/lib/streamlit/elements/write.py\nindex 5046d8fb0394..d06dd9ef4a28 100644\n--- a/lib/streamlit/elements/write.py\n+++ b/lib/streamlit/elements/write.py\n@@ -16,23 +16,20 @@\n \n import dataclasses\n import inspect\n-import json\n import types\n+from collections import ChainMap, UserDict\n from io import StringIO\n from typing import TYPE_CHECKING, Any, Callable, Final, Generator, Iterable, List, cast\n \n from streamlit import dataframe_util, type_util\n from streamlit.errors import StreamlitAPIException\n from streamlit.logger import get_logger\n-from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders\n from streamlit.runtime.metrics_util import gather_metrics\n-from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy\n from streamlit.string_util import (\n is_mem_address_str,\n max_char_sequence,\n probably_contains_html_tags,\n )\n-from streamlit.user_info import UserInfoProxy\n \n if TYPE_CHECKING:\n from streamlit.delta_generator import DeltaGenerator\n@@ -252,7 +249,8 @@ def write(self, *args: Any, unsafe_allow_html: bool = False, **kwargs) -> None:\n - write(string) : Prints the formatted Markdown string, with\n support for LaTeX expression, emoji shortcodes, and colored text.\n See docs for st.markdown for more.\n- - write(data_frame) : Displays the DataFrame as a table.\n+ - write(data_frame) : Displays any dataframe-compatible value\n+ as read-only table.\n - write(error) : Prints an exception specially.\n - write(func) : Displays information about a function.\n - write(module) : Displays information about the module.\n@@ -408,9 +406,7 @@ def flush_buffer():\n elif isinstance(arg, Exception):\n flush_buffer()\n self.dg.exception(arg)\n- elif dataframe_util.is_dataframe_like(\n- arg\n- ) or dataframe_util.is_snowpark_row_list(arg):\n+ elif dataframe_util.is_dataframe_like(arg):\n flush_buffer()\n self.dg.dataframe(arg)\n elif type_util.is_altair_chart(arg):\n@@ -440,23 +436,24 @@ def flush_buffer():\n flush_buffer()\n dot = vis_utils.model_to_dot(arg)\n self.dg.graphviz_chart(dot.to_string())\n- elif isinstance(\n- arg,\n- (\n- dict,\n- list,\n- SessionStateProxy,\n- UserInfoProxy,\n- QueryParamsProxy,\n- StreamlitHeaders,\n- StreamlitCookies,\n- ),\n+ elif (\n+ isinstance(\n+ arg,\n+ (\n+ dict,\n+ list,\n+ map,\n+ enumerate,\n+ types.MappingProxyType,\n+ UserDict,\n+ ChainMap,\n+ ),\n+ )\n+ or type_util.is_custom_dict(arg)\n+ or type_util.is_namedtuple(arg)\n ):\n flush_buffer()\n self.dg.json(arg)\n- elif type_util.is_namedtuple(arg):\n- flush_buffer()\n- self.dg.json(json.dumps(arg._asdict()))\n elif type_util.is_pydeck(arg):\n flush_buffer()\n self.dg.pydeck_chart(arg)\n@@ -482,16 +479,12 @@ def flush_buffer():\n # https://github.com/python/mypy/issues/12933\n self.dg.help(cast(type, arg))\n elif (\n- hasattr(arg, \"_repr_html_\")\n- and callable(arg._repr_html_)\n+ type_util.has_callable_attr(arg, \"_repr_html_\")\n and (repr_html := arg._repr_html_())\n and (unsafe_allow_html or not probably_contains_html_tags(repr_html))\n ):\n # We either explicitly allow HTML or infer it's not HTML\n self.dg.markdown(repr_html, unsafe_allow_html=unsafe_allow_html)\n- elif type_util.is_streamlit_secrets_class(arg):\n- flush_buffer()\n- self.dg.json(arg.to_dict())\n else:\n stringified_arg = str(arg)\n \ndiff --git a/lib/streamlit/runtime/caching/cache_utils.py b/lib/streamlit/runtime/caching/cache_utils.py\nindex 97a86e8654c1..51e2d8a38dcd 100644\n--- a/lib/streamlit/runtime/caching/cache_utils.py\n+++ b/lib/streamlit/runtime/caching/cache_utils.py\n@@ -299,20 +299,23 @@ def _handle_cache_miss(\n try:\n cache.write_result(value_key, computed_value, messages)\n return computed_value\n- except (CacheError, RuntimeError):\n- # An exception was thrown while we tried to write to the cache. Report it to the user.\n- # (We catch `RuntimeError` here because it will be raised by Apache Spark if we do not\n- # collect dataframe before using `st.cache_data`.)\n+ except (CacheError, RuntimeError) as ex:\n+ # An exception was thrown while we tried to write to the cache. Report\n+ # it to the user. (We catch `RuntimeError` here because it will be\n+ # raised by Apache Spark if we do not collect dataframe before\n+ # using `st.cache_data`.)\n if is_unevaluated_data_object(computed_value):\n # If the returned value is an unevaluated dataframe, raise an error.\n # Unevaluated dataframes are not yet in the local memory, which also\n # means they cannot be properly cached (serialized).\n raise UnevaluatedDataFrameError(\n- f\"\"\"\n- The function {get_cached_func_name_md(self._info.func)} is decorated with `st.cache_data` but it returns an unevaluated dataframe\n- of type `{type_util.get_fqn_type(computed_value)}`. Please call `collect()` or `to_pandas()` on the dataframe before returning it,\n- so `st.cache_data` can serialize and cache it.\"\"\"\n- )\n+ f\"The function {get_cached_func_name_md(self._info.func)} is \"\n+ \"decorated with `st.cache_data` but it returns an unevaluated \"\n+ f\"data object of type `{type_util.get_fqn_type(computed_value)}`. \"\n+ \"Please convert the object to a serializable format \"\n+ \"(e.g. Pandas DataFrame) before returning it, so \"\n+ \"`st.cache_data` can serialize and cache it.\"\n+ ) from ex\n raise UnserializableReturnValueError(\n return_value=computed_value, func=self._info.func\n )\ndiff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py\nindex 8f3d169d4894..1985deacb107 100644\n--- a/lib/streamlit/type_util.py\n+++ b/lib/streamlit/type_util.py\n@@ -16,6 +16,7 @@\n \n from __future__ import annotations\n \n+import dataclasses\n import re\n import types\n from typing import (\n@@ -42,7 +43,6 @@\n from plotly.graph_objs import Figure\n from pydeck import Deck\n \n- from streamlit.runtime.secrets import Secrets\n \n T = TypeVar(\"T\")\n \n@@ -51,6 +51,16 @@ class SupportsStr(Protocol):\n def __str__(self) -> str: ...\n \n \n+class CustomDict(Protocol):\n+ \"\"\"Protocol for Streamlit native custom dictionaries (e.g. session state, secrets, query params).\n+ that can be converted to a dict.\n+\n+ All these implementations should provide a to_dict method.\n+ \"\"\"\n+\n+ def to_dict(self) -> dict[str, Any]: ...\n+\n+\n @overload\n def is_type(\n obj: object, fqn_type_pattern: Literal[\"pydeck.bindings.deck.Deck\"]\n@@ -246,15 +256,22 @@ def is_function(x: object) -> TypeGuard[types.FunctionType]:\n return isinstance(x, types.FunctionType)\n \n \n+def has_callable_attr(obj: object, name: str) -> bool:\n+ \"\"\"True if obj has the specified attribute that is callable.\"\"\"\n+ return hasattr(obj, name) and callable(getattr(obj, name))\n+\n+\n def is_namedtuple(x: object) -> TypeGuard[NamedTuple]:\n- t = type(x)\n- b = t.__bases__\n- if len(b) != 1 or b[0] is not tuple:\n- return False\n- f = getattr(t, \"_fields\", None)\n- if not isinstance(f, tuple):\n- return False\n- return all(type(n).__name__ == \"str\" for n in f)\n+ \"\"\"True if obj is an instance of a namedtuple.\"\"\"\n+ return isinstance(x, tuple) and has_callable_attr(x, \"_asdict\")\n+\n+\n+def is_dataclass_instance(obj: object) -> bool:\n+ \"\"\"True if obj is an instance of a dataclass.\"\"\"\n+ # The not isinstance(obj, type) check is needed to make sure that this\n+ # is an instance of a dataclass and not the class itself.\n+ # dataclasses.is_dataclass returns True for either instance or class.\n+ return dataclasses.is_dataclass(obj) and not isinstance(obj, type)\n \n \n def is_pydeck(obj: object) -> TypeGuard[Deck]:\n@@ -262,6 +279,26 @@ def is_pydeck(obj: object) -> TypeGuard[Deck]:\n return is_type(obj, \"pydeck.bindings.deck.Deck\")\n \n \n+def is_custom_dict(obj: object) -> TypeGuard[CustomDict]:\n+ \"\"\"True if input looks like one of the Streamlit custom dictionaries.\"\"\"\n+ from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders\n+ from streamlit.runtime.secrets import Secrets\n+ from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy\n+ from streamlit.user_info import UserInfoProxy\n+\n+ return isinstance(\n+ obj,\n+ (\n+ SessionStateProxy,\n+ UserInfoProxy,\n+ QueryParamsProxy,\n+ StreamlitHeaders,\n+ StreamlitCookies,\n+ Secrets,\n+ ),\n+ ) and has_callable_attr(obj, \"to_dict\")\n+\n+\n def is_iterable(obj: object) -> TypeGuard[Iterable[Any]]:\n try:\n # The ignore statement here is intentional, as this is a\n@@ -272,11 +309,6 @@ def is_iterable(obj: object) -> TypeGuard[Iterable[Any]]:\n return True\n \n \n-def is_streamlit_secrets_class(obj: object) -> TypeGuard[Secrets]:\n- \"\"\"True if obj is a Streamlit Secrets object.\"\"\"\n- return is_type(obj, \"streamlit.runtime.secrets.Secrets\")\n-\n-\n def is_sequence(seq: Any) -> bool:\n \"\"\"True if input looks like a sequence.\"\"\"\n if isinstance(seq, str):\ndiff --git a/lib/tests/streamlit/data_mocks.py b/lib/tests/streamlit/data_mocks.py\nindex 8c26caa0aca0..f1bc83860fcb 100644\n--- a/lib/tests/streamlit/data_mocks.py\n+++ b/lib/tests/streamlit/data_mocks.py\n@@ -14,225 +14,885 @@\n \n from __future__ import annotations\n \n+import array\n import enum\n import random\n+from collections import ChainMap, Counter, OrderedDict, UserDict, defaultdict, deque\n+from dataclasses import dataclass\n from datetime import date\n-from typing import NamedTuple\n+from types import MappingProxyType\n+from typing import Any, Literal, NamedTuple, TypedDict\n \n import numpy as np\n import pandas as pd\n import pyarrow as pa\n \n from streamlit.dataframe_util import DataFormat\n-from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame\n+from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame\n+from tests.streamlit.modin_mocks import Series as ModinSeries\n+from tests.streamlit.pyspark_mocks import DataFrame as PySparkDataFrame\n from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame\n from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries\n from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame\n+from tests.streamlit.snowpark_mocks import Row as SnowparkRow\n from tests.streamlit.snowpark_mocks import Table as SnowparkTable\n \n np.random.seed(0)\n random.seed(0)\n \n \n-class TestCaseMetadata(NamedTuple):\n+class CaseMetadata(NamedTuple):\n+ # The expected number of rows\n expected_rows: int\n+ # The expected number of columns (doesn't include index columns)\n expected_cols: int\n+ # The expected data format\n expected_data_format: DataFormat\n+ # The expected sequence when the data is converted to a sequence\n+ # If None, the sequence is not checked.\n+ expected_sequence: list[Any] | None\n+ # The expected command used when the data is written via `st.write`\n+ expected_write_command: Literal[\n+ \"markdown\", \"dataframe\", \"json\", \"help\", \"write_stream\"\n+ ]\n+ # Whether the data structure is unevaluated and will be truncated\n+ # if it is too large.\n+ is_unevaluated: bool\n+ # The expected return type of the data when it is\n+ # returned from the `st.data_editor` function.\n+ expected_type: type | None = None\n \n- # Tell pytest this is not a TestClass despite having \"Test\" in the name.\n- __test__ = False\n-\n-\n-SHARED_TEST_CASES = [\n- # None:\n- (None, TestCaseMetadata(0, 0, DataFormat.EMPTY)),\n- # Empty list:\n- ([], TestCaseMetadata(0, 0, DataFormat.LIST_OF_VALUES)),\n- # Empty tuple:\n- ((), TestCaseMetadata(0, 0, DataFormat.TUPLE_OF_VALUES)),\n- # Empty dict (not a an empty set!)\n- ({}, TestCaseMetadata(0, 0, DataFormat.KEY_VALUE_DICT)),\n- # Empty set:\n- (set(), TestCaseMetadata(0, 0, DataFormat.SET_OF_VALUES)),\n- # Empty numpy array:\n- # for unknown reasons, pd.DataFrame initializes empty numpy arrays with a single column\n- (np.ndarray(0), TestCaseMetadata(0, 1, DataFormat.NUMPY_LIST)),\n- # Empty column value mapping with columns:\n- ({\"name\": [], \"type\": []}, TestCaseMetadata(0, 2, DataFormat.COLUMN_VALUE_MAPPING)),\n- # Empty dataframe:\n- (pd.DataFrame(), TestCaseMetadata(0, 0, DataFormat.PANDAS_DATAFRAME)),\n- # Empty dataframe with columns:\n+\n+@dataclass\n+class ElementDataClass:\n+ name: str\n+ is_widget: bool\n+ usage: float\n+\n+\n+class ElementNamedTuple(NamedTuple):\n+ name: str\n+ is_widget: bool\n+ usage: float\n+\n+\n+class ElementTypedDict(TypedDict):\n+ name: str\n+ is_widget: bool\n+ usage: float\n+\n+\n+class UserDictExample(UserDict): # type: ignore\n+ pass\n+\n+\n+class TestObject:\n+ def __str__(self):\n+ return \"TestObject\"\n+\n+\n+class StrTestEnum(str, enum.Enum):\n+ NUMBER_INPUT = \"st.number_input\"\n+ TEXT_AREA = \"st.text_area\"\n+ TEXT_INPUT = \"st.text_input\"\n+\n+\n+class TestEnum(enum.Enum):\n+ NUMBER_INPUT = \"st.number_input\"\n+ TEXT_AREA = \"st.text_area\"\n+ TEXT_INPUT = \"st.text_input\"\n+\n+\n+def data_generator():\n+ yield \"st.number_input\"\n+ yield \"st.text_area\"\n+ yield \"st.text_input\"\n+\n+\n+SHARED_TEST_CASES: list[tuple[str, Any, CaseMetadata]] = [\n+ ###################################\n+ ####### Native Python Types #######\n+ ###################################\n (\n- pd.DataFrame(\n- columns=[\"name\", \"type\"], index=pd.RangeIndex(start=0, step=1)\n- ), # Explicitly set the range index to have the same behavior across versions\n- TestCaseMetadata(0, 2, DataFormat.PANDAS_DATAFRAME),\n+ \"None\",\n+ None,\n+ CaseMetadata(0, 0, DataFormat.EMPTY, [], \"markdown\", False, pd.DataFrame),\n ),\n- # Pandas DataFrame:\n (\n- pd.DataFrame([\"st.text_area\", \"st.markdown\"]),\n- TestCaseMetadata(2, 1, DataFormat.PANDAS_DATAFRAME),\n+ \"Empty list\",\n+ [],\n+ CaseMetadata(0, 0, DataFormat.LIST_OF_VALUES, [], \"json\", False),\n ),\n- # List of strings (List[str]):\n (\n+ \"Empty tuple\",\n+ (),\n+ CaseMetadata(0, 0, DataFormat.TUPLE_OF_VALUES, [], \"markdown\", False),\n+ ),\n+ (\n+ \"Empty dict\",\n+ {},\n+ CaseMetadata(0, 0, DataFormat.KEY_VALUE_DICT, [], \"json\", False),\n+ ),\n+ (\n+ \"Empty set\",\n+ set(),\n+ CaseMetadata(0, 0, DataFormat.SET_OF_VALUES, [], \"markdown\", False),\n+ ),\n+ (\n+ \"List[str]\",\n [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n- TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES),\n- ),\n- # List of integers (List[int]):\n- ([1, 2, 3], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)),\n- # List of floats (List[float]):\n- ([1.0, 2.0, 3.0], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)),\n- # List of booleans (List[bool]):\n- ([True, False, True], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)),\n- # List of Nones (List[None]):\n- ([None, None, None], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)),\n- # List of dates (List[date]):\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n+ \"json\",\n+ False,\n+ ),\n+ ),\n (\n+ \"List[int]\",\n+ [1, 2, 3],\n+ CaseMetadata(3, 1, DataFormat.LIST_OF_VALUES, [1, 2, 3], \"json\", False),\n+ ),\n+ (\n+ \"List[float]\",\n+ [1.1, 2.2, 3.3],\n+ CaseMetadata(3, 1, DataFormat.LIST_OF_VALUES, [1.1, 2.2, 3.3], \"json\", False),\n+ ),\n+ (\n+ \"List[bool]\",\n+ [True, False, True],\n+ CaseMetadata(\n+ 3, 1, DataFormat.LIST_OF_VALUES, [True, False, True], \"json\", False\n+ ),\n+ ),\n+ (\n+ \"List[None]\",\n+ [None, None, None],\n+ CaseMetadata(\n+ 3, 1, DataFormat.LIST_OF_VALUES, [None, None, None], \"json\", False\n+ ),\n+ ),\n+ (\n+ \"List[date]\",\n [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 3)],\n- TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 3)],\n+ \"json\",\n+ False,\n+ ),\n ),\n- # Set of strings (Set[str]):\n- # Set does not have a stable order across different Python version.\n- # Therefore, we are only testing this with one item.\n (\n+ \"Set[str]\",\n+ # Set does not have a stable order across different Python version.\n+ # Therefore, we are only testing this with one item.\n {\"st.number_input\", \"st.number_input\"}, # noqa: B033\n- TestCaseMetadata(1, 1, DataFormat.SET_OF_VALUES),\n+ CaseMetadata(\n+ 1, 1, DataFormat.SET_OF_VALUES, [\"st.number_input\"], \"markdown\", False\n+ ),\n ),\n- # Tuple of strings (Tuple[str]):\n (\n+ \"Tuple[str]\",\n (\"st.text_area\", \"st.number_input\", \"st.text_input\"),\n- TestCaseMetadata(3, 1, DataFormat.TUPLE_OF_VALUES),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.TUPLE_OF_VALUES,\n+ [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n+ \"markdown\",\n+ False,\n+ ),\n ),\n- # Numpy list / 1D numpy array (np.array[str]):\n (\n- np.array([\"st.text_area\", \"st.number_input\", \"st.text_input\"]),\n- TestCaseMetadata(3, 1, DataFormat.NUMPY_LIST),\n+ \"Frozenset[str]\",\n+ # Set does not have a stable order across different Python version.\n+ # Therefore, we are only testing this with one item.\n+ frozenset({\"st.number_input\", \"st.number_input\"}), # noqa: B033\n+ CaseMetadata(\n+ 1,\n+ 1,\n+ DataFormat.SET_OF_VALUES,\n+ [\"st.number_input\"],\n+ \"markdown\",\n+ False,\n+ set,\n+ ),\n ),\n- # np.array[int]:\n- (np.array([1, 2, 3]), TestCaseMetadata(3, 1, DataFormat.NUMPY_LIST)),\n- # Multi-dimensional numpy array (np.array[List[Scalar]])\n (\n- np.array(\n+ \"Empty frozenset\",\n+ frozenset(),\n+ CaseMetadata(0, 0, DataFormat.SET_OF_VALUES, [], \"markdown\", False, set),\n+ ),\n+ (\n+ \"Range\",\n+ range(3),\n+ CaseMetadata(\n+ 3, 1, DataFormat.LIST_OF_VALUES, [0, 1, 2], \"markdown\", False, list\n+ ),\n+ ),\n+ (\n+ \"Dict Keys\",\n+ {\n+ \"st.number_input\": \"number\",\n+ \"st.text_area\": \"text\",\n+ \"st.text_input\": \"text\",\n+ }.keys(),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"markdown\",\n+ False,\n+ list,\n+ ),\n+ ),\n+ (\n+ \"Dict Values\",\n+ {\n+ \"st.number_input\": \"number\",\n+ \"st.text_area\": \"text\",\n+ \"st.text_input\": \"text\",\n+ }.values(),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [\"number\", \"text\", \"text\"],\n+ \"markdown\",\n+ False,\n+ list,\n+ ),\n+ ),\n+ (\n+ \"Dict Items\",\n+ {\n+ \"st.number_input\": \"number\",\n+ \"st.text_area\": \"text\",\n+ \"st.text_input\": \"text\",\n+ }.items(),\n+ CaseMetadata(\n+ 3,\n+ 2,\n+ DataFormat.LIST_OF_ROWS,\n+ None,\n+ \"markdown\",\n+ False,\n+ list,\n+ ),\n+ ),\n+ (\n+ \"collections.OrderedDict\",\n+ OrderedDict(\n [\n- [\"st.text_area\", \"widget\"],\n- [\"st.markdown\", \"element\"],\n+ (\"st.number_input\", \"number\"),\n+ (\"st.text_area\", \"text\"),\n ]\n ),\n- TestCaseMetadata(2, 2, DataFormat.NUMPY_MATRIX),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.number_input\", \"st.text_area\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n ),\n- # np.array[List[str]]:\n (\n- np.array([[\"st.text_area\"], [\"st.number_input\"], [\"st.text_input\"]]),\n- TestCaseMetadata(3, 1, DataFormat.NUMPY_MATRIX),\n+ \"collections.defaultdict\",\n+ defaultdict(\n+ lambda: \"Not Present\",\n+ {\"st.text_area\": \"widget\", \"st.markdown\": \"element\"},\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n ),\n- # Pandas Series (pd.Series):\n (\n- pd.Series([\"st.text_area\", \"st.number_input\", \"st.text_input\"], name=\"widgets\"),\n- TestCaseMetadata(3, 1, DataFormat.PANDAS_SERIES),\n+ \"collections.Counter\",\n+ Counter({\"st.number_input\": 4, \"st.text_area\": 2}),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.number_input\", \"st.text_area\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n ),\n- # Pandas Styler (pd.Styler):\n (\n- pd.DataFrame([\"st.text_area\", \"st.markdown\"]).style,\n- TestCaseMetadata(2, 1, DataFormat.PANDAS_STYLER),\n+ \"collections.deque\",\n+ deque([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"markdown\",\n+ False,\n+ list,\n+ ),\n ),\n- # Pandas Index (pd.Index):\n (\n- pd.Index([\"st.text_area\", \"st.markdown\"]),\n- TestCaseMetadata(2, 1, DataFormat.PANDAS_INDEX),\n+ \"collections.ChainMap\",\n+ ChainMap(\n+ {\"st.number_input\": \"number\", \"st.text_area\": \"text\"},\n+ {\"st.text_input\": \"text\"},\n+ ),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"number\", \"text\", \"text\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n ),\n- # Pyarrow Table (pyarrow.Table):\n (\n- pa.Table.from_pandas(pd.DataFrame([\"st.text_area\", \"st.markdown\"])),\n- TestCaseMetadata(2, 1, DataFormat.PYARROW_TABLE),\n+ \"Dataclass\",\n+ ElementDataClass(\"st.number_input\", is_widget=True, usage=0.32),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.number_input\", True, 0.32],\n+ \"help\",\n+ False,\n+ dict,\n+ ),\n+ ),\n+ (\n+ \"TypedDict\",\n+ ElementTypedDict(name=\"st.number_input\", is_widget=True, usage=0.32),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"name\", \"is_widget\", \"usage\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n+ ),\n+ (\n+ \"NamedTuple\",\n+ ElementNamedTuple(\"st.number_input\", is_widget=True, usage=0.32),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.number_input\", True, 0.32],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n+ ),\n+ (\n+ \"String Enum\",\n+ StrTestEnum,\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"help\",\n+ False,\n+ list,\n+ ),\n+ ),\n+ (\n+ \"Enum\",\n+ TestEnum,\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.LIST_OF_VALUES,\n+ [TestEnum.NUMBER_INPUT, TestEnum.TEXT_AREA, TestEnum.TEXT_INPUT],\n+ \"help\",\n+ False,\n+ list,\n+ ),\n+ ),\n+ (\n+ \"Generator Function\",\n+ data_generator,\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.UNKNOWN,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"write_stream\",\n+ True,\n+ ),\n+ ),\n+ (\n+ \"Empty column value mapping\",\n+ {\"name\": [], \"type\": []},\n+ CaseMetadata(\n+ 0, 2, DataFormat.COLUMN_VALUE_MAPPING, [\"name\", \"type\"], \"json\", False\n+ ),\n+ ),\n+ (\n+ \"array.array\",\n+ array.array(\"i\", [1, 2, 3]),\n+ CaseMetadata(\n+ 3, 1, DataFormat.LIST_OF_VALUES, [1, 2, 3], \"markdown\", False, list\n+ ),\n+ ),\n+ (\n+ \"MappingProxyType\",\n+ MappingProxyType({\"st.text_area\": \"widget\", \"st.markdown\": \"element\"}),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"widget\", \"element\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n+ ),\n+ (\n+ \"UserDict\",\n+ UserDictExample({\"st.text_area\": \"widget\", \"st.markdown\": \"element\"}),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"widget\", \"element\"],\n+ \"json\",\n+ False,\n+ dict,\n+ ),\n ),\n- # List of rows (List[List[Scalar]]):\n (\n+ \"List of rows\", # List[list[scalar]]\n [[\"st.text_area\", \"widget\"], [\"st.markdown\", \"element\"]],\n- TestCaseMetadata(2, 2, DataFormat.LIST_OF_ROWS),\n+ CaseMetadata(2, 2, DataFormat.LIST_OF_ROWS, None, \"json\", False),\n ),\n- # List of records (List[Dict[str, Scalar]]):\n (\n+ \"List of records\", # List[Dict[str, Scalar]]\n [\n {\"name\": \"st.text_area\", \"type\": \"widget\"},\n {\"name\": \"st.markdown\", \"type\": \"element\"},\n ],\n- TestCaseMetadata(2, 2, DataFormat.LIST_OF_RECORDS),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.LIST_OF_RECORDS,\n+ None,\n+ \"json\",\n+ False,\n+ ),\n ),\n- # Column-index mapping ({column: {index: value}}):\n (\n+ \"Column-index mapping\", # ({column: {index: value}})\n {\n \"type\": {\"st.text_area\": \"widget\", \"st.markdown\": \"element\"},\n \"usage\": {\"st.text_area\": 4.92, \"st.markdown\": 47.22},\n },\n- TestCaseMetadata(2, 2, DataFormat.COLUMN_INDEX_MAPPING),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.COLUMN_INDEX_MAPPING,\n+ [\"type\", \"usage\"],\n+ \"json\",\n+ False,\n+ ),\n ),\n- # Column-value mapping ({column: List[values]}}):\n (\n+ \"Column-value mapping\", # ({column: List[values]}})\n {\n \"name\": [\"st.text_area\", \"st.markdown\"],\n \"type\": [\"widget\", \"element\"],\n },\n- TestCaseMetadata(2, 2, DataFormat.COLUMN_VALUE_MAPPING),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.COLUMN_VALUE_MAPPING,\n+ [\"name\", \"type\"],\n+ \"json\",\n+ False,\n+ ),\n ),\n- # Column-series mapping ({column: Series(values)}):\n (\n+ \"Column-series mapping\", # ({column: Series(values)})\n {\n \"name\": pd.Series([\"st.text_area\", \"st.markdown\"], name=\"name\"),\n \"type\": pd.Series([\"widget\", \"element\"], name=\"type\"),\n },\n- TestCaseMetadata(2, 2, DataFormat.COLUMN_SERIES_MAPPING),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.COLUMN_SERIES_MAPPING,\n+ [\"name\", \"type\"],\n+ \"dataframe\",\n+ False,\n+ ),\n ),\n- # Key-value dict ({index: value}):\n (\n+ \"Key-value dict\", # ({index: value})\n {\"st.text_area\": \"widget\", \"st.markdown\": \"element\"},\n- TestCaseMetadata(2, 1, DataFormat.KEY_VALUE_DICT),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.KEY_VALUE_DICT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"json\",\n+ False,\n+ ),\n+ ),\n+ ###################################\n+ ########## Pandas Types ###########\n+ ###################################\n+ (\n+ \"Empty pd.Dataframe\",\n+ pd.DataFrame(),\n+ CaseMetadata(0, 0, DataFormat.PANDAS_DATAFRAME, [], \"dataframe\", False),\n+ ),\n+ (\n+ \"Empty pd.Dataframe with columns\",\n+ pd.DataFrame(\n+ columns=[\"name\", \"type\"], index=pd.RangeIndex(start=0, step=1)\n+ ), # Explicitly set the range index to have the same behavior across versions\n+ CaseMetadata(0, 2, DataFormat.PANDAS_DATAFRAME, [], \"dataframe\", False),\n+ ),\n+ (\n+ \"pd.Dataframe\",\n+ pd.DataFrame([\"st.text_area\", \"st.markdown\"]),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.PANDAS_DATAFRAME,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ (\n+ \"pd.Series[str]\",\n+ pd.Series(\n+ [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n+ name=\"widgets\",\n+ ),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.PANDAS_SERIES,\n+ [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ (\n+ \"pd.Index\",\n+ pd.Index([\"st.text_area\", \"st.markdown\"]),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.PANDAS_INDEX,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"Pandas Styler\",\n+ pd.DataFrame([\"st.text_area\", \"st.markdown\"]).style,\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.PANDAS_STYLER,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"pd.array\",\n+ pd.array([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.PANDAS_ARRAY,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"dataframe\",\n+ False,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"pd.DatetimeIndex\",\n+ pd.DatetimeIndex([\"1/1/2020 10:00:00+00:00\", \"2/1/2020 11:00:00+00:00\"]),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.PANDAS_INDEX,\n+ [\n+ pd.Timestamp(\"2020-01-01 10:00:00+0000\", tz=\"UTC\"),\n+ pd.Timestamp(\"2020-02-01 11:00:00+0000\", tz=\"UTC\"),\n+ ],\n+ \"dataframe\",\n+ False,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"pd.RangeIndex\",\n+ pd.RangeIndex(start=0, stop=3, step=1),\n+ CaseMetadata(\n+ 3, 1, DataFormat.PANDAS_INDEX, [0, 1, 2], \"dataframe\", False, pd.DataFrame\n+ ),\n+ ),\n+ ###################################\n+ ########### Numpy Types ###########\n+ ###################################\n+ (\n+ \"Empty np.array\",\n+ # For unknown reasons, pd.DataFrame initializes empty numpy arrays with a single column\n+ np.ndarray(0),\n+ CaseMetadata(0, 1, DataFormat.NUMPY_LIST, [], \"dataframe\", False),\n+ ),\n+ (\n+ \"np.array[str]\",\n+ np.array([\"st.text_area\", \"st.number_input\", \"st.text_input\"]),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.NUMPY_LIST,\n+ [\"st.text_area\", \"st.number_input\", \"st.text_input\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ (\n+ \"np.array[int]\",\n+ np.array([1, 2, 3]),\n+ CaseMetadata(3, 1, DataFormat.NUMPY_LIST, [1, 2, 3], \"dataframe\", False),\n+ ),\n+ (\n+ \"np.array[list[scalar]]\",\n+ np.array(\n+ [\n+ [\"st.text_area\", \"widget\"],\n+ [\"st.markdown\", \"element\"],\n+ ]\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.NUMPY_MATRIX,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ (\n+ \"np.array[list[str]]\", # numpy matrix\n+ np.array(\n+ [\n+ [\"st.text_area\", \"widget\"],\n+ [\"st.markdown\", \"element\"],\n+ ]\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.NUMPY_MATRIX,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ ),\n ),\n- # Snowpark DataFrame:\n+ ###################################\n+ ########## Pyarrow Types ##########\n+ ###################################\n (\n- SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),\n- TestCaseMetadata(2, 2, DataFormat.SNOWPARK_OBJECT),\n+ \"Pyarrow Table\",\n+ pa.Table.from_pandas(pd.DataFrame([\"st.text_area\", \"st.markdown\"])),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.PYARROW_TABLE,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ (\n+ \"Pyarrow Array\",\n+ pa.array([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n+ CaseMetadata(\n+ 3,\n+ 1,\n+ DataFormat.PYARROW_ARRAY,\n+ [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n+ \"dataframe\",\n+ False,\n+ ),\n+ ),\n+ ###################################\n+ ##### Snowflake Types (Mocks) #####\n+ ###################################\n+ (\n+ \"Snowpark DataFrame\",\n+ SnowparkDataFrame(\n+ pd.DataFrame(\n+ [\n+ {\"name\": \"st.text_area\", \"type\": \"widget\"},\n+ {\"name\": \"st.markdown\", \"type\": \"element\"},\n+ ]\n+ )\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.SNOWPARK_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"Snowpark Table\",\n+ SnowparkTable(\n+ pd.DataFrame(\n+ [\n+ {\"name\": \"st.text_area\", \"type\": \"widget\"},\n+ {\"name\": \"st.markdown\", \"type\": \"element\"},\n+ ]\n+ )\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.SNOWPARK_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"Snowpark Row List\",\n+ [\n+ SnowparkRow({\"name\": \"st.text_area\", \"type\": \"widget\"}),\n+ SnowparkRow({\"name\": \"st.markdown\", \"type\": \"element\"}),\n+ SnowparkRow({\"name\": \"st.text_input\", \"type\": \"text\"}),\n+ ],\n+ CaseMetadata(\n+ 3,\n+ 2,\n+ DataFormat.SNOWPARK_OBJECT,\n+ [\"st.text_area\", \"st.markdown\", \"st.text_input\"],\n+ \"dataframe\",\n+ False,\n+ pd.DataFrame,\n+ ),\n+ ),\n+ (\n+ \"Snowpandas DataFrame\",\n+ SnowpandasDataFrame(\n+ pd.DataFrame(\n+ [\n+ {\"name\": \"st.text_area\", \"type\": \"widget\"},\n+ {\"name\": \"st.markdown\", \"type\": \"element\"},\n+ ]\n+ )\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.SNOWPANDAS_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n ),\n- # Snowpark Table:\n (\n- SnowparkTable(pd.DataFrame(np.random.randn(2, 2))),\n- TestCaseMetadata(2, 2, DataFormat.SNOWPARK_OBJECT),\n+ \"Snowpandas Series\",\n+ SnowpandasSeries(pd.Series([\"st.text_area\", \"st.markdown\"])),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.SNOWPANDAS_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n ),\n- # Snowpark Pandas DataFrame:\n (\n- SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))),\n- TestCaseMetadata(2, 2, DataFormat.SNOWPANDAS_OBJECT),\n+ \"Modin DataFrame\",\n+ ModinDataFrame(\n+ pd.DataFrame(\n+ [\n+ {\"name\": \"st.text_area\", \"type\": \"widget\"},\n+ {\"name\": \"st.markdown\", \"type\": \"element\"},\n+ ]\n+ )\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.MODIN_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n ),\n- # Snowpark Pandas Series:\n (\n- SnowpandasSeries(pd.Series(np.random.randn(2))),\n- TestCaseMetadata(2, 1, DataFormat.SNOWPANDAS_OBJECT),\n+ \"Modin Series\",\n+ ModinSeries(pd.Series([\"st.text_area\", \"st.markdown\"])),\n+ CaseMetadata(\n+ 2,\n+ 1,\n+ DataFormat.MODIN_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n ),\n- # Pyspark Dataframe:\n+ ###################################\n+ ##### External Types (Mocks) ######\n+ ###################################\n (\n- PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),\n- TestCaseMetadata(2, 2, DataFormat.PYSPARK_OBJECT),\n+ \"Pyspark DataFrame\",\n+ PySparkDataFrame(\n+ pd.DataFrame(\n+ [\n+ {\"name\": \"st.text_area\", \"type\": \"widget\"},\n+ {\"name\": \"st.markdown\", \"type\": \"element\"},\n+ ]\n+ )\n+ ),\n+ CaseMetadata(\n+ 2,\n+ 2,\n+ DataFormat.PYSPARK_OBJECT,\n+ [\"st.text_area\", \"st.markdown\"],\n+ \"dataframe\",\n+ True,\n+ pd.DataFrame,\n+ ),\n ),\n ]\n-\n-\n-class TestObject:\n- def __str__(self):\n- return \"TestObject\"\n-\n-\n-class StrTestEnum(str, enum.Enum):\n- NUMBER_INPUT = \"st.number_input\"\n- TEXT_AREA = \"st.text_area\"\n- TEXT_INPUT = \"st.text_input\"\n-\n-\n-class TestEnum(enum.Enum):\n- NUMBER_INPUT = \"st.number_input\"\n- TEXT_AREA = \"st.text_area\"\n- TEXT_INPUT = \"st.text_input\"\n-\n-\n-def data_generator():\n- yield \"st.number_input\"\n- yield \"st.text_area\"\n- yield \"st.text_input\"\ndiff --git a/lib/tests/streamlit/dataframe_util_test.py b/lib/tests/streamlit/dataframe_util_test.py\nindex bf66bf3e744a..6ecceaee698c 100644\n--- a/lib/tests/streamlit/dataframe_util_test.py\n+++ b/lib/tests/streamlit/dataframe_util_test.py\n@@ -16,7 +16,6 @@\n \n import enum\n import unittest\n-from collections import OrderedDict\n from datetime import date\n from decimal import Decimal\n from typing import Any\n@@ -34,20 +33,13 @@\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n from tests.streamlit.data_mocks import (\n SHARED_TEST_CASES,\n- StrTestEnum,\n- TestCaseMetadata,\n- TestEnum,\n+ CaseMetadata,\n TestObject,\n- data_generator,\n )\n-from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame\n-from tests.streamlit.modin_mocks import Series as ModinSeries\n-from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame\n from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame\n from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries\n from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame\n from tests.streamlit.snowpark_mocks import Row as SnowparkRow\n-from tests.streamlit.snowpark_mocks import Table as SnowparkTable\n from tests.testutil import create_snowpark_session, patch_config_options\n \n \n@@ -66,8 +58,9 @@ def test_convert_pandas_df_to_arrow_bytes(self):\n )\n def test_convert_anything_to_pandas_df(\n self,\n+ name: str,\n input_data: Any,\n- metadata: TestCaseMetadata,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that `convert_anything_to_pandas_df` correctly converts\n a variety of types to a DataFrame.\n@@ -78,30 +71,33 @@ def test_convert_anything_to_pandas_df(\n self.assertEqual(converted_df.shape[1], metadata.expected_cols)\n \n @parameterized.expand(\n- [\n- (ModinDataFrame(pd.DataFrame(np.random.randn(2000, 2))),),\n- (ModinSeries(pd.Series(np.random.randn(2000))),),\n- (PysparkDataFrame(pd.DataFrame(np.random.randn(2000, 2))),),\n- (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2000, 2))),),\n- (SnowpandasSeries(pd.Series(np.random.randn(2000))),),\n- (SnowparkDataFrame(pd.DataFrame(np.random.randn(2000, 2))),),\n- (SnowparkTable(pd.DataFrame(np.random.randn(2000, 2))),),\n- ]\n+ SHARED_TEST_CASES,\n )\n- def test_convert_anything_to_pandas_df_show_warning_for_unevaluated_df(\n+ def test_unevaluated_dataframe_handling(\n self,\n+ name: str,\n input_data: Any,\n+ metadata: CaseMetadata,\n ):\n- \"\"\"Test that `convert_anything_to_pandas_df` correctly converts\n- a variety unevaluated dataframes and shows a warning if\n- the row count is > 1000.\n+ \"\"\"Test that unevaluated data objects are correctly detected and\n+ handled by limiting the number of rows to be displayed.\n \"\"\"\n- with patch(\"streamlit.caption\") as mock:\n- converted_df = dataframe_util.convert_anything_to_pandas_df(\n- input_data, max_unevaluated_rows=1000\n- )\n- self.assertIsInstance(converted_df, pd.DataFrame)\n- mock.assert_called_once()\n+ with patch(\"streamlit.dataframe_util._show_data_information\") as mock:\n+ if metadata.is_unevaluated:\n+ assert dataframe_util.is_unevaluated_data_object(input_data) is True\n+ converted_df = dataframe_util.convert_anything_to_pandas_df(\n+ input_data, max_unevaluated_rows=1\n+ )\n+ assert isinstance(converted_df, pd.DataFrame)\n+ assert converted_df.shape[0] <= 1\n+ mock.assert_called_once()\n+ else:\n+ assert dataframe_util.is_unevaluated_data_object(input_data) is False\n+ converted_df = dataframe_util.convert_anything_to_pandas_df(\n+ input_data, max_unevaluated_rows=1\n+ )\n+ assert converted_df.shape[0] == metadata.expected_rows\n+ mock.assert_not_called()\n \n def test_convert_anything_to_pandas_df_ensure_copy(self):\n \"\"\"Test that `convert_anything_to_pandas_df` creates a copy of the original\n@@ -138,11 +134,13 @@ def test_convert_anything_to_pandas_df_supports_key_value_dicts(self):\n \"\"\"\n data = {\"a\": 1, \"b\": 2}\n df = dataframe_util.convert_anything_to_pandas_df(data)\n- pd.testing.assert_frame_equal(df, pd.DataFrame.from_dict(data, orient=\"index\"))\n+ pd.testing.assert_frame_equal(\n+ df, pd.DataFrame.from_dict(data, orient=\"index\", columns=[\"value\"])\n+ )\n \n def test_convert_anything_to_pandas_df_converts_stylers(self):\n- \"\"\"Test that `convert_anything_to_pandas_df` correctly converts Stylers to DF, without cloning the\n- data.\n+ \"\"\"Test that `convert_anything_to_pandas_df` correctly converts Stylers to DF,\n+ without cloning the data.\n \"\"\"\n original_df = pd.DataFrame(\n {\n@@ -194,8 +192,9 @@ def to_pandas(self):\n )\n def test_convert_anything_to_arrow_bytes(\n self,\n+ name: str,\n input_data: Any,\n- metadata: TestCaseMetadata,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that `convert_anything_to_arrow_bytes` correctly converts\n a variety of types to Arrow bytes.\n@@ -415,7 +414,8 @@ class DummyClass:\n self.assertTrue(\n dataframe_util.is_snowpark_row_list(\n [\n- SnowparkRow(),\n+ SnowparkRow({\"col1\": 1, \"col2\": \"foo\"}),\n+ SnowparkRow({\"col1\": 2, \"col2\": \"bar\"}),\n ]\n )\n )\n@@ -440,22 +440,36 @@ def test_is_snowpark_dataframe(self):\n self.assertTrue(dataframe_util.is_snowpark_data_object(SnowparkDataFrame(df)))\n \n @pytest.mark.require_snowflake\n- def test_is_snowpark_dataframe_integration(self):\n+ def test_verify_snowpark_integration(self):\n+ \"\"\"Integration test snowpark object handling.\n+ This is in addition to the tests using the mocks to verify that\n+ the latest version of the library is still supported.\n+ \"\"\"\n with create_snowpark_session() as snowpark_session:\n- self.assertTrue(\n- dataframe_util.is_snowpark_data_object(\n- snowpark_session.sql(\"SELECT 40+2 as COL1\")\n- )\n+ snowpark_df = snowpark_session.sql(\"SELECT 40+2 as COL1\")\n+\n+ assert dataframe_util.is_snowpark_data_object(snowpark_df) is True\n+ assert isinstance(\n+ dataframe_util.convert_anything_to_pandas_df(snowpark_df),\n+ pd.DataFrame,\n )\n- self.assertTrue(\n- dataframe_util.is_snowpark_data_object(\n- snowpark_session.sql(\"SELECT 40+2 as COL1\").cache_result()\n- )\n+\n+ snowpark_cached_result = snowpark_session.sql(\n+ \"SELECT 40+2 as COL1\"\n+ ).cache_result()\n+ assert (\n+ dataframe_util.is_snowpark_data_object(snowpark_cached_result) is True\n )\n- self.assertTrue(\n- dataframe_util.is_snowpark_row_list(\n- snowpark_session.sql(\"SELECT 40+2 as COL1\").collect()\n- )\n+ assert isinstance(\n+ dataframe_util.convert_anything_to_pandas_df(snowpark_cached_result),\n+ pd.DataFrame,\n+ )\n+\n+ snowpark_row_list = snowpark_session.sql(\"SELECT 40+2 as COL1\").collect()\n+ assert dataframe_util.is_snowpark_row_list(snowpark_row_list) is True\n+ assert isinstance(\n+ dataframe_util.convert_anything_to_pandas_df(snowpark_row_list),\n+ pd.DataFrame,\n )\n \n @parameterized.expand(\n@@ -463,8 +477,9 @@ def test_is_snowpark_dataframe_integration(self):\n )\n def test_determine_data_format(\n self,\n+ name: str,\n input_data: Any,\n- metadata: TestCaseMetadata,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that `determine_data_format` correctly determines the\n data format of a variety of data structures/types.\n@@ -481,8 +496,9 @@ def test_determine_data_format(\n )\n def test_convert_pandas_df_to_data_format(\n self,\n+ name: str,\n input_data: Any,\n- metadata: TestCaseMetadata,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that `convert_pandas_df_to_data_format` correctly converts a\n DataFrame to the specified data format.\n@@ -502,32 +518,26 @@ def test_convert_pandas_df_to_data_format(\n converted_df, metadata.expected_data_format\n )\n \n- # Some data formats are converted to DataFrames instead of\n- # the original data type/structure.\n- if metadata.expected_data_format in [\n- dataframe_util.DataFormat.SNOWPARK_OBJECT,\n- dataframe_util.DataFormat.PYSPARK_OBJECT,\n- dataframe_util.DataFormat.PANDAS_INDEX,\n- dataframe_util.DataFormat.PANDAS_STYLER,\n- dataframe_util.DataFormat.SNOWPANDAS_OBJECT,\n- dataframe_util.DataFormat.MODIN_OBJECT,\n- dataframe_util.DataFormat.EMPTY,\n- ]:\n- assert isinstance(converted_data, pd.DataFrame)\n+ self.assertEqual(\n+ type(converted_data),\n+ type(input_data)\n+ if metadata.expected_type is None\n+ else metadata.expected_type,\n+ )\n+\n+ if isinstance(converted_data, pd.DataFrame):\n self.assertEqual(converted_data.shape[0], metadata.expected_rows)\n self.assertEqual(converted_data.shape[1], metadata.expected_cols)\n- else:\n- self.assertEqual(type(converted_data), type(input_data))\n+ elif (\n # Sets in python are unordered, so we can't compare them this way.\n- if (\n- metadata.expected_data_format\n- != dataframe_util.DataFormat.SET_OF_VALUES\n- ):\n- self.assertEqual(str(converted_data), str(input_data))\n- pd.testing.assert_frame_equal(\n- converted_df,\n- dataframe_util.convert_anything_to_pandas_df(converted_data),\n- )\n+ metadata.expected_data_format != dataframe_util.DataFormat.SET_OF_VALUES\n+ and metadata.expected_type is None\n+ ):\n+ self.assertEqual(str(converted_data), str(input_data))\n+ pd.testing.assert_frame_equal(\n+ converted_df,\n+ dataframe_util.convert_anything_to_pandas_df(converted_data),\n+ )\n \n def test_convert_pandas_df_to_data_format_with_unknown_data_format(self):\n \"\"\"Test that `convert_df_to_data_format` raises a ValueError when\n@@ -643,245 +653,25 @@ class StrOpt(str, enum.Enum):\n self.assertEqual(list(StrOpt), converted_list)\n \n @parameterized.expand(\n- [\n- (None, []),\n- # List:\n- ([], []),\n- (\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- (\n- [1, 2, 3],\n- [1, 2, 3],\n- ),\n- # Reversed list:\n- (\n- reversed([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [\"st.text_input\", \"st.text_area\", \"st.number_input\"],\n- ),\n- # Set:\n- (set(), []),\n- (\n- {\"st.number_input\", \"st.text_area\", \"st.text_input\"},\n- [\"st.text_input\", \"st.number_input\", \"st.text_area\"],\n- ),\n- # Tuple:\n- ((), []),\n- (\n- (\"st.number_input\", \"st.text_area\", \"st.text_input\"),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Dict:\n- ({}, []),\n- (\n- {\n- \"st.number_input\": \"number\",\n- \"st.text_area\": \"text\",\n- \"st.text_input\": \"text\",\n- },\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Dict keys:\n- (\n- {\n- \"st.number_input\": \"number\",\n- \"st.text_area\": \"text\",\n- \"st.text_input\": \"text\",\n- }.keys(),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Dict values:\n- (\n- {\n- \"st.number_input\": \"number\",\n- \"st.text_area\": \"text\",\n- \"st.text_input\": \"text\",\n- }.values(),\n- [\"number\", \"text\", \"text\"],\n- ),\n- # OrderedDict:\n- (\n- OrderedDict(\n- [\n- (\"st.number_input\", \"number\"),\n- (\"st.text_area\", \"text\"),\n- (\"st.text_input\", \"text\"),\n- ]\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Enum:\n- (\n- TestEnum,\n- [TestEnum.NUMBER_INPUT, TestEnum.TEXT_AREA, TestEnum.TEXT_INPUT],\n- ),\n- (StrTestEnum, [\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- # Generator:\n- (data_generator(), [\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- # String:\n- (\"abc\", [\"a\", \"b\", \"c\"]),\n- # Enumerate:\n- (\n- enumerate([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [0, 1, 2],\n- ),\n- # Range:\n- (range(3), [0, 1, 2]),\n- # Pandas Dataframe:\n- (\n- pd.DataFrame(),\n- [],\n- ),\n- (\n- pd.DataFrame(\n- columns=[\"name\", \"type\"], index=pd.RangeIndex(start=0, step=1)\n- ),\n- [],\n- ),\n- (\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Dataframe with multiple columns (widgets & types)\n- # The first column is expected to be selected as the sequence.\n- (\n- pd.DataFrame(\n- {\n- \"widgets\": [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- \"types\": [\"number\", \"text\", \"text\"],\n- }\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pandas Series (pd.Series):\n- (\n- pd.Series(\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"], name=\"widgets\"\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pandas Index (pd.Index):\n- (\n- pd.Index([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pandas Styler (pd.Styler):\n- (\n- pd.DataFrame(\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"]\n- ).style,\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pandas Categorical (pd.Categorical):\n- (\n- pd.Categorical([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pandas DatetimeIndex (pd.DatetimeIndex):\n- (\n- pd.DatetimeIndex(\n- [\"1/1/2020 10:00:00+00:00\", \"2/1/2020 11:00:00+00:00\"]\n- ),\n- [\n- pd.Timestamp(\"2020-01-01 10:00:00+0000\", tz=\"UTC\"),\n- pd.Timestamp(\"2020-02-01 11:00:00+0000\", tz=\"UTC\"),\n- ],\n- ),\n- # Pandas DatetimeArrayรฅ:\n- (\n- pd.arrays.DatetimeArray(\n- pd.DatetimeIndex(\n- [\"1/1/2020 10:00:00+00:00\", \"2/1/2020 11:00:00+00:00\"]\n- ),\n- ),\n- [\n- pd.Timestamp(\"2020-01-01 10:00:00+0000\", tz=\"UTC\"),\n- pd.Timestamp(\"2020-02-01 11:00:00+0000\", tz=\"UTC\"),\n- ],\n- ),\n- # Pandas RangeIndex (pd.RangeIndex):\n- (\n- pd.RangeIndex(start=0, stop=3, step=1),\n- [0, 1, 2],\n- ),\n- # Numpy array:\n- (\n- np.array([]),\n- [],\n- ),\n- (\n- np.array([\"st.number_input\", \"st.text_area\", \"st.text_input\"]),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pyarrow Table:\n- (\n- pa.Table.from_pandas(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Snowpark Table:\n- (\n- SnowparkTable(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Snowpark DataFrame:\n- (\n- SnowparkDataFrame(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Snowpark Pandas DataFrame:\n- (\n- SnowpandasDataFrame(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Snowpark Pandas Series:\n- (\n- SnowpandasSeries(\n- pd.Series([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Pyspark Dataframe:\n- (\n- PysparkDataFrame(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Modin Dataframe:\n- (\n- ModinDataFrame(\n- pd.DataFrame([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- # Modin Series:\n- (\n- ModinSeries(\n- pd.Series([\"st.number_input\", \"st.text_area\", \"st.text_input\"])\n- ),\n- [\"st.number_input\", \"st.text_area\", \"st.text_input\"],\n- ),\n- ]\n+ SHARED_TEST_CASES,\n )\n def test_convert_anything_to_sequence(\n- self, input_data: Any, result_sequence: list[Any]\n+ self,\n+ name: str,\n+ input_data: Any,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that `convert_anything_to_sequence` correctly converts\n a variety of types to a sequence.\n \"\"\"\n+ if metadata.expected_sequence is None:\n+ # Skip all cases where we don't have an expected sequence.\n+ return\n+\n converted_sequence = dataframe_util.convert_anything_to_sequence(input_data)\n # We convert to a set for the check since some of the formats don't\n # have a guaranteed order.\n- self.assertEqual(set(converted_sequence), set(result_sequence))\n+ self.assertEqual(set(converted_sequence), set(metadata.expected_sequence))\n # Check that it is a new object and not the same as the input:\n assert converted_sequence is not input_data\n \ndiff --git a/lib/tests/streamlit/elements/arrow_dataframe_test.py b/lib/tests/streamlit/elements/arrow_dataframe_test.py\nindex 58321a5d69c7..2d5e582da44c 100644\n--- a/lib/tests/streamlit/elements/arrow_dataframe_test.py\n+++ b/lib/tests/streamlit/elements/arrow_dataframe_test.py\n@@ -15,24 +15,22 @@\n \"\"\"Arrow DataFrame tests.\"\"\"\n \n import json\n+from typing import Any\n from unittest.mock import MagicMock, patch\n \n import numpy as np\n import pandas as pd\n-import pyarrow as pa\n-import pytest\n from pandas.io.formats.style_render import StylerRenderer as Styler\n from parameterized import parameterized\n \n import streamlit as st\n from streamlit.dataframe_util import (\n convert_arrow_bytes_to_pandas_df,\n- convert_arrow_table_to_arrow_bytes,\n )\n from streamlit.elements.lib.column_config_utils import INDEX_IDENTIFIER\n from streamlit.errors import StreamlitAPIException\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n-from tests.testutil import create_snowpark_session\n+from tests.streamlit.data_mocks import SHARED_TEST_CASES, CaseMetadata\n \n \n def mock_data_frame():\n@@ -67,13 +65,20 @@ def test_empty_column_order_parameter(self):\n proto = self.get_delta_from_queue().new_element.arrow_data_frame\n self.assertEqual(proto.column_order, [])\n \n- def test_pyarrow_table_data(self):\n- df = mock_data_frame()\n- table = pa.Table.from_pandas(df)\n- st.dataframe(table)\n+ @parameterized.expand(SHARED_TEST_CASES)\n+ def test_with_compatible_data(\n+ self,\n+ name: str,\n+ input_data: Any,\n+ metadata: CaseMetadata,\n+ ):\n+ \"\"\"Test that it can be called with compatible data.\"\"\"\n+ st.dataframe(input_data)\n \n proto = self.get_delta_from_queue().new_element.arrow_data_frame\n- self.assertEqual(proto.data, convert_arrow_table_to_arrow_bytes(table))\n+ reconstructed_df = convert_arrow_bytes_to_pandas_df(proto.data)\n+ self.assertEqual(reconstructed_df.shape[0], metadata.expected_rows)\n+ self.assertEqual(reconstructed_df.shape[1], metadata.expected_cols)\n \n def test_hide_index_true(self):\n \"\"\"Test that it can be called with hide_index=True param.\"\"\"\n@@ -190,28 +195,6 @@ def test_dataframe_uses_convert_anything_to_df(self):\n st.dataframe(df)\n convert_anything_to_df.assert_called_once()\n \n- @pytest.mark.require_snowflake\n- def test_snowpark_uncollected(self):\n- \"\"\"Tests that data can be read from Snowpark's uncollected Dataframe\"\"\"\n- with create_snowpark_session() as snowpark_session:\n- df = snowpark_session.sql(\"SELECT 42 as COL1\")\n-\n- st.dataframe(df)\n-\n- proto = self.get_delta_from_queue().new_element.arrow_data_frame\n- self.assertEqual(convert_arrow_bytes_to_pandas_df(proto.data).iat[0, 0], 42)\n-\n- @pytest.mark.require_snowflake\n- def test_snowpark_collected(self):\n- \"\"\"Tests that data can be read from Snowpark's collected Dataframe\"\"\"\n- with create_snowpark_session() as snowpark_session:\n- df = snowpark_session.sql(\"SELECT 42 as COL1\").collect()\n-\n- st.dataframe(df)\n-\n- proto = self.get_delta_from_queue().new_element.arrow_data_frame\n- self.assertEqual(convert_arrow_bytes_to_pandas_df(proto.data).iat[0, 0], 42)\n-\n def test_dataframe_on_select_initial_returns(self):\n \"\"\"Test st.dataframe returns an empty selection as initial result.\"\"\"\n \ndiff --git a/lib/tests/streamlit/elements/data_editor_test.py b/lib/tests/streamlit/elements/data_editor_test.py\nindex ec12aa284d5e..c374595a02eb 100644\n--- a/lib/tests/streamlit/elements/data_editor_test.py\n+++ b/lib/tests/streamlit/elements/data_editor_test.py\n@@ -48,7 +48,7 @@\n from streamlit.proto.Arrow_pb2 import Arrow as ArrowProto\n from streamlit.type_util import is_pandas_version_less_than\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n-from tests.streamlit.data_mocks import SHARED_TEST_CASES, TestCaseMetadata\n+from tests.streamlit.data_mocks import SHARED_TEST_CASES, CaseMetadata\n \n \n def _get_arrow_schema(df: pd.DataFrame) -> pa.Schema:\n@@ -410,10 +410,16 @@ def test_with_dataframe_data(self):\n @parameterized.expand(SHARED_TEST_CASES)\n def test_with_compatible_data(\n self,\n+ name: str,\n input_data: Any,\n- metadata: TestCaseMetadata,\n+ metadata: CaseMetadata,\n ):\n \"\"\"Test that it can be called with compatible data.\"\"\"\n+ if metadata.expected_data_format == DataFormat.UNKNOWN:\n+ # We can skip formats where the expected format is unknown\n+ # since these cases are not expected to work.\n+ return\n+\n return_data = st.data_editor(input_data)\n \n proto = self.get_delta_from_queue().new_element.arrow_data_frame\n@@ -421,25 +427,22 @@ def test_with_compatible_data(\n self.assertEqual(reconstructed_df.shape[0], metadata.expected_rows)\n self.assertEqual(reconstructed_df.shape[1], metadata.expected_cols)\n \n- # Some data formats are converted to DataFrames instead of\n- # the original data type/structure.\n- if metadata.expected_data_format in [\n- DataFormat.EMPTY,\n- DataFormat.MODIN_OBJECT,\n- DataFormat.PANDAS_INDEX,\n- DataFormat.PANDAS_STYLER,\n- DataFormat.PYSPARK_OBJECT,\n- DataFormat.SNOWPANDAS_OBJECT,\n- DataFormat.SNOWPARK_OBJECT,\n- ]:\n- assert isinstance(return_data, pd.DataFrame)\n+ self.assertEqual(\n+ type(return_data),\n+ type(input_data)\n+ if metadata.expected_type is None\n+ else metadata.expected_type,\n+ )\n+\n+ if isinstance(return_data, pd.DataFrame):\n self.assertEqual(return_data.shape[0], metadata.expected_rows)\n self.assertEqual(return_data.shape[1], metadata.expected_cols)\n- else:\n- self.assertEqual(type(return_data), type(input_data))\n+ elif (\n # Sets in python are unordered, so we can't compare them this way.\n- if metadata.expected_data_format != DataFormat.SET_OF_VALUES:\n- self.assertEqual(str(return_data), str(input_data))\n+ metadata.expected_data_format != DataFormat.SET_OF_VALUES\n+ and metadata.expected_type is None\n+ ):\n+ self.assertEqual(str(return_data), str(input_data))\n \n @parameterized.expand(\n [\n@@ -455,6 +458,26 @@ def test_with_invalid_data(self, input_data: Any):\n with self.assertRaises(StreamlitAPIException):\n st.data_editor(input_data)\n \n+ def test_disables_columns_when_incompatible(self):\n+ \"\"\"Test that Arrow incompatible columns are disabled (configured as non-editable).\"\"\"\n+ data_df = pd.DataFrame(\n+ {\n+ \"a\": pd.Series([1, 2]),\n+ \"b\": pd.Series([\"foo\", \"bar\"]),\n+ \"c\": pd.Series([1, \"foo\"]), # Incompatible\n+ \"d\": pd.Series([1 + 2j, 3 + 4j]), # Incompatible\n+ }\n+ )\n+ st.data_editor(data_df)\n+\n+ proto = self.get_delta_from_queue().new_element.arrow_data_frame\n+ columns_config = json.loads(proto.columns)\n+\n+ self.assertNotIn(\"a\", columns_config)\n+ self.assertNotIn(\"b\", columns_config)\n+ self.assertTrue(columns_config[\"c\"][\"disabled\"])\n+ self.assertTrue(columns_config[\"d\"][\"disabled\"])\n+\n @parameterized.expand(\n [\n (pd.CategoricalIndex([\"a\", \"b\", \"c\"]),),\ndiff --git a/lib/tests/streamlit/elements/lib/column_config_utils_test.py b/lib/tests/streamlit/elements/lib/column_config_utils_test.py\nindex 257fbe10b925..43221e8d09b7 100644\n--- a/lib/tests/streamlit/elements/lib/column_config_utils_test.py\n+++ b/lib/tests/streamlit/elements/lib/column_config_utils_test.py\n@@ -504,8 +504,7 @@ def test_apply_data_specific_configs_hides_index(\n ):\n \"\"\"Test that the index is hidden for some data formats.\"\"\"\n columns_config: ColumnConfigMapping = {}\n- data_df = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n- apply_data_specific_configs(columns_config, data_df, data_format)\n+ apply_data_specific_configs(columns_config, data_format)\n \n if hidden:\n self.assertEqual(\n@@ -516,81 +515,6 @@ def test_apply_data_specific_configs_hides_index(\n else:\n self.assertNotIn(INDEX_IDENTIFIER, columns_config)\n \n- def test_apply_data_specific_configs_makes_index_required(self):\n- \"\"\"Test that a non-range index gets configured as required.\"\"\"\n- columns_config: ColumnConfigMapping = {}\n- data_df = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]}, index=[\"a\", \"b\", \"c\"])\n- apply_data_specific_configs(\n- columns_config, data_df, DataFormat.PANDAS_DATAFRAME\n- )\n- self.assertEqual(\n- columns_config[INDEX_IDENTIFIER][\"required\"],\n- True,\n- f\"Index of type {type(data_df.index)} should be configured as required.\",\n- )\n-\n- @parameterized.expand(\n- [\n- (DataFormat.SET_OF_VALUES, True),\n- (DataFormat.TUPLE_OF_VALUES, True),\n- (DataFormat.LIST_OF_VALUES, True),\n- (DataFormat.NUMPY_LIST, True),\n- (DataFormat.KEY_VALUE_DICT, True),\n- # Most other data formats which should not rename the first column:\n- (DataFormat.PANDAS_DATAFRAME, False),\n- (DataFormat.PANDAS_SERIES, False),\n- (DataFormat.PANDAS_INDEX, False),\n- (DataFormat.PYARROW_TABLE, False),\n- (DataFormat.PANDAS_STYLER, False),\n- (DataFormat.COLUMN_INDEX_MAPPING, False),\n- (DataFormat.COLUMN_SERIES_MAPPING, False),\n- (DataFormat.LIST_OF_RECORDS, False),\n- (DataFormat.LIST_OF_ROWS, False),\n- (DataFormat.COLUMN_VALUE_MAPPING, False),\n- ]\n- )\n- def test_apply_data_specific_configs_renames_column(\n- self, data_format: DataFormat, renames: bool\n- ):\n- \"\"\"Test that the column names are changed for some data formats.\"\"\"\n- data_df = pd.DataFrame([1, 2, 3])\n- apply_data_specific_configs({}, data_df, data_format)\n- if renames:\n- self.assertEqual(\n- data_df.columns[0],\n- \"value\",\n- f\"Data of type {data_format} should be renamed to 'value'\",\n- )\n- else:\n- self.assertEqual(\n- data_df.columns[0],\n- 0,\n- f\"Data of type {data_format} should not be renamed.\",\n- )\n-\n- def test_apply_data_specific_configs_disables_columns(self):\n- \"\"\"Test that Arrow incompatible columns are disabled (configured as non-editable).\"\"\"\n- columns_config: ColumnConfigMapping = {}\n- data_df = pd.DataFrame(\n- {\n- \"a\": pd.Series([1, 2]),\n- \"b\": pd.Series([\"foo\", \"bar\"]),\n- \"c\": pd.Series([1, \"foo\"]), # Incompatible\n- \"d\": pd.Series([1 + 2j, 3 + 4j]), # Incompatible\n- }\n- )\n-\n- apply_data_specific_configs(\n- columns_config,\n- data_df,\n- DataFormat.PANDAS_DATAFRAME,\n- check_arrow_compatibility=True,\n- )\n- self.assertNotIn(\"a\", columns_config)\n- self.assertNotIn(\"b\", columns_config)\n- self.assertTrue(columns_config[\"c\"][\"disabled\"])\n- self.assertTrue(columns_config[\"d\"][\"disabled\"])\n-\n def test_nan_as_value_raises_exception(self):\n \"\"\"Test that the usage of `nan` as value in column config raises an exception.\"\"\"\n \ndiff --git a/lib/tests/streamlit/elements/map_test.py b/lib/tests/streamlit/elements/map_test.py\nindex 06fbd078123c..3cd39d203929 100644\n--- a/lib/tests/streamlit/elements/map_test.py\n+++ b/lib/tests/streamlit/elements/map_test.py\n@@ -20,14 +20,13 @@\n \n import numpy as np\n import pandas as pd\n-import pytest\n from parameterized import parameterized\n \n import streamlit as st\n from streamlit.elements.map import _DEFAULT_MAP, _DEFAULT_ZOOM_LEVEL\n from streamlit.errors import StreamlitAPIException\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n-from tests.testutil import create_snowpark_session, patch_config_options\n+from tests.testutil import patch_config_options\n \n mock_df = pd.DataFrame({\"lat\": [1, 2, 3, 4], \"lon\": [10, 20, 30, 40]})\n \n@@ -348,44 +347,6 @@ def test_nan_exception(self):\n \n self.assertIn(\"not allowed to contain null values\", str(ctx.exception))\n \n- @pytest.mark.require_snowflake\n- def test_unevaluated_snowpark_table_integration(self):\n- \"\"\"Test st.map with unevaluated Snowpark DataFrame using real Snowflake instance\"\"\"\n- with create_snowpark_session() as snowpark_session:\n- table = snowpark_session.sql(\n- \"\"\"\n- SELECT V1.$1 AS \"lat\", V1.$2 AS \"lon\" FROM\n- (\n- VALUES (1, 10), (2, 20), (3, 30), (4, 40)\n- ) AS V1\n- \"\"\"\n- ).cache_result()\n- st.map(table)\n-\n- c = json.loads(self.get_delta_from_queue().new_element.deck_gl_json_chart.json)\n-\n- \"\"\"Check if map data have 4 rows\"\"\"\n- self.assertEqual(len(c[\"layers\"][0][\"data\"]), 4)\n-\n- @pytest.mark.require_snowflake\n- def test_unevaluated_snowpark_dataframe_integration(self):\n- \"\"\"Test st.map with unevaluated Snowpark DataFrame using real Snowflake instance\"\"\"\n- with create_snowpark_session() as snowpark_session:\n- df = snowpark_session.sql(\n- \"\"\"\n- SELECT V1.$1 AS \"lat\", V1.$2 AS \"lon\" FROM\n- (\n- VALUES (1, 10), (2, 20), (3, 30), (4, 40)\n- ) AS V1\n- \"\"\"\n- )\n- st.map(df)\n-\n- c = json.loads(self.get_delta_from_queue().new_element.deck_gl_json_chart.json)\n-\n- \"\"\"Check if map data have 4 rows\"\"\"\n- self.assertEqual(len(c[\"layers\"][0][\"data\"]), 4)\n-\n def test_id_changes_when_data_changes(self):\n st.map()\n \ndiff --git a/lib/tests/streamlit/runtime/caching/cache_errors_test.py b/lib/tests/streamlit/runtime/caching/cache_errors_test.py\nindex 0ace64eb3417..e91e7194386d 100644\n--- a/lib/tests/streamlit/runtime/caching/cache_errors_test.py\n+++ b/lib/tests/streamlit/runtime/caching/cache_errors_test.py\n@@ -13,30 +13,17 @@\n # limitations under the License.\n \n import threading\n-from typing import Any\n-\n-import numpy as np\n-import pandas as pd\n-from parameterized import parameterized\n \n import streamlit as st\n from streamlit.elements import exception\n from streamlit.proto.Exception_pb2 import Exception as ExceptionProto\n from streamlit.runtime.caching.cache_errors import (\n- UnevaluatedDataFrameError,\n UnhashableParamError,\n UnserializableReturnValueError,\n get_return_value_type,\n )\n from tests import testutil\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n-from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame\n-from tests.streamlit.modin_mocks import Series as ModinSeries\n-from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame\n-from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame\n-from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries\n-from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame\n-from tests.streamlit.snowpark_mocks import Table as SnowparkTable\n \n \n class CacheErrorsTest(DeltaGeneratorTestCase):\n@@ -46,8 +33,6 @@ class CacheErrorsTest(DeltaGeneratorTestCase):\n are testing them word-for-word as much as possible. Even though this\n *feels* like an antipattern, it isn't: we're making sure the codepaths\n that pull useful debug info from the code are working.\n-\n- TODO: parameterize these tests for both memo + singleton\n \"\"\"\n \n maxDiff = None\n@@ -111,32 +96,3 @@ def unserializable_return_value_func():\n )\n self.assertEqual(ep.message_is_markdown, True)\n self.assertEqual(ep.is_warning, False)\n-\n- @parameterized.expand(\n- [\n- (ModinDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (ModinSeries(pd.Series(np.random.randn(2))),),\n- (PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (SnowpandasSeries(pd.Series(np.random.randn(2))),),\n- (SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (SnowparkTable(pd.DataFrame(np.random.randn(2, 2))),),\n- ]\n- )\n- def test_unevaluated_dataframe_error(self, data: Any):\n- @st.cache_data\n- def unevaluated_dataframe_func():\n- return data\n-\n- with self.assertRaises(UnevaluatedDataFrameError) as cm:\n- unevaluated_dataframe_func()\n-\n- ep = ExceptionProto()\n- exception.marshall(ep, cm.exception)\n-\n- self.assertEqual(ep.type, \"UnevaluatedDataFrameError\")\n-\n- expected_message = \"Please call `collect()` or `to_pandas()` on the dataframe before returning it\"\n- self.assertTrue(expected_message in ep.message)\n- self.assertEqual(ep.message_is_markdown, True)\n- self.assertEqual(ep.is_warning, False)\ndiff --git a/lib/tests/streamlit/snowpandas_mocks.py b/lib/tests/streamlit/snowpandas_mocks.py\nindex 9a8b13113065..7d93b55e38cf 100644\n--- a/lib/tests/streamlit/snowpandas_mocks.py\n+++ b/lib/tests/streamlit/snowpandas_mocks.py\n@@ -41,7 +41,11 @@ def to_pandas(self) -> pd.DataFrame:\n \n def head(self, n: int) -> DataFrame:\n \"\"\"Returns the top n element of a mock version of Snowpark Pandas DataFrame\"\"\"\n- return DataFrame(self._data.head(n))\n+ return DataFrame(self[:n])\n+\n+ def __getitem__(self, key: slice | int) -> DataFrame:\n+ # Allow slicing and integer indexing\n+ return DataFrame(self._data[key])\n \n \n class Series:\n@@ -65,4 +69,8 @@ def to_pandas(self) -> pd.Series:\n \n def head(self, n: int) -> Series:\n \"\"\"Returns the top n element of a mock version of Snowpark Pandas Series\"\"\"\n- return Series(self._data.head(n))\n+ return Series(self[:n])\n+\n+ def __getitem__(self, key: slice | int) -> Series:\n+ # Allow slicing and integer indexing\n+ return Series(self._data[key])\ndiff --git a/lib/tests/streamlit/snowpark_mocks.py b/lib/tests/streamlit/snowpark_mocks.py\nindex 83ecc1c7ee37..555bd55605ea 100644\n--- a/lib/tests/streamlit/snowpark_mocks.py\n+++ b/lib/tests/streamlit/snowpark_mocks.py\n@@ -14,7 +14,7 @@\n \n from __future__ import annotations\n \n-from typing import TYPE_CHECKING\n+from typing import TYPE_CHECKING, Any\n \n if TYPE_CHECKING:\n import pandas as pd\n@@ -74,3 +74,9 @@ class Row:\n for testing purposes.\"\"\"\n \n __module__ = \"snowflake.snowpark.row\"\n+\n+ def __init__(self, row_data: dict[str, Any]):\n+ self._row_data: dict[str, Any] = row_data\n+\n+ def as_dict(self) -> dict[str, Any]:\n+ return self._row_data\ndiff --git a/lib/tests/streamlit/type_util_test.py b/lib/tests/streamlit/type_util_test.py\nindex f43d9de856b0..0731484e9c17 100644\n--- a/lib/tests/streamlit/type_util_test.py\n+++ b/lib/tests/streamlit/type_util_test.py\n@@ -16,6 +16,7 @@\n \n import unittest\n from collections import namedtuple\n+from typing import Any\n from unittest.mock import patch\n \n import numpy as np\n@@ -26,6 +27,10 @@\n \n from streamlit import type_util\n from streamlit.errors import StreamlitAPIException\n+from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders\n+from streamlit.runtime.secrets import Secrets\n+from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy\n+from streamlit.user_info import UserInfoProxy\n \n \n class TypeUtilTest(unittest.TestCase):\n@@ -129,3 +134,35 @@ def test_check_python_comparable_exception(self, sequence, type_str):\n ),\n str(exception_message.value),\n )\n+\n+ def test_has_callable_attr(self):\n+ class TestClass:\n+ def __init__(self) -> None:\n+ self.also_not_callable = \"I am not callable\"\n+\n+ def callable_attr(self):\n+ pass\n+\n+ @property\n+ def not_callable_attr(self):\n+ return \"I am a property\"\n+\n+ assert type_util.has_callable_attr(TestClass, \"callable_attr\") is True\n+ assert type_util.has_callable_attr(TestClass, \"not_callable_attr\") is False\n+ assert type_util.has_callable_attr(TestClass, \"also_not_callable\") is False\n+ assert type_util.has_callable_attr(TestClass, \"not_a_real_attr\") is False\n+\n+ @parameterized.expand(\n+ [\n+ ({\"key\": \"value\"}, False),\n+ (Secrets(), True),\n+ (QueryParamsProxy(), True),\n+ (SessionStateProxy(), True),\n+ (StreamlitCookies({}), True),\n+ (StreamlitHeaders({}), True),\n+ (UserInfoProxy(), True),\n+ ]\n+ )\n+ def test_is_custom_dict(self, dict_obj: Any, is_custom_dict: bool):\n+ \"\"\"Test that `is_custom_dict` returns True for all Streamlit custom dicts.\"\"\"\n+ assert type_util.is_custom_dict(dict_obj) is is_custom_dict\ndiff --git a/lib/tests/streamlit/write_test.py b/lib/tests/streamlit/write_test.py\nindex 151ebfad86db..852f982fd582 100644\n--- a/lib/tests/streamlit/write_test.py\n+++ b/lib/tests/streamlit/write_test.py\n@@ -34,15 +34,11 @@\n from streamlit.error_util import handle_uncaught_app_exception\n from streamlit.errors import StreamlitAPIException\n from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy\n-from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame\n-from tests.streamlit.modin_mocks import Series as ModinSeries\n-from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame\n+from tests.streamlit.data_mocks import (\n+ SHARED_TEST_CASES,\n+ CaseMetadata,\n+)\n from tests.streamlit.runtime.secrets_test import MOCK_TOML\n-from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame\n-from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries\n-from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame\n-from tests.streamlit.snowpark_mocks import Row as SnowparkRow\n-from tests.streamlit.snowpark_mocks import Table as SnowparkTable\n \n \n class StreamlitWriteTest(unittest.TestCase):\n@@ -168,6 +164,24 @@ class FakePyplot:\n \n p.assert_called_once()\n \n+ @parameterized.expand(\n+ SHARED_TEST_CASES,\n+ )\n+ def test_input_data(\n+ self,\n+ name: str,\n+ input_data: Any,\n+ metadata: CaseMetadata,\n+ ):\n+ \"\"\"Test st.write with various input data and check that it uses\n+ the expected command.\"\"\"\n+ with patch(\n+ f\"streamlit.delta_generator.DeltaGenerator.{metadata.expected_write_command}\"\n+ ) as p:\n+ st.write(input_data)\n+\n+ p.assert_called_once()\n+\n def test_plotly(self):\n import plotly.graph_objs as go\n \n@@ -177,13 +191,6 @@ def test_plotly(self):\n \n p.assert_called_once()\n \n- def test_dict(self):\n- \"\"\"Test st.write with dict.\"\"\"\n- with patch(\"streamlit.delta_generator.DeltaGenerator.json\") as p:\n- st.write({\"a\": 1, \"b\": 2})\n-\n- p.assert_called_once()\n-\n def test_pil_image(self):\n \"\"\"Test st.write with PIL image objects.\"\"\"\n with patch(\"streamlit.delta_generator.DeltaGenerator.image\") as p:\n@@ -223,13 +230,6 @@ class FakeOpenaiStream:\n \n p.assert_called_once()\n \n- def test_list(self):\n- \"\"\"Test st.write with list.\"\"\"\n- with patch(\"streamlit.delta_generator.DeltaGenerator.json\") as p:\n- st.write([1, 2, 3])\n-\n- p.assert_called_once()\n-\n def test_namedtuple(self):\n \"\"\"Test st.write with list.\"\"\"\n with patch(\"streamlit.delta_generator.DeltaGenerator.json\") as p:\n@@ -261,31 +261,6 @@ def test_streamlit_secrets(self, *mocks):\n \n p.assert_called_once()\n \n- @parameterized.expand(\n- [\n- (pd.DataFrame([[20, 30, 50]], columns=[\"a\", \"b\", \"c\"]),),\n- (pd.Series(np.array([\"a\", \"b\", \"c\"])),),\n- (pd.Index(list(\"abc\")),),\n- (pd.DataFrame({\"a\": [1], \"b\": [2]}).style.format(\"{:.2%}\"),),\n- (np.array([\"a\", \"b\", \"c\"]),),\n- (SnowpandasSeries(pd.Series(np.random.randn(2))),),\n- (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (SnowparkTable(pd.DataFrame(np.random.randn(2, 2))),),\n- (SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- (ModinSeries(pd.Series(np.random.randn(2))),),\n- (ModinDataFrame(pd.DataFrame(np.random.randn(2, 2))),),\n- ([SnowparkRow()],),\n- ]\n- )\n- def test_write_compatible_dataframe(\n- self,\n- input_data: Any,\n- ):\n- with patch(\"streamlit.delta_generator.DeltaGenerator.dataframe\") as p:\n- st.write(input_data)\n- p.assert_called_once()\n-\n @patch(\"streamlit.delta_generator.DeltaGenerator.markdown\")\n @patch(\"streamlit.delta_generator.DeltaGenerator.json\")\n def test_dict_and_string(self, mock_json, mock_markdown):\n" }
[ { "diff_hunk": "@@ -599,8 +594,10 @@ def table(self, data: Data = None) -> DeltaGenerator:\n \n \"\"\"\n \n- # Check if data is uncollected, and collect it but with 100 rows max, instead of 10k rows, which is done in all other cases.\n- # Avoid this and use 100 rows in st.table, because large tables render slowly, take too much screen space, and can crush the app.\n+ # Check if data is uncollected, and collect it but with 100 rows max, instead of\n+ # 10k rows, which is done in all other cases.\n+ # We usse 100 rows in st.table, because large tables render slowly,", "line": null, "original_line": 599, "original_start_line": null, "path": "lib/streamlit/elements/arrow.py", "start_line": null, "text": "@user1:\nnit: usse -> use\n\n@author:\nfixed ๐Ÿ‘" }, { "diff_hunk": "@@ -817,15 +817,31 @@ def data_editor(\n \n # Convert the user provided column config into the frontend compatible format:\n column_config_mapping = process_config_mapping(column_config)\n- apply_data_specific_configs(\n- column_config_mapping, data_df, data_format, check_arrow_compatibility=True\n- )\n+\n+ # Deactivate editing for columns that are not compatible with arrow\n+ for column_name, column_data in data_df.items():\n+ if dataframe_util.is_colum_type_arrow_incompatible(column_data):\n+ update_column_config(\n+ column_config_mapping, column_name, {\"disabled\": True}\n+ )\n+ # Convert incompatible type to string\n+ data_df[column_name] = column_data.astype(\"string\")\n+\n+ apply_data_specific_configs(column_config_mapping, data_format)\n \n # Fix the column headers to work correctly for data editing:\n _fix_column_headers(data_df)\n- # Temporary workaround: We hide range indices if num_rows is dynamic.\n- # since the current way of handling this index during editing is a bit confusing.\n- if isinstance(data_df.index, pd.RangeIndex) and num_rows == \"dynamic\":\n+\n+ if not isinstance(data_df.index, pd.RangeIndex):\n+ # If the index is not a range index, we will configure it as required\n+ # since the user is required to provide a (unique) value for editing.\n+ update_column_config(\n+ column_config_mapping, INDEX_IDENTIFIER, {\"required\": True}\n+ )\n+ elif num_rows == \"dynamic\":", "line": null, "original_line": 841, "original_start_line": null, "path": "lib/streamlit/elements/widgets/data_editor.py", "start_line": null, "text": "@user1:\nit looks like you could merge the `elif` branch with the `if hide_index` [check below](https://github.com/streamlit/streamlit/pull/9205/files#diff-e591171c22c1c88a8244dbf9fd17e4f30b2d9d9cd93f8bac5479cd1ecd992448R849) since `hidden` will be overridden by that check anyways (which seems to make the comment also a little bit inaccurate).\r\n```python\r\nif hide_index is not None and num_rows == \"dynamic\":\r\n```\n\n@author:\nI think it is not as easy to merge since: 1) this should only happen if the index is a range index and 2) the user should have the possibility to overwrite; and 3) if none of these conditions are True, it shouldn't set hidden at all. \r\n\r\nI pushed a small change to improve readability a bit." } ]
215d380016bd2a70a307d2a9b1f9c299841db5d7
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V_co = TypeVar( "V_co", covariant=True, # https://peps.python.org/pep-0484/#covariance-and-contravariance @@ -111,9 +122,11 @@ class DataFormat(Enum): PANDAS_DATAFRAME = auto() # pd.DataFrame PANDAS_SERIES = auto() # pd.Series PANDAS_INDEX = auto() # pd.Index + PANDAS_ARRAY = auto() # pd.array NUMPY_LIST = auto() # np.array[Scalar] NUMPY_MATRIX = auto() # np.array[List[Scalar]] PYARROW_TABLE = auto() # pyarrow.Table + PYARROW_ARRAY = auto() # pyarrow.Array SNOWPARK_OBJECT = auto() # Snowpark DataFrame, Table, List[Row] PYSPARK_OBJECT = auto() # pyspark.DataFrame MODIN_OBJECT = auto() # Modin DataFrame, Series @@ -136,9 +149,9 @@ def is_dataframe_like(obj: object) -> bool: This does not include basic collection types like list, dict, tuple, etc. """ - if obj is None or isinstance( - obj, (list, tuple, set, dict, str, bytes, int, float, bool) - ): + # We exclude list and dict here since there are some cases where a list or dict is + # considered a dataframe-like object. + if obj is None or isinstance(obj, (tuple, set, str, bytes, int, float, bool)): # Basic types are not considered dataframe-like, so we can # return False early to avoid unnecessary checks. return False @@ -148,13 +161,16 @@ def is_dataframe_like(obj: object) -> bool: DataFormat.PANDAS_SERIES, DataFormat.PANDAS_INDEX, DataFormat.PANDAS_STYLER, + DataFormat.PANDAS_ARRAY, DataFormat.NUMPY_LIST, DataFormat.NUMPY_MATRIX, DataFormat.PYARROW_TABLE, + DataFormat.PYARROW_ARRAY, DataFormat.SNOWPARK_OBJECT, DataFormat.PYSPARK_OBJECT, DataFormat.MODIN_OBJECT, DataFormat.SNOWPANDAS_OBJECT, + DataFormat.COLUMN_SERIES_MAPPING, ] @@ -166,6 +182,7 @@ def is_unevaluated_data_object(obj: object) -> bool: - PySpark DataFrame - Modin DataFrame / Series - Snowpandas DataFrame / Series + - Generator functions Unevaluated means that the data is not yet in the local memory. Unevaluated data objects are treated differently from other data objects by only @@ -176,9 +193,15 @@ def is_unevaluated_data_object(obj: object) -> bool: or is_pyspark_data_object(obj) or is_snowpandas_data_object(obj) or is_modin_data_object(obj) + or inspect.isgeneratorfunction(obj) ) +def is_pandas_data_object(obj: object) -> bool: + """True if obj is a Pandas object (e.g. DataFrame, Series, Index, Styler, ...).""" + return is_type(obj, _PANDAS_DATA_OBJECT_TYPE_RE) + + def is_snowpark_data_object(obj: object) -> bool: """True if obj is a Snowpark DataFrame or Table.""" return is_type(obj, _SNOWPARK_TABLE_TYPE_STR) or is_type(obj, _SNOWPARK_DF_TYPE_STR) @@ -186,13 +209,12 @@ def is_snowpark_data_object(obj: object) -> bool: def is_snowpark_row_list(obj: object) -> bool: """True if obj is a list of snowflake.snowpark.row.Row.""" - if not isinstance(obj, list): - return False - if len(obj) < 1: - return False - if not hasattr(obj[0], "__class__"): - return False - return is_type(obj[0], _SNOWPARK_DF_ROW_TYPE_STR) + return ( + isinstance(obj, list) + and len(obj) > 0 + and is_type(obj[0], _SNOWPARK_DF_ROW_TYPE_STR) + and has_callable_attr(obj[0], "as_dict") + ) def is_pyspark_data_object(obj: object) -> bool: @@ -221,6 +243,78 @@ def is_pandas_styler(obj: object) -> TypeGuard[Styler]: return is_type(obj, _PANDAS_STYLER_TYPE_STR) +def _is_list_of_scalars(data: Iterable[Any]) -> bool: + """Check if the list only contains scalar values.""" + from pandas.api.types import infer_dtype + + # Overview on all value that are interpreted as scalar: + # https://pandas.pydata.org/docs/reference/api/pandas.api.types.is_scalar.html + return infer_dtype(data, skipna=True) not in ["mixed", "unknown-array"] + + +def _iterable_to_list( + iterable: Iterable[Any], max_iterations: int | None = None +) -> list[Any]: + """Convert an iterable to a list. + + Parameters + ---------- + iterable : Iterable + The iterable to convert to a list. + + max_iterations : int or None + The maximum number of iterations to perform. If None, all iterations are performed. + + Returns + ------- + list + The converted list. + """ + if max_iterations is None: + return list(iterable) + + result = [] + for i, item in enumerate(iterable): + if i >= max_iterations: + break + result.append(item) + return result + + +def _fix_column_naming(data_df: DataFrame) -> DataFrame: + """Rename the first column to "value" if it is not named + and if there is only one column in the dataframe. + + The default name of the first column is 0 if it is not named + which is not very descriptive. + """ + + if len(data_df.columns) == 1 and data_df.columns[0] == 0: + # Pandas automatically names the first column with 0 if it is not named. + # We rename it to "value" to make it more descriptive if there is only + # one column in the dataframe. + data_df.rename(columns={0: "value"}, inplace=True) + return data_df + + +def _dict_to_pandas_df(data: dict[Any, Any]) -> DataFrame: + """Convert a key-value dict to a Pandas DataFrame. + + Parameters + ---------- + data : dict + The dict to convert to a Pandas DataFrame. + + Returns + ------- + pandas.DataFrame + The converted Pandas DataFrame. + """ + import pandas as pd + + return _fix_column_naming(pd.DataFrame.from_dict(data, orient="index")) + + def convert_anything_to_pandas_df( data: Any, max_unevaluated_rows: int = _MAX_UNEVALUATED_DF_ROWS, @@ -246,81 +340,123 @@ def convert_anything_to_pandas_df( pandas.DataFrame """ + import array + import numpy as np import pandas as pd if isinstance(data, pd.DataFrame): return data.copy() if ensure_copy else cast(pd.DataFrame, data) - if isinstance(data, (pd.Series, pd.Index)): + if isinstance(data, (pd.Series, pd.Index, pd.api.extensions.ExtensionArray)): return pd.DataFrame(data) if is_pandas_styler(data): return cast(pd.DataFrame, data.data.copy() if ensure_copy else data.data) if isinstance(data, np.ndarray): - return pd.DataFrame([]) if len(data.shape) == 0 else pd.DataFrame(data) + return ( + pd.DataFrame([]) + if len(data.shape) == 0 + else _fix_column_naming(pd.DataFrame(data)) + ) if is_modin_data_object(data): data = data.head(max_unevaluated_rows)._to_pandas() - if isinstance(data, pd.Series): + if isinstance(data, (pd.Series, pd.Index)): data = data.to_frame() if data.shape[0] == max_unevaluated_rows: - st.caption( + _show_data_information( f"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} " - "rows. Call `_to_pandas()` on the dataframe to show more." + "rows. Call `_to_pandas()` on the data object to show more." ) return cast(pd.DataFrame, data) if is_pyspark_data_object(data): data = data.limit(max_unevaluated_rows).toPandas() if data.shape[0] == max_unevaluated_rows: - st.caption( + _show_data_information( + f"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} " + "rows. Call `toPandas()` on the data object to show more." + ) + return cast(pd.DataFrame, data) + + if is_snowpandas_data_object(data): + data = data[:max_unevaluated_rows].to_pandas() + + if isinstance(data, (pd.Series, pd.Index)): + data = data.to_frame() + + if data.shape[0] == max_unevaluated_rows: + _show_data_information( f"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} " - "rows. Call `toPandas()` on the dataframe to show more." + "rows. Call `to_pandas()` on the data object to show more." ) return cast(pd.DataFrame, data) if is_snowpark_data_object(data): data = data.limit(max_unevaluated_rows).to_pandas() if data.shape[0] == max_unevaluated_rows: - st.caption( + _show_data_information( f"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} " - "rows. Call `to_pandas()` on the dataframe to show more." + "rows. Call `to_pandas()` on the data object to show more." ) return cast(pd.DataFrame, data) - if is_snowpandas_data_object(data): - data = data.head(max_unevaluated_rows).to_pandas() + if is_snowpark_row_list(data): + return pd.DataFrame([row.as_dict() for row in data]) - if isinstance(data, pd.Series): - data = data.to_frame() + if has_callable_attr(data, "to_pandas"): + return pd.DataFrame(data.to_pandas()) + + # Support for generator functions + if inspect.isgeneratorfunction(data): + data = _fix_column_naming( + pd.DataFrame(_iterable_to_list(data(), max_iterations=max_unevaluated_rows)) + ) if data.shape[0] == max_unevaluated_rows: - st.caption( + _show_data_information( f"โš ๏ธ Showing only {string_util.simplify_number(max_unevaluated_rows)} " - "rows. Call `to_pandas()` on the dataframe to show more." + "rows. Convert the data to a list to show more." ) - return cast(pd.DataFrame, data) + return data - # This is inefficient when data is a pyarrow.Table as it will be converted - # back to Arrow when marshalled to protobuf, but area/bar/line charts need - # DataFrame magic to generate the correct output. - if hasattr(data, "to_pandas"): - return pd.DataFrame(data.to_pandas()) + if isinstance(data, EnumMeta): + # Support for enum classes + return _fix_column_naming(pd.DataFrame([c.value for c in data])) # type: ignore + + # Support for some list like objects + if isinstance(data, (deque, map, array.ArrayType)): + return _fix_column_naming(pd.DataFrame(list(data))) + + # Support for Streamlit's custom dict-like objects + if is_custom_dict(data): + return _dict_to_pandas_df(data.to_dict()) + + # Support for named tuples + if is_namedtuple(data): + return _dict_to_pandas_df(data._asdict()) + + # Support for dataclass instances + if is_dataclass_instance(data): + return _dict_to_pandas_df(dataclasses.asdict(data)) + + # Support for dict-like objects + if isinstance(data, (ChainMap, MappingProxyType, UserDict)): + return _dict_to_pandas_df(dict(data)) # Try to convert to pandas.DataFrame. This will raise an error is df is not # compatible with the pandas.DataFrame constructor. try: - return pd.DataFrame(data) - + return _fix_column_naming(pd.DataFrame(data)) except ValueError as ex: if isinstance(data, dict): with contextlib.suppress(ValueError): # Try to use index orient as back-up to support key-value dicts - return pd.DataFrame.from_dict(data, orient="index") + return _dict_to_pandas_df(data) raise errors.StreamlitAPIException( f""" Unable to convert object of type `{type(data)}` to `pandas.DataFrame`. @@ -419,6 +555,14 @@ def convert_arrow_bytes_to_pandas_df(source: bytes) -> DataFrame: return reader.read_pandas() +def _show_data_information(msg: str) -> None: + """Show a message to the user with important information + about the processed dataset.""" + from streamlit.delta_generator import main_dg + + main_dg.caption(msg) + + def convert_anything_to_arrow_bytes( data: Any, max_unevaluated_rows: int = _MAX_UNEVALUATED_DF_ROWS, @@ -449,8 +593,16 @@ def convert_anything_to_arrow_bytes( if isinstance(data, pa.Table): return convert_arrow_table_to_arrow_bytes(data) + if is_pandas_data_object(data): + # All pandas data objects should be handled via our pandas + # conversion logic. We are already calling it here + # to ensure that its not handled via the interchange + # protocol support below. + df = convert_anything_to_pandas_df(data, max_unevaluated_rows) + return convert_pandas_df_to_arrow_bytes(df) + # Fallback: try to convert to pandas DataFrame - # and then to Arrow bytes + # and then to Arrow bytes. df = convert_anything_to_pandas_df(data, max_unevaluated_rows) return convert_pandas_df_to_arrow_bytes(df) @@ -475,7 +627,9 @@ def convert_anything_to_sequence(obj: OptionSequence[V_co]) -> Sequence[V_co]: if obj is None: return [] # type: ignore - if isinstance(obj, (str, list, tuple, set, range, EnumMeta)): + if isinstance( + obj, (str, list, tuple, set, range, EnumMeta, deque, map) + ) and not is_snowpark_row_list(obj): # This also ensures that the sequence is copied to prevent # potential mutations to the original object. return list(obj) @@ -569,8 +723,7 @@ def _maybe_truncate_table( # we just display the exact numbers. displayed_rows = str(table.num_rows) total_rows = str(table.num_rows + truncated_rows) - - st.caption( + _show_data_information( f"โš ๏ธ Showing {displayed_rows} out of {total_rows} " "rows due to data size limitations." ) @@ -579,7 +732,8 @@ def _maybe_truncate_table( def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool: - """Return True if the column type is known to cause issues during Arrow conversion.""" + """Return True if the column type is known to cause issues during + Arrow conversion.""" from pandas.api.types import infer_dtype, is_dict_like, is_list_like if column.dtype.kind in [ @@ -610,7 +764,8 @@ def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool: ]: return True elif inferred_type == "mixed": - # This includes most of the more complex/custom types (objects, dicts, lists, ...) + # This includes most of the more complex/custom types (objects, dicts, + # lists, ...) if len(column) == 0 or not hasattr(column, "iloc"): # The column seems to be invalid, so we assume it is incompatible. # But this would most likely never happen since empty columns @@ -622,7 +777,8 @@ def is_colum_type_arrow_incompatible(column: Series[Any] | Index) -> bool: if ( not is_list_like(first_value) - # dicts are list-like, but have issues in Arrow JS (see comments in Quiver.ts) + # dicts are list-like, but have issues in Arrow JS (see comments in + # Quiver.ts) or is_dict_like(first_value) # Frozensets are list-like, but are not compatible with pyarrow. or isinstance(first_value, frozenset) @@ -684,15 +840,6 @@ def fix_arrow_incompatible_column_types( return df_copy if df_copy is not None else df -def _is_list_of_scalars(data: Iterable[Any]) -> bool: - """Check if the list only contains scalar values.""" - from pandas.api.types import infer_dtype - - # Overview on all value that are interpreted as scalar: - # https://pandas.pydata.org/docs/reference/api/pandas.api.types.is_scalar.html - return infer_dtype(data, skipna=True) not in ["mixed", "unknown-array"] - - def determine_data_format(input_data: Any) -> DataFormat: """Determine the data format of the input data. @@ -706,6 +853,8 @@ def determine_data_format(input_data: Any) -> DataFormat: DataFormat The data format of the input data. """ + import array + import numpy as np import pandas as pd import pyarrow as pa @@ -722,26 +871,43 @@ def determine_data_format(input_data: Any) -> DataFormat: return DataFormat.NUMPY_MATRIX elif isinstance(input_data, pa.Table): return DataFormat.PYARROW_TABLE + elif isinstance(input_data, pa.Array): + return DataFormat.PYARROW_ARRAY elif isinstance(input_data, pd.Series): return DataFormat.PANDAS_SERIES elif isinstance(input_data, pd.Index): return DataFormat.PANDAS_INDEX elif is_pandas_styler(input_data): return DataFormat.PANDAS_STYLER - elif is_snowpark_data_object(input_data): - return DataFormat.SNOWPARK_OBJECT + elif isinstance(input_data, pd.api.extensions.ExtensionArray): + return DataFormat.PANDAS_ARRAY elif is_modin_data_object(input_data): return DataFormat.MODIN_OBJECT elif is_snowpandas_data_object(input_data): return DataFormat.SNOWPANDAS_OBJECT elif is_pyspark_data_object(input_data): return DataFormat.PYSPARK_OBJECT - elif isinstance(input_data, (list, tuple, set)): + elif is_snowpark_data_object(input_data) or is_snowpark_row_list(input_data): + return DataFormat.SNOWPARK_OBJECT + elif isinstance( + input_data, (range, EnumMeta, KeysView, ValuesView, deque, map, array.ArrayType) + ): + return DataFormat.LIST_OF_VALUES + elif ( + isinstance(input_data, (ChainMap, MappingProxyType, UserDict)) + or is_dataclass_instance(input_data) + or is_namedtuple(input_data) + or is_custom_dict(input_data) + ): + return DataFormat.KEY_VALUE_DICT + elif isinstance(input_data, (ItemsView, enumerate)): + return DataFormat.LIST_OF_ROWS + elif isinstance(input_data, (list, tuple, set, frozenset)): if _is_list_of_scalars(input_data): # -> one-dimensional data structure if isinstance(input_data, tuple): return DataFormat.TUPLE_OF_VALUES - if isinstance(input_data, set): + if isinstance(input_data, (set, frozenset)): return DataFormat.SET_OF_VALUES return DataFormat.LIST_OF_VALUES else: @@ -751,23 +917,23 @@ def determine_data_format(input_data: Any) -> DataFormat: first_element = next(iter(input_data)) if isinstance(first_element, dict): return DataFormat.LIST_OF_RECORDS - if isinstance(first_element, (list, tuple, set)): + if isinstance(first_element, (list, tuple, set, frozenset)): return DataFormat.LIST_OF_ROWS elif isinstance(input_data, dict): if not input_data: return DataFormat.KEY_VALUE_DICT if len(input_data) > 0: first_value = next(iter(input_data.values())) + # In the future, we could potentially also support tight & split formats if isinstance(first_value, dict): return DataFormat.COLUMN_INDEX_MAPPING if isinstance(first_value, (list, tuple)): return DataFormat.COLUMN_VALUE_MAPPING if isinstance(first_value, pd.Series): return DataFormat.COLUMN_SERIES_MAPPING - # In the future, we could potentially also support the tight & split formats here - if _is_list_of_scalars(input_data.values()): - # Only use the key-value dict format if the values are only scalar values - return DataFormat.KEY_VALUE_DICT + # Use key-value dict as fallback. However, if the values of the dict + # contains mixed types, it will become non-editable in the frontend. + return DataFormat.KEY_VALUE_DICT return DataFormat.UNKNOWN @@ -783,12 +949,30 @@ def _unify_missing_values(df: DataFrame) -> DataFrame: return df.fillna(np.nan).replace([np.nan], [None]) +def _pandas_df_to_series(df: DataFrame) -> Series[Any]: + """Convert a Pandas DataFrame to a Pandas Series by selecting the first column. + + Raises + ------ + ValueError + If the DataFrame has more than one column. + """ + # Select first column in dataframe and create a new series based on the values + if len(df.columns) != 1: + raise ValueError( + "DataFrame is expected to have a single column but " + f"has {len(df.columns)}." + ) + return df[df.columns[0]] + + def convert_pandas_df_to_data_format( df: DataFrame, data_format: DataFormat ) -> ( DataFrame | Series[Any] | pa.Table + | pa.Array | np.ndarray[Any, np.dtype[Any]] | tuple[Any] | list[Any] @@ -818,6 +1002,7 @@ def convert_pandas_df_to_data_format( DataFormat.PYSPARK_OBJECT, DataFormat.PANDAS_INDEX, DataFormat.PANDAS_STYLER, + DataFormat.PANDAS_ARRAY, DataFormat.MODIN_OBJECT, DataFormat.SNOWPANDAS_OBJECT, ]: @@ -838,13 +1023,12 @@ def convert_pandas_df_to_data_format( import pyarrow as pa return pa.Table.from_pandas(df) + elif data_format == DataFormat.PYARROW_ARRAY: + import pyarrow as pa + + return pa.Array.from_pandas(_pandas_df_to_series(df)) elif data_format == DataFormat.PANDAS_SERIES: - # Select first column in dataframe and create a new series based on the values - if len(df.columns) != 1: - raise ValueError( - f"DataFrame is expected to have a single column but has {len(df.columns)}." - ) - return df[df.columns[0]] + return _pandas_df_to_series(df) elif data_format == DataFormat.LIST_OF_RECORDS: return _unify_missing_values(df).to_dict(orient="records") elif data_format == DataFormat.LIST_OF_ROWS: @@ -868,7 +1052,8 @@ def convert_pandas_df_to_data_format( return_list = df[df.columns[0]].tolist() elif len(df.columns) >= 1: raise ValueError( - f"DataFrame is expected to have a single column but has {len(df.columns)}." + "DataFrame is expected to have a single column but " + f"has {len(df.columns)}." ) if data_format == DataFormat.TUPLE_OF_VALUES: return tuple(return_list) diff --git a/lib/streamlit/delta_generator.py b/lib/streamlit/delta_generator.py index 063b5c385997..e05fa0a0c0e6 100644 --- a/lib/streamlit/delta_generator.py +++ b/lib/streamlit/delta_generator.py @@ -708,7 +708,8 @@ def _prep_data_for_add_rows( ) -> tuple[Data, AddRowsMetadata | None]: if not add_rows_metadata: if dataframe_util.is_pandas_styler(data): - # When calling add_rows on st.table or st.dataframe we want styles to pass through. + # When calling add_rows on st.table or st.dataframe we want styles to + # pass through. return data, None return dataframe_util.convert_anything_to_pandas_df(data), None diff --git a/lib/streamlit/elements/arrow.py b/lib/streamlit/elements/arrow.py index ef978847530a..5ce7ced70b85 100644 --- a/lib/streamlit/elements/arrow.py +++ b/lib/streamlit/elements/arrow.py @@ -513,12 +513,7 @@ def dataframe( data_df = dataframe_util.convert_anything_to_pandas_df( data, ensure_copy=False ) - apply_data_specific_configs( - column_config_mapping, - data_df, - data_format, - check_arrow_compatibility=False, - ) + apply_data_specific_configs(column_config_mapping, data_format) # Serialize the data to bytes: proto.data = dataframe_util.convert_pandas_df_to_arrow_bytes(data_df) @@ -599,8 +594,10 @@ def table(self, data: Data = None) -> DeltaGenerator: """ - # Check if data is uncollected, and collect it but with 100 rows max, instead of 10k rows, which is done in all other cases. - # Avoid this and use 100 rows in st.table, because large tables render slowly, take too much screen space, and can crush the app. + # Check if data is uncollected, and collect it but with 100 rows max, instead of + # 10k rows, which is done in all other cases. + # We use 100 rows in st.table, because large tables render slowly, + # take too much screen space, and can crush the app. if dataframe_util.is_unevaluated_data_object(data): data = dataframe_util.convert_anything_to_pandas_df( data, max_unevaluated_rows=100 diff --git a/lib/streamlit/elements/json.py b/lib/streamlit/elements/json.py index 79b2aec908d9..cbcd9bf33053 100644 --- a/lib/streamlit/elements/json.py +++ b/lib/streamlit/elements/json.py @@ -15,13 +15,13 @@ from __future__ import annotations import json +import types +from collections import ChainMap, UserDict from typing import TYPE_CHECKING, Any, cast from streamlit.proto.Json_pb2 import Json as JsonProto -from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders from streamlit.runtime.metrics_util import gather_metrics -from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy -from streamlit.user_info import UserInfoProxy +from streamlit.type_util import is_custom_dict, is_namedtuple if TYPE_CHECKING: from streamlit.delta_generator import DeltaGenerator @@ -79,18 +79,18 @@ def json( """ import streamlit as st - if isinstance( - body, - ( - SessionStateProxy, - UserInfoProxy, - QueryParamsProxy, - StreamlitHeaders, - StreamlitCookies, - ), - ): + if is_custom_dict(body): body = body.to_dict() + if is_namedtuple(body): + body = body._asdict() + + if isinstance(body, (map, enumerate)): + body = list(body) + + if isinstance(body, (ChainMap, types.MappingProxyType, UserDict)): + body = dict(body) + if not isinstance(body, str): try: # Serialize body to string and try to interpret sets as lists diff --git a/lib/streamlit/elements/lib/column_config_utils.py b/lib/streamlit/elements/lib/column_config_utils.py index 8e39d3f0f4a8..4c27c47241c7 100644 --- a/lib/streamlit/elements/lib/column_config_utils.py +++ b/lib/streamlit/elements/lib/column_config_utils.py @@ -21,7 +21,7 @@ from typing_extensions import TypeAlias -from streamlit.dataframe_util import DataFormat, is_colum_type_arrow_incompatible +from streamlit.dataframe_util import DataFormat from streamlit.elements.lib.column_types import ColumnConfig, ColumnType from streamlit.elements.lib.dicttools import remove_none_values from streamlit.errors import StreamlitAPIException @@ -466,9 +466,7 @@ def update_column_config( def apply_data_specific_configs( columns_config: ColumnConfigMapping, - data_df: DataFrame, data_format: DataFormat, - check_arrow_compatibility: bool = False, ) -> None: """Apply data specific configurations to the provided dataframe. @@ -480,24 +478,9 @@ def apply_data_specific_configs( columns_config : ColumnConfigMapping A mapping of column names/ids to column configurations. - data_df : pd.DataFrame - The dataframe to apply the configurations to. - data_format : DataFormat The format of the data. - - check_arrow_compatibility : bool - Whether to check if the data is compatible with arrow. """ - import pandas as pd - - # Deactivate editing for columns that are not compatible with arrow - if check_arrow_compatibility: - for column_name, column_data in data_df.items(): - if is_colum_type_arrow_incompatible(column_data): - update_column_config(columns_config, column_name, {"disabled": True}) - # Convert incompatible type to string - data_df[column_name] = column_data.astype("string") # Pandas adds a range index as default to all datastructures # but for most of the non-pandas data objects it is unnecessary @@ -514,23 +497,6 @@ def apply_data_specific_configs( ]: update_column_config(columns_config, INDEX_IDENTIFIER, {"hidden": True}) - # Rename the first column to "value" for some of the data formats - if data_format in [ - DataFormat.SET_OF_VALUES, - DataFormat.TUPLE_OF_VALUES, - DataFormat.LIST_OF_VALUES, - DataFormat.NUMPY_LIST, - DataFormat.KEY_VALUE_DICT, - ]: - # Pandas automatically names the first column "0" - # We rename it to "value" in selected cases to make it more descriptive - data_df.rename(columns={0: "value"}, inplace=True) - - if not isinstance(data_df.index, pd.RangeIndex): - # If the index is not a range index, we will configure it as required - # since the user is required to provide a (unique) value for editing. - update_column_config(columns_config, INDEX_IDENTIFIER, {"required": True}) - def _convert_column_config_to_json(column_config_mapping: ColumnConfigMapping) -> str: try: diff --git a/lib/streamlit/elements/widgets/data_editor.py b/lib/streamlit/elements/widgets/data_editor.py index d9241790b80c..420df9f85e2d 100644 --- a/lib/streamlit/elements/widgets/data_editor.py +++ b/lib/streamlit/elements/widgets/data_editor.py @@ -415,8 +415,8 @@ def _is_supported_index(df_index: pd.Index) -> bool: # Period type isn't editable currently: # pd.PeriodIndex, ] - # We need to check these index types without importing, since they are deprecated - # and planned to be removed soon. + # We need to check these index types without importing, since they are + # deprecated and planned to be removed soon. or is_type(df_index, "pandas.core.indexes.numeric.Int64Index") or is_type(df_index, "pandas.core.indexes.numeric.Float64Index") or is_type(df_index, "pandas.core.indexes.numeric.UInt64Index") @@ -817,19 +817,38 @@ def data_editor( # Convert the user provided column config into the frontend compatible format: column_config_mapping = process_config_mapping(column_config) - apply_data_specific_configs( - column_config_mapping, data_df, data_format, check_arrow_compatibility=True - ) + + # Deactivate editing for columns that are not compatible with arrow + for column_name, column_data in data_df.items(): + if dataframe_util.is_colum_type_arrow_incompatible(column_data): + update_column_config( + column_config_mapping, column_name, {"disabled": True} + ) + # Convert incompatible type to string + data_df[column_name] = column_data.astype("string") + + apply_data_specific_configs(column_config_mapping, data_format) # Fix the column headers to work correctly for data editing: _fix_column_headers(data_df) - # Temporary workaround: We hide range indices if num_rows is dynamic. - # since the current way of handling this index during editing is a bit confusing. - if isinstance(data_df.index, pd.RangeIndex) and num_rows == "dynamic": + + has_range_index = isinstance(data_df.index, pd.RangeIndex) + + if not has_range_index: + # If the index is not a range index, we will configure it as required + # since the user is required to provide a (unique) value for editing. update_column_config( - column_config_mapping, INDEX_IDENTIFIER, {"hidden": True} + column_config_mapping, INDEX_IDENTIFIER, {"required": True} ) + if hide_index is None and has_range_index and num_rows == "dynamic": + # Temporary workaround: + # We hide range indices if num_rows is dynamic. + # since the current way of handling this index during editing is a + # bit confusing. The user can still decide to show the index by + # setting hide_index explicitly to False. + hide_index = True + if hide_index is not None: update_column_config( column_config_mapping, INDEX_IDENTIFIER, {"hidden": hide_index} diff --git a/lib/streamlit/elements/write.py b/lib/streamlit/elements/write.py index 5046d8fb0394..d06dd9ef4a28 100644 --- a/lib/streamlit/elements/write.py +++ b/lib/streamlit/elements/write.py @@ -16,23 +16,20 @@ import dataclasses import inspect -import json import types +from collections import ChainMap, UserDict from io import StringIO from typing import TYPE_CHECKING, Any, Callable, Final, Generator, Iterable, List, cast from streamlit import dataframe_util, type_util from streamlit.errors import StreamlitAPIException from streamlit.logger import get_logger -from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders from streamlit.runtime.metrics_util import gather_metrics -from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy from streamlit.string_util import ( is_mem_address_str, max_char_sequence, probably_contains_html_tags, ) -from streamlit.user_info import UserInfoProxy if TYPE_CHECKING: from streamlit.delta_generator import DeltaGenerator @@ -252,7 +249,8 @@ def write(self, *args: Any, unsafe_allow_html: bool = False, **kwargs) -> None: - write(string) : Prints the formatted Markdown string, with support for LaTeX expression, emoji shortcodes, and colored text. See docs for st.markdown for more. - - write(data_frame) : Displays the DataFrame as a table. + - write(data_frame) : Displays any dataframe-compatible value + as read-only table. - write(error) : Prints an exception specially. - write(func) : Displays information about a function. - write(module) : Displays information about the module. @@ -408,9 +406,7 @@ def flush_buffer(): elif isinstance(arg, Exception): flush_buffer() self.dg.exception(arg) - elif dataframe_util.is_dataframe_like( - arg - ) or dataframe_util.is_snowpark_row_list(arg): + elif dataframe_util.is_dataframe_like(arg): flush_buffer() self.dg.dataframe(arg) elif type_util.is_altair_chart(arg): @@ -440,23 +436,24 @@ def flush_buffer(): flush_buffer() dot = vis_utils.model_to_dot(arg) self.dg.graphviz_chart(dot.to_string()) - elif isinstance( - arg, - ( - dict, - list, - SessionStateProxy, - UserInfoProxy, - QueryParamsProxy, - StreamlitHeaders, - StreamlitCookies, - ), + elif ( + isinstance( + arg, + ( + dict, + list, + map, + enumerate, + types.MappingProxyType, + UserDict, + ChainMap, + ), + ) + or type_util.is_custom_dict(arg) + or type_util.is_namedtuple(arg) ): flush_buffer() self.dg.json(arg) - elif type_util.is_namedtuple(arg): - flush_buffer() - self.dg.json(json.dumps(arg._asdict())) elif type_util.is_pydeck(arg): flush_buffer() self.dg.pydeck_chart(arg) @@ -482,16 +479,12 @@ def flush_buffer(): # https://github.com/python/mypy/issues/12933 self.dg.help(cast(type, arg)) elif ( - hasattr(arg, "_repr_html_") - and callable(arg._repr_html_) + type_util.has_callable_attr(arg, "_repr_html_") and (repr_html := arg._repr_html_()) and (unsafe_allow_html or not probably_contains_html_tags(repr_html)) ): # We either explicitly allow HTML or infer it's not HTML self.dg.markdown(repr_html, unsafe_allow_html=unsafe_allow_html) - elif type_util.is_streamlit_secrets_class(arg): - flush_buffer() - self.dg.json(arg.to_dict()) else: stringified_arg = str(arg) diff --git a/lib/streamlit/runtime/caching/cache_utils.py b/lib/streamlit/runtime/caching/cache_utils.py index 97a86e8654c1..51e2d8a38dcd 100644 --- a/lib/streamlit/runtime/caching/cache_utils.py +++ b/lib/streamlit/runtime/caching/cache_utils.py @@ -299,20 +299,23 @@ def _handle_cache_miss( try: cache.write_result(value_key, computed_value, messages) return computed_value - except (CacheError, RuntimeError): - # An exception was thrown while we tried to write to the cache. Report it to the user. - # (We catch `RuntimeError` here because it will be raised by Apache Spark if we do not - # collect dataframe before using `st.cache_data`.) + except (CacheError, RuntimeError) as ex: + # An exception was thrown while we tried to write to the cache. Report + # it to the user. (We catch `RuntimeError` here because it will be + # raised by Apache Spark if we do not collect dataframe before + # using `st.cache_data`.) if is_unevaluated_data_object(computed_value): # If the returned value is an unevaluated dataframe, raise an error. # Unevaluated dataframes are not yet in the local memory, which also # means they cannot be properly cached (serialized). raise UnevaluatedDataFrameError( - f""" - The function {get_cached_func_name_md(self._info.func)} is decorated with `st.cache_data` but it returns an unevaluated dataframe - of type `{type_util.get_fqn_type(computed_value)}`. Please call `collect()` or `to_pandas()` on the dataframe before returning it, - so `st.cache_data` can serialize and cache it.""" - ) + f"The function {get_cached_func_name_md(self._info.func)} is " + "decorated with `st.cache_data` but it returns an unevaluated " + f"data object of type `{type_util.get_fqn_type(computed_value)}`. " + "Please convert the object to a serializable format " + "(e.g. Pandas DataFrame) before returning it, so " + "`st.cache_data` can serialize and cache it." + ) from ex raise UnserializableReturnValueError( return_value=computed_value, func=self._info.func ) diff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py index 8f3d169d4894..1985deacb107 100644 --- a/lib/streamlit/type_util.py +++ b/lib/streamlit/type_util.py @@ -16,6 +16,7 @@ from __future__ import annotations +import dataclasses import re import types from typing import ( @@ -42,7 +43,6 @@ from plotly.graph_objs import Figure from pydeck import Deck - from streamlit.runtime.secrets import Secrets T = TypeVar("T") @@ -51,6 +51,16 @@ class SupportsStr(Protocol): def __str__(self) -> str: ... +class CustomDict(Protocol): + """Protocol for Streamlit native custom dictionaries (e.g. session state, secrets, query params). + that can be converted to a dict. + + All these implementations should provide a to_dict method. + """ + + def to_dict(self) -> dict[str, Any]: ... + + @overload def is_type( obj: object, fqn_type_pattern: Literal["pydeck.bindings.deck.Deck"] @@ -246,15 +256,22 @@ def is_function(x: object) -> TypeGuard[types.FunctionType]: return isinstance(x, types.FunctionType) +def has_callable_attr(obj: object, name: str) -> bool: + """True if obj has the specified attribute that is callable.""" + return hasattr(obj, name) and callable(getattr(obj, name)) + + def is_namedtuple(x: object) -> TypeGuard[NamedTuple]: - t = type(x) - b = t.__bases__ - if len(b) != 1 or b[0] is not tuple: - return False - f = getattr(t, "_fields", None) - if not isinstance(f, tuple): - return False - return all(type(n).__name__ == "str" for n in f) + """True if obj is an instance of a namedtuple.""" + return isinstance(x, tuple) and has_callable_attr(x, "_asdict") + + +def is_dataclass_instance(obj: object) -> bool: + """True if obj is an instance of a dataclass.""" + # The not isinstance(obj, type) check is needed to make sure that this + # is an instance of a dataclass and not the class itself. + # dataclasses.is_dataclass returns True for either instance or class. + return dataclasses.is_dataclass(obj) and not isinstance(obj, type) def is_pydeck(obj: object) -> TypeGuard[Deck]: @@ -262,6 +279,26 @@ def is_pydeck(obj: object) -> TypeGuard[Deck]: return is_type(obj, "pydeck.bindings.deck.Deck") +def is_custom_dict(obj: object) -> TypeGuard[CustomDict]: + """True if input looks like one of the Streamlit custom dictionaries.""" + from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders + from streamlit.runtime.secrets import Secrets + from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy + from streamlit.user_info import UserInfoProxy + + return isinstance( + obj, + ( + SessionStateProxy, + UserInfoProxy, + QueryParamsProxy, + StreamlitHeaders, + StreamlitCookies, + Secrets, + ), + ) and has_callable_attr(obj, "to_dict") + + def is_iterable(obj: object) -> TypeGuard[Iterable[Any]]: try: # The ignore statement here is intentional, as this is a @@ -272,11 +309,6 @@ def is_iterable(obj: object) -> TypeGuard[Iterable[Any]]: return True -def is_streamlit_secrets_class(obj: object) -> TypeGuard[Secrets]: - """True if obj is a Streamlit Secrets object.""" - return is_type(obj, "streamlit.runtime.secrets.Secrets") - - def is_sequence(seq: Any) -> bool: """True if input looks like a sequence.""" if isinstance(seq, str): diff --git a/lib/tests/streamlit/data_mocks.py b/lib/tests/streamlit/data_mocks.py index 8c26caa0aca0..f1bc83860fcb 100644 --- a/lib/tests/streamlit/data_mocks.py +++ b/lib/tests/streamlit/data_mocks.py @@ -14,225 +14,885 @@ from __future__ import annotations +import array import enum import random +from collections import ChainMap, Counter, OrderedDict, UserDict, defaultdict, deque +from dataclasses import dataclass from datetime import date -from typing import NamedTuple +from types import MappingProxyType +from typing import Any, Literal, NamedTuple, TypedDict import numpy as np import pandas as pd import pyarrow as pa from streamlit.dataframe_util import DataFormat -from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame +from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame +from tests.streamlit.modin_mocks import Series as ModinSeries +from tests.streamlit.pyspark_mocks import DataFrame as PySparkDataFrame from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame +from tests.streamlit.snowpark_mocks import Row as SnowparkRow from tests.streamlit.snowpark_mocks import Table as SnowparkTable np.random.seed(0) random.seed(0) -class TestCaseMetadata(NamedTuple): +class CaseMetadata(NamedTuple): + # The expected number of rows expected_rows: int + # The expected number of columns (doesn't include index columns) expected_cols: int + # The expected data format expected_data_format: DataFormat + # The expected sequence when the data is converted to a sequence + # If None, the sequence is not checked. + expected_sequence: list[Any] | None + # The expected command used when the data is written via `st.write` + expected_write_command: Literal[ + "markdown", "dataframe", "json", "help", "write_stream" + ] + # Whether the data structure is unevaluated and will be truncated + # if it is too large. + is_unevaluated: bool + # The expected return type of the data when it is + # returned from the `st.data_editor` function. + expected_type: type | None = None - # Tell pytest this is not a TestClass despite having "Test" in the name. - __test__ = False - - -SHARED_TEST_CASES = [ - # None: - (None, TestCaseMetadata(0, 0, DataFormat.EMPTY)), - # Empty list: - ([], TestCaseMetadata(0, 0, DataFormat.LIST_OF_VALUES)), - # Empty tuple: - ((), TestCaseMetadata(0, 0, DataFormat.TUPLE_OF_VALUES)), - # Empty dict (not a an empty set!) - ({}, TestCaseMetadata(0, 0, DataFormat.KEY_VALUE_DICT)), - # Empty set: - (set(), TestCaseMetadata(0, 0, DataFormat.SET_OF_VALUES)), - # Empty numpy array: - # for unknown reasons, pd.DataFrame initializes empty numpy arrays with a single column - (np.ndarray(0), TestCaseMetadata(0, 1, DataFormat.NUMPY_LIST)), - # Empty column value mapping with columns: - ({"name": [], "type": []}, TestCaseMetadata(0, 2, DataFormat.COLUMN_VALUE_MAPPING)), - # Empty dataframe: - (pd.DataFrame(), TestCaseMetadata(0, 0, DataFormat.PANDAS_DATAFRAME)), - # Empty dataframe with columns: + +@dataclass +class ElementDataClass: + name: str + is_widget: bool + usage: float + + +class ElementNamedTuple(NamedTuple): + name: str + is_widget: bool + usage: float + + +class ElementTypedDict(TypedDict): + name: str + is_widget: bool + usage: float + + +class UserDictExample(UserDict): # type: ignore + pass + + +class TestObject: + def __str__(self): + return "TestObject" + + +class StrTestEnum(str, enum.Enum): + NUMBER_INPUT = "st.number_input" + TEXT_AREA = "st.text_area" + TEXT_INPUT = "st.text_input" + + +class TestEnum(enum.Enum): + NUMBER_INPUT = "st.number_input" + TEXT_AREA = "st.text_area" + TEXT_INPUT = "st.text_input" + + +def data_generator(): + yield "st.number_input" + yield "st.text_area" + yield "st.text_input" + + +SHARED_TEST_CASES: list[tuple[str, Any, CaseMetadata]] = [ + ################################### + ####### Native Python Types ####### + ################################### ( - pd.DataFrame( - columns=["name", "type"], index=pd.RangeIndex(start=0, step=1) - ), # Explicitly set the range index to have the same behavior across versions - TestCaseMetadata(0, 2, DataFormat.PANDAS_DATAFRAME), + "None", + None, + CaseMetadata(0, 0, DataFormat.EMPTY, [], "markdown", False, pd.DataFrame), ), - # Pandas DataFrame: ( - pd.DataFrame(["st.text_area", "st.markdown"]), - TestCaseMetadata(2, 1, DataFormat.PANDAS_DATAFRAME), + "Empty list", + [], + CaseMetadata(0, 0, DataFormat.LIST_OF_VALUES, [], "json", False), ), - # List of strings (List[str]): ( + "Empty tuple", + (), + CaseMetadata(0, 0, DataFormat.TUPLE_OF_VALUES, [], "markdown", False), + ), + ( + "Empty dict", + {}, + CaseMetadata(0, 0, DataFormat.KEY_VALUE_DICT, [], "json", False), + ), + ( + "Empty set", + set(), + CaseMetadata(0, 0, DataFormat.SET_OF_VALUES, [], "markdown", False), + ), + ( + "List[str]", ["st.text_area", "st.number_input", "st.text_input"], - TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES), - ), - # List of integers (List[int]): - ([1, 2, 3], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)), - # List of floats (List[float]): - ([1.0, 2.0, 3.0], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)), - # List of booleans (List[bool]): - ([True, False, True], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)), - # List of Nones (List[None]): - ([None, None, None], TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES)), - # List of dates (List[date]): + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + ["st.text_area", "st.number_input", "st.text_input"], + "json", + False, + ), + ), ( + "List[int]", + [1, 2, 3], + CaseMetadata(3, 1, DataFormat.LIST_OF_VALUES, [1, 2, 3], "json", False), + ), + ( + "List[float]", + [1.1, 2.2, 3.3], + CaseMetadata(3, 1, DataFormat.LIST_OF_VALUES, [1.1, 2.2, 3.3], "json", False), + ), + ( + "List[bool]", + [True, False, True], + CaseMetadata( + 3, 1, DataFormat.LIST_OF_VALUES, [True, False, True], "json", False + ), + ), + ( + "List[None]", + [None, None, None], + CaseMetadata( + 3, 1, DataFormat.LIST_OF_VALUES, [None, None, None], "json", False + ), + ), + ( + "List[date]", [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 3)], - TestCaseMetadata(3, 1, DataFormat.LIST_OF_VALUES), + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 3)], + "json", + False, + ), ), - # Set of strings (Set[str]): - # Set does not have a stable order across different Python version. - # Therefore, we are only testing this with one item. ( + "Set[str]", + # Set does not have a stable order across different Python version. + # Therefore, we are only testing this with one item. {"st.number_input", "st.number_input"}, # noqa: B033 - TestCaseMetadata(1, 1, DataFormat.SET_OF_VALUES), + CaseMetadata( + 1, 1, DataFormat.SET_OF_VALUES, ["st.number_input"], "markdown", False + ), ), - # Tuple of strings (Tuple[str]): ( + "Tuple[str]", ("st.text_area", "st.number_input", "st.text_input"), - TestCaseMetadata(3, 1, DataFormat.TUPLE_OF_VALUES), + CaseMetadata( + 3, + 1, + DataFormat.TUPLE_OF_VALUES, + ["st.text_area", "st.number_input", "st.text_input"], + "markdown", + False, + ), ), - # Numpy list / 1D numpy array (np.array[str]): ( - np.array(["st.text_area", "st.number_input", "st.text_input"]), - TestCaseMetadata(3, 1, DataFormat.NUMPY_LIST), + "Frozenset[str]", + # Set does not have a stable order across different Python version. + # Therefore, we are only testing this with one item. + frozenset({"st.number_input", "st.number_input"}), # noqa: B033 + CaseMetadata( + 1, + 1, + DataFormat.SET_OF_VALUES, + ["st.number_input"], + "markdown", + False, + set, + ), ), - # np.array[int]: - (np.array([1, 2, 3]), TestCaseMetadata(3, 1, DataFormat.NUMPY_LIST)), - # Multi-dimensional numpy array (np.array[List[Scalar]]) ( - np.array( + "Empty frozenset", + frozenset(), + CaseMetadata(0, 0, DataFormat.SET_OF_VALUES, [], "markdown", False, set), + ), + ( + "Range", + range(3), + CaseMetadata( + 3, 1, DataFormat.LIST_OF_VALUES, [0, 1, 2], "markdown", False, list + ), + ), + ( + "Dict Keys", + { + "st.number_input": "number", + "st.text_area": "text", + "st.text_input": "text", + }.keys(), + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + ["st.number_input", "st.text_area", "st.text_input"], + "markdown", + False, + list, + ), + ), + ( + "Dict Values", + { + "st.number_input": "number", + "st.text_area": "text", + "st.text_input": "text", + }.values(), + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + ["number", "text", "text"], + "markdown", + False, + list, + ), + ), + ( + "Dict Items", + { + "st.number_input": "number", + "st.text_area": "text", + "st.text_input": "text", + }.items(), + CaseMetadata( + 3, + 2, + DataFormat.LIST_OF_ROWS, + None, + "markdown", + False, + list, + ), + ), + ( + "collections.OrderedDict", + OrderedDict( [ - ["st.text_area", "widget"], - ["st.markdown", "element"], + ("st.number_input", "number"), + ("st.text_area", "text"), ] ), - TestCaseMetadata(2, 2, DataFormat.NUMPY_MATRIX), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.number_input", "st.text_area"], + "json", + False, + dict, + ), ), - # np.array[List[str]]: ( - np.array([["st.text_area"], ["st.number_input"], ["st.text_input"]]), - TestCaseMetadata(3, 1, DataFormat.NUMPY_MATRIX), + "collections.defaultdict", + defaultdict( + lambda: "Not Present", + {"st.text_area": "widget", "st.markdown": "element"}, + ), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.text_area", "st.markdown"], + "json", + False, + dict, + ), ), - # Pandas Series (pd.Series): ( - pd.Series(["st.text_area", "st.number_input", "st.text_input"], name="widgets"), - TestCaseMetadata(3, 1, DataFormat.PANDAS_SERIES), + "collections.Counter", + Counter({"st.number_input": 4, "st.text_area": 2}), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.number_input", "st.text_area"], + "json", + False, + dict, + ), ), - # Pandas Styler (pd.Styler): ( - pd.DataFrame(["st.text_area", "st.markdown"]).style, - TestCaseMetadata(2, 1, DataFormat.PANDAS_STYLER), + "collections.deque", + deque(["st.number_input", "st.text_area", "st.text_input"]), + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + ["st.number_input", "st.text_area", "st.text_input"], + "markdown", + False, + list, + ), ), - # Pandas Index (pd.Index): ( - pd.Index(["st.text_area", "st.markdown"]), - TestCaseMetadata(2, 1, DataFormat.PANDAS_INDEX), + "collections.ChainMap", + ChainMap( + {"st.number_input": "number", "st.text_area": "text"}, + {"st.text_input": "text"}, + ), + CaseMetadata( + 3, + 1, + DataFormat.KEY_VALUE_DICT, + ["number", "text", "text"], + "json", + False, + dict, + ), ), - # Pyarrow Table (pyarrow.Table): ( - pa.Table.from_pandas(pd.DataFrame(["st.text_area", "st.markdown"])), - TestCaseMetadata(2, 1, DataFormat.PYARROW_TABLE), + "Dataclass", + ElementDataClass("st.number_input", is_widget=True, usage=0.32), + CaseMetadata( + 3, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.number_input", True, 0.32], + "help", + False, + dict, + ), + ), + ( + "TypedDict", + ElementTypedDict(name="st.number_input", is_widget=True, usage=0.32), + CaseMetadata( + 3, + 1, + DataFormat.KEY_VALUE_DICT, + ["name", "is_widget", "usage"], + "json", + False, + dict, + ), + ), + ( + "NamedTuple", + ElementNamedTuple("st.number_input", is_widget=True, usage=0.32), + CaseMetadata( + 3, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.number_input", True, 0.32], + "json", + False, + dict, + ), + ), + ( + "String Enum", + StrTestEnum, + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + ["st.number_input", "st.text_area", "st.text_input"], + "help", + False, + list, + ), + ), + ( + "Enum", + TestEnum, + CaseMetadata( + 3, + 1, + DataFormat.LIST_OF_VALUES, + [TestEnum.NUMBER_INPUT, TestEnum.TEXT_AREA, TestEnum.TEXT_INPUT], + "help", + False, + list, + ), + ), + ( + "Generator Function", + data_generator, + CaseMetadata( + 3, + 1, + DataFormat.UNKNOWN, + ["st.number_input", "st.text_area", "st.text_input"], + "write_stream", + True, + ), + ), + ( + "Empty column value mapping", + {"name": [], "type": []}, + CaseMetadata( + 0, 2, DataFormat.COLUMN_VALUE_MAPPING, ["name", "type"], "json", False + ), + ), + ( + "array.array", + array.array("i", [1, 2, 3]), + CaseMetadata( + 3, 1, DataFormat.LIST_OF_VALUES, [1, 2, 3], "markdown", False, list + ), + ), + ( + "MappingProxyType", + MappingProxyType({"st.text_area": "widget", "st.markdown": "element"}), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["widget", "element"], + "json", + False, + dict, + ), + ), + ( + "UserDict", + UserDictExample({"st.text_area": "widget", "st.markdown": "element"}), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["widget", "element"], + "json", + False, + dict, + ), ), - # List of rows (List[List[Scalar]]): ( + "List of rows", # List[list[scalar]] [["st.text_area", "widget"], ["st.markdown", "element"]], - TestCaseMetadata(2, 2, DataFormat.LIST_OF_ROWS), + CaseMetadata(2, 2, DataFormat.LIST_OF_ROWS, None, "json", False), ), - # List of records (List[Dict[str, Scalar]]): ( + "List of records", # List[Dict[str, Scalar]] [ {"name": "st.text_area", "type": "widget"}, {"name": "st.markdown", "type": "element"}, ], - TestCaseMetadata(2, 2, DataFormat.LIST_OF_RECORDS), + CaseMetadata( + 2, + 2, + DataFormat.LIST_OF_RECORDS, + None, + "json", + False, + ), ), - # Column-index mapping ({column: {index: value}}): ( + "Column-index mapping", # ({column: {index: value}}) { "type": {"st.text_area": "widget", "st.markdown": "element"}, "usage": {"st.text_area": 4.92, "st.markdown": 47.22}, }, - TestCaseMetadata(2, 2, DataFormat.COLUMN_INDEX_MAPPING), + CaseMetadata( + 2, + 2, + DataFormat.COLUMN_INDEX_MAPPING, + ["type", "usage"], + "json", + False, + ), ), - # Column-value mapping ({column: List[values]}}): ( + "Column-value mapping", # ({column: List[values]}}) { "name": ["st.text_area", "st.markdown"], "type": ["widget", "element"], }, - TestCaseMetadata(2, 2, DataFormat.COLUMN_VALUE_MAPPING), + CaseMetadata( + 2, + 2, + DataFormat.COLUMN_VALUE_MAPPING, + ["name", "type"], + "json", + False, + ), ), - # Column-series mapping ({column: Series(values)}): ( + "Column-series mapping", # ({column: Series(values)}) { "name": pd.Series(["st.text_area", "st.markdown"], name="name"), "type": pd.Series(["widget", "element"], name="type"), }, - TestCaseMetadata(2, 2, DataFormat.COLUMN_SERIES_MAPPING), + CaseMetadata( + 2, + 2, + DataFormat.COLUMN_SERIES_MAPPING, + ["name", "type"], + "dataframe", + False, + ), ), - # Key-value dict ({index: value}): ( + "Key-value dict", # ({index: value}) {"st.text_area": "widget", "st.markdown": "element"}, - TestCaseMetadata(2, 1, DataFormat.KEY_VALUE_DICT), + CaseMetadata( + 2, + 1, + DataFormat.KEY_VALUE_DICT, + ["st.text_area", "st.markdown"], + "json", + False, + ), + ), + ################################### + ########## Pandas Types ########### + ################################### + ( + "Empty pd.Dataframe", + pd.DataFrame(), + CaseMetadata(0, 0, DataFormat.PANDAS_DATAFRAME, [], "dataframe", False), + ), + ( + "Empty pd.Dataframe with columns", + pd.DataFrame( + columns=["name", "type"], index=pd.RangeIndex(start=0, step=1) + ), # Explicitly set the range index to have the same behavior across versions + CaseMetadata(0, 2, DataFormat.PANDAS_DATAFRAME, [], "dataframe", False), + ), + ( + "pd.Dataframe", + pd.DataFrame(["st.text_area", "st.markdown"]), + CaseMetadata( + 2, + 1, + DataFormat.PANDAS_DATAFRAME, + ["st.text_area", "st.markdown"], + "dataframe", + False, + ), + ), + ( + "pd.Series[str]", + pd.Series( + ["st.text_area", "st.number_input", "st.text_input"], + name="widgets", + ), + CaseMetadata( + 3, + 1, + DataFormat.PANDAS_SERIES, + ["st.text_area", "st.number_input", "st.text_input"], + "dataframe", + False, + ), + ), + ( + "pd.Index", + pd.Index(["st.text_area", "st.markdown"]), + CaseMetadata( + 2, + 1, + DataFormat.PANDAS_INDEX, + ["st.text_area", "st.markdown"], + "dataframe", + False, + pd.DataFrame, + ), + ), + ( + "Pandas Styler", + pd.DataFrame(["st.text_area", "st.markdown"]).style, + CaseMetadata( + 2, + 1, + DataFormat.PANDAS_STYLER, + ["st.text_area", "st.markdown"], + "dataframe", + False, + pd.DataFrame, + ), + ), + ( + "pd.array", + pd.array(["st.number_input", "st.text_area", "st.text_input"]), + CaseMetadata( + 3, + 1, + DataFormat.PANDAS_ARRAY, + ["st.number_input", "st.text_area", "st.text_input"], + "dataframe", + False, + pd.DataFrame, + ), + ), + ( + "pd.DatetimeIndex", + pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]), + CaseMetadata( + 2, + 1, + DataFormat.PANDAS_INDEX, + [ + pd.Timestamp("2020-01-01 10:00:00+0000", tz="UTC"), + pd.Timestamp("2020-02-01 11:00:00+0000", tz="UTC"), + ], + "dataframe", + False, + pd.DataFrame, + ), + ), + ( + "pd.RangeIndex", + pd.RangeIndex(start=0, stop=3, step=1), + CaseMetadata( + 3, 1, DataFormat.PANDAS_INDEX, [0, 1, 2], "dataframe", False, pd.DataFrame + ), + ), + ################################### + ########### Numpy Types ########### + ################################### + ( + "Empty np.array", + # For unknown reasons, pd.DataFrame initializes empty numpy arrays with a single column + np.ndarray(0), + CaseMetadata(0, 1, DataFormat.NUMPY_LIST, [], "dataframe", False), + ), + ( + "np.array[str]", + np.array(["st.text_area", "st.number_input", "st.text_input"]), + CaseMetadata( + 3, + 1, + DataFormat.NUMPY_LIST, + ["st.text_area", "st.number_input", "st.text_input"], + "dataframe", + False, + ), + ), + ( + "np.array[int]", + np.array([1, 2, 3]), + CaseMetadata(3, 1, DataFormat.NUMPY_LIST, [1, 2, 3], "dataframe", False), + ), + ( + "np.array[list[scalar]]", + np.array( + [ + ["st.text_area", "widget"], + ["st.markdown", "element"], + ] + ), + CaseMetadata( + 2, + 2, + DataFormat.NUMPY_MATRIX, + ["st.text_area", "st.markdown"], + "dataframe", + False, + ), + ), + ( + "np.array[list[str]]", # numpy matrix + np.array( + [ + ["st.text_area", "widget"], + ["st.markdown", "element"], + ] + ), + CaseMetadata( + 2, + 2, + DataFormat.NUMPY_MATRIX, + ["st.text_area", "st.markdown"], + "dataframe", + False, + ), ), - # Snowpark DataFrame: + ################################### + ########## Pyarrow Types ########## + ################################### ( - SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))), - TestCaseMetadata(2, 2, DataFormat.SNOWPARK_OBJECT), + "Pyarrow Table", + pa.Table.from_pandas(pd.DataFrame(["st.text_area", "st.markdown"])), + CaseMetadata( + 2, + 1, + DataFormat.PYARROW_TABLE, + ["st.text_area", "st.markdown"], + "dataframe", + False, + ), + ), + ( + "Pyarrow Array", + pa.array(["st.number_input", "st.text_area", "st.text_input"]), + CaseMetadata( + 3, + 1, + DataFormat.PYARROW_ARRAY, + ["st.number_input", "st.text_area", "st.text_input"], + "dataframe", + False, + ), + ), + ################################### + ##### Snowflake Types (Mocks) ##### + ################################### + ( + "Snowpark DataFrame", + SnowparkDataFrame( + pd.DataFrame( + [ + {"name": "st.text_area", "type": "widget"}, + {"name": "st.markdown", "type": "element"}, + ] + ) + ), + CaseMetadata( + 2, + 2, + DataFormat.SNOWPARK_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), + ), + ( + "Snowpark Table", + SnowparkTable( + pd.DataFrame( + [ + {"name": "st.text_area", "type": "widget"}, + {"name": "st.markdown", "type": "element"}, + ] + ) + ), + CaseMetadata( + 2, + 2, + DataFormat.SNOWPARK_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), + ), + ( + "Snowpark Row List", + [ + SnowparkRow({"name": "st.text_area", "type": "widget"}), + SnowparkRow({"name": "st.markdown", "type": "element"}), + SnowparkRow({"name": "st.text_input", "type": "text"}), + ], + CaseMetadata( + 3, + 2, + DataFormat.SNOWPARK_OBJECT, + ["st.text_area", "st.markdown", "st.text_input"], + "dataframe", + False, + pd.DataFrame, + ), + ), + ( + "Snowpandas DataFrame", + SnowpandasDataFrame( + pd.DataFrame( + [ + {"name": "st.text_area", "type": "widget"}, + {"name": "st.markdown", "type": "element"}, + ] + ) + ), + CaseMetadata( + 2, + 2, + DataFormat.SNOWPANDAS_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), ), - # Snowpark Table: ( - SnowparkTable(pd.DataFrame(np.random.randn(2, 2))), - TestCaseMetadata(2, 2, DataFormat.SNOWPARK_OBJECT), + "Snowpandas Series", + SnowpandasSeries(pd.Series(["st.text_area", "st.markdown"])), + CaseMetadata( + 2, + 1, + DataFormat.SNOWPANDAS_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), ), - # Snowpark Pandas DataFrame: ( - SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))), - TestCaseMetadata(2, 2, DataFormat.SNOWPANDAS_OBJECT), + "Modin DataFrame", + ModinDataFrame( + pd.DataFrame( + [ + {"name": "st.text_area", "type": "widget"}, + {"name": "st.markdown", "type": "element"}, + ] + ) + ), + CaseMetadata( + 2, + 2, + DataFormat.MODIN_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), ), - # Snowpark Pandas Series: ( - SnowpandasSeries(pd.Series(np.random.randn(2))), - TestCaseMetadata(2, 1, DataFormat.SNOWPANDAS_OBJECT), + "Modin Series", + ModinSeries(pd.Series(["st.text_area", "st.markdown"])), + CaseMetadata( + 2, + 1, + DataFormat.MODIN_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), ), - # Pyspark Dataframe: + ################################### + ##### External Types (Mocks) ###### + ################################### ( - PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))), - TestCaseMetadata(2, 2, DataFormat.PYSPARK_OBJECT), + "Pyspark DataFrame", + PySparkDataFrame( + pd.DataFrame( + [ + {"name": "st.text_area", "type": "widget"}, + {"name": "st.markdown", "type": "element"}, + ] + ) + ), + CaseMetadata( + 2, + 2, + DataFormat.PYSPARK_OBJECT, + ["st.text_area", "st.markdown"], + "dataframe", + True, + pd.DataFrame, + ), ), ] - - -class TestObject: - def __str__(self): - return "TestObject" - - -class StrTestEnum(str, enum.Enum): - NUMBER_INPUT = "st.number_input" - TEXT_AREA = "st.text_area" - TEXT_INPUT = "st.text_input" - - -class TestEnum(enum.Enum): - NUMBER_INPUT = "st.number_input" - TEXT_AREA = "st.text_area" - TEXT_INPUT = "st.text_input" - - -def data_generator(): - yield "st.number_input" - yield "st.text_area" - yield "st.text_input" diff --git a/lib/tests/streamlit/dataframe_util_test.py b/lib/tests/streamlit/dataframe_util_test.py index bf66bf3e744a..fd07868abfc6 100644 --- a/lib/tests/streamlit/dataframe_util_test.py +++ b/lib/tests/streamlit/dataframe_util_test.py @@ -16,7 +16,6 @@ import enum import unittest -from collections import OrderedDict from datetime import date from decimal import Decimal from typing import Any @@ -34,20 +33,13 @@ from tests.delta_generator_test_case import DeltaGeneratorTestCase from tests.streamlit.data_mocks import ( SHARED_TEST_CASES, - StrTestEnum, - TestCaseMetadata, - TestEnum, + CaseMetadata, TestObject, - data_generator, ) -from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame -from tests.streamlit.modin_mocks import Series as ModinSeries -from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame from tests.streamlit.snowpark_mocks import Row as SnowparkRow -from tests.streamlit.snowpark_mocks import Table as SnowparkTable from tests.testutil import create_snowpark_session, patch_config_options @@ -66,8 +58,9 @@ def test_convert_pandas_df_to_arrow_bytes(self): ) def test_convert_anything_to_pandas_df( self, + name: str, input_data: Any, - metadata: TestCaseMetadata, + metadata: CaseMetadata, ): """Test that `convert_anything_to_pandas_df` correctly converts a variety of types to a DataFrame. @@ -78,30 +71,33 @@ def test_convert_anything_to_pandas_df( self.assertEqual(converted_df.shape[1], metadata.expected_cols) @parameterized.expand( - [ - (ModinDataFrame(pd.DataFrame(np.random.randn(2000, 2))),), - (ModinSeries(pd.Series(np.random.randn(2000))),), - (PysparkDataFrame(pd.DataFrame(np.random.randn(2000, 2))),), - (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2000, 2))),), - (SnowpandasSeries(pd.Series(np.random.randn(2000))),), - (SnowparkDataFrame(pd.DataFrame(np.random.randn(2000, 2))),), - (SnowparkTable(pd.DataFrame(np.random.randn(2000, 2))),), - ] + SHARED_TEST_CASES, ) - def test_convert_anything_to_pandas_df_show_warning_for_unevaluated_df( + def test_unevaluated_dataframe_handling( self, + name: str, input_data: Any, + metadata: CaseMetadata, ): - """Test that `convert_anything_to_pandas_df` correctly converts - a variety unevaluated dataframes and shows a warning if - the row count is > 1000. + """Test that unevaluated data objects are correctly detected and + handled by limiting the number of rows to be displayed. """ - with patch("streamlit.caption") as mock: - converted_df = dataframe_util.convert_anything_to_pandas_df( - input_data, max_unevaluated_rows=1000 - ) - self.assertIsInstance(converted_df, pd.DataFrame) - mock.assert_called_once() + with patch("streamlit.dataframe_util._show_data_information") as mock: + if metadata.is_unevaluated: + assert dataframe_util.is_unevaluated_data_object(input_data) is True + converted_df = dataframe_util.convert_anything_to_pandas_df( + input_data, max_unevaluated_rows=1 + ) + assert isinstance(converted_df, pd.DataFrame) + assert converted_df.shape[0] <= 1 + mock.assert_called_once() + else: + assert dataframe_util.is_unevaluated_data_object(input_data) is False + converted_df = dataframe_util.convert_anything_to_pandas_df( + input_data, max_unevaluated_rows=1 + ) + assert converted_df.shape[0] == metadata.expected_rows + mock.assert_not_called() def test_convert_anything_to_pandas_df_ensure_copy(self): """Test that `convert_anything_to_pandas_df` creates a copy of the original @@ -138,11 +134,13 @@ def test_convert_anything_to_pandas_df_supports_key_value_dicts(self): """ data = {"a": 1, "b": 2} df = dataframe_util.convert_anything_to_pandas_df(data) - pd.testing.assert_frame_equal(df, pd.DataFrame.from_dict(data, orient="index")) + pd.testing.assert_frame_equal( + df, pd.DataFrame.from_dict(data, orient="index", columns=["value"]) + ) def test_convert_anything_to_pandas_df_converts_stylers(self): - """Test that `convert_anything_to_pandas_df` correctly converts Stylers to DF, without cloning the - data. + """Test that `convert_anything_to_pandas_df` correctly converts Stylers to DF, + without cloning the data. """ original_df = pd.DataFrame( { @@ -194,8 +192,9 @@ def to_pandas(self): ) def test_convert_anything_to_arrow_bytes( self, + name: str, input_data: Any, - metadata: TestCaseMetadata, + metadata: CaseMetadata, ): """Test that `convert_anything_to_arrow_bytes` correctly converts a variety of types to Arrow bytes. @@ -370,6 +369,14 @@ def test_data_frame_with_unsupported_column_types(self): f"Unsupported types of this dataframe should have been automatically fixed: {ex}" ) + def test_is_pandas_data_object(self): + """Test that `is_pandas_data_object` correctly detects pandas data objects.""" + assert dataframe_util.is_pandas_data_object(pd.DataFrame()) is True + assert dataframe_util.is_pandas_data_object(pd.Series()) is True + assert dataframe_util.is_pandas_data_object(pd.Index(["a", "b"])) is True + assert dataframe_util.is_pandas_data_object(pd.array(["a", "b"])) is True + assert dataframe_util.is_pandas_data_object(["a", "b"]) is False + def test_is_snowpandas_data_object(self): df = pd.DataFrame([1, 2, 3]) @@ -415,7 +422,8 @@ class DummyClass: self.assertTrue( dataframe_util.is_snowpark_row_list( [ - SnowparkRow(), + SnowparkRow({"col1": 1, "col2": "foo"}), + SnowparkRow({"col1": 2, "col2": "bar"}), ] ) ) @@ -440,22 +448,36 @@ def test_is_snowpark_dataframe(self): self.assertTrue(dataframe_util.is_snowpark_data_object(SnowparkDataFrame(df))) @pytest.mark.require_snowflake - def test_is_snowpark_dataframe_integration(self): + def test_verify_snowpark_integration(self): + """Integration test snowpark object handling. + This is in addition to the tests using the mocks to verify that + the latest version of the library is still supported. + """ with create_snowpark_session() as snowpark_session: - self.assertTrue( - dataframe_util.is_snowpark_data_object( - snowpark_session.sql("SELECT 40+2 as COL1") - ) + snowpark_df = snowpark_session.sql("SELECT 40+2 as COL1") + + assert dataframe_util.is_snowpark_data_object(snowpark_df) is True + assert isinstance( + dataframe_util.convert_anything_to_pandas_df(snowpark_df), + pd.DataFrame, ) - self.assertTrue( - dataframe_util.is_snowpark_data_object( - snowpark_session.sql("SELECT 40+2 as COL1").cache_result() - ) + + snowpark_cached_result = snowpark_session.sql( + "SELECT 40+2 as COL1" + ).cache_result() + assert ( + dataframe_util.is_snowpark_data_object(snowpark_cached_result) is True ) - self.assertTrue( - dataframe_util.is_snowpark_row_list( - snowpark_session.sql("SELECT 40+2 as COL1").collect() - ) + assert isinstance( + dataframe_util.convert_anything_to_pandas_df(snowpark_cached_result), + pd.DataFrame, + ) + + snowpark_row_list = snowpark_session.sql("SELECT 40+2 as COL1").collect() + assert dataframe_util.is_snowpark_row_list(snowpark_row_list) is True + assert isinstance( + dataframe_util.convert_anything_to_pandas_df(snowpark_row_list), + pd.DataFrame, ) @parameterized.expand( @@ -463,8 +485,9 @@ def test_is_snowpark_dataframe_integration(self): ) def test_determine_data_format( self, + name: str, input_data: Any, - metadata: TestCaseMetadata, + metadata: CaseMetadata, ): """Test that `determine_data_format` correctly determines the data format of a variety of data structures/types. @@ -481,8 +504,9 @@ def test_determine_data_format( ) def test_convert_pandas_df_to_data_format( self, + name: str, input_data: Any, - metadata: TestCaseMetadata, + metadata: CaseMetadata, ): """Test that `convert_pandas_df_to_data_format` correctly converts a DataFrame to the specified data format. @@ -502,32 +526,26 @@ def test_convert_pandas_df_to_data_format( converted_df, metadata.expected_data_format ) - # Some data formats are converted to DataFrames instead of - # the original data type/structure. - if metadata.expected_data_format in [ - dataframe_util.DataFormat.SNOWPARK_OBJECT, - dataframe_util.DataFormat.PYSPARK_OBJECT, - dataframe_util.DataFormat.PANDAS_INDEX, - dataframe_util.DataFormat.PANDAS_STYLER, - dataframe_util.DataFormat.SNOWPANDAS_OBJECT, - dataframe_util.DataFormat.MODIN_OBJECT, - dataframe_util.DataFormat.EMPTY, - ]: - assert isinstance(converted_data, pd.DataFrame) + self.assertEqual( + type(converted_data), + type(input_data) + if metadata.expected_type is None + else metadata.expected_type, + ) + + if isinstance(converted_data, pd.DataFrame): self.assertEqual(converted_data.shape[0], metadata.expected_rows) self.assertEqual(converted_data.shape[1], metadata.expected_cols) - else: - self.assertEqual(type(converted_data), type(input_data)) + elif ( # Sets in python are unordered, so we can't compare them this way. - if ( - metadata.expected_data_format - != dataframe_util.DataFormat.SET_OF_VALUES - ): - self.assertEqual(str(converted_data), str(input_data)) - pd.testing.assert_frame_equal( - converted_df, - dataframe_util.convert_anything_to_pandas_df(converted_data), - ) + metadata.expected_data_format != dataframe_util.DataFormat.SET_OF_VALUES + and metadata.expected_type is None + ): + self.assertEqual(str(converted_data), str(input_data)) + pd.testing.assert_frame_equal( + converted_df, + dataframe_util.convert_anything_to_pandas_df(converted_data), + ) def test_convert_pandas_df_to_data_format_with_unknown_data_format(self): """Test that `convert_df_to_data_format` raises a ValueError when @@ -643,245 +661,25 @@ class StrOpt(str, enum.Enum): self.assertEqual(list(StrOpt), converted_list) @parameterized.expand( - [ - (None, []), - # List: - ([], []), - ( - ["st.number_input", "st.text_area", "st.text_input"], - ["st.number_input", "st.text_area", "st.text_input"], - ), - ( - [1, 2, 3], - [1, 2, 3], - ), - # Reversed list: - ( - reversed(["st.number_input", "st.text_area", "st.text_input"]), - ["st.text_input", "st.text_area", "st.number_input"], - ), - # Set: - (set(), []), - ( - {"st.number_input", "st.text_area", "st.text_input"}, - ["st.text_input", "st.number_input", "st.text_area"], - ), - # Tuple: - ((), []), - ( - ("st.number_input", "st.text_area", "st.text_input"), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Dict: - ({}, []), - ( - { - "st.number_input": "number", - "st.text_area": "text", - "st.text_input": "text", - }, - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Dict keys: - ( - { - "st.number_input": "number", - "st.text_area": "text", - "st.text_input": "text", - }.keys(), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Dict values: - ( - { - "st.number_input": "number", - "st.text_area": "text", - "st.text_input": "text", - }.values(), - ["number", "text", "text"], - ), - # OrderedDict: - ( - OrderedDict( - [ - ("st.number_input", "number"), - ("st.text_area", "text"), - ("st.text_input", "text"), - ] - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Enum: - ( - TestEnum, - [TestEnum.NUMBER_INPUT, TestEnum.TEXT_AREA, TestEnum.TEXT_INPUT], - ), - (StrTestEnum, ["st.number_input", "st.text_area", "st.text_input"]), - # Generator: - (data_generator(), ["st.number_input", "st.text_area", "st.text_input"]), - # String: - ("abc", ["a", "b", "c"]), - # Enumerate: - ( - enumerate(["st.number_input", "st.text_area", "st.text_input"]), - [0, 1, 2], - ), - # Range: - (range(3), [0, 1, 2]), - # Pandas Dataframe: - ( - pd.DataFrame(), - [], - ), - ( - pd.DataFrame( - columns=["name", "type"], index=pd.RangeIndex(start=0, step=1) - ), - [], - ), - ( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Dataframe with multiple columns (widgets & types) - # The first column is expected to be selected as the sequence. - ( - pd.DataFrame( - { - "widgets": ["st.number_input", "st.text_area", "st.text_input"], - "types": ["number", "text", "text"], - } - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pandas Series (pd.Series): - ( - pd.Series( - ["st.number_input", "st.text_area", "st.text_input"], name="widgets" - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pandas Index (pd.Index): - ( - pd.Index(["st.number_input", "st.text_area", "st.text_input"]), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pandas Styler (pd.Styler): - ( - pd.DataFrame( - ["st.number_input", "st.text_area", "st.text_input"] - ).style, - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pandas Categorical (pd.Categorical): - ( - pd.Categorical(["st.number_input", "st.text_area", "st.text_input"]), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pandas DatetimeIndex (pd.DatetimeIndex): - ( - pd.DatetimeIndex( - ["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"] - ), - [ - pd.Timestamp("2020-01-01 10:00:00+0000", tz="UTC"), - pd.Timestamp("2020-02-01 11:00:00+0000", tz="UTC"), - ], - ), - # Pandas DatetimeArrayรฅ: - ( - pd.arrays.DatetimeArray( - pd.DatetimeIndex( - ["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"] - ), - ), - [ - pd.Timestamp("2020-01-01 10:00:00+0000", tz="UTC"), - pd.Timestamp("2020-02-01 11:00:00+0000", tz="UTC"), - ], - ), - # Pandas RangeIndex (pd.RangeIndex): - ( - pd.RangeIndex(start=0, stop=3, step=1), - [0, 1, 2], - ), - # Numpy array: - ( - np.array([]), - [], - ), - ( - np.array(["st.number_input", "st.text_area", "st.text_input"]), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pyarrow Table: - ( - pa.Table.from_pandas( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Snowpark Table: - ( - SnowparkTable( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Snowpark DataFrame: - ( - SnowparkDataFrame( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Snowpark Pandas DataFrame: - ( - SnowpandasDataFrame( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Snowpark Pandas Series: - ( - SnowpandasSeries( - pd.Series(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Pyspark Dataframe: - ( - PysparkDataFrame( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Modin Dataframe: - ( - ModinDataFrame( - pd.DataFrame(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - # Modin Series: - ( - ModinSeries( - pd.Series(["st.number_input", "st.text_area", "st.text_input"]) - ), - ["st.number_input", "st.text_area", "st.text_input"], - ), - ] + SHARED_TEST_CASES, ) def test_convert_anything_to_sequence( - self, input_data: Any, result_sequence: list[Any] + self, + name: str, + input_data: Any, + metadata: CaseMetadata, ): """Test that `convert_anything_to_sequence` correctly converts a variety of types to a sequence. """ + if metadata.expected_sequence is None: + # Skip all cases where we don't have an expected sequence. + return + converted_sequence = dataframe_util.convert_anything_to_sequence(input_data) # We convert to a set for the check since some of the formats don't # have a guaranteed order. - self.assertEqual(set(converted_sequence), set(result_sequence)) + self.assertEqual(set(converted_sequence), set(metadata.expected_sequence)) # Check that it is a new object and not the same as the input: assert converted_sequence is not input_data diff --git a/lib/tests/streamlit/elements/arrow_dataframe_test.py b/lib/tests/streamlit/elements/arrow_dataframe_test.py index 58321a5d69c7..2d5e582da44c 100644 --- a/lib/tests/streamlit/elements/arrow_dataframe_test.py +++ b/lib/tests/streamlit/elements/arrow_dataframe_test.py @@ -15,24 +15,22 @@ """Arrow DataFrame tests.""" import json +from typing import Any from unittest.mock import MagicMock, patch import numpy as np import pandas as pd -import pyarrow as pa -import pytest from pandas.io.formats.style_render import StylerRenderer as Styler from parameterized import parameterized import streamlit as st from streamlit.dataframe_util import ( convert_arrow_bytes_to_pandas_df, - convert_arrow_table_to_arrow_bytes, ) from streamlit.elements.lib.column_config_utils import INDEX_IDENTIFIER from streamlit.errors import StreamlitAPIException from tests.delta_generator_test_case import DeltaGeneratorTestCase -from tests.testutil import create_snowpark_session +from tests.streamlit.data_mocks import SHARED_TEST_CASES, CaseMetadata def mock_data_frame(): @@ -67,13 +65,20 @@ def test_empty_column_order_parameter(self): proto = self.get_delta_from_queue().new_element.arrow_data_frame self.assertEqual(proto.column_order, []) - def test_pyarrow_table_data(self): - df = mock_data_frame() - table = pa.Table.from_pandas(df) - st.dataframe(table) + @parameterized.expand(SHARED_TEST_CASES) + def test_with_compatible_data( + self, + name: str, + input_data: Any, + metadata: CaseMetadata, + ): + """Test that it can be called with compatible data.""" + st.dataframe(input_data) proto = self.get_delta_from_queue().new_element.arrow_data_frame - self.assertEqual(proto.data, convert_arrow_table_to_arrow_bytes(table)) + reconstructed_df = convert_arrow_bytes_to_pandas_df(proto.data) + self.assertEqual(reconstructed_df.shape[0], metadata.expected_rows) + self.assertEqual(reconstructed_df.shape[1], metadata.expected_cols) def test_hide_index_true(self): """Test that it can be called with hide_index=True param.""" @@ -190,28 +195,6 @@ def test_dataframe_uses_convert_anything_to_df(self): st.dataframe(df) convert_anything_to_df.assert_called_once() - @pytest.mark.require_snowflake - def test_snowpark_uncollected(self): - """Tests that data can be read from Snowpark's uncollected Dataframe""" - with create_snowpark_session() as snowpark_session: - df = snowpark_session.sql("SELECT 42 as COL1") - - st.dataframe(df) - - proto = self.get_delta_from_queue().new_element.arrow_data_frame - self.assertEqual(convert_arrow_bytes_to_pandas_df(proto.data).iat[0, 0], 42) - - @pytest.mark.require_snowflake - def test_snowpark_collected(self): - """Tests that data can be read from Snowpark's collected Dataframe""" - with create_snowpark_session() as snowpark_session: - df = snowpark_session.sql("SELECT 42 as COL1").collect() - - st.dataframe(df) - - proto = self.get_delta_from_queue().new_element.arrow_data_frame - self.assertEqual(convert_arrow_bytes_to_pandas_df(proto.data).iat[0, 0], 42) - def test_dataframe_on_select_initial_returns(self): """Test st.dataframe returns an empty selection as initial result.""" diff --git a/lib/tests/streamlit/elements/data_editor_test.py b/lib/tests/streamlit/elements/data_editor_test.py index ec12aa284d5e..c374595a02eb 100644 --- a/lib/tests/streamlit/elements/data_editor_test.py +++ b/lib/tests/streamlit/elements/data_editor_test.py @@ -48,7 +48,7 @@ from streamlit.proto.Arrow_pb2 import Arrow as ArrowProto from streamlit.type_util import is_pandas_version_less_than from tests.delta_generator_test_case import DeltaGeneratorTestCase -from tests.streamlit.data_mocks import SHARED_TEST_CASES, TestCaseMetadata +from tests.streamlit.data_mocks import SHARED_TEST_CASES, CaseMetadata def _get_arrow_schema(df: pd.DataFrame) -> pa.Schema: @@ -410,10 +410,16 @@ def test_with_dataframe_data(self): @parameterized.expand(SHARED_TEST_CASES) def test_with_compatible_data( self, + name: str, input_data: Any, - metadata: TestCaseMetadata, + metadata: CaseMetadata, ): """Test that it can be called with compatible data.""" + if metadata.expected_data_format == DataFormat.UNKNOWN: + # We can skip formats where the expected format is unknown + # since these cases are not expected to work. + return + return_data = st.data_editor(input_data) proto = self.get_delta_from_queue().new_element.arrow_data_frame @@ -421,25 +427,22 @@ def test_with_compatible_data( self.assertEqual(reconstructed_df.shape[0], metadata.expected_rows) self.assertEqual(reconstructed_df.shape[1], metadata.expected_cols) - # Some data formats are converted to DataFrames instead of - # the original data type/structure. - if metadata.expected_data_format in [ - DataFormat.EMPTY, - DataFormat.MODIN_OBJECT, - DataFormat.PANDAS_INDEX, - DataFormat.PANDAS_STYLER, - DataFormat.PYSPARK_OBJECT, - DataFormat.SNOWPANDAS_OBJECT, - DataFormat.SNOWPARK_OBJECT, - ]: - assert isinstance(return_data, pd.DataFrame) + self.assertEqual( + type(return_data), + type(input_data) + if metadata.expected_type is None + else metadata.expected_type, + ) + + if isinstance(return_data, pd.DataFrame): self.assertEqual(return_data.shape[0], metadata.expected_rows) self.assertEqual(return_data.shape[1], metadata.expected_cols) - else: - self.assertEqual(type(return_data), type(input_data)) + elif ( # Sets in python are unordered, so we can't compare them this way. - if metadata.expected_data_format != DataFormat.SET_OF_VALUES: - self.assertEqual(str(return_data), str(input_data)) + metadata.expected_data_format != DataFormat.SET_OF_VALUES + and metadata.expected_type is None + ): + self.assertEqual(str(return_data), str(input_data)) @parameterized.expand( [ @@ -455,6 +458,26 @@ def test_with_invalid_data(self, input_data: Any): with self.assertRaises(StreamlitAPIException): st.data_editor(input_data) + def test_disables_columns_when_incompatible(self): + """Test that Arrow incompatible columns are disabled (configured as non-editable).""" + data_df = pd.DataFrame( + { + "a": pd.Series([1, 2]), + "b": pd.Series(["foo", "bar"]), + "c": pd.Series([1, "foo"]), # Incompatible + "d": pd.Series([1 + 2j, 3 + 4j]), # Incompatible + } + ) + st.data_editor(data_df) + + proto = self.get_delta_from_queue().new_element.arrow_data_frame + columns_config = json.loads(proto.columns) + + self.assertNotIn("a", columns_config) + self.assertNotIn("b", columns_config) + self.assertTrue(columns_config["c"]["disabled"]) + self.assertTrue(columns_config["d"]["disabled"]) + @parameterized.expand( [ (pd.CategoricalIndex(["a", "b", "c"]),), diff --git a/lib/tests/streamlit/elements/lib/column_config_utils_test.py b/lib/tests/streamlit/elements/lib/column_config_utils_test.py index 257fbe10b925..43221e8d09b7 100644 --- a/lib/tests/streamlit/elements/lib/column_config_utils_test.py +++ b/lib/tests/streamlit/elements/lib/column_config_utils_test.py @@ -504,8 +504,7 @@ def test_apply_data_specific_configs_hides_index( ): """Test that the index is hidden for some data formats.""" columns_config: ColumnConfigMapping = {} - data_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - apply_data_specific_configs(columns_config, data_df, data_format) + apply_data_specific_configs(columns_config, data_format) if hidden: self.assertEqual( @@ -516,81 +515,6 @@ def test_apply_data_specific_configs_hides_index( else: self.assertNotIn(INDEX_IDENTIFIER, columns_config) - def test_apply_data_specific_configs_makes_index_required(self): - """Test that a non-range index gets configured as required.""" - columns_config: ColumnConfigMapping = {} - data_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=["a", "b", "c"]) - apply_data_specific_configs( - columns_config, data_df, DataFormat.PANDAS_DATAFRAME - ) - self.assertEqual( - columns_config[INDEX_IDENTIFIER]["required"], - True, - f"Index of type {type(data_df.index)} should be configured as required.", - ) - - @parameterized.expand( - [ - (DataFormat.SET_OF_VALUES, True), - (DataFormat.TUPLE_OF_VALUES, True), - (DataFormat.LIST_OF_VALUES, True), - (DataFormat.NUMPY_LIST, True), - (DataFormat.KEY_VALUE_DICT, True), - # Most other data formats which should not rename the first column: - (DataFormat.PANDAS_DATAFRAME, False), - (DataFormat.PANDAS_SERIES, False), - (DataFormat.PANDAS_INDEX, False), - (DataFormat.PYARROW_TABLE, False), - (DataFormat.PANDAS_STYLER, False), - (DataFormat.COLUMN_INDEX_MAPPING, False), - (DataFormat.COLUMN_SERIES_MAPPING, False), - (DataFormat.LIST_OF_RECORDS, False), - (DataFormat.LIST_OF_ROWS, False), - (DataFormat.COLUMN_VALUE_MAPPING, False), - ] - ) - def test_apply_data_specific_configs_renames_column( - self, data_format: DataFormat, renames: bool - ): - """Test that the column names are changed for some data formats.""" - data_df = pd.DataFrame([1, 2, 3]) - apply_data_specific_configs({}, data_df, data_format) - if renames: - self.assertEqual( - data_df.columns[0], - "value", - f"Data of type {data_format} should be renamed to 'value'", - ) - else: - self.assertEqual( - data_df.columns[0], - 0, - f"Data of type {data_format} should not be renamed.", - ) - - def test_apply_data_specific_configs_disables_columns(self): - """Test that Arrow incompatible columns are disabled (configured as non-editable).""" - columns_config: ColumnConfigMapping = {} - data_df = pd.DataFrame( - { - "a": pd.Series([1, 2]), - "b": pd.Series(["foo", "bar"]), - "c": pd.Series([1, "foo"]), # Incompatible - "d": pd.Series([1 + 2j, 3 + 4j]), # Incompatible - } - ) - - apply_data_specific_configs( - columns_config, - data_df, - DataFormat.PANDAS_DATAFRAME, - check_arrow_compatibility=True, - ) - self.assertNotIn("a", columns_config) - self.assertNotIn("b", columns_config) - self.assertTrue(columns_config["c"]["disabled"]) - self.assertTrue(columns_config["d"]["disabled"]) - def test_nan_as_value_raises_exception(self): """Test that the usage of `nan` as value in column config raises an exception.""" diff --git a/lib/tests/streamlit/elements/map_test.py b/lib/tests/streamlit/elements/map_test.py index 06fbd078123c..3cd39d203929 100644 --- a/lib/tests/streamlit/elements/map_test.py +++ b/lib/tests/streamlit/elements/map_test.py @@ -20,14 +20,13 @@ import numpy as np import pandas as pd -import pytest from parameterized import parameterized import streamlit as st from streamlit.elements.map import _DEFAULT_MAP, _DEFAULT_ZOOM_LEVEL from streamlit.errors import StreamlitAPIException from tests.delta_generator_test_case import DeltaGeneratorTestCase -from tests.testutil import create_snowpark_session, patch_config_options +from tests.testutil import patch_config_options mock_df = pd.DataFrame({"lat": [1, 2, 3, 4], "lon": [10, 20, 30, 40]}) @@ -348,44 +347,6 @@ def test_nan_exception(self): self.assertIn("not allowed to contain null values", str(ctx.exception)) - @pytest.mark.require_snowflake - def test_unevaluated_snowpark_table_integration(self): - """Test st.map with unevaluated Snowpark DataFrame using real Snowflake instance""" - with create_snowpark_session() as snowpark_session: - table = snowpark_session.sql( - """ - SELECT V1.$1 AS "lat", V1.$2 AS "lon" FROM - ( - VALUES (1, 10), (2, 20), (3, 30), (4, 40) - ) AS V1 - """ - ).cache_result() - st.map(table) - - c = json.loads(self.get_delta_from_queue().new_element.deck_gl_json_chart.json) - - """Check if map data have 4 rows""" - self.assertEqual(len(c["layers"][0]["data"]), 4) - - @pytest.mark.require_snowflake - def test_unevaluated_snowpark_dataframe_integration(self): - """Test st.map with unevaluated Snowpark DataFrame using real Snowflake instance""" - with create_snowpark_session() as snowpark_session: - df = snowpark_session.sql( - """ - SELECT V1.$1 AS "lat", V1.$2 AS "lon" FROM - ( - VALUES (1, 10), (2, 20), (3, 30), (4, 40) - ) AS V1 - """ - ) - st.map(df) - - c = json.loads(self.get_delta_from_queue().new_element.deck_gl_json_chart.json) - - """Check if map data have 4 rows""" - self.assertEqual(len(c["layers"][0]["data"]), 4) - def test_id_changes_when_data_changes(self): st.map() diff --git a/lib/tests/streamlit/runtime/caching/cache_errors_test.py b/lib/tests/streamlit/runtime/caching/cache_errors_test.py index 0ace64eb3417..e91e7194386d 100644 --- a/lib/tests/streamlit/runtime/caching/cache_errors_test.py +++ b/lib/tests/streamlit/runtime/caching/cache_errors_test.py @@ -13,30 +13,17 @@ # limitations under the License. import threading -from typing import Any - -import numpy as np -import pandas as pd -from parameterized import parameterized import streamlit as st from streamlit.elements import exception from streamlit.proto.Exception_pb2 import Exception as ExceptionProto from streamlit.runtime.caching.cache_errors import ( - UnevaluatedDataFrameError, UnhashableParamError, UnserializableReturnValueError, get_return_value_type, ) from tests import testutil from tests.delta_generator_test_case import DeltaGeneratorTestCase -from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame -from tests.streamlit.modin_mocks import Series as ModinSeries -from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame -from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame -from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries -from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame -from tests.streamlit.snowpark_mocks import Table as SnowparkTable class CacheErrorsTest(DeltaGeneratorTestCase): @@ -46,8 +33,6 @@ class CacheErrorsTest(DeltaGeneratorTestCase): are testing them word-for-word as much as possible. Even though this *feels* like an antipattern, it isn't: we're making sure the codepaths that pull useful debug info from the code are working. - - TODO: parameterize these tests for both memo + singleton """ maxDiff = None @@ -111,32 +96,3 @@ def unserializable_return_value_func(): ) self.assertEqual(ep.message_is_markdown, True) self.assertEqual(ep.is_warning, False) - - @parameterized.expand( - [ - (ModinDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (ModinSeries(pd.Series(np.random.randn(2))),), - (PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (SnowpandasSeries(pd.Series(np.random.randn(2))),), - (SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (SnowparkTable(pd.DataFrame(np.random.randn(2, 2))),), - ] - ) - def test_unevaluated_dataframe_error(self, data: Any): - @st.cache_data - def unevaluated_dataframe_func(): - return data - - with self.assertRaises(UnevaluatedDataFrameError) as cm: - unevaluated_dataframe_func() - - ep = ExceptionProto() - exception.marshall(ep, cm.exception) - - self.assertEqual(ep.type, "UnevaluatedDataFrameError") - - expected_message = "Please call `collect()` or `to_pandas()` on the dataframe before returning it" - self.assertTrue(expected_message in ep.message) - self.assertEqual(ep.message_is_markdown, True) - self.assertEqual(ep.is_warning, False) diff --git a/lib/tests/streamlit/snowpandas_mocks.py b/lib/tests/streamlit/snowpandas_mocks.py index 9a8b13113065..7d93b55e38cf 100644 --- a/lib/tests/streamlit/snowpandas_mocks.py +++ b/lib/tests/streamlit/snowpandas_mocks.py @@ -41,7 +41,11 @@ def to_pandas(self) -> pd.DataFrame: def head(self, n: int) -> DataFrame: """Returns the top n element of a mock version of Snowpark Pandas DataFrame""" - return DataFrame(self._data.head(n)) + return DataFrame(self[:n]) + + def __getitem__(self, key: slice | int) -> DataFrame: + # Allow slicing and integer indexing + return DataFrame(self._data[key]) class Series: @@ -65,4 +69,8 @@ def to_pandas(self) -> pd.Series: def head(self, n: int) -> Series: """Returns the top n element of a mock version of Snowpark Pandas Series""" - return Series(self._data.head(n)) + return Series(self[:n]) + + def __getitem__(self, key: slice | int) -> Series: + # Allow slicing and integer indexing + return Series(self._data[key]) diff --git a/lib/tests/streamlit/snowpark_mocks.py b/lib/tests/streamlit/snowpark_mocks.py index 83ecc1c7ee37..555bd55605ea 100644 --- a/lib/tests/streamlit/snowpark_mocks.py +++ b/lib/tests/streamlit/snowpark_mocks.py @@ -14,7 +14,7 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any if TYPE_CHECKING: import pandas as pd @@ -74,3 +74,9 @@ class Row: for testing purposes.""" __module__ = "snowflake.snowpark.row" + + def __init__(self, row_data: dict[str, Any]): + self._row_data: dict[str, Any] = row_data + + def as_dict(self) -> dict[str, Any]: + return self._row_data diff --git a/lib/tests/streamlit/type_util_test.py b/lib/tests/streamlit/type_util_test.py index f43d9de856b0..0731484e9c17 100644 --- a/lib/tests/streamlit/type_util_test.py +++ b/lib/tests/streamlit/type_util_test.py @@ -16,6 +16,7 @@ import unittest from collections import namedtuple +from typing import Any from unittest.mock import patch import numpy as np @@ -26,6 +27,10 @@ from streamlit import type_util from streamlit.errors import StreamlitAPIException +from streamlit.runtime.context import StreamlitCookies, StreamlitHeaders +from streamlit.runtime.secrets import Secrets +from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy +from streamlit.user_info import UserInfoProxy class TypeUtilTest(unittest.TestCase): @@ -129,3 +134,35 @@ def test_check_python_comparable_exception(self, sequence, type_str): ), str(exception_message.value), ) + + def test_has_callable_attr(self): + class TestClass: + def __init__(self) -> None: + self.also_not_callable = "I am not callable" + + def callable_attr(self): + pass + + @property + def not_callable_attr(self): + return "I am a property" + + assert type_util.has_callable_attr(TestClass, "callable_attr") is True + assert type_util.has_callable_attr(TestClass, "not_callable_attr") is False + assert type_util.has_callable_attr(TestClass, "also_not_callable") is False + assert type_util.has_callable_attr(TestClass, "not_a_real_attr") is False + + @parameterized.expand( + [ + ({"key": "value"}, False), + (Secrets(), True), + (QueryParamsProxy(), True), + (SessionStateProxy(), True), + (StreamlitCookies({}), True), + (StreamlitHeaders({}), True), + (UserInfoProxy(), True), + ] + ) + def test_is_custom_dict(self, dict_obj: Any, is_custom_dict: bool): + """Test that `is_custom_dict` returns True for all Streamlit custom dicts.""" + assert type_util.is_custom_dict(dict_obj) is is_custom_dict diff --git a/lib/tests/streamlit/write_test.py b/lib/tests/streamlit/write_test.py index 151ebfad86db..852f982fd582 100644 --- a/lib/tests/streamlit/write_test.py +++ b/lib/tests/streamlit/write_test.py @@ -34,15 +34,11 @@ from streamlit.error_util import handle_uncaught_app_exception from streamlit.errors import StreamlitAPIException from streamlit.runtime.state import QueryParamsProxy, SessionStateProxy -from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame -from tests.streamlit.modin_mocks import Series as ModinSeries -from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame +from tests.streamlit.data_mocks import ( + SHARED_TEST_CASES, + CaseMetadata, +) from tests.streamlit.runtime.secrets_test import MOCK_TOML -from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame -from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries -from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame -from tests.streamlit.snowpark_mocks import Row as SnowparkRow -from tests.streamlit.snowpark_mocks import Table as SnowparkTable class StreamlitWriteTest(unittest.TestCase): @@ -168,6 +164,24 @@ class FakePyplot: p.assert_called_once() + @parameterized.expand( + SHARED_TEST_CASES, + ) + def test_input_data( + self, + name: str, + input_data: Any, + metadata: CaseMetadata, + ): + """Test st.write with various input data and check that it uses + the expected command.""" + with patch( + f"streamlit.delta_generator.DeltaGenerator.{metadata.expected_write_command}" + ) as p: + st.write(input_data) + + p.assert_called_once() + def test_plotly(self): import plotly.graph_objs as go @@ -177,13 +191,6 @@ def test_plotly(self): p.assert_called_once() - def test_dict(self): - """Test st.write with dict.""" - with patch("streamlit.delta_generator.DeltaGenerator.json") as p: - st.write({"a": 1, "b": 2}) - - p.assert_called_once() - def test_pil_image(self): """Test st.write with PIL image objects.""" with patch("streamlit.delta_generator.DeltaGenerator.image") as p: @@ -223,13 +230,6 @@ class FakeOpenaiStream: p.assert_called_once() - def test_list(self): - """Test st.write with list.""" - with patch("streamlit.delta_generator.DeltaGenerator.json") as p: - st.write([1, 2, 3]) - - p.assert_called_once() - def test_namedtuple(self): """Test st.write with list.""" with patch("streamlit.delta_generator.DeltaGenerator.json") as p: @@ -261,31 +261,6 @@ def test_streamlit_secrets(self, *mocks): p.assert_called_once() - @parameterized.expand( - [ - (pd.DataFrame([[20, 30, 50]], columns=["a", "b", "c"]),), - (pd.Series(np.array(["a", "b", "c"])),), - (pd.Index(list("abc")),), - (pd.DataFrame({"a": [1], "b": [2]}).style.format("{:.2%}"),), - (np.array(["a", "b", "c"]),), - (SnowpandasSeries(pd.Series(np.random.randn(2))),), - (SnowpandasDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (SnowparkTable(pd.DataFrame(np.random.randn(2, 2))),), - (SnowparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (PysparkDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - (ModinSeries(pd.Series(np.random.randn(2))),), - (ModinDataFrame(pd.DataFrame(np.random.randn(2, 2))),), - ([SnowparkRow()],), - ] - ) - def test_write_compatible_dataframe( - self, - input_data: Any, - ): - with patch("streamlit.delta_generator.DeltaGenerator.dataframe") as p: - st.write(input_data) - p.assert_called_once() - @patch("streamlit.delta_generator.DeltaGenerator.markdown") @patch("streamlit.delta_generator.DeltaGenerator.json") def test_dict_and_string(self, mock_json, mock_markdown):
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
sympy__sympy-13773@bcce686
sympy/sympy
Python
13,773
@/__matmul__ now only performs matrix multiplication
<!-- Your title above should be a short description of what was changed. Do not include the issue number in the title. --> Add check to allow __matmul__ to perform only matrix multiplication and throws ValueError if scalars are used as operators or operands. #### References to other Issues or PRs This PR will fix #13766 . #### Brief description of what is fixed or changed ``` >>> B = Matrix([[2, 3], [1, 2]]) >>> 2@B ``` and ``` >>> B@2 ``` now throws ValueError: Scalar operands are not allowed, use '*' instead
2017-12-19T10:44:38Z
@ (__matmul__) should fail if one argument is not a matrix ``` >>> A = Matrix([[1, 2], [3, 4]]) >>> B = Matrix([[2, 3], [1, 2]]) >>> A@B Matrix([ [ 4, 7], [10, 17]]) >>> 2@B Matrix([ [4, 6], [2, 4]]) ``` Right now `@` (`__matmul__`) just copies `__mul__`, but it should actually only work if the multiplication is actually a matrix multiplication. This is also how NumPy works ``` >>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> 2*a array([[2, 4], [6, 8]]) >>> 2@a Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: Scalar operands are not allowed, use '*' instead ```
Note to anyone fixing this: `@`/`__matmul__` only works in Python 3.5+. I would like to work on this issue.
[ { "body": "```\r\n>>> A = Matrix([[1, 2], [3, 4]])\r\n>>> B = Matrix([[2, 3], [1, 2]])\r\n>>> A@B\r\nMatrix([\r\n[ 4, 7],\r\n[10, 17]])\r\n>>> 2@B\r\nMatrix([\r\n[4, 6],\r\n[2, 4]])\r\n```\r\n\r\nRight now `@` (`__matmul__`) just copies `__mul__`, but it should actually only work if the multiplication is actually a matrix multiplication. \r\n\r\nThis is also how NumPy works\r\n\r\n```\r\n>>> import numpy as np\r\n>>> a = np.array([[1, 2], [3, 4]])\r\n>>> 2*a\r\narray([[2, 4],\r\n [6, 8]])\r\n>>> 2@a\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nValueError: Scalar operands are not allowed, use '*' instead\r\n```", "number": 13766, "title": "@ (__matmul__) should fail if one argument is not a matrix" } ]
7121bdf1facdd90d05b6994b4c2e5b2865a4638a
{ "head_commit": "bcce6867ac7646ec2e3059b4a41ffb28d2265cac", "head_commit_message": "@/__matmul__ now only performs matrix multiplication", "patch_to_review": "diff --git a/sympy/matrices/common.py b/sympy/matrices/common.py\nindex 8c4e21fcfbe0..3061f9a8a591 100644\n--- a/sympy/matrices/common.py\n+++ b/sympy/matrices/common.py\n@@ -1973,6 +1973,13 @@ def __div__(self, other):\n \n @call_highest_priority('__rmatmul__')\n def __matmul__(self, other):\n+ other = _matrixify(other)\n+ # matrix-like objects can have shapes. This is our check.\n+ # Non matrice operands will throw error\n+ if not(hasattr(other, 'shape')):\n+ raise ValueError(\"Scalar operands are not allowed, use '*' instead\");\n+ return\n+\n return self.__mul__(other)\n \n @call_highest_priority('__rmul__')\n@@ -2066,6 +2073,12 @@ def __radd__(self, other):\n \n @call_highest_priority('__matmul__')\n def __rmatmul__(self, other):\n+ other = _matrixify(other)\n+ # matrix-like objects can have shapes. This is our check.\n+ # Non matrice operands will throw error\n+ if not(hasattr(other, 'shape')):\n+ raise ValueError(\"Scalar operands are not allowed, use '*' instead\");\n+ return\n return self.__rmul__(other)\n \n @call_highest_priority('__mul__')\ndiff --git a/sympy/matrices/tests/test_commonmatrix.py b/sympy/matrices/tests/test_commonmatrix.py\nindex a7554d92956c..17e76291508b 100644\n--- a/sympy/matrices/tests/test_commonmatrix.py\n+++ b/sympy/matrices/tests/test_commonmatrix.py\n@@ -674,6 +674,13 @@ def test_multiplication():\n assert c[1, 0] == 3*5\n assert c[1, 1] == 0\n \n+def test_matmul():\n+ a = Matrix([[1, 2], [3, 4]])\n+ b = Matrix([[2, 3], [1, 2]])\n+ raises(ValueError, lambda: a@2)\n+ raises(ValueError, lambda: 2@a)\n+ \n+\n def test_power():\n raises(NonSquareMatrixError, lambda: Matrix((1, 2))**2)\n \n" }
[ { "diff_hunk": "@@ -1973,6 +1973,13 @@ def __div__(self, other):\n \n @call_highest_priority('__rmatmul__')\n def __matmul__(self, other):\n+ other = _matrixify(other)\n+ # matrix-like objects can have shapes. This is our check.\n+ # Non matrice operands will throw error\n+ if not(hasattr(other, 'shape')):\n+ raise ValueError(\"Scalar operands are not allowed, use '*' instead\");", "line": null, "original_line": 1980, "original_start_line": null, "path": "sympy/matrices/common.py", "start_line": null, "text": "@user1:\nDon't put semicolons at the end of lines." }, { "diff_hunk": "@@ -1973,6 +1973,13 @@ def __div__(self, other):\n \n @call_highest_priority('__rmatmul__')\n def __matmul__(self, other):\n+ other = _matrixify(other)\n+ # matrix-like objects can have shapes. This is our check.\n+ # Non matrice operands will throw error\n+ if not(hasattr(other, 'shape')):\n+ raise ValueError(\"Scalar operands are not allowed, use '*' instead\");\n+ return", "line": null, "original_line": 1981, "original_start_line": null, "path": "sympy/matrices/common.py", "start_line": null, "text": "@user1:\nRemove the `return`." } ]
d74a605e453a63c28ce6ad15dc86735e75ba8173
diff --git a/sympy/matrices/common.py b/sympy/matrices/common.py index 8c4e21fcfbe0..a6f996be10d7 100644 --- a/sympy/matrices/common.py +++ b/sympy/matrices/common.py @@ -1973,6 +1973,10 @@ def __div__(self, other): @call_highest_priority('__rmatmul__') def __matmul__(self, other): + other = _matrixify(other) + if not getattr(other, 'is_Matrix', False) and not getattr(other, 'is_MatrixLike', False): + return NotImplemented + return self.__mul__(other) @call_highest_priority('__rmul__') @@ -2066,6 +2070,10 @@ def __radd__(self, other): @call_highest_priority('__matmul__') def __rmatmul__(self, other): + other = _matrixify(other) + if not getattr(other, 'is_Matrix', False) and not getattr(other, 'is_MatrixLike', False): + return NotImplemented + return self.__rmul__(other) @call_highest_priority('__mul__') diff --git a/sympy/matrices/tests/test_commonmatrix.py b/sympy/matrices/tests/test_commonmatrix.py index a7554d92956c..a976d844312f 100644 --- a/sympy/matrices/tests/test_commonmatrix.py +++ b/sympy/matrices/tests/test_commonmatrix.py @@ -674,6 +674,30 @@ def test_multiplication(): assert c[1, 0] == 3*5 assert c[1, 1] == 0 +def test_matmul(): + a = Matrix([[1, 2], [3, 4]]) + + assert a.__matmul__(2) == NotImplemented + + assert a.__rmatmul__(2) == NotImplemented + + #This is done this way because @ is only supported in Python 3.5+ + #To check 2@a case + try: + eval('2 @ a') + except SyntaxError: + pass + except TypeError: #TypeError is raised in case of NotImplemented is returned + pass + + #Check a@2 case + try: + eval('a @ 2') + except SyntaxError: + pass + except TypeError: #TypeError is raised in case of NotImplemented is returned + pass + def test_power(): raises(NonSquareMatrixError, lambda: Matrix((1, 2))**2)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13564@6401bb3
sympy/sympy
Python
13,564
Resolves RecursionError in latex representation
fixes #13559. It was due to factor of `1/1` ```python expr = parse_expr('5/1', evaluate=False) expr.args ``` it gives `(5 , 1/1)` , which was not handled properly in latex conversion . So everytime an expression was divided by 1 , it resulted in RecursionError
2017-11-04T17:08:52Z
LaTeX representation raises RecursionError LaTeX representation of an expression that has division by 1 causes `RecursionError`. SymPy version: 1.1.1. Code to reproduce the bug: ```python from sympy import latex from sympy.parsing.sympy_parser import parse_expr expr = parse_expr('2/1', evaluate=False) latex(expr) ``` Output: ``` ... ~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/printer.py in _print(self, expr, *args, **kwargs) 255 printmethod = '_print_' + cls.__name__ 256 if hasattr(self, printmethod): --> 257 return getattr(self, printmethod)(expr, *args, **kwargs) 258 # Unknown object, fall back to the emptyPrinter. 259 return self.emptyPrinter(expr) ~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in _print_Pow(self, expr) 466 elif expr.exp.is_Rational and expr.exp.is_negative and expr.base.is_commutative: 467 # Things like 1/x --> 468 return self._print_Mul(expr) 469 else: 470 if expr.base.is_Function: ~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in _print_Mul(self, expr) 396 # use the original expression here, since fraction() may have 397 # altered it when producing numer and denom --> 398 tex += convert(expr) 399 else: 400 snumer = convert(numer) ~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in convert(expr) 366 def convert(expr): 367 if not expr.is_Mul: --> 368 return str(self._print(expr)) 369 else: 370 _tex = last_term_tex = "" ... last 4 frames repeated, from the frame below ... ~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/printer.py in _print(self, expr, *args, **kwargs) 255 printmethod = '_print_' + cls.__name__ 256 if hasattr(self, printmethod): --> 257 return getattr(self, printmethod)(expr, *args, **kwargs) 258 # Unknown object, fall back to the emptyPrinter. 259 return self.emptyPrinter(expr) RecursionError: maximum recursion depth exceeded in comparison ```
[ { "body": "LaTeX representation of an expression that has division by 1 causes `RecursionError`. SymPy version: 1.1.1.\r\n\r\nCode to reproduce the bug:\r\n```python\r\nfrom sympy import latex\r\nfrom sympy.parsing.sympy_parser import parse_expr\r\n\r\nexpr = parse_expr('2/1', evaluate=False)\r\nlatex(expr)\r\n```\r\nOutput:\r\n```\r\n...\r\n\r\n~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/printer.py in _print(self, expr, *args, **kwargs)\r\n 255 printmethod = '_print_' + cls.__name__\r\n 256 if hasattr(self, printmethod):\r\n--> 257 return getattr(self, printmethod)(expr, *args, **kwargs)\r\n 258 # Unknown object, fall back to the emptyPrinter.\r\n 259 return self.emptyPrinter(expr)\r\n\r\n~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in _print_Pow(self, expr)\r\n 466 elif expr.exp.is_Rational and expr.exp.is_negative and expr.base.is_commutative:\r\n 467 # Things like 1/x\r\n--> 468 return self._print_Mul(expr)\r\n 469 else:\r\n 470 if expr.base.is_Function:\r\n\r\n~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in _print_Mul(self, expr)\r\n 396 # use the original expression here, since fraction() may have\r\n 397 # altered it when producing numer and denom\r\n--> 398 tex += convert(expr)\r\n 399 else:\r\n 400 snumer = convert(numer)\r\n\r\n~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/latex.py in convert(expr)\r\n 366 def convert(expr):\r\n 367 if not expr.is_Mul:\r\n--> 368 return str(self._print(expr))\r\n 369 else:\r\n 370 _tex = last_term_tex = \"\"\r\n\r\n... last 4 frames repeated, from the frame below ...\r\n\r\n~/.virtualenvs/sympy/lib/python3.5/site-packages/sympy/printing/printer.py in _print(self, expr, *args, **kwargs)\r\n 255 printmethod = '_print_' + cls.__name__\r\n 256 if hasattr(self, printmethod):\r\n--> 257 return getattr(self, printmethod)(expr, *args, **kwargs)\r\n 258 # Unknown object, fall back to the emptyPrinter.\r\n 259 return self.emptyPrinter(expr)\r\n\r\nRecursionError: maximum recursion depth exceeded in comparison\r\n```\r\n", "number": 13559, "title": "LaTeX representation raises RecursionError" } ]
36d9c850c642bec047e2cbad0a07c48f4fc7c779
{ "head_commit": "6401bb3207756d5d097e419c9be6d57df224757e", "head_commit_message": "added tests for changes in latex", "patch_to_review": "diff --git a/sympy/printing/latex.py b/sympy/printing/latex.py\nindex 51c1e67f2a1e..dab13173fbb7 100644\n--- a/sympy/printing/latex.py\n+++ b/sympy/printing/latex.py\n@@ -372,6 +372,7 @@ def _print_Gradient(self, expr):\n return r\"\\nabla\\cdot %s\" % self.parenthesize(func, PRECEDENCE['Mul'])\n \n def _print_Mul(self, expr):\n+ from sympy.parsing.sympy_parser import parse_expr\n include_parens = False\n if _coeff_isneg(expr):\n expr = -expr\n@@ -415,12 +416,9 @@ def convert(expr):\n _tex += term_tex\n last_term_tex = term_tex\n return _tex\n+ one_by_one = parse_expr('1/1', evaluate=False)\n \n- if denom is S.One:\n- # use the original expression here, since fraction() may have\n- # altered it when producing numer and denom\n- tex += convert(expr)\n- else:\n+ if not denom is S.One or one_by_one in expr.args:\n snumer = convert(numer)\n sdenom = convert(denom)\n ldenom = len(sdenom.split())\n@@ -459,6 +457,11 @@ def convert(expr):\n else:\n tex += r\"\\frac{%s}{%s}\" % (snumer, sdenom)\n \n+ elif denom is S.One:\n+ # use the original expression here, since fraction() may have\n+ # altered it when producing numer and denom\n+ tex += convert(expr)\n+\n if include_parens:\n tex += \")\"\n return tex\ndiff --git a/sympy/printing/tests/test_latex.py b/sympy/printing/tests/test_latex.py\nindex 17196cb5d375..52ccacd10ace 100644\n--- a/sympy/printing/tests/test_latex.py\n+++ b/sympy/printing/tests/test_latex.py\n@@ -1629,6 +1629,12 @@ def test_issue_12886():\n assert latex(m__1**2 + l__1**2) == r'\\left(l^{1}\\right)^{2} + \\left(m^{1}\\right)^{2}'\n \n \n+def test_issue_13559():\n+ from sympy.parsing.sympy_parser import parse_expr\n+ expr = parse_expr('5/1', evaluate=False)\n+ assert latex(expr) == r\"\\frac{5}{1}\"\n+\n+\n def test_latex_UnevaluatedExpr():\n x = symbols(\"x\")\n he = UnevaluatedExpr(1/x)\n" }
[ { "diff_hunk": "@@ -415,12 +416,9 @@ def convert(expr):\n _tex += term_tex\n last_term_tex = term_tex\n return _tex\n+ one_by_one = parse_expr('1/1', evaluate=False)", "line": null, "original_line": 419, "original_start_line": null, "path": "sympy/printing/latex.py", "start_line": null, "text": "@user1:\nYou can do `Pow(1, -1, evaluate=False)`." } ]
c39ae02943ee0e91f6d9430541413a77fd4cdb80
diff --git a/sympy/printing/latex.py b/sympy/printing/latex.py index 51c1e67f2a1e..8a07803d8575 100644 --- a/sympy/printing/latex.py +++ b/sympy/printing/latex.py @@ -372,6 +372,7 @@ def _print_Gradient(self, expr): return r"\nabla\cdot %s" % self.parenthesize(func, PRECEDENCE['Mul']) def _print_Mul(self, expr): + from sympy.core.power import Pow include_parens = False if _coeff_isneg(expr): expr = -expr @@ -416,10 +417,11 @@ def convert(expr): last_term_tex = term_tex return _tex - if denom is S.One: + if denom is S.One and Pow(1, -1, evaluate=False) not in expr.args: # use the original expression here, since fraction() may have # altered it when producing numer and denom tex += convert(expr) + else: snumer = convert(numer) sdenom = convert(denom) diff --git a/sympy/printing/tests/test_latex.py b/sympy/printing/tests/test_latex.py index 17196cb5d375..52ccacd10ace 100644 --- a/sympy/printing/tests/test_latex.py +++ b/sympy/printing/tests/test_latex.py @@ -1629,6 +1629,12 @@ def test_issue_12886(): assert latex(m__1**2 + l__1**2) == r'\left(l^{1}\right)^{2} + \left(m^{1}\right)^{2}' +def test_issue_13559(): + from sympy.parsing.sympy_parser import parse_expr + expr = parse_expr('5/1', evaluate=False) + assert latex(expr) == r"\frac{5}{1}" + + def test_latex_UnevaluatedExpr(): x = symbols("x") he = UnevaluatedExpr(1/x)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13709@82f9445
sympy/sympy
Python
13,709
imported filldedent in line.py
fixes https://github.com/sympy/sympy/issues/13705 @smichr please review <!-- Please give this pull request a descriptive title. Pull requests with descriptive titles are more likely to receive reviews. Describe what you changed! A title that only references an issue number is not descriptive. --> <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below. -->
2017-12-08T21:07:03Z
import filldedent in line.py The following should raise a ValueError: ``` >>> Line((x,1),(2,3)).arbitrary_point(x) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "sympy\geometry\line.py", line 236, in arbitrary_point raise ValueError(filldedent(''' NameError: global name 'filldedent' is not defined ``` The following mod to line.py should be made and a test of the above added to the place where `arbitrary_point` is tested in the `test_line.py` file. ```diff diff --git a/sympy/geometry/line.py b/sympy/geometry/line.py index a542ddb..8ac0dc8 100644 --- a/sympy/geometry/line.py +++ b/sympy/geometry/line.py @@ -33,7 +33,7 @@ from .entity import GeometryEntity, GeometrySet from .point import Point, Point3D -from sympy.utilities.misc import Undecidable +from sympy.utilities.misc import Undecidable, filldedent class LinearEntity(GeometrySet): ```
@smichr I would like to work on this issue. I have looked into the arbitrary_point tests and was wondering what would be the best way to write a test for the ValueError, since that error is raised in the arbitrary_point method in the line.py file as shown below ``` if t.name in (f.name for f in self.free_symbols): raise ValueError(filldedent(''' Symbol %s already appears in object and cannot be used as a parameter. ''' % t.name)) ```
[ { "body": "The following should raise a ValueError:\r\n```\r\n>>> Line((x,1),(2,3)).arbitrary_point(x)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"sympy\\geometry\\line.py\", line 236, in arbitrary_point\r\n raise ValueError(filldedent('''\r\nNameError: global name 'filldedent' is not defined\r\n```\r\nThe following mod to line.py should be made and a test of the above added to the place where `arbitrary_point` is tested in the `test_line.py` file.\r\n\r\n```diff\r\ndiff --git a/sympy/geometry/line.py b/sympy/geometry/line.py\r\nindex a542ddb..8ac0dc8 100644\r\n--- a/sympy/geometry/line.py\r\n+++ b/sympy/geometry/line.py\r\n@@ -33,7 +33,7 @@\r\n\r\n from .entity import GeometryEntity, GeometrySet\r\n from .point import Point, Point3D\r\n-from sympy.utilities.misc import Undecidable\r\n+from sympy.utilities.misc import Undecidable, filldedent\r\n\r\n\r\n class LinearEntity(GeometrySet):\r\n```", "number": 13705, "title": "import filldedent in line.py" } ]
e01729f7e7f9d43df711084dde431aad4461b792
{ "head_commit": "82f9445670b1ef0249bcde42b386589cf22ca3ec", "head_commit_message": "Merge pull request #1 from TheGluck/filldedent\n\nAdds test", "patch_to_review": "diff --git a/sympy/geometry/line.py b/sympy/geometry/line.py\nindex 6af9079a9657..ab2cc8a9e569 100644\n--- a/sympy/geometry/line.py\n+++ b/sympy/geometry/line.py\n@@ -32,7 +32,7 @@\n \n from .entity import GeometryEntity, GeometrySet\n from .point import Point, Point3D\n-from sympy.utilities.misc import Undecidable\n+from sympy.utilities.misc import Undecidable, filldedent\n \n \n class LinearEntity(GeometrySet):\ndiff --git a/sympy/geometry/tests/test_line.py b/sympy/geometry/tests/test_line.py\nindex 2475838189ca..766106a1c182 100644\n--- a/sympy/geometry/tests/test_line.py\n+++ b/sympy/geometry/tests/test_line.py\n@@ -33,7 +33,7 @@ def warn_with_traceback(message, category, filename, lineno, file=None, line=Non\n \n \n warnings.showwarning = warn_with_traceback\n-warnings.simplefilter('always', UserWarning) # make sure to show warnings every time they occurr\n+warnings.simplefilter('always', UserWarning) # make sure to show warnings every time they occur\n \n \n def feq(a, b):\n@@ -76,6 +76,12 @@ def test_arbitrary_point():\n assert Segment3D(Point3D(x1, x1, x1), Point3D(y1, y1, y1)).length == sqrt(3) * sqrt((x1 - y1) ** 2)\n assert Segment3D((1, 1, 1), (2, 3, 4)).arbitrary_point() == \\\n Point3D(t + 1, 2 * t + 1, 3 * t + 1)\n+ try:\n+ Line((x,1),(2,3)).arbitrary_point(x)\n+ except ValueError:\n+ assert True\n+ except BaseException:\n+ assert False\n \n \n def test_are_concurrent_2d():\n" }
[ { "diff_hunk": "@@ -76,6 +76,12 @@ def test_arbitrary_point():\n assert Segment3D(Point3D(x1, x1, x1), Point3D(y1, y1, y1)).length == sqrt(3) * sqrt((x1 - y1) ** 2)\n assert Segment3D((1, 1, 1), (2, 3, 4)).arbitrary_point() == \\\n Point3D(t + 1, 2 * t + 1, 3 * t + 1)\n+ try:\n+ Line((x,1),(2,3)).arbitrary_point(x)\n+ except ValueError:\n+ assert True\n+ except BaseException:\n+ assert False", "line": null, "original_line": 84, "original_start_line": null, "path": "sympy/geometry/tests/test_line.py", "start_line": null, "text": "@user1:\nYou can use `raises` from `sympy.utilities.pytest` to test correct error is raised. See `test_raises` around line 649.\n\n@user2:\n[BaseException](https://docs.python.org/2/library/exceptions.html#exceptions.BaseException) is only used as a base class for other exceptions.\n\n@user3:\n@user1 The code as written is progmatically correct, the test is passed if a ValueError is thrown and fails if any other error is thrown. BaseException is the superclass of all exceptions in python therefore when an exception is inevitably thrown, either it will be a ValueError and will be caught by the first except clause, or it will be caught by the second except clause. This functions as intended.\n\n@user4:\n@user2 is telling you how we test such things in the test suite. The `raises` function will do what you are saying with less noise.\n\n@user3:\nI see what you mean now, what do you mean by noise though?" } ]
dc50cd7e061dcde9241d036f07b303cadaa5c1ea
diff --git a/sympy/geometry/line.py b/sympy/geometry/line.py index 6af9079a9657..ab2cc8a9e569 100644 --- a/sympy/geometry/line.py +++ b/sympy/geometry/line.py @@ -32,7 +32,7 @@ from .entity import GeometryEntity, GeometrySet from .point import Point, Point3D -from sympy.utilities.misc import Undecidable +from sympy.utilities.misc import Undecidable, filldedent class LinearEntity(GeometrySet): diff --git a/sympy/geometry/tests/test_line.py b/sympy/geometry/tests/test_line.py index 2475838189ca..ac17dd717a5e 100644 --- a/sympy/geometry/tests/test_line.py +++ b/sympy/geometry/tests/test_line.py @@ -33,7 +33,7 @@ def warn_with_traceback(message, category, filename, lineno, file=None, line=Non warnings.showwarning = warn_with_traceback -warnings.simplefilter('always', UserWarning) # make sure to show warnings every time they occurr +warnings.simplefilter('always', UserWarning) # make sure to show warnings every time they occur def feq(a, b): @@ -76,7 +76,7 @@ def test_arbitrary_point(): assert Segment3D(Point3D(x1, x1, x1), Point3D(y1, y1, y1)).length == sqrt(3) * sqrt((x1 - y1) ** 2) assert Segment3D((1, 1, 1), (2, 3, 4)).arbitrary_point() == \ Point3D(t + 1, 2 * t + 1, 3 * t + 1) - + raises(ValueError, (lambda: Line((x,1),(2,3)).arbitrary_point(x))) def test_are_concurrent_2d(): l1 = Line(Point(0, 0), Point(1, 1))
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
sympy__sympy-13553@666be46
sympy/sympy
Python
13,553
Wrong result with solveset. Fix issue 13550
Fix #13550. The expression `solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0))` returns `EmptySet()` instead of `FiniteSet(-3)`. The bug is due to the function `_invert(f_x, y, x, domain=S.Complexes)` in `solveset.py`. It returns `x1, s.intersection(domain)` but in such example `x1` is `x**2 - 2*x`, so that `s` does not represent the inversion. Hence the intersection with the domain is meaningless. In the case `x1` is not equal to the symbol, i.e. the function is not inverted, it is better to return `x1, s` A test is added. <!-- Please give this pull request a descriptive title. Pull requests with descriptive titles are more likely to receive reviews. Describe what you changed! A title that only references an issue number is not descriptive. --> <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below. -->
2017-10-30T16:24:51Z
Wrong result with solveset ``` >>> from sympy import * >>> x = Symbol('x') >>> solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) EmptySet() ``` This is incorrect. The correct output for the above code should be `FiniteSet(-3)`. When the `domain = S.Reals`, `FiniteSet(-3, 5)` is returned.
[ { "body": "```\r\n>>> from sympy import *\r\n>>> x = Symbol('x')\r\n>>> solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0))\r\nEmptySet()\r\n```\r\nThis is incorrect. The correct output for the above code should be `FiniteSet(-3)`. When the `domain = S.Reals`, `FiniteSet(-3, 5)` is returned.\r\n", "number": 13550, "title": "Wrong result with solveset" } ]
9476425b9e34363c2d9ac38e9f04aa75ae54a775
{ "head_commit": "666be46c58a8e23962f21bb05918049953c59668", "head_commit_message": "Wrong result with solveset. Issue 13350\n\nFix 13350.\nThe expression solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0))\nreturns EmptySet() instead of FiniteSet(-3). The bug is due to the\nfunction _invert(f_x, y, x, domain=S.Complexes). It returns\nx1, s.intersection(domain)\nbut in such example x1 is x**2 - 2*x, so that 's' does not represent\nthe solution. Hence the intersection with the domain is meaningless.\n\nIn the case x1 is not equal to the symbol, it is better to return x1, s\n\nA test is added.", "patch_to_review": "diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py\nindex 885918210d50..7d02714cd666 100644\n--- a/sympy/solvers/solveset.py\n+++ b/sympy/solvers/solveset.py\n@@ -108,6 +108,9 @@ def _invert(f_x, y, x, domain=S.Complexes):\n if not isinstance(s, FiniteSet) or x1 == f_x:\n return x1, s\n \n+ if not x1 == x:\n+ return x1, s\n+\n return x1, s.intersection(domain)\n \n \ndiff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py\nindex b84df55e3c00..a4527e63ad99 100644\n--- a/sympy/solvers/tests/test_solveset.py\n+++ b/sympy/solvers/tests/test_solveset.py\n@@ -1633,3 +1633,6 @@ def test__is_finite_with_finite_vars():\n assert all(f(1/x) is None for x in (\n Dummy(), Dummy(real=True), Dummy(complex=True)))\n assert f(1/Dummy(real=False)) is True # b/c it's finite but not 0\n+\n+def test_issue_13550():\n+ assert solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) == FiniteSet(-3)\n" }
[ { "diff_hunk": "@@ -108,6 +108,9 @@ def _invert(f_x, y, x, domain=S.Complexes):\n if not isinstance(s, FiniteSet) or x1 == f_x:", "line": null, "original_line": 108, "original_start_line": null, "path": "sympy/solvers/solveset.py", "start_line": null, "text": "@user1:\nI guess the condition `or x1 != x` could be appended here instead of adding a new block below.\n\n@author:\nThanks. You are right. Modification done." } ]
071fed89e78af5544bbb07e5109dcefb6c7e6e60
diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py index 885918210d50..59b60b2e47b9 100644 --- a/sympy/solvers/solveset.py +++ b/sympy/solvers/solveset.py @@ -105,7 +105,7 @@ def _invert(f_x, y, x, domain=S.Complexes): else: x1, s = _invert_complex(f_x, FiniteSet(y), x) - if not isinstance(s, FiniteSet) or x1 == f_x: + if not isinstance(s, FiniteSet) or x1 == f_x or x1 != x: return x1, s return x1, s.intersection(domain) diff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py index b84df55e3c00..a4527e63ad99 100644 --- a/sympy/solvers/tests/test_solveset.py +++ b/sympy/solvers/tests/test_solveset.py @@ -1633,3 +1633,6 @@ def test__is_finite_with_finite_vars(): assert all(f(1/x) is None for x in ( Dummy(), Dummy(real=True), Dummy(complex=True))) assert f(1/Dummy(real=False)) is True # b/c it's finite but not 0 + +def test_issue_13550(): + assert solveset(x**2 - 2*x - 15, symbol = x, domain = Interval(-oo, 0)) == FiniteSet(-3)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13173@a2b464e
sympy/sympy
Python
13,173
degree(multivariate): multivariate expression now requires generator to specified
multivariate expressions could be passed into the degree expression, in which case the degree was returned for the first generator. As of this pull request, when giving the degree function multivariate expressions, a generator is mandatory to be specified, else a TypeError is raised. Example: ``` >>> degree(x+x**2) 2 >>>degree(x**2+y**3,y) 3 >>> degree(x**2+y) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "sympy/polys/polytools.py", line 4414, in degree raise TypeError("generator needs to be specified for degree() for multivariate polynomials") TypeError: generator needs to be specified for degree() for multivariate polynomials ``` <!-- Please give this pull request a descriptive title. Pull requests with descriptive titles are more likely to receive reviews. Describe what you changed! A title that only references an issue number is not descriptive. --> <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below. --> Fixes #13129
2017-08-22T16:57:46Z
degree(multivariate) -> degree of first generator When giving the degree function a multivariate expression, the default degree returned is for the first generator which is chosen canonically but arbitrarily. When the degree method of Poly is called, this is not so bad because one would/should know what the generators are. For the case of using the function, however, I believe it allows ambiguity to pass silently by not requiring the generator to be specified. ``` degree(x + x**2) -> 2 this is ok, there is no ambiguity ``` Here, the return value depends on the symbols chosen ``` degree(a + b**2) -> 1 degree(b + a**2) -> 2 ```
[ { "body": "When giving the degree function a multivariate expression, the default degree returned is for the first generator which is chosen canonically but arbitrarily. When the degree method of Poly is called, this is not so bad because one would/should know what the generators are. For the case of using the function, however, I believe it allows ambiguity to pass silently by not requiring the generator to be specified.\r\n\r\n```\r\ndegree(x + x**2) -> 2 this is ok, there is no ambiguity\r\n```\r\nHere, the return value depends on the symbols chosen\r\n\r\n```\r\ndegree(a + b**2) -> 1\r\ndegree(b + a**2) -> 2\r\n```", "number": 13129, "title": "degree(multivariate) -> degree of first generator" } ]
0ccd0b8aec14b4212d25a3171c5a5b6b5a23c79a
{ "head_commit": "a2b464ea134099b69b1b499ac3bf386c329ed72b", "head_commit_message": "degree(multivariate): Modified if clause and edited error message\n\nThe if clause added previously is changed to an elif clause. The error\nmessage is modified so as to give an example specific to the user's passed\nmultivariate expression, instead of a generic expression. White spaces are\nadded to test_util.py where needed.", "patch_to_review": "diff --git a/sympy/polys/polytools.py b/sympy/polys/polytools.py\nindex ff6fedc17b71..364f933d7623 100644\n--- a/sympy/polys/polytools.py\n+++ b/sympy/polys/polytools.py\n@@ -45,7 +45,7 @@\n GeneratorsError,\n )\n \n-from sympy.utilities import group, sift, public\n+from sympy.utilities import group, sift, public, filldedent\n \n import sympy.polys\n import mpmath\n@@ -56,8 +56,7 @@\n \n from sympy.polys import polyoptions as options\n \n-from sympy.core.compatibility import iterable, range\n-\n+from sympy.core.compatibility import iterable, range, ordered\n \n @public\n class Poly(Expr):\n@@ -4427,6 +4426,13 @@ def degree(f, gen=0):\n if gen not in p.gens:\n return S.Zero\n \n+ elif len(f.free_symbols) > 1 and sympify(gen).is_Number and not f.is_Poly:\n+ raise TypeError(filldedent('''\n+ A symbolic generator of interest is required for a multivariate\n+ expression like func = %s, e.g. degree(func, gen = %s) instead of\n+ degree(func, gen = %s).\n+ ''' % (f, next(ordered(f.free_symbols)), gen)))\n+\n return Integer(p.degree(gen))\n \n \ndiff --git a/sympy/polys/tests/test_polytools.py b/sympy/polys/tests/test_polytools.py\nindex 0af574396244..f448bf201e65 100644\n--- a/sympy/polys/tests/test_polytools.py\n+++ b/sympy/polys/tests/test_polytools.py\n@@ -1166,10 +1166,10 @@ def test_Poly_degree():\n assert degree(x*y**2, y) == 2\n assert degree(x*y**2, z) == 0\n \n- assert degree(y**2+x**3) == 3\n- assert degree(y**2+x**3, 1) == 2\n assert degree(pi) == 1\n \n+ raises(TypeError, lambda: degree(y**2 + x**3))\n+ raises(TypeError, lambda: degree(y**2 + x**3, 1))\n raises(PolynomialError, lambda: degree(x, 1.1))\n \n assert degree(Poly(0,x),z) == -oo\n" }
[ { "diff_hunk": "@@ -4427,6 +4426,13 @@ def degree(f, gen=0):\n if gen not in p.gens:\n return S.Zero\n \n+ elif len(f.free_symbols) > 1 and sympify(gen).is_Number and not f.is_Poly:", "line": null, "original_line": 4429, "original_start_line": null, "path": "sympy/polys/polytools.py", "start_line": null, "text": "@user1:\nThis `elif` should read `elif not f.is_Poly and len(f.free_symbols) > 1:` because we already know that `sympify(gen).is_Number` is true because of the `if` clause. Also, there is no need to traverse the expression if we know that f is a Poly (and that, I assume is faster than finding the free symbols) -- put cheaper tests first.\n\n@author:\nThank you. Will take care of this next time." } ]
b8ac4ba112863382878961a372b162cc2e9fc72b
diff --git a/sympy/polys/polytools.py b/sympy/polys/polytools.py index ff6fedc17b71..4091e39da233 100644 --- a/sympy/polys/polytools.py +++ b/sympy/polys/polytools.py @@ -45,7 +45,7 @@ GeneratorsError, ) -from sympy.utilities import group, sift, public +from sympy.utilities import group, sift, public, filldedent import sympy.polys import mpmath @@ -56,8 +56,7 @@ from sympy.polys import polyoptions as options -from sympy.core.compatibility import iterable, range - +from sympy.core.compatibility import iterable, range, ordered @public class Poly(Expr): @@ -4426,6 +4425,12 @@ def degree(f, gen=0): p, _ = poly_from_expr(f.as_expr()) if gen not in p.gens: return S.Zero + elif not f.is_Poly and len(f.free_symbols) > 1: + raise TypeError(filldedent(''' + A symbolic generator of interest is required for a multivariate + expression like func = %s, e.g. degree(func, gen = %s) instead of + degree(func, gen = %s). + ''' % (f, next(ordered(f.free_symbols)), gen))) return Integer(p.degree(gen)) diff --git a/sympy/polys/tests/test_polytools.py b/sympy/polys/tests/test_polytools.py index 0af574396244..f448bf201e65 100644 --- a/sympy/polys/tests/test_polytools.py +++ b/sympy/polys/tests/test_polytools.py @@ -1166,10 +1166,10 @@ def test_Poly_degree(): assert degree(x*y**2, y) == 2 assert degree(x*y**2, z) == 0 - assert degree(y**2+x**3) == 3 - assert degree(y**2+x**3, 1) == 2 assert degree(pi) == 1 + raises(TypeError, lambda: degree(y**2 + x**3)) + raises(TypeError, lambda: degree(y**2 + x**3, 1)) raises(PolynomialError, lambda: degree(x, 1.1)) assert degree(Poly(0,x),z) == -oo
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
zulip__zulip-32359@2584ca6
zulip/zulip
Python
32,359
puppet: Require libldap-common be installed.
Zulip instances without a database included, like the Docker image, would not fail to use TLS properly, since `TLS_REQCERT` was not set in `/etc/ldap/ldap.conf`. While there's a few other ways we could fix this, just installing libldap-common on app frontend instances seems like a good solution, and has no impact on other Zulip systems, and it was already being installed through a "Recommends" tier apt dependency indirectly from the PostgreSQL server package. Fixes zulip/docker-zulip#454.
2024-11-14T23:52:48Z
Version 9.0 and 9.1 "ZulipLDAPAuthBackend" Issue with TLS on ldaps 636 Hello, we are running zulip docker for about a year now, starting from Version 7 and really enjoying the product! Good work! Now that we upgraded our "dev" machine to version 9.0 to test the upgrade we are facing issues with authenticating through ldap. In the logs we just see: `ldap.SERVER_DOWN: {'result': -1, 'desc': "Can't contact LDAP server", 'ctrls': [], 'info': '(unknown error code)'}` also we found out that if you add `echo 'TLS_REQCERT allow' >> /etc/ldap/ldap.conf` inside the zulip docker container, it works again. But we don't want to ignore certificate checks for ldap tls. We use a official publicly trusted certificate on ldaps which is bought from GoDaddy, which properly verifys when using openssl command even from inside the zulip docker cli. We also tried version 9.1 but it is the same result. Please help us :) Thank you! BR, Felix
We are facing the same issue. How do we can add a SSL certificate to avoid such behavior? Also would be nice, if LOGS will be more informative over this issue Facing the same issue, would be nice to have a fix for this Hi, can we get an update on this? Please :) BR, Felix Hello, we were facing the same issue. As a workaround, it helped to add the following two lines to `Dockerfile` in order to point ldap to `/etc/ssl/certs`: ``` RUN mkdir /etc/ldap && \ echo 'TLS_CACERTDIR /etc/ssl/certs' >> /etc/ldap/ldap.conf ``` This is maybe a bit more sensible than to just accept all certificates. (And then of course you have to rebuild & restart the container) Also encountered this issue, which broke a previously-working setup on upgrade since both LDAP+STARTTLS and LDAPS stopped working. [This issue from another Docker repo](https://github.com/netbox-community/netbox-docker/issues/799) suggests that installing `libldap-common` in the Dockerfile is a sufficient fix. I'm going to test this myself (though it's very painful to rebuild the image.) Folks, [chat.zulip.org](https://zulip.com/development-community/) is the best place to get help with issues setting up a server. @ivanbakel did your test determine that installing `libldap-common` fixed this issue for you? If so, that seems like an easy tweak to the `Dockerfile` that we could include in 9.3. I can confirm that installing `libldap-common` leads to the `/etc/ldap/ldap.conf` file being present with the desired cert settings on the image, and this fixes the LDAP issue. It looks like the reason this hasn't come up outside Docker is that a postgres database ends up pulling this in: ``` $ aptitude why libldap-common i postgresql-16 Depends libldap-2.5-0 (>= 2.5.4) i A libldap-2.5-0 Recommends libldap-common ```
[ { "body": "Hello,\r\n\r\nwe are running zulip docker for about a year now, starting from Version 7 and really enjoying the product! Good work!\r\n\r\nNow that we upgraded our \"dev\" machine to version 9.0 to test the upgrade we are facing issues with authenticating through ldap. In the logs we just see:\r\n\r\n`ldap.SERVER_DOWN: {'result': -1, 'desc': \"Can't contact LDAP server\", 'ctrls': [], 'info': '(unknown error code)'}`\r\n\r\nalso we found out that if you add \r\n\r\n`echo 'TLS_REQCERT allow' >> /etc/ldap/ldap.conf`\r\n\r\ninside the zulip docker container, it works again. But we don't want to ignore certificate checks for ldap tls. \r\nWe use a official publicly trusted certificate on ldaps which is bought from GoDaddy, which properly verifys when using openssl command even from inside the zulip docker cli. We also tried version 9.1 but it is the same result.\r\n\r\nPlease help us :) \r\nThank you!\r\n\r\nBR,\r\nFelix", "number": 454, "title": "Version 9.0 and 9.1 \"ZulipLDAPAuthBackend\" Issue with TLS on ldaps 636" } ]
8356a9d9e488c876811119b9e2dc740ee6da0f8e
{ "head_commit": "2584ca6e0617f83bfec4bbc0466b6ceb1be691cf", "head_commit_message": "puppet: Require libldap-common be installed.\n\nZulip instances without a database included, like the Docker image,\nwould not fail to use TLS properly, since `TLS_REQCERT` was not set in\n`/etc/ldap/ldap.conf`. While there's a few other ways we could fix\nthis, just installing libldap-common on app frontend instances seems\nlike a good solution, and has no impact on other Zulip systems, and it\nwas already being installed through a \"Recommends\" tier apt dependency\nindirectly from the PostgreSQL server package.\n\nFixes zulip/docker-zulip#454.", "patch_to_review": "diff --git a/puppet/zulip/manifests/app_frontend_base.pp b/puppet/zulip/manifests/app_frontend_base.pp\nindex 4bf5a8f696fd8..9d723533b225b 100644\n--- a/puppet/zulip/manifests/app_frontend_base.pp\n+++ b/puppet/zulip/manifests/app_frontend_base.pp\n@@ -14,8 +14,20 @@\n # package already includes the client.\n include zulip::postgresql_client\n }\n- # For Slack import\n- zulip::safepackage { 'unzip': ensure => installed }\n+ zulip::safepackage {\n+ [\n+ # For Slack import.\n+ 'unzip',\n+ # Ensures `/etc/ldap/ldap.conf` exists; the default\n+ # `TLS_CACERTDIR` specified there is necessary for LDAP\n+ # authentication to work. This package is \"Recommended\" by\n+ # `libldap` where is required by postgres, so has been on most\n+ # Zulip servers by default; adding it here explicitly ensures it\n+ # is available on those that don't include the database server.\n+ 'libldap-common'\n+ ]:\n+ ensure => installed,\n+ }\n \n file { '/etc/nginx/zulip-include/app':\n require => Package[$zulip::common::nginx],\n" }
[ { "diff_hunk": "@@ -14,8 +14,20 @@\n # package already includes the client.\n include zulip::postgresql_client\n }\n- # For Slack import\n- zulip::safepackage { 'unzip': ensure => installed }\n+ zulip::safepackage {\n+ [\n+ # For Slack import.\n+ 'unzip',\n+ # Ensures `/etc/ldap/ldap.conf` exists; the default\n+ # `TLS_CACERTDIR` specified there is necessary for LDAP\n+ # authentication to work. This package is \"Recommended\" by\n+ # `libldap` where is required by postgres, so has been on most\n+ # Zulip servers by default; adding it here explicitly ensures it\n+ # is available on those that don't include the database server.\n+ 'libldap-common'\n+ ]:\n+ ensure => installed,", "line": null, "original_line": 29, "original_start_line": 27, "path": "puppet/zulip/manifests/app_frontend_base.pp", "start_line": null, "text": "@user1:\n```suggestion\r\n 'libldap-common',\r\n ]:\r\n ensure => installed,\r\n```" } ]
a39edb16e0a61f98af40592fa057f58bd53845ae
diff --git a/puppet/zulip/manifests/app_frontend_base.pp b/puppet/zulip/manifests/app_frontend_base.pp index 4bf5a8f696fd8..5eac856133884 100644 --- a/puppet/zulip/manifests/app_frontend_base.pp +++ b/puppet/zulip/manifests/app_frontend_base.pp @@ -14,8 +14,20 @@ # package already includes the client. include zulip::postgresql_client } - # For Slack import - zulip::safepackage { 'unzip': ensure => installed } + zulip::safepackage { + [ + # For Slack import. + 'unzip', + # Ensures `/etc/ldap/ldap.conf` exists; the default + # `TLS_CACERTDIR` specified there is necessary for LDAP + # authentication to work. This package is "Recommended" by + # `libldap` where is required by postgres, so has been on most + # Zulip servers by default; adding it here explicitly ensures it + # is available on those that don't include the database server. + 'libldap-common', + ]: + ensure => installed, + } file { '/etc/nginx/zulip-include/app': require => Package[$zulip::common::nginx],
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
streamlit__streamlit-8877@7924890
streamlit/streamlit
Python
8,877
Horizontal `st.bar_chart`
## Describe your changes PR adds a boolean kwarg `horizontal=False|True` to `st.bar_chart`. The default is `False`, which keeps the current behavior. ## Testing Plan - E2E Tests - โœ… - Manual Testing - โœ… **Note: The substantial # of snapshot changes are a consequence of the test file organization** Added 2 horizontal bar chart examples in sequence so charts indexed 22 on have all been shifted down - `st_builtin_chart-22.png` is now `st_builtin_chart-24.png` and so on. ---- **Before (Vertical only):** <img width="300" alt="Screenshot 2024-06-11 at 2 36 30โ€ฏPM" src="https://github.com/streamlit/streamlit/assets/63436329/3f9df8fb-5787-466c-b7da-fba144c622c0"> <img width="300" alt="Screenshot 2024-06-11 at 2 36 00โ€ฏPM" src="https://github.com/streamlit/streamlit/assets/63436329/0f70e765-8a89-4355-9b85-af04e071c019"> **After (`horizontal=true`):** <img width="500" alt="Screenshot 2024-06-11 at 2 36 21โ€ฏPM" src="https://github.com/streamlit/streamlit/assets/63436329/9d1c69d6-20fa-4755-a68e-496b48b75dfc"> <img width="500" alt="Screenshot 2024-06-11 at 2 35 50โ€ฏPM" src="https://github.com/streamlit/streamlit/assets/63436329/3c5ec144-77a7-4217-84f9-f0e2ac27a5d8">
2024-06-11T21:40:03Z
Horizontal bar charts ### Problem When having a bar chart with string indices it is very hard to read them. ### Solution Support `barh` type chart, where the bars are horizontal rather than vertical. So new interface would be: ``` streamlit.bar_chart(data=None, orientation='vertical', width=0, height=0) ``` ### Additional context In `pandas` it is very simple `df.plot.barh()` --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
Hi @AmitMY, thanks a lot for the feature request. We will triage and add to our roadmap accordingly to our prioritization. In the meantime, we do support charting libraries like `altair` and also `matplotlib` that you can use to customize a chart. In this way you can get a more advance chart with little effort. Doc link [here](https://streamlit.io/docs/api.html#display-charts). For example, `st.st.pyplot()` will just render any matplotlib figure and `st.altair_chart(my_chart)` will render an altair chart, `my_chart` defined with `altair` (https://altair-viz.github.io/), so you can use all the expressive power of these libraries and then display the charts with Streamlit. Still, we need the basic charting options to be surfaced in Streamlit, so this is a really useful feature request. Best, Matteo
[ { "body": "### Problem \r\n\r\nWhen having a bar chart with string indices it is very hard to read them.\r\n\r\n### Solution\r\n\r\nSupport `barh` type chart, where the bars are horizontal rather than vertical.\r\n\r\nSo new interface would be:\r\n```\r\nstreamlit.bar_chart(data=None, orientation='vertical', width=0, height=0)\r\n```\r\n\r\n### Additional context\r\n\r\nIn `pandas` it is very simple `df.plot.barh()`\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**", "number": 716, "title": "Horizontal bar charts" } ]
51cd0c91dfd9b2a0e90a147a4e09ce8bbc31d87f
{ "head_commit": "792489065e58a683c1c121f62e664ca6b4347c6e", "head_commit_message": "More snaps", "patch_to_review": "diff --git a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[chromium].png b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[chromium].png\nindex 9f31492e1cba..54eaa5af708e 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[chromium].png and b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[chromium].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[firefox].png b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[firefox].png\nindex 7103ccaa3eba..4073a50f18ae 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-22[firefox].png and 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b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-chromium].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png\nindex b074879d10c6..21041f7f0e47 100644\nBinary files a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png and b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png differ\ndiff --git a/e2e_playwright/st_builtin_chart.py b/e2e_playwright/st_builtin_chart.py\nindex ea9ffcaaace9..2211cd745361 100644\n--- a/e2e_playwright/st_builtin_chart.py\n+++ b/e2e_playwright/st_builtin_chart.py\n@@ -83,6 +83,8 @@\n st.bar_chart(utc_df)\n st.bar_chart(color_df, x=\"a\", y=\"b\", color=\"e\")\n st.bar_chart(df, x_label=\"X Axis Label\", y_label=\"Y Axis Label\")\n+st.bar_chart(df, horizontal=True)\n+st.bar_chart(df, horizontal=True, x_label=\"X Label\", y_label=\"Y Label\")\n \n st.header(\"Line Chart\")\n \ndiff --git a/e2e_playwright/st_builtin_chart_test.py b/e2e_playwright/st_builtin_chart_test.py\nindex 33134941bfbd..d713af46a805 100644\n--- a/e2e_playwright/st_builtin_chart_test.py\n+++ b/e2e_playwright/st_builtin_chart_test.py\n@@ -16,7 +16,7 @@\n \n from e2e_playwright.conftest import ImageCompareFunction\n \n-TOTAL_CHARTS = 46\n+TOTAL_CHARTS = 48\n \n \n def test_built_in_chart_rendering(app: Page, assert_snapshot: ImageCompareFunction):\n@@ -44,7 +44,7 @@ def test_themed_chart_rendering(\n # Only test a single selected chart per built-in chart type:\n assert_snapshot(chart_elements.nth(1), name=\"st_builtin_chart-area_chart_themed\")\n assert_snapshot(chart_elements.nth(12), name=\"st_builtin_chart-bar_chart_themed\")\n- assert_snapshot(chart_elements.nth(23), name=\"st_builtin_chart-line_chart_themed\")\n+ assert_snapshot(chart_elements.nth(25), name=\"st_builtin_chart-line_chart_themed\")\n assert_snapshot(\n- chart_elements.nth(34), name=\"st_builtin_chart-scatter_chart_themed\"\n+ chart_elements.nth(36), name=\"st_builtin_chart-scatter_chart_themed\"\n )\ndiff --git a/lib/streamlit/elements/lib/built_in_chart_utils.py b/lib/streamlit/elements/lib/built_in_chart_utils.py\nindex 5e83d237feae..ba95e80c2502 100644\n--- a/lib/streamlit/elements/lib/built_in_chart_utils.py\n+++ b/lib/streamlit/elements/lib/built_in_chart_utils.py\n@@ -122,6 +122,8 @@ def generate_chart(\n size_from_user: str | float | None = None,\n width: int | None = None,\n height: int | None = None,\n+ # For bar charts only:\n+ horizontal: bool = False,\n ) -> tuple[alt.Chart, AddRowsMetadata]:\n \"\"\"Function to use the chart's type, data columns and indices to figure out the chart's spec.\"\"\"\n import altair as alt\n@@ -165,6 +167,18 @@ def generate_chart(\n \n # At this point, x_column is only None if user did not provide one AND df is empty.\n \n+ if not horizontal:\n+ x_encoding = _get_x_encoding(\n+ df, x_column, x_from_user, x_axis_label, chart_type\n+ )\n+ y_encoding = _get_y_encoding(df, y_column, y_from_user, y_axis_label)\n+ else:\n+ # Handle horizontal bar chart - switches x and y encodings:\n+ x_encoding = _get_y_encoding(df, y_column, y_from_user, x_axis_label) # type: ignore[assignment]\n+ y_encoding = _get_x_encoding(\n+ df, x_column, x_from_user, y_axis_label, chart_type\n+ ) # type: ignore[assignment]\n+\n # Create a Chart with x and y encodings.\n chart = alt.Chart(\n data=df,\n@@ -172,8 +186,8 @@ def generate_chart(\n width=width or 0,\n height=height or 0,\n ).encode(\n- x=_get_x_encoding(df, x_column, x_from_user, x_axis_label, chart_type),\n- y=_get_y_encoding(df, y_column, y_from_user, y_axis_label),\n+ x=x_encoding,\n+ y=y_encoding,\n )\n \n # Set up opacity encoding.\ndiff --git a/lib/streamlit/elements/vega_charts.py b/lib/streamlit/elements/vega_charts.py\nindex 4408eed345d7..1a71fadb013b 100644\n--- a/lib/streamlit/elements/vega_charts.py\n+++ b/lib/streamlit/elements/vega_charts.py\n@@ -936,6 +936,7 @@ def bar_chart(\n x_label: str | None = None,\n y_label: str | None = None,\n color: str | Color | list[Color] | None = None,\n+ horizontal: bool = False,\n width: int | None = None,\n height: int | None = None,\n use_container_width: bool = True,\n@@ -1010,6 +1011,11 @@ def bar_chart(\n as the number of y values (e.g. ``color=[\"#fd0\", \"#f0f\", \"#04f\"]``\n for three lines).\n \n+ horizontal : bool\n+ Determines the orientation of the chart:\n+ * True: Displays the chart horizontally, with the x-axis and y-axis swapped.\n+ * False: Displays the chart vertically (default).\n+\n width : int or None\n Desired width of the chart expressed in pixels. If ``width`` is\n ``None`` (default), Streamlit sets the width of the chart to fit\n@@ -1101,6 +1107,7 @@ def bar_chart(\n size_from_user=None,\n width=width,\n height=height,\n+ horizontal=horizontal,\n )\n return cast(\n \"DeltaGenerator\",\n" }
[ { "diff_hunk": "@@ -165,15 +167,27 @@ def generate_chart(\n \n # At this point, x_column is only None if user did not provide one AND df is empty.\n \n+ if not horizontal:\n+ x_encoding = _get_x_encoding(\n+ df, x_column, x_from_user, x_axis_label, chart_type\n+ )\n+ y_encoding = _get_y_encoding(df, y_column, y_from_user, y_axis_label)\n+ else:\n+ # Handle horizontal bar chart - switches x and y encodings:\n+ x_encoding = _get_y_encoding(df, y_column, y_from_user, x_axis_label) # type: ignore[assignment]\n+ y_encoding = _get_x_encoding(\n+ df, x_column, x_from_user, y_axis_label, chart_type\n+ ) # type: ignore[assignment]", "line": null, "original_line": 180, "original_start_line": 177, "path": "lib/streamlit/elements/lib/built_in_chart_utils.py", "start_line": null, "text": "@user1:\nThis feels a bit hacky since it will assign `alt.X` to y and `alt.Y` to x. It probably works because they are extending the same base class without too many changes, but it could break if things change in Altair. Would it be possible to just change the parameters? \r\n\r\n```python\r\n x_encoding = _get_x_encoding(df, y_column, y_from_user, x_axis_label, chart_type) \r\n```\n\n@author:\nIt does seem hacky, but it was the only solution I found to reliably create a horizontal bar chart ๐Ÿ˜“ \r\nJust changing the param produced the below:\r\n<img width=500 src=https://github.com/streamlit/streamlit/assets/63436329/3f392aa4-f8fd-4c53-8e26-38b4a310f442 />\n\n@user1:\nOh, I believe this might be the reason why it's not working when you just change the parameters:\r\n\r\nhttps://github.com/streamlit/streamlit/blob/a94106dcbe4f667b8bfec0140615bbd1d3938e69/lib/streamlit/elements/lib/built_in_chart_utils.py#L894-L897\r\n\r\nIt's making the wrong column ordinal \n\n@user1:\nOne alternative might be to replace `ChartType.BAR` with \r\n\r\n```\r\n VERTICAL_BAR = {\"mark_type\": \"bar\", \"command\": \"bar_chart\", \"horizontal\": False}\r\n HORIZONTAL_BAR = {\"mark_type\": \"bar\", \"command\": \"bar_chart\", \"horizontal\": True}\r\n```\r\n\r\nand then do this in `_get_x_encoding_type`:\r\n\r\n```\r\nif chart_type == ChartType.VERTICAL_BAR and not _is_date_column(df, x_column): \r\n return \"ordinal\"\r\n```\r\n\r\nand this in `_get_y_encoding_type`:\r\n\r\n```\r\nif chart_type == ChartType.HORIZONTAL_BAR and not _is_date_column(df, y_column): \r\n return \"ordinal\"\r\n```\r\n\r\nAnd just do the selection of `VERTICAL_BAR` vs `HORIZONTAL_BAR` type in the `bar_chart` command." } ]
fb12e006c271d875b97ca0a9cf8ea356abf3187b
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--git a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png index b074879d10c6..21041f7f0e47 100644 Binary files a/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png and b/e2e_playwright/__snapshots__/linux/st_builtin_chart_test/st_builtin_chart-scatter_chart_themed[light_theme-firefox].png differ diff --git a/e2e_playwright/st_builtin_chart.py b/e2e_playwright/st_builtin_chart.py index ea9ffcaaace9..2211cd745361 100644 --- a/e2e_playwright/st_builtin_chart.py +++ b/e2e_playwright/st_builtin_chart.py @@ -83,6 +83,8 @@ st.bar_chart(utc_df) st.bar_chart(color_df, x="a", y="b", color="e") st.bar_chart(df, x_label="X Axis Label", y_label="Y Axis Label") +st.bar_chart(df, horizontal=True) +st.bar_chart(df, horizontal=True, x_label="X Label", y_label="Y Label") st.header("Line Chart") diff --git a/e2e_playwright/st_builtin_chart_test.py b/e2e_playwright/st_builtin_chart_test.py index 33134941bfbd..d713af46a805 100644 --- a/e2e_playwright/st_builtin_chart_test.py +++ b/e2e_playwright/st_builtin_chart_test.py @@ -16,7 +16,7 @@ from e2e_playwright.conftest import ImageCompareFunction -TOTAL_CHARTS = 46 +TOTAL_CHARTS = 48 def test_built_in_chart_rendering(app: Page, assert_snapshot: ImageCompareFunction): @@ -44,7 +44,7 @@ def test_themed_chart_rendering( # Only test a single selected chart per built-in chart type: assert_snapshot(chart_elements.nth(1), name="st_builtin_chart-area_chart_themed") assert_snapshot(chart_elements.nth(12), name="st_builtin_chart-bar_chart_themed") - assert_snapshot(chart_elements.nth(23), name="st_builtin_chart-line_chart_themed") + assert_snapshot(chart_elements.nth(25), name="st_builtin_chart-line_chart_themed") assert_snapshot( - chart_elements.nth(34), name="st_builtin_chart-scatter_chart_themed" + chart_elements.nth(36), name="st_builtin_chart-scatter_chart_themed" ) diff --git a/lib/streamlit/elements/lib/built_in_chart_utils.py b/lib/streamlit/elements/lib/built_in_chart_utils.py index 5e83d237feae..f87b3fcf562c 100644 --- a/lib/streamlit/elements/lib/built_in_chart_utils.py +++ b/lib/streamlit/elements/lib/built_in_chart_utils.py @@ -71,7 +71,8 @@ class AddRowsMetadata: class ChartType(Enum): AREA = {"mark_type": "area", "command": "area_chart"} - BAR = {"mark_type": "bar", "command": "bar_chart"} + VERTICAL_BAR = {"mark_type": "bar", "command": "bar_chart", "horizontal": False} + HORIZONTAL_BAR = {"mark_type": "bar", "command": "bar_chart", "horizontal": True} LINE = {"mark_type": "line", "command": "line_chart"} SCATTER = {"mark_type": "circle", "command": "scatter_chart"} @@ -165,6 +166,22 @@ def generate_chart( # At this point, x_column is only None if user did not provide one AND df is empty. + if chart_type == ChartType.HORIZONTAL_BAR: + # Handle horizontal bar chart - switches x and y data: + x_encoding = _get_x_encoding( + df, y_column, y_from_user, x_axis_label, chart_type + ) + y_encoding = _get_y_encoding( + df, x_column, x_from_user, y_axis_label, chart_type + ) + else: + x_encoding = _get_x_encoding( + df, x_column, x_from_user, x_axis_label, chart_type + ) + y_encoding = _get_y_encoding( + df, y_column, y_from_user, y_axis_label, chart_type + ) + # Create a Chart with x and y encodings. chart = alt.Chart( data=df, @@ -172,8 +189,8 @@ def generate_chart( width=width or 0, height=height or 0, ).encode( - x=_get_x_encoding(df, x_column, x_from_user, x_axis_label, chart_type), - y=_get_y_encoding(df, y_column, y_from_user, y_axis_label), + x=x_encoding, + y=y_encoding, ) # Set up opacity encoding. @@ -620,7 +637,7 @@ def _maybe_melt( def _get_x_encoding( df: pd.DataFrame, x_column: str | None, - x_from_user: str | None, + x_from_user: str | Sequence[str] | None, x_axis_label: str | None, chart_type: ChartType, ) -> alt.X: @@ -653,12 +670,15 @@ def _get_x_encoding( if x_axis_label is not None: x_title = x_axis_label + # grid lines on x axis for horizontal bar charts only + grid = True if chart_type == ChartType.HORIZONTAL_BAR else False + return alt.X( x_field, title=x_title, type=_get_x_encoding_type(df, chart_type, x_column), scale=alt.Scale(), - axis=_get_axis_config(df, x_column, grid=False), + axis=_get_axis_config(df, x_column, grid=grid), ) @@ -667,6 +687,7 @@ def _get_y_encoding( y_column: str | None, y_from_user: str | Sequence[str] | None, y_axis_label: str | None, + chart_type: ChartType, ) -> alt.Y: import altair as alt @@ -697,12 +718,15 @@ def _get_y_encoding( if y_axis_label is not None: y_title = y_axis_label + # grid lines on y axis for all charts except horizontal bar charts + grid = False if chart_type == ChartType.HORIZONTAL_BAR else True + return alt.Y( field=y_field, title=y_title, - type=_get_y_encoding_type(df, y_column), + type=_get_y_encoding_type(df, chart_type, y_column), scale=alt.Scale(), - axis=_get_axis_config(df, y_column, grid=True), + axis=_get_axis_config(df, y_column, grid=grid), ) @@ -877,17 +901,21 @@ def _get_x_encoding_type( if x_column is None: return "quantitative" # Anything. If None, Vega-Lite may hide the axis. - # Bar charts should have a discrete (ordinal) x-axis, UNLESS type is date/time + # Vertical bar charts should have a discrete (ordinal) x-axis, UNLESS type is date/time # https://github.com/streamlit/streamlit/pull/2097#issuecomment-714802475 - if chart_type == ChartType.BAR and not _is_date_column(df, x_column): + if chart_type == ChartType.VERTICAL_BAR and not _is_date_column(df, x_column): return "ordinal" return type_util.infer_vegalite_type(df[x_column]) def _get_y_encoding_type( - df: pd.DataFrame, y_column: str | None + df: pd.DataFrame, chart_type: ChartType, y_column: str | None ) -> type_util.VegaLiteType: + # Horizontal bar charts should have a discrete (ordinal) y-axis, UNLESS type is date/time + if chart_type == ChartType.HORIZONTAL_BAR and not _is_date_column(df, y_column): + return "ordinal" + if y_column: return type_util.infer_vegalite_type(df[y_column]) diff --git a/lib/streamlit/elements/vega_charts.py b/lib/streamlit/elements/vega_charts.py index 4408eed345d7..d5d7303de5cd 100644 --- a/lib/streamlit/elements/vega_charts.py +++ b/lib/streamlit/elements/vega_charts.py @@ -936,6 +936,7 @@ def bar_chart( x_label: str | None = None, y_label: str | None = None, color: str | Color | list[Color] | None = None, + horizontal: bool = False, width: int | None = None, height: int | None = None, use_container_width: bool = True, @@ -1010,6 +1011,11 @@ def bar_chart( as the number of y values (e.g. ``color=["#fd0", "#f0f", "#04f"]`` for three lines). + horizontal : bool + Determines the orientation of the chart: + * True: Displays the chart horizontally, with the x-axis and y-axis swapped. + * False: Displays the chart vertically (default). + width : int or None Desired width of the chart expressed in pixels. If ``width`` is ``None`` (default), Streamlit sets the width of the chart to fit @@ -1090,8 +1096,12 @@ def bar_chart( """ + bar_chart_type = ( + ChartType.HORIZONTAL_BAR if horizontal else ChartType.VERTICAL_BAR + ) + chart, add_rows_metadata = generate_chart( - chart_type=ChartType.BAR, + chart_type=bar_chart_type, data=data, x_from_user=x, y_from_user=y,
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
sympy__sympy-13131@2cc8481
sympy/sympy
Python
13,131
Fix powsimp issues #9183, #10095, and KeyError
- Fixed powsimp raising ValueError when base is float and Pow is argument of Mul. Since multiplicity expects integer or rational but powsimp doesn't checks it, it raises ValueError: fixes #9183 - Fixed Pow.as_numer_denom() returning nan when either numerator or denominator of base is 1 and exp is infinite. Since 1**oo is NaN, numerator or denominator becomes NaN: fixes #10095 - Fixed powsimp raising KeyError in common_b[b] = common_b[b] + e, when one base is Float and another is equal Rational(Integer). This seems to be caused by #11707. ~**Thing(s) to consider:** Should we automatically evaluate k**oo to zero where k is in (-1, 1), i.e., `(1/(2*E))**oo`?~
2017-08-15T16:39:38Z
simplify throws unexpected ValueError The following error was unexpected for me and I could not find anything about it in the docs so I thought I'd report it. Hope it helps. kind regards, Jesse this works fine: > > > a=sympy.sympify('0.12**d+43') - sympy.sympify('0.12*d+43') > > > a.simplify() > > > 0.12**d - 0.12*d however, the reverse throws an exception: > > > a=sympy.sympify('0.12_d+43') - sympy.sympify('0.12__d+43') > > > a > > > -0.12__d + 0.12_d > > > a.simplify() > > > Traceback (most recent call last): > > > File "<interactive input>", line 1, in <module> > > > File "C:\Python34\lib\site-packages\sympy\core\expr.py", line 2922, in simplify > > > return simplify(self, ratio, measure) > > > File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 3684, in simplify > > > expr = Mul(_powsimp(expr).as_content_primitive()) > > > File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2549, in powsimp > > > expr = expr.func(_[recurse(w) for w in expr.args]) > > > File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2549, in <listcomp> > > > expr = expr.func(*[recurse(w) for w in expr.args]) > > > File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2540, in recurse > > > return powsimp(arg, _deep, _combine, _force, _measure) > > > File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2597, in powsimp > > > m = multiplicity(abs(b), abs(coeff)) > > > File "C:\Python34\lib\site-packages\sympy\ntheory\factor_.py", line 230, in multiplicity > > > raise ValueError('expecting ints or fractions, got %s and %s' % (p, n)) > > > ValueError: expecting ints or fractions, got 0.120000000000000 and 1 simplify((1/(2*E))**oo) returns `nan` ``` >>> (1/(2*E))**oo (1/(2*E))**oo >>> simplify((1/(2*E))**oo) nan >>> powsimp((1/(2*E))**oo) (1/(2*E))**oo ```
sorry, I'm new to github. I don't see the asterisks in my above code anymore (why?), but I really did type in 0.12 asterisk d and 0.12 asterisk asterisk d Please read more about [markdown](https://guides.github.com/features/mastering-markdown/). Enclosing the text in triple backticks ("annotate as code") should do the trick. Works: ``` >>> a=sympy.sympify('0.12**d+43') - sympy.sympify('0.12*d+43') >>> a.simplify() 0.12**d - 0.12*d ``` Fails: ``` >>> a=sympy.sympify('0.12*d+43') - sympy.sympify('0.12**d+43') >>> a -0.12**d + 0.12*d >>> a.simplify() Traceback (most recent call last): File "<interactive input>", line 1, in <module> File "C:\Python34\lib\site-packages\sympy\core\expr.py", line 2922, in simplify return simplify(self, ratio, measure) File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 3684, in simplify expr = Mul(*powsimp(expr).as_content_primitive()) File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2549, in powsimp expr = expr.func(*[recurse(w) for w in expr.args]) File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2549, in <listcomp> expr = expr.func(*[recurse(w) for w in expr.args]) File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2540, in recurse return powsimp(arg, _deep, _combine, _force, _measure) File "C:\Python34\lib\site-packages\sympy\simplify\simplify.py", line 2597, in powsimp m = multiplicity(abs(b), abs(coeff)) File "C:\Python34\lib\site-packages\sympy\ntheory\factor_.py", line 230, in multiplicity raise ValueError('expecting ints or fractions, got %s and %s' % (p, n)) ValueError: expecting ints or fractions, got 0.120000000000000 and 1 ``` I am having a related issue I believe, however I am not sure this is supposed to be supported yet or not, or if I am doing something wrong. I deeply appreciate any input on this. If I try to integrate without symbols (using values for the interval) the computation is performed. ``` python X1 = Normal('X1',50, 28.3) X2 = Normal('X2',50, 28.3) x1 = Symbol('x1') x2 = Symbol('x2') f = density(X1)(x1)*density(X2)(x2) s = Symbol('s') Q = Symbol('Q') integrate(f, (x1, s+Q-50, +oo), (x2, Q, +oo)) ``` The traceback: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/site-packages/sympy/utilities/decorator.py", line 35, in threaded_func return func(expr, *args, **kwargs) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 1232, in integrate risch=risch, manual=manual) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 487, in doit conds=conds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 760, in _eval_integral return result + i.doit(risch=False) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 487, in doit conds=conds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 876, in _eval_integral h = meijerint_indefinite(g, x) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/meijerint.py", line 1596, in meijerint_indefinite res = _meijerint_indefinite_1(f.subs(x, x + a), x) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/meijerint.py", line 1611, in _meijerint_indefinite_1 gs = _rewrite1(f, x) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/meijerint.py", line 1548, in _rewrite1 g = _rewrite_single(g, x, recursive) File "/usr/local/lib/python2.7/site-packages/sympy/core/cache.py", line 93, in wrapper retval = cfunc(*args, **kwargs) File "/usr/local/lib/python2.7/site-packages/sympy/core/compatibility.py", line 872, in wrapper result = user_function(*args, **kwds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/meijerint.py", line 1503, in _rewrite_single simplify=False, needeval=True) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 353, in mellin_transform return MellinTransform(f, x, s).doit(**hints) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 129, in doit for x in fn.args] File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 118, in doit self.function_variable, self.transform_variable, **hints) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 295, in _compute_transform return _mellin_transform(f, x, s, **hints) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 196, in wrapper res = func(*args, **kwargs) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/transforms.py", line 220, in _mellin_transform F = integrator(x**(s - 1) * f, x) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/meijerint.py", line 1497, in my_integrator res = _my_unpolarify(hyperexpand(res, rewrite='nonrepsmall')) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/hyperexpand.py", line 2482, in hyperexpand return f.replace(hyper, do_replace).replace(meijerg, do_meijer) File "/usr/local/lib/python2.7/site-packages/sympy/core/basic.py", line 1351, in replace rv = bottom_up(self, rec_replace, atoms=True) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 4079, in bottom_up for a in rv.args]) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 4082, in bottom_up rv = F(rv) File "/usr/local/lib/python2.7/site-packages/sympy/core/basic.py", line 1336, in rec_replace new = _value(expr, result) File "/usr/local/lib/python2.7/site-packages/sympy/core/basic.py", line 1280, in <lambda> _value = lambda expr, result: value(*expr.args) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/hyperexpand.py", line 2479, in do_meijer allow_hyper, rewrite=rewrite) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/hyperexpand.py", line 2378, in _meijergexpand slater2 = powdenest(slater2.subs(t, 1/z0), polar=True) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 2443, in powdenest return unpolarify(powdenest(unpolarify(eq, exponents_only=True)), rep) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 2445, in powdenest new = powsimp(sympify(eq)) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 2550, in powsimp expr = expr.func(*[recurse(w) for w in expr.args]) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 2541, in recurse return powsimp(arg, _deep, _combine, _force, _measure) File "/usr/local/lib/python2.7/site-packages/sympy/simplify/simplify.py", line 2601, in powsimp m = multiplicity(abs(b), abs(coeff)) File "/usr/local/lib/python2.7/site-packages/sympy/ntheory/factor_.py", line 231, in multiplicity raise ValueError('expecting ints or fractions, got %s and %s' % (p, n)) ValueError: expecting ints or fractions, got 1.56076365043889 and 2 ``` As the integrand is a product, the integral can be computed by multiplying two independent integrals. (There is only limited support for multidimensional integrals.) ``` integrate(density(X1)(x1), (x1, s + Q - 50, oo))*integrate(density(X2)(x2), (x2, Q, oo)) ``` Thanks for the reply jksuom. The thing is that they are not independent (I realise now that in the example they are, I was following a buggy article), but what I need to do is actually: `integrate(f, (x1, s+Q-x2, +oo), (x2, Q, +oo))` So the inner integral depends on the outer. This actually gives a different error: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/site-packages/sympy/utilities/decorator.py", line 35, in threaded_func return func(expr, *args, **kwargs) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 1232, in integrate risch=risch, manual=manual) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 487, in doit conds=conds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/integrals.py", line 849, in _eval_integral h, i = risch_integrate(g, x, separate_integral=True, conds=conds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/risch.py", line 1678, in risch_integrate ans, i, b = integrate_hyperexponential(fa, fd, DE, conds=conds) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/risch.py", line 1408, in integrate_hyperexponential g1, h, r = hermite_reduce(a, d, DE) File "/usr/local/lib/python2.7/site-packages/sympy/integrals/risch.py", line 997, in hermite_reduce q, r = a.div(d) File "/usr/local/lib/python2.7/site-packages/sympy/polys/polytools.py", line 1529, in div dom, per, F, G = f._unify(g) File "/usr/local/lib/python2.7/site-packages/sympy/polys/polytools.py", line 358, in _unify dom, lev = f.rep.dom.unify(g.rep.dom, gens), len(gens) - 1 File "/usr/local/lib/python2.7/site-packages/sympy/polys/domains/domain.py", line 250, in unify return K0.unify_with_symbols(K1, symbols) File "/usr/local/lib/python2.7/site-packages/sympy/polys/domains/domain.py", line 230, in unify_with_symbols return K0.unify(K1) File "/usr/local/lib/python2.7/site-packages/sympy/polys/domains/domain.py", line 274, in unify domain = domain.get_ring() File "/usr/local/lib/python2.7/site-packages/sympy/polys/domains/realfield.py", line 106, in get_ring raise DomainError('there is no ring associated with %s' % self) sympy.polys.polyerrors.DomainError: there is no ring associated with RR ``` Should I start a discussion in the googlegroups forum? The cause of this DomainError is 28.3. Python will automatically transform it to a floating point number and some integrators in SymPy cannot deal with floats. The error does not appear if the rational number `S(283)/10` is used instead. Unfortunately, this does not help to compute the integral. It appears currently too hard for SymPy. I see what you mean. I tried to use an integer instead, and I've been waiting for the result for half an hour (I am guessing it will throw an error sooner or later, or I'll just have to kill it). For this specific case, I was trying to build an expression (which has that integral) that will be derived wrt s and Q iteratively. I know it is a rather complex task. In order to solve it manually we use a Taylor series approximation. Can I do this with sympy? I just got the same `sympy.polys.polyerrors.DomainError: there is no ring associated with RR` error when trying the following ``` py from sympy import symbols, integrate, exp, nsimplify a, b, c, d, f, g, z = symbols('a, b, c, d, f, g, z', real=True) src = a*exp((1/2)*b**(-2)*(-c + d)**2 - (-(1/2)*f + (1/2)*z)/g) int_src = integrate(src, (z, 0, 1)) ``` Although rewriting to ``` py src = exp((1/2)*b**(-2)*(-c + d)**2 - (-(1/2)*f + (1/2)*z)/g) ``` did not throw the error. Also ``` py from sympy import nsimplify src = nsimplify(src) ``` made it work (as suggested by @jksuom). > some integrators in SymPy cannot deal with floats Is there any plans to make this happen? I guess this very bug could be the cause of major frustrations for many users (at least it did for me). The basic issue here appears to be in `powsimp()`: ```python >>> sympy.sympify('0.1*d-0.1**d').powsimp() ValueError Traceback (most recent call last) ... ValueError: expecting ints or fractions, got 0.100000000000000 and 1 ``` This is really frustrating as there is no clear path on how to prevent this when simplifying complex expressions. This works, ``` >>> limit((1/(2*E))**x, x, oo) 0 ``` @asmeurer was the automatic simplification of `1/(2*E)**oo` is not done. Is that so because that makes the `core` slow? Or is that a bug? ``` >>> from sympy import * >>> (1/(2*E))**oo (exp(-1)/2)**oo >>> simplify((exp(-1)/2)**oo) nan ``` So, I don't think that it is a bug.. Perhaps you should calculate the limit instead.
[ { "body": "The following error was unexpected for me and I could not find anything about it in the docs so I thought I'd report it. Hope it helps.\n\nkind regards,\nJesse\n\nthis works fine:\n\n> > > a=sympy.sympify('0.12**d+43') - sympy.sympify('0.12*d+43')\n> > > a.simplify()\n> > > 0.12**d - 0.12*d\n\nhowever, the reverse throws an exception:\n\n> > > a=sympy.sympify('0.12_d+43') - sympy.sympify('0.12__d+43')\n> > > a\n> > > -0.12__d + 0.12_d\n> > > a.simplify()\n> > > Traceback (most recent call last):\n> > > File \"<interactive input>\", line 1, in <module>\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\core\\expr.py\", line 2922, in simplify\n> > > return simplify(self, ratio, measure)\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\simplify\\simplify.py\", line 3684, in simplify\n> > > expr = Mul(_powsimp(expr).as_content_primitive())\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\simplify\\simplify.py\", line 2549, in powsimp\n> > > expr = expr.func(_[recurse(w) for w in expr.args])\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\simplify\\simplify.py\", line 2549, in <listcomp>\n> > > expr = expr.func(*[recurse(w) for w in expr.args])\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\simplify\\simplify.py\", line 2540, in recurse\n> > > return powsimp(arg, _deep, _combine, _force, _measure)\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\simplify\\simplify.py\", line 2597, in powsimp\n> > > m = multiplicity(abs(b), abs(coeff))\n> > > File \"C:\\Python34\\lib\\site-packages\\sympy\\ntheory\\factor_.py\", line 230, in multiplicity\n> > > raise ValueError('expecting ints or fractions, got %s and %s' % (p, n))\n> > > ValueError: expecting ints or fractions, got 0.120000000000000 and 1\n", "number": 9183, "title": "simplify throws unexpected ValueError" }, { "body": "```\n>>> (1/(2*E))**oo\n(1/(2*E))**oo\n>>> simplify((1/(2*E))**oo)\nnan\n>>> powsimp((1/(2*E))**oo)\n(1/(2*E))**oo\n```\n", "number": 10095, "title": "simplify((1/(2*E))**oo) returns `nan`" } ]
dceb708ca035c568c816d9457af1b7ca9e57c0a5
{ "head_commit": "2cc8481d38a28009b32e962021ac60a9c5e7f0e7", "head_commit_message": "Fix powsimp raising KeyError when base has Float\n\nFixed powsimp raising KeyError in common_b[b] = common_b[b] + e, when\none base has Float and another has its Rational(Integer) counterpart.\nThis seems to be caused by sympy/sympy#11707.", "patch_to_review": "diff --git a/sympy/core/power.py b/sympy/core/power.py\nindex 82bc545f3307..2c31134dc460 100644\n--- a/sympy/core/power.py\n+++ b/sympy/core/power.py\n@@ -1174,6 +1174,11 @@ def as_numer_denom(self):\n if neg_exp:\n n, d = d, n\n exp = -exp\n+ if exp.is_infinite:\n+ if n is S.One and d is not S.One:\n+ return n, self.func(d, exp)\n+ if n is not S.One and d is S.One:\n+ return self.func(n, exp), d\n return self.func(n, exp), self.func(d, exp)\n \n def matches(self, expr, repl_dict={}, old=False):\ndiff --git a/sympy/simplify/powsimp.py b/sympy/simplify/powsimp.py\nindex 3cf646ca4dd7..8367dca7eb8b 100644\n--- a/sympy/simplify/powsimp.py\n+++ b/sympy/simplify/powsimp.py\n@@ -163,9 +163,9 @@ def recurse(arg, **kwargs):\n # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are\n # Numbers since autoevaluation will undo it, e.g.\n # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4\n- if (b and b.is_Number and not all(ei.is_Number for ei in e) and \\\n- coeff is not S.One and\n- b not in (S.One, S.NegativeOne)):\n+ if (b and b.is_Number and b.is_rational and not all(\n+ ei.is_Number for ei in e) and coeff is not S.One\n+ and b not in (S.One, S.NegativeOne)):\n m = multiplicity(abs(b), abs(coeff))\n if m:\n e.append(m)\n@@ -276,7 +276,7 @@ def update(b):\n bases = []\n for b, e in c_powers:\n b, e = bkey(b, e)\n- if b in common_b.keys():\n+ if b in common_b:\n common_b[b] = common_b[b] + e\n else:\n common_b[b] = e\ndiff --git a/sympy/simplify/tests/test_powsimp.py b/sympy/simplify/tests/test_powsimp.py\nindex 8c80867ddfb6..6a41c23637bf 100644\n--- a/sympy/simplify/tests/test_powsimp.py\n+++ b/sympy/simplify/tests/test_powsimp.py\n@@ -1,7 +1,7 @@\n from sympy import (\n symbols, powsimp, symbols, MatrixSymbol, sqrt, pi, Mul, gamma, Function,\n S, I, exp, simplify, sin, E, log, hyper, Symbol, Dummy, powdenest, root,\n- Rational)\n+ Rational, oo)\n \n from sympy.abc import x, y, z, t, a, b, c, d, e, f, g, h, i, k\n \n@@ -98,6 +98,16 @@ def test_powsimp():\n # issue 8836\n assert str( powsimp(exp(I*pi/3)*root(-1,3)) ) == '(-1)**(2/3)'\n \n+ # issue 9183\n+ assert powsimp(-0.1**x) == -0.1**x\n+\n+ # issue 10095\n+ assert powsimp((1/(2*E))**oo) == (exp(-1)/2)**oo\n+\n+ # PR 13131\n+ eq = sin(2*x)**2*sin(2.0*x)**2\n+ assert powsimp(eq) == eq\n+\n \n def test_powsimp_negated_base():\n assert powsimp((-x + y)/sqrt(x - y)) == -sqrt(x - y)\n" }
[ { "diff_hunk": "@@ -163,9 +163,9 @@ def recurse(arg, **kwargs):\n # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are\n # Numbers since autoevaluation will undo it, e.g.\n # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4\n- if (b and b.is_Number and not all(ei.is_Number for ei in e) and \\\n- coeff is not S.One and\n- b not in (S.One, S.NegativeOne)):\n+ if (b and b.is_Number and b.is_rational and not all(", "line": null, "original_line": 166, "original_start_line": null, "path": "sympy/simplify/powsimp.py", "start_line": null, "text": "@user1:\nIf you need an actual rational number you should use is_Rational or isinstance(b, Rational). is_rational will also be True for things like `Symbol('x', rational=True)`." } ]
309a2620c6f130fd8ed4ec3239ead736a55f4d08
diff --git a/sympy/core/power.py b/sympy/core/power.py index 82bc545f3307..2c31134dc460 100644 --- a/sympy/core/power.py +++ b/sympy/core/power.py @@ -1174,6 +1174,11 @@ def as_numer_denom(self): if neg_exp: n, d = d, n exp = -exp + if exp.is_infinite: + if n is S.One and d is not S.One: + return n, self.func(d, exp) + if n is not S.One and d is S.One: + return self.func(n, exp), d return self.func(n, exp), self.func(d, exp) def matches(self, expr, repl_dict={}, old=False): diff --git a/sympy/simplify/powsimp.py b/sympy/simplify/powsimp.py index 3cf646ca4dd7..045c04760dad 100644 --- a/sympy/simplify/powsimp.py +++ b/sympy/simplify/powsimp.py @@ -163,7 +163,7 @@ def recurse(arg, **kwargs): # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are # Numbers since autoevaluation will undo it, e.g. # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4 - if (b and b.is_Number and not all(ei.is_Number for ei in e) and \ + if (b and b.is_Rational and not all(ei.is_Number for ei in e) and \ coeff is not S.One and b not in (S.One, S.NegativeOne)): m = multiplicity(abs(b), abs(coeff)) @@ -276,7 +276,7 @@ def update(b): bases = [] for b, e in c_powers: b, e = bkey(b, e) - if b in common_b.keys(): + if b in common_b: common_b[b] = common_b[b] + e else: common_b[b] = e diff --git a/sympy/simplify/tests/test_powsimp.py b/sympy/simplify/tests/test_powsimp.py index 8c80867ddfb6..6a41c23637bf 100644 --- a/sympy/simplify/tests/test_powsimp.py +++ b/sympy/simplify/tests/test_powsimp.py @@ -1,7 +1,7 @@ from sympy import ( symbols, powsimp, symbols, MatrixSymbol, sqrt, pi, Mul, gamma, Function, S, I, exp, simplify, sin, E, log, hyper, Symbol, Dummy, powdenest, root, - Rational) + Rational, oo) from sympy.abc import x, y, z, t, a, b, c, d, e, f, g, h, i, k @@ -98,6 +98,16 @@ def test_powsimp(): # issue 8836 assert str( powsimp(exp(I*pi/3)*root(-1,3)) ) == '(-1)**(2/3)' + # issue 9183 + assert powsimp(-0.1**x) == -0.1**x + + # issue 10095 + assert powsimp((1/(2*E))**oo) == (exp(-1)/2)**oo + + # PR 13131 + eq = sin(2*x)**2*sin(2.0*x)**2 + assert powsimp(eq) == eq + def test_powsimp_negated_base(): assert powsimp((-x + y)/sqrt(x - y)) == -sqrt(x - y)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13531@cc6b61c
sympy/sympy
Python
13,531
manualintegrate modifications
fixes #9687 closes #10611 as a replacement and modification
2017-10-25T01:49:29Z
integrate(1/((x**2+4)*sqrt(4*x**2+1)), x) raises `TypeError: 'NoneType' object has no attribute '__getitem__'` ``` In [1]: integrate(1/((x**2+4)*sqrt(4*x**2+1)), x) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-1-b25b3537657d> in <module>() ----> 1 integrate(1/((x**2+4)*sqrt(4*x**2+1)), x) /home/ondrej/repos/sympy/sympy/utilities/decorator.py in threaded_func(expr, *args, **kwargs) 35 func(expr.rhs, *args, **kwargs)) 36 else: ---> 37 return func(expr, *args, **kwargs) 38 39 return threaded_func /home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in integrate(*args, **kwargs) 1253 if isinstance(integral, Integral): 1254 return integral.doit(deep=False, meijerg=meijerg, conds=conds, -> 1255 risch=risch, manual=manual) 1256 else: 1257 return integral /home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in doit(self, **hints) 480 function, xab[0], 481 meijerg=meijerg1, risch=risch, manual=manual, --> 482 conds=conds) 483 if antideriv is None and meijerg1 is True: 484 ret = try_meijerg(function, xab) /home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in _eval_integral(self, f, x, meijerg, risch, manual, conds) 884 if h is None and manual is not False: 885 try: --> 886 result = manualintegrate(g, x) 887 if result is not None and not isinstance(result, Integral): 888 if result.has(Integral): /home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in manualintegrate(f, var) 1143 sympy.integrals.integrals.Integral 1144 """ -> 1145 return _manualintegrate(integral_steps(f, var)) /home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in integral_steps(integrand, symbol, **options) 944 null_safe(trig_substitution_rule) 945 ), --> 946 fallback_rule)(integral) 947 del _integral_cache[cachekey] 948 return result /home/ondrej/repos/sympy/sympy/strategies/core.pyc in do_one_rl(expr) 83 def do_one_rl(expr): 84 for rl in rules: ---> 85 result = rl(expr) 86 if result != expr: 87 return result /home/ondrej/repos/sympy/sympy/strategies/core.pyc in do_one_rl(expr) 83 def do_one_rl(expr): 84 for rl in rules: ---> 85 result = rl(expr) 86 if result != expr: 87 return result /home/ondrej/repos/sympy/sympy/strategies/core.pyc in null_safe_rl(expr) 63 """ Return original expr if rule returns None """ 64 def null_safe_rl(expr): ---> 65 result = rule(expr) 66 if result is None: 67 return expr /home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in trig_substitution_rule(integral) 688 for expr in matches: 689 match = expr.match(a + b*symbol**2) --> 690 a = match[a] 691 b = match[b] 692 TypeError: 'NoneType' object has no attribute '__getitem__' ```
@lidavidm, do you have any idea what is going on here? The error seems to come from `manualintegrate`. It's assuming that `subexpr.match(query)` is not `None` for each `subexpr in expr.find(query)`. I guess it should check if the `match` is `None`, and if so, move on to the next subexpression.
[ { "body": "```\nIn [1]: integrate(1/((x**2+4)*sqrt(4*x**2+1)), x)\n---------------------------------------------------------------------------\nTypeError Traceback (most recent call last)\n<ipython-input-1-b25b3537657d> in <module>()\n----> 1 integrate(1/((x**2+4)*sqrt(4*x**2+1)), x)\n\n/home/ondrej/repos/sympy/sympy/utilities/decorator.py in threaded_func(expr, *args, **kwargs)\n 35 func(expr.rhs, *args, **kwargs))\n 36 else:\n---> 37 return func(expr, *args, **kwargs)\n 38 \n 39 return threaded_func\n\n/home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in integrate(*args, **kwargs)\n 1253 if isinstance(integral, Integral):\n 1254 return integral.doit(deep=False, meijerg=meijerg, conds=conds,\n-> 1255 risch=risch, manual=manual)\n 1256 else:\n 1257 return integral\n\n/home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in doit(self, **hints)\n 480 function, xab[0],\n 481 meijerg=meijerg1, risch=risch, manual=manual,\n--> 482 conds=conds)\n 483 if antideriv is None and meijerg1 is True:\n 484 ret = try_meijerg(function, xab)\n\n/home/ondrej/repos/sympy/sympy/integrals/integrals.pyc in _eval_integral(self, f, x, meijerg, risch, manual, conds)\n 884 if h is None and manual is not False:\n 885 try:\n--> 886 result = manualintegrate(g, x)\n 887 if result is not None and not isinstance(result, Integral):\n 888 if result.has(Integral):\n\n/home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in manualintegrate(f, var)\n 1143 sympy.integrals.integrals.Integral\n 1144 \"\"\"\n-> 1145 return _manualintegrate(integral_steps(f, var))\n\n/home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in integral_steps(integrand, symbol, **options)\n 944 null_safe(trig_substitution_rule)\n 945 ),\n--> 946 fallback_rule)(integral)\n 947 del _integral_cache[cachekey]\n 948 return result\n\n/home/ondrej/repos/sympy/sympy/strategies/core.pyc in do_one_rl(expr)\n 83 def do_one_rl(expr):\n 84 for rl in rules:\n---> 85 result = rl(expr)\n 86 if result != expr:\n 87 return result\n\n/home/ondrej/repos/sympy/sympy/strategies/core.pyc in do_one_rl(expr)\n 83 def do_one_rl(expr):\n 84 for rl in rules:\n---> 85 result = rl(expr)\n 86 if result != expr:\n 87 return result\n\n/home/ondrej/repos/sympy/sympy/strategies/core.pyc in null_safe_rl(expr)\n 63 \"\"\" Return original expr if rule returns None \"\"\"\n 64 def null_safe_rl(expr):\n---> 65 result = rule(expr)\n 66 if result is None:\n 67 return expr\n\n/home/ondrej/repos/sympy/sympy/integrals/manualintegrate.py in trig_substitution_rule(integral)\n 688 for expr in matches:\n 689 match = expr.match(a + b*symbol**2)\n--> 690 a = match[a]\n 691 b = match[b]\n 692 \n\nTypeError: 'NoneType' object has no attribute '__getitem__'\n```\n", "number": 9687, "title": "integrate(1/((x**2+4)*sqrt(4*x**2+1)), x) raises `TypeError: 'NoneType' object has no attribute '__getitem__'`" } ]
bc6c481dc04e360d83c32296d02fb7b74d980822
{ "head_commit": "cc6b61cf4d06790ebeec7d690a020bd556b9513e", "head_commit_message": "fix ode tests", "patch_to_review": "diff --git a/sympy/integrals/manualintegrate.py b/sympy/integrals/manualintegrate.py\nindex 8ef504501bbd..8040a16949b6 100644\n--- a/sympy/integrals/manualintegrate.py\n+++ b/sympy/integrals/manualintegrate.py\n@@ -28,6 +28,8 @@\n from sympy.strategies.core import switch, do_one, null_safe, condition\n \n \n+ZERO = sympy.S.Zero\n+\n def Rule(name, props=\"\"):\n # GOTCHA: namedtuple class name not considered!\n def __eq__(self, other):\n@@ -300,8 +302,7 @@ def make_inverse_trig(RuleClass, base_exp, a, sign_a, b, sign_b):\n substep = ConstantTimesRule(constant, factored, substep, integrand, symbol)\n return substep\n \n- a, b = match[a], match[b]\n-\n+ a, b = [match.get(i, ZERO) for i in (a, b)]\n # list of (rule, base_exp, a, sign_a, b, sign_b, condition)\n possibilities = []\n \n@@ -678,14 +679,16 @@ def trig_sincos_rule(integral):\n if any(integrand.has(f) for f in (sympy.sin, sympy.cos)):\n pattern, a, b, m, n = sincos_pattern(symbol)\n match = integrand.match(pattern)\n+ if not match:\n+ return\n \n- if match:\n- a, b, m, n = match.get(a, 0), match.get(b, 0), match.get(m, 0), match.get(n, 0)\n- return multiplexer({\n- sincos_botheven_condition: sincos_botheven,\n- sincos_sinodd_condition: sincos_sinodd,\n- sincos_cosodd_condition: sincos_cosodd\n- })((a, b, m, n, integrand, symbol))\n+ return multiplexer({\n+ sincos_botheven_condition: sincos_botheven,\n+ sincos_sinodd_condition: sincos_sinodd,\n+ sincos_cosodd_condition: sincos_cosodd\n+ })(tuple(\n+ [match.get(i, ZERO) for i in (a, b, m, n)] +\n+ [integrand, symbol]))\n \n def trig_tansec_rule(integral):\n integrand, symbol = integral\n@@ -697,14 +700,16 @@ def trig_tansec_rule(integral):\n if any(integrand.has(f) for f in (sympy.tan, sympy.sec)):\n pattern, a, b, m, n = tansec_pattern(symbol)\n match = integrand.match(pattern)\n+ if not match:\n+ return\n \n- if match:\n- a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0)\n- return multiplexer({\n- tansec_tanodd_condition: tansec_tanodd,\n- tansec_seceven_condition: tansec_seceven,\n- tan_tansquared_condition: tan_tansquared\n- })((a, b, m, n, integrand, symbol))\n+ return multiplexer({\n+ tansec_tanodd_condition: tansec_tanodd,\n+ tansec_seceven_condition: tansec_seceven,\n+ tan_tansquared_condition: tan_tansquared\n+ })(tuple(\n+ [match.get(i, ZERO) for i in (a, b, m, n)] +\n+ [integrand, symbol]))\n \n def trig_cotcsc_rule(integral):\n integrand, symbol = integral\n@@ -717,13 +722,15 @@ def trig_cotcsc_rule(integral):\n if any(integrand.has(f) for f in (sympy.cot, sympy.csc)):\n pattern, a, b, m, n = cotcsc_pattern(symbol)\n match = integrand.match(pattern)\n+ if not match:\n+ return\n \n- if match:\n- a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0)\n- return multiplexer({\n- cotcsc_cotodd_condition: cotcsc_cotodd,\n- cotcsc_csceven_condition: cotcsc_csceven\n- })((a, b, m, n, integrand, symbol))\n+ return multiplexer({\n+ cotcsc_cotodd_condition: cotcsc_cotodd,\n+ cotcsc_csceven_condition: cotcsc_csceven\n+ })(tuple(\n+ [match.get(i, ZERO) for i in (a, b, m, n)] +\n+ [integrand, symbol]))\n \n def trig_powers_products_rule(integral):\n return do_one(null_safe(trig_sincos_rule),\n@@ -732,19 +739,18 @@ def trig_powers_products_rule(integral):\n \n def trig_substitution_rule(integral):\n integrand, symbol = integral\n- a = sympy.Wild('a', exclude=[0, symbol])\n- b = sympy.Wild('b', exclude=[0, symbol])\n+ A = sympy.Wild('a', exclude=[0, symbol])\n+ B = sympy.Wild('b', exclude=[0, symbol])\n theta = sympy.Dummy(\"theta\")\n \n- matches = integrand.find(a + b*symbol**2)\n+ matches = integrand.find(A + B*symbol**2)\n if matches:\n for expr in matches:\n- match = expr.match(a + b*symbol**2)\n- a = match[a]\n- b = match[b]\n+ match = expr.match(A + B*symbol**2) or {}\n+ a, b = [match.get(i, ZERO) for i in (A, B)]\n+ a_positive = a.is_positive\n+ b_positive = b.is_positive\n \n- a_positive = ((a.is_number and a > 0) or a.is_positive)\n- b_positive = ((b.is_number and b > 0) or b.is_positive)\n x_func = None\n if a_positive and b_positive:\n # a**2 + b*x**2. Assume sec(theta) > 0, -pi/2 < theta < pi/2\n@@ -753,12 +759,12 @@ def trig_substitution_rule(integral):\n # value on the interval -pi/2 < theta < pi/2 so x takes on\n # any value\n restriction = True\n- elif a_positive and not b_positive:\n+ elif a_positive:\n # a**2 - b*x**2. Assume cos(theta) > 0, -pi/2 < theta < pi/2\n constant = sympy.sqrt(a)/sympy.sqrt(-b)\n x_func = constant * sympy.sin(theta)\n restriction = sympy.And(symbol > -constant, symbol < constant)\n- elif not a_positive and b_positive:\n+ elif b_positive:\n # b*x**2 - a**2. Assume sin(theta) > 0, 0 < theta < pi\n constant = sympy.sqrt(-a)/sympy.sqrt(b)\n x_func = constant * sympy.sec(theta)\ndiff --git a/sympy/integrals/tests/test_manual.py b/sympy/integrals/tests/test_manual.py\nindex b26858d3e534..75d7b09a3788 100644\n--- a/sympy/integrals/tests/test_manual.py\n+++ b/sympy/integrals/tests/test_manual.py\n@@ -261,11 +261,21 @@ def test_issue_6746():\n assert manualintegrate((y + 1)**(n*x), x) == \\\n (y + 1)**(n*x)/(n*log(y + 1))\n a = Symbol('a', negative=True)\n- assert manualintegrate(1 / (a + b*x**2), x) == \\\n+ b = Symbol('b')\n+ assert manualintegrate(1/(a + b*x**2), x) == \\\n Piecewise((atan(x/sqrt(a/b))/(b*sqrt(a/b)), a/b > 0), \\\n (-acoth(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 > -a/b)), \\\n (-atanh(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 < -a/b)))\n-\n+ b = Symbol('b', negative=True)\n+ assert manualintegrate(1/(a + b*x**2), x) == \\\n+ atan(x/(sqrt(-a)*sqrt(-1/b)))/(b*sqrt(-a)*sqrt(-1/b))\n+ assert manualintegrate(1/((x**a + y**b + 4)*sqrt(a*x**2 + 1)), x) == \\\n+ y**(-b)*Integral(x**(-a)/(y**(-b)*sqrt(a*x**2 + 1) +\n+ x**(-a)*sqrt(a*x**2 + 1) + 4*x**(-a)*y**(-b)*sqrt(a*x**2 + 1)), x)\n+ assert manualintegrate(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x) == \\\n+ Integral(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x)\n+ assert manualintegrate(1/(x - a**x + x*b**2), x) == \\\n+ Integral(1/(-a**x + b**2*x + x), x)\n \n \n def test_issue_2850():\ndiff --git a/sympy/solvers/tests/test_ode.py b/sympy/solvers/tests/test_ode.py\nindex 97d3aa156908..cfb33d428276 100644\n--- a/sympy/solvers/tests/test_ode.py\n+++ b/sympy/solvers/tests/test_ode.py\n@@ -542,21 +542,17 @@ def test_checksysodesol():\n \n @slow\n def test_nonlinear_3eq_order1():\n- x, y, z = symbols('x, y, z', function=True)\n+ x, y, z, C3 = symbols('x, y, z, C3', function=True)\n t = Symbol('t')\n- eq1 = (4*diff(x(t),t) + 2*y(t)*z(t), 3*diff(y(t),t) - z(t)*x(t), 5*diff(z(t),t) - x(t)*y(t))\n- sol1 = \"[Eq(Integral(4/(sqrt(-4*_y**2 - 3*C1 + C2)*sqrt(-4*_y**2 + 5*C1 - C2)), (_y, x(t))), \"\\\n- \"C3 + Integral(-sqrt(15)/15, t)), Eq(Integral(3/(sqrt(-6*_y**2 - C1 + 5*C2)*sqrt(3*_y**2 + C1 - 4*C2)), \"\\\n- \"(_y, y(t))), C3 + Integral(sqrt(5)/10, t)), Eq(Integral(5/(sqrt(-10*_y**2 - 3*C1 + C2)*\"\\\n- \"sqrt(5*_y**2 + 4*C1 - C2)), (_y, z(t))), C3 + Integral(sqrt(3)/6, t))]\"\n- assert str(dsolve(eq1)) == sol1\n-\n- eq2 = (4*diff(x(t),t) + 2*y(t)*z(t)*sin(t), 3*diff(y(t),t) - z(t)*x(t)*sin(t), 5*diff(z(t),t) - x(t)*y(t)*sin(t))\n- sol2 = \"[Eq(Integral(3/(sqrt(-6*_y**2 - C1 + 5*C2)*sqrt(3*_y**2 + C1 - 4*C2)), (_y, x(t))), C3 + \"\\\n- \"Integral(-sqrt(5)*sin(t)/10, t)), Eq(Integral(4/(sqrt(-4*_y**2 - 3*C1 + C2)*sqrt(-4*_y**2 + 5*C1 - C2)), \"\\\n- \"(_y, y(t))), C3 + Integral(sqrt(15)*sin(t)/15, t)), Eq(Integral(5/(sqrt(-10*_y**2 - 3*C1 + C2)*\"\\\n- \"sqrt(5*_y**2 + 4*C1 - C2)), (_y, z(t))), C3 + Integral(-sqrt(3)*sin(t)/6, t))]\"\n- assert str(dsolve(eq2)) == sol2\n+ eq1 = (4*diff(x(t),t) + 2*y(t)*z(t), 3*diff(y(t),t)\n+ - z(t)*x(t), 5*diff(z(t),t) - x(t)*y(t))\n+ assert dsolve(eq1) == [C3 - sqrt(15)*t/15,\n+ C3 + sqrt(5)*t/10, C3 + sqrt(3)*t/6]\n+\n+ eq2 = (4*diff(x(t),t) + 2*y(t)*z(t)*sin(t), 3*diff(y(t),t)\n+ - z(t)*x(t)*sin(t), 5*diff(z(t),t) - x(t)*y(t)*sin(t))\n+ assert str(dsolve(eq2)) == [C3 + sqrt(5)*cos(t)/10,\n+ C3 - sqrt(15)*cos(t)/15, C3 + sqrt(3)*cos(t)/6]\n \n \n def test_checkodesol():\n" }
[ { "diff_hunk": "@@ -542,21 +542,17 @@ def test_checksysodesol():\n \n @slow\n def test_nonlinear_3eq_order1():\n- x, y, z = symbols('x, y, z', function=True)\n+ x, y, z, C3 = symbols('x, y, z, C3', function=True)\n t = Symbol('t')\n- eq1 = (4*diff(x(t),t) + 2*y(t)*z(t), 3*diff(y(t),t) - z(t)*x(t), 5*diff(z(t),t) - x(t)*y(t))\n- sol1 = \"[Eq(Integral(4/(sqrt(-4*_y**2 - 3*C1 + C2)*sqrt(-4*_y**2 + 5*C1 - C2)), (_y, x(t))), \"\\\n- \"C3 + Integral(-sqrt(15)/15, t)), Eq(Integral(3/(sqrt(-6*_y**2 - C1 + 5*C2)*sqrt(3*_y**2 + C1 - 4*C2)), \"\\\n- \"(_y, y(t))), C3 + Integral(sqrt(5)/10, t)), Eq(Integral(5/(sqrt(-10*_y**2 - 3*C1 + C2)*\"\\\n- \"sqrt(5*_y**2 + 4*C1 - C2)), (_y, z(t))), C3 + Integral(sqrt(3)/6, t))]\"\n- assert str(dsolve(eq1)) == sol1\n-\n- eq2 = (4*diff(x(t),t) + 2*y(t)*z(t)*sin(t), 3*diff(y(t),t) - z(t)*x(t)*sin(t), 5*diff(z(t),t) - x(t)*y(t)*sin(t))\n- sol2 = \"[Eq(Integral(3/(sqrt(-6*_y**2 - C1 + 5*C2)*sqrt(3*_y**2 + C1 - 4*C2)), (_y, x(t))), C3 + \"\\\n- \"Integral(-sqrt(5)*sin(t)/10, t)), Eq(Integral(4/(sqrt(-4*_y**2 - 3*C1 + C2)*sqrt(-4*_y**2 + 5*C1 - C2)), \"\\\n- \"(_y, y(t))), C3 + Integral(sqrt(15)*sin(t)/15, t)), Eq(Integral(5/(sqrt(-10*_y**2 - 3*C1 + C2)*\"\\\n- \"sqrt(5*_y**2 + 4*C1 - C2)), (_y, z(t))), C3 + Integral(-sqrt(3)*sin(t)/6, t))]\"\n- assert str(dsolve(eq2)) == sol2\n+ eq1 = (4*diff(x(t),t) + 2*y(t)*z(t), 3*diff(y(t),t)", "line": null, "original_line": 547, "original_start_line": null, "path": "sympy/solvers/tests/test_ode.py", "start_line": null, "text": "@author:\nThis doesn't satisfy the original ode, eq1, so it must be wrong." } ]
e476995a5feb9011fe93ac3f0dffdb66845b68f2
diff --git a/sympy/integrals/manualintegrate.py b/sympy/integrals/manualintegrate.py index 5bab3f5ded6b..ec16c456d900 100644 --- a/sympy/integrals/manualintegrate.py +++ b/sympy/integrals/manualintegrate.py @@ -28,6 +28,8 @@ from sympy.strategies.core import switch, do_one, null_safe, condition +ZERO = sympy.S.Zero + def Rule(name, props=""): # GOTCHA: namedtuple class name not considered! def __eq__(self, other): @@ -300,8 +302,7 @@ def make_inverse_trig(RuleClass, base_exp, a, sign_a, b, sign_b): substep = ConstantTimesRule(constant, factored, substep, integrand, symbol) return substep - a, b = match[a], match[b] - + a, b = [match.get(i, ZERO) for i in (a, b)] # list of (rule, base_exp, a, sign_a, b, sign_b, condition) possibilities = [] @@ -678,14 +679,16 @@ def trig_sincos_rule(integral): if any(integrand.has(f) for f in (sympy.sin, sympy.cos)): pattern, a, b, m, n = sincos_pattern(symbol) match = integrand.match(pattern) + if not match: + return - if match: - a, b, m, n = match.get(a, 0), match.get(b, 0), match.get(m, 0), match.get(n, 0) - return multiplexer({ - sincos_botheven_condition: sincos_botheven, - sincos_sinodd_condition: sincos_sinodd, - sincos_cosodd_condition: sincos_cosodd - })((a, b, m, n, integrand, symbol)) + return multiplexer({ + sincos_botheven_condition: sincos_botheven, + sincos_sinodd_condition: sincos_sinodd, + sincos_cosodd_condition: sincos_cosodd + })(tuple( + [match.get(i, ZERO) for i in (a, b, m, n)] + + [integrand, symbol])) def trig_tansec_rule(integral): integrand, symbol = integral @@ -697,14 +700,16 @@ def trig_tansec_rule(integral): if any(integrand.has(f) for f in (sympy.tan, sympy.sec)): pattern, a, b, m, n = tansec_pattern(symbol) match = integrand.match(pattern) + if not match: + return - if match: - a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0) - return multiplexer({ - tansec_tanodd_condition: tansec_tanodd, - tansec_seceven_condition: tansec_seceven, - tan_tansquared_condition: tan_tansquared - })((a, b, m, n, integrand, symbol)) + return multiplexer({ + tansec_tanodd_condition: tansec_tanodd, + tansec_seceven_condition: tansec_seceven, + tan_tansquared_condition: tan_tansquared + })(tuple( + [match.get(i, ZERO) for i in (a, b, m, n)] + + [integrand, symbol])) def trig_cotcsc_rule(integral): integrand, symbol = integral @@ -717,13 +722,15 @@ def trig_cotcsc_rule(integral): if any(integrand.has(f) for f in (sympy.cot, sympy.csc)): pattern, a, b, m, n = cotcsc_pattern(symbol) match = integrand.match(pattern) + if not match: + return - if match: - a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0) - return multiplexer({ - cotcsc_cotodd_condition: cotcsc_cotodd, - cotcsc_csceven_condition: cotcsc_csceven - })((a, b, m, n, integrand, symbol)) + return multiplexer({ + cotcsc_cotodd_condition: cotcsc_cotodd, + cotcsc_csceven_condition: cotcsc_csceven + })(tuple( + [match.get(i, ZERO) for i in (a, b, m, n)] + + [integrand, symbol])) def trig_powers_products_rule(integral): return do_one(null_safe(trig_sincos_rule), @@ -732,16 +739,16 @@ def trig_powers_products_rule(integral): def trig_substitution_rule(integral): integrand, symbol = integral - a_wild = sympy.Wild('a', exclude=[0, symbol]) - b_wild = sympy.Wild('b', exclude=[0, symbol]) + A = sympy.Wild('a', exclude=[0, symbol]) + B = sympy.Wild('b', exclude=[0, symbol]) theta = sympy.Dummy("theta") - target_pattern = a_wild + b_wild*symbol**2 + target_pattern = A + B*symbol**2 matches = integrand.find(target_pattern) for expr in matches: match = expr.match(target_pattern) - a = match[a_wild] - b = match[b_wild] + a = match.get(A, ZERO) + b = match.get(B, ZERO) a_positive = ((a.is_number and a > 0) or a.is_positive) b_positive = ((b.is_number and b > 0) or b.is_positive) diff --git a/sympy/integrals/tests/test_manual.py b/sympy/integrals/tests/test_manual.py index b26858d3e534..75d7b09a3788 100644 --- a/sympy/integrals/tests/test_manual.py +++ b/sympy/integrals/tests/test_manual.py @@ -261,11 +261,21 @@ def test_issue_6746(): assert manualintegrate((y + 1)**(n*x), x) == \ (y + 1)**(n*x)/(n*log(y + 1)) a = Symbol('a', negative=True) - assert manualintegrate(1 / (a + b*x**2), x) == \ + b = Symbol('b') + assert manualintegrate(1/(a + b*x**2), x) == \ Piecewise((atan(x/sqrt(a/b))/(b*sqrt(a/b)), a/b > 0), \ (-acoth(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 > -a/b)), \ (-atanh(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 < -a/b))) - + b = Symbol('b', negative=True) + assert manualintegrate(1/(a + b*x**2), x) == \ + atan(x/(sqrt(-a)*sqrt(-1/b)))/(b*sqrt(-a)*sqrt(-1/b)) + assert manualintegrate(1/((x**a + y**b + 4)*sqrt(a*x**2 + 1)), x) == \ + y**(-b)*Integral(x**(-a)/(y**(-b)*sqrt(a*x**2 + 1) + + x**(-a)*sqrt(a*x**2 + 1) + 4*x**(-a)*y**(-b)*sqrt(a*x**2 + 1)), x) + assert manualintegrate(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x) == \ + Integral(1/((x**2 + 4)*sqrt(4*x**2 + 1)), x) + assert manualintegrate(1/(x - a**x + x*b**2), x) == \ + Integral(1/(-a**x + b**2*x + x), x) def test_issue_2850(): diff --git a/sympy/solvers/tests/test_ode.py b/sympy/solvers/tests/test_ode.py index 7e52276242c6..86cc1098f7f4 100644 --- a/sympy/solvers/tests/test_ode.py +++ b/sympy/solvers/tests/test_ode.py @@ -542,7 +542,7 @@ def test_checksysodesol(): @slow def test_nonlinear_3eq_order1(): - x, y, z = symbols('x, y, z', function=True) + x, y, z, C3 = symbols('x, y, z, C3', function=True) t = Symbol('t') eq1 = (4*diff(x(t),t) + 2*y(t)*z(t), 3*diff(y(t),t) - z(t)*x(t), 5*diff(z(t),t) - x(t)*y(t)) sol1 = "[Eq(4*Integral(1/(sqrt(-4*_y**2 - 3*C1 + C2)*sqrt(-4*_y**2 + 5*C1 - C2)), (_y, x(t))), "\
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
sympy__sympy-12228@dd59fe6
sympy/sympy
Python
12,228
differentiate_finite: set evaluate kwarg default to False (fix gh-12186)
In `differentiate_finite` , the default of the kwarg in `evaluate` is changed from `True` to `False` and hence fixes gh-12186. "Fortunately", the default value of `evaluate` was undocumented so I think we can skip a deprecation cycle in this case. @halpernf I would be grateful to hear if you have any feedback on the wording / suggestions for improvement.
2017-02-27T17:51:27Z
default behavior for product rule in differentiate_finite is incorrect Hi everybody, First time user of SymPy. So far I am quite enjoying this and I was able to generate Arakawa brackets of arbitrary order with just a few lines of SymPy. I wanted to report on an issue I came across. The following expression from the SymPy documentation shows use of the product rule for continuous function applied on a discrete function. This behavior is incorrect, e.g. ``>>> x = symbols('x')`` ``>>> f, g = symbols('f g', cls=Function)`` ``>>> differentiate_finite(f(x)*g(x))`` ``(-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x)`` A large part of the applied mathematics literature is devoted to designing numerical schemes that preserve a "discrete product rule." Examples are summation-by-parts and mimetic finite differences. A better default behavior in SymPy would be to force ``evaluate=False``, which gives ``>>> differentiate_finite(f(x)*g(x), evaluate=False)`` ``-f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2)`` The latter expression is the correct answer.
I am +1 for just changing the default to `evaluate=False`, @halpernf would you mind opening a PR changing this and perhaps changing the docstring to include the point you've made in this issue? @bjodah I would like to work on this issue, kindly refer to any starting point on this . Thanks @mohit3011 are you familiar with MFD? I would do this myself if I had experience with MFD, but since I don't, I think it's better that someone who has updates the documentation. @bjodah What's the full form of MFD? @bjodah sorry about my ignorance, by PR you mean pull request? @bjodah @mohit3011 no need to implement MFD here. Changing the default to `evaluate=False` should be enough for the time being... I'll have some time later on this week. It should be easy enough to do even if I don't know the code that well.
[ { "body": "Hi everybody,\r\n\r\nFirst time user of SymPy. So far I am quite enjoying this and I was able to generate Arakawa brackets of arbitrary order with just a few lines of SymPy. I wanted to report on an issue I came across.\r\n\r\nThe following expression from the SymPy documentation shows use of the product rule for continuous function applied on a discrete function. This behavior is incorrect, e.g.\r\n\r\n``>>> x = symbols('x')``\r\n``>>> f, g = symbols('f g', cls=Function)``\r\n``>>> differentiate_finite(f(x)*g(x))``\r\n``(-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x)``\r\n\r\nA large part of the applied mathematics literature is devoted to designing numerical schemes that preserve a \"discrete product rule.\" Examples are summation-by-parts and mimetic finite differences. A better default behavior in SymPy would be to force ``evaluate=False``, which gives\r\n\r\n``>>> differentiate_finite(f(x)*g(x), evaluate=False)``\r\n``-f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2)``\r\n\r\nThe latter expression is the correct answer.\r\n", "number": 12186, "title": "default behavior for product rule in differentiate_finite is incorrect" } ]
d60497958f6dea7f5e25bc41e9107a6a63694d01
{ "head_commit": "dd59fe6337d145b826eb2e9bd4acc5a1d18b857f", "head_commit_message": "Change evaluate kwarg default to False (fixes gh-12186)", "patch_to_review": "diff --git a/doc/src/tutorial/calculus.rst b/doc/src/tutorial/calculus.rst\nindex 1c2c3c336561..61ac1077901e 100644\n--- a/doc/src/tutorial/calculus.rst\n+++ b/doc/src/tutorial/calculus.rst\n@@ -323,14 +323,15 @@ the ``differentiate_finite`` function:\n \n >>> f, g = symbols('f g', cls=Function)\n >>> differentiate_finite(f(x)*g(x))\n- (-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x)\n+ -f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2)\n \n-If we don't want to expand the intermediate derivative we may pass the\n+If we want to expand the intermediate derivative we may pass the\n flag ``evaluate=False``:\n \n- >>> differentiate_finite(f(x)*g(x), evaluate=False)\n- -f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2)\n+ >>> differentiate_finite(f(x)*g(x), evaluate=True)\n+ (-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x)\n \n+This form however does not respect the product rule.\n If you already have a ``Derivative`` instance, you can use the\n ``as_finite_difference`` method to generate approximations of the\n derivative to arbitrary order:\ndiff --git a/sympy/calculus/finite_diff.py b/sympy/calculus/finite_diff.py\nindex d9cfbb1eab16..1647386c20f1 100644\n--- a/sympy/calculus/finite_diff.py\n+++ b/sympy/calculus/finite_diff.py\n@@ -430,8 +430,8 @@ def differentiate_finite(expr, *symbols,\n wrt: Symbol, optional\n see ``Derivative.as_finite_difference``\n evaluate : bool\n- kwarg passed on to ``diff`` (whether or not to\n- evaluate the Derivative intermediately).\n+ kwarg passed on to ``diff``, whether or not to\n+ evaluate the Derivative intermediately (default: ``False``).\n \n \n Examples\n@@ -439,26 +439,33 @@ def differentiate_finite(expr, *symbols,\n \n >>> from sympy import cos, sin, Function, differentiate_finite\n >>> from sympy.abc import x, y, h\n- >>> f = Function('f')\n+ >>> f, g = Function('f'), Function('g')\n+ >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h])\n+ -f(-h + x)*g(-h + x)/(2*h) + f(h + x)*g(h + x)/(2*h)\n+\n+ Note that the above form preserves the product rule in discrete form.\n+ If we want we can pass ``evaluate=True`` to get another form (which is\n+ usually no what we want):\n+\n+ >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h], evaluate=True).simplify()\n+ -((f(-h + x) - f(h + x))*g(x) + (g(-h + x) - g(h + x))*f(x))/(2*h)\n+\n+ ``differentiate_finite`` works on any expression:\n+\n >>> differentiate_finite(f(x) + sin(x), x, 2)\n- -2*f(x) + f(x - 1) + f(x + 1) - sin(x)\n- >>> differentiate_finite(f(x) + sin(x), x, 2, evaluate=False)\n -2*f(x) + f(x - 1) + f(x + 1) - 2*sin(x) + sin(x - 1) + sin(x + 1)\n+ >>> differentiate_finite(f(x) + sin(x), x, 2, evaluate=True)\n+ -2*f(x) + f(x - 1) + f(x + 1) - sin(x)\n >>> differentiate_finite(f(x, y), x, y)\n f(x - 1/2, y - 1/2) - f(x - 1/2, y + 1/2) - f(x + 1/2, y - 1/2) + \\\n f(x + 1/2, y + 1/2)\n- >>> g = Function('g')\n- >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h]).simplify()\n- -((f(-h + x) - f(h + x))*g(x) + (g(-h + x) - g(h + x))*f(x))/(2*h)\n- >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h], evaluate=False)\n- -f(-h + x)*g(-h + x)/(2*h) + f(h + x)*g(h + x)/(2*h)\n \n \"\"\"\n # Key-word only arguments only available in Python 3\n points = kwargs.pop('points', 1)\n x0 = kwargs.pop('x0', None)\n wrt = kwargs.pop('wrt', None)\n- evaluate = kwargs.pop('evaluate', True)\n+ evaluate = kwargs.pop('evaluate', False)\n if kwargs != {}:\n raise ValueError(\"Unknown kwargs: %s\" % kwargs)\n \ndiff --git a/sympy/calculus/tests/test_finite_diff.py b/sympy/calculus/tests/test_finite_diff.py\nindex a75b92927fef..b70ea5fa152f 100644\n--- a/sympy/calculus/tests/test_finite_diff.py\n+++ b/sympy/calculus/tests/test_finite_diff.py\n@@ -114,18 +114,17 @@ def test_as_finite_diff():\n def test_differentiate_finite():\n x, y = symbols('x y')\n f = Function('f')\n- res0 = differentiate_finite(f(x, y) + exp(42), x, y)\n+ res0 = differentiate_finite(f(x, y) + exp(42), x, y, evaluate=True)\n xm, xp, ym, yp = [v + sign*S(1)/2 for v, sign in product([x, y], [-1, 1])]\n ref0 = f(xm, ym) + f(xp, yp) - f(xm, yp) - f(xp, ym)\n assert (res0 - ref0).simplify() == 0\n \n g = Function('g')\n- res1 = differentiate_finite(f(x)*g(x) + 42, x)\n+ res1 = differentiate_finite(f(x)*g(x) + 42, x, evaluate=True)\n ref1 = (-f(x - S(1)/2) + f(x + S(1)/2))*g(x) + \\\n (-g(x - S(1)/2) + g(x + S(1)/2))*f(x)\n assert (res1 - ref1).simplify() == 0\n \n- res2 = differentiate_finite(f(x) + x**3 + 42, x, points=[x-1, x+1],\n- evaluate=False)\n+ res2 = differentiate_finite(f(x) + x**3 + 42, x, points=[x-1, x+1])\n ref2 = (f(x + 1) + (x + 1)**3 - f(x - 1) - (x - 1)**3)/2\n assert (res2 - ref2).simplify() == 0\n" }
[ { "diff_hunk": "@@ -430,35 +430,42 @@ def differentiate_finite(expr, *symbols,\n wrt: Symbol, optional\n see ``Derivative.as_finite_difference``\n evaluate : bool\n- kwarg passed on to ``diff`` (whether or not to\n- evaluate the Derivative intermediately).\n+ kwarg passed on to ``diff``, whether or not to\n+ evaluate the Derivative intermediately (default: ``False``).\n \n \n Examples\n ========\n \n >>> from sympy import cos, sin, Function, differentiate_finite\n >>> from sympy.abc import x, y, h\n- >>> f = Function('f')\n+ >>> f, g = Function('f'), Function('g')\n+ >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h])\n+ -f(-h + x)*g(-h + x)/(2*h) + f(h + x)*g(h + x)/(2*h)\n+\n+ Note that the above form preserves the product rule in discrete form.\n+ If we want we can pass ``evaluate=True`` to get another form (which is\n+ usually no what we want):", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/calculus/finite_diff.py", "start_line": null, "text": "@user1:\nTypo" } ]
2a44518358e27ded79e945f226a5cb1821294db1
diff --git a/doc/src/tutorial/calculus.rst b/doc/src/tutorial/calculus.rst index 1c2c3c336561..fcfa4a6e0f03 100644 --- a/doc/src/tutorial/calculus.rst +++ b/doc/src/tutorial/calculus.rst @@ -323,13 +323,15 @@ the ``differentiate_finite`` function: >>> f, g = symbols('f g', cls=Function) >>> differentiate_finite(f(x)*g(x)) - (-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x) + -f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2) -If we don't want to expand the intermediate derivative we may pass the -flag ``evaluate=False``: +If we want to expand the intermediate derivative we may pass the +flag ``evaluate=True``: - >>> differentiate_finite(f(x)*g(x), evaluate=False) - -f(x - 1/2)โ‹…g(x - 1/2) + f(x + 1/2)โ‹…g(x + 1/2) + >>> differentiate_finite(f(x)*g(x), evaluate=True) + (-f(x - 1/2) + f(x + 1/2))โ‹…g(x) + (-g(x - 1/2) + g(x + 1/2))โ‹…f(x) + +This form however does not respect the product rule. If you already have a ``Derivative`` instance, you can use the ``as_finite_difference`` method to generate approximations of the diff --git a/sympy/calculus/finite_diff.py b/sympy/calculus/finite_diff.py index d9cfbb1eab16..7190009d816a 100644 --- a/sympy/calculus/finite_diff.py +++ b/sympy/calculus/finite_diff.py @@ -430,8 +430,8 @@ def differentiate_finite(expr, *symbols, wrt: Symbol, optional see ``Derivative.as_finite_difference`` evaluate : bool - kwarg passed on to ``diff`` (whether or not to - evaluate the Derivative intermediately). + kwarg passed on to ``diff``, whether or not to + evaluate the Derivative intermediately (default: ``False``). Examples @@ -439,26 +439,33 @@ def differentiate_finite(expr, *symbols, >>> from sympy import cos, sin, Function, differentiate_finite >>> from sympy.abc import x, y, h - >>> f = Function('f') + >>> f, g = Function('f'), Function('g') + >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h]) + -f(-h + x)*g(-h + x)/(2*h) + f(h + x)*g(h + x)/(2*h) + + Note that the above form preserves the product rule in discrete form. + If we want we can pass ``evaluate=True`` to get another form (which is + usually not what we want): + + >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h], evaluate=True).simplify() + -((f(-h + x) - f(h + x))*g(x) + (g(-h + x) - g(h + x))*f(x))/(2*h) + + ``differentiate_finite`` works on any expression: + >>> differentiate_finite(f(x) + sin(x), x, 2) - -2*f(x) + f(x - 1) + f(x + 1) - sin(x) - >>> differentiate_finite(f(x) + sin(x), x, 2, evaluate=False) -2*f(x) + f(x - 1) + f(x + 1) - 2*sin(x) + sin(x - 1) + sin(x + 1) + >>> differentiate_finite(f(x) + sin(x), x, 2, evaluate=True) + -2*f(x) + f(x - 1) + f(x + 1) - sin(x) >>> differentiate_finite(f(x, y), x, y) f(x - 1/2, y - 1/2) - f(x - 1/2, y + 1/2) - f(x + 1/2, y - 1/2) + \ f(x + 1/2, y + 1/2) - >>> g = Function('g') - >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h]).simplify() - -((f(-h + x) - f(h + x))*g(x) + (g(-h + x) - g(h + x))*f(x))/(2*h) - >>> differentiate_finite(f(x)*g(x), x, points=[x-h, x+h], evaluate=False) - -f(-h + x)*g(-h + x)/(2*h) + f(h + x)*g(h + x)/(2*h) """ # Key-word only arguments only available in Python 3 points = kwargs.pop('points', 1) x0 = kwargs.pop('x0', None) wrt = kwargs.pop('wrt', None) - evaluate = kwargs.pop('evaluate', True) + evaluate = kwargs.pop('evaluate', False) if kwargs != {}: raise ValueError("Unknown kwargs: %s" % kwargs) diff --git a/sympy/calculus/tests/test_finite_diff.py b/sympy/calculus/tests/test_finite_diff.py index a75b92927fef..b70ea5fa152f 100644 --- a/sympy/calculus/tests/test_finite_diff.py +++ b/sympy/calculus/tests/test_finite_diff.py @@ -114,18 +114,17 @@ def test_as_finite_diff(): def test_differentiate_finite(): x, y = symbols('x y') f = Function('f') - res0 = differentiate_finite(f(x, y) + exp(42), x, y) + res0 = differentiate_finite(f(x, y) + exp(42), x, y, evaluate=True) xm, xp, ym, yp = [v + sign*S(1)/2 for v, sign in product([x, y], [-1, 1])] ref0 = f(xm, ym) + f(xp, yp) - f(xm, yp) - f(xp, ym) assert (res0 - ref0).simplify() == 0 g = Function('g') - res1 = differentiate_finite(f(x)*g(x) + 42, x) + res1 = differentiate_finite(f(x)*g(x) + 42, x, evaluate=True) ref1 = (-f(x - S(1)/2) + f(x + S(1)/2))*g(x) + \ (-g(x - S(1)/2) + g(x + S(1)/2))*f(x) assert (res1 - ref1).simplify() == 0 - res2 = differentiate_finite(f(x) + x**3 + 42, x, points=[x-1, x+1], - evaluate=False) + res2 = differentiate_finite(f(x) + x**3 + 42, x, points=[x-1, x+1]) ref2 = (f(x + 1) + (x + 1)**3 - f(x - 1) - (x - 1)**3)/2 assert (res2 - ref2).simplify() == 0
{ "difficulty": "medium", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
sympy__sympy-12194@1e0c8ff
sympy/sympy
Python
12,194
ntheory : Add functionality to return list of all factors
Passing in the argument `multiple=True` to `factorint()` or `factorrat()` returns a list of all the prime factors constituting the number along with multiplicity. PR #12124 got messed up, so I opened a new PR. Example: ``` >>> factorint(222,multiple=True) [2, 3, 37] >>>factorrat(S(25)/14, multiple=True) [1/2, 5, 5, 1/7] ``` Fixes #12101 <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below.> Fixes #12101
2017-02-18T13:56:49Z
List of prime factors including multiplicity is missing The vast majority of the time when using prime factorization in algorithms I'm interested in a list of prime factors. E.g. for 24 it's `[2, 2, 2, 3]`. However sympy (to my knowledge) only provides `sympy.factorint` which returns a dictionary. I always end up writing a helper function: factorlist = lambda fac: sum(([p] * fac[p] for p in sorted(fac)), []) It would be really nice if factorizations in this format were directly provided by sympy, either by an option to `factorint` (e.g. `sympy.factorint(24, aslist=True)`) or under a new function name entirely.
A possibility is: ``` >>> from sympy.utilities.iterables import multiset_combinations >>> d={'a':3,'b':2,'c':2} >>> list(multiset_combinations(d, sum(d.values())))[0] ['a', 'a', 'a', 'b', 'b', 'c', 'c'] >>> d = factoring(24) >>> list(multiset_combinations(d, sum(d.values())))[0] [2, 2, 2, 3] ``` But maybe an argument `multiple=True` for `factorint` would be preferable. @smichr `multiple=True` for `factorint` is definitely better than my suggestion. I really do think that providing this functionality straight up without a helper function is an improvement for sympy.
[ { "body": "The vast majority of the time when using prime factorization in algorithms I'm interested in a list of prime factors. E.g. for 24 it's `[2, 2, 2, 3]`. However sympy (to my knowledge) only provides `sympy.factorint` which returns a dictionary. I always end up writing a helper function:\r\n\r\n factorlist = lambda fac: sum(([p] * fac[p] for p in sorted(fac)), [])\r\n\r\nIt would be really nice if factorizations in this format were directly provided by sympy, either by an option to `factorint` (e.g. `sympy.factorint(24, aslist=True)`) or under a new function name entirely.", "number": 12101, "title": "List of prime factors including multiplicity is missing" } ]
62b1996e353fedc1e3fb51f626ffaae9583b74a6
{ "head_commit": "1e0c8ff1eb5298bbd754a2df0ad82e7e7f2383d4", "head_commit_message": "ntheory: Add multiple=True to return list of factors", "patch_to_review": "diff --git a/sympy/ntheory/factor_.py b/sympy/ntheory/factor_.py\nindex 67c2c18ce7db..fe078c375fb4 100644\n--- a/sympy/ntheory/factor_.py\n+++ b/sympy/ntheory/factor_.py\n@@ -803,7 +803,7 @@ def done(n, d):\n \n \n def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n- verbose=False, visual=None):\n+ verbose=False, visual=None, multiple=False):\n r\"\"\"\n Given a positive integer ``n``, ``factorint(n)`` returns a dict containing\n the prime factors of ``n`` as keys and their respective multiplicities\n@@ -942,6 +942,15 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n smoothness, smoothness_p, divisors\n \n \"\"\"\n+ if multiple:\n+ fac = factorint(n, limit=limit, use_trial=use_trial,\n+ use_rho=use_rho, use_pm1=use_pm1,\n+ verbose=verbose, visual=False, multiple=False)\n+ factorlist = sum(([p] * fac[p]\n+ if fac[p] > 0 else [S(1)/p]*(-1 * fac[p])\n+ for p in sorted(fac)), [])\n+ return factorlist\n+\n factordict = {}\n if visual and not isinstance(n, Mul) and not isinstance(n, dict):\n factordict = factorint(n, limit=limit, use_trial=use_trial,\n@@ -1172,7 +1181,7 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n \n \n def factorrat(rat, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n- verbose=False, visual=None):\n+ verbose=False, visual=None, multiple=False):\n r\"\"\"\n Given a Rational ``r``, ``factorrat(r)`` returns a dict containing\n the prime factors of ``r`` as keys and their respective multiplicities\n@@ -1196,6 +1205,15 @@ def factorrat(rat, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n - ``visual``: Toggle product form of output\n \"\"\"\n from collections import defaultdict\n+ if multiple:\n+ fac = factorrat(rat, limit=limit, use_trial=use_trial,\n+ use_rho=use_rho, use_pm1=use_pm1,\n+ verbose=verbose, visual=False,multiple=False)\n+ factorlist = sum(([p] * fac[p]\n+ if fac[p] > 0 else [S(1)/p]*(-1 * fac[p])\n+ for p in sorted(fac)), [])\n+ return factorlist\n+\n f = factorint(rat.p, limit=limit, use_trial=use_trial,\n use_rho=use_rho, use_pm1=use_pm1,\n verbose=verbose).copy()\ndiff --git a/sympy/ntheory/tests/test_factor_.py b/sympy/ntheory/tests/test_factor_.py\nindex aed3766babaf..5fede7819c56 100644\n--- a/sympy/ntheory/tests/test_factor_.py\n+++ b/sympy/ntheory/tests/test_factor_.py\n@@ -154,6 +154,20 @@ def test_factorint():\n assert factorint(5951757) == {3: 1, 7: 1, 29: 2, 337: 1}\n assert factorint(64015937) == {7993: 1, 8009: 1}\n assert factorint(2**(2**6) + 1) == {274177: 1, 67280421310721: 1}\n+\n+ assert factorint(0, multiple=True) == [0]\n+ assert factorint(1, multiple=True) == []\n+ assert factorint(-1, multiple=True) == [-1]\n+ assert factorint(-2, multiple=True) == [-1, 2]\n+ assert factorint(-16, multiple=True) == [-1, 2, 2, 2, 2]\n+ assert factorint(2, multiple=True) == [2]\n+ assert factorint(24, multiple=True) == [2, 2, 2, 3]\n+ assert factorint(126, multiple=True) == [2, 3, 3, 7]\n+ assert factorint(123456, multiple=True) == [2, 2, 2, 2, 2, 2, 3, 643]\n+ assert factorint(5951757, multiple=True) == [3, 7, 29, 29, 337]\n+ assert factorint(64015937, multiple=True) == [7993, 8009]\n+ assert factorint(2**(2**6) + 1, multiple=True) == [274177, 67280421310721]\n+\n assert multiproduct(factorint(fac(200))) == fac(200)\n for b, e in factorint(fac(150)).items():\n assert e == fac_multiplicity(150, b)\n@@ -424,6 +438,13 @@ def test_factorrat():\n assert str(factorrat(S(25)/14, visual=True)) == '5**2/(2*7)'\n assert str(factorrat(S(-25)/14/9, visual=True)) == '-5**2/(2*3**2*7)'\n \n+ assert factorrat(S(12)/1, multiple=True) == [2, 2, 3]\n+ assert factorrat(S(1)/1, multiple=True) == []\n+ assert factorrat(S(25)/14, multiple=True) == [1/2, 5, 5, 1/7]\n+ assert factorrat(S(12)/1, multiple=True) == [2, 2, 3]\n+ assert factorrat(S(-25)/14/9, multiple=True) == \\\n+ [-1, 1/2, 1/3, 1/3, 5, 5, 1/7]\n+\n \n def test_visual_io():\n sm = smoothness_p\n" }
[ { "diff_hunk": "@@ -942,6 +942,15 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True,\n smoothness, smoothness_p, divisors\n \n \"\"\"\n+ if multiple:\n+ fac = factorint(n, limit=limit, use_trial=use_trial,\n+ use_rho=use_rho, use_pm1=use_pm1,\n+ verbose=verbose, visual=False, multiple=False)\n+ factorlist = sum(([p] * fac[p]\n+ if fac[p] > 0 else [S(1)/p]*(-1 * fac[p])\n+ for p in sorted(fac)), [])", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/ntheory/factor_.py", "start_line": null, "text": "@user1:\ninstead of straight sorting, perhaps `for p, _ in sorted(fac.items(), key=lambda (p, m): p if m > 0 else 1/p)` would be better so you would get `[1/7, 1/2, 5, 5]` for `factorrat(S(25)/14, multiple=True)` instead of `[1/2, 5, 5, 1/7]`." } ]
00d420521775e72c73966ce8c38ce2739fd12bd0
diff --git a/sympy/ntheory/factor_.py b/sympy/ntheory/factor_.py index 67c2c18ce7db..32b91c99be7c 100644 --- a/sympy/ntheory/factor_.py +++ b/sympy/ntheory/factor_.py @@ -803,7 +803,7 @@ def done(n, d): def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True, - verbose=False, visual=None): + verbose=False, visual=None, multiple=False): r""" Given a positive integer ``n``, ``factorint(n)`` returns a dict containing the prime factors of ``n`` as keys and their respective multiplicities @@ -850,6 +850,14 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True, >>> factorint(3*101**7, limit=5) {3: 1, 101: 7} + List of Factors: + + If ``multiple`` is set to ``True`` then a list containing the + prime factors including multiplicities is returned. + + >>> factorint(24, multiple=True) + [2, 2, 2, 3] + Visual Factorization: If ``visual`` is set to ``True``, then it will return a visual @@ -942,6 +950,14 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True, smoothness, smoothness_p, divisors """ + if multiple: + fac = factorint(n, limit=limit, use_trial=use_trial, + use_rho=use_rho, use_pm1=use_pm1, + verbose=verbose, visual=False, multiple=False) + factorlist = sum(([p] * fac[p] if fac[p] > 0 else [S(1)/p]*(-1*fac[p]) + for p in sorted(fac)), []) + return factorlist + factordict = {} if visual and not isinstance(n, Mul) and not isinstance(n, dict): factordict = factorint(n, limit=limit, use_trial=use_trial, @@ -1172,7 +1188,7 @@ def factorint(n, limit=None, use_trial=True, use_rho=True, use_pm1=True, def factorrat(rat, limit=None, use_trial=True, use_rho=True, use_pm1=True, - verbose=False, visual=None): + verbose=False, visual=None, multiple=False): r""" Given a Rational ``r``, ``factorrat(r)`` returns a dict containing the prime factors of ``r`` as keys and their respective multiplicities @@ -1193,9 +1209,21 @@ def factorrat(rat, limit=None, use_trial=True, use_rho=True, use_pm1=True, - ``use_rho``: Toggle use of Pollard's rho method - ``use_pm1``: Toggle use of Pollard's p-1 method - ``verbose``: Toggle detailed printing of progress + - ``multiple``: Toggle returning a list of factors or dict - ``visual``: Toggle product form of output """ from collections import defaultdict + if multiple: + fac = factorrat(rat, limit=limit, use_trial=use_trial, + use_rho=use_rho, use_pm1=use_pm1, + verbose=verbose, visual=False,multiple=False) + factorlist = sum(([p] * fac[p] if fac[p] > 0 else [S(1)/p]*(-1*fac[p]) + for p, _ in sorted(fac.items(), + key=lambda elem: elem[0] + if elem[1] > 0 + else 1/elem[0])), []) + return factorlist + f = factorint(rat.p, limit=limit, use_trial=use_trial, use_rho=use_rho, use_pm1=use_pm1, verbose=verbose).copy() diff --git a/sympy/ntheory/tests/test_factor_.py b/sympy/ntheory/tests/test_factor_.py index aed3766babaf..7b4c4fbd5665 100644 --- a/sympy/ntheory/tests/test_factor_.py +++ b/sympy/ntheory/tests/test_factor_.py @@ -154,6 +154,20 @@ def test_factorint(): assert factorint(5951757) == {3: 1, 7: 1, 29: 2, 337: 1} assert factorint(64015937) == {7993: 1, 8009: 1} assert factorint(2**(2**6) + 1) == {274177: 1, 67280421310721: 1} + + assert factorint(0, multiple=True) == [0] + assert factorint(1, multiple=True) == [] + assert factorint(-1, multiple=True) == [-1] + assert factorint(-2, multiple=True) == [-1, 2] + assert factorint(-16, multiple=True) == [-1, 2, 2, 2, 2] + assert factorint(2, multiple=True) == [2] + assert factorint(24, multiple=True) == [2, 2, 2, 3] + assert factorint(126, multiple=True) == [2, 3, 3, 7] + assert factorint(123456, multiple=True) == [2, 2, 2, 2, 2, 2, 3, 643] + assert factorint(5951757, multiple=True) == [3, 7, 29, 29, 337] + assert factorint(64015937, multiple=True) == [7993, 8009] + assert factorint(2**(2**6) + 1, multiple=True) == [274177, 67280421310721] + assert multiproduct(factorint(fac(200))) == fac(200) for b, e in factorint(fac(150)).items(): assert e == fac_multiplicity(150, b) @@ -424,6 +438,13 @@ def test_factorrat(): assert str(factorrat(S(25)/14, visual=True)) == '5**2/(2*7)' assert str(factorrat(S(-25)/14/9, visual=True)) == '-5**2/(2*3**2*7)' + assert factorrat(S(12)/1, multiple=True) == [2, 2, 3] + assert factorrat(S(1)/1, multiple=True) == [] + assert factorrat(S(25)/14, multiple=True) == [1/7, 1/2, 5, 5] + assert factorrat(S(12)/1, multiple=True) == [2, 2, 3] + assert factorrat(S(-25)/14/9, multiple=True) == \ + [-1, 1/7, 1/3, 1/3, 1/2, 5, 5] + def test_visual_io(): sm = smoothness_p
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
sympy__sympy-12066@0ab8193
sympy/sympy
Python
12,066
Fix Subs._eval_derivative function for higher order derivative
Fixes #12005 , one of the test cases(only one) taken from diofant. If this fix has any similarity with the code of skirpichev that is probably a coincidence. I have only used the test case from his PR. <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below.>
2017-01-19T21:22:22Z
Subs._eval_derivative doubles derivatives This issue appears in the computation of higher derivatives with respect to functions. ``` >>> x = symbols('x') >>> f = symbols('f', cls=Function) >>> e = Subs(Derivative(f(x), x), (x,), (x,)) >>> e._eval_derivative(x) 2*Derivative(f(x), x, x) ``` Apparently, the derivative is computed in both of the two parts of the returned sum: ``` return self.func(self.expr.diff(s), self.variables, self.point).doit() \ + Add(*[ Subs(point.diff(s) * self.expr.diff(arg), self.variables, self.point).doit() for arg, point in zip(self.variables, self.point) ]) ```
This is the reason for the error encountered in #11799.
[ { "body": "This issue appears in the computation of higher derivatives with respect to functions.\r\n```\r\n>>> x = symbols('x')\r\n>>> f = symbols('f', cls=Function)\r\n>>> e = Subs(Derivative(f(x), x), (x,), (x,))\r\n>>> e._eval_derivative(x)\r\n2*Derivative(f(x), x, x)\r\n```\r\nApparently, the derivative is computed in both of the two parts of the returned sum:\r\n```\r\n return self.func(self.expr.diff(s), self.variables, self.point).doit() \\\r\n + Add(*[ Subs(point.diff(s) * self.expr.diff(arg),\r\n self.variables, self.point).doit() for arg,\r\n point in zip(self.variables, self.point) ])\r\n```\r\n", "number": 12005, "title": "Subs._eval_derivative doubles derivatives" } ]
8b3fcd87c83de5b55a959cf18501c2a4f4d8e289
{ "head_commit": "0ab8193b0914b4f9643c50380f56ec82705dd1b5", "head_commit_message": "Fix Subs._eval_derivative function for higher order derivative\n\nFixes #12005, one of the test cases(only one) taken from diofant", "patch_to_review": "diff --git a/sympy/core/function.py b/sympy/core/function.py\nindex 2d33d96b94b3..262690a5aa92 100644\n--- a/sympy/core/function.py\n+++ b/sympy/core/function.py\n@@ -1738,10 +1738,9 @@ def _eval_subs(self, old, new):\n def _eval_derivative(self, s):\n if s not in self.free_symbols:\n return S.Zero\n- return self.func(self.expr.diff(s), self.variables, self.point).doit() \\\n- + Add(*[ Subs(point.diff(s) * self.expr.diff(arg),\n- self.variables, self.point).doit() for arg,\n- point in zip(self.variables, self.point) ])\n+ return Add(*[point.diff(s)*self.func(self.expr.diff(var), self.variables, self.point).doit() + \\\n+ self.func(self.expr.diff(s) if s != var else S.Zero, self.variables, self.point).doit() \\\n+ for var, point in zip(self.variables, self.point)])\n \n def _eval_nseries(self, x, n, logx):\n if x in self.point:\ndiff --git a/sympy/core/tests/test_function.py b/sympy/core/tests/test_function.py\nindex d1fdd97ef814..c91d37768804 100644\n--- a/sympy/core/tests/test_function.py\n+++ b/sympy/core/tests/test_function.py\n@@ -811,3 +811,13 @@ def test_issue_11159():\n expr0 = expr1 * expr1\n expr1 = expr0.subs(expr1,expr0)\n assert expr0 == expr1\n+\n+\n+def test_issue_12005():\n+ e1 = Subs(Derivative(f(x), x), (x,), (x,))\n+ assert e1.diff(x) == Derivative(f(x), x, x)\n+\n+ e2 = Subs(Derivative(f(x), x), (x,), (x**2 + 1,))\n+ assert e2.diff(x) == 2*x*Subs(Derivative(f(x), x, x), (x,), (x**2 + 1,))\n+\n+ assert f(g(x)).diff(g(x), g(x)) == Subs(Derivative(f(y), y, y), (y,), (g(x),))\n" }
[ { "diff_hunk": "@@ -1738,10 +1738,9 @@ def _eval_subs(self, old, new):\n def _eval_derivative(self, s):\n if s not in self.free_symbols:\n return S.Zero\n- return self.func(self.expr.diff(s), self.variables, self.point).doit() \\\n- + Add(*[ Subs(point.diff(s) * self.expr.diff(arg),\n- self.variables, self.point).doit() for arg,\n- point in zip(self.variables, self.point) ])\n+ return Add(*[point.diff(s)*self.func(self.expr.diff(var), self.variables, self.point).doit() + \\\n+ self.func(self.expr.diff(s) if s != var else S.Zero, self.variables, self.point).doit() \\", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/core/function.py", "start_line": null, "text": "@user1:\nIt looks like this term may occur several times.\r\n```\r\nIn [42]: e = Subs(Derivative(f(x), x), (y, z), (y, z))\r\n\r\nIn [43]: print(e.diff(x))\r\n2*Derivative(f(x), x, x)\r\n\r\nIn [45]: e = Subs(Derivative(f(x), x), (y, z, t), (y, z, t))\r\n\r\nIn [46]: print(e.diff(x))\r\n3*Derivative(f(x), x, x)\r\n```\r\nThe backslashes are probably not needed." } ]
1d1fc47a61a8951f47253a52d47569d76bcc3581
diff --git a/sympy/core/function.py b/sympy/core/function.py index 2d33d96b94b3..1b8005e1d230 100644 --- a/sympy/core/function.py +++ b/sympy/core/function.py @@ -1738,10 +1738,10 @@ def _eval_subs(self, old, new): def _eval_derivative(self, s): if s not in self.free_symbols: return S.Zero - return self.func(self.expr.diff(s), self.variables, self.point).doit() \ - + Add(*[ Subs(point.diff(s) * self.expr.diff(arg), - self.variables, self.point).doit() for arg, - point in zip(self.variables, self.point) ]) + return Add((Subs(self.expr.diff(s), self.variables, self.point).doit() + if s not in self.variables else S.Zero), + *[p.diff(s) * Subs(self.expr.diff(v), self.variables, + self.point).doit() for v, p in zip(self.variables, self.point)]) def _eval_nseries(self, x, n, logx): if x in self.point: diff --git a/sympy/core/tests/test_function.py b/sympy/core/tests/test_function.py index d1fdd97ef814..0288a80c46b3 100644 --- a/sympy/core/tests/test_function.py +++ b/sympy/core/tests/test_function.py @@ -698,6 +698,7 @@ def test_mexpand(): assert _mexpand(1) is S.One assert _mexpand(x*(x + 1)**2) == (x*(x + 1)**2).expand() + def test_issue_8469(): # This should not take forever to run N = 40 @@ -708,6 +709,7 @@ def g(w, theta): import functools expr = functools.reduce(g,ws) + def test_should_evalf(): # This should not take forever to run (see #8506) assert isinstance(sin((1.0 + 1.0*I)**10000 + 1), sin) @@ -805,9 +807,24 @@ def test_Derivative_as_finite_difference(): ref2 = f(xm, ym) + f(xp, yp) - f(xp, ym) - f(xm, yp) assert (d2fdxdy.as_finite_difference() - ref2).simplify() == 0 + def test_issue_11159(): # Tests Application._eval_subs expr1 = E expr0 = expr1 * expr1 expr1 = expr0.subs(expr1,expr0) assert expr0 == expr1 + + +def test_issue_12005(): + e1 = Subs(Derivative(f(x), x), (x,), (x,)) + assert e1.diff(x) == Derivative(f(x), x, x) + e2 = Subs(Derivative(f(x), x), (x,), (x**2 + 1,)) + assert e2.diff(x) == 2*x*Subs(Derivative(f(x), x, x), (x,), (x**2 + 1,)) + e3 = Subs(Derivative(f(x) + y**2 - y, y), (y,), (y**2,)) + assert e3.diff(y) == 4*y + e4 = Subs(Derivative(f(x + y), y), (y,), (x**2)) + assert e4.diff(y) == S.Zero + e5 = Subs(Derivative(f(x), x), (y, z), (y, z)) + assert e5.diff(x) == Derivative(f(x), x, x) + assert f(g(x)).diff(g(x), g(x)) == Subs(Derivative(f(y), y, y), (y,), (g(x),))
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
sympy__sympy-11951@e7a7ff3
sympy/sympy
Python
11,951
Matrix hstack/vstack improved
fixes #11944
2016-12-16T09:56:49Z
matrix vstack/hstack can fail with immutable matrix as first argument ```` >>> A = Matrix([[1, 2], [pi, 4]]) >>> AIm = sympify(A) >>> type(A) sympy.matrices.dense.MutableDenseMatrix >>> type(AIm) sympy.matrices.immutable.ImmutableMatrix >>> Matrix.vstack(A, AIm) โŽก1 2โŽค โŽข โŽฅ โŽขฯ€ 4โŽฅ โŽข โŽฅ โŽข1 2โŽฅ โŽข โŽฅ โŽฃฯ€ 4โŽฆ >>> Matrix.vstack(AIm, A) ('hello vstack, cls=', <class 'sympy.matrices.dense.MutableDenseMatrix'>) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-37-aec335eeafd0> in <module>() ----> 1 Matrix.vstack(AIm, A) /home/cbm/work/octsympy/octsympy.git/inst/sympy/matrices/matrices.py in vstack(cls, *args) 4541 """ -> 4542 return reduce(cls.col_join, args) 4543 4544 def xreplace(self, rule): # should mirror core.basic.xreplace TypeError: unbound method col_join() must be called with MutableDenseMatrix instance as first argument (got ImmutableMatrix instance instead) ```` Note it will work if one uses `MatrixBase.vstack`. I don't think this is really about immutable matrices; its about `cls.col_join` not being the right method, depending on what is in `args`. One way to fix this would be to take `cls` from the type of `args[0]` instead: ```` kls = type(args[0]) reduce(kls.col_join, args) ```` Although I'm not sure that's quite right. Another option is to avoid `reduce` and use: ```` M = args.pop(0) for i = range(len(args)): M = M.col_join(args.pop(0)) ```` (or more pythonic version thereof). Whatever we do `hstack` needs the same fix.
I want to try this. @cbm755 this the output in my system and seems to be fine. ``` In [36]: A=Matrix([[1,2],[pi,4]]) In [37]: AIm=sympify(A) In [38]: type(A) Out[38]: sympy.matrices.dense.MutableDenseMatrix In [39]: type(AIm) Out[39]: sympy.matrices.immutable.ImmutableMatrix In [40]: Matrix.vstack(AIm,A) Out[40]: ?1 2? ? ? ?ฯ€ 4? ? ? ?1 2? ? ? ?ฯ€ 4? In [41]: Matrix.hstack(AIm,A) Out[41]: ?1 2 1 2? ? ? ?ฯ€ 4 ฯ€ 4? ``` Thanks for checking. I've tried it and I can reproduce with Python 2 on both Sympy 1.0.1 and the current git master. But on Python 3, it works as you show: nice catch. > I want to try this. Go for it. It should be an easy fix, I'm just not enough of a Python expert to know which fix is the best. Please add a test and read the https://github.com/sympy/sympy/wiki/Development-workflow. Hi @Abdullahjavednesar . Let me know in case you're not working on it :) This is fixed in https://github.com/sympy/sympy/pull/11592 , for what it's worth :-) @siefkenj that's great! I wondered when I saw your refactor but I haven't had a chance to look yet. Thanks. @Abdullahjavednesar if that PR goes in first, we'll need to update your PR #11951 to just add tests---this issue will still need tests even if its fixed elsewhere.
[ { "body": "````\r\n>>> A = Matrix([[1, 2], [pi, 4]])\r\n>>> AIm = sympify(A)\r\n\r\n>>> type(A)\r\nsympy.matrices.dense.MutableDenseMatrix\r\n\r\n>>> type(AIm)\r\nsympy.matrices.immutable.ImmutableMatrix\r\n\r\n>>> Matrix.vstack(A, AIm)\r\nโŽก1 2โŽค\r\nโŽข โŽฅ\r\nโŽขฯ€ 4โŽฅ\r\nโŽข โŽฅ\r\nโŽข1 2โŽฅ\r\nโŽข โŽฅ\r\nโŽฃฯ€ 4โŽฆ\r\n\r\n>>> Matrix.vstack(AIm, A)\r\n('hello vstack, cls=', <class 'sympy.matrices.dense.MutableDenseMatrix'>)\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-37-aec335eeafd0> in <module>()\r\n----> 1 Matrix.vstack(AIm, A)\r\n\r\n/home/cbm/work/octsympy/octsympy.git/inst/sympy/matrices/matrices.py in vstack(cls, *args)\r\n 4541 \"\"\"\r\n-> 4542 return reduce(cls.col_join, args)\r\n 4543 \r\n 4544 def xreplace(self, rule): # should mirror core.basic.xreplace\r\n\r\nTypeError: unbound method col_join() must be called with MutableDenseMatrix instance as first argument (got ImmutableMatrix instance instead)\r\n````\r\nNote it will work if one uses `MatrixBase.vstack`.\r\n\r\nI don't think this is really about immutable matrices; its about `cls.col_join` not being the right method, depending on what is in `args`.\r\n\r\nOne way to fix this would be to take `cls` from the type of `args[0]` instead:\r\n````\r\nkls = type(args[0])\r\nreduce(kls.col_join, args)\r\n````\r\nAlthough I'm not sure that's quite right.\r\n\r\nAnother option is to avoid `reduce` and use:\r\n````\r\nM = args.pop(0)\r\nfor i = range(len(args)):\r\n M = M.col_join(args.pop(0))\r\n````\r\n(or more pythonic version thereof).\r\n\r\nWhatever we do `hstack` needs the same fix.", "number": 11944, "title": "matrix vstack/hstack can fail with immutable matrix as first argument" } ]
b1f55f2ad770cd656ebcb0207f10605f996c63cf
{ "head_commit": "e7a7ff33e6fba08bf4fb56e7129e44df39e5a919", "head_commit_message": "Tests corrected", "patch_to_review": "diff --git a/sympy/matrices/matrices.py b/sympy/matrices/matrices.py\nindex 2fd4efa8ca06..edd9d9fd6008 100644\n--- a/sympy/matrices/matrices.py\n+++ b/sympy/matrices/matrices.py\n@@ -2035,7 +2035,8 @@ def hstack(cls, *args):\n [1, 0, 2, 0],\n [0, 1, 0, 2]])\n \"\"\"\n- return reduce(cls.row_join, args)\n+ kls = type(args[0])\n+ return reduce(kls.row_join, args)\n \n def integrate(self, *args):\n \"\"\"Integrate each element of the matrix.\n@@ -4531,7 +4532,8 @@ def vstack(cls, *args):\n [2, 0],\n [0, 2]])\n \"\"\"\n- return reduce(cls.col_join, args)\n+ kls = type(args[0])\n+ return reduce(kls.col_join, args)\n \n def xreplace(self, rule): # should mirror core.basic.xreplace\n \"\"\"Return a new matrix with xreplace applied to each entry.\ndiff --git a/sympy/matrices/tests/test_matrices.py b/sympy/matrices/tests/test_matrices.py\nindex 8c2481580650..7a2c30a50473 100644\n--- a/sympy/matrices/tests/test_matrices.py\n+++ b/sympy/matrices/tests/test_matrices.py\n@@ -2378,6 +2378,13 @@ def test_issue_5320():\n [0, 1, 0, 2]\n ])\n \n+def test_issue_11944():\n+ A = Matrix([[1]])\n+ AIm = sympify(A)\n+ assert Matrix.hstack(AIm, A) == Matrix([[1, 1]])\n+ assert Matrix.vstack(AIm, A) == Matrix([\n+ [1, ],\n+ [1, ]])\n \n def test_cross():\n a = [1, 2, 3]\n" }
[ { "diff_hunk": "@@ -2378,6 +2378,13 @@ def test_issue_5320():\n [0, 1, 0, 2]\n ])\n \n+def test_issue_11944():\n+ A = Matrix([[1]])\n+ AIm = sympify(A)\n+ assert Matrix.hstack(AIm, A) == Matrix([[1, 1]])\n+ assert Matrix.vstack(AIm, A) == Matrix([\n+ [1, ],\n+ [1, ]])", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/matrices/tests/test_matrices.py", "start_line": null, "text": "@user1:\n`== Matrix([[1], [1]])` please\n\n@author:\nOkay" } ]
8c1de45cd73d9c6708f6f05e2c92e21e4d3d1350
diff --git a/sympy/matrices/matrices.py b/sympy/matrices/matrices.py index e7d0d4998fb9..71026d1297a6 100644 --- a/sympy/matrices/matrices.py +++ b/sympy/matrices/matrices.py @@ -3170,7 +3170,8 @@ def hstack(cls, *args): [1, 0, 2, 0], [0, 1, 0, 2]]) """ - return reduce(cls.row_join, args) + kls = type(args[0]) + return reduce(kls.row_join, args) def integrate(self, *args): """Integrate each element of the matrix. @@ -5042,7 +5043,8 @@ def vstack(cls, *args): [2, 0], [0, 2]]) """ - return reduce(cls.col_join, args) + kls = type(args[0]) + return reduce(kls.col_join, args) _eval_simplify = simplify diff --git a/sympy/matrices/tests/test_matrices.py b/sympy/matrices/tests/test_matrices.py index 73cd03bcdf63..bb0c8197e5d9 100644 --- a/sympy/matrices/tests/test_matrices.py +++ b/sympy/matrices/tests/test_matrices.py @@ -2388,6 +2388,11 @@ def test_issue_5320(): [0, 1, 0, 2] ]) +def test_issue_11944(): + A = Matrix([[1]]) + AIm = sympify(A) + assert Matrix.hstack(AIm, A) == Matrix([[1, 1]]) + assert Matrix.vstack(AIm, A) == Matrix([[1], [1]]) def test_cross(): a = [1, 2, 3]
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-11818@ab1bcd5
sympy/sympy
Python
11,818
Fixed behaviour of S.Complexes in unions (#11730)
This PR fixes #11730
2016-11-06T19:54:19Z
Union(FiniteSet(oo), S.Complexes) gives S.Complexes (should remain unevaluated) Hi, well searching i found this: ``` python >>> oo in S.UniversalSet True >>> oo in S.Complexes False >>> Union(FiniteSet(oo), S.Complexes) S.Complexes ``` i don't know with this where `oo` belongs, is part of Complexes or not? Thx. Cya.
UniversalSet doesn't come into play here. It's just a formal set that always returns True for any containment check. `Union(FiniteSet(oo), S.Complexes)` giving `S.Complexes` is a bug. (Optimistically setting this as easy to fix. I suspect it isn't difficult, but there is a chance I am wrong) After working for a bit on this, there's also a related bug with Intersection ``` python >>> Intersection(S.Reals, ComplexRegion(Interval(0,1)**2)) โ„ ``` One would expect this to be [0,1]. I have tried fixing this [here](https://github.com/MrOverlord/sympy/commit/efa9a618bb277612c1520eaea01264a866d654b0#diff-c1c17bdd28aafd400bf9415c7d048382), but this causes a failure in test_ComplexRegion_union : ``` python c1 = ComplexRegion(Interval(0, 1)*Interval(0, 2*S.Pi), polar=True) #... assert c1.union(Interval(2, 4)) == Union(c1, Interval(2, 4), evaluate=False) ``` I think sympy is not being consistent here. Tests expect treating real numbers as complex numbers when making union with S.Complexes, yet also expect them to be completely separate when making union with _any_ other subset of complex numbers (In fact, at the moment, S.Complexes in unions behaves a bit like S.UniversalSet). ``` python >>>Interval(0,1).union(ComplexRegion(Interval(-5,5)**2)) [0,1] โˆช { x + yi | x,y โˆˆ [โˆ’5,5]^2 } >>>Interval(0,1).union(S.Complexes) โ„‚ >>> Union(S.Complexes, FiniteSet(x)) โ„‚ ``` Should I treat real numbers as complex ones in my fix? If I do, I'd need to modify the aforementioned test. If the new test output is still mathematically correct, then just change the test. union returning Union is the default behavior. If it can return something more specific instead, that is preferred.
[ { "body": "Hi, well searching i found this:\n\n``` python\n>>> oo in S.UniversalSet\nTrue\n>>> oo in S.Complexes\nFalse\n>>> Union(FiniteSet(oo), S.Complexes)\nS.Complexes\n```\n\ni don't know with this where `oo` belongs, is part of Complexes or not?\n\nThx. Cya.\n", "number": 11730, "title": "Union(FiniteSet(oo), S.Complexes) gives S.Complexes (should remain unevaluated)" } ]
a221c376f3f382d251a600aa336b45e95f92b7fe
{ "head_commit": "ab1bcd52134398f569a28184f6ff40688052c939", "head_commit_message": "Add unit test for issue 11730\n\n Modified test test_ComplexRegion_union to be consistent with other\n tests.", "patch_to_review": "diff --git a/sympy/sets/fancysets.py b/sympy/sets/fancysets.py\nindex a009f3f7ab95..5f88e53a5edd 100644\n--- a/sympy/sets/fancysets.py\n+++ b/sympy/sets/fancysets.py\n@@ -1409,8 +1409,6 @@ def _intersect(self, other):\n return ComplexRegion(new_r_interval*new_theta_interval,\n polar=True)\n \n- if other is S.Reals:\n- return other\n \n if other.is_subset(S.Reals):\n new_interval = []\n@@ -1443,8 +1441,13 @@ def _union(self, other):\n elif self.polar and other.polar:\n return ComplexRegion(Union(self.sets, other.sets), polar=True)\n \n- if self == S.Complexes:\n- return self\n+ # treat a subset of Reals as complex numbers\n+ if other.is_subset(S.Reals):\n+ as_complex = other * FiniteSet(0)\n+ if as_complex.is_subset(self.sets):\n+ return self\n+ else:\n+ return ComplexRegion(Union(self.sets, as_complex), polar=self.polar)\n \n return None\n \ndiff --git a/sympy/sets/tests/test_fancysets.py b/sympy/sets/tests/test_fancysets.py\nindex 0d26c0b65032..45832cde5781 100644\n--- a/sympy/sets/tests/test_fancysets.py\n+++ b/sympy/sets/tests/test_fancysets.py\n@@ -5,7 +5,7 @@\n Intersection)\n from sympy.simplify.simplify import simplify\n from sympy import (S, Symbol, Lambda, symbols, cos, sin, pi, oo, Basic,\n- Rational, sqrt, tan, log, exp, Abs, I, Tuple)\n+ Rational, sqrt, tan, log, exp, Abs, I, Tuple, eye)\n from sympy.utilities.iterables import cartes\n from sympy.utilities.pytest import XFAIL, raises\n from sympy.abc import x, y, z, t\n@@ -593,8 +593,8 @@ def test_ComplexRegion_union():\n assert c5.union(c6) == ComplexRegion(p3)\n assert c7.union(c8) == ComplexRegion(p4)\n \n- assert c1.union(Interval(2, 4)) == Union(c1, Interval(2, 4), evaluate=False)\n- assert c5.union(Interval(2, 4)) == Union(c5, Interval(2, 4), evaluate=False)\n+ assert c1.union(Interval(2, 4)) == Union.reduce(Union(c1, Interval(2, 4), evaluate=False).args)\n+ assert c5.union(Interval(2, 4)) == Union.reduce(Union(c5, Interval(2, 4), evaluate=False).args)\n \n \n def test_ComplexRegion_measure():\n@@ -698,3 +698,13 @@ def test_issue_11732():\n assert (pointComplex in S.Naturals0) == False\n assert (pointComplex in S.Integers) == False\n assert (pointComplex in S.Complexes) == True\n+\n+\n+def test_issue_11730():\n+ unit = Interval(0, 1)\n+ square = ComplexRegion(unit ** 2)\n+\n+ assert Union(S.Complexes, FiniteSet(oo)) != S.Complexes\n+ assert Union(S.Complexes, FiniteSet(eye(4))) != S.Complexes\n+ assert Union(unit, square) == square\n+ assert Intersection(S.Reals, square) == unit\n" }
[ { "diff_hunk": "@@ -1443,8 +1441,13 @@ def _union(self, other):\n elif self.polar and other.polar:\n return ComplexRegion(Union(self.sets, other.sets), polar=True)\n \n- if self == S.Complexes:\n- return self\n+ # treat a subset of Reals as complex numbers\n+ if other.is_subset(S.Reals):", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/sets/fancysets.py", "start_line": null, "text": "@user1:\nThe method could probably check this first and coerce other into a ComplexRegion before asking `if other.is_ComplexRegion`.\n\n@author:\nI don't think converting other to ComplexRegion is necessary, since under the `if other.is_ComplexRegion:` we are only using `other.sets` which just returns set passed in constructor, and this way we don't incur overhead of ComplexRegion's constructor.\r\n\r\nIf you think this makes logic of what's happening clearer, I'll change it.\n\n@user1:\n> this makes logic of what's happening clearer\r\n\r\nYes, that is what I think. We should avoid having to use any knowledge of the internal representation of ComplexRegion. Hence also the conversion should be carried out by ComplexRegion despite of some overhead. That could be done by overloading `ComplexRegion.__new__` to accept a subset of Reals, or, maybe preferably, by a new method `ComplexRegion.from_real`." } ]
d6e6e8335ae22f09a3c9b9177333a7fb84cd2be1
diff --git a/sympy/sets/fancysets.py b/sympy/sets/fancysets.py index a009f3f7ab95..cae6eb7d429c 100644 --- a/sympy/sets/fancysets.py +++ b/sympy/sets/fancysets.py @@ -1353,6 +1353,25 @@ def _measure(self): """ return self.sets._measure + @classmethod + def from_real(cls, sets): + """ + Converts given subset of real numbers to a complex region. + + Examples + ======== + + >>> from sympy import Interval, ComplexRegion + >>> unit = Interval(0,1) + >>> ComplexRegion.from_real(unit) + ComplexRegion([0, 1] x {0}, False) + + """ + if not sets.is_subset(S.Reals): + raise ValueError("sets must be a subset of the real line") + + return cls(sets * FiniteSet(0)) + def _contains(self, other): from sympy.functions import arg, Abs from sympy.core.containers import Tuple @@ -1409,8 +1428,6 @@ def _intersect(self, other): return ComplexRegion(new_r_interval*new_theta_interval, polar=True) - if other is S.Reals: - return other if other.is_subset(S.Reals): new_interval = [] @@ -1433,6 +1450,10 @@ def _intersect(self, other): def _union(self, other): + if other.is_subset(S.Reals): + # treat a subset of reals as a complex region + other = ComplexRegion.from_real(other) + if other.is_ComplexRegion: # self in rectangular form @@ -1443,9 +1464,6 @@ def _union(self, other): elif self.polar and other.polar: return ComplexRegion(Union(self.sets, other.sets), polar=True) - if self == S.Complexes: - return self - return None diff --git a/sympy/sets/sets.py b/sympy/sets/sets.py index bb9d9b32e6d1..032186255723 100644 --- a/sympy/sets/sets.py +++ b/sympy/sets/sets.py @@ -633,6 +633,8 @@ def _intersect(self, other): for a, b in zip(self.sets, other.sets)) def _union(self, other): + if other.is_subset(self): + return self if not other.is_ProductSet: return None if len(other.args) != len(self.args): diff --git a/sympy/sets/tests/test_fancysets.py b/sympy/sets/tests/test_fancysets.py index 0d26c0b65032..165bfe253056 100644 --- a/sympy/sets/tests/test_fancysets.py +++ b/sympy/sets/tests/test_fancysets.py @@ -5,7 +5,7 @@ Intersection) from sympy.simplify.simplify import simplify from sympy import (S, Symbol, Lambda, symbols, cos, sin, pi, oo, Basic, - Rational, sqrt, tan, log, exp, Abs, I, Tuple) + Rational, sqrt, tan, log, exp, Abs, I, Tuple, eye) from sympy.utilities.iterables import cartes from sympy.utilities.pytest import XFAIL, raises from sympy.abc import x, y, z, t @@ -594,7 +594,7 @@ def test_ComplexRegion_union(): assert c7.union(c8) == ComplexRegion(p4) assert c1.union(Interval(2, 4)) == Union(c1, Interval(2, 4), evaluate=False) - assert c5.union(Interval(2, 4)) == Union(c5, Interval(2, 4), evaluate=False) + assert c5.union(Interval(2, 4)) == Union(c5, ComplexRegion.from_real(Interval(2, 4))) def test_ComplexRegion_measure(): @@ -698,3 +698,13 @@ def test_issue_11732(): assert (pointComplex in S.Naturals0) == False assert (pointComplex in S.Integers) == False assert (pointComplex in S.Complexes) == True + + +def test_issue_11730(): + unit = Interval(0, 1) + square = ComplexRegion(unit ** 2) + + assert Union(S.Complexes, FiniteSet(oo)) != S.Complexes + assert Union(S.Complexes, FiniteSet(eye(4))) != S.Complexes + assert Union(unit, square) == square + assert Intersection(S.Reals, square) == unit
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-13016@98a97d7
sympy/sympy
Python
13,016
making invert_real to work for 0**x + a type equations
This PR tries to fix [issue#11536](https://github.com/sympy/sympy/issues/11536). The approach I applied solves the issue but other `test cases` fail, and I am not able to figure it out what might have caused the failure. ping @kshitij10496
2017-07-20T19:32:13Z
Solution of 0**x + a should be EmptySet There is some discrepancy in the behaviour of `0**x` and the solving technique for the following equations in solveset: ``` python In []: solveset(0**x - 1, x, S.Reals) Out[]: {0} # correct result In []: solveset(0**x - 100, x, S.Reals) Out[]: {0} # should be EmptySet In []: solveset(0**x + 100, x, S.Reals) Out[]: {0} # should be EmptySet In [17]: solve(0**x - 100, x) # raises NotImplementedError ```
For ``` python solve(0**x - 100, x) ``` SymPy 1.0 returns ``` python ConditionSet(x, Eq(0**x - 100, 0), Complexes((-oo, oo) x (-oo, oo), False)) ``` Which version of SymPy are you using? I agree with you @kshitij10496. Should it be fixed? If so I would like to work on it. > Should it be fixed? If so I would like to work on it. Yes, it needs to be fixed. The function `0**x` is a transcendental function. For now, I would suggest we prevent `solveset` from returning an incorrect solution by returning a `ConditionSet` object instead. However, this case needs to be addressed in the future while implementing `transolve`. @nikoskaragiannakis I think you might have confused between `solve` and `solveset` here. In spite of that, I agree with you that `solveset` works fine in the `S.Complexes` domain. However, the issue here is that an incorrect solution is returned in case of real domain. ```python In []: solve(0**x - 100, x) # raises NotImplementedError In []: solveset(0**x - 100, x) Out[]: ConditionSet(x, Eq(0**x - 100, 0), Complexes((-oo, oo) x (-oo, oo), False)) # correct answer in Complex domain In []: solveset(0**x - 100, x, S.Reals) Out[]: {0} # wrong result in the Real domain ``` @kshitij10496 I could fix this to return `ConditionSet` for time being. Right now I am trying to study `solve's` transcendental eq solver. Also looking forward to contribute in `transolve` in future. @kshitij10496 I was thinking to use `checksol` to check whether the the result satisfies the given eq `f`, but many tests were failing, I implemented it in line 692 of `solveset` module. ``` chk = checksol(f, symbol, rhs_s.args[0]) if not chk: result = ConditionSet(symbol, f, domain) ``` I guess its not the right place to use `checksol` or is there any other way to implement. What do you suggest? Checking for the solution inside `_solveset` won't exactly address this behavior. I would suggest you fix this by adding checks inside `invert_real` function. These are indeterminate forms : ``` In [1]: 0**0 Out[1]: 1 # must be nan In [2]: oo/oo Out[2]: nan In [3]: 0/0 --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) <ipython-input-3-6549dea6d1ae> in <module>() ----> 1 0/0 ZeroDivisionError: division by zero # must be nan In [4]: 0*oo Out[4]: nan In [5]: oo-oo Out[5]: nan In [6]: 1**oo Out[6]: nan In [7]: oo**0 Out[7]: 1 # must be nan ``` So [0**x - 1 = 0 for x ](http://www.wolframalpha.com/input/?i=0**x++-+1+%3D+0+for+x) should return no solution. Right ? We can have a look [here](https://math.stackexchange.com/questions/11150/zero-to-the-zero-power-is-00-1) @jksuom @asmeurer As @Shekharrajak mentioned above,`0**0` returns `1`. Is this by design or a bug?
[ { "body": "There is some discrepancy in the behaviour of `0**x` and the solving technique for the following equations in solveset:\n\n``` python\nIn []: solveset(0**x - 1, x, S.Reals)\nOut[]: {0} # correct result\n\nIn []: solveset(0**x - 100, x, S.Reals)\nOut[]: {0} # should be EmptySet \n\nIn []: solveset(0**x + 100, x, S.Reals)\nOut[]: {0} # should be EmptySet\n\nIn [17]: solve(0**x - 100, x) # raises NotImplementedError\n```\n", "number": 11536, "title": "Solution of 0**x + a should be EmptySet" } ]
2f3eba1cd616166d3b11c7a0aa2bdb4d453093e5
{ "head_commit": "98a97d7680cce5fe7d3a2996814417d96c474b12", "head_commit_message": "re-implemented the logic", "patch_to_review": "diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py\nindex 7d5dc1254a81..84d8c415c590 100644\n--- a/sympy/solvers/solveset.py\n+++ b/sympy/solvers/solveset.py\n@@ -117,7 +117,6 @@ def invert_real(f_x, y, x, domain=S.Reals):\n \"\"\"\n return _invert(f_x, y, x, domain)\n \n-\n def _invert_real(f, g_ys, symbol):\n \"\"\"Helper function for _invert.\"\"\"\n \n@@ -188,8 +187,13 @@ def _invert_real(f, g_ys, symbol):\n return _invert_real(base, res, symbol)\n \n if not base_has_sym:\n- return _invert_real(expo,\n- imageset(Lambda(n, log(n)/log(base)), g_ys), symbol)\n+ if base is not S.Zero:\n+ return _invert_real(expo,\n+ imageset(Lambda(n, log(n)/log(base)), g_ys), symbol)\n+ if base is S.Zero and g_ys.args[0] is S.One:\n+ #special case: 0**x - 1\n+ return (expo, FiniteSet(0))\n+\n \n if isinstance(f, TrigonometricFunction):\n if isinstance(g_ys, FiniteSet):\ndiff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py\nindex bdbd25c216c4..3ec3adc7e71b 100644\n--- a/sympy/solvers/tests/test_solveset.py\n+++ b/sympy/solvers/tests/test_solveset.py\n@@ -177,6 +177,9 @@ def test_domain_check():\n assert domain_check(x, x, oo) is False\n assert domain_check(0, x, oo) is False\n \n+def test_issue_11536():\n+ assert solveset(0**x - 100, x, S.Reals) == ConditionSet(x, Eq(0**x - 100, 0), S.Reals)\n+ assert solveset(0**x - 1, x, S.Reals) == FiniteSet(0)\n \n def test_is_function_class_equation():\n from sympy.abc import x, a\n" }
[ { "diff_hunk": "@@ -188,8 +187,13 @@ def _invert_real(f, g_ys, symbol):\n return _invert_real(base, res, symbol)\n \n if not base_has_sym:\n- return _invert_real(expo,\n- imageset(Lambda(n, log(n)/log(base)), g_ys), symbol)\n+ if base is not S.Zero:\n+ return _invert_real(expo,\n+ imageset(Lambda(n, log(n)/log(base)), g_ys), symbol)\n+ if base is S.Zero and g_ys.args[0] is S.One:", "line": null, "original_line": 193, "original_start_line": null, "path": "sympy/solvers/solveset.py", "start_line": null, "text": "@user1:\n`if ...` -> `elif g_ys.args[0] is S.One:`\n\n@user1:\nI'm not sure what `g_ys` is -- a Tuple? Would it ever have args[0] be 1 but other terms also present? 1 in position other than 0?\n\n@author:\n> I'm not sure what g_ys is\r\n\r\n`g_ys` is actually a `FiniteSet` that is returned by every recursive call.\r\n\r\n> Would it ever have args[0] be 1 but other terms also present?\r\n\r\nI don't think there will be more than one args in `g_ys` because it is a sub-condition of `if f.is_Pow` which will always have single element in finiteset.\r\n\r\n> 1 in position other than 0?\r\n\r\nI guess this is a very special case `1` will be always present in position 0" } ]
f87af89ef8eb3b1539a5b57e4f4126a40f7ac707
diff --git a/sympy/solvers/solveset.py b/sympy/solvers/solveset.py index 7d5dc1254a81..af0f1c1c3170 100644 --- a/sympy/solvers/solveset.py +++ b/sympy/solvers/solveset.py @@ -117,7 +117,6 @@ def invert_real(f_x, y, x, domain=S.Reals): """ return _invert(f_x, y, x, domain) - def _invert_real(f, g_ys, symbol): """Helper function for _invert.""" @@ -188,8 +187,13 @@ def _invert_real(f, g_ys, symbol): return _invert_real(base, res, symbol) if not base_has_sym: - return _invert_real(expo, - imageset(Lambda(n, log(n)/log(base)), g_ys), symbol) + if base is not S.Zero: + return _invert_real(expo, + imageset(Lambda(n, log(n)/log(base)), g_ys), symbol) + elif g_ys.args[0] is S.One: + #special case: 0**x - 1 + return (expo, FiniteSet(0)) + if isinstance(f, TrigonometricFunction): if isinstance(g_ys, FiniteSet): diff --git a/sympy/solvers/tests/test_solveset.py b/sympy/solvers/tests/test_solveset.py index bdbd25c216c4..3ec3adc7e71b 100644 --- a/sympy/solvers/tests/test_solveset.py +++ b/sympy/solvers/tests/test_solveset.py @@ -177,6 +177,9 @@ def test_domain_check(): assert domain_check(x, x, oo) is False assert domain_check(0, x, oo) is False +def test_issue_11536(): + assert solveset(0**x - 100, x, S.Reals) == ConditionSet(x, Eq(0**x - 100, 0), S.Reals) + assert solveset(0**x - 1, x, S.Reals) == FiniteSet(0) def test_is_function_class_equation(): from sympy.abc import x, a
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-12888@498ebe7
sympy/sympy
Python
12,888
Fix Idx bounds assumptions
This PR fixes #12780
2017-07-04T16:31:23Z
Idx should not require integer assumptions ```py >>> n = symbols('n') >>> Idx('i', (0, n)) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "./sympy/tensor/indexed.py", line 585, in __new__ raise TypeError("Idx object requires integer bounds.") TypeError: Idx object requires integer bounds. ``` Requiring assumptions is an antipattern. It should only fail if `is_integer` if False.
Hi @asmeurer, Going through the `Idx` docstring, it states that > Note: the ``Idx`` constructor is rather pedantic in that it only accepts integer arguments. The only exception is that you can use ``-oo`` and ``oo`` to specify an unbounded range. For all other cases, both label and bounds must be declared as integers, e.g. if ``n`` is given as an argument then ``n.is_integer`` must return ``True``. Therefore it appears that you must declare your symbols as integers, in your case: ```python n = symbol('n', integer=True) ``` in which case `Idx('i', (0, n))` will return i. Is this the solution that you were looking for? If not could you guide me through a possible solution, as I'm fairly new to sympy. Yes, this is the current behavior. But it should work with just `symbols('n')`. It should only check if `is_integer` if `False`, and not fail if it is `None`.
[ { "body": "```py\r\n>>> n = symbols('n')\r\n>>> Idx('i', (0, n))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"./sympy/tensor/indexed.py\", line 585, in __new__\r\n raise TypeError(\"Idx object requires integer bounds.\")\r\nTypeError: Idx object requires integer bounds.\r\n```\r\n\r\nRequiring assumptions is an antipattern. It should only fail if `is_integer` if False. ", "number": 12780, "title": "Idx should not require integer assumptions" } ]
c87c0fbf1223c28db8590f2591f64db75d5bdd66
{ "head_commit": "498ebe772fa1de889e51376dbcd5262d6b449544", "head_commit_message": "Update docs, add test for issue 12780", "patch_to_review": "diff --git a/sympy/tensor/indexed.py b/sympy/tensor/indexed.py\nindex 7e40f1554c7e..debcffa38029 100644\n--- a/sympy/tensor/indexed.py\n+++ b/sympy/tensor/indexed.py\n@@ -510,11 +510,9 @@ class Idx(Expr):\n * ``tuple``: The two elements are interpreted as the lower and upper\n bounds of the range, respectively.\n \n- Note: the ``Idx`` constructor is rather pedantic in that it only accepts\n- integer arguments. The only exception is that you can use ``-oo`` and\n- ``oo`` to specify an unbounded range. For all other cases, both label and\n- bounds must be declared as integers, e.g. if ``n`` is given as an argument\n- then ``n.is_integer`` must return ``True``.\n+ Note: bounds of the range are assumed to be either integer or infinite (oo\n+ and -oo are allowed to specify an unbounded range). If ``n`` is given as a\n+ bound, then ``n.is_integer`` must not return false.\n \n For convenience, if the label is given as a string it is automatically\n converted to an integer symbol. (Note: this conversion is not done for\n@@ -581,7 +579,7 @@ def __new__(cls, label, range=None, **kw_args):\n raise ValueError(filldedent(\"\"\"\n Idx range tuple must have length 2, but got %s\"\"\" % len(range)))\n for bound in range:\n- if not (bound.is_integer or abs(bound) is S.Infinity):\n+ if not (bound.is_integer != False or abs(bound) is S.Infinity):\n raise TypeError(\"Idx object requires integer bounds.\")\n args = label, Tuple(*range)\n elif isinstance(range, Expr):\ndiff --git a/sympy/tensor/tests/test_indexed.py b/sympy/tensor/tests/test_indexed.py\nindex 78716a3c64b8..09f2b2fe19cc 100644\n--- a/sympy/tensor/tests/test_indexed.py\n+++ b/sympy/tensor/tests/test_indexed.py\n@@ -14,7 +14,7 @@ def test_Idx_construction():\n assert Idx(i, a) == Idx(i, (0, a - 1))\n assert Idx(i, oo) == Idx(i, (0, oo))\n \n- x = symbols('x')\n+ x = symbols('x', integer=False)\n raises(TypeError, lambda: Idx(x))\n raises(TypeError, lambda: Idx(0.5))\n raises(TypeError, lambda: Idx(i, x))\n@@ -362,3 +362,9 @@ def test_issue_12533():\n assert d[0].subs(d, range(5)) == 0\n assert d[1].subs(d, range(5)) == 1\n assert Indexed(Range(5), 2) == 2\n+\n+\n+def test_issue_12780():\n+ n = symbols(\"n\")\n+ i = Idx(\"i\", (0, n))\n+ raises(TypeError, lambda: i.subs(n, 1.5))\n" }
[ { "diff_hunk": "@@ -581,7 +579,7 @@ def __new__(cls, label, range=None, **kw_args):\n raise ValueError(filldedent(\"\"\"\n Idx range tuple must have length 2, but got %s\"\"\" % len(range)))\n for bound in range:\n- if not (bound.is_integer or abs(bound) is S.Infinity):\n+ if not (bound.is_integer != False or abs(bound) is S.Infinity):", "line": null, "original_line": 582, "original_start_line": null, "path": "sympy/tensor/indexed.py", "start_line": null, "text": "@user1:\nSince `oo.is_integer == None` it's enough to write: `if bound.is_integer is False:`" } ]
25bbdbd912c010f0ae550f4e222bcf94f85f6a31
diff --git a/sympy/tensor/indexed.py b/sympy/tensor/indexed.py index 7e40f1554c7e..123a47843510 100644 --- a/sympy/tensor/indexed.py +++ b/sympy/tensor/indexed.py @@ -510,11 +510,9 @@ class Idx(Expr): * ``tuple``: The two elements are interpreted as the lower and upper bounds of the range, respectively. - Note: the ``Idx`` constructor is rather pedantic in that it only accepts - integer arguments. The only exception is that you can use ``-oo`` and - ``oo`` to specify an unbounded range. For all other cases, both label and - bounds must be declared as integers, e.g. if ``n`` is given as an argument - then ``n.is_integer`` must return ``True``. + Note: bounds of the range are assumed to be either integer or infinite (oo + and -oo are allowed to specify an unbounded range). If ``n`` is given as a + bound, then ``n.is_integer`` must not return false. For convenience, if the label is given as a string it is automatically converted to an integer symbol. (Note: this conversion is not done for @@ -581,7 +579,7 @@ def __new__(cls, label, range=None, **kw_args): raise ValueError(filldedent(""" Idx range tuple must have length 2, but got %s""" % len(range))) for bound in range: - if not (bound.is_integer or abs(bound) is S.Infinity): + if bound.is_integer is False: raise TypeError("Idx object requires integer bounds.") args = label, Tuple(*range) elif isinstance(range, Expr): diff --git a/sympy/tensor/tests/test_indexed.py b/sympy/tensor/tests/test_indexed.py index 78716a3c64b8..09f2b2fe19cc 100644 --- a/sympy/tensor/tests/test_indexed.py +++ b/sympy/tensor/tests/test_indexed.py @@ -14,7 +14,7 @@ def test_Idx_construction(): assert Idx(i, a) == Idx(i, (0, a - 1)) assert Idx(i, oo) == Idx(i, (0, oo)) - x = symbols('x') + x = symbols('x', integer=False) raises(TypeError, lambda: Idx(x)) raises(TypeError, lambda: Idx(0.5)) raises(TypeError, lambda: Idx(i, x)) @@ -362,3 +362,9 @@ def test_issue_12533(): assert d[0].subs(d, range(5)) == 0 assert d[1].subs(d, range(5)) == 1 assert Indexed(Range(5), 2) == 2 + + +def test_issue_12780(): + n = symbols("n") + i = Idx("i", (0, n)) + raises(TypeError, lambda: i.subs(n, 1.5))
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
sympy__sympy-12490@a08a034
sympy/sympy
Python
12,490
ccode: Add capability to _print_Indexed() to use user-defined strided schemes and offsets
<!-- Please give this pull request a descriptive title. Pull requests with descriptive titles are more likely to receive reviews. Describe what you changed! A title that only references an issue number is not descriptive. --> As mentioned in #12464 , `ccode` unrolled 2D `Indexed` objects in row-major format. This PR adds support for unrolling in column-major format as well. Example : ``` >>> from sympy import * >>> A = IndexedBase('A', shape=(5,3)) >>> i, j = Idx('i'), Idx('j') >>> ccode(A[i,j]) 'A[3*i + j]' >>> A = IndexedBase('A', shape=(5,3), ordering=1) >>> ccode(A[i,j]) 'A[i + 5*j]' ``` UPDATE : Now support for strided schemes and offset have been added <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below. --> Fixes #12464
2017-04-04T11:19:33Z
CCodePrinter._print_Indexed assumes row-major ordering When working with BLAS and LAPACK it's quite common to work with column-major ordering. To avoid unneccessary transposes `_print_Indexed` should also support column-major ordering. It is probably easiest to add either an `ordering` or `strides` property to `Indexed`.
Can you show me a simple example of an `Indexed` object which would use this method when translated to C code? I have gone through [this page](http://docs.sympy.org/dev/modules/tensor/indexed.html) but any `Indexed` object I'm trying out seems to lack some or the other attribute required in `_print_Indexed`. Sure, ``` >>> A = IndexedBase('A') >>> A = IndexedBase('A', shape=(5,3)) >>> i, j = Idx('i'), Idx('j') >>> ccode(A[i,j]) 'A[3*i + j]' ``` @bjodah I think `CCodePrinter` assumes row major ordering because accessing matrices through rows is faster than accessing them through columns. @SatyaPrakashDwibedi no, which is faster depends on ordering. Generally speaking, iterating over the dimension with the lowest stride gives fastest access due to cache locality.
[ { "body": "When working with BLAS and LAPACK it's quite common to work with column-major ordering.\r\nTo avoid unneccessary transposes `_print_Indexed` should also support column-major ordering.\r\nIt is probably easiest to add either an `ordering` or `strides` property to `Indexed`.", "number": 12464, "title": "CCodePrinter._print_Indexed assumes row-major ordering" } ]
5f7df4e47c7b36f3795456afdd2022c1463ca2a2
{ "head_commit": "a08a034a47ad797059e46930feeb7863ddac71be", "head_commit_message": "Rectify doctest", "patch_to_review": "diff --git a/sympy/printing/ccode.py b/sympy/printing/ccode.py\nindex ee21ea62a954..ecf2905fabfa 100644\n--- a/sympy/printing/ccode.py\n+++ b/sympy/printing/ccode.py\n@@ -170,13 +170,33 @@ def _print_Rational(self, expr):\n \n def _print_Indexed(self, expr):\n # calculate index for 1d array\n- dims = expr.shape\n- elem = S.Zero\n- offset = S.One\n- for i in reversed(range(expr.rank)):\n- elem += expr.indices[i]*offset\n- offset *= dims[i]\n- return \"%s[%s]\" % (self._print(expr.base.label), self._print(elem))\n+ offset = expr.offset\n+ strides = expr.strides\n+\n+ if strides is None or isinstance(strides, str):\n+ dims = expr.shape\n+ elem = S.Zero\n+ shift = S.One\n+ if strides == 'C' or strides is None:\n+ traversal = reversed(range(expr.rank))\n+ elif strides == 'F':\n+ traversal = range(expr.rank)\n+\n+ for i in traversal:\n+ elem += expr.indices[i]*shift\n+ shift *= dims[i]\n+\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),\n+ self._print(elem) + str(offset))\n+ elif isinstance(strides, tuple):\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),\n+ str(sum([x[0]*x[1]\n+ for x in zip(expr.indices, strides)])) +\n+ \" + \" + str(offset))\n \n def _print_Idx(self, expr):\n return self._print(expr.label)\ndiff --git a/sympy/printing/tests/test_ccode.py b/sympy/printing/tests/test_ccode.py\nindex 86a46ade151b..2f5dd7d9d835 100644\n--- a/sympy/printing/tests/test_ccode.py\n+++ b/sympy/printing/tests/test_ccode.py\n@@ -249,7 +249,7 @@ def test_ccode_settings():\n def test_ccode_Indexed():\n from sympy.tensor import IndexedBase, Idx\n from sympy import symbols\n- n, m, o = symbols('n m o', integer=True)\n+ s, n, m, o = symbols('s n m o', integer=True)\n i, j, k = Idx('i', n), Idx('j', m), Idx('k', o)\n \n x = IndexedBase('x')[j]\n@@ -266,7 +266,16 @@ def test_ccode_Indexed():\n assert p._print_Indexed(B) == 'B[%s]' % (i*o*m+j*o+k)\n assert p._not_c == set()\n \n+ A = IndexedBase('A', shape=(5,3))[i, j]\n+ assert p._print_Indexed(A) == 'A[%s]' % (3*i + j)\n+ A = IndexedBase('A', shape=(5,3), strides='F')[i, j]\n+ assert p._print_Indexed(A) == 'A[%s]' % (i + 5*j)\n \n+ A = IndexedBase('A', shape=(29,29), strides=(1, s), offset=o)[i, j]\n+ assert p._print_Indexed(A) == 'A[s*j + i + o]'\n+\n+ A = IndexedBase('A', strides=(s, m, n), offset=o)[i, j, k]\n+ assert p._print_Indexed(A) == 'A[m*j + n*k + s*i + o]'\n \n def test_ccode_Indexed_without_looking_for_contraction():\n len_y = 5\ndiff --git a/sympy/tensor/indexed.py b/sympy/tensor/indexed.py\nindex c8b958ea566e..6eaebd28c9f1 100644\n--- a/sympy/tensor/indexed.py\n+++ b/sympy/tensor/indexed.py\n@@ -270,6 +270,14 @@ def shape(self):\n Shape cannot be inferred from Idx with\n undefined range: %s\"\"\" % self))\n \n+ @property\n+ def strides(self):\n+ return self.base._strides\n+\n+ @property\n+ def offset(self):\n+ return self.base._offset\n+\n @property\n def ranges(self):\n \"\"\"Returns a list of tuples with lower and upper range of each index.\n@@ -363,7 +371,7 @@ class IndexedBase(Expr, NotIterable):\n is_symbol = True\n is_Atom = True\n \n- def __new__(cls, label, shape=None, **kw_args):\n+ def __new__(cls, label, shape=None, strides=None, offset=None, **kw_args):\n if isinstance(label, string_types):\n label = Symbol(label)\n elif isinstance(label, Symbol):\n@@ -376,11 +384,18 @@ def __new__(cls, label, shape=None, **kw_args):\n elif shape is not None:\n shape = Tuple(shape)\n \n+ if not(isinstance(strides, tuple) or strides in ['C', 'F'] or\n+ strides is None):\n+ raise TypeError(filldedent(\"\"\"Stride scheme not understood.\n+ Should be tuple or string('C' or 'F' : denotes\n+ row-major or column-major respectively.\"\"\"))\n if shape is not None:\n obj = Expr.__new__(cls, label, shape, **kw_args)\n else:\n obj = Expr.__new__(cls, label, **kw_args)\n obj._shape = shape\n+ obj._strides = strides\n+ obj._offset = offset\n return obj\n \n def __getitem__(self, indices, **kw_args):\n@@ -421,6 +436,44 @@ def shape(self):\n \"\"\"\n return self._shape\n \n+ @property\n+ def strides(self):\n+ \"\"\"Returns the strided scheme for the ``IndexedBase`` object.\n+\n+ Normally this is a tuple denoting the number of\n+ steps to take in the respective dimension when traversing\n+ an array. For code generation purposes strides='C' and\n+ strides='F' can also be used.\n+\n+ strides='C' would mean that code printer would unroll\n+ in row-major order and 'F' means unroll in column major\n+ order.\n+\n+ \"\"\"\n+ return self._strides\n+\n+ @property\n+ def offset(self):\n+ \"\"\"Returns the offset for the ``IndexedBase`` object.\n+\n+ This is the value added to the resulting index when the\n+ 2D Indexed object is unrolled to a 1D form. Used in code\n+ generation.\n+\n+ Examples\n+ ==========\n+ >>> from sympy.printing import ccode\n+ >>> from sympy.tensor import IndexedBase, Idx\n+ >>> from sympy import symbols\n+ >>> l, m, n, o = symbols('l m n o', integer=True)\n+ >>> A = IndexedBase('A', strides=(l, m, n), offset=o)\n+ >>> i, j, k = Idx('i'), Idx('j'), Idx('k')\n+ >>> ccode(A[i, j, k])\n+ 'A[l*i + m*j + n*k + o]'\n+\n+ \"\"\"\n+ return self._offset\n+\n @property\n def label(self):\n \"\"\"Returns the label of the ``IndexedBase`` object.\n" }
[ { "diff_hunk": "@@ -170,13 +170,33 @@ def _print_Rational(self, expr):\n \n def _print_Indexed(self, expr):\n # calculate index for 1d array\n- dims = expr.shape\n- elem = S.Zero\n- offset = S.One\n- for i in reversed(range(expr.rank)):\n- elem += expr.indices[i]*offset\n- offset *= dims[i]\n- return \"%s[%s]\" % (self._print(expr.base.label), self._print(elem))\n+ offset = expr.offset\n+ strides = expr.strides\n+\n+ if strides is None or isinstance(strides, str):\n+ dims = expr.shape\n+ elem = S.Zero\n+ shift = S.One\n+ if strides == 'C' or strides is None:\n+ traversal = reversed(range(expr.rank))\n+ elif strides == 'F':\n+ traversal = range(expr.rank)\n+\n+ for i in traversal:\n+ elem += expr.indices[i]*shift\n+ shift *= dims[i]\n+\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),\n+ self._print(elem) + str(offset))\n+ elif isinstance(strides, tuple):\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),\n+ str(sum([x[0]*x[1]", "line": null, "original_line": 197, "original_start_line": null, "path": "sympy/printing/ccode.py", "start_line": null, "text": "@user1:\ninstead of `str` you can use `self._print` here too (it is most probably safe to use `str`, but better safe than sorry if the user calculates indices in some fancy way)" }, { "diff_hunk": "@@ -170,13 +170,33 @@ def _print_Rational(self, expr):\n \n def _print_Indexed(self, expr):\n # calculate index for 1d array\n- dims = expr.shape\n- elem = S.Zero\n- offset = S.One\n- for i in reversed(range(expr.rank)):\n- elem += expr.indices[i]*offset\n- offset *= dims[i]\n- return \"%s[%s]\" % (self._print(expr.base.label), self._print(elem))\n+ offset = expr.offset\n+ strides = expr.strides\n+\n+ if strides is None or isinstance(strides, str):\n+ dims = expr.shape\n+ elem = S.Zero\n+ shift = S.One\n+ if strides == 'C' or strides is None:\n+ traversal = reversed(range(expr.rank))\n+ elif strides == 'F':\n+ traversal = range(expr.rank)\n+\n+ for i in traversal:\n+ elem += expr.indices[i]*shift\n+ shift *= dims[i]\n+\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),\n+ self._print(elem) + str(offset))\n+ elif isinstance(strides, tuple):\n+ if offset is None:\n+ offset = \"\"", "line": null, "original_line": 195, "original_start_line": null, "path": "sympy/printing/ccode.py", "start_line": null, "text": "@user1:\nyou can let offset be 0 by default and add it to the sum, i.e. `sum(...) + offset`" }, { "diff_hunk": "@@ -170,13 +170,33 @@ def _print_Rational(self, expr):\n \n def _print_Indexed(self, expr):\n # calculate index for 1d array\n- dims = expr.shape\n- elem = S.Zero\n- offset = S.One\n- for i in reversed(range(expr.rank)):\n- elem += expr.indices[i]*offset\n- offset *= dims[i]\n- return \"%s[%s]\" % (self._print(expr.base.label), self._print(elem))\n+ offset = expr.offset\n+ strides = expr.strides\n+\n+ if strides is None or isinstance(strides, str):\n+ dims = expr.shape\n+ elem = S.Zero\n+ shift = S.One\n+ if strides == 'C' or strides is None:\n+ traversal = reversed(range(expr.rank))\n+ elif strides == 'F':\n+ traversal = range(expr.rank)\n+\n+ for i in traversal:\n+ elem += expr.indices[i]*shift\n+ shift *= dims[i]\n+\n+ if offset is None:\n+ offset = \"\"\n+ return \"%s[%s]\" % (self._print(expr.base.label),", "line": null, "original_line": 191, "original_start_line": null, "path": "sympy/printing/ccode.py", "start_line": null, "text": "@user1:\ninstead of having a separate return statement we can calculate the missing `strides` here and let the method use the same logic further down as when strides is given explicitly." } ]
e79cc4b47377d50b090eadd9949e92ea84200d2c
diff --git a/sympy/printing/ccode.py b/sympy/printing/ccode.py index ee21ea62a954..e3914f5ff9a6 100644 --- a/sympy/printing/ccode.py +++ b/sympy/printing/ccode.py @@ -170,13 +170,26 @@ def _print_Rational(self, expr): def _print_Indexed(self, expr): # calculate index for 1d array - dims = expr.shape - elem = S.Zero - offset = S.One - for i in reversed(range(expr.rank)): - elem += expr.indices[i]*offset - offset *= dims[i] - return "%s[%s]" % (self._print(expr.base.label), self._print(elem)) + offset = getattr(expr.base, 'offset', S.Zero) + strides = getattr(expr.base, 'strides', None) + indices = expr.indices + + if strides is None or isinstance(strides, str): + dims = expr.shape + shift = S.One + temp = tuple() + if strides == 'C' or strides is None: + traversal = reversed(range(expr.rank)) + indices = indices[::-1] + elif strides == 'F': + traversal = range(expr.rank) + + for i in traversal: + temp += (shift,) + shift *= dims[i] + strides = temp + flat_index = sum([x[0]*x[1] for x in zip(indices, strides)]) + offset + return "%s[%s]" % (self._print(expr.base.label), self._print(flat_index)) def _print_Idx(self, expr): return self._print(expr.label) diff --git a/sympy/printing/tests/test_ccode.py b/sympy/printing/tests/test_ccode.py index 86a46ade151b..1e3b7b86b598 100644 --- a/sympy/printing/tests/test_ccode.py +++ b/sympy/printing/tests/test_ccode.py @@ -249,7 +249,7 @@ def test_ccode_settings(): def test_ccode_Indexed(): from sympy.tensor import IndexedBase, Idx from sympy import symbols - n, m, o = symbols('n m o', integer=True) + s, n, m, o = symbols('s n m o', integer=True) i, j, k = Idx('i', n), Idx('j', m), Idx('k', o) x = IndexedBase('x')[j] @@ -266,6 +266,18 @@ def test_ccode_Indexed(): assert p._print_Indexed(B) == 'B[%s]' % (i*o*m+j*o+k) assert p._not_c == set() + A = IndexedBase('A', shape=(5,3))[i, j] + assert p._print_Indexed(A) == 'A[%s]' % (3*i + j) + + A = IndexedBase('A', shape=(5,3), strides='F')[i, j] + assert ccode(A) == 'A[%s]' % (i + 5*j) + + A = IndexedBase('A', shape=(29,29), strides=(1, s), offset=o)[i, j] + assert ccode(A) == 'A[o + s*j + i]' + + Abase = IndexedBase('A', strides=(s, m, n), offset=o) + assert ccode(Abase[i, j, k]) == 'A[m*j + n*k + o + s*i]' + assert ccode(Abase[2, 3, k]) == 'A[3*m + n*k + o + 2*s]' def test_ccode_Indexed_without_looking_for_contraction(): diff --git a/sympy/tensor/indexed.py b/sympy/tensor/indexed.py index c8b958ea566e..3e0c2dfbadd1 100644 --- a/sympy/tensor/indexed.py +++ b/sympy/tensor/indexed.py @@ -155,6 +155,7 @@ def __new__(cls, base, *args, **kw_args): args = list(map(sympify, args)) if isinstance(base, (NDimArray, collections.Iterable, Tuple, MatrixBase)) and all([i.is_number for i in args]): return base[args] + return Expr.__new__(cls, base, *args, **kw_args) @property @@ -376,11 +377,16 @@ def __new__(cls, label, shape=None, **kw_args): elif shape is not None: shape = Tuple(shape) + offset = kw_args.pop('offset', S.Zero) + strides = kw_args.pop('strides', None) + if shape is not None: obj = Expr.__new__(cls, label, shape, **kw_args) else: obj = Expr.__new__(cls, label, **kw_args) obj._shape = shape + obj._offset = offset + obj._strides = strides return obj def __getitem__(self, indices, **kw_args): @@ -421,6 +427,45 @@ def shape(self): """ return self._shape + @property + def strides(self): + """Returns the strided scheme for the ``IndexedBase`` object. + + Normally this is a tuple denoting the number of + steps to take in the respective dimension when traversing + an array. For code generation purposes strides='C' and + strides='F' can also be used. + + strides='C' would mean that code printer would unroll + in row-major order and 'F' means unroll in column major + order. + + """ + + return self._strides + + @property + def offset(self): + """Returns the offset for the ``IndexedBase`` object. + + This is the value added to the resulting index when the + 2D Indexed object is unrolled to a 1D form. Used in code + generation. + + Examples + ========== + >>> from sympy.printing import ccode + >>> from sympy.tensor import IndexedBase, Idx + >>> from sympy import symbols + >>> l, m, n, o = symbols('l m n o', integer=True) + >>> A = IndexedBase('A', strides=(l, m, n), offset=o) + >>> i, j, k = map(Idx, 'ijk') + >>> ccode(A[i, j, k]) + 'A[l*i + m*j + n*k + o]' + + """ + return self._offset + @property def label(self): """Returns the label of the ``IndexedBase`` object.
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
sympy__sympy-12286@d62a240
sympy/sympy
Python
12,286
recreatable Dummy, simplified
Fixes #11847. This is a simplification of #12144, only the last commit is new. This version has slightly weaker guarantees but is much simpler and in particular doesn't keep an extra dict around to track the ordering of Dummys. Summary of issues to consider [added by cps from below, updated again by cbm] ---- There are two types of collisions we might worry about: Type 1: we automatically generate a Dummy that collides with a user-specified-dummy-index Dummy. Type 2: user-specified-dummy-index unintentionally collides with an automatically generated one. We need to be able to execute `eval(srepr(x))`, even if a new session is started between the `srepr` and the `eval` (which would mean that the `Dummy._count` and other internals may have changed). It may be desirable for `eval(srepr(x))` to equal `x`, but perhaps not essential. (The most important issue to fix here is #11847: if `f` is an expression with one Dummy appearing twice, then `eval(srepr(f))` should also have one Dummy appearing twice, not two Dummys'.)
2017-03-10T23:10:16Z
Dummy fails when is re-evaluated in S and srepr Hi all! Well, i found this little problem, Dummy is a nice function but in the way is implemented exist this: ``` >>> alpha = Dummy("alpha") >>> i = Integral(1/sqrt(1 - sin(alpha)**2), (alpha, 0, pi/2)) >>> N(i) 0.e+2 >>> i = S(srepr(i)) >>> N(i) Integral(1/sqrt(-sin(_alpha)**2 + 1), (_alpha, 0, pi/2)) ``` Basically, if you works with Dummy, and you get the expression with srepr or similar, when you eval it every Dummy will be interpreted as a new Dummy, so it fails, in the example you can see we can't eval the second expression because the 2 Dummy expression are interpreted as differents, other ex: ``` >>> m=Dummy("m") >>> a=Matrix([m, m]) >>> a[0]==a[1] True >>> b=S(srepr(a)) >>> b[0]==b[1] False ``` So thinking a solution can be use a hash or similar function, like: ``` >>> m=Dummy("m") >>> srepr(m) "Dummy('m', hash=987654321)" >>> m=Dummy("m") >>> srepr(m) "Dummy('m', hash=754619474)" ``` Always will exist the hash collision, but at least to cover all possible can be sympy check the existents Dummy expressions and check the hash never by equal to other, the probability of the hash collision, to be equal, in the same context, with the same name, and in the same expression, i think is very low. Maybe instead use a hash can be a random expression, with chars like ```8waerfn23jb89a```, that can help to avoid hash collision. Thx. Cya.
I can confirm this. Quoting `printing/repr.py` > srepr returns a string so that the relation eval(srepr(expr))=expr holds in an appropriate environment. Here's my minimal example: ``` >>> d = Dummy('d') >>> A = Add(d, d, evaluate=False) >>> srepr(A) # can see what the problem will be "Add(Dummy('d'), Dummy('d'))" >>> B = S(srepr(A)) >>> B _d + _d >>> A.doit() 2*_d >>> B.doit() # cannot, has two different Dummys _d + _d ``` Note that Dummy does seem to maintain its identity across pickling: ``` >>> import cPickle >>> d1 = Dummy() >>> s1 = cPickle.dumps(d1) >>> d2 = cPickle.loads(s1) >>> d1 == d2 True >>> d1 is d2 False ``` You have to set it to have the same dummy_index attribute. Right now there isn't a way to do that, but a (private) keyword argument could be added. Not sure I agree that srepr should maintain this, though. mm, not support this i think is a vary bad idea, because this means all expressions with Dummy, used internally or externally will break all data export.......... (like the examples posted here) And not all expressions can not have a Dummy, is used a lot in subs, and similars, and other common expression are integrals without elementary functions... (ex, all elliptic functions), we will be unable to represent it in the right way............ In my perspective i can see 2 ways, one, error when we send a dummy to srepr, second fix dummy in srepr, i actually have a fix for this, but i don't know if you will like it.... Well the fix is easy. Allow `Dummy('x', _dummy_index=10)` to override the `dummy_index` that is set from the class variable, and make srepr print that. But the whole point of dummy is that they shouldn't be equal to other instances of the same name. So the question is, should objects created with srepr be considered new or not? Also, if you do recreate an object in a new session, bets are off, because the same dummy_index could represent a completely different Dummy created somewhere else. This would only apply if you serialize and deserialize an expression in the same session. But I think srepr's primary use-case is for separate sessions (or any serialization really. Why would you serialize something if you're in the same session? Just reuse it). @asmeurer I think the point is that a single object could contain the same Dummy more than once (think of a variable of integration or my silly `evaluate=False` example). Pre-serialization this is fine. But upon `S(srepr(x))`, they become two distinct objects. Bad news for the integral, whose meaning has just completely changed. (There is a related concern about two distinct objects depending on the same Dummy, and serialized separately. I agree that's a much greyer area). Ah, I see. So in that case, I do think this is a good idea, but caveated that dummy objects are only guaranteed to be the same in a single srepr'd expression, not across expressions. Hi, can you give me an example of across expressions plis? @latot, see my example above but consider: ``` A2 = 3*A ``` Now `A2` and `A` are two expressions which contain the same Dummy (the Dummy is shared "across expressions". Now do `B = S(srepr(A))` and `B2 = S(srepr(A2))`: we want each of those to "DTRT". But perhaps we do not guarantee that both `B` and `B2` contain the same Dummy (the Dummy might not be shared across B and B2). mm, i think both dummy should be equals, only for one reason, if don't are equals will break the user env...... (ex, if is trying to export it)
[ { "body": "Hi all!\r\n\r\nWell, i found this little problem, Dummy is a nice function but in the way is implemented exist this:\r\n```\r\n>>> alpha = Dummy(\"alpha\")\r\n>>> i = Integral(1/sqrt(1 - sin(alpha)**2), (alpha, 0, pi/2))\r\n>>> N(i)\r\n0.e+2\r\n>>> i = S(srepr(i))\r\n>>> N(i)\r\nIntegral(1/sqrt(-sin(_alpha)**2 + 1), (_alpha, 0, pi/2))\r\n```\r\nBasically, if you works with Dummy, and you get the expression with srepr or similar, when you eval it every Dummy will be interpreted as a new Dummy, so it fails, in the example you can see we can't eval the second expression because the 2 Dummy expression are interpreted as differents, other ex:\r\n```\r\n>>> m=Dummy(\"m\")\r\n>>> a=Matrix([m, m])\r\n>>> a[0]==a[1]\r\nTrue\r\n>>> b=S(srepr(a))\r\n>>> b[0]==b[1]\r\nFalse\r\n```\r\n\r\nSo thinking a solution can be use a hash or similar function, like:\r\n```\r\n>>> m=Dummy(\"m\")\r\n>>> srepr(m)\r\n\"Dummy('m', hash=987654321)\"\r\n>>> m=Dummy(\"m\")\r\n>>> srepr(m)\r\n\"Dummy('m', hash=754619474)\"\r\n```\r\nAlways will exist the hash collision, but at least to cover all possible can be sympy check the existents Dummy expressions and check the hash never by equal to other, the probability of the hash collision, to be equal, in the same context, with the same name, and in the same expression, i think is very low.\r\n\r\nMaybe instead use a hash can be a random expression, with chars like ```8waerfn23jb89a```, that can help to avoid hash collision.\r\n\r\nThx. Cya. ", "number": 11847, "title": "Dummy fails when is re-evaluated in S and srepr" } ]
24ba5c32d9a6383e67cc159740152f60c48c2b2b
{ "head_commit": "d62a2407600412a63d96831ffb98c25623d23797", "head_commit_message": "Dummy: remove untrue comment from test\n\n[skip-ci]", "patch_to_review": "diff --git a/sympy/core/function.py b/sympy/core/function.py\nindex eec86a8540d5..c7688f5829bc 100644\n--- a/sympy/core/function.py\n+++ b/sympy/core/function.py\n@@ -707,8 +707,7 @@ def fdiff(self, argindex=1):\n else:\n return Derivative(self, self.args[argindex - 1], evaluate=False)\n # See issue 4624 and issue 4719 and issue 5600\n- arg_dummy = Dummy('xi_%i' % argindex)\n- arg_dummy.dummy_index = hash(self.args[argindex - 1])\n+ arg_dummy = Dummy('xi_%i' % argindex, dummy_index=hash(self.args[argindex - 1]))\n new_args = [arg for arg in self.args]\n new_args[argindex-1] = arg_dummy\n return Subs(Derivative(self.func(*new_args), arg_dummy),\n@@ -1178,8 +1177,7 @@ def __new__(cls, expr, *variables, **assumptions):\n obj = None\n else:\n if not is_symbol:\n- new_v = Dummy('xi_%i' % i)\n- new_v.dummy_index = hash(v)\n+ new_v = Dummy('xi_%i' % i, dummy_index=hash(v))\n expr = expr.xreplace({v: new_v})\n old_v = v\n v = new_v\ndiff --git a/sympy/core/symbol.py b/sympy/core/symbol.py\nindex 75e749f2e1fd..61b012b62980 100644\n--- a/sympy/core/symbol.py\n+++ b/sympy/core/symbol.py\n@@ -14,6 +14,7 @@\n \n import string\n import re as _re\n+import random\n \n \n class Symbol(AtomicExpr, Boolean):\n@@ -178,13 +179,13 @@ def free_symbols(self):\n \n \n class Dummy(Symbol):\n- \"\"\"Dummy symbols are each unique, identified by an internal count index:\n+ \"\"\"Dummy symbols are each unique, even if they have the same name:\n \n >>> from sympy import Dummy\n >>> bool(Dummy(\"x\") == Dummy(\"x\")) == True\n False\n \n- If a name is not supplied then a string value of the count index will be\n+ If a name is not supplied then a string value of an internal count will be\n used. This is useful when a temporary variable is needed and the name\n of the variable used in the expression is not important.\n \n@@ -193,21 +194,41 @@ class Dummy(Symbol):\n \n \"\"\"\n \n+ # In the rare event that a Dummy object needs to be recreated, both the\n+ # `name` and `dummy_index` should be passed. This is used by `srepr` for\n+ # example:\n+ # >>> d1 = Dummy()\n+ # >>> d2 = eval(srepr(d1))\n+ # >>> d2 == d1\n+ # True\n+ #\n+ # If a new session is started between `srepr` and `eval`, there is a very\n+ # small chance that `d2` will be equal to a previously-created Dummy.\n+\n _count = 0\n+ _prng = random.Random()\n+ _base_dummy_index = _prng.randint(10**6, 9*10**6)\n \n __slots__ = ['dummy_index']\n \n is_Dummy = True\n \n- def __new__(cls, name=None, **assumptions):\n+ def __new__(cls, name=None, dummy_index=None, **assumptions):\n+ if dummy_index is not None:\n+ assert name is not None, \"If you specify a dummy_index, you must also provide a name\"\n+\n if name is None:\n name = \"Dummy_\" + str(Dummy._count)\n \n+ if dummy_index is None:\n+ dummy_index = Dummy._base_dummy_index + Dummy._count\n+\n cls._sanitize(assumptions, cls)\n obj = Symbol.__xnew__(cls, name, **assumptions)\n \n+ obj.dummy_index = dummy_index\n+\n Dummy._count += 1\n- obj.dummy_index = Dummy._count\n return obj\n \n def __getstate__(self):\ndiff --git a/sympy/core/tests/test_symbol.py b/sympy/core/tests/test_symbol.py\nindex 69288934664e..c60123582027 100644\n--- a/sympy/core/tests/test_symbol.py\n+++ b/sympy/core/tests/test_symbol.py\n@@ -30,10 +30,17 @@ def test_Symbol():\n \n def test_Dummy():\n assert Dummy() != Dummy()\n- Dummy._count = 0\n- d1 = Dummy()\n- Dummy._count = 0\n- assert d1 == Dummy()\n+\n+\n+def test_Dummy_force_dummy_index():\n+ raises(AssertionError, lambda: Dummy(dummy_index=1))\n+ assert Dummy('d', dummy_index=2) == Dummy('d', dummy_index=2)\n+ assert Dummy('d1', dummy_index=2) != Dummy('d2', dummy_index=2)\n+ d1 = Dummy('d', dummy_index=3)\n+ d2 = Dummy('d')\n+ assert d1 != d2\n+ d3 = Dummy('d', dummy_index=3)\n+ assert d1 == d3\n \n \n def test_as_dummy():\ndiff --git a/sympy/printing/repr.py b/sympy/printing/repr.py\nindex a3488630e2e3..d09ec71f8471 100644\n--- a/sympy/printing/repr.py\n+++ b/sympy/printing/repr.py\n@@ -145,6 +145,10 @@ def _print_Sum2(self, expr):\n \n def _print_Symbol(self, expr):\n d = expr._assumptions.generator\n+ # print the dummy_index like it was an assumption\n+ if expr.is_Dummy:\n+ d['dummy_index'] = expr.dummy_index\n+\n if d == {}:\n return \"%s(%s)\" % (expr.__class__.__name__, self._print(expr.name))\n else:\ndiff --git a/sympy/printing/tests/test_repr.py b/sympy/printing/tests/test_repr.py\nindex e8fd9ff7569e..1f05385916cf 100644\n--- a/sympy/printing/tests/test_repr.py\n+++ b/sympy/printing/tests/test_repr.py\n@@ -137,16 +137,25 @@ def test_Wild():\n \n \n def test_Dummy():\n- # cannot use sT here\n+ d = Dummy('d')\n+ sT(d, \"Dummy('d', dummy_index=%s)\" % str(d.dummy_index))\n+\n+\n+def test_Dummy_assumption():\n d = Dummy('d', nonzero=True)\n- assert srepr(d) == \"Dummy('d', nonzero=True)\"\n+ assert d == eval(srepr(d))\n+ s1 = \"Dummy('d', dummy_index=%s, nonzero=True)\" % str(d.dummy_index)\n+ s2 = \"Dummy('d', nonzero=True, dummy_index=%s)\" % str(d.dummy_index)\n+ assert srepr(d) in (s1, s2)\n \n \n def test_Dummy_from_Symbol():\n # should not get the full dictionary of assumptions\n n = Symbol('n', integer=True)\n d = n.as_dummy()\n- assert srepr(d) == \"Dummy('n', integer=True)\"\n+ s1 = \"Dummy('n', dummy_index=%s, integer=True)\" % str(d.dummy_index)\n+ s2 = \"Dummy('n', integer=True, dummy_index=%s)\" % str(d.dummy_index)\n+ assert srepr(d) in (s1, s2)\n \n \n def test_tuple():\n" }
[ { "diff_hunk": "@@ -178,13 +179,13 @@ def free_symbols(self):\n \n \n class Dummy(Symbol):\n- \"\"\"Dummy symbols are each unique, identified by an internal count index:\n+ \"\"\"Dummy symbols are each unique, even if they have the same name:\n \n >>> from sympy import Dummy\n >>> bool(Dummy(\"x\") == Dummy(\"x\")) == True", "line": null, "original_line": null, "original_start_line": null, "path": "sympy/core/symbol.py", "start_line": null, "text": "@user1:\nI wonder if we could just simplify that as \r\n\r\n```\r\n>>> Dummy('x') == Dummy('x')\r\nFalse\r\n```\n\n@user1:\nI did so in the PR that I sent.\n\n@author:\nthanks, I agree and I'll take a look." } ]
6c79fd29972d5a5bbde311b014cf423de14d6e8e
diff --git a/sympy/core/function.py b/sympy/core/function.py index eec86a8540d5..c7688f5829bc 100644 --- a/sympy/core/function.py +++ b/sympy/core/function.py @@ -707,8 +707,7 @@ def fdiff(self, argindex=1): else: return Derivative(self, self.args[argindex - 1], evaluate=False) # See issue 4624 and issue 4719 and issue 5600 - arg_dummy = Dummy('xi_%i' % argindex) - arg_dummy.dummy_index = hash(self.args[argindex - 1]) + arg_dummy = Dummy('xi_%i' % argindex, dummy_index=hash(self.args[argindex - 1])) new_args = [arg for arg in self.args] new_args[argindex-1] = arg_dummy return Subs(Derivative(self.func(*new_args), arg_dummy), @@ -1178,8 +1177,7 @@ def __new__(cls, expr, *variables, **assumptions): obj = None else: if not is_symbol: - new_v = Dummy('xi_%i' % i) - new_v.dummy_index = hash(v) + new_v = Dummy('xi_%i' % i, dummy_index=hash(v)) expr = expr.xreplace({v: new_v}) old_v = v v = new_v diff --git a/sympy/core/symbol.py b/sympy/core/symbol.py index 75e749f2e1fd..6294f5a23c67 100644 --- a/sympy/core/symbol.py +++ b/sympy/core/symbol.py @@ -14,6 +14,7 @@ import string import re as _re +import random class Symbol(AtomicExpr, Boolean): @@ -178,13 +179,13 @@ def free_symbols(self): class Dummy(Symbol): - """Dummy symbols are each unique, identified by an internal count index: + """Dummy symbols are each unique, even if they have the same name: >>> from sympy import Dummy - >>> bool(Dummy("x") == Dummy("x")) == True + >>> Dummy("x") == Dummy("x") False - If a name is not supplied then a string value of the count index will be + If a name is not supplied then a string value of an internal count will be used. This is useful when a temporary variable is needed and the name of the variable used in the expression is not important. @@ -193,21 +194,41 @@ class Dummy(Symbol): """ + # In the rare event that a Dummy object needs to be recreated, both the + # `name` and `dummy_index` should be passed. This is used by `srepr` for + # example: + # >>> d1 = Dummy() + # >>> d2 = eval(srepr(d1)) + # >>> d2 == d1 + # True + # + # If a new session is started between `srepr` and `eval`, there is a very + # small chance that `d2` will be equal to a previously-created Dummy. + _count = 0 + _prng = random.Random() + _base_dummy_index = _prng.randint(10**6, 9*10**6) __slots__ = ['dummy_index'] is_Dummy = True - def __new__(cls, name=None, **assumptions): + def __new__(cls, name=None, dummy_index=None, **assumptions): + if dummy_index is not None: + assert name is not None, "If you specify a dummy_index, you must also provide a name" + if name is None: name = "Dummy_" + str(Dummy._count) + if dummy_index is None: + dummy_index = Dummy._base_dummy_index + Dummy._count + Dummy._count += 1 + cls._sanitize(assumptions, cls) obj = Symbol.__xnew__(cls, name, **assumptions) - Dummy._count += 1 - obj.dummy_index = Dummy._count + obj.dummy_index = dummy_index + return obj def __getstate__(self): diff --git a/sympy/core/tests/test_symbol.py b/sympy/core/tests/test_symbol.py index 69288934664e..164f6ef362b9 100644 --- a/sympy/core/tests/test_symbol.py +++ b/sympy/core/tests/test_symbol.py @@ -30,10 +30,19 @@ def test_Symbol(): def test_Dummy(): assert Dummy() != Dummy() - Dummy._count = 0 - d1 = Dummy() - Dummy._count = 0 - assert d1 == Dummy() + + +def test_Dummy_force_dummy_index(): + raises(AssertionError, lambda: Dummy(dummy_index=1)) + assert Dummy('d', dummy_index=2) == Dummy('d', dummy_index=2) + assert Dummy('d1', dummy_index=2) != Dummy('d2', dummy_index=2) + d1 = Dummy('d', dummy_index=3) + d2 = Dummy('d') + # might fail if d1 were created with dummy_index >= 10**6 + assert d1 != d2 + d3 = Dummy('d', dummy_index=3) + assert d1 == d3 + assert Dummy()._count == Dummy('d', dummy_index=3)._count def test_as_dummy(): diff --git a/sympy/printing/repr.py b/sympy/printing/repr.py index a3488630e2e3..d09ec71f8471 100644 --- a/sympy/printing/repr.py +++ b/sympy/printing/repr.py @@ -145,6 +145,10 @@ def _print_Sum2(self, expr): def _print_Symbol(self, expr): d = expr._assumptions.generator + # print the dummy_index like it was an assumption + if expr.is_Dummy: + d['dummy_index'] = expr.dummy_index + if d == {}: return "%s(%s)" % (expr.__class__.__name__, self._print(expr.name)) else: diff --git a/sympy/printing/tests/test_repr.py b/sympy/printing/tests/test_repr.py index e8fd9ff7569e..1f05385916cf 100644 --- a/sympy/printing/tests/test_repr.py +++ b/sympy/printing/tests/test_repr.py @@ -137,16 +137,25 @@ def test_Wild(): def test_Dummy(): - # cannot use sT here + d = Dummy('d') + sT(d, "Dummy('d', dummy_index=%s)" % str(d.dummy_index)) + + +def test_Dummy_assumption(): d = Dummy('d', nonzero=True) - assert srepr(d) == "Dummy('d', nonzero=True)" + assert d == eval(srepr(d)) + s1 = "Dummy('d', dummy_index=%s, nonzero=True)" % str(d.dummy_index) + s2 = "Dummy('d', nonzero=True, dummy_index=%s)" % str(d.dummy_index) + assert srepr(d) in (s1, s2) def test_Dummy_from_Symbol(): # should not get the full dictionary of assumptions n = Symbol('n', integer=True) d = n.as_dummy() - assert srepr(d) == "Dummy('n', integer=True)" + s1 = "Dummy('n', dummy_index=%s, integer=True)" % str(d.dummy_index) + s2 = "Dummy('n', integer=True, dummy_index=%s)" % str(d.dummy_index) + assert srepr(d) in (s1, s2) def test_tuple():
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Code Refactoring / Architectural Improvement" }
sympy__sympy-12147@afc48d4
sympy/sympy
Python
12,147
check_assumptions: Improvement in the return value of the function
closes: #12146
2017-02-10T11:55:26Z
Improvement in check_assumptions in solvers check_assumption just returns True or False. So rather than just returning False if the assumption about the expression that is incorrect is also returned the user would know which of all the set of assumptions is False. This would probably help him determining the expression correctly.
[ { "body": "check_assumption just returns True or False. So rather than just returning False if the assumption about the expression that is incorrect is also returned the user would know which of all the set of assumptions is False. This would probably help him determining the expression correctly.\r\n", "number": 12146, "title": "Improvement in check_assumptions in solvers" } ]
69d64a71b9678aff5e8e94f38f097eaefd0f4a32
{ "head_commit": "afc48d4478fd1176d8d08be59865d4715bd86114", "head_commit_message": "added `failing_assumptions()` as a helper for `check_assumptions()` and tests", "patch_to_review": "diff --git a/sympy/solvers/solvers.py b/sympy/solvers/solvers.py\nindex 5723f14c63b0..6000854756bf 100644\n--- a/sympy/solvers/solvers.py\n+++ b/sympy/solvers/solvers.py\n@@ -361,6 +361,50 @@ def checksol(f, symbol, sol=None, **flags):\n # TODO: improve solution testing\n \n \n+def failing_assumptions(expr, against=None, **assumptions):\n+ \"\"\"\n+ Helper for ``check_assumptions``.\n+\n+ Returns empty list `[]` if all assumptions are True, or a list\n+ of assumption(s) that the expression does not satisfy or\n+ `None` if it can't conclude.\n+\n+ Examples\n+ ========\n+\n+ >>> from sympy import Symbol\n+ >>> from sympy.solvers.solvers import failing_assumptions\n+ >>> failing_assumptions(-5, integer=False, positive=True)\n+ ['integer', 'positive']\n+\n+ >>> x = Symbol('x', positive=True)\n+ >>> y = Symbol('y')\n+ >>> failing_assumptions(6*x, positive=True)\n+ []\n+\n+ >>> failing_assumptions(y, real=True)\n+ \"\"\"\n+ if against is not None:\n+ assumptions = against.assumptions0\n+\n+ expr = sympify(expr)\n+\n+ flag = True\n+ fail_list = []\n+ for key, expected in ordered(assumptions.items()):\n+ if expected is None:\n+ continue\n+ test = getattr(expr, 'is_' + key, None)\n+ if test is expected:\n+ continue\n+ elif test is not None:\n+ fail_list.append(key)\n+ flag = True\n+ continue\n+ flag = False\n+ return fail_list if flag else None\n+\n+\n def check_assumptions(expr, against=None, **assumptions):\n \"\"\"Checks whether expression `expr` satisfies all assumptions.\n \n@@ -398,22 +442,8 @@ def check_assumptions(expr, against=None, **assumptions):\n >>> z = Symbol('z')\n >>> check_assumptions(z, real=True)\n \"\"\"\n- if against is not None:\n- assumptions = against.assumptions0\n-\n- expr = sympify(expr)\n-\n- result = True\n- for key, expected in assumptions.items():\n- if expected is None:\n- continue\n- test = getattr(expr, 'is_' + key, None)\n- if test is expected:\n- continue\n- elif test is not None:\n- return False\n- result = None # Can't conclude, unless an other test fails.\n- return result\n+ ans = failing_assumptions(expr, against, **assumptions)\n+ return None if ans is None else not ans\n \n \n def solve(f, *symbols, **flags):\ndiff --git a/sympy/solvers/tests/test_solvers.py b/sympy/solvers/tests/test_solvers.py\nindex 44c574a96b60..880e35031559 100644\n--- a/sympy/solvers/tests/test_solvers.py\n+++ b/sympy/solvers/tests/test_solvers.py\n@@ -12,7 +12,8 @@\n from sympy.solvers import solve_linear_system, solve_linear_system_LU, \\\n solve_undetermined_coeffs\n from sympy.solvers.solvers import _invert, unrad, checksol, posify, _ispow, \\\n- det_quick, det_perm, det_minor, _simple_dens, check_assumptions, denoms\n+ det_quick, det_perm, det_minor, _simple_dens, check_assumptions, denoms, \\\n+ failing_assumptions\n \n from sympy.physics.units import cm\n from sympy.polys.rootoftools import CRootOf\n@@ -1301,6 +1302,14 @@ def test_check_assumptions():\n assert solve(x**2 - 1) == [1]\n assert check_assumptions(1, x) == True\n \n+def test_failing_assumptions():\n+ x = Symbol('x', real=True, positive=True)\n+ y = Symbol('y')\n+ assert failing_assumptions(6*x + y, x) == None\n+ assert failing_assumptions(x**2 - 1, positive=True) == None\n+ assert failing_assumptions(-2*x - 6, real=True, positive=True) == ['positive']\n+ assert failing_assumptions(x**2, positive=True) == []\n+\n \n def test_issue_6056():\n assert solve(tanh(x + 3)*tanh(x - 3) - 1) == []\n" }
[ { "diff_hunk": "@@ -361,7 +361,51 @@ def checksol(f, symbol, sol=None, **flags):\n # TODO: improve solution testing\n \n \n-def check_assumptions(expr, against=None, failing_assumption=False, **assumptions):\n+def failing_assumptions(expr, against=None, **assumptions):\n+ \"\"\"\n+ Helper for ``check_assumptions``.\n+\n+ Returns empty list `[]` if all assumptions are True, or a list\n+ of assumption(s) that the expression does not satisfy or\n+ `None` if it can't conclude.\n+\n+ Examples\n+ ========\n+\n+ >>> from sympy import Symbol\n+ >>> from sympy.solvers.solvers import failing_assumptions\n+ >>> failing_assumptions(-5, integer=False, positive=True)\n+ ['integer', 'positive']\n+\n+ >>> x = Symbol('x', positive=True)\n+ >>> y = Symbol('y')\n+ >>> failing_assumptions(6*x, positive=True)\n+ []\n+\n+ >>> failing_assumptions(y, real=True)\n+ \"\"\"\n+ if against is not None:\n+ assumptions = against.assumptions0\n+\n+ expr = sympify(expr)\n+\n+ flag = True", "line": null, "original_line": 392, "original_start_line": null, "path": "sympy/solvers/solvers.py", "start_line": null, "text": "@user1:\nWhat do you think about this (no frills version)?\r\n\r\n```\r\ndef failing_assumptions(expr, **assumptions):\r\n failed = {}\r\n for key in list(assumptions.keys()):\r\n test = getattr(expr, 'is_%s' % key, None)\r\n if test is not assumptions[key]:\r\n failed[key] = test\r\n return failed # {} or {assumption: value != desired}\r\n\r\ndef check_assumptions(expr, against=None, **assumptions):\r\n if against:\r\n assert not assumptions\r\n assert against.is_Symbol # ?\r\n assumptions = against.assumptions0\r\n a = assumptions\r\n return all(getattr(expr, 'is_%s' % k, None) is a[k] for k in a)\r\n\r\n>>> x = Symbol('x', real=True, positive=True)\r\n>>> y = Symbol('y')\r\n>>> failing_assumptions(6*x + y, real=True, positive=True)\r\n{'real': None, 'positive': None}\r\n>>> failing_assumptions(6*x + y, **x.assumptions0) == {\r\n 'real': None,\r\n 'imaginary': None,\r\n 'complex': None,\r\n 'hermitian': None,\r\n 'positive': None, \r\n 'nonpositive': None, \r\n 'nonnegative': None, \r\n 'nonzero': None, \r\n 'negative': None, \r\n 'zero': None}\r\n>>> failing_assumptions(x**2 - 1, positive=True)\r\n{'positive': None}\r\n>>> failing_assumptions(-2*x - 6, real=True, positive=True)\r\n{'positive': False}\r\n>>> failing_assumptions(x**2, positive=True)\r\n{}\r\n```\r\n\r\nMy thought is that if you are going to go through the trouble of returning what is wrong, you might as well tell how it is wrong. Imagine this being used interactively as a tutor and the task is to create an expression that is unknown to be positive. So you call `failing_assumptions(given, positive=None)`. Let's say they give a result that is positive. The session would then report that \"no, your expression is positive\" without having to recompute the value. Of course in the version you presented, only True and False values are returned so the key along with the passed values are sufficient for reconstruction. But it doesn't seem unreasonable to pass in an assumption that is None (as in the example given).\n\n@author:\nTo see if we both are on the same page, what I understood is you want `failing_assumptions` to return correct value of the key along with the key instead of a key only. Is it correct?\r\n\r\nIn examples above I see if an `expr` returns `None`, all assumptions are made `None`. I am not sure but I don't think this should be done because `None` comes out when atleast one (or maybe all)assumption cannot be concluded and rest are `True`. like:\r\n`(6*x + y, real=True, positive=True)` in this real=True is correct but positive=True cannot be determined therfore the result is `None`. I think the result should be `{'real': True, 'positive': None}`\r\n\r\n> without having to recompute the value\r\n\r\nI am not clear on this.\n\n@author:\n> What do you think about this (no frills version)?\r\n\r\nI think it seems to be good, but the only question that comes to my mind is how effective this would be means will it be of any positive use(like to carry out any further computation), or will it just be a functionality that we have.\n\n@user1:\n> (6*x + y, real=True, positive=True) in this real=True is correct\r\n\r\nMaybe you meant to make y real b/c as a vanilla y, the expression is not known to be real:\r\n\r\n```\r\n>>> x = Symbol('x', real=True, positive=True)\r\n>>> y = Symbol('y')\r\n>>> eq=6*x + y\r\n>>> eq.is_real\r\n>>> eq.is_positive\r\n>>>\r\n```\r\n\r\n> if an expr returns None, all assumptions are made None\r\n\r\nI'm not sure what you mean here. In this case, for all assumptions that y had the expression happened to have values of None, but this would not always be the case. For example, if we make y be an integer, it will have 11 assumptions. The expression `6*x + y` will have 6 of them that are None, not all of them:\r\n\r\n```\r\n>>> x = Symbol('x', real=True, positive=True)\r\n>>> y = Symbol('y', integer=True)\r\n>>> failing_assumptions(6*x + y, **y.assumptions0)\r\n>>> failing_assumptions(6*x+y, **y.assumptions0)\r\n{'integer': None, 'rational': None, 'noninteger': None, 'algebraic': None, 'irrational': None, 'transcendental': None}\r\n>>> len(_), len(y.assumptions0)\r\n(6, 11)\r\n```\r\n\r\n> will it be of any positive use\r\n\r\nWhat is the use case that you are thinking of for the function -- it isn't explicitly state in the issue. (I gave an example of how one might use the information, e.g. with an interactive tutor program in which it would be useful to know the incorrect assumption and value so it can be reported to the user. Still...this seems of limited use. That's why I'm interested in seeing how you imagine such functionality to be used.)" } ]
b5c4925421cfa56646c6b01017a00945c280b277
diff --git a/sympy/solvers/__init__.py b/sympy/solvers/__init__.py index c7ae36fe188e..a498d56e9699 100644 --- a/sympy/solvers/__init__.py +++ b/sympy/solvers/__init__.py @@ -10,7 +10,7 @@ """ from .solvers import solve, solve_linear_system, solve_linear_system_LU, \ solve_undetermined_coeffs, nsolve, solve_linear, checksol, \ - det_quick, inv_quick, check_assumptions + det_quick, inv_quick, check_assumptions, failing_assumptions from .diophantine import diophantine diff --git a/sympy/solvers/solvers.py b/sympy/solvers/solvers.py index 5723f14c63b0..728fd35d6132 100644 --- a/sympy/solvers/solvers.py +++ b/sympy/solvers/solvers.py @@ -26,7 +26,7 @@ from sympy.integrals.integrals import Integral from sympy.core.numbers import ilcm, Float from sympy.core.relational import Relational, Ge, _canonical -from sympy.core.logic import fuzzy_not +from sympy.core.logic import fuzzy_not, fuzzy_and from sympy.logic.boolalg import And, Or, BooleanAtom from sympy.core.basic import preorder_traversal @@ -361,6 +361,37 @@ def checksol(f, symbol, sol=None, **flags): # TODO: improve solution testing +def failing_assumptions(expr, **assumptions): + """Return a dictionary containing assumptions with values not + matching those of the passed assumptions. + + Examples + ======== + + >>> from sympy import failing_assumptions, Symbol + + >>> x = Symbol('x', real=True, positive=True) + >>> y = Symbol('y') + >>> failing_assumptions(6*x + y, real=True, positive=True) + {'positive': None, 'real': None} + + >>> failing_assumptions(x**2 - 1, positive=True) + {'positive': None} + + If all assumptions satisfy the `expr` an empty dictionary is returned. + + >>> failing_assumptions(x**2, positive=True) + {} + """ + expr = sympify(expr) + failed = {} + for key in list(assumptions.keys()): + test = getattr(expr, 'is_%s' % key, None) + if test is not assumptions[key]: + failed[key] = test + return failed # {} or {assumption: value != desired} + + def check_assumptions(expr, against=None, **assumptions): """Checks whether expression `expr` satisfies all assumptions. @@ -397,23 +428,23 @@ def check_assumptions(expr, against=None, **assumptions): >>> check_assumptions(2*x - 1, real=True, positive=True) >>> z = Symbol('z') >>> check_assumptions(z, real=True) - """ - if against is not None: - assumptions = against.assumptions0 + See Also + ======== + failing_assumptions + """ expr = sympify(expr) - - result = True - for key, expected in assumptions.items(): - if expected is None: - continue - test = getattr(expr, 'is_' + key, None) - if test is expected: - continue - elif test is not None: - return False - result = None # Can't conclude, unless an other test fails. - return result + if against: + if not isinstance(against, Symbol): + raise TypeError('against should be of type Symbol') + if assumptions: + raise AssertionError('No assumptions should be specified') + assumptions = against.assumptions0 + def _test(key): + v = getattr(expr, 'is_' + key, None) + if v is not None: + return assumptions[key] is v + return fuzzy_and(_test(key) for key in assumptions) def solve(f, *symbols, **flags): diff --git a/sympy/solvers/tests/test_solvers.py b/sympy/solvers/tests/test_solvers.py index 44c574a96b60..3d24642edf88 100644 --- a/sympy/solvers/tests/test_solvers.py +++ b/sympy/solvers/tests/test_solvers.py @@ -12,7 +12,8 @@ from sympy.solvers import solve_linear_system, solve_linear_system_LU, \ solve_undetermined_coeffs from sympy.solvers.solvers import _invert, unrad, checksol, posify, _ispow, \ - det_quick, det_perm, det_minor, _simple_dens, check_assumptions, denoms + det_quick, det_perm, det_minor, _simple_dens, check_assumptions, denoms, \ + failing_assumptions from sympy.physics.units import cm from sympy.polys.rootoftools import CRootOf @@ -1300,7 +1301,16 @@ def test_check_assumptions(): x = symbols('x', positive=True) assert solve(x**2 - 1) == [1] assert check_assumptions(1, x) == True + raises(AssertionError, lambda: check_assumptions(2*x, x, positive=True)) + raises(TypeError, lambda: check_assumptions(1, 1)) +def test_failing_assumptions(): + x = Symbol('x', real=True, positive=True) + y = Symbol('y') + assert failing_assumptions(6*x + y, **x.assumptions0) == \ + {'real': None, 'imaginary': None, 'complex': None, 'hermitian': None, + 'positive': None, 'nonpositive': None, 'nonnegative': None, 'nonzero': None, + 'negative': None, 'zero': None} def test_issue_6056(): assert solve(tanh(x + 3)*tanh(x - 3) - 1) == []
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-7270@3a8b1b8
streamlit/streamlit
Python
7,270
Allow `None` as index for `st.selectbox`
## Describe your changes This PR adds support for setting `None` as the index in `st.selectbox`. Using `index=None` will render an empty selectbox with no option selected, which returns `None` as long as the user hasn't selected an option: <img width="718" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/47e38536-7457-48b8-acc5-f94db03dd058"> - Spec: https://www.notion.so/snowflake-corp/Spec-v2-b00de8d56f99464693e7ddfdea0674e9 ## GitHub Issue Link (if applicable) - Closes #949 ## Testing Plan - Unit Tests: โœ… - E2E Tests: โœ… --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-09-02T16:41:25Z
st.selectbox - No selection per default ### Problem I believe it is currently not possible to use a selectbox that has nothing selected per default. This may be undesirable in some cases. ### Solution Replace the `index`parameter with a `default` parameter just like the multiselect item does. Problem: This is a breaking change. Possibly better: Instead of throwing an error when the provided index is not within the list, return `None` and show an empty selection in the UI instead of throwing an error. Problem: No explicit error is being raised in case the index out of bound in an unwanted scenario. Possibly possibly better: An index of `-1` indicates an empty selection and `None` is returned. This might be a good balance since negative values are currently forbidden. --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
Here is a workaround: Example: display a map only if a valid zoom level is selected: ``` map_zoom = st.selectbox('Zoom level', ['<select>', 12, 11, 10, 9, 8, 7], 0) # default value = index 0 if map_zoom != '<select>': st.map(data, zoom=map_zoom) ``` That's correct there's no actual way to have an empty selectbox, but for now you could do something like this using an empty string plus the `format_func` or even using just the empty string ``` import streamlit as st selected = st.selectbox('Select one option:', ['', 'First one', 'Second one'], format_func=lambda x: 'Select an option' if x == '' else x) if selected: st.success('Yay! ๐ŸŽ‰') else: st.warning('No option is selected') ``` A clean solution would probably need to have a dedicated default value that cannot be mistaken for any actual value ( `` could possibly be a valid option to select). I didn't think of the `format_func` to actually format this exceptional value and thus had to use a string, but it's a very good point. For reference, you'll find below the wrapper I wrote in December to handle a very similar use case. ```python DEFAULT = '< PICK A VALUE >' def selectbox_with_default(text, values, default=DEFAULT, sidebar=False): func = st.sidebar.selectbox if sidebar else st.selectbox return func(text, np.insert(np.array(values, object), 0, default)) ``` I'd then call it from the `app.py` code with ```python res = selectbox_with_default('some title', some_values) if res == DEFAULT: st.warning("Please fill all the fields !") raise StopException ``` I would find it very useful to be able to have an selection box without a default value selected! declare the variable before the select box res=some_values res = selectbox_with_default('some title', some_values) [Here](https://extras.streamlit.app/No-Default%20Selectbox) is a third-party solution which has this behavior. This is part of the [streamlit-extras](https://github.com/arnaudmiribel/streamlit-extras) package. ```python from streamlit_extras.no_default_selectbox import selectbox result = selectbox("Select an option", ["A", "B", "C"]) st.write("Result:", result) ``` This returns `None` until the user selects one of the options. We have this on our roadmap. No concrete date yet but I'd love to get it out soon-ish. Any idea in case of Striemlit selectbox component how we can handle showing the section option from start instead show based on the option select in select box.. We're working on this right now. It will work by setting `index=None`. See a preview here: https://github.com/streamlit/streamlit/assets/127552870/f25119e3-c16d-4246-85e9-a0321af8d974 @jrieke "We're working on this right now. It will work by setting `index=None`. See a preview here:" which version is this working in? As I tried the same with version 1.24.0 as below but it's not working throws an error. ` option = st.selectbox( 'How would you like to be contacted?', ('Email', 'Home phone', 'Mobile phone'),index=None) st.write('You selected:', option) `
[ { "body": "### Problem\r\n\r\nI believe it is currently not possible to use a selectbox that has nothing selected per default. This may be undesirable in some cases.\r\n\r\n### Solution\r\n\r\nReplace the `index`parameter with a `default` parameter just like the multiselect item does. Problem: This is a breaking change.\r\n\r\nPossibly better: Instead of throwing an error when the provided index is not within the list, return `None` and show an empty selection in the UI instead of throwing an error. Problem: No explicit error is being raised in case the index out of bound in an unwanted scenario.\r\n\r\nPossibly possibly better: An index of `-1` indicates an empty selection and `None` is returned. This might be a good balance since negative values are currently forbidden.\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**\r\n", "number": 949, "title": "st.selectbox - No selection per default " } ]
4f45c18a4323a796440d651ba77b5eb29409cb2b
{ "head_commit": "3a8b1b8bed4c0e4afd0ea1611759bcb1ec7e72d5", "head_commit_message": "Fix test", "patch_to_review": "diff --git a/e2e/scripts/st_selectbox.py b/e2e/scripts/st_selectbox.py\nindex 8ba9860fefdc..46b53f98c2a0 100644\n--- a/e2e/scripts/st_selectbox.py\n+++ b/e2e/scripts/st_selectbox.py\n@@ -23,7 +23,7 @@\n i2 = st.selectbox(\"selectbox 2\", options, 0, format_func=lambda x: x.capitalize())\n st.write(\"value 2:\", i2)\n \n-i3 = st.selectbox(\"selectbox 3\", [])\n+i3: None = st.selectbox(\"selectbox 3\", [])\n st.write(\"value 3:\", i3)\n \n more_options = [\ndiff --git a/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-callback_help[dark_theme-chromium].png b/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-callback_help[dark_theme-chromium].png\nnew file mode 100644\nindex 000000000000..ab62d96948b7\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-callback_help[dark_theme-chromium].png 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b/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-selection_dropdown[firefox].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-selection_dropdown[webkit].png b/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-selection_dropdown[webkit].png\nnew file mode 100644\nindex 000000000000..784bc8b75ad1\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_selectbox_test/st_selectbox-selection_dropdown[webkit].png differ\ndiff --git a/e2e_playwright/st_selectbox.py b/e2e_playwright/st_selectbox.py\nnew file mode 100644\nindex 000000000000..8e453072946f\n--- /dev/null\n+++ b/e2e_playwright/st_selectbox.py\n@@ -0,0 +1,92 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import pandas as pd\n+\n+import streamlit as st\n+from streamlit import runtime\n+\n+options = (\"male\", \"female\")\n+i1 = st.selectbox(\"selectbox 1 (default)\", options)\n+st.write(\"value 1:\", i1)\n+\n+i2 = st.selectbox(\n+ \"selectbox 2 (formatted options)\", options, 1, format_func=lambda x: x.capitalize()\n+)\n+st.write(\"value 2:\", i2)\n+\n+i3: None = st.selectbox(\"selectbox 3 (no options)\", [])\n+st.write(\"value 3:\", i3)\n+\n+more_options = [\n+ \"e2e/scripts/components_iframe.py\",\n+ \"e2e/scripts/st_warning.py\",\n+ \"This is a very very very long option label that should be truncated when it is showing in the dropdown menu.\",\n+ \"e2e/scripts/st_container.py\",\n+ \"e2e/scripts/st_dataframe_sort_column.py\",\n+ \"e2e/scripts/app_hotkeys.py\",\n+ \"e2e/scripts/st_info.py\",\n+ \"e2e/scripts/st_echo.py\",\n+ \"e2e/scripts/st_json.py\",\n+ \"e2e/scripts/st_experimental_get_query_params.py\",\n+ \"e2e/scripts/st_markdown.py\",\n+ \"e2e/scripts/st_color_picker.py\",\n+ \"e2e/scripts/st_expander.py\",\n+]\n+i4 = st.selectbox(\"selectbox 4 (more options)\", more_options, 0)\n+st.write(\"value 4:\", i4)\n+\n+i5 = st.selectbox(\"selectbox 5 (disabled)\", options, disabled=True)\n+st.write(\"value 5:\", i5)\n+\n+i6 = st.selectbox(\"selectbox 6 (hidden label)\", options, label_visibility=\"hidden\")\n+st.write(\"value 6:\", i6)\n+\n+i7 = st.selectbox(\n+ \"selectbox 7 (collapsed label)\", options, label_visibility=\"collapsed\"\n+)\n+st.write(\"value 7:\", i7)\n+\n+if runtime.exists():\n+\n+ def on_change():\n+ st.session_state.selectbox_changed = True\n+ st.text(\"Selectbox widget callback triggered\")\n+\n+ st.selectbox(\n+ \"selectbox 8 (with callback, help)\",\n+ options,\n+ 1,\n+ key=\"selectbox8\",\n+ on_change=on_change,\n+ help=\"Help text\",\n+ )\n+ st.write(\"value 8:\", st.session_state.selectbox8)\n+ st.write(\"selectbox changed:\", \"selectbox_changed\" in st.session_state)\n+\n+i9 = st.selectbox(\"selectbox 9 (empty selection)\", options, index=None)\n+st.write(\"value 9:\", i9)\n+\n+i10 = st.selectbox(\n+ \"selectbox 10 (empty, custom placeholder)\",\n+ options,\n+ index=None,\n+ placeholder=\"Select one of the options...\",\n+)\n+st.write(\"value 10:\", i10)\n+\n+i11 = st.selectbox(\n+ \"selectbox 11 (options from dataframe)\", pd.DataFrame({\"foo\": list(options)})\n+)\n+st.write(\"value 11:\", i11)\ndiff --git a/e2e_playwright/st_selectbox_test.py b/e2e_playwright/st_selectbox_test.py\nnew file mode 100644\nindex 000000000000..3abeb001282f\n--- /dev/null\n+++ b/e2e_playwright/st_selectbox_test.py\n@@ -0,0 +1,190 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from playwright.sync_api import Page, expect\n+\n+from e2e_playwright.conftest import ImageCompareFunction\n+\n+\n+def test_selectbox_widget_rendering(\n+ themed_app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that the selectbox widgets are correctly rendered via screenshot matching.\"\"\"\n+ selectbox_widgets = themed_app.get_by_test_id(\"stSelectbox\")\n+ expect(selectbox_widgets).to_have_count(11)\n+\n+ assert_snapshot(selectbox_widgets.nth(0), name=\"st_selectbox-default\")\n+ assert_snapshot(selectbox_widgets.nth(1), name=\"st_selectbox-formatted_options\")\n+ assert_snapshot(selectbox_widgets.nth(2), name=\"st_selectbox-no_options\")\n+ assert_snapshot(selectbox_widgets.nth(3), name=\"st_selectbox-more_options\")\n+ assert_snapshot(selectbox_widgets.nth(4), name=\"st_selectbox-disabled\")\n+ assert_snapshot(selectbox_widgets.nth(5), name=\"st_selectbox-hidden_label\")\n+ assert_snapshot(selectbox_widgets.nth(6), name=\"st_selectbox-collapsed_label\")\n+ assert_snapshot(selectbox_widgets.nth(7), name=\"st_selectbox-callback_help\")\n+ assert_snapshot(selectbox_widgets.nth(8), name=\"st_selectbox-empty_selection\")\n+ assert_snapshot(\n+ selectbox_widgets.nth(9), name=\"st_selectbox-empty_selection_placeholder\"\n+ )\n+ assert_snapshot(selectbox_widgets.nth(10), name=\"st_selectbox-dataframe_options\")\n+\n+\n+def test_selectbox_has_correct_initial_values(app: Page):\n+ \"\"\"Test that st.selectbox returns the correct initial values.\"\"\"\n+ markdown_elements = app.get_by_test_id(\"stMarkdown\")\n+ expect(markdown_elements).to_have_count(12)\n+\n+ expected = [\n+ \"value 1: male\",\n+ \"value 2: female\",\n+ \"value 3: None\",\n+ \"value 4: e2e/scripts/components_iframe.py\",\n+ \"value 5: male\",\n+ \"value 6: male\",\n+ \"value 7: male\",\n+ \"value 8: female\",\n+ \"selectbox changed: False\",\n+ \"value 9: None\",\n+ \"value 10: None\",\n+ \"value 11: male\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(markdown_elements.all(), expected):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_handles_option_selection(app: Page, assert_snapshot: ImageCompareFunction):\n+ \"\"\"Test that selection of an option via the dropdown works correctly.\"\"\"\n+ app.get_by_test_id(\"stSelectbox\").nth(3).locator(\"input\").click()\n+\n+ # Take a snapshot of the selection dropdown:\n+ selection_dropdown = app.locator('[data-baseweb=\"popover\"]').first\n+ assert_snapshot(selection_dropdown, name=\"st_selectbox-selection_dropdown\")\n+ # Select last option:\n+ selection_dropdown.locator(\"li\").nth(1).click()\n+ # Check that selection worked:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(3)).to_have_text(\n+ \"value 4: e2e/scripts/st_warning.py\", use_inner_text=True\n+ )\n+\n+\n+def test_handles_option_selection_via_typing(app: Page):\n+ \"\"\"Test that selection of an option via typing works correctly.\"\"\"\n+ selectbox_input = app.get_by_test_id(\"stSelectbox\").nth(3).locator(\"input\")\n+\n+ # Type an option:\n+ selectbox_input.type(\"e2e/scripts/st_warning.py\")\n+ selectbox_input.press(\"Enter\")\n+\n+ # Check that selection worked:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(3)).to_have_text(\n+ \"value 4: e2e/scripts/st_warning.py\", use_inner_text=True\n+ )\n+\n+\n+def test_shows_correct_options_via_fuzzy_search(\n+ app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that the fuzzy matching of options via typing works correctly.\"\"\"\n+ selectbox_input = app.get_by_test_id(\"stSelectbox\").nth(3).locator(\"input\")\n+\n+ # Start typing:\n+ selectbox_input.type(\"exp\")\n+\n+ # Check filtered options\n+ selection_dropdown = app.locator('[data-baseweb=\"popover\"]').first\n+ assert_snapshot(selection_dropdown, name=\"st_selectbox-fuzzy_matching\")\n+\n+\n+def test_empty_selectbox_behaves_correctly(\n+ app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that st.selectbox behaves correctly when empty (no initial selection).\"\"\"\n+ # Enter 10 in the first empty input:\n+ empty_selectbox_input = app.get_by_test_id(\"stSelectbox\").locator(\"input\").nth(8)\n+\n+ # Type an option:\n+ empty_selectbox_input.type(\"male\")\n+ empty_selectbox_input.press(\"Enter\")\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(9)).to_have_text(\n+ \"value 9: male\", use_inner_text=True\n+ )\n+\n+ assert_snapshot(\n+ app.get_by_test_id(\"stSelectbox\").nth(8), name=\"st_selectbox-clearable_input\"\n+ )\n+\n+ empty_selectbox_input.focus()\n+ empty_selectbox_input.press(\"Escape\")\n+\n+ # Should be empty again:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(9)).to_have_text(\n+ \"value 9: None\", use_inner_text=True\n+ )\n+\n+\n+def test_keeps_value_on_selection_close(app: Page):\n+ \"\"\"Test that the fuzzy matching of options via typing works correctly.\"\"\"\n+ app.get_by_test_id(\"stSelectbox\").nth(3).locator(\"input\").click()\n+\n+ # Take a snapshot of the selection dropdown:\n+ expect(app.locator('[data-baseweb=\"popover\"]').first).to_be_visible()\n+\n+ # Click outside to close the dropdown:\n+ app.get_by_test_id(\"stMarkdown\").first.click()\n+\n+ # Check if value is still initial value:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(3)).to_have_text(\n+ \"value 4: e2e/scripts/components_iframe.py\", use_inner_text=True\n+ )\n+\n+\n+def test_handles_callback_on_change_correctly(app: Page):\n+ \"\"\"Test that it correctly calls the callback on change.\"\"\"\n+ # Check initial state:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(7)).to_have_text(\n+ \"value 8: female\", use_inner_text=True\n+ )\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(8)).to_have_text(\n+ \"selectbox changed: False\", use_inner_text=True\n+ )\n+\n+ app.get_by_test_id(\"stSelectbox\").nth(7).locator(\"input\").click()\n+\n+ # Take a snapshot of the selection dropdown:\n+ selection_dropdown = app.locator('[data-baseweb=\"popover\"]').first\n+ # Select last option:\n+ selection_dropdown.locator(\"li\").first.click()\n+\n+ # Check that selection worked:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(7)).to_have_text(\n+ \"value 8: male\", use_inner_text=True\n+ )\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(8)).to_have_text(\n+ \"selectbox changed: True\", use_inner_text=True\n+ )\n+\n+ # Change different date input to trigger delta path change\n+ empty_selectbox_input = app.get_by_test_id(\"stSelectbox\").locator(\"input\").first\n+\n+ # Type an option:\n+ empty_selectbox_input.type(\"female\")\n+ empty_selectbox_input.press(\"Enter\")\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"value 1: female\", use_inner_text=True\n+ )\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(7)).to_have_text(\n+ \"value 8: male\", use_inner_text=True\n+ )\ndiff --git a/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx b/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx\nindex 0413545e3425..5da266e1cc52 100644\n--- a/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx\n+++ b/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx\n@@ -174,10 +174,10 @@ export class SettingsDialog extends PureComponent<Props, UserSettings> {\n this.changeSingleSetting(e.target.name, e.target.checked)\n }\n \n- private handleThemeChange = (index: number): void => {\n+ private handleThemeChange = (index: number | null): void => {\n const { activeTheme: oldTheme, availableThemes }: LibContextProps =\n this.context\n- const newTheme = availableThemes[index]\n+ const newTheme = availableThemes[index ?? 0]\n \n this.props.metricsMgr.enqueue(\"themeChanged\", {\n oldThemeName: oldTheme.name,\ndiff --git a/frontend/lib/src/components/elements/Markdown/Markdown.tsx b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\nindex c0a932c4f327..00bab744812e 100644\n--- a/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n+++ b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n@@ -37,7 +37,7 @@ export default function Markdown({\n }: MarkdownProps): ReactElement {\n const styleProp = { width }\n return (\n- <div className=\"stMarkdown\" style={styleProp}>\n+ <div className=\"stMarkdown\" style={styleProp} data-testid=\"stMarkdown\">\n {element.help ? (\n <StyledLabelHelpWrapper>\n <StreamlitMarkdown\ndiff --git a/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx b/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx\nindex a93a1aff55f8..ab8f8238101f 100644\n--- a/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx\n+++ b/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx\n@@ -20,7 +20,8 @@ import { shallow, mount } from \"@streamlit/lib/src/test_util\"\n \n import { Select as UISelect } from \"baseui/select\"\n import { LabelVisibilityOptions } from \"@streamlit/lib/src/util/utils\"\n-import Selectbox, { Props, fuzzyFilterSelectOptions } from \"./Selectbox\"\n+import { Selectbox, Props, fuzzyFilterSelectOptions } from \"./Selectbox\"\n+import { mockTheme } from \"@streamlit/lib/src/mocks/mockTheme\"\n \n jest.mock(\"@streamlit/lib/src/WidgetStateManager\")\n \n@@ -31,6 +32,7 @@ const getProps = (props: Partial<Props> = {}): Props => ({\n width: 0,\n disabled: false,\n onChange: jest.fn(),\n+ theme: mockTheme.emotion,\n placeholder: \"Select...\",\n ...props,\n })\ndiff --git a/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx b/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx\nindex a7052d03d7e3..e3324c1ee034 100644\n--- a/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx\n+++ b/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx\n@@ -18,11 +18,15 @@ import React from \"react\"\n import { isMobile } from \"react-device-detect\"\n import { ChevronDown } from \"baseui/icon\"\n import { Select as UISelect, OnChangeParams, Option } from \"baseui/select\"\n-import { logWarning } from \"@streamlit/lib/src/util/log\"\n-import VirtualDropdown from \"@streamlit/lib/src/components/shared/Dropdown/VirtualDropdown\"\n+import { withTheme } from \"@emotion/react\"\n import { hasMatch, score } from \"fzy.js\"\n import _ from \"lodash\"\n-import { LabelVisibilityOptions } from \"@streamlit/lib/src/util/utils\"\n+\n+import VirtualDropdown from \"@streamlit/lib/src/components/shared/Dropdown/VirtualDropdown\"\n+import {\n+ LabelVisibilityOptions,\n+ isNullOrUndefined,\n+} from \"@streamlit/lib/src/util/utils\"\n import { Placement } from \"@streamlit/lib/src/components/shared/Tooltip\"\n import TooltipIcon from \"@streamlit/lib/src/components/shared/TooltipIcon\"\n import {\n@@ -30,17 +34,22 @@ import {\n StyledWidgetLabelHelp,\n } from \"@streamlit/lib/src/components/widgets/BaseWidget\"\n import { iconSizes } from \"@streamlit/lib/src/theme/primitives\"\n+import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n+\n+const NO_OPTIONS_MSG = \"No options to select.\"\n \n export interface Props {\n disabled: boolean\n width?: number\n- value: number\n- onChange: (value: number) => void\n+ value: number | null\n+ onChange: (value: number | null) => void\n options: any[]\n label?: string | null\n labelVisibility?: LabelVisibilityOptions\n help?: string\n placeholder?: string\n+ clearable?: boolean\n+ theme: EmotionTheme\n }\n \n interface State {\n@@ -50,7 +59,7 @@ interface State {\n * The value specified by the user via the UI. If the user didn't touch this\n * widget's UI, the default value is used.\n */\n- value: number\n+ value: number | null\n }\n \n interface SelectOption {\n@@ -78,7 +87,7 @@ export function fuzzyFilterSelectOptions(\n .value()\n }\n \n-class Selectbox extends React.PureComponent<Props, State> {\n+export class Selectbox extends React.PureComponent<Props, State> {\n public state: State = {\n isEmpty: false,\n value: this.props.value,\n@@ -97,7 +106,7 @@ class Selectbox extends React.PureComponent<Props, State> {\n \n private onChange = (params: OnChangeParams): void => {\n if (params.value.length === 0) {\n- logWarning(\"No value selected!\")\n+ this.setState({ value: null }, () => this.props.onChange(null))\n return\n }\n \n@@ -138,25 +147,24 @@ class Selectbox extends React.PureComponent<Props, State> {\n \n public render(): React.ReactNode {\n const style = { width: this.props.width }\n- const { label, labelVisibility, help, placeholder } = this.props\n+ const { label, labelVisibility, help, placeholder, theme, clearable } =\n+ this.props\n let { disabled, options } = this.props\n \n- let value = [\n- {\n- label:\n- options.length > 0\n- ? options[this.state.value]\n- : \"No options to select.\",\n- value: this.state.value.toString(),\n- },\n- ]\n+ let value: Option[] = []\n \n- if (this.state.isEmpty) {\n- value = []\n+ if (!isNullOrUndefined(this.state.value) && !this.state.isEmpty) {\n+ value = [\n+ {\n+ label:\n+ options.length > 0 ? options[this.state.value] : NO_OPTIONS_MSG,\n+ value: this.state.value.toString(),\n+ },\n+ ]\n }\n \n if (options.length === 0) {\n- options = [\"No options to select.\"]\n+ options = [NO_OPTIONS_MSG]\n disabled = true\n }\n \n@@ -172,7 +180,11 @@ class Selectbox extends React.PureComponent<Props, State> {\n const showKeyboardOnMobile = options.length > 10\n \n return (\n- <div className=\"row-widget stSelectbox\" style={style}>\n+ <div\n+ className=\"row-widget stSelectbox\"\n+ data-testid=\"stSelectbox\"\n+ style={style}\n+ >\n <WidgetLabel\n label={label}\n labelVisibility={labelVisibility}\n@@ -185,7 +197,6 @@ class Selectbox extends React.PureComponent<Props, State> {\n )}\n </WidgetLabel>\n <UISelect\n- clearable={false}\n disabled={disabled}\n labelKey=\"label\"\n aria-label={label || \"\"}\n@@ -194,6 +205,8 @@ class Selectbox extends React.PureComponent<Props, State> {\n onClose={this.onClose}\n options={selectOptions}\n filterOptions={this.filterOptions}\n+ clearable={clearable || false}\n+ escapeClearsValue={clearable || false}\n value={value}\n valueKey=\"value\"\n placeholder={placeholder}\n@@ -204,7 +217,26 @@ class Selectbox extends React.PureComponent<Props, State> {\n }),\n },\n Dropdown: { component: VirtualDropdown },\n-\n+ ClearIcon: {\n+ props: {\n+ overrides: {\n+ Svg: {\n+ style: {\n+ color: theme.colors.darkGray,\n+ // Since the close icon is an SVG, and we can't control its viewbox nor its attributes,\n+ // Let's use a scale transform effect to make it bigger.\n+ // The width property only enlarges its bounding box, so it's easier to click.\n+ marginRight: \"3px\",\n+ transform: \"scale(1.25)\",\n+ width: theme.spacing.twoXL,\n+ \":hover\": {\n+ fill: theme.colors.bodyText,\n+ },\n+ },\n+ },\n+ },\n+ },\n+ },\n ControlContainer: {\n style: () => ({\n // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings.\n@@ -278,4 +310,4 @@ class Selectbox extends React.PureComponent<Props, State> {\n }\n }\n \n-export default Selectbox\n+export default withTheme(Selectbox)\ndiff --git a/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx b/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx\nindex 2b2e6c256f5e..e596f60386ce 100644\n--- a/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx\n+++ b/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx\n@@ -20,7 +20,8 @@ import { WidgetStateManager } from \"@streamlit/lib/src/WidgetStateManager\"\n \n import { Select as UISelect } from \"baseui/select\"\n import { Selectbox as SelectboxProto } from \"@streamlit/lib/src/proto\"\n-import Selectbox, { Props } from \"./Selectbox\"\n+import { Selectbox, Props } from \"./Selectbox\"\n+import { mockTheme } from \"@streamlit/lib/src/mocks/mockTheme\"\n \n const getProps = (elementProps: Partial<SelectboxProto> = {}): Props => ({\n element: SelectboxProto.create({\n@@ -32,6 +33,7 @@ const getProps = (elementProps: Partial<SelectboxProto> = {}): Props => ({\n }),\n width: 0,\n disabled: false,\n+ theme: mockTheme.emotion,\n widgetMgr: new WidgetStateManager({\n sendRerunBackMsg: jest.fn(),\n formsDataChanged: jest.fn(),\ndiff --git a/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx b/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx\nindex d82f80d9ca35..d21561f1e717 100644\n--- a/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx\n+++ b/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx\n@@ -15,6 +15,8 @@\n */\n \n import React from \"react\"\n+\n+import { withTheme } from \"@emotion/react\"\n import { Selectbox as SelectboxProto } from \"@streamlit/lib/src/proto\"\n import { FormClearHelper } from \"@streamlit/lib/src/components/widgets/Form\"\n import {\n@@ -22,13 +24,18 @@ import {\n Source,\n } from \"@streamlit/lib/src/WidgetStateManager\"\n import UISelectbox from \"@streamlit/lib/src/components/shared/Dropdown\"\n-import { labelVisibilityProtoValueToEnum } from \"@streamlit/lib/src/util/utils\"\n+import {\n+ labelVisibilityProtoValueToEnum,\n+ isNullOrUndefined,\n+} from \"@streamlit/lib/src/util/utils\"\n+import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n \n export interface Props {\n disabled: boolean\n element: SelectboxProto\n widgetMgr: WidgetStateManager\n width: number\n+ theme: EmotionTheme\n }\n \n interface State {\n@@ -36,21 +43,21 @@ interface State {\n * The value specified by the user via the UI. If the user didn't touch this\n * widget's UI, the default value is used.\n */\n- value: number\n+ value: number | null\n }\n \n-class Selectbox extends React.PureComponent<Props, State> {\n+export class Selectbox extends React.PureComponent<Props, State> {\n private readonly formClearHelper = new FormClearHelper()\n \n public state: State = {\n value: this.initialValue,\n }\n \n- get initialValue(): number {\n+ get initialValue(): number | null {\n // If WidgetStateManager knew a value for this widget, initialize to that.\n // Otherwise, use the default value from the widget protobuf.\n const storedValue = this.props.widgetMgr.getIntValue(this.props.element)\n- return storedValue !== undefined ? storedValue : this.props.element.default\n+ return storedValue ?? this.props.element.default ?? null\n }\n \n public componentDidMount(): void {\n@@ -79,7 +86,7 @@ class Selectbox extends React.PureComponent<Props, State> {\n private updateFromProtobuf(): void {\n const { value } = this.props.element\n this.props.element.setValue = false\n- this.setState({ value }, () => {\n+ this.setState({ value: value ?? null }, () => {\n this.commitWidgetValue({ fromUi: false })\n })\n }\n@@ -100,13 +107,13 @@ class Selectbox extends React.PureComponent<Props, State> {\n private onFormCleared = (): void => {\n this.setState(\n (_, prevProps) => {\n- return { value: prevProps.element.default }\n+ return { value: prevProps.element.default ?? null }\n },\n () => this.commitWidgetValue({ fromUi: true })\n )\n }\n \n- private onChange = (value: number): void => {\n+ private onChange = (value: number | null): void => {\n this.setState({ value }, () => this.commitWidgetValue({ fromUi: true }))\n }\n \n@@ -114,6 +121,8 @@ class Selectbox extends React.PureComponent<Props, State> {\n const { options, help, label, labelVisibility, formId, placeholder } =\n this.props.element\n const { disabled, widgetMgr } = this.props\n+ const clearable =\n+ isNullOrUndefined(this.props.element.default) && !disabled\n \n // Manage our form-clear event handler.\n this.formClearHelper.manageFormClearListener(\n@@ -135,9 +144,10 @@ class Selectbox extends React.PureComponent<Props, State> {\n value={this.state.value}\n help={help}\n placeholder={placeholder}\n+ clearable={clearable}\n />\n )\n }\n }\n \n-export default Selectbox\n+export default withTheme(Selectbox)\ndiff --git a/lib/streamlit/elements/widgets/selectbox.py b/lib/streamlit/elements/widgets/selectbox.py\nindex f39c88b316ec..4f5c44877269 100644\n--- a/lib/streamlit/elements/widgets/selectbox.py\n+++ b/lib/streamlit/elements/widgets/selectbox.py\n@@ -11,10 +11,11 @@\n # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n # See the License for the specific language governing permissions and\n # limitations under the License.\n+from __future__ import annotations\n \n from dataclasses import dataclass\n from textwrap import dedent\n-from typing import TYPE_CHECKING, Any, Callable, Generic, Optional, Sequence, cast\n+from typing import TYPE_CHECKING, Any, Callable, Generic, Sequence, cast, overload\n \n from streamlit.elements.form import current_form_id\n from streamlit.elements.utils import (\n@@ -51,41 +52,80 @@\n @dataclass\n class SelectboxSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n \n- def serialize(self, v: object) -> int:\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n if len(self.options) == 0:\n return 0\n return index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n- idx: int = ui_value if ui_value is not None else self.index\n-\n- return self.options[idx] if len(self.options) > 0 else None\n+ ) -> T | None:\n+ idx = ui_value if ui_value is not None else self.index\n+ return self.options[idx] if idx is not None and len(self.options) > 0 else None\n \n \n class SelectboxMixin:\n- @gather_metrics(\"selectbox\")\n+ @overload\n def selectbox(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only arguments:\n+ placeholder: str = \"Select...\",\n+ disabled: bool = False,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:\n+ pass\n+\n+ @overload\n+ def selectbox(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: None = None,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n placeholder: str = \"Select...\",\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n+ pass\n+\n+ @gather_metrics(\"selectbox\")\n+ def selectbox(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: int | None = 0,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only arguments:\n+ placeholder: str = \"Select...\",\n+ disabled: bool = False,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T | None:\n r\"\"\"Display a select widget.\n \n Parameters\n@@ -120,7 +160,9 @@ def selectbox(\n Labels for the select options. This will be cast to str internally\n by default. For pandas.DataFrame, the first column is selected.\n index : int\n- The index of the preselected option on first render.\n+ The index of the preselected option on first render. If ``None``,\n+ will initialize empty and return ``None`` until the user selects an option.\n+ Defaults to 0 (the first option).\n format_func : function\n Function to modify the display of the labels. It receives the option\n as an argument and its output will be cast to str.\n@@ -139,10 +181,6 @@ def selectbox(\n An optional dict of kwargs to pass to the callback.\n placeholder : str\n A string to display when no options are selected. Defaults to 'Select...'.\n-\n- A selectbox can't be empty, so a placeholder only displays while a\n- user's cursor is in a selectbox after manually deleting the current\n- selection. A future update will allow selectboxes to be empty.\n disabled : bool\n An optional boolean, which disables the selectbox if set to True.\n The default is False. This argument can only be supplied by keyword.\n@@ -155,7 +193,7 @@ def selectbox(\n Returns\n -------\n any\n- The selected option\n+ The selected option or ``None`` if no option is selected.\n \n Example\n -------\n@@ -171,6 +209,23 @@ def selectbox(\n https://doc-selectbox.streamlit.app/\n height: 320px\n \n+ To initialize an empty selectbox, use ``None`` as the index value:\n+\n+ >>> import streamlit as st\n+ >>>\n+ >>> option = st.selectbox(\n+ ... \"How would you like to be contacted?\",\n+ ... (\"Email\", \"Home phone\", \"Mobile phone\"),\n+ ... index=None,\n+ ... placeholder=\"Select contact method...\",\n+ ... )\n+ >>>\n+ >>> st.write('You selected:', option)\n+\n+ .. output::\n+ https://doc-selectbox-empty.streamlit.app/\n+ height: 320px\n+\n \"\"\"\n ctx = get_script_run_ctx()\n return self._selectbox(\n@@ -193,19 +248,19 @@ def _selectbox(\n self,\n label: str,\n options: OptionSequence[T],\n- index: int = 0,\n+ index: int | None = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n placeholder: str = \"Select...\",\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- ctx: Optional[ScriptRunContext] = None,\n- ) -> Optional[T]:\n+ ctx: ScriptRunContext | None = None,\n+ ) -> T | None:\n key = to_key(key)\n check_callback_rules(self.dg, on_change)\n check_session_state_rules(default_value=None if index == 0 else index, key=key)\n@@ -227,12 +282,12 @@ def _selectbox(\n page=ctx.page_script_hash if ctx else None,\n )\n \n- if not isinstance(index, int):\n+ if not isinstance(index, int) and index is not None:\n raise StreamlitAPIException(\n \"Selectbox Value has invalid type: %s\" % type(index).__name__\n )\n \n- if len(opt) > 0 and not 0 <= index < len(opt):\n+ if index is not None and len(opt) > 0 and not 0 <= index < len(opt):\n raise StreamlitAPIException(\n \"Selectbox index must be between 0 and length of options\"\n )\n@@ -240,7 +295,8 @@ def _selectbox(\n selectbox_proto = SelectboxProto()\n selectbox_proto.id = id\n selectbox_proto.label = label\n- selectbox_proto.default = index\n+ if index is not None:\n+ selectbox_proto.default = index\n selectbox_proto.options[:] = [str(format_func(option)) for option in opt]\n selectbox_proto.form_id = current_form_id(self.dg)\n selectbox_proto.placeholder = placeholder\n@@ -267,7 +323,9 @@ def _selectbox(\n )\n \n if widget_state.value_changed:\n- selectbox_proto.value = serde.serialize(widget_state.value)\n+ serialized_value = serde.serialize(widget_state.value)\n+ if serialized_value is not None:\n+ selectbox_proto.value = serialized_value\n selectbox_proto.set_value = True\n \n self.dg._enqueue(\"selectbox\", selectbox_proto)\ndiff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py\nindex 7e5787435524..d9f3b594daea 100644\n--- a/lib/streamlit/testing/element_tree.py\n+++ b/lib/streamlit/testing/element_tree.py\n@@ -646,7 +646,7 @@ def widget_state(self) -> WidgetState:\n \n @dataclass(repr=False)\n class Selectbox(Widget, Generic[T]):\n- _value: T | None\n+ _value: T | None | InitialValue\n \n proto: SelectboxProto = field(repr=False)\n options: list[str]\n@@ -654,7 +654,7 @@ class Selectbox(Widget, Generic[T]):\n def __init__(self, proto: SelectboxProto, root: ElementTree):\n self.proto = proto\n self.root = root\n- self._value = None\n+ self._value = InitialValue()\n \n self.type = \"selectbox\"\n self.id = proto.id\n@@ -666,22 +666,25 @@ def __init__(self, proto: SelectboxProto, root: ElementTree):\n self.key = user_key_from_widget_id(self.id)\n \n @property\n- def index(self) -> int:\n+ def index(self) -> int | None:\n+ if self.value is None:\n+ return None\n+\n if len(self.options) == 0:\n return 0\n return self.options.index(str(self.value))\n \n @property\n- def value(self) -> T:\n+ def value(self) -> T | None:\n \"\"\"The currently selected value from the options.\"\"\"\n- if self._value is not None:\n+ if not isinstance(self._value, InitialValue):\n return self._value\n else:\n state = self.root.session_state\n assert state\n return cast(T, state[self.id])\n \n- def set_value(self, v: T) -> Selectbox[T]:\n+ def set_value(self, v: T | None) -> Selectbox[T]:\n \"\"\"\n Set the value of the selectbox.\n Implementation note: set_value not work correctly if `format_func` is also\n@@ -691,10 +694,12 @@ def set_value(self, v: T) -> Selectbox[T]:\n self._value = v\n return self\n \n- def select(self, v: T) -> Selectbox[T]:\n+ def select(self, v: T | None) -> Selectbox[T]:\n return self.set_value(v)\n \n- def select_index(self, index: int) -> Selectbox[T]:\n+ def select_index(self, index: int | None) -> Selectbox[T]:\n+ if index is None:\n+ return self.set_value(None)\n return self.set_value(cast(T, self.options[index]))\n \n def widget_state(self) -> WidgetState:\n@@ -704,7 +709,8 @@ def widget_state(self) -> WidgetState:\n \"\"\"\n ws = WidgetState()\n ws.id = self.id\n- ws.int_value = self.index\n+ if self.index is not None:\n+ ws.int_value = self.index\n return ws\n \n \ndiff --git a/lib/tests/streamlit/elements/selectbox_test.py b/lib/tests/streamlit/elements/selectbox_test.py\nindex 5fce6aede9fe..1da07ee11fc1 100644\n--- a/lib/tests/streamlit/elements/selectbox_test.py\n+++ b/lib/tests/streamlit/elements/selectbox_test.py\n@@ -23,6 +23,7 @@\n import streamlit as st\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage\n+from streamlit.testing.script_interactions import InteractiveScriptTests\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n \n \n@@ -40,6 +41,7 @@ def test_just_label(self):\n LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE,\n )\n self.assertEqual(c.default, 0)\n+ self.assertEqual(c.HasField(\"default\"), True)\n self.assertEqual(c.disabled, False)\n self.assertEqual(c.placeholder, \"Select...\")\n \n@@ -58,6 +60,17 @@ def test_valid_value(self):\n self.assertEqual(c.label, \"the label\")\n self.assertEqual(c.default, 1)\n \n+ def test_none_index(self):\n+ \"\"\"Test that it can be called with None as index value.\"\"\"\n+ st.selectbox(\"the label\", (\"m\", \"f\"), index=None)\n+\n+ c = self.get_delta_from_queue().new_element.selectbox\n+ self.assertEqual(c.label, \"the label\")\n+ # If a proto property is null is not determined by this value,\n+ # but by the check via the HasField method:\n+ self.assertEqual(c.default, 0)\n+ self.assertEqual(c.HasField(\"default\"), False)\n+\n def test_noneType_option(self):\n \"\"\"Test NoneType option value.\"\"\"\n current_value = st.selectbox(\"the label\", (None, \"selected\"), 0)\n@@ -188,3 +201,27 @@ def test_placeholder(self):\n \n c = self.get_delta_from_queue().new_element.selectbox\n self.assertEqual(c.placeholder, \"Please select\")\n+\n+\n+class SelectboxInteractiveTest(InteractiveScriptTests):\n+ def test_selectbox_interaction(self):\n+ \"\"\"Test interactions with an empty selectbox widget.\"\"\"\n+ script = self.script_from_string(\n+ \"\"\"\n+ import streamlit as st\n+ st.selectbox(\"the label\", (\"m\", \"f\"), index=None)\n+ \"\"\"\n+ )\n+ sr = script.run()\n+ selectbox = sr.selectbox[0]\n+ assert selectbox.value is None\n+\n+ # Select option m\n+ sr2 = selectbox.set_value(\"m\").run()\n+ selectbox = sr2.selectbox[0]\n+ assert selectbox.value == \"m\"\n+\n+ # # Clear the value\n+ sr3 = selectbox.set_value(None).run()\n+ selectbox = sr3.selectbox[0]\n+ assert selectbox.value is None\ndiff --git a/proto/streamlit/proto/Selectbox.proto b/proto/streamlit/proto/Selectbox.proto\nindex 0f67cc1c6602..7e469c304875 100644\n--- a/proto/streamlit/proto/Selectbox.proto\n+++ b/proto/streamlit/proto/Selectbox.proto\n@@ -21,11 +21,11 @@ import \"streamlit/proto/LabelVisibilityMessage.proto\";\n message Selectbox {\n string id = 1;\n string label = 2;\n- int32 default = 3;\n+ optional int32 default = 3;\n repeated string options = 4;\n string help = 5;\n string form_id = 6;\n- int32 value = 7;\n+ optional int32 value = 7;\n bool set_value = 8;\n bool disabled = 9;\n LabelVisibilityMessage label_visibility = 10;\n" }
[ { "diff_hunk": "@@ -51,41 +52,80 @@\n @dataclass\n class SelectboxSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n \n- def serialize(self, v: object) -> int:\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n if len(self.options) == 0:\n return 0\n return index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n- idx: int = ui_value if ui_value is not None else self.index\n-\n- return self.options[idx] if len(self.options) > 0 else None\n+ ) -> T | None:\n+ idx = ui_value if ui_value is not None else self.index\n+ return self.options[idx] if idx is not None and len(self.options) > 0 else None\n \n \n class SelectboxMixin:\n- @gather_metrics(\"selectbox\")\n+ @overload\n def selectbox(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only arguments:\n+ placeholder: str = \"Select...\",\n+ disabled: bool = False,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:\n+ pass\n+\n+ @overload\n+ def selectbox(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: None = None,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n placeholder: str = \"Select...\",\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n+ pass", "line": null, "original_line": 110, "original_start_line": 74, "path": "lib/streamlit/elements/widgets/selectbox.py", "start_line": null, "text": "@user1:\nI think my comments on #7269 should also apply here:\r\n* https://github.com/streamlit/streamlit/pull/7269#discussion_r1316611057\r\n* https://github.com/streamlit/streamlit/pull/7269#discussion_r1316611561\n\n@author:\nRemoved the overloads for `st.radio` and `st.selectbox` as mentioned in the comment in `st.radio`." } ]
c424fa238c7cadd38f5d4248d9260c5329dd5b4f
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(2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd + +import streamlit as st +from streamlit import runtime + +options = ("male", "female") +i1 = st.selectbox("selectbox 1 (default)", options) +st.write("value 1:", i1) + +i2 = st.selectbox( + "selectbox 2 (formatted options)", options, 1, format_func=lambda x: x.capitalize() +) +st.write("value 2:", i2) + +i3 = st.selectbox("selectbox 3 (no options)", []) +st.write("value 3:", i3) + +more_options = [ + "e2e/scripts/components_iframe.py", + "e2e/scripts/st_warning.py", + "This is a very very very long option label that should be truncated when it is showing in the dropdown menu.", + "e2e/scripts/st_container.py", + "e2e/scripts/st_dataframe_sort_column.py", + "e2e/scripts/app_hotkeys.py", + "e2e/scripts/st_info.py", + "e2e/scripts/st_echo.py", + "e2e/scripts/st_json.py", + "e2e/scripts/st_experimental_get_query_params.py", + "e2e/scripts/st_markdown.py", + "e2e/scripts/st_color_picker.py", + "e2e/scripts/st_expander.py", +] +i4 = st.selectbox("selectbox 4 (more options)", more_options, 0) +st.write("value 4:", i4) + +i5 = st.selectbox("selectbox 5 (disabled)", options, disabled=True) +st.write("value 5:", i5) + +i6 = st.selectbox("selectbox 6 (hidden label)", options, label_visibility="hidden") +st.write("value 6:", i6) + +i7 = st.selectbox( + "selectbox 7 (collapsed label)", options, label_visibility="collapsed" +) +st.write("value 7:", i7) + +if runtime.exists(): + + def on_change(): + st.session_state.selectbox_changed = True + st.text("Selectbox widget callback triggered") + + st.selectbox( + "selectbox 8 (with callback, help)", + options, + 1, + key="selectbox8", + on_change=on_change, + help="Help text", + ) + st.write("value 8:", st.session_state.selectbox8) + st.write("selectbox changed:", "selectbox_changed" in st.session_state) + +i9 = st.selectbox("selectbox 9 (empty selection)", options, index=None) +st.write("value 9:", i9) + +i10 = st.selectbox( + "selectbox 10 (empty, custom placeholder)", + options, + index=None, + placeholder="Select one of the options...", +) +st.write("value 10:", i10) + +i11 = st.selectbox( + "selectbox 11 (options from dataframe)", pd.DataFrame({"foo": list(options)}) +) +st.write("value 11:", i11) diff --git a/e2e_playwright/st_selectbox_test.py b/e2e_playwright/st_selectbox_test.py new file mode 100644 index 000000000000..3abeb001282f --- /dev/null +++ b/e2e_playwright/st_selectbox_test.py @@ -0,0 +1,190 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from playwright.sync_api import Page, expect + +from e2e_playwright.conftest import ImageCompareFunction + + +def test_selectbox_widget_rendering( + themed_app: Page, assert_snapshot: ImageCompareFunction +): + """Test that the selectbox widgets are correctly rendered via screenshot matching.""" + selectbox_widgets = themed_app.get_by_test_id("stSelectbox") + expect(selectbox_widgets).to_have_count(11) + + assert_snapshot(selectbox_widgets.nth(0), name="st_selectbox-default") + assert_snapshot(selectbox_widgets.nth(1), name="st_selectbox-formatted_options") + assert_snapshot(selectbox_widgets.nth(2), name="st_selectbox-no_options") + assert_snapshot(selectbox_widgets.nth(3), name="st_selectbox-more_options") + assert_snapshot(selectbox_widgets.nth(4), name="st_selectbox-disabled") + assert_snapshot(selectbox_widgets.nth(5), name="st_selectbox-hidden_label") + assert_snapshot(selectbox_widgets.nth(6), name="st_selectbox-collapsed_label") + assert_snapshot(selectbox_widgets.nth(7), name="st_selectbox-callback_help") + assert_snapshot(selectbox_widgets.nth(8), name="st_selectbox-empty_selection") + assert_snapshot( + selectbox_widgets.nth(9), name="st_selectbox-empty_selection_placeholder" + ) + assert_snapshot(selectbox_widgets.nth(10), name="st_selectbox-dataframe_options") + + +def test_selectbox_has_correct_initial_values(app: Page): + """Test that st.selectbox returns the correct initial values.""" + markdown_elements = app.get_by_test_id("stMarkdown") + expect(markdown_elements).to_have_count(12) + + expected = [ + "value 1: male", + "value 2: female", + "value 3: None", + "value 4: e2e/scripts/components_iframe.py", + "value 5: male", + "value 6: male", + "value 7: male", + "value 8: female", + "selectbox changed: False", + "value 9: None", + "value 10: None", + "value 11: male", + ] + + for markdown_element, expected_text in zip(markdown_elements.all(), expected): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_handles_option_selection(app: Page, assert_snapshot: ImageCompareFunction): + """Test that selection of an option via the dropdown works correctly.""" + app.get_by_test_id("stSelectbox").nth(3).locator("input").click() + + # Take a snapshot of the selection dropdown: + selection_dropdown = app.locator('[data-baseweb="popover"]').first + assert_snapshot(selection_dropdown, name="st_selectbox-selection_dropdown") + # Select last option: + selection_dropdown.locator("li").nth(1).click() + # Check that selection worked: + expect(app.get_by_test_id("stMarkdown").nth(3)).to_have_text( + "value 4: e2e/scripts/st_warning.py", use_inner_text=True + ) + + +def test_handles_option_selection_via_typing(app: Page): + """Test that selection of an option via typing works correctly.""" + selectbox_input = app.get_by_test_id("stSelectbox").nth(3).locator("input") + + # Type an option: + selectbox_input.type("e2e/scripts/st_warning.py") + selectbox_input.press("Enter") + + # Check that selection worked: + expect(app.get_by_test_id("stMarkdown").nth(3)).to_have_text( + "value 4: e2e/scripts/st_warning.py", use_inner_text=True + ) + + +def test_shows_correct_options_via_fuzzy_search( + app: Page, assert_snapshot: ImageCompareFunction +): + """Test that the fuzzy matching of options via typing works correctly.""" + selectbox_input = app.get_by_test_id("stSelectbox").nth(3).locator("input") + + # Start typing: + selectbox_input.type("exp") + + # Check filtered options + selection_dropdown = app.locator('[data-baseweb="popover"]').first + assert_snapshot(selection_dropdown, name="st_selectbox-fuzzy_matching") + + +def test_empty_selectbox_behaves_correctly( + app: Page, assert_snapshot: ImageCompareFunction +): + """Test that st.selectbox behaves correctly when empty (no initial selection).""" + # Enter 10 in the first empty input: + empty_selectbox_input = app.get_by_test_id("stSelectbox").locator("input").nth(8) + + # Type an option: + empty_selectbox_input.type("male") + empty_selectbox_input.press("Enter") + + expect(app.get_by_test_id("stMarkdown").nth(9)).to_have_text( + "value 9: male", use_inner_text=True + ) + + assert_snapshot( + app.get_by_test_id("stSelectbox").nth(8), name="st_selectbox-clearable_input" + ) + + empty_selectbox_input.focus() + empty_selectbox_input.press("Escape") + + # Should be empty again: + expect(app.get_by_test_id("stMarkdown").nth(9)).to_have_text( + "value 9: None", use_inner_text=True + ) + + +def test_keeps_value_on_selection_close(app: Page): + """Test that the fuzzy matching of options via typing works correctly.""" + app.get_by_test_id("stSelectbox").nth(3).locator("input").click() + + # Take a snapshot of the selection dropdown: + expect(app.locator('[data-baseweb="popover"]').first).to_be_visible() + + # Click outside to close the dropdown: + app.get_by_test_id("stMarkdown").first.click() + + # Check if value is still initial value: + expect(app.get_by_test_id("stMarkdown").nth(3)).to_have_text( + "value 4: e2e/scripts/components_iframe.py", use_inner_text=True + ) + + +def test_handles_callback_on_change_correctly(app: Page): + """Test that it correctly calls the callback on change.""" + # Check initial state: + expect(app.get_by_test_id("stMarkdown").nth(7)).to_have_text( + "value 8: female", use_inner_text=True + ) + expect(app.get_by_test_id("stMarkdown").nth(8)).to_have_text( + "selectbox changed: False", use_inner_text=True + ) + + app.get_by_test_id("stSelectbox").nth(7).locator("input").click() + + # Take a snapshot of the selection dropdown: + selection_dropdown = app.locator('[data-baseweb="popover"]').first + # Select last option: + selection_dropdown.locator("li").first.click() + + # Check that selection worked: + expect(app.get_by_test_id("stMarkdown").nth(7)).to_have_text( + "value 8: male", use_inner_text=True + ) + expect(app.get_by_test_id("stMarkdown").nth(8)).to_have_text( + "selectbox changed: True", use_inner_text=True + ) + + # Change different date input to trigger delta path change + empty_selectbox_input = app.get_by_test_id("stSelectbox").locator("input").first + + # Type an option: + empty_selectbox_input.type("female") + empty_selectbox_input.press("Enter") + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "value 1: female", use_inner_text=True + ) + expect(app.get_by_test_id("stMarkdown").nth(7)).to_have_text( + "value 8: male", use_inner_text=True + ) diff --git a/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx b/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx index 0413545e3425..5da266e1cc52 100644 --- a/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx +++ b/frontend/app/src/components/StreamlitDialog/SettingsDialog.tsx @@ -174,10 +174,10 @@ export class SettingsDialog extends PureComponent<Props, UserSettings> { this.changeSingleSetting(e.target.name, e.target.checked) } - private handleThemeChange = (index: number): void => { + private handleThemeChange = (index: number | null): void => { const { activeTheme: oldTheme, availableThemes }: LibContextProps = this.context - const newTheme = availableThemes[index] + const newTheme = availableThemes[index ?? 0] this.props.metricsMgr.enqueue("themeChanged", { oldThemeName: oldTheme.name, diff --git a/frontend/lib/src/components/elements/Markdown/Markdown.tsx b/frontend/lib/src/components/elements/Markdown/Markdown.tsx index c0a932c4f327..00bab744812e 100644 --- a/frontend/lib/src/components/elements/Markdown/Markdown.tsx +++ b/frontend/lib/src/components/elements/Markdown/Markdown.tsx @@ -37,7 +37,7 @@ export default function Markdown({ }: MarkdownProps): ReactElement { const styleProp = { width } return ( - <div className="stMarkdown" style={styleProp}> + <div className="stMarkdown" style={styleProp} data-testid="stMarkdown"> {element.help ? ( <StyledLabelHelpWrapper> <StreamlitMarkdown diff --git a/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx b/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx index a93a1aff55f8..ab8f8238101f 100644 --- a/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx +++ b/frontend/lib/src/components/shared/Dropdown/Selectbox.test.tsx @@ -20,7 +20,8 @@ import { shallow, mount } from "@streamlit/lib/src/test_util" import { Select as UISelect } from "baseui/select" import { LabelVisibilityOptions } from "@streamlit/lib/src/util/utils" -import Selectbox, { Props, fuzzyFilterSelectOptions } from "./Selectbox" +import { Selectbox, Props, fuzzyFilterSelectOptions } from "./Selectbox" +import { mockTheme } from "@streamlit/lib/src/mocks/mockTheme" jest.mock("@streamlit/lib/src/WidgetStateManager") @@ -31,6 +32,7 @@ const getProps = (props: Partial<Props> = {}): Props => ({ width: 0, disabled: false, onChange: jest.fn(), + theme: mockTheme.emotion, placeholder: "Select...", ...props, }) diff --git a/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx b/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx index a7052d03d7e3..e3324c1ee034 100644 --- a/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx +++ b/frontend/lib/src/components/shared/Dropdown/Selectbox.tsx @@ -18,11 +18,15 @@ import React from "react" import { isMobile } from "react-device-detect" import { ChevronDown } from "baseui/icon" import { Select as UISelect, OnChangeParams, Option } from "baseui/select" -import { logWarning } from "@streamlit/lib/src/util/log" -import VirtualDropdown from "@streamlit/lib/src/components/shared/Dropdown/VirtualDropdown" +import { withTheme } from "@emotion/react" import { hasMatch, score } from "fzy.js" import _ from "lodash" -import { LabelVisibilityOptions } from "@streamlit/lib/src/util/utils" + +import VirtualDropdown from "@streamlit/lib/src/components/shared/Dropdown/VirtualDropdown" +import { + LabelVisibilityOptions, + isNullOrUndefined, +} from "@streamlit/lib/src/util/utils" import { Placement } from "@streamlit/lib/src/components/shared/Tooltip" import TooltipIcon from "@streamlit/lib/src/components/shared/TooltipIcon" import { @@ -30,17 +34,22 @@ import { StyledWidgetLabelHelp, } from "@streamlit/lib/src/components/widgets/BaseWidget" import { iconSizes } from "@streamlit/lib/src/theme/primitives" +import { EmotionTheme } from "@streamlit/lib/src/theme" + +const NO_OPTIONS_MSG = "No options to select." export interface Props { disabled: boolean width?: number - value: number - onChange: (value: number) => void + value: number | null + onChange: (value: number | null) => void options: any[] label?: string | null labelVisibility?: LabelVisibilityOptions help?: string placeholder?: string + clearable?: boolean + theme: EmotionTheme } interface State { @@ -50,7 +59,7 @@ interface State { * The value specified by the user via the UI. If the user didn't touch this * widget's UI, the default value is used. */ - value: number + value: number | null } interface SelectOption { @@ -78,7 +87,7 @@ export function fuzzyFilterSelectOptions( .value() } -class Selectbox extends React.PureComponent<Props, State> { +export class Selectbox extends React.PureComponent<Props, State> { public state: State = { isEmpty: false, value: this.props.value, @@ -97,7 +106,7 @@ class Selectbox extends React.PureComponent<Props, State> { private onChange = (params: OnChangeParams): void => { if (params.value.length === 0) { - logWarning("No value selected!") + this.setState({ value: null }, () => this.props.onChange(null)) return } @@ -138,25 +147,24 @@ class Selectbox extends React.PureComponent<Props, State> { public render(): React.ReactNode { const style = { width: this.props.width } - const { label, labelVisibility, help, placeholder } = this.props + const { label, labelVisibility, help, placeholder, theme, clearable } = + this.props let { disabled, options } = this.props - let value = [ - { - label: - options.length > 0 - ? options[this.state.value] - : "No options to select.", - value: this.state.value.toString(), - }, - ] + let value: Option[] = [] - if (this.state.isEmpty) { - value = [] + if (!isNullOrUndefined(this.state.value) && !this.state.isEmpty) { + value = [ + { + label: + options.length > 0 ? options[this.state.value] : NO_OPTIONS_MSG, + value: this.state.value.toString(), + }, + ] } if (options.length === 0) { - options = ["No options to select."] + options = [NO_OPTIONS_MSG] disabled = true } @@ -172,7 +180,11 @@ class Selectbox extends React.PureComponent<Props, State> { const showKeyboardOnMobile = options.length > 10 return ( - <div className="row-widget stSelectbox" style={style}> + <div + className="row-widget stSelectbox" + data-testid="stSelectbox" + style={style} + > <WidgetLabel label={label} labelVisibility={labelVisibility} @@ -185,7 +197,6 @@ class Selectbox extends React.PureComponent<Props, State> { )} </WidgetLabel> <UISelect - clearable={false} disabled={disabled} labelKey="label" aria-label={label || ""} @@ -194,6 +205,8 @@ class Selectbox extends React.PureComponent<Props, State> { onClose={this.onClose} options={selectOptions} filterOptions={this.filterOptions} + clearable={clearable || false} + escapeClearsValue={clearable || false} value={value} valueKey="value" placeholder={placeholder} @@ -204,7 +217,26 @@ class Selectbox extends React.PureComponent<Props, State> { }), }, Dropdown: { component: VirtualDropdown }, - + ClearIcon: { + props: { + overrides: { + Svg: { + style: { + color: theme.colors.darkGray, + // Since the close icon is an SVG, and we can't control its viewbox nor its attributes, + // Let's use a scale transform effect to make it bigger. + // The width property only enlarges its bounding box, so it's easier to click. + marginRight: "3px", + transform: "scale(1.25)", + width: theme.spacing.twoXL, + ":hover": { + fill: theme.colors.bodyText, + }, + }, + }, + }, + }, + }, ControlContainer: { style: () => ({ // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings. @@ -278,4 +310,4 @@ class Selectbox extends React.PureComponent<Props, State> { } } -export default Selectbox +export default withTheme(Selectbox) diff --git a/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx b/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx index 2b2e6c256f5e..e596f60386ce 100644 --- a/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx +++ b/frontend/lib/src/components/widgets/Selectbox/Selectbox.test.tsx @@ -20,7 +20,8 @@ import { WidgetStateManager } from "@streamlit/lib/src/WidgetStateManager" import { Select as UISelect } from "baseui/select" import { Selectbox as SelectboxProto } from "@streamlit/lib/src/proto" -import Selectbox, { Props } from "./Selectbox" +import { Selectbox, Props } from "./Selectbox" +import { mockTheme } from "@streamlit/lib/src/mocks/mockTheme" const getProps = (elementProps: Partial<SelectboxProto> = {}): Props => ({ element: SelectboxProto.create({ @@ -32,6 +33,7 @@ const getProps = (elementProps: Partial<SelectboxProto> = {}): Props => ({ }), width: 0, disabled: false, + theme: mockTheme.emotion, widgetMgr: new WidgetStateManager({ sendRerunBackMsg: jest.fn(), formsDataChanged: jest.fn(), diff --git a/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx b/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx index d82f80d9ca35..d21561f1e717 100644 --- a/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx +++ b/frontend/lib/src/components/widgets/Selectbox/Selectbox.tsx @@ -15,6 +15,8 @@ */ import React from "react" + +import { withTheme } from "@emotion/react" import { Selectbox as SelectboxProto } from "@streamlit/lib/src/proto" import { FormClearHelper } from "@streamlit/lib/src/components/widgets/Form" import { @@ -22,13 +24,18 @@ import { Source, } from "@streamlit/lib/src/WidgetStateManager" import UISelectbox from "@streamlit/lib/src/components/shared/Dropdown" -import { labelVisibilityProtoValueToEnum } from "@streamlit/lib/src/util/utils" +import { + labelVisibilityProtoValueToEnum, + isNullOrUndefined, +} from "@streamlit/lib/src/util/utils" +import { EmotionTheme } from "@streamlit/lib/src/theme" export interface Props { disabled: boolean element: SelectboxProto widgetMgr: WidgetStateManager width: number + theme: EmotionTheme } interface State { @@ -36,21 +43,21 @@ interface State { * The value specified by the user via the UI. If the user didn't touch this * widget's UI, the default value is used. */ - value: number + value: number | null } -class Selectbox extends React.PureComponent<Props, State> { +export class Selectbox extends React.PureComponent<Props, State> { private readonly formClearHelper = new FormClearHelper() public state: State = { value: this.initialValue, } - get initialValue(): number { + get initialValue(): number | null { // If WidgetStateManager knew a value for this widget, initialize to that. // Otherwise, use the default value from the widget protobuf. const storedValue = this.props.widgetMgr.getIntValue(this.props.element) - return storedValue !== undefined ? storedValue : this.props.element.default + return storedValue ?? this.props.element.default ?? null } public componentDidMount(): void { @@ -79,7 +86,7 @@ class Selectbox extends React.PureComponent<Props, State> { private updateFromProtobuf(): void { const { value } = this.props.element this.props.element.setValue = false - this.setState({ value }, () => { + this.setState({ value: value ?? null }, () => { this.commitWidgetValue({ fromUi: false }) }) } @@ -100,13 +107,13 @@ class Selectbox extends React.PureComponent<Props, State> { private onFormCleared = (): void => { this.setState( (_, prevProps) => { - return { value: prevProps.element.default } + return { value: prevProps.element.default ?? null } }, () => this.commitWidgetValue({ fromUi: true }) ) } - private onChange = (value: number): void => { + private onChange = (value: number | null): void => { this.setState({ value }, () => this.commitWidgetValue({ fromUi: true })) } @@ -114,6 +121,8 @@ class Selectbox extends React.PureComponent<Props, State> { const { options, help, label, labelVisibility, formId, placeholder } = this.props.element const { disabled, widgetMgr } = this.props + const clearable = + isNullOrUndefined(this.props.element.default) && !disabled // Manage our form-clear event handler. this.formClearHelper.manageFormClearListener( @@ -135,9 +144,10 @@ class Selectbox extends React.PureComponent<Props, State> { value={this.state.value} help={help} placeholder={placeholder} + clearable={clearable} /> ) } } -export default Selectbox +export default withTheme(Selectbox) diff --git a/lib/streamlit/elements/widgets/selectbox.py b/lib/streamlit/elements/widgets/selectbox.py index f39c88b316ec..95cb71581eb3 100644 --- a/lib/streamlit/elements/widgets/selectbox.py +++ b/lib/streamlit/elements/widgets/selectbox.py @@ -11,10 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations from dataclasses import dataclass from textwrap import dedent -from typing import TYPE_CHECKING, Any, Callable, Generic, Optional, Sequence, cast +from typing import TYPE_CHECKING, Any, Callable, Generic, Sequence, cast from streamlit.elements.form import current_form_id from streamlit.elements.utils import ( @@ -51,21 +52,22 @@ @dataclass class SelectboxSerde(Generic[T]): options: Sequence[T] - index: int + index: int | None - def serialize(self, v: object) -> int: + def serialize(self, v: object) -> int | None: + if v is None: + return None if len(self.options) == 0: return 0 return index_(self.options, v) def deserialize( self, - ui_value: Optional[int], + ui_value: int | None, widget_id: str = "", - ) -> Optional[T]: - idx: int = ui_value if ui_value is not None else self.index - - return self.options[idx] if len(self.options) > 0 else None + ) -> T | None: + idx = ui_value if ui_value is not None else self.index + return self.options[idx] if idx is not None and len(self.options) > 0 else None class SelectboxMixin: @@ -74,18 +76,18 @@ def selectbox( self, label: str, options: OptionSequence[T], - index: int = 0, + index: int | None = 0, format_func: Callable[[Any], Any] = str, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: placeholder: str = "Select...", disabled: bool = False, label_visibility: LabelVisibility = "visible", - ) -> Optional[T]: + ) -> T | None: r"""Display a select widget. Parameters @@ -120,7 +122,9 @@ def selectbox( Labels for the select options. This will be cast to str internally by default. For pandas.DataFrame, the first column is selected. index : int - The index of the preselected option on first render. + The index of the preselected option on first render. If ``None``, + will initialize empty and return ``None`` until the user selects an option. + Defaults to 0 (the first option). format_func : function Function to modify the display of the labels. It receives the option as an argument and its output will be cast to str. @@ -139,10 +143,6 @@ def selectbox( An optional dict of kwargs to pass to the callback. placeholder : str A string to display when no options are selected. Defaults to 'Select...'. - - A selectbox can't be empty, so a placeholder only displays while a - user's cursor is in a selectbox after manually deleting the current - selection. A future update will allow selectboxes to be empty. disabled : bool An optional boolean, which disables the selectbox if set to True. The default is False. This argument can only be supplied by keyword. @@ -155,7 +155,7 @@ def selectbox( Returns ------- any - The selected option + The selected option or ``None`` if no option is selected. Example ------- @@ -171,6 +171,23 @@ def selectbox( https://doc-selectbox.streamlit.app/ height: 320px + To initialize an empty selectbox, use ``None`` as the index value: + + >>> import streamlit as st + >>> + >>> option = st.selectbox( + ... "How would you like to be contacted?", + ... ("Email", "Home phone", "Mobile phone"), + ... index=None, + ... placeholder="Select contact method...", + ... ) + >>> + >>> st.write('You selected:', option) + + .. output:: + https://doc-selectbox-empty.streamlit.app/ + height: 320px + """ ctx = get_script_run_ctx() return self._selectbox( @@ -193,19 +210,19 @@ def _selectbox( self, label: str, options: OptionSequence[T], - index: int = 0, + index: int | None = 0, format_func: Callable[[Any], Any] = str, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: placeholder: str = "Select...", disabled: bool = False, label_visibility: LabelVisibility = "visible", - ctx: Optional[ScriptRunContext] = None, - ) -> Optional[T]: + ctx: ScriptRunContext | None = None, + ) -> T | None: key = to_key(key) check_callback_rules(self.dg, on_change) check_session_state_rules(default_value=None if index == 0 else index, key=key) @@ -227,12 +244,12 @@ def _selectbox( page=ctx.page_script_hash if ctx else None, ) - if not isinstance(index, int): + if not isinstance(index, int) and index is not None: raise StreamlitAPIException( "Selectbox Value has invalid type: %s" % type(index).__name__ ) - if len(opt) > 0 and not 0 <= index < len(opt): + if index is not None and len(opt) > 0 and not 0 <= index < len(opt): raise StreamlitAPIException( "Selectbox index must be between 0 and length of options" ) @@ -240,7 +257,8 @@ def _selectbox( selectbox_proto = SelectboxProto() selectbox_proto.id = id selectbox_proto.label = label - selectbox_proto.default = index + if index is not None: + selectbox_proto.default = index selectbox_proto.options[:] = [str(format_func(option)) for option in opt] selectbox_proto.form_id = current_form_id(self.dg) selectbox_proto.placeholder = placeholder @@ -267,7 +285,9 @@ def _selectbox( ) if widget_state.value_changed: - selectbox_proto.value = serde.serialize(widget_state.value) + serialized_value = serde.serialize(widget_state.value) + if serialized_value is not None: + selectbox_proto.value = serialized_value selectbox_proto.set_value = True self.dg._enqueue("selectbox", selectbox_proto) diff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py index 7e5787435524..d9f3b594daea 100644 --- a/lib/streamlit/testing/element_tree.py +++ b/lib/streamlit/testing/element_tree.py @@ -646,7 +646,7 @@ def widget_state(self) -> WidgetState: @dataclass(repr=False) class Selectbox(Widget, Generic[T]): - _value: T | None + _value: T | None | InitialValue proto: SelectboxProto = field(repr=False) options: list[str] @@ -654,7 +654,7 @@ class Selectbox(Widget, Generic[T]): def __init__(self, proto: SelectboxProto, root: ElementTree): self.proto = proto self.root = root - self._value = None + self._value = InitialValue() self.type = "selectbox" self.id = proto.id @@ -666,22 +666,25 @@ def __init__(self, proto: SelectboxProto, root: ElementTree): self.key = user_key_from_widget_id(self.id) @property - def index(self) -> int: + def index(self) -> int | None: + if self.value is None: + return None + if len(self.options) == 0: return 0 return self.options.index(str(self.value)) @property - def value(self) -> T: + def value(self) -> T | None: """The currently selected value from the options.""" - if self._value is not None: + if not isinstance(self._value, InitialValue): return self._value else: state = self.root.session_state assert state return cast(T, state[self.id]) - def set_value(self, v: T) -> Selectbox[T]: + def set_value(self, v: T | None) -> Selectbox[T]: """ Set the value of the selectbox. Implementation note: set_value not work correctly if `format_func` is also @@ -691,10 +694,12 @@ def set_value(self, v: T) -> Selectbox[T]: self._value = v return self - def select(self, v: T) -> Selectbox[T]: + def select(self, v: T | None) -> Selectbox[T]: return self.set_value(v) - def select_index(self, index: int) -> Selectbox[T]: + def select_index(self, index: int | None) -> Selectbox[T]: + if index is None: + return self.set_value(None) return self.set_value(cast(T, self.options[index])) def widget_state(self) -> WidgetState: @@ -704,7 +709,8 @@ def widget_state(self) -> WidgetState: """ ws = WidgetState() ws.id = self.id - ws.int_value = self.index + if self.index is not None: + ws.int_value = self.index return ws diff --git a/lib/tests/streamlit/elements/selectbox_test.py b/lib/tests/streamlit/elements/selectbox_test.py index 5fce6aede9fe..1da07ee11fc1 100644 --- a/lib/tests/streamlit/elements/selectbox_test.py +++ b/lib/tests/streamlit/elements/selectbox_test.py @@ -23,6 +23,7 @@ import streamlit as st from streamlit.errors import StreamlitAPIException from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage +from streamlit.testing.script_interactions import InteractiveScriptTests from tests.delta_generator_test_case import DeltaGeneratorTestCase @@ -40,6 +41,7 @@ def test_just_label(self): LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE, ) self.assertEqual(c.default, 0) + self.assertEqual(c.HasField("default"), True) self.assertEqual(c.disabled, False) self.assertEqual(c.placeholder, "Select...") @@ -58,6 +60,17 @@ def test_valid_value(self): self.assertEqual(c.label, "the label") self.assertEqual(c.default, 1) + def test_none_index(self): + """Test that it can be called with None as index value.""" + st.selectbox("the label", ("m", "f"), index=None) + + c = self.get_delta_from_queue().new_element.selectbox + self.assertEqual(c.label, "the label") + # If a proto property is null is not determined by this value, + # but by the check via the HasField method: + self.assertEqual(c.default, 0) + self.assertEqual(c.HasField("default"), False) + def test_noneType_option(self): """Test NoneType option value.""" current_value = st.selectbox("the label", (None, "selected"), 0) @@ -188,3 +201,27 @@ def test_placeholder(self): c = self.get_delta_from_queue().new_element.selectbox self.assertEqual(c.placeholder, "Please select") + + +class SelectboxInteractiveTest(InteractiveScriptTests): + def test_selectbox_interaction(self): + """Test interactions with an empty selectbox widget.""" + script = self.script_from_string( + """ + import streamlit as st + st.selectbox("the label", ("m", "f"), index=None) + """ + ) + sr = script.run() + selectbox = sr.selectbox[0] + assert selectbox.value is None + + # Select option m + sr2 = selectbox.set_value("m").run() + selectbox = sr2.selectbox[0] + assert selectbox.value == "m" + + # # Clear the value + sr3 = selectbox.set_value(None).run() + selectbox = sr3.selectbox[0] + assert selectbox.value is None diff --git a/proto/streamlit/proto/Selectbox.proto b/proto/streamlit/proto/Selectbox.proto index 0f67cc1c6602..7e469c304875 100644 --- a/proto/streamlit/proto/Selectbox.proto +++ b/proto/streamlit/proto/Selectbox.proto @@ -21,11 +21,11 @@ import "streamlit/proto/LabelVisibilityMessage.proto"; message Selectbox { string id = 1; string label = 2; - int32 default = 3; + optional int32 default = 3; repeated string options = 4; string help = 5; string form_id = 6; - int32 value = 7; + optional int32 value = 7; bool set_value = 8; bool disabled = 9; LabelVisibilityMessage label_visibility = 10;
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-7840@be146db
streamlit/streamlit
Python
7,840
Add check that individual elements are "python comparable"
<!-- โš ๏ธ BEFORE CONTRIBUTING PLEASE READ OUR CONTRIBUTING GUIDELINES! https://github.com/streamlit/streamlit/wiki/Contributing --> ## Describe your changes Add check that individual elements are comparable and comparison return boolean ## GitHub Issue Link (if applicable) Closes: #3714 ## Testing Plan - Explanation of why no additional tests are needed - Unit Tests (JS and/or Python) Added โœ… - E2E Tests - Any manual testing needed? --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-12-14T14:47:07Z
Exception "The truth value of a Series is ambiguous" when using Pandas row object as widget state ### Summary Note: this is a new issue when I upgraded streamlit to the current version which added session state. Suppose I use a pandas object as the selected state for a widget like a radio button (code sample below). Then when changes occur in the widget, an exception is thrown: ``` ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). ``` ### Steps to reproduce Create a widget like a radio selector where the pandas row object (technically a `pd.Series`) is used as the selected object. Certainly one could use something like an id instead and then look up from the DataFrame, but this used to work and I had code that did things this way. It seemed convenient. ``` selected_object = st.radio("", [o for i, o in df.iterrows()], format_func=lambda o: o["someField"]) ``` With the session state update, any selection of a different radio button triggers the exception, because in `session_state.py`, the `_widget_changed` method does a boolean comparison of the selected object. This isn't allowed with `pd.Series` objects. ``` def _widget_changed(self, widget_id: str) -> bool: new_value = self._new_widget_state.get(widget_id) old_value = self._old_state.get(widget_id) changed: bool = new_value != old_value return changed ``` **Expected behavior:** Support pd.Series objects as widget selected values. **Actual behavior:** Exception is thrown, application fails to function. ### Is this a regression? Yes, worked before session state was added. ### Debug info Seen in streamlit 0.86.0
Thanks for reporting this @kahliburke. I'll look into this to see if there's a reasonable fix that doesn't require us to have to special-case the scenario where the widget values are `pd.Series` objects, although I have a hunch that there's not too much else we can do to support this. Thanks for the response. That is how I have have patched it locally and I can submit a PR if you'd like? @kahliburke that'd be much appreciated, thanks! I admittedly haven't put too much time into thinking of a solution that doesn't involve special-casing the behavior for `pd.Series` objects, but I think it'd be worth it to do that for now since it both strictly improves the current situation where things are broken and is a small change we can easily rework in the future if a more clever way of handling this does get figured out.
[ { "body": "### Summary\r\n\r\nNote: this is a new issue when I upgraded streamlit to the current version which added session state.\r\n\r\nSuppose I use a pandas object as the selected state for a widget like a radio button (code sample below). Then when changes occur in the widget, an exception is thrown:\r\n\r\n```\r\nValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\r\n```\r\n\r\n### Steps to reproduce\r\nCreate a widget like a radio selector where the pandas row object (technically a `pd.Series`) is used as the selected object. Certainly one could use something like an id instead and then look up from the DataFrame, but this used to work and I had code that did things this way. It seemed convenient.\r\n\r\n```\r\nselected_object = st.radio(\"\", [o for i, o in df.iterrows()], format_func=lambda o: o[\"someField\"])\r\n```\r\n\r\nWith the session state update, any selection of a different radio button triggers the exception, because in `session_state.py`, the `_widget_changed` method does a boolean comparison of the selected object. This isn't allowed with `pd.Series` objects.\r\n\r\n```\r\n def _widget_changed(self, widget_id: str) -> bool:\r\n new_value = self._new_widget_state.get(widget_id)\r\n old_value = self._old_state.get(widget_id)\r\n changed: bool = new_value != old_value\r\n return changed\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nSupport pd.Series objects as widget selected values.\r\n\r\n**Actual behavior:**\r\n\r\nException is thrown, application fails to function.\r\n\r\n### Is this a regression?\r\n\r\nYes, worked before session state was added.\r\n\r\n### Debug info\r\n\r\nSeen in streamlit 0.86.0\r\n", "number": 3714, "title": "Exception \"The truth value of a Series is ambiguous\" when using Pandas row object as widget state" } ]
9cfb2fc9684cbbe9928b34689f29a73ea3673df1
{ "head_commit": "be146db11629ed2d7571277e3c00e710b3c978c9", "head_commit_message": "Add check that individual elements are comparable and comparison return boolean", "patch_to_review": "diff --git a/lib/streamlit/elements/widgets/multiselect.py b/lib/streamlit/elements/widgets/multiselect.py\nindex 186d81065323..f859a7055b05 100644\n--- a/lib/streamlit/elements/widgets/multiselect.py\n+++ b/lib/streamlit/elements/widgets/multiselect.py\n@@ -50,6 +50,7 @@\n LabelVisibility,\n OptionSequence,\n T,\n+ check_python_comparable,\n ensure_indexable,\n is_iterable,\n is_type,\n@@ -294,6 +295,7 @@ def _multiselect(\n check_session_state_rules(default_value=default, key=key)\n \n opt = ensure_indexable(options)\n+ check_python_comparable(opt)\n maybe_raise_label_warnings(label, label_visibility)\n \n indices = _check_and_convert_to_indices(opt, default)\ndiff --git a/lib/streamlit/elements/widgets/radio.py b/lib/streamlit/elements/widgets/radio.py\nindex d9e5b37ab8d4..c9fd2121a87a 100644\n--- a/lib/streamlit/elements/widgets/radio.py\n+++ b/lib/streamlit/elements/widgets/radio.py\n@@ -41,6 +41,7 @@\n LabelVisibility,\n OptionSequence,\n T,\n+ check_python_comparable,\n ensure_indexable,\n maybe_raise_label_warnings,\n to_key,\n@@ -252,6 +253,7 @@ def _radio(\n check_session_state_rules(default_value=None if index == 0 else index, key=key)\n maybe_raise_label_warnings(label, label_visibility)\n opt = ensure_indexable(options)\n+ check_python_comparable(opt)\n \n id = compute_widget_id(\n \"radio\",\ndiff --git a/lib/streamlit/elements/widgets/select_slider.py b/lib/streamlit/elements/widgets/select_slider.py\nindex 72b2efabf360..d7f97b121f95 100644\n--- a/lib/streamlit/elements/widgets/select_slider.py\n+++ b/lib/streamlit/elements/widgets/select_slider.py\n@@ -53,6 +53,7 @@\n LabelVisibility,\n OptionSequence,\n T,\n+ check_python_comparable,\n ensure_indexable,\n maybe_raise_label_warnings,\n to_key,\n@@ -264,6 +265,7 @@ def _select_slider(\n check_session_state_rules(default_value=value, key=key)\n maybe_raise_label_warnings(label, label_visibility)\n opt = ensure_indexable(options)\n+ check_python_comparable(opt)\n \n if len(opt) == 0:\n raise StreamlitAPIException(\"The `options` argument needs to be non-empty\")\ndiff --git a/lib/streamlit/elements/widgets/selectbox.py b/lib/streamlit/elements/widgets/selectbox.py\nindex cca2781cde2b..87f715f30e73 100644\n--- a/lib/streamlit/elements/widgets/selectbox.py\n+++ b/lib/streamlit/elements/widgets/selectbox.py\n@@ -40,6 +40,7 @@\n LabelVisibility,\n OptionSequence,\n T,\n+ check_python_comparable,\n ensure_indexable,\n maybe_raise_label_warnings,\n to_key,\n@@ -234,6 +235,7 @@ def _selectbox(\n maybe_raise_label_warnings(label, label_visibility)\n \n opt = ensure_indexable(options)\n+ check_python_comparable(opt)\n \n id = compute_widget_id(\n \"selectbox\",\ndiff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py\nindex c1eba6e872ec..7f0b202c161e 100644\n--- a/lib/streamlit/type_util.py\n+++ b/lib/streamlit/type_util.py\n@@ -49,6 +49,7 @@\n from streamlit import config, errors\n from streamlit import logger as _logger\n from streamlit import string_util\n+from streamlit.errors import StreamlitAPIException\n \n if TYPE_CHECKING:\n import graphviz\n@@ -656,6 +657,23 @@ def ensure_indexable(obj: OptionSequence[V_co]) -> Sequence[V_co]:\n return list(it)\n \n \n+def check_python_comparable(seq: Sequence) -> None:\n+ \"\"\"Check if the sequence elements support \"python comparison\".\n+ That means that the equality operator (==) returns a boolean value.\n+ Which is not True for e.g. numpy arrays and pandas series.\n+ In case of empty sequences, the check not raise an exception.\"\"\"\n+ try:\n+ bool(seq[0] == seq[0])\n+ except LookupError:\n+ pass\n+ except ValueError:\n+ raise StreamlitAPIException(\n+ f\"Options must be python comparable. Got **{type(seq[0]).__name__}**. \\n\\n\"\n+ \"Please refactor your code, to use a comparable type, e.g. \"\n+ \"using index numbers instead.\"\n+ )\n+\n+\n def is_pandas_version_less_than(v: str) -> bool:\n \"\"\"Return True if the current Pandas version is less than the input version.\n \n" }
[ { "diff_hunk": "@@ -656,6 +657,23 @@\n return list(it)\n \n \n+def check_python_comparable(seq: Sequence) -> None:\n+ \"\"\"Check if the sequence elements support \"python comparison\".\n+ That means that the equality operator (==) returns a boolean value.\n+ Which is not True for e.g. numpy arrays and pandas series.\n+ In case of empty sequences, the check not raise an exception.\"\"\"\n+ try:\n+ bool(seq[0] == seq[0])\n+ except LookupError:", "line": 666, "original_line": 667, "original_start_line": null, "path": "lib/streamlit/type_util.py", "start_line": null, "text": "@user1:\n## Empty except\n\n'except' clause does nothing but pass and there is no explanatory comment.\n\n[Show more details](https://github.com/streamlit/streamlit/security/code-scanning/8175)" } ]
85ab9f34fc36d890c361eefcbbd2d1144ea773de
diff --git a/lib/streamlit/elements/widgets/multiselect.py b/lib/streamlit/elements/widgets/multiselect.py index 186d81065323..f859a7055b05 100644 --- a/lib/streamlit/elements/widgets/multiselect.py +++ b/lib/streamlit/elements/widgets/multiselect.py @@ -50,6 +50,7 @@ LabelVisibility, OptionSequence, T, + check_python_comparable, ensure_indexable, is_iterable, is_type, @@ -294,6 +295,7 @@ def _multiselect( check_session_state_rules(default_value=default, key=key) opt = ensure_indexable(options) + check_python_comparable(opt) maybe_raise_label_warnings(label, label_visibility) indices = _check_and_convert_to_indices(opt, default) diff --git a/lib/streamlit/elements/widgets/radio.py b/lib/streamlit/elements/widgets/radio.py index d9e5b37ab8d4..c9fd2121a87a 100644 --- a/lib/streamlit/elements/widgets/radio.py +++ b/lib/streamlit/elements/widgets/radio.py @@ -41,6 +41,7 @@ LabelVisibility, OptionSequence, T, + check_python_comparable, ensure_indexable, maybe_raise_label_warnings, to_key, @@ -252,6 +253,7 @@ def _radio( check_session_state_rules(default_value=None if index == 0 else index, key=key) maybe_raise_label_warnings(label, label_visibility) opt = ensure_indexable(options) + check_python_comparable(opt) id = compute_widget_id( "radio", diff --git a/lib/streamlit/elements/widgets/select_slider.py b/lib/streamlit/elements/widgets/select_slider.py index 72b2efabf360..d7f97b121f95 100644 --- a/lib/streamlit/elements/widgets/select_slider.py +++ b/lib/streamlit/elements/widgets/select_slider.py @@ -53,6 +53,7 @@ LabelVisibility, OptionSequence, T, + check_python_comparable, ensure_indexable, maybe_raise_label_warnings, to_key, @@ -264,6 +265,7 @@ def _select_slider( check_session_state_rules(default_value=value, key=key) maybe_raise_label_warnings(label, label_visibility) opt = ensure_indexable(options) + check_python_comparable(opt) if len(opt) == 0: raise StreamlitAPIException("The `options` argument needs to be non-empty") diff --git a/lib/streamlit/elements/widgets/selectbox.py b/lib/streamlit/elements/widgets/selectbox.py index cca2781cde2b..87f715f30e73 100644 --- a/lib/streamlit/elements/widgets/selectbox.py +++ b/lib/streamlit/elements/widgets/selectbox.py @@ -40,6 +40,7 @@ LabelVisibility, OptionSequence, T, + check_python_comparable, ensure_indexable, maybe_raise_label_warnings, to_key, @@ -234,6 +235,7 @@ def _selectbox( maybe_raise_label_warnings(label, label_visibility) opt = ensure_indexable(options) + check_python_comparable(opt) id = compute_widget_id( "selectbox", diff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py index c1eba6e872ec..23695edf4b2f 100644 --- a/lib/streamlit/type_util.py +++ b/lib/streamlit/type_util.py @@ -49,6 +49,7 @@ from streamlit import config, errors from streamlit import logger as _logger from streamlit import string_util +from streamlit.errors import StreamlitAPIException if TYPE_CHECKING: import graphviz @@ -656,6 +657,24 @@ def ensure_indexable(obj: OptionSequence[V_co]) -> Sequence[V_co]: return list(it) +def check_python_comparable(seq: Sequence[Any]) -> None: + """Check if the sequence elements support "python comparison". + That means that the equality operator (==) returns a boolean value. + Which is not True for e.g. numpy arrays and pandas series.""" + try: + bool(seq[0] == seq[0]) + except LookupError: + # In case of empty sequences, the check not raise an exception. + pass + except ValueError: + raise StreamlitAPIException( + "Invalid option type provided. Options must be comparable, returning a " + f"boolean when used with *==*. \n\nGot **{type(seq[0]).__name__}**, " + "which cannot be compared. Refactor your code to use elements of " + "comparable types as options, e.g. use indices instead." + ) + + def is_pandas_version_less_than(v: str) -> bool: """Return True if the current Pandas version is less than the input version. diff --git a/lib/tests/streamlit/type_util_test.py b/lib/tests/streamlit/type_util_test.py index 85a8b89db912..0e27de23797e 100644 --- a/lib/tests/streamlit/type_util_test.py +++ b/lib/tests/streamlit/type_util_test.py @@ -28,6 +28,7 @@ from parameterized import parameterized from streamlit import errors, type_util +from streamlit.errors import StreamlitAPIException from tests.streamlit.data_mocks import ( BASE_TYPES_DF, DATETIME_TYPES_DF, @@ -247,6 +248,41 @@ def test_convert_anything_to_df_converts_stylers_and_clones_data(self): self.assertNotEqual(id(original_df), id(out)) pd.testing.assert_frame_equal(original_df, out) + @parameterized.expand( + [ + ([1, 2, 3],), + (["foo", "bar", "baz"],), + (np.array([1, 2, 3, 4]),), + (pd.Series([1, 2, 3]),), + ] + ) + def test_check_python_comparable(self, sequence): + """Test that `check_python_comparable` not raises exception + when elements of sequence returns bool when compared.""" + + # Just check that it should not raise any exception + type_util.check_python_comparable(sequence) + + @parameterized.expand( + [ + (np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), "ndarray"), + ([pd.Series([1, 2, 3]), pd.Series([4, 5, 6])], "Series"), + ] + ) + def test_check_python_comparable_exception(self, sequence, type_str): + """Test that `check_python_comparable` raises an exception if ndarray.""" + with pytest.raises(StreamlitAPIException) as exception_message: + type_util.check_python_comparable(sequence) + self.assertEqual( + ( + "Invalid option type provided. Options must be comparable, returning a " + f"boolean when used with *==*. \n\nGot **{type_str}**, which cannot be " + "compared. Refactor your code to use elements of comparable types as " + "options, e.g. use indices instead." + ), + str(exception_message.value), + ) + def test_convert_anything_to_df_calls_to_pandas_when_available(self): class DataFrameIsh: def to_pandas(self):
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
zulip__zulip-31199@0dddf15
zulip/zulip
Python
31,199
Defer computing last_edit_timestr until tooltip renders.
Earlier, we used to compute last_edit_timestr as data-tippy-content when rendering the whole message feed. This commit changes the behaviour by computing the `last_edit_timestr` when a user hovers over the message_edit_notice. Fixes: zulip#27240. <!-- Describe your pull request here.--> <!-- If the PR makes UI changes, always include one or more still screenshots to demonstrate your changes. If it seems helpful, add a screen capture of the new functionality as well. Tooling tips: https://zulip.readthedocs.io/en/latest/tutorials/screenshot-and-gif-software.html --> **Screenshots and screen captures:** <details> <summary>Self-review checklist</summary> <!-- Prior to submitting a PR, follow our step-by-step guide to review your own code: https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code --> <!-- Once you create the PR, check off all the steps below that you have completed. If any of these steps are not relevant or you have not completed, leave them unchecked.--> - [x] [Self-reviewed](https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code) the changes for clarity and maintainability (variable names, code reuse, readability, etc.). Communicate decisions, questions, and potential concerns. - [x] Explains differences from previous plans (e.g., issue description). - [x] Highlights technical choices and bugs encountered. - [x] Calls out remaining decisions and concerns. - [ ] Automated tests verify logic where appropriate. Individual commits are ready for review (see [commit discipline](https://zulip.readthedocs.io/en/latest/contributing/commit-discipline.html)). - [x] Each commit is a coherent idea. - [x] Commit message(s) explain reasoning and motivation for changes. Completed manual review and testing of the following: - [x] Visual appearance of the changes. - [x] Responsiveness and internationalization. - [x] Strings and tooltips. - [x] End-to-end functionality of buttons, interactions and flows. - [x] Corner cases, error conditions, and easily imagined bugs. </details>
2024-07-31T21:53:52Z
Defer rendering last_edit_timestr until actually hovering over a message At present, we have logic that computes the `last_edit_timestr` to display as a Tippy tooltip when hovering over a message date element: ``` $ cat web/templates/edited_notice.hbs {{#if msg/local_edit_timestamp}} <div class="message_edit_notice" data-tippy-content="{{t 'Last edited {last_edit_timestr}.' }}"> {{t "SAVING" }} </div> {{else if moved}} <div class="message_edit_notice" data-tippy-content="{{t 'Last moved {last_edit_timestr}.' }}"> {{t "MOVED" }} </div> {{else}} <div class="message_edit_notice" data-tippy-content="{{t 'Last edited {last_edit_timestr}.' }}"> {{t "EDITED" }} </div> {{/if}} ``` When rendering a message feed containing a lot of messages that have been moved, computing all these localized time renderings in a loop is expensive. We should start deferring that work to when the user actually hovers it. We already have a function that is called on such a hover that could be extended to call `_get_msg_timestring` on the message object after looking it up in the message list data structure. ``` message_list_tooltip(".message_edit_notice", { trigger: "mouseenter", delay: LONG_HOVER_DELAY, popperOptions: { modifiers: [ { name: "flip", options: { fallbackPlacements: "bottom", }, }, ], }, onShow(instance) { const $elem = $(instance.reference); const edited_notice_str = $elem.attr("data-tippy-content"); instance.setContent( parse_html( render_message_edit_notice_tooltip({ edited_notice_str, realm_allow_edit_history: page_params.realm_allow_edit_history, }), ), ); }, onHidden(instance) { instance.destroy(); }, }); ```
[ { "body": "At present, we have logic that computes the `last_edit_timestr` to display as a Tippy tooltip when hovering over a message date element:\r\n\r\n```\r\n$ cat web/templates/edited_notice.hbs\r\n{{#if msg/local_edit_timestamp}}\r\n<div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last edited {last_edit_timestr}.' }}\">\r\n {{t \"SAVING\" }}\r\n</div>\r\n{{else if moved}}\r\n<div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last moved {last_edit_timestr}.' }}\">\r\n {{t \"MOVED\" }}\r\n</div>\r\n{{else}}\r\n<div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last edited {last_edit_timestr}.' }}\">\r\n {{t \"EDITED\" }}\r\n</div>\r\n{{/if}}\r\n```\r\n\r\nWhen rendering a message feed containing a lot of messages that have been moved, computing all these localized time renderings in a loop is expensive. We should start deferring that work to when the user actually hovers it.\r\n\r\nWe already have a function that is called on such a hover that could be extended to call `_get_msg_timestring` on the message object after looking it up in the message list data structure.\r\n\r\n```\r\n message_list_tooltip(\".message_edit_notice\", { \r\n trigger: \"mouseenter\", \r\n delay: LONG_HOVER_DELAY, \r\n popperOptions: { \r\n modifiers: [ \r\n { \r\n name: \"flip\", \r\n options: { \r\n fallbackPlacements: \"bottom\", \r\n }, \r\n }, \r\n ], \r\n }, \r\n onShow(instance) { \r\n const $elem = $(instance.reference); \r\n const edited_notice_str = $elem.attr(\"data-tippy-content\"); \r\n instance.setContent( \r\n parse_html( \r\n render_message_edit_notice_tooltip({ \r\n edited_notice_str, \r\n realm_allow_edit_history: page_params.realm_allow_edit_history, \r\n }), \r\n ), \r\n ); \r\n }, \r\n onHidden(instance) { \r\n instance.destroy(); \r\n }, \r\n }); \r\n```", "number": 27240, "title": "Defer rendering last_edit_timestr until actually hovering over a message" } ]
4b25425adfb95584271f867ea995a58dd335c5fd
{ "head_commit": "0dddf152d7bf332c2874d62b907d847bfe7c6385", "head_commit_message": "tooltips: Defer computing last_edit_timestr until tooltip renders.\n\nEarlier, we used to compute last_edit_timestr as data-tippy-content\nwhen rendering the whole message feed.\n\nThis commit changes the behaviour by computing the `last_edit_timestr`\nwhen a user hovers over the message_edit_notice.\n\nFixes: zulip#27240.", "patch_to_review": "diff --git a/web/src/message_list_tooltips.ts b/web/src/message_list_tooltips.ts\nindex 58992b8a4a234..6081d3db54336 100644\n--- a/web/src/message_list_tooltips.ts\n+++ b/web/src/message_list_tooltips.ts\n@@ -6,7 +6,9 @@ import render_message_edit_notice_tooltip from \"../templates/message_edit_notice\n import render_message_inline_image_tooltip from \"../templates/message_inline_image_tooltip.hbs\";\n import render_narrow_tooltip from \"../templates/narrow_tooltip.hbs\";\n \n+import {$t} from \"./i18n\";\n import * as message_lists from \"./message_lists\";\n+import type {Message} from \"./message_store\";\n import * as popover_menus from \"./popover_menus\";\n import * as reactions from \"./reactions\";\n import * as rows from \"./rows\";\n@@ -120,6 +122,30 @@ export function destroy_all_message_list_tooltips(): void {\n message_list_tippy_instances.clear();\n }\n \n+function get_last_edit_timestr(message: Message): string {\n+ let last_edit_timestamp;\n+ if (message.local_edit_timestamp !== undefined) {\n+ last_edit_timestamp = message.local_edit_timestamp;\n+ } else {\n+ last_edit_timestamp = message.last_edit_timestamp!;\n+ }\n+ const last_edit_time = new Date(last_edit_timestamp * 1000);\n+ let date = timerender.render_date(last_edit_time).textContent;\n+ // If the date is today or yesterday, we don't want to show the date as capitalized.\n+ // Thus, we need to check if the date string contains a digit or not using regex,\n+ // since any other date except today/yesterday will contain a digit.\n+ if (date && !/\\d/.test(date)) {\n+ date = date.toLowerCase();\n+ }\n+ return $t(\n+ {defaultMessage: \"{date} at {time}\"},\n+ {\n+ date,\n+ time: timerender.stringify_time(last_edit_time),\n+ },\n+ );\n+}\n+\n export function initialize(): void {\n message_list_tooltip(\".tippy-narrow-tooltip\", {\n delay: LONG_HOVER_DELAY,\n@@ -338,11 +364,16 @@ export function initialize(): void {\n },\n onShow(instance) {\n const $elem = $(instance.reference);\n- const edited_notice_str = $elem.attr(\"data-tippy-content\");\n+ assert(message_lists.current !== undefined);\n+ const message_id = Number($elem.closest(\".message_row\").attr(\"data-message-id\"));\n+ const message_container = message_lists.current.view.message_containers.get(message_id);\n+ assert(message_container !== undefined);\n+ const last_edit_timestr = get_last_edit_timestr(message_container.msg);\n instance.setContent(\n parse_html(\n render_message_edit_notice_tooltip({\n- edited_notice_str,\n+ message_container,\n+ last_edit_timestr,\n realm_allow_edit_history: realm.realm_allow_edit_history,\n }),\n ),\ndiff --git a/web/src/message_list_view.ts b/web/src/message_list_view.ts\nindex 931aac5a1396d..5b97f94d0fd32 100644\n--- a/web/src/message_list_view.ts\n+++ b/web/src/message_list_view.ts\n@@ -52,7 +52,7 @@ export type MessageContainer = {\n include_recipient: boolean;\n include_sender: boolean;\n is_hidden: boolean;\n- last_edit_timestr: string | undefined;\n+ last_edit_timestamp: number | undefined;\n mention_classname: string | undefined;\n message_edit_notices_in_left_col: boolean;\n message_edit_notices_alongside_sender: boolean;\n@@ -150,7 +150,7 @@ function same_recipient(a: MessageContainer | undefined, b: MessageContainer | u\n \n function analyze_edit_history(\n message: Message,\n- last_edit_timestr: string | undefined,\n+ last_edit_timestamp: number | undefined,\n ): {\n edited: boolean;\n moved: boolean;\n@@ -201,9 +201,9 @@ function analyze_edit_history(\n moved = true;\n }\n }\n- } else if (last_edit_timestr !== undefined) {\n+ } else if (last_edit_timestamp !== undefined) {\n // When the edit_history is disabled for the organization, we do not receive the edit_history\n- // variable in the message object. In this case, we will check if the last_edit_timestr is\n+ // variable in the message object. In this case, we will check if the last_edit_timestamp is\n // available or not. Since we don't have the edit_history, we can't determine if the message\n // was moved or edited. Therefore, we simply mark the messages as edited.\n edited = true;\n@@ -582,59 +582,34 @@ export class MessageListView {\n );\n }\n \n- _get_msg_timestring(message: Message): string | undefined {\n+ _get_message_edited_vars(message: Message): {\n+ last_edit_timestamp: number | undefined;\n+ moved: boolean;\n+ modified: boolean;\n+ } {\n let last_edit_timestamp;\n if (message.local_edit_timestamp !== undefined) {\n last_edit_timestamp = message.local_edit_timestamp;\n } else {\n last_edit_timestamp = message.last_edit_timestamp;\n }\n- if (last_edit_timestamp !== undefined) {\n- const last_edit_time = new Date(last_edit_timestamp * 1000);\n- let date = timerender.render_date(last_edit_time).textContent;\n- // If the date is today or yesterday, we don't want to show the date as capitalized.\n- // Thus, we need to check if the date string contains a digit or not using regex,\n- // since any other date except today/yesterday will contain a digit.\n- if (date && !/\\d/.test(date)) {\n- date = date.toLowerCase();\n- }\n- return $t(\n- {defaultMessage: \"{date} at {time}\"},\n- {\n- date,\n- time: timerender.stringify_time(last_edit_time),\n- },\n- );\n- }\n- return undefined;\n- }\n-\n- _get_message_edited_vars(message: Message): {\n- last_edit_timestr: string | undefined;\n- moved: boolean;\n- modified: boolean;\n- } {\n- const last_edit_timestr = this._get_msg_timestring(message);\n- const edit_history_details = analyze_edit_history(message, last_edit_timestr);\n+ const edit_history_details = analyze_edit_history(message, last_edit_timestamp);\n \n- if (\n- last_edit_timestr === undefined ||\n- !(edit_history_details.moved || edit_history_details.edited)\n- ) {\n+ if (!last_edit_timestamp || !(edit_history_details.moved || edit_history_details.edited)) {\n // For messages whose edit history at most includes\n // resolving topics, we don't display an EDITED/MOVED\n // notice at all. (The message actions popover will still\n // display an edit history option, so you can see when it\n // was marked as resolved if you need to).\n return {\n- last_edit_timestr: undefined,\n+ last_edit_timestamp: undefined,\n moved: false,\n modified: false,\n };\n }\n \n return {\n- last_edit_timestr,\n+ last_edit_timestamp,\n moved: edit_history_details.moved && !edit_history_details.edited,\n modified: true,\n };\n@@ -659,7 +634,7 @@ export class MessageListView {\n mention_classname: string | undefined;\n include_sender: boolean;\n status_message: string | false;\n- last_edit_timestr: string | undefined;\n+ last_edit_timestamp: number | undefined;\n moved: boolean;\n modified: boolean;\n } {\ndiff --git a/web/templates/edited_notice.hbs b/web/templates/edited_notice.hbs\nindex d7e25e4e190bd..a4931cfccce9e 100644\n--- a/web/templates/edited_notice.hbs\n+++ b/web/templates/edited_notice.hbs\n@@ -1,14 +1,14 @@\n {{#if modified}}\n {{#if msg/local_edit_timestamp}}\n- <div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last edited {last_edit_timestr}.'}}\">\n+ <div class=\"message_edit_notice\">\n {{t \"SAVING\"}}\n </div>\n {{else if moved}}\n- <div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last moved {last_edit_timestr}.'}}\">\n+ <div class=\"message_edit_notice\">\n {{t \"MOVED\"}}\n </div>\n {{else}}\n- <div class=\"message_edit_notice\" data-tippy-content=\"{{t 'Last edited {last_edit_timestr}.'}}\">\n+ <div class=\"message_edit_notice\">\n {{t \"EDITED\"}}\n </div>\n {{/if}}\ndiff --git a/web/templates/message_edit_notice_tooltip.hbs b/web/templates/message_edit_notice_tooltip.hbs\nindex 69ff0dd48917b..9f0f6418ff100 100644\n--- a/web/templates/message_edit_notice_tooltip.hbs\n+++ b/web/templates/message_edit_notice_tooltip.hbs\n@@ -1,7 +1,11 @@\n <div>\n <div>{{t \"View edit history\"}}</div>\n {{#if realm_allow_edit_history}}\n- <div class=\"tooltip-inner-content italic\">{{edited_notice_str}}</div>\n+ {{#if message_container/moved}}\n+ <div class=\"tooltip-inner-content italic\">{{t 'Last moved {last_edit_timestr}.'}}</div>\n+ {{else}}\n+ <div class=\"tooltip-inner-content italic\">{{t 'Last edited {last_edit_timestr}.'}}</div>\n+ {{/if}}\n {{/if}}\n </div>\n {{tooltip_hotkey_hints \"Shift\" \"H\"}}\n" }
[ { "diff_hunk": "@@ -1,7 +1,11 @@\n <div>\n <div>{{t \"View edit history\"}}</div>\n {{#if realm_allow_edit_history}}", "line": 2, "original_line": 2, "original_start_line": null, "path": "web/templates/message_edit_notice_tooltip.hbs", "start_line": null, "text": "@user2:\nThis preexisting check is in the wrong place. Looks like @user1 introduced this in abece73c5a580e0bc6a27c7603974c1e281df6dd, which swapped the strings around without bringing the conditionals along." } ]
b18dee2c9e35d5743b71564b6080418a804e8ea6
diff --git a/web/src/message_list_tooltips.ts b/web/src/message_list_tooltips.ts index 58992b8a4a234..5abe9e111fd60 100644 --- a/web/src/message_list_tooltips.ts +++ b/web/src/message_list_tooltips.ts @@ -6,7 +6,9 @@ import render_message_edit_notice_tooltip from "../templates/message_edit_notice import render_message_inline_image_tooltip from "../templates/message_inline_image_tooltip.hbs"; import render_narrow_tooltip from "../templates/narrow_tooltip.hbs"; +import {$t} from "./i18n"; import * as message_lists from "./message_lists"; +import type {Message} from "./message_store"; import * as popover_menus from "./popover_menus"; import * as reactions from "./reactions"; import * as rows from "./rows"; @@ -120,6 +122,30 @@ export function destroy_all_message_list_tooltips(): void { message_list_tippy_instances.clear(); } +function get_last_edit_timestr(message: Message): string { + let last_edit_timestamp; + if (message.local_edit_timestamp !== undefined) { + last_edit_timestamp = message.local_edit_timestamp; + } else { + last_edit_timestamp = message.last_edit_timestamp!; + } + const last_edit_time = new Date(last_edit_timestamp * 1000); + let date = timerender.render_date(last_edit_time).textContent; + // If the date is today or yesterday, we don't want to show the date as capitalized. + // Thus, we need to check if the date string contains a digit or not using regex, + // since any other date except today/yesterday will contain a digit. + if (date && !/\d/.test(date)) { + date = date.toLowerCase(); + } + return $t( + {defaultMessage: "{date} at {time}"}, + { + date, + time: timerender.stringify_time(last_edit_time), + }, + ); +} + export function initialize(): void { message_list_tooltip(".tippy-narrow-tooltip", { delay: LONG_HOVER_DELAY, @@ -338,11 +364,17 @@ export function initialize(): void { }, onShow(instance) { const $elem = $(instance.reference); - const edited_notice_str = $elem.attr("data-tippy-content"); + const message_id = Number($elem.closest(".message_row").attr("data-message-id")); + + assert(message_lists.current !== undefined); + const message_container = message_lists.current.view.message_containers.get(message_id); + assert(message_container !== undefined); + const last_edit_timestr = get_last_edit_timestr(message_container.msg); instance.setContent( parse_html( render_message_edit_notice_tooltip({ - edited_notice_str, + moved: message_container.moved, + last_edit_timestr, realm_allow_edit_history: realm.realm_allow_edit_history, }), ), diff --git a/web/src/message_list_view.ts b/web/src/message_list_view.ts index 931aac5a1396d..5b97f94d0fd32 100644 --- a/web/src/message_list_view.ts +++ b/web/src/message_list_view.ts @@ -52,7 +52,7 @@ export type MessageContainer = { include_recipient: boolean; include_sender: boolean; is_hidden: boolean; - last_edit_timestr: string | undefined; + last_edit_timestamp: number | undefined; mention_classname: string | undefined; message_edit_notices_in_left_col: boolean; message_edit_notices_alongside_sender: boolean; @@ -150,7 +150,7 @@ function same_recipient(a: MessageContainer | undefined, b: MessageContainer | u function analyze_edit_history( message: Message, - last_edit_timestr: string | undefined, + last_edit_timestamp: number | undefined, ): { edited: boolean; moved: boolean; @@ -201,9 +201,9 @@ function analyze_edit_history( moved = true; } } - } else if (last_edit_timestr !== undefined) { + } else if (last_edit_timestamp !== undefined) { // When the edit_history is disabled for the organization, we do not receive the edit_history - // variable in the message object. In this case, we will check if the last_edit_timestr is + // variable in the message object. In this case, we will check if the last_edit_timestamp is // available or not. Since we don't have the edit_history, we can't determine if the message // was moved or edited. Therefore, we simply mark the messages as edited. edited = true; @@ -582,59 +582,34 @@ export class MessageListView { ); } - _get_msg_timestring(message: Message): string | undefined { + _get_message_edited_vars(message: Message): { + last_edit_timestamp: number | undefined; + moved: boolean; + modified: boolean; + } { let last_edit_timestamp; if (message.local_edit_timestamp !== undefined) { last_edit_timestamp = message.local_edit_timestamp; } else { last_edit_timestamp = message.last_edit_timestamp; } - if (last_edit_timestamp !== undefined) { - const last_edit_time = new Date(last_edit_timestamp * 1000); - let date = timerender.render_date(last_edit_time).textContent; - // If the date is today or yesterday, we don't want to show the date as capitalized. - // Thus, we need to check if the date string contains a digit or not using regex, - // since any other date except today/yesterday will contain a digit. - if (date && !/\d/.test(date)) { - date = date.toLowerCase(); - } - return $t( - {defaultMessage: "{date} at {time}"}, - { - date, - time: timerender.stringify_time(last_edit_time), - }, - ); - } - return undefined; - } - - _get_message_edited_vars(message: Message): { - last_edit_timestr: string | undefined; - moved: boolean; - modified: boolean; - } { - const last_edit_timestr = this._get_msg_timestring(message); - const edit_history_details = analyze_edit_history(message, last_edit_timestr); + const edit_history_details = analyze_edit_history(message, last_edit_timestamp); - if ( - last_edit_timestr === undefined || - !(edit_history_details.moved || edit_history_details.edited) - ) { + if (!last_edit_timestamp || !(edit_history_details.moved || edit_history_details.edited)) { // For messages whose edit history at most includes // resolving topics, we don't display an EDITED/MOVED // notice at all. (The message actions popover will still // display an edit history option, so you can see when it // was marked as resolved if you need to). return { - last_edit_timestr: undefined, + last_edit_timestamp: undefined, moved: false, modified: false, }; } return { - last_edit_timestr, + last_edit_timestamp, moved: edit_history_details.moved && !edit_history_details.edited, modified: true, }; @@ -659,7 +634,7 @@ export class MessageListView { mention_classname: string | undefined; include_sender: boolean; status_message: string | false; - last_edit_timestr: string | undefined; + last_edit_timestamp: number | undefined; moved: boolean; modified: boolean; } { diff --git a/web/templates/edited_notice.hbs b/web/templates/edited_notice.hbs index d7e25e4e190bd..a4931cfccce9e 100644 --- a/web/templates/edited_notice.hbs +++ b/web/templates/edited_notice.hbs @@ -1,14 +1,14 @@ {{#if modified}} {{#if msg/local_edit_timestamp}} - <div class="message_edit_notice" data-tippy-content="{{t 'Last edited {last_edit_timestr}.'}}"> + <div class="message_edit_notice"> {{t "SAVING"}} </div> {{else if moved}} - <div class="message_edit_notice" data-tippy-content="{{t 'Last moved {last_edit_timestr}.'}}"> + <div class="message_edit_notice"> {{t "MOVED"}} </div> {{else}} - <div class="message_edit_notice" data-tippy-content="{{t 'Last edited {last_edit_timestr}.'}}"> + <div class="message_edit_notice"> {{t "EDITED"}} </div> {{/if}} diff --git a/web/templates/message_edit_notice_tooltip.hbs b/web/templates/message_edit_notice_tooltip.hbs index 69ff0dd48917b..ca9e500914dab 100644 --- a/web/templates/message_edit_notice_tooltip.hbs +++ b/web/templates/message_edit_notice_tooltip.hbs @@ -1,7 +1,15 @@ <div> - <div>{{t "View edit history"}}</div> {{#if realm_allow_edit_history}} - <div class="tooltip-inner-content italic">{{edited_notice_str}}</div> + <div>{{t "View edit history"}}</div> {{/if}} + <div class="tooltip-inner-content italic"> + {{#if moved}} + {{t 'Last moved {last_edit_timestr}.'}} + {{else}} + {{t 'Last edited {last_edit_timestr}.'}} + {{/if}} + </div> </div> +{{#if realm_allow_edit_history}} {{tooltip_hotkey_hints "Shift" "H"}} +{{/if}}
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Performance Optimizations" }
streamlit__streamlit-7269@007fb8e
streamlit/streamlit
Python
7,269
Allow `None` as index for `st.radio`
## Describe your changes This PR adds support for setting `None` as the index in `st.radio`. Using `index=None` will render an empty radio with no option selected which returns `None` as long as the user hasn't selected an option: <img width="219" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/877bce2b-5d69-4184-888e-134da711c321"> - Spec: https://www.notion.so/snowflake-corp/Spec-v2-b00de8d56f99464693e7ddfdea0674e9 ## GitHub Issue Link (if applicable) - Closes #5220 ## Testing Plan - Unit Tests: โœ… - E2E Tests: โœ… --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-09-02T12:14:56Z
Radio button without pre-selection This option is possible as I saw in other platforms. It would be cool if you also add this option. --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
Yes, this would a convenient feature. I currently insert a blank element as the 1st radio button element, as a current workaround. @kiansina Thanks for your suggestion! This sounds like a useful enhancement ๐Ÿ‘ I will forward this feature request to our product team. For other users reading this enhancement: please upvote the issue (๐Ÿ‘) if you also want to have this implemented! As a temporary solution I am using hacked checkboxes. No preselected box and only one can be picked at a time. Hope you find it useful ๐Ÿ˜„ . ```python import streamlit as st def disable_other_checkboxes(*other_checkboxes_keys): for checkbox_key in other_checkboxes_keys: st.session_state[checkbox_key] = False option_1 = st.checkbox( "Option_1", key="op1", on_change=disable_other_checkboxes, args=("op2", "op3", "op4"), ) option_2 = st.checkbox( "Option_2", key="op2", on_change=disable_other_checkboxes, args=("op1", "op3", "op4"), ) option_3 = st.checkbox( "Option_3", key="op3", on_change=disable_other_checkboxes, args=("op2", "op1", "op4"), ) option_4 = st.checkbox( "Option_4", key="op4", on_change=disable_other_checkboxes, args=("op2", "op3", "op1"), ) st.write(option_1, option_2, option_3, option_4) ``` <img width="408" alt="Screenshot 2022-08-31 at 18 25 55" src="https://user-images.githubusercontent.com/41453558/187730074-8b8e095b-1ee9-4ccf-a6b5-550d79137127.png"> Can you all describe what exactly you want this for? This would help us decide if we want to support it or not. My use case would be for a survey where we want to make sure users have responded to a question. I was planning on tracking if they've responded using session state but that doesn't account for the situation where the pre-selected option is what the user would actually like to respond to. Basically just trying to hack a way to get "required" questions when using `st.forms` Hey all, just wanted to give a quick update. This issue is on our radar. The blocker for it is that (unfortunately!) the UI framework we use under the hood (BaseWeb) doesnโ€™t support radio buttons without a default selection. So if we want to fix this, weโ€™d need to either use a different UI framework (lots of work) or build our own component (also lots of work). Itโ€™s definitely possible weโ€™ll do this at some point but for right now it seems like too big of a time investment.
[ { "body": "This option is possible as I saw in other platforms. It would be cool if you also add this option. \r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**", "number": 5220, "title": "Radio button without pre-selection" } ]
b617e07d0ee9bfe13a3d898f2a4dfb5ae8effb72
{ "head_commit": "007fb8eff70ad9d371582c321b2a6c9135102288", "head_commit_message": "Use testid", "patch_to_review": "diff --git a/e2e/scripts/st_radio.py b/e2e/scripts/st_radio.py\nindex 586c292e5114..9d0edb0dc894 100644\n--- a/e2e/scripts/st_radio.py\n+++ b/e2e/scripts/st_radio.py\n@@ -35,7 +35,7 @@\n i2 = st.radio(\"radio 2\", options, 0, format_func=lambda x: x.capitalize())\n st.write(\"value 2:\", i2)\n \n-i3 = st.radio(\"radio 3\", [])\n+i3: None = st.radio(\"radio 3\", [])\n st.write(\"value 3:\", i3)\n \n i4 = st.radio(\"radio 4\", options, disabled=True)\ndiff --git a/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-callback_help[dark_theme-chromium].png b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-callback_help[dark_theme-chromium].png\nnew file mode 100644\nindex 000000000000..711e80ddd432\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-callback_help[dark_theme-chromium].png differ\ndiff --git 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b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-no_options[light_theme-firefox].png\nnew file mode 100644\nindex 000000000000..802e0f7c9d1b\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-no_options[light_theme-firefox].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-no_options[light_theme-webkit].png b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-no_options[light_theme-webkit].png\nnew file mode 100644\nindex 000000000000..fe8b784c2ac0\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_radio_test/st_radio-no_options[light_theme-webkit].png differ\ndiff --git a/e2e_playwright/st_radio.py b/e2e_playwright/st_radio.py\nnew file mode 100644\nindex 000000000000..0ae28f3425f4\n--- /dev/null\n+++ b/e2e_playwright/st_radio.py\n@@ -0,0 +1,97 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import pandas as pd\n+\n+import streamlit as st\n+from streamlit import runtime\n+\n+options = (\"female\", \"male\")\n+markdown_options = (\n+ \"**bold text**\",\n+ \"*italics text*\",\n+ \"~strikethrough text~\",\n+ \"shortcode: :blush:\",\n+ # link should not work in radio options\n+ \"[link text](www.example.com)\",\n+ \"`code text`\",\n+ \":red[red] :blue[blue] :green[green] :violet[violet] :orange[orange]\",\n+)\n+\n+i1 = st.radio(\"radio 1 (default)\", options)\n+st.write(\"value 1:\", i1)\n+\n+i2 = st.radio(\n+ \"radio 2 (Formatted options)\",\n+ options,\n+ 1,\n+ format_func=lambda x: x.capitalize(),\n+)\n+st.write(\"value 2:\", i2)\n+\n+i3: None = st.radio(\"radio 3 (no options)\", [])\n+st.write(\"value 3:\", i3)\n+\n+i4 = st.radio(\"radio 4 (disabled)\", options, disabled=True)\n+st.write(\"value 4:\", i4)\n+\n+i5 = st.radio(\"radio 5 (horizontal)\", options, horizontal=True)\n+st.write(\"value 5:\", i5)\n+\n+i6 = st.radio(\"radio 6 (options from dataframe)\", pd.DataFrame({\"foo\": list(options)}))\n+st.write(\"value 6:\", i6)\n+\n+i7 = st.radio(\"radio 7 (hidden label)\", options, label_visibility=\"hidden\")\n+st.write(\"value 7:\", i7)\n+\n+i8 = st.radio(\"radio 8 (collapsed label)\", options, label_visibility=\"collapsed\")\n+st.write(\"value 8:\", i8)\n+\n+i9 = st.radio(\"radio 9 (markdown options)\", options=markdown_options)\n+st.write(\"value 9:\", i9)\n+\n+i10 = st.radio(\n+ \"radio 10 (with captions)\",\n+ [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"],\n+ captions=markdown_options,\n+)\n+st.write(\"value 10:\", i10)\n+\n+i11 = st.radio(\n+ \"radio 11 (horizontal, captions)\",\n+ [\"yes\", \"maybe\", \"no\"],\n+ captions=[\"Opt in\", \"\", \"Opt out\"],\n+ horizontal=True,\n+)\n+st.write(\"value 11:\", i11)\n+\n+if runtime.exists():\n+\n+ def on_change():\n+ st.session_state.radio_changed = True\n+ st.text(\"Radio widget callback triggered\")\n+\n+ st.radio(\n+ \"radio 12 (with callback, help)\",\n+ options,\n+ 1,\n+ key=\"radio12\",\n+ on_change=on_change,\n+ help=\"help text\",\n+ )\n+ st.write(\"value 12:\", st.session_state.radio12)\n+ st.write(\"radio changed:\", \"radio_changed\" in st.session_state)\n+\n+i13 = st.radio(\"radio 13 (empty selection)\", options, index=None)\n+st.write(\"value 13:\", i13)\ndiff --git a/e2e_playwright/st_radio_test.py b/e2e_playwright/st_radio_test.py\nnew file mode 100644\nindex 000000000000..5aec97a8beb2\n--- /dev/null\n+++ b/e2e_playwright/st_radio_test.py\n@@ -0,0 +1,124 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from playwright.sync_api import Page, expect\n+\n+from e2e_playwright.conftest import ImageCompareFunction\n+\n+\n+def test_radio_widget_rendering(\n+ themed_app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that the radio widgets are correctly rendered via screenshot matching.\"\"\"\n+ radio_widgets = themed_app.get_by_test_id(\"stRadio\")\n+ expect(radio_widgets).to_have_count(13)\n+\n+ assert_snapshot(radio_widgets.nth(0), name=\"st_radio-default\")\n+ assert_snapshot(radio_widgets.nth(1), name=\"st_radio-formatted_options\")\n+ assert_snapshot(radio_widgets.nth(2), name=\"st_radio-no_options\")\n+ assert_snapshot(radio_widgets.nth(3), name=\"st_radio-disabled\")\n+ assert_snapshot(radio_widgets.nth(4), name=\"st_radio-horizontal\")\n+ assert_snapshot(radio_widgets.nth(5), name=\"st_radio-dataframe_options\")\n+ assert_snapshot(radio_widgets.nth(6), name=\"st_radio-hidden_label\")\n+ assert_snapshot(radio_widgets.nth(7), name=\"st_radio-collapsed_label\")\n+ assert_snapshot(radio_widgets.nth(8), name=\"st_radio-markdown_options\")\n+ assert_snapshot(radio_widgets.nth(9), name=\"st_radio-captions\")\n+ assert_snapshot(radio_widgets.nth(10), name=\"st_radio-horizontal_captions\")\n+ assert_snapshot(radio_widgets.nth(11), name=\"st_radio-callback_help\")\n+ assert_snapshot(radio_widgets.nth(12), name=\"st_radio-empty_selection\")\n+\n+\n+def test_radio_has_correct_default_values(app: Page):\n+ \"\"\"Test that st.radio returns the correct initial values.\"\"\"\n+ markdown_elements = app.get_by_test_id(\"stMarkdown\")\n+ expect(markdown_elements).to_have_count(14)\n+\n+ expected = [\n+ \"value 1: female\",\n+ \"value 2: male\",\n+ \"value 3: None\",\n+ \"value 4: female\",\n+ \"value 5: female\",\n+ \"value 6: female\",\n+ \"value 7: female\",\n+ \"value 8: female\",\n+ \"value 9: bold text\",\n+ \"value 10: A\",\n+ \"value 11: yes\",\n+ \"value 12: male\",\n+ \"radio changed: False\",\n+ \"value 13: None\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(markdown_elements.all(), expected):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_set_value_correctly_when_click(app: Page):\n+ \"\"\"Test that st.radio returns the correct values when the selection is changed.\"\"\"\n+ for index, element in enumerate(app.get_by_test_id(\"stRadio\").all()):\n+ if index != 3: # skip disabled widget\n+ element.locator('label[data-baseweb=\"radio\"]').last.click(force=True)\n+\n+ expected = [\n+ \"value 1: male\",\n+ \"value 2: male\",\n+ \"value 3: None\",\n+ \"value 4: female\",\n+ \"value 5: male\",\n+ \"value 6: male\",\n+ \"value 7: male\",\n+ \"value 8: male\",\n+ \"value 9: red blue green violet orange\",\n+ \"value 10: G\",\n+ \"value 11: no\",\n+ \"value 12: male\",\n+ \"radio changed: False\",\n+ \"value 13: male\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(\n+ app.get_by_test_id(\"stMarkdown\").all(), expected\n+ ):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_calls_callback_on_change(app: Page):\n+ \"\"\"Test that it correctly calls the callback on change.\"\"\"\n+ radio_widget = app.get_by_test_id(\"stRadio\").nth(11)\n+\n+ radio_widget.locator('label[data-baseweb=\"radio\"]').first.click(force=True)\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(11)).to_have_text(\n+ \"value 12: female\",\n+ use_inner_text=True,\n+ )\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(12)).to_have_text(\n+ \"radio changed: True\",\n+ use_inner_text=True,\n+ )\n+\n+ # Change different date input to trigger delta path change\n+ first_date_input_field = app.get_by_test_id(\"stRadio\").first\n+ first_date_input_field.locator('label[data-baseweb=\"radio\"]').last.click(force=True)\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"value 1: male\", use_inner_text=True\n+ )\n+\n+ # Test if value is still correct after delta path change\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(11)).to_have_text(\n+ \"value 12: female\",\n+ use_inner_text=True,\n+ )\ndiff --git a/frontend/lib/src/components/elements/Markdown/Markdown.tsx b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\nindex c0a932c4f327..00bab744812e 100644\n--- a/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n+++ b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n@@ -37,7 +37,7 @@ export default function Markdown({\n }: MarkdownProps): ReactElement {\n const styleProp = { width }\n return (\n- <div className=\"stMarkdown\" style={styleProp}>\n+ <div className=\"stMarkdown\" style={styleProp} data-testid=\"stMarkdown\">\n {element.help ? (\n <StyledLabelHelpWrapper>\n <StreamlitMarkdown\ndiff --git a/frontend/lib/src/components/shared/Radio/Radio.test.tsx b/frontend/lib/src/components/shared/Radio/Radio.test.tsx\nindex 6a28638fd83e..8bbc19dcfe1d 100644\n--- a/frontend/lib/src/components/shared/Radio/Radio.test.tsx\n+++ b/frontend/lib/src/components/shared/Radio/Radio.test.tsx\n@@ -101,6 +101,7 @@ describe(\"Radio widget\", () => {\n const radioOptions = screen.getAllByRole(\"radio\")\n expect(radioOptions).toHaveLength(3)\n \n+ // @ts-expect-error\n const checked = radioOptions[props.value]\n expect(checked).toBeChecked()\n })\ndiff --git a/frontend/lib/src/components/shared/Radio/Radio.tsx b/frontend/lib/src/components/shared/Radio/Radio.tsx\nindex c01abe44d02a..92083af79b2c 100644\n--- a/frontend/lib/src/components/shared/Radio/Radio.tsx\n+++ b/frontend/lib/src/components/shared/Radio/Radio.tsx\n@@ -32,7 +32,7 @@ export interface Props {\n horizontal: boolean\n theme: EmotionTheme\n width?: number\n- value: number\n+ value: number | null\n onChange: (selectedIndex: number) => any\n options: any[]\n captions: any[]\n@@ -46,12 +46,12 @@ interface State {\n * The value specified by the user via the UI. If the user didn't touch this\n * widget's UI, the default value is used.\n */\n- value: number\n+ value: number | null\n }\n \n class Radio extends React.PureComponent<Props, State> {\n public state: State = {\n- value: this.props.value,\n+ value: this.props.value ?? null,\n }\n \n public componentDidUpdate(prevProps: Props): void {\n@@ -61,7 +61,7 @@ class Radio extends React.PureComponent<Props, State> {\n this.props.value !== this.state.value\n ) {\n this.setState((_, prevProps) => {\n- return { value: prevProps.value }\n+ return { value: prevProps.value ?? null }\n })\n }\n }\n@@ -110,7 +110,9 @@ class Radio extends React.PureComponent<Props, State> {\n </WidgetLabel>\n <RadioGroup\n onChange={this.onChange}\n- value={this.state.value.toString()}\n+ value={\n+ this.state.value !== null ? this.state.value.toString() : undefined\n+ }\n disabled={disabled}\n align={horizontal ? ALIGN.horizontal : ALIGN.vertical}\n aria-label={label}\ndiff --git a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\nindex 50dd53dcc914..12410be47527 100644\n--- a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\n+++ b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\n@@ -91,6 +91,7 @@ describe(\"Radio widget\", () => {\n const radioOptions = screen.getAllByRole(\"radio\")\n expect(radioOptions).toHaveLength(3)\n \n+ // @ts-expect-error\n const checked = radioOptions[props.element.default]\n expect(checked).toBeChecked()\n })\n@@ -192,6 +193,7 @@ describe(\"Radio widget\", () => {\n props.widgetMgr.submitForm(\"form\")\n \n // Our widget should be reset, and the widgetMgr should be updated\n+ // @ts-expect-error\n const defaultValue = radioOptions[props.element.default]\n expect(defaultValue).toBeChecked()\n \ndiff --git a/frontend/lib/src/components/widgets/Radio/Radio.tsx b/frontend/lib/src/components/widgets/Radio/Radio.tsx\nindex ee17cd4b9068..dddb729c7408 100644\n--- a/frontend/lib/src/components/widgets/Radio/Radio.tsx\n+++ b/frontend/lib/src/components/widgets/Radio/Radio.tsx\n@@ -36,7 +36,7 @@ interface State {\n * The value specified by the user via the UI. If the user didn't touch this\n * widget's UI, the default value is used.\n */\n- value: number\n+ value: number | null\n }\n \n class Radio extends React.PureComponent<Props, State> {\n@@ -46,11 +46,11 @@ class Radio extends React.PureComponent<Props, State> {\n value: this.initialValue,\n }\n \n- get initialValue(): number {\n+ get initialValue(): number | null {\n // If WidgetStateManager knew a value for this widget, initialize to that.\n // Otherwise, use the default value from the widget protobuf.\n const storedValue = this.props.widgetMgr.getIntValue(this.props.element)\n- return storedValue !== undefined ? storedValue : this.props.element.default\n+ return storedValue ?? this.props.element.default ?? null\n }\n \n public componentDidMount(): void {\n@@ -79,7 +79,7 @@ class Radio extends React.PureComponent<Props, State> {\n private updateFromProtobuf(): void {\n const { value } = this.props.element\n this.props.element.setValue = false\n- this.setState({ value }, () => {\n+ this.setState({ value: value ?? null }, () => {\n this.commitWidgetValue({ fromUi: false })\n })\n }\n@@ -100,7 +100,7 @@ class Radio extends React.PureComponent<Props, State> {\n private onFormCleared = (): void => {\n this.setState(\n (_, prevProps) => {\n- return { value: prevProps.element.default }\n+ return { value: prevProps.element.default ?? null }\n },\n () => this.commitWidgetValue({ fromUi: true })\n )\ndiff --git a/lib/streamlit/elements/widgets/radio.py b/lib/streamlit/elements/widgets/radio.py\nindex 51b89e303b49..c5a1db0aa394 100644\n--- a/lib/streamlit/elements/widgets/radio.py\n+++ b/lib/streamlit/elements/widgets/radio.py\n@@ -12,9 +12,11 @@\n # See the License for the specific language governing permissions and\n # limitations under the License.\n \n+from __future__ import annotations\n+\n from dataclasses import dataclass\n from textwrap import dedent\n-from typing import TYPE_CHECKING, Any, Callable, Generic, Optional, Sequence, cast\n+from typing import TYPE_CHECKING, Any, Callable, Generic, Sequence, cast, overload\n \n from streamlit.elements.form import current_form_id\n from streamlit.elements.utils import (\n@@ -51,46 +53,89 @@\n @dataclass\n class RadioSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n+\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n \n- def serialize(self, v: object) -> int:\n- if len(self.options) == 0:\n- return 0\n- return index_(self.options, v)\n+ return 0 if len(self.options) == 0 else index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n idx = ui_value if ui_value is not None else self.index\n \n return (\n self.options[idx]\n- if len(self.options) > 0 and self.options[idx] is not None\n+ if idx is not None\n+ and len(self.options) > 0\n+ and self.options[idx] is not None\n else None\n )\n \n \n class RadioMixin:\n- @gather_metrics(\"radio\")\n+ @overload\n def radio(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:\n+ pass\n+\n+ @overload\n+ def radio(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: None = None,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T | None:\n+ pass\n+\n+ @gather_metrics(\"radio\")\n+ def radio(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: int | None = 0,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only args:\n disabled: bool = False,\n horizontal: bool = False,\n- captions: Optional[Sequence[str]] = None,\n+ captions: Sequence[str] | None = None,\n label_visibility: LabelVisibility = \"visible\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n r\"\"\"Display a radio button widget.\n \n Parameters\n@@ -126,8 +171,10 @@ def radio(\n described in the ``label`` parameter and will be cast to str\n internally by default. For pandas.DataFrame, the first column is\n selected.\n- index : int\n- The index of the preselected option on first render.\n+ index : int or None\n+ The index of the preselected option on first render. If ``None``,\n+ will initialize empty and return ``None`` until the user selects an option.\n+ Defaults to 0 (the first option).\n format_func : function\n Function to modify the display of radio options. It receives\n the raw option as an argument and should output the label to be\n@@ -166,7 +213,7 @@ def radio(\n Returns\n -------\n any\n- The selected option.\n+ The selected option or ``None`` if no option is selected.\n \n Example\n -------\n@@ -186,6 +233,22 @@ def radio(\n https://doc-radio.streamlit.app/\n height: 300px\n \n+ To initialize an empty radio widget, use ``None`` as the index value:\n+\n+ >>> import streamlit as st\n+ >>>\n+ >>> genre = st.radio(\n+ ... \"What's your favorite movie genre\",\n+ ... [\":rainbow[Comedy]\", \"***Drama***\", \"Documentary :movie_camera:\"],\n+ ... index=None,\n+ ... )\n+ >>>\n+ >>> st.write(\"You selected:\", genre)\n+\n+ .. output::\n+ https://doc-radio-empty.streamlit.app/\n+ height: 300px\n+\n \"\"\"\n ctx = get_script_run_ctx()\n return self._radio(\n@@ -209,20 +272,20 @@ def _radio(\n self,\n label: str,\n options: OptionSequence[T],\n- index: int = 0,\n+ index: int | None = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only args:\n disabled: bool = False,\n horizontal: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- captions: Optional[Sequence[str]] = None,\n- ctx: Optional[ScriptRunContext],\n- ) -> Optional[T]:\n+ captions: Sequence[str] | None = None,\n+ ctx: ScriptRunContext | None,\n+ ) -> T | None:\n key = to_key(key)\n check_callback_rules(self.dg, on_change)\n check_session_state_rules(default_value=None if index == 0 else index, key=key)\n@@ -243,17 +306,17 @@ def _radio(\n page=ctx.page_script_hash if ctx else None,\n )\n \n- if not isinstance(index, int):\n+ if not isinstance(index, int) and index is not None:\n raise StreamlitAPIException(\n \"Radio Value has invalid type: %s\" % type(index).__name__\n )\n \n- if len(opt) > 0 and not 0 <= index < len(opt):\n+ if index is not None and len(opt) > 0 and not 0 <= index < len(opt):\n raise StreamlitAPIException(\n \"Radio index must be between 0 and length of options\"\n )\n \n- def handle_captions(caption: Optional[str]) -> str:\n+ def handle_captions(caption: str | None) -> str:\n if caption is None:\n return \"\"\n elif isinstance(caption, str):\n@@ -266,7 +329,8 @@ def handle_captions(caption: Optional[str]) -> str:\n radio_proto = RadioProto()\n radio_proto.id = id\n radio_proto.label = label\n- radio_proto.default = index\n+ if index is not None:\n+ radio_proto.default = index\n radio_proto.options[:] = [str(format_func(option)) for option in opt]\n radio_proto.form_id = current_form_id(self.dg)\n radio_proto.horizontal = horizontal\n@@ -296,7 +360,10 @@ def handle_captions(caption: Optional[str]) -> str:\n )\n \n if widget_state.value_changed:\n- radio_proto.value = serde.serialize(widget_state.value)\n+ if widget_state.value is not None:\n+ serialized_value = serde.serialize(widget_state.value)\n+ if serialized_value is not None:\n+ radio_proto.value = serialized_value\n radio_proto.set_value = True\n \n self.dg._enqueue(\"radio\", radio_proto)\ndiff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py\nindex 7e5787435524..2e870ce6ade6 100644\n--- a/lib/streamlit/testing/element_tree.py\n+++ b/lib/streamlit/testing/element_tree.py\n@@ -62,6 +62,7 @@\n T = TypeVar(\"T\")\n \n \n+@dataclass\n class InitialValue:\n \"\"\"This class is used to represent the initial value of a widget.\"\"\"\n \n@@ -594,7 +595,7 @@ def decrement(self) -> NumberInput:\n \n @dataclass(repr=False)\n class Radio(Widget, Generic[T]):\n- _value: T | None\n+ _value: T | None | InitialValue\n \n proto: RadioProto\n options: list[str]\n@@ -603,7 +604,7 @@ class Radio(Widget, Generic[T]):\n def __init__(self, proto: RadioProto, root: ElementTree):\n self.proto = proto\n self.root = root\n- self._value = None\n+ self._value = InitialValue()\n \n self.type = \"radio\"\n self.id = proto.id\n@@ -616,20 +617,22 @@ def __init__(self, proto: RadioProto, root: ElementTree):\n self.key = user_key_from_widget_id(self.id)\n \n @property\n- def index(self) -> int:\n+ def index(self) -> int | None:\n+ if self.value is None:\n+ return None\n return self.options.index(str(self.value))\n \n @property\n- def value(self) -> T:\n+ def value(self) -> T | None:\n \"\"\"The currently selected value from the options.\"\"\"\n- if self._value is not None:\n+ if not isinstance(self._value, InitialValue):\n return self._value\n else:\n state = self.root.session_state\n assert state\n return cast(T, state[self.id])\n \n- def set_value(self, v: T) -> Radio[T]:\n+ def set_value(self, v: T | None) -> Radio[T]:\n self._value = v\n return self\n \n@@ -640,7 +643,8 @@ def widget_state(self) -> WidgetState:\n \"\"\"\n ws = WidgetState()\n ws.id = self.id\n- ws.int_value = self.index\n+ if self.index is not None:\n+ ws.int_value = self.index\n return ws\n \n \ndiff --git a/lib/tests/streamlit/elements/radio_test.py b/lib/tests/streamlit/elements/radio_test.py\nindex 2d23c149806f..48aabff2261c 100644\n--- a/lib/tests/streamlit/elements/radio_test.py\n+++ b/lib/tests/streamlit/elements/radio_test.py\n@@ -23,6 +23,7 @@\n import streamlit as st\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage\n+from streamlit.testing.script_interactions import InteractiveScriptTests\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n \n \n@@ -41,6 +42,7 @@ def test_just_label(self):\n )\n self.assertEqual(c.default, 0)\n self.assertEqual(c.disabled, False)\n+ self.assertEqual(c.HasField(\"default\"), True)\n self.assertEqual(c.captions, [])\n \n def test_just_disabled(self):\n@@ -50,6 +52,17 @@ def test_just_disabled(self):\n c = self.get_delta_from_queue().new_element.radio\n self.assertEqual(c.disabled, True)\n \n+ def test_none_value(self):\n+ \"\"\"Test that it can be called with None as index value.\"\"\"\n+ st.radio(\"the label\", (\"m\", \"f\"), index=None)\n+\n+ c = self.get_delta_from_queue().new_element.radio\n+ self.assertEqual(c.label, \"the label\")\n+ # If a proto property is null is not determined by this value,\n+ # but by the check via the HasField method:\n+ self.assertEqual(c.default, 0)\n+ self.assertEqual(c.HasField(\"default\"), False)\n+\n def test_horizontal(self):\n \"\"\"Test that it can be called with horizontal param.\"\"\"\n st.radio(\"the label\", (\"m\", \"f\"), horizontal=True)\n@@ -235,3 +248,28 @@ def test_some_captions(self):\n self.assertEqual(c.label, \"the label\")\n self.assertEqual(c.default, 0)\n self.assertEqual(c.captions, [\"first caption\", \"\", \"\", \"last caption\"])\n+\n+\n+class RadioInteractiveTest(InteractiveScriptTests):\n+ def test_radio_interaction(self):\n+ \"\"\"Test interactions with an empty radio widget.\"\"\"\n+ script = self.script_from_string(\n+ \"\"\"\n+ import streamlit as st\n+\n+ st.radio(\"the label\", (\"m\", \"f\"), index=None)\n+ \"\"\"\n+ )\n+ sr = script.run()\n+ radio = sr.radio[0]\n+ assert radio.value is None\n+\n+ # Select option m\n+ sr2 = radio.set_value(\"m\").run()\n+ radio = sr2.radio[0]\n+ assert radio.value == \"m\"\n+\n+ # # Clear the value\n+ sr3 = radio.set_value(None).run()\n+ radio = sr3.radio[0]\n+ assert radio.value is None\ndiff --git a/proto/streamlit/proto/Radio.proto b/proto/streamlit/proto/Radio.proto\nindex d88e9ebe8eea..95abb238f78d 100644\n--- a/proto/streamlit/proto/Radio.proto\n+++ b/proto/streamlit/proto/Radio.proto\n@@ -21,11 +21,11 @@ import \"streamlit/proto/LabelVisibilityMessage.proto\";\n message Radio {\n string id = 1;\n string label = 2;\n- int32 default = 3;\n+ optional int32 default = 3;\n repeated string options = 4;\n string help = 5;\n string form_id = 6;\n- int32 value = 7;\n+ optional int32 value = 7;\n bool set_value = 8;\n bool disabled = 9;\n bool horizontal = 10;\n" }
[ { "diff_hunk": "@@ -51,46 +53,89 @@\n @dataclass\n class RadioSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n+\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n \n- def serialize(self, v: object) -> int:\n- if len(self.options) == 0:\n- return 0\n- return index_(self.options, v)\n+ return 0 if len(self.options) == 0 else index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n idx = ui_value if ui_value is not None else self.index\n \n return (\n self.options[idx]\n- if len(self.options) > 0 and self.options[idx] is not None\n+ if idx is not None\n+ and len(self.options) > 0\n+ and self.options[idx] is not None\n else None\n )\n \n \n class RadioMixin:\n- @gather_metrics(\"radio\")\n+ @overload\n def radio(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:\n+ pass\n+\n+ @overload\n+ def radio(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: None = None,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T | None:\n+ pass", "line": null, "original_line": 119, "original_start_line": 81, "path": "lib/streamlit/elements/widgets/radio.py", "start_line": null, "text": "@user1:\nAre these duplicates necessary?\n\n@author:\nNot strictly, but this signature overload improves typing for users since radio cannot return `None` if index was not `None`. It's not a big problem for radio, since radio was already annotated with `Optional`. But for some of the other widgets, some users may run into typing issues if we just add `None` to the return types without overloading. \n\n@author:\nIt's a small typing benefit for users that has the cost of some redundancy... but I'm not sure if it is worth it.\n\n@user1:\nNot sure where I land on whether its worth it, but could you add a comment if you choose to leave it?\n\n@author:\nI decided to remove the overloads for radio and selectbox since they cannot fully cover the cases anyways (since empty list also returns `None` as mentioned by Vincent below)." }, { "diff_hunk": "@@ -51,46 +53,89 @@\n @dataclass\n class RadioSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n+\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n \n- def serialize(self, v: object) -> int:\n- if len(self.options) == 0:\n- return 0\n- return index_(self.options, v)\n+ return 0 if len(self.options) == 0 else index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n idx = ui_value if ui_value is not None else self.index\n \n return (\n self.options[idx]\n- if len(self.options) > 0 and self.options[idx] is not None\n+ if idx is not None\n+ and len(self.options) > 0\n+ and self.options[idx] is not None\n else None\n )\n \n \n class RadioMixin:\n- @gather_metrics(\"radio\")\n+ @overload\n def radio(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:", "line": null, "original_line": 98, "original_start_line": null, "path": "lib/streamlit/elements/widgets/radio.py", "start_line": null, "text": "@user1:\nHm, is the return type here correct? I think there's a case where `st.radio` is passed an empty options sequence and unspecified index (which thus defaults to 0) where the return type of the call to `st.radio` will be `None`.\r\n\r\nThis will probably be fine most of the time as `T` will often be unbound, but a user could have explicitly typed the `OptionsSequence` doing something like the following\r\n\r\n```python\r\nmy_options: List[str] = []\r\n\r\n# Some conditional logic which doesn't append anything to `my_options`\r\n# In this script run\r\nif False:\r\n my_options.append(\"option 1\")\r\n\r\nchoice = st.radio('pick an option', my_options)\r\n```\r\n\r\nIn the code snippet above, I think `mypy` will complain that a `str` is not returned by\r\n`st.radio`, but I would consider the above code valid.\r\n\r\nI think the other overload + the signature used in the function implementation itself should be enough to cover all cases.\n\n@author:\nOh, you are right. Empty option list also returns `None` and there doesn't seem to be a good way to type an empty list. So, I think we can just remove the overload since it always needs to be typed with `None` & the type of the options." }, { "diff_hunk": "@@ -51,46 +53,89 @@\n @dataclass\n class RadioSerde(Generic[T]):\n options: Sequence[T]\n- index: int\n+ index: int | None\n+\n+ def serialize(self, v: object) -> int | None:\n+ if v is None:\n+ return None\n \n- def serialize(self, v: object) -> int:\n- if len(self.options) == 0:\n- return 0\n- return index_(self.options, v)\n+ return 0 if len(self.options) == 0 else index_(self.options, v)\n \n def deserialize(\n self,\n- ui_value: Optional[int],\n+ ui_value: int | None,\n widget_id: str = \"\",\n- ) -> Optional[T]:\n+ ) -> T | None:\n idx = ui_value if ui_value is not None else self.index\n \n return (\n self.options[idx]\n- if len(self.options) > 0 and self.options[idx] is not None\n+ if idx is not None\n+ and len(self.options) > 0\n+ and self.options[idx] is not None\n else None\n )\n \n \n class RadioMixin:\n- @gather_metrics(\"radio\")\n+ @overload\n def radio(\n self,\n label: str,\n options: OptionSequence[T],\n index: int = 0,\n format_func: Callable[[Any], Any] = str,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T:\n+ pass\n+\n+ @overload\n+ def radio(\n+ self,\n+ label: str,\n+ options: OptionSequence[T],\n+ index: None = None,\n+ format_func: Callable[[Any], Any] = str,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only args:\n+ disabled: bool = False,\n+ horizontal: bool = False,\n+ captions: Sequence[str] | None = None,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> T | None:", "line": null, "original_line": 118, "original_start_line": 101, "path": "lib/streamlit/elements/widgets/radio.py", "start_line": null, "text": "@user1:\nHm, I think in this case the return type could probably be narrowed further to `None` since we know that we can't get back a `T` if `index is None`.\n\n@author:\nOnce the user has selected a value, it won't return `None` anymore. So I think it still needs to have both return types." } ]
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new file mode 100644 index 000000000000..b70f5503d053 --- /dev/null +++ b/e2e_playwright/st_radio.py @@ -0,0 +1,97 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pandas as pd + +import streamlit as st +from streamlit import runtime + +options = ("female", "male") +markdown_options = ( + "**bold text**", + "*italics text*", + "~strikethrough text~", + "shortcode: :blush:", + # link should not work in radio options + "[link text](www.example.com)", + "`code text`", + ":red[red] :blue[blue] :green[green] :violet[violet] :orange[orange]", +) + +i1 = st.radio("radio 1 (default)", options) +st.write("value 1:", i1) + +i2 = st.radio( + "radio 2 (Formatted options)", + options, + 1, + format_func=lambda x: x.capitalize(), +) +st.write("value 2:", i2) + +i3 = st.radio("radio 3 (no options)", []) +st.write("value 3:", i3) + +i4 = st.radio("radio 4 (disabled)", options, disabled=True) +st.write("value 4:", i4) + +i5 = st.radio("radio 5 (horizontal)", options, horizontal=True) +st.write("value 5:", i5) + +i6 = st.radio("radio 6 (options from dataframe)", pd.DataFrame({"foo": list(options)})) +st.write("value 6:", i6) + +i7 = st.radio("radio 7 (hidden label)", options, label_visibility="hidden") +st.write("value 7:", i7) + +i8 = st.radio("radio 8 (collapsed label)", options, label_visibility="collapsed") +st.write("value 8:", i8) + +i9 = st.radio("radio 9 (markdown options)", options=markdown_options) +st.write("value 9:", i9) + +i10 = st.radio( + "radio 10 (with captions)", + ["A", "B", "C", "D", "E", "F", "G"], + captions=markdown_options, +) +st.write("value 10:", i10) + +i11 = st.radio( + "radio 11 (horizontal, captions)", + ["yes", "maybe", "no"], + captions=["Opt in", "", "Opt out"], + horizontal=True, +) +st.write("value 11:", i11) + +if runtime.exists(): + + def on_change(): + st.session_state.radio_changed = True + st.text("Radio widget callback triggered") + + st.radio( + "radio 12 (with callback, help)", + options, + 1, + key="radio12", + on_change=on_change, + help="help text", + ) + st.write("value 12:", st.session_state.radio12) + st.write("radio changed:", "radio_changed" in st.session_state) + +i13 = st.radio("radio 13 (empty selection)", options, index=None) +st.write("value 13:", i13) diff --git a/e2e_playwright/st_radio_test.py b/e2e_playwright/st_radio_test.py new file mode 100644 index 000000000000..5aec97a8beb2 --- /dev/null +++ b/e2e_playwright/st_radio_test.py @@ -0,0 +1,124 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from playwright.sync_api import Page, expect + +from e2e_playwright.conftest import ImageCompareFunction + + +def test_radio_widget_rendering( + themed_app: Page, assert_snapshot: ImageCompareFunction +): + """Test that the radio widgets are correctly rendered via screenshot matching.""" + radio_widgets = themed_app.get_by_test_id("stRadio") + expect(radio_widgets).to_have_count(13) + + assert_snapshot(radio_widgets.nth(0), name="st_radio-default") + assert_snapshot(radio_widgets.nth(1), name="st_radio-formatted_options") + assert_snapshot(radio_widgets.nth(2), name="st_radio-no_options") + assert_snapshot(radio_widgets.nth(3), name="st_radio-disabled") + assert_snapshot(radio_widgets.nth(4), name="st_radio-horizontal") + assert_snapshot(radio_widgets.nth(5), name="st_radio-dataframe_options") + assert_snapshot(radio_widgets.nth(6), name="st_radio-hidden_label") + assert_snapshot(radio_widgets.nth(7), name="st_radio-collapsed_label") + assert_snapshot(radio_widgets.nth(8), name="st_radio-markdown_options") + assert_snapshot(radio_widgets.nth(9), name="st_radio-captions") + assert_snapshot(radio_widgets.nth(10), name="st_radio-horizontal_captions") + assert_snapshot(radio_widgets.nth(11), name="st_radio-callback_help") + assert_snapshot(radio_widgets.nth(12), name="st_radio-empty_selection") + + +def test_radio_has_correct_default_values(app: Page): + """Test that st.radio returns the correct initial values.""" + markdown_elements = app.get_by_test_id("stMarkdown") + expect(markdown_elements).to_have_count(14) + + expected = [ + "value 1: female", + "value 2: male", + "value 3: None", + "value 4: female", + "value 5: female", + "value 6: female", + "value 7: female", + "value 8: female", + "value 9: bold text", + "value 10: A", + "value 11: yes", + "value 12: male", + "radio changed: False", + "value 13: None", + ] + + for markdown_element, expected_text in zip(markdown_elements.all(), expected): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_set_value_correctly_when_click(app: Page): + """Test that st.radio returns the correct values when the selection is changed.""" + for index, element in enumerate(app.get_by_test_id("stRadio").all()): + if index != 3: # skip disabled widget + element.locator('label[data-baseweb="radio"]').last.click(force=True) + + expected = [ + "value 1: male", + "value 2: male", + "value 3: None", + "value 4: female", + "value 5: male", + "value 6: male", + "value 7: male", + "value 8: male", + "value 9: red blue green violet orange", + "value 10: G", + "value 11: no", + "value 12: male", + "radio changed: False", + "value 13: male", + ] + + for markdown_element, expected_text in zip( + app.get_by_test_id("stMarkdown").all(), expected + ): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_calls_callback_on_change(app: Page): + """Test that it correctly calls the callback on change.""" + radio_widget = app.get_by_test_id("stRadio").nth(11) + + radio_widget.locator('label[data-baseweb="radio"]').first.click(force=True) + + expect(app.get_by_test_id("stMarkdown").nth(11)).to_have_text( + "value 12: female", + use_inner_text=True, + ) + expect(app.get_by_test_id("stMarkdown").nth(12)).to_have_text( + "radio changed: True", + use_inner_text=True, + ) + + # Change different date input to trigger delta path change + first_date_input_field = app.get_by_test_id("stRadio").first + first_date_input_field.locator('label[data-baseweb="radio"]').last.click(force=True) + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "value 1: male", use_inner_text=True + ) + + # Test if value is still correct after delta path change + expect(app.get_by_test_id("stMarkdown").nth(11)).to_have_text( + "value 12: female", + use_inner_text=True, + ) diff --git a/frontend/lib/src/components/elements/Markdown/Markdown.tsx b/frontend/lib/src/components/elements/Markdown/Markdown.tsx index c0a932c4f327..00bab744812e 100644 --- a/frontend/lib/src/components/elements/Markdown/Markdown.tsx +++ b/frontend/lib/src/components/elements/Markdown/Markdown.tsx @@ -37,7 +37,7 @@ export default function Markdown({ }: MarkdownProps): ReactElement { const styleProp = { width } return ( - <div className="stMarkdown" style={styleProp}> + <div className="stMarkdown" style={styleProp} data-testid="stMarkdown"> {element.help ? ( <StyledLabelHelpWrapper> <StreamlitMarkdown diff --git a/frontend/lib/src/components/shared/Radio/Radio.test.tsx b/frontend/lib/src/components/shared/Radio/Radio.test.tsx index 6a28638fd83e..8bbc19dcfe1d 100644 --- a/frontend/lib/src/components/shared/Radio/Radio.test.tsx +++ b/frontend/lib/src/components/shared/Radio/Radio.test.tsx @@ -101,6 +101,7 @@ describe("Radio widget", () => { const radioOptions = screen.getAllByRole("radio") expect(radioOptions).toHaveLength(3) + // @ts-expect-error const checked = radioOptions[props.value] expect(checked).toBeChecked() }) diff --git a/frontend/lib/src/components/shared/Radio/Radio.tsx b/frontend/lib/src/components/shared/Radio/Radio.tsx index c01abe44d02a..92083af79b2c 100644 --- a/frontend/lib/src/components/shared/Radio/Radio.tsx +++ b/frontend/lib/src/components/shared/Radio/Radio.tsx @@ -32,7 +32,7 @@ export interface Props { horizontal: boolean theme: EmotionTheme width?: number - value: number + value: number | null onChange: (selectedIndex: number) => any options: any[] captions: any[] @@ -46,12 +46,12 @@ interface State { * The value specified by the user via the UI. If the user didn't touch this * widget's UI, the default value is used. */ - value: number + value: number | null } class Radio extends React.PureComponent<Props, State> { public state: State = { - value: this.props.value, + value: this.props.value ?? null, } public componentDidUpdate(prevProps: Props): void { @@ -61,7 +61,7 @@ class Radio extends React.PureComponent<Props, State> { this.props.value !== this.state.value ) { this.setState((_, prevProps) => { - return { value: prevProps.value } + return { value: prevProps.value ?? null } }) } } @@ -110,7 +110,9 @@ class Radio extends React.PureComponent<Props, State> { </WidgetLabel> <RadioGroup onChange={this.onChange} - value={this.state.value.toString()} + value={ + this.state.value !== null ? this.state.value.toString() : undefined + } disabled={disabled} align={horizontal ? ALIGN.horizontal : ALIGN.vertical} aria-label={label} diff --git a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx index 50dd53dcc914..12410be47527 100644 --- a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx +++ b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx @@ -91,6 +91,7 @@ describe("Radio widget", () => { const radioOptions = screen.getAllByRole("radio") expect(radioOptions).toHaveLength(3) + // @ts-expect-error const checked = radioOptions[props.element.default] expect(checked).toBeChecked() }) @@ -192,6 +193,7 @@ describe("Radio widget", () => { props.widgetMgr.submitForm("form") // Our widget should be reset, and the widgetMgr should be updated + // @ts-expect-error const defaultValue = radioOptions[props.element.default] expect(defaultValue).toBeChecked() diff --git a/frontend/lib/src/components/widgets/Radio/Radio.tsx b/frontend/lib/src/components/widgets/Radio/Radio.tsx index ee17cd4b9068..dddb729c7408 100644 --- a/frontend/lib/src/components/widgets/Radio/Radio.tsx +++ b/frontend/lib/src/components/widgets/Radio/Radio.tsx @@ -36,7 +36,7 @@ interface State { * The value specified by the user via the UI. If the user didn't touch this * widget's UI, the default value is used. */ - value: number + value: number | null } class Radio extends React.PureComponent<Props, State> { @@ -46,11 +46,11 @@ class Radio extends React.PureComponent<Props, State> { value: this.initialValue, } - get initialValue(): number { + get initialValue(): number | null { // If WidgetStateManager knew a value for this widget, initialize to that. // Otherwise, use the default value from the widget protobuf. const storedValue = this.props.widgetMgr.getIntValue(this.props.element) - return storedValue !== undefined ? storedValue : this.props.element.default + return storedValue ?? this.props.element.default ?? null } public componentDidMount(): void { @@ -79,7 +79,7 @@ class Radio extends React.PureComponent<Props, State> { private updateFromProtobuf(): void { const { value } = this.props.element this.props.element.setValue = false - this.setState({ value }, () => { + this.setState({ value: value ?? null }, () => { this.commitWidgetValue({ fromUi: false }) }) } @@ -100,7 +100,7 @@ class Radio extends React.PureComponent<Props, State> { private onFormCleared = (): void => { this.setState( (_, prevProps) => { - return { value: prevProps.element.default } + return { value: prevProps.element.default ?? null } }, () => this.commitWidgetValue({ fromUi: true }) ) diff --git a/lib/streamlit/elements/widgets/radio.py b/lib/streamlit/elements/widgets/radio.py index 51b89e303b49..d4672398039a 100644 --- a/lib/streamlit/elements/widgets/radio.py +++ b/lib/streamlit/elements/widgets/radio.py @@ -12,9 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + from dataclasses import dataclass from textwrap import dedent -from typing import TYPE_CHECKING, Any, Callable, Generic, Optional, Sequence, cast +from typing import TYPE_CHECKING, Any, Callable, Generic, Sequence, cast from streamlit.elements.form import current_form_id from streamlit.elements.utils import ( @@ -51,23 +53,26 @@ @dataclass class RadioSerde(Generic[T]): options: Sequence[T] - index: int + index: int | None + + def serialize(self, v: object) -> int | None: + if v is None: + return None - def serialize(self, v: object) -> int: - if len(self.options) == 0: - return 0 - return index_(self.options, v) + return 0 if len(self.options) == 0 else index_(self.options, v) def deserialize( self, - ui_value: Optional[int], + ui_value: int | None, widget_id: str = "", - ) -> Optional[T]: + ) -> T | None: idx = ui_value if ui_value is not None else self.index return ( self.options[idx] - if len(self.options) > 0 and self.options[idx] is not None + if idx is not None + and len(self.options) > 0 + and self.options[idx] is not None else None ) @@ -78,19 +83,19 @@ def radio( self, label: str, options: OptionSequence[T], - index: int = 0, + index: int | None = 0, format_func: Callable[[Any], Any] = str, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only args: disabled: bool = False, horizontal: bool = False, - captions: Optional[Sequence[str]] = None, + captions: Sequence[str] | None = None, label_visibility: LabelVisibility = "visible", - ) -> Optional[T]: + ) -> T | None: r"""Display a radio button widget. Parameters @@ -126,8 +131,10 @@ def radio( described in the ``label`` parameter and will be cast to str internally by default. For pandas.DataFrame, the first column is selected. - index : int - The index of the preselected option on first render. + index : int or None + The index of the preselected option on first render. If ``None``, + will initialize empty and return ``None`` until the user selects an option. + Defaults to 0 (the first option). format_func : function Function to modify the display of radio options. It receives the raw option as an argument and should output the label to be @@ -166,7 +173,7 @@ def radio( Returns ------- any - The selected option. + The selected option or ``None`` if no option is selected. Example ------- @@ -186,6 +193,22 @@ def radio( https://doc-radio.streamlit.app/ height: 300px + To initialize an empty radio widget, use ``None`` as the index value: + + >>> import streamlit as st + >>> + >>> genre = st.radio( + ... "What's your favorite movie genre", + ... [":rainbow[Comedy]", "***Drama***", "Documentary :movie_camera:"], + ... index=None, + ... ) + >>> + >>> st.write("You selected:", genre) + + .. output:: + https://doc-radio-empty.streamlit.app/ + height: 300px + """ ctx = get_script_run_ctx() return self._radio( @@ -209,20 +232,20 @@ def _radio( self, label: str, options: OptionSequence[T], - index: int = 0, + index: int | None = 0, format_func: Callable[[Any], Any] = str, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only args: disabled: bool = False, horizontal: bool = False, label_visibility: LabelVisibility = "visible", - captions: Optional[Sequence[str]] = None, - ctx: Optional[ScriptRunContext], - ) -> Optional[T]: + captions: Sequence[str] | None = None, + ctx: ScriptRunContext | None, + ) -> T | None: key = to_key(key) check_callback_rules(self.dg, on_change) check_session_state_rules(default_value=None if index == 0 else index, key=key) @@ -243,17 +266,17 @@ def _radio( page=ctx.page_script_hash if ctx else None, ) - if not isinstance(index, int): + if not isinstance(index, int) and index is not None: raise StreamlitAPIException( "Radio Value has invalid type: %s" % type(index).__name__ ) - if len(opt) > 0 and not 0 <= index < len(opt): + if index is not None and len(opt) > 0 and not 0 <= index < len(opt): raise StreamlitAPIException( "Radio index must be between 0 and length of options" ) - def handle_captions(caption: Optional[str]) -> str: + def handle_captions(caption: str | None) -> str: if caption is None: return "" elif isinstance(caption, str): @@ -266,7 +289,8 @@ def handle_captions(caption: Optional[str]) -> str: radio_proto = RadioProto() radio_proto.id = id radio_proto.label = label - radio_proto.default = index + if index is not None: + radio_proto.default = index radio_proto.options[:] = [str(format_func(option)) for option in opt] radio_proto.form_id = current_form_id(self.dg) radio_proto.horizontal = horizontal @@ -296,7 +320,10 @@ def handle_captions(caption: Optional[str]) -> str: ) if widget_state.value_changed: - radio_proto.value = serde.serialize(widget_state.value) + if widget_state.value is not None: + serialized_value = serde.serialize(widget_state.value) + if serialized_value is not None: + radio_proto.value = serialized_value radio_proto.set_value = True self.dg._enqueue("radio", radio_proto) diff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py index 7e5787435524..2e870ce6ade6 100644 --- a/lib/streamlit/testing/element_tree.py +++ b/lib/streamlit/testing/element_tree.py @@ -62,6 +62,7 @@ T = TypeVar("T") +@dataclass class InitialValue: """This class is used to represent the initial value of a widget.""" @@ -594,7 +595,7 @@ def decrement(self) -> NumberInput: @dataclass(repr=False) class Radio(Widget, Generic[T]): - _value: T | None + _value: T | None | InitialValue proto: RadioProto options: list[str] @@ -603,7 +604,7 @@ class Radio(Widget, Generic[T]): def __init__(self, proto: RadioProto, root: ElementTree): self.proto = proto self.root = root - self._value = None + self._value = InitialValue() self.type = "radio" self.id = proto.id @@ -616,20 +617,22 @@ def __init__(self, proto: RadioProto, root: ElementTree): self.key = user_key_from_widget_id(self.id) @property - def index(self) -> int: + def index(self) -> int | None: + if self.value is None: + return None return self.options.index(str(self.value)) @property - def value(self) -> T: + def value(self) -> T | None: """The currently selected value from the options.""" - if self._value is not None: + if not isinstance(self._value, InitialValue): return self._value else: state = self.root.session_state assert state return cast(T, state[self.id]) - def set_value(self, v: T) -> Radio[T]: + def set_value(self, v: T | None) -> Radio[T]: self._value = v return self @@ -640,7 +643,8 @@ def widget_state(self) -> WidgetState: """ ws = WidgetState() ws.id = self.id - ws.int_value = self.index + if self.index is not None: + ws.int_value = self.index return ws diff --git a/lib/tests/streamlit/elements/radio_test.py b/lib/tests/streamlit/elements/radio_test.py index 2d23c149806f..48aabff2261c 100644 --- a/lib/tests/streamlit/elements/radio_test.py +++ b/lib/tests/streamlit/elements/radio_test.py @@ -23,6 +23,7 @@ import streamlit as st from streamlit.errors import StreamlitAPIException from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage +from streamlit.testing.script_interactions import InteractiveScriptTests from tests.delta_generator_test_case import DeltaGeneratorTestCase @@ -41,6 +42,7 @@ def test_just_label(self): ) self.assertEqual(c.default, 0) self.assertEqual(c.disabled, False) + self.assertEqual(c.HasField("default"), True) self.assertEqual(c.captions, []) def test_just_disabled(self): @@ -50,6 +52,17 @@ def test_just_disabled(self): c = self.get_delta_from_queue().new_element.radio self.assertEqual(c.disabled, True) + def test_none_value(self): + """Test that it can be called with None as index value.""" + st.radio("the label", ("m", "f"), index=None) + + c = self.get_delta_from_queue().new_element.radio + self.assertEqual(c.label, "the label") + # If a proto property is null is not determined by this value, + # but by the check via the HasField method: + self.assertEqual(c.default, 0) + self.assertEqual(c.HasField("default"), False) + def test_horizontal(self): """Test that it can be called with horizontal param.""" st.radio("the label", ("m", "f"), horizontal=True) @@ -235,3 +248,28 @@ def test_some_captions(self): self.assertEqual(c.label, "the label") self.assertEqual(c.default, 0) self.assertEqual(c.captions, ["first caption", "", "", "last caption"]) + + +class RadioInteractiveTest(InteractiveScriptTests): + def test_radio_interaction(self): + """Test interactions with an empty radio widget.""" + script = self.script_from_string( + """ + import streamlit as st + + st.radio("the label", ("m", "f"), index=None) + """ + ) + sr = script.run() + radio = sr.radio[0] + assert radio.value is None + + # Select option m + sr2 = radio.set_value("m").run() + radio = sr2.radio[0] + assert radio.value == "m" + + # # Clear the value + sr3 = radio.set_value(None).run() + radio = sr3.radio[0] + assert radio.value is None diff --git a/proto/streamlit/proto/Radio.proto b/proto/streamlit/proto/Radio.proto index d88e9ebe8eea..95abb238f78d 100644 --- a/proto/streamlit/proto/Radio.proto +++ b/proto/streamlit/proto/Radio.proto @@ -21,11 +21,11 @@ import "streamlit/proto/LabelVisibilityMessage.proto"; message Radio { string id = 1; string label = 2; - int32 default = 3; + optional int32 default = 3; repeated string options = 4; string help = 5; string form_id = 6; - int32 value = 7; + optional int32 value = 7; bool set_value = 8; bool disabled = 9; bool horizontal = 10;
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
sympy__sympy-12380@cf2e3da
sympy/sympy
Python
12,380
Runtime Warning changed to an error
<!-- Please give this pull request a descriptive title. Pull requests with descriptive titles are more likely to receive reviews. Describe what you changed! A title that only references an issue number is not descriptive. --> <!-- If this pull request fixes an issue please indicate which issue by typing "Fixes #NNNN" below. --> Fixes #11727
2017-03-21T12:07:02Z
RuntimeWarning: divide by zero encountered in true_divide in lambdify tests ``` $./bin/test lambdify ==================================================================== test process starts ===================================================================== executable: /Users/aaronmeurer/anaconda3/bin/python (3.5.2-final-0) [CPython] architecture: 64-bit cache: yes ground types: gmpy 2.0.7 random seed: 92687094 hash randomization: on (PYTHONHASHSEED=2305149269) sympy/utilities/tests/test_lambdify.py[65] ...........s.................../Users/aaronmeurer/anaconda3/lib/python3.5/site-packages/numpy/__init__.py:1: RuntimeWarning: divide by zero encountered in true_divide """ ........ssssssss.................. [OK] =================================================== tests finished: 56 passed, 9 skipped, in 1.87 seconds ==================================================== ``` The RuntimeWarning should not be printed. We might want to make them cause an error in the tests.
Hi I'm new here. Can I work on this? Seems like I should be able to figure this out if I dig around.. Any suggestions is appreciated Hi, I encountered neither an error nor an exception. If there is a real problem can you give me a reference to a file so that I can try to fix it. @asmeurer ![screenshot from 2017-01-27 20-32-35 png 1366x768](https://cloud.githubusercontent.com/assets/6770064/22375441/0f2d33b6-e4d0-11e6-83b0-a98edbd16ddd.png) @akshaym96 make sure you have NumPy installed. Otherwise it will just skip those tests (that's what the `s`s are in your test run. Hello, I'd like to work on this issue. I'm new and looking around the codebase. What should I do to get started on this problem? @asmeurer Yeah I have installed numpy and got the same issue. Can please suggest some steps suggest some steps so that I can fix the problem. Which file do I need to start with? @asmeurer Is this issue still open ? I would want to solve it if it is. @asmeurer , I tried a fix. Is that what you expect from this test ? ![screenshot from 2017-03-07 18-45-14](https://cloud.githubusercontent.com/assets/18261500/23657911/6f3f75a2-0366-11e7-8d01-5ed9312aab30.png) Yes, but the test should also be fixed to not fail. The original issue https://github.com/sympy/sympy/issues/11306 may be useful to read up on here. @asmeurer , Yeah i got it and i'm working on it, thanks. Will get back to you with a PR.
[ { "body": "```\n$./bin/test lambdify\n==================================================================== test process starts =====================================================================\nexecutable: /Users/aaronmeurer/anaconda3/bin/python (3.5.2-final-0) [CPython]\narchitecture: 64-bit\ncache: yes\nground types: gmpy 2.0.7\nrandom seed: 92687094\nhash randomization: on (PYTHONHASHSEED=2305149269)\n\nsympy/utilities/tests/test_lambdify.py[65] ...........s.................../Users/aaronmeurer/anaconda3/lib/python3.5/site-packages/numpy/__init__.py:1: RuntimeWarning: divide by zero encountered in true_divide\n \"\"\"\n........ssssssss.................. [OK]\n\n=================================================== tests finished: 56 passed, 9 skipped, in 1.87 seconds ====================================================\n```\n\nThe RuntimeWarning should not be printed. We might want to make them cause an error in the tests. \n", "number": 11727, "title": "RuntimeWarning: divide by zero encountered in true_divide in lambdify tests" } ]
22e21ed0240d529aadfe5b4fa5ea5d61fd4ec565
{ "head_commit": "cf2e3da63922d50d275929ead1e9fbf27f09ad5c", "head_commit_message": "Runtime Warning changed to an error", "patch_to_review": "diff --git a/sympy/utilities/tests/test_lambdify.py b/sympy/utilities/tests/test_lambdify.py\nindex 8749c667c587..91d8db7f4833 100644\n--- a/sympy/utilities/tests/test_lambdify.py\n+++ b/sympy/utilities/tests/test_lambdify.py\n@@ -364,8 +364,11 @@ def test_numpy_old_matrix():\n def test_python_div_zero_issue_11306():\n if not numpy:\n skip(\"numpy not installed.\")\n- p = Piecewise((1 / x, y < -1), (x, y <= 1), (1 / x, True))\n- lambdify([x, y], p, modules='numpy')(0, 1)\n+ p = Piecewise((1 / x, y < -1), (x, y < 1), (1 / x, True))\n+ f = lambdify([x, y], p, modules='numpy')\n+ numpy.seterr(all='ignore')\n+ assert str(float(f(0,1))) == 'inf'\n+ numpy.seterr(all='raise')\n \n def test_issue9474():\n mods = [None, 'math']\n" }
[ { "diff_hunk": "@@ -364,8 +364,11 @@ def test_numpy_old_matrix():\n def test_python_div_zero_issue_11306():\n if not numpy:\n skip(\"numpy not installed.\")\n- p = Piecewise((1 / x, y < -1), (x, y <= 1), (1 / x, True))\n- lambdify([x, y], p, modules='numpy')(0, 1)\n+ p = Piecewise((1 / x, y < -1), (x, y < 1), (1 / x, True))\n+ f = lambdify([x, y], p, modules='numpy')\n+ numpy.seterr(all='ignore')", "line": null, "original_line": 369, "original_start_line": null, "path": "sympy/utilities/tests/test_lambdify.py", "start_line": null, "text": "@user1:\nWould it suffice to ignore divide only?\r\n\n\n@author:\nYes, i guess so because the assert statement makes sure that the wrapper produces an output as `inf`.\r\nAny specific cases you think it can give an error in ?" } ]
8f7e4bcfd1f50d1137e686f06adf15b5b1035495
diff --git a/sympy/utilities/tests/test_lambdify.py b/sympy/utilities/tests/test_lambdify.py index 94133c6c3f7d..eaebc5d19ab0 100644 --- a/sympy/utilities/tests/test_lambdify.py +++ b/sympy/utilities/tests/test_lambdify.py @@ -364,8 +364,11 @@ def test_numpy_old_matrix(): def test_python_div_zero_issue_11306(): if not numpy: skip("numpy not installed.") - p = Piecewise((1 / x, y < -1), (x, y <= 1), (1 / x, True)) - lambdify([x, y], p, modules='numpy')(0, 1) + p = Piecewise((1 / x, y < -1), (x, y < 1), (1 / x, True)) + f = lambdify([x, y], p, modules='numpy') + numpy.seterr(divide='ignore') + assert str(float(f(0,1))) == 'inf' + numpy.seterr(divide='warn') def test_issue9474(): mods = [None, 'math']
{ "difficulty": "medium", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
streamlit__streamlit-7265@585c373
streamlit/streamlit
Python
7,265
Allow `None` as value for `st.date_input`
## Describe your changes This PR adds support for setting `None` as the value in `st.date_input`. Using `value=None` will render an empty date input that returns `None` as long as the user hasn't specified a date: <img width="723" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/1cf90c9a-8ca3-4ed0-b4d0-ef7df4e12614"> If a user has specified a date, the user can clear this value again to reset the input to `None`: <img width="723" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/47d34a1b-4da9-4c8f-b103-e4941059845d"> - Spec: https://www.notion.so/snowflake-corp/Spec-v2-b00de8d56f99464693e7ddfdea0674e9 ## GitHub Issue Link (if applicable) - Closes #6719 ## Testing Plan - Unit Tests: โœ… - E2E Tests: โœ… --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-09-01T13:35:13Z
Data Widgets to start with no initial value Can all data widgets have no initial selected value? 1. Can a date widget have no default date value on first start / load? It is only after the user selects a date, can it contain a date value. To get around this I have to use a text_input widget instead, and add date validation logic to check if the date is valid in terms of syntax (in addition to other other conditions such as end dates to be greater than start dates...) 2. Can radio buttons start with no value? It is only after a use selects one of the options does the widget have a selected value etc... To get around this I use a dummy 1st option to default as no selection. 3. ... Use case: data from widgets can be stored back into the database only if the user explicitly selects a value, not otherwise. There is no way for me to otherwise tell if today's date, in a date widget, was explicitly selected by the end user or is a result of the default widget value.
This seems related to #4645 (the idea of making widgets required so as to conclusively get a value from the user). Certainly the select-type widgets can do this (in effect), but other widgets don't have simple ways to accomplish this. For things like number input or color picker where the default choice might indeed be the user's choice, I've had to rely on a button to get confirmation. This is currently in work here: https://github.com/streamlit/streamlit/pull/6596. It will allow setting the initial value to `None`, which will result in an empty state. Thanks @LukasMasuch! (No love for color_picker yet?) ๐Ÿ˜„ @MathCatsAnd not yet ๐Ÿ˜ฌ Adding an empty state to color_picker is a bit more complicated Thanks both, for your prompt responses :-)
[ { "body": "Can all data widgets have no initial selected value?\r\n\r\n1. Can a date widget have no default date value on first start / load? It is only after the user selects a date, can it contain a date value. To get around this I have to use a text_input widget instead, and add date validation logic to check if the date is valid in terms of syntax (in addition to other other conditions such as end dates to be greater than start dates...)\r\n2. Can radio buttons start with no value? It is only after a use selects one of the options does the widget have a selected value\r\netc... To get around this I use a dummy 1st option to default as no selection.\r\n3. ...\r\n\r\nUse case: data from widgets can be stored back into the database only if the user explicitly selects a value, not otherwise. \r\n\r\nThere is no way for me to otherwise tell if today's date, in a date widget, was explicitly selected by the end user or is a result of the default widget value.", "number": 6719, "title": "Data Widgets to start with no initial value" } ]
7a3ba24da57dfbf713b857a78d5699e300f616a7
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differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[firefox].png b/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[firefox].png\nnew file mode 100644\nindex 000000000000..dc972f3c1e35\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[firefox].png differ\ndiff --git a/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[webkit].png b/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[webkit].png\nnew file mode 100644\nindex 000000000000..bd6a96ae7e05\nBinary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_date_input_test/st_date_input-clearable_input[webkit].png differ\ndiff --git a/e2e_playwright/conftest.py b/e2e_playwright/conftest.py\nindex 2868897c4035..e672e51c7bc0 100644\n--- a/e2e_playwright/conftest.py\n+++ b/e2e_playwright/conftest.py\n@@ -396,7 +396,7 @@ def compare(\n # Update this in updates folder:\n snapshot_updates_file_path.parent.mkdir(parents=True, exist_ok=True)\n snapshot_updates_file_path.write_bytes(img_bytes)\n- pytest.fail(f\"Snapshot matching failed: {ex}\")\n+ pytest.fail(f\"Snapshot matching for {snapshot_file_name} failed: {ex}\")\n max_diff_pixels = int(image_threshold * img_a.size[0] * img_a.size[1])\n \n if mismatch < max_diff_pixels:\ndiff --git a/e2e_playwright/st_date_input.py b/e2e_playwright/st_date_input.py\nnew file mode 100644\nindex 000000000000..76a034873072\n--- /dev/null\n+++ b/e2e_playwright/st_date_input.py\n@@ -0,0 +1,79 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from datetime import date, datetime\n+\n+import streamlit as st\n+from streamlit import runtime\n+\n+d1 = st.date_input(\"Single date\", date(1970, 1, 1), min_value=date(1970, 1, 1))\n+st.write(\"Value 1:\", d1)\n+\n+d2 = st.date_input(\"Single datetime\", datetime(2019, 7, 6, 21, 15), help=\"Help text\")\n+st.write(\"Value 2:\", d2)\n+\n+d3 = st.date_input(\"Range, no date\", [])\n+st.write(\"Value 3:\", d3)\n+\n+d4 = st.date_input(\"Range, one date\", [date(2019, 7, 6)])\n+st.write(\"Value 4:\", d4)\n+\n+d5 = st.date_input(\"Range, two dates\", [date(2019, 7, 6), date(2019, 7, 8)])\n+st.write(\"Value 5:\", d5)\n+\n+d6 = st.date_input(\"Disabled, no date\", [], disabled=True)\n+st.write(\"Value 6:\", d6)\n+\n+d7 = st.date_input(\n+ \"Label hidden\", datetime(2019, 7, 6, 21, 15), label_visibility=\"hidden\"\n+)\n+st.write(\"Value 7:\", d7)\n+\n+d8 = st.date_input(\n+ \"Label collapsed\", datetime(2019, 7, 6, 21, 15), label_visibility=\"collapsed\"\n+)\n+st.write(\"Value 8:\", d8)\n+\n+d9 = st.date_input(\"Single date with format\", date(1970, 1, 1), format=\"MM-DD-YYYY\")\n+st.write(\"Value 9:\", d9)\n+\n+d10 = st.date_input(\n+ \"Range, two dates with format\",\n+ [date(2019, 7, 6), date(2019, 7, 8)],\n+ format=\"MM/DD/YYYY\",\n+)\n+st.write(\"Value 10:\", d10)\n+\n+d11 = st.date_input(\"Range, no date with format\", [], format=\"DD.MM.YYYY\")\n+st.write(\"Value 11:\", d11)\n+\n+\n+if runtime.exists():\n+\n+ def on_change():\n+ st.session_state.date_input_changed = True\n+ st.text(\"Date input changed callback\")\n+\n+ st.date_input(\n+ \"Single date with callback\",\n+ date(1970, 1, 1),\n+ min_value=date(1970, 1, 1),\n+ key=\"date_input12\",\n+ on_change=on_change,\n+ )\n+ st.write(\"Value 12:\", st.session_state.date_input12)\n+ st.write(\"Date Input Changed:\", \"date_input_changed\" in st.session_state)\n+\n+d13 = st.date_input(\"Empty value\", value=None)\n+st.write(\"Value 13:\", d13)\ndiff --git a/e2e_playwright/st_date_input_test.py b/e2e_playwright/st_date_input_test.py\nnew file mode 100644\nindex 000000000000..507a1e9acc4e\n--- /dev/null\n+++ b/e2e_playwright/st_date_input_test.py\n@@ -0,0 +1,285 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from playwright.sync_api import Page, expect\n+\n+from e2e_playwright.conftest import ImageCompareFunction\n+\n+\n+def test_date_input_rendering(themed_app: Page, assert_snapshot: ImageCompareFunction):\n+ \"\"\"Test that st.date_input renders correctly via screenshots matching.\"\"\"\n+ date_time_widgets = themed_app.get_by_test_id(\"stDateInput\")\n+ expect(date_time_widgets).to_have_count(13)\n+\n+ assert_snapshot(date_time_widgets.nth(0), name=\"date_input-single_date\")\n+ assert_snapshot(date_time_widgets.nth(1), name=\"date_input-single_datetime\")\n+ assert_snapshot(date_time_widgets.nth(2), name=\"date_input-range_no_date\")\n+ assert_snapshot(date_time_widgets.nth(3), name=\"date_input-range_one_date\")\n+ assert_snapshot(date_time_widgets.nth(4), name=\"date_input-range_two_dates\")\n+ assert_snapshot(date_time_widgets.nth(5), name=\"date_input-disabled_no_date\")\n+ assert_snapshot(date_time_widgets.nth(6), name=\"date_input-label_hidden\")\n+ assert_snapshot(date_time_widgets.nth(7), name=\"date_input-label_collapsed\")\n+ assert_snapshot(date_time_widgets.nth(8), name=\"date_input-single_date_format\")\n+ assert_snapshot(date_time_widgets.nth(9), name=\"date_input-range_two_dates_format\")\n+ assert_snapshot(date_time_widgets.nth(10), name=\"date_input-range_no_date_format\")\n+ assert_snapshot(date_time_widgets.nth(11), name=\"date_input-single_date_callback\")\n+ assert_snapshot(date_time_widgets.nth(12), name=\"date_input-empty_value\")\n+\n+\n+def test_date_input_has_correct_initial_values(app: Page):\n+ \"\"\"Test that st.date_input has the correct initial values.\"\"\"\n+ markdown_elements = app.get_by_test_id(\"stMarkdown\")\n+ expect(markdown_elements).to_have_count(14)\n+\n+ expected = [\n+ \"Value 1: 1970-01-01\",\n+ \"Value 2: 2019-07-06\",\n+ \"Value 3: ()\",\n+ \"Value 4: (datetime.date(2019, 7, 6),)\",\n+ \"Value 5: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 8))\",\n+ \"Value 6: ()\",\n+ \"Value 7: 2019-07-06\",\n+ \"Value 8: 2019-07-06\",\n+ \"Value 9: 1970-01-01\",\n+ \"Value 10: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 8))\",\n+ \"Value 11: ()\",\n+ \"Value 12: 1970-01-01\",\n+ \"Date Input Changed: False\",\n+ \"Value 13: None\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(markdown_elements.all(), expected):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_handles_date_selection(app: Page):\n+ \"\"\"Test that selection of a date on the calendar works as expected.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").first.click()\n+\n+ # Select '1970/01/02':\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Friday, January 2nd 1970.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"Value 1: 1970-01-02\", use_inner_text=True\n+ )\n+\n+\n+def test_handle_value_changes(app: Page):\n+ \"\"\"Test that st.date_input has the correct value after typing in a date.\"\"\"\n+\n+ first_date_input_field = app.locator(\".stDateInput input\").first\n+ first_date_input_field.type(\"1970/01/02\")\n+ first_date_input_field.blur()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"Value 1: 1970-01-02\", use_inner_text=True\n+ )\n+\n+\n+def test_empty_date_input_behaves_correctly(\n+ app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that st.date_input behaves correctly when empty.\"\"\"\n+ # Enter 10 in the first empty input:\n+ empty_number_input = app.locator(\".stDateInput input\").nth(12)\n+ empty_number_input.type(\"1970/01/02\")\n+ empty_number_input.press(\"Enter\")\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(13)).to_have_text(\n+ \"Value 13: 1970-01-02\", use_inner_text=True\n+ )\n+\n+ # Click outside to remove focus:\n+ app.get_by_test_id(\"stMarkdown\").nth(13).click()\n+\n+ # Screenshot match clearable input:\n+ assert_snapshot(\n+ app.get_by_test_id(\"stDateInput\").nth(12), name=\"st_date_input-clearable_input\"\n+ )\n+\n+ # Press escape to clear value:\n+ empty_number_input = app.locator(\".stDateInput input\").nth(12)\n+ empty_number_input.focus()\n+ empty_number_input.press(\"Escape\")\n+ # Click outside to enter value:\n+ app.get_by_test_id(\"stMarkdown\").nth(13).click()\n+\n+ # Should be empty again:\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(13)).to_have_text(\n+ \"Value 13: None\", use_inner_text=True\n+ )\n+\n+\n+def test_handles_range_end_date_changes(app: Page):\n+ \"\"\"Test that it correctly handles changes to the end date of a range.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").nth(3).click()\n+\n+ # Select '2019/07/10'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Wednesday, July 10th 2019.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(3)).to_have_text(\n+ \"Value 4: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 10))\",\n+ use_inner_text=True,\n+ )\n+\n+\n+def test_handles_range_start_end_date_changes(app: Page):\n+ \"\"\"Test that it correctly handles changes to the start and end date of a range.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").nth(4).click()\n+\n+ # Select start date: '2019/07/10'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Wednesday, July 10th 2019.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(4)).to_have_text(\n+ \"Value 5: (datetime.date(2019, 7, 10),)\",\n+ use_inner_text=True,\n+ )\n+\n+ # Select end date: '2019/07/12'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Friday, July 12th 2019.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(4)).to_have_text(\n+ \"Value 5: (datetime.date(2019, 7, 10), datetime.date(2019, 7, 12))\",\n+ use_inner_text=True,\n+ )\n+\n+\n+def test_calls_callback_on_change(app: Page):\n+ \"\"\"Test that it correctly calls the callback on change.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").nth(11).click()\n+\n+ # Select '1970/01/02'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Friday, January 2nd 1970.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(11)).to_have_text(\n+ \"Value 12: 1970-01-02\",\n+ use_inner_text=True,\n+ )\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(12)).to_have_text(\n+ \"Date Input Changed: True\",\n+ use_inner_text=True,\n+ )\n+\n+ # Change different date input to trigger delta path change\n+ first_date_input_field = app.locator(\".stDateInput input\").first\n+ first_date_input_field.type(\"1971/01/03\")\n+ first_date_input_field.blur()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"Value 1: 1971-01-03\", use_inner_text=True\n+ )\n+\n+ # Test if value is still correct after delta path change\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(11)).to_have_text(\n+ \"Value 12: 1970-01-02\",\n+ use_inner_text=True,\n+ )\n+\n+\n+def test_single_date_calendar_picker_rendering(\n+ themed_app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that the single value calendar picker renders correctly via screenshots matching.\"\"\"\n+ themed_app.get_by_test_id(\"stDateInput\").first.click()\n+ assert_snapshot(\n+ themed_app.locator('[data-baseweb=\"calendar\"]').first,\n+ name=\"date_input-single_date_calendar\",\n+ )\n+\n+\n+def test_range_date_calendar_picker_rendering(\n+ themed_app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that the range calendar picker renders correctly via screenshots matching.\"\"\"\n+ themed_app.get_by_test_id(\"stDateInput\").nth(4).click()\n+ assert_snapshot(\n+ themed_app.locator('[data-baseweb=\"calendar\"]').first,\n+ name=\"date_input-range_two_dates_calendar\",\n+ )\n+\n+\n+def test_resets_to_default_single_value_if_calendar_closed_empty(app: Page):\n+ \"\"\"Test that single value is reset to default if calendar closed empty.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").first.click()\n+\n+ # Select '1970/01/02'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Friday, January 2nd 1970.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"Value 1: 1970-01-02\", use_inner_text=True\n+ )\n+\n+ # Close calendar without selecting a date\n+ date_input_field = app.locator(\".stDateInput input\").first\n+ date_input_field.focus()\n+ date_input_field.clear()\n+\n+ # Click outside of the calendar to submit value\n+ app.get_by_test_id(\"stMarkdown\").first.click(delay=100)\n+\n+ # Value should be reset to default\n+ expect(app.get_by_test_id(\"stMarkdown\").first).to_have_text(\n+ \"Value 1: 1970-01-01\", use_inner_text=True\n+ )\n+\n+\n+def test_range_is_empty_if_calendar_closed_empty(app: Page):\n+ \"\"\"Test that range value is empty of calendar closed empty.\"\"\"\n+ app.get_by_test_id(\"stDateInput\").nth(4).click()\n+\n+ # Select start date: '2019/07/10'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Wednesday, July 10th 2019.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(4)).to_have_text(\n+ \"Value 5: (datetime.date(2019, 7, 10),)\",\n+ use_inner_text=True,\n+ )\n+\n+ # Select end date: '2019/07/12'\n+ app.locator(\n+ '[data-baseweb=\"calendar\"] [aria-label^=\"Choose Friday, July 12th 2019.\"]'\n+ ).first.click()\n+\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(4)).to_have_text(\n+ \"Value 5: (datetime.date(2019, 7, 10), datetime.date(2019, 7, 12))\",\n+ use_inner_text=True,\n+ )\n+\n+ # Close calendar without selecting a date\n+ date_input_field = app.locator(\".stDateInput input\").nth(4)\n+ date_input_field.focus()\n+ date_input_field.clear()\n+\n+ # Click outside of the calendar to submit value\n+ app.get_by_test_id(\"stMarkdown\").nth(4).click()\n+\n+ # Range should be empty\n+ expect(app.get_by_test_id(\"stMarkdown\").nth(4)).to_have_text(\n+ \"Value 5: ()\",\n+ use_inner_text=True,\n+ )\ndiff --git a/frontend/lib/src/components/elements/Markdown/Markdown.tsx b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\nindex c0a932c4f327..00bab744812e 100644\n--- a/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n+++ b/frontend/lib/src/components/elements/Markdown/Markdown.tsx\n@@ -37,7 +37,7 @@ export default function Markdown({\n }: MarkdownProps): ReactElement {\n const styleProp = { width }\n return (\n- <div className=\"stMarkdown\" style={styleProp}>\n+ <div className=\"stMarkdown\" style={styleProp} data-testid=\"stMarkdown\">\n {element.help ? (\n <StyledLabelHelpWrapper>\n <StreamlitMarkdown\ndiff --git a/frontend/lib/src/components/widgets/DateInput/DateInput.tsx b/frontend/lib/src/components/widgets/DateInput/DateInput.tsx\nindex 40d217939393..a3737eb8752b 100644\n--- a/frontend/lib/src/components/widgets/DateInput/DateInput.tsx\n+++ b/frontend/lib/src/components/widgets/DateInput/DateInput.tsx\n@@ -66,6 +66,9 @@ function stringsToDates(strings: string[]): Date[] {\n \n /** Convert an array of dates to an array of strings. */\n function datesToStrings(dates: Date[]): string[] {\n+ if (!dates) {\n+ return []\n+ }\n return dates.map((value: Date) => moment(value as Date).format(DATE_FORMAT))\n }\n \n@@ -85,7 +88,9 @@ class DateInput extends React.PureComponent<Props, State> {\n this.props.element\n )\n const stringArray =\n- storedValue !== undefined ? storedValue : this.props.element.default\n+ storedValue !== undefined\n+ ? storedValue\n+ : this.props.element.default || []\n return stringsToDates(stringArray)\n }\n \n@@ -214,6 +219,7 @@ class DateInput extends React.PureComponent<Props, State> {\n const style = { width }\n const minDate = moment(element.min, DATE_FORMAT).toDate()\n const maxDate = this.getMaxDate()\n+ const clearable = element.default.length === 0 && !disabled\n \n // We need to extract the mask and format (date-fns notation) from the provided format string\n // The user configured date format is based on the momentJS notation and is only allowed to contain\n@@ -235,7 +241,7 @@ class DateInput extends React.PureComponent<Props, State> {\n )\n \n return (\n- <div className=\"stDateInput\" style={style}>\n+ <div className=\"stDateInput\" style={style} data-testid=\"stDateInput\">\n <WidgetLabel\n label={element.label}\n disabled={disabled}\n@@ -368,7 +374,26 @@ class DateInput extends React.PureComponent<Props, State> {\n borderBottomWidth: \"1px\",\n },\n },\n-\n+ ClearIcon: {\n+ props: {\n+ overrides: {\n+ Svg: {\n+ style: {\n+ color: theme.colors.darkGray,\n+ // Since the close icon is an SVG, and we can't control its viewbox nor its attributes,\n+ // Let's use a scale transform effect to make it bigger.\n+ // The width property only enlarges its bounding box, so it's easier to click.\n+ transform: \"scale(1.41)\",\n+ width: theme.spacing.twoXL,\n+ marginRight: \"-8px\",\n+ \":hover\": {\n+ fill: theme.colors.bodyText,\n+ },\n+ },\n+ },\n+ },\n+ },\n+ },\n Input: {\n style: {\n // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings.\n@@ -386,6 +411,7 @@ class DateInput extends React.PureComponent<Props, State> {\n minDate={minDate}\n maxDate={maxDate}\n range={isRange}\n+ clearable={clearable}\n />\n </div>\n )\ndiff --git a/lib/streamlit/elements/widgets/number_input.py b/lib/streamlit/elements/widgets/number_input.py\nindex 56ba71b10de5..5a39f20a9479 100644\n--- a/lib/streamlit/elements/widgets/number_input.py\n+++ b/lib/streamlit/elements/widgets/number_input.py\n@@ -169,8 +169,8 @@ def number_input(\n If None, there will be no maximum.\n value : int, float, or None\n The value of this widget when it first renders. If ``None``, the widget\n- will be initialized empty and returns ``None`` as long as the user didn't\n- provide an input. Defaults to min_value, or 0.0 if min_value is None.\n+ will initialize empty and return ``None`` until the user provides input.\n+ Defaults to min_value, or 0.0 if min_value is None.\n step : int, float, or None\n The stepping interval.\n Defaults to 1 if the value is an int, 0.01 otherwise.\ndiff --git a/lib/streamlit/elements/widgets/time_widgets.py b/lib/streamlit/elements/widgets/time_widgets.py\nindex 3d7b75589f7d..cb4cca563db7 100644\n--- a/lib/streamlit/elements/widgets/time_widgets.py\n+++ b/lib/streamlit/elements/widgets/time_widgets.py\n@@ -34,6 +34,7 @@\n from streamlit.runtime.metrics_util import gather_metrics\n from streamlit.runtime.scriptrunner import ScriptRunContext, get_script_run_ctx\n from streamlit.runtime.state import (\n+ DefaultValue,\n WidgetArgs,\n WidgetCallback,\n WidgetKwargs,\n@@ -43,12 +44,16 @@\n from streamlit.type_util import Key, LabelVisibility, maybe_raise_label_warnings, to_key\n \n if TYPE_CHECKING:\n+ from builtins import ellipsis\n+\n from streamlit.delta_generator import DeltaGenerator\n \n TimeValue: TypeAlias = Union[time, datetime, None]\n SingleDateValue: TypeAlias = Union[date, datetime, None]\n DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue]]\n-DateWidgetReturn: TypeAlias = Union[date, Tuple[()], Tuple[date], Tuple[date, date]]\n+DateWidgetReturn: TypeAlias = Union[\n+ date, Tuple[()], Tuple[date], Tuple[date, date], None\n+]\n \n DEFAULT_STEP_MINUTES = 15\n ALLOWED_DATE_FORMATS = re.compile(\n@@ -56,10 +61,12 @@\n )\n \n \n-def _parse_date_value(value: DateValue) -> Tuple[List[date], bool]:\n+def _parse_date_value(value: DateValue | ellipsis) -> Tuple[List[date] | None, bool]:\n parsed_dates: List[date]\n range_value: bool = False\n if value is None:\n+ return None, range_value\n+ if value is DefaultValue:\n # Set value default.\n parsed_dates = [datetime.now().date()]\n elif isinstance(value, datetime):\n@@ -85,7 +92,7 @@ def _parse_date_value(value: DateValue) -> Tuple[List[date], bool]:\n \n def _parse_min_date(\n min_value: SingleDateValue,\n- parsed_dates: Sequence[date],\n+ parsed_dates: Sequence[date] | None,\n ) -> date:\n parsed_min_date: date\n if isinstance(min_value, datetime):\n@@ -106,7 +113,7 @@ def _parse_min_date(\n \n def _parse_max_date(\n max_value: SingleDateValue,\n- parsed_dates: Sequence[date],\n+ parsed_dates: Sequence[date] | None,\n ) -> date:\n parsed_max_date: date\n if isinstance(max_value, datetime):\n@@ -127,7 +134,7 @@ def _parse_max_date(\n \n @dataclass(frozen=True)\n class _DateInputValues:\n- value: Sequence[date]\n+ value: Sequence[date] | None\n is_range: bool\n max: date\n min: date\n@@ -135,7 +142,7 @@ class _DateInputValues:\n @classmethod\n def from_raw_values(\n cls,\n- value: DateValue,\n+ value: DateValue | ellipsis,\n min_value: SingleDateValue,\n max_value: SingleDateValue,\n ) -> \"_DateInputValues\":\n@@ -198,7 +205,7 @@ def deserialize(\n ui_value: Any,\n widget_id: str = \"\",\n ) -> DateWidgetReturn:\n- return_value: Sequence[date]\n+ return_value: Sequence[date] | None\n if ui_value is not None:\n return_value = tuple(\n datetime.strptime(v, \"%Y/%m/%d\").date() for v in ui_value\n@@ -206,11 +213,17 @@ def deserialize(\n else:\n return_value = self.value.value\n \n+ if return_value is None or len(return_value) == 0:\n+ return () if self.value.is_range else None\n+\n if not self.value.is_range:\n return return_value[0]\n return cast(DateWidgetReturn, tuple(return_value))\n \n def serialize(self, v: DateWidgetReturn) -> List[str]:\n+ if v is None:\n+ return []\n+\n to_serialize = list(v) if isinstance(v, (list, tuple)) else [v]\n return [date.strftime(v, \"%Y/%m/%d\") for v in to_serialize]\n \n@@ -417,14 +430,14 @@ def _time_input(\n def date_input(\n self,\n label: str,\n- value: DateValue = None,\n+ value: DateValue | ellipsis = ...,\n min_value: SingleDateValue = None,\n max_value: SingleDateValue = None,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n format: str = \"YYYY/MM/DD\",\n disabled: bool = False,\n@@ -463,7 +476,9 @@ def date_input(\n value : datetime.date or datetime.datetime or list/tuple of datetime.date or datetime.datetime or None\n The value of this widget when it first renders. If a list/tuple with\n 0 to 2 date/datetime values is provided, the datepicker will allow\n- users to provide a range. Defaults to today as a single-date picker.\n+ users to provide a range. If ``None``, will initialize empty and\n+ return ``None`` until the user provides input. Defaults to today as\n+ a single-date picker.\n min_value : datetime.date or datetime.datetime\n The minimum selectable date. If value is a date, defaults to value - 10 years.\n If value is the interval [start, end], defaults to start - 10 years.\n@@ -499,8 +514,9 @@ def date_input(\n \n Returns\n -------\n- datetime.date or a tuple with 0-2 dates\n- The current value of the date input widget.\n+ datetime.date or a tuple with 0-2 dates or None\n+ The current value of the date input widget or ``None`` if no date has been\n+ selected.\n \n Examples\n --------\n@@ -535,6 +551,16 @@ def date_input(\n https://doc-date-input1.streamlit.app/\n height: 380px\n \n+ >>> import datetime\n+ >>> import streamlit as st\n+ >>>\n+ >>> d = st.date_input(\"When's your birthday\", value=None)\n+ >>> st.write('Your birthday is:', d)\n+\n+ .. output::\n+ https://doc-date-input-empty.streamlit.app/\n+ height: 380px\n+\n \"\"\"\n ctx = get_script_run_ctx()\n return self._date_input(\n@@ -556,19 +582,19 @@ def date_input(\n def _date_input(\n self,\n label: str,\n- value: DateValue = None,\n+ value: DateValue | ellipsis = ...,\n min_value: SingleDateValue = None,\n max_value: SingleDateValue = None,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n format: str = \"YYYY/MM/DD\",\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- ctx: Optional[ScriptRunContext] = None,\n+ ctx: ScriptRunContext | None = None,\n ) -> DateWidgetReturn:\n key = to_key(key)\n check_callback_rules(self.dg, on_change)\n@@ -576,19 +602,21 @@ def _date_input(\n \n maybe_raise_label_warnings(label, label_visibility)\n \n- def parse_date_deterministic(v: SingleDateValue) -> str | None:\n- if v is None:\n- return None\n- elif isinstance(v, datetime):\n+ def parse_date_deterministic(v: SingleDateValue | ellipsis) -> str | None:\n+ if isinstance(v, datetime):\n return date.strftime(v.date(), \"%Y/%m/%d\")\n elif isinstance(v, date):\n return date.strftime(v, \"%Y/%m/%d\")\n+ return None\n \n parsed_min_date = parse_date_deterministic(min_value)\n parsed_max_date = parse_date_deterministic(max_value)\n \n- if isinstance(value, datetime) or isinstance(value, date) or value is None:\n- parsed: str | None | List[str | None] = parse_date_deterministic(value)\n+ parsed: str | None | List[str | None]\n+ if value is DefaultValue or value is None:\n+ parsed = None\n+ elif isinstance(value, (datetime, date)):\n+ parsed = parse_date_deterministic(value)\n else:\n parsed = [parse_date_deterministic(v) for v in value]\n \n@@ -630,9 +658,12 @@ def parse_date_deterministic(v: SingleDateValue) -> str | None:\n )\n date_input_proto.format = format\n date_input_proto.label = label\n- date_input_proto.default[:] = [\n- date.strftime(v, \"%Y/%m/%d\") for v in parsed_values.value\n- ]\n+ if parsed_values.value is None:\n+ date_input_proto.default[:] = []\n+ else:\n+ date_input_proto.default[:] = [\n+ date.strftime(v, \"%Y/%m/%d\") for v in parsed_values.value\n+ ]\n date_input_proto.min = date.strftime(parsed_values.min, \"%Y/%m/%d\")\n date_input_proto.max = date.strftime(parsed_values.max, \"%Y/%m/%d\")\n date_input_proto.form_id = current_form_id(self.dg)\ndiff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py\nindex 7e5787435524..d5483f89f87a 100644\n--- a/lib/streamlit/testing/element_tree.py\n+++ b/lib/streamlit/testing/element_tree.py\n@@ -292,12 +292,12 @@ def pick(self, v: str) -> ColorPicker:\n \n \n SingleDateValue: TypeAlias = Union[date, datetime]\n-DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue]]\n+DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue], None]\n \n \n @dataclass(repr=False)\n class DateInput(Widget):\n- _value: DateValue | None\n+ _value: DateValue | None | InitialValue\n proto: DateInputProto\n min: date\n max: date\n@@ -306,7 +306,7 @@ class DateInput(Widget):\n def __init__(self, proto: DateInputProto, root: ElementTree):\n self.proto = proto\n self.root = root\n- self._value = None\n+ self._value = InitialValue()\n \n self.type = \"date_input\"\n self.id = proto.id\n@@ -333,9 +333,9 @@ def widget_state(self) -> WidgetState:\n \n @property\n def value(self) -> DateWidgetReturn:\n- if self._value is not None:\n+ if not isinstance(self._value, InitialValue):\n parsed, _ = _parse_date_value(self._value)\n- return tuple(parsed) # type: ignore\n+ return tuple(parsed) if parsed is not None else None # type: ignore\n else:\n state = self.root.session_state\n assert state\ndiff --git a/lib/tests/streamlit/elements/date_input_test.py b/lib/tests/streamlit/elements/date_input_test.py\nindex 7be7d3aff97b..6b791fd97cfd 100644\n--- a/lib/tests/streamlit/elements/date_input_test.py\n+++ b/lib/tests/streamlit/elements/date_input_test.py\n@@ -22,6 +22,7 @@\n import streamlit as st\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage\n+from streamlit.testing.script_interactions import InteractiveScriptTests\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n \n \n@@ -50,6 +51,16 @@ def test_just_disabled(self):\n c = self.get_delta_from_queue().new_element.date_input\n self.assertEqual(c.disabled, True)\n \n+ def test_none_value(self):\n+ \"\"\"Test that it can be called with None as value.\"\"\"\n+ st.date_input(\"the label\", value=None)\n+\n+ c = self.get_delta_from_queue().new_element.date_input\n+ self.assertEqual(c.label, \"the label\")\n+ # If a proto property is null is not determined by this value,\n+ # but by the check via the HasField method:\n+ self.assertEqual(c.default, [])\n+\n @parameterized.expand(\n [\n (date(1970, 1, 1), [\"1970/01/01\"]),\n@@ -254,3 +265,28 @@ def test_invalid_date_format_values(self, format: str):\n with self.assertRaises(StreamlitAPIException) as ex:\n st.date_input(\"the label\", format=format)\n self.assertTrue(str(ex.exception).startswith(\"The provided format\"))\n+\n+\n+class DateInputInteractiveTest(InteractiveScriptTests):\n+ def test_date_input_interaction(self):\n+ \"\"\"Test interactions with an empty date_input widget.\"\"\"\n+ script = self.script_from_string(\n+ \"\"\"\n+ import streamlit as st\n+\n+ st.date_input(\"the label\", value=None)\n+ \"\"\"\n+ )\n+ sr = script.run()\n+ date_input = sr.date_input[0]\n+ assert date_input.value is None\n+\n+ # Set the value to 10\n+ sr2 = date_input.set_value(date(2012, 1, 3)).run()\n+ date_input = sr2.date_input[0]\n+ assert date_input.value == date(2012, 1, 3)\n+\n+ # # Clear the value\n+ sr3 = date_input.set_value(None).run()\n+ date_input = sr3.date_input[0]\n+ assert date_input.value is None\ndiff --git a/lib/tests/streamlit/elements/element_utils_test.py b/lib/tests/streamlit/elements/element_utils_test.py\nindex 31a5a7082203..fbf9576325f2 100644\n--- a/lib/tests/streamlit/elements/element_utils_test.py\n+++ b/lib/tests/streamlit/elements/element_utils_test.py\n@@ -20,6 +20,7 @@\n import pytest\n \n from streamlit import config\n+from streamlit.elements import utils\n from streamlit.elements.utils import check_callback_rules, check_session_state_rules\n from streamlit.errors import StreamlitAPIException\n \n@@ -107,6 +108,8 @@ def test_check_session_state_rules_prints_warning(\n mock_session_state = MagicMock()\n mock_session_state.is_new_state_value.return_value = True\n patched_get_session_state.return_value = mock_session_state\n+ # Reset globale flag:\n+ utils._shown_default_value_warning = False\n \n check_session_state_rules(5, key=\"the key\")\n \n" }
[ { "diff_hunk": "@@ -254,3 +265,28 @@ def test_invalid_date_format_values(self, format: str):\n with self.assertRaises(StreamlitAPIException) as ex:\n st.date_input(\"the label\", format=format)\n self.assertTrue(str(ex.exception).startswith(\"The provided format\"))\n+\n+\n+class DateInputInteractiveTest(InteractiveScriptTests):\n+ def test_date_input_interaction(self):\n+ \"\"\"Test interactions with an empty date_input widget.\"\"\"\n+ script = self.script_from_string(\n+ \"\"\"\n+ import streamlit as st\n+\n+ st.date_input(\"the label\", value=None)\n+ \"\"\"\n+ )\n+ sr = script.run()\n+ date_input = sr.date_input[0]\n+ assert date_input.value is None\n+\n+ # Set the value to 10", "line": null, "original_line": 284, "original_start_line": null, "path": "lib/tests/streamlit/elements/date_input_test.py", "start_line": null, "text": "@user1:\nNit: Set it to 10?\n\n@author:\nWhoops, this is from number input copy/paste..." } ]
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in updates folder: snapshot_updates_file_path.parent.mkdir(parents=True, exist_ok=True) snapshot_updates_file_path.write_bytes(img_bytes) - pytest.fail(f"Snapshot matching failed: {ex}") + pytest.fail(f"Snapshot matching for {snapshot_file_name} failed: {ex}") max_diff_pixels = int(image_threshold * img_a.size[0] * img_a.size[1]) if mismatch < max_diff_pixels: diff --git a/e2e_playwright/st_date_input.py b/e2e_playwright/st_date_input.py new file mode 100644 index 000000000000..76a034873072 --- /dev/null +++ b/e2e_playwright/st_date_input.py @@ -0,0 +1,79 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from datetime import date, datetime + +import streamlit as st +from streamlit import runtime + +d1 = st.date_input("Single date", date(1970, 1, 1), min_value=date(1970, 1, 1)) +st.write("Value 1:", d1) + +d2 = st.date_input("Single datetime", datetime(2019, 7, 6, 21, 15), help="Help text") +st.write("Value 2:", d2) + +d3 = st.date_input("Range, no date", []) +st.write("Value 3:", d3) + +d4 = st.date_input("Range, one date", [date(2019, 7, 6)]) +st.write("Value 4:", d4) + +d5 = st.date_input("Range, two dates", [date(2019, 7, 6), date(2019, 7, 8)]) +st.write("Value 5:", d5) + +d6 = st.date_input("Disabled, no date", [], disabled=True) +st.write("Value 6:", d6) + +d7 = st.date_input( + "Label hidden", datetime(2019, 7, 6, 21, 15), label_visibility="hidden" +) +st.write("Value 7:", d7) + +d8 = st.date_input( + "Label collapsed", datetime(2019, 7, 6, 21, 15), label_visibility="collapsed" +) +st.write("Value 8:", d8) + +d9 = st.date_input("Single date with format", date(1970, 1, 1), format="MM-DD-YYYY") +st.write("Value 9:", d9) + +d10 = st.date_input( + "Range, two dates with format", + [date(2019, 7, 6), date(2019, 7, 8)], + format="MM/DD/YYYY", +) +st.write("Value 10:", d10) + +d11 = st.date_input("Range, no date with format", [], format="DD.MM.YYYY") +st.write("Value 11:", d11) + + +if runtime.exists(): + + def on_change(): + st.session_state.date_input_changed = True + st.text("Date input changed callback") + + st.date_input( + "Single date with callback", + date(1970, 1, 1), + min_value=date(1970, 1, 1), + key="date_input12", + on_change=on_change, + ) + st.write("Value 12:", st.session_state.date_input12) + st.write("Date Input Changed:", "date_input_changed" in st.session_state) + +d13 = st.date_input("Empty value", value=None) +st.write("Value 13:", d13) diff --git a/e2e_playwright/st_date_input_test.py b/e2e_playwright/st_date_input_test.py new file mode 100644 index 000000000000..507a1e9acc4e --- /dev/null +++ b/e2e_playwright/st_date_input_test.py @@ -0,0 +1,285 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from playwright.sync_api import Page, expect + +from e2e_playwright.conftest import ImageCompareFunction + + +def test_date_input_rendering(themed_app: Page, assert_snapshot: ImageCompareFunction): + """Test that st.date_input renders correctly via screenshots matching.""" + date_time_widgets = themed_app.get_by_test_id("stDateInput") + expect(date_time_widgets).to_have_count(13) + + assert_snapshot(date_time_widgets.nth(0), name="date_input-single_date") + assert_snapshot(date_time_widgets.nth(1), name="date_input-single_datetime") + assert_snapshot(date_time_widgets.nth(2), name="date_input-range_no_date") + assert_snapshot(date_time_widgets.nth(3), name="date_input-range_one_date") + assert_snapshot(date_time_widgets.nth(4), name="date_input-range_two_dates") + assert_snapshot(date_time_widgets.nth(5), name="date_input-disabled_no_date") + assert_snapshot(date_time_widgets.nth(6), name="date_input-label_hidden") + assert_snapshot(date_time_widgets.nth(7), name="date_input-label_collapsed") + assert_snapshot(date_time_widgets.nth(8), name="date_input-single_date_format") + assert_snapshot(date_time_widgets.nth(9), name="date_input-range_two_dates_format") + assert_snapshot(date_time_widgets.nth(10), name="date_input-range_no_date_format") + assert_snapshot(date_time_widgets.nth(11), name="date_input-single_date_callback") + assert_snapshot(date_time_widgets.nth(12), name="date_input-empty_value") + + +def test_date_input_has_correct_initial_values(app: Page): + """Test that st.date_input has the correct initial values.""" + markdown_elements = app.get_by_test_id("stMarkdown") + expect(markdown_elements).to_have_count(14) + + expected = [ + "Value 1: 1970-01-01", + "Value 2: 2019-07-06", + "Value 3: ()", + "Value 4: (datetime.date(2019, 7, 6),)", + "Value 5: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 8))", + "Value 6: ()", + "Value 7: 2019-07-06", + "Value 8: 2019-07-06", + "Value 9: 1970-01-01", + "Value 10: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 8))", + "Value 11: ()", + "Value 12: 1970-01-01", + "Date Input Changed: False", + "Value 13: None", + ] + + for markdown_element, expected_text in zip(markdown_elements.all(), expected): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_handles_date_selection(app: Page): + """Test that selection of a date on the calendar works as expected.""" + app.get_by_test_id("stDateInput").first.click() + + # Select '1970/01/02': + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Friday, January 2nd 1970."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "Value 1: 1970-01-02", use_inner_text=True + ) + + +def test_handle_value_changes(app: Page): + """Test that st.date_input has the correct value after typing in a date.""" + + first_date_input_field = app.locator(".stDateInput input").first + first_date_input_field.type("1970/01/02") + first_date_input_field.blur() + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "Value 1: 1970-01-02", use_inner_text=True + ) + + +def test_empty_date_input_behaves_correctly( + app: Page, assert_snapshot: ImageCompareFunction +): + """Test that st.date_input behaves correctly when empty.""" + # Enter 10 in the first empty input: + empty_number_input = app.locator(".stDateInput input").nth(12) + empty_number_input.type("1970/01/02") + empty_number_input.press("Enter") + + expect(app.get_by_test_id("stMarkdown").nth(13)).to_have_text( + "Value 13: 1970-01-02", use_inner_text=True + ) + + # Click outside to remove focus: + app.get_by_test_id("stMarkdown").nth(13).click() + + # Screenshot match clearable input: + assert_snapshot( + app.get_by_test_id("stDateInput").nth(12), name="st_date_input-clearable_input" + ) + + # Press escape to clear value: + empty_number_input = app.locator(".stDateInput input").nth(12) + empty_number_input.focus() + empty_number_input.press("Escape") + # Click outside to enter value: + app.get_by_test_id("stMarkdown").nth(13).click() + + # Should be empty again: + expect(app.get_by_test_id("stMarkdown").nth(13)).to_have_text( + "Value 13: None", use_inner_text=True + ) + + +def test_handles_range_end_date_changes(app: Page): + """Test that it correctly handles changes to the end date of a range.""" + app.get_by_test_id("stDateInput").nth(3).click() + + # Select '2019/07/10' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Wednesday, July 10th 2019."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(3)).to_have_text( + "Value 4: (datetime.date(2019, 7, 6), datetime.date(2019, 7, 10))", + use_inner_text=True, + ) + + +def test_handles_range_start_end_date_changes(app: Page): + """Test that it correctly handles changes to the start and end date of a range.""" + app.get_by_test_id("stDateInput").nth(4).click() + + # Select start date: '2019/07/10' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Wednesday, July 10th 2019."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(4)).to_have_text( + "Value 5: (datetime.date(2019, 7, 10),)", + use_inner_text=True, + ) + + # Select end date: '2019/07/12' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Friday, July 12th 2019."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(4)).to_have_text( + "Value 5: (datetime.date(2019, 7, 10), datetime.date(2019, 7, 12))", + use_inner_text=True, + ) + + +def test_calls_callback_on_change(app: Page): + """Test that it correctly calls the callback on change.""" + app.get_by_test_id("stDateInput").nth(11).click() + + # Select '1970/01/02' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Friday, January 2nd 1970."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(11)).to_have_text( + "Value 12: 1970-01-02", + use_inner_text=True, + ) + expect(app.get_by_test_id("stMarkdown").nth(12)).to_have_text( + "Date Input Changed: True", + use_inner_text=True, + ) + + # Change different date input to trigger delta path change + first_date_input_field = app.locator(".stDateInput input").first + first_date_input_field.type("1971/01/03") + first_date_input_field.blur() + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "Value 1: 1971-01-03", use_inner_text=True + ) + + # Test if value is still correct after delta path change + expect(app.get_by_test_id("stMarkdown").nth(11)).to_have_text( + "Value 12: 1970-01-02", + use_inner_text=True, + ) + + +def test_single_date_calendar_picker_rendering( + themed_app: Page, assert_snapshot: ImageCompareFunction +): + """Test that the single value calendar picker renders correctly via screenshots matching.""" + themed_app.get_by_test_id("stDateInput").first.click() + assert_snapshot( + themed_app.locator('[data-baseweb="calendar"]').first, + name="date_input-single_date_calendar", + ) + + +def test_range_date_calendar_picker_rendering( + themed_app: Page, assert_snapshot: ImageCompareFunction +): + """Test that the range calendar picker renders correctly via screenshots matching.""" + themed_app.get_by_test_id("stDateInput").nth(4).click() + assert_snapshot( + themed_app.locator('[data-baseweb="calendar"]').first, + name="date_input-range_two_dates_calendar", + ) + + +def test_resets_to_default_single_value_if_calendar_closed_empty(app: Page): + """Test that single value is reset to default if calendar closed empty.""" + app.get_by_test_id("stDateInput").first.click() + + # Select '1970/01/02' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Friday, January 2nd 1970."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "Value 1: 1970-01-02", use_inner_text=True + ) + + # Close calendar without selecting a date + date_input_field = app.locator(".stDateInput input").first + date_input_field.focus() + date_input_field.clear() + + # Click outside of the calendar to submit value + app.get_by_test_id("stMarkdown").first.click(delay=100) + + # Value should be reset to default + expect(app.get_by_test_id("stMarkdown").first).to_have_text( + "Value 1: 1970-01-01", use_inner_text=True + ) + + +def test_range_is_empty_if_calendar_closed_empty(app: Page): + """Test that range value is empty of calendar closed empty.""" + app.get_by_test_id("stDateInput").nth(4).click() + + # Select start date: '2019/07/10' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Wednesday, July 10th 2019."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(4)).to_have_text( + "Value 5: (datetime.date(2019, 7, 10),)", + use_inner_text=True, + ) + + # Select end date: '2019/07/12' + app.locator( + '[data-baseweb="calendar"] [aria-label^="Choose Friday, July 12th 2019."]' + ).first.click() + + expect(app.get_by_test_id("stMarkdown").nth(4)).to_have_text( + "Value 5: (datetime.date(2019, 7, 10), datetime.date(2019, 7, 12))", + use_inner_text=True, + ) + + # Close calendar without selecting a date + date_input_field = app.locator(".stDateInput input").nth(4) + date_input_field.focus() + date_input_field.clear() + + # Click outside of the calendar to submit value + app.get_by_test_id("stMarkdown").nth(4).click() + + # Range should be empty + expect(app.get_by_test_id("stMarkdown").nth(4)).to_have_text( + "Value 5: ()", + use_inner_text=True, + ) diff --git a/frontend/lib/src/components/widgets/DateInput/DateInput.tsx b/frontend/lib/src/components/widgets/DateInput/DateInput.tsx index 40d217939393..a3737eb8752b 100644 --- a/frontend/lib/src/components/widgets/DateInput/DateInput.tsx +++ b/frontend/lib/src/components/widgets/DateInput/DateInput.tsx @@ -66,6 +66,9 @@ function stringsToDates(strings: string[]): Date[] { /** Convert an array of dates to an array of strings. */ function datesToStrings(dates: Date[]): string[] { + if (!dates) { + return [] + } return dates.map((value: Date) => moment(value as Date).format(DATE_FORMAT)) } @@ -85,7 +88,9 @@ class DateInput extends React.PureComponent<Props, State> { this.props.element ) const stringArray = - storedValue !== undefined ? storedValue : this.props.element.default + storedValue !== undefined + ? storedValue + : this.props.element.default || [] return stringsToDates(stringArray) } @@ -214,6 +219,7 @@ class DateInput extends React.PureComponent<Props, State> { const style = { width } const minDate = moment(element.min, DATE_FORMAT).toDate() const maxDate = this.getMaxDate() + const clearable = element.default.length === 0 && !disabled // We need to extract the mask and format (date-fns notation) from the provided format string // The user configured date format is based on the momentJS notation and is only allowed to contain @@ -235,7 +241,7 @@ class DateInput extends React.PureComponent<Props, State> { ) return ( - <div className="stDateInput" style={style}> + <div className="stDateInput" style={style} data-testid="stDateInput"> <WidgetLabel label={element.label} disabled={disabled} @@ -368,7 +374,26 @@ class DateInput extends React.PureComponent<Props, State> { borderBottomWidth: "1px", }, }, - + ClearIcon: { + props: { + overrides: { + Svg: { + style: { + color: theme.colors.darkGray, + // Since the close icon is an SVG, and we can't control its viewbox nor its attributes, + // Let's use a scale transform effect to make it bigger. + // The width property only enlarges its bounding box, so it's easier to click. + transform: "scale(1.41)", + width: theme.spacing.twoXL, + marginRight: "-8px", + ":hover": { + fill: theme.colors.bodyText, + }, + }, + }, + }, + }, + }, Input: { style: { // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings. @@ -386,6 +411,7 @@ class DateInput extends React.PureComponent<Props, State> { minDate={minDate} maxDate={maxDate} range={isRange} + clearable={clearable} /> </div> ) diff --git a/lib/streamlit/elements/widgets/number_input.py b/lib/streamlit/elements/widgets/number_input.py index 56ba71b10de5..eb0a1dc95d5a 100644 --- a/lib/streamlit/elements/widgets/number_input.py +++ b/lib/streamlit/elements/widgets/number_input.py @@ -169,8 +169,8 @@ def number_input( If None, there will be no maximum. value : int, float, or None The value of this widget when it first renders. If ``None``, the widget - will be initialized empty and returns ``None`` as long as the user didn't - provide an input. Defaults to min_value, or 0.0 if min_value is None. + will initialize empty and return ``None`` until the user provides input. + Defaults to min_value, or 0.0 if min_value is None. step : int, float, or None The stepping interval. Defaults to 1 if the value is an int, 0.01 otherwise. @@ -204,9 +204,9 @@ def number_input( Returns ------- - int or float - The current value of the numeric input widget. The return type - will match the data type of the value parameter. + int or float or None + The current value of the numeric input widget or ``None`` if the widget + is empty. The return type will match the data type of the value parameter. Example ------- @@ -219,6 +219,17 @@ def number_input( https://doc-number-input.streamlit.app/ height: 260px + To initialize an empty number input, use ``None`` as the value: + + >>> import streamlit as st + >>> + >>> number = st.number_input('Insert a number', value=None) + >>> st.write('The current number is ', number) + + .. output:: + https://doc-number-input-empty.streamlit.app/ + height: 260px + """ ctx = get_script_run_ctx() return self._number_input( diff --git a/lib/streamlit/elements/widgets/time_widgets.py b/lib/streamlit/elements/widgets/time_widgets.py index 3d7b75589f7d..cd92bee9ff80 100644 --- a/lib/streamlit/elements/widgets/time_widgets.py +++ b/lib/streamlit/elements/widgets/time_widgets.py @@ -17,7 +17,17 @@ from dataclasses import dataclass from datetime import date, datetime, time, timedelta from textwrap import dedent -from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Tuple, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + List, + Literal, + Optional, + Sequence, + Tuple, + Union, + cast, +) from dateutil import relativedelta from typing_extensions import TypeAlias @@ -48,7 +58,9 @@ TimeValue: TypeAlias = Union[time, datetime, None] SingleDateValue: TypeAlias = Union[date, datetime, None] DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue]] -DateWidgetReturn: TypeAlias = Union[date, Tuple[()], Tuple[date], Tuple[date, date]] +DateWidgetReturn: TypeAlias = Union[ + date, Tuple[()], Tuple[date], Tuple[date, date], None +] DEFAULT_STEP_MINUTES = 15 ALLOWED_DATE_FORMATS = re.compile( @@ -56,10 +68,14 @@ ) -def _parse_date_value(value: DateValue) -> Tuple[List[date], bool]: +def _parse_date_value( + value: DateValue | Literal["today"], +) -> Tuple[List[date] | None, bool]: parsed_dates: List[date] range_value: bool = False if value is None: + return None, range_value + if value == "today": # Set value default. parsed_dates = [datetime.now().date()] elif isinstance(value, datetime): @@ -85,7 +101,7 @@ def _parse_date_value(value: DateValue) -> Tuple[List[date], bool]: def _parse_min_date( min_value: SingleDateValue, - parsed_dates: Sequence[date], + parsed_dates: Sequence[date] | None, ) -> date: parsed_min_date: date if isinstance(min_value, datetime): @@ -106,7 +122,7 @@ def _parse_min_date( def _parse_max_date( max_value: SingleDateValue, - parsed_dates: Sequence[date], + parsed_dates: Sequence[date] | None, ) -> date: parsed_max_date: date if isinstance(max_value, datetime): @@ -127,7 +143,7 @@ def _parse_max_date( @dataclass(frozen=True) class _DateInputValues: - value: Sequence[date] + value: Sequence[date] | None is_range: bool max: date min: date @@ -135,7 +151,7 @@ class _DateInputValues: @classmethod def from_raw_values( cls, - value: DateValue, + value: DateValue | Literal["today"], min_value: SingleDateValue, max_value: SingleDateValue, ) -> "_DateInputValues": @@ -198,7 +214,7 @@ def deserialize( ui_value: Any, widget_id: str = "", ) -> DateWidgetReturn: - return_value: Sequence[date] + return_value: Sequence[date] | None if ui_value is not None: return_value = tuple( datetime.strptime(v, "%Y/%m/%d").date() for v in ui_value @@ -206,11 +222,17 @@ def deserialize( else: return_value = self.value.value + if return_value is None or len(return_value) == 0: + return () if self.value.is_range else None + if not self.value.is_range: return return_value[0] return cast(DateWidgetReturn, tuple(return_value)) def serialize(self, v: DateWidgetReturn) -> List[str]: + if v is None: + return [] + to_serialize = list(v) if isinstance(v, (list, tuple)) else [v] return [date.strftime(v, "%Y/%m/%d") for v in to_serialize] @@ -417,14 +439,14 @@ def _time_input( def date_input( self, label: str, - value: DateValue = None, + value: DateValue | Literal["today"] = "today", min_value: SingleDateValue = None, max_value: SingleDateValue = None, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: format: str = "YYYY/MM/DD", disabled: bool = False, @@ -460,10 +482,12 @@ def date_input( For accessibility reasons, you should never set an empty label (label="") but hide it with label_visibility if needed. In the future, we may disallow empty labels by raising an exception. - value : datetime.date or datetime.datetime or list/tuple of datetime.date or datetime.datetime or None + value : datetime.date or datetime.datetime or list/tuple of datetime.date or datetime.datetime, "today", or None The value of this widget when it first renders. If a list/tuple with 0 to 2 date/datetime values is provided, the datepicker will allow - users to provide a range. Defaults to today as a single-date picker. + users to provide a range. If ``None``, will initialize empty and + return ``None`` until the user provides input. If "today" (default), + will initialize with today as a single-date picker. min_value : datetime.date or datetime.datetime The minimum selectable date. If value is a date, defaults to value - 10 years. If value is the interval [start, end], defaults to start - 10 years. @@ -499,8 +523,9 @@ def date_input( Returns ------- - datetime.date or a tuple with 0-2 dates - The current value of the date input widget. + datetime.date or a tuple with 0-2 dates or None + The current value of the date input widget or ``None`` if no date has been + selected. Examples -------- @@ -535,6 +560,18 @@ def date_input( https://doc-date-input1.streamlit.app/ height: 380px + To initialize an empty date input, use ``None`` as the value: + + >>> import datetime + >>> import streamlit as st + >>> + >>> d = st.date_input("When's your birthday", value=None) + >>> st.write('Your birthday is:', d) + + .. output:: + https://doc-date-input-empty.streamlit.app/ + height: 380px + """ ctx = get_script_run_ctx() return self._date_input( @@ -556,19 +593,19 @@ def date_input( def _date_input( self, label: str, - value: DateValue = None, + value: DateValue | Literal["today"] = "today", min_value: SingleDateValue = None, max_value: SingleDateValue = None, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: format: str = "YYYY/MM/DD", disabled: bool = False, label_visibility: LabelVisibility = "visible", - ctx: Optional[ScriptRunContext] = None, + ctx: ScriptRunContext | None = None, ) -> DateWidgetReturn: key = to_key(key) check_callback_rules(self.dg, on_change) @@ -576,19 +613,23 @@ def _date_input( maybe_raise_label_warnings(label, label_visibility) - def parse_date_deterministic(v: SingleDateValue) -> str | None: - if v is None: - return None - elif isinstance(v, datetime): + def parse_date_deterministic( + v: SingleDateValue | Literal["today"], + ) -> str | None: + if isinstance(v, datetime): return date.strftime(v.date(), "%Y/%m/%d") elif isinstance(v, date): return date.strftime(v, "%Y/%m/%d") + return None parsed_min_date = parse_date_deterministic(min_value) parsed_max_date = parse_date_deterministic(max_value) - if isinstance(value, datetime) or isinstance(value, date) or value is None: - parsed: str | None | List[str | None] = parse_date_deterministic(value) + parsed: str | None | List[str | None] + if value == "today" or value is None: + parsed = None + elif isinstance(value, (datetime, date)): + parsed = parse_date_deterministic(value) else: parsed = [parse_date_deterministic(v) for v in value] @@ -630,9 +671,15 @@ def parse_date_deterministic(v: SingleDateValue) -> str | None: ) date_input_proto.format = format date_input_proto.label = label - date_input_proto.default[:] = [ - date.strftime(v, "%Y/%m/%d") for v in parsed_values.value - ] + if parsed_values.value is None: + # An empty array represents the empty state. The reason for using an empty + # array here is that we cannot optional keyword for repeated fields + # in protobuf. + date_input_proto.default[:] = [] + else: + date_input_proto.default[:] = [ + date.strftime(v, "%Y/%m/%d") for v in parsed_values.value + ] date_input_proto.min = date.strftime(parsed_values.min, "%Y/%m/%d") date_input_proto.max = date.strftime(parsed_values.max, "%Y/%m/%d") date_input_proto.form_id = current_form_id(self.dg) diff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py index e78e1799e066..7204478fbb4c 100644 --- a/lib/streamlit/testing/element_tree.py +++ b/lib/streamlit/testing/element_tree.py @@ -293,12 +293,12 @@ def pick(self, v: str) -> ColorPicker: SingleDateValue: TypeAlias = Union[date, datetime] -DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue]] +DateValue: TypeAlias = Union[SingleDateValue, Sequence[SingleDateValue], None] @dataclass(repr=False) class DateInput(Widget): - _value: DateValue | None + _value: DateValue | None | InitialValue proto: DateInputProto min: date max: date @@ -307,7 +307,7 @@ class DateInput(Widget): def __init__(self, proto: DateInputProto, root: ElementTree): self.proto = proto self.root = root - self._value = None + self._value = InitialValue() self.type = "date_input" self.id = proto.id @@ -334,9 +334,9 @@ def widget_state(self) -> WidgetState: @property def value(self) -> DateWidgetReturn: - if self._value is not None: + if not isinstance(self._value, InitialValue): parsed, _ = _parse_date_value(self._value) - return tuple(parsed) # type: ignore + return tuple(parsed) if parsed is not None else None # type: ignore else: state = self.root.session_state assert state diff --git a/lib/tests/streamlit/elements/date_input_test.py b/lib/tests/streamlit/elements/date_input_test.py index 7be7d3aff97b..dcd80c543036 100644 --- a/lib/tests/streamlit/elements/date_input_test.py +++ b/lib/tests/streamlit/elements/date_input_test.py @@ -22,6 +22,7 @@ import streamlit as st from streamlit.errors import StreamlitAPIException from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage +from streamlit.testing.script_interactions import InteractiveScriptTests from tests.delta_generator_test_case import DeltaGeneratorTestCase @@ -50,6 +51,16 @@ def test_just_disabled(self): c = self.get_delta_from_queue().new_element.date_input self.assertEqual(c.disabled, True) + def test_none_value(self): + """Test that it can be called with None as value.""" + st.date_input("the label", value=None) + + c = self.get_delta_from_queue().new_element.date_input + self.assertEqual(c.label, "the label") + # If a proto property is null is not determined by this value, + # but by the check via the HasField method: + self.assertEqual(c.default, []) + @parameterized.expand( [ (date(1970, 1, 1), ["1970/01/01"]), @@ -254,3 +265,28 @@ def test_invalid_date_format_values(self, format: str): with self.assertRaises(StreamlitAPIException) as ex: st.date_input("the label", format=format) self.assertTrue(str(ex.exception).startswith("The provided format")) + + +class DateInputInteractiveTest(InteractiveScriptTests): + def test_date_input_interaction(self): + """Test interactions with an empty date_input widget.""" + script = self.script_from_string( + """ + import streamlit as st + + st.date_input("the label", value=None) + """ + ) + sr = script.run() + date_input = sr.date_input[0] + assert date_input.value is None + + # Set the value to a specific date + sr2 = date_input.set_value(date(2012, 1, 3)).run() + date_input = sr2.date_input[0] + assert date_input.value == date(2012, 1, 3) + + # # Clear the value + sr3 = date_input.set_value(None).run() + date_input = sr3.date_input[0] + assert date_input.value is None diff --git a/lib/tests/streamlit/elements/element_utils_test.py b/lib/tests/streamlit/elements/element_utils_test.py index 31a5a7082203..fbf9576325f2 100644 --- a/lib/tests/streamlit/elements/element_utils_test.py +++ b/lib/tests/streamlit/elements/element_utils_test.py @@ -20,6 +20,7 @@ import pytest from streamlit import config +from streamlit.elements import utils from streamlit.elements.utils import check_callback_rules, check_session_state_rules from streamlit.errors import StreamlitAPIException @@ -107,6 +108,8 @@ def test_check_session_state_rules_prints_warning( mock_session_state = MagicMock() mock_session_state.is_new_state_value.return_value = True patched_get_session_state.return_value = mock_session_state + # Reset globale flag: + utils._shown_default_value_warning = False check_session_state_rules(5, key="the key")
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-7262@8cd45fd
streamlit/streamlit
Python
7,262
Allow `None` as value for `st.number_input`
## Describe your changes This PR supports setting `None` as the value in `st.number_input`. Using `value=None` will render an empty number input that returns `None` as long as the user hasn't specified a number: <img width="599" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/5fca5660-4c3c-4503-9472-c83e6559736c"> If a user has specified a number, the user can clear this value again to reset the input to `None`: <img width="599" alt="image" src="https://github.com/streamlit/streamlit/assets/2852129/073ccda3-0f63-493e-a0d6-dbea8cfc5715"> - Spec: https://www.notion.so/snowflake-corp/Spec-v2-b00de8d56f99464693e7ddfdea0674e9 ## GitHub Issue Link (if applicable) Closes https://github.com/streamlit/streamlit/issues/4650 ## Testing Plan - Added e2e tests and unit tests for value=None behaviour. --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-08-31T16:58:25Z
Number input does not accept None as value parameter ### Summary It is not possible to set the value parameter of the number_input to None, even though it is listed in the API as possible and it works for all other input widgets. ### Steps to reproduce ``` import streamlit as st input = st.number_input('Number input', value = None) ``` **Expected behavior:** Shows the number input widget without a set default value, same as if it was not set at all. **Actual behavior:** Throws the following error: streamlit.errors.StreamlitAPIException: Default value for number_input should be an int or a float. ### Is this a regression? Don't know ### Debug info - Streamlit version: 1.8.1 - Python version: 3.9.12 - Streamlit installed with pip - OS version: Windows 10 - Browser version: Chrome
Hi @ChristophBoerlin ๐Ÿ‘‹๐Ÿผ Appreciate your submission, and for helping to make Streamlit better! I was able to confirm the conflict in behavior vs. our `st.number_input` documentation ๐Ÿ˜“ We will take a look at how we can incorporate this fix into our future development plans. Thanks again! any update on this? +1 Is there an update on this? The behavior is still happening. Yet happening although docs specify it can be None Hi, I would like to volunteer to work on this issue and I appreciate any suggestions! Hey all! We don't want to fix this for now since we are thinking about enabling setting `None` *as a feature*. I.e. you could set `value=None` and the widget would not show its current default value, but stay completely empty and also return `None`. Unfortunately, that has a few implications that we'd need to think through first (especially how this works on other widgets). So no promises on when we'll get to it.
[ { "body": "### Summary\r\n\r\nIt is not possible to set the value parameter of the number_input to None, even though it is listed in the API as possible and it works for all other input widgets.\r\n\r\n### Steps to reproduce\r\n```\r\nimport streamlit as st\r\ninput = st.number_input('Number input', value = None)\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nShows the number input widget without a set default value, same as if it was not set at all.\r\n\r\n**Actual behavior:**\r\n\r\nThrows the following error:\r\nstreamlit.errors.StreamlitAPIException: Default value for number_input should be an int or a float. \r\n\r\n### Is this a regression?\r\n\r\nDon't know\r\n\r\n### Debug info\r\n\r\n- Streamlit version: 1.8.1\r\n- Python version: 3.9.12\r\n- Streamlit installed with pip\r\n- OS version: Windows 10\r\n- Browser version: Chrome \r\n", "number": 4650, "title": "Number input does not accept None as value parameter" } ]
45a9273a1fe642db7fc7750c82d393e5e3bf1e39
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b/e2e_playwright/__snapshots__/linux/st_number_input_test/st_number_input-value_none_min_1[light_theme-webkit].png differ\ndiff --git a/e2e_playwright/st_code_test.py b/e2e_playwright/st_code_test.py\nindex f08c75dbbb82..1b120487c7d1 100644\n--- a/e2e_playwright/st_code_test.py\n+++ b/e2e_playwright/st_code_test.py\n@@ -47,7 +47,7 @@ def test_code_blocks_render_correctly(\n def test_code_in_markdown(app: Page, assert_snapshot: ImageCompareFunction):\n \"\"\"Test that the syntax highlighting is applied correctly to the code block.\"\"\"\n # This test seems to be a little flaky without additional timeout:\n- app.wait_for_timeout(250)\n+ app.wait_for_timeout(500)\n \n expander_with_code = app.get_by_test_id(\"stExpander\")\n \ndiff --git a/e2e_playwright/st_number_input.py b/e2e_playwright/st_number_input.py\nnew file mode 100644\nindex 000000000000..1b688f6c4f74\n--- /dev/null\n+++ b/e2e_playwright/st_number_input.py\n@@ -0,0 +1,74 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import streamlit as st\n+from streamlit import runtime\n+\n+i1 = st.number_input(\"number input 1 (default)\")\n+st.write(\"number input 1 (default) - value: \", i1)\n+\n+i2 = st.number_input(\"number input 2 (value=1)\", value=1)\n+st.write(\"number input 2 (value=1) - value: \", i2)\n+\n+i3 = st.number_input(\"number input 3 (min & max)\", 1, 10)\n+st.write(\"number input 3 (min & max) - value: \", i3)\n+\n+i4 = st.number_input(\"number input 4 (step=2)\", step=2)\n+st.write(\"number input 4 (step=2) - value: \", i4)\n+\n+i5 = st.number_input(\"number input 5 (max=10)\", max_value=10)\n+st.write(\"number input 5 (max=10) - value: \", i5)\n+\n+i6 = st.number_input(\"number input 6 (disabled=True)\", disabled=True)\n+st.write(\"number input 6 (disabled=True) - value: \", i6)\n+\n+i7 = st.number_input(\"number input 7 (label=hidden)\", label_visibility=\"hidden\")\n+st.write(\"number input 7 (label=hidden) - value: \", i7)\n+\n+i8 = st.number_input(\"number input 8 (label=collapsed)\", label_visibility=\"collapsed\")\n+st.write(\"number input 8 (label=collapsed) - value: \", i8)\n+\n+if runtime.exists():\n+\n+ def on_change():\n+ st.session_state.number_input_changed = True\n+\n+ st.number_input(\n+ \"number input 9 (on_change)\", key=\"number_input9\", on_change=on_change\n+ )\n+ st.write(\"number input 9 (on_change) - value: \", st.session_state.number_input9)\n+ st.write(\n+ \"number input 9 (on_change) - changed:\",\n+ \"number_input_changed\" in st.session_state,\n+ )\n+\n+[col1, col2, col3, col4, col5, col6] = st.columns(6)\n+\n+with col1:\n+ i10 = st.number_input(\"number input 10 (small width)\", max_value=10)\n+ st.write(\"number input 10 (small width) - value: \", i10)\n+\n+i11 = st.number_input(\"number input 11 (value=None)\", value=None)\n+st.write(\"number input 11 (value=None) - value: \", i11)\n+\n+if \"number_input12\" not in st.session_state:\n+ st.session_state[\"number_input12\"] = 10\n+\n+i12 = st.number_input(\n+ \"number input 12 (value from state & min=1)\",\n+ value=None,\n+ min_value=1,\n+ key=\"number_input12\",\n+)\n+st.write(\"number input 12 (value from state & min=1) - value: \", i12)\ndiff --git a/e2e_playwright/st_number_input_test.py b/e2e_playwright/st_number_input_test.py\nnew file mode 100644\nindex 000000000000..9bdfb0d66544\n--- /dev/null\n+++ b/e2e_playwright/st_number_input_test.py\n@@ -0,0 +1,179 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+\n+from playwright.sync_api import Page, expect\n+\n+from e2e_playwright.conftest import ImageCompareFunction\n+\n+\n+def test_number_input_widget_display(\n+ themed_app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that st.number_input renders correctly.\"\"\"\n+ number_input_elements = themed_app.get_by_test_id(\"stNumberInput\")\n+ expect(number_input_elements).to_have_count(12)\n+\n+ assert_snapshot(number_input_elements.nth(0), name=\"st_number_input-default\")\n+ assert_snapshot(number_input_elements.nth(1), name=\"st_number_input-value_1\")\n+ assert_snapshot(number_input_elements.nth(2), name=\"st_number_input-min_max\")\n+ assert_snapshot(number_input_elements.nth(3), name=\"st_number_input-step_2\")\n+ assert_snapshot(number_input_elements.nth(4), name=\"st_number_input-max_10\")\n+ assert_snapshot(number_input_elements.nth(5), name=\"st_number_input-disabled_true\")\n+ assert_snapshot(number_input_elements.nth(6), name=\"st_number_input-label_hidden\")\n+ assert_snapshot(\n+ number_input_elements.nth(7), name=\"st_number_input-label_collapsed\"\n+ )\n+ assert_snapshot(number_input_elements.nth(8), name=\"st_number_input-on_change\")\n+ assert_snapshot(number_input_elements.nth(9), name=\"st_number_input-small_width\")\n+ assert_snapshot(number_input_elements.nth(10), name=\"st_number_input-value_none\")\n+ assert_snapshot(\n+ number_input_elements.nth(11), name=\"st_number_input-value_none_min_1\"\n+ )\n+\n+\n+def test_number_input_has_correct_default_values(app: Page):\n+ \"\"\"Test that st.number_input has the correct initial values.\"\"\"\n+ markdown_elements = app.locator(\".stMarkdown\")\n+ expect(markdown_elements).to_have_count(13)\n+\n+ expected = [\n+ \"number input 1 (default) - value: 0.0\",\n+ \"number input 2 (value=1) - value: 1\",\n+ \"number input 3 (min & max) - value: 1\",\n+ \"number input 4 (step=2) - value: 0\",\n+ \"number input 5 (max=10) - value: 0\",\n+ \"number input 6 (disabled=True) - value: 0.0\",\n+ \"number input 7 (label=hidden) - value: 0.0\",\n+ \"number input 8 (label=collapsed) - value: 0.0\",\n+ \"number input 9 (on_change) - value: 0.0\",\n+ \"number input 9 (on_change) - changed: False\",\n+ \"number input 10 (small width) - value: 0\",\n+ \"number input 11 (value=None) - value: None\",\n+ \"number input 12 (value from state & min=1) - value: 10\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(markdown_elements.all(), expected):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_number_input_shows_instructions_when_dirty(\n+ app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that st.number_input shows the instructions correctly when dirty.\"\"\"\n+ first_number_input_field = app.locator(\".stNumberInput input\").nth(0)\n+ first_number_input_field.fill(\"10\")\n+\n+ assert_snapshot(first_number_input_field, name=\"st_number_input-input_instructions\")\n+\n+\n+def test_number_input_updates_value_correctly_on_enter(app: Page):\n+ \"\"\"Test that st.number_input updates the value correctly on enter.\"\"\"\n+ first_number_input_field = app.locator(\".stNumberInput input\").nth(0)\n+ first_number_input_field.fill(\"10\")\n+ first_number_input_field.press(\"Enter\")\n+\n+ expect(app.locator(\".stMarkdown\").nth(0)).to_have_text(\n+ \"number input 1 (default) - value: 10.0\", use_inner_text=True\n+ )\n+\n+\n+def test_number_input_has_correct_value_on_increment_click(app: Page):\n+ \"\"\"Test that st.number_input has the correct value on increment click.\"\"\"\n+ number_input_up_buttons = app.locator(\".stNumberInput button.step-up\")\n+ expect(number_input_up_buttons).to_have_count(11)\n+ for i, button in enumerate(number_input_up_buttons.all()):\n+ if i not in [5, 9]:\n+ button.click()\n+\n+ markdown_elements = app.locator(\".stMarkdown\")\n+\n+ expected = [\n+ \"number input 1 (default) - value: 0.01\",\n+ \"number input 2 (value=1) - value: 2\",\n+ \"number input 3 (min & max) - value: 2\",\n+ \"number input 4 (step=2) - value: 2\",\n+ \"number input 5 (max=10) - value: 1\",\n+ \"number input 6 (disabled=True) - value: 0.0\",\n+ \"number input 7 (label=hidden) - value: 0.01\",\n+ \"number input 8 (label=collapsed) - value: 0.01\",\n+ \"number input 9 (on_change) - value: 0.01\",\n+ \"number input 9 (on_change) - changed: True\",\n+ \"number input 10 (small width) - value: 0\",\n+ \"number input 11 (value=None) - value: None\",\n+ \"number input 12 (value from state & min=1) - value: 11\",\n+ ]\n+\n+ for markdown_element, expected_text in zip(markdown_elements.all(), expected):\n+ expect(markdown_element).to_have_text(expected_text, use_inner_text=True)\n+\n+\n+def test_number_input_has_correct_value_on_arrow_up(app: Page):\n+ \"\"\"Test that st.number_input has the correct value on arrow up.\"\"\"\n+ first_number_input_field = app.locator(\".stNumberInput input\").nth(0)\n+ first_number_input_field.press(\"ArrowUp\")\n+\n+ expect(app.locator(\".stMarkdown\").nth(0)).to_have_text(\n+ \"number input 1 (default) - value: 0.01\", use_inner_text=True\n+ )\n+\n+\n+def test_number_input_has_correct_value_on_blur(app: Page):\n+ \"\"\"Test that st.number_input has the correct value on blur.\"\"\"\n+\n+ first_number_input_field = app.locator(\".stNumberInput input\").nth(0)\n+ first_number_input_field.focus()\n+ first_number_input_field.fill(\"10\")\n+ first_number_input_field.blur()\n+\n+ expect(app.locator(\".stMarkdown\").nth(0)).to_have_text(\n+ \"number input 1 (default) - value: 10.0\", use_inner_text=True\n+ )\n+\n+\n+def test_empty_number_input_behaves_correctly(\n+ app: Page, assert_snapshot: ImageCompareFunction\n+):\n+ \"\"\"Test that st.number_input behaves correctly when empty.\"\"\"\n+ # Enter 10 in the first empty input:\n+ empty_number_input = app.locator(\".stNumberInput input\").nth(10)\n+ empty_number_input.fill(\"10\")\n+ empty_number_input.press(\"Enter\")\n+\n+ expect(app.locator(\".stMarkdown\").nth(11)).to_have_text(\n+ \"number input 11 (value=None) - value: 10.0\", use_inner_text=True\n+ )\n+\n+ assert_snapshot(empty_number_input, name=\"st_number_input-clearable_input\")\n+\n+ # Press escape to clear value:\n+ empty_number_input = app.locator(\".stNumberInput\").nth(10)\n+ empty_number_input.focus()\n+ empty_number_input.press(\"Escape\")\n+ empty_number_input.press(\"Enter\")\n+\n+ # Should be empty again:\n+ expect(app.locator(\".stMarkdown\").nth(11)).to_have_text(\n+ \"number input 11 (value=None) - value: None\", use_inner_text=True\n+ )\n+\n+ # Check with second empty input, this one should be integer since the min_value was\n+ # set to an integer:\n+ empty_number_input_with_min = app.locator(\".stNumberInput input\").nth(11)\n+ empty_number_input_with_min.fill(\"15\")\n+ empty_number_input_with_min.press(\"Enter\")\n+\n+ expect(app.locator(\".stMarkdown\").nth(12)).to_have_text(\n+ \"number input 12 (value from state & min=1) - value: 15\", use_inner_text=True\n+ )\ndiff --git a/frontend/lib/src/WidgetStateManager.ts b/frontend/lib/src/WidgetStateManager.ts\nindex dcfd184bf5c4..16e11b3b87fe 100644\n--- a/frontend/lib/src/WidgetStateManager.ts\n+++ b/frontend/lib/src/WidgetStateManager.ts\n@@ -309,7 +309,11 @@ export class WidgetStateManager {\n return undefined\n }\n \n- public setIntValue(widget: WidgetInfo, value: number, source: Source): void {\n+ public setIntValue(\n+ widget: WidgetInfo,\n+ value: number | null,\n+ source: Source\n+ ): void {\n this.createWidgetState(widget, source).intValue = value\n this.onWidgetValueChanged(widget.formId, source)\n }\n@@ -325,7 +329,7 @@ export class WidgetStateManager {\n \n public setDoubleValue(\n widget: WidgetInfo,\n- value: number,\n+ value: number | null,\n source: Source\n ): void {\n this.createWidgetState(widget, source).doubleValue = value\ndiff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\nindex a88ad32292f8..26461e22c63d 100644\n--- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\n+++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\n@@ -13,29 +13,32 @@\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n-\n+import React from \"react\"\n import { ShallowWrapper } from \"enzyme\"\n import {\n LabelVisibilityMessage as LabelVisibilityMessageProto,\n NumberInput as NumberInputProto,\n } from \"@streamlit/lib/src/proto\"\n-import React from \"react\"\n+\n import { mount, shallow } from \"@streamlit/lib/src/test_util\"\n import { StyledInputControls } from \"@streamlit/lib/src/components/widgets/NumberInput/styled-components\"\n import { Input as UIInput } from \"baseui/input\"\n import { WidgetStateManager } from \"@streamlit/lib/src/WidgetStateManager\"\n+import { mockTheme } from \"@streamlit/lib/src/mocks/mockTheme\"\n \n-import NumberInput, { Props, State } from \"./NumberInput\"\n+import { NumberInput, Props, State } from \"./NumberInput\"\n \n const getProps = (elementProps: Partial<NumberInputProto> = {}): Props => ({\n element: NumberInputProto.create({\n label: \"Label\",\n+ default: 0,\n hasMin: false,\n hasMax: false,\n ...elementProps,\n }),\n width: 300,\n disabled: false,\n+ theme: mockTheme.emotion,\n widgetMgr: new WidgetStateManager({\n sendRerunBackMsg: jest.fn(),\n formsDataChanged: jest.fn(),\ndiff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\nindex 06c3098d25ee..974f30a8283b 100644\n--- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\n+++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\n@@ -16,7 +16,9 @@\n \n import React from \"react\"\n import { Plus, Minus } from \"@emotion-icons/open-iconic\"\n+import { withTheme } from \"@emotion/react\"\n import { sprintf } from \"sprintf-js\"\n+\n import { FormClearHelper } from \"@streamlit/lib/src/components/widgets/Form\"\n import { logWarning } from \"@streamlit/lib/src/util/log\"\n import { NumberInput as NumberInputProto } from \"@streamlit/lib/src/proto\"\n@@ -27,7 +29,6 @@ import {\n } from \"@streamlit/lib/src/WidgetStateManager\"\n import TooltipIcon from \"@streamlit/lib/src/components/shared/TooltipIcon\"\n import { Placement } from \"@streamlit/lib/src/components/shared/Tooltip\"\n-\n import Icon from \"@streamlit/lib/src/components/shared/Icon\"\n import { Input as UIInput } from \"baseui/input\"\n import InputInstructions from \"@streamlit/lib/src/components/shared/InputInstructions/InputInstructions\"\n@@ -35,10 +36,12 @@ import {\n WidgetLabel,\n StyledWidgetLabelHelp,\n } from \"@streamlit/lib/src/components/widgets/BaseWidget\"\n-\n+import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n import {\n isInForm,\n labelVisibilityProtoValueToEnum,\n+ isNullOrUndefined,\n+ notNullOrUndefined,\n } from \"@streamlit/lib/src/util/utils\"\n \n import {\n@@ -53,6 +56,7 @@ export interface Props {\n element: NumberInputProto\n widgetMgr: WidgetStateManager\n width: number\n+ theme: EmotionTheme\n }\n \n export interface State {\n@@ -65,12 +69,12 @@ export interface State {\n * The value specified by the user via the UI. If the user didn't touch this\n * widget's UI, the default value is used.\n */\n- value: number\n+ value: number | null\n \n /**\n * The value with applied format that is going to be shown to the user\n */\n- formattedValue: string\n+ formattedValue: string | null\n \n /**\n * True if the input is selected\n@@ -78,7 +82,7 @@ export interface State {\n isFocused: boolean\n }\n \n-class NumberInput extends React.PureComponent<Props, State> {\n+export class NumberInput extends React.PureComponent<Props, State> {\n private readonly formClearHelper = new FormClearHelper()\n \n private inputRef = React.createRef<HTMLInputElement | HTMLTextAreaElement>()\n@@ -94,14 +98,14 @@ class NumberInput extends React.PureComponent<Props, State> {\n }\n }\n \n- get initialValue(): number {\n+ get initialValue(): number | null {\n // If WidgetStateManager knew a value for this widget, initialize to that.\n // Otherwise, use the default value from the widget protobuf\n const storedValue = this.isIntData()\n ? this.props.widgetMgr.getIntValue(this.props.element)\n : this.props.widgetMgr.getDoubleValue(this.props.element)\n \n- return storedValue !== undefined ? storedValue : this.props.element.default\n+ return storedValue ?? this.props.element.default ?? null\n }\n \n public componentDidMount(): void {\n@@ -130,12 +134,22 @@ class NumberInput extends React.PureComponent<Props, State> {\n private updateFromProtobuf(): void {\n const { value } = this.props.element\n this.props.element.setValue = false\n- this.setState({ value, formattedValue: this.formatValue(value) }, () => {\n- this.commitWidgetValue({ fromUi: false })\n- })\n+ this.setState(\n+ {\n+ value: value ?? null,\n+ formattedValue: this.formatValue(value ?? null),\n+ },\n+ () => {\n+ this.commitWidgetValue({ fromUi: false })\n+ }\n+ )\n }\n \n- private formatValue = (value: number): string => {\n+ private formatValue = (value: number | null): string | null => {\n+ if (isNullOrUndefined(value)) {\n+ return null\n+ }\n+\n const format = getNonEmptyString(this.props.element.format)\n if (format == null) {\n return value.toString()\n@@ -183,13 +197,13 @@ class NumberInput extends React.PureComponent<Props, State> {\n const min = this.getMin()\n const max = this.getMax()\n \n- if (min > value || value > max) {\n+ if (notNullOrUndefined(value) && (min > value || value > max)) {\n const node = this.inputRef.current\n if (node) {\n node.reportValidity()\n }\n } else {\n- const valueToBeSaved = value || value === 0 ? value : data.default\n+ const valueToBeSaved = value ?? data.default ?? null\n \n if (this.isIntData()) {\n widgetMgr.setIntValue(element, valueToBeSaved, source)\n@@ -212,7 +226,7 @@ class NumberInput extends React.PureComponent<Props, State> {\n private onFormCleared = (): void => {\n this.setState(\n (_, prevProps) => {\n- return { value: prevProps.element.default }\n+ return { value: prevProps.element.default ?? null }\n },\n () => this.commitWidgetValue({ fromUi: true })\n )\n@@ -235,19 +249,27 @@ class NumberInput extends React.PureComponent<Props, State> {\n ): void => {\n const { value } = e.target\n \n- let numValue: number\n-\n- if (this.isIntData()) {\n- numValue = parseInt(value, 10)\n+ if (value === \"\") {\n+ this.setState({\n+ dirty: true,\n+ value: null,\n+ formattedValue: null,\n+ })\n } else {\n- numValue = parseFloat(value)\n- }\n+ let numValue: number\n \n- this.setState({\n- dirty: true,\n- value: numValue,\n- formattedValue: value,\n- })\n+ if (this.isIntData()) {\n+ numValue = parseInt(value, 10)\n+ } else {\n+ numValue = parseFloat(value)\n+ }\n+\n+ this.setState({\n+ dirty: true,\n+ value: numValue,\n+ formattedValue: value,\n+ })\n+ }\n }\n \n private onKeyDown = (\n@@ -285,11 +307,19 @@ class NumberInput extends React.PureComponent<Props, State> {\n \n /** True if the input's current value can be decremented by its step. */\n private get canDecrement(): boolean {\n+ if (isNullOrUndefined(this.state.value)) {\n+ return false\n+ }\n+\n return this.state.value - this.getStep() >= this.getMin()\n }\n \n /** True if the input's current value can be incremented by its step. */\n private get canIncrement(): boolean {\n+ if (isNullOrUndefined(this.state.value)) {\n+ return false\n+ }\n+\n return this.state.value + this.getStep() <= this.getMax()\n }\n \n@@ -305,7 +335,7 @@ class NumberInput extends React.PureComponent<Props, State> {\n this.setState(\n {\n dirty: true,\n- value: value + step,\n+ value: (value ?? this.getMin()) + step,\n },\n () => {\n this.commitWidgetValue({ fromUi: true })\n@@ -318,7 +348,7 @@ class NumberInput extends React.PureComponent<Props, State> {\n this.setState(\n {\n dirty: true,\n- value: value - step,\n+ value: (value ?? this.getMax()) - step,\n },\n () => {\n this.commitWidgetValue({ fromUi: true })\n@@ -331,13 +361,14 @@ class NumberInput extends React.PureComponent<Props, State> {\n }\n \n public render(): React.ReactNode {\n- const { element, width, disabled, widgetMgr } = this.props\n+ const { element, width, disabled, widgetMgr, theme } = this.props\n const { formattedValue, dirty, isFocused } = this.state\n \n const style = { width }\n \n const disableDecrement = !this.canDecrement || disabled\n const disableIncrement = !this.canIncrement || disabled\n+ const clearable = isNullOrUndefined(element.default) && !disabled\n \n // Manage our form-clear event handler.\n this.formClearHelper.manageFormClearListener(\n@@ -347,7 +378,7 @@ class NumberInput extends React.PureComponent<Props, State> {\n )\n \n return (\n- <div className=\"stNumberInput\" style={style}>\n+ <div className=\"stNumberInput\" style={style} data-testid=\"stNumberInput\">\n <WidgetLabel\n label={element.label}\n disabled={disabled}\n@@ -368,15 +399,38 @@ class NumberInput extends React.PureComponent<Props, State> {\n <UIInput\n type=\"number\"\n inputRef={this.inputRef}\n- value={formattedValue}\n+ value={formattedValue || undefined}\n onBlur={this.onBlur}\n onFocus={this.onFocus}\n onChange={this.onChange}\n onKeyPress={this.onKeyPress}\n onKeyDown={this.onKeyDown}\n+ clearable={clearable}\n+ clearOnEscape={clearable}\n disabled={disabled}\n aria-label={element.label}\n overrides={{\n+ ClearIcon: {\n+ props: {\n+ overrides: {\n+ Svg: {\n+ style: {\n+ color: theme.colors.darkGray,\n+ // Since the close icon is an SVG, and we can't control its viewbox nor its attributes,\n+ // Let's use a scale transform effect to make it bigger.\n+ // The width property only enlarges its bounding box, so it's easier to click.\n+ transform: \"scale(1.20)\",\n+ width: theme.spacing.twoXL,\n+ marginRight: \"-1.3em\",\n+\n+ \":hover\": {\n+ fill: theme.colors.bodyText,\n+ },\n+ },\n+ },\n+ },\n+ },\n+ },\n Input: {\n props: {\n step: this.getStep(),\n@@ -445,7 +499,7 @@ class NumberInput extends React.PureComponent<Props, State> {\n <StyledInstructionsContainer>\n <InputInstructions\n dirty={dirty}\n- value={formattedValue}\n+ value={formattedValue ?? \"\"}\n className=\"input-instructions\"\n inForm={isInForm({ formId: element.formId })}\n />\n@@ -465,4 +519,4 @@ function getNonEmptyString(\n return value == null || value === \"\" ? undefined : value\n }\n \n-export default NumberInput\n+export default withTheme(NumberInput)\ndiff --git a/lib/streamlit/elements/widgets/number_input.py b/lib/streamlit/elements/widgets/number_input.py\nindex 8d7db13ad45b..b3e072c70104 100644\n--- a/lib/streamlit/elements/widgets/number_input.py\n+++ b/lib/streamlit/elements/widgets/number_input.py\n@@ -12,10 +12,12 @@\n # See the License for the specific language governing permissions and\n # limitations under the License.\n \n+from __future__ import annotations\n+\n import numbers\n from dataclasses import dataclass\n from textwrap import dedent\n-from typing import Optional, Union, cast\n+from typing import Union, cast, overload\n \n import streamlit\n from streamlit.elements.form import current_form_id\n@@ -30,7 +32,7 @@\n from streamlit.runtime.metrics_util import gather_metrics\n from streamlit.runtime.scriptrunner import ScriptRunContext, get_script_run_ctx\n from streamlit.runtime.state import (\n- NoValue,\n+ DefaultValue,\n WidgetArgs,\n WidgetCallback,\n WidgetKwargs,\n@@ -44,44 +46,82 @@\n \n @dataclass\n class NumberInputSerde:\n- value: Union[int, float]\n+ value: Number | None\n data_type: int\n \n- def serialize(self, v: Number) -> Number:\n+ def serialize(self, v: Number | None) -> Number | None:\n return v\n \n- def deserialize(self, ui_value: Optional[Number], widget_id: str = \"\") -> Number:\n- if ui_value is not None:\n- val = ui_value\n- else:\n- # Widget has not been used; fallback to the original value,\n- val = self.value\n+ def deserialize(\n+ self, ui_value: Number | None, widget_id: str = \"\"\n+ ) -> Number | None:\n+ val: Number | None = ui_value if ui_value is not None else self.value\n \n- if self.data_type == NumberInputProto.INT:\n+ if val is not None and self.data_type == NumberInputProto.INT:\n val = int(val)\n \n return val\n \n \n class NumberInputMixin:\n- @gather_metrics(\"number_input\")\n+ @overload\n def number_input(\n self,\n label: str,\n- min_value: Optional[Number] = None,\n- max_value: Optional[Number] = None,\n- value: Union[NoValue, Number, None] = NoValue(),\n- step: Optional[Number] = None,\n- format: Optional[str] = None,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ min_value: Number | None = None,\n+ max_value: Number | None = None,\n+ value: DefaultValue | Number = DefaultValue(),\n+ step: Number | None = None,\n+ format: str | None = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n ) -> Number:\n+ pass\n+\n+ @overload\n+ def number_input(\n+ self,\n+ label: str,\n+ min_value: Number | None = None,\n+ max_value: Number | None = None,\n+ value: None = None,\n+ step: Number | None = None,\n+ format: str | None = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only arguments:\n+ disabled: bool = False,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> Number | None:\n+ pass\n+\n+ @gather_metrics(\"number_input\")\n+ def number_input(\n+ self,\n+ label: str,\n+ min_value: Number | None = None,\n+ max_value: Number | None = None,\n+ value: DefaultValue | Number | None = DefaultValue(),\n+ step: Number | None = None,\n+ format: str | None = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ *, # keyword-only arguments:\n+ disabled: bool = False,\n+ label_visibility: LabelVisibility = \"visible\",\n+ ) -> Number | None:\n r\"\"\"Display a numeric input widget.\n \n .. note::\n@@ -125,8 +165,9 @@ def number_input(\n The maximum permitted value.\n If None, there will be no maximum.\n value : int, float, or None\n- The value of this widget when it first renders.\n- Defaults to min_value, or 0.0 if min_value is None\n+ The value of this widget when it first renders. If ``None``, the widget\n+ will be initialized as empty and returns ``None`` as long as the viewer\n+ didn't provide input. Defaults to min_value, or 0.0 if min_value is None.\n step : int, float, or None\n The stepping interval.\n Defaults to 1 if the value is an int, 0.01 otherwise.\n@@ -197,25 +238,25 @@ def number_input(\n def _number_input(\n self,\n label: str,\n- min_value: Optional[Number] = None,\n- max_value: Optional[Number] = None,\n- value: Union[NoValue, Number, None] = NoValue(),\n- step: Optional[Number] = None,\n- format: Optional[str] = None,\n- key: Optional[Key] = None,\n- help: Optional[str] = None,\n- on_change: Optional[WidgetCallback] = None,\n- args: Optional[WidgetArgs] = None,\n- kwargs: Optional[WidgetKwargs] = None,\n+ min_value: Number | None = None,\n+ max_value: Number | None = None,\n+ value: DefaultValue | Number | None = DefaultValue(),\n+ step: Number | None = None,\n+ format: str | None = None,\n+ key: Key | None = None,\n+ help: str | None = None,\n+ on_change: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n *, # keyword-only arguments:\n disabled: bool = False,\n label_visibility: LabelVisibility = \"visible\",\n- ctx: Optional[ScriptRunContext] = None,\n- ) -> Number:\n+ ctx: ScriptRunContext | None = None,\n+ ) -> Number | None:\n key = to_key(key)\n check_callback_rules(self.dg, on_change)\n check_session_state_rules(\n- default_value=None if isinstance(value, NoValue) else value, key=key\n+ default_value=None if isinstance(value, DefaultValue) else value, key=key\n )\n maybe_raise_label_warnings(label, label_visibility)\n \n@@ -237,12 +278,12 @@ def _number_input(\n number_input_args = [min_value, max_value, value, step]\n \n int_args = all(\n- isinstance(a, (numbers.Integral, type(None), NoValue))\n+ isinstance(a, (numbers.Integral, type(None), DefaultValue))\n for a in number_input_args\n )\n \n float_args = all(\n- isinstance(a, (float, type(None), NoValue)) for a in number_input_args\n+ isinstance(a, (float, type(None), DefaultValue)) for a in number_input_args\n )\n \n if not int_args and not float_args:\n@@ -254,7 +295,7 @@ def _number_input(\n f\"\\n`step` has {type(step).__name__} type.\"\n )\n \n- if isinstance(value, NoValue):\n+ if isinstance(value, DefaultValue):\n if min_value is not None:\n value = min_value\n elif int_args and float_args:\n@@ -268,28 +309,31 @@ def _number_input(\n float_value = isinstance(value, float)\n \n if value is None:\n- raise StreamlitAPIException(\n- \"Default value for number_input should be an int or a float.\"\n+ if int_args and not float_args:\n+ # Select int type if all relevant args are ints:\n+ int_value = True\n+ else:\n+ # Otherwise, defaults to float:\n+ float_value = True\n+\n+ if format is None:\n+ format = \"%d\" if int_value else \"%0.2f\"\n+\n+ # Warn user if they format an int type as a float or vice versa.\n+ if format in [\"%d\", \"%u\", \"%i\"] and float_value:\n+ import streamlit as st\n+\n+ st.warning(\n+ \"Warning: NumberInput value below has type float,\"\n+ f\" but format {format} displays as integer.\"\n+ )\n+ elif format[-1] == \"f\" and int_value:\n+ import streamlit as st\n+\n+ st.warning(\n+ \"Warning: NumberInput value below has type int so is\"\n+ f\" displayed as int despite format string {format}.\"\n )\n- else:\n- if format is None:\n- format = \"%d\" if int_value else \"%0.2f\"\n-\n- # Warn user if they format an int type as a float or vice versa.\n- if format in [\"%d\", \"%u\", \"%i\"] and float_value:\n- import streamlit as st\n-\n- st.warning(\n- \"Warning: NumberInput value below has type float,\"\n- f\" but format {format} displays as integer.\"\n- )\n- elif format[-1] == \"f\" and int_value:\n- import streamlit as st\n-\n- st.warning(\n- \"Warning: NumberInput value below has type int so is\"\n- f\" displayed as int despite format string {format}.\"\n- )\n \n if step is None:\n step = 1 if int_value else 0.01\n@@ -309,11 +353,10 @@ def _number_input(\n raise StreamlitAPIException(\n f\"The default `value` {value} must be greater than or equal to the `min_value` {min_value}\"\n )\n- if max_value is not None and value is not None:\n- if max_value < value:\n- raise StreamlitAPIException(\n- f\"The default `value` {value} must be less than or equal to the `max_value` {max_value}\"\n- )\n+ if max_value is not None and value is not None and max_value < value:\n+ raise StreamlitAPIException(\n+ f\"The default `value` {value} must be less than or equal to the `max_value` {max_value}\"\n+ )\n \n # Bounds checks. JSNumber produces human-readable exceptions that\n # we simply re-package as StreamlitAPIExceptions.\n@@ -329,7 +372,8 @@ def _number_input(\n )\n if step is not None:\n JSNumber.validate_int_bounds(step, \"`step`\") # type: ignore\n- JSNumber.validate_int_bounds(value, \"`value`\") # type: ignore\n+ if value is not None:\n+ JSNumber.validate_int_bounds(value, \"`value`\") # type: ignore\n else:\n if min_value is not None:\n JSNumber.validate_float_bounds(min_value, \"`min_value`\")\n@@ -337,7 +381,8 @@ def _number_input(\n JSNumber.validate_float_bounds(max_value, \"`max_value`\")\n if step is not None:\n JSNumber.validate_float_bounds(step, \"`step`\")\n- JSNumber.validate_float_bounds(value, \"`value`\")\n+ if value is not None:\n+ JSNumber.validate_float_bounds(value, \"`value`\")\n except JSNumberBoundsException as e:\n raise StreamlitAPIException(str(e))\n \n@@ -347,7 +392,8 @@ def _number_input(\n number_input_proto.id = id\n number_input_proto.data_type = data_type\n number_input_proto.label = label\n- number_input_proto.default = value\n+ if value is not None:\n+ number_input_proto.default = value\n number_input_proto.form_id = current_form_id(self.dg)\n number_input_proto.disabled = disabled\n number_input_proto.label_visibility.value = get_label_visibility_proto_value(\n@@ -385,7 +431,8 @@ def _number_input(\n )\n \n if widget_state.value_changed:\n- number_input_proto.value = widget_state.value\n+ if widget_state.value is not None:\n+ number_input_proto.value = widget_state.value\n number_input_proto.set_value = True\n \n self.dg._enqueue(\"number_input\", number_input_proto)\ndiff --git a/lib/streamlit/runtime/state/__init__.py b/lib/streamlit/runtime/state/__init__.py\nindex 8a86e9168299..109704084f71 100644\n--- a/lib/streamlit/runtime/state/__init__.py\n+++ b/lib/streamlit/runtime/state/__init__.py\n@@ -33,6 +33,7 @@\n from streamlit.runtime.state.session_state_proxy import (\n get_session_state as get_session_state,\n )\n+from streamlit.runtime.state.widgets import DefaultValue as DefaultValue\n from streamlit.runtime.state.widgets import NoValue as NoValue\n from streamlit.runtime.state.widgets import (\n coalesce_widget_states as coalesce_widget_states,\ndiff --git a/lib/streamlit/runtime/state/common.py b/lib/streamlit/runtime/state/common.py\nindex 57f541b943ca..ff6d725fa82d 100644\n--- a/lib/streamlit/runtime/state/common.py\n+++ b/lib/streamlit/runtime/state/common.py\n@@ -57,7 +57,7 @@\n from streamlit.type_util import ValueFieldName\n \n if TYPE_CHECKING:\n- from streamlit.runtime.state.widgets import NoValue\n+ from streamlit.runtime.state.widgets import DefaultValue, NoValue\n \n \n # Protobuf types for all widgets.\n@@ -157,7 +157,15 @@ def failure(\n \n PROTO_SCALAR_VALUE = Union[float, int, bool, str, bytes]\n SAFE_VALUES = Union[\n- date, time, datetime, timedelta, None, \"NoValue\", Message, PROTO_SCALAR_VALUE\n+ date,\n+ time,\n+ datetime,\n+ timedelta,\n+ None,\n+ \"NoValue\",\n+ \"DefaultValue\",\n+ Message,\n+ PROTO_SCALAR_VALUE,\n ]\n \n \ndiff --git a/lib/streamlit/runtime/state/session_state.py b/lib/streamlit/runtime/state/session_state.py\nindex d0e6f056f3de..07475c483f46 100644\n--- a/lib/streamlit/runtime/state/session_state.py\n+++ b/lib/streamlit/runtime/state/session_state.py\n@@ -112,7 +112,11 @@ def __getitem__(self, k: str) -> Any:\n ValueFieldName,\n wstate.value.WhichOneof(\"value\"),\n )\n- value = wstate.value.__getattribute__(value_field_name)\n+ value = (\n+ wstate.value.__getattribute__(value_field_name)\n+ if value_field_name # Field name is None if the widget value was cleared\n+ else None\n+ )\n \n if is_array_value_field_name(value_field_name):\n # Array types are messages with data in a `data` field\n@@ -210,6 +214,7 @@ def get_serialized(self, k: str) -> WidgetStateProto | None:\n \n field = metadata.value_type\n serialized = metadata.serializer(item.value)\n+\n if is_array_value_field_name(field):\n arr = getattr(widget, field)\n arr.data.extend(serialized)\n@@ -219,7 +224,11 @@ def get_serialized(self, k: str) -> WidgetStateProto | None:\n widget.file_uploader_state_value.CopyFrom(serialized)\n elif field == \"string_trigger_value\":\n widget.string_trigger_value.CopyFrom(serialized)\n- else:\n+ elif field is not None and serialized is not None:\n+ # If the field is None, the widget value was cleared\n+ # by the user and therefore is None. But we cannot\n+ # set it to None here, since the proto properties are\n+ # not nullable. So we just don't set it.\n setattr(widget, field, serialized)\n \n return widget\ndiff --git a/lib/streamlit/runtime/state/widgets.py b/lib/streamlit/runtime/state/widgets.py\nindex cf305bbf5b1f..30255bda4f1c 100644\n--- a/lib/streamlit/runtime/state/widgets.py\n+++ b/lib/streamlit/runtime/state/widgets.py\n@@ -72,6 +72,15 @@\n )\n \n \n+class DefaultValue:\n+ \"\"\"Used as default value for element parameters to indicate that the user\n+ didn't explicitly specify a value. This usually means that the user wants\n+ to use a default value.\n+ \"\"\"\n+\n+ pass\n+\n+\n class NoValue:\n \"\"\"Return this from DeltaGenerator.foo_widget() when you want the st.foo_widget()\n call to return None. This is needed because `DeltaGenerator._enqueue`\ndiff --git a/lib/tests/streamlit/elements/number_input_test.py b/lib/tests/streamlit/elements/number_input_test.py\nindex 7987117d5af7..0b6ebcf7f21b 100644\n--- a/lib/tests/streamlit/elements/number_input_test.py\n+++ b/lib/tests/streamlit/elements/number_input_test.py\n@@ -58,6 +58,7 @@ def test_just_label(self):\n LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE,\n )\n self.assertEqual(c.default, 0.0)\n+ self.assertEqual(c.HasField(\"default\"), True)\n self.assertEqual(c.has_min, False)\n self.assertEqual(c.has_max, False)\n self.assertEqual(c.disabled, False)\n@@ -69,6 +70,32 @@ def test_just_disabled(self):\n c = self.get_delta_from_queue().new_element.number_input\n self.assertEqual(c.disabled, True)\n \n+ def test_none_value(self):\n+ \"\"\"Test that it can be called with None as value.\"\"\"\n+ st.number_input(\"the label\", value=None)\n+\n+ c = self.get_delta_from_queue().new_element.number_input\n+ self.assertEqual(c.label, \"the label\")\n+ # If a proto property is null is not determined by this value,\n+ # but by the check via the HasField method:\n+ self.assertEqual(c.default, 0.0)\n+ self.assertEqual(c.HasField(\"default\"), False)\n+\n+ def test_none_value_with_int_min(self):\n+ \"\"\"Test that it can be called with None as value and\n+ will be interpreted as integer if min_value is set to int.\"\"\"\n+ st.number_input(\"the label\", value=None, min_value=1)\n+\n+ c = self.get_delta_from_queue().new_element.number_input\n+ self.assertEqual(c.label, \"the label\")\n+ # If a proto property is null is not determined by this value,\n+ # but by the check via the HasField method:\n+ self.assertEqual(c.default, 0.0)\n+ self.assertEqual(c.HasField(\"default\"), False)\n+ self.assertEqual(c.has_min, True)\n+ self.assertEqual(c.min, 1)\n+ self.assertEqual(c.data_type, NumberInput.INT)\n+\n def test_default_value_when_min_is_passed(self):\n st.number_input(\"the label\", min_value=1, max_value=10)\n \ndiff --git a/proto/streamlit/proto/NumberInput.proto b/proto/streamlit/proto/NumberInput.proto\nindex 7e4c86720245..309e5646690d 100644\n--- a/proto/streamlit/proto/NumberInput.proto\n+++ b/proto/streamlit/proto/NumberInput.proto\n@@ -35,12 +35,12 @@ message NumberInput {\n bool has_max = 12;\n \n DataType data_type = 13;\n- double default = 14;\n+ optional double default = 14;\n double step = 15;\n double min = 16;\n double max = 17;\n string help = 18;\n- double value = 19;\n+ optional double value = 19;\n bool set_value = 20;\n bool disabled = 21;\n LabelVisibilityMessage label_visibility = 22;\n" }
[ { "diff_hunk": "@@ -72,6 +72,15 @@\n )\n \n \n+class DefaultValue:", "line": null, "original_line": 75, "original_start_line": null, "path": "lib/streamlit/runtime/state/widgets.py", "start_line": null, "text": "@user1:\nIf we make this a dataclass, we can simplify some `isinstance` checks to `==` equality checks, which seems more semantically fitting.\n\n@author:\nOh, good point. I also just checked the spec and it specified to use ellipsis here instead of a class. I will check both options, change to dataclass or change to ellipsis tomorrow." } ]
b3c659ddbd7617703ec6e62a19c84952cac1b8b4
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a/e2e_playwright/__snapshots__/linux/st_number_input_test/st_number_input-value_none_min_1[light_theme-webkit].png b/e2e_playwright/__snapshots__/linux/st_number_input_test/st_number_input-value_none_min_1[light_theme-webkit].png new file mode 100644 index 000000000000..d9bbf698c946 Binary files /dev/null and b/e2e_playwright/__snapshots__/linux/st_number_input_test/st_number_input-value_none_min_1[light_theme-webkit].png differ diff --git a/e2e_playwright/st_code_test.py b/e2e_playwright/st_code_test.py index f08c75dbbb82..8365a438b3a5 100644 --- a/e2e_playwright/st_code_test.py +++ b/e2e_playwright/st_code_test.py @@ -47,7 +47,7 @@ def test_code_blocks_render_correctly( def test_code_in_markdown(app: Page, assert_snapshot: ImageCompareFunction): """Test that the syntax highlighting is applied correctly to the code block.""" # This test seems to be a little flaky without additional timeout: - app.wait_for_timeout(250) + app.wait_for_timeout(1000) expander_with_code = app.get_by_test_id("stExpander") diff --git a/e2e_playwright/st_number_input.py b/e2e_playwright/st_number_input.py new file mode 100644 index 000000000000..1b688f6c4f74 --- /dev/null +++ b/e2e_playwright/st_number_input.py @@ -0,0 +1,74 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import streamlit as st +from streamlit import runtime + +i1 = st.number_input("number input 1 (default)") +st.write("number input 1 (default) - value: ", i1) + +i2 = st.number_input("number input 2 (value=1)", value=1) +st.write("number input 2 (value=1) - value: ", i2) + +i3 = st.number_input("number input 3 (min & max)", 1, 10) +st.write("number input 3 (min & max) - value: ", i3) + +i4 = st.number_input("number input 4 (step=2)", step=2) +st.write("number input 4 (step=2) - value: ", i4) + +i5 = st.number_input("number input 5 (max=10)", max_value=10) +st.write("number input 5 (max=10) - value: ", i5) + +i6 = st.number_input("number input 6 (disabled=True)", disabled=True) +st.write("number input 6 (disabled=True) - value: ", i6) + +i7 = st.number_input("number input 7 (label=hidden)", label_visibility="hidden") +st.write("number input 7 (label=hidden) - value: ", i7) + +i8 = st.number_input("number input 8 (label=collapsed)", label_visibility="collapsed") +st.write("number input 8 (label=collapsed) - value: ", i8) + +if runtime.exists(): + + def on_change(): + st.session_state.number_input_changed = True + + st.number_input( + "number input 9 (on_change)", key="number_input9", on_change=on_change + ) + st.write("number input 9 (on_change) - value: ", st.session_state.number_input9) + st.write( + "number input 9 (on_change) - changed:", + "number_input_changed" in st.session_state, + ) + +[col1, col2, col3, col4, col5, col6] = st.columns(6) + +with col1: + i10 = st.number_input("number input 10 (small width)", max_value=10) + st.write("number input 10 (small width) - value: ", i10) + +i11 = st.number_input("number input 11 (value=None)", value=None) +st.write("number input 11 (value=None) - value: ", i11) + +if "number_input12" not in st.session_state: + st.session_state["number_input12"] = 10 + +i12 = st.number_input( + "number input 12 (value from state & min=1)", + value=None, + min_value=1, + key="number_input12", +) +st.write("number input 12 (value from state & min=1) - value: ", i12) diff --git a/e2e_playwright/st_number_input_test.py b/e2e_playwright/st_number_input_test.py new file mode 100644 index 000000000000..9bdfb0d66544 --- /dev/null +++ b/e2e_playwright/st_number_input_test.py @@ -0,0 +1,179 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from playwright.sync_api import Page, expect + +from e2e_playwright.conftest import ImageCompareFunction + + +def test_number_input_widget_display( + themed_app: Page, assert_snapshot: ImageCompareFunction +): + """Test that st.number_input renders correctly.""" + number_input_elements = themed_app.get_by_test_id("stNumberInput") + expect(number_input_elements).to_have_count(12) + + assert_snapshot(number_input_elements.nth(0), name="st_number_input-default") + assert_snapshot(number_input_elements.nth(1), name="st_number_input-value_1") + assert_snapshot(number_input_elements.nth(2), name="st_number_input-min_max") + assert_snapshot(number_input_elements.nth(3), name="st_number_input-step_2") + assert_snapshot(number_input_elements.nth(4), name="st_number_input-max_10") + assert_snapshot(number_input_elements.nth(5), name="st_number_input-disabled_true") + assert_snapshot(number_input_elements.nth(6), name="st_number_input-label_hidden") + assert_snapshot( + number_input_elements.nth(7), name="st_number_input-label_collapsed" + ) + assert_snapshot(number_input_elements.nth(8), name="st_number_input-on_change") + assert_snapshot(number_input_elements.nth(9), name="st_number_input-small_width") + assert_snapshot(number_input_elements.nth(10), name="st_number_input-value_none") + assert_snapshot( + number_input_elements.nth(11), name="st_number_input-value_none_min_1" + ) + + +def test_number_input_has_correct_default_values(app: Page): + """Test that st.number_input has the correct initial values.""" + markdown_elements = app.locator(".stMarkdown") + expect(markdown_elements).to_have_count(13) + + expected = [ + "number input 1 (default) - value: 0.0", + "number input 2 (value=1) - value: 1", + "number input 3 (min & max) - value: 1", + "number input 4 (step=2) - value: 0", + "number input 5 (max=10) - value: 0", + "number input 6 (disabled=True) - value: 0.0", + "number input 7 (label=hidden) - value: 0.0", + "number input 8 (label=collapsed) - value: 0.0", + "number input 9 (on_change) - value: 0.0", + "number input 9 (on_change) - changed: False", + "number input 10 (small width) - value: 0", + "number input 11 (value=None) - value: None", + "number input 12 (value from state & min=1) - value: 10", + ] + + for markdown_element, expected_text in zip(markdown_elements.all(), expected): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_number_input_shows_instructions_when_dirty( + app: Page, assert_snapshot: ImageCompareFunction +): + """Test that st.number_input shows the instructions correctly when dirty.""" + first_number_input_field = app.locator(".stNumberInput input").nth(0) + first_number_input_field.fill("10") + + assert_snapshot(first_number_input_field, name="st_number_input-input_instructions") + + +def test_number_input_updates_value_correctly_on_enter(app: Page): + """Test that st.number_input updates the value correctly on enter.""" + first_number_input_field = app.locator(".stNumberInput input").nth(0) + first_number_input_field.fill("10") + first_number_input_field.press("Enter") + + expect(app.locator(".stMarkdown").nth(0)).to_have_text( + "number input 1 (default) - value: 10.0", use_inner_text=True + ) + + +def test_number_input_has_correct_value_on_increment_click(app: Page): + """Test that st.number_input has the correct value on increment click.""" + number_input_up_buttons = app.locator(".stNumberInput button.step-up") + expect(number_input_up_buttons).to_have_count(11) + for i, button in enumerate(number_input_up_buttons.all()): + if i not in [5, 9]: + button.click() + + markdown_elements = app.locator(".stMarkdown") + + expected = [ + "number input 1 (default) - value: 0.01", + "number input 2 (value=1) - value: 2", + "number input 3 (min & max) - value: 2", + "number input 4 (step=2) - value: 2", + "number input 5 (max=10) - value: 1", + "number input 6 (disabled=True) - value: 0.0", + "number input 7 (label=hidden) - value: 0.01", + "number input 8 (label=collapsed) - value: 0.01", + "number input 9 (on_change) - value: 0.01", + "number input 9 (on_change) - changed: True", + "number input 10 (small width) - value: 0", + "number input 11 (value=None) - value: None", + "number input 12 (value from state & min=1) - value: 11", + ] + + for markdown_element, expected_text in zip(markdown_elements.all(), expected): + expect(markdown_element).to_have_text(expected_text, use_inner_text=True) + + +def test_number_input_has_correct_value_on_arrow_up(app: Page): + """Test that st.number_input has the correct value on arrow up.""" + first_number_input_field = app.locator(".stNumberInput input").nth(0) + first_number_input_field.press("ArrowUp") + + expect(app.locator(".stMarkdown").nth(0)).to_have_text( + "number input 1 (default) - value: 0.01", use_inner_text=True + ) + + +def test_number_input_has_correct_value_on_blur(app: Page): + """Test that st.number_input has the correct value on blur.""" + + first_number_input_field = app.locator(".stNumberInput input").nth(0) + first_number_input_field.focus() + first_number_input_field.fill("10") + first_number_input_field.blur() + + expect(app.locator(".stMarkdown").nth(0)).to_have_text( + "number input 1 (default) - value: 10.0", use_inner_text=True + ) + + +def test_empty_number_input_behaves_correctly( + app: Page, assert_snapshot: ImageCompareFunction +): + """Test that st.number_input behaves correctly when empty.""" + # Enter 10 in the first empty input: + empty_number_input = app.locator(".stNumberInput input").nth(10) + empty_number_input.fill("10") + empty_number_input.press("Enter") + + expect(app.locator(".stMarkdown").nth(11)).to_have_text( + "number input 11 (value=None) - value: 10.0", use_inner_text=True + ) + + assert_snapshot(empty_number_input, name="st_number_input-clearable_input") + + # Press escape to clear value: + empty_number_input = app.locator(".stNumberInput").nth(10) + empty_number_input.focus() + empty_number_input.press("Escape") + empty_number_input.press("Enter") + + # Should be empty again: + expect(app.locator(".stMarkdown").nth(11)).to_have_text( + "number input 11 (value=None) - value: None", use_inner_text=True + ) + + # Check with second empty input, this one should be integer since the min_value was + # set to an integer: + empty_number_input_with_min = app.locator(".stNumberInput input").nth(11) + empty_number_input_with_min.fill("15") + empty_number_input_with_min.press("Enter") + + expect(app.locator(".stMarkdown").nth(12)).to_have_text( + "number input 12 (value from state & min=1) - value: 15", use_inner_text=True + ) diff --git a/frontend/lib/src/WidgetStateManager.ts b/frontend/lib/src/WidgetStateManager.ts index dcfd184bf5c4..16e11b3b87fe 100644 --- a/frontend/lib/src/WidgetStateManager.ts +++ b/frontend/lib/src/WidgetStateManager.ts @@ -309,7 +309,11 @@ export class WidgetStateManager { return undefined } - public setIntValue(widget: WidgetInfo, value: number, source: Source): void { + public setIntValue( + widget: WidgetInfo, + value: number | null, + source: Source + ): void { this.createWidgetState(widget, source).intValue = value this.onWidgetValueChanged(widget.formId, source) } @@ -325,7 +329,7 @@ export class WidgetStateManager { public setDoubleValue( widget: WidgetInfo, - value: number, + value: number | null, source: Source ): void { this.createWidgetState(widget, source).doubleValue = value diff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx index fbab0da9a5aa..0a961421635f 100644 --- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx +++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx @@ -13,13 +13,13 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - +import React from "react" import { ShallowWrapper } from "enzyme" import { LabelVisibilityMessage as LabelVisibilityMessageProto, NumberInput as NumberInputProto, } from "@streamlit/lib/src/proto" -import React from "react" + import { mount, shallow } from "@streamlit/lib/src/test_util" import { StyledInputControls, @@ -27,18 +27,21 @@ import { } from "@streamlit/lib/src/components/widgets/NumberInput/styled-components" import { Input as UIInput } from "baseui/input" import { WidgetStateManager } from "@streamlit/lib/src/WidgetStateManager" +import { mockTheme } from "@streamlit/lib/src/mocks/mockTheme" -import NumberInput, { Props, State } from "./NumberInput" +import { NumberInput, Props, State } from "./NumberInput" const getProps = (elementProps: Partial<NumberInputProto> = {}): Props => ({ element: NumberInputProto.create({ label: "Label", + default: 0, hasMin: false, hasMax: false, ...elementProps, }), width: 300, disabled: false, + theme: mockTheme.emotion, widgetMgr: new WidgetStateManager({ sendRerunBackMsg: jest.fn(), formsDataChanged: jest.fn(), diff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx index 7bd31e07c1ff..2fc3a9d87b38 100644 --- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx +++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx @@ -16,7 +16,9 @@ import React from "react" import { Plus, Minus } from "@emotion-icons/open-iconic" +import { withTheme } from "@emotion/react" import { sprintf } from "sprintf-js" + import { FormClearHelper } from "@streamlit/lib/src/components/widgets/Form" import { logWarning } from "@streamlit/lib/src/util/log" import { NumberInput as NumberInputProto } from "@streamlit/lib/src/proto" @@ -27,7 +29,6 @@ import { } from "@streamlit/lib/src/WidgetStateManager" import TooltipIcon from "@streamlit/lib/src/components/shared/TooltipIcon" import { Placement } from "@streamlit/lib/src/components/shared/Tooltip" - import Icon from "@streamlit/lib/src/components/shared/Icon" import { Input as UIInput } from "baseui/input" import InputInstructions from "@streamlit/lib/src/components/shared/InputInstructions/InputInstructions" @@ -35,10 +36,12 @@ import { WidgetLabel, StyledWidgetLabelHelp, } from "@streamlit/lib/src/components/widgets/BaseWidget" - +import { EmotionTheme } from "@streamlit/lib/src/theme" import { isInForm, labelVisibilityProtoValueToEnum, + isNullOrUndefined, + notNullOrUndefined, } from "@streamlit/lib/src/util/utils" import { @@ -53,6 +56,7 @@ export interface Props { element: NumberInputProto widgetMgr: WidgetStateManager width: number + theme: EmotionTheme } export interface State { @@ -65,12 +69,12 @@ export interface State { * The value specified by the user via the UI. If the user didn't touch this * widget's UI, the default value is used. */ - value: number + value: number | null /** * The value with applied format that is going to be shown to the user */ - formattedValue: string + formattedValue: string | null /** * True if the input is selected @@ -78,7 +82,7 @@ export interface State { isFocused: boolean } -class NumberInput extends React.PureComponent<Props, State> { +export class NumberInput extends React.PureComponent<Props, State> { private readonly formClearHelper = new FormClearHelper() private inputRef = React.createRef<HTMLInputElement | HTMLTextAreaElement>() @@ -94,14 +98,14 @@ class NumberInput extends React.PureComponent<Props, State> { } } - get initialValue(): number { + get initialValue(): number | null { // If WidgetStateManager knew a value for this widget, initialize to that. // Otherwise, use the default value from the widget protobuf const storedValue = this.isIntData() ? this.props.widgetMgr.getIntValue(this.props.element) : this.props.widgetMgr.getDoubleValue(this.props.element) - return storedValue !== undefined ? storedValue : this.props.element.default + return storedValue ?? this.props.element.default ?? null } public componentDidMount(): void { @@ -130,12 +134,22 @@ class NumberInput extends React.PureComponent<Props, State> { private updateFromProtobuf(): void { const { value } = this.props.element this.props.element.setValue = false - this.setState({ value, formattedValue: this.formatValue(value) }, () => { - this.commitWidgetValue({ fromUi: false }) - }) + this.setState( + { + value: value ?? null, + formattedValue: this.formatValue(value ?? null), + }, + () => { + this.commitWidgetValue({ fromUi: false }) + } + ) } - private formatValue = (value: number): string => { + private formatValue = (value: number | null): string | null => { + if (isNullOrUndefined(value)) { + return null + } + const format = getNonEmptyString(this.props.element.format) if (format == null) { return value.toString() @@ -183,13 +197,13 @@ class NumberInput extends React.PureComponent<Props, State> { const min = this.getMin() const max = this.getMax() - if (min > value || value > max) { + if (notNullOrUndefined(value) && (min > value || value > max)) { const node = this.inputRef.current if (node) { node.reportValidity() } } else { - const valueToBeSaved = value || value === 0 ? value : data.default + const valueToBeSaved = value ?? data.default ?? null if (this.isIntData()) { widgetMgr.setIntValue(element, valueToBeSaved, source) @@ -212,7 +226,7 @@ class NumberInput extends React.PureComponent<Props, State> { private onFormCleared = (): void => { this.setState( (_, prevProps) => { - return { value: prevProps.element.default } + return { value: prevProps.element.default ?? null } }, () => this.commitWidgetValue({ fromUi: true }) ) @@ -235,19 +249,27 @@ class NumberInput extends React.PureComponent<Props, State> { ): void => { const { value } = e.target - let numValue: number - - if (this.isIntData()) { - numValue = parseInt(value, 10) + if (value === "") { + this.setState({ + dirty: true, + value: null, + formattedValue: null, + }) } else { - numValue = parseFloat(value) - } + let numValue: number - this.setState({ - dirty: true, - value: numValue, - formattedValue: value, - }) + if (this.isIntData()) { + numValue = parseInt(value, 10) + } else { + numValue = parseFloat(value) + } + + this.setState({ + dirty: true, + value: numValue, + formattedValue: value, + }) + } } private onKeyDown = ( @@ -285,11 +307,19 @@ class NumberInput extends React.PureComponent<Props, State> { /** True if the input's current value can be decremented by its step. */ private get canDecrement(): boolean { + if (isNullOrUndefined(this.state.value)) { + return false + } + return this.state.value - this.getStep() >= this.getMin() } /** True if the input's current value can be incremented by its step. */ private get canIncrement(): boolean { + if (isNullOrUndefined(this.state.value)) { + return false + } + return this.state.value + this.getStep() <= this.getMax() } @@ -305,7 +335,7 @@ class NumberInput extends React.PureComponent<Props, State> { this.setState( { dirty: true, - value: value + step, + value: (value ?? this.getMin()) + step, }, () => { this.commitWidgetValue({ fromUi: true }) @@ -318,7 +348,7 @@ class NumberInput extends React.PureComponent<Props, State> { this.setState( { dirty: true, - value: value - step, + value: (value ?? this.getMax()) - step, }, () => { this.commitWidgetValue({ fromUi: true }) @@ -331,13 +361,14 @@ class NumberInput extends React.PureComponent<Props, State> { } public render(): React.ReactNode { - const { element, width, disabled, widgetMgr } = this.props + const { element, width, disabled, widgetMgr, theme } = this.props const { formattedValue, dirty, isFocused } = this.state const style = { width } const disableDecrement = !this.canDecrement || disabled const disableIncrement = !this.canIncrement || disabled + const clearable = isNullOrUndefined(element.default) && !disabled // Manage our form-clear event handler. this.formClearHelper.manageFormClearListener( @@ -347,7 +378,7 @@ class NumberInput extends React.PureComponent<Props, State> { ) return ( - <div className="stNumberInput" style={style}> + <div className="stNumberInput" style={style} data-testid="stNumberInput"> <WidgetLabel label={element.label} disabled={disabled} @@ -368,15 +399,38 @@ class NumberInput extends React.PureComponent<Props, State> { <UIInput type="number" inputRef={this.inputRef} - value={formattedValue} + value={formattedValue || undefined} onBlur={this.onBlur} onFocus={this.onFocus} onChange={this.onChange} onKeyPress={this.onKeyPress} onKeyDown={this.onKeyDown} + clearable={clearable} + clearOnEscape={clearable} disabled={disabled} aria-label={element.label} overrides={{ + ClearIcon: { + props: { + overrides: { + Svg: { + style: { + color: theme.colors.darkGray, + // Since the close icon is an SVG, and we can't control its viewbox nor its attributes, + // Let's use a scale transform effect to make it bigger. + // The width property only enlarges its bounding box, so it's easier to click. + transform: "scale(1.4)", + width: theme.spacing.twoXL, + marginRight: "-1.25em", + + ":hover": { + fill: theme.colors.bodyText, + }, + }, + }, + }, + }, + }, Input: { props: { step: this.getStep(), @@ -447,7 +501,7 @@ class NumberInput extends React.PureComponent<Props, State> { <StyledInstructionsContainer> <InputInstructions dirty={dirty} - value={formattedValue} + value={formattedValue ?? ""} className="input-instructions" inForm={isInForm({ formId: element.formId })} /> @@ -468,4 +522,4 @@ function getNonEmptyString( return value == null || value === "" ? undefined : value } -export default NumberInput +export default withTheme(NumberInput) diff --git a/lib/streamlit/elements/widgets/number_input.py b/lib/streamlit/elements/widgets/number_input.py index 3174c68ae0b1..56ba71b10de5 100644 --- a/lib/streamlit/elements/widgets/number_input.py +++ b/lib/streamlit/elements/widgets/number_input.py @@ -12,10 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import numbers from dataclasses import dataclass from textwrap import dedent -from typing import Optional, Union, cast +from typing import TYPE_CHECKING, Union, cast, overload import streamlit from streamlit.elements.form import current_form_id @@ -30,7 +32,7 @@ from streamlit.runtime.metrics_util import gather_metrics from streamlit.runtime.scriptrunner import ScriptRunContext, get_script_run_ctx from streamlit.runtime.state import ( - NoValue, + DefaultValue, WidgetArgs, WidgetCallback, WidgetKwargs, @@ -41,47 +43,88 @@ Number = Union[int, float] +if TYPE_CHECKING: + from builtins import ellipsis + @dataclass class NumberInputSerde: - value: Union[int, float] + value: Number | None data_type: int - def serialize(self, v: Number) -> Number: + def serialize(self, v: Number | None) -> Number | None: return v - def deserialize(self, ui_value: Optional[Number], widget_id: str = "") -> Number: - if ui_value is not None: - val = ui_value - else: - # Widget has not been used; fallback to the original value, - val = self.value + def deserialize( + self, ui_value: Number | None, widget_id: str = "" + ) -> Number | None: + val: Number | None = ui_value if ui_value is not None else self.value - if self.data_type == NumberInputProto.INT: + if val is not None and self.data_type == NumberInputProto.INT: val = int(val) return val class NumberInputMixin: - @gather_metrics("number_input") + @overload def number_input( self, label: str, - min_value: Optional[Number] = None, - max_value: Optional[Number] = None, - value: Union[NoValue, Number, None] = NoValue(), - step: Optional[Number] = None, - format: Optional[str] = None, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + min_value: Number | None = None, + max_value: Number | None = None, + value: ellipsis | Number = ..., + step: Number | None = None, + format: str | None = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: disabled: bool = False, label_visibility: LabelVisibility = "visible", ) -> Number: + pass + + @overload + def number_input( + self, + label: str, + min_value: Number | None = None, + max_value: Number | None = None, + value: None = None, + step: Number | None = None, + format: str | None = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, + *, # keyword-only arguments: + disabled: bool = False, + label_visibility: LabelVisibility = "visible", + ) -> Number | None: + pass + + @gather_metrics("number_input") + def number_input( + self, + label: str, + min_value: Number | None = None, + max_value: Number | None = None, + value: ellipsis | Number | None = ..., + step: Number | None = None, + format: str | None = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, + *, # keyword-only arguments: + disabled: bool = False, + label_visibility: LabelVisibility = "visible", + ) -> Number | None: r"""Display a numeric input widget. .. note:: @@ -125,8 +168,9 @@ def number_input( The maximum permitted value. If None, there will be no maximum. value : int, float, or None - The value of this widget when it first renders. - Defaults to min_value, or 0.0 if min_value is None + The value of this widget when it first renders. If ``None``, the widget + will be initialized empty and returns ``None`` as long as the user didn't + provide an input. Defaults to min_value, or 0.0 if min_value is None. step : int, float, or None The stepping interval. Defaults to 1 if the value is an int, 0.01 otherwise. @@ -197,25 +241,25 @@ def number_input( def _number_input( self, label: str, - min_value: Optional[Number] = None, - max_value: Optional[Number] = None, - value: Union[NoValue, Number, None] = NoValue(), - step: Optional[Number] = None, - format: Optional[str] = None, - key: Optional[Key] = None, - help: Optional[str] = None, - on_change: Optional[WidgetCallback] = None, - args: Optional[WidgetArgs] = None, - kwargs: Optional[WidgetKwargs] = None, + min_value: Number | None = None, + max_value: Number | None = None, + value: ellipsis | Number | None = ..., + step: Number | None = None, + format: str | None = None, + key: Key | None = None, + help: str | None = None, + on_change: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, *, # keyword-only arguments: disabled: bool = False, label_visibility: LabelVisibility = "visible", - ctx: Optional[ScriptRunContext] = None, - ) -> Number: + ctx: ScriptRunContext | None = None, + ) -> Number | None: key = to_key(key) check_callback_rules(self.dg, on_change) check_session_state_rules( - default_value=None if isinstance(value, NoValue) else value, key=key + default_value=None if value is DefaultValue else value, key=key ) maybe_raise_label_warnings(label, label_visibility) @@ -238,12 +282,13 @@ def _number_input( number_input_args = [min_value, max_value, value, step] int_args = all( - isinstance(a, (numbers.Integral, type(None), NoValue)) + isinstance(a, (numbers.Integral, type(None), type(DefaultValue))) for a in number_input_args ) float_args = all( - isinstance(a, (float, type(None), NoValue)) for a in number_input_args + isinstance(a, (float, type(None), type(DefaultValue))) + for a in number_input_args ) if not int_args and not float_args: @@ -255,7 +300,7 @@ def _number_input( f"\n`step` has {type(step).__name__} type." ) - if isinstance(value, NoValue): + if value is DefaultValue: if min_value is not None: value = min_value elif int_args and float_args: @@ -269,28 +314,31 @@ def _number_input( float_value = isinstance(value, float) if value is None: - raise StreamlitAPIException( - "Default value for number_input should be an int or a float." + if int_args and not float_args: + # Select int type if all relevant args are ints: + int_value = True + else: + # Otherwise, defaults to float: + float_value = True + + if format is None: + format = "%d" if int_value else "%0.2f" + + # Warn user if they format an int type as a float or vice versa. + if format in ["%d", "%u", "%i"] and float_value: + import streamlit as st + + st.warning( + "Warning: NumberInput value below has type float," + f" but format {format} displays as integer." + ) + elif format[-1] == "f" and int_value: + import streamlit as st + + st.warning( + "Warning: NumberInput value below has type int so is" + f" displayed as int despite format string {format}." ) - else: - if format is None: - format = "%d" if int_value else "%0.2f" - - # Warn user if they format an int type as a float or vice versa. - if format in ["%d", "%u", "%i"] and float_value: - import streamlit as st - - st.warning( - "Warning: NumberInput value below has type float," - f" but format {format} displays as integer." - ) - elif format[-1] == "f" and int_value: - import streamlit as st - - st.warning( - "Warning: NumberInput value below has type int so is" - f" displayed as int despite format string {format}." - ) if step is None: step = 1 if int_value else 0.01 @@ -310,11 +358,10 @@ def _number_input( raise StreamlitAPIException( f"The default `value` {value} must be greater than or equal to the `min_value` {min_value}" ) - if max_value is not None and value is not None: - if max_value < value: - raise StreamlitAPIException( - f"The default `value` {value} must be less than or equal to the `max_value` {max_value}" - ) + if max_value is not None and value is not None and max_value < value: + raise StreamlitAPIException( + f"The default `value` {value} must be less than or equal to the `max_value` {max_value}" + ) # Bounds checks. JSNumber produces human-readable exceptions that # we simply re-package as StreamlitAPIExceptions. @@ -330,7 +377,8 @@ def _number_input( ) if step is not None: JSNumber.validate_int_bounds(step, "`step`") # type: ignore - JSNumber.validate_int_bounds(value, "`value`") # type: ignore + if value is not None: + JSNumber.validate_int_bounds(value, "`value`") # type: ignore else: if min_value is not None: JSNumber.validate_float_bounds(min_value, "`min_value`") @@ -338,7 +386,8 @@ def _number_input( JSNumber.validate_float_bounds(max_value, "`max_value`") if step is not None: JSNumber.validate_float_bounds(step, "`step`") - JSNumber.validate_float_bounds(value, "`value`") + if value is not None: + JSNumber.validate_float_bounds(value, "`value`") except JSNumberBoundsException as e: raise StreamlitAPIException(str(e)) @@ -348,7 +397,8 @@ def _number_input( number_input_proto.id = id number_input_proto.data_type = data_type number_input_proto.label = label - number_input_proto.default = value + if value is not None: + number_input_proto.default = value number_input_proto.form_id = current_form_id(self.dg) number_input_proto.disabled = disabled number_input_proto.label_visibility.value = get_label_visibility_proto_value( @@ -386,7 +436,8 @@ def _number_input( ) if widget_state.value_changed: - number_input_proto.value = widget_state.value + if widget_state.value is not None: + number_input_proto.value = widget_state.value number_input_proto.set_value = True self.dg._enqueue("number_input", number_input_proto) diff --git a/lib/streamlit/runtime/state/__init__.py b/lib/streamlit/runtime/state/__init__.py index 8a86e9168299..109704084f71 100644 --- a/lib/streamlit/runtime/state/__init__.py +++ b/lib/streamlit/runtime/state/__init__.py @@ -33,6 +33,7 @@ from streamlit.runtime.state.session_state_proxy import ( get_session_state as get_session_state, ) +from streamlit.runtime.state.widgets import DefaultValue as DefaultValue from streamlit.runtime.state.widgets import NoValue as NoValue from streamlit.runtime.state.widgets import ( coalesce_widget_states as coalesce_widget_states, diff --git a/lib/streamlit/runtime/state/common.py b/lib/streamlit/runtime/state/common.py index 57f541b943ca..606480bd149d 100644 --- a/lib/streamlit/runtime/state/common.py +++ b/lib/streamlit/runtime/state/common.py @@ -57,6 +57,8 @@ from streamlit.type_util import ValueFieldName if TYPE_CHECKING: + from builtins import ellipsis + from streamlit.runtime.state.widgets import NoValue @@ -157,7 +159,15 @@ def failure( PROTO_SCALAR_VALUE = Union[float, int, bool, str, bytes] SAFE_VALUES = Union[ - date, time, datetime, timedelta, None, "NoValue", Message, PROTO_SCALAR_VALUE + date, + time, + datetime, + timedelta, + None, + "NoValue", + "ellipsis", + Message, + PROTO_SCALAR_VALUE, ] diff --git a/lib/streamlit/runtime/state/session_state.py b/lib/streamlit/runtime/state/session_state.py index d0e6f056f3de..07475c483f46 100644 --- a/lib/streamlit/runtime/state/session_state.py +++ b/lib/streamlit/runtime/state/session_state.py @@ -112,7 +112,11 @@ def __getitem__(self, k: str) -> Any: ValueFieldName, wstate.value.WhichOneof("value"), ) - value = wstate.value.__getattribute__(value_field_name) + value = ( + wstate.value.__getattribute__(value_field_name) + if value_field_name # Field name is None if the widget value was cleared + else None + ) if is_array_value_field_name(value_field_name): # Array types are messages with data in a `data` field @@ -210,6 +214,7 @@ def get_serialized(self, k: str) -> WidgetStateProto | None: field = metadata.value_type serialized = metadata.serializer(item.value) + if is_array_value_field_name(field): arr = getattr(widget, field) arr.data.extend(serialized) @@ -219,7 +224,11 @@ def get_serialized(self, k: str) -> WidgetStateProto | None: widget.file_uploader_state_value.CopyFrom(serialized) elif field == "string_trigger_value": widget.string_trigger_value.CopyFrom(serialized) - else: + elif field is not None and serialized is not None: + # If the field is None, the widget value was cleared + # by the user and therefore is None. But we cannot + # set it to None here, since the proto properties are + # not nullable. So we just don't set it. setattr(widget, field, serialized) return widget diff --git a/lib/streamlit/runtime/state/widgets.py b/lib/streamlit/runtime/state/widgets.py index cf305bbf5b1f..cfce927765d2 100644 --- a/lib/streamlit/runtime/state/widgets.py +++ b/lib/streamlit/runtime/state/widgets.py @@ -71,6 +71,10 @@ } ) +# Used to indicate that an element parameter should be set to its default value. +# This needs to be type via `builtins.ellipsis`. +DefaultValue = Ellipsis # same as ... + class NoValue: """Return this from DeltaGenerator.foo_widget() when you want the st.foo_widget() diff --git a/lib/streamlit/testing/element_tree.py b/lib/streamlit/testing/element_tree.py index 766578e7913d..7e5787435524 100644 --- a/lib/streamlit/testing/element_tree.py +++ b/lib/streamlit/testing/element_tree.py @@ -62,6 +62,12 @@ T = TypeVar("T") +class InitialValue: + """This class is used to represent the initial value of a widget.""" + + pass + + # TODO This class serves as a fallback option for elements that have not # been implemented yet, as well as providing implementations of some # trivial methods. It may have significantly reduced scope, or be removed @@ -527,54 +533,62 @@ def unselect(self, v: T) -> Multiselect[T]: @dataclass(repr=False) class NumberInput(Widget): - _value: Number | None + _value: Number | None | InitialValue proto: NumberInputProto - min_value: Number - max_value: Number + min_value: Number | None + max_value: Number | None step: Number def __init__(self, proto: NumberInputProto, root: ElementTree): self.proto = proto self.root = root - self._value = None + self._value = InitialValue() self.type = "number_input" self.id = proto.id self.label = proto.label - self.min_value = proto.min - self.max_value = proto.max + self.min_value = proto.min if proto.has_min else None + self.max_value = proto.max if proto.has_max else None self.step = proto.step self.help = proto.help self.form_id = proto.form_id self.disabled = proto.disabled self.key = user_key_from_widget_id(self.id) - def set_value(self, v: Number) -> NumberInput: + def set_value(self, v: Number | None) -> NumberInput: self._value = v return self def widget_state(self) -> WidgetState: ws = WidgetState() ws.id = self.id - ws.double_value = self.value + if self.value is not None: + ws.double_value = self.value return ws @property - def value(self) -> Number: - if self._value is not None: + def value(self) -> Number | None: + if not isinstance(self._value, InitialValue): return self._value else: state = self.root.session_state assert state + # Awkward to do this with `cast` return state[self.id] # type: ignore def increment(self) -> NumberInput: - v = min(self.value + self.step, self.max_value) + if self.value is None: + return self + + v = min(self.value + self.step, self.max_value or float("inf")) return self.set_value(v) def decrement(self) -> NumberInput: - v = max(self.value - self.step, self.min_value) + if self.value is None: + return self + + v = max(self.value - self.step, self.min_value or float("-inf")) return self.set_value(v) diff --git a/lib/tests/streamlit/elements/number_input_test.py b/lib/tests/streamlit/elements/number_input_test.py index 7987117d5af7..1e473dec6f0a 100644 --- a/lib/tests/streamlit/elements/number_input_test.py +++ b/lib/tests/streamlit/elements/number_input_test.py @@ -26,6 +26,7 @@ from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage from streamlit.proto.NumberInput_pb2 import NumberInput from streamlit.proto.WidgetStates_pb2 import WidgetState +from streamlit.testing.script_interactions import InteractiveScriptTests from tests.delta_generator_test_case import DeltaGeneratorTestCase @@ -58,6 +59,7 @@ def test_just_label(self): LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE, ) self.assertEqual(c.default, 0.0) + self.assertEqual(c.HasField("default"), True) self.assertEqual(c.has_min, False) self.assertEqual(c.has_max, False) self.assertEqual(c.disabled, False) @@ -69,6 +71,32 @@ def test_just_disabled(self): c = self.get_delta_from_queue().new_element.number_input self.assertEqual(c.disabled, True) + def test_none_value(self): + """Test that it can be called with None as value.""" + st.number_input("the label", value=None) + + c = self.get_delta_from_queue().new_element.number_input + self.assertEqual(c.label, "the label") + # If a proto property is null is not determined by this value, + # but by the check via the HasField method: + self.assertEqual(c.default, 0.0) + self.assertEqual(c.HasField("default"), False) + + def test_none_value_with_int_min(self): + """Test that it can be called with None as value and + will be interpreted as integer if min_value is set to int.""" + st.number_input("the label", value=None, min_value=1) + + c = self.get_delta_from_queue().new_element.number_input + self.assertEqual(c.label, "the label") + # If a proto property is null is not determined by this value, + # but by the check via the HasField method: + self.assertEqual(c.default, 0.0) + self.assertEqual(c.HasField("default"), False) + self.assertEqual(c.has_min, True) + self.assertEqual(c.min, 1) + self.assertEqual(c.data_type, NumberInput.INT) + def test_default_value_when_min_is_passed(self): st.number_input("the label", min_value=1, max_value=10) @@ -333,3 +361,33 @@ def test_should_raise_exception_when_default_gt_max_and_min_is_none(self): max_value = 10 with pytest.raises(StreamlitAPIException): st.number_input("My Label", value=value, max_value=max_value) + + +class NumberInputInteractiveTest(InteractiveScriptTests): + def test_number_input_interaction(self): + """Test interactions with an empty number input widget.""" + script = self.script_from_string( + """ + import streamlit as st + + st.number_input("the label", value=None) + """ + ) + sr = script.run() + number_input = sr.number_input[0] + assert number_input.value is None + + # Set the value to 10 + sr2 = number_input.set_value(10).run() + number_input = sr2.number_input[0] + assert number_input.value == 10 + + # # Increment the value + sr3 = number_input.increment().run() + number_input = sr3.number_input[0] + assert number_input.value == 10.01 + + # # Clear the value + sr4 = number_input.set_value(None).run() + number_input = sr4.number_input[0] + assert number_input.value is None diff --git a/proto/streamlit/proto/NumberInput.proto b/proto/streamlit/proto/NumberInput.proto index 7e4c86720245..309e5646690d 100644 --- a/proto/streamlit/proto/NumberInput.proto +++ b/proto/streamlit/proto/NumberInput.proto @@ -35,12 +35,12 @@ message NumberInput { bool has_max = 12; DataType data_type = 13; - double default = 14; + optional double default = 14; double step = 15; double min = 16; double max = 17; string help = 18; - double value = 19; + optional double value = 19; bool set_value = 20; bool disabled = 21; LabelVisibilityMessage label_visibility = 22;
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
zulip__zulip-29326@890303a
zulip/zulip
Python
29,326
push_notifs: Make push_notifications_enabled more resistant to flapping.
Fixes #28403 Uses redis to remember the last time push notifications were experienced working. This needs to work across processes, so can't be done just in memory.
2024-03-18T00:21:22Z
Make `push_notifications_enabled` more resistant to flapping Edited to remove the part that we determined is not actually a bug. ~~The `maybe_mark_pushes_disabled` logic is asymmetric, in that it checks if push notifications appear to not be working on every request, but only consider marking them as disabled -- they will only be switched to enabled when the hourly cron job runs.~~ ~~This is not intended and needs to be fixed for 8.1.~~ It might also be nice to use some local state to avoid flapping with networking issues; if there have been both successful and unsuccessful requests to the push bouncer service in the last hour, then a (potentially transient) networking failure trying to send a push notification should probably not mark push notifications as not working.
Is the conclusion that this issue was opened in error because we already have that logic? @timabbott I believe so. The other point raised here: > It might also be nice to use some local state to avoid flapping with networking issues; if there have been both successful and unsuccessful requests to the push bouncer service in the last hour, then a (potentially transient) networking failure trying to send a push notification should probably not mark push notifications as not working. May warrant keeping this issue up after renaming it, or just opening a new one. Not sure if the plan would still be to try to merge this for 8.1 if I can make a PR for it in time? I think we can leave that for 8.2. I'll retitle the issue. @mateuszmandera You have been unassigned from this issue because you have not made any updates for over 14 days. Please feel free to reclaim the issue if you decide to pick up again. Thanks! @timabbott, @mateuszmandera You have been unassigned from this issue because you have not made any updates for over 14 days. Please feel free to reclaim the issue if you decide to pick up again. Thanks! @mateuszmandera You have been unassigned from this issue because you have not made any updates for over 14 days. Please feel free to reclaim the issue if you decide to pick up again. Thanks!
[ { "body": "Edited to remove the part that we determined is not actually a bug.\r\n\r\n~~The `maybe_mark_pushes_disabled` logic is asymmetric, in that it checks if push notifications appear to not be working on every request, but only consider marking them as disabled -- they will only be switched to enabled when the hourly cron job runs.~~\r\n\r\n~~This is not intended and needs to be fixed for 8.1.~~\r\n\r\nIt might also be nice to use some local state to avoid flapping with networking issues; if there have been both successful and unsuccessful requests to the push bouncer service in the last hour, then a (potentially transient) networking failure trying to send a push notification should probably not mark push notifications as not working.", "number": 28403, "title": "Make `push_notifications_enabled` more resistant to flapping" } ]
b71f4b9342db3247ee2db32c49dd3b4b5f70d603
{ "head_commit": "890303a2d77e072fd4a66ac77e9d199f804b3277", "head_commit_message": "rate_limiter: Extract KEY_PREFIX to redis_utils.", "patch_to_review": "diff --git a/zerver/lib/rate_limiter.py b/zerver/lib/rate_limiter.py\nindex c086b1fe3e0a3..6381058a72385 100644\n--- a/zerver/lib/rate_limiter.py\n+++ b/zerver/lib/rate_limiter.py\n@@ -1,5 +1,4 @@\n import logging\n-import os\n import time\n from abc import ABC, abstractmethod\n from typing import Dict, List, Optional, Set, Tuple, Type, cast\n@@ -11,6 +10,7 @@\n from django.http import HttpRequest\n from typing_extensions import override\n \n+from zerver.lib import redis_utils\n from zerver.lib.cache import cache_with_key\n from zerver.lib.exceptions import RateLimitedError\n from zerver.lib.redis_utils import get_redis_client\n@@ -22,8 +22,6 @@\n client = get_redis_client()\n rules: Dict[str, List[Tuple[int, int]]] = settings.RATE_LIMITING_RULES\n \n-KEY_PREFIX = \"\"\n-\n logger = logging.getLogger(__name__)\n \n \n@@ -159,11 +157,6 @@ def rules(self) -> List[Tuple[int, int]]:\n return rules[self.domain]\n \n \n-def bounce_redis_key_prefix_for_testing(test_name: str) -> None:\n- global KEY_PREFIX\n- KEY_PREFIX = test_name + \":\" + str(os.getpid()) + \":\"\n-\n-\n class RateLimiterBackend(ABC):\n @classmethod\n @abstractmethod\n@@ -323,7 +316,8 @@ class RedisRateLimiterBackend(RateLimiterBackend):\n @classmethod\n def get_keys(cls, entity_key: str) -> List[str]:\n return [\n- f\"{KEY_PREFIX}ratelimit:{entity_key}:{keytype}\" for keytype in [\"list\", \"zset\", \"block\"]\n+ f\"{redis_utils.REDIS_KEY_PREFIX}ratelimit:{entity_key}:{keytype}\"\n+ for keytype in [\"list\", \"zset\", \"block\"]\n ]\n \n @classmethod\ndiff --git a/zerver/lib/redis_utils.py b/zerver/lib/redis_utils.py\nindex 16d9100bb07f9..deddca2a091c1 100644\n--- a/zerver/lib/redis_utils.py\n+++ b/zerver/lib/redis_utils.py\n@@ -1,3 +1,4 @@\n+import os\n import re\n import secrets\n from typing import Any, Dict, Mapping, Optional\n@@ -84,3 +85,11 @@ def validate_key_fits_format(key: str, key_format: str) -> None:\n \n if not re.fullmatch(regex, key):\n raise ZulipRedisKeyOfWrongFormatError(f\"{key} does not match format {key_format}\")\n+\n+\n+REDIS_KEY_PREFIX = \"\"\n+\n+\n+def bounce_redis_key_prefix_for_testing(test_name: str) -> None:\n+ global REDIS_KEY_PREFIX\n+ REDIS_KEY_PREFIX = test_name + \":\" + str(os.getpid()) + \":\"\ndiff --git a/zerver/lib/test_classes.py b/zerver/lib/test_classes.py\nindex d897f5a6dbb06..c8be32bef4801 100644\n--- a/zerver/lib/test_classes.py\n+++ b/zerver/lib/test_classes.py\n@@ -66,7 +66,7 @@\n from zerver.lib.message import access_message\n from zerver.lib.notification_data import UserMessageNotificationsData\n from zerver.lib.per_request_cache import flush_per_request_caches\n-from zerver.lib.rate_limiter import bounce_redis_key_prefix_for_testing\n+from zerver.lib.redis_utils import bounce_redis_key_prefix_for_testing\n from zerver.lib.sessions import get_session_dict_user\n from zerver.lib.soft_deactivation import do_soft_deactivate_users\n from zerver.lib.stream_subscription import get_subscribed_stream_ids_for_user\n" }
[ { "diff_hunk": "@@ -245,6 +250,29 @@ def send_json_to_push_bouncer(\n )\n \n \n+PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY = \"push_notifications_recently_working_ts\"\n+\n+\n+def record_push_notifications_recently_working() -> None:\n+ # Record the timestamp in redisp, marking that push notifications", "line": null, "original_line": 257, "original_start_line": null, "path": "zerver/lib/remote_server.py", "start_line": null, "text": "@user1:\n```suggestion\r\n # Record the timestamp in redis, marking that push notifications\r\n```" } ]
fdf1f5541d2bcfab541b244baebe98257399bcb5
diff --git a/zerver/lib/push_notifications.py b/zerver/lib/push_notifications.py index 85bf6cb06fa9f..52a66a98ac9d0 100644 --- a/zerver/lib/push_notifications.py +++ b/zerver/lib/push_notifications.py @@ -45,6 +45,7 @@ from zerver.lib.message import access_message, huddle_users from zerver.lib.outgoing_http import OutgoingSession from zerver.lib.remote_server import ( + record_push_notifications_recently_working, send_json_to_push_bouncer, send_server_data_to_push_bouncer, send_to_push_bouncer, @@ -662,15 +663,18 @@ def send_notifications_to_bouncer( # The server may have updated our understanding of whether # push notifications will work. assert isinstance(remote_realm_dict, dict) + can_push = remote_realm_dict["can_push"] do_set_realm_property( user_profile.realm, "push_notifications_enabled", - remote_realm_dict["can_push"], + can_push, acting_user=None, ) do_set_push_notifications_enabled_end_timestamp( user_profile.realm, remote_realm_dict["expected_end_timestamp"], acting_user=None ) + if can_push: + record_push_notifications_recently_working() logger.info( "Sent mobile push notifications for user %s through bouncer: %s via FCM devices, %s via APNs devices", diff --git a/zerver/lib/rate_limiter.py b/zerver/lib/rate_limiter.py index c086b1fe3e0a3..6381058a72385 100644 --- a/zerver/lib/rate_limiter.py +++ b/zerver/lib/rate_limiter.py @@ -1,5 +1,4 @@ import logging -import os import time from abc import ABC, abstractmethod from typing import Dict, List, Optional, Set, Tuple, Type, cast @@ -11,6 +10,7 @@ from django.http import HttpRequest from typing_extensions import override +from zerver.lib import redis_utils from zerver.lib.cache import cache_with_key from zerver.lib.exceptions import RateLimitedError from zerver.lib.redis_utils import get_redis_client @@ -22,8 +22,6 @@ client = get_redis_client() rules: Dict[str, List[Tuple[int, int]]] = settings.RATE_LIMITING_RULES -KEY_PREFIX = "" - logger = logging.getLogger(__name__) @@ -159,11 +157,6 @@ def rules(self) -> List[Tuple[int, int]]: return rules[self.domain] -def bounce_redis_key_prefix_for_testing(test_name: str) -> None: - global KEY_PREFIX - KEY_PREFIX = test_name + ":" + str(os.getpid()) + ":" - - class RateLimiterBackend(ABC): @classmethod @abstractmethod @@ -323,7 +316,8 @@ class RedisRateLimiterBackend(RateLimiterBackend): @classmethod def get_keys(cls, entity_key: str) -> List[str]: return [ - f"{KEY_PREFIX}ratelimit:{entity_key}:{keytype}" for keytype in ["list", "zset", "block"] + f"{redis_utils.REDIS_KEY_PREFIX}ratelimit:{entity_key}:{keytype}" + for keytype in ["list", "zset", "block"] ] @classmethod diff --git a/zerver/lib/redis_utils.py b/zerver/lib/redis_utils.py index 16d9100bb07f9..deddca2a091c1 100644 --- a/zerver/lib/redis_utils.py +++ b/zerver/lib/redis_utils.py @@ -1,3 +1,4 @@ +import os import re import secrets from typing import Any, Dict, Mapping, Optional @@ -84,3 +85,11 @@ def validate_key_fits_format(key: str, key_format: str) -> None: if not re.fullmatch(regex, key): raise ZulipRedisKeyOfWrongFormatError(f"{key} does not match format {key_format}") + + +REDIS_KEY_PREFIX = "" + + +def bounce_redis_key_prefix_for_testing(test_name: str) -> None: + global REDIS_KEY_PREFIX + REDIS_KEY_PREFIX = test_name + ":" + str(os.getpid()) + ":" diff --git a/zerver/lib/remote_server.py b/zerver/lib/remote_server.py index 27cb0d7f025d0..9fd10697c7e42 100644 --- a/zerver/lib/remote_server.py +++ b/zerver/lib/remote_server.py @@ -6,6 +6,7 @@ import requests from django.conf import settings from django.db.models import QuerySet +from django.utils.timezone import now as timezone_now from django.utils.translation import gettext as _ from pydantic import UUID4, BaseModel, ConfigDict, Field, Json, field_validator @@ -16,6 +17,7 @@ do_set_push_notifications_enabled_end_timestamp, do_set_realm_property, ) +from zerver.lib import redis_utils from zerver.lib.exceptions import ( JsonableError, MissingRemoteRealmError, @@ -23,9 +25,12 @@ ) from zerver.lib.outgoing_http import OutgoingSession from zerver.lib.queue import queue_event_on_commit +from zerver.lib.redis_utils import get_redis_client from zerver.models import Realm, RealmAuditLog from zerver.models.realms import OrgTypeEnum +redis_client = get_redis_client() + class PushBouncerSession(OutgoingSession): def __init__(self, timeout: int = 15) -> None: @@ -245,6 +250,29 @@ def send_json_to_push_bouncer( ) +PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY = "push_notifications_recently_working_ts" + + +def record_push_notifications_recently_working() -> None: + # Record the timestamp in redis, marking that push notifications + # were working as of this moment. + + redis_key = redis_utils.REDIS_KEY_PREFIX + PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY + # Keep this record around for 24h in case it's useful for debugging. + redis_client.set(redis_key, str(timezone_now().timestamp()), ex=60 * 60 * 24) + + +def check_push_notifications_recently_working() -> bool: + # Check in redis whether push notifications were working in the last hour. + redis_key = redis_utils.REDIS_KEY_PREFIX + PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY + timestamp = redis_client.get(redis_key) + if timestamp is None: + return False + + # If the timestamp is within the last hour, we consider push notifications to be working. + return timezone_now().timestamp() - float(timestamp) < 60 * 60 + + def maybe_mark_pushes_disabled( e: Union[JsonableError, orjson.JSONDecodeError], logger: logging.Logger ) -> None: @@ -259,11 +287,18 @@ def maybe_mark_pushes_disabled( logger.exception("Exception communicating with %s", settings.PUSH_NOTIFICATION_BOUNCER_URL) # An exception was thrown talking to the push bouncer. There may - # be certain transient failures that we could ignore here, but the - # default explanation is that there is something wrong either with - # our credentials being corrupted or our ability to reach the - # bouncer service over the network, so we immediately move to + # be certain transient failures that we could ignore here - + # therefore we check whether push notifications were recently working + # and if so, the error can be treated as transient. + # Otherwise, the assumed explanation is that there is something wrong + # either with our credentials being corrupted or our ability to reach the + # bouncer service over the network, so we move to # reporting push notifications as likely not working. + if check_push_notifications_recently_working(): + # Push notifications were recently observed working, so we + # assume this is likely a transient failure. + return + for realm in Realm.objects.filter(push_notifications_enabled=True): do_set_realm_property(realm, "push_notifications_enabled", False, acting_user=None) do_set_push_notifications_enabled_end_timestamp(realm, None, acting_user=None) diff --git a/zerver/lib/test_classes.py b/zerver/lib/test_classes.py index d897f5a6dbb06..c8be32bef4801 100644 --- a/zerver/lib/test_classes.py +++ b/zerver/lib/test_classes.py @@ -66,7 +66,7 @@ from zerver.lib.message import access_message from zerver.lib.notification_data import UserMessageNotificationsData from zerver.lib.per_request_cache import flush_per_request_caches -from zerver.lib.rate_limiter import bounce_redis_key_prefix_for_testing +from zerver.lib.redis_utils import bounce_redis_key_prefix_for_testing from zerver.lib.sessions import get_session_dict_user from zerver.lib.soft_deactivation import do_soft_deactivate_users from zerver.lib.stream_subscription import get_subscribed_stream_ids_for_user diff --git a/zerver/tests/test_push_notifications.py b/zerver/tests/test_push_notifications.py index f5343669ccc91..ecb48706cc519 100644 --- a/zerver/tests/test_push_notifications.py +++ b/zerver/tests/test_push_notifications.py @@ -39,6 +39,7 @@ from zerver.actions.user_groups import check_add_user_group from zerver.actions.user_settings import do_change_user_setting, do_regenerate_api_key from zerver.actions.user_topics import do_set_user_topic_visibility_policy +from zerver.lib import redis_utils from zerver.lib.avatar import absolute_avatar_url, get_avatar_for_inaccessible_user from zerver.lib.exceptions import JsonableError from zerver.lib.push_notifications import ( @@ -64,12 +65,15 @@ send_notifications_to_bouncer, ) from zerver.lib.remote_server import ( + PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY, AnalyticsRequest, PushNotificationBouncerError, PushNotificationBouncerRetryLaterError, PushNotificationBouncerServerError, build_analytics_data, get_realms_info_for_push_bouncer, + record_push_notifications_recently_working, + redis_client, send_server_data_to_push_bouncer, send_to_push_bouncer, ) @@ -1329,6 +1333,12 @@ def assertPushNotificationsAre(self, should_be: bool) -> None: ), ) + @override + def setUp(self) -> None: + redis_client.delete(PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY) + + return super().setUp() + @override_settings(PUSH_NOTIFICATION_BOUNCER_URL="https://push.zulip.org.example.com") @responses.activate def test_analytics_failure_api(self) -> None: @@ -1349,6 +1359,40 @@ def test_analytics_failure_api(self) -> None: self.assertTrue(resp.assert_call_count(ANALYTICS_STATUS_URL, 1)) self.assertPushNotificationsAre(False) + # Simulate ConnectionError again, but this time with a redis record indicating + # that push notifications have recently worked fine. + with responses.RequestsMock() as resp, self.assertLogs( + "zulip.analytics", level="WARNING" + ) as mock_warning: + resp.add(responses.GET, ANALYTICS_STATUS_URL, body=ConnectionError()) + Realm.objects.all().update(push_notifications_enabled=True) + record_push_notifications_recently_working() + + send_server_data_to_push_bouncer() + self.assertEqual( + "WARNING:zulip.analytics:ConnectionError while trying to connect to push notification bouncer", + mock_warning.output[0], + ) + self.assertTrue(resp.assert_call_count(ANALYTICS_STATUS_URL, 1)) + # push_notifications_enabled shouldn't get set to False, because this is treated + # as a transient error. + self.assertPushNotificationsAre(True) + + # However after an hour has passed without seeing push notifications + # working, we take the error seriously. + with time_machine.travel(now() + timedelta(minutes=61), tick=False): + send_server_data_to_push_bouncer() + self.assertEqual( + "WARNING:zulip.analytics:ConnectionError while trying to connect to push notification bouncer", + mock_warning.output[1], + ) + self.assertTrue(resp.assert_call_count(ANALYTICS_STATUS_URL, 2)) + self.assertPushNotificationsAre(False) + + redis_client.delete( + redis_utils.REDIS_KEY_PREFIX + PUSH_NOTIFICATIONS_RECENTLY_WORKING_REDIS_KEY + ) + with responses.RequestsMock() as resp, self.assertLogs( "zulip.analytics", level="WARNING" ) as mock_warning:
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Code Refactoring / Architectural Improvement" }
streamlit__streamlit-7050@d08729f
streamlit/streamlit
Python
7,050
Assorted visual tweaks
## Describe your changes This PR addresses a handful of visual tweaks, namely: 1. Makes library links blue, to make them consistent with the app interface (reported by @jrieke); 2. Fixes the small bottom padding after scrolling in the sidebar (reported by Andreas Braendhaugen); 3. Hides the `-` and `+` buttons for `st.number_input` when its width is smaller than `120px` (reported by @jrieke). Closes #894 **Before:** ![Screenshot 2023-07-20 at 2 23 48 PM](https://github.com/streamlit/streamlit/assets/103376966/b76e95b5-8f79-4694-a9ef-e6fbf2713749) <img width="2494" alt="Untitled (1)" src="https://github.com/streamlit/streamlit/assets/103376966/e35a0d3c-dc89-4573-99d7-d0e06863f5ff"> <img width="130" alt="Untitled" src="https://github.com/streamlit/streamlit/assets/103376966/01fab15b-614c-46ed-bb46-03ee933158d8"> **After:** ![Screenshot 2023-07-20 at 2 26 50 PM](https://github.com/streamlit/streamlit/assets/103376966/c374690e-5ba9-4d96-a0d2-ea9e28d00afd) https://github.com/streamlit/streamlit/assets/103376966/5eede343-f284-4a3e-8130-48a98e6cb351 ![Screenshot 2023-07-20 at 2 54 50 PM](https://github.com/streamlit/streamlit/assets/103376966/6a2ef8d6-3690-4937-815a-9190a64a418d) ## Notion links * https://www.notion.so/snowflake-corp/Change-link-color-in-About-dialog-46201c1b2f0144d2b961d25053f9d006?pvs=4 * https://www.notion.so/Bottom-padding-on-sidebar-disappears-when-content-overflows-9455d42dea104505b749e36f3b36b88b * https://www.notion.so/snowflake-corp/Papercut-Request-icons-in-st-number_input-c58552f88b1b448eb3857fde197ce058?pvs=4 ## Testing Plan - Explanation of why no additional tests are needed: N/A - Unit Tests (JS and/or Python): Added - E2E Tests: Added - Any manual testing needed? Yes --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-07-20T18:01:56Z
Remove number_input -/+ step toggles option Is there an option to remove the -/+ number_input step toggles? If not, I would suggest that for a future release. Thank you! Also, is it possible to increase the precision? Right now I am just using a text_input and type casting to float to get around this. --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
Hi @ajberlier , There's no option to remove the +/- toggles at the moment. I'll leave this issue open as a feature request! You can use the `step` attribute to modify the precision of the toggles. If you just want to be able to enter floats in general, use a float as the initial value. `st.number_input("number input", step=0.1, value=1.0)` The default `step` and `value` are floats already. `st.number_input("number input")` @jrhone I am not sure how hard it would be to implement, but I think a good way to use this (currently non-existent) feature would be to set step=0. Hi @RedFrez , I like the idea suggestion :) Pinging our product team to ensure they have eyes on this @nthmost One reason to having the ability to remove the buttons, is if the page will be printed out. In "paper" format the buttons have no value, and just add some visual clutter to the page. With a live site, if reviewing the data is more than altering it, then you may want to reduce the usability/visibility of the buttons. __My idea for implementation:__ - Anyone who cares about altering the steps of the buttons would not be setting them to 0. This also passes my logic test of if you don't want buttons, then you want the steps to nothing, null, or 0. - The step could be converted to receive a `null` value, but that would require altering the Proto and more logic in order to process how it should be handled. - There could be another parameter added to number_input, but this would add overall complexity to the code, which I think is unnecessary. This could also be in the future if desired with minimal impact. Using the `step=0` option, only adds two additional lines to the number_input widget, not taking into account the lines added for testing. Since it checks `element.step` for conditional rendering of the buttons, it has no impact on how `step` is processed or used for any other aspect of the input. I thought it was important to still allow for use of the up and down arrows for controlling the input. Since `0` is a Falsy value, it will still generate the default steps based on number type of the input. @ajberlier : Curious to hear, what is your use case for removing the -/+ number_input step toggles? For us, we would use as a 'free' number input and not as a "range". For example: `st.number_input("How much did it cost?", min_value=0.0, step=0.01)` We want from the user that write out the number in the specified format (like `12499.99`), where '+' and '-' buttons has no meaning. Hi @asaini , I thought I would describe a common use-case of mine for removing the step buttons. A lot of my Streamlit apps include some preliminary data manipulation functionality, like inputting various minimum/maximum constraints which then get applied to, say, columns of a dataframe. For one app, I'd like to be able to do this in the sidebar, since it isn't the main functionality of the app, and having it centered would clutter the interface. I have found that the best way to accomplish this, from a layout perspective, is to use `st.beta_columns` and populate 2 columns with `st.number_input` for the minimum and maximum values. These 2 column "units" are then placed in rows for as many constraints are needed. However, when these columns are placed in the sidebar, the low sidebar width causes the increment/decrement buttons to occupy more screen space than the actual input field. The user cannot see the entirety of the numbers they are entering, and the layout is cluttered and distracting. I see three workarounds, each problematic in their own way: 1. Use `st.slider`. This works to an extent, but the flaw with using sliders occurs when the slider range is extracted from the range of the data itself. Sometimes, extreme outliers in the data will skew one end of the slider so far to the left or right side that a majority of the data is compressed to within some small % of the slider space. This is frustrating, and often means that in the course of interaction the user has to move the ends of the sliders so close together that the numbers bleed together and obscure each other (the numbers which float above the little draggable circle). Additionally, if there are a sufficient number of digits in the maximum value for slider, then the sidebar gains a horizontal scroll bar (at least in Firefox) since these digits go off the edge of the sidebar. 2. Use `st.text_input`. This removes the visual clutter of the increment/decrement buttons present with `st.number_input`, but is essentially untenable. It requires manually handling numeric input validation, and in addition losing out on the real-time validation and protection that `st.number_input` provides, it also leaves the user without the clean hint regarding the min/max value specification (i.e., the pop-up message saying "Please select a value that is no less/more than *X*" which occurs when you enter an out-of-range value into an `st.number_input`). 3. [Increasing the sidebar width](https://github.com/streamlit/streamlit/issues/2058). This allows the user to see the numbers in each field, but at the expense of the visual experience of the app. It means that the sidebar is no longer "in the background", as it competes with the main space and pushes it further to the right side of the screen. On a larger screen with high DPI, this can look mostly OK, but becomes a problem at lower resolutions or aspect ratios. In sum, I feel that having an option to just simply not render the increment/decrement buttons on `st.number_input` would allow for more compact side-by-side layouts by taking better advantage of screen estate. I don't have anything to suggest regarding implementation, but this is just my use-case. Anyways--I hope this feels concrete and well motivated. Let me know if you have any questions. @jrhone @kmcgrady are there any news on this 2 year old much wanted feature? :) I'm also interested in any updates Any update on this request? As mentioned, the + and - buttons sometimes occupies a large fraction of the number_input field, making it impossible to read the number typed.
[ { "body": "Is there an option to remove the -/+ number_input step toggles? If not, I would suggest that for a future release. Thank you! \r\n\r\nAlso, is it possible to increase the precision? \r\n\r\nRight now I am just using a text_input and type casting to float to get around this.\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**", "number": 894, "title": "Remove number_input -/+ step toggles option" } ]
56c1f3e0fb4be112c8c28f22720abdd4a29be23a
{ "head_commit": "d08729fc2456d1989260c56dacaf4e3fcb15de02", "head_commit_message": "Add modal snapshots", "patch_to_review": "diff --git a/e2e/scripts/st_number_input.py b/e2e/scripts/st_number_input.py\nindex c135fe48ed32..0aebaeb7dcfe 100644\n--- a/e2e/scripts/st_number_input.py\n+++ b/e2e/scripts/st_number_input.py\n@@ -47,3 +47,9 @@ def on_change():\n st.number_input(\"number input 9\", key=\"number_input9\", on_change=on_change)\n st.write('value 9: \"', st.session_state.number_input9, '\"')\n st.write(\"number input changed:\", \"number_input_changed\" in st.session_state)\n+\n+[col1, col2, col3, col4, col5, col6] = st.columns(6)\n+\n+with col1:\n+ i10 = st.number_input(\"number input 10\", max_value=10)\n+ st.write('value 10: \"', i10, '\"')\ndiff --git a/e2e/specs/st_number_input.spec.js b/e2e/specs/st_number_input.spec.js\nindex fa378afed8de..768d318b109f 100644\n--- a/e2e/specs/st_number_input.spec.js\n+++ b/e2e/specs/st_number_input.spec.js\n@@ -22,7 +22,7 @@ describe(\"st.number_input\", () => {\n });\n \n it(\"shows widget correctly\", () => {\n- cy.get(\".stNumberInput\").should(\"have.length\", 9);\n+ cy.get(\".stNumberInput\").should(\"have.length\", 10);\n \n cy.get(\".stNumberInput\").each((el, idx) => {\n // @ts-expect-error\n@@ -42,7 +42,8 @@ describe(\"st.number_input\", () => {\n 'value 7: \" 0.0 \"' +\n 'value 8: \" 0.0 \"' +\n 'value 9: \" 0.0 \"' +\n- \"number input changed: False\"\n+ \"number input changed: False\" +\n+ 'value 10: \" 0 \"'\n );\n });\n \n@@ -76,7 +77,8 @@ describe(\"st.number_input\", () => {\n 'value 7: \" 0.0 \"' +\n 'value 8: \" 0.0 \"' +\n 'value 9: \" 0.0 \"' +\n- \"number input changed: False\"\n+ \"number input changed: False\" +\n+ 'value 10: \" 0 \"'\n );\n });\n \n@@ -98,7 +100,8 @@ describe(\"st.number_input\", () => {\n 'value 7: \" 0.0 \"' +\n 'value 8: \" 0.0 \"' +\n 'value 9: \" 0.0 \"' +\n- \"number input changed: False\"\n+ \"number input changed: False\" +\n+ 'value 10: \" 0 \"'\n );\n });\n \n@@ -126,7 +129,8 @@ describe(\"st.number_input\", () => {\n 'value 7: \" 0.01 \"' +\n 'value 8: \" 0.01 \"' +\n 'value 9: \" 0.01 \"' +\n- \"number input changed: True\"\n+ \"number input changed: True\" +\n+ 'value 10: \" 0 \"'\n );\n });\n \n@@ -146,7 +150,8 @@ describe(\"st.number_input\", () => {\n 'value 7: \" 0.0 \"' +\n 'value 8: \" 0.0 \"' +\n 'value 9: \" 0.0 \"' +\n- \"number input changed: False\"\n+ \"number input changed: False\" +\n+ 'value 10: \" 0 \"'\n );\n });\n });\ndiff --git a/frontend/app/src/components/Sidebar/styled-components.ts b/frontend/app/src/components/Sidebar/styled-components.ts\nindex 6f6cd63ae742..4a8ece81fe12 100644\n--- a/frontend/app/src/components/Sidebar/styled-components.ts\n+++ b/frontend/app/src/components/Sidebar/styled-components.ts\n@@ -243,7 +243,7 @@ export const StyledSidebarUserContent =\n paddingTop: hasPageNavAbove\n ? theme.spacing.lg\n : theme.sizes.sidebarTopSpace,\n- paddingBottom: theme.spacing.twoXL,\n+ paddingBottom: theme.sizes.sidebarTopSpace,\n paddingLeft: theme.spacing.lg,\n paddingRight: theme.spacing.lg,\n \ndiff --git a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png\nindex ca546193101a..e27f66955617 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png\nindex 51d93644053e..3aeffb72c315 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png and b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png\nnew file mode 100644\nindex 000000000000..70a08d77135d\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png\nnew file mode 100644\nindex 000000000000..c190b16f269e\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png differ\ndiff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\nindex 4c6bff9ba4a3..39549adab768 100644\n--- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\n+++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx\n@@ -33,7 +33,7 @@ const getProps = (elementProps: Partial<NumberInputProto> = {}): Props => ({\n hasMax: false,\n ...elementProps,\n }),\n- width: 0,\n+ width: 300,\n disabled: false,\n widgetMgr: new WidgetStateManager({\n sendRerunBackMsg: jest.fn(),\n@@ -452,5 +452,12 @@ describe(\"NumberInput widget\", () => {\n expect(wrapper.state(\"value\")).toBe(2)\n expect(stepUpButton(wrapper).prop(\"disabled\")).toBe(true)\n })\n+\n+ it(\"hides stepUp and stepDown buttons when width is smaller than 120px\", () => {\n+ const props = getIntProps({ default: 1, step: 1, max: 2, hasMax: true })\n+ const wrapper = shallow(<NumberInput {...props} width={100} />)\n+\n+ expect(wrapper.find(\"button\").exists()).toBe(false)\n+ })\n })\n })\ndiff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\nindex ca991b455e76..712b6816c74f 100644\n--- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\n+++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx\n@@ -410,30 +410,32 @@ class NumberInput extends React.PureComponent<Props, State> {\n },\n }}\n />\n- <StyledInputControls>\n- <StyledInputControl\n- className=\"step-down\"\n- onClick={this.modifyValueUsingStep(\"decrement\")}\n- disabled={disableDecrement}\n- >\n- <Icon\n- content={Minus}\n- size=\"xs\"\n- color={this.canDecrement ? \"inherit\" : \"disabled\"}\n- />\n- </StyledInputControl>\n- <StyledInputControl\n- className=\"step-up\"\n- onClick={this.modifyValueUsingStep(\"increment\")}\n- disabled={disableIncrement}\n- >\n- <Icon\n- content={Plus}\n- size=\"xs\"\n- color={this.canIncrement ? \"inherit\" : \"disabled\"}\n- />\n- </StyledInputControl>\n- </StyledInputControls>\n+ {width > 120 && (\n+ <StyledInputControls>\n+ <StyledInputControl\n+ className=\"step-down\"\n+ onClick={this.modifyValueUsingStep(\"decrement\")}\n+ disabled={disableDecrement}\n+ >\n+ <Icon\n+ content={Minus}\n+ size=\"xs\"\n+ color={this.canDecrement ? \"inherit\" : \"disabled\"}\n+ />\n+ </StyledInputControl>\n+ <StyledInputControl\n+ className=\"step-up\"\n+ onClick={this.modifyValueUsingStep(\"increment\")}\n+ disabled={disableIncrement}\n+ >\n+ <Icon\n+ content={Plus}\n+ size=\"xs\"\n+ color={this.canIncrement ? \"inherit\" : \"disabled\"}\n+ />\n+ </StyledInputControl>\n+ </StyledInputControls>\n+ )}\n </StyledInputContainer>\n <StyledInstructionsContainer>\n <InputInstructions\ndiff --git a/frontend/lib/src/theme/globalStyles.ts b/frontend/lib/src/theme/globalStyles.ts\nindex 363cb98d0968..58e55e6b2742 100644\n--- a/frontend/lib/src/theme/globalStyles.ts\n+++ b/frontend/lib/src/theme/globalStyles.ts\n@@ -21,7 +21,7 @@ import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n export const globalStyles = (theme: EmotionTheme): SerializedStyles => css`\n a,\n a:visited {\n- color: ${theme.colors.primary};\n+ color: ${theme.colors.linkText};\n }\n \n // Override the base font-size value here.\n@@ -32,7 +32,7 @@ export const globalStyles = (theme: EmotionTheme): SerializedStyles => css`\n \n a:hover,\n a:active {\n- color: ${theme.colors.primary};\n+ color: ${theme.colors.linkText};\n text-decoration: underline;\n }\n \n@@ -337,11 +337,11 @@ export const globalStyles = (theme: EmotionTheme): SerializedStyles => css`\n // Links\n \n a {\n- color: ${theme.colors.primary};\n+ color: ${theme.colors.linkText};\n text-decoration: underline;\n \n &:hover {\n- color: ${darken(theme.colors.primary, 0.15)};\n+ color: ${darken(theme.colors.linkText, 0.15)};\n }\n }\n \n" }
[ { "diff_hunk": "@@ -410,30 +410,32 @@ class NumberInput extends React.PureComponent<Props, State> {\n },\n }}\n />\n- <StyledInputControls>\n- <StyledInputControl\n- className=\"step-down\"\n- onClick={this.modifyValueUsingStep(\"decrement\")}\n- disabled={disableDecrement}\n- >\n- <Icon\n- content={Minus}\n- size=\"xs\"\n- color={this.canDecrement ? \"inherit\" : \"disabled\"}\n- />\n- </StyledInputControl>\n- <StyledInputControl\n- className=\"step-up\"\n- onClick={this.modifyValueUsingStep(\"increment\")}\n- disabled={disableIncrement}\n- >\n- <Icon\n- content={Plus}\n- size=\"xs\"\n- color={this.canIncrement ? \"inherit\" : \"disabled\"}\n- />\n- </StyledInputControl>\n- </StyledInputControls>\n+ {width > 120 && (", "line": null, "original_line": 413, "original_start_line": null, "path": "frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx", "start_line": null, "text": "@user1:\nIs there a reasonable place to put this breakpoint in theme where it is easily accessible here? Maybe `frontend/lib/src/theme/primitives/breakpoints.ts`? Also please add a note there as to what its used for, perhaps why 120px.\r\n\r\nAlso think we should make this a variable above the render with explanatory comment above it, something along the lines of:\r\n```\r\n// We only want to show the increment/decrement controls when there is sufficient room\r\n// to display the value & these controls\r\nconst showControls = theme.breakpoints.numberInput > 120\r\n```\n\n@author:\nSorry for the poor job at explaining how this works: Updated in [95b0381](https://github.com/streamlit/streamlit/pull/7050/commits/95b038195ea6e4b474252fa5dc83bd102bcd26b1)" }, { "diff_hunk": "@@ -21,7 +21,7 @@ import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n export const globalStyles = (theme: EmotionTheme): SerializedStyles => css`\n a,\n a:visited {\n- color: ${theme.colors.primary};\n+ color: ${theme.colors.linkText};", "line": null, "original_line": 24, "original_start_line": null, "path": "frontend/lib/src/theme/globalStyles.ts", "start_line": null, "text": "@user1:\nglobal style changes always going to make me a little nervous, but it is our intent to change all links right?\n\n@author:\nYeah, I definitively feel the same way... The request mentions the link color in the about dialog only, but it also mentions `it should be the normal blue that we use everywhere else`, which makes me think we want all links to be the same color as the actual app links. @user2 maybe you can shed a bit more light as to what was your thinking for this request?\n\n@user2:\nOh sorry for the confusion. Just change the dialog, we don't need to change anything else. (I thought we already use the same color for links everywhere else). \n\n@author:\nMakes sense! Reverted and fixed on [c30d2b4](https://github.com/streamlit/streamlit/pull/7050/commits/c30d2b4f567657dcd8b526b9dcf6ce2c16716410)" } ]
c30d2b4f567657dcd8b526b9dcf6ce2c16716410
diff --git a/e2e/scripts/st_number_input.py b/e2e/scripts/st_number_input.py index c135fe48ed32..0aebaeb7dcfe 100644 --- a/e2e/scripts/st_number_input.py +++ b/e2e/scripts/st_number_input.py @@ -47,3 +47,9 @@ def on_change(): st.number_input("number input 9", key="number_input9", on_change=on_change) st.write('value 9: "', st.session_state.number_input9, '"') st.write("number input changed:", "number_input_changed" in st.session_state) + +[col1, col2, col3, col4, col5, col6] = st.columns(6) + +with col1: + i10 = st.number_input("number input 10", max_value=10) + st.write('value 10: "', i10, '"') diff --git a/e2e/specs/st_number_input.spec.js b/e2e/specs/st_number_input.spec.js index fa378afed8de..768d318b109f 100644 --- a/e2e/specs/st_number_input.spec.js +++ b/e2e/specs/st_number_input.spec.js @@ -22,7 +22,7 @@ describe("st.number_input", () => { }); it("shows widget correctly", () => { - cy.get(".stNumberInput").should("have.length", 9); + cy.get(".stNumberInput").should("have.length", 10); cy.get(".stNumberInput").each((el, idx) => { // @ts-expect-error @@ -42,7 +42,8 @@ describe("st.number_input", () => { 'value 7: " 0.0 "' + 'value 8: " 0.0 "' + 'value 9: " 0.0 "' + - "number input changed: False" + "number input changed: False" + + 'value 10: " 0 "' ); }); @@ -76,7 +77,8 @@ describe("st.number_input", () => { 'value 7: " 0.0 "' + 'value 8: " 0.0 "' + 'value 9: " 0.0 "' + - "number input changed: False" + "number input changed: False" + + 'value 10: " 0 "' ); }); @@ -98,7 +100,8 @@ describe("st.number_input", () => { 'value 7: " 0.0 "' + 'value 8: " 0.0 "' + 'value 9: " 0.0 "' + - "number input changed: False" + "number input changed: False" + + 'value 10: " 0 "' ); }); @@ -126,7 +129,8 @@ describe("st.number_input", () => { 'value 7: " 0.01 "' + 'value 8: " 0.01 "' + 'value 9: " 0.01 "' + - "number input changed: True" + "number input changed: True" + + 'value 10: " 0 "' ); }); @@ -146,7 +150,8 @@ describe("st.number_input", () => { 'value 7: " 0.0 "' + 'value 8: " 0.0 "' + 'value 9: " 0.0 "' + - "number input changed: False" + "number input changed: False" + + 'value 10: " 0 "' ); }); }); diff --git a/frontend/app/src/components/Sidebar/styled-components.ts b/frontend/app/src/components/Sidebar/styled-components.ts index 6f6cd63ae742..4a8ece81fe12 100644 --- a/frontend/app/src/components/Sidebar/styled-components.ts +++ b/frontend/app/src/components/Sidebar/styled-components.ts @@ -243,7 +243,7 @@ export const StyledSidebarUserContent = paddingTop: hasPageNavAbove ? theme.spacing.lg : theme.sizes.sidebarTopSpace, - paddingBottom: theme.spacing.twoXL, + paddingBottom: theme.sizes.sidebarTopSpace, paddingLeft: theme.spacing.lg, paddingRight: theme.spacing.lg, diff --git a/frontend/app/src/components/StreamlitDialog/StreamlitDialog.tsx b/frontend/app/src/components/StreamlitDialog/StreamlitDialog.tsx index f51f8ef8fd6b..f486920f5aa9 100644 --- a/frontend/app/src/components/StreamlitDialog/StreamlitDialog.tsx +++ b/frontend/app/src/components/StreamlitDialog/StreamlitDialog.tsx @@ -44,6 +44,7 @@ import { StyledCommandLine, StyledDeployErrorContent, StyledAboutInfo, + StyledAboutLink, } from "./styled-components" export type PlainEventHandler = () => void @@ -172,7 +173,9 @@ function aboutDialog(props: AboutProps): ReactElement { <br /> </> )} - <a href={STREAMLIT_HOME_URL}>{STREAMLIT_HOME_URL}</a> + <StyledAboutLink href={STREAMLIT_HOME_URL}> + {STREAMLIT_HOME_URL} + </StyledAboutLink> <br /> Copyright {new Date().getFullYear()} Snowflake Inc. All rights reserved. diff --git a/frontend/app/src/components/StreamlitDialog/styled-components.ts b/frontend/app/src/components/StreamlitDialog/styled-components.ts index af3b91250928..afb9be0d155b 100644 --- a/frontend/app/src/components/StreamlitDialog/styled-components.ts +++ b/frontend/app/src/components/StreamlitDialog/styled-components.ts @@ -16,6 +16,7 @@ import styled from "@emotion/styled" import { ChevronLeft } from "react-feather" +import { darken } from "color2k" import { Small } from "@streamlit/lib" export const StyledRerunHeader = styled.div(({ theme }) => ({ @@ -149,3 +150,11 @@ export const StyledAboutInfo = styled.div(() => ({ padding: "0 0 1rem 0", overflowY: "scroll", })) + +export const StyledAboutLink = styled.a(({ theme }) => ({ + color: `${theme.colors.linkText} !important`, + + "&:hover": { + color: `${darken(theme.colors.linkText, 0.15)} !important`, + }, +})) diff --git a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png index ca546193101a..e27f66955617 100644 Binary files a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png index 51d93644053e..3aeffb72c315 100644 Binary files a/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png and b/frontend/cypress/snapshots/linux/2x/modals.spec.js/about.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png new file mode 100644 index 000000000000..70a08d77135d Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png new file mode 100644 index 000000000000..c190b16f269e Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_number_input.spec.js/number_input9.snap.png differ diff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx index 4c6bff9ba4a3..a88ad32292f8 100644 --- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx +++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.test.tsx @@ -21,6 +21,7 @@ import { } from "@streamlit/lib/src/proto" import React from "react" import { mount, shallow } from "@streamlit/lib/src/test_util" +import { StyledInputControls } from "@streamlit/lib/src/components/widgets/NumberInput/styled-components" import { Input as UIInput } from "baseui/input" import { WidgetStateManager } from "@streamlit/lib/src/WidgetStateManager" @@ -33,7 +34,7 @@ const getProps = (elementProps: Partial<NumberInputProto> = {}): Props => ({ hasMax: false, ...elementProps, }), - width: 0, + width: 300, disabled: false, widgetMgr: new WidgetStateManager({ sendRerunBackMsg: jest.fn(), @@ -452,5 +453,19 @@ describe("NumberInput widget", () => { expect(wrapper.state("value")).toBe(2) expect(stepUpButton(wrapper).prop("disabled")).toBe(true) }) + + it("hides stepUp and stepDown buttons when width is smaller than 120px", () => { + const props = getIntProps({ default: 1, step: 1, max: 2, hasMax: true }) + const wrapper = shallow(<NumberInput {...props} width={100} />) + + expect(wrapper.find(StyledInputControls).exists()).toBe(false) + }) + + it("shows stepUp and stepDown buttons when width is bigger than 120px", () => { + const props = getIntProps({ default: 1, step: 1, max: 2, hasMax: true }) + const wrapper = shallow(<NumberInput {...props} width={125} />) + + expect(wrapper.find(StyledInputControls).exists()).toBe(true) + }) }) }) diff --git a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx index ca991b455e76..27ccb5291b27 100644 --- a/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx +++ b/frontend/lib/src/components/widgets/NumberInput/NumberInput.tsx @@ -20,6 +20,7 @@ import { sprintf } from "sprintf-js" import { FormClearHelper } from "@streamlit/lib/src/components/widgets/Form" import { logWarning } from "@streamlit/lib/src/util/log" import { NumberInput as NumberInputProto } from "@streamlit/lib/src/proto" +import { breakpoints } from "@streamlit/lib/src/theme/primitives/breakpoints" import { WidgetStateManager, Source, @@ -410,30 +411,34 @@ class NumberInput extends React.PureComponent<Props, State> { }, }} /> - <StyledInputControls> - <StyledInputControl - className="step-down" - onClick={this.modifyValueUsingStep("decrement")} - disabled={disableDecrement} - > - <Icon - content={Minus} - size="xs" - color={this.canDecrement ? "inherit" : "disabled"} - /> - </StyledInputControl> - <StyledInputControl - className="step-up" - onClick={this.modifyValueUsingStep("increment")} - disabled={disableIncrement} - > - <Icon - content={Plus} - size="xs" - color={this.canIncrement ? "inherit" : "disabled"} - /> - </StyledInputControl> - </StyledInputControls> + + {/* We only want to show the increment/decrement controls when there is sufficient room to display the value and these controls. */} + {width > breakpoints.numberInputControls && ( + <StyledInputControls> + <StyledInputControl + className="step-down" + onClick={this.modifyValueUsingStep("decrement")} + disabled={disableDecrement} + > + <Icon + content={Minus} + size="xs" + color={this.canDecrement ? "inherit" : "disabled"} + /> + </StyledInputControl> + <StyledInputControl + className="step-up" + onClick={this.modifyValueUsingStep("increment")} + disabled={disableIncrement} + > + <Icon + content={Plus} + size="xs" + color={this.canIncrement ? "inherit" : "disabled"} + /> + </StyledInputControl> + </StyledInputControls> + )} </StyledInputContainer> <StyledInstructionsContainer> <InputInstructions diff --git a/frontend/lib/src/theme/primitives/breakpoints.ts b/frontend/lib/src/theme/primitives/breakpoints.ts index e12ca856606b..820e497136d1 100644 --- a/frontend/lib/src/theme/primitives/breakpoints.ts +++ b/frontend/lib/src/theme/primitives/breakpoints.ts @@ -16,6 +16,9 @@ export const breakpoints = { toast: "68px", + // This value is used to determine whether we should show/hide the - and + buttons in st.number_input. + // We use 120px because at ~100px three-digit numbers (like 0.00) start to be hidden by these controls. + numberInputControls: 120, sm: "576px", columns: "640px", md: "768px",
{ "difficulty": "low", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-6865@9ed8e67
streamlit/streamlit
Python
6,865
Add chat component
## Describe your changes This PR adds two new commands optimized for a chat UI: - `st.chat_message`: Displays a container styled like a chat message with a configurable avatar. - `st.chat_input`: An input widget optimized for chat apps. E.g. it autogrows, sends the message on `enter`, clears the message after it is submitted and has a submit button. ## GitHub Issue Link Closes: https://github.com/streamlit/streamlit/issues/6320 ## Testing Plan Implemented various e2e, Python & Typescript unit tests. --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-06-20T11:19:12Z
Chat component Since ChatGPT or some other LLM modules are so popular recently, lot of users want to make a web app with conversational interface. Now if I want to make one with streamlit, I can use streamlit component st-chat and st.text_input. But the user experience is not as good as streamlit official components, so I'm wondering will you make some official component for conversational interface? --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
Yup we are discussing this at the moment! No concrete plan yet but stay tuned. We're all just as excited about GPT as you are ๐Ÿ˜‰
[ { "body": "Since ChatGPT or some other LLM modules are so popular recently, lot of users want to make a web app with conversational interface. Now if I want to make one with streamlit, I can use streamlit component st-chat and st.text_input. But the user experience is not as good as streamlit official components, so I'm wondering will you make some official component for conversational interface?\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**\r\n\r\n", "number": 6320, "title": "Chat component" } ]
fec6a7f1ab66328342512f2232bad39ac50f983a
{ "head_commit": "9ed8e67af06ad85ce18fb6ca669471a85b524f95", "head_commit_message": "Fix reStructuredText formatting issues", "patch_to_review": "diff --git a/e2e/scripts/st_chat_input.py b/e2e/scripts/st_chat_input.py\nnew file mode 100644\nindex 000000000000..b2411d648295\n--- /dev/null\n+++ b/e2e/scripts/st_chat_input.py\n@@ -0,0 +1,17 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import streamlit as st\n+\n+st.chat_input(placeholder=\"Please enter your message here...\", max_chars=200)\ndiff --git a/e2e/scripts/st_chat_message.py b/e2e/scripts/st_chat_message.py\nnew file mode 100644\nindex 000000000000..6deb542168a2\n--- /dev/null\n+++ b/e2e/scripts/st_chat_message.py\n@@ -0,0 +1,69 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import numpy as np\n+import pandas as pd\n+\n+import streamlit as st\n+\n+np.random.seed(0)\n+\n+\n+# Generate a random dataframe\n+df = pd.DataFrame(\n+ np.random.randn(5, 5),\n+ columns=(\"col_%d\" % i for i in range(5)),\n+)\n+\n+\n+with st.chat_message(\"user\"):\n+ st.write(\"Helloโ€ฆ\")\n+\n+with st.chat_message(\"assistant\"):\n+ st.write(\n+ \"\"\"\n+Hello, here is a code snippet:\n+\n+```python\n+import streamlit as st\n+with st.chat_message(\"assistant\"):\n+ st.write(\"Hello, here is a code snippet...\")\n+```\n+\"\"\"\n+ )\n+\n+with st.chat_message(\"user\", avatar=\"๐Ÿง‘\"):\n+ st.write(\n+ \"\"\"\n+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris tristique est\n+at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque\n+at. Ut et dui molestie, iaculis magna sed.\n+\"\"\"\n+ )\n+\n+with st.chat_message(\"dog\", avatar=\"https://static.streamlit.io/examples/dog.jpg\"):\n+ st.write(\"Woof woof! I'm a dog and I like charts:\")\n+ st.line_chart(df, use_container_width=True)\n+\n+cat = st.chat_message(\"cat\", avatar=\"https://static.streamlit.io/examples/cat.jpg\")\n+cat.write(\"I'm a cat and I like this dataset:\")\n+cat.dataframe(df, use_container_width=True)\n+cat.text_input(\"What's your name?\")\n+\n+\n+with st.chat_message(\"Bot\"):\n+ with st.expander(\"See more\", expanded=True):\n+ st.write(\"Lorem ipsum dolor sit amet\")\n+\n+st.chat_message(\"user\")\ndiff --git a/e2e/specs/st_chat_input.spec.js b/e2e/specs/st_chat_input.spec.js\nnew file mode 100644\nindex 000000000000..a30f74b81c9c\n--- /dev/null\n+++ b/e2e/specs/st_chat_input.spec.js\n@@ -0,0 +1,76 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+describe(\"st.chat_input\", () => {\n+ before(() => {\n+ cy.loadApp(\"http://localhost:3000/\");\n+ cy.prepForElementSnapshots();\n+ cy.get(\".stChatInputContainer\").should(\"have.length\", 1);\n+ });\n+\n+ it(\"renders the default state correctly\", () => {\n+ cy.get(\".stChatInputContainer\").matchThemedSnapshots(\"chatInput\");\n+ });\n+\n+ it(\"renders the focus state correctly\", () => {\n+ // Light Theme:\n+ cy.get(\".stChatInputContainer textarea\").click();\n+ cy.get(\".stChatInputContainer\").matchImageSnapshot(\"chatInput-focused-light\");\n+ // Dark Theme:\n+ cy.changeTheme(\"Dark\")\n+ // refocus\n+ cy.get(\".stChatInputContainer textarea\").click();\n+ cy.get(\".stChatInputContainer\").matchImageSnapshot(\"chatInput-focused-dark\");\n+ });\n+\n+ it(\"Shift+Enter creates a new line\", () => {\n+ // Clear the text input & Shift+Enter\n+ cy.get(\".stChatInputContainer textarea\").clear().type(`{shift+enter}New Line`);\n+ cy.get(\".stChatInputContainer\").matchThemedSnapshots(\"chatInput-shiftEnter\");\n+ });\n+\n+ it(\"Enter submits/clears input\", () => {\n+ // Types a message & then Enter\n+ cy.get(\".stChatInputContainer textarea\").clear().type(`Corgi{enter}`);\n+ cy.get('.stChatInputContainer textarea').invoke('val').should('eq', '');\n+ });\n+\n+ it(\"can click button to submit & clear input\", () => {\n+ // Types a message\n+ cy.get(\".stChatInputContainer textarea\").clear().type(`Corgi`);\n+ // Clicks the submit button\n+ cy.get(\".stChatInputContainer button\").click();\n+ cy.get('.stChatInputContainer textarea').invoke('val').should('eq', '');\n+ });\n+\n+ it(\"grows when input text is long & shrinks when deleted\", () => {\n+ // Type a long message (but < 200 chars)\n+ cy.get(\".stChatInputContainer textarea\").type(`Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna.`);\n+ cy.get(\".stChatInputContainer\").matchThemedSnapshots(\"chatInput-grows\");\n+\n+ // Remove characters on third line\n+ cy.get(\".stChatInputContainer textarea\").type('{backspace}{backspace}{backspace}{backspace}{backspace}{backspace}{backspace}');\n+ cy.get(\".stChatInputContainer\").matchThemedSnapshots(\"chatInput-shrinks\");\n+ });\n+\n+ it(\"max characters enforced\", () => {\n+ // Try to type a message over the 200 char limit\n+ cy.get(\".stChatInputContainer textarea\").clear().type(`Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna sed. This text shouldn't appear in the input.`);\n+ // Check that the message is truncated\n+ cy.get('.stChatInputContainer textarea').invoke('val').should('eq', 'Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna se')\n+ cy.get(\".stChatInputContainer\").matchThemedSnapshots(\"chatInput-maxChars\");\n+ });\n+});\ndiff --git a/e2e/specs/st_chat_message.spec.js b/e2e/specs/st_chat_message.spec.js\nnew file mode 100644\nindex 000000000000..b414b9d41c7b\n--- /dev/null\n+++ b/e2e/specs/st_chat_message.spec.js\n@@ -0,0 +1,32 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+describe(\"st.chat_message\", () => {\n+ before(() => {\n+ cy.loadApp(\"http://localhost:3000/\");\n+ cy.prepForElementSnapshots();\n+ // Make the toolbar disappear to not interfere with snapshots (in wide mode)\n+ cy.get(\"[data-testid='stToolbar']\").invoke(\"css\", \"opacity\", 0);\n+ });\n+\n+ it(\"renders chat messages correctly\", () => {\n+ cy.get(\".stChatMessage\").should(\"have.length\", 7);\n+\n+ cy.get(\".stChatMessage\").each((el, idx) => {\n+ return cy.wrap(el).matchThemedSnapshots(\"chat_message-\" + idx);\n+ });\n+ });\n+});\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png\nnew file mode 100644\nindex 000000000000..4a61ca6ae14d\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png\nnew file mode 100644\nindex 000000000000..c105f308c869\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png\nnew file mode 100644\nindex 000000000000..26c52ddaa4ad\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png\nnew file mode 100644\nindex 000000000000..790fe325f745\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png\nnew file mode 100644\nindex 000000000000..59e0a1c07bc3\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars-dark.snap.png\nnew file mode 100644\nindex 000000000000..7c6d877c77e2\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars.snap.png\nnew file mode 100644\nindex 000000000000..dff5b9c438bf\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-maxChars.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter-dark.snap.png\nnew file mode 100644\nindex 000000000000..7f9d296c1383\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter.snap.png\nnew file mode 100644\nindex 000000000000..71bed4b8e916\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shiftEnter.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shrinks-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shrinks-dark.snap.png\nnew file mode 100644\nindex 000000000000..afa4f4391be0\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-shrinks-dark.snap.png differ\ndiff --git 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b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-5.snap.png\nnew file mode 100644\nindex 000000000000..10430f47bbdf\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-5.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6-dark.snap.png\nnew file mode 100644\nindex 000000000000..719f05973f21\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6.snap.png\nnew file mode 100644\nindex 000000000000..6b5b3e3e0449\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_message.spec.js/chat_message-6.snap.png differ\ndiff --git a/frontend/src/app/components/AppView/AppView.test.tsx b/frontend/src/app/components/AppView/AppView.test.tsx\nindex 4e39bbe1ef86..b9ef3cdce246 100644\n--- a/frontend/src/app/components/AppView/AppView.test.tsx\n+++ b/frontend/src/app/components/AppView/AppView.test.tsx\n@@ -15,7 +15,19 @@\n */\n \n import React from \"react\"\n-import { Block as BlockProto, ForwardMsgMetadata } from \"src/lib/proto\"\n+import { screen } from \"@testing-library/react\"\n+import \"@testing-library/jest-dom\"\n+import {\n+ AppContext,\n+ Props as AppContextProps,\n+} from \"src/app/components/AppContext\"\n+import {\n+ Block as BlockProto,\n+ ForwardMsgMetadata,\n+ PageConfig,\n+ Element,\n+ ChatInput as ChatInputProto,\n+} from \"src/lib/proto\"\n import { ScriptRunState } from \"src/lib/ScriptRunState\"\n import { BlockNode, ElementNode, AppRoot } from \"src/lib/AppNode\"\n import { FileUploadClient } from \"src/lib/FileUploadClient\"\n@@ -26,9 +38,25 @@ import {\n import { makeElementWithInfoText } from \"src/lib/util/utils\"\n import { ComponentRegistry } from \"src/lib/components/widgets/CustomComponent\"\n import { mockEndpoints, mockSessionInfo } from \"src/lib/mocks/mocks\"\n-import { render, shallow } from \"src/lib/test_util\"\n+import { render } from \"src/lib/test_util\"\n import AppView, { AppViewProps } from \"./AppView\"\n \n+function getContextOutput(context: Partial<AppContextProps>): AppContextProps {\n+ return {\n+ wideMode: false,\n+ initialSidebarState: PageConfig.SidebarState.AUTO,\n+ embedded: false,\n+ showPadding: false,\n+ disableScrolling: false,\n+ showFooter: false,\n+ showToolbar: false,\n+ showColoredLine: false,\n+ pageLinkBaseUrl: \"\",\n+ sidebarChevronDownshift: 0,\n+ ...context,\n+ }\n+}\n+\n function getProps(props: Partial<AppViewProps> = {}): AppViewProps {\n const formsData = createFormsData()\n \n@@ -68,17 +96,15 @@ describe(\"AppView element\", () => {\n })\n \n it(\"renders without crashing\", () => {\n- const props = getProps()\n- const wrapper = shallow(<AppView {...props} />)\n-\n- expect(wrapper).toBeDefined()\n+ render(<AppView {...getProps()} />)\n })\n \n it(\"does not render a sidebar when there are no elements and only one page\", () => {\n const props = getProps()\n- const wrapper = shallow(<AppView {...props} />)\n+ render(<AppView {...props} />)\n \n- expect(wrapper.find(\"[data-testid='stSidebar']\").exists()).toBe(false)\n+ const sidebar = screen.queryByTestId(\"stSidebar\")\n+ expect(sidebar).not.toBeInTheDocument()\n })\n \n it(\"renders a sidebar when there are elements and only one page\", () => {\n@@ -98,14 +124,10 @@ describe(\"AppView element\", () => {\n const props = getProps({\n elements: new AppRoot(new BlockNode([main, sidebar])),\n })\n- const wrapper = shallow(<AppView {...props} />)\n+ render(<AppView {...props} />)\n \n- expect(wrapper.find(\"ThemedSidebar\").exists()).toBe(true)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"hasElements\")).toBe(true)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"appPages\")).toHaveLength(1)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"currentPageScriptHash\")).toBe(\n- \"main_page_script_hash\"\n- )\n+ const sidebarDOMElement = screen.queryByTestId(\"stSidebar\")\n+ expect(sidebarDOMElement).toBeInTheDocument()\n })\n \n it(\"renders a sidebar when there are no elements but multiple pages\", () => {\n@@ -113,11 +135,10 @@ describe(\"AppView element\", () => {\n { pageName: \"streamlit_app\", pageScriptHash: \"page_hash\" },\n { pageName: \"streamlit_app2\", pageScriptHash: \"page_hash2\" },\n ]\n- const wrapper = shallow(<AppView {...getProps({ appPages })} />)\n+ render(<AppView {...getProps({ appPages })} />)\n \n- expect(wrapper.find(\"ThemedSidebar\").exists()).toBe(true)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"hasElements\")).toBe(false)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"appPages\")).toEqual(appPages)\n+ const sidebarDOMElement = screen.queryByTestId(\"stSidebar\")\n+ expect(sidebarDOMElement).toBeInTheDocument()\n })\n \n it(\"renders a sidebar when there are elements and multiple pages\", () => {\n@@ -142,11 +163,10 @@ describe(\"AppView element\", () => {\n elements: new AppRoot(new BlockNode([main, sidebar])),\n appPages,\n })\n- const wrapper = shallow(<AppView {...props} />)\n+ render(<AppView {...props} />)\n \n- expect(wrapper.find(\"ThemedSidebar\").exists()).toBe(true)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"hasElements\")).toBe(true)\n- expect(wrapper.find(\"ThemedSidebar\").prop(\"appPages\")).toEqual(appPages)\n+ const sidebarDOMElement = screen.queryByTestId(\"stSidebar\")\n+ expect(sidebarDOMElement).toBeInTheDocument()\n })\n \n it(\"does not render the sidebar if there are no elements, multiple pages but hideSidebarNav is true\", () => {\n@@ -158,65 +178,86 @@ describe(\"AppView element\", () => {\n appPages,\n hideSidebarNav: true,\n })\n- const wrapper = shallow(<AppView {...props} />)\n+ render(<AppView {...props} />)\n \n- expect(wrapper.find(\"ThemedSidebar\").exists()).toBe(false)\n+ const sidebar = screen.queryByTestId(\"stSidebar\")\n+ expect(sidebar).not.toBeInTheDocument()\n })\n \n it(\"does not render the wide class\", () => {\n- jest\n- .spyOn(React, \"useContext\")\n- .mockImplementation(() => ({ wideMode: false, embedded: false }))\n- const wrapper = shallow(<AppView {...getProps()} />)\n+ const realUseContext = React.useContext\n+ jest.spyOn(React, \"useContext\").mockImplementation(input => {\n+ if (input === AppContext) {\n+ return getContextOutput({ wideMode: false, embedded: false })\n+ }\n+\n+ return realUseContext(input)\n+ })\n \n- expect(\n- wrapper.find(\"StyledAppViewBlockContainer\").prop(\"isWideMode\")\n- ).toBe(false)\n+ const main = new BlockNode([], new BlockProto({ allowEmpty: true }))\n+ const sidebar = new BlockNode([], new BlockProto({ allowEmpty: true }))\n+\n+ const props = getProps({\n+ elements: new AppRoot(new BlockNode([main, sidebar])),\n+ })\n+ const { getByTestId } = render(<AppView {...props} />)\n \n- expect(wrapper.find(\"StyledAppViewFooter\").prop(\"isWideMode\")).toBe(false)\n+ const style = window.getComputedStyle(getByTestId(\"block-container\"))\n+ expect(style.maxWidth).not.toEqual(\"initial\")\n })\n \n it(\"does render the wide class when specified\", () => {\n- jest\n- .spyOn(React, \"useContext\")\n- .mockImplementation(() => ({ wideMode: true, embedded: false }))\n- const wrapper = shallow(<AppView {...getProps()} />)\n+ const realUseContext = React.useContext\n+ jest.spyOn(React, \"useContext\").mockImplementation(input => {\n+ if (input === AppContext) {\n+ return getContextOutput({ wideMode: true, embedded: false })\n+ }\n \n- expect(\n- wrapper.find(\"StyledAppViewBlockContainer\").prop(\"isWideMode\")\n- ).toBe(true)\n+ return realUseContext(input)\n+ })\n+ const { getByTestId } = render(<AppView {...getProps()} />)\n+ const style = window.getComputedStyle(getByTestId(\"block-container\"))\n \n- expect(wrapper.find(\"StyledAppViewFooter\").prop(\"isWideMode\")).toBe(true)\n+ expect(style.maxWidth).toEqual(\"initial\")\n })\n \n it(\"opens link to streamlit.io in new tab\", () => {\n- const wrapper = shallow(<AppView {...getProps()} />)\n- expect(wrapper.find(\"StyledAppViewFooterLink\").props()).toEqual(\n- expect.objectContaining({\n- href: \"//streamlit.io\",\n- target: \"_blank\",\n- })\n- )\n+ render(<AppView {...getProps()} />)\n+ const link = screen.getByRole(\"link\", { name: \"Streamlit\" })\n+ expect(link).toHaveAttribute(\"href\", \"//streamlit.io\")\n+ expect(link).toHaveAttribute(\"target\", \"_blank\")\n })\n \n it(\"renders the Spacer and Footer when not embedded\", () => {\n- jest\n- .spyOn(React, \"useContext\")\n- .mockImplementation(() => ({ wideMode: false, embedded: false }))\n- const wrapper = shallow(<AppView {...getProps()} />)\n+ const realUseContext = React.useContext\n+ jest.spyOn(React, \"useContext\").mockImplementation(input => {\n+ if (input === AppContext) {\n+ return getContextOutput({ wideMode: false, embedded: false })\n+ }\n+\n+ return realUseContext(input)\n+ })\n+\n+ const { getByRole, getByTestId } = render(<AppView {...getProps()} />)\n \n- expect(wrapper.find(\"StyledAppViewBlockSpacer\").exists()).toBe(true)\n- expect(wrapper.find(\"StyledAppViewFooter\").exists()).toBe(true)\n+ expect(getByTestId(\"AppViewBlockSpacer\")).toBeInTheDocument()\n+ expect(getByRole(\"contentinfo\")).toBeInTheDocument()\n })\n \n it(\"does not render the Spacer and Footer when embedded\", () => {\n- jest\n- .spyOn(React, \"useContext\")\n- .mockImplementation(() => ({ wideMode: false, embedded: true }))\n- const wrapper = shallow(<AppView {...getProps()} />)\n+ const realUseContext = React.useContext\n+ jest.spyOn(React, \"useContext\").mockImplementation(input => {\n+ if (input === AppContext) {\n+ return getContextOutput({ wideMode: false, embedded: true })\n+ }\n \n- expect(wrapper.find(\"StyledAppViewBlockSpacer\").exists()).toBe(false)\n- expect(wrapper.find(\"StyledAppViewFooter\").exists()).toBe(false)\n+ return realUseContext(input)\n+ })\n+\n+ const { queryByRole, queryByTestId } = render(<AppView {...getProps()} />)\n+\n+ expect(queryByTestId(\"AppViewBlockSpacer\")).not.toBeInTheDocument()\n+ expect(queryByRole(\"contentinfo\")).not.toBeInTheDocument()\n })\n \n describe(\"when window.location.hash changes\", () => {\n@@ -236,4 +277,43 @@ describe(\"AppView element\", () => {\n })\n })\n })\n+\n+ it(\"does not render a Scroll To Bottom container when no chat input is present\", () => {\n+ const props = getProps()\n+ render(<AppView {...props} />)\n+\n+ const stbContainer = screen.queryByTestId(\"ScrollToBottomContainer\")\n+ expect(stbContainer).not.toBeInTheDocument()\n+ })\n+\n+ it(\"renders a Scroll To Bottom container when a chat input is present\", () => {\n+ const chatInputElement = new ElementNode(\n+ new Element({\n+ chatInput: {\n+ id: \"123\",\n+ placeholder: \"Enter Text Here\",\n+ disabled: false,\n+ default: \"\",\n+ position: ChatInputProto.Position.BOTTOM,\n+ },\n+ }),\n+ ForwardMsgMetadata.create({}),\n+ \"no script run id\"\n+ )\n+\n+ const sidebar = new BlockNode([], new BlockProto({ allowEmpty: true }))\n+\n+ const main = new BlockNode(\n+ [chatInputElement],\n+ new BlockProto({ allowEmpty: true })\n+ )\n+ const props = getProps({\n+ elements: new AppRoot(new BlockNode([main, sidebar])),\n+ })\n+\n+ render(<AppView {...props} />)\n+\n+ const stbContainer = screen.queryByTestId(\"ScrollToBottomContainer\")\n+ expect(stbContainer).toBeInTheDocument()\n+ })\n })\ndiff --git a/frontend/src/app/components/AppView/AppView.tsx b/frontend/src/app/components/AppView/AppView.tsx\nindex 451bd5893e23..a444450b7f42 100644\n--- a/frontend/src/app/components/AppView/AppView.tsx\n+++ b/frontend/src/app/components/AppView/AppView.tsx\n@@ -39,6 +39,7 @@ import {\n StyledIFrameResizerAnchor,\n StyledAppViewBlockSpacer,\n } from \"./styled-components\"\n+import ScrollToBottomContainer from \"./ScrollToBottomContainer\"\n \n export interface AppViewProps {\n elements: AppRoot\n@@ -96,6 +97,16 @@ function AppView(props: AppViewProps): ReactElement {\n endpoints,\n } = props\n \n+ // TODO: This works for scroll to bottom, but we will need\n+ // to revisit this when we support multiple position options\n+ const containsChatInput =\n+ Array.from(elements.main.getElements()).find(element => {\n+ return element.type === \"chatInput\"\n+ }) !== undefined\n+ const Component = containsChatInput\n+ ? ScrollToBottomContainer\n+ : StyledAppViewMain\n+\n React.useEffect(() => {\n const listener = (): void => {\n sendMessageToHost({\n@@ -120,6 +131,7 @@ function AppView(props: AppViewProps): ReactElement {\n const renderBlock = (node: BlockNode): ReactElement => (\n <StyledAppViewBlockContainer\n className=\"block-container\"\n+ data-testid=\"block-container\"\n isWideMode={wideMode}\n showPadding={showPadding}\n addPaddingForHeader={showToolbar || showColoredLine}\n@@ -164,7 +176,7 @@ function AppView(props: AppViewProps): ReactElement {\n {renderBlock(elements.sidebar)}\n </ThemedSidebar>\n )}\n- <StyledAppViewMain\n+ <Component\n tabIndex={0}\n isEmbedded={embedded}\n disableScrolling={disableScrolling}\n@@ -179,8 +191,10 @@ function AppView(props: AppViewProps): ReactElement {\n />\n {/* Spacer fills up dead space to ensure the footer remains at the\n bottom of the page in larger views */}\n- {(!embedded || showFooter) && <StyledAppViewBlockSpacer />}\n {(!embedded || showFooter) && (\n+ <StyledAppViewBlockSpacer data-testid=\"AppViewBlockSpacer\" />\n+ )}\n+ {(!embedded || showFooter) && !containsChatInput && (\n <StyledAppViewFooter isWideMode={wideMode}>\n Made with{\" \"}\n <StyledAppViewFooterLink href=\"//streamlit.io\" target=\"_blank\">\n@@ -188,7 +202,7 @@ function AppView(props: AppViewProps): ReactElement {\n </StyledAppViewFooterLink>\n </StyledAppViewFooter>\n )}\n- </StyledAppViewMain>\n+ </Component>\n </StyledAppViewContainer>\n )\n }\ndiff --git a/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx b/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx\nnew file mode 100644\nindex 000000000000..afac2e6d6dda\n--- /dev/null\n+++ b/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx\n@@ -0,0 +1,45 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React, { ReactElement, ReactNode } from \"react\"\n+import { StyledAppViewMain } from \"./styled-components\"\n+import useScrollToBottom from \"src/lib/hooks/useScrollToBottom\"\n+\n+export interface Props {\n+ className: string\n+ tabIndex: number\n+ isEmbedded: boolean\n+ disableScrolling: boolean\n+ children: ReactNode\n+}\n+\n+export default function ScrollToBottomContainer(props: Props): ReactElement {\n+ const { className, tabIndex, children, isEmbedded, disableScrolling } = props\n+ const scrollContainerRef = useScrollToBottom()\n+\n+ return (\n+ <StyledAppViewMain\n+ tabIndex={tabIndex}\n+ className={className}\n+ isEmbedded={isEmbedded}\n+ disableScrolling={disableScrolling}\n+ ref={scrollContainerRef}\n+ data-testid=\"ScrollToBottomContainer\"\n+ >\n+ {children}\n+ </StyledAppViewMain>\n+ )\n+}\ndiff --git a/frontend/src/lib/WidgetStateManager.test.ts b/frontend/src/lib/WidgetStateManager.test.ts\nindex bbbdc5e8f619..d8423613c782 100644\n--- a/frontend/src/lib/WidgetStateManager.test.ts\n+++ b/frontend/src/lib/WidgetStateManager.test.ts\n@@ -152,6 +152,18 @@ describe(\"Widget State Manager\", () => {\n assertCallbacks({ insideForm: false })\n })\n \n+ /**\n+ * String Triggers can't be used within forms, so this test\n+ * is not parameterized on insideForm.\n+ */\n+ it(\"sets string trigger value correctly\", () => {\n+ const widget = getWidget({ insideForm: false })\n+ widgetMgr.setStringTriggerValue(widget, \"sample string\", { fromUi: true })\n+ // @ts-expect-error\n+ expect(widgetMgr.getWidgetState(widget)).toBe(undefined)\n+ assertCallbacks({ insideForm: false })\n+ })\n+\n it.each([false, true])(\n \"sets string array value correctly (insideForm=%p)\",\n insideForm => {\ndiff --git a/frontend/src/lib/WidgetStateManager.ts b/frontend/src/lib/WidgetStateManager.ts\nindex 081a7359f90c..568e6a051b4f 100644\n--- a/frontend/src/lib/WidgetStateManager.ts\n+++ b/frontend/src/lib/WidgetStateManager.ts\n@@ -23,6 +23,7 @@ import {\n IFileUploaderState,\n SInt64Array,\n StringArray,\n+ StringTriggerValue,\n WidgetState,\n WidgetStates,\n } from \"src/lib/proto\"\n@@ -36,7 +37,7 @@ export interface Source {\n /** Common widget protobuf fields that are used by the WidgetStateManager. */\n export interface WidgetInfo {\n id: string\n- formId: string\n+ formId?: string\n }\n \n /**\n@@ -241,6 +242,22 @@ export class WidgetStateManager {\n }\n }\n \n+ /**\n+ * Sets the string trigger value for the given widget ID to a string value,\n+ * sends a rerunScript message to the server, and then immediately unsets the\n+ * string trigger value to None/null.\n+ */\n+ public setStringTriggerValue(\n+ widget: WidgetInfo,\n+ value: string,\n+ source: Source\n+ ): void {\n+ this.createWidgetState(widget, source).stringTriggerValue =\n+ new StringTriggerValue({ data: value })\n+ this.onWidgetValueChanged(widget.formId, source)\n+ this.deleteWidgetState(widget.id)\n+ }\n+\n /**\n * Sets the trigger value for the given widget ID to true, sends a rerunScript message\n * to the server, and then immediately unsets the trigger value.\n@@ -527,7 +544,7 @@ export class WidgetStateManager {\n private createWidgetState(widget: WidgetInfo, source: Source): WidgetState {\n const addToForm = isValidFormId(widget.formId) && source.fromUi\n const widgetStateDict = addToForm\n- ? this.getOrCreateFormState(widget.formId).widgetStates\n+ ? this.getOrCreateFormState(widget.formId as string).widgetStates\n : this.widgetStates\n \n return widgetStateDict.createState(widget.id)\ndiff --git a/frontend/src/lib/components/core/Block/Block.tsx b/frontend/src/lib/components/core/Block/Block.tsx\nindex 513ebea86ad6..feadb8ec9514 100644\n--- a/frontend/src/lib/components/core/Block/Block.tsx\n+++ b/frontend/src/lib/components/core/Block/Block.tsx\n@@ -22,6 +22,7 @@ import { BlockNode, AppNode, ElementNode } from \"src/lib/AppNode\"\n import { getElementWidgetID } from \"src/lib/util/utils\"\n import withExpandable from \"src/lib/hocs/withExpandable\"\n import { Form } from \"src/lib/components/widgets/Form\"\n+import ChatMessage from \"src/lib/components/elements/ChatMessage\"\n import Tabs, { TabProps } from \"src/lib/components/elements/Tabs\"\n \n import {\n@@ -53,8 +54,13 @@ interface BlockPropsWithWidth extends BaseBlockProps {\n const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => {\n const { node } = props\n \n- // Allow columns to create the specified space regardless of empty state\n- if (node.isEmpty && !node.deltaBlock.column) {\n+ // Allow columns and chat messages to create the specified space regardless of empty state\n+ // TODO: Maybe we can simplify this to: node.isEmpty && !node.deltaBlock.allowEmpty?\n+ if (\n+ node.isEmpty &&\n+ !node.deltaBlock.column &&\n+ !node.deltaBlock.chatMessage\n+ ) {\n return <></>\n }\n \n@@ -101,6 +107,16 @@ const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => {\n )\n }\n \n+ if (node.deltaBlock.chatMessage) {\n+ return (\n+ <ChatMessage\n+ element={node.deltaBlock.chatMessage as BlockProto.ChatMessage}\n+ >\n+ {child}\n+ </ChatMessage>\n+ )\n+ }\n+\n if (node.deltaBlock.column) {\n return (\n <StyledColumn\ndiff --git a/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx b/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx\nindex 11399baa467f..4dceeac1d09f 100644\n--- a/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx\n+++ b/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx\n@@ -22,6 +22,7 @@ import {\n Button as ButtonProto,\n DownloadButton as DownloadButtonProto,\n CameraInput as CameraInputProto,\n+ ChatInput as ChatInputProto,\n Checkbox as CheckboxProto,\n Code as CodeProto,\n ColorPicker as ColorPickerProto,\n@@ -106,6 +107,7 @@ const ArrowVegaLiteChart = React.lazy(\n const BokehChart = React.lazy(\n () => import(\"src/lib/components/elements/BokehChart\")\n )\n+\n const DebouncedBokehChart = debounceRender(BokehChart, 100)\n \n const DataFrame = React.lazy(\n@@ -137,6 +139,9 @@ const DownloadButton = React.lazy(\n const CameraInput = React.lazy(\n () => import(\"src/lib/components/widgets/CameraInput\")\n )\n+const ChatInput = React.lazy(\n+ () => import(\"src/lib/components/widgets/ChatInput\")\n+)\n const Checkbox = React.lazy(\n () => import(\"src/lib/components/widgets/Checkbox\")\n )\n@@ -481,6 +486,19 @@ const RawElementNodeRenderer = (\n )\n }\n \n+ case \"chatInput\": {\n+ const chatInputProto = node.element.chatInput as ChatInputProto\n+ widgetProps.disabled = widgetProps.disabled || chatInputProto.disabled\n+ return (\n+ <ChatInput\n+ key={chatInputProto.id}\n+ element={chatInputProto}\n+ width={width}\n+ {...widgetProps}\n+ />\n+ )\n+ }\n+\n case \"checkbox\": {\n const checkboxProto = node.element.checkbox as CheckboxProto\n widgetProps.disabled = widgetProps.disabled || checkboxProto.disabled\ndiff --git a/frontend/src/lib/components/core/Block/styled-components.ts b/frontend/src/lib/components/core/Block/styled-components.ts\nindex 434c10acc512..9a17857fadfe 100644\n--- a/frontend/src/lib/components/core/Block/styled-components.ts\n+++ b/frontend/src/lib/components/core/Block/styled-components.ts\n@@ -54,6 +54,7 @@ export interface StyledElementContainerProps {\n elementType: string\n }\n \n+const GLOBAL_ELEMENTS = [\"balloons\", \"snow\", \"chatInput\"]\n export const StyledElementContainer = styled.div<StyledElementContainerProps>(\n ({ theme, isStale, width, elementType }) => ({\n width,\n@@ -68,7 +69,9 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>(\n overflow: \"visible\",\n },\n \n- ...(isStale\n+ // We do not want the chat input to be faded out.\n+ // TODO: Reconsider this when we implement fixed-sized chat containers\n+ ...(isStale && elementType !== \"chatInput\"\n ? {\n opacity: 0.33,\n transition: \"opacity 1s ease-in 0.5s\",\n@@ -80,10 +83,12 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>(\n display: \"none\",\n }\n : {}),\n- ...(elementType === \"balloons\"\n+ ...(GLOBAL_ELEMENTS.includes(elementType)\n ? {\n- // Apply negative bottom margin to remove the flexbox gap.\n- // display: none does not work for balloons, since it needs to be visible.\n+ // Global elements are rendered in their delta position, but they\n+ // are not part of the flexbox layout. We apply a negative margin\n+ // to remove the flexbox gap. display: none does not work for these,\n+ // since they needs to be visible.\n marginBottom: `-${theme.spacing.lg}`,\n }\n : {}),\ndiff --git a/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx\nnew file mode 100644\nindex 000000000000..0496e51546b9\n--- /dev/null\n+++ b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx\n@@ -0,0 +1,127 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React from \"react\"\n+import \"@testing-library/jest-dom\"\n+\n+import { render } from \"src/lib/test_util\"\n+import { Block as BlockProto } from \"src/lib/proto\"\n+\n+import ChatMessage, { ChatMessageProps } from \"./ChatMessage\"\n+\n+const getProps = (\n+ elementProps: Partial<BlockProto.ChatMessage> = {}\n+): ChatMessageProps => ({\n+ element: BlockProto.ChatMessage.create({\n+ participant: \"user\",\n+ avatarType: BlockProto.ChatMessage.AvatarType.ICON,\n+ avatar: \"user\",\n+ ...elementProps,\n+ }),\n+})\n+\n+describe(\"ChatMessage\", () => {\n+ it(\"renders without crashing\", () => {\n+ const props = getProps()\n+ const rtlResults = render(<ChatMessage {...props} />)\n+ expect(rtlResults).toBeDefined()\n+ })\n+\n+ it(\"renders message children content\", () => {\n+ const props = getProps()\n+ const { getByLabelText } = render(\n+ <ChatMessage {...props}>Hello, world!</ChatMessage>\n+ )\n+ expect(getByLabelText(\"Chat message from user\").textContent).toBe(\n+ \"Hello, world!\"\n+ )\n+ })\n+\n+ it(\"renders with an emoji avatar\", () => {\n+ const props = getProps({\n+ avatar: \"๐Ÿ˜ƒ\",\n+ avatarType: BlockProto.ChatMessage.AvatarType.EMOJI,\n+ })\n+ const rtlResults = render(<ChatMessage {...props} />)\n+ expect(rtlResults.getByText(\"๐Ÿ˜ƒ\")).toBeTruthy()\n+ })\n+\n+ it(\"renders with an image avatar\", () => {\n+ const props = getProps({\n+ avatar: \"http://example.com/avatar.jpg\",\n+ avatarType: BlockProto.ChatMessage.AvatarType.IMAGE,\n+ })\n+ const { container } = render(<ChatMessage {...props} />)\n+ const images = container.getElementsByTagName(\"img\")\n+ expect(images.length).toEqual(1)\n+ expect(images[0].src).toBe(\"http://example.com/avatar.jpg\")\n+ })\n+\n+ it(\"renders with a participant label character as fallback\", () => {\n+ const props = getProps({\n+ avatar: undefined,\n+ avatarType: undefined,\n+ participant: \"test\",\n+ })\n+ const { getByText } = render(<ChatMessage {...props} />)\n+ expect(getByText(\"T\")).toBeTruthy()\n+ })\n+\n+ it(\"renders with a 'user' icon avatar\", () => {\n+ const props = getProps({\n+ avatar: \"user\",\n+ avatarType: BlockProto.ChatMessage.AvatarType.ICON,\n+ participant: \"foo\",\n+ })\n+ const { container } = render(<ChatMessage {...props} />)\n+\n+ const svgs = container.getElementsByTagName(\"svg\")\n+ expect(svgs.length).toEqual(1)\n+ })\n+\n+ it(\"renders with a 'assistant' icon avatar\", () => {\n+ const props = getProps({\n+ avatar: \"assistant\",\n+ avatarType: BlockProto.ChatMessage.AvatarType.ICON,\n+ participant: \"foo\",\n+ })\n+ const { container } = render(<ChatMessage {...props} />)\n+\n+ const svgs = container.getElementsByTagName(\"svg\")\n+ expect(svgs.length).toEqual(1)\n+ })\n+\n+ it(\"renders with a grey background when participant is 'user'\", () => {\n+ const props = getProps({\n+ participant: \"user\",\n+ })\n+ const { container } = render(<ChatMessage {...props} />)\n+ const messageContainer = container.firstChild\n+ expect(messageContainer).toHaveStyle(\n+ \"background-color: rgba(240, 242, 246, 0.5)\"\n+ )\n+ })\n+\n+ it(\"sets an aria label on the chat message\", () => {\n+ const props = getProps()\n+ const { getByTestId } = render(<ChatMessage {...props} />)\n+\n+ const chatMessageContent = getByTestId(\"stChatMessageContent\")\n+ expect(chatMessageContent.getAttribute(\"aria-label\")).toEqual(\n+ \"Chat message from user\"\n+ )\n+ })\n+})\ndiff --git a/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx\nnew file mode 100644\nindex 000000000000..2bbf1b1c018d\n--- /dev/null\n+++ b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx\n@@ -0,0 +1,105 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React, { ReactElement } from \"react\"\n+import { useTheme } from \"@emotion/react\"\n+import { Face, SmartToy } from \"@emotion-icons/material-outlined\"\n+\n+import { Block as BlockProto } from \"src/lib/proto\"\n+import Icon from \"src/lib/components/shared/Icon\"\n+import { EmotionTheme } from \"src/lib/theme\"\n+\n+import {\n+ StyledChatMessageContainer,\n+ StyledMessageContent,\n+ StyledAvatarImage,\n+ StyledAvatarIcon,\n+ StyledAvatarBackground,\n+} from \"./styled-components\"\n+\n+interface ChatMessageAvatarProps {\n+ participant: string\n+ avatar?: string\n+ avatarType?: BlockProto.ChatMessage.AvatarType\n+}\n+\n+function ChatMessageAvatar(props: ChatMessageAvatarProps): ReactElement {\n+ const { avatar, avatarType, participant } = props\n+ const theme: EmotionTheme = useTheme()\n+\n+ if (avatar) {\n+ switch (avatarType) {\n+ case BlockProto.ChatMessage.AvatarType.IMAGE:\n+ return <StyledAvatarImage src={avatar} alt={`${participant} avatar`} />\n+ case BlockProto.ChatMessage.AvatarType.EMOJI:\n+ return <StyledAvatarBackground>{avatar}</StyledAvatarBackground>\n+ case BlockProto.ChatMessage.AvatarType.ICON:\n+ if (avatar === \"user\") {\n+ return (\n+ <StyledAvatarIcon background={theme.colors.red60}>\n+ <Icon content={Face} size=\"lg\" />\n+ </StyledAvatarIcon>\n+ )\n+ } else if (avatar === \"assistant\") {\n+ return (\n+ <StyledAvatarIcon background={theme.colors.orange60}>\n+ <Icon content={SmartToy} size=\"lg\" />\n+ </StyledAvatarIcon>\n+ )\n+ }\n+ }\n+ }\n+\n+ // Fallback to first character of the participant label if nothing else can be matched:\n+ return (\n+ <StyledAvatarBackground>\n+ {participant ? participant.charAt(0).toUpperCase() : \"๐Ÿง‘โ€๐Ÿ’ป\"}\n+ </StyledAvatarBackground>\n+ )\n+}\n+\n+export interface ChatMessageProps {\n+ element: BlockProto.ChatMessage\n+}\n+\n+const ChatMessage: React.FC<ChatMessageProps> = ({\n+ element,\n+ children,\n+}): ReactElement => {\n+ const { avatar, avatarType, participant } = element\n+\n+ return (\n+ <StyledChatMessageContainer\n+ className=\"stChatMessage\"\n+ background={participant.toLowerCase() === \"user\"}\n+ >\n+ <ChatMessageAvatar\n+ participant={participant}\n+ avatar={avatar}\n+ avatarType={avatarType}\n+ data-testid=\"stChatMessageAvatar\"\n+ />\n+ <StyledMessageContent\n+ data-testid=\"stChatMessageContent\"\n+ aria-label={`Chat message from ${participant}`}\n+ >\n+ {children}\n+ </StyledMessageContent>\n+ </StyledChatMessageContainer>\n+ )\n+}\n+\n+export default ChatMessage\ndiff --git a/frontend/src/lib/components/elements/ChatMessage/index.tsx b/frontend/src/lib/components/elements/ChatMessage/index.tsx\nnew file mode 100644\nindex 000000000000..810c6f9ca039\n--- /dev/null\n+++ b/frontend/src/lib/components/elements/ChatMessage/index.tsx\n@@ -0,0 +1,17 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+export { default } from \"./ChatMessage\"\ndiff --git a/frontend/src/lib/components/elements/ChatMessage/styled-components.ts b/frontend/src/lib/components/elements/ChatMessage/styled-components.ts\nnew file mode 100644\nindex 000000000000..77daf65b4a3b\n--- /dev/null\n+++ b/frontend/src/lib/components/elements/ChatMessage/styled-components.ts\n@@ -0,0 +1,99 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import styled from \"@emotion/styled\"\n+import { transparentize } from \"color2k\"\n+\n+import { hasLightBackgroundColor } from \"src/lib/theme\"\n+\n+export interface StyledChatMessageContainerProps {\n+ background: boolean\n+}\n+\n+export const StyledChatMessageContainer =\n+ styled.div<StyledChatMessageContainerProps>(({ theme, background }) => {\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ return {\n+ display: \"flex\",\n+ alignItems: \"flex-start\",\n+ gap: theme.spacing.sm,\n+ padding: theme.spacing.lg,\n+ paddingRight: background ? theme.spacing.lg : 0,\n+ borderRadius: theme.radii.lg,\n+ ...(background\n+ ? {\n+ backgroundColor: lightTheme\n+ ? transparentize(theme.colors.gray20, 0.5)\n+ : transparentize(theme.colors.gray90, 0.5),\n+ }\n+ : {}),\n+ }\n+ })\n+\n+export const StyledMessageContent = styled.div(({ theme }) => ({\n+ color: theme.colors.bodyText,\n+ margin: \"auto\",\n+ flexGrow: 1,\n+}))\n+\n+export const StyledAvatarBackground = styled.div(({ theme }) => {\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ return {\n+ display: \"flex\",\n+ border: `1px solid ${\n+ lightTheme ? theme.colors.gray40 : theme.colors.gray85\n+ }`,\n+ backgroundColor: lightTheme ? theme.colors.white : theme.colors.gray100,\n+ color: lightTheme ? theme.colors.gray90 : theme.colors.white,\n+ lineHeight: \"1\",\n+ fontSize: theme.fontSizes.md,\n+ width: \"2rem\",\n+ height: \"2rem\",\n+ borderRadius: theme.radii.lg,\n+ alignItems: \"center\",\n+ justifyContent: \"center\",\n+ }\n+})\n+\n+export interface StyledAvatarIconProps {\n+ background: string\n+}\n+\n+export const StyledAvatarIcon = styled.div<StyledAvatarIconProps>(\n+ ({ theme, background }) => {\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ return {\n+ display: \"flex\",\n+ width: \"2rem\",\n+ height: \"2rem\",\n+ borderRadius: theme.radii.lg,\n+ alignItems: \"center\",\n+ justifyContent: \"center\",\n+ backgroundColor: background,\n+ color: lightTheme ? theme.colors.white : theme.colors.gray100,\n+ }\n+ }\n+)\n+\n+export const StyledAvatarImage = styled.img(({ theme }) => {\n+ return {\n+ width: \"2rem\",\n+ height: \"2rem\",\n+ borderRadius: theme.radii.lg,\n+ objectFit: \"cover\",\n+ display: \"flex\",\n+ }\n+})\ndiff --git a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx\nindex d8d7be2715d5..a12076809cbe 100644\n--- a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx\n+++ b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx\n@@ -15,7 +15,7 @@\n */\n \n import React from \"react\"\n-import { shallow } from \"src/lib/test_util\"\n+import { render } from \"src/lib/test_util\"\n \n import InputInstructions, { Props } from \"./InputInstructions\"\n \n@@ -27,24 +27,31 @@ const getProps = (props: Partial<Props> = {}): Props => ({\n \n describe(\"InputInstructions\", () => {\n const props = getProps()\n- const wrapper = shallow(<InputInstructions {...props} />)\n \n it(\"renders without crashing\", () => {\n- expect(wrapper.text()).toBeDefined()\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+\n+ expect(getByTestId(\"InputInstructions\").textContent).toBeDefined()\n })\n \n it(\"should show Enter instructions\", () => {\n- expect(wrapper.text()).toBe(\"Press Enter to apply\")\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\n+ \"Press Enter to apply\"\n+ )\n })\n \n describe(\"Multiline type\", () => {\n const props = getProps({\n type: \"multiline\",\n })\n- const wrapper = shallow(<InputInstructions {...props} />)\n \n it(\"should show Ctrl+Enter instructions\", () => {\n- expect(wrapper.text()).toBe(\"Press Ctrl+Enter to apply\")\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\n+ \"Press Ctrl+Enter to apply\"\n+ )\n })\n \n it(\"show โŒ˜+Enter instructions\", () => {\n@@ -56,9 +63,11 @@ describe(\"InputInstructions\", () => {\n const props = getProps({\n type: \"multiline\",\n })\n- const wrapper = shallow(<InputInstructions {...props} />)\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n \n- expect(wrapper.text()).toBe(\"Press โŒ˜+Enter to apply\")\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\n+ \"Press โŒ˜+Enter to apply\"\n+ )\n })\n \n it(\"should show instructions for max length\", () => {\n@@ -66,9 +75,11 @@ describe(\"InputInstructions\", () => {\n type: \"multiline\",\n maxLength: 3,\n })\n- const wrapper = shallow(<InputInstructions {...props} />)\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n \n- expect(wrapper.text()).toBe(\"Press โŒ˜+Enter to apply3/3\")\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\n+ \"Press โŒ˜+Enter to apply3/3\"\n+ )\n })\n })\n \n@@ -76,8 +87,31 @@ describe(\"InputInstructions\", () => {\n const props = getProps({\n maxLength: 3,\n })\n- const wrapper = shallow(<InputInstructions {...props} />)\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\n+ \"Press Enter to apply3/3\"\n+ )\n+ })\n+\n+ describe(\"Chat type\", () => {\n+ const props = getProps({\n+ type: \"chat\",\n+ })\n \n- expect(wrapper.text()).toBe(\"Press Enter to apply3/3\")\n+ it(\"should not show instructions\", () => {\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\"\")\n+ })\n+\n+ it(\"should show instructions for max length\", () => {\n+ const props = getProps({\n+ type: \"chat\",\n+ maxLength: 3,\n+ })\n+ const { getByTestId } = render(<InputInstructions {...props} />)\n+\n+ expect(getByTestId(\"InputInstructions\").textContent).toBe(\"3/3\")\n+ })\n })\n })\ndiff --git a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx\nindex 752b95721e9b..c7ec2478d189 100644\n--- a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx\n+++ b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx\n@@ -24,7 +24,7 @@ export interface Props {\n value: string\n maxLength?: number\n className?: string\n- type?: \"multiline\" | \"single\"\n+ type?: \"multiline\" | \"single\" | \"chat\"\n }\n \n const InputInstructions = ({\n@@ -54,12 +54,12 @@ const InputInstructions = ({\n } else {\n addMessage(\"Press Ctrl+Enter to apply\")\n }\n- } else {\n+ } else if (type === \"single\") {\n addMessage(\"Press Enter to apply\")\n }\n }\n \n- if (maxLength) {\n+ if (maxLength && (type !== \"chat\" || dirty)) {\n addMessage(\n `${value.length}/${maxLength}`,\n dirty && value.length >= maxLength\n@@ -67,7 +67,10 @@ const InputInstructions = ({\n }\n \n return (\n- <StyledWidgetInstructions className={className}>\n+ <StyledWidgetInstructions\n+ data-testid=\"InputInstructions\"\n+ className={className}\n+ >\n {messages}\n </StyledWidgetInstructions>\n )\ndiff --git a/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx b/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx\nnew file mode 100644\nindex 000000000000..aa6ec9ba133f\n--- /dev/null\n+++ b/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx\n@@ -0,0 +1,249 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React from \"react\"\n+import \"@testing-library/jest-dom\"\n+import { fireEvent } from \"@testing-library/react\"\n+import { render } from \"src/lib/test_util\"\n+import { ChatInput as ChatInputProto } from \"src/lib/proto\"\n+import { WidgetStateManager } from \"src/lib/WidgetStateManager\"\n+\n+import ChatInput, { Props } from \"./ChatInput\"\n+\n+const getProps = (elementProps: Partial<ChatInputProto> = {}): Props => ({\n+ element: ChatInputProto.create({\n+ id: \"123\",\n+ placeholder: \"Enter Text Here\",\n+ disabled: false,\n+ default: \"\",\n+ position: ChatInputProto.Position.BOTTOM,\n+ ...elementProps,\n+ }),\n+ width: 0,\n+ disabled: false,\n+ widgetMgr: new WidgetStateManager({\n+ sendRerunBackMsg: jest.fn(),\n+ formsDataChanged: jest.fn(),\n+ }),\n+})\n+\n+describe(\"ChatInput widget\", () => {\n+ afterEach(() => {\n+ jest.restoreAllMocks()\n+ })\n+\n+ it(\"renders without crashing\", () => {\n+ const props = getProps()\n+ const rtlResults = render(<ChatInput {...props} />)\n+ expect(rtlResults).toBeDefined()\n+ })\n+\n+ it(\"shows a placeholder\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0].placeholder).toEqual(props.element.placeholder)\n+ })\n+\n+ it(\"sets the aria label to the placeholder\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0].getAttribute(\"aria-label\")).toEqual(\n+ props.element.placeholder\n+ )\n+ })\n+\n+ it(\"sets the value intially to the element default\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0].value).toEqual(props.element.default)\n+ })\n+\n+ it(\"sets the value when values are typed in\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"Sample text\" } })\n+ expect(textareas[0].value).toEqual(\"Sample text\")\n+ })\n+\n+ it(\"does not increase text value when maxChars is set\", () => {\n+ const props = getProps({ maxChars: 10 })\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ fireEvent.change(textareas[0], { target: { value: \"12345678901\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ })\n+\n+ it(\"sends and resets the value on enter\", () => {\n+ const props = getProps()\n+ const spy = jest.spyOn(props.widgetMgr, \"setStringTriggerValue\")\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ fireEvent.keyDown(textareas[0], { key: \"Enter\" })\n+ expect(spy).toHaveBeenCalledWith(props.element, \"1234567890\", {\n+ fromUi: true,\n+ })\n+ expect(textareas[0].value).toEqual(\"\")\n+ })\n+\n+ it(\"will not send an empty value on enter if empty\", () => {\n+ const props = getProps()\n+ const spy = jest.spyOn(props.widgetMgr, \"setStringTriggerValue\")\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.keyDown(textareas[0], { key: \"Enter\" })\n+ expect(spy).not.toHaveBeenCalledWith(props.element, \"\", {\n+ fromUi: true,\n+ })\n+ expect(textareas[0].value).toEqual(\"\")\n+ })\n+\n+ it(\"will not show instructions when the text has changed\", () => {\n+ const props = getProps()\n+ const { container, getAllByTestId } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ const instructions = getAllByTestId(\"InputInstructions\")\n+ expect(instructions.length).toEqual(1)\n+ expect(instructions[0].textContent).toEqual(\"\")\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(instructions[0].textContent).toEqual(\"\")\n+ })\n+\n+ it(\"does not send/clear on shift + enter\", () => {\n+ const props = getProps()\n+ const spy = jest.spyOn(props.widgetMgr, \"setStringTriggerValue\")\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ fireEvent.keyDown(textareas[0], { key: \"Enter\", shiftKey: true })\n+ // We cannot test the value to be changed cause that is essentially a\n+ // change event.\n+ expect(textareas[0].value).not.toEqual(\"\")\n+ expect(spy).not.toHaveBeenCalled()\n+ })\n+\n+ it(\"does not send/clear on ctrl + enter\", () => {\n+ const props = getProps()\n+ const spy = jest.spyOn(props.widgetMgr, \"setStringTriggerValue\")\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ fireEvent.keyDown(textareas[0], { key: \"Enter\", ctrlKey: true })\n+ // We cannot test the value to be changed cause that is essentially a\n+ // change event.\n+ expect(textareas[0].value).not.toEqual(\"\")\n+ expect(spy).not.toHaveBeenCalled()\n+ })\n+\n+ it(\"does not send/clear on meta + enter\", () => {\n+ const props = getProps()\n+ const spy = jest.spyOn(props.widgetMgr, \"setStringTriggerValue\")\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"1234567890\" } })\n+ expect(textareas[0].value).toEqual(\"1234567890\")\n+ fireEvent.keyDown(textareas[0], { key: \"Enter\", metaKey: true })\n+ // We cannot test the value to be changed cause that is essentially a\n+ // change event.\n+ expect(textareas[0].value).not.toEqual(\"\")\n+ expect(spy).not.toHaveBeenCalled()\n+ })\n+\n+ it(\"does sets the value if specified from protobuf to set it\", () => {\n+ const props = getProps({ value: \"12345\", setValue: true })\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0].value).toEqual(\"12345\")\n+ })\n+\n+ it(\"does not set the value if protobuf does not specify to set it\", () => {\n+ const props = getProps({ value: \"12345\", setValue: false })\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0].value).toEqual(\"\")\n+ })\n+\n+ it(\"disables the textarea and button\", () => {\n+ const props = getProps({ disabled: true })\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0]).toBeDisabled()\n+\n+ const button = container.getElementsByTagName(\"button\")\n+ expect(button.length).toEqual(1)\n+ expect(button[0]).toBeDisabled()\n+ })\n+\n+ it(\"not disable the textarea by default\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ expect(textareas[0]).not.toBeDisabled()\n+\n+ const button = container.getElementsByTagName(\"button\")\n+ expect(button.length).toEqual(1)\n+ expect(button[0]).toBeDisabled()\n+ })\n+\n+ it(\"disables the send button by default since there's no text\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+\n+ const button = container.getElementsByTagName(\"button\")\n+ expect(button.length).toEqual(1)\n+ expect(button[0]).toBeDisabled()\n+ })\n+\n+ it(\"enables the send button when text is set, disables it when removed\", () => {\n+ const props = getProps()\n+ const { container } = render(<ChatInput {...props} />)\n+ const textareas = container.getElementsByTagName(\"textarea\")\n+ expect(textareas.length).toEqual(1)\n+ fireEvent.change(textareas[0], { target: { value: \"Sample text\" } })\n+\n+ const button = container.getElementsByTagName(\"button\")\n+ expect(button.length).toEqual(1)\n+ expect(button[0]).not.toBeDisabled()\n+\n+ fireEvent.change(textareas[0], { target: { value: \"\" } })\n+ expect(button.length).toEqual(1)\n+ expect(button[0]).toBeDisabled()\n+ })\n+})\ndiff --git a/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx b/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx\nnew file mode 100644\nindex 000000000000..6596018b196d\n--- /dev/null\n+++ b/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx\n@@ -0,0 +1,238 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React, {\n+ useEffect,\n+ useRef,\n+ useState,\n+ ChangeEvent,\n+ KeyboardEvent,\n+} from \"react\"\n+import { useTheme } from \"@emotion/react\"\n+import { Send } from \"@emotion-icons/material-rounded\"\n+import { Textarea as UITextArea } from \"baseui/textarea\"\n+\n+import { ChatInput as ChatInputProto } from \"src/lib/proto\"\n+import { WidgetStateManager } from \"src/lib/WidgetStateManager\"\n+import Icon from \"src/lib/components/shared/Icon\"\n+import InputInstructions from \"src/lib/components/shared/InputInstructions/InputInstructions\"\n+import { hasLightBackgroundColor } from \"src/lib/theme\"\n+\n+import {\n+ StyledChatInputContainer,\n+ StyledChatInput,\n+ StyledFloatingChatInputContainer,\n+ StyledInputInstructionsContainer,\n+ StyledSendIconButton,\n+ StyledSendIconButtonContainer,\n+} from \"./styled-components\"\n+\n+export interface Props {\n+ disabled: boolean\n+ element: ChatInputProto\n+ widgetMgr: WidgetStateManager\n+ width: number\n+}\n+\n+// We want to show easily that there's scrolling so we deliberately choose\n+// a half size.\n+const MAX_VISIBLE_NUM_LINES = 6.5\n+// Rounding errors can arbitrarily create scrollbars. We add a rounding offset\n+// to manage it better.\n+const ROUNDING_OFFSET = 1\n+\n+const isEnterKeyPressed = (\n+ event: KeyboardEvent<HTMLTextAreaElement>\n+): boolean => {\n+ // Using keyCode as well due to some different behaviors on Windows\n+ // https://bugs.chromium.org/p/chromium/issues/detail?id=79407\n+\n+ const { keyCode, key } = event\n+ return key === \"Enter\" || keyCode === 13 || keyCode === 10\n+}\n+\n+function ChatInput({ width, element, widgetMgr }: Props): React.ReactElement {\n+ const theme = useTheme()\n+ // True if the user-specified state.value has not yet been synced to the WidgetStateManager.\n+ const [dirty, setDirty] = useState(false)\n+ // The value specified by the user via the UI. If the user didn't touch this widget's UI, the default value is used.\n+ const [value, setValue] = useState(element.default)\n+ // The value of the height of the textarea. It depends on a variety of factors including the default height, and autogrowing\n+ const [scrollHeight, setScrollHeight] = useState(0)\n+ const chatInputRef = useRef<HTMLTextAreaElement>(null)\n+ const heightGuidance = useRef({ minHeight: 0, maxHeight: 0 })\n+\n+ const getScrollHeight = (): number => {\n+ let scrollHeight = 0\n+ const { current: textarea } = chatInputRef\n+ if (textarea) {\n+ const placeholder = textarea.placeholder\n+ textarea.placeholder = \"\"\n+ textarea.style.height = \"auto\"\n+ scrollHeight = textarea.scrollHeight\n+ textarea.placeholder = placeholder\n+ textarea.style.height = \"\"\n+ }\n+\n+ return scrollHeight\n+ }\n+\n+ const handleSubmit = (): void => {\n+ if (!value) {\n+ return\n+ }\n+\n+ widgetMgr.setStringTriggerValue(element, value, { fromUi: true })\n+ setDirty(false)\n+ setValue(\"\")\n+ setScrollHeight(0)\n+ }\n+\n+ const handleKeyDown = (e: KeyboardEvent<HTMLTextAreaElement>): void => {\n+ const { metaKey, ctrlKey, shiftKey } = e\n+ const shouldSubmit =\n+ isEnterKeyPressed(e) && !shiftKey && !ctrlKey && !metaKey\n+\n+ if (shouldSubmit) {\n+ e.preventDefault()\n+\n+ handleSubmit()\n+ }\n+ }\n+\n+ const handleChange = (e: ChangeEvent<HTMLTextAreaElement>): void => {\n+ const { value } = e.target\n+ const { maxChars } = element\n+\n+ if (maxChars !== 0 && value.length > maxChars) {\n+ return\n+ }\n+\n+ setDirty(value !== \"\")\n+ setValue(value)\n+ setScrollHeight(getScrollHeight())\n+ }\n+\n+ useEffect(() => {\n+ if (element.setValue) {\n+ // We are intentionally setting this to avoid regularly calling this effect.\n+ element.setValue = false\n+ const val = element.value || \"\"\n+ setValue(val)\n+ setDirty(val !== \"\")\n+ }\n+ }, [element])\n+\n+ useEffect(() => {\n+ if (chatInputRef.current) {\n+ const { offsetHeight } = chatInputRef.current\n+ heightGuidance.current.minHeight = offsetHeight\n+ heightGuidance.current.maxHeight = offsetHeight * MAX_VISIBLE_NUM_LINES\n+ }\n+ }, [chatInputRef])\n+\n+ const { disabled, placeholder, maxChars, position } = element\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ const { minHeight, maxHeight } = heightGuidance.current\n+ const placeholderColor = lightTheme\n+ ? theme.colors.gray70\n+ : theme.colors.gray80\n+\n+ const isInputExtended =\n+ scrollHeight > 0 && chatInputRef.current\n+ ? Math.abs(scrollHeight - minHeight) > ROUNDING_OFFSET\n+ : false\n+\n+ return (\n+ <StyledFloatingChatInputContainer className=\"stChatFloatingInputContainer\">\n+ <StyledChatInputContainer\n+ className=\"stChatInputContainer\"\n+ width={width}\n+ position={position}\n+ >\n+ <StyledChatInput>\n+ <UITextArea\n+ data-testid=\"stChatInput\"\n+ inputRef={chatInputRef}\n+ value={value}\n+ placeholder={placeholder}\n+ onChange={handleChange}\n+ onKeyDown={handleKeyDown}\n+ aria-label={placeholder}\n+ disabled={disabled}\n+ rows={1}\n+ overrides={{\n+ Root: {\n+ style: {\n+ outline: \"none\",\n+ backgroundColor: theme.colors.transparent,\n+ // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings.\n+ borderLeftWidth: \"1px\",\n+ borderRightWidth: \"1px\",\n+ borderTopWidth: \"1px\",\n+ borderBottomWidth: \"1px\",\n+ width: `${width}px`,\n+ },\n+ },\n+ InputContainer: {\n+ style: {\n+ backgroundColor: theme.colors.transparent,\n+ },\n+ },\n+ Input: {\n+ style: {\n+ lineHeight: \"1.4\",\n+ backgroundColor: theme.colors.transparent,\n+ \"::placeholder\": {\n+ color: placeholderColor,\n+ },\n+ height: isInputExtended\n+ ? `${scrollHeight + ROUNDING_OFFSET}px`\n+ : \"auto\",\n+ maxHeight: maxHeight ? `${maxHeight}px` : \"none\",\n+ // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings.\n+ paddingRight: \"3rem\",\n+ paddingLeft: theme.spacing.sm,\n+ paddingBottom: theme.spacing.sm,\n+ paddingTop: theme.spacing.sm,\n+ },\n+ },\n+ }}\n+ />\n+ <StyledInputInstructionsContainer>\n+ <InputInstructions\n+ dirty={dirty}\n+ value={value}\n+ maxLength={maxChars}\n+ type=\"chat\"\n+ />\n+ </StyledInputInstructionsContainer>\n+ <StyledSendIconButtonContainer>\n+ <StyledSendIconButton\n+ onClick={handleSubmit}\n+ disabled={!dirty || disabled}\n+ extended={isInputExtended}\n+ >\n+ <Icon content={Send} size=\"xl\" color=\"inherit\" />\n+ </StyledSendIconButton>\n+ </StyledSendIconButtonContainer>\n+ </StyledChatInput>\n+ </StyledChatInputContainer>\n+ </StyledFloatingChatInputContainer>\n+ )\n+}\n+\n+export default ChatInput\ndiff --git a/frontend/src/lib/components/widgets/ChatInput/index.tsx b/frontend/src/lib/components/widgets/ChatInput/index.tsx\nnew file mode 100644\nindex 000000000000..65754e81dc92\n--- /dev/null\n+++ b/frontend/src/lib/components/widgets/ChatInput/index.tsx\n@@ -0,0 +1,16 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+export { default } from \"./ChatInput\"\ndiff --git a/frontend/src/lib/components/widgets/ChatInput/styled-components.ts b/frontend/src/lib/components/widgets/ChatInput/styled-components.ts\nnew file mode 100644\nindex 000000000000..65e15fa99d44\n--- /dev/null\n+++ b/frontend/src/lib/components/widgets/ChatInput/styled-components.ts\n@@ -0,0 +1,129 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+import styled from \"@emotion/styled\"\n+import { ChatInput as ChatInputProto } from \"src/lib/proto\"\n+import { hasLightBackgroundColor } from \"src/lib/theme\"\n+\n+export interface StyledChatInputContainerProps {\n+ width: number\n+ position: ChatInputProto.Position\n+}\n+\n+export const StyledChatInputContainer =\n+ styled.div<StyledChatInputContainerProps>(({ theme, width, position }) => {\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ return {\n+ borderRadius: theme.radii.lg,\n+ display: \"flex\",\n+ ...(position === ChatInputProto.Position.BOTTOM && {\n+ backgroundColor: lightTheme\n+ ? theme.colors.gray20\n+ : theme.colors.gray90,\n+ }),\n+ width: `${width}px`,\n+ }\n+ })\n+\n+export const StyledChatInput = styled.div(({ theme }) => {\n+ return {\n+ backgroundColor: theme.colors.transparent,\n+ position: \"relative\",\n+ flexGrow: 1,\n+ borderRadius: theme.radii.lg,\n+ display: \"flex\",\n+ alignItems: \"center\",\n+ }\n+})\n+\n+interface StyledSendIconButtonProps {\n+ disabled: boolean\n+ extended: boolean\n+}\n+\n+export const StyledSendIconButton = styled.button<StyledSendIconButtonProps>(\n+ ({ theme, disabled, extended }) => {\n+ const lightTheme = hasLightBackgroundColor(theme)\n+ const [cleanIconColor, dirtyIconColor] = lightTheme\n+ ? [theme.colors.gray60, theme.colors.gray80]\n+ : [theme.colors.gray80, theme.colors.gray40]\n+ return {\n+ border: \"none\",\n+ backgroundColor: theme.colors.transparent,\n+ borderTopRightRadius: extended ? theme.radii.none : theme.radii.lg,\n+ borderTopLeftRadius: extended ? theme.radii.lg : theme.radii.none,\n+ borderBottomRightRadius: theme.radii.lg,\n+ display: \"inline-flex\",\n+ alignItems: \"center\",\n+ justifyContent: \"center\",\n+ lineHeight: 1,\n+ margin: 0,\n+ padding: theme.spacing.sm,\n+ color: disabled ? cleanIconColor : dirtyIconColor,\n+ pointerEvents: \"auto\",\n+ \"&:focus\": {\n+ outline: \"none\",\n+ },\n+ \":focus\": {\n+ outline: \"none\",\n+ },\n+ \"&:focus-visible\": {\n+ backgroundColor: lightTheme\n+ ? theme.colors.gray10\n+ : theme.colors.gray90,\n+ },\n+ \"&:hover\": {\n+ backgroundColor: theme.colors.primary,\n+ color: theme.colors.white,\n+ },\n+ \"&:disabled, &:disabled:hover, &:disabled:active\": {\n+ backgroundColor: theme.colors.transparent,\n+ borderColor: theme.colors.transparent,\n+ color: theme.colors.gray,\n+ },\n+ }\n+ }\n+)\n+\n+export const StyledFloatingChatInputContainer = styled.div(({ theme }) => ({\n+ position: \"fixed\",\n+ bottom: \"0px\",\n+ paddingBottom: \"70px\",\n+ paddingTop: theme.spacing.lg,\n+ backgroundColor: theme.colors.bgColor,\n+ zIndex: theme.zIndices.chatInput,\n+ [`@media (max-width: ${theme.breakpoints.md})`]: {\n+ display: \"flex\",\n+ flexDirection: \"column\",\n+ alignItems: \"center\",\n+ left: 0,\n+ width: \"100vw\",\n+ },\n+}))\n+\n+export const StyledSendIconButtonContainer = styled.div(() => ({\n+ display: \"flex\",\n+ alignItems: \"flex-end\",\n+ height: \"100%\",\n+ position: \"absolute\",\n+ right: \"0px\",\n+ pointerEvents: \"none\",\n+}))\n+\n+export const StyledInputInstructionsContainer = styled.div({\n+ position: \"absolute\",\n+ bottom: \"0px\",\n+ right: \"3rem\",\n+})\ndiff --git a/frontend/src/lib/hooks/useScrollAnimation.test.ts b/frontend/src/lib/hooks/useScrollAnimation.test.ts\nnew file mode 100644\nindex 000000000000..5fbb8d4c6f02\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollAnimation.test.ts\n@@ -0,0 +1,126 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { renderHook, act } from \"@testing-library/react-hooks\"\n+import useScrollAnimation from \"./useScrollAnimation\"\n+\n+describe(\"useScrollAnimation\", () => {\n+ let targetElement: HTMLElement\n+ let onEndMock: () => void\n+ let scrollHeight: number\n+ let offsetHeight: number\n+\n+ beforeEach(() => {\n+ targetElement = document.createElement(\"div\")\n+ targetElement.scrollTop = 0\n+ scrollHeight = 200\n+ offsetHeight = 100\n+ Object.defineProperty(targetElement, \"scrollHeight\", {\n+ set: value => (scrollHeight = value),\n+ get: () => scrollHeight,\n+ })\n+ Object.defineProperty(targetElement, \"offsetHeight\", {\n+ set: value => (offsetHeight = value),\n+ get: () => offsetHeight,\n+ })\n+ targetElement.addEventListener = jest.fn()\n+ targetElement.removeEventListener = jest.fn()\n+ onEndMock = jest.fn()\n+ })\n+\n+ afterEach(() => {\n+ jest.clearAllMocks()\n+ })\n+\n+ it(\"should animate scroll\", () => {\n+ jest.useFakeTimers()\n+\n+ renderHook(() => useScrollAnimation(targetElement, onEndMock, true))\n+\n+ // Simulate scroll animation\n+ act(() => {\n+ jest.advanceTimersByTime(5)\n+ // Trigger the callback of requestAnimationFrame\n+ act(() => {\n+ jest.runOnlyPendingTimers()\n+ })\n+ })\n+\n+ // Assert the updated scrollTop value\n+ expect(targetElement.scrollTop).toBeGreaterThan(0)\n+\n+ // Simulate reaching the end of animation\n+ act(() => {\n+ jest.advanceTimersByTime(100)\n+ // Trigger the callback of requestAnimationFrame\n+ act(() => {\n+ jest.runOnlyPendingTimers()\n+ })\n+ })\n+\n+ // Assert that onEnd callback is called\n+ expect(onEndMock).toHaveBeenCalled()\n+\n+ jest.useRealTimers()\n+ })\n+\n+ it(\"should register and deregister the correct events\", () => {\n+ jest.useFakeTimers()\n+\n+ const { unmount } = renderHook(() =>\n+ useScrollAnimation(targetElement, onEndMock, true)\n+ )\n+\n+ expect(targetElement.addEventListener).toHaveBeenCalledTimes(2)\n+ expect(targetElement.addEventListener).toHaveBeenCalledWith(\n+ \"pointerdown\",\n+ expect.any(Function),\n+ { passive: true }\n+ )\n+ expect(targetElement.addEventListener).toHaveBeenCalledWith(\n+ \"wheel\",\n+ expect.any(Function),\n+ { passive: true }\n+ )\n+\n+ unmount()\n+\n+ // Cleanup\n+ expect(targetElement.removeEventListener).toHaveBeenCalledTimes(2)\n+ expect(targetElement.removeEventListener).toHaveBeenCalledWith(\n+ \"pointerdown\",\n+ expect.any(Function)\n+ )\n+ expect(targetElement.removeEventListener).toHaveBeenCalledWith(\n+ \"wheel\",\n+ expect.any(Function)\n+ )\n+\n+ jest.useRealTimers()\n+ })\n+\n+ it(\"should not animate scroll if target element is null\", () => {\n+ renderHook(() => useScrollAnimation(null, onEndMock, true))\n+\n+ expect(targetElement.addEventListener).not.toHaveBeenCalled()\n+ })\n+\n+ it(\"should not animate scroll if isAnimating is false\", () => {\n+ renderHook(() => useScrollAnimation(targetElement, onEndMock, false))\n+\n+ expect(targetElement.addEventListener).not.toHaveBeenCalled()\n+ })\n+})\ndiff --git a/frontend/src/lib/hooks/useScrollAnimation.ts b/frontend/src/lib/hooks/useScrollAnimation.ts\nnew file mode 100644\nindex 000000000000..05d911934e63\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollAnimation.ts\n@@ -0,0 +1,146 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { useCallback, useLayoutEffect, useRef } from \"react\"\n+\n+/**\n+ * Computes next step in a square-root based animation sequence. Step size is\n+ * square root of absolute difference between current and target values. Can be\n+ * used as a stepper function for other higher-level stepping functions.\n+ *\n+ * @param {number} current - Current value in animation sequence.\n+ * @param {number} to - Target value of animation sequence.\n+ * @returns {number} Next value in animation sequence.\n+ */\n+function squareStepper(current: number, to: number): number {\n+ const sign = Math.sign(to - current)\n+ const step = Math.sqrt(Math.abs(to - current))\n+ const next = current + step * sign\n+\n+ if (sign > 0) {\n+ return Math.min(to, next)\n+ }\n+\n+ return Math.max(to, next)\n+}\n+\n+/**\n+ * Computes sequence of steps in animation by repeatedly applying a stepper\n+ * function.\n+ *\n+ * @param {number} from - Initial value in animation sequence.\n+ * @param {number} to - Target value of animation sequence.\n+ * @param {function} stepper - Function computing next value given current\n+ * and target values.\n+ * @param {number} index - Number of steps to compute.\n+ * @returns {number} Value at given index in animation sequence.\n+ */\n+function step(\n+ from: number,\n+ to: number,\n+ stepper: (x: number, y: number) => number,\n+ index: number\n+): number {\n+ let next = from\n+\n+ for (let i = 0; i < index; i++) {\n+ next = stepper(next, to)\n+ }\n+\n+ return next\n+}\n+\n+/**\n+ * Handles scroll animation for a given target HTMLElement. Uses a square-root\n+ * based stepping function to compute scroll animation. Stops animation if\n+ * target's scrollTop has reached scrollHeight or if user interacts with target\n+ * (mousedown or mousewheel). Can also be cancelled by caller.\n+ *\n+ * @export\n+ * @param {HTMLElement | null} target - HTML element to animate scroll of. If\n+ * null, no animation is performed.\n+ * @param {() => void} onEnd - Callback when animation ends or is cancelled.\n+ * @param {boolean} isAnimating - Boolean to start or stop animation. If false,\n+ * no animation is performed.\n+ * @returns {void}\n+ */\n+export default function useScrollAnimation(\n+ target: HTMLElement | null,\n+ onEnd: () => void,\n+ isAnimating: boolean\n+): void {\n+ const animator = useRef(0)\n+\n+ const animate = useCallback(\n+ (from, index, start = Date.now()) => {\n+ cancelAnimationFrame(animator.current)\n+\n+ animator.current = requestAnimationFrame(() => {\n+ if (target) {\n+ const toNumber = target.scrollHeight - target.offsetHeight\n+ let nextValue = step(\n+ from,\n+ toNumber,\n+ squareStepper,\n+ (Date.now() - start) / 5\n+ )\n+\n+ if (Math.abs(toNumber - nextValue) < 1.5) {\n+ nextValue = toNumber\n+ }\n+\n+ target.scrollTop = nextValue\n+\n+ if (toNumber === nextValue) {\n+ onEnd()\n+ } else {\n+ animate(from, index + 1, start)\n+ }\n+ }\n+ })\n+ },\n+ [animator, onEnd, target]\n+ )\n+\n+ const handleCancelAnimation = useCallback(() => {\n+ cancelAnimationFrame(animator.current)\n+ onEnd()\n+ }, [onEnd])\n+\n+ useLayoutEffect(() => {\n+ if (!target || !isAnimating) {\n+ return\n+ }\n+ animate(target.scrollTop, 1)\n+\n+ if (target) {\n+ target.addEventListener(\"pointerdown\", handleCancelAnimation, {\n+ passive: true,\n+ })\n+ target.addEventListener(\"wheel\", handleCancelAnimation, {\n+ passive: true,\n+ })\n+\n+ return () => {\n+ target.removeEventListener(\"pointerdown\", handleCancelAnimation)\n+ target.removeEventListener(\"wheel\", handleCancelAnimation)\n+ cancelAnimationFrame(animator.current)\n+ }\n+ }\n+\n+ return () => cancelAnimationFrame(animator.current)\n+ }, [animate, animator, handleCancelAnimation, target, isAnimating])\n+}\ndiff --git a/frontend/src/lib/hooks/useScrollSpy.test.ts b/frontend/src/lib/hooks/useScrollSpy.test.ts\nnew file mode 100644\nindex 000000000000..1beaa2cb9613\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollSpy.test.ts\n@@ -0,0 +1,137 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { renderHook, act } from \"@testing-library/react-hooks\"\n+import useScrollSpy, { debounce } from \"./useScrollSpy\"\n+\n+describe(\"debounce function\", () => {\n+ beforeEach(() => {\n+ jest.useFakeTimers()\n+ })\n+ it(\"should call the function immediately when no delay is provided\", () => {\n+ const fn = jest.fn()\n+ const debouncedFn = debounce(fn, 0)\n+\n+ debouncedFn(\"arg1\", \"arg2\")\n+\n+ expect(fn).toHaveBeenCalledTimes(1)\n+ expect(fn).toHaveBeenCalledWith(\"arg1\", \"arg2\")\n+ })\n+\n+ it(\"should delay the function call when a delay is provided\", () => {\n+ const fn = jest.fn()\n+ const debouncedFn = debounce(fn, 100)\n+ debouncedFn(\"arg1\", \"arg2\")\n+ expect(fn).toHaveBeenCalledTimes(1)\n+ expect(fn).toHaveBeenCalledWith(\"arg1\", \"arg2\")\n+\n+ debouncedFn(\"arg3\", \"arg4\")\n+ expect(fn).not.toHaveBeenCalledWith(\"arg3\", \"arg4\")\n+ jest.advanceTimersByTime(99)\n+ expect(fn).not.toHaveBeenCalledWith(\"arg3\", \"arg4\")\n+\n+ jest.advanceTimersByTime(1)\n+ expect(fn).toHaveBeenCalledTimes(2)\n+ expect(fn).toHaveBeenCalledWith(\"arg3\", \"arg4\")\n+ })\n+\n+ it(\"should cancel the delay when the function is called again\", () => {\n+ const fn = jest.fn()\n+ const debouncedFn = debounce(fn, 100)\n+ debouncedFn(\"arg1\", \"arg2\")\n+ expect(fn).toHaveBeenCalledTimes(1)\n+ expect(fn).toHaveBeenCalledWith(\"arg1\", \"arg2\")\n+\n+ debouncedFn(\"arg3\", \"arg4\")\n+ jest.advanceTimersByTime(99)\n+\n+ debouncedFn(\"arg5\", \"arg6\")\n+ expect(fn).not.toHaveBeenCalledWith(\"arg5\", \"arg6\")\n+\n+ jest.advanceTimersByTime(1)\n+ expect(fn).toHaveBeenCalledTimes(2)\n+ expect(fn).toHaveBeenCalledWith(\"arg5\", \"arg6\")\n+ })\n+})\n+\n+describe(\"useScrollSpy hook\", () => {\n+ let target: HTMLElement\n+ let eventHandler: ({ timeStampLow }: any) => void\n+\n+ beforeEach(() => {\n+ jest.useFakeTimers()\n+ target = document.createElement(\"div\")\n+ eventHandler = jest.fn()\n+\n+ document.body.appendChild(target)\n+ jest.spyOn(target, \"addEventListener\")\n+ jest.spyOn(target, \"removeEventListener\")\n+ })\n+\n+ afterEach(() => {\n+ document.body.removeChild(target)\n+ jest.clearAllMocks()\n+ })\n+\n+ it(\"should set up and clean up event listeners\", () => {\n+ const { unmount } = renderHook(() => useScrollSpy(target, eventHandler))\n+\n+ expect(target.addEventListener).toHaveBeenCalledWith(\n+ \"scroll\",\n+ expect.any(Function),\n+ { passive: true }\n+ )\n+ expect(eventHandler).toHaveBeenCalledWith({\n+ target,\n+ timeStampLow: expect.any(Number),\n+ })\n+\n+ unmount()\n+\n+ expect(target.removeEventListener).toHaveBeenCalledWith(\n+ \"scroll\",\n+ expect.any(Function)\n+ )\n+ })\n+\n+ it(\"should not set up event listeners if no target is provided\", () => {\n+ renderHook(() => useScrollSpy(null, eventHandler))\n+\n+ expect(target.addEventListener).not.toHaveBeenCalled()\n+ expect(eventHandler).not.toHaveBeenCalled()\n+ })\n+\n+ it(\"should debounce events\", () => {\n+ renderHook(() => useScrollSpy(target, eventHandler))\n+\n+ const scrollEvent = new Event(\"scroll\")\n+ act(() => {\n+ target.dispatchEvent(scrollEvent)\n+ })\n+ expect(eventHandler).toHaveBeenCalledTimes(1)\n+\n+ act(() => {\n+ target.dispatchEvent(scrollEvent)\n+ })\n+ expect(eventHandler).toHaveBeenCalledTimes(1)\n+\n+ jest.advanceTimersByTime(99)\n+ expect(eventHandler).toHaveBeenCalledTimes(1)\n+\n+ jest.advanceTimersByTime(1)\n+ expect(eventHandler).toHaveBeenCalledTimes(2)\n+ })\n+})\ndiff --git a/frontend/src/lib/hooks/useScrollSpy.ts b/frontend/src/lib/hooks/useScrollSpy.ts\nnew file mode 100644\nindex 000000000000..2f369e665f33\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollSpy.ts\n@@ -0,0 +1,130 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { useLayoutEffect, useMemo, useRef, useCallback } from \"react\"\n+\n+/**\n+ * Creates a debounced function that delays invoking `fn` until after `ms`\n+ * milliseconds have passed since the last time the debounced function was\n+ * invoked.\n+ *\n+ * @param fn - A function to debounce. `fn` is called after the debounced\n+ * function has not been called for `ms` milliseconds.\n+ *\n+ * @param ms - The delay in milliseconds before `fn` is executed.\n+ *\n+ * The debounced function behaves as follows:\n+ * - It will be invoked immediately if 0 is passed for `ms`.\n+ * - It will be invoked immediately for the first call in any case.\n+ * - For subsequent calls, if less than `ms` milliseconds have passed since\n+ * the last invocation, a new invocation of `fn` is scheduled for `ms`\n+ * milliseconds after the last invocation.\n+ * - If it is invoked and `ms` milliseconds have passed since the last\n+ * invocation, `fn` is executed immediately and the timestamp is updated.\n+ * - If a new invocation of the debounced function is scheduled and it\n+ * is invoked again before the scheduled invocation, the scheduled\n+ * invocation is canceled and a new one is scheduled `ms` milliseconds\n+ * after the latest invocation.\n+ *\n+ * TODO: This has very similar but different behavior than our debounce function\n+ * in utils.ts. This behavior ensures that the debounced function is called on\n+ * some interval. Our other debounce function ensures that the function is\n+ * delayed until the user stops calling it. We should probably unify these\n+ *\n+ * @returns A debounced version of the `fn` function.\n+ */\n+export function debounce(\n+ fn: (...args: any[]) => void,\n+ ms: number\n+): (...args: any[]) => void {\n+ if (!ms) {\n+ return fn\n+ }\n+\n+ let last = 0\n+ let timeout: ReturnType<typeof setTimeout> | null = null\n+\n+ return (...args) => {\n+ const now = Date.now()\n+\n+ if (now - last > ms) {\n+ fn(...args)\n+ last = now\n+ } else {\n+ if (timeout) {\n+ clearTimeout(timeout)\n+ }\n+\n+ timeout = setTimeout(() => {\n+ fn(...args)\n+ last = Date.now()\n+ }, Math.max(0, ms - now + last))\n+ }\n+ }\n+}\n+const DEFAULT_DEBOUNCE_MS = 100\n+\n+/**\n+ * A hook to add a scroll event listener to a target element with debouncing.\n+ *\n+ * @param target - The target HTMLElement to attach the scroll listener to.\n+ * @param eventHandler - The callback function to execute on scroll.\n+ *\n+ * The hook behaves as follows:\n+ * - The eventHandler callback is wrapped in a debounce function, which\n+ * ensures the callback is not executed too frequently.\n+ * - A scroll event listener is added to the target element on mount.\n+ * - A 'timeStampLow' property is added to the event object before it's\n+ * passed to the eventHandler.\n+ * - The scroll event listener is removed from the target when the component\n+ * unmounts.\n+ *\n+ * @returns void.\n+ */\n+export default function useScrollSpy(\n+ target: HTMLElement | null,\n+ eventHandler: ({ timeStampLow }: any) => void\n+): void {\n+ const onEventRef = useRef(eventHandler)\n+\n+ const debouncer = useMemo(\n+ () =>\n+ debounce(event => {\n+ onEventRef.current(event)\n+ }, DEFAULT_DEBOUNCE_MS),\n+ [onEventRef]\n+ )\n+\n+ const handleEvent = useCallback(\n+ event => {\n+ event.timeStampLow = Date.now()\n+\n+ debouncer(event)\n+ },\n+ [debouncer]\n+ )\n+\n+ useLayoutEffect(() => {\n+ if (!target) {\n+ return () => {}\n+ }\n+\n+ target.addEventListener(\"scroll\", handleEvent, { passive: true })\n+ handleEvent({ target })\n+\n+ return () => target.removeEventListener(\"scroll\", handleEvent)\n+ }, [handleEvent, target])\n+}\ndiff --git a/frontend/src/lib/hooks/useScrollToBottom.test.ts b/frontend/src/lib/hooks/useScrollToBottom.test.ts\nnew file mode 100644\nindex 000000000000..f61d13bec541\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollToBottom.test.ts\n@@ -0,0 +1,44 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { renderHook } from \"@testing-library/react-hooks\"\n+import useScrollToBottom from \"./useScrollToBottom\"\n+import useStateRef from \"./useStateRef\"\n+\n+jest.mock(\"./useScrollSpy\")\n+jest.mock(\"./useScrollAnimation\")\n+jest.mock(\"./useStateRef\")\n+\n+describe(\"useScrollToBottom\", () => {\n+ beforeEach(() => {\n+ jest.clearAllMocks()\n+ })\n+\n+ it(\"should initialize with proper values\", () => {\n+ const mockedUseStateRef = useStateRef as jest.MockedFunction<\n+ typeof useStateRef\n+ >\n+ mockedUseStateRef.mockImplementation(initialValue => [\n+ initialValue,\n+ jest.fn(),\n+ { current: initialValue },\n+ ])\n+ const { result } = renderHook(() => useScrollToBottom())\n+\n+ expect(result.current).not.toBeNull()\n+ expect(result.current.current).toBeNull()\n+ })\n+})\ndiff --git a/frontend/src/lib/hooks/useScrollToBottom.ts b/frontend/src/lib/hooks/useScrollToBottom.ts\nnew file mode 100644\nindex 000000000000..877d0872ee6b\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useScrollToBottom.ts\n@@ -0,0 +1,250 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { useEffect, useRef, useCallback, RefObject } from \"react\"\n+import useScrollSpy from \"./useScrollSpy\"\n+import useScrollAnimation from \"./useScrollAnimation\"\n+import useStateRef from \"./useStateRef\"\n+\n+export interface ScrollToBottomOptions {\n+ bottomThreshold?: number\n+ debounceMs?: number\n+}\n+\n+const DEFAULT_BOTTOM_THRESHOLD = 1\n+const SCROLL_DECISION_DURATION = 34 // 2 frames\n+const MIN_CHECK_INTERVAL = 17 // 1 frame\n+\n+function setImmediateInterval(fn: () => void, ms: number): NodeJS.Timeout {\n+ fn()\n+\n+ return setInterval(fn, ms)\n+}\n+\n+function isAtBottom({\n+ scrollHeight,\n+ offsetHeight,\n+ scrollTop,\n+}: HTMLElement): boolean {\n+ return scrollHeight - scrollTop - offsetHeight < DEFAULT_BOTTOM_THRESHOLD\n+}\n+\n+/**\n+ * useScrollToBottom is a custom React hook managing automatic\n+ * scrolling behavior for an HTML element. It keeps the scroll view\n+ * at the bottom unless a user scrolls up, then stops auto-scroll.\n+ *\n+ * This hook returns a ref object attached to the HTML element.\n+ * It maintains several pieces of state:\n+ * - isSticky: a boolean for whether the scroll position should\n+ * \"stick\" to the bottom.\n+ * - isAnimating: a boolean for whether the scroll view is animating.\n+ *\n+ * It has two major functions:\n+ * - handleScrollToBottomFinished: resets stickiness if necessary.\n+ * - handleScroll: adjusts isSticky based on user scroll behavior.\n+ *\n+ * The hook includes side effects with the useEffect hook:\n+ * - The first effect sets an interval to check the scroll position\n+ * and adjust stickiness and animating state.\n+ * - The second effect attaches a focus event listener to update\n+ * the scrollHeight value.\n+ */\n+function useScrollToBottom<T extends HTMLElement>(): RefObject<T> {\n+ const scrollableRef = useRef<T>(null)\n+ const [isSticky, setIsSticky, isStickyRef] = useStateRef(false)\n+ const [isAnimating, setIsAnimating, isAnimatingRef] = useStateRef(true)\n+\n+ // Internal context\n+ const ignoreScrollEventBeforeRef = useRef(0)\n+ const offsetHeightRef = useRef(0)\n+ const scrollHeightRef = useRef(0)\n+\n+ // Once, we have determined we are at the bottom, we can reset\n+ // the ignoring of scroll events.\n+ const handleScrollToBottomFinished = useCallback(() => {\n+ ignoreScrollEventBeforeRef.current = Date.now()\n+\n+ // handleScrollToBottomFinished may end at a position which should lose stickiness.\n+ // In that case, we will need to set sticky to false to stop the interval check.\n+ if (!isAnimatingRef.current) {\n+ // Essentially we are not suppose to be animating cause a scroll\n+ // occurred before we finished animating.\n+ setIsSticky(false)\n+ }\n+\n+ setIsAnimating(false)\n+ }, [ignoreScrollEventBeforeRef, isAnimatingRef, setIsAnimating, setIsSticky])\n+\n+ const handleScroll = useCallback(\n+ ({ timeStampLow }) => {\n+ const { current: target } = scrollableRef\n+ const animating = isAnimatingRef.current\n+\n+ // Currently, there are no reliable way to check if the \"scroll\" event is trigger due to\n+ // user gesture, programmatic scrolling, or Chrome-synthesized \"scroll\" event to compensate size change.\n+ // Thus, we use our best-effort to guess if it is triggered by user gesture, and disable sticky if it is heading towards the start direction.\n+\n+ if (timeStampLow <= ignoreScrollEventBeforeRef.current || !target) {\n+ // Since we debounce \"scroll\" event, this handler might be called after spineTo.onEnd (a.k.a. artificial scrolling).\n+ // We should ignore debounced event fired after scrollEnd, because without skipping them, the userInitiatedScroll calculated below will not be accurate.\n+ // Thus, on a fast machine, adding elements super fast will lose the \"stickiness\".\n+\n+ return\n+ }\n+\n+ const atBottom = isAtBottom(target)\n+\n+ // Chrome will emit \"synthetic\" scroll event if the container is resized or an element is added\n+ // We need to ignore these \"synthetic\" events\n+ const {\n+ offsetHeight: nextOffsetHeight,\n+ scrollHeight: nextScrollHeight,\n+ } = target\n+ const { current: offsetHeight } = offsetHeightRef\n+ const { current: scrollHeight } = scrollHeightRef\n+ const offsetHeightChanged = nextOffsetHeight !== offsetHeight\n+ const scrollHeightChanged = nextScrollHeight !== scrollHeight\n+\n+ if (offsetHeightChanged) {\n+ offsetHeightRef.current = nextOffsetHeight\n+ }\n+\n+ if (scrollHeightChanged) {\n+ scrollHeightRef.current = nextScrollHeight\n+ }\n+\n+ // Sticky means:\n+ // - If it is scrolled programatically, we are still in sticky mode\n+ // - If it is scrolled by the user, then sticky means if we are at the end\n+\n+ // Only update stickiness if the scroll event is not due to synthetic scroll done by Chrome\n+ if (!offsetHeightChanged && !scrollHeightChanged) {\n+ // We are sticky if we are animating to the end, or we are already at the end.\n+ // We can be \"animating but not sticky\" by calling \"scrollTo(100)\" where the container scrollHeight is 200px.\n+ const nextSticky = animating || atBottom\n+\n+ if (isStickyRef.current !== nextSticky) {\n+ setIsSticky(nextSticky)\n+ }\n+ } else if (isStickyRef.current) {\n+ setIsAnimating(true)\n+ setIsSticky(true)\n+ }\n+ },\n+ [\n+ ignoreScrollEventBeforeRef,\n+ offsetHeightRef,\n+ scrollHeightRef,\n+ isAnimatingRef,\n+ isStickyRef,\n+ setIsAnimating,\n+ setIsSticky,\n+ ]\n+ )\n+\n+ useEffect(() => {\n+ if (scrollableRef.current) {\n+ let stickyButNotAtEndSince = 0\n+\n+ const timeout = setImmediateInterval(() => {\n+ const { current: target } = scrollableRef\n+ const animating = isAnimatingRef.current\n+\n+ if (isStickyRef.current && target) {\n+ if (!isAtBottom(target)) {\n+ if (!stickyButNotAtEndSince) {\n+ stickyButNotAtEndSince = Date.now()\n+ } else if (\n+ Date.now() - stickyButNotAtEndSince >\n+ SCROLL_DECISION_DURATION\n+ ) {\n+ // Quirks: In Firefox, after user scroll down, Firefox do two things:\n+ // 1. Set to a new \"scrollTop\"\n+ // 2. Fire \"scroll\" event\n+ // For what we observed, #1 is fired about 20ms before #2. There is a chance that this stickyCheckTimeout is being scheduled between 1 and 2.\n+ // That means, if we just look at #1 to decide if we should scroll, we will always scroll, in oppose to the user's intention.\n+ // Repro: Open Firefox, set checkInterval to a lower number, and try to scroll by dragging the scroll handler. It will jump back.\n+\n+ // The \"animating\" check will make sure stickiness is not lost when elements are adding at a very fast pace.\n+ if (!animating) {\n+ setIsAnimating(true)\n+ setIsSticky(true)\n+ }\n+\n+ stickyButNotAtEndSince = 0\n+ }\n+ } else {\n+ stickyButNotAtEndSince = 0\n+ }\n+ } else if (\n+ target &&\n+ target.scrollHeight <= target.offsetHeight &&\n+ !isStickyRef.current\n+ ) {\n+ // When the container is emptied, we will set sticky back to true.\n+ setIsSticky(true)\n+ }\n+ }, MIN_CHECK_INTERVAL)\n+\n+ return () => clearInterval(timeout)\n+ }\n+ }, [\n+ scrollableRef,\n+ isSticky,\n+ isAnimating,\n+ isAnimatingRef,\n+ isStickyRef,\n+ setIsSticky,\n+ setIsAnimating,\n+ ])\n+\n+ useEffect(() => {\n+ // We need to update the \"scrollHeight\" value to latest when the user do a focus inside the box.\n+ //\n+ // This is because:\n+ // - In our code that mitigate Chrome synthetic scrolling, that code will look at whether \"scrollHeight\" value is latest or not.\n+ // - That code only run on \"scroll\" event.\n+ // - That means, on every \"scroll\" event, if the \"scrollHeight\" value is not latest, we will skip modifying the stickiness.\n+ // - That means, if the user \"focus\" to an element that cause the scroll view to scroll to the bottom, the user agent will fire \"scroll\" event.\n+ // Since the \"scrollHeight\" is not latest value, this \"scroll\" event will be ignored and stickiness will not be modified.\n+ // - That means, if the user \"focus\" to a newly added element that is at the end of the scroll view, the \"scroll to bottom\" button will continue to show.\n+ const target = scrollableRef.current\n+ if (target) {\n+ const handleFocus = (): void => {\n+ scrollHeightRef.current = target.scrollHeight\n+ }\n+\n+ target.addEventListener(\"focus\", handleFocus, {\n+ capture: true,\n+ passive: true,\n+ })\n+\n+ return () => target.removeEventListener(\"focus\", handleFocus)\n+ }\n+ }, [scrollableRef])\n+\n+ useScrollSpy(scrollableRef.current, handleScroll)\n+ useScrollAnimation(\n+ scrollableRef.current,\n+ handleScrollToBottomFinished,\n+ isAnimating\n+ )\n+\n+ return scrollableRef\n+}\n+\n+export default useScrollToBottom\ndiff --git a/frontend/src/lib/hooks/useStateRef.test.ts b/frontend/src/lib/hooks/useStateRef.test.ts\nnew file mode 100644\nindex 000000000000..673db27f5873\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useStateRef.test.ts\n@@ -0,0 +1,92 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import { act, renderHook } from \"@testing-library/react-hooks\"\n+import useStateRef from \"./useStateRef\" // import the hook\n+\n+describe(\"useStateRef hook\", () => {\n+ it(\"should initialize correctly with initial state\", () => {\n+ const { result } = renderHook(() => useStateRef(10))\n+\n+ const [state, , stateRef] = result.current\n+\n+ // Initial state is set correctly\n+ expect(state).toEqual(10)\n+ expect(stateRef.current).toEqual(10)\n+ })\n+\n+ it(\"should set state correctly\", () => {\n+ const { result } = renderHook(() => useStateRef(10))\n+ const [, setState, stateRef] = result.current\n+\n+ act(() => {\n+ setState(20)\n+ })\n+\n+ // State is updated\n+ const state = result.current[0]\n+ expect(state).toEqual(20)\n+ expect(stateRef.current).toEqual(20)\n+ })\n+\n+ it(\"should handle function update correctly\", () => {\n+ const { result } = renderHook(() => useStateRef(10))\n+ const [, setState, stateRef] = result.current\n+\n+ act(() => {\n+ setState(prev => prev + 10)\n+ })\n+\n+ // State is updated\n+ const state = result.current[0]\n+ expect(state).toEqual(20)\n+ expect(stateRef.current).toEqual(20)\n+ })\n+\n+ it(\"should maintain reference correctly when state changes\", () => {\n+ const { result } = renderHook(() => useStateRef(10))\n+ const [, setValue, initialRef] = result.current\n+\n+ act(() => {\n+ setValue(20)\n+ })\n+\n+ // Reference has not changed\n+ expect(result.current[2]).toBe(initialRef)\n+ })\n+\n+ it(\"should allow ref to be set independently from state\", () => {\n+ const { result } = renderHook(() => useStateRef(10))\n+ const [, setState, stateRef] = result.current\n+\n+ act(() => {\n+ stateRef.current = 20\n+ })\n+\n+ let state = result.current[0]\n+ expect(state).toEqual(10)\n+ expect(stateRef.current).toEqual(20)\n+\n+ act(() => {\n+ setState(20)\n+ })\n+\n+ // State is updated\n+ state = result.current[0]\n+ expect(state).toEqual(20)\n+ expect(stateRef.current).toEqual(20)\n+ })\n+})\ndiff --git a/frontend/src/lib/hooks/useStateRef.ts b/frontend/src/lib/hooks/useStateRef.ts\nnew file mode 100644\nindex 000000000000..7a7d162eb655\n--- /dev/null\n+++ b/frontend/src/lib/hooks/useStateRef.ts\n@@ -0,0 +1,63 @@\n+/**\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import {\n+ useRef,\n+ useState,\n+ MutableRefObject,\n+ Dispatch,\n+ SetStateAction,\n+} from \"react\"\n+\n+/**\n+ * A custom React Hook that extends useState by providing a mutable ref object\n+ * to track the current state value.\n+ *\n+ * @template T The type of the state value.\n+ * @param {T} initialState The initial state value.\n+ * @returns {[T, Dispatch<SetStateAction<T>>, MutableRefObject<T>]} A tuple containing the\n+ * current state value, a function to update the state, and a mutable ref object.\n+ *\n+ * @example\n+ * // Usage inside a component:\n+ * const [count, setCount, countRef] = useStateRef(0);\n+ *\n+ * // Accessing the current state value:\n+ * console.log(count); // Output: 0\n+ * console.log(countRef.current); // Output: 0\n+ *\n+ * // Modifying the state value and updating the ref:\n+ * setCount(10);\n+ * console.log(count); // Output: 10\n+ * console.log(countRef.current); // Output: 10\n+ *\n+ * // Comparing previous and current state values:\n+ * const previousCount = useRef(countRef.current);\n+ * console.log(previousCount.current); // Output: 10\n+ * console.log(previousCount.current === countRef.current); // Output: true\n+ *\n+ * // Sharing the state value with other components:\n+ * <ChildComponent countRef={countRef} />\n+ */\n+export default function useStateRef<T>(\n+ initialState: T\n+): [T, Dispatch<SetStateAction<T>>, MutableRefObject<T>] {\n+ const [state, setState] = useState<T>(initialState)\n+ const ref = useRef<T>(initialState)\n+ ref.current = state\n+\n+ return [state, setState, ref]\n+}\ndiff --git a/frontend/src/lib/theme/primitives/zIndices.ts b/frontend/src/lib/theme/primitives/zIndices.ts\nindex ac104c497d24..a18419d346db 100644\n--- a/frontend/src/lib/theme/primitives/zIndices.ts\n+++ b/frontend/src/lib/theme/primitives/zIndices.ts\n@@ -18,6 +18,7 @@ const sidebar = 100\n const menuButton = sidebar + 10\n const balloons = 1000000\n const header = balloons - 10\n+const chatInput = sidebar - 1\n const sidebarMobile = balloons - 5\n const popupMenu = balloons + 40\n const fullscreenWrapper = balloons + 50\n@@ -35,4 +36,5 @@ export const zIndices = {\n popupMenu,\n fullscreenWrapper,\n tablePortal,\n+ chatInput,\n }\ndiff --git a/lib/streamlit/__init__.py b/lib/streamlit/__init__.py\nindex fbd1e21d3645..d914d2f012e6 100644\n--- a/lib/streamlit/__init__.py\n+++ b/lib/streamlit/__init__.py\n@@ -118,6 +118,8 @@ def _update_logger() -> None:\n button = _main.button\n caption = _main.caption\n camera_input = _main.camera_input\n+chat_message = _main.chat_message\n+chat_input = _main.chat_input\n checkbox = _main.checkbox\n code = _main.code\n columns = _main.columns\ndiff --git a/lib/streamlit/delta_generator.py b/lib/streamlit/delta_generator.py\nindex f83005282d52..dff9a8b694ff 100644\n--- a/lib/streamlit/delta_generator.py\n+++ b/lib/streamlit/delta_generator.py\n@@ -46,6 +46,7 @@\n from streamlit.elements.bokeh_chart import BokehMixin\n from streamlit.elements.button import ButtonMixin\n from streamlit.elements.camera_input import CameraInputMixin\n+from streamlit.elements.chat import ChatMixin\n from streamlit.elements.checkbox import CheckboxMixin\n from streamlit.elements.code import CodeMixin\n from streamlit.elements.color_picker import ColorPickerMixin\n@@ -156,6 +157,7 @@ class DeltaGenerator(\n BokehMixin,\n ButtonMixin,\n CameraInputMixin,\n+ ChatMixin,\n CheckboxMixin,\n CodeMixin,\n ColorPickerMixin,\n@@ -278,7 +280,7 @@ def __init__(\n # Change the module of all mixin'ed functions to be st.delta_generator,\n # instead of the original module (e.g. st.elements.markdown)\n for mixin in self.__class__.__bases__:\n- for (name, func) in mixin.__dict__.items():\n+ for name, func in mixin.__dict__.items():\n if callable(func):\n func.__module__ = self.__module__\n \n@@ -599,6 +601,10 @@ def _block(\n raise StreamlitAPIException(\n \"Columns can only be placed inside other columns up to one level of nesting.\"\n )\n+ if block_type == \"chat_message\" and block_type in frozenset(parent_block_types):\n+ raise StreamlitAPIException(\n+ \"Chat messages cannot nested inside other chat messages.\"\n+ )\n if block_type == \"expandable\" and block_type in frozenset(parent_block_types):\n raise StreamlitAPIException(\n \"Expanders may not be nested inside other expanders.\"\ndiff --git a/lib/streamlit/elements/__init__.py b/lib/streamlit/elements/__init__.py\nindex a8de2ef39318..73ab16b4fc1f 100644\n--- a/lib/streamlit/elements/__init__.py\n+++ b/lib/streamlit/elements/__init__.py\n@@ -15,6 +15,7 @@\n WIDGETS = [\n \"button\",\n \"camera_input\",\n+ \"chat_input\",\n \"checkbox\",\n \"color_picker\",\n \"component_instance\",\ndiff --git a/lib/streamlit/elements/chat.py b/lib/streamlit/elements/chat.py\nnew file mode 100644\nindex 000000000000..acfb344fa4ff\n--- /dev/null\n+++ b/lib/streamlit/elements/chat.py\n@@ -0,0 +1,326 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+from __future__ import annotations\n+\n+from dataclasses import dataclass\n+from enum import Enum\n+from typing import TYPE_CHECKING, Optional, Tuple, cast\n+\n+from typing_extensions import Literal\n+\n+from streamlit import runtime\n+from streamlit.elements.image import AtomicImage, WidthBehaviour, image_to_url\n+from streamlit.elements.utils import check_callback_rules, check_session_state_rules\n+from streamlit.errors import StreamlitAPIException\n+from streamlit.proto.Block_pb2 import Block as BlockProto\n+from streamlit.proto.ChatInput_pb2 import ChatInput as ChatInputProto\n+from streamlit.proto.Common_pb2 import StringTriggerValue as StringTriggerValueProto\n+from streamlit.proto.RootContainer_pb2 import RootContainer\n+from streamlit.runtime.metrics_util import gather_metrics\n+from streamlit.runtime.scriptrunner import get_script_run_ctx\n+from streamlit.runtime.state import (\n+ WidgetArgs,\n+ WidgetCallback,\n+ WidgetKwargs,\n+ register_widget,\n+)\n+from streamlit.string_util import is_emoji\n+from streamlit.type_util import Key, to_key\n+\n+if TYPE_CHECKING:\n+ from streamlit.delta_generator import DeltaGenerator\n+\n+\n+class PresetLabels(str, Enum):\n+ USER = \"user\"\n+ ASSISTANT = \"assistant\"\n+\n+\n+def _process_avatar_input(\n+ avatar: str | AtomicImage | None = None,\n+) -> Tuple[BlockProto.ChatMessage.AvatarType.ValueType, str]:\n+ \"\"\"Detects the avatar type and prepares the avatar data for the frontend.\n+\n+ Parameters\n+ ----------\n+ avatar :\n+ The avatar that was provided by the user.\n+\n+ Returns\n+ -------\n+ Tuple[AvatarType, str]\n+ The detected avatar type and the prepared avatar data.\n+ \"\"\"\n+ AvatarType = BlockProto.ChatMessage.AvatarType\n+\n+ if avatar is None:\n+ return AvatarType.ICON, \"\"\n+ elif isinstance(avatar, str) and avatar in [\n+ PresetLabels.USER,\n+ PresetLabels.ASSISTANT,\n+ ]:\n+ return AvatarType.ICON, avatar\n+ elif isinstance(avatar, str) and is_emoji(avatar):\n+ return AvatarType.EMOJI, avatar\n+ else:\n+ try:\n+ # TODO(lukasmasuch): Pure SVGs are not yet supported here.\n+ # They have a special handling in `st.image` and might require some refactoring.\n+ return AvatarType.IMAGE, image_to_url(\n+ avatar,\n+ width=WidthBehaviour.ORIGINAL,\n+ clamp=False,\n+ channels=\"RGB\",\n+ output_format=\"auto\",\n+ image_id=\"\",\n+ )\n+ except Exception as ex:\n+ raise StreamlitAPIException(\n+ \"Failed to load the provided avatar value as an image.\"\n+ ) from ex\n+\n+\n+DISALLOWED_CONTAINERS_ERROR_TEXT = \"`st.chat_input()` can't be used inside an `st.expander`, `st.form`, `st.tabs`, `st.columns`, or `st.sidebar`.\"\n+\n+\n+@dataclass\n+class ChatInputSerde:\n+ def deserialize(\n+ self, ui_value: Optional[StringTriggerValueProto], widget_id: str = \"\"\n+ ) -> str | None:\n+ if ui_value is None or not ui_value.HasField(\"data\"):\n+ return None\n+\n+ return ui_value.data\n+\n+ def serialize(self, v: str | None) -> StringTriggerValueProto:\n+ return StringTriggerValueProto(data=v)\n+\n+\n+class ChatMixin:\n+ @gather_metrics(\"chat_message\")\n+ def chat_message(\n+ self,\n+ participant: Literal[\"user\", \"assistant\"] | str,\n+ *,\n+ avatar: Literal[\"user\", \"assistant\"] | str | AtomicImage | None = None,\n+ ) -> \"DeltaGenerator\":\n+ \"\"\"Insert a chat message container.\n+\n+ To add elements to the returned container, you can use ``with`` notation\n+ (preferred) or just call methods directly on the returned object. See the\n+ examples below.\n+\n+ Parameters\n+ ----------\n+ participant : \"user\", \"assistant\", or str\n+ The name of the chat participant. Can be \"user\" or \"assistant\" to enable\n+ preset styling and avatars. For accessibility reasons, you should not\n+ use an empty string.\n+\n+ avatar : str, numpy.ndarray, or BytesIO\n+ The avatar shown next to the message. Can be one of:\n+\n+ * A single emoji, e.g. \"๐Ÿง‘โ€๐Ÿ’ป\", \"๐Ÿค–\", \"๐Ÿฆ–\". Shortcodes are not supported.\n+\n+ * An image using one of the formats allowed for ``st.image``: path of a local\n+ image file; URL to fetch the image from; array of shape (w,h) or (w,h,1)\n+ for a monochrome image, (w,h,3) for a color image, or (w,h,4) for an RGBA image.\n+\n+ If None (default), uses default icons if ``participant`` is \"user\" or\n+ \"assistant\", or the first letter of the ``participant`` value.\n+\n+ Returns\n+ -------\n+ Container\n+ A single container that can hold multiple elements.\n+\n+ Examples\n+ --------\n+ You can use ``with`` notation to insert any element into an expander\n+\n+ >>> import streamlit as st\n+ >>> import numpy as np\n+ >>>\n+ >>> with st.chat_message(\"user\"):\n+ ... st.write(\"Hello ๐Ÿ‘‹\")\n+ ... st.line_chart(np.random.randn(30, 3))\n+\n+ .. output ::\n+ https://doc-chat-message-user.streamlit.app/\n+ height: 450px\n+\n+ Or you can just call methods directly in the returned objects:\n+\n+ >>> import streamlit as st\n+ >>> import numpy as np\n+ >>>\n+ >>> message = st.chat_message(\"assistant\"):\n+ >>> message.write(\"Hello human\")\n+ >>> message.bar_chart(np.random.randn(30, 3))\n+\n+ .. output ::\n+ https://doc-chat-message-assistant.streamlit.app/\n+ height: 450px\n+\n+ \"\"\"\n+ if participant is None:\n+ raise StreamlitAPIException(\"A participant is required for a chat message\")\n+\n+ if avatar is None and (\n+ participant.lower()\n+ in [\n+ PresetLabels.USER,\n+ PresetLabels.ASSISTANT,\n+ ]\n+ or is_emoji(participant)\n+ ):\n+ # For selected labels, we are mapping the label to an avatar\n+ avatar = participant.lower()\n+ avatar_type, converted_avatar = _process_avatar_input(avatar)\n+\n+ message_container_proto = BlockProto.ChatMessage()\n+ message_container_proto.participant = participant\n+ message_container_proto.avatar = converted_avatar\n+ message_container_proto.avatar_type = avatar_type\n+ block_proto = BlockProto()\n+ block_proto.allow_empty = True\n+ block_proto.chat_message.CopyFrom(message_container_proto)\n+\n+ return self.dg._block(block_proto=block_proto)\n+\n+ @gather_metrics(\"chat_input\")\n+ def chat_input(\n+ self,\n+ placeholder: str = \"Your message\",\n+ *,\n+ key: Key | None = None,\n+ max_chars: int | None = None,\n+ disabled: bool = False,\n+ on_submit: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ ) -> str | None:\n+ \"\"\"Display a chat input widget.\n+\n+ .. warning::\n+ Chat input can only be used once per app page and inside the main area of the app.\n+ It cannot be used in the sidebar, columns, expanders, forms or tabs.\n+ We plan to support this in the future.\n+\n+ Parameters\n+ ----------\n+ placeholder : str\n+ A placeholder text shown when the chat input is empty. Defaults to\n+ \"Your message\". For accessibility reasons, you should not use an\n+ empty string.\n+\n+ key : str or int\n+ An optional string or integer to use as the unique key for the widget.\n+ If this is omitted, a key will be generated for the widget based on\n+ its content. Multiple widgets of the same type may not share the same key.\n+\n+ max_chars : int or None\n+ The maximum number of characters that can be entered. If None\n+ (default), there will be no maximum.\n+\n+ disabled : bool\n+ Whether the chat input should be disabled. Defaults to False.\n+\n+ on_submit : callable\n+ An optional callback invoked when the chat input's value is submitted.\n+\n+ args : tuple\n+ An optional tuple of args to pass to the callback.\n+\n+ kwargs : dict\n+ An optional dict of kwargs to pass to the callback.\n+\n+ Returns\n+ -------\n+ str or None\n+ The current (non-empty) value of the text input widget on the last\n+ run of the app, None otherwise.\n+\n+ Examples\n+ --------\n+ >>> import streamlit as st\n+ >>>\n+ >>> prompt = st.chat_input(\"Say something\")\n+ >>> if prompt:\n+ ... st.write(f\"User has sent the following prompt: {prompt}\")\n+\n+ .. output ::\n+ https://doc-chat-input.streamlit.app/\n+ height: 350px\n+\n+ \"\"\"\n+ # We default to an empty string here and disallow user choice intentionally\n+ default = \"\"\n+ key = to_key(key)\n+ check_callback_rules(self.dg, on_submit)\n+ check_session_state_rules(default_value=default, key=key, writes_allowed=False)\n+\n+ # We omit this check for scripts running outside streamlit, because\n+ # they will have no script_run_ctx.\n+ if runtime.exists():\n+ if (\n+ len(list(self.dg._active_dg._parent_block_types)) > 0\n+ or self.dg._active_dg._root_container == RootContainer.SIDEBAR\n+ ):\n+ # TODO: This allows the user to create a chat_input inside a\n+ # container but it still cannot be in other containers. This is\n+ # a result of the Vertical field not being set in Blocks by\n+ # default and seems like a \"bug\", but one that is not producing\n+ # major issues. We should look into what's the correct behavior\n+ # to implement.\n+ raise StreamlitAPIException(DISALLOWED_CONTAINERS_ERROR_TEXT)\n+\n+ chat_input_proto = ChatInputProto()\n+ chat_input_proto.placeholder = str(placeholder)\n+\n+ if max_chars is not None:\n+ chat_input_proto.max_chars = max_chars\n+\n+ chat_input_proto.default = default\n+ chat_input_proto.position = ChatInputProto.Position.BOTTOM\n+\n+ ctx = get_script_run_ctx()\n+\n+ serde = ChatInputSerde()\n+ widget_state = register_widget(\n+ \"chat_input\",\n+ chat_input_proto,\n+ user_key=key,\n+ on_change_handler=on_submit,\n+ args=args,\n+ kwargs=kwargs,\n+ deserializer=serde.deserialize,\n+ serializer=serde.serialize,\n+ ctx=ctx,\n+ )\n+\n+ chat_input_proto.disabled = disabled\n+ if widget_state.value_changed and widget_state.value is not None:\n+ chat_input_proto.value = widget_state.value\n+ chat_input_proto.set_value = True\n+\n+ self.dg._enqueue(\"chat_input\", chat_input_proto)\n+ return widget_state.value if not widget_state.value_changed else None\n+\n+ @property\n+ def dg(self) -> \"DeltaGenerator\":\n+ \"\"\"Get our DeltaGenerator.\"\"\"\n+ return cast(\"DeltaGenerator\", self)\n+ return cast(\"DeltaGenerator\", self)\ndiff --git a/lib/streamlit/elements/layouts.py b/lib/streamlit/elements/layouts.py\nindex 61e84559fa91..1137c0aba57a 100644\n--- a/lib/streamlit/elements/layouts.py\n+++ b/lib/streamlit/elements/layouts.py\n@@ -11,10 +11,8 @@\n # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n # See the License for the specific language governing permissions and\n # limitations under the License.\n-\n from typing import TYPE_CHECKING, List, Optional, Sequence, Union, cast\n \n-from streamlit.deprecation_util import deprecate_func_name\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto.Block_pb2 import Block as BlockProto\n from streamlit.runtime.metrics_util import gather_metrics\ndiff --git a/lib/streamlit/elements/utils.py b/lib/streamlit/elements/utils.py\nindex 0b089fc66afe..b1a5f32a2919 100644\n--- a/lib/streamlit/elements/utils.py\n+++ b/lib/streamlit/elements/utils.py\n@@ -50,6 +50,11 @@ def check_callback_rules(\n \n _shown_default_value_warning: bool = False\n \n+SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT = \"\"\"\n+Values for st.button, st.download_button, st.file_uploader, st.data_editor,\n+st.chat_input, and st.form cannot be set using st.session_state.\n+\"\"\"\n+\n \n def check_session_state_rules(\n default_value: Any, key: Optional[str], writes_allowed: bool = True\n@@ -64,10 +69,7 @@ def check_session_state_rules(\n return\n \n if not writes_allowed:\n- raise StreamlitAPIException(\n- \"Values for st.button, st.download_button, st.file_uploader, st.data_editor\"\n- \" and st.form cannot be set using st.session_state.\"\n- )\n+ raise StreamlitAPIException(SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT)\n \n if (\n default_value is not None\ndiff --git a/lib/streamlit/runtime/state/common.py b/lib/streamlit/runtime/state/common.py\nindex 9ff9e80b2ce9..7f02a2732836 100644\n--- a/lib/streamlit/runtime/state/common.py\n+++ b/lib/streamlit/runtime/state/common.py\n@@ -26,6 +26,7 @@\n from streamlit.proto.Arrow_pb2 import Arrow\n from streamlit.proto.Button_pb2 import Button\n from streamlit.proto.CameraInput_pb2 import CameraInput\n+from streamlit.proto.ChatInput_pb2 import ChatInput\n from streamlit.proto.Checkbox_pb2 import Checkbox\n from streamlit.proto.ColorPicker_pb2 import ColorPicker\n from streamlit.proto.Components_pb2 import ComponentInstance\n@@ -47,6 +48,7 @@\n Arrow,\n Button,\n CameraInput,\n+ ChatInput,\n Checkbox,\n ColorPicker,\n ComponentInstance,\ndiff --git a/lib/streamlit/runtime/state/session_state.py b/lib/streamlit/runtime/state/session_state.py\nindex da281b76bc51..74d6bf4f2b20 100644\n--- a/lib/streamlit/runtime/state/session_state.py\n+++ b/lib/streamlit/runtime/state/session_state.py\n@@ -215,6 +215,8 @@ def get_serialized(self, k: str) -> WidgetStateProto | None:\n setattr(widget, field, json.dumps(serialized))\n elif field == \"file_uploader_state_value\":\n widget.file_uploader_state_value.CopyFrom(serialized)\n+ elif field == \"string_trigger_value\":\n+ widget.string_trigger_value.CopyFrom(serialized)\n else:\n setattr(widget, field, serialized)\n \n@@ -525,13 +527,19 @@ def _reset_triggers(self) -> None:\n \"\"\"Set all trigger values in our state dictionary to False.\"\"\"\n for state_id in self._new_widget_state:\n metadata = self._new_widget_state.widget_metadata.get(state_id)\n- if metadata is not None and metadata.value_type == \"trigger_value\":\n- self._new_widget_state[state_id] = Value(False)\n+ if metadata is not None:\n+ if metadata.value_type == \"trigger_value\":\n+ self._new_widget_state[state_id] = Value(False)\n+ elif metadata.value_type == \"string_trigger_value\":\n+ self._new_widget_state[state_id] = Value(None)\n \n for state_id in self._old_state:\n metadata = self._new_widget_state.widget_metadata.get(state_id)\n- if metadata is not None and metadata.value_type == \"trigger_value\":\n- self._old_state[state_id] = False\n+ if metadata is not None:\n+ if metadata.value_type == \"trigger_value\":\n+ self._old_state[state_id] = False\n+ elif metadata.value_type == \"string_trigger_value\":\n+ self._old_state[state_id] = None\n \n def _remove_stale_widgets(self, active_widget_ids: set[str]) -> None:\n \"\"\"Remove widget state for widgets whose ids aren't in `active_widget_ids`.\"\"\"\ndiff --git a/lib/streamlit/runtime/state/widgets.py b/lib/streamlit/runtime/state/widgets.py\nindex 2f0b79667505..2b051d6987da 100644\n--- a/lib/streamlit/runtime/state/widgets.py\n+++ b/lib/streamlit/runtime/state/widgets.py\n@@ -53,6 +53,7 @@\n \"button\": \"trigger_value\",\n \"download_button\": \"trigger_value\",\n \"checkbox\": \"bool_value\",\n+ \"chat_input\": \"string_trigger_value\",\n \"camera_input\": \"file_uploader_state_value\",\n \"color_picker\": \"string_value\",\n \"date_input\": \"string_array_value\",\n@@ -232,18 +233,22 @@ def coalesce_widget_states(\n wstate.id: wstate for wstate in new_states.widgets\n }\n \n+ trigger_value_types = [(\"trigger_value\", False), (\"string_trigger_value\", None)]\n for old_state in old_states.widgets:\n- if old_state.WhichOneof(\"value\") == \"trigger_value\" and old_state.trigger_value:\n-\n- # Ensure the corresponding new_state is also a trigger;\n- # otherwise, a widget that was previously a button but no longer is\n- # could get a bad value.\n- new_trigger_val = states_by_id.get(old_state.id)\n+ for trigger_value_type, unset_value in trigger_value_types:\n if (\n- new_trigger_val\n- and new_trigger_val.WhichOneof(\"value\") == \"trigger_value\"\n+ old_state.WhichOneof(\"value\") == trigger_value_type\n+ and old_state.trigger_value != unset_value\n ):\n- states_by_id[old_state.id] = old_state\n+ # Ensure the corresponding new_state is also a trigger;\n+ # otherwise, a widget that was previously a button but no longer is\n+ # could get a bad value.\n+ new_trigger_val = states_by_id.get(old_state.id)\n+ if (\n+ new_trigger_val\n+ and new_trigger_val.WhichOneof(\"value\") == trigger_value_type\n+ ):\n+ states_by_id[old_state.id] = old_state\n \n coalesced = WidgetStates()\n coalesced.widgets.extend(states_by_id.values())\ndiff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py\nindex 4f4aa8126b96..c95543aa0136 100644\n--- a/lib/streamlit/type_util.py\n+++ b/lib/streamlit/type_util.py\n@@ -102,6 +102,7 @@\n \"json_value\",\n \"string_value\",\n \"trigger_value\",\n+ \"string_trigger_value\",\n ]\n \n V_co = TypeVar(\ndiff --git a/lib/tests/streamlit/delta_generator_test.py b/lib/tests/streamlit/delta_generator_test.py\nindex ea26f5d02571..0dc0480e334e 100644\n--- a/lib/tests/streamlit/delta_generator_test.py\n+++ b/lib/tests/streamlit/delta_generator_test.py\n@@ -96,6 +96,8 @@ def test_public_api(self):\n \"button\",\n \"camera_input\",\n \"caption\",\n+ \"chat_input\",\n+ \"chat_message\",\n \"checkbox\",\n \"code\",\n \"color_picker\",\ndiff --git a/lib/tests/streamlit/elements/chat_test.py b/lib/tests/streamlit/elements/chat_test.py\nnew file mode 100644\nindex 000000000000..0f300fcd381e\n--- /dev/null\n+++ b/lib/tests/streamlit/elements/chat_test.py\n@@ -0,0 +1,207 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+\"\"\"chat input and message unit tests.\"\"\"\n+\n+import pytest\n+from parameterized import parameterized\n+\n+import streamlit as st\n+from streamlit.elements.chat import DISALLOWED_CONTAINERS_ERROR_TEXT\n+from streamlit.elements.utils import SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT\n+from streamlit.errors import StreamlitAPIException\n+from streamlit.proto.Block_pb2 import Block as BlockProto\n+from streamlit.proto.ChatInput_pb2 import ChatInput as ChatInputProto\n+from tests.delta_generator_test_case import DeltaGeneratorTestCase\n+\n+\n+class ChatTest(DeltaGeneratorTestCase):\n+ \"\"\"Test ability to marshall ChatInput and ChatMessage protos.\"\"\"\n+\n+ def test_label_required(self):\n+ \"\"\"Test that label is required\"\"\"\n+ with self.assertRaises(TypeError):\n+ st.chat_message()\n+\n+ def test_nesting_is_disallowed(self):\n+ \"\"\"Test that it is not allowed to be nested.\"\"\"\n+ with self.assertRaises(StreamlitAPIException):\n+ with st.chat_message(\"user\"):\n+ with st.chat_message(\"assistant\"):\n+ st.write(\"hello\")\n+\n+ def test_user_message(self):\n+ \"\"\"Test that the user message is correct.\"\"\"\n+ message = st.chat_message(\"user\")\n+\n+ with message:\n+ pass\n+\n+ message_block = self.get_delta_from_queue()\n+\n+ self.assertEqual(message_block.add_block.chat_message.participant, \"user\")\n+ self.assertEqual(message_block.add_block.chat_message.avatar, \"user\")\n+ self.assertEqual(\n+ message_block.add_block.chat_message.avatar_type,\n+ BlockProto.ChatMessage.AvatarType.ICON,\n+ )\n+\n+ def test_assistant_message(self):\n+ \"\"\"Test that the assistant message is correct.\"\"\"\n+ message = st.chat_message(\"assistant\")\n+\n+ with message:\n+ pass\n+\n+ message_block = self.get_delta_from_queue()\n+\n+ self.assertEqual(message_block.add_block.chat_message.participant, \"assistant\")\n+ self.assertEqual(message_block.add_block.chat_message.avatar, \"assistant\")\n+ self.assertEqual(\n+ message_block.add_block.chat_message.avatar_type,\n+ BlockProto.ChatMessage.AvatarType.ICON,\n+ )\n+\n+ def test_emoji_avatar(self):\n+ \"\"\"Test that it is possible to set an emoji as avatar.\"\"\"\n+\n+ message = st.chat_message(\"user\", avatar=\"๐Ÿ‘‹\")\n+\n+ with message:\n+ pass\n+\n+ message_block = self.get_delta_from_queue()\n+\n+ self.assertEqual(message_block.add_block.chat_message.participant, \"user\")\n+ self.assertEqual(message_block.add_block.chat_message.avatar, \"๐Ÿ‘‹\")\n+ self.assertEqual(\n+ message_block.add_block.chat_message.avatar_type,\n+ BlockProto.ChatMessage.AvatarType.EMOJI,\n+ )\n+\n+ def test_image_avatar(self):\n+ \"\"\"Test that it is possible to set an image as avatar.\"\"\"\n+\n+ message = st.chat_message(\n+ \"cat\",\n+ avatar=\"https://static.streamlit.io/examples/cat.jpg\",\n+ )\n+\n+ with message:\n+ pass\n+\n+ message_block = self.get_delta_from_queue()\n+ self.assertEqual(message_block.add_block.chat_message.participant, \"cat\")\n+ self.assertEqual(\n+ message_block.add_block.chat_message.avatar,\n+ \"https://static.streamlit.io/examples/cat.jpg\",\n+ )\n+ self.assertEqual(\n+ message_block.add_block.chat_message.avatar_type,\n+ BlockProto.ChatMessage.AvatarType.IMAGE,\n+ )\n+\n+ def test_throws_invalid_avatar_exception(self):\n+ \"\"\"Test that chat_message throws an StreamlitAPIException on invalid avatar input.\"\"\"\n+ with pytest.raises(StreamlitAPIException):\n+ st.chat_message(\"user\", avatar=\"FOOO\")\n+\n+ def test_chat_input(self):\n+ \"\"\"Test that it can be called.\"\"\"\n+ st.chat_input(\"Placeholder\")\n+\n+ c = self.get_delta_from_queue().new_element.chat_input\n+ self.assertEqual(c.placeholder, \"Placeholder\")\n+ self.assertEqual(c.default, \"\")\n+ self.assertEqual(c.value, \"\")\n+ self.assertEqual(c.set_value, False)\n+ self.assertEqual(c.max_chars, 0)\n+ self.assertEqual(c.disabled, False)\n+ self.assertEqual(c.position, ChatInputProto.Position.BOTTOM)\n+\n+ def test_chat_input_disabled(self):\n+ \"\"\"Test that it sets disabled correctly.\"\"\"\n+ st.chat_input(\"Placeholder\", disabled=True)\n+\n+ c = self.get_delta_from_queue().new_element.chat_input\n+ self.assertEqual(c.placeholder, \"Placeholder\")\n+ self.assertEqual(c.default, \"\")\n+ self.assertEqual(c.value, \"\")\n+ self.assertEqual(c.set_value, False)\n+ self.assertEqual(c.max_chars, 0)\n+ self.assertEqual(c.disabled, True)\n+ self.assertEqual(c.position, ChatInputProto.Position.BOTTOM)\n+\n+ def test_chat_input_max_chars(self):\n+ \"\"\"Test that it sets max chars correctly.\"\"\"\n+ st.chat_input(\"Placeholder\", max_chars=100)\n+\n+ c = self.get_delta_from_queue().new_element.chat_input\n+ self.assertEqual(c.placeholder, \"Placeholder\")\n+ self.assertEqual(c.default, \"\")\n+ self.assertEqual(c.value, \"\")\n+ self.assertEqual(c.set_value, False)\n+ self.assertEqual(c.max_chars, 100)\n+ self.assertEqual(c.disabled, False)\n+ self.assertEqual(c.position, ChatInputProto.Position.BOTTOM)\n+\n+ @parameterized.expand(\n+ [\n+ lambda: st.columns(2)[0],\n+ lambda: st.tabs([\"Tab1\", \"Tab2\"])[0],\n+ lambda: st.expander(\"Expand Me\"),\n+ lambda: st.form(\"Form Key\"),\n+ lambda: st.sidebar,\n+ ]\n+ )\n+ def test_chat_not_allowed_in_containers(self, container_call):\n+ \"\"\"Test that it disallows being called in containers.\"\"\"\n+ with pytest.raises(StreamlitAPIException) as exception_message:\n+ container_call().chat_input(\"Placeholder\")\n+\n+ self.assertEqual(\n+ DISALLOWED_CONTAINERS_ERROR_TEXT,\n+ str(exception_message.value),\n+ )\n+\n+ @parameterized.expand(\n+ [\n+ lambda: st.columns(2)[0],\n+ lambda: st.tabs([\"Tab1\", \"Tab2\"])[0],\n+ lambda: st.expander(\"Expand Me\"),\n+ lambda: st.form(\"Form Key\"),\n+ lambda: st.sidebar,\n+ ]\n+ )\n+ def test_chat_not_allowed_in_with_containers(self, container_call):\n+ \"\"\"Test that it disallows being called in containers (using with syntax).\"\"\"\n+ with pytest.raises(StreamlitAPIException) as exception_message:\n+ with container_call():\n+ st.chat_input(\"Placeholder\")\n+\n+ self.assertEqual(\n+ DISALLOWED_CONTAINERS_ERROR_TEXT,\n+ str(exception_message.value),\n+ )\n+\n+ def test_session_state_rules(self):\n+ \"\"\"Test that it disallows being called in containers (using with syntax).\"\"\"\n+ with pytest.raises(StreamlitAPIException) as exception_message:\n+ st.session_state.my_key = \"Foo\"\n+ st.chat_input(\"Placeholder\", key=\"my_key\")\n+\n+ self.assertEqual(\n+ SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT,\n+ str(exception_message.value),\n+ )\ndiff --git a/lib/tests/streamlit/runtime/state/widgets_test.py b/lib/tests/streamlit/runtime/state/widgets_test.py\nindex 9b8becdbb08b..7a9d4a5b0ad4 100644\n--- a/lib/tests/streamlit/runtime/state/widgets_test.py\n+++ b/lib/tests/streamlit/runtime/state/widgets_test.py\n@@ -22,6 +22,7 @@\n import streamlit as st\n from streamlit import errors\n from streamlit.proto.Button_pb2 import Button as ButtonProto\n+from streamlit.proto.Common_pb2 import StringTriggerValue as StringTriggerValueProto\n from streamlit.proto.WidgetStates_pb2 import WidgetStates\n from streamlit.runtime.scriptrunner.script_run_context import get_script_run_ctx\n from streamlit.runtime.state import coalesce_widget_states\n@@ -181,6 +182,32 @@ def test_reset_triggers(self):\n self.assertFalse(session_state[\"trigger\"])\n self.assertEqual(123, session_state[\"int\"])\n \n+ def test_reset_string_triggers(self):\n+ states = WidgetStates()\n+ session_state = SessionState()\n+\n+ _create_widget(\"string_trigger\", states).string_trigger_value.CopyFrom(\n+ StringTriggerValueProto(data=\"Some Value\")\n+ )\n+ _create_widget(\"int\", states).int_value = 123\n+ session_state.set_widgets_from_proto(states)\n+ session_state._set_widget_metadata(\n+ WidgetMetadata(\n+ \"string_trigger\", lambda x, s: x, None, \"string_trigger_value\"\n+ )\n+ )\n+ session_state._set_widget_metadata(\n+ WidgetMetadata(\"int\", lambda x, s: x, None, \"int_value\")\n+ )\n+\n+ self.assertEqual(\"Some Value\", session_state[\"string_trigger\"].data)\n+ self.assertEqual(123, session_state[\"int\"])\n+\n+ session_state._reset_triggers()\n+\n+ self.assertIsNone(session_state[\"string_trigger\"])\n+ self.assertEqual(123, session_state[\"int\"])\n+\n def test_coalesce_widget_states(self):\n session_state = SessionState()\n \n@@ -188,6 +215,15 @@ def test_coalesce_widget_states(self):\n \n _create_widget(\"old_set_trigger\", old_states).trigger_value = True\n _create_widget(\"old_unset_trigger\", old_states).trigger_value = False\n+ _create_widget(\n+ \"old_set_string_trigger\", old_states\n+ ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=\"Some String\"))\n+ _create_widget(\n+ \"old_set_empty_string_trigger\", old_states\n+ ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=\"\"))\n+ _create_widget(\n+ \"old_unset_string_trigger\", old_states\n+ ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None))\n _create_widget(\"missing_in_new\", old_states).int_value = 123\n _create_widget(\"shape_changing_trigger\", old_states).trigger_value = True\n \n@@ -197,6 +233,15 @@ def test_coalesce_widget_states(self):\n session_state._set_widget_metadata(\n create_metadata(\"old_unset_trigger\", \"trigger_value\")\n )\n+ session_state._set_widget_metadata(\n+ create_metadata(\"old_set_string_trigger\", \"string_trigger_value\")\n+ )\n+ session_state._set_widget_metadata(\n+ create_metadata(\"old_set_empty_string_trigger\", \"string_trigger_value\")\n+ )\n+ session_state._set_widget_metadata(\n+ create_metadata(\"old_unset_string_trigger\", \"string_trigger_value\")\n+ )\n session_state._set_widget_metadata(\n create_metadata(\"missing_in_new\", \"int_value\")\n )\n@@ -208,11 +253,25 @@ def test_coalesce_widget_states(self):\n \n _create_widget(\"old_set_trigger\", new_states).trigger_value = False\n _create_widget(\"new_set_trigger\", new_states).trigger_value = True\n+ _create_widget(\n+ \"old_set_string_trigger\", new_states\n+ ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None))\n+ _create_widget(\n+ \"old_set_empty_string_trigger\", new_states\n+ ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None))\n+ _create_widget(\n+ \"new_set_string_trigger\", new_states\n+ ).string_trigger_value.CopyFrom(\n+ StringTriggerValueProto(data=\"Some other string\")\n+ )\n _create_widget(\"added_in_new\", new_states).int_value = 456\n _create_widget(\"shape_changing_trigger\", new_states).int_value = 3\n session_state._set_widget_metadata(\n create_metadata(\"new_set_trigger\", \"trigger_value\")\n )\n+ session_state._set_widget_metadata(\n+ create_metadata(\"new_set_string_trigger\", \"string_trigger_value\")\n+ )\n session_state._set_widget_metadata(create_metadata(\"added_in_new\", \"int_value\"))\n session_state._set_widget_metadata(\n create_metadata(\"shape_changing_trigger\", \"int_value\")\n@@ -224,10 +283,16 @@ def test_coalesce_widget_states(self):\n \n self.assertRaises(KeyError, lambda: session_state[\"old_unset_trigger\"])\n self.assertRaises(KeyError, lambda: session_state[\"missing_in_new\"])\n+ self.assertRaises(KeyError, lambda: session_state[\"old_unset_string_trigger\"])\n \n self.assertEqual(True, session_state[\"old_set_trigger\"])\n self.assertEqual(True, session_state[\"new_set_trigger\"])\n self.assertEqual(456, session_state[\"added_in_new\"])\n+ self.assertEqual(\"Some String\", session_state[\"old_set_string_trigger\"].data)\n+ self.assertEqual(\"\", session_state[\"old_set_empty_string_trigger\"].data)\n+ self.assertEqual(\n+ \"Some other string\", session_state[\"new_set_string_trigger\"].data\n+ )\n \n # Widgets that were triggers before, but no longer are, will *not*\n # be coalesced\ndiff --git a/lib/tests/streamlit/streamlit_test.py b/lib/tests/streamlit/streamlit_test.py\nindex 62aa12744511..aab6dbd477c8 100644\n--- a/lib/tests/streamlit/streamlit_test.py\n+++ b/lib/tests/streamlit/streamlit_test.py\n@@ -70,6 +70,8 @@ def test_public_api(self):\n \"button\",\n \"caption\",\n \"camera_input\",\n+ \"chat_input\",\n+ \"chat_message\",\n \"checkbox\",\n \"code\",\n \"columns\",\ndiff --git a/proto/streamlit/proto/Block.proto b/proto/streamlit/proto/Block.proto\nindex 635ef6b162ca..8cfdc979cd9b 100644\n--- a/proto/streamlit/proto/Block.proto\n+++ b/proto/streamlit/proto/Block.proto\n@@ -25,6 +25,7 @@ message Block {\n Form form = 5;\n TabContainer tab_container = 6;\n Tab tab = 7;\n+ ChatMessage chat_message = 9;\n }\n \n bool allow_empty = 8;\n@@ -58,5 +59,17 @@ message Block {\n string label = 1;\n }\n \n- // Next ID: 9\n+ message ChatMessage {\n+ enum AvatarType {\n+ IMAGE = 0;\n+ EMOJI = 1;\n+ ICON = 2;\n+ }\n+\n+ string participant = 1;\n+ string avatar = 2;\n+ AvatarType avatar_type = 3;\n+ }\n+\n+ // Next ID: 10\n }\ndiff --git a/proto/streamlit/proto/ChatInput.proto b/proto/streamlit/proto/ChatInput.proto\nnew file mode 100644\nindex 000000000000..fc23d4bb872e\n--- /dev/null\n+++ b/proto/streamlit/proto/ChatInput.proto\n@@ -0,0 +1,32 @@\n+/**!\n+ * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+syntax = \"proto3\";\n+\n+message ChatInput {\n+ enum Position {\n+ BOTTOM = 0;\n+ }\n+\n+ string id = 1;\n+ string placeholder = 2;\n+ uint32 max_chars = 3;\n+ bool disabled = 4;\n+ string value = 5;\n+ bool set_value = 6;\n+ string default = 7;\n+ Position position = 8;\n+}\ndiff --git a/proto/streamlit/proto/Common.proto b/proto/streamlit/proto/Common.proto\nindex 68430a2a8407..05b4cd64d7e1 100644\n--- a/proto/streamlit/proto/Common.proto\n+++ b/proto/streamlit/proto/Common.proto\n@@ -42,6 +42,10 @@ message UInt32Array {\n repeated uint32 data = 1;\n }\n \n+message StringTriggerValue {\n+ optional string data = 1;\n+}\n+\n // Information on a file uploaded via the file_uploader widget.\n message UploadedFileInfo {\n sint64 id = 1;\ndiff --git a/proto/streamlit/proto/Element.proto b/proto/streamlit/proto/Element.proto\nindex 489d5da96a7e..6148cf8fef6d 100644\n--- a/proto/streamlit/proto/Element.proto\n+++ b/proto/streamlit/proto/Element.proto\n@@ -25,6 +25,7 @@ import \"streamlit/proto/BokehChart.proto\";\n import \"streamlit/proto/Button.proto\";\n import \"streamlit/proto/DownloadButton.proto\";\n import \"streamlit/proto/CameraInput.proto\";\n+import \"streamlit/proto/ChatInput.proto\";\n import \"streamlit/proto/Checkbox.proto\";\n import \"streamlit/proto/Code.proto\";\n import \"streamlit/proto/ColorPicker.proto\";\n@@ -75,6 +76,7 @@ message Element {\n Button button = 19;\n DownloadButton download_button = 43;\n CameraInput camera_input = 45;\n+ ChatInput chat_input = 49;\n Checkbox checkbox = 20;\n ColorPicker color_picker = 35;\n ComponentInstance component_instance = 37;\n@@ -110,7 +112,7 @@ message Element {\n Video video = 14;\n Heading heading = 47;\n Code code = 48;\n- // Next ID: 49\n+ // Next ID: 50\n }\n \n reserved 9;\ndiff --git a/proto/streamlit/proto/WidgetStates.proto b/proto/streamlit/proto/WidgetStates.proto\nindex 39c44b0fbb28..8ca074cd6c48 100644\n--- a/proto/streamlit/proto/WidgetStates.proto\n+++ b/proto/streamlit/proto/WidgetStates.proto\n@@ -48,5 +48,8 @@ message WidgetState {\n ArrowTable arrow_value = 11;\n bytes bytes_value = 12;\n FileUploaderState file_uploader_state_value = 13;\n+ // String value that resets itself to empty after the script has been run.\n+ // This is used for the chat_input widget.\n+ StringTriggerValue string_trigger_value = 14;\n }\n }\n" }
[ { "diff_hunk": "@@ -0,0 +1,326 @@\n+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+from __future__ import annotations\n+\n+from dataclasses import dataclass\n+from enum import Enum\n+from typing import TYPE_CHECKING, Optional, Tuple, cast\n+\n+from typing_extensions import Literal\n+\n+from streamlit import runtime\n+from streamlit.elements.image import AtomicImage, WidthBehaviour, image_to_url\n+from streamlit.elements.utils import check_callback_rules, check_session_state_rules\n+from streamlit.errors import StreamlitAPIException\n+from streamlit.proto.Block_pb2 import Block as BlockProto\n+from streamlit.proto.ChatInput_pb2 import ChatInput as ChatInputProto\n+from streamlit.proto.Common_pb2 import StringTriggerValue as StringTriggerValueProto\n+from streamlit.proto.RootContainer_pb2 import RootContainer\n+from streamlit.runtime.metrics_util import gather_metrics\n+from streamlit.runtime.scriptrunner import get_script_run_ctx\n+from streamlit.runtime.state import (\n+ WidgetArgs,\n+ WidgetCallback,\n+ WidgetKwargs,\n+ register_widget,\n+)\n+from streamlit.string_util import is_emoji\n+from streamlit.type_util import Key, to_key\n+\n+if TYPE_CHECKING:\n+ from streamlit.delta_generator import DeltaGenerator\n+\n+\n+class PresetLabels(str, Enum):\n+ USER = \"user\"\n+ ASSISTANT = \"assistant\"\n+\n+\n+def _process_avatar_input(\n+ avatar: str | AtomicImage | None = None,\n+) -> Tuple[BlockProto.ChatMessage.AvatarType.ValueType, str]:\n+ \"\"\"Detects the avatar type and prepares the avatar data for the frontend.\n+\n+ Parameters\n+ ----------\n+ avatar :\n+ The avatar that was provided by the user.\n+\n+ Returns\n+ -------\n+ Tuple[AvatarType, str]\n+ The detected avatar type and the prepared avatar data.\n+ \"\"\"\n+ AvatarType = BlockProto.ChatMessage.AvatarType\n+\n+ if avatar is None:\n+ return AvatarType.ICON, \"\"\n+ elif isinstance(avatar, str) and avatar in [\n+ PresetLabels.USER,\n+ PresetLabels.ASSISTANT,\n+ ]:\n+ return AvatarType.ICON, avatar\n+ elif isinstance(avatar, str) and is_emoji(avatar):\n+ return AvatarType.EMOJI, avatar\n+ else:\n+ try:\n+ # TODO(lukasmasuch): Pure SVGs are not yet supported here.\n+ # They have a special handling in `st.image` and might require some refactoring.\n+ return AvatarType.IMAGE, image_to_url(\n+ avatar,\n+ width=WidthBehaviour.ORIGINAL,\n+ clamp=False,\n+ channels=\"RGB\",\n+ output_format=\"auto\",\n+ image_id=\"\",\n+ )\n+ except Exception as ex:\n+ raise StreamlitAPIException(\n+ \"Failed to load the provided avatar value as an image.\"\n+ ) from ex\n+\n+\n+DISALLOWED_CONTAINERS_ERROR_TEXT = \"`st.chat_input()` can't be used inside an `st.expander`, `st.form`, `st.tabs`, `st.columns`, or `st.sidebar`.\"\n+\n+\n+@dataclass\n+class ChatInputSerde:\n+ def deserialize(\n+ self, ui_value: Optional[StringTriggerValueProto], widget_id: str = \"\"\n+ ) -> str | None:\n+ if ui_value is None or not ui_value.HasField(\"data\"):\n+ return None\n+\n+ return ui_value.data\n+\n+ def serialize(self, v: str | None) -> StringTriggerValueProto:\n+ return StringTriggerValueProto(data=v)\n+\n+\n+class ChatMixin:\n+ @gather_metrics(\"chat_message\")\n+ def chat_message(\n+ self,\n+ participant: Literal[\"user\", \"assistant\"] | str,\n+ *,\n+ avatar: Literal[\"user\", \"assistant\"] | str | AtomicImage | None = None,\n+ ) -> \"DeltaGenerator\":\n+ \"\"\"Insert a chat message container.\n+\n+ To add elements to the returned container, you can use ``with`` notation\n+ (preferred) or just call methods directly on the returned object. See the\n+ examples below.\n+\n+ Parameters\n+ ----------\n+ participant : \"user\", \"assistant\", or str\n+ The name of the chat participant. Can be \"user\" or \"assistant\" to enable\n+ preset styling and avatars. For accessibility reasons, you should not\n+ use an empty string.\n+\n+ avatar : str, numpy.ndarray, or BytesIO\n+ The avatar shown next to the message. Can be one of:\n+\n+ * A single emoji, e.g. \"๐Ÿง‘โ€๐Ÿ’ป\", \"๐Ÿค–\", \"๐Ÿฆ–\". Shortcodes are not supported.\n+\n+ * An image using one of the formats allowed for ``st.image``: path of a local\n+ image file; URL to fetch the image from; array of shape (w,h) or (w,h,1)\n+ for a monochrome image, (w,h,3) for a color image, or (w,h,4) for an RGBA image.\n+\n+ If None (default), uses default icons if ``participant`` is \"user\" or\n+ \"assistant\", or the first letter of the ``participant`` value.\n+\n+ Returns\n+ -------\n+ Container\n+ A single container that can hold multiple elements.\n+\n+ Examples\n+ --------\n+ You can use ``with`` notation to insert any element into an expander\n+\n+ >>> import streamlit as st\n+ >>> import numpy as np\n+ >>>\n+ >>> with st.chat_message(\"user\"):\n+ ... st.write(\"Hello ๐Ÿ‘‹\")\n+ ... st.line_chart(np.random.randn(30, 3))\n+\n+ .. output ::\n+ https://doc-chat-message-user.streamlit.app/\n+ height: 450px\n+\n+ Or you can just call methods directly in the returned objects:\n+\n+ >>> import streamlit as st\n+ >>> import numpy as np\n+ >>>\n+ >>> message = st.chat_message(\"assistant\"):\n+ >>> message.write(\"Hello human\")\n+ >>> message.bar_chart(np.random.randn(30, 3))\n+\n+ .. output ::\n+ https://doc-chat-message-assistant.streamlit.app/\n+ height: 450px\n+\n+ \"\"\"\n+ if participant is None:\n+ raise StreamlitAPIException(\"A participant is required for a chat message\")\n+\n+ if avatar is None and (\n+ participant.lower()\n+ in [\n+ PresetLabels.USER,\n+ PresetLabels.ASSISTANT,\n+ ]\n+ or is_emoji(participant)\n+ ):\n+ # For selected labels, we are mapping the label to an avatar\n+ avatar = participant.lower()\n+ avatar_type, converted_avatar = _process_avatar_input(avatar)\n+\n+ message_container_proto = BlockProto.ChatMessage()\n+ message_container_proto.participant = participant\n+ message_container_proto.avatar = converted_avatar\n+ message_container_proto.avatar_type = avatar_type\n+ block_proto = BlockProto()\n+ block_proto.allow_empty = True\n+ block_proto.chat_message.CopyFrom(message_container_proto)\n+\n+ return self.dg._block(block_proto=block_proto)\n+\n+ @gather_metrics(\"chat_input\")\n+ def chat_input(\n+ self,\n+ placeholder: str = \"Your message\",\n+ *,\n+ key: Key | None = None,\n+ max_chars: int | None = None,\n+ disabled: bool = False,\n+ on_submit: WidgetCallback | None = None,\n+ args: WidgetArgs | None = None,\n+ kwargs: WidgetKwargs | None = None,\n+ ) -> str | None:\n+ \"\"\"Display a chat input widget.\n+\n+ .. warning::\n+ Chat input can only be used once per app page and inside the main area of the app.\n+ It cannot be used in the sidebar, columns, expanders, forms or tabs.\n+ We plan to support this in the future.\n+\n+ Parameters\n+ ----------\n+ placeholder : str\n+ A placeholder text shown when the chat input is empty. Defaults to\n+ \"Your message\". For accessibility reasons, you should not use an\n+ empty string.\n+\n+ key : str or int\n+ An optional string or integer to use as the unique key for the widget.\n+ If this is omitted, a key will be generated for the widget based on\n+ its content. Multiple widgets of the same type may not share the same key.\n+\n+ max_chars : int or None\n+ The maximum number of characters that can be entered. If None\n+ (default), there will be no maximum.\n+\n+ disabled : bool\n+ Whether the chat input should be disabled. Defaults to False.\n+\n+ on_submit : callable\n+ An optional callback invoked when the chat input's value is submitted.\n+\n+ args : tuple\n+ An optional tuple of args to pass to the callback.\n+\n+ kwargs : dict\n+ An optional dict of kwargs to pass to the callback.\n+\n+ Returns\n+ -------\n+ str or None\n+ The current (non-empty) value of the text input widget on the last\n+ run of the app, None otherwise.\n+\n+ Examples\n+ --------\n+ >>> import streamlit as st\n+ >>>\n+ >>> prompt = st.chat_input(\"Say something\")\n+ >>> if prompt:\n+ ... st.write(f\"User has sent the following prompt: {prompt}\")\n+\n+ .. output ::\n+ https://doc-chat-input.streamlit.app/\n+ height: 350px\n+\n+ \"\"\"\n+ # We default to an empty string here and disallow user choice intentionally\n+ default = \"\"\n+ key = to_key(key)\n+ check_callback_rules(self.dg, on_submit)\n+ check_session_state_rules(default_value=default, key=key, writes_allowed=False)\n+\n+ # We omit this check for scripts running outside streamlit, because\n+ # they will have no script_run_ctx.\n+ if runtime.exists():\n+ if (\n+ len(list(self.dg._active_dg._parent_block_types)) > 0\n+ or self.dg._active_dg._root_container == RootContainer.SIDEBAR\n+ ):\n+ # TODO: This allows the user to create a chat_input inside a\n+ # container but it still cannot be in other containers. This is\n+ # a result of the Vertical field not being set in Blocks by\n+ # default and seems like a \"bug\", but one that is not producing\n+ # major issues. We should look into what's the correct behavior\n+ # to implement.\n+ raise StreamlitAPIException(DISALLOWED_CONTAINERS_ERROR_TEXT)\n+\n+ chat_input_proto = ChatInputProto()\n+ chat_input_proto.placeholder = str(placeholder)\n+\n+ if max_chars is not None:\n+ chat_input_proto.max_chars = max_chars\n+\n+ chat_input_proto.default = default\n+ chat_input_proto.position = ChatInputProto.Position.BOTTOM\n+\n+ ctx = get_script_run_ctx()\n+\n+ serde = ChatInputSerde()\n+ widget_state = register_widget(\n+ \"chat_input\",\n+ chat_input_proto,\n+ user_key=key,\n+ on_change_handler=on_submit,\n+ args=args,\n+ kwargs=kwargs,\n+ deserializer=serde.deserialize,\n+ serializer=serde.serialize,\n+ ctx=ctx,\n+ )\n+\n+ chat_input_proto.disabled = disabled\n+ if widget_state.value_changed and widget_state.value is not None:\n+ chat_input_proto.value = widget_state.value\n+ chat_input_proto.set_value = True\n+\n+ self.dg._enqueue(\"chat_input\", chat_input_proto)\n+ return widget_state.value if not widget_state.value_changed else None\n+\n+ @property\n+ def dg(self) -> \"DeltaGenerator\":\n+ \"\"\"Get our DeltaGenerator.\"\"\"\n+ return cast(\"DeltaGenerator\", self)\n+ return cast(\"DeltaGenerator\", self)", "line": null, "original_line": 326, "original_start_line": null, "path": "lib/streamlit/elements/chat.py", "start_line": null, "text": "@user1:\n## Unreachable code\n\nThis statement is unreachable.\n\n[Show more details](https://github.com/streamlit/streamlit/security/code-scanning/7761)\n\n@user2:\nRemoved on [e389710](https://github.com/streamlit/streamlit/pull/6865/commits/e389710796f154eacc209616f72d5dbb74e2f88d), so dismissing" } ]
ae457bcae5b577c96607231cb24add3a33eb841f
diff --git a/e2e/scripts/st_chat_input.py b/e2e/scripts/st_chat_input.py new file mode 100644 index 000000000000..b2411d648295 --- /dev/null +++ b/e2e/scripts/st_chat_input.py @@ -0,0 +1,17 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import streamlit as st + +st.chat_input(placeholder="Please enter your message here...", max_chars=200) diff --git a/e2e/scripts/st_chat_message.py b/e2e/scripts/st_chat_message.py new file mode 100644 index 000000000000..6deb542168a2 --- /dev/null +++ b/e2e/scripts/st_chat_message.py @@ -0,0 +1,69 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pandas as pd + +import streamlit as st + +np.random.seed(0) + + +# Generate a random dataframe +df = pd.DataFrame( + np.random.randn(5, 5), + columns=("col_%d" % i for i in range(5)), +) + + +with st.chat_message("user"): + st.write("Helloโ€ฆ") + +with st.chat_message("assistant"): + st.write( + """ +Hello, here is a code snippet: + +```python +import streamlit as st +with st.chat_message("assistant"): + st.write("Hello, here is a code snippet...") +``` +""" + ) + +with st.chat_message("user", avatar="๐Ÿง‘"): + st.write( + """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris tristique est +at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque +at. Ut et dui molestie, iaculis magna sed. +""" + ) + +with st.chat_message("dog", avatar="https://static.streamlit.io/examples/dog.jpg"): + st.write("Woof woof! I'm a dog and I like charts:") + st.line_chart(df, use_container_width=True) + +cat = st.chat_message("cat", avatar="https://static.streamlit.io/examples/cat.jpg") +cat.write("I'm a cat and I like this dataset:") +cat.dataframe(df, use_container_width=True) +cat.text_input("What's your name?") + + +with st.chat_message("Bot"): + with st.expander("See more", expanded=True): + st.write("Lorem ipsum dolor sit amet") + +st.chat_message("user") diff --git a/e2e/specs/st_chat_input.spec.js b/e2e/specs/st_chat_input.spec.js new file mode 100644 index 000000000000..a30f74b81c9c --- /dev/null +++ b/e2e/specs/st_chat_input.spec.js @@ -0,0 +1,76 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +describe("st.chat_input", () => { + before(() => { + cy.loadApp("http://localhost:3000/"); + cy.prepForElementSnapshots(); + cy.get(".stChatInputContainer").should("have.length", 1); + }); + + it("renders the default state correctly", () => { + cy.get(".stChatInputContainer").matchThemedSnapshots("chatInput"); + }); + + it("renders the focus state correctly", () => { + // Light Theme: + cy.get(".stChatInputContainer textarea").click(); + cy.get(".stChatInputContainer").matchImageSnapshot("chatInput-focused-light"); + // Dark Theme: + cy.changeTheme("Dark") + // refocus + cy.get(".stChatInputContainer textarea").click(); + cy.get(".stChatInputContainer").matchImageSnapshot("chatInput-focused-dark"); + }); + + it("Shift+Enter creates a new line", () => { + // Clear the text input & Shift+Enter + cy.get(".stChatInputContainer textarea").clear().type(`{shift+enter}New Line`); + cy.get(".stChatInputContainer").matchThemedSnapshots("chatInput-shiftEnter"); + }); + + it("Enter submits/clears input", () => { + // Types a message & then Enter + cy.get(".stChatInputContainer textarea").clear().type(`Corgi{enter}`); + cy.get('.stChatInputContainer textarea').invoke('val').should('eq', ''); + }); + + it("can click button to submit & clear input", () => { + // Types a message + cy.get(".stChatInputContainer textarea").clear().type(`Corgi`); + // Clicks the submit button + cy.get(".stChatInputContainer button").click(); + cy.get('.stChatInputContainer textarea').invoke('val').should('eq', ''); + }); + + it("grows when input text is long & shrinks when deleted", () => { + // Type a long message (but < 200 chars) + cy.get(".stChatInputContainer textarea").type(`Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna.`); + cy.get(".stChatInputContainer").matchThemedSnapshots("chatInput-grows"); + + // Remove characters on third line + cy.get(".stChatInputContainer textarea").type('{backspace}{backspace}{backspace}{backspace}{backspace}{backspace}{backspace}'); + cy.get(".stChatInputContainer").matchThemedSnapshots("chatInput-shrinks"); + }); + + it("max characters enforced", () => { + // Try to type a message over the 200 char limit + cy.get(".stChatInputContainer textarea").clear().type(`Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna sed. This text shouldn't appear in the input.`); + // Check that the message is truncated + cy.get('.stChatInputContainer textarea').invoke('val').should('eq', 'Lorem ipsum dolor amet, consectetur adipiscing elit. Mauris tristique est at tincidunt pul vinar. Nam pulvinar neque sapien, eu pellentesque metus pellentesque at. Ut et dui molestie, iaculis magna se') + cy.get(".stChatInputContainer").matchThemedSnapshots("chatInput-maxChars"); + }); +}); diff --git a/e2e/specs/st_chat_message.spec.js b/e2e/specs/st_chat_message.spec.js new file mode 100644 index 000000000000..b414b9d41c7b --- /dev/null +++ b/e2e/specs/st_chat_message.spec.js @@ -0,0 +1,32 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +describe("st.chat_message", () => { + before(() => { + cy.loadApp("http://localhost:3000/"); + cy.prepForElementSnapshots(); + // Make the toolbar disappear to not interfere with snapshots (in wide mode) + cy.get("[data-testid='stToolbar']").invoke("css", "opacity", 0); + }); + + it("renders chat messages correctly", () => { + cy.get(".stChatMessage").should("have.length", 7); + + cy.get(".stChatMessage").each((el, idx) => { + return cy.wrap(el).matchThemedSnapshots("chat_message-" + idx); + }); + }); +}); diff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png new file mode 100644 index 000000000000..4a61ca6ae14d Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png new file mode 100644 index 000000000000..c105f308c869 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png new file mode 100644 index 000000000000..26c52ddaa4ad Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-focused-light.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png new file mode 100644 index 000000000000..790fe325f745 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png new file mode 100644 index 000000000000..59e0a1c07bc3 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_chat_input.spec.js/chatInput-grows.snap.png differ 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a/frontend/src/app/components/AppView/AppView.test.tsx +++ b/frontend/src/app/components/AppView/AppView.test.tsx @@ -15,7 +15,19 @@ */ import React from "react" -import { Block as BlockProto, ForwardMsgMetadata } from "src/lib/proto" +import { screen } from "@testing-library/react" +import "@testing-library/jest-dom" +import { + AppContext, + Props as AppContextProps, +} from "src/app/components/AppContext" +import { + Block as BlockProto, + ForwardMsgMetadata, + PageConfig, + Element, + ChatInput as ChatInputProto, +} from "src/lib/proto" import { ScriptRunState } from "src/lib/ScriptRunState" import { BlockNode, ElementNode, AppRoot } from "src/lib/AppNode" import { FileUploadClient } from "src/lib/FileUploadClient" @@ -26,9 +38,25 @@ import { import { makeElementWithInfoText } from "src/lib/util/utils" import { ComponentRegistry } from "src/lib/components/widgets/CustomComponent" import { mockEndpoints, mockSessionInfo } from "src/lib/mocks/mocks" -import { render, shallow } from "src/lib/test_util" +import { render } from "src/lib/test_util" import AppView, { AppViewProps } from "./AppView" +function getContextOutput(context: Partial<AppContextProps>): AppContextProps { + return { + wideMode: false, + initialSidebarState: PageConfig.SidebarState.AUTO, + embedded: false, + showPadding: false, + disableScrolling: false, + showFooter: false, + showToolbar: false, + showColoredLine: false, + pageLinkBaseUrl: "", + sidebarChevronDownshift: 0, + ...context, + } +} + function getProps(props: Partial<AppViewProps> = {}): AppViewProps { const formsData = createFormsData() @@ -68,17 +96,15 @@ describe("AppView element", () => { }) it("renders without crashing", () => { - const props = getProps() - const wrapper = shallow(<AppView {...props} />) - - expect(wrapper).toBeDefined() + render(<AppView {...getProps()} />) }) it("does not render a sidebar when there are no elements and only one page", () => { const props = getProps() - const wrapper = shallow(<AppView {...props} />) + render(<AppView {...props} />) - expect(wrapper.find("[data-testid='stSidebar']").exists()).toBe(false) + const sidebar = screen.queryByTestId("stSidebar") + expect(sidebar).not.toBeInTheDocument() }) it("renders a sidebar when there are elements and only one page", () => { @@ -98,14 +124,10 @@ describe("AppView element", () => { const props = getProps({ elements: new AppRoot(new BlockNode([main, sidebar])), }) - const wrapper = shallow(<AppView {...props} />) + render(<AppView {...props} />) - expect(wrapper.find("ThemedSidebar").exists()).toBe(true) - expect(wrapper.find("ThemedSidebar").prop("hasElements")).toBe(true) - expect(wrapper.find("ThemedSidebar").prop("appPages")).toHaveLength(1) - expect(wrapper.find("ThemedSidebar").prop("currentPageScriptHash")).toBe( - "main_page_script_hash" - ) + const sidebarDOMElement = screen.queryByTestId("stSidebar") + expect(sidebarDOMElement).toBeInTheDocument() }) it("renders a sidebar when there are no elements but multiple pages", () => { @@ -113,11 +135,10 @@ describe("AppView element", () => { { pageName: "streamlit_app", pageScriptHash: "page_hash" }, { pageName: "streamlit_app2", pageScriptHash: "page_hash2" }, ] - const wrapper = shallow(<AppView {...getProps({ appPages })} />) + render(<AppView {...getProps({ appPages })} />) - expect(wrapper.find("ThemedSidebar").exists()).toBe(true) - expect(wrapper.find("ThemedSidebar").prop("hasElements")).toBe(false) - expect(wrapper.find("ThemedSidebar").prop("appPages")).toEqual(appPages) + const sidebarDOMElement = screen.queryByTestId("stSidebar") + expect(sidebarDOMElement).toBeInTheDocument() }) it("renders a sidebar when there are elements and multiple pages", () => { @@ -142,11 +163,10 @@ describe("AppView element", () => { elements: new AppRoot(new BlockNode([main, sidebar])), appPages, }) - const wrapper = shallow(<AppView {...props} />) + render(<AppView {...props} />) - expect(wrapper.find("ThemedSidebar").exists()).toBe(true) - expect(wrapper.find("ThemedSidebar").prop("hasElements")).toBe(true) - expect(wrapper.find("ThemedSidebar").prop("appPages")).toEqual(appPages) + const sidebarDOMElement = screen.queryByTestId("stSidebar") + expect(sidebarDOMElement).toBeInTheDocument() }) it("does not render the sidebar if there are no elements, multiple pages but hideSidebarNav is true", () => { @@ -158,65 +178,86 @@ describe("AppView element", () => { appPages, hideSidebarNav: true, }) - const wrapper = shallow(<AppView {...props} />) + render(<AppView {...props} />) - expect(wrapper.find("ThemedSidebar").exists()).toBe(false) + const sidebar = screen.queryByTestId("stSidebar") + expect(sidebar).not.toBeInTheDocument() }) it("does not render the wide class", () => { - jest - .spyOn(React, "useContext") - .mockImplementation(() => ({ wideMode: false, embedded: false })) - const wrapper = shallow(<AppView {...getProps()} />) + const realUseContext = React.useContext + jest.spyOn(React, "useContext").mockImplementation(input => { + if (input === AppContext) { + return getContextOutput({ wideMode: false, embedded: false }) + } + + return realUseContext(input) + }) - expect( - wrapper.find("StyledAppViewBlockContainer").prop("isWideMode") - ).toBe(false) + const main = new BlockNode([], new BlockProto({ allowEmpty: true })) + const sidebar = new BlockNode([], new BlockProto({ allowEmpty: true })) + + const props = getProps({ + elements: new AppRoot(new BlockNode([main, sidebar])), + }) + const { getByTestId } = render(<AppView {...props} />) - expect(wrapper.find("StyledAppViewFooter").prop("isWideMode")).toBe(false) + const style = window.getComputedStyle(getByTestId("block-container")) + expect(style.maxWidth).not.toEqual("initial") }) it("does render the wide class when specified", () => { - jest - .spyOn(React, "useContext") - .mockImplementation(() => ({ wideMode: true, embedded: false })) - const wrapper = shallow(<AppView {...getProps()} />) + const realUseContext = React.useContext + jest.spyOn(React, "useContext").mockImplementation(input => { + if (input === AppContext) { + return getContextOutput({ wideMode: true, embedded: false }) + } - expect( - wrapper.find("StyledAppViewBlockContainer").prop("isWideMode") - ).toBe(true) + return realUseContext(input) + }) + const { getByTestId } = render(<AppView {...getProps()} />) + const style = window.getComputedStyle(getByTestId("block-container")) - expect(wrapper.find("StyledAppViewFooter").prop("isWideMode")).toBe(true) + expect(style.maxWidth).toEqual("initial") }) it("opens link to streamlit.io in new tab", () => { - const wrapper = shallow(<AppView {...getProps()} />) - expect(wrapper.find("StyledAppViewFooterLink").props()).toEqual( - expect.objectContaining({ - href: "//streamlit.io", - target: "_blank", - }) - ) + render(<AppView {...getProps()} />) + const link = screen.getByRole("link", { name: "Streamlit" }) + expect(link).toHaveAttribute("href", "//streamlit.io") + expect(link).toHaveAttribute("target", "_blank") }) it("renders the Spacer and Footer when not embedded", () => { - jest - .spyOn(React, "useContext") - .mockImplementation(() => ({ wideMode: false, embedded: false })) - const wrapper = shallow(<AppView {...getProps()} />) + const realUseContext = React.useContext + jest.spyOn(React, "useContext").mockImplementation(input => { + if (input === AppContext) { + return getContextOutput({ wideMode: false, embedded: false }) + } + + return realUseContext(input) + }) + + const { getByRole, getByTestId } = render(<AppView {...getProps()} />) - expect(wrapper.find("StyledAppViewBlockSpacer").exists()).toBe(true) - expect(wrapper.find("StyledAppViewFooter").exists()).toBe(true) + expect(getByTestId("AppViewBlockSpacer")).toBeInTheDocument() + expect(getByRole("contentinfo")).toBeInTheDocument() }) it("does not render the Spacer and Footer when embedded", () => { - jest - .spyOn(React, "useContext") - .mockImplementation(() => ({ wideMode: false, embedded: true })) - const wrapper = shallow(<AppView {...getProps()} />) + const realUseContext = React.useContext + jest.spyOn(React, "useContext").mockImplementation(input => { + if (input === AppContext) { + return getContextOutput({ wideMode: false, embedded: true }) + } - expect(wrapper.find("StyledAppViewBlockSpacer").exists()).toBe(false) - expect(wrapper.find("StyledAppViewFooter").exists()).toBe(false) + return realUseContext(input) + }) + + const { queryByRole, queryByTestId } = render(<AppView {...getProps()} />) + + expect(queryByTestId("AppViewBlockSpacer")).not.toBeInTheDocument() + expect(queryByRole("contentinfo")).not.toBeInTheDocument() }) describe("when window.location.hash changes", () => { @@ -236,4 +277,43 @@ describe("AppView element", () => { }) }) }) + + it("does not render a Scroll To Bottom container when no chat input is present", () => { + const props = getProps() + render(<AppView {...props} />) + + const stbContainer = screen.queryByTestId("ScrollToBottomContainer") + expect(stbContainer).not.toBeInTheDocument() + }) + + it("renders a Scroll To Bottom container when a chat input is present", () => { + const chatInputElement = new ElementNode( + new Element({ + chatInput: { + id: "123", + placeholder: "Enter Text Here", + disabled: false, + default: "", + position: ChatInputProto.Position.BOTTOM, + }, + }), + ForwardMsgMetadata.create({}), + "no script run id" + ) + + const sidebar = new BlockNode([], new BlockProto({ allowEmpty: true })) + + const main = new BlockNode( + [chatInputElement], + new BlockProto({ allowEmpty: true }) + ) + const props = getProps({ + elements: new AppRoot(new BlockNode([main, sidebar])), + }) + + render(<AppView {...props} />) + + const stbContainer = screen.queryByTestId("ScrollToBottomContainer") + expect(stbContainer).toBeInTheDocument() + }) }) diff --git a/frontend/src/app/components/AppView/AppView.tsx b/frontend/src/app/components/AppView/AppView.tsx index 451bd5893e23..a444450b7f42 100644 --- a/frontend/src/app/components/AppView/AppView.tsx +++ b/frontend/src/app/components/AppView/AppView.tsx @@ -39,6 +39,7 @@ import { StyledIFrameResizerAnchor, StyledAppViewBlockSpacer, } from "./styled-components" +import ScrollToBottomContainer from "./ScrollToBottomContainer" export interface AppViewProps { elements: AppRoot @@ -96,6 +97,16 @@ function AppView(props: AppViewProps): ReactElement { endpoints, } = props + // TODO: This works for scroll to bottom, but we will need + // to revisit this when we support multiple position options + const containsChatInput = + Array.from(elements.main.getElements()).find(element => { + return element.type === "chatInput" + }) !== undefined + const Component = containsChatInput + ? ScrollToBottomContainer + : StyledAppViewMain + React.useEffect(() => { const listener = (): void => { sendMessageToHost({ @@ -120,6 +131,7 @@ function AppView(props: AppViewProps): ReactElement { const renderBlock = (node: BlockNode): ReactElement => ( <StyledAppViewBlockContainer className="block-container" + data-testid="block-container" isWideMode={wideMode} showPadding={showPadding} addPaddingForHeader={showToolbar || showColoredLine} @@ -164,7 +176,7 @@ function AppView(props: AppViewProps): ReactElement { {renderBlock(elements.sidebar)} </ThemedSidebar> )} - <StyledAppViewMain + <Component tabIndex={0} isEmbedded={embedded} disableScrolling={disableScrolling} @@ -179,8 +191,10 @@ function AppView(props: AppViewProps): ReactElement { /> {/* Spacer fills up dead space to ensure the footer remains at the bottom of the page in larger views */} - {(!embedded || showFooter) && <StyledAppViewBlockSpacer />} {(!embedded || showFooter) && ( + <StyledAppViewBlockSpacer data-testid="AppViewBlockSpacer" /> + )} + {(!embedded || showFooter) && !containsChatInput && ( <StyledAppViewFooter isWideMode={wideMode}> Made with{" "} <StyledAppViewFooterLink href="//streamlit.io" target="_blank"> @@ -188,7 +202,7 @@ function AppView(props: AppViewProps): ReactElement { </StyledAppViewFooterLink> </StyledAppViewFooter> )} - </StyledAppViewMain> + </Component> </StyledAppViewContainer> ) } diff --git a/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx b/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx new file mode 100644 index 000000000000..afac2e6d6dda --- /dev/null +++ b/frontend/src/app/components/AppView/ScrollToBottomContainer.tsx @@ -0,0 +1,45 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React, { ReactElement, ReactNode } from "react" +import { StyledAppViewMain } from "./styled-components" +import useScrollToBottom from "src/lib/hooks/useScrollToBottom" + +export interface Props { + className: string + tabIndex: number + isEmbedded: boolean + disableScrolling: boolean + children: ReactNode +} + +export default function ScrollToBottomContainer(props: Props): ReactElement { + const { className, tabIndex, children, isEmbedded, disableScrolling } = props + const scrollContainerRef = useScrollToBottom() + + return ( + <StyledAppViewMain + tabIndex={tabIndex} + className={className} + isEmbedded={isEmbedded} + disableScrolling={disableScrolling} + ref={scrollContainerRef} + data-testid="ScrollToBottomContainer" + > + {children} + </StyledAppViewMain> + ) +} diff --git a/frontend/src/lib/WidgetStateManager.test.ts b/frontend/src/lib/WidgetStateManager.test.ts index bbbdc5e8f619..d8423613c782 100644 --- a/frontend/src/lib/WidgetStateManager.test.ts +++ b/frontend/src/lib/WidgetStateManager.test.ts @@ -152,6 +152,18 @@ describe("Widget State Manager", () => { assertCallbacks({ insideForm: false }) }) + /** + * String Triggers can't be used within forms, so this test + * is not parameterized on insideForm. + */ + it("sets string trigger value correctly", () => { + const widget = getWidget({ insideForm: false }) + widgetMgr.setStringTriggerValue(widget, "sample string", { fromUi: true }) + // @ts-expect-error + expect(widgetMgr.getWidgetState(widget)).toBe(undefined) + assertCallbacks({ insideForm: false }) + }) + it.each([false, true])( "sets string array value correctly (insideForm=%p)", insideForm => { diff --git a/frontend/src/lib/WidgetStateManager.ts b/frontend/src/lib/WidgetStateManager.ts index 081a7359f90c..568e6a051b4f 100644 --- a/frontend/src/lib/WidgetStateManager.ts +++ b/frontend/src/lib/WidgetStateManager.ts @@ -23,6 +23,7 @@ import { IFileUploaderState, SInt64Array, StringArray, + StringTriggerValue, WidgetState, WidgetStates, } from "src/lib/proto" @@ -36,7 +37,7 @@ export interface Source { /** Common widget protobuf fields that are used by the WidgetStateManager. */ export interface WidgetInfo { id: string - formId: string + formId?: string } /** @@ -241,6 +242,22 @@ export class WidgetStateManager { } } + /** + * Sets the string trigger value for the given widget ID to a string value, + * sends a rerunScript message to the server, and then immediately unsets the + * string trigger value to None/null. + */ + public setStringTriggerValue( + widget: WidgetInfo, + value: string, + source: Source + ): void { + this.createWidgetState(widget, source).stringTriggerValue = + new StringTriggerValue({ data: value }) + this.onWidgetValueChanged(widget.formId, source) + this.deleteWidgetState(widget.id) + } + /** * Sets the trigger value for the given widget ID to true, sends a rerunScript message * to the server, and then immediately unsets the trigger value. @@ -527,7 +544,7 @@ export class WidgetStateManager { private createWidgetState(widget: WidgetInfo, source: Source): WidgetState { const addToForm = isValidFormId(widget.formId) && source.fromUi const widgetStateDict = addToForm - ? this.getOrCreateFormState(widget.formId).widgetStates + ? this.getOrCreateFormState(widget.formId as string).widgetStates : this.widgetStates return widgetStateDict.createState(widget.id) diff --git a/frontend/src/lib/components/core/Block/Block.tsx b/frontend/src/lib/components/core/Block/Block.tsx index 513ebea86ad6..feadb8ec9514 100644 --- a/frontend/src/lib/components/core/Block/Block.tsx +++ b/frontend/src/lib/components/core/Block/Block.tsx @@ -22,6 +22,7 @@ import { BlockNode, AppNode, ElementNode } from "src/lib/AppNode" import { getElementWidgetID } from "src/lib/util/utils" import withExpandable from "src/lib/hocs/withExpandable" import { Form } from "src/lib/components/widgets/Form" +import ChatMessage from "src/lib/components/elements/ChatMessage" import Tabs, { TabProps } from "src/lib/components/elements/Tabs" import { @@ -53,8 +54,13 @@ interface BlockPropsWithWidth extends BaseBlockProps { const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => { const { node } = props - // Allow columns to create the specified space regardless of empty state - if (node.isEmpty && !node.deltaBlock.column) { + // Allow columns and chat messages to create the specified space regardless of empty state + // TODO: Maybe we can simplify this to: node.isEmpty && !node.deltaBlock.allowEmpty? + if ( + node.isEmpty && + !node.deltaBlock.column && + !node.deltaBlock.chatMessage + ) { return <></> } @@ -101,6 +107,16 @@ const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => { ) } + if (node.deltaBlock.chatMessage) { + return ( + <ChatMessage + element={node.deltaBlock.chatMessage as BlockProto.ChatMessage} + > + {child} + </ChatMessage> + ) + } + if (node.deltaBlock.column) { return ( <StyledColumn diff --git a/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx b/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx index 11399baa467f..4dceeac1d09f 100644 --- a/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx +++ b/frontend/src/lib/components/core/Block/ElementNodeRenderer.tsx @@ -22,6 +22,7 @@ import { Button as ButtonProto, DownloadButton as DownloadButtonProto, CameraInput as CameraInputProto, + ChatInput as ChatInputProto, Checkbox as CheckboxProto, Code as CodeProto, ColorPicker as ColorPickerProto, @@ -106,6 +107,7 @@ const ArrowVegaLiteChart = React.lazy( const BokehChart = React.lazy( () => import("src/lib/components/elements/BokehChart") ) + const DebouncedBokehChart = debounceRender(BokehChart, 100) const DataFrame = React.lazy( @@ -137,6 +139,9 @@ const DownloadButton = React.lazy( const CameraInput = React.lazy( () => import("src/lib/components/widgets/CameraInput") ) +const ChatInput = React.lazy( + () => import("src/lib/components/widgets/ChatInput") +) const Checkbox = React.lazy( () => import("src/lib/components/widgets/Checkbox") ) @@ -481,6 +486,19 @@ const RawElementNodeRenderer = ( ) } + case "chatInput": { + const chatInputProto = node.element.chatInput as ChatInputProto + widgetProps.disabled = widgetProps.disabled || chatInputProto.disabled + return ( + <ChatInput + key={chatInputProto.id} + element={chatInputProto} + width={width} + {...widgetProps} + /> + ) + } + case "checkbox": { const checkboxProto = node.element.checkbox as CheckboxProto widgetProps.disabled = widgetProps.disabled || checkboxProto.disabled diff --git a/frontend/src/lib/components/core/Block/styled-components.ts b/frontend/src/lib/components/core/Block/styled-components.ts index 434c10acc512..9a17857fadfe 100644 --- a/frontend/src/lib/components/core/Block/styled-components.ts +++ b/frontend/src/lib/components/core/Block/styled-components.ts @@ -54,6 +54,7 @@ export interface StyledElementContainerProps { elementType: string } +const GLOBAL_ELEMENTS = ["balloons", "snow", "chatInput"] export const StyledElementContainer = styled.div<StyledElementContainerProps>( ({ theme, isStale, width, elementType }) => ({ width, @@ -68,7 +69,9 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>( overflow: "visible", }, - ...(isStale + // We do not want the chat input to be faded out. + // TODO: Reconsider this when we implement fixed-sized chat containers + ...(isStale && elementType !== "chatInput" ? { opacity: 0.33, transition: "opacity 1s ease-in 0.5s", @@ -80,10 +83,12 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>( display: "none", } : {}), - ...(elementType === "balloons" + ...(GLOBAL_ELEMENTS.includes(elementType) ? { - // Apply negative bottom margin to remove the flexbox gap. - // display: none does not work for balloons, since it needs to be visible. + // Global elements are rendered in their delta position, but they + // are not part of the flexbox layout. We apply a negative margin + // to remove the flexbox gap. display: none does not work for these, + // since they needs to be visible. marginBottom: `-${theme.spacing.lg}`, } : {}), diff --git a/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx new file mode 100644 index 000000000000..5bdd3009d76b --- /dev/null +++ b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.test.tsx @@ -0,0 +1,127 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React from "react" +import "@testing-library/jest-dom" + +import { render } from "src/lib/test_util" +import { Block as BlockProto } from "src/lib/proto" + +import ChatMessage, { ChatMessageProps } from "./ChatMessage" + +const getProps = ( + elementProps: Partial<BlockProto.ChatMessage> = {} +): ChatMessageProps => ({ + element: BlockProto.ChatMessage.create({ + name: "user", + avatarType: BlockProto.ChatMessage.AvatarType.ICON, + avatar: "user", + ...elementProps, + }), +}) + +describe("ChatMessage", () => { + it("renders without crashing", () => { + const props = getProps() + const rtlResults = render(<ChatMessage {...props} />) + expect(rtlResults).toBeDefined() + }) + + it("renders message children content", () => { + const props = getProps() + const { getByLabelText } = render( + <ChatMessage {...props}>Hello, world!</ChatMessage> + ) + expect(getByLabelText("Chat message from user").textContent).toBe( + "Hello, world!" + ) + }) + + it("renders with an emoji avatar", () => { + const props = getProps({ + avatar: "๐Ÿ˜ƒ", + avatarType: BlockProto.ChatMessage.AvatarType.EMOJI, + }) + const rtlResults = render(<ChatMessage {...props} />) + expect(rtlResults.getByText("๐Ÿ˜ƒ")).toBeTruthy() + }) + + it("renders with an image avatar", () => { + const props = getProps({ + avatar: "http://example.com/avatar.jpg", + avatarType: BlockProto.ChatMessage.AvatarType.IMAGE, + }) + const { container } = render(<ChatMessage {...props} />) + const images = container.getElementsByTagName("img") + expect(images.length).toEqual(1) + expect(images[0].src).toBe("http://example.com/avatar.jpg") + }) + + it("renders with a name label character as fallback", () => { + const props = getProps({ + avatar: undefined, + avatarType: undefined, + name: "test", + }) + const { getByText } = render(<ChatMessage {...props} />) + expect(getByText("T")).toBeTruthy() + }) + + it("renders with a 'user' icon avatar", () => { + const props = getProps({ + avatar: "user", + avatarType: BlockProto.ChatMessage.AvatarType.ICON, + name: "foo", + }) + const { container } = render(<ChatMessage {...props} />) + + const svgs = container.getElementsByTagName("svg") + expect(svgs.length).toEqual(1) + }) + + it("renders with a 'assistant' icon avatar", () => { + const props = getProps({ + avatar: "assistant", + avatarType: BlockProto.ChatMessage.AvatarType.ICON, + name: "foo", + }) + const { container } = render(<ChatMessage {...props} />) + + const svgs = container.getElementsByTagName("svg") + expect(svgs.length).toEqual(1) + }) + + it("renders with a grey background when name is 'user'", () => { + const props = getProps({ + name: "user", + }) + const { container } = render(<ChatMessage {...props} />) + const messageContainer = container.firstChild + expect(messageContainer).toHaveStyle( + "background-color: rgba(240, 242, 246, 0.5)" + ) + }) + + it("sets an aria label on the chat message", () => { + const props = getProps() + const { getByTestId } = render(<ChatMessage {...props} />) + + const chatMessageContent = getByTestId("stChatMessageContent") + expect(chatMessageContent.getAttribute("aria-label")).toEqual( + "Chat message from user" + ) + }) +}) diff --git a/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx new file mode 100644 index 000000000000..cf64a5479895 --- /dev/null +++ b/frontend/src/lib/components/elements/ChatMessage/ChatMessage.tsx @@ -0,0 +1,105 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React, { ReactElement } from "react" +import { useTheme } from "@emotion/react" +import { Face, SmartToy } from "@emotion-icons/material-outlined" + +import { Block as BlockProto } from "src/lib/proto" +import Icon from "src/lib/components/shared/Icon" +import { EmotionTheme } from "src/lib/theme" + +import { + StyledChatMessageContainer, + StyledMessageContent, + StyledAvatarImage, + StyledAvatarIcon, + StyledAvatarBackground, +} from "./styled-components" + +interface ChatMessageAvatarProps { + name: string + avatar?: string + avatarType?: BlockProto.ChatMessage.AvatarType +} + +function ChatMessageAvatar(props: ChatMessageAvatarProps): ReactElement { + const { avatar, avatarType, name } = props + const theme: EmotionTheme = useTheme() + + if (avatar) { + switch (avatarType) { + case BlockProto.ChatMessage.AvatarType.IMAGE: + return <StyledAvatarImage src={avatar} alt={`${name} avatar`} /> + case BlockProto.ChatMessage.AvatarType.EMOJI: + return <StyledAvatarBackground>{avatar}</StyledAvatarBackground> + case BlockProto.ChatMessage.AvatarType.ICON: + if (avatar === "user") { + return ( + <StyledAvatarIcon background={theme.colors.red60}> + <Icon content={Face} size="lg" /> + </StyledAvatarIcon> + ) + } else if (avatar === "assistant") { + return ( + <StyledAvatarIcon background={theme.colors.orange60}> + <Icon content={SmartToy} size="lg" /> + </StyledAvatarIcon> + ) + } + } + } + + // Fallback to first character of the name label if nothing else can be matched: + return ( + <StyledAvatarBackground> + {name ? name.charAt(0).toUpperCase() : "๐Ÿง‘โ€๐Ÿ’ป"} + </StyledAvatarBackground> + ) +} + +export interface ChatMessageProps { + element: BlockProto.ChatMessage +} + +const ChatMessage: React.FC<ChatMessageProps> = ({ + element, + children, +}): ReactElement => { + const { avatar, avatarType, name } = element + + return ( + <StyledChatMessageContainer + className="stChatMessage" + background={name.toLowerCase() === "user"} + > + <ChatMessageAvatar + name={name} + avatar={avatar} + avatarType={avatarType} + data-testid="stChatMessageAvatar" + /> + <StyledMessageContent + data-testid="stChatMessageContent" + aria-label={`Chat message from ${name}`} + > + {children} + </StyledMessageContent> + </StyledChatMessageContainer> + ) +} + +export default ChatMessage diff --git a/frontend/src/lib/components/elements/ChatMessage/index.tsx b/frontend/src/lib/components/elements/ChatMessage/index.tsx new file mode 100644 index 000000000000..810c6f9ca039 --- /dev/null +++ b/frontend/src/lib/components/elements/ChatMessage/index.tsx @@ -0,0 +1,17 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +export { default } from "./ChatMessage" diff --git a/frontend/src/lib/components/elements/ChatMessage/styled-components.ts b/frontend/src/lib/components/elements/ChatMessage/styled-components.ts new file mode 100644 index 000000000000..77daf65b4a3b --- /dev/null +++ b/frontend/src/lib/components/elements/ChatMessage/styled-components.ts @@ -0,0 +1,99 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import styled from "@emotion/styled" +import { transparentize } from "color2k" + +import { hasLightBackgroundColor } from "src/lib/theme" + +export interface StyledChatMessageContainerProps { + background: boolean +} + +export const StyledChatMessageContainer = + styled.div<StyledChatMessageContainerProps>(({ theme, background }) => { + const lightTheme = hasLightBackgroundColor(theme) + return { + display: "flex", + alignItems: "flex-start", + gap: theme.spacing.sm, + padding: theme.spacing.lg, + paddingRight: background ? theme.spacing.lg : 0, + borderRadius: theme.radii.lg, + ...(background + ? { + backgroundColor: lightTheme + ? transparentize(theme.colors.gray20, 0.5) + : transparentize(theme.colors.gray90, 0.5), + } + : {}), + } + }) + +export const StyledMessageContent = styled.div(({ theme }) => ({ + color: theme.colors.bodyText, + margin: "auto", + flexGrow: 1, +})) + +export const StyledAvatarBackground = styled.div(({ theme }) => { + const lightTheme = hasLightBackgroundColor(theme) + return { + display: "flex", + border: `1px solid ${ + lightTheme ? theme.colors.gray40 : theme.colors.gray85 + }`, + backgroundColor: lightTheme ? theme.colors.white : theme.colors.gray100, + color: lightTheme ? theme.colors.gray90 : theme.colors.white, + lineHeight: "1", + fontSize: theme.fontSizes.md, + width: "2rem", + height: "2rem", + borderRadius: theme.radii.lg, + alignItems: "center", + justifyContent: "center", + } +}) + +export interface StyledAvatarIconProps { + background: string +} + +export const StyledAvatarIcon = styled.div<StyledAvatarIconProps>( + ({ theme, background }) => { + const lightTheme = hasLightBackgroundColor(theme) + return { + display: "flex", + width: "2rem", + height: "2rem", + borderRadius: theme.radii.lg, + alignItems: "center", + justifyContent: "center", + backgroundColor: background, + color: lightTheme ? theme.colors.white : theme.colors.gray100, + } + } +) + +export const StyledAvatarImage = styled.img(({ theme }) => { + return { + width: "2rem", + height: "2rem", + borderRadius: theme.radii.lg, + objectFit: "cover", + display: "flex", + } +}) diff --git a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx index d8d7be2715d5..a12076809cbe 100644 --- a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx +++ b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.test.tsx @@ -15,7 +15,7 @@ */ import React from "react" -import { shallow } from "src/lib/test_util" +import { render } from "src/lib/test_util" import InputInstructions, { Props } from "./InputInstructions" @@ -27,24 +27,31 @@ const getProps = (props: Partial<Props> = {}): Props => ({ describe("InputInstructions", () => { const props = getProps() - const wrapper = shallow(<InputInstructions {...props} />) it("renders without crashing", () => { - expect(wrapper.text()).toBeDefined() + const { getByTestId } = render(<InputInstructions {...props} />) + + expect(getByTestId("InputInstructions").textContent).toBeDefined() }) it("should show Enter instructions", () => { - expect(wrapper.text()).toBe("Press Enter to apply") + const { getByTestId } = render(<InputInstructions {...props} />) + + expect(getByTestId("InputInstructions").textContent).toBe( + "Press Enter to apply" + ) }) describe("Multiline type", () => { const props = getProps({ type: "multiline", }) - const wrapper = shallow(<InputInstructions {...props} />) it("should show Ctrl+Enter instructions", () => { - expect(wrapper.text()).toBe("Press Ctrl+Enter to apply") + const { getByTestId } = render(<InputInstructions {...props} />) + expect(getByTestId("InputInstructions").textContent).toBe( + "Press Ctrl+Enter to apply" + ) }) it("show โŒ˜+Enter instructions", () => { @@ -56,9 +63,11 @@ describe("InputInstructions", () => { const props = getProps({ type: "multiline", }) - const wrapper = shallow(<InputInstructions {...props} />) + const { getByTestId } = render(<InputInstructions {...props} />) - expect(wrapper.text()).toBe("Press โŒ˜+Enter to apply") + expect(getByTestId("InputInstructions").textContent).toBe( + "Press โŒ˜+Enter to apply" + ) }) it("should show instructions for max length", () => { @@ -66,9 +75,11 @@ describe("InputInstructions", () => { type: "multiline", maxLength: 3, }) - const wrapper = shallow(<InputInstructions {...props} />) + const { getByTestId } = render(<InputInstructions {...props} />) - expect(wrapper.text()).toBe("Press โŒ˜+Enter to apply3/3") + expect(getByTestId("InputInstructions").textContent).toBe( + "Press โŒ˜+Enter to apply3/3" + ) }) }) @@ -76,8 +87,31 @@ describe("InputInstructions", () => { const props = getProps({ maxLength: 3, }) - const wrapper = shallow(<InputInstructions {...props} />) + const { getByTestId } = render(<InputInstructions {...props} />) + + expect(getByTestId("InputInstructions").textContent).toBe( + "Press Enter to apply3/3" + ) + }) + + describe("Chat type", () => { + const props = getProps({ + type: "chat", + }) - expect(wrapper.text()).toBe("Press Enter to apply3/3") + it("should not show instructions", () => { + const { getByTestId } = render(<InputInstructions {...props} />) + expect(getByTestId("InputInstructions").textContent).toBe("") + }) + + it("should show instructions for max length", () => { + const props = getProps({ + type: "chat", + maxLength: 3, + }) + const { getByTestId } = render(<InputInstructions {...props} />) + + expect(getByTestId("InputInstructions").textContent).toBe("3/3") + }) }) }) diff --git a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx index 752b95721e9b..c7ec2478d189 100644 --- a/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx +++ b/frontend/src/lib/components/shared/InputInstructions/InputInstructions.tsx @@ -24,7 +24,7 @@ export interface Props { value: string maxLength?: number className?: string - type?: "multiline" | "single" + type?: "multiline" | "single" | "chat" } const InputInstructions = ({ @@ -54,12 +54,12 @@ const InputInstructions = ({ } else { addMessage("Press Ctrl+Enter to apply") } - } else { + } else if (type === "single") { addMessage("Press Enter to apply") } } - if (maxLength) { + if (maxLength && (type !== "chat" || dirty)) { addMessage( `${value.length}/${maxLength}`, dirty && value.length >= maxLength @@ -67,7 +67,10 @@ const InputInstructions = ({ } return ( - <StyledWidgetInstructions className={className}> + <StyledWidgetInstructions + data-testid="InputInstructions" + className={className} + > {messages} </StyledWidgetInstructions> ) diff --git a/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx b/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx new file mode 100644 index 000000000000..aa6ec9ba133f --- /dev/null +++ b/frontend/src/lib/components/widgets/ChatInput/ChatInput.test.tsx @@ -0,0 +1,249 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React from "react" +import "@testing-library/jest-dom" +import { fireEvent } from "@testing-library/react" +import { render } from "src/lib/test_util" +import { ChatInput as ChatInputProto } from "src/lib/proto" +import { WidgetStateManager } from "src/lib/WidgetStateManager" + +import ChatInput, { Props } from "./ChatInput" + +const getProps = (elementProps: Partial<ChatInputProto> = {}): Props => ({ + element: ChatInputProto.create({ + id: "123", + placeholder: "Enter Text Here", + disabled: false, + default: "", + position: ChatInputProto.Position.BOTTOM, + ...elementProps, + }), + width: 0, + disabled: false, + widgetMgr: new WidgetStateManager({ + sendRerunBackMsg: jest.fn(), + formsDataChanged: jest.fn(), + }), +}) + +describe("ChatInput widget", () => { + afterEach(() => { + jest.restoreAllMocks() + }) + + it("renders without crashing", () => { + const props = getProps() + const rtlResults = render(<ChatInput {...props} />) + expect(rtlResults).toBeDefined() + }) + + it("shows a placeholder", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0].placeholder).toEqual(props.element.placeholder) + }) + + it("sets the aria label to the placeholder", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0].getAttribute("aria-label")).toEqual( + props.element.placeholder + ) + }) + + it("sets the value intially to the element default", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0].value).toEqual(props.element.default) + }) + + it("sets the value when values are typed in", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "Sample text" } }) + expect(textareas[0].value).toEqual("Sample text") + }) + + it("does not increase text value when maxChars is set", () => { + const props = getProps({ maxChars: 10 }) + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(textareas[0].value).toEqual("1234567890") + fireEvent.change(textareas[0], { target: { value: "12345678901" } }) + expect(textareas[0].value).toEqual("1234567890") + }) + + it("sends and resets the value on enter", () => { + const props = getProps() + const spy = jest.spyOn(props.widgetMgr, "setStringTriggerValue") + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(textareas[0].value).toEqual("1234567890") + fireEvent.keyDown(textareas[0], { key: "Enter" }) + expect(spy).toHaveBeenCalledWith(props.element, "1234567890", { + fromUi: true, + }) + expect(textareas[0].value).toEqual("") + }) + + it("will not send an empty value on enter if empty", () => { + const props = getProps() + const spy = jest.spyOn(props.widgetMgr, "setStringTriggerValue") + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.keyDown(textareas[0], { key: "Enter" }) + expect(spy).not.toHaveBeenCalledWith(props.element, "", { + fromUi: true, + }) + expect(textareas[0].value).toEqual("") + }) + + it("will not show instructions when the text has changed", () => { + const props = getProps() + const { container, getAllByTestId } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + const instructions = getAllByTestId("InputInstructions") + expect(instructions.length).toEqual(1) + expect(instructions[0].textContent).toEqual("") + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(instructions[0].textContent).toEqual("") + }) + + it("does not send/clear on shift + enter", () => { + const props = getProps() + const spy = jest.spyOn(props.widgetMgr, "setStringTriggerValue") + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(textareas[0].value).toEqual("1234567890") + fireEvent.keyDown(textareas[0], { key: "Enter", shiftKey: true }) + // We cannot test the value to be changed cause that is essentially a + // change event. + expect(textareas[0].value).not.toEqual("") + expect(spy).not.toHaveBeenCalled() + }) + + it("does not send/clear on ctrl + enter", () => { + const props = getProps() + const spy = jest.spyOn(props.widgetMgr, "setStringTriggerValue") + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(textareas[0].value).toEqual("1234567890") + fireEvent.keyDown(textareas[0], { key: "Enter", ctrlKey: true }) + // We cannot test the value to be changed cause that is essentially a + // change event. + expect(textareas[0].value).not.toEqual("") + expect(spy).not.toHaveBeenCalled() + }) + + it("does not send/clear on meta + enter", () => { + const props = getProps() + const spy = jest.spyOn(props.widgetMgr, "setStringTriggerValue") + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "1234567890" } }) + expect(textareas[0].value).toEqual("1234567890") + fireEvent.keyDown(textareas[0], { key: "Enter", metaKey: true }) + // We cannot test the value to be changed cause that is essentially a + // change event. + expect(textareas[0].value).not.toEqual("") + expect(spy).not.toHaveBeenCalled() + }) + + it("does sets the value if specified from protobuf to set it", () => { + const props = getProps({ value: "12345", setValue: true }) + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0].value).toEqual("12345") + }) + + it("does not set the value if protobuf does not specify to set it", () => { + const props = getProps({ value: "12345", setValue: false }) + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0].value).toEqual("") + }) + + it("disables the textarea and button", () => { + const props = getProps({ disabled: true }) + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0]).toBeDisabled() + + const button = container.getElementsByTagName("button") + expect(button.length).toEqual(1) + expect(button[0]).toBeDisabled() + }) + + it("not disable the textarea by default", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + expect(textareas[0]).not.toBeDisabled() + + const button = container.getElementsByTagName("button") + expect(button.length).toEqual(1) + expect(button[0]).toBeDisabled() + }) + + it("disables the send button by default since there's no text", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + + const button = container.getElementsByTagName("button") + expect(button.length).toEqual(1) + expect(button[0]).toBeDisabled() + }) + + it("enables the send button when text is set, disables it when removed", () => { + const props = getProps() + const { container } = render(<ChatInput {...props} />) + const textareas = container.getElementsByTagName("textarea") + expect(textareas.length).toEqual(1) + fireEvent.change(textareas[0], { target: { value: "Sample text" } }) + + const button = container.getElementsByTagName("button") + expect(button.length).toEqual(1) + expect(button[0]).not.toBeDisabled() + + fireEvent.change(textareas[0], { target: { value: "" } }) + expect(button.length).toEqual(1) + expect(button[0]).toBeDisabled() + }) +}) diff --git a/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx b/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx new file mode 100644 index 000000000000..6596018b196d --- /dev/null +++ b/frontend/src/lib/components/widgets/ChatInput/ChatInput.tsx @@ -0,0 +1,238 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React, { + useEffect, + useRef, + useState, + ChangeEvent, + KeyboardEvent, +} from "react" +import { useTheme } from "@emotion/react" +import { Send } from "@emotion-icons/material-rounded" +import { Textarea as UITextArea } from "baseui/textarea" + +import { ChatInput as ChatInputProto } from "src/lib/proto" +import { WidgetStateManager } from "src/lib/WidgetStateManager" +import Icon from "src/lib/components/shared/Icon" +import InputInstructions from "src/lib/components/shared/InputInstructions/InputInstructions" +import { hasLightBackgroundColor } from "src/lib/theme" + +import { + StyledChatInputContainer, + StyledChatInput, + StyledFloatingChatInputContainer, + StyledInputInstructionsContainer, + StyledSendIconButton, + StyledSendIconButtonContainer, +} from "./styled-components" + +export interface Props { + disabled: boolean + element: ChatInputProto + widgetMgr: WidgetStateManager + width: number +} + +// We want to show easily that there's scrolling so we deliberately choose +// a half size. +const MAX_VISIBLE_NUM_LINES = 6.5 +// Rounding errors can arbitrarily create scrollbars. We add a rounding offset +// to manage it better. +const ROUNDING_OFFSET = 1 + +const isEnterKeyPressed = ( + event: KeyboardEvent<HTMLTextAreaElement> +): boolean => { + // Using keyCode as well due to some different behaviors on Windows + // https://bugs.chromium.org/p/chromium/issues/detail?id=79407 + + const { keyCode, key } = event + return key === "Enter" || keyCode === 13 || keyCode === 10 +} + +function ChatInput({ width, element, widgetMgr }: Props): React.ReactElement { + const theme = useTheme() + // True if the user-specified state.value has not yet been synced to the WidgetStateManager. + const [dirty, setDirty] = useState(false) + // The value specified by the user via the UI. If the user didn't touch this widget's UI, the default value is used. + const [value, setValue] = useState(element.default) + // The value of the height of the textarea. It depends on a variety of factors including the default height, and autogrowing + const [scrollHeight, setScrollHeight] = useState(0) + const chatInputRef = useRef<HTMLTextAreaElement>(null) + const heightGuidance = useRef({ minHeight: 0, maxHeight: 0 }) + + const getScrollHeight = (): number => { + let scrollHeight = 0 + const { current: textarea } = chatInputRef + if (textarea) { + const placeholder = textarea.placeholder + textarea.placeholder = "" + textarea.style.height = "auto" + scrollHeight = textarea.scrollHeight + textarea.placeholder = placeholder + textarea.style.height = "" + } + + return scrollHeight + } + + const handleSubmit = (): void => { + if (!value) { + return + } + + widgetMgr.setStringTriggerValue(element, value, { fromUi: true }) + setDirty(false) + setValue("") + setScrollHeight(0) + } + + const handleKeyDown = (e: KeyboardEvent<HTMLTextAreaElement>): void => { + const { metaKey, ctrlKey, shiftKey } = e + const shouldSubmit = + isEnterKeyPressed(e) && !shiftKey && !ctrlKey && !metaKey + + if (shouldSubmit) { + e.preventDefault() + + handleSubmit() + } + } + + const handleChange = (e: ChangeEvent<HTMLTextAreaElement>): void => { + const { value } = e.target + const { maxChars } = element + + if (maxChars !== 0 && value.length > maxChars) { + return + } + + setDirty(value !== "") + setValue(value) + setScrollHeight(getScrollHeight()) + } + + useEffect(() => { + if (element.setValue) { + // We are intentionally setting this to avoid regularly calling this effect. + element.setValue = false + const val = element.value || "" + setValue(val) + setDirty(val !== "") + } + }, [element]) + + useEffect(() => { + if (chatInputRef.current) { + const { offsetHeight } = chatInputRef.current + heightGuidance.current.minHeight = offsetHeight + heightGuidance.current.maxHeight = offsetHeight * MAX_VISIBLE_NUM_LINES + } + }, [chatInputRef]) + + const { disabled, placeholder, maxChars, position } = element + const lightTheme = hasLightBackgroundColor(theme) + const { minHeight, maxHeight } = heightGuidance.current + const placeholderColor = lightTheme + ? theme.colors.gray70 + : theme.colors.gray80 + + const isInputExtended = + scrollHeight > 0 && chatInputRef.current + ? Math.abs(scrollHeight - minHeight) > ROUNDING_OFFSET + : false + + return ( + <StyledFloatingChatInputContainer className="stChatFloatingInputContainer"> + <StyledChatInputContainer + className="stChatInputContainer" + width={width} + position={position} + > + <StyledChatInput> + <UITextArea + data-testid="stChatInput" + inputRef={chatInputRef} + value={value} + placeholder={placeholder} + onChange={handleChange} + onKeyDown={handleKeyDown} + aria-label={placeholder} + disabled={disabled} + rows={1} + overrides={{ + Root: { + style: { + outline: "none", + backgroundColor: theme.colors.transparent, + // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings. + borderLeftWidth: "1px", + borderRightWidth: "1px", + borderTopWidth: "1px", + borderBottomWidth: "1px", + width: `${width}px`, + }, + }, + InputContainer: { + style: { + backgroundColor: theme.colors.transparent, + }, + }, + Input: { + style: { + lineHeight: "1.4", + backgroundColor: theme.colors.transparent, + "::placeholder": { + color: placeholderColor, + }, + height: isInputExtended + ? `${scrollHeight + ROUNDING_OFFSET}px` + : "auto", + maxHeight: maxHeight ? `${maxHeight}px` : "none", + // Baseweb requires long-hand props, short-hand leads to weird bugs & warnings. + paddingRight: "3rem", + paddingLeft: theme.spacing.sm, + paddingBottom: theme.spacing.sm, + paddingTop: theme.spacing.sm, + }, + }, + }} + /> + <StyledInputInstructionsContainer> + <InputInstructions + dirty={dirty} + value={value} + maxLength={maxChars} + type="chat" + /> + </StyledInputInstructionsContainer> + <StyledSendIconButtonContainer> + <StyledSendIconButton + onClick={handleSubmit} + disabled={!dirty || disabled} + extended={isInputExtended} + > + <Icon content={Send} size="xl" color="inherit" /> + </StyledSendIconButton> + </StyledSendIconButtonContainer> + </StyledChatInput> + </StyledChatInputContainer> + </StyledFloatingChatInputContainer> + ) +} + +export default ChatInput diff --git a/frontend/src/lib/components/widgets/ChatInput/index.tsx b/frontend/src/lib/components/widgets/ChatInput/index.tsx new file mode 100644 index 000000000000..65754e81dc92 --- /dev/null +++ b/frontend/src/lib/components/widgets/ChatInput/index.tsx @@ -0,0 +1,16 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +export { default } from "./ChatInput" diff --git a/frontend/src/lib/components/widgets/ChatInput/styled-components.ts b/frontend/src/lib/components/widgets/ChatInput/styled-components.ts new file mode 100644 index 000000000000..65e15fa99d44 --- /dev/null +++ b/frontend/src/lib/components/widgets/ChatInput/styled-components.ts @@ -0,0 +1,129 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +import styled from "@emotion/styled" +import { ChatInput as ChatInputProto } from "src/lib/proto" +import { hasLightBackgroundColor } from "src/lib/theme" + +export interface StyledChatInputContainerProps { + width: number + position: ChatInputProto.Position +} + +export const StyledChatInputContainer = + styled.div<StyledChatInputContainerProps>(({ theme, width, position }) => { + const lightTheme = hasLightBackgroundColor(theme) + return { + borderRadius: theme.radii.lg, + display: "flex", + ...(position === ChatInputProto.Position.BOTTOM && { + backgroundColor: lightTheme + ? theme.colors.gray20 + : theme.colors.gray90, + }), + width: `${width}px`, + } + }) + +export const StyledChatInput = styled.div(({ theme }) => { + return { + backgroundColor: theme.colors.transparent, + position: "relative", + flexGrow: 1, + borderRadius: theme.radii.lg, + display: "flex", + alignItems: "center", + } +}) + +interface StyledSendIconButtonProps { + disabled: boolean + extended: boolean +} + +export const StyledSendIconButton = styled.button<StyledSendIconButtonProps>( + ({ theme, disabled, extended }) => { + const lightTheme = hasLightBackgroundColor(theme) + const [cleanIconColor, dirtyIconColor] = lightTheme + ? [theme.colors.gray60, theme.colors.gray80] + : [theme.colors.gray80, theme.colors.gray40] + return { + border: "none", + backgroundColor: theme.colors.transparent, + borderTopRightRadius: extended ? theme.radii.none : theme.radii.lg, + borderTopLeftRadius: extended ? theme.radii.lg : theme.radii.none, + borderBottomRightRadius: theme.radii.lg, + display: "inline-flex", + alignItems: "center", + justifyContent: "center", + lineHeight: 1, + margin: 0, + padding: theme.spacing.sm, + color: disabled ? cleanIconColor : dirtyIconColor, + pointerEvents: "auto", + "&:focus": { + outline: "none", + }, + ":focus": { + outline: "none", + }, + "&:focus-visible": { + backgroundColor: lightTheme + ? theme.colors.gray10 + : theme.colors.gray90, + }, + "&:hover": { + backgroundColor: theme.colors.primary, + color: theme.colors.white, + }, + "&:disabled, &:disabled:hover, &:disabled:active": { + backgroundColor: theme.colors.transparent, + borderColor: theme.colors.transparent, + color: theme.colors.gray, + }, + } + } +) + +export const StyledFloatingChatInputContainer = styled.div(({ theme }) => ({ + position: "fixed", + bottom: "0px", + paddingBottom: "70px", + paddingTop: theme.spacing.lg, + backgroundColor: theme.colors.bgColor, + zIndex: theme.zIndices.chatInput, + [`@media (max-width: ${theme.breakpoints.md})`]: { + display: "flex", + flexDirection: "column", + alignItems: "center", + left: 0, + width: "100vw", + }, +})) + +export const StyledSendIconButtonContainer = styled.div(() => ({ + display: "flex", + alignItems: "flex-end", + height: "100%", + position: "absolute", + right: "0px", + pointerEvents: "none", +})) + +export const StyledInputInstructionsContainer = styled.div({ + position: "absolute", + bottom: "0px", + right: "3rem", +}) diff --git a/frontend/src/lib/hooks/useScrollAnimation.test.ts b/frontend/src/lib/hooks/useScrollAnimation.test.ts new file mode 100644 index 000000000000..5fbb8d4c6f02 --- /dev/null +++ b/frontend/src/lib/hooks/useScrollAnimation.test.ts @@ -0,0 +1,126 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { renderHook, act } from "@testing-library/react-hooks" +import useScrollAnimation from "./useScrollAnimation" + +describe("useScrollAnimation", () => { + let targetElement: HTMLElement + let onEndMock: () => void + let scrollHeight: number + let offsetHeight: number + + beforeEach(() => { + targetElement = document.createElement("div") + targetElement.scrollTop = 0 + scrollHeight = 200 + offsetHeight = 100 + Object.defineProperty(targetElement, "scrollHeight", { + set: value => (scrollHeight = value), + get: () => scrollHeight, + }) + Object.defineProperty(targetElement, "offsetHeight", { + set: value => (offsetHeight = value), + get: () => offsetHeight, + }) + targetElement.addEventListener = jest.fn() + targetElement.removeEventListener = jest.fn() + onEndMock = jest.fn() + }) + + afterEach(() => { + jest.clearAllMocks() + }) + + it("should animate scroll", () => { + jest.useFakeTimers() + + renderHook(() => useScrollAnimation(targetElement, onEndMock, true)) + + // Simulate scroll animation + act(() => { + jest.advanceTimersByTime(5) + // Trigger the callback of requestAnimationFrame + act(() => { + jest.runOnlyPendingTimers() + }) + }) + + // Assert the updated scrollTop value + expect(targetElement.scrollTop).toBeGreaterThan(0) + + // Simulate reaching the end of animation + act(() => { + jest.advanceTimersByTime(100) + // Trigger the callback of requestAnimationFrame + act(() => { + jest.runOnlyPendingTimers() + }) + }) + + // Assert that onEnd callback is called + expect(onEndMock).toHaveBeenCalled() + + jest.useRealTimers() + }) + + it("should register and deregister the correct events", () => { + jest.useFakeTimers() + + const { unmount } = renderHook(() => + useScrollAnimation(targetElement, onEndMock, true) + ) + + expect(targetElement.addEventListener).toHaveBeenCalledTimes(2) + expect(targetElement.addEventListener).toHaveBeenCalledWith( + "pointerdown", + expect.any(Function), + { passive: true } + ) + expect(targetElement.addEventListener).toHaveBeenCalledWith( + "wheel", + expect.any(Function), + { passive: true } + ) + + unmount() + + // Cleanup + expect(targetElement.removeEventListener).toHaveBeenCalledTimes(2) + expect(targetElement.removeEventListener).toHaveBeenCalledWith( + "pointerdown", + expect.any(Function) + ) + expect(targetElement.removeEventListener).toHaveBeenCalledWith( + "wheel", + expect.any(Function) + ) + + jest.useRealTimers() + }) + + it("should not animate scroll if target element is null", () => { + renderHook(() => useScrollAnimation(null, onEndMock, true)) + + expect(targetElement.addEventListener).not.toHaveBeenCalled() + }) + + it("should not animate scroll if isAnimating is false", () => { + renderHook(() => useScrollAnimation(targetElement, onEndMock, false)) + + expect(targetElement.addEventListener).not.toHaveBeenCalled() + }) +}) diff --git a/frontend/src/lib/hooks/useScrollAnimation.ts b/frontend/src/lib/hooks/useScrollAnimation.ts new file mode 100644 index 000000000000..05d911934e63 --- /dev/null +++ b/frontend/src/lib/hooks/useScrollAnimation.ts @@ -0,0 +1,146 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { useCallback, useLayoutEffect, useRef } from "react" + +/** + * Computes next step in a square-root based animation sequence. Step size is + * square root of absolute difference between current and target values. Can be + * used as a stepper function for other higher-level stepping functions. + * + * @param {number} current - Current value in animation sequence. + * @param {number} to - Target value of animation sequence. + * @returns {number} Next value in animation sequence. + */ +function squareStepper(current: number, to: number): number { + const sign = Math.sign(to - current) + const step = Math.sqrt(Math.abs(to - current)) + const next = current + step * sign + + if (sign > 0) { + return Math.min(to, next) + } + + return Math.max(to, next) +} + +/** + * Computes sequence of steps in animation by repeatedly applying a stepper + * function. + * + * @param {number} from - Initial value in animation sequence. + * @param {number} to - Target value of animation sequence. + * @param {function} stepper - Function computing next value given current + * and target values. + * @param {number} index - Number of steps to compute. + * @returns {number} Value at given index in animation sequence. + */ +function step( + from: number, + to: number, + stepper: (x: number, y: number) => number, + index: number +): number { + let next = from + + for (let i = 0; i < index; i++) { + next = stepper(next, to) + } + + return next +} + +/** + * Handles scroll animation for a given target HTMLElement. Uses a square-root + * based stepping function to compute scroll animation. Stops animation if + * target's scrollTop has reached scrollHeight or if user interacts with target + * (mousedown or mousewheel). Can also be cancelled by caller. + * + * @export + * @param {HTMLElement | null} target - HTML element to animate scroll of. If + * null, no animation is performed. + * @param {() => void} onEnd - Callback when animation ends or is cancelled. + * @param {boolean} isAnimating - Boolean to start or stop animation. If false, + * no animation is performed. + * @returns {void} + */ +export default function useScrollAnimation( + target: HTMLElement | null, + onEnd: () => void, + isAnimating: boolean +): void { + const animator = useRef(0) + + const animate = useCallback( + (from, index, start = Date.now()) => { + cancelAnimationFrame(animator.current) + + animator.current = requestAnimationFrame(() => { + if (target) { + const toNumber = target.scrollHeight - target.offsetHeight + let nextValue = step( + from, + toNumber, + squareStepper, + (Date.now() - start) / 5 + ) + + if (Math.abs(toNumber - nextValue) < 1.5) { + nextValue = toNumber + } + + target.scrollTop = nextValue + + if (toNumber === nextValue) { + onEnd() + } else { + animate(from, index + 1, start) + } + } + }) + }, + [animator, onEnd, target] + ) + + const handleCancelAnimation = useCallback(() => { + cancelAnimationFrame(animator.current) + onEnd() + }, [onEnd]) + + useLayoutEffect(() => { + if (!target || !isAnimating) { + return + } + animate(target.scrollTop, 1) + + if (target) { + target.addEventListener("pointerdown", handleCancelAnimation, { + passive: true, + }) + target.addEventListener("wheel", handleCancelAnimation, { + passive: true, + }) + + return () => { + target.removeEventListener("pointerdown", handleCancelAnimation) + target.removeEventListener("wheel", handleCancelAnimation) + cancelAnimationFrame(animator.current) + } + } + + return () => cancelAnimationFrame(animator.current) + }, [animate, animator, handleCancelAnimation, target, isAnimating]) +} diff --git a/frontend/src/lib/hooks/useScrollSpy.test.ts b/frontend/src/lib/hooks/useScrollSpy.test.ts new file mode 100644 index 000000000000..1beaa2cb9613 --- /dev/null +++ b/frontend/src/lib/hooks/useScrollSpy.test.ts @@ -0,0 +1,137 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { renderHook, act } from "@testing-library/react-hooks" +import useScrollSpy, { debounce } from "./useScrollSpy" + +describe("debounce function", () => { + beforeEach(() => { + jest.useFakeTimers() + }) + it("should call the function immediately when no delay is provided", () => { + const fn = jest.fn() + const debouncedFn = debounce(fn, 0) + + debouncedFn("arg1", "arg2") + + expect(fn).toHaveBeenCalledTimes(1) + expect(fn).toHaveBeenCalledWith("arg1", "arg2") + }) + + it("should delay the function call when a delay is provided", () => { + const fn = jest.fn() + const debouncedFn = debounce(fn, 100) + debouncedFn("arg1", "arg2") + expect(fn).toHaveBeenCalledTimes(1) + expect(fn).toHaveBeenCalledWith("arg1", "arg2") + + debouncedFn("arg3", "arg4") + expect(fn).not.toHaveBeenCalledWith("arg3", "arg4") + jest.advanceTimersByTime(99) + expect(fn).not.toHaveBeenCalledWith("arg3", "arg4") + + jest.advanceTimersByTime(1) + expect(fn).toHaveBeenCalledTimes(2) + expect(fn).toHaveBeenCalledWith("arg3", "arg4") + }) + + it("should cancel the delay when the function is called again", () => { + const fn = jest.fn() + const debouncedFn = debounce(fn, 100) + debouncedFn("arg1", "arg2") + expect(fn).toHaveBeenCalledTimes(1) + expect(fn).toHaveBeenCalledWith("arg1", "arg2") + + debouncedFn("arg3", "arg4") + jest.advanceTimersByTime(99) + + debouncedFn("arg5", "arg6") + expect(fn).not.toHaveBeenCalledWith("arg5", "arg6") + + jest.advanceTimersByTime(1) + expect(fn).toHaveBeenCalledTimes(2) + expect(fn).toHaveBeenCalledWith("arg5", "arg6") + }) +}) + +describe("useScrollSpy hook", () => { + let target: HTMLElement + let eventHandler: ({ timeStampLow }: any) => void + + beforeEach(() => { + jest.useFakeTimers() + target = document.createElement("div") + eventHandler = jest.fn() + + document.body.appendChild(target) + jest.spyOn(target, "addEventListener") + jest.spyOn(target, "removeEventListener") + }) + + afterEach(() => { + document.body.removeChild(target) + jest.clearAllMocks() + }) + + it("should set up and clean up event listeners", () => { + const { unmount } = renderHook(() => useScrollSpy(target, eventHandler)) + + expect(target.addEventListener).toHaveBeenCalledWith( + "scroll", + expect.any(Function), + { passive: true } + ) + expect(eventHandler).toHaveBeenCalledWith({ + target, + timeStampLow: expect.any(Number), + }) + + unmount() + + expect(target.removeEventListener).toHaveBeenCalledWith( + "scroll", + expect.any(Function) + ) + }) + + it("should not set up event listeners if no target is provided", () => { + renderHook(() => useScrollSpy(null, eventHandler)) + + expect(target.addEventListener).not.toHaveBeenCalled() + expect(eventHandler).not.toHaveBeenCalled() + }) + + it("should debounce events", () => { + renderHook(() => useScrollSpy(target, eventHandler)) + + const scrollEvent = new Event("scroll") + act(() => { + target.dispatchEvent(scrollEvent) + }) + expect(eventHandler).toHaveBeenCalledTimes(1) + + act(() => { + target.dispatchEvent(scrollEvent) + }) + expect(eventHandler).toHaveBeenCalledTimes(1) + + jest.advanceTimersByTime(99) + expect(eventHandler).toHaveBeenCalledTimes(1) + + jest.advanceTimersByTime(1) + expect(eventHandler).toHaveBeenCalledTimes(2) + }) +}) diff --git a/frontend/src/lib/hooks/useScrollSpy.ts b/frontend/src/lib/hooks/useScrollSpy.ts new file mode 100644 index 000000000000..2f369e665f33 --- /dev/null +++ b/frontend/src/lib/hooks/useScrollSpy.ts @@ -0,0 +1,130 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { useLayoutEffect, useMemo, useRef, useCallback } from "react" + +/** + * Creates a debounced function that delays invoking `fn` until after `ms` + * milliseconds have passed since the last time the debounced function was + * invoked. + * + * @param fn - A function to debounce. `fn` is called after the debounced + * function has not been called for `ms` milliseconds. + * + * @param ms - The delay in milliseconds before `fn` is executed. + * + * The debounced function behaves as follows: + * - It will be invoked immediately if 0 is passed for `ms`. + * - It will be invoked immediately for the first call in any case. + * - For subsequent calls, if less than `ms` milliseconds have passed since + * the last invocation, a new invocation of `fn` is scheduled for `ms` + * milliseconds after the last invocation. + * - If it is invoked and `ms` milliseconds have passed since the last + * invocation, `fn` is executed immediately and the timestamp is updated. + * - If a new invocation of the debounced function is scheduled and it + * is invoked again before the scheduled invocation, the scheduled + * invocation is canceled and a new one is scheduled `ms` milliseconds + * after the latest invocation. + * + * TODO: This has very similar but different behavior than our debounce function + * in utils.ts. This behavior ensures that the debounced function is called on + * some interval. Our other debounce function ensures that the function is + * delayed until the user stops calling it. We should probably unify these + * + * @returns A debounced version of the `fn` function. + */ +export function debounce( + fn: (...args: any[]) => void, + ms: number +): (...args: any[]) => void { + if (!ms) { + return fn + } + + let last = 0 + let timeout: ReturnType<typeof setTimeout> | null = null + + return (...args) => { + const now = Date.now() + + if (now - last > ms) { + fn(...args) + last = now + } else { + if (timeout) { + clearTimeout(timeout) + } + + timeout = setTimeout(() => { + fn(...args) + last = Date.now() + }, Math.max(0, ms - now + last)) + } + } +} +const DEFAULT_DEBOUNCE_MS = 100 + +/** + * A hook to add a scroll event listener to a target element with debouncing. + * + * @param target - The target HTMLElement to attach the scroll listener to. + * @param eventHandler - The callback function to execute on scroll. + * + * The hook behaves as follows: + * - The eventHandler callback is wrapped in a debounce function, which + * ensures the callback is not executed too frequently. + * - A scroll event listener is added to the target element on mount. + * - A 'timeStampLow' property is added to the event object before it's + * passed to the eventHandler. + * - The scroll event listener is removed from the target when the component + * unmounts. + * + * @returns void. + */ +export default function useScrollSpy( + target: HTMLElement | null, + eventHandler: ({ timeStampLow }: any) => void +): void { + const onEventRef = useRef(eventHandler) + + const debouncer = useMemo( + () => + debounce(event => { + onEventRef.current(event) + }, DEFAULT_DEBOUNCE_MS), + [onEventRef] + ) + + const handleEvent = useCallback( + event => { + event.timeStampLow = Date.now() + + debouncer(event) + }, + [debouncer] + ) + + useLayoutEffect(() => { + if (!target) { + return () => {} + } + + target.addEventListener("scroll", handleEvent, { passive: true }) + handleEvent({ target }) + + return () => target.removeEventListener("scroll", handleEvent) + }, [handleEvent, target]) +} diff --git a/frontend/src/lib/hooks/useScrollToBottom.test.ts b/frontend/src/lib/hooks/useScrollToBottom.test.ts new file mode 100644 index 000000000000..f61d13bec541 --- /dev/null +++ b/frontend/src/lib/hooks/useScrollToBottom.test.ts @@ -0,0 +1,44 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { renderHook } from "@testing-library/react-hooks" +import useScrollToBottom from "./useScrollToBottom" +import useStateRef from "./useStateRef" + +jest.mock("./useScrollSpy") +jest.mock("./useScrollAnimation") +jest.mock("./useStateRef") + +describe("useScrollToBottom", () => { + beforeEach(() => { + jest.clearAllMocks() + }) + + it("should initialize with proper values", () => { + const mockedUseStateRef = useStateRef as jest.MockedFunction< + typeof useStateRef + > + mockedUseStateRef.mockImplementation(initialValue => [ + initialValue, + jest.fn(), + { current: initialValue }, + ]) + const { result } = renderHook(() => useScrollToBottom()) + + expect(result.current).not.toBeNull() + expect(result.current.current).toBeNull() + }) +}) diff --git a/frontend/src/lib/hooks/useScrollToBottom.ts b/frontend/src/lib/hooks/useScrollToBottom.ts new file mode 100644 index 000000000000..877d0872ee6b --- /dev/null +++ b/frontend/src/lib/hooks/useScrollToBottom.ts @@ -0,0 +1,250 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { useEffect, useRef, useCallback, RefObject } from "react" +import useScrollSpy from "./useScrollSpy" +import useScrollAnimation from "./useScrollAnimation" +import useStateRef from "./useStateRef" + +export interface ScrollToBottomOptions { + bottomThreshold?: number + debounceMs?: number +} + +const DEFAULT_BOTTOM_THRESHOLD = 1 +const SCROLL_DECISION_DURATION = 34 // 2 frames +const MIN_CHECK_INTERVAL = 17 // 1 frame + +function setImmediateInterval(fn: () => void, ms: number): NodeJS.Timeout { + fn() + + return setInterval(fn, ms) +} + +function isAtBottom({ + scrollHeight, + offsetHeight, + scrollTop, +}: HTMLElement): boolean { + return scrollHeight - scrollTop - offsetHeight < DEFAULT_BOTTOM_THRESHOLD +} + +/** + * useScrollToBottom is a custom React hook managing automatic + * scrolling behavior for an HTML element. It keeps the scroll view + * at the bottom unless a user scrolls up, then stops auto-scroll. + * + * This hook returns a ref object attached to the HTML element. + * It maintains several pieces of state: + * - isSticky: a boolean for whether the scroll position should + * "stick" to the bottom. + * - isAnimating: a boolean for whether the scroll view is animating. + * + * It has two major functions: + * - handleScrollToBottomFinished: resets stickiness if necessary. + * - handleScroll: adjusts isSticky based on user scroll behavior. + * + * The hook includes side effects with the useEffect hook: + * - The first effect sets an interval to check the scroll position + * and adjust stickiness and animating state. + * - The second effect attaches a focus event listener to update + * the scrollHeight value. + */ +function useScrollToBottom<T extends HTMLElement>(): RefObject<T> { + const scrollableRef = useRef<T>(null) + const [isSticky, setIsSticky, isStickyRef] = useStateRef(false) + const [isAnimating, setIsAnimating, isAnimatingRef] = useStateRef(true) + + // Internal context + const ignoreScrollEventBeforeRef = useRef(0) + const offsetHeightRef = useRef(0) + const scrollHeightRef = useRef(0) + + // Once, we have determined we are at the bottom, we can reset + // the ignoring of scroll events. + const handleScrollToBottomFinished = useCallback(() => { + ignoreScrollEventBeforeRef.current = Date.now() + + // handleScrollToBottomFinished may end at a position which should lose stickiness. + // In that case, we will need to set sticky to false to stop the interval check. + if (!isAnimatingRef.current) { + // Essentially we are not suppose to be animating cause a scroll + // occurred before we finished animating. + setIsSticky(false) + } + + setIsAnimating(false) + }, [ignoreScrollEventBeforeRef, isAnimatingRef, setIsAnimating, setIsSticky]) + + const handleScroll = useCallback( + ({ timeStampLow }) => { + const { current: target } = scrollableRef + const animating = isAnimatingRef.current + + // Currently, there are no reliable way to check if the "scroll" event is trigger due to + // user gesture, programmatic scrolling, or Chrome-synthesized "scroll" event to compensate size change. + // Thus, we use our best-effort to guess if it is triggered by user gesture, and disable sticky if it is heading towards the start direction. + + if (timeStampLow <= ignoreScrollEventBeforeRef.current || !target) { + // Since we debounce "scroll" event, this handler might be called after spineTo.onEnd (a.k.a. artificial scrolling). + // We should ignore debounced event fired after scrollEnd, because without skipping them, the userInitiatedScroll calculated below will not be accurate. + // Thus, on a fast machine, adding elements super fast will lose the "stickiness". + + return + } + + const atBottom = isAtBottom(target) + + // Chrome will emit "synthetic" scroll event if the container is resized or an element is added + // We need to ignore these "synthetic" events + const { + offsetHeight: nextOffsetHeight, + scrollHeight: nextScrollHeight, + } = target + const { current: offsetHeight } = offsetHeightRef + const { current: scrollHeight } = scrollHeightRef + const offsetHeightChanged = nextOffsetHeight !== offsetHeight + const scrollHeightChanged = nextScrollHeight !== scrollHeight + + if (offsetHeightChanged) { + offsetHeightRef.current = nextOffsetHeight + } + + if (scrollHeightChanged) { + scrollHeightRef.current = nextScrollHeight + } + + // Sticky means: + // - If it is scrolled programatically, we are still in sticky mode + // - If it is scrolled by the user, then sticky means if we are at the end + + // Only update stickiness if the scroll event is not due to synthetic scroll done by Chrome + if (!offsetHeightChanged && !scrollHeightChanged) { + // We are sticky if we are animating to the end, or we are already at the end. + // We can be "animating but not sticky" by calling "scrollTo(100)" where the container scrollHeight is 200px. + const nextSticky = animating || atBottom + + if (isStickyRef.current !== nextSticky) { + setIsSticky(nextSticky) + } + } else if (isStickyRef.current) { + setIsAnimating(true) + setIsSticky(true) + } + }, + [ + ignoreScrollEventBeforeRef, + offsetHeightRef, + scrollHeightRef, + isAnimatingRef, + isStickyRef, + setIsAnimating, + setIsSticky, + ] + ) + + useEffect(() => { + if (scrollableRef.current) { + let stickyButNotAtEndSince = 0 + + const timeout = setImmediateInterval(() => { + const { current: target } = scrollableRef + const animating = isAnimatingRef.current + + if (isStickyRef.current && target) { + if (!isAtBottom(target)) { + if (!stickyButNotAtEndSince) { + stickyButNotAtEndSince = Date.now() + } else if ( + Date.now() - stickyButNotAtEndSince > + SCROLL_DECISION_DURATION + ) { + // Quirks: In Firefox, after user scroll down, Firefox do two things: + // 1. Set to a new "scrollTop" + // 2. Fire "scroll" event + // For what we observed, #1 is fired about 20ms before #2. There is a chance that this stickyCheckTimeout is being scheduled between 1 and 2. + // That means, if we just look at #1 to decide if we should scroll, we will always scroll, in oppose to the user's intention. + // Repro: Open Firefox, set checkInterval to a lower number, and try to scroll by dragging the scroll handler. It will jump back. + + // The "animating" check will make sure stickiness is not lost when elements are adding at a very fast pace. + if (!animating) { + setIsAnimating(true) + setIsSticky(true) + } + + stickyButNotAtEndSince = 0 + } + } else { + stickyButNotAtEndSince = 0 + } + } else if ( + target && + target.scrollHeight <= target.offsetHeight && + !isStickyRef.current + ) { + // When the container is emptied, we will set sticky back to true. + setIsSticky(true) + } + }, MIN_CHECK_INTERVAL) + + return () => clearInterval(timeout) + } + }, [ + scrollableRef, + isSticky, + isAnimating, + isAnimatingRef, + isStickyRef, + setIsSticky, + setIsAnimating, + ]) + + useEffect(() => { + // We need to update the "scrollHeight" value to latest when the user do a focus inside the box. + // + // This is because: + // - In our code that mitigate Chrome synthetic scrolling, that code will look at whether "scrollHeight" value is latest or not. + // - That code only run on "scroll" event. + // - That means, on every "scroll" event, if the "scrollHeight" value is not latest, we will skip modifying the stickiness. + // - That means, if the user "focus" to an element that cause the scroll view to scroll to the bottom, the user agent will fire "scroll" event. + // Since the "scrollHeight" is not latest value, this "scroll" event will be ignored and stickiness will not be modified. + // - That means, if the user "focus" to a newly added element that is at the end of the scroll view, the "scroll to bottom" button will continue to show. + const target = scrollableRef.current + if (target) { + const handleFocus = (): void => { + scrollHeightRef.current = target.scrollHeight + } + + target.addEventListener("focus", handleFocus, { + capture: true, + passive: true, + }) + + return () => target.removeEventListener("focus", handleFocus) + } + }, [scrollableRef]) + + useScrollSpy(scrollableRef.current, handleScroll) + useScrollAnimation( + scrollableRef.current, + handleScrollToBottomFinished, + isAnimating + ) + + return scrollableRef +} + +export default useScrollToBottom diff --git a/frontend/src/lib/hooks/useStateRef.test.ts b/frontend/src/lib/hooks/useStateRef.test.ts new file mode 100644 index 000000000000..673db27f5873 --- /dev/null +++ b/frontend/src/lib/hooks/useStateRef.test.ts @@ -0,0 +1,92 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { act, renderHook } from "@testing-library/react-hooks" +import useStateRef from "./useStateRef" // import the hook + +describe("useStateRef hook", () => { + it("should initialize correctly with initial state", () => { + const { result } = renderHook(() => useStateRef(10)) + + const [state, , stateRef] = result.current + + // Initial state is set correctly + expect(state).toEqual(10) + expect(stateRef.current).toEqual(10) + }) + + it("should set state correctly", () => { + const { result } = renderHook(() => useStateRef(10)) + const [, setState, stateRef] = result.current + + act(() => { + setState(20) + }) + + // State is updated + const state = result.current[0] + expect(state).toEqual(20) + expect(stateRef.current).toEqual(20) + }) + + it("should handle function update correctly", () => { + const { result } = renderHook(() => useStateRef(10)) + const [, setState, stateRef] = result.current + + act(() => { + setState(prev => prev + 10) + }) + + // State is updated + const state = result.current[0] + expect(state).toEqual(20) + expect(stateRef.current).toEqual(20) + }) + + it("should maintain reference correctly when state changes", () => { + const { result } = renderHook(() => useStateRef(10)) + const [, setValue, initialRef] = result.current + + act(() => { + setValue(20) + }) + + // Reference has not changed + expect(result.current[2]).toBe(initialRef) + }) + + it("should allow ref to be set independently from state", () => { + const { result } = renderHook(() => useStateRef(10)) + const [, setState, stateRef] = result.current + + act(() => { + stateRef.current = 20 + }) + + let state = result.current[0] + expect(state).toEqual(10) + expect(stateRef.current).toEqual(20) + + act(() => { + setState(20) + }) + + // State is updated + state = result.current[0] + expect(state).toEqual(20) + expect(stateRef.current).toEqual(20) + }) +}) diff --git a/frontend/src/lib/hooks/useStateRef.ts b/frontend/src/lib/hooks/useStateRef.ts new file mode 100644 index 000000000000..7a7d162eb655 --- /dev/null +++ b/frontend/src/lib/hooks/useStateRef.ts @@ -0,0 +1,63 @@ +/** + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import { + useRef, + useState, + MutableRefObject, + Dispatch, + SetStateAction, +} from "react" + +/** + * A custom React Hook that extends useState by providing a mutable ref object + * to track the current state value. + * + * @template T The type of the state value. + * @param {T} initialState The initial state value. + * @returns {[T, Dispatch<SetStateAction<T>>, MutableRefObject<T>]} A tuple containing the + * current state value, a function to update the state, and a mutable ref object. + * + * @example + * // Usage inside a component: + * const [count, setCount, countRef] = useStateRef(0); + * + * // Accessing the current state value: + * console.log(count); // Output: 0 + * console.log(countRef.current); // Output: 0 + * + * // Modifying the state value and updating the ref: + * setCount(10); + * console.log(count); // Output: 10 + * console.log(countRef.current); // Output: 10 + * + * // Comparing previous and current state values: + * const previousCount = useRef(countRef.current); + * console.log(previousCount.current); // Output: 10 + * console.log(previousCount.current === countRef.current); // Output: true + * + * // Sharing the state value with other components: + * <ChildComponent countRef={countRef} /> + */ +export default function useStateRef<T>( + initialState: T +): [T, Dispatch<SetStateAction<T>>, MutableRefObject<T>] { + const [state, setState] = useState<T>(initialState) + const ref = useRef<T>(initialState) + ref.current = state + + return [state, setState, ref] +} diff --git a/frontend/src/lib/theme/primitives/zIndices.ts b/frontend/src/lib/theme/primitives/zIndices.ts index ac104c497d24..a18419d346db 100644 --- a/frontend/src/lib/theme/primitives/zIndices.ts +++ b/frontend/src/lib/theme/primitives/zIndices.ts @@ -18,6 +18,7 @@ const sidebar = 100 const menuButton = sidebar + 10 const balloons = 1000000 const header = balloons - 10 +const chatInput = sidebar - 1 const sidebarMobile = balloons - 5 const popupMenu = balloons + 40 const fullscreenWrapper = balloons + 50 @@ -35,4 +36,5 @@ export const zIndices = { popupMenu, fullscreenWrapper, tablePortal, + chatInput, } diff --git a/lib/streamlit/__init__.py b/lib/streamlit/__init__.py index fbd1e21d3645..d914d2f012e6 100644 --- a/lib/streamlit/__init__.py +++ b/lib/streamlit/__init__.py @@ -118,6 +118,8 @@ def _update_logger() -> None: button = _main.button caption = _main.caption camera_input = _main.camera_input +chat_message = _main.chat_message +chat_input = _main.chat_input checkbox = _main.checkbox code = _main.code columns = _main.columns diff --git a/lib/streamlit/delta_generator.py b/lib/streamlit/delta_generator.py index f83005282d52..dff9a8b694ff 100644 --- a/lib/streamlit/delta_generator.py +++ b/lib/streamlit/delta_generator.py @@ -46,6 +46,7 @@ from streamlit.elements.bokeh_chart import BokehMixin from streamlit.elements.button import ButtonMixin from streamlit.elements.camera_input import CameraInputMixin +from streamlit.elements.chat import ChatMixin from streamlit.elements.checkbox import CheckboxMixin from streamlit.elements.code import CodeMixin from streamlit.elements.color_picker import ColorPickerMixin @@ -156,6 +157,7 @@ class DeltaGenerator( BokehMixin, ButtonMixin, CameraInputMixin, + ChatMixin, CheckboxMixin, CodeMixin, ColorPickerMixin, @@ -278,7 +280,7 @@ def __init__( # Change the module of all mixin'ed functions to be st.delta_generator, # instead of the original module (e.g. st.elements.markdown) for mixin in self.__class__.__bases__: - for (name, func) in mixin.__dict__.items(): + for name, func in mixin.__dict__.items(): if callable(func): func.__module__ = self.__module__ @@ -599,6 +601,10 @@ def _block( raise StreamlitAPIException( "Columns can only be placed inside other columns up to one level of nesting." ) + if block_type == "chat_message" and block_type in frozenset(parent_block_types): + raise StreamlitAPIException( + "Chat messages cannot nested inside other chat messages." + ) if block_type == "expandable" and block_type in frozenset(parent_block_types): raise StreamlitAPIException( "Expanders may not be nested inside other expanders." diff --git a/lib/streamlit/elements/__init__.py b/lib/streamlit/elements/__init__.py index a8de2ef39318..73ab16b4fc1f 100644 --- a/lib/streamlit/elements/__init__.py +++ b/lib/streamlit/elements/__init__.py @@ -15,6 +15,7 @@ WIDGETS = [ "button", "camera_input", + "chat_input", "checkbox", "color_picker", "component_instance", diff --git a/lib/streamlit/elements/chat.py b/lib/streamlit/elements/chat.py new file mode 100644 index 000000000000..0a1627dbf7ac --- /dev/null +++ b/lib/streamlit/elements/chat.py @@ -0,0 +1,330 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum +from typing import TYPE_CHECKING, Optional, Tuple, cast + +from typing_extensions import Literal + +from streamlit import runtime +from streamlit.elements.image import AtomicImage, WidthBehaviour, image_to_url +from streamlit.elements.utils import check_callback_rules, check_session_state_rules +from streamlit.errors import StreamlitAPIException +from streamlit.proto.Block_pb2 import Block as BlockProto +from streamlit.proto.ChatInput_pb2 import ChatInput as ChatInputProto +from streamlit.proto.Common_pb2 import StringTriggerValue as StringTriggerValueProto +from streamlit.proto.RootContainer_pb2 import RootContainer +from streamlit.runtime.metrics_util import gather_metrics +from streamlit.runtime.scriptrunner import get_script_run_ctx +from streamlit.runtime.state import ( + WidgetArgs, + WidgetCallback, + WidgetKwargs, + register_widget, +) +from streamlit.string_util import is_emoji +from streamlit.type_util import Key, to_key + +if TYPE_CHECKING: + from streamlit.delta_generator import DeltaGenerator + + +class PresetNames(str, Enum): + USER = "user" + ASSISTANT = "assistant" + + +def _process_avatar_input( + avatar: str | AtomicImage | None = None, +) -> Tuple[BlockProto.ChatMessage.AvatarType.ValueType, str]: + """Detects the avatar type and prepares the avatar data for the frontend. + + Parameters + ---------- + avatar : + The avatar that was provided by the user. + + Returns + ------- + Tuple[AvatarType, str] + The detected avatar type and the prepared avatar data. + """ + AvatarType = BlockProto.ChatMessage.AvatarType + + if avatar is None: + return AvatarType.ICON, "" + elif isinstance(avatar, str) and avatar in [ + PresetNames.USER, + PresetNames.ASSISTANT, + ]: + return AvatarType.ICON, avatar + elif isinstance(avatar, str) and is_emoji(avatar): + return AvatarType.EMOJI, avatar + else: + try: + # TODO(lukasmasuch): Pure SVGs are not yet supported here. + # They have a special handling in `st.image` and might require some refactoring. + return AvatarType.IMAGE, image_to_url( + avatar, + width=WidthBehaviour.ORIGINAL, + clamp=False, + channels="RGB", + output_format="auto", + image_id="", + ) + except Exception as ex: + raise StreamlitAPIException( + "Failed to load the provided avatar value as an image." + ) from ex + + +DISALLOWED_CONTAINERS_ERROR_TEXT = "`st.chat_input()` can't be used inside an `st.expander`, `st.form`, `st.tabs`, `st.columns`, or `st.sidebar`." + + +@dataclass +class ChatInputSerde: + def deserialize( + self, ui_value: Optional[StringTriggerValueProto], widget_id: str = "" + ) -> str | None: + if ui_value is None or not ui_value.HasField("data"): + return None + + return ui_value.data + + def serialize(self, v: str | None) -> StringTriggerValueProto: + return StringTriggerValueProto(data=v) + + +class ChatMixin: + @gather_metrics("chat_message") + def chat_message( + self, + name: Literal["user", "assistant"] | str, + *, + avatar: Literal["user", "assistant"] | str | AtomicImage | None = None, + ) -> "DeltaGenerator": + """Insert a chat message container. + + To add elements to the returned container, you can use ``with`` notation + (preferred) or just call methods directly on the returned object. See the + examples below. + + Parameters + ---------- + name : "user", "assistant", or str + The name of the message author. Can be โ€œuserโ€ or โ€œassistantโ€ to + enable preset styling and avatars. + + Currently, the name is not shown in the UI but is only set as an + accessibility label. For accessibility reasons, you should not use + an empty string. + + avatar : str, numpy.ndarray, or BytesIO + The avatar shown next to the message. Can be one of: + + * A single emoji, e.g. "๐Ÿง‘โ€๐Ÿ’ป", "๐Ÿค–", "๐Ÿฆ–". Shortcodes are not supported. + + * An image using one of the formats allowed for ``st.image``: path of a local + image file; URL to fetch the image from; array of shape (w,h) or (w,h,1) + for a monochrome image, (w,h,3) for a color image, or (w,h,4) for an RGBA image. + + If None (default), uses default icons if ``name`` is "user" or + "assistant", or the first letter of the ``name`` value. + + Returns + ------- + Container + A single container that can hold multiple elements. + + Examples + -------- + You can use ``with`` notation to insert any element into an expander + + >>> import streamlit as st + >>> import numpy as np + >>> + >>> with st.chat_message("user"): + ... st.write("Hello ๐Ÿ‘‹") + ... st.line_chart(np.random.randn(30, 3)) + + .. output :: + https://doc-chat-message-user.streamlit.app/ + height: 450px + + Or you can just call methods directly in the returned objects: + + >>> import streamlit as st + >>> import numpy as np + >>> + >>> message = st.chat_message("assistant") + >>> message.write("Hello human") + >>> message.bar_chart(np.random.randn(30, 3)) + + .. output :: + https://doc-chat-message-user1.streamlit.app/ + height: 450px + + """ + if name is None: + raise StreamlitAPIException( + "The author name is required for a chat message, please set it via the parameter `name`." + ) + + if avatar is None and ( + name.lower() + in [ + PresetNames.USER, + PresetNames.ASSISTANT, + ] + or is_emoji(name) + ): + # For selected labels, we are mapping the label to an avatar + avatar = name.lower() + avatar_type, converted_avatar = _process_avatar_input(avatar) + + message_container_proto = BlockProto.ChatMessage() + message_container_proto.name = name + message_container_proto.avatar = converted_avatar + message_container_proto.avatar_type = avatar_type + block_proto = BlockProto() + block_proto.allow_empty = True + block_proto.chat_message.CopyFrom(message_container_proto) + + return self.dg._block(block_proto=block_proto) + + @gather_metrics("chat_input") + def chat_input( + self, + placeholder: str = "Your message", + *, + key: Key | None = None, + max_chars: int | None = None, + disabled: bool = False, + on_submit: WidgetCallback | None = None, + args: WidgetArgs | None = None, + kwargs: WidgetKwargs | None = None, + ) -> str | None: + """Display a chat input widget. + + .. warning:: + Chat input can only be used once per app page and inside the main area of the app. + It cannot be used in the sidebar, columns, expanders, forms or tabs. + We plan to support this in the future. + + Parameters + ---------- + placeholder : str + A placeholder text shown when the chat input is empty. Defaults to + "Your message". For accessibility reasons, you should not use an + empty string. + + key : str or int + An optional string or integer to use as the unique key for the widget. + If this is omitted, a key will be generated for the widget based on + its content. Multiple widgets of the same type may not share the same key. + + max_chars : int or None + The maximum number of characters that can be entered. If None + (default), there will be no maximum. + + disabled : bool + Whether the chat input should be disabled. Defaults to False. + + on_submit : callable + An optional callback invoked when the chat input's value is submitted. + + args : tuple + An optional tuple of args to pass to the callback. + + kwargs : dict + An optional dict of kwargs to pass to the callback. + + Returns + ------- + str or None + The current (non-empty) value of the text input widget on the last + run of the app, None otherwise. + + Examples + -------- + >>> import streamlit as st + >>> + >>> prompt = st.chat_input("Say something") + >>> if prompt: + ... st.write(f"User has sent the following prompt: {prompt}") + + .. output :: + https://doc-chat-input.streamlit.app/ + height: 350px + + """ + # We default to an empty string here and disallow user choice intentionally + default = "" + key = to_key(key) + check_callback_rules(self.dg, on_submit) + check_session_state_rules(default_value=default, key=key, writes_allowed=False) + + # We omit this check for scripts running outside streamlit, because + # they will have no script_run_ctx. + if runtime.exists(): + if ( + len(list(self.dg._active_dg._parent_block_types)) > 0 + or self.dg._active_dg._root_container == RootContainer.SIDEBAR + ): + # TODO: This allows the user to create a chat_input inside a + # container but it still cannot be in other containers. This is + # a result of the Vertical field not being set in Blocks by + # default and seems like a "bug", but one that is not producing + # major issues. We should look into what's the correct behavior + # to implement. + raise StreamlitAPIException(DISALLOWED_CONTAINERS_ERROR_TEXT) + + chat_input_proto = ChatInputProto() + chat_input_proto.placeholder = str(placeholder) + + if max_chars is not None: + chat_input_proto.max_chars = max_chars + + chat_input_proto.default = default + chat_input_proto.position = ChatInputProto.Position.BOTTOM + + ctx = get_script_run_ctx() + + serde = ChatInputSerde() + widget_state = register_widget( + "chat_input", + chat_input_proto, + user_key=key, + on_change_handler=on_submit, + args=args, + kwargs=kwargs, + deserializer=serde.deserialize, + serializer=serde.serialize, + ctx=ctx, + ) + + chat_input_proto.disabled = disabled + if widget_state.value_changed and widget_state.value is not None: + chat_input_proto.value = widget_state.value + chat_input_proto.set_value = True + + self.dg._enqueue("chat_input", chat_input_proto) + return widget_state.value if not widget_state.value_changed else None + + @property + def dg(self) -> "DeltaGenerator": + """Get our DeltaGenerator.""" + return cast("DeltaGenerator", self) diff --git a/lib/streamlit/elements/layouts.py b/lib/streamlit/elements/layouts.py index 61e84559fa91..1137c0aba57a 100644 --- a/lib/streamlit/elements/layouts.py +++ b/lib/streamlit/elements/layouts.py @@ -11,10 +11,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING, List, Optional, Sequence, Union, cast -from streamlit.deprecation_util import deprecate_func_name from streamlit.errors import StreamlitAPIException from streamlit.proto.Block_pb2 import Block as BlockProto from streamlit.runtime.metrics_util import gather_metrics diff --git a/lib/streamlit/elements/utils.py b/lib/streamlit/elements/utils.py index 0b089fc66afe..b1a5f32a2919 100644 --- a/lib/streamlit/elements/utils.py +++ b/lib/streamlit/elements/utils.py @@ -50,6 +50,11 @@ def check_callback_rules( _shown_default_value_warning: bool = False +SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT = """ +Values for st.button, st.download_button, st.file_uploader, st.data_editor, +st.chat_input, and st.form cannot be set using st.session_state. +""" + def check_session_state_rules( default_value: Any, key: Optional[str], writes_allowed: bool = True @@ -64,10 +69,7 @@ def check_session_state_rules( return if not writes_allowed: - raise StreamlitAPIException( - "Values for st.button, st.download_button, st.file_uploader, st.data_editor" - " and st.form cannot be set using st.session_state." - ) + raise StreamlitAPIException(SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT) if ( default_value is not None diff --git a/lib/streamlit/runtime/state/common.py b/lib/streamlit/runtime/state/common.py index 9ff9e80b2ce9..7f02a2732836 100644 --- a/lib/streamlit/runtime/state/common.py +++ b/lib/streamlit/runtime/state/common.py @@ -26,6 +26,7 @@ from streamlit.proto.Arrow_pb2 import Arrow from streamlit.proto.Button_pb2 import Button from streamlit.proto.CameraInput_pb2 import CameraInput +from streamlit.proto.ChatInput_pb2 import ChatInput from streamlit.proto.Checkbox_pb2 import Checkbox from streamlit.proto.ColorPicker_pb2 import ColorPicker from streamlit.proto.Components_pb2 import ComponentInstance @@ -47,6 +48,7 @@ Arrow, Button, CameraInput, + ChatInput, Checkbox, ColorPicker, ComponentInstance, diff --git a/lib/streamlit/runtime/state/session_state.py b/lib/streamlit/runtime/state/session_state.py index da281b76bc51..74d6bf4f2b20 100644 --- a/lib/streamlit/runtime/state/session_state.py +++ b/lib/streamlit/runtime/state/session_state.py @@ -215,6 +215,8 @@ def get_serialized(self, k: str) -> WidgetStateProto | None: setattr(widget, field, json.dumps(serialized)) elif field == "file_uploader_state_value": widget.file_uploader_state_value.CopyFrom(serialized) + elif field == "string_trigger_value": + widget.string_trigger_value.CopyFrom(serialized) else: setattr(widget, field, serialized) @@ -525,13 +527,19 @@ def _reset_triggers(self) -> None: """Set all trigger values in our state dictionary to False.""" for state_id in self._new_widget_state: metadata = self._new_widget_state.widget_metadata.get(state_id) - if metadata is not None and metadata.value_type == "trigger_value": - self._new_widget_state[state_id] = Value(False) + if metadata is not None: + if metadata.value_type == "trigger_value": + self._new_widget_state[state_id] = Value(False) + elif metadata.value_type == "string_trigger_value": + self._new_widget_state[state_id] = Value(None) for state_id in self._old_state: metadata = self._new_widget_state.widget_metadata.get(state_id) - if metadata is not None and metadata.value_type == "trigger_value": - self._old_state[state_id] = False + if metadata is not None: + if metadata.value_type == "trigger_value": + self._old_state[state_id] = False + elif metadata.value_type == "string_trigger_value": + self._old_state[state_id] = None def _remove_stale_widgets(self, active_widget_ids: set[str]) -> None: """Remove widget state for widgets whose ids aren't in `active_widget_ids`.""" diff --git a/lib/streamlit/runtime/state/widgets.py b/lib/streamlit/runtime/state/widgets.py index 2f0b79667505..2b051d6987da 100644 --- a/lib/streamlit/runtime/state/widgets.py +++ b/lib/streamlit/runtime/state/widgets.py @@ -53,6 +53,7 @@ "button": "trigger_value", "download_button": "trigger_value", "checkbox": "bool_value", + "chat_input": "string_trigger_value", "camera_input": "file_uploader_state_value", "color_picker": "string_value", "date_input": "string_array_value", @@ -232,18 +233,22 @@ def coalesce_widget_states( wstate.id: wstate for wstate in new_states.widgets } + trigger_value_types = [("trigger_value", False), ("string_trigger_value", None)] for old_state in old_states.widgets: - if old_state.WhichOneof("value") == "trigger_value" and old_state.trigger_value: - - # Ensure the corresponding new_state is also a trigger; - # otherwise, a widget that was previously a button but no longer is - # could get a bad value. - new_trigger_val = states_by_id.get(old_state.id) + for trigger_value_type, unset_value in trigger_value_types: if ( - new_trigger_val - and new_trigger_val.WhichOneof("value") == "trigger_value" + old_state.WhichOneof("value") == trigger_value_type + and old_state.trigger_value != unset_value ): - states_by_id[old_state.id] = old_state + # Ensure the corresponding new_state is also a trigger; + # otherwise, a widget that was previously a button but no longer is + # could get a bad value. + new_trigger_val = states_by_id.get(old_state.id) + if ( + new_trigger_val + and new_trigger_val.WhichOneof("value") == trigger_value_type + ): + states_by_id[old_state.id] = old_state coalesced = WidgetStates() coalesced.widgets.extend(states_by_id.values()) diff --git a/lib/streamlit/type_util.py b/lib/streamlit/type_util.py index 4f4aa8126b96..c95543aa0136 100644 --- a/lib/streamlit/type_util.py +++ b/lib/streamlit/type_util.py @@ -102,6 +102,7 @@ "json_value", "string_value", "trigger_value", + "string_trigger_value", ] V_co = TypeVar( diff --git a/lib/tests/streamlit/delta_generator_test.py b/lib/tests/streamlit/delta_generator_test.py index ea26f5d02571..0dc0480e334e 100644 --- a/lib/tests/streamlit/delta_generator_test.py +++ b/lib/tests/streamlit/delta_generator_test.py @@ -96,6 +96,8 @@ def test_public_api(self): "button", "camera_input", "caption", + "chat_input", + "chat_message", "checkbox", "code", "color_picker", diff --git a/lib/tests/streamlit/elements/chat_test.py b/lib/tests/streamlit/elements/chat_test.py new file mode 100644 index 000000000000..6071c180cbb6 --- /dev/null +++ b/lib/tests/streamlit/elements/chat_test.py @@ -0,0 +1,207 @@ +# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""chat input and message unit tests.""" + +import pytest +from parameterized import parameterized + +import streamlit as st +from streamlit.elements.chat import DISALLOWED_CONTAINERS_ERROR_TEXT +from streamlit.elements.utils import SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT +from streamlit.errors import StreamlitAPIException +from streamlit.proto.Block_pb2 import Block as BlockProto +from streamlit.proto.ChatInput_pb2 import ChatInput as ChatInputProto +from tests.delta_generator_test_case import DeltaGeneratorTestCase + + +class ChatTest(DeltaGeneratorTestCase): + """Test ability to marshall ChatInput and ChatMessage protos.""" + + def test_label_required(self): + """Test that label is required""" + with self.assertRaises(TypeError): + st.chat_message() + + def test_nesting_is_disallowed(self): + """Test that it is not allowed to be nested.""" + with self.assertRaises(StreamlitAPIException): + with st.chat_message("user"): + with st.chat_message("assistant"): + st.write("hello") + + def test_user_message(self): + """Test that the user message is correct.""" + message = st.chat_message("user") + + with message: + pass + + message_block = self.get_delta_from_queue() + + self.assertEqual(message_block.add_block.chat_message.name, "user") + self.assertEqual(message_block.add_block.chat_message.avatar, "user") + self.assertEqual( + message_block.add_block.chat_message.avatar_type, + BlockProto.ChatMessage.AvatarType.ICON, + ) + + def test_assistant_message(self): + """Test that the assistant message is correct.""" + message = st.chat_message("assistant") + + with message: + pass + + message_block = self.get_delta_from_queue() + + self.assertEqual(message_block.add_block.chat_message.name, "assistant") + self.assertEqual(message_block.add_block.chat_message.avatar, "assistant") + self.assertEqual( + message_block.add_block.chat_message.avatar_type, + BlockProto.ChatMessage.AvatarType.ICON, + ) + + def test_emoji_avatar(self): + """Test that it is possible to set an emoji as avatar.""" + + message = st.chat_message("user", avatar="๐Ÿ‘‹") + + with message: + pass + + message_block = self.get_delta_from_queue() + + self.assertEqual(message_block.add_block.chat_message.name, "user") + self.assertEqual(message_block.add_block.chat_message.avatar, "๐Ÿ‘‹") + self.assertEqual( + message_block.add_block.chat_message.avatar_type, + BlockProto.ChatMessage.AvatarType.EMOJI, + ) + + def test_image_avatar(self): + """Test that it is possible to set an image as avatar.""" + + message = st.chat_message( + "cat", + avatar="https://static.streamlit.io/examples/cat.jpg", + ) + + with message: + pass + + message_block = self.get_delta_from_queue() + self.assertEqual(message_block.add_block.chat_message.name, "cat") + self.assertEqual( + message_block.add_block.chat_message.avatar, + "https://static.streamlit.io/examples/cat.jpg", + ) + self.assertEqual( + message_block.add_block.chat_message.avatar_type, + BlockProto.ChatMessage.AvatarType.IMAGE, + ) + + def test_throws_invalid_avatar_exception(self): + """Test that chat_message throws an StreamlitAPIException on invalid avatar input.""" + with pytest.raises(StreamlitAPIException): + st.chat_message("user", avatar="FOOO") + + def test_chat_input(self): + """Test that it can be called.""" + st.chat_input("Placeholder") + + c = self.get_delta_from_queue().new_element.chat_input + self.assertEqual(c.placeholder, "Placeholder") + self.assertEqual(c.default, "") + self.assertEqual(c.value, "") + self.assertEqual(c.set_value, False) + self.assertEqual(c.max_chars, 0) + self.assertEqual(c.disabled, False) + self.assertEqual(c.position, ChatInputProto.Position.BOTTOM) + + def test_chat_input_disabled(self): + """Test that it sets disabled correctly.""" + st.chat_input("Placeholder", disabled=True) + + c = self.get_delta_from_queue().new_element.chat_input + self.assertEqual(c.placeholder, "Placeholder") + self.assertEqual(c.default, "") + self.assertEqual(c.value, "") + self.assertEqual(c.set_value, False) + self.assertEqual(c.max_chars, 0) + self.assertEqual(c.disabled, True) + self.assertEqual(c.position, ChatInputProto.Position.BOTTOM) + + def test_chat_input_max_chars(self): + """Test that it sets max chars correctly.""" + st.chat_input("Placeholder", max_chars=100) + + c = self.get_delta_from_queue().new_element.chat_input + self.assertEqual(c.placeholder, "Placeholder") + self.assertEqual(c.default, "") + self.assertEqual(c.value, "") + self.assertEqual(c.set_value, False) + self.assertEqual(c.max_chars, 100) + self.assertEqual(c.disabled, False) + self.assertEqual(c.position, ChatInputProto.Position.BOTTOM) + + @parameterized.expand( + [ + lambda: st.columns(2)[0], + lambda: st.tabs(["Tab1", "Tab2"])[0], + lambda: st.expander("Expand Me"), + lambda: st.form("Form Key"), + lambda: st.sidebar, + ] + ) + def test_chat_not_allowed_in_containers(self, container_call): + """Test that it disallows being called in containers.""" + with pytest.raises(StreamlitAPIException) as exception_message: + container_call().chat_input("Placeholder") + + self.assertEqual( + DISALLOWED_CONTAINERS_ERROR_TEXT, + str(exception_message.value), + ) + + @parameterized.expand( + [ + lambda: st.columns(2)[0], + lambda: st.tabs(["Tab1", "Tab2"])[0], + lambda: st.expander("Expand Me"), + lambda: st.form("Form Key"), + lambda: st.sidebar, + ] + ) + def test_chat_not_allowed_in_with_containers(self, container_call): + """Test that it disallows being called in containers (using with syntax).""" + with pytest.raises(StreamlitAPIException) as exception_message: + with container_call(): + st.chat_input("Placeholder") + + self.assertEqual( + DISALLOWED_CONTAINERS_ERROR_TEXT, + str(exception_message.value), + ) + + def test_session_state_rules(self): + """Test that it disallows being called in containers (using with syntax).""" + with pytest.raises(StreamlitAPIException) as exception_message: + st.session_state.my_key = "Foo" + st.chat_input("Placeholder", key="my_key") + + self.assertEqual( + SESSION_STATE_WRITES_NOT_ALLOWED_ERROR_TEXT, + str(exception_message.value), + ) diff --git a/lib/tests/streamlit/runtime/state/widgets_test.py b/lib/tests/streamlit/runtime/state/widgets_test.py index 9b8becdbb08b..7a9d4a5b0ad4 100644 --- a/lib/tests/streamlit/runtime/state/widgets_test.py +++ b/lib/tests/streamlit/runtime/state/widgets_test.py @@ -22,6 +22,7 @@ import streamlit as st from streamlit import errors from streamlit.proto.Button_pb2 import Button as ButtonProto +from streamlit.proto.Common_pb2 import StringTriggerValue as StringTriggerValueProto from streamlit.proto.WidgetStates_pb2 import WidgetStates from streamlit.runtime.scriptrunner.script_run_context import get_script_run_ctx from streamlit.runtime.state import coalesce_widget_states @@ -181,6 +182,32 @@ def test_reset_triggers(self): self.assertFalse(session_state["trigger"]) self.assertEqual(123, session_state["int"]) + def test_reset_string_triggers(self): + states = WidgetStates() + session_state = SessionState() + + _create_widget("string_trigger", states).string_trigger_value.CopyFrom( + StringTriggerValueProto(data="Some Value") + ) + _create_widget("int", states).int_value = 123 + session_state.set_widgets_from_proto(states) + session_state._set_widget_metadata( + WidgetMetadata( + "string_trigger", lambda x, s: x, None, "string_trigger_value" + ) + ) + session_state._set_widget_metadata( + WidgetMetadata("int", lambda x, s: x, None, "int_value") + ) + + self.assertEqual("Some Value", session_state["string_trigger"].data) + self.assertEqual(123, session_state["int"]) + + session_state._reset_triggers() + + self.assertIsNone(session_state["string_trigger"]) + self.assertEqual(123, session_state["int"]) + def test_coalesce_widget_states(self): session_state = SessionState() @@ -188,6 +215,15 @@ def test_coalesce_widget_states(self): _create_widget("old_set_trigger", old_states).trigger_value = True _create_widget("old_unset_trigger", old_states).trigger_value = False + _create_widget( + "old_set_string_trigger", old_states + ).string_trigger_value.CopyFrom(StringTriggerValueProto(data="Some String")) + _create_widget( + "old_set_empty_string_trigger", old_states + ).string_trigger_value.CopyFrom(StringTriggerValueProto(data="")) + _create_widget( + "old_unset_string_trigger", old_states + ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None)) _create_widget("missing_in_new", old_states).int_value = 123 _create_widget("shape_changing_trigger", old_states).trigger_value = True @@ -197,6 +233,15 @@ def test_coalesce_widget_states(self): session_state._set_widget_metadata( create_metadata("old_unset_trigger", "trigger_value") ) + session_state._set_widget_metadata( + create_metadata("old_set_string_trigger", "string_trigger_value") + ) + session_state._set_widget_metadata( + create_metadata("old_set_empty_string_trigger", "string_trigger_value") + ) + session_state._set_widget_metadata( + create_metadata("old_unset_string_trigger", "string_trigger_value") + ) session_state._set_widget_metadata( create_metadata("missing_in_new", "int_value") ) @@ -208,11 +253,25 @@ def test_coalesce_widget_states(self): _create_widget("old_set_trigger", new_states).trigger_value = False _create_widget("new_set_trigger", new_states).trigger_value = True + _create_widget( + "old_set_string_trigger", new_states + ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None)) + _create_widget( + "old_set_empty_string_trigger", new_states + ).string_trigger_value.CopyFrom(StringTriggerValueProto(data=None)) + _create_widget( + "new_set_string_trigger", new_states + ).string_trigger_value.CopyFrom( + StringTriggerValueProto(data="Some other string") + ) _create_widget("added_in_new", new_states).int_value = 456 _create_widget("shape_changing_trigger", new_states).int_value = 3 session_state._set_widget_metadata( create_metadata("new_set_trigger", "trigger_value") ) + session_state._set_widget_metadata( + create_metadata("new_set_string_trigger", "string_trigger_value") + ) session_state._set_widget_metadata(create_metadata("added_in_new", "int_value")) session_state._set_widget_metadata( create_metadata("shape_changing_trigger", "int_value") @@ -224,10 +283,16 @@ def test_coalesce_widget_states(self): self.assertRaises(KeyError, lambda: session_state["old_unset_trigger"]) self.assertRaises(KeyError, lambda: session_state["missing_in_new"]) + self.assertRaises(KeyError, lambda: session_state["old_unset_string_trigger"]) self.assertEqual(True, session_state["old_set_trigger"]) self.assertEqual(True, session_state["new_set_trigger"]) self.assertEqual(456, session_state["added_in_new"]) + self.assertEqual("Some String", session_state["old_set_string_trigger"].data) + self.assertEqual("", session_state["old_set_empty_string_trigger"].data) + self.assertEqual( + "Some other string", session_state["new_set_string_trigger"].data + ) # Widgets that were triggers before, but no longer are, will *not* # be coalesced diff --git a/lib/tests/streamlit/streamlit_test.py b/lib/tests/streamlit/streamlit_test.py index 62aa12744511..aab6dbd477c8 100644 --- a/lib/tests/streamlit/streamlit_test.py +++ b/lib/tests/streamlit/streamlit_test.py @@ -70,6 +70,8 @@ def test_public_api(self): "button", "caption", "camera_input", + "chat_input", + "chat_message", "checkbox", "code", "columns", diff --git a/proto/streamlit/proto/Block.proto b/proto/streamlit/proto/Block.proto index 635ef6b162ca..e5e464f55938 100644 --- a/proto/streamlit/proto/Block.proto +++ b/proto/streamlit/proto/Block.proto @@ -25,6 +25,7 @@ message Block { Form form = 5; TabContainer tab_container = 6; Tab tab = 7; + ChatMessage chat_message = 9; } bool allow_empty = 8; @@ -58,5 +59,17 @@ message Block { string label = 1; } - // Next ID: 9 + message ChatMessage { + enum AvatarType { + IMAGE = 0; + EMOJI = 1; + ICON = 2; + } + + string name = 1; + string avatar = 2; + AvatarType avatar_type = 3; + } + + // Next ID: 10 } diff --git a/proto/streamlit/proto/ChatInput.proto b/proto/streamlit/proto/ChatInput.proto new file mode 100644 index 000000000000..fc23d4bb872e --- /dev/null +++ b/proto/streamlit/proto/ChatInput.proto @@ -0,0 +1,32 @@ +/**! + * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022) + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +syntax = "proto3"; + +message ChatInput { + enum Position { + BOTTOM = 0; + } + + string id = 1; + string placeholder = 2; + uint32 max_chars = 3; + bool disabled = 4; + string value = 5; + bool set_value = 6; + string default = 7; + Position position = 8; +} diff --git a/proto/streamlit/proto/Common.proto b/proto/streamlit/proto/Common.proto index 68430a2a8407..05b4cd64d7e1 100644 --- a/proto/streamlit/proto/Common.proto +++ b/proto/streamlit/proto/Common.proto @@ -42,6 +42,10 @@ message UInt32Array { repeated uint32 data = 1; } +message StringTriggerValue { + optional string data = 1; +} + // Information on a file uploaded via the file_uploader widget. message UploadedFileInfo { sint64 id = 1; diff --git a/proto/streamlit/proto/Element.proto b/proto/streamlit/proto/Element.proto index 489d5da96a7e..6148cf8fef6d 100644 --- a/proto/streamlit/proto/Element.proto +++ b/proto/streamlit/proto/Element.proto @@ -25,6 +25,7 @@ import "streamlit/proto/BokehChart.proto"; import "streamlit/proto/Button.proto"; import "streamlit/proto/DownloadButton.proto"; import "streamlit/proto/CameraInput.proto"; +import "streamlit/proto/ChatInput.proto"; import "streamlit/proto/Checkbox.proto"; import "streamlit/proto/Code.proto"; import "streamlit/proto/ColorPicker.proto"; @@ -75,6 +76,7 @@ message Element { Button button = 19; DownloadButton download_button = 43; CameraInput camera_input = 45; + ChatInput chat_input = 49; Checkbox checkbox = 20; ColorPicker color_picker = 35; ComponentInstance component_instance = 37; @@ -110,7 +112,7 @@ message Element { Video video = 14; Heading heading = 47; Code code = 48; - // Next ID: 49 + // Next ID: 50 } reserved 9; diff --git a/proto/streamlit/proto/WidgetStates.proto b/proto/streamlit/proto/WidgetStates.proto index 39c44b0fbb28..8ca074cd6c48 100644 --- a/proto/streamlit/proto/WidgetStates.proto +++ b/proto/streamlit/proto/WidgetStates.proto @@ -48,5 +48,8 @@ message WidgetState { ArrowTable arrow_value = 11; bytes bytes_value = 12; FileUploaderState file_uploader_state_value = 13; + // String value that resets itself to empty after the script has been run. + // This is used for the chat_input widget. + StringTriggerValue string_trigger_value = 14; } }
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
streamlit__streamlit-5789@1c8465c
streamlit/streamlit
Python
5,789
Add the label_visibility parameter to st.metric
## ๐Ÿ“š Context Adds the `label_visibility` keyword-only parameter to `st.metric`, to be consistent with other elements that have the `label` parameter. - What kind of change does this PR introduce? - [x] Bugfix - [x] Feature - [ ] Refactoring - [ ] Other, please describe: ## ๐Ÿง  Description of Changes - [ ] This is a breaking API change - [x] This is a visible (user-facing) change **Visible:** ![image](https://user-images.githubusercontent.com/20672874/204869733-062efe35-71a0-49cd-88a6-e01848f37b97.png) **Hidden:** ![image](https://user-images.githubusercontent.com/20672874/204869810-33746e2f-f154-4fd5-b56f-6cf3ed787417.png) **Collapsed:** ![image](https://user-images.githubusercontent.com/20672874/204869926-a1a2d18b-959c-4c9c-b6a7-c0499943b231.png) ## ๐Ÿงช Testing Done - [x] Screenshots included - [x] Added/Updated unit tests - [x] Added/Updated e2e tests ## ๐ŸŒ References _Does this depend on other work, documents, or tickets?_ - **Issue**: Closes #5786 --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2022-11-30T02:05:06Z
`st.metric` should have `label_visibility` parameter We had that in the original spec but probably forgot it during implementation. Let's fix it. Not super urgent but would be good to do soon so we don't forget. --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
[ { "body": "We had that in the original spec but probably forgot it during implementation. Let's fix it. Not super urgent but would be good to do soon so we don't forget. \r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**\r\n", "number": 5786, "title": "`st.metric` should have `label_visibility` parameter" } ]
0b28bfdf50a7da392b9e83a1131cfe8a5e07351a
{ "head_commit": "1c8465ccaf2574e417ec4a0da5dfc768e2e643a4", "head_commit_message": "Add unit tests for st.metric label_visibility", "patch_to_review": "diff --git a/frontend/src/components/elements/Metric/Metric.test.tsx b/frontend/src/components/elements/Metric/Metric.test.tsx\nindex 7c479bc2a4bf..8be6a20c4a5c 100644\n--- a/frontend/src/components/elements/Metric/Metric.test.tsx\n+++ b/frontend/src/components/elements/Metric/Metric.test.tsx\n@@ -19,7 +19,10 @@ import { mount } from \"src/lib/test_util\"\n \n import StreamlitMarkdown from \"src/components/shared/StreamlitMarkdown\"\n \n-import { Metric as MetricProto } from \"src/autogen/proto\"\n+import {\n+ Metric as MetricProto,\n+ LabelVisibilityMessage as LabelVisibilityMessageProto,\n+} from \"src/autogen/proto\"\n import Metric, { MetricProps } from \"./Metric\"\n \n const getProps = (elementProps: Partial<MetricProto> = {}): MetricProps => ({\n@@ -47,6 +50,30 @@ describe(\"Metric element\", () => {\n expect(wrappedMetricLabel.props().isLabel).toBe(true)\n })\n \n+ it(\"pass labelVisibility prop to StyledTruncateText correctly when hidden\", () => {\n+ const props = getProps({\n+ labelVisibility: {\n+ value: LabelVisibilityMessageProto.LabelVisibilityOptions.HIDDEN,\n+ },\n+ })\n+ const wrapper = mount(<Metric {...props} />)\n+ expect(\n+ wrapper.find(\"StyledTruncateText\").first().prop(\"visibility\")\n+ ).toEqual(LabelVisibilityMessageProto.LabelVisibilityOptions.HIDDEN)\n+ })\n+\n+ it(\"pass labelVisibility prop to StyledTruncateText correctly when collapsed\", () => {\n+ const props = getProps({\n+ labelVisibility: {\n+ value: LabelVisibilityMessageProto.LabelVisibilityOptions.COLLAPSED,\n+ },\n+ })\n+ const wrapper = mount(<Metric {...props} />)\n+ expect(\n+ wrapper.find(\"StyledTruncateText\").first().prop(\"visibility\")\n+ ).toEqual(LabelVisibilityMessageProto.LabelVisibilityOptions.COLLAPSED)\n+ })\n+\n it(\"renders direction icon based on props\", () => {\n const props = getProps()\n const wrapper = mount(<Metric {...props} />)\ndiff --git a/frontend/src/components/elements/Metric/Metric.tsx b/frontend/src/components/elements/Metric/Metric.tsx\nindex d13877427620..f78fb46fce04 100644\n--- a/frontend/src/components/elements/Metric/Metric.tsx\n+++ b/frontend/src/components/elements/Metric/Metric.tsx\n@@ -17,6 +17,7 @@\n import React, { ReactElement } from \"react\"\n import { Metric as MetricProto } from \"src/autogen/proto\"\n import { Theme } from \"src/theme\"\n+import { labelVisibilityProtoValueToEnum } from \"src/lib/utils\"\n import Icon from \"src/components/shared/Icon\"\n import { useTheme } from \"@emotion/react\"\n import { ArrowDownward, ArrowUpward } from \"@emotion-icons/material-outlined\"\n@@ -75,7 +76,11 @@ export default function Metric({ element }: MetricProps): ReactElement {\n return (\n <div data-testid=\"metric-container\">\n <StyledMetricLabelText data-testid=\"stMetricLabel\">\n- <StyledTruncateText>\n+ <StyledTruncateText\n+ visibility={labelVisibilityProtoValueToEnum(\n+ element.labelVisibility?.value\n+ )}\n+ >\n <StreamlitMarkdown\n source={element.label}\n allowHTML={false}\ndiff --git a/frontend/src/components/elements/Metric/styled-components.ts b/frontend/src/components/elements/Metric/styled-components.ts\nindex 43d2f605e0fd..3f600b0ce00e 100644\n--- a/frontend/src/components/elements/Metric/styled-components.ts\n+++ b/frontend/src/components/elements/Metric/styled-components.ts\n@@ -16,20 +16,29 @@\n \n import styled from \"@emotion/styled\"\n import { StyledWidgetLabel } from \"src/components/widgets/BaseWidget/styled-components\"\n+import { LabelVisibilityOptions } from \"src/lib/utils\"\n \n-export const StyledTruncateText = styled.div(({ theme }) => ({\n- overflowWrap: \"normal\",\n- textOverflow: \"ellipsis\",\n- width: \"100%\",\n- overflow: \"hidden\",\n- whiteSpace: \"nowrap\",\n- fontFamily: theme.genericFonts.bodyFont,\n- lineHeight: theme.lineHeights.normal,\n- verticalAlign: \"middle\",\n- display: \"flex\",\n- flexDirection: \"row\",\n- alignItems: \"center\",\n-}))\n+export interface StyledTruncateTextProps {\n+ visibility?: LabelVisibilityOptions\n+}\n+\n+export const StyledTruncateText = styled.div<StyledTruncateTextProps>(\n+ ({ theme, visibility }) => ({\n+ overflowWrap: \"normal\",\n+ textOverflow: \"ellipsis\",\n+ width: \"100%\",\n+ overflow: \"hidden\",\n+ whiteSpace: \"nowrap\",\n+ fontFamily: theme.genericFonts.bodyFont,\n+ lineHeight: theme.lineHeights.normal,\n+ verticalAlign: \"middle\",\n+ display: visibility === LabelVisibilityOptions.Collapsed ? \"none\" : \"flex\",\n+ visibility:\n+ visibility === LabelVisibilityOptions.Hidden ? \"hidden\" : \"visible\",\n+ flexDirection: \"row\",\n+ alignItems: \"center\",\n+ })\n+)\n \n export const StyledMetricLabelText = styled(StyledWidgetLabel)(() => ({\n marginBottom: 0,\ndiff --git a/lib/streamlit/elements/metric.py b/lib/streamlit/elements/metric.py\nindex 5f65b4e65b04..d86339e01cca 100644\n--- a/lib/streamlit/elements/metric.py\n+++ b/lib/streamlit/elements/metric.py\n@@ -18,10 +18,12 @@\n \n from typing_extensions import Literal, TypeAlias\n \n+from streamlit.elements.utils import get_label_visibility_proto_value\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto.Metric_pb2 import Metric as MetricProto\n from streamlit.runtime.metrics_util import gather_metrics\n from streamlit.string_util import clean_text\n+from streamlit.type_util import LabelVisibility, maybe_raise_label_warnings\n \n if TYPE_CHECKING:\n import numpy as np\n@@ -49,6 +51,7 @@ def metric(\n delta: Delta = None,\n delta_color: DeltaColor = \"normal\",\n help: Optional[str] = None,\n+ label_visibility: LabelVisibility = \"visible\",\n ) -> \"DeltaGenerator\":\n \"\"\"Display a metric in big bold font, with an optional indicator of how the metric changed.\n \n@@ -79,6 +82,11 @@ def metric(\n regardless of its value.\n help : str\n An optional tooltip that gets displayed next to the metric label.\n+ label_visibility : \"visible\" or \"hidden\" or \"collapsed\"\n+ The visibility of the label. If \"hidden\", the label doesn't show but there\n+ is still empty space for it (equivalent to label=\"\").\n+ If \"collapsed\", both the label and the space are removed. Default is\n+ \"visible\". This argument can only be supplied by keyword.\n \n Example\n -------\n@@ -112,6 +120,8 @@ def metric(\n height: 320px\n \n \"\"\"\n+ maybe_raise_label_warnings(label, label_visibility)\n+\n metric_proto = MetricProto()\n metric_proto.body = self.parse_value(value)\n metric_proto.label = self.parse_label(label)\n@@ -124,6 +134,9 @@ def metric(\n )\n metric_proto.color = color_and_direction.color\n metric_proto.direction = color_and_direction.direction\n+ metric_proto.label_visibility.value = get_label_visibility_proto_value(\n+ label_visibility\n+ )\n \n return self.dg._enqueue(\"metric\", metric_proto)\n \ndiff --git a/lib/tests/streamlit/elements/metric_test.py b/lib/tests/streamlit/elements/metric_test.py\nindex f754a6919d55..fbeaa6d72d07 100644\n--- a/lib/tests/streamlit/elements/metric_test.py\n+++ b/lib/tests/streamlit/elements/metric_test.py\n@@ -13,9 +13,13 @@\n # limitations under the License.\n \n \"\"\"metric unit tests.\"\"\"\n+from parameterized import parameterized\n+\n import streamlit as st\n from streamlit.errors import StreamlitAPIException\n+from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage\n from streamlit.proto.Metric_pb2 import Metric as MetricProto\n+from streamlit.type_util import _LOGGER\n from tests.delta_generator_test_case import DeltaGeneratorTestCase\n \n \n@@ -28,6 +32,10 @@ def test_no_value(self):\n self.assertEqual(c.label, \"label_test\")\n # This is an em dash. Not a regular \"-\"\n self.assertEqual(c.body, \"โ€”\")\n+ self.assertEqual(\n+ c.label_visibility.value,\n+ LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE,\n+ )\n \n def test_label_and_value(self):\n \"\"\"Test that metric can be called with label and value passed in.\"\"\"\n@@ -39,6 +47,22 @@ def test_label_and_value(self):\n self.assertEqual(c.color, MetricProto.MetricColor.GRAY)\n self.assertEqual(c.direction, MetricProto.MetricDirection.NONE)\n \n+ @parameterized.expand(\n+ [\n+ (\"visible\", LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE),\n+ (\"hidden\", LabelVisibilityMessage.LabelVisibilityOptions.HIDDEN),\n+ (\"collapsed\", LabelVisibilityMessage.LabelVisibilityOptions.COLLAPSED),\n+ ]\n+ )\n+ def test_label_visibility(self, label_visibility_value, proto_value):\n+ \"\"\"Test that metric can be called with label_visibility param.\"\"\"\n+ st.metric(\"label_test\", \"123\", label_visibility=label_visibility_value)\n+\n+ c = self.get_delta_from_queue().new_element.metric\n+ self.assertEqual(c.label, \"label_test\")\n+ self.assertEqual(c.body, \"123\")\n+ self.assertEqual(c.label_visibility.value, proto_value)\n+\n def test_label_and_value_and_delta_and_delta_color(self):\n \"\"\"Test that metric can be called with label, value, delta, and delta\n colors passed in.\"\"\"\n@@ -157,6 +181,26 @@ def test_invalid_label(self):\n str(exc.exception),\n )\n \n+ def test_invalid_label_visibility(self):\n+ with self.assertRaises(StreamlitAPIException) as e:\n+ st.metric(\"label_test\", \"123\", label_visibility=\"wrong_value\")\n+ self.assertEqual(\n+ str(e.exception),\n+ \"Unsupported label_visibility option 'wrong_value'. Valid values are \"\n+ \"'visible', 'hidden' or 'collapsed'.\",\n+ )\n+\n+ def test_empty_label_warning(self):\n+ \"\"\"Test that a warning is logged if st.metric was called with empty label.\"\"\"\n+\n+ with self.assertLogs(_LOGGER) as logs:\n+ st.metric(label=\"\", value=\"123\")\n+\n+ self.assertIn(\n+ \"`label` got an empty value. This is discouraged for accessibility reasons\",\n+ logs.records[0].msg,\n+ )\n+\n def test_invalid_value(self):\n with self.assertRaises(TypeError) as exc:\n st.metric(\"Testing\", [1, 2, 3])\ndiff --git a/proto/streamlit/proto/Metric.proto b/proto/streamlit/proto/Metric.proto\nindex 8d0543344722..f40b684912e1 100644\n--- a/proto/streamlit/proto/Metric.proto\n+++ b/proto/streamlit/proto/Metric.proto\n@@ -16,6 +16,8 @@\n \n syntax = \"proto3\";\n \n+import \"streamlit/proto/LabelVisibilityMessage.proto\";\n+\n message Metric {\n string label = 1;\n string body = 2;\n@@ -23,6 +25,7 @@ message Metric {\n MetricDirection direction = 4;\n MetricColor color = 5;\n string help = 6;\n+ LabelVisibilityMessage label_visibility = 7;\n \n enum MetricColor{\n RED = 0;\n" }
[ { "diff_hunk": "@@ -16,20 +16,29 @@\n \n import styled from \"@emotion/styled\"\n import { StyledWidgetLabel } from \"src/components/widgets/BaseWidget/styled-components\"\n+import { LabelVisibilityOptions } from \"src/lib/utils\"\n \n-export const StyledTruncateText = styled.div(({ theme }) => ({\n- overflowWrap: \"normal\",\n- textOverflow: \"ellipsis\",\n- width: \"100%\",\n- overflow: \"hidden\",\n- whiteSpace: \"nowrap\",\n- fontFamily: theme.genericFonts.bodyFont,\n- lineHeight: theme.lineHeights.normal,\n- verticalAlign: \"middle\",\n- display: \"flex\",\n- flexDirection: \"row\",\n- alignItems: \"center\",\n-}))\n+export interface StyledTruncateTextProps {\n+ visibility?: LabelVisibilityOptions\n+}\n+\n+export const StyledTruncateText = styled.div<StyledTruncateTextProps>(\n+ ({ theme, visibility }) => ({\n+ overflowWrap: \"normal\",\n+ textOverflow: \"ellipsis\",\n+ width: \"100%\",\n+ overflow: \"hidden\",\n+ whiteSpace: \"nowrap\",\n+ fontFamily: theme.genericFonts.bodyFont,\n+ lineHeight: theme.lineHeights.normal,\n+ verticalAlign: \"middle\",\n+ display: visibility === LabelVisibilityOptions.Collapsed ? \"none\" : \"flex\",\n+ visibility:\n+ visibility === LabelVisibilityOptions.Hidden ? \"hidden\" : \"visible\",\n+ flexDirection: \"row\",\n+ alignItems: \"center\",\n+ })\n+)\n \n export const StyledMetricLabelText = styled(StyledWidgetLabel)(() => ({\n marginBottom: 0,", "line": 42, "original_line": 44, "original_start_line": null, "path": "frontend/src/components/elements/Metric/styled-components.ts", "start_line": null, "text": "@user1:\n\r\nCould you please add \r\n```\r\n display: visibility === LabelVisibilityOptions.Collapsed ? \"none\" : \"flex\",\r\n visibility: visibility === LabelVisibilityOptions.Hidden ? \"hidden\" : \"visible\", \r\n ```\r\n to the `StyledMetricLabelText` class (`StyledTruncateText` changes could be reverted )\r\n \r\n This should fix an issue with spacing when `Collapsed`.\r\n \r\n And big thanks again for this PR!\n\n@author:\nThanks Karen! Fixed on a04ccae." } ]
1f4c3a590491d926724f9af0ec6f9eeb9cd7ee0e
diff --git a/e2e/scripts/st_metric.py b/e2e/scripts/st_metric.py index 1f3689999887..f2caec9d5296 100644 --- a/e2e/scripts/st_metric.py +++ b/e2e/scripts/st_metric.py @@ -33,3 +33,11 @@ st.metric("Test 2", -4.56, 1.23, "inverse") with col3: st.slider("Pick another") + + +with col1: + st.metric("Test 3", -4.56, 1.23, label_visibility="visible") +with col2: + st.metric("Test 4", -4.56, 1.23, label_visibility="hidden") +with col3: + st.metric("Test 5", -4.56, 1.23, label_visibility="collapsed") diff --git a/e2e/specs/st_metric.spec.js b/e2e/specs/st_metric.spec.js index d63cefb89dd2..d6ee6bdf7da5 100644 --- a/e2e/specs/st_metric.spec.js +++ b/e2e/specs/st_metric.spec.js @@ -114,4 +114,32 @@ describe("st.metric", () => { ).matchThemedSnapshots("metric-container-gray"); }); }); + + describe("Hides label correctly when label_visibility set to hidden", () => { + it("Check Metric Snapshot", () => { + cy.getIndexed("[data-testid='stMetricLabel']", 5).should( + "have.text", + "Test 4" + ); + + cy.getIndexed( + '[data-testid="metric-container"]', + 5 + ).matchThemedSnapshots("metric-label-hidden"); + }); + }); + + describe("Collapses label correctly when label_visibility set to collapse", () => { + it("Check Metric Snapshot", () => { + cy.getIndexed("[data-testid='stMetricLabel']", 6).should( + "have.text", + "Test 5" + ); + + cy.getIndexed( + '[data-testid="metric-container"]', + 6 + ).matchThemedSnapshots("metric-label-collapse"); + }); + }); }); diff --git a/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse-dark.snap.png new file mode 100644 index 000000000000..0cb9d51a7316 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse.snap.png b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse.snap.png new file mode 100644 index 000000000000..0a4037e7a722 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-collapse.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden-dark.snap.png new file mode 100644 index 000000000000..fc238725294e Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden.snap.png b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden.snap.png new file mode 100644 index 000000000000..0f421c33fa3d Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_metric.spec.js/metric-label-hidden.snap.png differ diff --git a/frontend/src/components/elements/Metric/Metric.test.tsx b/frontend/src/components/elements/Metric/Metric.test.tsx index 7c479bc2a4bf..3290026fc53b 100644 --- a/frontend/src/components/elements/Metric/Metric.test.tsx +++ b/frontend/src/components/elements/Metric/Metric.test.tsx @@ -19,7 +19,10 @@ import { mount } from "src/lib/test_util" import StreamlitMarkdown from "src/components/shared/StreamlitMarkdown" -import { Metric as MetricProto } from "src/autogen/proto" +import { + Metric as MetricProto, + LabelVisibilityMessage as LabelVisibilityMessageProto, +} from "src/autogen/proto" import Metric, { MetricProps } from "./Metric" const getProps = (elementProps: Partial<MetricProto> = {}): MetricProps => ({ @@ -47,6 +50,30 @@ describe("Metric element", () => { expect(wrappedMetricLabel.props().isLabel).toBe(true) }) + it("pass labelVisibility prop to StyledMetricLabelText correctly when hidden", () => { + const props = getProps({ + labelVisibility: { + value: LabelVisibilityMessageProto.LabelVisibilityOptions.HIDDEN, + }, + }) + const wrapper = mount(<Metric {...props} />) + expect(wrapper.find("StyledMetricLabelText").prop("visibility")).toEqual( + LabelVisibilityMessageProto.LabelVisibilityOptions.HIDDEN + ) + }) + + it("pass labelVisibility prop to StyledMetricLabelText correctly when collapsed", () => { + const props = getProps({ + labelVisibility: { + value: LabelVisibilityMessageProto.LabelVisibilityOptions.COLLAPSED, + }, + }) + const wrapper = mount(<Metric {...props} />) + expect(wrapper.find("StyledMetricLabelText").prop("visibility")).toEqual( + LabelVisibilityMessageProto.LabelVisibilityOptions.COLLAPSED + ) + }) + it("renders direction icon based on props", () => { const props = getProps() const wrapper = mount(<Metric {...props} />) diff --git a/frontend/src/components/elements/Metric/Metric.tsx b/frontend/src/components/elements/Metric/Metric.tsx index d13877427620..fd61bf32c774 100644 --- a/frontend/src/components/elements/Metric/Metric.tsx +++ b/frontend/src/components/elements/Metric/Metric.tsx @@ -17,6 +17,7 @@ import React, { ReactElement } from "react" import { Metric as MetricProto } from "src/autogen/proto" import { Theme } from "src/theme" +import { labelVisibilityProtoValueToEnum } from "src/lib/utils" import Icon from "src/components/shared/Icon" import { useTheme } from "@emotion/react" import { ArrowDownward, ArrowUpward } from "@emotion-icons/material-outlined" @@ -74,7 +75,12 @@ export default function Metric({ element }: MetricProps): ReactElement { return ( <div data-testid="metric-container"> - <StyledMetricLabelText data-testid="stMetricLabel"> + <StyledMetricLabelText + data-testid="stMetricLabel" + visibility={labelVisibilityProtoValueToEnum( + element.labelVisibility?.value + )} + > <StyledTruncateText> <StreamlitMarkdown source={element.label} diff --git a/frontend/src/components/elements/Metric/styled-components.ts b/frontend/src/components/elements/Metric/styled-components.ts index 43d2f605e0fd..5bed2a55c3a9 100644 --- a/frontend/src/components/elements/Metric/styled-components.ts +++ b/frontend/src/components/elements/Metric/styled-components.ts @@ -16,6 +16,11 @@ import styled from "@emotion/styled" import { StyledWidgetLabel } from "src/components/widgets/BaseWidget/styled-components" +import { LabelVisibilityOptions } from "src/lib/utils" + +export interface StyledMetricLabelTextProps { + visibility?: LabelVisibilityOptions +} export const StyledTruncateText = styled.div(({ theme }) => ({ overflowWrap: "normal", @@ -31,8 +36,13 @@ export const StyledTruncateText = styled.div(({ theme }) => ({ alignItems: "center", })) -export const StyledMetricLabelText = styled(StyledWidgetLabel)(() => ({ +export const StyledMetricLabelText = styled( + StyledWidgetLabel +)<StyledMetricLabelTextProps>(({ visibility }) => ({ marginBottom: 0, + display: visibility === LabelVisibilityOptions.Collapsed ? "none" : "flex", + visibility: + visibility === LabelVisibilityOptions.Hidden ? "hidden" : "visible", })) export const StyledMetricValueText = styled.div(({ theme }) => ({ diff --git a/lib/streamlit/elements/metric.py b/lib/streamlit/elements/metric.py index 5f65b4e65b04..d86339e01cca 100644 --- a/lib/streamlit/elements/metric.py +++ b/lib/streamlit/elements/metric.py @@ -18,10 +18,12 @@ from typing_extensions import Literal, TypeAlias +from streamlit.elements.utils import get_label_visibility_proto_value from streamlit.errors import StreamlitAPIException from streamlit.proto.Metric_pb2 import Metric as MetricProto from streamlit.runtime.metrics_util import gather_metrics from streamlit.string_util import clean_text +from streamlit.type_util import LabelVisibility, maybe_raise_label_warnings if TYPE_CHECKING: import numpy as np @@ -49,6 +51,7 @@ def metric( delta: Delta = None, delta_color: DeltaColor = "normal", help: Optional[str] = None, + label_visibility: LabelVisibility = "visible", ) -> "DeltaGenerator": """Display a metric in big bold font, with an optional indicator of how the metric changed. @@ -79,6 +82,11 @@ def metric( regardless of its value. help : str An optional tooltip that gets displayed next to the metric label. + label_visibility : "visible" or "hidden" or "collapsed" + The visibility of the label. If "hidden", the label doesn't show but there + is still empty space for it (equivalent to label=""). + If "collapsed", both the label and the space are removed. Default is + "visible". This argument can only be supplied by keyword. Example ------- @@ -112,6 +120,8 @@ def metric( height: 320px """ + maybe_raise_label_warnings(label, label_visibility) + metric_proto = MetricProto() metric_proto.body = self.parse_value(value) metric_proto.label = self.parse_label(label) @@ -124,6 +134,9 @@ def metric( ) metric_proto.color = color_and_direction.color metric_proto.direction = color_and_direction.direction + metric_proto.label_visibility.value = get_label_visibility_proto_value( + label_visibility + ) return self.dg._enqueue("metric", metric_proto) diff --git a/lib/tests/streamlit/elements/metric_test.py b/lib/tests/streamlit/elements/metric_test.py index f754a6919d55..fbeaa6d72d07 100644 --- a/lib/tests/streamlit/elements/metric_test.py +++ b/lib/tests/streamlit/elements/metric_test.py @@ -13,9 +13,13 @@ # limitations under the License. """metric unit tests.""" +from parameterized import parameterized + import streamlit as st from streamlit.errors import StreamlitAPIException +from streamlit.proto.LabelVisibilityMessage_pb2 import LabelVisibilityMessage from streamlit.proto.Metric_pb2 import Metric as MetricProto +from streamlit.type_util import _LOGGER from tests.delta_generator_test_case import DeltaGeneratorTestCase @@ -28,6 +32,10 @@ def test_no_value(self): self.assertEqual(c.label, "label_test") # This is an em dash. Not a regular "-" self.assertEqual(c.body, "โ€”") + self.assertEqual( + c.label_visibility.value, + LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE, + ) def test_label_and_value(self): """Test that metric can be called with label and value passed in.""" @@ -39,6 +47,22 @@ def test_label_and_value(self): self.assertEqual(c.color, MetricProto.MetricColor.GRAY) self.assertEqual(c.direction, MetricProto.MetricDirection.NONE) + @parameterized.expand( + [ + ("visible", LabelVisibilityMessage.LabelVisibilityOptions.VISIBLE), + ("hidden", LabelVisibilityMessage.LabelVisibilityOptions.HIDDEN), + ("collapsed", LabelVisibilityMessage.LabelVisibilityOptions.COLLAPSED), + ] + ) + def test_label_visibility(self, label_visibility_value, proto_value): + """Test that metric can be called with label_visibility param.""" + st.metric("label_test", "123", label_visibility=label_visibility_value) + + c = self.get_delta_from_queue().new_element.metric + self.assertEqual(c.label, "label_test") + self.assertEqual(c.body, "123") + self.assertEqual(c.label_visibility.value, proto_value) + def test_label_and_value_and_delta_and_delta_color(self): """Test that metric can be called with label, value, delta, and delta colors passed in.""" @@ -157,6 +181,26 @@ def test_invalid_label(self): str(exc.exception), ) + def test_invalid_label_visibility(self): + with self.assertRaises(StreamlitAPIException) as e: + st.metric("label_test", "123", label_visibility="wrong_value") + self.assertEqual( + str(e.exception), + "Unsupported label_visibility option 'wrong_value'. Valid values are " + "'visible', 'hidden' or 'collapsed'.", + ) + + def test_empty_label_warning(self): + """Test that a warning is logged if st.metric was called with empty label.""" + + with self.assertLogs(_LOGGER) as logs: + st.metric(label="", value="123") + + self.assertIn( + "`label` got an empty value. This is discouraged for accessibility reasons", + logs.records[0].msg, + ) + def test_invalid_value(self): with self.assertRaises(TypeError) as exc: st.metric("Testing", [1, 2, 3]) diff --git a/proto/streamlit/proto/Metric.proto b/proto/streamlit/proto/Metric.proto index 8d0543344722..f40b684912e1 100644 --- a/proto/streamlit/proto/Metric.proto +++ b/proto/streamlit/proto/Metric.proto @@ -16,6 +16,8 @@ syntax = "proto3"; +import "streamlit/proto/LabelVisibilityMessage.proto"; + message Metric { string label = 1; string body = 2; @@ -23,6 +25,7 @@ message Metric { MetricDirection direction = 4; MetricColor color = 5; string help = 6; + LabelVisibilityMessage label_visibility = 7; enum MetricColor{ RED = 0;
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-7018@a5a50db
streamlit/streamlit
Python
7,018
Markdown support for radio button labels
## Describe your changes Allows markdown in the options of `st.radio` - the same ones as permitted in Radio's label, with the exception of links because it is a clickable element. ## GitHub Issue Link (if applicable) Closes #6085 ## Testing Plan - FE/JS unit tests: Re-wrote both `Radio.test.tsx` files to to RTL - E2E/Snapshot tests: Added snapshot test w/ markdown in radio options **Before:** ![BeforeRadioMarkdownScreenshot](https://github.com/streamlit/streamlit/assets/63436329/b6192dc3-1050-437a-abb8-fc8070426c3c) **After:** ![After - no links](https://github.com/streamlit/streamlit/assets/63436329/edb25652-485b-4546-a1d7-e772751550d3) --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2023-07-14T23:42:10Z
Markdown support for radio buttons ### Problem Colored text and other markdown elements work in the label of `st.radio` but not in the texts of the radio elements. This is a bit weird since we do support it in the texts of checkboxes (where the text next to the checkbox is the label). ### Solution Allow markdown in the options of `st.radio`. --- Community voting on feature requests enables the Streamlit team to understand which features are most important to our users. **If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**
[ { "body": "### Problem\r\n\r\nColored text and other markdown elements work in the label of `st.radio` but not in the texts of the radio elements. This is a bit weird since we do support it in the texts of checkboxes (where the text next to the checkbox is the label). \r\n\r\n\r\n### Solution\r\n\r\nAllow markdown in the options of `st.radio`. \r\n\r\n\r\n---\r\n\r\nCommunity voting on feature requests enables the Streamlit team to understand which features are most important to our users.\r\n\r\n**If you'd like the Streamlit team to prioritize this feature request, please use the ๐Ÿ‘ (thumbs up emoji) reaction in response to the initial post.**\r\n", "number": 6085, "title": "Markdown support for radio buttons" } ]
0ae6c716a2b6bc770568eb34b907deb5f1ff1e25
{ "head_commit": "a5a50db3bd19b27518ffea1e447b5adee4a6b5c4", "head_commit_message": "Add updated snaps", "patch_to_review": "diff --git a/e2e/scripts/st_radio.py b/e2e/scripts/st_radio.py\nindex 2394c567ff76..0eb963c656aa 100644\n--- a/e2e/scripts/st_radio.py\n+++ b/e2e/scripts/st_radio.py\n@@ -19,6 +19,16 @@\n from tests.streamlit import pyspark_mocks\n \n options = (\"female\", \"male\")\n+markdown_options = (\n+ \"**bold text**\",\n+ \"*italics text*\",\n+ \"~strikethrough text~\",\n+ \"shortcode: :blush:\",\n+ # link should not work in radio options\n+ \"[link text](www.example.com)\",\n+ \"`code text`\",\n+ \":red[red] :blue[blue] :green[green] :violet[violet] :orange[orange]\",\n+)\n i1 = st.radio(\"radio 1\", options, 1)\n st.write(\"value 1:\", i1)\n \n@@ -43,14 +53,16 @@\n i8 = st.radio(\"radio 8\", options, label_visibility=\"collapsed\")\n st.write(\"value 8:\", i8)\n \n+i9 = st.radio(\"radio 9\", markdown_options)\n+st.write(\"value 9:\", i9)\n \n if runtime.exists():\n \n def on_change():\n st.session_state.radio_changed = True\n \n- st.radio(\"radio 9\", options, 1, key=\"radio9\", on_change=on_change)\n- st.write(\"value 9:\", st.session_state.radio9)\n+ st.radio(\"radio 10\", options, 1, key=\"radio10\", on_change=on_change)\n+ st.write(\"value 10:\", st.session_state.radio10)\n st.write(\"radio changed:\", \"radio_changed\" in st.session_state)\n \n st.radio(\"PySpark radio\", pyspark_mocks.DataFrame()) # type: ignore\ndiff --git a/e2e/specs/st_radio.spec.js b/e2e/specs/st_radio.spec.js\nindex 87373c438a00..672232444a22 100644\n--- a/e2e/specs/st_radio.spec.js\n+++ b/e2e/specs/st_radio.spec.js\n@@ -22,7 +22,7 @@ describe(\"st.radio\", () => {\n });\n \n it(\"shows widget correctly\", () => {\n- cy.get(\".stRadio\").should(\"have.length\", 10);\n+ cy.get(\".stRadio\").should(\"have.length\", 11);\n \n cy.get(\".stRadio\").each((el, idx) => {\n return cy.wrap(el).matchThemedSnapshots(\"radio\" + idx);\n@@ -95,7 +95,8 @@ describe(\"st.radio\", () => {\n \"value 6: female\" +\n \"value 7: female\" +\n \"value 8: female\" +\n- \"value 9: male\" +\n+ \"value 9: bold text\" +\n+ \"value 10: male\" +\n \"radio changed: False\"\n );\n });\n@@ -139,13 +140,14 @@ describe(\"st.radio\", () => {\n \"value 6: male\" +\n \"value 7: male\" +\n \"value 8: male\" +\n- \"value 9: male\" +\n+ \"value 9: red blue green violet orange\" +\n+ \"value 10: male\" +\n \"radio changed: False\"\n );\n });\n \n it(\"calls callback if one is registered\", () => {\n- cy.getIndexed(\".stRadio\", 8).then(el => {\n+ cy.getIndexed(\".stRadio\", 9).then(el => {\n return cy\n .wrap(el)\n .find(\"input\")\n@@ -163,7 +165,8 @@ describe(\"st.radio\", () => {\n \"value 6: female\" +\n \"value 7: female\" +\n \"value 8: female\" +\n- \"value 9: female\" +\n+ \"value 9: bold text\" +\n+ \"value 10: female\" +\n \"radio changed: True\"\n );\n });\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png\nnew file mode 100644\nindex 000000000000..1bde91ed3278\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png\nnew file mode 100644\nindex 000000000000..d5e0bb5f92a0\nBinary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png\nindex 6ab9115d4181..2388ad3f3c56 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png\nindex 91de8ceb40d0..a82cf77ab72e 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png\nindex aa675f07ea3d..d44eae20a0e1 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png\nindex c55d3ce0451e..42d5de1d07ba 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png differ\ndiff --git a/frontend/lib/src/components/shared/Radio/Radio.test.tsx b/frontend/lib/src/components/shared/Radio/Radio.test.tsx\nindex a69cc7f74eb8..f2bce73125e1 100644\n--- a/frontend/lib/src/components/shared/Radio/Radio.test.tsx\n+++ b/frontend/lib/src/components/shared/Radio/Radio.test.tsx\n@@ -15,9 +15,10 @@\n */\n \n import React from \"react\"\n-import { mount } from \"@streamlit/lib/src/test_util\"\n+import { render } from \"@streamlit/lib/src/test_util\"\n+import { screen, fireEvent } from \"@testing-library/react\"\n+import \"@testing-library/jest-dom\"\n \n-import { Radio as UIRadio, RadioGroup, ALIGN } from \"baseui/radio\"\n import { LabelVisibilityOptions } from \"@streamlit/lib/src/util/utils\"\n import { mockTheme } from \"@streamlit/lib/src/mocks/mockTheme\"\n import Radio, { Props } from \"./Radio\"\n@@ -37,112 +38,110 @@ const getProps = (props: Partial<Props> = {}): Props => ({\n describe(\"Radio widget\", () => {\n it(\"renders without crashing\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n+ const radioGroup = screen.getByRole(\"radiogroup\")\n+ const radioOptions = screen.getAllByRole(\"radio\")\n \n- expect(wrapper.find(RadioGroup).length).toBe(1)\n- expect(wrapper.find(UIRadio).length).toBe(3)\n+ expect(radioGroup).toBeInTheDocument()\n+ expect(radioOptions).toHaveLength(3)\n })\n \n it(\"renders without crashing if no label is provided\", () => {\n const props = getProps({ label: undefined })\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).length).toBe(1)\n- expect(wrapper.find(UIRadio).length).toBe(3)\n+ render(<Radio {...props} />)\n+ const widgetLabel = screen.queryByText(\"Label\")\n+ const radioOptions = screen.getByRole(\"radiogroup\")\n+\n+ expect(widgetLabel).toBeNull()\n+ expect(radioOptions).toBeInTheDocument()\n })\n \n it(\"pass labelVisibility prop to StyledWidgetLabel correctly when hidden\", () => {\n const props = getProps({\n labelVisibility: LabelVisibilityOptions.Hidden,\n })\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(\"StyledWidgetLabel\").prop(\"labelVisibility\")).toEqual(\n- LabelVisibilityOptions.Hidden\n- )\n+ render(<Radio {...props} />)\n+\n+ const widgetLabel = screen.getByText(\"Label\")\n+ expect(widgetLabel).toHaveStyle(\"visibility: hidden\")\n+ expect(widgetLabel).not.toBeVisible()\n })\n \n it(\"pass labelVisibility prop to StyledWidgetLabel correctly when collapsed\", () => {\n const props = getProps({\n labelVisibility: LabelVisibilityOptions.Collapsed,\n })\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(\"StyledWidgetLabel\").prop(\"labelVisibility\")).toEqual(\n- LabelVisibilityOptions.Collapsed\n- )\n+ render(<Radio {...props} />)\n+ const widgetLabel = screen.getByText(\"Label\")\n+ expect(widgetLabel).not.toBeVisible()\n })\n \n it(\"has correct className and style\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- const wrappedDiv = wrapper.find(\"div\").first()\n-\n- const { className, style } = wrappedDiv.props()\n- // @ts-expect-error\n- const splittedClassName = className.split(\" \")\n+ render(<Radio {...props} />)\n+ const radioElement = screen.getByTestId(\"stRadio\")\n \n- expect(splittedClassName).toContain(\"row-widget\")\n- expect(splittedClassName).toContain(\"stRadio\")\n-\n- // @ts-expect-error\n- expect(style.width).toBe(getProps().width)\n+ expect(radioElement).toHaveClass(\"row-widget\")\n+ expect(radioElement).toHaveClass(\"stRadio\")\n+ expect(radioElement).toHaveStyle(`width: ${props.width}px`)\n })\n \n it(\"renders a label\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(\"StyledWidgetLabel\").text()).toBe(props.label)\n+ render(<Radio {...props} />)\n+ const widgetLabel = screen.queryByText(`${props.label}`)\n+\n+ expect(widgetLabel).toBeInTheDocument()\n })\n \n it(\"has a default value\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(props.value.toString())\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ expect(radioOptions).toHaveLength(3)\n+\n+ const checked = radioOptions[props.value]\n+ expect(checked).toBeChecked()\n })\n \n it(\"can be disabled\", () => {\n- const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).prop(\"disabled\")).toBe(props.disabled)\n- })\n+ const props = getProps({ disabled: true })\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n \n- it(\"can be horizontally aligned\", () => {\n- const props = getProps({ horizontal: true })\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).prop(\"align\")).toBe(ALIGN.horizontal)\n+ radioOptions.forEach(option => {\n+ expect(option).toBeDisabled()\n+ })\n })\n \n it(\"has the correct options\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- const options = wrapper.find(UIRadio)\n+ render(<Radio {...props} />)\n \n- options.forEach((option, index) => {\n- expect(option.prop(\"value\")).toBe(index.toString())\n- expect(option.prop(\"children\")).toBe(props.options[index])\n+ props.options.forEach(option => {\n+ expect(screen.getByText(option)).toBeInTheDocument()\n })\n })\n \n it(\"shows a message when there are no options to be shown\", () => {\n const props = getProps({ options: [] })\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ const noOptionLabel = screen.getByText(\"No options to select.\")\n \n- expect(wrapper.find(UIRadio).length).toBe(1)\n- expect(wrapper.find(UIRadio).prop(\"children\")).toBe(\n- \"No options to select.\"\n- )\n+ expect(radioOptions).toHaveLength(1)\n+ expect(noOptionLabel).toBeInTheDocument()\n })\n \n it(\"handles value changes\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+\n+ const secondOption = radioOptions[1]\n \n- // @ts-expect-error\n- wrapper.find(RadioGroup).prop(\"onChange\")({\n- target: {\n- value: \"1\",\n- },\n- } as React.ChangeEvent<HTMLInputElement>)\n- wrapper.update()\n+ fireEvent.click(secondOption)\n \n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(\"1\")\n+ expect(secondOption).toBeChecked()\n })\n })\ndiff --git a/frontend/lib/src/components/shared/Radio/Radio.tsx b/frontend/lib/src/components/shared/Radio/Radio.tsx\nindex 7f162dbd6068..b86fc8cbe708 100644\n--- a/frontend/lib/src/components/shared/Radio/Radio.tsx\n+++ b/frontend/lib/src/components/shared/Radio/Radio.tsx\n@@ -25,6 +25,7 @@ import TooltipIcon from \"@streamlit/lib/src/components/shared/TooltipIcon\"\n import { LabelVisibilityOptions } from \"@streamlit/lib/src/util/utils\"\n import { Placement } from \"@streamlit/lib/src/components/shared/Tooltip\"\n import { EmotionTheme } from \"@streamlit/lib/src/theme\"\n+import StreamlitMarkdown from \"@streamlit/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown\"\n \n export interface Props {\n disabled: boolean\n@@ -85,7 +86,7 @@ class Radio extends React.PureComponent<Props, State> {\n }\n \n return (\n- <div className=\"row-widget stRadio\" style={style}>\n+ <div className=\"row-widget stRadio\" data-testid=\"stRadio\" style={style}>\n <WidgetLabel\n label={label}\n disabled={disabled}\n@@ -103,6 +104,7 @@ class Radio extends React.PureComponent<Props, State> {\n disabled={disabled}\n align={horizontal ? ALIGN.horizontal : ALIGN.vertical}\n aria-label={label}\n+ data-testid=\"stRadioGroup\"\n >\n {options.map((option: string, index: number) => (\n <UIRadio\n@@ -158,7 +160,12 @@ class Radio extends React.PureComponent<Props, State> {\n },\n }}\n >\n- {option}\n+ <StreamlitMarkdown\n+ source={option}\n+ allowHTML={false}\n+ isLabel\n+ isButton\n+ />\n </UIRadio>\n ))}\n </RadioGroup>\ndiff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx\nindex 425673421830..c68b0b340dc7 100644\n--- a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx\n+++ b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx\n@@ -248,14 +248,13 @@ describe(\"StreamlitMarkdown\", () => {\n \n it(\"doesn't render links when isButton is true\", () => {\n // Valid markdown further restricted with buttons to eliminate links\n- const source = \"Link: [text](www.example.com)\"\n- const wrapper = render(\n+ const source = \"[Link text](www.example.com)\"\n+ render(\n <StreamlitMarkdown source={source} allowHTML={false} isLabel isButton />\n )\n- const container = wrapper.getByTestId(\"stMarkdownContainer\")\n- const invalidTag = container.querySelector(\"a\")\n- expect(invalidTag).toBeNull()\n- expect(container).toHaveTextContent(\"Link: \")\n+ const tag = screen.getByText(\"Link text\")\n+ const isLinkTag = tag instanceof HTMLAnchorElement ? true : false\n+ expect(isLinkTag).toBe(false)\n })\n \n it(\"renders smaller text sizing when isToast is true\", () => {\n@@ -266,6 +265,21 @@ describe(\"StreamlitMarkdown\", () => {\n expect(textTag).toHaveStyle(\"font-size: 14px\")\n })\n \n+ it(\"renders regular text sizing when largerLabel is true\", () => {\n+ const source = \"Here is some checkbox label text\"\n+ render(\n+ <StreamlitMarkdown\n+ source={source}\n+ allowHTML={false}\n+ isLabel\n+ largerLabel\n+ />\n+ )\n+\n+ const textTag = screen.getByText(\"Here is some checkbox label text\")\n+ expect(textTag).toHaveStyle(\"font-size: inherit\")\n+ })\n+\n it(\"colours text properly\", () => {\n const colorMapping = new Map([\n [\"red\", colors.red80],\ndiff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx\nindex 16ed58a91f25..44e23bee7c07 100644\n--- a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx\n+++ b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx\n@@ -87,14 +87,14 @@ export interface Props {\n isLabel?: boolean\n \n /**\n- * Does not allow links\n+ * Checkbox labels have larger font sizing\n */\n- isButton?: boolean\n+ largerLabel?: boolean\n \n /**\n- * Checkbox has larger label font sizing\n+ * Does not allow links & has larger font sizing\n */\n- isCheckbox?: boolean\n+ isButton?: boolean\n \n /**\n * Toast has smaller font sizing\n@@ -233,7 +233,7 @@ export interface RenderedMarkdownProps {\n isLabel?: boolean\n \n /**\n- * Does not allow links\n+ * Does not allow links & has larger font sizing\n */\n isButton?: boolean\n }\n@@ -387,8 +387,8 @@ class StreamlitMarkdown extends PureComponent<Props> {\n style,\n isCaption,\n isLabel,\n+ largerLabel,\n isButton,\n- isCheckbox,\n isToast,\n } = this.props\n const isInSidebar = this.context\n@@ -398,8 +398,8 @@ class StreamlitMarkdown extends PureComponent<Props> {\n isCaption={Boolean(isCaption)}\n isInSidebar={isInSidebar}\n isLabel={isLabel}\n+ largerLabel={largerLabel}\n isButton={isButton}\n- isCheckbox={isCheckbox}\n isToast={isToast}\n style={style}\n data-testid={isCaption ? \"stCaptionContainer\" : \"stMarkdownContainer\"}\ndiff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts b/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts\nindex 6b3e50d65ccb..ee75d979e77e 100644\n--- a/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts\n+++ b/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts\n@@ -21,8 +21,8 @@ export interface StyledStreamlitMarkdownProps {\n isCaption: boolean\n isInSidebar: boolean\n isLabel?: boolean\n+ largerLabel?: boolean\n isButton?: boolean\n- isCheckbox?: boolean\n isToast?: boolean\n }\n \n@@ -45,13 +45,13 @@ export const StyledStreamlitMarkdown =\n isCaption,\n isInSidebar,\n isLabel,\n+ largerLabel,\n isButton,\n- isCheckbox,\n isToast,\n }) => {\n- // Widget Labels have smaller font size with exception of Buttons/Checkboxes\n+ // Widget Labels have smaller font size with exception of Button/Checkbox/Radio Button labels\n // Toasts also have smaller font size\n- const labelFontSize = (isLabel && !isCheckbox && !isButton) || isToast\n+ const labelFontSize = (isLabel && !largerLabel && !isButton) || isToast\n return {\n fontFamily: theme.genericFonts.bodyFont,\n marginBottom: isLabel ? \"\" : `-${theme.spacing.lg}`,\ndiff --git a/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx b/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx\nindex fec67ff6d5bf..637e79734758 100644\n--- a/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx\n+++ b/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx\n@@ -211,7 +211,7 @@ class Checkbox extends React.PureComponent<Props, State> {\n source={element.label}\n allowHTML={false}\n isLabel\n- isCheckbox\n+ largerLabel\n />\n {element.help && (\n <StyledWidgetLabelHelpInline color={color}>\ndiff --git a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\nindex 6e624ecce2c7..6e772a745e84 100644\n--- a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\n+++ b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx\n@@ -15,14 +15,18 @@\n */\n \n import React from \"react\"\n-import { mount } from \"@streamlit/lib/src/test_util\"\n-import { WidgetStateManager } from \"@streamlit/lib/src/WidgetStateManager\"\n+import { render } from \"@streamlit/lib/src/test_util\"\n+import { screen, fireEvent } from \"@testing-library/react\"\n+import \"@testing-library/jest-dom\"\n \n-import { Radio as UIRadio, RadioGroup } from \"baseui/radio\"\n+import { WidgetStateManager } from \"@streamlit/lib/src/WidgetStateManager\"\n import { Radio as RadioProto } from \"@streamlit/lib/src/proto\"\n import Radio, { Props } from \"./Radio\"\n \n-const getProps = (elementProps: Partial<RadioProto> = {}): Props => ({\n+const getProps = (\n+ elementProps: Partial<RadioProto> = {},\n+ otherProps: Partial<Props> = {}\n+): Props => ({\n element: RadioProto.create({\n id: \"1\",\n label: \"Label\",\n@@ -36,21 +40,24 @@ const getProps = (elementProps: Partial<RadioProto> = {}): Props => ({\n sendRerunBackMsg: jest.fn(),\n formsDataChanged: jest.fn(),\n }),\n+ ...otherProps,\n })\n \n describe(\"Radio widget\", () => {\n it(\"renders without crashing\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n+ const radioGroup = screen.getByRole(\"radiogroup\")\n+ const radioOptions = screen.getAllByRole(\"radio\")\n \n- expect(wrapper.find(RadioGroup).length).toBe(1)\n- expect(wrapper.find(UIRadio).length).toBe(3)\n+ expect(radioGroup).toBeInTheDocument()\n+ expect(radioOptions).toHaveLength(3)\n })\n \n it(\"sets widget value on mount\", () => {\n const props = getProps()\n jest.spyOn(props.widgetMgr, \"setIntValue\")\n- mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n \n expect(props.widgetMgr.setIntValue).toHaveBeenCalledWith(\n props.element,\n@@ -61,78 +68,77 @@ describe(\"Radio widget\", () => {\n \n it(\"has correct className and style\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- const wrappedDiv = wrapper.find(\"div\").first()\n-\n- const { className, style } = wrappedDiv.props()\n- // @ts-expect-error\n- const splittedClassName = className.split(\" \")\n-\n- expect(splittedClassName).toContain(\"row-widget\")\n- expect(splittedClassName).toContain(\"stRadio\")\n+ render(<Radio {...props} />)\n+ const radioElement = screen.getByTestId(\"stRadio\")\n \n- // @ts-expect-error\n- expect(style.width).toBe(getProps().width)\n+ expect(radioElement).toHaveClass(\"row-widget\")\n+ expect(radioElement).toHaveClass(\"stRadio\")\n+ expect(radioElement).toHaveStyle(`width: ${props.width}px`)\n })\n \n it(\"renders a label\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(\"StyledWidgetLabel\").text()).toBe(props.element.label)\n+ render(<Radio {...props} />)\n+ const widgetLabel = screen.queryByText(`${props.element.label}`)\n+\n+ expect(widgetLabel).toBeInTheDocument()\n })\n \n it(\"has a default value\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(\n- props.element.default.toString()\n- )\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ expect(radioOptions).toHaveLength(3)\n+\n+ const checked = radioOptions[props.element.default]\n+ expect(checked).toBeChecked()\n })\n \n it(\"can be disabled\", () => {\n- const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- expect(wrapper.find(RadioGroup).prop(\"disabled\")).toBe(props.disabled)\n+ const props = getProps({}, { disabled: true })\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+\n+ radioOptions.forEach(option => {\n+ expect(option).toBeDisabled()\n+ })\n })\n \n it(\"has the correct options\", () => {\n const props = getProps()\n- const wrapper = mount(<Radio {...props} />)\n- const options = wrapper.find(UIRadio)\n+ render(<Radio {...props} />)\n \n- options.forEach((option, index) => {\n- expect(option.prop(\"value\")).toBe(index.toString())\n- expect(option.prop(\"children\")).toBe(props.element.options[index])\n+ props.element.options.forEach(option => {\n+ expect(screen.getByText(option)).toBeInTheDocument()\n })\n })\n \n it(\"shows a message when there are no options to be shown\", () => {\n const props = getProps({ options: [] })\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n \n- expect(wrapper.find(UIRadio).length).toBe(1)\n- expect(wrapper.find(UIRadio).prop(\"children\")).toBe(\n- \"No options to select.\"\n- )\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ const noOptionLabel = screen.getByText(\"No options to select.\")\n+\n+ expect(radioOptions).toHaveLength(1)\n+ expect(noOptionLabel).toBeInTheDocument()\n })\n \n it(\"sets the widget value when an option is selected\", () => {\n const props = getProps()\n jest.spyOn(props.widgetMgr, \"setIntValue\")\n- const wrapper = mount(<Radio {...props} />)\n+ render(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ const secondOption = radioOptions[1]\n \n- // @ts-expect-error\n- wrapper.find(RadioGroup).prop(\"onChange\")({\n- target: { value: \"1\" },\n- } as React.ChangeEvent<HTMLInputElement>)\n- wrapper.update()\n+ fireEvent.click(secondOption)\n \n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(\"1\")\n expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith(\n props.element,\n 1,\n { fromUi: true }\n )\n+ expect(secondOption).toBeChecked()\n })\n \n it(\"resets its value when form is cleared\", () => {\n@@ -141,17 +147,15 @@ describe(\"Radio widget\", () => {\n props.widgetMgr.setFormClearOnSubmit(\"form\", true)\n \n jest.spyOn(props.widgetMgr, \"setIntValue\")\n+ render(<Radio {...props} />)\n \n- const wrapper = mount(<Radio {...props} />)\n+ const radioOptions = screen.getAllByRole(\"radio\")\n+ const secondOption = radioOptions[1]\n \n // Change the widget value\n- // @ts-expect-error\n- wrapper.find(RadioGroup).prop(\"onChange\")({\n- target: { value: \"1\" },\n- } as React.ChangeEvent<HTMLInputElement>)\n- wrapper.update()\n+ fireEvent.click(secondOption)\n+ expect(secondOption).toBeChecked()\n \n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(\"1\")\n expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith(\n props.element,\n 1,\n@@ -160,12 +164,11 @@ describe(\"Radio widget\", () => {\n \n // \"Submit\" the form\n props.widgetMgr.submitForm(\"form\")\n- wrapper.update()\n \n // Our widget should be reset, and the widgetMgr should be updated\n- expect(wrapper.find(RadioGroup).prop(\"value\")).toBe(\n- props.element.default.toString()\n- )\n+ const defaultValue = radioOptions[props.element.default]\n+ expect(defaultValue).toBeChecked()\n+\n expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith(\n props.element,\n props.element.default,\n" }
[ { "diff_hunk": "@@ -248,14 +248,13 @@ describe(\"StreamlitMarkdown\", () => {\n \n it(\"doesn't render links when isButton is true\", () => {\n // Valid markdown further restricted with buttons to eliminate links\n- const source = \"Link: [text](www.example.com)\"\n- const wrapper = render(\n+ const source = \"[Link text](www.example.com)\"\n+ render(\n <StreamlitMarkdown source={source} allowHTML={false} isLabel isButton />\n )\n- const container = wrapper.getByTestId(\"stMarkdownContainer\")\n- const invalidTag = container.querySelector(\"a\")\n- expect(invalidTag).toBeNull()\n- expect(container).toHaveTextContent(\"Link: \")\n+ const tag = screen.getByText(\"Link text\")\n+ const isLinkTag = tag instanceof HTMLAnchorElement ? true : false\n+ expect(isLinkTag).toBe(false)", "line": null, "original_line": 257, "original_start_line": 256, "path": "frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx", "start_line": null, "text": "@user1:\nnit: I think this can just be `expect(tag instanceof HTMLAnchorElement).toBe(false)`, or alternatively\r\n\r\n```typescript\r\n const isLinkTag = tag instanceof HTMLAnchorElement\r\n expect(isLinkTag).toBe(false)\r\n```\r\n\r\nif you want to keep the intermediate variable name for clarity" } ]
294a83ed763c7a8f2ea7ebb05ec754efd0704a2a
diff --git a/e2e/scripts/st_radio.py b/e2e/scripts/st_radio.py index 2394c567ff76..0eb963c656aa 100644 --- a/e2e/scripts/st_radio.py +++ b/e2e/scripts/st_radio.py @@ -19,6 +19,16 @@ from tests.streamlit import pyspark_mocks options = ("female", "male") +markdown_options = ( + "**bold text**", + "*italics text*", + "~strikethrough text~", + "shortcode: :blush:", + # link should not work in radio options + "[link text](www.example.com)", + "`code text`", + ":red[red] :blue[blue] :green[green] :violet[violet] :orange[orange]", +) i1 = st.radio("radio 1", options, 1) st.write("value 1:", i1) @@ -43,14 +53,16 @@ i8 = st.radio("radio 8", options, label_visibility="collapsed") st.write("value 8:", i8) +i9 = st.radio("radio 9", markdown_options) +st.write("value 9:", i9) if runtime.exists(): def on_change(): st.session_state.radio_changed = True - st.radio("radio 9", options, 1, key="radio9", on_change=on_change) - st.write("value 9:", st.session_state.radio9) + st.radio("radio 10", options, 1, key="radio10", on_change=on_change) + st.write("value 10:", st.session_state.radio10) st.write("radio changed:", "radio_changed" in st.session_state) st.radio("PySpark radio", pyspark_mocks.DataFrame()) # type: ignore diff --git a/e2e/specs/st_radio.spec.js b/e2e/specs/st_radio.spec.js index 87373c438a00..672232444a22 100644 --- a/e2e/specs/st_radio.spec.js +++ b/e2e/specs/st_radio.spec.js @@ -22,7 +22,7 @@ describe("st.radio", () => { }); it("shows widget correctly", () => { - cy.get(".stRadio").should("have.length", 10); + cy.get(".stRadio").should("have.length", 11); cy.get(".stRadio").each((el, idx) => { return cy.wrap(el).matchThemedSnapshots("radio" + idx); @@ -95,7 +95,8 @@ describe("st.radio", () => { "value 6: female" + "value 7: female" + "value 8: female" + - "value 9: male" + + "value 9: bold text" + + "value 10: male" + "radio changed: False" ); }); @@ -139,13 +140,14 @@ describe("st.radio", () => { "value 6: male" + "value 7: male" + "value 8: male" + - "value 9: male" + + "value 9: red blue green violet orange" + + "value 10: male" + "radio changed: False" ); }); it("calls callback if one is registered", () => { - cy.getIndexed(".stRadio", 8).then(el => { + cy.getIndexed(".stRadio", 9).then(el => { return cy .wrap(el) .find("input") @@ -163,7 +165,8 @@ describe("st.radio", () => { "value 6: female" + "value 7: female" + "value 8: female" + - "value 9: female" + + "value 9: bold text" + + "value 10: female" + "radio changed: True" ); }); diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png new file mode 100644 index 000000000000..1bde91ed3278 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png new file mode 100644 index 000000000000..d5e0bb5f92a0 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio10.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png index 6ab9115d4181..2388ad3f3c56 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png index 91de8ceb40d0..a82cf77ab72e 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio8.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png index aa675f07ea3d..d44eae20a0e1 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png index c55d3ce0451e..42d5de1d07ba 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png and b/frontend/cypress/snapshots/linux/2x/st_radio.spec.js/radio9.snap.png differ diff --git a/frontend/lib/src/components/shared/Radio/Radio.test.tsx b/frontend/lib/src/components/shared/Radio/Radio.test.tsx index a69cc7f74eb8..f2bce73125e1 100644 --- a/frontend/lib/src/components/shared/Radio/Radio.test.tsx +++ b/frontend/lib/src/components/shared/Radio/Radio.test.tsx @@ -15,9 +15,10 @@ */ import React from "react" -import { mount } from "@streamlit/lib/src/test_util" +import { render } from "@streamlit/lib/src/test_util" +import { screen, fireEvent } from "@testing-library/react" +import "@testing-library/jest-dom" -import { Radio as UIRadio, RadioGroup, ALIGN } from "baseui/radio" import { LabelVisibilityOptions } from "@streamlit/lib/src/util/utils" import { mockTheme } from "@streamlit/lib/src/mocks/mockTheme" import Radio, { Props } from "./Radio" @@ -37,112 +38,110 @@ const getProps = (props: Partial<Props> = {}): Props => ({ describe("Radio widget", () => { it("renders without crashing", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) + const radioGroup = screen.getByRole("radiogroup") + const radioOptions = screen.getAllByRole("radio") - expect(wrapper.find(RadioGroup).length).toBe(1) - expect(wrapper.find(UIRadio).length).toBe(3) + expect(radioGroup).toBeInTheDocument() + expect(radioOptions).toHaveLength(3) }) it("renders without crashing if no label is provided", () => { const props = getProps({ label: undefined }) - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).length).toBe(1) - expect(wrapper.find(UIRadio).length).toBe(3) + render(<Radio {...props} />) + const widgetLabel = screen.queryByText("Label") + const radioOptions = screen.getByRole("radiogroup") + + expect(widgetLabel).toBeNull() + expect(radioOptions).toBeInTheDocument() }) it("pass labelVisibility prop to StyledWidgetLabel correctly when hidden", () => { const props = getProps({ labelVisibility: LabelVisibilityOptions.Hidden, }) - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find("StyledWidgetLabel").prop("labelVisibility")).toEqual( - LabelVisibilityOptions.Hidden - ) + render(<Radio {...props} />) + + const widgetLabel = screen.getByText("Label") + expect(widgetLabel).toHaveStyle("visibility: hidden") + expect(widgetLabel).not.toBeVisible() }) it("pass labelVisibility prop to StyledWidgetLabel correctly when collapsed", () => { const props = getProps({ labelVisibility: LabelVisibilityOptions.Collapsed, }) - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find("StyledWidgetLabel").prop("labelVisibility")).toEqual( - LabelVisibilityOptions.Collapsed - ) + render(<Radio {...props} />) + const widgetLabel = screen.getByText("Label") + expect(widgetLabel).not.toBeVisible() }) it("has correct className and style", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - const wrappedDiv = wrapper.find("div").first() - - const { className, style } = wrappedDiv.props() - // @ts-expect-error - const splittedClassName = className.split(" ") + render(<Radio {...props} />) + const radioElement = screen.getByTestId("stRadio") - expect(splittedClassName).toContain("row-widget") - expect(splittedClassName).toContain("stRadio") - - // @ts-expect-error - expect(style.width).toBe(getProps().width) + expect(radioElement).toHaveClass("row-widget") + expect(radioElement).toHaveClass("stRadio") + expect(radioElement).toHaveStyle(`width: ${props.width}px`) }) it("renders a label", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find("StyledWidgetLabel").text()).toBe(props.label) + render(<Radio {...props} />) + const widgetLabel = screen.queryByText(`${props.label}`) + + expect(widgetLabel).toBeInTheDocument() }) it("has a default value", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).prop("value")).toBe(props.value.toString()) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + expect(radioOptions).toHaveLength(3) + + const checked = radioOptions[props.value] + expect(checked).toBeChecked() }) it("can be disabled", () => { - const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).prop("disabled")).toBe(props.disabled) - }) + const props = getProps({ disabled: true }) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") - it("can be horizontally aligned", () => { - const props = getProps({ horizontal: true }) - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).prop("align")).toBe(ALIGN.horizontal) + radioOptions.forEach(option => { + expect(option).toBeDisabled() + }) }) it("has the correct options", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - const options = wrapper.find(UIRadio) + render(<Radio {...props} />) - options.forEach((option, index) => { - expect(option.prop("value")).toBe(index.toString()) - expect(option.prop("children")).toBe(props.options[index]) + props.options.forEach(option => { + expect(screen.getByText(option)).toBeInTheDocument() }) }) it("shows a message when there are no options to be shown", () => { const props = getProps({ options: [] }) - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + const noOptionLabel = screen.getByText("No options to select.") - expect(wrapper.find(UIRadio).length).toBe(1) - expect(wrapper.find(UIRadio).prop("children")).toBe( - "No options to select." - ) + expect(radioOptions).toHaveLength(1) + expect(noOptionLabel).toBeInTheDocument() }) it("handles value changes", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + + const secondOption = radioOptions[1] - // @ts-expect-error - wrapper.find(RadioGroup).prop("onChange")({ - target: { - value: "1", - }, - } as React.ChangeEvent<HTMLInputElement>) - wrapper.update() + fireEvent.click(secondOption) - expect(wrapper.find(RadioGroup).prop("value")).toBe("1") + expect(secondOption).toBeChecked() }) }) diff --git a/frontend/lib/src/components/shared/Radio/Radio.tsx b/frontend/lib/src/components/shared/Radio/Radio.tsx index 7f162dbd6068..b86fc8cbe708 100644 --- a/frontend/lib/src/components/shared/Radio/Radio.tsx +++ b/frontend/lib/src/components/shared/Radio/Radio.tsx @@ -25,6 +25,7 @@ import TooltipIcon from "@streamlit/lib/src/components/shared/TooltipIcon" import { LabelVisibilityOptions } from "@streamlit/lib/src/util/utils" import { Placement } from "@streamlit/lib/src/components/shared/Tooltip" import { EmotionTheme } from "@streamlit/lib/src/theme" +import StreamlitMarkdown from "@streamlit/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown" export interface Props { disabled: boolean @@ -85,7 +86,7 @@ class Radio extends React.PureComponent<Props, State> { } return ( - <div className="row-widget stRadio" style={style}> + <div className="row-widget stRadio" data-testid="stRadio" style={style}> <WidgetLabel label={label} disabled={disabled} @@ -103,6 +104,7 @@ class Radio extends React.PureComponent<Props, State> { disabled={disabled} align={horizontal ? ALIGN.horizontal : ALIGN.vertical} aria-label={label} + data-testid="stRadioGroup" > {options.map((option: string, index: number) => ( <UIRadio @@ -158,7 +160,12 @@ class Radio extends React.PureComponent<Props, State> { }, }} > - {option} + <StreamlitMarkdown + source={option} + allowHTML={false} + isLabel + isButton + /> </UIRadio> ))} </RadioGroup> diff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx index 5a8e318afb90..418c98cb368c 100644 --- a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx +++ b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.test.tsx @@ -249,13 +249,12 @@ describe("StreamlitMarkdown", () => { it("doesn't render links when isButton is true", () => { // Valid markdown further restricted with buttons to eliminate links - const source = "[text](www.example.com)" + const source = "[Link text](www.example.com)" render( <StreamlitMarkdown source={source} allowHTML={false} isLabel isButton /> ) - const markdown = screen.getByText("text") - const tagName = markdown.nodeName.toLowerCase() - expect(tagName).not.toBe("a") + const tag = screen.getByText("Link text") + expect(tag instanceof HTMLAnchorElement).toBe(false) }) it("renders smaller text sizing when isToast is true", () => { @@ -266,6 +265,21 @@ describe("StreamlitMarkdown", () => { expect(textTag).toHaveStyle("font-size: 14px") }) + it("renders regular text sizing when largerLabel is true", () => { + const source = "Here is some checkbox label text" + render( + <StreamlitMarkdown + source={source} + allowHTML={false} + isLabel + largerLabel + /> + ) + + const textTag = screen.getByText("Here is some checkbox label text") + expect(textTag).toHaveStyle("font-size: inherit") + }) + it("colours text properly", () => { const colorMapping = new Map([ ["red", colors.red80], diff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx index 16ed58a91f25..44e23bee7c07 100644 --- a/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx +++ b/frontend/lib/src/components/shared/StreamlitMarkdown/StreamlitMarkdown.tsx @@ -87,14 +87,14 @@ export interface Props { isLabel?: boolean /** - * Does not allow links + * Checkbox labels have larger font sizing */ - isButton?: boolean + largerLabel?: boolean /** - * Checkbox has larger label font sizing + * Does not allow links & has larger font sizing */ - isCheckbox?: boolean + isButton?: boolean /** * Toast has smaller font sizing @@ -233,7 +233,7 @@ export interface RenderedMarkdownProps { isLabel?: boolean /** - * Does not allow links + * Does not allow links & has larger font sizing */ isButton?: boolean } @@ -387,8 +387,8 @@ class StreamlitMarkdown extends PureComponent<Props> { style, isCaption, isLabel, + largerLabel, isButton, - isCheckbox, isToast, } = this.props const isInSidebar = this.context @@ -398,8 +398,8 @@ class StreamlitMarkdown extends PureComponent<Props> { isCaption={Boolean(isCaption)} isInSidebar={isInSidebar} isLabel={isLabel} + largerLabel={largerLabel} isButton={isButton} - isCheckbox={isCheckbox} isToast={isToast} style={style} data-testid={isCaption ? "stCaptionContainer" : "stMarkdownContainer"} diff --git a/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts b/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts index 6b3e50d65ccb..ee75d979e77e 100644 --- a/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts +++ b/frontend/lib/src/components/shared/StreamlitMarkdown/styled-components.ts @@ -21,8 +21,8 @@ export interface StyledStreamlitMarkdownProps { isCaption: boolean isInSidebar: boolean isLabel?: boolean + largerLabel?: boolean isButton?: boolean - isCheckbox?: boolean isToast?: boolean } @@ -45,13 +45,13 @@ export const StyledStreamlitMarkdown = isCaption, isInSidebar, isLabel, + largerLabel, isButton, - isCheckbox, isToast, }) => { - // Widget Labels have smaller font size with exception of Buttons/Checkboxes + // Widget Labels have smaller font size with exception of Button/Checkbox/Radio Button labels // Toasts also have smaller font size - const labelFontSize = (isLabel && !isCheckbox && !isButton) || isToast + const labelFontSize = (isLabel && !largerLabel && !isButton) || isToast return { fontFamily: theme.genericFonts.bodyFont, marginBottom: isLabel ? "" : `-${theme.spacing.lg}`, diff --git a/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx b/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx index fec67ff6d5bf..637e79734758 100644 --- a/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx +++ b/frontend/lib/src/components/widgets/Checkbox/Checkbox.tsx @@ -211,7 +211,7 @@ class Checkbox extends React.PureComponent<Props, State> { source={element.label} allowHTML={false} isLabel - isCheckbox + largerLabel /> {element.help && ( <StyledWidgetLabelHelpInline color={color}> diff --git a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx index 6e624ecce2c7..6e772a745e84 100644 --- a/frontend/lib/src/components/widgets/Radio/Radio.test.tsx +++ b/frontend/lib/src/components/widgets/Radio/Radio.test.tsx @@ -15,14 +15,18 @@ */ import React from "react" -import { mount } from "@streamlit/lib/src/test_util" -import { WidgetStateManager } from "@streamlit/lib/src/WidgetStateManager" +import { render } from "@streamlit/lib/src/test_util" +import { screen, fireEvent } from "@testing-library/react" +import "@testing-library/jest-dom" -import { Radio as UIRadio, RadioGroup } from "baseui/radio" +import { WidgetStateManager } from "@streamlit/lib/src/WidgetStateManager" import { Radio as RadioProto } from "@streamlit/lib/src/proto" import Radio, { Props } from "./Radio" -const getProps = (elementProps: Partial<RadioProto> = {}): Props => ({ +const getProps = ( + elementProps: Partial<RadioProto> = {}, + otherProps: Partial<Props> = {} +): Props => ({ element: RadioProto.create({ id: "1", label: "Label", @@ -36,21 +40,24 @@ const getProps = (elementProps: Partial<RadioProto> = {}): Props => ({ sendRerunBackMsg: jest.fn(), formsDataChanged: jest.fn(), }), + ...otherProps, }) describe("Radio widget", () => { it("renders without crashing", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) + const radioGroup = screen.getByRole("radiogroup") + const radioOptions = screen.getAllByRole("radio") - expect(wrapper.find(RadioGroup).length).toBe(1) - expect(wrapper.find(UIRadio).length).toBe(3) + expect(radioGroup).toBeInTheDocument() + expect(radioOptions).toHaveLength(3) }) it("sets widget value on mount", () => { const props = getProps() jest.spyOn(props.widgetMgr, "setIntValue") - mount(<Radio {...props} />) + render(<Radio {...props} />) expect(props.widgetMgr.setIntValue).toHaveBeenCalledWith( props.element, @@ -61,78 +68,77 @@ describe("Radio widget", () => { it("has correct className and style", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - const wrappedDiv = wrapper.find("div").first() - - const { className, style } = wrappedDiv.props() - // @ts-expect-error - const splittedClassName = className.split(" ") - - expect(splittedClassName).toContain("row-widget") - expect(splittedClassName).toContain("stRadio") + render(<Radio {...props} />) + const radioElement = screen.getByTestId("stRadio") - // @ts-expect-error - expect(style.width).toBe(getProps().width) + expect(radioElement).toHaveClass("row-widget") + expect(radioElement).toHaveClass("stRadio") + expect(radioElement).toHaveStyle(`width: ${props.width}px`) }) it("renders a label", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find("StyledWidgetLabel").text()).toBe(props.element.label) + render(<Radio {...props} />) + const widgetLabel = screen.queryByText(`${props.element.label}`) + + expect(widgetLabel).toBeInTheDocument() }) it("has a default value", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).prop("value")).toBe( - props.element.default.toString() - ) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + expect(radioOptions).toHaveLength(3) + + const checked = radioOptions[props.element.default] + expect(checked).toBeChecked() }) it("can be disabled", () => { - const props = getProps() - const wrapper = mount(<Radio {...props} />) - expect(wrapper.find(RadioGroup).prop("disabled")).toBe(props.disabled) + const props = getProps({}, { disabled: true }) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + + radioOptions.forEach(option => { + expect(option).toBeDisabled() + }) }) it("has the correct options", () => { const props = getProps() - const wrapper = mount(<Radio {...props} />) - const options = wrapper.find(UIRadio) + render(<Radio {...props} />) - options.forEach((option, index) => { - expect(option.prop("value")).toBe(index.toString()) - expect(option.prop("children")).toBe(props.element.options[index]) + props.element.options.forEach(option => { + expect(screen.getByText(option)).toBeInTheDocument() }) }) it("shows a message when there are no options to be shown", () => { const props = getProps({ options: [] }) - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) - expect(wrapper.find(UIRadio).length).toBe(1) - expect(wrapper.find(UIRadio).prop("children")).toBe( - "No options to select." - ) + const radioOptions = screen.getAllByRole("radio") + const noOptionLabel = screen.getByText("No options to select.") + + expect(radioOptions).toHaveLength(1) + expect(noOptionLabel).toBeInTheDocument() }) it("sets the widget value when an option is selected", () => { const props = getProps() jest.spyOn(props.widgetMgr, "setIntValue") - const wrapper = mount(<Radio {...props} />) + render(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + const secondOption = radioOptions[1] - // @ts-expect-error - wrapper.find(RadioGroup).prop("onChange")({ - target: { value: "1" }, - } as React.ChangeEvent<HTMLInputElement>) - wrapper.update() + fireEvent.click(secondOption) - expect(wrapper.find(RadioGroup).prop("value")).toBe("1") expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith( props.element, 1, { fromUi: true } ) + expect(secondOption).toBeChecked() }) it("resets its value when form is cleared", () => { @@ -141,17 +147,15 @@ describe("Radio widget", () => { props.widgetMgr.setFormClearOnSubmit("form", true) jest.spyOn(props.widgetMgr, "setIntValue") + render(<Radio {...props} />) - const wrapper = mount(<Radio {...props} />) + const radioOptions = screen.getAllByRole("radio") + const secondOption = radioOptions[1] // Change the widget value - // @ts-expect-error - wrapper.find(RadioGroup).prop("onChange")({ - target: { value: "1" }, - } as React.ChangeEvent<HTMLInputElement>) - wrapper.update() + fireEvent.click(secondOption) + expect(secondOption).toBeChecked() - expect(wrapper.find(RadioGroup).prop("value")).toBe("1") expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith( props.element, 1, @@ -160,12 +164,11 @@ describe("Radio widget", () => { // "Submit" the form props.widgetMgr.submitForm("form") - wrapper.update() // Our widget should be reset, and the widgetMgr should be updated - expect(wrapper.find(RadioGroup).prop("value")).toBe( - props.element.default.toString() - ) + const defaultValue = radioOptions[props.element.default] + expect(defaultValue).toBeChecked() + expect(props.widgetMgr.setIntValue).toHaveBeenLastCalledWith( props.element, props.element.default,
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
zulip__zulip-26294@a42972f
zulip/zulip
Python
26,294
compose: Allow URLs to be pasted onto selected text.
This PR enables URL-pasting onto selected compose text. The logic checks for the presence of a URL on the clipboard following a `"paste"` event, and equally for a text selection in the compose area. Absent either of those things, no action is taken. Otherwise, a Markdown string of the selected text is formatted using the selection in the textarea, and the cursor is placed at the end of the new Markdown string. The PR opens with a testing-related prep commit to remove brittle, self-referential event-testing logic from the copy-and-paste tests, which will break anyway once there is need to reach into the event object--as this PR does in retrieving the textarea in question. Fixes: #18692 [Recent CZO discussion](https://chat.zulip.org/#narrow/stream/137-feedback/topic/Linkify.20text.20when.20pasting.20an.20url/near/1608530) **Compose area:** ![copy-and-paste-compose](https://github.com/zulip/zulip/assets/170719/758d5f28-680b-4d7a-8351-2c1b2192668d) **Edit area:** ![copy-and-paste-edit](https://github.com/zulip/zulip/assets/170719/9f9f8efb-0b14-445a-a347-f58da2c5989c) <details> <summary>Self-review checklist</summary> <!-- Prior to submitting a PR, follow our step-by-step guide to review your own code: https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code --> <!-- Once you create the PR, check off all the steps below that you have completed. If any of these steps are not relevant or you have not completed, leave them unchecked.--> - [x] [Self-reviewed](https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code) the changes for clarity and maintainability (variable names, code reuse, readability, etc.). Communicate decisions, questions, and potential concerns. - [x] Explains differences from previous plans (e.g., issue description). - [x] Highlights technical choices and bugs encountered. - [x] Calls out remaining decisions and concerns. - [x] Automated tests verify logic where appropriate. Individual commits are ready for review (see [commit discipline](https://zulip.readthedocs.io/en/latest/contributing/commit-discipline.html)). - [x] Each commit is a coherent idea. - [x] Commit message(s) explain reasoning and motivation for changes. Completed manual review and testing of the following: - [x] Visual appearance of the changes. - [x] Responsiveness and internationalization. - [x] Strings and tooltips. - [x] End-to-end functionality of buttons, interactions and flows. - [x] Corner cases, error conditions, and easily imagined bugs. </details>
2023-07-19T15:47:21Z
Paste URL to create a named link At present, highlighting text in the compose box and pasting a URL replaces the text with that URL. Instead, when a user highlights text in the compose and pastes a URL, it should turn the highlighted text into a named link to that URL. It's a really nice UX for making links, which you can try out in a GitHub comment or in Dropbox Paper to get a feel for it.
Hello @zulip/server-compose members, this issue was labeled with the "area: compose" label, so you may want to check it out! <!-- areaLabelAddition --> I really like this idea actually. This is a very helpful feature of dropbox. @zulipbot claim Reopened by #19180. **ERROR:** This active issue has no assignee. **ERROR:** This active issue has no assignee. **ERROR:** This active issue has no assignee. **ERROR:** This active issue has no assignee. @zulipbot remove "in progress" @karlstolley You have been unassigned from this issue because you have not made any updates for over 14 days. Please feel free to reclaim the issue if you decide to pick up again. Thanks!
[ { "body": "At present, highlighting text in the compose box and pasting a URL replaces the text with that URL.\r\n\r\nInstead, when a user highlights text in the compose and pastes a URL, it should turn the highlighted text into a named link to that URL.\r\n\r\nIt's a really nice UX for making links, which you can try out in a GitHub comment or in Dropbox Paper to get a feel for it.", "number": 18692, "title": "Paste URL to create a named link" } ]
ca40e60469d0ac7d7eac3fdeecbd4023d0b27dd2
{ "head_commit": "a42972f0cbd53c3b2bfb38193d8cb05c7527e764", "head_commit_message": "copy_paste: Strip back tests to only test handler.\n\nThis gets us out of the brittle business of trying to mock a\ncomplex event like \"paste\"--which mocking basically means we\nare testing against the mock more than a real event.\n\nThe test name is also changed to clarify the handler being\ntested.\n\nSee CZO discussion behind this change:\nhttps://chat.zulip.org/#narrow/stream/43-automated-testing/topic/mocking.20browser.20events.3F/near/1615110", "patch_to_review": "diff --git a/web/tests/copy_and_paste.test.js b/web/tests/copy_and_paste.test.js\nindex 85c16e37dd0a3..d85c95307e0c0 100644\n--- a/web/tests/copy_and_paste.test.js\n+++ b/web/tests/copy_and_paste.test.js\n@@ -2,43 +2,13 @@\n \n const {strict: assert} = require(\"assert\");\n \n-const {JSDOM} = require(\"jsdom\");\n-\n-const {mock_esm, set_global, zrequire} = require(\"./lib/namespace\");\n-const jquery = require(\"./lib/real_jquery\");\n+const {zrequire} = require(\"./lib/namespace\");\n const {run_test} = require(\"./lib/test\");\n const {page_params} = require(\"./lib/zpage_params\");\n \n-const {window} = new JSDOM(\"<!DOCTYPE html><p>Hello world</p>\");\n-\n-const {document} = window;\n-const $ = jquery(window);\n-\n-const compose_ui = mock_esm(\"../src/compose_ui\");\n-set_global(\"document\", document);\n-\n const copy_and_paste = zrequire(\"copy_and_paste\");\n \n-// Super stripped down version of the code in the drag-mock library\n-// https://github.com/andywer/drag-mock/blob/6d46c7c0ffd6a4d685e6612a90cd58cda80f30fc/src/DataTransfer.js\n-class DataTransfer {\n- dataByFormat = {};\n- getData(dataFormat) {\n- return this.dataByFormat[dataFormat];\n- }\n- setData(dataFormat, data) {\n- this.dataByFormat[dataFormat] = data;\n- }\n-}\n-\n-const createPasteEvent = function () {\n- const clipboardData = new DataTransfer();\n- const pasteEvent = new window.Event(\"paste\");\n- pasteEvent.clipboardData = clipboardData;\n- return new $.Event(pasteEvent);\n-};\n-\n-run_test(\"paste_handler\", () => {\n+run_test(\"paste_handler_converter\", () => {\n page_params.development_environment = true;\n let input =\n '<meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\"><span style=\"color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;\"><span> </span>love the<span> </span><b>Zulip</b><b> </b></span><b style=\"color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;\">Organization</b><span style=\"color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;\">.</span>';\n@@ -90,22 +60,4 @@ run_test(\"paste_handler\", () => {\n copy_and_paste.paste_handler_converter(input),\n \"Test list:\\n* Item 1\\n* Item 2\",\n );\n-\n- let data = \"<p>text</p>\";\n- let event = createPasteEvent();\n- event.originalEvent.clipboardData.setData(\"text/html\", data);\n- let insert_syntax_and_focus_called = false;\n- compose_ui.insert_syntax_and_focus = function () {\n- insert_syntax_and_focus_called = true;\n- };\n- copy_and_paste.paste_handler(event);\n- assert.ok(insert_syntax_and_focus_called);\n-\n- data =\n- '<meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\"><img src=\"http://localhost:9991/thumbnail?url=user_uploads%2F1%2Fe2%2FHPMCcGWOG9rS2M4ybHN8sEzh%2Fpasted_image.png&amp;size=full\"/>';\n- event = createPasteEvent();\n- event.originalEvent.clipboardData.setData(\"text/html\", data);\n- insert_syntax_and_focus_called = false;\n- copy_and_paste.paste_handler(event);\n- assert.ok(!insert_syntax_and_focus_called);\n });\n" }
[ { "diff_hunk": "@@ -341,7 +341,28 @@ export function paste_handler(event) {\n }\n \n if (clipboardData.getData) {\n+ const $textarea = $(event.currentTarget);\n+ const paste_text = clipboardData.getData(\"text\");\n const paste_html = clipboardData.getData(\"text/html\");\n+ // Trim the paste_text to accommodate sloppy copying\n+ const trimmed_paste_text = paste_text.trim();\n+\n+ // Match for anything that resembles a URL, something://something\n+ const url_regex = /^\\w+:\\/\\/\\S+$/;", "line": null, "original_line": 351, "original_start_line": null, "path": "web/src/copy_and_paste.js", "start_line": null, "text": "@user2:\nSurely we have a better \"is this probably a URL\" check we can do, either via native libraries or already in the codebase; @user1 do you have something to suggest?\n\n@author:\nGood point. I didn't see anything in the frontend code that looked like it was checking for URLs, so I've pushed an update using the [is-url](https://github.com/segmentio/is-url) package, which looks pretty good and has a nice set of tests.\r\n\r\nI'm still interested in Anders's opinion, too." } ]
3507c6d07153ba5dc107c6f3a3aca4ee209ff051
diff --git a/package.json b/package.json index 103ba0eadceb6..b56b4899d01ab 100644 --- a/package.json +++ b/package.json @@ -44,6 +44,7 @@ "handlebars-loader": "^1.7.1", "html-webpack-plugin": "^5.3.2", "intl-messageformat": "^10.3.0", + "is-url": "^1.2.4", "jquery": "^3.6.3", "jquery-caret-plugin": "^1.5.2", "jquery-validation": "^1.19.0", diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index 03467ce0a98c5..438134d4e2ed5 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -134,6 +134,9 @@ dependencies: intl-messageformat: specifier: ^10.3.0 version: 10.5.0 + is-url: + specifier: ^1.2.4 + version: 1.2.4 jquery: specifier: ^3.6.3 version: 3.7.0 @@ -7455,6 +7458,10 @@ packages: unc-path-regex: 0.1.2 dev: false + /[email protected]: + resolution: {integrity: sha512-ITvGim8FhRiYe4IQ5uHSkj7pVaPDrCTkNd3yq3cV7iZAcJdHTUMPMEHcqSOy9xZ9qFenQCvi+2wjH9a1nXqHww==} + dev: false + /[email protected]: resolution: {integrity: sha512-rMYPYvCzsXywIsldgLaSoPlw5PfoB/ssr7hY4pLfcodrA5M/eArza1a9VmTiNIBNMjOGr1Ow9mTyU2o69U6U9Q==} dev: false diff --git a/web/src/compose_ui.js b/web/src/compose_ui.js index 73c9064a23b7d..8059dc085c6b9 100644 --- a/web/src/compose_ui.js +++ b/web/src/compose_ui.js @@ -339,7 +339,7 @@ export function handle_keyup(_event, $textarea) { rtl.set_rtl_class_for_textarea($textarea); } -export function format_text($textarea, type) { +export function format_text($textarea, type, inserted_content) { const italic_syntax = "*"; const bold_syntax = "**"; const bold_and_italic_syntax = "***"; @@ -503,6 +503,14 @@ export function format_text($textarea, type) { field.setSelectionRange(new_start, new_end); break; } + case "linked": { + // From a paste event with a URL as inserted content + wrapSelection(field, "[", `](${inserted_content})`); + // Put the cursor at the end of the selection range + // and all wrapped material + $textarea.caret(range.end + `[](${inserted_content})`.length); + break; + } } } diff --git a/web/src/copy_and_paste.js b/web/src/copy_and_paste.js index 11400708fbe04..6a7d7d6ca9432 100644 --- a/web/src/copy_and_paste.js +++ b/web/src/copy_and_paste.js @@ -1,3 +1,4 @@ +import isUrl from "is-url"; import $ from "jquery"; import TurndownService from "turndown"; @@ -341,7 +342,25 @@ export function paste_handler(event) { } if (clipboardData.getData) { + const $textarea = $(event.currentTarget); + const paste_text = clipboardData.getData("text"); const paste_html = clipboardData.getData("text/html"); + // Trim the paste_text to accommodate sloppy copying + const trimmed_paste_text = paste_text.trim(); + const range = $textarea.range(); + + // Only try to generate formatted links when dealing with a URL + // and a range selection. Note that even clipboards with "text/html" + // have a "text" equivalent, so we need an if statement that checks + // for more than a value on `trimmed_paste_text` + if (isUrl(trimmed_paste_text) && range.text) { + event.preventDefault(); + event.stopPropagation(); + const url = trimmed_paste_text; + compose_ui.format_text($textarea, "linked", url); + return; + } + if (paste_html && page_params.development_environment) { const text = paste_handler_converter(paste_html); const mdImageRegex = /^!\[.*]\(.*\)$/; @@ -359,5 +378,5 @@ export function paste_handler(event) { export function initialize() { $("#compose-textarea").on("paste", paste_handler); - $("body").on("paste", ".message_edit_form", paste_handler); + $("body").on("paste", ".message_edit_content", paste_handler); } diff --git a/web/tests/copy_and_paste.test.js b/web/tests/copy_and_paste.test.js index 85c16e37dd0a3..d85c95307e0c0 100644 --- a/web/tests/copy_and_paste.test.js +++ b/web/tests/copy_and_paste.test.js @@ -2,43 +2,13 @@ const {strict: assert} = require("assert"); -const {JSDOM} = require("jsdom"); - -const {mock_esm, set_global, zrequire} = require("./lib/namespace"); -const jquery = require("./lib/real_jquery"); +const {zrequire} = require("./lib/namespace"); const {run_test} = require("./lib/test"); const {page_params} = require("./lib/zpage_params"); -const {window} = new JSDOM("<!DOCTYPE html><p>Hello world</p>"); - -const {document} = window; -const $ = jquery(window); - -const compose_ui = mock_esm("../src/compose_ui"); -set_global("document", document); - const copy_and_paste = zrequire("copy_and_paste"); -// Super stripped down version of the code in the drag-mock library -// https://github.com/andywer/drag-mock/blob/6d46c7c0ffd6a4d685e6612a90cd58cda80f30fc/src/DataTransfer.js -class DataTransfer { - dataByFormat = {}; - getData(dataFormat) { - return this.dataByFormat[dataFormat]; - } - setData(dataFormat, data) { - this.dataByFormat[dataFormat] = data; - } -} - -const createPasteEvent = function () { - const clipboardData = new DataTransfer(); - const pasteEvent = new window.Event("paste"); - pasteEvent.clipboardData = clipboardData; - return new $.Event(pasteEvent); -}; - -run_test("paste_handler", () => { +run_test("paste_handler_converter", () => { page_params.development_environment = true; let input = '<meta http-equiv="content-type" content="text/html; charset=utf-8"><span style="color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;"><span> </span>love the<span> </span><b>Zulip</b><b> </b></span><b style="color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;">Organization</b><span style="color: hsl(0, 0%, 13%); font-family: arial, sans-serif; font-size: 12.8px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: hsl(0, 0%, 100%); text-decoration-style: initial; text-decoration-color: initial;">.</span>'; @@ -90,22 +60,4 @@ run_test("paste_handler", () => { copy_and_paste.paste_handler_converter(input), "Test list:\n* Item 1\n* Item 2", ); - - let data = "<p>text</p>"; - let event = createPasteEvent(); - event.originalEvent.clipboardData.setData("text/html", data); - let insert_syntax_and_focus_called = false; - compose_ui.insert_syntax_and_focus = function () { - insert_syntax_and_focus_called = true; - }; - copy_and_paste.paste_handler(event); - assert.ok(insert_syntax_and_focus_called); - - data = - '<meta http-equiv="content-type" content="text/html; charset=utf-8"><img src="http://localhost:9991/thumbnail?url=user_uploads%2F1%2Fe2%2FHPMCcGWOG9rS2M4ybHN8sEzh%2Fpasted_image.png&amp;size=full"/>'; - event = createPasteEvent(); - event.originalEvent.clipboardData.setData("text/html", data); - insert_syntax_and_focus_called = false; - copy_and_paste.paste_handler(event); - assert.ok(!insert_syntax_and_focus_called); });
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
zulip__zulip-26231@4162342
zulip/zulip
Python
26,231
integrations: Add support for "Test plugin" in Sentry integration.
If a user tries to create a webhook using the Webhooks plugin in Sentry and uses the "Test plugin" to test the webhook, the server would send a 500 error, even though the integration worked perfectly. This led users to believe that the integration was not working. This PR fixes that behaviour and makes sure the sample event is handled properly. Fixes: #26173 <details> <summary>Self-review checklist</summary> <!-- Prior to submitting a PR, follow our step-by-step guide to review your own code: https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code --> <!-- Once you create the PR, check off all the steps below that you have completed. If any of these steps are not relevant or you have not completed, leave them unchecked.--> - [x] [Self-reviewed](https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code) the changes for clarity and maintainability (variable names, code reuse, readability, etc.). Communicate decisions, questions, and potential concerns. - [ ] Explains differences from previous plans (e.g., issue description). - [ ] Highlights technical choices and bugs encountered. - [ ] Calls out remaining decisions and concerns. - [ ] Automated tests verify logic where appropriate. Individual commits are ready for review (see [commit discipline](https://zulip.readthedocs.io/en/latest/contributing/commit-discipline.html)). - [x] Each commit is a coherent idea. - [x] Commit message(s) explain reasoning and motivation for changes. Completed manual review and testing of the following: - [ ] Visual appearance of the changes. - [ ] Responsiveness and internationalization. - [ ] Strings and tooltips. - [ ] End-to-end functionality of buttons, interactions and flows. - [ ] Corner cases, error conditions, and easily imagined bugs. </details>
2023-07-10T20:46:37Z
Sentry integration "Test Plugin" failure Hello, I'm setting up Sentry.io integration and got this error when I tried "Test Plugin" (meaning "Test Outgoing Webhook to Zulip"); I'm assuming it's the response payload that came back from Zulip Cloud: >"There was an internal error with the Plugin, {\"result\":\"error\",\"msg\":\"The 'Raven SDK' event isn't currently supported by the Sentry webhook\",\"webhook_name\":\"Sentry\",\"event_type\":\"Raven SDK\",\"code\":\"UNSUPPORTED_WEBHOOK_EVENT_TYPE\"}\n" I'm not sure if there are any events that do work because I'm new to Sentry and not sure how to trigger test events other than the Test Plugin event. **Zulip Server and web app version:** - [x] Zulip Cloud (`*.zulipchat.com`) - [ ] Zulip Server 7.0+ - [ ] Zulip Server 6.0+ - [ ] Zulip Server 5.0 or older - [ ] Other or not sure
@zulipbot add "area: integrations" Hello @zulip/server-integrations members, this issue was labeled with the "area: integrations" label, so you may want to check it out! <!-- areaLabelAddition --> @aryairani Thanks for reporting this issue. This issue was recently fixed in 16563a321. I tried to reproduce this by pressing the **Send Test Notification** button while creating an alert rule and a notification was received as expected. I also inspected the received webhook and all seems to be alright. Can you confirm if you are creating an "Alert Rule" as mentioned in the [documentation](https://zulip.com/integrations/doc/sentry)? Hi @sbansal1999, This is under `https://<myorg>.sentry.io/settings/projects/<myproj>/plugins/webhooks/` Pressing [Test Plugin], with Callback Url `https://<myzulip>.zulipchat.com/api/v1/external/sentry?api_key=<mykey>&stream=<mystream>` Is this expected to work? I'm working on getting permission to "Create New Integration"; fwiw the don't currently have permissions to create a custom integration for the whole org, I'm "only" admin for the project. The webhook also appears under` https://<myorg>.sentry.io/settings/projects/<myproj>/alerts/`. Is this also not right? Ok thanks! Alright, I guess I got it sorted out, thanks for the hint. I wonder if it might be worthwhile to add to a note that the Sentry Webhook integration is not the right way to configure Sentry alerts via Webhook to Zulip? @aryairani Our [Sentry documentation](https://zulip.com/integrations/doc/sentry) says: >NOTE: Zulip also supports configuring this as a webhook in Sentry โ€” which, while easier to configure (Navigate to Settings > Integrations > WebHooks) may not include the full breadth of event types. I tried creating a webhook using the `Integrations` list -- as you did. I was able to trigger issues on my sample project and then the events resulted in correct messages being sent by the bot. But as you pointed out when testing it by using the "Test Plugin" button it returns an error. So practically speaking, the webhook will still be able to receive events related to issues but won't respond to the test The only problematic thing here is that Sentry doesn't explicitly mention its condition for the sample event; hence making it difficult to identify them. So, I don't think there is a need to delete this from the documentation. I see, sorry I missed that. Fwiw I find the integration docs to have a bit of a "wall of text" vibe, which makes it hard to follow (not just the Sentry integration docs); but I'm not a designer myself so I don't have any concrete suggestions on that front. Thanks for your help! Should I close the ticket? #25976 is actively being worked on, which should help address the big block of text in various integrations documentation pages for generating the URL. But until that's done, I don't think there's much to be done about that block of text in step 2. I did a little revision pass of the Sentry specific instructions (steps 3 and 4), and have #26224 up with an updated version of the current documentation that breaks up that text a bit and emphasized that warning/note about the alternative implementation via Sentry webhook. @laurynmm Thanks for making some needed changes to the documentation. I also went ahead and opened #26231 to fix the behavior that was reported in the issue. After this is merged whenever the user will test the webhook using the "Test plugin" button (which the author also did and is also a documented way), the integration will not return a 500 and will lead to a meaningful notification being sent.
[ { "body": "Hello, I'm setting up Sentry.io integration and got this error when I tried \"Test Plugin\" (meaning \"Test Outgoing Webhook to Zulip\"); I'm assuming it's the response payload that came back from Zulip Cloud:\r\n\r\n>\"There was an internal error with the Plugin, {\\\"result\\\":\\\"error\\\",\\\"msg\\\":\\\"The 'Raven SDK' event isn't currently supported by the Sentry webhook\\\",\\\"webhook_name\\\":\\\"Sentry\\\",\\\"event_type\\\":\\\"Raven SDK\\\",\\\"code\\\":\\\"UNSUPPORTED_WEBHOOK_EVENT_TYPE\\\"}\\n\"\r\n\r\nI'm not sure if there are any events that do work because I'm new to Sentry and not sure how to trigger test events other than the Test Plugin event.\r\n\r\n**Zulip Server and web app version:**\r\n\r\n- [x] Zulip Cloud (`*.zulipchat.com`)\r\n- [ ] Zulip Server 7.0+\r\n- [ ] Zulip Server 6.0+\r\n- [ ] Zulip Server 5.0 or older\r\n- [ ] Other or not sure\r\n", "number": 26173, "title": "Sentry integration \"Test Plugin\" failure" } ]
88b200c298d66fcb8a9e437d4670e568c10d9032
{ "head_commit": "416234269ad8de403384dffabe436e0cde059771", "head_commit_message": "integrations: Add Raven SDK test to Sentry Integration.", "patch_to_review": "diff --git a/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json b/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json\nnew file mode 100644\nindex 0000000000000..d267f1561569f\n--- /dev/null\n+++ b/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json\n@@ -0,0 +1,134 @@\n+{\n+ \"id\": \"4311163364\",\n+ \"project\": \"python-raven\",\n+ \"project_name\": \"python-raven\",\n+ \"project_slug\": \"python-raven\",\n+ \"logger\": null,\n+ \"level\": \"error\",\n+ \"culprit\": \"__main__ in <module>\",\n+ \"message\": \"ZeroDivisionError: division by zero\",\n+ \"url\": \"https://nitk-46.sentry.io/issues/4311163364/?referrer=webhooks_plugin\",\n+ \"triggering_rules\": [\n+ \"a\"\n+ ],\n+ \"event\": {\n+ \"event_id\": \"b465c7ca9581475093a6f5c3c6a523d2\",\n+ \"level\": \"error\",\n+ \"version\": \"6\",\n+ \"type\": \"error\",\n+ \"fingerprint\": [\n+ \"{{ default }}\"\n+ ],\n+ \"culprit\": \"__main__ in <module>\",\n+ \"logentry\": {\n+ \"formatted\": \"ZeroDivisionError: division by zero\"\n+ },\n+ \"logger\": \"\",\n+ \"modules\": {\n+ \"python\": \"3.10.6\"\n+ },\n+ \"platform\": \"python\",\n+ \"timestamp\": 1689194871.0,\n+ \"received\": 1689194872.041476,\n+ \"exception\": {\n+ \"values\": [\n+ {\n+ \"type\": \"ZeroDivisionError\",\n+ \"value\": \"division by zero\",\n+ \"module\": \"builtins\",\n+ \"stacktrace\": {\n+ \"frames\": [\n+ {\n+ \"function\": \"<module>\",\n+ \"module\": \"__main__\",\n+ \"filename\": \"sentry/temp.py\",\n+ \"abs_path\": \"/home/sbansal1999/p/sentry/temp.py\",\n+ \"lineno\": 6,\n+ \"pre_context\": [\n+ \"from raven import Client\",\n+ \"\",\n+ \"client = Client('https://6130e69dc19e426e87deaa54744be1cd@o4505261800423424.ingest.sentry.io/4505518370586624')\",\n+ \"\",\n+ \"try:\"\n+ ],\n+ \"context_line\": \" 1 / 0\",\n+ \"post_context\": [\n+ \"except ZeroDivisionError:\",\n+ \" client.captureException()\"\n+ ],\n+ \"in_app\": false,\n+ \"vars\": {\n+ \"Client\": \"<class 'raven.base.Client'>\",\n+ \"__annotations__\": {},\n+ \"__builtins__\": \"<module 'builtins' (built-in)>\",\n+ \"__cached__\": null,\n+ \"__doc__\": null,\n+ \"__file__\": \"'/home/sbansal1999/p/sentry/temp.py'\",\n+ \"__loader__\": \"<_frozen_importlib_external.SourceFileLoader object at 0x7fd40cfdd930>\",\n+ \"__name__\": \"'__main__'\",\n+ \"__package__\": null,\n+ \"__spec__\": null,\n+ \"client\": \"<raven.base.Client object at 0x7fd40d107c10>\"\n+ },\n+ \"data\": {\n+ \"orig_in_app\": -1\n+ }\n+ }\n+ ]\n+ }\n+ }\n+ ]\n+ },\n+ \"tags\": [\n+ [\n+ \"level\",\n+ \"error\"\n+ ],\n+ [\n+ \"server_name\",\n+ \"sbansal1999-OMEN-Laptop-15-en0xxx\"\n+ ]\n+ ],\n+ \"extra\": {\n+ \"sys.argv\": [\n+ \"'temp.py'\"\n+ ]\n+ },\n+ \"sdk\": {\n+ \"name\": \"raven-python\",\n+ \"version\": \"6.10.0\"\n+ },\n+ \"ingest_path\": [\n+ {\n+ \"version\": \"23.6.2\",\n+ \"public_key\": \"XE7QiyuNlja9PZ7I9qJlwQotzecWrUIN91BAO7Q5R38\"\n+ }\n+ ],\n+ \"key_id\": \"3267509\",\n+ \"project\": 4505518370586624,\n+ \"grouping_config\": {\n+ \"enhancements\": \"eJybzDRxc15qeXFJZU6qlZGBkbGugaGuoeEEAHJMCAM\",\n+ \"id\": \"newstyle:2023-01-11\"\n+ },\n+ \"_metrics\": {\n+ \"bytes.ingested.event\": 1549,\n+ \"bytes.stored.event\": 2356\n+ },\n+ \"_ref\": 4505518370586624,\n+ \"_ref_version\": 2,\n+ \"hashes\": [\n+ \"cac8c3af9240e70448922144a14f55b3\"\n+ ],\n+ \"location\": \"sentry/temp.py\",\n+ \"metadata\": {\n+ \"display_title_with_tree_label\": false,\n+ \"filename\": \"sentry/temp.py\",\n+ \"function\": \"<module>\",\n+ \"type\": \"ZeroDivisionError\",\n+ \"value\": \"division by zero\"\n+ },\n+ \"nodestore_insert\": 1689194873.112506,\n+ \"title\": \"ZeroDivisionError: division by zero\",\n+ \"id\": \"b465c7ca9581475093a6f5c3c6a523d2\"\n+ }\n+}\ndiff --git a/zerver/webhooks/sentry/fixtures/sample_event.json b/zerver/webhooks/sentry/fixtures/sample_event_through_alert.json\nsimilarity index 100%\nrename from zerver/webhooks/sentry/fixtures/sample_event.json\nrename to zerver/webhooks/sentry/fixtures/sample_event_through_alert.json\ndiff --git a/zerver/webhooks/sentry/tests.py b/zerver/webhooks/sentry/tests.py\nindex 22d886eeff721..b1f342ee18e3c 100644\n--- a/zerver/webhooks/sentry/tests.py\n+++ b/zerver/webhooks/sentry/tests.py\n@@ -251,7 +251,7 @@ def test_deprecated_exception_message(self) -> None:\n ```\"\"\"\n self.check_webhook(\"deprecated_exception_message\", expected_topic, expected_message)\n \n- def test_sample_event(self) -> None:\n+ def test_sample_event_through_alert(self) -> None:\n expected_topic = \"This is an example Python exception\"\n expected_message = \"\"\"\\\n **New message event:** [This is an example Python exception](https://sentry.io/organizations/nitk-46/issues/4218258981/events/b6eff1a49b1f4132850b1238d968da70/)\n@@ -259,4 +259,17 @@ def test_sample_event(self) -> None:\n **level:** error\n **timestamp:** 2023-05-31 11:06:16\n ```\"\"\"\n- self.check_webhook(\"sample_event\", expected_topic, expected_message)\n+ self.check_webhook(\"sample_event_through_alert\", expected_topic, expected_message)\n+\n+ def test_raven_sdk_python_event(self) -> None:\n+ payload = self.get_body(\"raven_sdk_python_event\")\n+ result = self.client_post(\n+ self.url,\n+ payload,\n+ content_type=\"application/json\",\n+ )\n+ self.assert_json_success(result)\n+ self.assert_in_response(\n+ \"The 'Raven SDK' event isn't currently supported by the Sentry webhook; ignoring\",\n+ result,\n+ )\n" }
[ { "diff_hunk": "@@ -81,6 +81,20 @@\n }\n \n \n+def is_sample_event(event: Dict[str, Any]) -> bool:\n+ # These are just some workarounds; there is no documented way to check\n+ # if an event is a sample event or not.\n+ tags = event.get(\"tags\", [])\n+ if [\"sample_event\", \"yes\"] in tags:\n+ return True\n+ title = event.get(\"title\", \"\")\n+ if title == \"This is an example Python exception\":\n+ return True", "line": null, "original_line": 89, "original_start_line": 85, "path": "zerver/webhooks/sentry/view.py", "start_line": null, "text": "@user1:\nI actually wasn't suggesting that you remove the other check -- I said they \"seem potentially redundant unless one knows the history here.\" I feel that, if we could only use it, the tags solution is better because it's less likely to have false-positives. I can see users typing `raise(\"This is an example Python exception\")` in their code when trying it out, and I'd rather that we not _think_ it's s sample-from-Sentry. Right now, the only effect is that we'd accept a Raven SDK submission, but code does drift and if we, say, started doing other things based on `is_sample_event`, false-positives would get more dangerous.\r\n\r\nSince we _have_ to use the title, I'm fine with dropping the tag support, but I'd prefer to adjust the \"This is just a workaround\" comment to make clearer that this is a _heuristic_ which may false-positive, so we should not rely on it for important behaviour decisions.\r\n\r\n> When the integration is set through Alerts there is no response body shown to the user but when it is set through the Webhook plugin a response body is shown only when an error is returned. So I don't think we can hint the user as such.\r\n\r\nBummer.\r\n\r\n> I think since it's clearly mentioned in the [documentation](https://zulip.com/integrations/doc/sentry) that setting the integration through the Webhook plugin is not the best idea hence, there is no need for this.\r\n\r\nIt's documented, but _people will not always read the documentation_ -- which is what made us need this PR in the first place. :) So we should always think about if it's simple to have the user interface (here, through the Sentry UI) give good feedback. So while there's no \"need\" for this, in that it's not a blocker, it certainly would have improved the product experience if we could.\r\n\r\n> I think you meant to say via the Webhook plugin because if someone sets an integration through that that's when the integration doesn't provide all the events.\r\n\r\nAh, yeah, I said that backwards. Sorry for any confusion!\n\n@author:\n> Since we have to use the title, I'm fine with dropping the tag support, but I'd prefer to adjust the \"This is just a workaround\" comment to make clearer that this is a heuristic which may false-positive, so we should not rely on it for important behaviour decisions.\r\n\r\nYes, I think that makes perfect sense.\r\n\r\n>So while there's no \"need\" for this, in that it's not a blocker, it certainly would have improved the product experience if we could.\r\n\r\nYou are correct that would have certainly improved the overall experience. Since there is no way right now to do that, I feel we can leave this for now." } ]
3ac624404f83ec02a0904593f6b576ea395778f2
diff --git a/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json b/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json new file mode 100644 index 0000000000000..d267f1561569f --- /dev/null +++ b/zerver/webhooks/sentry/fixtures/raven_sdk_python_event.json @@ -0,0 +1,134 @@ +{ + "id": "4311163364", + "project": "python-raven", + "project_name": "python-raven", + "project_slug": "python-raven", + "logger": null, + "level": "error", + "culprit": "__main__ in <module>", + "message": "ZeroDivisionError: division by zero", + "url": "https://nitk-46.sentry.io/issues/4311163364/?referrer=webhooks_plugin", + "triggering_rules": [ + "a" + ], + "event": { + "event_id": "b465c7ca9581475093a6f5c3c6a523d2", + "level": "error", + "version": "6", + "type": "error", + "fingerprint": [ + "{{ default }}" + ], + "culprit": "__main__ in <module>", + "logentry": { + "formatted": "ZeroDivisionError: division by zero" + }, + "logger": "", + "modules": { + "python": "3.10.6" + }, + "platform": "python", + "timestamp": 1689194871.0, + "received": 1689194872.041476, + "exception": { + "values": [ + { + "type": "ZeroDivisionError", + "value": "division by zero", + "module": "builtins", + "stacktrace": { + "frames": [ + { + "function": "<module>", + "module": "__main__", + "filename": "sentry/temp.py", + "abs_path": "/home/sbansal1999/p/sentry/temp.py", + "lineno": 6, + "pre_context": [ + "from raven import Client", + "", + "client = Client('https://6130e69dc19e426e87deaa54744be1cd@o4505261800423424.ingest.sentry.io/4505518370586624')", + "", + "try:" + ], + "context_line": " 1 / 0", + "post_context": [ + "except ZeroDivisionError:", + " client.captureException()" + ], + "in_app": false, + "vars": { + "Client": "<class 'raven.base.Client'>", + "__annotations__": {}, + "__builtins__": "<module 'builtins' (built-in)>", + "__cached__": null, + "__doc__": null, + "__file__": "'/home/sbansal1999/p/sentry/temp.py'", + "__loader__": "<_frozen_importlib_external.SourceFileLoader object at 0x7fd40cfdd930>", + "__name__": "'__main__'", + "__package__": null, + "__spec__": null, + "client": "<raven.base.Client object at 0x7fd40d107c10>" + }, + "data": { + "orig_in_app": -1 + } + } + ] + } + } + ] + }, + "tags": [ + [ + "level", + "error" + ], + [ + "server_name", + "sbansal1999-OMEN-Laptop-15-en0xxx" + ] + ], + "extra": { + "sys.argv": [ + "'temp.py'" + ] + }, + "sdk": { + "name": "raven-python", + "version": "6.10.0" + }, + "ingest_path": [ + { + "version": "23.6.2", + "public_key": "XE7QiyuNlja9PZ7I9qJlwQotzecWrUIN91BAO7Q5R38" + } + ], + "key_id": "3267509", + "project": 4505518370586624, + "grouping_config": { + "enhancements": "eJybzDRxc15qeXFJZU6qlZGBkbGugaGuoeEEAHJMCAM", + "id": "newstyle:2023-01-11" + }, + "_metrics": { + "bytes.ingested.event": 1549, + "bytes.stored.event": 2356 + }, + "_ref": 4505518370586624, + "_ref_version": 2, + "hashes": [ + "cac8c3af9240e70448922144a14f55b3" + ], + "location": "sentry/temp.py", + "metadata": { + "display_title_with_tree_label": false, + "filename": "sentry/temp.py", + "function": "<module>", + "type": "ZeroDivisionError", + "value": "division by zero" + }, + "nodestore_insert": 1689194873.112506, + "title": "ZeroDivisionError: division by zero", + "id": "b465c7ca9581475093a6f5c3c6a523d2" + } +} diff --git a/zerver/webhooks/sentry/fixtures/sample_event.json b/zerver/webhooks/sentry/fixtures/sample_event_through_alert.json similarity index 100% rename from zerver/webhooks/sentry/fixtures/sample_event.json rename to zerver/webhooks/sentry/fixtures/sample_event_through_alert.json diff --git a/zerver/webhooks/sentry/fixtures/sample_event_through_plugin.json b/zerver/webhooks/sentry/fixtures/sample_event_through_plugin.json new file mode 100644 index 0000000000000..998ac00a42cca --- /dev/null +++ b/zerver/webhooks/sentry/fixtures/sample_event_through_plugin.json @@ -0,0 +1,471 @@ +{ + "id": "4218258981", + "project": "python", + "project_name": "python", + "project_slug": "python", + "logger": null, + "level": "error", + "culprit": "raven.scripts.runner in main", + "message": "This is an example Python exception", + "url": "https://nitk-46.sentry.io/issues/4218258981/?referrer=webhooks_plugin", + "triggering_rules": [], + "event": { + "event_id": "4dc4fc2858aa450eb658be9e5b8ad149", + "level": "error", + "version": "5", + "type": "default", + "logentry": { + "formatted": "This is an example Python exception", + "message": null, + "params": null + }, + "logger": "", + "modules": { + "my.package": "1.0.0" + }, + "platform": "python", + "timestamp": 1688935284.772, + "received": 1688935344.773181, + "environment": "prod", + "user": { + "id": "1", + "email": "[email protected]", + "ip_address": "127.0.0.1", + "username": "sentry", + "name": "Sentry", + "geo": { + "country_code": "GB", + "city": "London", + "region": "H9" + } + }, + "request": { + "url": "http://example.com/foo", + "method": "GET", + "data": { + "hello": "world" + }, + "query_string": [ + [ + "foo", + "bar" + ] + ], + "cookies": [ + [ + "foo", + "bar" + ], + [ + "biz", + "baz" + ] + ], + "headers": [ + [ + "Content-Type", + "application/json" + ], + [ + "Referer", + "http://example.com" + ], + [ + "User-Agent", + "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.72 Safari/537.36" + ] + ], + "env": { + "ENV": "prod" + }, + "inferred_content_type": "application/json", + "api_target": null, + "fragment": null + }, + "contexts": { + "browser": { + "name": "Chrome", + "version": "28.0.1500", + "type": "browser" + }, + "client_os": { + "name": "Windows", + "version": "8", + "type": "os" + } + }, + "stacktrace": { + "frames": [ + { + "function": "build_msg", + "module": "raven.base", + "filename": "raven/base.py", + "abs_path": "/home/ubuntu/.virtualenvs/getsentry/src/raven/raven/base.py", + "lineno": 303, + "pre_context": [ + " frames = stack", + "", + " data.update({", + " 'sentry.interfaces.Stacktrace': {", + " 'frames': get_stack_info(frames," + ], + "context_line": " transformer=self.transform)", + "post_context": [ + " },", + " })", + "", + " if 'sentry.interfaces.Stacktrace' in data:", + " if self.include_paths:" + ], + "in_app": false, + "vars": { + "'culprit'": null, + "'data'": { + "'message'": "u'This is a test message generated using ``raven test``'", + "'sentry.interfaces.Message'": { + "'message'": "u'This is a test message generated using ``raven test``'", + "'params'": [] + } + }, + "'date'": "datetime.datetime(2013, 8, 13, 3, 8, 24, 880386)", + "'event_id'": "'54a322436e1b47b88e239b78998ae742'", + "'event_type'": "'raven.events.Message'", + "'extra'": { + "'go_deeper'": [ + [ + "{\"'bar'\":[\"'baz'\"],\"'foo'\":\"'bar'\"}" + ] + ], + "'loadavg'": [ + 0.37255859375, + 0.5341796875, + 0.62939453125 + ], + "'user'": "'dcramer'" + }, + "'frames'": "<generator object iter_stack_frames at 0x107bcc3c0>", + "'handler'": "<raven.events.Message object at 0x107bd0890>", + "'k'": "'sentry.interfaces.Message'", + "'kwargs'": { + "'level'": 20, + "'message'": "'This is a test message generated using ``raven test``'" + }, + "'public_key'": null, + "'result'": { + "'message'": "u'This is a test message generated using ``raven test``'", + "'sentry.interfaces.Message'": { + "'message'": "u'This is a test message generated using ``raven test``'", + "'params'": [] + } + }, + "'self'": "<raven.base.Client object at 0x107bb8210>", + "'stack'": true, + "'tags'": null, + "'time_spent'": null, + "'v'": { + "'message'": "u'This is a test message generated using ``raven test``'", + "'params'": [] + } + }, + "colno": null, + "data": null, + "errors": null, + "raw_function": null, + "image_addr": null, + "instruction_addr": null, + "addr_mode": null, + "package": null, + "platform": null, + "symbol": null, + "symbol_addr": null, + "trust": null, + "snapshot": null, + "lock": null + }, + { + "function": "capture", + "module": "raven.base", + "filename": "raven/base.py", + "abs_path": "/home/ubuntu/.virtualenvs/getsentry/src/raven/raven/base.py", + "lineno": 459, + "pre_context": [ + " if not self.is_enabled():", + " return", + "", + " data = self.build_msg(", + " event_type, data, date, time_spent, extra, stack, tags=tags," + ], + "context_line": " **kwargs)", + "post_context": [ + "", + " self.send(**data)", + "", + " return (data.get('event_id'),)", + "" + ], + "in_app": false, + "vars": { + "'data'": null, + "'date'": null, + "'event_type'": "'raven.events.Message'", + "'extra'": { + "'go_deeper'": [ + [ + "{\"'bar'\":[\"'baz'\"],\"'foo'\":\"'bar'\"}" + ] + ], + "'loadavg'": [ + 0.37255859375, + 0.5341796875, + 0.62939453125 + ], + "'user'": "'dcramer'" + }, + "'kwargs'": { + "'level'": 20, + "'message'": "'This is a test message generated using ``raven test``'" + }, + "'self'": "<raven.base.Client object at 0x107bb8210>", + "'stack'": true, + "'tags'": null, + "'time_spent'": null + }, + "colno": null, + "data": null, + "errors": null, + "raw_function": null, + "image_addr": null, + "instruction_addr": null, + "addr_mode": null, + "package": null, + "platform": null, + "symbol": null, + "symbol_addr": null, + "trust": null, + "snapshot": null, + "lock": null + }, + { + "function": "captureMessage", + "module": "raven.base", + "filename": "raven/base.py", + "abs_path": "/home/ubuntu/.virtualenvs/getsentry/src/raven/raven/base.py", + "lineno": 577, + "pre_context": [ + " \"\"\"", + " Creates an event from ``message``.", + "", + " >>> client.captureMessage('My event just happened!')", + " \"\"\"" + ], + "context_line": " return self.capture('raven.events.Message', message=message, **kwargs)", + "post_context": [ + "", + " def captureException(self, exc_info=None, **kwargs):", + " \"\"\"", + " Creates an event from an exception.", + "" + ], + "in_app": false, + "vars": { + "'kwargs'": { + "'data'": null, + "'extra'": { + "'go_deeper'": [ + "[{\"'bar'\":[\"'baz'\"],\"'foo'\":\"'bar'\"}]" + ], + "'loadavg'": [ + 0.37255859375, + 0.5341796875, + 0.62939453125 + ], + "'user'": "'dcramer'" + }, + "'level'": 20, + "'stack'": true, + "'tags'": null + }, + "'message'": "'This is a test message generated using ``raven test``'", + "'self'": "<raven.base.Client object at 0x107bb8210>" + }, + "colno": null, + "data": null, + "errors": null, + "raw_function": null, + "image_addr": null, + "instruction_addr": null, + "addr_mode": null, + "package": null, + "platform": null, + "symbol": null, + "symbol_addr": null, + "trust": null, + "snapshot": null, + "lock": null + }, + { + "function": "send_test_message", + "module": "raven.scripts.runner", + "filename": "raven/scripts/runner.py", + "abs_path": "/home/ubuntu/.virtualenvs/getsentry/src/raven/raven/scripts/runner.py", + "lineno": 77, + "pre_context": [ + " level=logging.INFO,", + " stack=True,", + " tags=options.get('tags', {}),", + " extra={", + " 'user': get_uid()," + ], + "context_line": " 'loadavg': get_loadavg(),", + "post_context": [ + " },", + " ))", + "", + " if client.state.did_fail():", + " print('error!')" + ], + "in_app": false, + "vars": { + "'client'": "<raven.base.Client object at 0x107bb8210>", + "'data'": null, + "'k'": "'secret_key'", + "'options'": { + "'data'": null, + "'tags'": null + } + }, + "colno": null, + "data": null, + "errors": null, + "raw_function": null, + "image_addr": null, + "instruction_addr": null, + "addr_mode": null, + "package": null, + "platform": null, + "symbol": null, + "symbol_addr": null, + "trust": null, + "snapshot": null, + "lock": null + }, + { + "function": "main", + "module": "raven.scripts.runner", + "filename": "raven/scripts/runner.py", + "abs_path": "/home/ubuntu/.virtualenvs/getsentry/src/raven/raven/scripts/runner.py", + "lineno": 112, + "pre_context": [ + " print(\"Using DSN configuration:\")", + " print(\" \", dsn)", + " print()", + "", + " client = Client(dsn, include_paths=['raven'])" + ], + "context_line": " send_test_message(client, opts.__dict__)", + "in_app": false, + "vars": { + "'args'": [ + "'test'", + "'https://ebc35f33e151401f9deac549978bda11:[email protected]/1'" + ], + "'client'": "<raven.base.Client object at 0x107bb8210>", + "'dsn'": "'https://ebc35f33e151401f9deac549978bda11:[email protected]/1'", + "'opts'": "<Values at 0x107ba3b00: {'data': None, 'tags': None}>", + "'parser'": "<optparse.OptionParser instance at 0x107ba3368>", + "'root'": "<logging.Logger object at 0x107ba5b10>" + }, + "colno": null, + "data": null, + "errors": null, + "raw_function": null, + "image_addr": null, + "instruction_addr": null, + "addr_mode": null, + "package": null, + "platform": null, + "post_context": null, + "symbol": null, + "symbol_addr": null, + "trust": null, + "snapshot": null, + "lock": null + } + ] + }, + "tags": [ + [ + "browser", + "Chrome 28.0.1500" + ], + [ + "browser.name", + "Chrome" + ], + [ + "client_os", + "Windows 8" + ], + [ + "client_os.name", + "Windows" + ], + [ + "environment", + "prod" + ], + [ + "level", + "error" + ], + [ + "sentry:user", + "id:1" + ], + [ + "server_name", + "web01.example.org" + ], + [ + "url", + "http://example.com/foo" + ] + ], + "extra": { + "emptyList": [], + "emptyMap": {}, + "length": 10837790, + "results": [ + 1, + 2, + 3, + 4, + 5 + ], + "session": { + "foo": "bar" + }, + "unauthorized": false, + "url": "http://example.org/foo/bar/" + }, + "fingerprint": [ + "{{ default }}" + ], + "hashes": [ + "3a2b45089d0211943e5a6645fb4cea3f" + ], + "culprit": "raven.scripts.runner in main", + "metadata": { + "title": "This is an example Python exception" + }, + "title": "This is an example Python exception", + "location": null, + "_ref": 4505278293147648, + "_ref_version": 2, + "_metrics": { + "bytes.stored.event": 8121 + }, + "nodestore_insert": 1688935345.484592, + "id": "4dc4fc2858aa450eb658be9e5b8ad149" + } +} diff --git a/zerver/webhooks/sentry/tests.py b/zerver/webhooks/sentry/tests.py index 22d886eeff721..793306c047a2d 100644 --- a/zerver/webhooks/sentry/tests.py +++ b/zerver/webhooks/sentry/tests.py @@ -251,7 +251,7 @@ def test_deprecated_exception_message(self) -> None: ```""" self.check_webhook("deprecated_exception_message", expected_topic, expected_message) - def test_sample_event(self) -> None: + def test_sample_event_through_alert(self) -> None: expected_topic = "This is an example Python exception" expected_message = """\ **New message event:** [This is an example Python exception](https://sentry.io/organizations/nitk-46/issues/4218258981/events/b6eff1a49b1f4132850b1238d968da70/) @@ -259,4 +259,27 @@ def test_sample_event(self) -> None: **level:** error **timestamp:** 2023-05-31 11:06:16 ```""" - self.check_webhook("sample_event", expected_topic, expected_message) + self.check_webhook("sample_event_through_alert", expected_topic, expected_message) + + def test_sample_event_through_plugin(self) -> None: + expected_topic = "This is an example Python exception" + expected_message = """\ +**New message event:** [This is an example Python exception](https://nitk-46.sentry.io/issues/4218258981/events/4dc4fc2858aa450eb658be9e5b8ad149/) +```quote +**level:** error +**timestamp:** 2023-07-09 20:41:24 +```""" + self.check_webhook("sample_event_through_plugin", expected_topic, expected_message) + + def test_raven_sdk_python_event(self) -> None: + payload = self.get_body("raven_sdk_python_event") + result = self.client_post( + self.url, + payload, + content_type="application/json", + ) + self.assert_json_success(result) + self.assert_in_response( + "The 'Raven SDK' event isn't currently supported by the Sentry webhook; ignoring", + result, + ) diff --git a/zerver/webhooks/sentry/view.py b/zerver/webhooks/sentry/view.py index bbe924949dcbb..a3f8c7d38a92b 100644 --- a/zerver/webhooks/sentry/view.py +++ b/zerver/webhooks/sentry/view.py @@ -81,6 +81,15 @@ } +def is_sample_event(event: Dict[str, Any]) -> bool: + # This is just a heuristic to detect the sample event, this should + # not be used for making important behavior decisions. + title = event.get("title", "") + if title == "This is an example Python exception": + return True + return False + + def convert_lines_to_traceback_string(lines: Optional[List[str]]) -> str: traceback = "" if lines is not None: @@ -103,12 +112,10 @@ def handle_event_payload(event: Dict[str, Any]) -> Tuple[str, str]: # We shouldn't support the officially deprecated Raven series of # Python SDKs. - if platform_name == "python" and int(event["version"]) < 7: + if platform_name == "python" and int(event["version"]) < 7 and not is_sample_event(event): # The sample event is still an old "version" -- accept it even # though we don't accept events from the old Python SDK. - tags = event.get("tags", []) - if ["sample_event", "yes"] not in tags: - raise UnsupportedWebhookEventTypeError("Raven SDK") + raise UnsupportedWebhookEventTypeError("Raven SDK") context = { "title": subject, "level": event["level"],
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
zulip__zulip-25738@4b7a8be
zulip/zulip
Python
25,738
compose_validate: Use correct stream_id value for different context.
This PR addresses the issue of relying on `compose_state` for retrieving the `stream_id` to display warning banners. Previously, when the compose box was closed, the warnings were not shown in the message edit box. Now, we utilize the `get_stream_id_for_textarea` function to obtain the correct `stream_id` value and resolve this problem. Fixes: #25410. **Screenshots and screen captures:** <details> <summary>Warning with compose box closed</summary> | Before | After | |--------|--------| | ![before](https://github.com/zulip/zulip/assets/53193850/5e0f5f17-03df-44ca-974c-95f6cbdbbd45) | ![after](https://github.com/zulip/zulip/assets/53193850/bae67cba-071a-43f8-a37c-2d2778e96508) | </details> <details> <summary>Private stream mention with compose box closed</summary> | Before | After | |--------|--------| | ![before](https://github.com/zulip/zulip/assets/53193850/fa4d4092-e4bb-4e0d-8d82-8bb352ec820d) | ![after](https://github.com/zulip/zulip/assets/53193850/240ce7e1-c143-4fa6-bed7-d41fa700aad9) | </details> <details> <summary>Different context</summary> | Before | After | |--------|--------| | ![before](https://github.com/zulip/zulip/assets/53193850/0820e27d-0966-4f08-9a2c-9d4d84f6782a) | ![after](https://github.com/zulip/zulip/assets/53193850/4e1b9110-1ce5-413a-87e9-f7436b130748) | </details>
2023-05-24T12:48:58Z
Edit message UI should use correct context for warnings When composing a message, we give warnings for some potentially problematic situations: 1. Mentioning a private stream to an audience where not everyone is subscribed to it. 2. Mentioning a user who is not subscribed to the stream being composed to. These warning are not triggered correctly in the message editing UI. In particular, the set of warnings shown is the ones that are applicable to the current compose box context, *not* to the context of the message being edited (which may be to a different narrow). If the compose box is closed, these warnings aren't shown at all while editing a message.
Hello @zulip/server-message-view members, this issue was labeled with the "area: message-editing" label, so you may want to check it out! <!-- areaLabelAddition --> @shameondev Could you please add a this to your todo list, to pick up when you free up if nobody else fixes it in the meantime? It's not a release goal, but should be fixed while we're thinking about this area. In both these cases, the core issue is that we need the call from `composebox_typeahead` into the `compose_banner` module to pass metadata about what component one is typing in, specifically the target stream, rather than having the `compose_banner` logic just look up the current subscribed stream. I'm not entirely sure how best to plumb this through the typeahead module; @N-Shar-ma or @aero31aero might have ideas, but worst case, `initialize_compose_typeahead` could accept a parameter that's the message ID being edited, if any, and either store that on the typeahead object or probably better just use a lambda to pass that through to `content_typeahead_selected`. The other idea, which might be even simpler, is to just find the `.message-row` ancestor, if any, if the edit form element, and use that to look up its message ID and from there determine the stream/PM context here instead of looking at the current compose recipient.
[ { "body": "When composing a message, we give warnings for some potentially problematic situations:\r\n\r\n1. Mentioning a private stream to an audience where not everyone is subscribed to it.\r\n2. Mentioning a user who is not subscribed to the stream being composed to.\r\n\r\nThese warning are not triggered correctly in the message editing UI. In particular, the set of warnings shown is the ones that are applicable to the current compose box context, *not* to the context of the message being edited (which may be to a different narrow). If the compose box is closed, these warnings aren't shown at all while editing a message.", "number": 25410, "title": "Edit message UI should use correct context for warnings" } ]
1beebb9fa101715a8949e74e7147da1fc1a1f663
{ "head_commit": "4b7a8bec17d1e997cc1f1523aeecf5c06aa4c591", "head_commit_message": "compose_validate: Extract the logic of getting stream_id.", "patch_to_review": "diff --git a/web/src/compose_validate.js b/web/src/compose_validate.js\nindex b7f72d16d0f98..7b7eb3717f85a 100644\n--- a/web/src/compose_validate.js\n+++ b/web/src/compose_validate.js\n@@ -59,6 +59,16 @@ export function needs_subscribe_warning(user_id, stream_id) {\n return true;\n }\n \n+function get_stream_id() {\n+ const stream_name = compose_state.stream_name();\n+\n+ if (!stream_name) {\n+ return undefined;\n+ }\n+\n+ return stream_data.get_sub(stream_name).stream_id;\n+}\n+\n export function warn_if_private_stream_is_linked(linked_stream, $textarea) {\n // For PMs, we currently don't warn about links to private\n // streams, since you are specifically sharing the existence of\n@@ -67,9 +77,8 @@ export function warn_if_private_stream_is_linked(linked_stream, $textarea) {\n if (compose_state.get_message_type() !== \"stream\") {\n return;\n }\n-\n- const compose_stream = stream_data.get_sub(compose_state.stream_name());\n- if (compose_stream === undefined) {\n+ const stream_id = get_stream_id();\n+ if (!stream_id) {\n // We have an invalid stream name, don't warn about this here as\n // we show an error to the user when they try to send the message.\n return;\n@@ -93,7 +102,7 @@ export function warn_if_private_stream_is_linked(linked_stream, $textarea) {\n // knows it exists. (But always warn Zephyr users, since\n // we may not know their stream's subscribers.)\n if (\n- peer_data.is_subscriber_subset(compose_stream.stream_id, linked_stream.stream_id) &&\n+ peer_data.is_subscriber_subset(stream_id, linked_stream.stream_id) &&\n !page_params.realm_is_zephyr_mirror_realm\n ) {\n return;\n@@ -124,19 +133,13 @@ export function warn_if_mentioning_unsubscribed_user(mentioned, $textarea) {\n return; // don't check if @all/@everyone/@stream\n }\n \n- const stream_name = compose_state.stream_name();\n-\n- if (!stream_name) {\n- return;\n- }\n-\n- const sub = stream_data.get_sub(stream_name);\n+ const stream_id = get_stream_id();\n \n- if (!sub) {\n+ if (!stream_id) {\n return;\n }\n \n- if (needs_subscribe_warning(user_id, sub.stream_id)) {\n+ if (needs_subscribe_warning(user_id, stream_id)) {\n const $banner_container = compose_banner.get_compose_banner_container($textarea);\n const $existing_invites_area = $banner_container.find(\n `.${CSS.escape(compose_banner.CLASSNAMES.recipient_not_subscribed)}`,\n@@ -151,7 +154,7 @@ export function warn_if_mentioning_unsubscribed_user(mentioned, $textarea) {\n if (!existing_invites.includes(user_id)) {\n const context = {\n user_id,\n- stream_id: sub.stream_id,\n+ stream_id,\n banner_type: compose_banner.WARNING,\n button_text: can_subscribe_other_users\n ? $t({defaultMessage: \"Subscribe them\"})\n" }
[ { "diff_hunk": "@@ -59,7 +59,16 @@ export function needs_subscribe_warning(user_id, stream_id) {\n return true;\n }\n \n-function get_stream_id() {\n+function get_stream_id_for_textarea($textarea) {\n+ const is_in_editing_area = $textarea.closest(\".message_row\").length > 0;\n+\n+ if (is_in_editing_area) {\n+ return Number.parseInt(\n+ $textarea.closest(\".recipient_row\").find(\".message_header\").attr(\"data-stream-id\"),\n+ 10,\n+ );", "line": null, "original_line": 69, "original_start_line": null, "path": "web/src/compose_validate.js", "start_line": null, "text": "@user1:\nThis is not great in the event that the message is a direct message -- we end up returning `NaN`, not `undefined`, which is falsey but seems like the kind of thing that would lead to bugs in the future.\r\n\r\nI wrote a proper comment documenting this function's interface, and also changed it to return a consistent type." } ]
acb4cd3ad61ff75c3739801006089a55bbac1dc6
diff --git a/web/src/compose_validate.js b/web/src/compose_validate.js index b7f72d16d0f98..e779733693a1d 100644 --- a/web/src/compose_validate.js +++ b/web/src/compose_validate.js @@ -59,19 +59,49 @@ export function needs_subscribe_warning(user_id, stream_id) { return true; } -export function warn_if_private_stream_is_linked(linked_stream, $textarea) { - // For PMs, we currently don't warn about links to private - // streams, since you are specifically sharing the existence of - // the private stream with someone. One could imagine changing - // this policy if user feedback suggested it was useful. - if (compose_state.get_message_type() !== "stream") { - return; +function get_stream_id_for_textarea($textarea) { + // Returns the stream ID, if any, associated with the textarea: + // The recipient of a message being edited, or the target + // recipient of a message being drafted in the compose box. + // Returns undefined if the appropriate context is a direct + // message conversation. + const is_in_editing_area = $textarea.closest(".message_row").length > 0; + + if (is_in_editing_area) { + const stream_id_str = $textarea + .closest(".recipient_row") + .find(".message_header") + .attr("data-stream-id"); + if (stream_id_str === undefined) { + // Direct messages don't have a data-stream-id. + return undefined; + } + return Number.parseInt(stream_id_str, 10); } - const compose_stream = stream_data.get_sub(compose_state.stream_name()); - if (compose_stream === undefined) { - // We have an invalid stream name, don't warn about this here as - // we show an error to the user when they try to send the message. + const stream_name = compose_state.stream_name(); + + if (!stream_name) { + return undefined; + } + + return stream_data.get_sub(stream_name).stream_id; +} + +export function warn_if_private_stream_is_linked(linked_stream, $textarea) { + const stream_id = get_stream_id_for_textarea($textarea); + + if (!stream_id) { + // There are two cases in which the `stream_id` will be + // omitted, and we want to exclude the warning banner: + // + // 1. We currently do not warn about links to private streams + // in direct messages; it would probably be an improvement to + // do so when one of the recipients is not subscribed. + // + // 2. If we have an invalid stream name, we do not warn about + // it here; we will show an error to the user when they try to + // send the message. return; } @@ -93,7 +123,7 @@ export function warn_if_private_stream_is_linked(linked_stream, $textarea) { // knows it exists. (But always warn Zephyr users, since // we may not know their stream's subscribers.) if ( - peer_data.is_subscriber_subset(compose_stream.stream_id, linked_stream.stream_id) && + peer_data.is_subscriber_subset(stream_id, linked_stream.stream_id) && !page_params.realm_is_zephyr_mirror_realm ) { return; @@ -109,10 +139,6 @@ export function warn_if_private_stream_is_linked(linked_stream, $textarea) { } export function warn_if_mentioning_unsubscribed_user(mentioned, $textarea) { - if (compose_state.get_message_type() !== "stream") { - return; - } - // Disable for Zephyr mirroring realms, since we never have subscriber lists there if (page_params.realm_is_zephyr_mirror_realm) { return; @@ -124,19 +150,13 @@ export function warn_if_mentioning_unsubscribed_user(mentioned, $textarea) { return; // don't check if @all/@everyone/@stream } - const stream_name = compose_state.stream_name(); + const stream_id = get_stream_id_for_textarea($textarea); - if (!stream_name) { - return; - } - - const sub = stream_data.get_sub(stream_name); - - if (!sub) { + if (!stream_id) { return; } - if (needs_subscribe_warning(user_id, sub.stream_id)) { + if (needs_subscribe_warning(user_id, stream_id)) { const $banner_container = compose_banner.get_compose_banner_container($textarea); const $existing_invites_area = $banner_container.find( `.${CSS.escape(compose_banner.CLASSNAMES.recipient_not_subscribed)}`, @@ -151,7 +171,7 @@ export function warn_if_mentioning_unsubscribed_user(mentioned, $textarea) { if (!existing_invites.includes(user_id)) { const context = { user_id, - stream_id: sub.stream_id, + stream_id, banner_type: compose_banner.WARNING, button_text: can_subscribe_other_users ? $t({defaultMessage: "Subscribe them"}) diff --git a/web/tests/compose_validate.test.js b/web/tests/compose_validate.test.js index f0137131a395d..891d8be1c9234 100644 --- a/web/tests/compose_validate.test.js +++ b/web/tests/compose_validate.test.js @@ -66,6 +66,15 @@ function test_ui(label, f) { }); } +function stub_message_row($textarea) { + const $stub = $.create("message_row_stub"); + $textarea.closest = (selector) => { + assert.equal(selector, ".message_row"); + $stub.length = 0; + return $stub; + }; +} + test_ui("validate_stream_message_address_info", ({mock_template}) => { mock_banners(); const sub = { @@ -622,6 +631,7 @@ test_ui("needs_subscribe_warning", () => { test_ui("warn_if_private_stream_is_linked", ({mock_template}) => { const $textarea = $("<textarea>").attr("id", "compose-textarea"); + stub_message_row($textarea); const test_sub = { name: compose_state.stream_name(), stream_id: 99, @@ -676,6 +686,7 @@ test_ui("warn_if_private_stream_is_linked", ({mock_template}) => { test_ui("warn_if_mentioning_unsubscribed_user", ({override, override_rewire, mock_template}) => { override_rewire(compose_recipient, "on_compose_select_recipient_update", () => {}); const $textarea = $("<textarea>").attr("id", "compose-textarea"); + stub_message_row($textarea); compose_state.set_stream_name(""); override(settings_data, "user_can_subscribe_other_users", () => true);
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-5116@71dc54b
streamlit/streamlit
Python
5,116
Fix tabs switching couse scrolling up
<!-- Before contributing (PLEASE READ!) โš ๏ธ If your contribution is more than a few lines of code, then prior to starting to code on it please post in the issue saying you want to volunteer, then wait for a positive response. And if there is no issue for it yet, create it first. This helps make sure: 1. Two people aren't working on the same thing 2. This is something Streamlit's maintainers believe should be implemented/fixed 3. Any API, UI, or deeper architectural changes that need to be implemented have been fully thought through by Streamlit's maintainers 4. Your time is well spent! More information in our wiki: https://github.com/streamlit/streamlit/wiki/Contributing --> ## ๐Ÿ“š Context _Please describe the project or issue background here_ - What kind of change does this PR introduce? - [x] Bugfix - [ ] Feature - [ ] Refactoring - [ ] Other, please describe: ## ๐Ÿง  Description of Changes It's a bug fix for [Issue-#5069](https://github.com/streamlit/streamlit/issues/5069). When using an HTML Video element with tabs it's initial height it's set to 0 and stays like this, which couses unexpected scrolling up on tab change. This PR fixes it by setting up HTML Video element height on loadedmetadata event inside `useEffect` method - _Add bullet points summarizing your changes here_ - [ ] This is a breaking API change - [x] This is a visible (user-facing) change **Revised:** _Insert screenshot of your updated UI/code here_ **Current:** _Insert screenshot of existing UI/code here_ ## ๐Ÿงช Testing Done - [ ] Screenshots included - [ ] Added/Updated unit tests - [ ] Added/Updated e2e tests ## ๐ŸŒ References _Does this depend on other work, documents, or tickets?_ - **Issue**: It's fixes part of [Issue-#5069](https://github.com/streamlit/streamlit/issues/5069). It is expected that other elements like `st.image` will couse similar issue, when their initial height is set to 0 --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2022-08-09T11:31:39Z
tabs switching cause scrolling up ### Summary app scrolls up when I have tabs after some widgets and switch them ### Steps to reproduce Code snippet: ``` import streamlit as st for i in range(20): st.markdown("dummy text") tab1, tab2 = st.tabs(["tab1", "tab2"]) with tab1: st.video(video_path) with tab2: st.video(video_path) ``` steps : 1. scroll down to the videos 2. switching tabs **Expected behavior:** tabs should be switched and focus stay on video **Actual behavior:** it scrolling up and I have to scroll down again ### Debug info - Streamlit version: 1.11.0 - Python version: 3.8.11
@RRaphaell Thanks for reporting this issue! I was able to reproduce it and have deployed a demo app [here](https://lukasmasuch-st-playground-issuesgh-5069app-tmwuwl.streamlitapp.com/). The underlying issue is that when changing tabs, the height of the actual tab content is not correct for a split second. This influences the full height of the app -> which in turn influences the maximum possible scroll position -> and this causes the unexpected up scroll. This does not happen if there is enough content underneath the tab (try out in the demo app), since in that case the scroll position will not be larger than the maximum possible. To find a solution here requires a bit more investigation. I tried out the `renderAll` property of the baseWeb component, but this only fixes it for a second tab change. thanks @LukasMasuch for your time. you are right but when I tested it by adding more content below it still happens if you scroll down to the new content and then switch tabs +1 .I have the same issue. @LukasMasuch is correct about the source of the issue, when I've setup fixed height of the `<video>` React component it fixed the issue, however I don't think we want to have the fixed `height` for every video. I'd like to set fixed `height` on first `componentDidMount`, what do you think about such proposition? @sfc-gh-tszerszen Thanks for investigating this! I think there are also a few other elements without a fixed height. For example, our built-in chart commands: ```python import streamlit as st import pandas as pd import numpy as np chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"]) for i in range(20): st.markdown("dummy text") tab1, tab2 = st.tabs(["tab1", "tab2"]) with tab1: st.line_chart(chart_data) with tab2: st.line_chart(chart_data) ``` So, we might need to figure out which elements are affected and apply the same fix. > I'd like to set fixed height on first componentDidMount, what do you think about such proposition? Would be great if we can fix it this way, but this needs some testing if there are any negative side effects. E.g. in some situations, components are reused for different content, so just fixing the height on `componentDidMount` might lead to wrong height in some situations.
[ { "body": "### Summary\r\n\r\napp scrolls up when I have tabs after some widgets and switch them\r\n\r\n\r\n### Steps to reproduce\r\n\r\nCode snippet:\r\n\r\n```\r\nimport streamlit as st\r\n\r\nfor i in range(20):\r\n st.markdown(\"dummy text\")\r\n\r\n\r\ntab1, tab2 = st.tabs([\"tab1\", \"tab2\"])\r\n\r\nwith tab1:\r\n st.video(video_path)\r\n\r\nwith tab2:\r\n st.video(video_path)\r\n\r\n```\r\n\r\nsteps :\r\n1. scroll down to the videos\r\n2. switching tabs\r\n\r\n**Expected behavior:**\r\n\r\ntabs should be switched and focus stay on video\r\n\r\n**Actual behavior:**\r\n\r\nit scrolling up and I have to scroll down again\r\n\r\n### Debug info\r\n\r\n- Streamlit version: 1.11.0\r\n- Python version: 3.8.11", "number": 5069, "title": "tabs switching cause scrolling up" } ]
eb72f758957ec1f0e401e2884649ff46f15a1ae8
{ "head_commit": "71dc54b84a7ce7ddb3a49f51f98f23e5eef78e5f", "head_commit_message": "update E2E Cypress st_tabs.spec.js to march renderAll property", "patch_to_review": "diff --git a/e2e/specs/st_tabs.spec.js b/e2e/specs/st_tabs.spec.js\nindex f747a79513fa..24a444e9804a 100644\n--- a/e2e/specs/st_tabs.spec.js\n+++ b/e2e/specs/st_tabs.spec.js\n@@ -36,11 +36,18 @@ describe(\"st.tabs\", () => {\n 2\n );\n \n- cy.get(\"[data-testid='stSidebar'] .stTabs\")\n+ cy.get(\"[data-testid='stSidebar'] .stTabs .st-cx\")\n .first()\n .within(() => {\n cy.get(\".stMarkdown\").should(\"have.text\", \"I am in the sidebar\");\n });\n+\n+ // text from every tab should be here because renderAll property is set to true\n+ cy.get(\"[data-testid='stSidebar'] .stTabs\")\n+ .first()\n+ .within(() => {\n+ cy.get(\".stMarkdown\").should(\"have.text\", \"I am in the sidebarI'm also in the sidebar\");\n+ });\n });\n \n it(\"changes rendered content on tab selection\", () => {\ndiff --git a/frontend/src/components/elements/Tabs/Tabs.tsx b/frontend/src/components/elements/Tabs/Tabs.tsx\nindex 7439316f7b58..9f69d74d5bc5 100644\n--- a/frontend/src/components/elements/Tabs/Tabs.tsx\n+++ b/frontend/src/components/elements/Tabs/Tabs.tsx\n@@ -62,6 +62,10 @@ function Tabs(props: Props): ReactElement {\n onChange={({ activeKey }) => {\n setActiveKey(activeKey)\n }}\n+ /* renderAll on UITabs should always be set to true to avoid scrolling issue\n+ https://github.com/streamlit/streamlit/issues/5069\n+ */\n+ renderAll={true}\n disabled={widgetsDisabled}\n overrides={{\n TabHighlight: {\ndiff --git a/frontend/src/components/elements/Video/Video.tsx b/frontend/src/components/elements/Video/Video.tsx\nindex 5b3813d41e52..e5ece8c10f77 100644\n--- a/frontend/src/components/elements/Video/Video.tsx\n+++ b/frontend/src/components/elements/Video/Video.tsx\n@@ -36,19 +36,26 @@ export default function Video({ element, width }: VideoProps): ReactElement {\n useEffect(() => {\n const videoNode = videoRef.current\n \n- const setStartTime: () => void = () => {\n+ const onLoadedMetadata: () => void = () => {\n if (videoNode) {\n+ // setStartTime\n videoNode.currentTime = element.startTime\n+\n+ /* height of HTML video element needs to be set to avoid scrolling issue: https://github.com/streamlit/streamlit/issues/5069\n+ initially HTML video has height 0 and when tabs are switched page auto scrolls up\n+ because of \"lack of content\" on the page, setting height of HTML video element fixes it\n+ */\n+ videoNode.height = videoNode.videoHeight\n }\n }\n \n if (videoNode) {\n- videoNode.addEventListener(\"loadedmetadata\", setStartTime)\n+ videoNode.addEventListener(\"loadedmetadata\", onLoadedMetadata)\n }\n \n return () => {\n if (videoNode) {\n- videoNode.removeEventListener(\"loadedmetadata\", setStartTime)\n+ videoNode.removeEventListener(\"loadedmetadata\", onLoadedMetadata)\n }\n }\n }, [element])\n" }
[ { "diff_hunk": "@@ -36,11 +36,18 @@ describe(\"st.tabs\", () => {\n 2\n );\n \n- cy.get(\"[data-testid='stSidebar'] .stTabs\")\n+ cy.get(\"[data-testid='stSidebar'] .stTabs .st-cx\")", "line": null, "original_line": 39, "original_start_line": null, "path": "e2e/specs/st_tabs.spec.js", "start_line": null, "text": "@user1:\nI'm not sure if we can or should use `.st-cx` since this is probably an internally generated class. I think it is fine to just select the first `.stMarkdown` within the `.stTabs` component: \r\n\r\n```javascript\r\ncy.get(\"[data-testid='stSidebar'] .stTabs\")\r\n.first()\r\n .within(() => {\r\n cy.get(\".stMarkdown\").first().should(\"have.text\", \"I am in the sidebar\");\r\n });\r\n```" } ]
21213c32f3b78824c683a82abd0e0fc190456b44
diff --git a/e2e/specs/st_tabs.spec.js b/e2e/specs/st_tabs.spec.js index f747a79513fa..0b09fce04ed5 100644 --- a/e2e/specs/st_tabs.spec.js +++ b/e2e/specs/st_tabs.spec.js @@ -36,10 +36,16 @@ describe("st.tabs", () => { 2 ); + cy.get("[data-testid='stSidebar'] .stTabs").first() + .within(() => { + cy.get(".stMarkdown").first().should("have.text", "I am in the sidebar"); + }); + + // text from every tab should be here because renderAll property is set to true cy.get("[data-testid='stSidebar'] .stTabs") .first() .within(() => { - cy.get(".stMarkdown").should("have.text", "I am in the sidebar"); + cy.get(".stMarkdown").should("have.text", "I am in the sidebarI'm also in the sidebar"); }); }); diff --git a/frontend/src/components/elements/Tabs/Tabs.tsx b/frontend/src/components/elements/Tabs/Tabs.tsx index 7439316f7b58..9f69d74d5bc5 100644 --- a/frontend/src/components/elements/Tabs/Tabs.tsx +++ b/frontend/src/components/elements/Tabs/Tabs.tsx @@ -62,6 +62,10 @@ function Tabs(props: Props): ReactElement { onChange={({ activeKey }) => { setActiveKey(activeKey) }} + /* renderAll on UITabs should always be set to true to avoid scrolling issue + https://github.com/streamlit/streamlit/issues/5069 + */ + renderAll={true} disabled={widgetsDisabled} overrides={{ TabHighlight: { diff --git a/frontend/src/components/elements/Video/Video.tsx b/frontend/src/components/elements/Video/Video.tsx index 5b3813d41e52..e5ece8c10f77 100644 --- a/frontend/src/components/elements/Video/Video.tsx +++ b/frontend/src/components/elements/Video/Video.tsx @@ -36,19 +36,26 @@ export default function Video({ element, width }: VideoProps): ReactElement { useEffect(() => { const videoNode = videoRef.current - const setStartTime: () => void = () => { + const onLoadedMetadata: () => void = () => { if (videoNode) { + // setStartTime videoNode.currentTime = element.startTime + + /* height of HTML video element needs to be set to avoid scrolling issue: https://github.com/streamlit/streamlit/issues/5069 + initially HTML video has height 0 and when tabs are switched page auto scrolls up + because of "lack of content" on the page, setting height of HTML video element fixes it + */ + videoNode.height = videoNode.videoHeight } } if (videoNode) { - videoNode.addEventListener("loadedmetadata", setStartTime) + videoNode.addEventListener("loadedmetadata", onLoadedMetadata) } return () => { if (videoNode) { - videoNode.removeEventListener("loadedmetadata", setStartTime) + videoNode.removeEventListener("loadedmetadata", onLoadedMetadata) } } }, [element])
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
zulip__zulip-25154@0de167b
zulip/zulip
Python
25,154
message-type: Add support for "direct" as value for `type` parameter.
For endpoints with a `type` parameter to indicate whether the message is a stream or direct message, [`POST /typing`](https://zulip.com/api/set-typing-status) and [`POST /messages`](https://zulip.com/api/send-message), adds support for passing `"direct"` as a value for direct messages. Maintains support for `"private"` as a deprecated alias for direct messages. Fixes https://github.com/zulip/zulip/issues/24960. --- **Notes**: - For the **Usage examples** in the `POST /messages` documentation: - Comments now use 'direct messages' or 'DMs' - Updated the curl and javascript examples - Did not update the python example for "direct". I wasn't sure if we wanted to update that in tandem with an update to zulip-python-api; see question below. - I added the validation check for the `type` parameter that was in `POST /typing` to also be in `POST /messages`. - I did the refactor in the prep commit because any follow-up changes we do can be a bit clearer in that all instances of `message_type_name` now refer to these parameters passed to the view functions from clients. See `git-grep` below. - for `POST /messages`, we use "private" for the `message_type` string that's then used for the various function calls. - for `POST /typing`, we use "direct" for the default and updated `message_type` string. - Updates these two API endpoint documentation pages to use "direct messages" or "DMs" and also added links to relevant help center documentation. <details> <summary>git-grep of `message_type_name` after changes</summary> ```console $ git grep message_type_name zerver/tests/test_message_send.py: message_type_name = ["direct", "private"] zerver/tests/test_message_send.py: for message_type in message_type_name: zerver/tests/test_message_send.py: message_type_name = ["direct", "private"] zerver/tests/test_message_send.py: for message_type in message_type_name: zerver/tests/test_typing.py: message_type_name = ["direct", "private"] zerver/tests/test_typing.py: for message_type in message_type_name: zerver/views/message_send.py: message_type_name: str = REQ("type", str_validator=check_string_in(VALID_MESSAGE_TYPES)), zerver/views/message_send.py: message_type = message_type_name zerver/views/typing.py: message_type_name: str = REQ( zerver/views/typing.py: message_type = message_type_name ``` </details> **Questions**: - In the **Changes** notes, I stated that "private" would be removed in a future release, but I wasn't sure if that's the case? - I added some TODO notes where `message_type` is set in both view functions, but these might not be something we plan on doing? - Again, as noted above, we'll likely want to update the [zulip-python-api](https://github.com/zulip/python-zulip-api/blob/02586f1d348dbcab3bc8d30b805fffd48a524e18/zulip/zulip/send.py#L89-L101)? --- **Screenshots and screen captures:** <details> <summary>API changelog update</summary> ![Screenshot from 2023-04-17 16-48-08](https://user-images.githubusercontent.com/63245456/232539073-ca9114d8-f9aa-471e-bd37-2e6ec6c4e8b7.png) </details> <details> <summary>Send a message</summary> [Current documentation](https://zulip.com/api/send-message) <details> <summary>Main endpoint description and usage examples</summary> ![Screenshot from 2023-04-17 16-50-21](https://user-images.githubusercontent.com/63245456/232538394-d2ed3339-3b7c-4f68-89dd-f5398c1f22d7.png) ![Screenshot from 2023-04-17 16-50-32](https://user-images.githubusercontent.com/63245456/232538398-492451a4-471a-4605-bc02-29875decb79a.png) ![Screenshot from 2023-04-17 16-50-40](https://user-images.githubusercontent.com/63245456/232538401-ba956f7a-9b0f-4f5c-b672-a306edb91c6b.png) ![Screenshot from 2023-04-17 16-50-47](https://user-images.githubusercontent.com/63245456/232538415-a9785037-5a4a-4249-8b1d-ef82f095fe85.png) </details> <details> <summary>Parameters and response</summary> ![Screenshot from 2023-04-17 16-50-57](https://user-images.githubusercontent.com/63245456/232538419-25edea0c-d051-4b30-ac9e-ae255836535b.png) ![Screenshot from 2023-04-17 16-51-06](https://user-images.githubusercontent.com/63245456/232538421-708e15bf-1344-4966-a219-9adb0c162fc7.png) </details> </details> <details> <summary>Set typing status</summary> [Current documentation](https://zulip.com/api/set-typing-status) <details> <summary>Main endpoint description</summary> ![Screenshot from 2023-04-17 16-49-12](https://user-images.githubusercontent.com/63245456/232540431-eb421fa7-c1de-4853-a0b8-f769eee22530.png) </details> <details> <summary>Usage examples</summary> ![Screenshot from 2023-04-17 16-49-30](https://user-images.githubusercontent.com/63245456/232540157-7b0b9d8f-2e16-45dd-b0db-d07446fda0e7.png) ![Screenshot from 2023-04-17 16-49-36](https://user-images.githubusercontent.com/63245456/232540163-f4c557e4-1224-4d22-a2d8-71c265527a3c.png) ![Screenshot from 2023-04-17 16-49-44](https://user-images.githubusercontent.com/63245456/232540167-bd604c45-5ee6-491b-a909-12d114a8499a.png) </details> <details> <summary>Parameters and response</summary> ![Screenshot from 2023-04-17 16-49-54](https://user-images.githubusercontent.com/63245456/232540171-d6a88467-b198-4309-932b-1bf93f9b60ed.png) ![Screenshot from 2023-04-17 16-50-02](https://user-images.githubusercontent.com/63245456/232540176-206d9437-f35a-4a75-b07a-7a9f67b2409d.png) </details> </details> --- <details> <summary>Self-review checklist</summary> <!-- Prior to submitting a PR, follow our step-by-step guide to review your own code: https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code --> <!-- Once you create the PR, check off all the steps below that you have completed. If any of these steps are not relevant or you have not completed, leave them unchecked.--> - [ ] [Self-reviewed](https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code) the changes for clarity and maintainability (variable names, code reuse, readability, etc.). Communicate decisions, questions, and potential concerns. - [ ] Explains differences from previous plans (e.g., issue description). - [ ] Highlights technical choices and bugs encountered. - [ ] Calls out remaining decisions and concerns. - [ ] Automated tests verify logic where appropriate. Individual commits are ready for review (see [commit discipline](https://zulip.readthedocs.io/en/latest/contributing/commit-discipline.html)). - [ ] Each commit is a coherent idea. - [ ] Commit message(s) explain reasoning and motivation for changes. Completed manual review and testing of the following: - [ ] Visual appearance of the changes. - [ ] Responsiveness and internationalization. - [ ] Strings and tooltips. - [ ] End-to-end functionality of buttons, interactions and flows. - [ ] Corner cases, error conditions, and easily imagined bugs. </details>
2023-04-17T16:26:43Z
Add API support for using "direct" as the message "type" for private messages Since we renamed "private messages" to "direct messages" in the UI, we should start thinking about API changes in line with this change. This issue is for the simplest one, which I think should be a Zulip 7.0 release goal: Allowing `send_message` (and similar things like typing notifications requests) to use `direct` rather than `private` for the `type` parameter. `message_type_name: str = REQ("type")` @laurynmm can I assign this to you? `git grep private zerver/views/` suggests it may just be those two endpoints that need attention here. There's a significantly more difficult project of supporting a `direct` message type in events, which likely requires a client capability; the goal for this issue is just do the (mostly documentation, I think?) part of allowing things that send messages via the API to use the new name.
Hello @zulip/server-api members, this issue was labeled with the "area: api" label, so you may want to check it out! <!-- areaLabelAddition --> @timabbott - Yeah, assigning to me makes sense. I already noted this down as a follow-up in the work I'm doing on #24806. @laurynmm We noticed that you have not made any updates to this issue or linked PRs for 10 days. Please comment here if you are still actively working on it. Otherwise, we'd appreciate a quick `@zulipbot abandon` comment so that someone else can claim this issue and continue from where you left off. If we don't hear back, you will be automatically unassigned in 4 days. Thanks! <!-- inactiveWarning -->
[ { "body": "Since we renamed \"private messages\" to \"direct messages\" in the UI, we should start thinking about API changes in line with this change. This issue is for the simplest one, which I think should be a Zulip 7.0 release goal: Allowing `send_message` (and similar things like typing notifications requests) to use `direct` rather than `private` for the `type` parameter. \r\n\r\n`message_type_name: str = REQ(\"type\")`\r\n\r\n@laurynmm can I assign this to you? `git grep private zerver/views/` suggests it may just be those two endpoints that need attention here.\r\n\r\nThere's a significantly more difficult project of supporting a `direct` message type in events, which likely requires a client capability; the goal for this issue is just do the (mostly documentation, I think?) part of allowing things that send messages via the API to use the new name.", "number": 24960, "title": "Add API support for using \"direct\" as the message \"type\" for private messages" } ]
3ad5e7dfc0f081d4cc7ebecb814c5102d359559f
{ "head_commit": "0de167b5b44bb542a8f868a54f4f789b2a7620ae", "head_commit_message": "message: Use `recipient_type_name` for API message type references.\n\nRefactors instances of `message_type_name` and `message_type`\nthat are referring to API message type value (\"stream\" or\n\"private\") to use `recipient_type_name` instead.\n\nPrep commit for adding \"direct\" as a value for endpoints with a\n`type` parameter to indicate whether the message is a stream or\ndirect message.", "patch_to_review": "diff --git a/zerver/actions/message_send.py b/zerver/actions/message_send.py\nindex 05e53e053e073..a5c316b3aab91 100644\n--- a/zerver/actions/message_send.py\n+++ b/zerver/actions/message_send.py\n@@ -1154,7 +1154,7 @@ def check_send_private_message(\n def check_send_message(\n sender: UserProfile,\n client: Client,\n- message_type_name: str,\n+ recipient_type_name: str,\n message_to: Union[Sequence[int], Sequence[str]],\n topic_name: Optional[str],\n message_content: str,\n@@ -1168,7 +1168,7 @@ def check_send_message(\n *,\n skip_stream_access_check: bool = False,\n ) -> int:\n- addressee = Addressee.legacy_build(sender, message_type_name, message_to, topic_name)\n+ addressee = Addressee.legacy_build(sender, recipient_type_name, message_to, topic_name)\n try:\n message = check_message(\n sender,\n@@ -1192,7 +1192,7 @@ def check_send_message(\n def check_schedule_message(\n sender: UserProfile,\n client: Client,\n- message_type_name: str,\n+ recipient_type_name: str,\n message_to: Union[Sequence[str], Sequence[int]],\n topic_name: Optional[str],\n message_content: str,\n@@ -1202,7 +1202,7 @@ def check_schedule_message(\n realm: Optional[Realm] = None,\n forwarder_user_profile: Optional[UserProfile] = None,\n ) -> int:\n- addressee = Addressee.legacy_build(sender, message_type_name, message_to, topic_name)\n+ addressee = Addressee.legacy_build(sender, recipient_type_name, message_to, topic_name)\n \n send_request = check_message(\n sender,\ndiff --git a/zerver/lib/addressee.py b/zerver/lib/addressee.py\nindex ada059d196a61..a5a172407c0ee 100644\n--- a/zerver/lib/addressee.py\n+++ b/zerver/lib/addressee.py\n@@ -97,7 +97,7 @@ def topic(self) -> str:\n @staticmethod\n def legacy_build(\n sender: UserProfile,\n- message_type_name: str,\n+ recipient_type_name: str,\n message_to: Union[Sequence[int], Sequence[str]],\n topic_name: Optional[str],\n realm: Optional[Realm] = None,\n@@ -108,7 +108,7 @@ def legacy_build(\n if realm is None:\n realm = sender.realm\n \n- if message_type_name == \"stream\":\n+ if recipient_type_name == \"stream\":\n if len(message_to) > 1:\n raise JsonableError(_(\"Cannot send to multiple streams\"))\n \n@@ -131,7 +131,7 @@ def legacy_build(\n return Addressee.for_stream_id(stream_name_or_id, topic_name)\n \n return Addressee.for_stream_name(stream_name_or_id, topic_name)\n- elif message_type_name == \"private\":\n+ elif recipient_type_name == \"private\":\n if not message_to:\n raise JsonableError(_(\"Message must have recipients\"))\n \ndiff --git a/zerver/lib/email_mirror.py b/zerver/lib/email_mirror.py\nindex 9778088dddbf0..71c2626100014 100644\n--- a/zerver/lib/email_mirror.py\n+++ b/zerver/lib/email_mirror.py\n@@ -198,7 +198,7 @@ def send_mm_reply_to_stream(\n check_send_message(\n sender=user_profile,\n client=get_client(\"Internal\"),\n- message_type_name=\"stream\",\n+ recipient_type_name=\"stream\",\n message_to=[stream.id],\n topic_name=topic,\n message_content=body,\ndiff --git a/zerver/lib/message.py b/zerver/lib/message.py\nindex e9e2a75c75362..f7bc7b521a8be 100644\n--- a/zerver/lib/message.py\n+++ b/zerver/lib/message.py\n@@ -1273,17 +1273,17 @@ def apply_unread_message_event(\n ) -> None:\n message_id = message[\"id\"]\n if message[\"type\"] == \"stream\":\n- message_type = \"stream\"\n+ recipient_type = \"stream\"\n elif message[\"type\"] == \"private\":\n others = [recip for recip in message[\"display_recipient\"] if recip[\"id\"] != user_profile.id]\n if len(others) <= 1:\n- message_type = \"private\"\n+ recipient_type = \"private\"\n else:\n- message_type = \"huddle\"\n+ recipient_type = \"huddle\"\n else:\n raise AssertionError(\"Invalid message type {}\".format(message[\"type\"]))\n \n- if message_type == \"stream\":\n+ if recipient_type == \"stream\":\n stream_id = message[\"stream_id\"]\n topic = message[TOPIC_NAME]\n state[\"stream_dict\"][message_id] = RawUnreadStreamDict(\n@@ -1300,7 +1300,7 @@ def apply_unread_message_event(\n ):\n state[\"unmuted_stream_msgs\"].add(message_id)\n \n- elif message_type == \"private\":\n+ elif recipient_type == \"private\":\n if len(others) == 1:\n other_user_id = others[0][\"id\"]\n else:\ndiff --git a/zerver/lib/outgoing_webhook.py b/zerver/lib/outgoing_webhook.py\nindex d866ce64268d4..3a7fa17588210 100644\n--- a/zerver/lib/outgoing_webhook.py\n+++ b/zerver/lib/outgoing_webhook.py\n@@ -191,7 +191,7 @@ def send_response_message(\n that might let someone send arbitrary messages to any stream through this.\n \"\"\"\n \n- message_type = message_info[\"type\"]\n+ recipient_type_name = message_info[\"type\"]\n display_recipient = message_info[\"display_recipient\"]\n try:\n topic_name: Optional[str] = get_topic_from_message_info(message_info)\n@@ -207,9 +207,9 @@ def send_response_message(\n \n widget_content = response_data.get(\"widget_content\")\n \n- if message_type == \"stream\":\n+ if recipient_type_name == \"stream\":\n message_to = [display_recipient]\n- elif message_type == \"private\":\n+ elif recipient_type_name == \"private\":\n message_to = [recipient[\"email\"] for recipient in display_recipient]\n else:\n raise JsonableError(_(\"Invalid message type\"))\n@@ -217,7 +217,7 @@ def send_response_message(\n check_send_message(\n sender=bot_user,\n client=client,\n- message_type_name=message_type,\n+ recipient_type_name=recipient_type_name,\n message_to=message_to,\n topic_name=topic_name,\n message_content=content,\ndiff --git a/zerver/tests/test_event_system.py b/zerver/tests/test_event_system.py\nindex 0bb597a206c17..70b02ea9645d1 100644\n--- a/zerver/tests/test_event_system.py\n+++ b/zerver/tests/test_event_system.py\n@@ -324,7 +324,7 @@ def test_get_events(self) -> None:\n check_send_message(\n sender=user_profile,\n client=get_client(\"whatever\"),\n- message_type_name=\"private\",\n+ recipient_type_name=\"private\",\n message_to=[recipient_email],\n topic_name=None,\n message_content=\"hello\",\n@@ -357,7 +357,7 @@ def test_get_events(self) -> None:\n check_send_message(\n sender=user_profile,\n client=get_client(\"whatever\"),\n- message_type_name=\"private\",\n+ recipient_type_name=\"private\",\n message_to=[recipient_email],\n topic_name=None,\n message_content=\"hello\",\ndiff --git a/zerver/tests/test_message_send.py b/zerver/tests/test_message_send.py\nindex 6aee993dc8fd3..78810d2e0b017 100644\n--- a/zerver/tests/test_message_send.py\n+++ b/zerver/tests/test_message_send.py\n@@ -2557,7 +2557,7 @@ def test_addressee_legacy_build_for_user_ids(self) -> None:\n \n result = Addressee.legacy_build(\n sender=self.example_user(\"hamlet\"),\n- message_type_name=\"private\",\n+ recipient_type_name=\"private\",\n message_to=user_ids,\n topic_name=\"random_topic\",\n realm=realm,\n@@ -2576,7 +2576,7 @@ def test_addressee_legacy_build_for_stream_id(self) -> None:\n \n result = Addressee.legacy_build(\n sender=sender,\n- message_type_name=\"stream\",\n+ recipient_type_name=\"stream\",\n message_to=[stream.id],\n topic_name=\"random_topic\",\n realm=realm,\ndiff --git a/zerver/tests/test_mirror_users.py b/zerver/tests/test_mirror_users.py\nindex 377256c898c2f..f9930935209b6 100644\n--- a/zerver/tests/test_mirror_users.py\n+++ b/zerver/tests/test_mirror_users.py\n@@ -19,11 +19,13 @@ def test_invalid_client(self) -> None:\n \n recipients: List[str] = []\n \n- message_type = \"private\"\n+ recipient_type_name = \"private\"\n client = get_client(\"banned_mirror\")\n \n with self.assertRaises(InvalidMirrorInputError):\n- create_mirrored_message_users(client, user, recipients, sender.email, message_type)\n+ create_mirrored_message_users(\n+ client, user, recipients, sender.email, recipient_type_name\n+ )\n \n def test_invalid_email(self) -> None:\n invalid_email = \"alice AT example.com\"\n@@ -33,13 +35,15 @@ def test_invalid_email(self) -> None:\n user = self.mit_user(\"starnine\")\n sender = user\n \n- message_type = \"private\"\n+ recipient_type_name = \"private\"\n \n for client_name in [\"zephyr_mirror\", \"irc_mirror\", \"jabber_mirror\"]:\n client = get_client(client_name)\n \n with self.assertRaises(InvalidMirrorInputError):\n- create_mirrored_message_users(client, user, recipients, sender.email, message_type)\n+ create_mirrored_message_users(\n+ client, user, recipients, sender.email, recipient_type_name\n+ )\n \n @mock.patch(\n \"DNS.dnslookup\",\n@@ -54,11 +58,11 @@ def test_zephyr_mirror_new_recipient(self, ignored: object) -> None:\n \n recipients = [user.email, new_user_email]\n \n- message_type = \"private\"\n+ recipient_type_name = \"private\"\n client = get_client(\"zephyr_mirror\")\n \n mirror_sender = create_mirrored_message_users(\n- client, user, recipients, sender.email, message_type\n+ client, user, recipients, sender.email, recipient_type_name\n )\n \n self.assertEqual(mirror_sender, sender)\n@@ -82,11 +86,11 @@ def test_zephyr_mirror_new_sender(self, ignored: object) -> None:\n \n recipients = [\"stream_name\"]\n \n- message_type = \"stream\"\n+ recipient_type_name = \"stream\"\n client = get_client(\"zephyr_mirror\")\n \n mirror_sender = create_mirrored_message_users(\n- client, user, recipients, sender_email, message_type\n+ client, user, recipients, sender_email, recipient_type_name\n )\n \n assert mirror_sender is not None\n@@ -105,11 +109,11 @@ def test_irc_mirror(self) -> None:\n self.nonreg_email(\"cordelia\"),\n ]\n \n- message_type = \"private\"\n+ recipient_type_name = \"private\"\n client = get_client(\"irc_mirror\")\n \n mirror_sender = create_mirrored_message_users(\n- client, user, recipients, sender.email, message_type\n+ client, user, recipients, sender.email, recipient_type_name\n )\n \n self.assertEqual(mirror_sender, sender)\n@@ -134,11 +138,11 @@ def test_jabber_mirror(self) -> None:\n self.nonreg_email(\"cordelia\"),\n ]\n \n- message_type = \"private\"\n+ recipient_type_name = \"private\"\n client = get_client(\"jabber_mirror\")\n \n mirror_sender = create_mirrored_message_users(\n- client, user, recipients, sender.email, message_type\n+ client, user, recipients, sender.email, recipient_type_name\n )\n \n self.assertEqual(mirror_sender, sender)\ndiff --git a/zerver/tornado/event_queue.py b/zerver/tornado/event_queue.py\nindex d750765fbbc25..3604f07ce1dda 100644\n--- a/zerver/tornado/event_queue.py\n+++ b/zerver/tornado/event_queue.py\n@@ -949,7 +949,7 @@ def process_message_event(\n \n sender_id: int = wide_dict[\"sender_id\"]\n message_id: int = wide_dict[\"id\"]\n- message_type: str = wide_dict[\"type\"]\n+ recipient_type_name: str = wide_dict[\"type\"]\n sending_client: str = wide_dict[\"client\"]\n \n @lru_cache(maxsize=None)\n@@ -970,7 +970,7 @@ def get_client_payload(apply_markdown: bool, client_gravatar: bool) -> Dict[str,\n \n # If the recipient was offline and the message was a single or group PM to them\n # or they were @-notified potentially notify more immediately\n- private_message = message_type == \"private\"\n+ private_message = recipient_type_name == \"private\"\n user_notifications_data = UserMessageNotificationsData.from_user_id_sets(\n user_id=user_profile_id,\n flags=flags,\ndiff --git a/zerver/views/message_send.py b/zerver/views/message_send.py\nindex bd402e02a9973..fabcee06018bb 100644\n--- a/zerver/views/message_send.py\n+++ b/zerver/views/message_send.py\n@@ -51,11 +51,11 @@ def create_mirrored_message_users(\n user_profile: UserProfile,\n recipients: Iterable[str],\n sender: str,\n- message_type: str,\n+ recipient_type_name: str,\n ) -> UserProfile:\n sender_email = sender.strip().lower()\n referenced_users = {sender_email}\n- if message_type == \"private\":\n+ if recipient_type_name == \"private\":\n for email in recipients:\n referenced_users.add(email.lower())\n \n@@ -142,7 +142,7 @@ def same_realm_jabber_user(user_profile: UserProfile, email: str) -> bool:\n def handle_deferred_message(\n sender: UserProfile,\n client: Client,\n- message_type_name: str,\n+ recipient_type_name: str,\n message_to: Union[Sequence[str], Sequence[int]],\n topic_name: Optional[str],\n message_content: str,\n@@ -176,7 +176,7 @@ def handle_deferred_message(\n check_schedule_message(\n sender,\n client,\n- message_type_name,\n+ recipient_type_name,\n message_to,\n topic_name,\n message_content,\n@@ -210,12 +210,14 @@ def send_message_backend(\n tz_guess: Optional[str] = REQ(\"tz_guess\", default=None, documentation_pending=True),\n time: Optional[float] = REQ(default=None, converter=to_float, documentation_pending=True),\n ) -> HttpResponse:\n+ recipient_type_name = message_type_name\n+\n # If req_to is None, then we default to an\n # empty list of recipients.\n message_to: Union[Sequence[int], Sequence[str]] = []\n \n if req_to is not None:\n- if message_type_name == \"stream\":\n+ if recipient_type_name == \"stream\":\n stream_indicator = extract_stream_indicator(req_to)\n \n # For legacy reasons check_send_message expects\n@@ -259,7 +261,7 @@ def send_message_backend(\n # same-realm constraint.\n if req_sender is None:\n raise JsonableError(_(\"Missing sender\"))\n- if message_type_name != \"private\" and not can_forge_sender:\n+ if recipient_type_name != \"private\" and not can_forge_sender:\n raise JsonableError(_(\"User not authorized for this query\"))\n \n # For now, mirroring only works with recipient emails, not for\n@@ -274,7 +276,7 @@ def send_message_backend(\n \n try:\n mirror_sender = create_mirrored_message_users(\n- client, user_profile, message_to, req_sender, message_type_name\n+ client, user_profile, message_to, req_sender, recipient_type_name\n )\n except InvalidMirrorInputError:\n raise JsonableError(_(\"Invalid mirrored message\"))\n@@ -294,7 +296,7 @@ def send_message_backend(\n deliver_at = handle_deferred_message(\n sender,\n client,\n- message_type_name,\n+ recipient_type_name,\n message_to,\n topic_name,\n message_content,\n@@ -310,7 +312,7 @@ def send_message_backend(\n ret = check_send_message(\n sender,\n client,\n- message_type_name,\n+ recipient_type_name,\n message_to,\n topic_name,\n message_content,\n" }
[ { "diff_hunk": "@@ -210,12 +210,14 @@ def send_message_backend(\n tz_guess: Optional[str] = REQ(\"tz_guess\", default=None, documentation_pending=True),\n time: Optional[float] = REQ(default=None, converter=to_float, documentation_pending=True),\n ) -> HttpResponse:\n+ recipient_type_name = message_type_name", "line": null, "original_line": 213, "original_start_line": null, "path": "zerver/views/message_send.py", "start_line": null, "text": "@user1:\nThis can be done a bit more cleanly be using the first positional argument to `REQ`, to not have set `message_type_name` at all.\n\n@user1:\nAhh, but it gets replaced in the next commit anyway, yeah this is pretty reasonable as an intermediate state before we redo the internals plumbing to support `\"direct\"` rather than `\"private\"`." } ]
2f3532bc146e003a36464b286d9222344db15eeb
diff --git a/api_docs/changelog.md b/api_docs/changelog.md index 5fdf340a3521f..0ab9db84dfb2b 100644 --- a/api_docs/changelog.md +++ b/api_docs/changelog.md @@ -20,6 +20,15 @@ format used by the Zulip server that they are interacting with. ## Changes in Zulip 7.0 +**Feature level 174**: + +* [`POST /typing`](/api/set-typing-status), [`POST /messages`](/api/send-message): + Added `"direct"` as the preferred way to indicate a direct message for the + `type` parameter, deprecating the original `"private"`. While `"private"` + is still supported for direct messages, clients are encouraged to use to + the modern convention with servers that support it, because support for + `"private"` will eventually be removed. + **Feature level 173**: * [`GET /scheduled_messages`](/api/get-scheduled-messages), [`DELETE diff --git a/api_docs/send-message.md b/api_docs/send-message.md index c71e6211f31c7..c256b863803da 100644 --- a/api_docs/send-message.md +++ b/api_docs/send-message.md @@ -19,10 +19,10 @@ curl -X POST {{ api_url }}/v1/messages \ --data-urlencode topic=Castle \ --data-urlencode 'content=I come not, friends, to steal away your hearts.' -# For private messages +# For direct messages curl -X POST {{ api_url }}/v1/messages \ -u BOT_EMAIL_ADDRESS:BOT_API_KEY \ - --data-urlencode type=private \ + --data-urlencode type=direct \ --data-urlencode 'to=[9]' \ --data-urlencode 'content=With mirth and laughter let old wrinkles come.' ``` @@ -38,7 +38,7 @@ the command-line, providing the message content via STDIN. zulip-send --stream Denmark --subject Castle \ --user [email protected] --api-key a0b1c2d3e4f5a6b7c8d9e0f1a2b3c4d5 -# For private messages +# For direct messages zulip-send [email protected] \ --user [email protected] --api-key a0b1c2d3e4f5a6b7c8d9e0f1a2b3c4d5 ``` diff --git a/version.py b/version.py index b016fcb542f79..c6ffa7497373b 100644 --- a/version.py +++ b/version.py @@ -33,7 +33,7 @@ # Changes should be accompanied by documentation explaining what the # new level means in api_docs/changelog.md, as well as "**Changes**" # entries in the endpoint's documentation in `zulip.yaml`. -API_FEATURE_LEVEL = 173 +API_FEATURE_LEVEL = 174 # Bump the minor PROVISION_VERSION to indicate that folks should provision # only when going from an old version of the code to a newer version. Bump diff --git a/zerver/actions/message_send.py b/zerver/actions/message_send.py index 05e53e053e073..a5c316b3aab91 100644 --- a/zerver/actions/message_send.py +++ b/zerver/actions/message_send.py @@ -1154,7 +1154,7 @@ def check_send_private_message( def check_send_message( sender: UserProfile, client: Client, - message_type_name: str, + recipient_type_name: str, message_to: Union[Sequence[int], Sequence[str]], topic_name: Optional[str], message_content: str, @@ -1168,7 +1168,7 @@ def check_send_message( *, skip_stream_access_check: bool = False, ) -> int: - addressee = Addressee.legacy_build(sender, message_type_name, message_to, topic_name) + addressee = Addressee.legacy_build(sender, recipient_type_name, message_to, topic_name) try: message = check_message( sender, @@ -1192,7 +1192,7 @@ def check_send_message( def check_schedule_message( sender: UserProfile, client: Client, - message_type_name: str, + recipient_type_name: str, message_to: Union[Sequence[str], Sequence[int]], topic_name: Optional[str], message_content: str, @@ -1202,7 +1202,7 @@ def check_schedule_message( realm: Optional[Realm] = None, forwarder_user_profile: Optional[UserProfile] = None, ) -> int: - addressee = Addressee.legacy_build(sender, message_type_name, message_to, topic_name) + addressee = Addressee.legacy_build(sender, recipient_type_name, message_to, topic_name) send_request = check_message( sender, diff --git a/zerver/lib/addressee.py b/zerver/lib/addressee.py index ada059d196a61..a5a172407c0ee 100644 --- a/zerver/lib/addressee.py +++ b/zerver/lib/addressee.py @@ -97,7 +97,7 @@ def topic(self) -> str: @staticmethod def legacy_build( sender: UserProfile, - message_type_name: str, + recipient_type_name: str, message_to: Union[Sequence[int], Sequence[str]], topic_name: Optional[str], realm: Optional[Realm] = None, @@ -108,7 +108,7 @@ def legacy_build( if realm is None: realm = sender.realm - if message_type_name == "stream": + if recipient_type_name == "stream": if len(message_to) > 1: raise JsonableError(_("Cannot send to multiple streams")) @@ -131,7 +131,7 @@ def legacy_build( return Addressee.for_stream_id(stream_name_or_id, topic_name) return Addressee.for_stream_name(stream_name_or_id, topic_name) - elif message_type_name == "private": + elif recipient_type_name == "private": if not message_to: raise JsonableError(_("Message must have recipients")) diff --git a/zerver/lib/email_mirror.py b/zerver/lib/email_mirror.py index 9778088dddbf0..71c2626100014 100644 --- a/zerver/lib/email_mirror.py +++ b/zerver/lib/email_mirror.py @@ -198,7 +198,7 @@ def send_mm_reply_to_stream( check_send_message( sender=user_profile, client=get_client("Internal"), - message_type_name="stream", + recipient_type_name="stream", message_to=[stream.id], topic_name=topic, message_content=body, diff --git a/zerver/lib/message.py b/zerver/lib/message.py index e9e2a75c75362..f7bc7b521a8be 100644 --- a/zerver/lib/message.py +++ b/zerver/lib/message.py @@ -1273,17 +1273,17 @@ def apply_unread_message_event( ) -> None: message_id = message["id"] if message["type"] == "stream": - message_type = "stream" + recipient_type = "stream" elif message["type"] == "private": others = [recip for recip in message["display_recipient"] if recip["id"] != user_profile.id] if len(others) <= 1: - message_type = "private" + recipient_type = "private" else: - message_type = "huddle" + recipient_type = "huddle" else: raise AssertionError("Invalid message type {}".format(message["type"])) - if message_type == "stream": + if recipient_type == "stream": stream_id = message["stream_id"] topic = message[TOPIC_NAME] state["stream_dict"][message_id] = RawUnreadStreamDict( @@ -1300,7 +1300,7 @@ def apply_unread_message_event( ): state["unmuted_stream_msgs"].add(message_id) - elif message_type == "private": + elif recipient_type == "private": if len(others) == 1: other_user_id = others[0]["id"] else: diff --git a/zerver/lib/outgoing_webhook.py b/zerver/lib/outgoing_webhook.py index d866ce64268d4..3a7fa17588210 100644 --- a/zerver/lib/outgoing_webhook.py +++ b/zerver/lib/outgoing_webhook.py @@ -191,7 +191,7 @@ def send_response_message( that might let someone send arbitrary messages to any stream through this. """ - message_type = message_info["type"] + recipient_type_name = message_info["type"] display_recipient = message_info["display_recipient"] try: topic_name: Optional[str] = get_topic_from_message_info(message_info) @@ -207,9 +207,9 @@ def send_response_message( widget_content = response_data.get("widget_content") - if message_type == "stream": + if recipient_type_name == "stream": message_to = [display_recipient] - elif message_type == "private": + elif recipient_type_name == "private": message_to = [recipient["email"] for recipient in display_recipient] else: raise JsonableError(_("Invalid message type")) @@ -217,7 +217,7 @@ def send_response_message( check_send_message( sender=bot_user, client=client, - message_type_name=message_type, + recipient_type_name=recipient_type_name, message_to=message_to, topic_name=topic_name, message_content=content, diff --git a/zerver/models.py b/zerver/models.py index 1b6b92cbf1552..0fb006d6414ca 100644 --- a/zerver/models.py +++ b/zerver/models.py @@ -3021,6 +3021,16 @@ class ArchivedMessage(AbstractMessage): class Message(AbstractMessage): + # Recipient types used when a Message object is provided to + # Zulip clients via the API. + # + # A detail worth noting: + # * "direct" was introduced in 2023 with the goal of + # deprecating the original "private" and becoming the + # preferred way to indicate a personal or huddle + # Recipient type via the API. + API_RECIPIENT_TYPES = ["direct", "private", "stream"] + search_tsvector = SearchVectorField(null=True) def topic_name(self) -> str: diff --git a/zerver/openapi/javascript_examples.js b/zerver/openapi/javascript_examples.js index 5db423b9e0aa7..f371e3ab4cc79 100644 --- a/zerver/openapi/javascript_examples.js +++ b/zerver/openapi/javascript_examples.js @@ -113,11 +113,11 @@ add_example("send_message", "/messages:post", 200, async (client, console) => { }; console.log(await client.messages.send(params)); - // Send a private message + // Send a direct message const user_id = 9; params = { to: [user_id], - type: "private", + type: "direct", content: "With mirth and laughter let old wrinkles come.", }; console.log(await client.messages.send(params)); @@ -252,7 +252,8 @@ add_example("set_typing_status", "/typing:post", 200, async (client, console) => to: [user_id1, user_id2], }; - // The user has started to type in the group PM with Iago and Polonius + // The user has started typing in the group direct message + // with Iago and Polonius console.log(await client.typing.send(typingParams)); // {code_example|end} }); diff --git a/zerver/openapi/python_examples.py b/zerver/openapi/python_examples.py index 03f2b8453dd0d..e6533a2c7713e 100644 --- a/zerver/openapi/python_examples.py +++ b/zerver/openapi/python_examples.py @@ -954,6 +954,7 @@ def send_message(client: Client) -> int: "content": "I come not, friends, to steal away your hearts.", } result = client.send_message(request) + # {code_example|end} validate_against_openapi_schema(result, "/messages", "post", "200") @@ -971,7 +972,7 @@ def send_message(client: Client) -> int: ensure_users([10], ["hamlet"]) # {code_example|start} - # Send a private message + # Send a direct message user_id = 10 request = { "type": "private", @@ -979,6 +980,7 @@ def send_message(client: Client) -> int: "content": "With mirth and laughter let old wrinkles come.", } result = client.send_message(request) + # {code_example|end} validate_against_openapi_schema(result, "/messages", "post", "200") @@ -1294,7 +1296,8 @@ def set_typing_status(client: Client) -> None: ensure_users([10, 11], ["hamlet", "iago"]) # {code_example|start} - # The user has started to type in the group PM with Iago and Polonius + # The user has started typing in the group direct message + # with Iago and Polonius user_id1 = 10 user_id2 = 11 @@ -1309,7 +1312,8 @@ def set_typing_status(client: Client) -> None: validate_against_openapi_schema(result, "/typing", "post", "200") # {code_example|start} - # The user has finished typing in the group PM with Iago and Polonius + # The user has finished typing in the group direct message + # with Iago and Polonius user_id1 = 10 user_id2 = 11 @@ -1324,7 +1328,8 @@ def set_typing_status(client: Client) -> None: validate_against_openapi_schema(result, "/typing", "post", "200") # {code_example|start} - # The user has started to type in topic "typing status" of stream "Denmark" + # The user has started to type in topic "typing status" + # of stream "Denmark" stream_id = client.get_stream_id("Denmark")["stream_id"] topic = "typing status" @@ -1341,7 +1346,8 @@ def set_typing_status(client: Client) -> None: validate_against_openapi_schema(result, "/typing", "post", "200") # {code_example|start} - # The user has finished typing in topic "typing status" of stream "Denmark" + # The user has finished typing in topic "typing status" + # of stream "Denmark" stream_id = client.get_stream_id("Denmark")["stream_id"] topic = "typing status" diff --git a/zerver/openapi/zulip.yaml b/zerver/openapi/zulip.yaml index 359165169c95a..631aa56276b44 100644 --- a/zerver/openapi/zulip.yaml +++ b/zerver/openapi/zulip.yaml @@ -5502,25 +5502,35 @@ paths: summary: Send a message tags: ["messages"] description: | - Send a stream or a private message. + Send a [stream message](/help/streams-and-topics) or a + [direct message](/help/direct-messages). parameters: - name: type in: query description: | - The type of message to be sent. `private` for a private message and - `stream` for a stream message. + The type of message to be sent. + + `"direct"` for a direct message and `"stream"` for a stream message. + + **Changes**: In Zulip 7.0 (feature level 174), `"direct"` was added as + the preferred way to request a direct message, deprecating the original + `"private"`. While `"private"` is still supported for requesting direct + messages, clients are encouraged to use to the modern convention with + servers that support it, because support for `"private"` will eventually + be removed. schema: type: string enum: - - private + - direct - stream - example: private + - private + example: direct required: true - name: to in: query description: | For stream messages, either the name or integer ID of the stream. For - private messages, either a list containing integer user IDs or a list + direct messages, either a list containing integer user IDs or a list containing string email addresses. **Changes**: Support for using user/stream IDs was added in Zulip 2.0.0. @@ -5624,7 +5634,7 @@ paths: "result": "error", } description: | - A typical failed JSON response for when a private message is sent to a user + A typical failed JSON response for when a direct message is sent to a user that does not exist: /messages/{message_id}/history: get: @@ -15056,7 +15066,7 @@ paths: summary: Set "typing" status tags: ["users"] description: | - Notify other users whether the current user is typing a message. + Notify other users whether the current user is [typing a message](/help/typing-notifications). Clients implementing Zulip's typing notifications protocol should work as follows: @@ -15100,15 +15110,21 @@ paths: description: | Type of the message being composed. - **Changes**: New in Zulip 4.0 (feature level 58). Previously, typing - notifications were only for private messages. + **Changes**: In Zulip 7.0 (feature level 174), `"direct"` was added + as the preferred way to indicate a direct message is being composed, + becoming the default value for this parameter and deprecating the + original `"private"`. + + New in Zulip 4.0 (feature level 58). Previously, typing notifications + were only for direct messages. schema: type: string enum: - - private + - direct - stream - default: private - example: private + - private + default: direct + example: direct - name: op in: query description: | @@ -15123,16 +15139,16 @@ paths: - name: to in: query description: | - For `"private"` type it is the user_ids of the recipients of the message being typed. - Send a JSON-encoded list of user_ids. (Use a list even if there is only one - recipient.) + For `"direct"` type it is the user IDs of the recipients of the message + being typed. Send a JSON-encoded list of user IDs. (Use a list even if + there is only one recipient.) For `"stream"` type it is a single element list containing ID of stream in which the message is being typed. **Changes**: Support for typing notifications for stream messages is new in Zulip 4.0 (feature level 58). Previously, typing - notifications were only for private messages. + notifications were only for direct messages. Before Zulip 2.0.0, this parameter accepted only a JSON-encoded list of email addresses. Support for the email address-based format was @@ -15149,10 +15165,10 @@ paths: in: query description: | Topic to which message is being typed. Required for the `"stream"` type. - Ignored in the case of `"private"` type. + Ignored in the case of `"direct"` type. **Changes**: New in Zulip 4.0 (feature level 58). Previously, typing - notifications were only for private messages. + notifications were only for direct messages. schema: type: string example: typing notifications diff --git a/zerver/tests/test_event_system.py b/zerver/tests/test_event_system.py index 0bb597a206c17..70b02ea9645d1 100644 --- a/zerver/tests/test_event_system.py +++ b/zerver/tests/test_event_system.py @@ -324,7 +324,7 @@ def test_get_events(self) -> None: check_send_message( sender=user_profile, client=get_client("whatever"), - message_type_name="private", + recipient_type_name="private", message_to=[recipient_email], topic_name=None, message_content="hello", @@ -357,7 +357,7 @@ def test_get_events(self) -> None: check_send_message( sender=user_profile, client=get_client("whatever"), - message_type_name="private", + recipient_type_name="private", message_to=[recipient_email], topic_name=None, message_content="hello", diff --git a/zerver/tests/test_message_send.py b/zerver/tests/test_message_send.py index 6aee993dc8fd3..1469ce8abee6f 100644 --- a/zerver/tests/test_message_send.py +++ b/zerver/tests/test_message_send.py @@ -586,7 +586,7 @@ def test_personal_message(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": orjson.dumps([othello.email]).decode(), }, @@ -605,7 +605,7 @@ def test_personal_message(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": orjson.dumps([user_profile.email]).decode(), }, @@ -628,52 +628,60 @@ def test_personal_message(self) -> None: def test_personal_message_by_id(self) -> None: """ - Sending a personal message to a valid user ID is successful. + Sending a personal message to a valid user ID is successful + for both valid strings for `type` parameter. """ self.login("hamlet") - result = self.client_post( - "/json/messages", - { - "type": "private", - "content": "Test message", - "to": orjson.dumps([self.example_user("othello").id]).decode(), - }, - ) - self.assert_json_success(result) + recipient_type_name = ["direct", "private"] - msg = self.get_last_message() - self.assertEqual("Test message", msg.content) - self.assertEqual(msg.recipient_id, self.example_user("othello").recipient_id) + for type in recipient_type_name: + result = self.client_post( + "/json/messages", + { + "type": type, + "content": "Test message", + "to": orjson.dumps([self.example_user("othello").id]).decode(), + }, + ) + self.assert_json_success(result) + + msg = self.get_last_message() + self.assertEqual("Test message", msg.content) + self.assertEqual(msg.recipient_id, self.example_user("othello").recipient_id) def test_group_personal_message_by_id(self) -> None: """ - Sending a personal message to a valid user ID is successful. + Sending a personal message to a valid user ID is successful + for both valid strings for `type` parameter. """ self.login("hamlet") - result = self.client_post( - "/json/messages", - { - "type": "private", - "content": "Test message", - "to": orjson.dumps( - [self.example_user("othello").id, self.example_user("cordelia").id] - ).decode(), - }, - ) - self.assert_json_success(result) + recipient_type_name = ["direct", "private"] - msg = self.get_last_message() - self.assertEqual("Test message", msg.content) - self.assertEqual( - msg.recipient_id, - get_huddle_recipient( + for type in recipient_type_name: + result = self.client_post( + "/json/messages", { - self.example_user("hamlet").id, - self.example_user("othello").id, - self.example_user("cordelia").id, - } - ).id, - ) + "type": type, + "content": "Test message", + "to": orjson.dumps( + [self.example_user("othello").id, self.example_user("cordelia").id] + ).decode(), + }, + ) + self.assert_json_success(result) + + msg = self.get_last_message() + self.assertEqual("Test message", msg.content) + self.assertEqual( + msg.recipient_id, + get_huddle_recipient( + { + self.example_user("hamlet").id, + self.example_user("othello").id, + self.example_user("cordelia").id, + } + ).id, + ) def test_personal_message_copying_self(self) -> None: """ @@ -686,7 +694,7 @@ def test_personal_message_copying_self(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": orjson.dumps([hamlet.id, othello.id]).decode(), }, @@ -704,7 +712,7 @@ def test_personal_message_to_nonexistent_user(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": "nonexistent", }, @@ -723,7 +731,7 @@ def test_personal_message_to_deactivated_user(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": orjson.dumps([othello.id]).decode(), }, @@ -733,7 +741,7 @@ def test_personal_message_to_deactivated_user(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "content": "Test message", "to": orjson.dumps([othello.id, cordelia.id]).decode(), }, @@ -754,7 +762,7 @@ def test_invalid_type(self) -> None: "to": othello.email, }, ) - self.assert_json_error(result, "Invalid message type") + self.assert_json_error(result, "Invalid type") def test_empty_message(self) -> None: """ @@ -764,7 +772,7 @@ def test_empty_message(self) -> None: othello = self.example_user("othello") result = self.client_post( "/json/messages", - {"type": "private", "content": " ", "to": othello.email}, + {"type": "direct", "content": " ", "to": othello.email}, ) self.assert_json_error(result, "Message must not be empty") @@ -824,9 +832,9 @@ def test_invalid_topic(self) -> None: ) self.assert_json_error(result, "Invalid character in topic, at position 5!") - def test_invalid_message_type(self) -> None: + def test_invalid_recipient_type(self) -> None: """ - Messages other than the type of "private" or "stream" are considered as invalid + Messages other than the type of "direct", "private" or "stream" are invalid. """ self.login("hamlet") result = self.client_post( @@ -838,7 +846,7 @@ def test_invalid_message_type(self) -> None: "topic": "Test topic", }, ) - self.assert_json_error(result, "Invalid message type") + self.assert_json_error(result, "Invalid type") def test_private_message_without_recipients(self) -> None: """ @@ -847,7 +855,7 @@ def test_private_message_without_recipients(self) -> None: self.login("hamlet") result = self.client_post( "/json/messages", - {"type": "private", "content": "Test content", "to": ""}, + {"type": "direct", "content": "Test content", "to": ""}, ) self.assert_json_error(result, "Message must have recipients") @@ -859,7 +867,7 @@ def test_mirrored_huddle(self) -> None: self.mit_user("starnine"), "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -879,7 +887,7 @@ def test_mirrored_personal(self) -> None: self.mit_user("starnine"), "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -898,7 +906,7 @@ def test_mirrored_personal_browser(self) -> None: result = self.client_post( "/json/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -916,7 +924,7 @@ def test_mirrored_personal_to_someone_else(self) -> None: self.mit_user("starnine"), "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -931,7 +939,7 @@ def test_duplicated_mirrored_huddle(self) -> None: Sending two mirrored huddles in the row return the same ID """ msg = { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -1065,7 +1073,7 @@ def test_send_message_when_sender_is_not_set(self) -> None: self.mit_user("starnine"), "/api/v1/messages", { - "type": "private", + "type": "direct", "content": "Test message", "client": "zephyr_mirror", "to": self.mit_email("starnine"), @@ -1079,7 +1087,7 @@ def test_send_message_as_not_superuser_when_type_is_not_private(self) -> None: self.mit_user("starnine"), "/api/v1/messages", { - "type": "not-private", + "type": "stream", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -1098,7 +1106,7 @@ def test_send_message_create_mirrored_message_user_returns_invalid_input( self.mit_user("starnine"), "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -1120,7 +1128,7 @@ def test_send_message_when_client_is_zephyr_mirror_but_string_id_is_not_zephyr( user, "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -1141,7 +1149,7 @@ def test_send_message_when_client_is_zephyr_mirror_but_recipient_is_user_id( user, "/api/v1/messages", { - "type": "private", + "type": "direct", "sender": self.mit_email("sipbtest"), "content": "Test message", "client": "zephyr_mirror", @@ -2557,7 +2565,7 @@ def test_addressee_legacy_build_for_user_ids(self) -> None: result = Addressee.legacy_build( sender=self.example_user("hamlet"), - message_type_name="private", + recipient_type_name="private", message_to=user_ids, topic_name="random_topic", realm=realm, @@ -2576,7 +2584,7 @@ def test_addressee_legacy_build_for_stream_id(self) -> None: result = Addressee.legacy_build( sender=sender, - message_type_name="stream", + recipient_type_name="stream", message_to=[stream.id], topic_name="random_topic", realm=realm, diff --git a/zerver/tests/test_mirror_users.py b/zerver/tests/test_mirror_users.py index 377256c898c2f..f9930935209b6 100644 --- a/zerver/tests/test_mirror_users.py +++ b/zerver/tests/test_mirror_users.py @@ -19,11 +19,13 @@ def test_invalid_client(self) -> None: recipients: List[str] = [] - message_type = "private" + recipient_type_name = "private" client = get_client("banned_mirror") with self.assertRaises(InvalidMirrorInputError): - create_mirrored_message_users(client, user, recipients, sender.email, message_type) + create_mirrored_message_users( + client, user, recipients, sender.email, recipient_type_name + ) def test_invalid_email(self) -> None: invalid_email = "alice AT example.com" @@ -33,13 +35,15 @@ def test_invalid_email(self) -> None: user = self.mit_user("starnine") sender = user - message_type = "private" + recipient_type_name = "private" for client_name in ["zephyr_mirror", "irc_mirror", "jabber_mirror"]: client = get_client(client_name) with self.assertRaises(InvalidMirrorInputError): - create_mirrored_message_users(client, user, recipients, sender.email, message_type) + create_mirrored_message_users( + client, user, recipients, sender.email, recipient_type_name + ) @mock.patch( "DNS.dnslookup", @@ -54,11 +58,11 @@ def test_zephyr_mirror_new_recipient(self, ignored: object) -> None: recipients = [user.email, new_user_email] - message_type = "private" + recipient_type_name = "private" client = get_client("zephyr_mirror") mirror_sender = create_mirrored_message_users( - client, user, recipients, sender.email, message_type + client, user, recipients, sender.email, recipient_type_name ) self.assertEqual(mirror_sender, sender) @@ -82,11 +86,11 @@ def test_zephyr_mirror_new_sender(self, ignored: object) -> None: recipients = ["stream_name"] - message_type = "stream" + recipient_type_name = "stream" client = get_client("zephyr_mirror") mirror_sender = create_mirrored_message_users( - client, user, recipients, sender_email, message_type + client, user, recipients, sender_email, recipient_type_name ) assert mirror_sender is not None @@ -105,11 +109,11 @@ def test_irc_mirror(self) -> None: self.nonreg_email("cordelia"), ] - message_type = "private" + recipient_type_name = "private" client = get_client("irc_mirror") mirror_sender = create_mirrored_message_users( - client, user, recipients, sender.email, message_type + client, user, recipients, sender.email, recipient_type_name ) self.assertEqual(mirror_sender, sender) @@ -134,11 +138,11 @@ def test_jabber_mirror(self) -> None: self.nonreg_email("cordelia"), ] - message_type = "private" + recipient_type_name = "private" client = get_client("jabber_mirror") mirror_sender = create_mirrored_message_users( - client, user, recipients, sender.email, message_type + client, user, recipients, sender.email, recipient_type_name ) self.assertEqual(mirror_sender, sender) diff --git a/zerver/tests/test_typing.py b/zerver/tests/test_typing.py index b78e0c672aecd..1cf1f51fe4c7e 100644 --- a/zerver/tests/test_typing.py +++ b/zerver/tests/test_typing.py @@ -132,6 +132,23 @@ def test_stream_doesnt_exist(self) -> None: class TypingHappyPathTestPMs(ZulipTestCase): + def test_valid_type_and_op_parameters(self) -> None: + recipient_type_name = ["direct", "private"] + operator_type = ["start", "stop"] + sender = self.example_user("hamlet") + recipient_user = self.example_user("othello") + + for type in recipient_type_name: + for operator in operator_type: + params = dict( + to=orjson.dumps([recipient_user.id]).decode(), + op=operator, + type=type, + ) + + result = self.api_post(sender, "/api/v1/typing", params) + self.assert_json_success(result) + def test_start_to_single_recipient(self) -> None: sender = self.example_user("hamlet") recipient_user = self.example_user("othello") diff --git a/zerver/tornado/event_queue.py b/zerver/tornado/event_queue.py index d750765fbbc25..3604f07ce1dda 100644 --- a/zerver/tornado/event_queue.py +++ b/zerver/tornado/event_queue.py @@ -949,7 +949,7 @@ def process_message_event( sender_id: int = wide_dict["sender_id"] message_id: int = wide_dict["id"] - message_type: str = wide_dict["type"] + recipient_type_name: str = wide_dict["type"] sending_client: str = wide_dict["client"] @lru_cache(maxsize=None) @@ -970,7 +970,7 @@ def get_client_payload(apply_markdown: bool, client_gravatar: bool) -> Dict[str, # If the recipient was offline and the message was a single or group PM to them # or they were @-notified potentially notify more immediately - private_message = message_type == "private" + private_message = recipient_type_name == "private" user_notifications_data = UserMessageNotificationsData.from_user_id_sets( user_id=user_profile_id, flags=flags, diff --git a/zerver/views/message_send.py b/zerver/views/message_send.py index bd402e02a9973..07fcf2f70491a 100644 --- a/zerver/views/message_send.py +++ b/zerver/views/message_send.py @@ -24,7 +24,7 @@ from zerver.lib.response import json_success from zerver.lib.timestamp import convert_to_UTC from zerver.lib.topic import REQ_topic -from zerver.lib.validator import check_int, to_float +from zerver.lib.validator import check_int, check_string_in, to_float from zerver.lib.zcommand import process_zcommands from zerver.lib.zephyr import compute_mit_user_fullname from zerver.models import ( @@ -51,11 +51,11 @@ def create_mirrored_message_users( user_profile: UserProfile, recipients: Iterable[str], sender: str, - message_type: str, + recipient_type_name: str, ) -> UserProfile: sender_email = sender.strip().lower() referenced_users = {sender_email} - if message_type == "private": + if recipient_type_name == "private": for email in recipients: referenced_users.add(email.lower()) @@ -142,7 +142,7 @@ def same_realm_jabber_user(user_profile: UserProfile, email: str) -> bool: def handle_deferred_message( sender: UserProfile, client: Client, - message_type_name: str, + recipient_type_name: str, message_to: Union[Sequence[str], Sequence[int]], topic_name: Optional[str], message_content: str, @@ -176,7 +176,7 @@ def handle_deferred_message( check_schedule_message( sender, client, - message_type_name, + recipient_type_name, message_to, topic_name, message_content, @@ -193,7 +193,7 @@ def handle_deferred_message( def send_message_backend( request: HttpRequest, user_profile: UserProfile, - message_type_name: str = REQ("type"), + req_type: str = REQ("type", str_validator=check_string_in(Message.API_RECIPIENT_TYPES)), req_to: Optional[str] = REQ("to", default=None), req_sender: Optional[str] = REQ("sender", default=None, documentation_pending=True), forged_str: Optional[str] = REQ("forged", default=None, documentation_pending=True), @@ -210,12 +210,19 @@ def send_message_backend( tz_guess: Optional[str] = REQ("tz_guess", default=None, documentation_pending=True), time: Optional[float] = REQ(default=None, converter=to_float, documentation_pending=True), ) -> HttpResponse: + recipient_type_name = req_type + if recipient_type_name == "direct": + # For now, use "private" from Message.API_RECIPIENT_TYPES. + # TODO: Use "direct" here, as well as in events and + # message (created, schdeduled, drafts) objects/dicts. + recipient_type_name = "private" + # If req_to is None, then we default to an # empty list of recipients. message_to: Union[Sequence[int], Sequence[str]] = [] if req_to is not None: - if message_type_name == "stream": + if recipient_type_name == "stream": stream_indicator = extract_stream_indicator(req_to) # For legacy reasons check_send_message expects @@ -259,7 +266,7 @@ def send_message_backend( # same-realm constraint. if req_sender is None: raise JsonableError(_("Missing sender")) - if message_type_name != "private" and not can_forge_sender: + if recipient_type_name != "private" and not can_forge_sender: raise JsonableError(_("User not authorized for this query")) # For now, mirroring only works with recipient emails, not for @@ -274,7 +281,7 @@ def send_message_backend( try: mirror_sender = create_mirrored_message_users( - client, user_profile, message_to, req_sender, message_type_name + client, user_profile, message_to, req_sender, recipient_type_name ) except InvalidMirrorInputError: raise JsonableError(_("Invalid mirrored message")) @@ -294,7 +301,7 @@ def send_message_backend( deliver_at = handle_deferred_message( sender, client, - message_type_name, + recipient_type_name, message_to, topic_name, message_content, @@ -310,7 +317,7 @@ def send_message_backend( ret = check_send_message( sender, client, - message_type_name, + recipient_type_name, message_to, topic_name, message_content, diff --git a/zerver/views/typing.py b/zerver/views/typing.py index 7c991dc52ce06..bc1ca7912f2e8 100644 --- a/zerver/views/typing.py +++ b/zerver/views/typing.py @@ -9,18 +9,17 @@ from zerver.lib.response import json_success from zerver.lib.streams import access_stream_by_id, access_stream_for_send_message from zerver.lib.validator import check_int, check_list, check_string_in -from zerver.models import UserProfile +from zerver.models import Message, UserProfile VALID_OPERATOR_TYPES = ["start", "stop"] -VALID_MESSAGE_TYPES = ["private", "stream"] @has_request_variables def send_notification_backend( request: HttpRequest, user_profile: UserProfile, - message_type: str = REQ( - "type", str_validator=check_string_in(VALID_MESSAGE_TYPES), default="private" + req_type: str = REQ( + "type", str_validator=check_string_in(Message.API_RECIPIENT_TYPES), default="direct" ), operator: str = REQ("op", str_validator=check_string_in(VALID_OPERATOR_TYPES)), notification_to: List[int] = REQ("to", json_validator=check_list(check_int)), @@ -31,7 +30,12 @@ def send_notification_backend( if to_length == 0: raise JsonableError(_("Empty 'to' list")) - if message_type == "stream": + recipient_type_name = req_type + if recipient_type_name == "private": + # TODO: Use "direct" in typing notification events. + recipient_type_name = "direct" + + if recipient_type_name == "stream": if to_length > 1: raise JsonableError(_("Cannot send to multiple streams"))
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
zulip__zulip-25349@67de2a4
zulip/zulip
Python
25,349
schedule: Remove 'Schedule' state of compose box.
Fixes #25340 This means that we now schedule the message simply after selecting time if the message is valid. Also, editing scheduled messages will now delete the scheduled message and open compose with scheduled message. TODO: - [x] Note that the user can always drop an accidentally scheduled message from the scheduled messages overlay, which is linked from the confirmation banner. We may also want to add an "Undo" button to that banner. - [x] Drop the "Scheduling this message..." compose banner. - [x] Live update schedule message overlay based on events.
2023-04-30T13:05:01Z
Simplify message scheduling flow In the current scheduled message implementation, we have a somewhat awkward state of a time having been picked without a message being scheduled. To simplify the experience, we should make selecting a time immediately schedule the message. As a result, we can: - Remove the "Cancel scheduling" option from the three-dot menu. - Never have the "Send" button be labeled as "Schedule". - Drop the "Scheduling this message..." compose banner. If the compose box is empty, clicking "Schedule message" should highlight it in red, just like trying to send an empty message. The scheduling modal should not open in this case. Note that the user can always drop an accidentally scheduled message from the scheduled messages overlay, which is linked from the confirmation banner. We may also want to add an "Undo" button to that banner.
Hello @zulip/server-compose members, this issue was labeled with the "area: compose" label, so you may want to check it out! <!-- areaLabelAddition --> @zulipbot claim Hello @akarsh-jain-790! Thanks for your interest in Zulip! You have attempted to claim an issue without the label "help wanted". You can only claim and submit pull requests for issues with the [help wanted](https://github.com/zulip/zulip/issues?q=is%3Aopen+is%3Aissue+no%3Aassignee+label%3A%22help+wanted%22) label. If this is your first time here, we recommend reading our [guide for new contributors](https://zulip.readthedocs.io/en/latest/overview/contributing.html) before getting started.
[ { "body": "In the current scheduled message implementation, we have a somewhat awkward state of a time having been picked without a message being scheduled. \r\n\r\nTo simplify the experience, we should make selecting a time immediately schedule the message. As a result, we can:\r\n\r\n- Remove the \"Cancel scheduling\" option from the three-dot menu.\r\n- Never have the \"Send\" button be labeled as \"Schedule\".\r\n- Drop the \"Scheduling this message...\" compose banner.\r\n\r\nIf the compose box is empty, clicking \"Schedule message\" should highlight it in red, just like trying to send an empty message. The scheduling modal should not open in this case.\r\n\r\nNote that the user can always drop an accidentally scheduled message from the scheduled messages overlay, which is linked from the confirmation banner. We may also want to add an \"Undo\" button to that banner.", "number": 25340, "title": "Simplify message scheduling flow" } ]
7425079814d674672b5e1208f0742f800ba470c7
{ "head_commit": "67de2a416521a98ac6b40ff448803d07d3c800d1", "head_commit_message": "scheduled_messages_overlay_ui: Use CSS to display no scheduled messages.", "patch_to_review": "diff --git a/web/src/compose.js b/web/src/compose.js\nindex 577ce1e58307d..55685f1126210 100644\n--- a/web/src/compose.js\n+++ b/web/src/compose.js\n@@ -188,7 +188,7 @@ export function clear_compose_box() {\n compose_banner.clear_errors();\n compose_banner.clear_warnings();\n compose_ui.hide_compose_spinner();\n- reset_compose_scheduling_state();\n+ popover_menus.reset_selected_schedule_time();\n }\n \n export function send_message_success(local_id, message_id, locally_echoed) {\n@@ -269,7 +269,6 @@ export function send_message(request = create_message_object()) {\n }\n \n transmit.send_message(request, success, error);\n- scheduled_messages.delete_scheduled_message_if_sent_directly();\n server_events.assert_get_events_running(\n \"Restarting get_events because it was not running during send\",\n );\n@@ -298,7 +297,7 @@ export function enter_with_preview_open(ctrl_pressed = false) {\n // Common entrypoint for asking the server to send the message\n // currently drafted in the compose box, including for scheduled\n // messages.\n-export function finish() {\n+export function finish(from_do_schedule_message = false) {\n if (compose_ui.compose_spinner_visible) {\n // Avoid sending a message twice in parallel in races where\n // the user clicks the `Send` button very quickly twice or\n@@ -330,7 +329,7 @@ export function finish() {\n return false;\n }\n \n- if (popover_menus.is_time_selected_for_schedule()) {\n+ if (from_do_schedule_message) {\n schedule_message_to_custom_date();\n } else {\n send_message();\n@@ -789,16 +788,6 @@ export function initialize() {\n }\n }\n \n-export function reset_compose_scheduling_state(reset_edit_state = true) {\n- $(\"#compose-textarea\").prop(\"disabled\", false);\n- $(\"#compose-schedule-confirm-button\").hide();\n- popover_menus.reset_selected_schedule_time();\n- $(\"#compose-send-button\").show();\n- if (reset_edit_state) {\n- $(\"#compose-textarea\").removeAttr(\"data-scheduled-message-id\");\n- }\n-}\n-\n function schedule_message_to_custom_date() {\n const compose_message_object = create_message_object();\n \n@@ -823,12 +812,5 @@ function schedule_message_to_custom_date() {\n scheduled_delivery_timestamp,\n };\n \n- // If this is an edit request `scheduled_message_id` will be defined.\n- if ($(\"#compose-textarea\").attr(\"data-scheduled-message-id\")) {\n- scheduled_message_data.scheduled_message_id = $(\"#compose-textarea\").attr(\n- \"data-scheduled-message-id\",\n- );\n- }\n-\n scheduled_messages.send_request_to_schedule_message(scheduled_message_data, deliver_at);\n }\ndiff --git a/web/src/compose_actions.js b/web/src/compose_actions.js\nindex e8a5c83501504..aacff10141bb2 100644\n--- a/web/src/compose_actions.js\n+++ b/web/src/compose_actions.js\n@@ -78,7 +78,6 @@ function clear_box() {\n compose_ui.autosize_textarea($(\"#compose-textarea\"));\n compose_banner.clear_errors();\n compose_banner.clear_warnings();\n- compose.reset_compose_scheduling_state();\n }\n \n export function autosize_message_content() {\ndiff --git a/web/src/compose_ui.js b/web/src/compose_ui.js\nindex cbfac1738f005..e4bfdfef2e5ea 100644\n--- a/web/src/compose_ui.js\n+++ b/web/src/compose_ui.js\n@@ -535,10 +535,3 @@ export function get_compose_click_target(e) {\n }\n return e.target;\n }\n-\n-export function get_submit_button() {\n- if (popover_menus.is_time_selected_for_schedule()) {\n- return $(\"#compose-schedule-confirm-button\");\n- }\n- return $(\"#compose-send-button\");\n-}\ndiff --git a/web/src/composebox_typeahead.js b/web/src/composebox_typeahead.js\nindex 8fc65a2fdf68c..0137b0979db27 100644\n--- a/web/src/composebox_typeahead.js\n+++ b/web/src/composebox_typeahead.js\n@@ -221,7 +221,7 @@ function handle_keydown(e) {\n // could result in focus being moved to the \"Send\n // button\" after sending the message, preventing\n // typing a next message!\n- compose_ui.get_submit_button().trigger(\"focus\");\n+ $(\"#compose-send-button\").trigger(\"focus\");\n \n e.preventDefault();\n e.stopPropagation();\n@@ -232,7 +232,7 @@ function handle_keydown(e) {\n e.preventDefault();\n if (\n compose_validate.validate_message_length() &&\n- !compose_ui.get_submit_button().prop(\"disabled\")\n+ !$(\"#compose-send-button\").prop(\"disabled\")\n ) {\n compose.finish();\n }\ndiff --git a/web/src/popover_menus.js b/web/src/popover_menus.js\nindex a6899cfab6208..67d2203e9d62b 100644\n--- a/web/src/popover_menus.js\n+++ b/web/src/popover_menus.js\n@@ -25,6 +25,7 @@ import * as channel from \"./channel\";\n import * as common from \"./common\";\n import * as compose from \"./compose\";\n import * as compose_actions from \"./compose_actions\";\n+import * as compose_validate from \"./compose_validate\";\n import * as condense from \"./condense\";\n import * as confirm_dialog from \"./confirm_dialog\";\n import * as drafts from \"./drafts\";\n@@ -80,10 +81,6 @@ export function get_topic_menu_popover() {\n return popover_instances.topics_menu;\n }\n \n-export function is_time_selected_for_schedule() {\n- return selected_send_later_time !== undefined;\n-}\n-\n export function get_selected_send_later_time() {\n if (!selected_send_later_time) {\n return undefined;\n@@ -98,6 +95,10 @@ export function get_formatted_selected_send_later_time() {\n return timerender.get_full_datetime(new Date(selected_send_later_time), \"time\");\n }\n \n+export function set_selected_schedule_time(scheduled_time) {\n+ selected_send_later_time = scheduled_time;\n+}\n+\n export function reset_selected_schedule_time() {\n selected_send_later_time = undefined;\n }\n@@ -229,11 +230,8 @@ export function toggle_message_actions_menu(message) {\n return true;\n }\n \n-export function show_schedule_confirm_button(send_at_time, not_from_flatpickr = false) {\n- // If no time was selected by the user, we don't show the schedule button.\n- if (!send_at_time) {\n- return;\n- }\n+export function do_schedule_message(send_at_time, not_from_flatpickr = false) {\n+ overlays.close_active_modal();\n \n // flatpickr.show_flatpickr doesn't pass any value for not_from_flatpickr,\n // making it false by default and we pass it as true in other cases.\n@@ -244,13 +242,7 @@ export function show_schedule_confirm_button(send_at_time, not_from_flatpickr =\n }\n \n selected_send_later_time = send_at_time;\n- $(\"#compose-schedule-confirm-button\").show();\n- $(\"#compose-send-button\").hide();\n-}\n-\n-function update_scheduled_date_from_modal(send_at_time, not_from_flatpickr = false) {\n- overlays.close_active_modal();\n- show_schedule_confirm_button(send_at_time, not_from_flatpickr);\n+ compose.finish(true);\n }\n \n export function initialize() {\n@@ -799,13 +791,6 @@ export function initialize() {\n },\n });\n \n- $(\"body\").on(\"click\", \"#compose-schedule-confirm-button\", (e) => {\n- compose.finish();\n-\n- e.preventDefault();\n- e.stopPropagation();\n- });\n-\n const send_later_today = {\n today_nine_am: {\n text: $t({defaultMessage: \"Today at 9:00 AM\"}),\n@@ -880,6 +865,10 @@ export function initialize() {\n const $popper = $(instance.popper);\n \n $popper.one(\"click\", \".open_send_later_modal\", () => {\n+ if (!compose_validate.validate()) {\n+ return;\n+ }\n+\n // Only show send later options that are possible today.\n const date = new Date();\n const hours = date.getHours();\n@@ -892,7 +881,7 @@ export function initialize() {\n possible_send_later_today = false;\n }\n \n- const formatted_send_later_time = get_selected_send_later_time();\n+ const formatted_send_later_time = get_formatted_selected_send_later_time();\n $(\"body\").append(\n render_send_later_modal({\n possible_send_later_today,\n@@ -914,7 +903,7 @@ export function initialize() {\n const current_time = new Date();\n flatpickr.show_flatpickr(\n $(\".send_later_custom\")[0],\n- update_scheduled_date_from_modal,\n+ do_schedule_message,\n new Date(current_time.getTime() + 60 * 60 * 1000),\n {minDate: new Date(current_time.getTime() + 5 * 60 * 1000)},\n );\n@@ -927,7 +916,18 @@ export function initialize() {\n (e) => {\n const send_at_time = set_compose_box_schedule(e.currentTarget);\n const not_from_flatpickr = true;\n- update_scheduled_date_from_modal(send_at_time, not_from_flatpickr);\n+ do_schedule_message(send_at_time, not_from_flatpickr);\n+ e.preventDefault();\n+ e.stopPropagation();\n+ },\n+ );\n+ $send_later_modal.one(\n+ \"click\",\n+ \".send_later_selected_send_later_time\",\n+ (e) => {\n+ const send_at_time = get_selected_send_later_time();\n+ const not_from_flatpickr = true;\n+ do_schedule_message(send_at_time, not_from_flatpickr);\n e.preventDefault();\n e.stopPropagation();\n },\n@@ -935,13 +935,6 @@ export function initialize() {\n },\n });\n });\n- $popper.one(\"click\", \"#clear_compose_schedule_state\", (e) => {\n- compose.reset_compose_scheduling_state(false);\n- // Close the popper instance when clearing scheduling:\n- instance.hide();\n- e.preventDefault();\n- e.stopPropagation();\n- });\n },\n onHidden(instance) {\n instance.destroy();\ndiff --git a/web/src/scheduled_messages.js b/web/src/scheduled_messages.js\nindex 50ffc4652ea81..bed203f0c99cb 100644\n--- a/web/src/scheduled_messages.js\n+++ b/web/src/scheduled_messages.js\n@@ -65,8 +65,7 @@ export function edit_scheduled_message(scheduled_msg_id) {\n compose.clear_compose_box();\n compose_actions.start(compose_args.type, compose_args);\n compose_ui.autosize_textarea($(\"#compose-textarea\"));\n- $(\"#compose-textarea\").attr(\"data-scheduled-message-id\", scheduled_msg_id);\n- popover_menus.show_schedule_confirm_button(scheduled_msg.formatted_send_at_time, true);\n+ popover_menus.set_selected_schedule_time(scheduled_msg.formatted_send_at_time);\n }\n \n export function send_request_to_schedule_message(scheduled_message_data, deliver_at) {\n@@ -99,7 +98,7 @@ export function send_request_to_schedule_message(scheduled_message_data, deliver\n });\n }\n \n-export function delete_scheduled_message(scheduled_msg_id) {\n+export function delete_scheduled_message(scheduled_msg_id, edit_if_delete_successs) {\n channel.del({\n url: \"/json/scheduled_messages/\" + scheduled_msg_id,\n success() {\n@@ -109,25 +108,9 @@ export function delete_scheduled_message(scheduled_msg_id) {\n `#scheduled_messages_overlay .scheduled-message-row[data-message-id=${scheduled_msg_id}]`,\n ).remove();\n }\n- if ($(\"#compose-textarea\").attr(\"data-scheduled-message-id\")) {\n- const compose_scheduled_msg_id = $(\"#compose-textarea\").attr(\n- \"data-scheduled-message-id\",\n- );\n- // If user deleted the scheduled message which is being edited in compose, we clear\n- // the scheduled message id from there which converts this editing state into a normal\n- // schedule message state. So, clicking \"Schedule\" will now create a new scheduled message.\n- if (compose_scheduled_msg_id === scheduled_msg_id) {\n- $(\"#compose-textarea\").removeAttr(\"data-scheduled-message-id\");\n- }\n+ if (edit_if_delete_successs) {\n+ edit_scheduled_message(scheduled_msg_id);\n }\n },\n });\n }\n-\n-export function delete_scheduled_message_if_sent_directly() {\n- // Delete old scheduled message if it was sent.\n- if ($(\"#compose-textarea\").attr(\"data-scheduled-message-id\")) {\n- delete_scheduled_message($(\"#compose-textarea\").attr(\"data-scheduled-message-id\"));\n- $(\"#compose-textarea\").removeAttr(\"data-scheduled-message-id\");\n- }\n-}\ndiff --git a/web/src/scheduled_messages_overlay_ui.js b/web/src/scheduled_messages_overlay_ui.js\nindex 1f666bcddc12e..dbdfb135d622f 100644\n--- a/web/src/scheduled_messages_overlay_ui.js\n+++ b/web/src/scheduled_messages_overlay_ui.js\n@@ -64,19 +64,15 @@ export function launch() {\n url: \"/json/scheduled_messages\",\n success(data) {\n hide_loading_indicator();\n- if (data.scheduled_messages.length === 0) {\n- $(\".no-overlay-messages\").show();\n- } else {\n- // Saving formatted data is helpful when user is trying to edit a scheduled message.\n- scheduled_messages.override_scheduled_messages_data(\n- format(data.scheduled_messages),\n- );\n- const rendered_list = render_scheduled_message({\n- scheduled_messages_data: scheduled_messages.scheduled_messages_data,\n- });\n- const $messages_list = $(\"#scheduled_messages_overlay .overlay-messages-list\");\n- $messages_list.append(rendered_list);\n- }\n+ // Saving formatted data is helpful when user is trying to edit a scheduled message.\n+ scheduled_messages.override_scheduled_messages_data(\n+ format(data.scheduled_messages),\n+ );\n+ const rendered_list = render_scheduled_message({\n+ scheduled_messages_data: scheduled_messages.scheduled_messages_data,\n+ });\n+ const $messages_list = $(\"#scheduled_messages_overlay .overlay-messages-list\");\n+ $messages_list.append(rendered_list);\n },\n error(xhr) {\n hide_loading_indicator();\n@@ -91,7 +87,7 @@ export function initialize() {\n .closest(\".scheduled-message-row\")\n .attr(\"data-message-id\");\n scheduled_msg_id = Number.parseInt(scheduled_msg_id, 10);\n- scheduled_messages.edit_scheduled_message(scheduled_msg_id);\n+ scheduled_messages.delete_scheduled_message(scheduled_msg_id, true);\n \n e.stopPropagation();\n e.preventDefault();\ndiff --git a/web/src/tippyjs.js b/web/src/tippyjs.js\nindex 139b8c495ccf9..f2c1bd7dfc4f7 100644\n--- a/web/src/tippyjs.js\n+++ b/web/src/tippyjs.js\n@@ -622,27 +622,6 @@ export function initialize() {\n },\n });\n \n- delegate(\"body\", {\n- target: \"#compose-schedule-confirm-button\",\n- onShow(instance) {\n- if (popover_menus.get_scheduled_messages_popover()) {\n- return false;\n- }\n-\n- const send_at_time = popover_menus.get_formatted_selected_send_later_time();\n- instance.setContent(\n- parse_html(\n- $t(\n- {defaultMessage: \"Schedule message for <br/> {send_at_time}\"},\n- {send_at_time},\n- ),\n- ),\n- );\n- return true;\n- },\n- appendTo: () => document.body,\n- });\n-\n delegate(\"body\", {\n target: \"#send_later\",\n delay: LONG_HOVER_DELAY,\ndiff --git a/web/styles/compose.css b/web/styles/compose.css\nindex f6501ab7e124f..1710eb2504107 100644\n--- a/web/styles/compose.css\n+++ b/web/styles/compose.css\n@@ -525,7 +525,6 @@ input.recipient_box {\n width: 100%;\n }\n \n-#compose-schedule-confirm-button,\n #compose-send-button {\n padding: 3px 12px;\n margin-bottom: 0;\ndiff --git a/web/styles/scheduled_messages.css b/web/styles/scheduled_messages.css\nindex 34f452375fca0..e2502e242bef7 100644\n--- a/web/styles/scheduled_messages.css\n+++ b/web/styles/scheduled_messages.css\n@@ -12,5 +12,9 @@\n \n .no-overlay-messages {\n display: none;\n+\n+ &:only-child {\n+ display: block;\n+ }\n }\n }\ndiff --git a/web/templates/compose.hbs b/web/templates/compose.hbs\nindex 342a1bb7bcced..15442d82ce080 100644\n--- a/web/templates/compose.hbs\n+++ b/web/templates/compose.hbs\n@@ -123,10 +123,6 @@\n <img class=\"loader\" alt=\"\" src=\"\" />\n <span>{{t 'Send' }}</span>\n </button>\n- <button id=\"compose-schedule-confirm-button\" class=\"button small compose-submit-button hide animated-purple-button\" tabindex=0>\n- <img class=\"loader\" alt=\"\" src=\"\" />\n- <span>{{t 'Schedule' }}</span>\n- </button>\n <button class=\"animated-purple-button message-control-button\" id=\"send_later\" tabindex=0 type=\"button\" data-tippy-content=\"{{t 'Send later' }}\">\n <i class=\"zulip-icon zulip-icon-ellipsis-v-solid\"></i>\n </button>\ndiff --git a/web/templates/scheduled_message.hbs b/web/templates/scheduled_message.hbs\nindex 22296860a9158..4b2188dcebfd4 100644\n--- a/web/templates/scheduled_message.hbs\n+++ b/web/templates/scheduled_message.hbs\n@@ -38,7 +38,7 @@\n <div class=\"overlay_message_controls\">\n <i class=\"fa fa-pencil fa-lg restore-overlay-message tippy-zulip-tooltip\" aria-hidden=\"true\" data-tooltip-template-id=\"restore-scheduled-message-tooltip-template\"></i>\n <template id=\"restore-scheduled-message-tooltip-template\">\n- {{t 'Edit or reschedule message' }}\n+ {{t 'Edit and reschedule message' }}\n </template>\n <i class=\"fa fa-trash-o fa-lg delete-overlay-message tippy-zulip-tooltip\" aria-hidden=\"true\" data-tooltip-template-id=\"delete-scheduled-message-tooltip-template\"></i>\n <template id=\"delete-scheduled-message-tooltip-template\">\ndiff --git a/web/templates/scheduled_messages_overlay.hbs b/web/templates/scheduled_messages_overlay.hbs\nindex 5a89635993104..27ab3093f617c 100644\n--- a/web/templates/scheduled_messages_overlay.hbs\n+++ b/web/templates/scheduled_messages_overlay.hbs\n@@ -8,7 +8,7 @@\n </div>\n <div class=\"removed-drafts\">\n {{#tr}}\n- Click on the pencil (<z-pencil-icon></z-pencil-icon>) icon to reschedule a message.\n+ Click on the pencil (<z-pencil-icon></z-pencil-icon>) icon to edit and reschedule a message.\n {{#*inline \"z-pencil-icon\"}}<i class=\"fa fa-pencil\"></i>{{/inline}}\n {{/tr}}\n </div>\ndiff --git a/web/templates/send_later_modal.hbs b/web/templates/send_later_modal.hbs\nindex f1e275cdb7b72..c87128a6b0076 100644\n--- a/web/templates/send_later_modal.hbs\n+++ b/web/templates/send_later_modal.hbs\n@@ -22,6 +22,11 @@\n <a id=\"{{@key}}\" class=\"send_later_tomorrow\" tabindex=\"0\">{{this.text}}</a>\n </li>\n {{/each}}\n+ {{#if formatted_send_later_time}}\n+ <li>\n+ <a class=\"send_later_selected_send_later_time\" tabindex=\"0\">{{formatted_send_later_time}}</a>\n+ </li>\n+ {{/if}}\n <li>\n <a class=\"send_later_custom\" tabindex=\"0\">{{t 'Custom time'}}</a>\n </li>\ndiff --git a/web/templates/send_later_popover.hbs b/web/templates/send_later_popover.hbs\nindex d5f4a8ea862c3..c17e603a1046a 100644\n--- a/web/templates/send_later_popover.hbs\n+++ b/web/templates/send_later_popover.hbs\n@@ -2,11 +2,6 @@\n <li>\n <a class=\"open_send_later_modal\" tabindex=\"0\">{{t \"Schedule message\" }}</a>\n </li>\n- {{#if formatted_send_later_time }}\n- <li>\n- <a id=\"clear_compose_schedule_state\" tabindex=\"0\">{{t \"Cancel scheduling\" }}</a>\n- </li>\n- {{/if}}\n <hr />\n <li>\n <a href=\"#scheduled\" tabindex=\"0\">{{t \"View scheduled messages\" }}</a>\n" }
[ { "diff_hunk": "@@ -8,28 +8,62 @@ import * as compose_ui from \"./compose_ui\";\n import {$t} from \"./i18n\";\n import * as narrow from \"./narrow\";\n import * as notifications from \"./notifications\";\n-import * as overlays from \"./overlays\";\n+import {page_params} from \"./page_params\";\n import * as people from \"./people\";\n import * as popover_menus from \"./popover_menus\";\n+import * as stream_data from \"./stream_data\";\n \n-// This is only updated when user opens the scheduled messages overlay.\n export let scheduled_messages_data = [];\n \n-export function override_scheduled_messages_data(data) {\n- scheduled_messages_data = data;\n+function sort_scheduled_messages_data() {\n+ scheduled_messages_data.sort(\n+ (msg1, msg2) => msg1.scheduled_delivery_timestamp - msg2.scheduled_delivery_timestamp,\n+ );\n }\n \n-export function edit_scheduled_message(scheduled_msg_id) {\n- const scheduled_msg = scheduled_messages_data.find(\n- (msg) => msg.scheduled_message_id === scheduled_msg_id,\n+export function add_scheduled_messages(scheduled_messages) {\n+ scheduled_messages_data.push(...scheduled_messages);\n+ sort_scheduled_messages_data();\n+}\n+\n+export function remove_scheduled_message(scheduled_message_id) {\n+ const msg_index = scheduled_messages_data.findIndex(\n+ (msg) => msg.scheduled_message_id === scheduled_message_id,\n );\n+ if (msg_index !== undefined) {\n+ scheduled_messages_data.splice(msg_index, 1);\n+ }\n+}\n \n- let compose_args;\n+export function update_scheduled_message(scheduled_message) {\n+ const msg_index = scheduled_messages_data.findIndex(\n+ (msg) => msg.scheduled_message_id === scheduled_message.scheduled_message_id,\n+ );\n+\n+ if (msg_index === undefined) {\n+ return;\n+ }\n+\n+ let needs_to_sorted = false;\n+ if (\n+ scheduled_messages_data[msg_index].scheduled_delivery_timestamp !==\n+ scheduled_message.scheduled_delivery_timestamp\n+ ) {\n+ needs_to_sorted = true;\n+ }\n+\n+ scheduled_messages_data[msg_index] = scheduled_message;\n+ if (needs_to_sorted) {", "line": null, "original_line": 56, "original_start_line": null, "path": "web/src/scheduled_messages.js", "start_line": null, "text": "@user1:\n`needs_resorting` would be a nicer variable name, but also probably we should just sort unconditionally; it's a really low value optimization, since there should never be a lot of these and they likely don't change often." }, { "diff_hunk": "@@ -103,15 +103,14 @@ export function open_scheduled_message_in_compose(scheduled_msg) {\n }\n \n export function send_request_to_schedule_message(scheduled_message_data, deliver_at) {\n- const success = function () {\n+ const success = function (data) {\n compose.clear_compose_box();\n- notifications.notify_above_composebox(\n- $t({defaultMessage: `Your message has been scheduled for {deliver_at}.`}, {deliver_at}),\n- \"scheduled_message_banner\",\n- \"/#scheduled\",\n- \"\",\n- $t({defaultMessage: \"View scheduled messages\"}),\n- );\n+ const new_row = render_succes_message_scheduled_banner({", "line": null, "original_line": 108, "original_start_line": null, "path": "web/src/scheduled_messages.js", "start_line": null, "text": "@user1:\nsuccess has a typo." }, { "diff_hunk": "@@ -56,29 +48,32 @@ export function launch() {\n browser_history.exit_overlay();\n },\n });\n- loading.make_indicator($(\"#scheduled_messages_overlay .loading-indicator\"), {\n- abs_positioned: true,\n+\n+ const rendered_list = render_scheduled_message({\n+ scheduled_messages_data: format(scheduled_messages.scheduled_messages_data),\n });\n+ const $messages_list = $(\"#scheduled_messages_overlay .overlay-messages-list\");\n+ $messages_list.append(rendered_list);\n+}\n \n- channel.get({\n- url: \"/json/scheduled_messages\",\n- success(data) {\n- hide_loading_indicator();\n- // Saving formatted data is helpful when user is trying to edit a scheduled message.\n- scheduled_messages.override_scheduled_messages_data(\n- format(data.scheduled_messages),\n- );\n- const rendered_list = render_scheduled_message({\n- scheduled_messages_data: scheduled_messages.scheduled_messages_data,\n- });\n- const $messages_list = $(\"#scheduled_messages_overlay .overlay-messages-list\");\n- $messages_list.append(rendered_list);\n- },\n- error(xhr) {\n- hide_loading_indicator();\n- blueslip.error(xhr);\n- },\n+export function rerender() {\n+ if (!overlays.scheduled_messages_open()) {\n+ return;\n+ }\n+ const rendered_list = render_scheduled_message({\n+ scheduled_messages_data: format(scheduled_messages.scheduled_messages_data),\n });\n+ const $messages_list = $(\"#scheduled_messages_overlay .overlay-messages-list\");\n+ $messages_list.find(\".scheduled-message-row\").remove();\n+ $messages_list.append(rendered_list);\n+}\n+\n+export function remove_scheduled_message_id(scheduled_msg_id) {\n+ if (overlays.scheduled_messages_open()) {\n+ $(\n+ `#scheduled_messages_overlay .scheduled-message-row[data-message-id=${scheduled_msg_id}]`,", "line": null, "original_line": 74, "original_start_line": null, "path": "web/src/scheduled_messages_overlay_ui.js", "start_line": null, "text": "@user1:\nThis can be a follow-up, but we should definitely call it `data-scheduled-message-id`, to avoid confusion -- they are not message IDs." } ]
102c68c211d3e565deff4bb04015234df7f062c6
diff --git a/web/src/compose.js b/web/src/compose.js index 577ce1e58307d..434cb470dfe0d 100644 --- a/web/src/compose.js +++ b/web/src/compose.js @@ -188,7 +188,7 @@ export function clear_compose_box() { compose_banner.clear_errors(); compose_banner.clear_warnings(); compose_ui.hide_compose_spinner(); - reset_compose_scheduling_state(); + popover_menus.reset_selected_schedule_timestamp(); } export function send_message_success(local_id, message_id, locally_echoed) { @@ -269,7 +269,6 @@ export function send_message(request = create_message_object()) { } transmit.send_message(request, success, error); - scheduled_messages.delete_scheduled_message_if_sent_directly(); server_events.assert_get_events_running( "Restarting get_events because it was not running during send", ); @@ -298,7 +297,7 @@ export function enter_with_preview_open(ctrl_pressed = false) { // Common entrypoint for asking the server to send the message // currently drafted in the compose box, including for scheduled // messages. -export function finish() { +export function finish(from_do_schedule_message = false) { if (compose_ui.compose_spinner_visible) { // Avoid sending a message twice in parallel in races where // the user clicks the `Send` button very quickly twice or @@ -330,7 +329,7 @@ export function finish() { return false; } - if (popover_menus.is_time_selected_for_schedule()) { + if (from_do_schedule_message) { schedule_message_to_custom_date(); } else { send_message(); @@ -789,22 +788,11 @@ export function initialize() { } } -export function reset_compose_scheduling_state(reset_edit_state = true) { - $("#compose-textarea").prop("disabled", false); - $("#compose-schedule-confirm-button").hide(); - popover_menus.reset_selected_schedule_time(); - $("#compose-send-button").show(); - if (reset_edit_state) { - $("#compose-textarea").removeAttr("data-scheduled-message-id"); - } -} - function schedule_message_to_custom_date() { const compose_message_object = create_message_object(); const deliver_at = popover_menus.get_formatted_selected_send_later_time(); - const send_later_time = popover_menus.get_selected_send_later_time(); - const scheduled_delivery_timestamp = Math.floor(Date.parse(send_later_time) / 1000); + const scheduled_delivery_timestamp = popover_menus.get_selected_send_later_timestamp(); const message_type = compose_message_object.type; let req_type; @@ -823,12 +811,5 @@ function schedule_message_to_custom_date() { scheduled_delivery_timestamp, }; - // If this is an edit request `scheduled_message_id` will be defined. - if ($("#compose-textarea").attr("data-scheduled-message-id")) { - scheduled_message_data.scheduled_message_id = $("#compose-textarea").attr( - "data-scheduled-message-id", - ); - } - scheduled_messages.send_request_to_schedule_message(scheduled_message_data, deliver_at); } diff --git a/web/src/compose_actions.js b/web/src/compose_actions.js index e8a5c83501504..aacff10141bb2 100644 --- a/web/src/compose_actions.js +++ b/web/src/compose_actions.js @@ -78,7 +78,6 @@ function clear_box() { compose_ui.autosize_textarea($("#compose-textarea")); compose_banner.clear_errors(); compose_banner.clear_warnings(); - compose.reset_compose_scheduling_state(); } export function autosize_message_content() { diff --git a/web/src/compose_banner.ts b/web/src/compose_banner.ts index a2b5ac0298294..a0e8afc8fbd1e 100644 --- a/web/src/compose_banner.ts +++ b/web/src/compose_banner.ts @@ -17,7 +17,7 @@ export const ERROR = "error"; const MESSAGE_SENT_CLASSNAMES = { sent_scroll_to_view: "sent_scroll_to_view", narrow_to_recipient: "narrow_to_recipient", - scheduled_message_banner: "scheduled_message_banner", + message_scheduled_success_compose_banner: "message_scheduled_success_compose_banner", }; // Technically, unmute_topic_notification is a message sent banner, but // it has distinct behavior / look - it has an associated action button, diff --git a/web/src/compose_ui.js b/web/src/compose_ui.js index cbfac1738f005..e4bfdfef2e5ea 100644 --- a/web/src/compose_ui.js +++ b/web/src/compose_ui.js @@ -535,10 +535,3 @@ export function get_compose_click_target(e) { } return e.target; } - -export function get_submit_button() { - if (popover_menus.is_time_selected_for_schedule()) { - return $("#compose-schedule-confirm-button"); - } - return $("#compose-send-button"); -} diff --git a/web/src/composebox_typeahead.js b/web/src/composebox_typeahead.js index 8fc65a2fdf68c..0137b0979db27 100644 --- a/web/src/composebox_typeahead.js +++ b/web/src/composebox_typeahead.js @@ -221,7 +221,7 @@ function handle_keydown(e) { // could result in focus being moved to the "Send // button" after sending the message, preventing // typing a next message! - compose_ui.get_submit_button().trigger("focus"); + $("#compose-send-button").trigger("focus"); e.preventDefault(); e.stopPropagation(); @@ -232,7 +232,7 @@ function handle_keydown(e) { e.preventDefault(); if ( compose_validate.validate_message_length() && - !compose_ui.get_submit_button().prop("disabled") + !$("#compose-send-button").prop("disabled") ) { compose.finish(); } diff --git a/web/src/popover_menus.js b/web/src/popover_menus.js index a6899cfab6208..5b003a7ec6794 100644 --- a/web/src/popover_menus.js +++ b/web/src/popover_menus.js @@ -25,6 +25,7 @@ import * as channel from "./channel"; import * as common from "./common"; import * as compose from "./compose"; import * as compose_actions from "./compose_actions"; +import * as compose_validate from "./compose_validate"; import * as condense from "./condense"; import * as confirm_dialog from "./confirm_dialog"; import * as drafts from "./drafts"; @@ -53,7 +54,7 @@ import {user_settings} from "./user_settings"; import * as user_topics from "./user_topics"; let message_actions_popover_keyboard_toggle = false; -let selected_send_later_time; +let selected_send_later_timestamp; const popover_instances = { compose_control_buttons: null, @@ -80,26 +81,26 @@ export function get_topic_menu_popover() { return popover_instances.topics_menu; } -export function is_time_selected_for_schedule() { - return selected_send_later_time !== undefined; -} - -export function get_selected_send_later_time() { - if (!selected_send_later_time) { +export function get_selected_send_later_timestamp() { + if (!selected_send_later_timestamp) { return undefined; } - return selected_send_later_time; + return selected_send_later_timestamp; } export function get_formatted_selected_send_later_time() { - if (!selected_send_later_time) { + if (!selected_send_later_timestamp) { return undefined; } - return timerender.get_full_datetime(new Date(selected_send_later_time), "time"); + return timerender.get_full_datetime(new Date(selected_send_later_timestamp * 1000), "time"); +} + +export function set_selected_schedule_timestamp(timestamp) { + selected_send_later_timestamp = timestamp; } -export function reset_selected_schedule_time() { - selected_send_later_time = undefined; +export function reset_selected_schedule_timestamp() { + selected_send_later_timestamp = undefined; } export function get_scheduled_messages_popover() { @@ -229,28 +230,15 @@ export function toggle_message_actions_menu(message) { return true; } -export function show_schedule_confirm_button(send_at_time, not_from_flatpickr = false) { - // If no time was selected by the user, we don't show the schedule button. - if (!send_at_time) { - return; - } +export function do_schedule_message(send_at_time) { + overlays.close_active_modal(); - // flatpickr.show_flatpickr doesn't pass any value for not_from_flatpickr, - // making it false by default and we pass it as true in other cases. - // This is used to determine if flatpickr was used to select time for the - // message to be sent. - if (!not_from_flatpickr) { - send_at_time = format(new Date(send_at_time), "MMM d yyyy h:mm a"); + if (!Number.isInteger(send_at_time)) { + // Convert to timestamp if this is not a timestamp. + send_at_time = Math.floor(Date.parse(send_at_time) / 1000); } - - selected_send_later_time = send_at_time; - $("#compose-schedule-confirm-button").show(); - $("#compose-send-button").hide(); -} - -function update_scheduled_date_from_modal(send_at_time, not_from_flatpickr = false) { - overlays.close_active_modal(); - show_schedule_confirm_button(send_at_time, not_from_flatpickr); + selected_send_later_timestamp = send_at_time; + compose.finish(true); } export function initialize() { @@ -799,13 +787,6 @@ export function initialize() { }, }); - $("body").on("click", "#compose-schedule-confirm-button", (e) => { - compose.finish(); - - e.preventDefault(); - e.stopPropagation(); - }); - const send_later_today = { today_nine_am: { text: $t({defaultMessage: "Today at 9:00 AM"}), @@ -878,8 +859,15 @@ export function initialize() { }, onMount(instance) { const $popper = $(instance.popper); - + $popper.one("click", ".send_later_selected_send_later_time", () => { + const send_at_timestamp = get_selected_send_later_timestamp(); + do_schedule_message(send_at_timestamp); + }); $popper.one("click", ".open_send_later_modal", () => { + if (!compose_validate.validate()) { + return; + } + // Only show send later options that are possible today. const date = new Date(); const hours = date.getHours(); @@ -892,13 +880,11 @@ export function initialize() { possible_send_later_today = false; } - const formatted_send_later_time = get_selected_send_later_time(); $("body").append( render_send_later_modal({ possible_send_later_today, send_later_tomorrow, send_later_custom, - formatted_send_later_time, }), ); overlays.open_modal("send_later_modal", { @@ -914,7 +900,7 @@ export function initialize() { const current_time = new Date(); flatpickr.show_flatpickr( $(".send_later_custom")[0], - update_scheduled_date_from_modal, + do_schedule_message, new Date(current_time.getTime() + 60 * 60 * 1000), {minDate: new Date(current_time.getTime() + 5 * 60 * 1000)}, ); @@ -926,8 +912,7 @@ export function initialize() { ".send_later_today, .send_later_tomorrow", (e) => { const send_at_time = set_compose_box_schedule(e.currentTarget); - const not_from_flatpickr = true; - update_scheduled_date_from_modal(send_at_time, not_from_flatpickr); + do_schedule_message(send_at_time); e.preventDefault(); e.stopPropagation(); }, @@ -935,13 +920,6 @@ export function initialize() { }, }); }); - $popper.one("click", "#clear_compose_schedule_state", (e) => { - compose.reset_compose_scheduling_state(false); - // Close the popper instance when clearing scheduling: - instance.hide(); - e.preventDefault(); - e.stopPropagation(); - }); }, onHidden(instance) { instance.destroy(); diff --git a/web/src/scheduled_messages.js b/web/src/scheduled_messages.js index 50ffc4652ea81..417f56bed4494 100644 --- a/web/src/scheduled_messages.js +++ b/web/src/scheduled_messages.js @@ -1,35 +1,59 @@ import $ from "jquery"; +import render_success_message_scheduled_banner from "../templates/compose_banner/success_message_scheduled_banner.hbs"; + import * as channel from "./channel"; import * as compose from "./compose"; import * as compose_actions from "./compose_actions"; import * as compose_banner from "./compose_banner"; import * as compose_ui from "./compose_ui"; -import {$t} from "./i18n"; import * as narrow from "./narrow"; -import * as notifications from "./notifications"; -import * as overlays from "./overlays"; +import {page_params} from "./page_params"; import * as people from "./people"; import * as popover_menus from "./popover_menus"; +import * as stream_data from "./stream_data"; -// This is only updated when user opens the scheduled messages overlay. export let scheduled_messages_data = []; -export function override_scheduled_messages_data(data) { - scheduled_messages_data = data; +function sort_scheduled_messages_data() { + scheduled_messages_data.sort( + (msg1, msg2) => msg1.scheduled_delivery_timestamp - msg2.scheduled_delivery_timestamp, + ); } -export function edit_scheduled_message(scheduled_msg_id) { - const scheduled_msg = scheduled_messages_data.find( - (msg) => msg.scheduled_message_id === scheduled_msg_id, +export function add_scheduled_messages(scheduled_messages) { + scheduled_messages_data.push(...scheduled_messages); + sort_scheduled_messages_data(); +} + +export function remove_scheduled_message(scheduled_message_id) { + const msg_index = scheduled_messages_data.findIndex( + (msg) => msg.scheduled_message_id === scheduled_message_id, + ); + if (msg_index !== undefined) { + scheduled_messages_data.splice(msg_index, 1); + } +} + +export function update_scheduled_message(scheduled_message) { + const msg_index = scheduled_messages_data.findIndex( + (msg) => msg.scheduled_message_id === scheduled_message.scheduled_message_id, ); - let compose_args; + if (msg_index === undefined) { + return; + } + scheduled_messages_data[msg_index] = scheduled_message; + sort_scheduled_messages_data(); +} + +export function open_scheduled_message_in_compose(scheduled_msg) { + let compose_args; if (scheduled_msg.type === "stream") { compose_args = { type: "stream", - stream: scheduled_msg.stream_name, + stream: stream_data.maybe_get_stream_name(scheduled_msg.to), topic: scheduled_msg.topic, content: scheduled_msg.content, }; @@ -61,24 +85,22 @@ export function edit_scheduled_message(scheduled_msg_id) { }); } - overlays.close_overlay("scheduled"); compose.clear_compose_box(); + compose_banner.clear_message_sent_banners(false); compose_actions.start(compose_args.type, compose_args); compose_ui.autosize_textarea($("#compose-textarea")); - $("#compose-textarea").attr("data-scheduled-message-id", scheduled_msg_id); - popover_menus.show_schedule_confirm_button(scheduled_msg.formatted_send_at_time, true); + popover_menus.set_selected_schedule_timestamp(scheduled_msg.scheduled_delivery_timestamp); } export function send_request_to_schedule_message(scheduled_message_data, deliver_at) { - const success = function () { + const success = function (data) { compose.clear_compose_box(); - notifications.notify_above_composebox( - $t({defaultMessage: `Your message has been scheduled for {deliver_at}.`}, {deliver_at}), - "scheduled_message_banner", - "/#scheduled", - "", - $t({defaultMessage: "View scheduled messages"}), - ); + const new_row = render_success_message_scheduled_banner({ + scheduled_message_id: data.scheduled_message_id, + deliver_at, + }); + compose_banner.clear_message_sent_banners(); + compose_banner.append_compose_banner_to_banner_list(new_row); }; const error = function (xhr) { @@ -99,35 +121,38 @@ export function send_request_to_schedule_message(scheduled_message_data, deliver }); } -export function delete_scheduled_message(scheduled_msg_id) { +export function edit_scheduled_message(scheduled_message_id) { + const scheduled_msg = scheduled_messages_data.find( + (msg) => msg.scheduled_message_id === scheduled_message_id, + ); + delete_scheduled_message(scheduled_message_id, () => + open_scheduled_message_in_compose(scheduled_msg), + ); +} + +export function delete_scheduled_message(scheduled_msg_id, success = () => {}) { channel.del({ url: "/json/scheduled_messages/" + scheduled_msg_id, - success() { - // TODO: Do this via events received from the server in server_events_dispatch. - if (overlays.scheduled_messages_open()) { - $( - `#scheduled_messages_overlay .scheduled-message-row[data-message-id=${scheduled_msg_id}]`, - ).remove(); - } - if ($("#compose-textarea").attr("data-scheduled-message-id")) { - const compose_scheduled_msg_id = $("#compose-textarea").attr( - "data-scheduled-message-id", - ); - // If user deleted the scheduled message which is being edited in compose, we clear - // the scheduled message id from there which converts this editing state into a normal - // schedule message state. So, clicking "Schedule" will now create a new scheduled message. - if (compose_scheduled_msg_id === scheduled_msg_id) { - $("#compose-textarea").removeAttr("data-scheduled-message-id"); - } - } - }, + success, }); } -export function delete_scheduled_message_if_sent_directly() { - // Delete old scheduled message if it was sent. - if ($("#compose-textarea").attr("data-scheduled-message-id")) { - delete_scheduled_message($("#compose-textarea").attr("data-scheduled-message-id")); - $("#compose-textarea").removeAttr("data-scheduled-message-id"); +export function initialize() { + if (scheduled_messages_data.length === 0) { + scheduled_messages_data = page_params.scheduled_messages; + } else { + add_scheduled_messages(page_params.scheduled_messages); } + + $("body").on("click", ".undo_scheduled_message", (e) => { + const scheduled_message_id = Number.parseInt( + $(e.target) + .parents(".message_scheduled_success_compose_banner") + .attr("data-scheduled-message-id"), + 10, + ); + edit_scheduled_message(scheduled_message_id); + e.preventDefault(); + e.stopPropagation(); + }); } diff --git a/web/src/scheduled_messages_overlay_ui.js b/web/src/scheduled_messages_overlay_ui.js index 1f666bcddc12e..b71280249fc3c 100644 --- a/web/src/scheduled_messages_overlay_ui.js +++ b/web/src/scheduled_messages_overlay_ui.js @@ -1,13 +1,9 @@ -import * as date_fns from "date-fns"; import $ from "jquery"; import render_scheduled_message from "../templates/scheduled_message.hbs"; import render_scheduled_messages_overlay from "../templates/scheduled_messages_overlay.hbs"; -import * as blueslip from "./blueslip"; import * as browser_history from "./browser_history"; -import * as channel from "./channel"; -import * as loading from "./loading"; import * as overlays from "./overlays"; import * as people from "./people"; import * as scheduled_messages from "./scheduled_messages"; @@ -15,11 +11,6 @@ import * as stream_color from "./stream_color"; import * as stream_data from "./stream_data"; import * as timerender from "./timerender"; -function hide_loading_indicator() { - loading.destroy_indicator($("#scheduled_messages_overlay .loading-indicator")); - $(".scheduled-messages-loading").hide(); -} - function format(scheduled_messages) { const formatted_msgs = []; for (const msg of scheduled_messages) { @@ -39,8 +30,7 @@ function format(scheduled_messages) { msg_render_context.recipients = people.get_recipients(msg.to.join(",")); } const time = new Date(msg.scheduled_delivery_timestamp * 1000); - msg_render_context.full_date_time = timerender.get_full_datetime(time); - msg_render_context.formatted_send_at_time = date_fns.format(time, "MMM d yyyy h:mm a"); + msg_render_context.formatted_send_at_time = timerender.get_full_datetime(time, "time"); formatted_msgs.push(msg_render_context); } return formatted_msgs; @@ -56,43 +46,42 @@ export function launch() { browser_history.exit_overlay(); }, }); - loading.make_indicator($("#scheduled_messages_overlay .loading-indicator"), { - abs_positioned: true, + + const rendered_list = render_scheduled_message({ + scheduled_messages_data: format(scheduled_messages.scheduled_messages_data), }); + const $messages_list = $("#scheduled_messages_overlay .overlay-messages-list"); + $messages_list.append(rendered_list); +} - channel.get({ - url: "/json/scheduled_messages", - success(data) { - hide_loading_indicator(); - if (data.scheduled_messages.length === 0) { - $(".no-overlay-messages").show(); - } else { - // Saving formatted data is helpful when user is trying to edit a scheduled message. - scheduled_messages.override_scheduled_messages_data( - format(data.scheduled_messages), - ); - const rendered_list = render_scheduled_message({ - scheduled_messages_data: scheduled_messages.scheduled_messages_data, - }); - const $messages_list = $("#scheduled_messages_overlay .overlay-messages-list"); - $messages_list.append(rendered_list); - } - }, - error(xhr) { - hide_loading_indicator(); - blueslip.error(xhr); - }, +export function rerender() { + if (!overlays.scheduled_messages_open()) { + return; + } + const rendered_list = render_scheduled_message({ + scheduled_messages_data: format(scheduled_messages.scheduled_messages_data), }); + const $messages_list = $("#scheduled_messages_overlay .overlay-messages-list"); + $messages_list.find(".scheduled-message-row").remove(); + $messages_list.append(rendered_list); +} + +export function remove_scheduled_message_id(scheduled_msg_id) { + if (overlays.scheduled_messages_open()) { + $( + `#scheduled_messages_overlay .scheduled-message-row[data-scheduled-message-id=${scheduled_msg_id}]`, + ).remove(); + } } export function initialize() { $("body").on("click", ".scheduled-message-row .restore-overlay-message", (e) => { let scheduled_msg_id = $(e.currentTarget) .closest(".scheduled-message-row") - .attr("data-message-id"); + .attr("data-scheduled-message-id"); scheduled_msg_id = Number.parseInt(scheduled_msg_id, 10); scheduled_messages.edit_scheduled_message(scheduled_msg_id); - + overlays.close_overlay("scheduled"); e.stopPropagation(); e.preventDefault(); }); @@ -100,7 +89,7 @@ export function initialize() { $("body").on("click", ".scheduled-message-row .delete-overlay-message", (e) => { const scheduled_msg_id = $(e.currentTarget) .closest(".scheduled-message-row") - .attr("data-message-id"); + .attr("data-scheduled-message-id"); scheduled_messages.delete_scheduled_message(scheduled_msg_id); e.stopPropagation(); diff --git a/web/src/server_events_dispatch.js b/web/src/server_events_dispatch.js index 9a1b71d88f527..139667af712e5 100644 --- a/web/src/server_events_dispatch.js +++ b/web/src/server_events_dispatch.js @@ -39,6 +39,8 @@ import * as realm_logo from "./realm_logo"; import * as realm_playground from "./realm_playground"; import {realm_user_settings_defaults} from "./realm_user_settings_defaults"; import * as reload from "./reload"; +import * as scheduled_messages from "./scheduled_messages"; +import * as scheduled_messages_overlay_ui from "./scheduled_messages_overlay_ui"; import * as scroll_bar from "./scroll_bar"; import * as settings_account from "./settings_account"; import * as settings_bots from "./settings_bots"; @@ -80,6 +82,29 @@ import * as user_status from "./user_status"; export function dispatch_normal_event(event) { const noop = function () {}; switch (event.type) { + case "scheduled_messages": + switch (event.op) { + case "add": { + scheduled_messages.add_scheduled_messages(event.scheduled_messages); + scheduled_messages_overlay_ui.rerender(); + break; + } + case "remove": { + scheduled_messages.remove_scheduled_message(event.scheduled_message_id); + scheduled_messages_overlay_ui.remove_scheduled_message_id( + event.scheduled_message_id, + ); + break; + } + case "update": { + scheduled_messages.update_scheduled_message(event.scheduled_message); + scheduled_messages_overlay_ui.rerender(); + break; + } + // No default + } + break; + case "alert_words": alert_words.set_words(event.alert_words); alert_words_ui.rerender_alert_words_ui(); diff --git a/web/src/tippyjs.js b/web/src/tippyjs.js index 139b8c495ccf9..f2c1bd7dfc4f7 100644 --- a/web/src/tippyjs.js +++ b/web/src/tippyjs.js @@ -622,27 +622,6 @@ export function initialize() { }, }); - delegate("body", { - target: "#compose-schedule-confirm-button", - onShow(instance) { - if (popover_menus.get_scheduled_messages_popover()) { - return false; - } - - const send_at_time = popover_menus.get_formatted_selected_send_later_time(); - instance.setContent( - parse_html( - $t( - {defaultMessage: "Schedule message for <br/> {send_at_time}"}, - {send_at_time}, - ), - ), - ); - return true; - }, - appendTo: () => document.body, - }); - delegate("body", { target: "#send_later", delay: LONG_HOVER_DELAY, diff --git a/web/src/ui_init.js b/web/src/ui_init.js index 65228561146c7..f68c46882da69 100644 --- a/web/src/ui_init.js +++ b/web/src/ui_init.js @@ -74,6 +74,7 @@ import * as reload from "./reload"; import * as rendered_markdown from "./rendered_markdown"; import * as resize from "./resize"; import * as rows from "./rows"; +import * as scheduled_messages from "./scheduled_messages"; import * as scheduled_messages_overlay_ui from "./scheduled_messages_overlay_ui"; import * as scroll_bar from "./scroll_bar"; import * as scroll_util from "./scroll_util"; @@ -588,6 +589,8 @@ export function initialize_everything() { i18n.initialize(i18n_params); tippyjs.initialize(); + // This populates data for scheduled messages. + scheduled_messages.initialize(); popovers.initialize(); popover_menus.initialize(); diff --git a/web/styles/compose.css b/web/styles/compose.css index f6501ab7e124f..0ddcdbbee5347 100644 --- a/web/styles/compose.css +++ b/web/styles/compose.css @@ -325,6 +325,19 @@ color: hsl(147deg 57% 25% / 75%); } } + + .compose_banner_action_button { + background-color: hsl(147deg 57% 25% / 10%); + color: inherit; + + &:hover { + background-color: hsl(147deg 57% 25% / 12%); + } + + &:active { + background-color: hsl(147deg 57% 25% / 15%); + } + } } /* warning and warning-style classes have the same CSS; this is since @@ -413,6 +426,32 @@ } } +.compose_banner.success.message_scheduled_success_compose_banner { + & a.open_scheduled_message_overlay { + line-height: normal; + display: inline-block; + box-sizing: border-box; + + &:hover, + &:focus { + text-decoration: none; + } + } + + .undo_scheduled_message { + color: hsl(38deg 44% 27%); + background-color: hsl(46.29deg 46.67% 85.29%); + + &:hover { + background-color: hsl(47.65deg 41.46% 83.92%); + } + + &:active { + background-color: hsl(47.65deg 37.78% 82.35%); + } + } +} + .upload_banner { overflow: hidden; @@ -525,7 +564,6 @@ input.recipient_box { width: 100%; } -#compose-schedule-confirm-button, #compose-send-button { padding: 3px 12px; margin-bottom: 0; diff --git a/web/styles/dark_theme.css b/web/styles/dark_theme.css index 4af4b2e78cb3c..adf2258575979 100644 --- a/web/styles/dark_theme.css +++ b/web/styles/dark_theme.css @@ -225,6 +225,19 @@ color: hsl(147deg 51% 55% / 75%); } } + + .compose_banner_action_button { + background-color: hsl(147deg 51% 55% / 10%); + color: inherit; + + &:hover { + background-color: hsl(147deg 51% 55% / 15%); + } + + &:active { + background-color: hsl(147deg 51% 55% / 20%); + } + } } &.warning, @@ -310,6 +323,21 @@ } } + .compose_banner.success.message_scheduled_success_compose_banner { + .undo_scheduled_message { + color: hsl(50deg 45% 61%); + background-color: hsl(54deg 75% 15.69%); + + &:hover { + background-color: hsl(52.13deg 64.21% 18.63%); + } + + &:active { + background-color: hsl(51.29deg 57.41% 21.18%); + } + } + } + .upload_banner { .moving_bar { background: hsl(204deg 63% 18%); diff --git a/web/styles/scheduled_messages.css b/web/styles/scheduled_messages.css index 34f452375fca0..e2502e242bef7 100644 --- a/web/styles/scheduled_messages.css +++ b/web/styles/scheduled_messages.css @@ -12,5 +12,9 @@ .no-overlay-messages { display: none; + + &:only-child { + display: block; + } } } diff --git a/web/templates/compose.hbs b/web/templates/compose.hbs index 342a1bb7bcced..15442d82ce080 100644 --- a/web/templates/compose.hbs +++ b/web/templates/compose.hbs @@ -123,10 +123,6 @@ <img class="loader" alt="" src="" /> <span>{{t 'Send' }}</span> </button> - <button id="compose-schedule-confirm-button" class="button small compose-submit-button hide animated-purple-button" tabindex=0> - <img class="loader" alt="" src="" /> - <span>{{t 'Schedule' }}</span> - </button> <button class="animated-purple-button message-control-button" id="send_later" tabindex=0 type="button" data-tippy-content="{{t 'Send later' }}"> <i class="zulip-icon zulip-icon-ellipsis-v-solid"></i> </button> diff --git a/web/templates/compose_banner/success_message_scheduled_banner.hbs b/web/templates/compose_banner/success_message_scheduled_banner.hbs new file mode 100644 index 0000000000000..b92f04bd83cfd --- /dev/null +++ b/web/templates/compose_banner/success_message_scheduled_banner.hbs @@ -0,0 +1,8 @@ +<div + class="compose_banner success message_scheduled_success_compose_banner" + data-scheduled-message-id="{{scheduled_message_id}}"> + <p class="banner_content">{{t 'Your message has been scheduled for {deliver_at}.' }}</p> + <a href="#scheduled" class="compose_banner_action_button open_scheduled_message_overlay">{{t "View scheduled messages" }}</a> + <button class="compose_banner_action_button undo_scheduled_message" >{{t "Undo"}}</button> + <a role="button" class="zulip-icon zulip-icon-close compose_banner_close_button"></a> +</div> diff --git a/web/templates/scheduled_message.hbs b/web/templates/scheduled_message.hbs index 22296860a9158..df54028278c1f 100644 --- a/web/templates/scheduled_message.hbs +++ b/web/templates/scheduled_message.hbs @@ -1,5 +1,5 @@ {{#each scheduled_messages_data}} - <div class="scheduled-message-row overlay-message-row" data-message-id="{{scheduled_message_id}}"> + <div class="scheduled-message-row overlay-message-row" data-scheduled-message-id="{{scheduled_message_id}}"> <div class="overlay-message-info-box" tabindex="0"> {{#if is_stream}} <div class="message_header message_header_stream"> @@ -38,7 +38,7 @@ <div class="overlay_message_controls"> <i class="fa fa-pencil fa-lg restore-overlay-message tippy-zulip-tooltip" aria-hidden="true" data-tooltip-template-id="restore-scheduled-message-tooltip-template"></i> <template id="restore-scheduled-message-tooltip-template"> - {{t 'Edit or reschedule message' }} + {{t 'Edit and reschedule message' }} </template> <i class="fa fa-trash-o fa-lg delete-overlay-message tippy-zulip-tooltip" aria-hidden="true" data-tooltip-template-id="delete-scheduled-message-tooltip-template"></i> <template id="delete-scheduled-message-tooltip-template"> diff --git a/web/templates/scheduled_messages_overlay.hbs b/web/templates/scheduled_messages_overlay.hbs index 5a89635993104..d08c53034715a 100644 --- a/web/templates/scheduled_messages_overlay.hbs +++ b/web/templates/scheduled_messages_overlay.hbs @@ -8,20 +8,11 @@ </div> <div class="removed-drafts"> {{#tr}} - Click on the pencil (<z-pencil-icon></z-pencil-icon>) icon to reschedule a message. + Click on the pencil (<z-pencil-icon></z-pencil-icon>) icon to edit and reschedule a message. {{#*inline "z-pencil-icon"}}<i class="fa fa-pencil"></i>{{/inline}} {{/tr}} </div> </div> - <div class="scheduled-messages-loading"> - <div class="scheduled-messages-loading-logo"> - <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 773.12 773.12"> - <circle cx="386.56" cy="386.56" r="386.56"></circle> - <path d="M566.66 527.25c0 33.03-24.23 60.05-53.84 60.05H260.29c-29.61 0-53.84-27.02-53.84-60.05 0-20.22 9.09-38.2 22.93-49.09l134.37-120c2.5-2.14 5.74 1.31 3.94 4.19l-49.29 98.69c-1.38 2.76.41 6.16 3.25 6.16h191.18c29.61 0 53.83 27.03 53.83 60.05zm0-281.39c0 20.22-9.09 38.2-22.93 49.09l-134.37 120c-2.5 2.14-5.74-1.31-3.94-4.19l49.29-98.69c1.38-2.76-.41-6.16-3.25-6.16H260.29c-29.61 0-53.84-27.02-53.84-60.05s24.23-60.05 53.84-60.05h252.54c29.61 0 53.83 27.02 53.83 60.05z"></path> - </svg> - </div> - <div class="loading-indicator"></div> - </div> <div class="overlay-messages-list"> <div class="no-overlay-messages"> {{t 'No scheduled messages.'}} diff --git a/web/templates/send_later_popover.hbs b/web/templates/send_later_popover.hbs index d5f4a8ea862c3..64747e83c088a 100644 --- a/web/templates/send_later_popover.hbs +++ b/web/templates/send_later_popover.hbs @@ -2,10 +2,10 @@ <li> <a class="open_send_later_modal" tabindex="0">{{t "Schedule message" }}</a> </li> - {{#if formatted_send_later_time }} - <li> - <a id="clear_compose_schedule_state" tabindex="0">{{t "Cancel scheduling" }}</a> - </li> + {{#if formatted_send_later_time}} + <li> + <a class="send_later_selected_send_later_time" tabindex="0">{{t 'Schedule for {formatted_send_later_time}' }}</a> + </li> {{/if}} <hr /> <li> diff --git a/web/tests/dispatch.test.js b/web/tests/dispatch.test.js index 2366ce5c549a3..427cd30046951 100644 --- a/web/tests/dispatch.test.js +++ b/web/tests/dispatch.test.js @@ -40,6 +40,8 @@ const realm_icon = mock_esm("../src/realm_icon"); const realm_logo = mock_esm("../src/realm_logo"); const realm_playground = mock_esm("../src/realm_playground"); const reload = mock_esm("../src/reload"); +const scheduled_messages = mock_esm("../src/scheduled_messages"); +const scheduled_messages_overlay_ui = mock_esm("../src/scheduled_messages_overlay_ui"); const scroll_bar = mock_esm("../src/scroll_bar"); const settings_account = mock_esm("../src/settings_account"); const settings_bots = mock_esm("../src/settings_bots"); @@ -371,6 +373,38 @@ run_test("reaction", ({override}) => { } }); +run_test("scheduled_messages", ({override}) => { + override(scheduled_messages_overlay_ui, "rerender", noop); + override(scheduled_messages_overlay_ui, "remove_scheduled_message_id", noop); + let event = event_fixtures.scheduled_messages__add; + { + const stub = make_stub(); + override(scheduled_messages, "add_scheduled_messages", stub.f); + dispatch(event); + assert.equal(stub.num_calls, 1); + const args = stub.get_args("scheduled_messages"); + assert_same(args.scheduled_messages, event.scheduled_messages); + } + event = event_fixtures.scheduled_messages__update; + { + const stub = make_stub(); + override(scheduled_messages, "update_scheduled_message", stub.f); + dispatch(event); + assert.equal(stub.num_calls, 1); + const args = stub.get_args("scheduled_message"); + assert_same(args.scheduled_message, event.scheduled_message); + } + event = event_fixtures.scheduled_messages__remove; + { + const stub = make_stub(); + override(scheduled_messages, "remove_scheduled_message", stub.f); + dispatch(event); + assert.equal(stub.num_calls, 1); + const args = stub.get_args("scheduled_message_id"); + assert_same(args.scheduled_message_id, event.scheduled_message_id); + } +}); + run_test("realm settings", ({override}) => { page_params.is_admin = true; diff --git a/web/tests/lib/events.js b/web/tests/lib/events.js index b05e6fdda5d1e..99b936b2f2c30 100644 --- a/web/tests/lib/events.js +++ b/web/tests/lib/events.js @@ -613,6 +613,40 @@ exports.fixtures = { immediate: true, }, + scheduled_messages__add: { + type: "scheduled_messages", + op: "add", + scheduled_messages: [ + { + scheduled_message_id: 17, + type: "private", + to: [6], + content: "Hello there!", + rendered_content: "<p>Hello there!</p>", + scheduled_delivery_timestamp: 1681662420, + }, + ], + }, + + scheduled_messages__remove: { + type: "scheduled_messages", + op: "remove", + scheduled_message_id: 17, + }, + + scheduled_messages__update: { + type: "scheduled_messages", + op: "update", + scheduled_message: { + scheduled_message_id: 17, + type: "private", + to: [6], + content: "Hello there!", + rendered_content: "<p>Hello there!</p>", + scheduled_delivery_timestamp: 1681662420, + }, + }, + stream__create: { type: "stream", op: "create",
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-4204@4ad7909
streamlit/streamlit
Python
4,204
Fix issue with hidden balloons (approach 2)
## ๐Ÿ“š Context The [`Block.tsx` refactoring](https://github.com/streamlit/streamlit/pull/4053) applies a `display: none` to the element containers of `empty` and `balloons` elements. The reason is to prevent the `gap` of the parent flexbox. However, the `balloon` element is never shown on the frontend due to `display: none`. This PR applies negative bottom margin to balloons in the same size of the gap to prevent the gap. With this workaround, we can render the `balloons` element. This PR also removes the `isHidden` flag, since it is not anymore needed as abstraction since `empty` and `balloons` are now treated differently. - What kind of change does this PR introduce? - [x] Bugfix - [ ] Feature - [ ] Refactoring - [ ] Other, please describe: ## ๐Ÿงช Testing Done - [ ] Screenshots included - [ ] Added/Updated unit tests - [ ] Added/Updated e2e tests --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2021-12-21T21:55:01Z
st.balloons() is not working in streamlit 1.3.0 ### Summary st.balloons() was working in 1.2.0 version, but not in 1.3.0. (The st.success text is executing all right on both versions) Code snippet: ``` with st.spinner("Analyzing..."): pass st.success("Done!") st.balloons() ``` ### Is this a regression? That is, did this use to work the way you expected in the past? yes ### Debug info - Streamlit version: 1.3.0 - Python version: 3.9
Thanks for reporting this @RohanKaran! This is known, and we have two proposed fixes out (#4199 and #4204) and are deciding between the two. We'll have a fix out ASAP.
[ { "body": "### Summary\r\n\r\nst.balloons() was working in 1.2.0 version, but not in 1.3.0.\r\n(The st.success text is executing all right on both versions)\r\n\r\nCode snippet:\r\n\r\n```\r\nwith st.spinner(\"Analyzing...\"):\r\n pass\r\nst.success(\"Done!\")\r\nst.balloons()\r\n```\r\n### Is this a regression?\r\n\r\nThat is, did this use to work the way you expected in the past?\r\nyes\r\n\r\n### Debug info\r\n\r\n- Streamlit version: 1.3.0\r\n- Python version: 3.9", "number": 4207, "title": "st.balloons() is not working in streamlit 1.3.0" } ]
435e110d457c06e6810793a3a7e0e3822042ac42
{ "head_commit": "4ad79094fd0a30e959d9b2318e79fca4fba61b6a", "head_commit_message": "Pin mypy to older version", "patch_to_review": "diff --git a/e2e/scripts/st_balloons.py b/e2e/scripts/st_balloons.py\nnew file mode 100644\nindex 000000000000..32af104a16e8\n--- /dev/null\n+++ b/e2e/scripts/st_balloons.py\n@@ -0,0 +1,17 @@\n+# Copyright 2018-2021 Streamlit Inc.\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import streamlit as st\n+\n+st.balloons()\ndiff --git a/e2e/specs/st_balloons.spec.js b/e2e/specs/st_balloons.spec.js\nnew file mode 100644\nindex 000000000000..da5ef5b0dc46\n--- /dev/null\n+++ b/e2e/specs/st_balloons.spec.js\n@@ -0,0 +1,35 @@\n+/**\n+ * @license\n+ * Copyright 2018-2021 Streamlit Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+describe(\"st.balloons\", () => {\n+ before(() => {\n+ cy.visit(\"http://localhost:3000/\");\n+ });\n+\n+ it(\"uses negative bottom margin styling\", () => {\n+ // balloons use negative bottom margin to prevent the flexbox gap (instead of display: none like st.empty)\n+ cy.get(\".balloons\")\n+ .eq(0)\n+ .parent()\n+ .should(\"have.css\", \"margin-bottom\");\n+\n+ cy.get(\".balloons\")\n+ .eq(0)\n+ .parent()\n+ .should(\"not.have.css\", \"display\", \"none\");\n+ });\n+});\ndiff --git a/e2e/specs/st_empty.spec.js b/e2e/specs/st_empty.spec.js\nindex 3eb787235650..03dd0f281426 100644\n--- a/e2e/specs/st_empty.spec.js\n+++ b/e2e/specs/st_empty.spec.js\n@@ -22,4 +22,11 @@ describe(\"st.empty\", () => {\n // Make the ribbon decoration line disappear\n cy.get(\"[data-testid='stDecoration']\").invoke(\"css\", \"display\", \"none\");\n });\n+\n+ it(\"uses display none styling\", () => {\n+ cy.get(\".stHidden\")\n+ .eq(0)\n+ .parent()\n+ .should(\"have.css\", \"display\", \"none\");\n+ });\n });\ndiff --git a/frontend/src/components/core/Block/Block.tsx b/frontend/src/components/core/Block/Block.tsx\nindex ff5314134940..78a75621c62f 100644\n--- a/frontend/src/components/core/Block/Block.tsx\n+++ b/frontend/src/components/core/Block/Block.tsx\n@@ -57,7 +57,7 @@ const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => {\n return <></>\n }\n \n- const enable = shouldComponentBeEnabled(false, props.reportRunState)\n+ const enable = shouldComponentBeEnabled(\"\", props.reportRunState)\n const isStale = isComponentStale(\n enable,\n node,\ndiff --git a/frontend/src/components/core/Block/ElementNodeRenderer.tsx b/frontend/src/components/core/Block/ElementNodeRenderer.tsx\nindex 749e368d59de..67d5f6d478ba 100644\n--- a/frontend/src/components/core/Block/ElementNodeRenderer.tsx\n+++ b/frontend/src/components/core/Block/ElementNodeRenderer.tsx\n@@ -570,9 +570,8 @@ const ElementNodeRenderer = (\n ): ReactElement => {\n const { node } = props\n \n- const elementType = node.element.type\n- const isHidden = elementType === \"empty\" || elementType === \"balloons\"\n- const enable = shouldComponentBeEnabled(isHidden, props.reportRunState)\n+ const elementType = node.element.type || \"\"\n+ const enable = shouldComponentBeEnabled(elementType, props.reportRunState)\n const isStale = isComponentStale(\n enable,\n node,\n@@ -595,9 +594,9 @@ const ElementNodeRenderer = (\n <StyledElementContainer\n data-stale={isStale}\n isStale={isStale}\n- isHidden={isHidden}\n+ width={width}\n className={\"element-container\"}\n- style={{ width, display: isHidden ? \"none\" : undefined }}\n+ elementType={elementType}\n >\n <ErrorBoundary width={width}>\n <Suspense\ndiff --git a/frontend/src/components/core/Block/styled-components.ts b/frontend/src/components/core/Block/styled-components.ts\nindex 0240742e3062..7d1ab98631c8 100644\n--- a/frontend/src/components/core/Block/styled-components.ts\n+++ b/frontend/src/components/core/Block/styled-components.ts\n@@ -32,11 +32,13 @@ export const StyledHorizontalBlock = styled.div(({ theme }) => ({\n \n export interface StyledElementContainerProps {\n isStale: boolean\n- isHidden: boolean\n+ width: number\n+ elementType: string\n }\n \n export const StyledElementContainer = styled.div<StyledElementContainerProps>(\n- ({ theme, isStale, isHidden }) => ({\n+ ({ theme, isStale, width, elementType }) => ({\n+ width,\n // Allows to have absolutely-positioned nodes inside report elements, like\n // floating buttons.\n position: \"relative\",\n@@ -54,6 +56,19 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>(\n transition: \"opacity 1s ease-in 0.5s\",\n }\n : {}),\n+ ...(elementType === \"empty\"\n+ ? {\n+ // Use display: none for empty elements to avoid the flexbox gap.\n+ display: \"none\",\n+ }\n+ : {}),\n+ ...(elementType === \"balloons\"\n+ ? {\n+ // Apply negative bottom margin to remove the flexbox gap.\n+ // display: none does not work for balloons, since it needs to be visible.\n+ marginBottom: `-${theme.spacing.lg}`,\n+ }\n+ : {}),\n })\n )\n \ndiff --git a/frontend/src/components/core/Block/utils.ts b/frontend/src/components/core/Block/utils.ts\nindex ecd58b97599d..2df49e5d2cbd 100644\n--- a/frontend/src/components/core/Block/utils.ts\n+++ b/frontend/src/components/core/Block/utils.ts\n@@ -22,10 +22,10 @@ import { FileUploadClient } from \"src/lib/FileUploadClient\"\n import { ComponentRegistry } from \"src/components/widgets/CustomComponent/\"\n \n export function shouldComponentBeEnabled(\n- isHidden: boolean,\n+ elementType: string,\n reportRunState: ReportRunState\n ): boolean {\n- return !isHidden || reportRunState !== ReportRunState.RUNNING\n+ return elementType !== \"empty\" || reportRunState !== ReportRunState.RUNNING\n }\n \n export function isElementStale(\ndiff --git a/lib/Pipfile b/lib/Pipfile\nindex 546a2f21326d..374abf2a1ea6 100644\n--- a/lib/Pipfile\n+++ b/lib/Pipfile\n@@ -9,7 +9,7 @@ black = \"==21.6b0\"\n boto3 = \"*\"\n botocore = \">=1.13.44\"\n hypothesis = \">=6.17.4\"\n-mypy = \">=0.910\"\n+mypy = \">=0.910, <0.930\"\n mypy-protobuf = \">=1.17\"\n parameterized = \"*\"\n pipenv = \"*\"\n" }
[ { "diff_hunk": "@@ -9,7 +9,7 @@ black = \"==21.6b0\"\n boto3 = \"*\"\n botocore = \">=1.13.44\"\n hypothesis = \">=6.17.4\"\n-mypy = \">=0.910\"\n+mypy = \">=0.910, <0.930\"", "line": null, "original_line": 12, "original_start_line": null, "path": "lib/Pipfile", "start_line": null, "text": "@user1:\nI don't think we should do this (in fact, if anything, we should pin `mypy = \">=0.930\"`)\n\n@author:\nThat's was just a test if my build or mypy is broken. And as it turns out, mypy `0.930` (which was released a few hours ago) breaks our build pipelines :(\n\n@author:\nSee the build logs here: https://app.circleci.com/pipelines/github/streamlit/streamlit/10654/workflows/9f4fcc98-06ec-474d-b7b8-84907ef9c7ca/jobs/34844/parallel-runs/0/steps/0-123\n\n@user2:\nI think it may just be necessary to pull in the latest changes from `develop`, since I believe https://github.com/streamlit/streamlit/pull/4191 fixed this\n\n@author:\nI will try that. However, the builds went fine yesterday and those issues only appeared after mypy 0.930 got released. So, I'm assuming there are additional issues ๐Ÿ˜ฌ\n\n@author:\nActually, the fixes from https://github.com/streamlit/streamlit/pull/4191 are already included in this branch.\n\n@user2:\nAh, #4191 fixed the issues that appeared with mypy 0.920\r\n\r\nI didn't realize this was a new set of issues that appeared with the release of 0.930 ๐Ÿ˜ฌ \n\n@user1:\nAnnoying!\r\n\r\nIt looks like all these issues are in the `legacy_caching/hashing.py` file - as a stopgap, we could add `# type: ignore` to the top of that file, along with a TODO to investigate after the break. (#core-platform can be in charge of fixing this issue.)\n\n@user3:\nWe got a solution with https://github.com/streamlit/streamlit/pull/4218 that fixes with the latest mypy. I've merged it in this one." } ]
f4630a9902c0824e548ba51c6cd392191cb2e71f
diff --git a/e2e/scripts/st_balloons.py b/e2e/scripts/st_balloons.py new file mode 100644 index 000000000000..32af104a16e8 --- /dev/null +++ b/e2e/scripts/st_balloons.py @@ -0,0 +1,17 @@ +# Copyright 2018-2021 Streamlit Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import streamlit as st + +st.balloons() diff --git a/e2e/specs/st_balloons.spec.js b/e2e/specs/st_balloons.spec.js new file mode 100644 index 000000000000..da5ef5b0dc46 --- /dev/null +++ b/e2e/specs/st_balloons.spec.js @@ -0,0 +1,35 @@ +/** + * @license + * Copyright 2018-2021 Streamlit Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +describe("st.balloons", () => { + before(() => { + cy.visit("http://localhost:3000/"); + }); + + it("uses negative bottom margin styling", () => { + // balloons use negative bottom margin to prevent the flexbox gap (instead of display: none like st.empty) + cy.get(".balloons") + .eq(0) + .parent() + .should("have.css", "margin-bottom"); + + cy.get(".balloons") + .eq(0) + .parent() + .should("not.have.css", "display", "none"); + }); +}); diff --git a/e2e/specs/st_empty.spec.js b/e2e/specs/st_empty.spec.js index 3eb787235650..03dd0f281426 100644 --- a/e2e/specs/st_empty.spec.js +++ b/e2e/specs/st_empty.spec.js @@ -22,4 +22,11 @@ describe("st.empty", () => { // Make the ribbon decoration line disappear cy.get("[data-testid='stDecoration']").invoke("css", "display", "none"); }); + + it("uses display none styling", () => { + cy.get(".stHidden") + .eq(0) + .parent() + .should("have.css", "display", "none"); + }); }); diff --git a/frontend/src/components/core/Block/Block.tsx b/frontend/src/components/core/Block/Block.tsx index ff5314134940..78a75621c62f 100644 --- a/frontend/src/components/core/Block/Block.tsx +++ b/frontend/src/components/core/Block/Block.tsx @@ -57,7 +57,7 @@ const BlockNodeRenderer = (props: BlockPropsWithWidth): ReactElement => { return <></> } - const enable = shouldComponentBeEnabled(false, props.reportRunState) + const enable = shouldComponentBeEnabled("", props.reportRunState) const isStale = isComponentStale( enable, node, diff --git a/frontend/src/components/core/Block/ElementNodeRenderer.tsx b/frontend/src/components/core/Block/ElementNodeRenderer.tsx index 749e368d59de..67d5f6d478ba 100644 --- a/frontend/src/components/core/Block/ElementNodeRenderer.tsx +++ b/frontend/src/components/core/Block/ElementNodeRenderer.tsx @@ -570,9 +570,8 @@ const ElementNodeRenderer = ( ): ReactElement => { const { node } = props - const elementType = node.element.type - const isHidden = elementType === "empty" || elementType === "balloons" - const enable = shouldComponentBeEnabled(isHidden, props.reportRunState) + const elementType = node.element.type || "" + const enable = shouldComponentBeEnabled(elementType, props.reportRunState) const isStale = isComponentStale( enable, node, @@ -595,9 +594,9 @@ const ElementNodeRenderer = ( <StyledElementContainer data-stale={isStale} isStale={isStale} - isHidden={isHidden} + width={width} className={"element-container"} - style={{ width, display: isHidden ? "none" : undefined }} + elementType={elementType} > <ErrorBoundary width={width}> <Suspense diff --git a/frontend/src/components/core/Block/styled-components.ts b/frontend/src/components/core/Block/styled-components.ts index 0240742e3062..7d1ab98631c8 100644 --- a/frontend/src/components/core/Block/styled-components.ts +++ b/frontend/src/components/core/Block/styled-components.ts @@ -32,11 +32,13 @@ export const StyledHorizontalBlock = styled.div(({ theme }) => ({ export interface StyledElementContainerProps { isStale: boolean - isHidden: boolean + width: number + elementType: string } export const StyledElementContainer = styled.div<StyledElementContainerProps>( - ({ theme, isStale, isHidden }) => ({ + ({ theme, isStale, width, elementType }) => ({ + width, // Allows to have absolutely-positioned nodes inside report elements, like // floating buttons. position: "relative", @@ -54,6 +56,19 @@ export const StyledElementContainer = styled.div<StyledElementContainerProps>( transition: "opacity 1s ease-in 0.5s", } : {}), + ...(elementType === "empty" + ? { + // Use display: none for empty elements to avoid the flexbox gap. + display: "none", + } + : {}), + ...(elementType === "balloons" + ? { + // Apply negative bottom margin to remove the flexbox gap. + // display: none does not work for balloons, since it needs to be visible. + marginBottom: `-${theme.spacing.lg}`, + } + : {}), }) ) diff --git a/frontend/src/components/core/Block/utils.ts b/frontend/src/components/core/Block/utils.ts index ecd58b97599d..2df49e5d2cbd 100644 --- a/frontend/src/components/core/Block/utils.ts +++ b/frontend/src/components/core/Block/utils.ts @@ -22,10 +22,10 @@ import { FileUploadClient } from "src/lib/FileUploadClient" import { ComponentRegistry } from "src/components/widgets/CustomComponent/" export function shouldComponentBeEnabled( - isHidden: boolean, + elementType: string, reportRunState: ReportRunState ): boolean { - return !isHidden || reportRunState !== ReportRunState.RUNNING + return elementType !== "empty" || reportRunState !== ReportRunState.RUNNING } export function isElementStale(
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-4358@83d68a3
streamlit/streamlit
Python
4,358
Fix timezone handling with slider
## ๐Ÿ“š Context If a slider is used with a `time`/`datetime` value, it will be converted to a UTC timestamp in microseconds. The frontend itself is not aware of the actual timezone used in the backend. Thereby, it currently does some timezone conversations that results in diverging information between the value shown on the frontend and the actual value in the backend. - What kind of change does this PR introduce? - [x] Bugfix - [ ] Feature - [ ] Refactoring - [ ] Other, please describe: ## ๐Ÿง  Description of Changes This PR removes any timezone conversation on the backend (it just replaces the `tzinfo` with UTC), and uses the UTC timezone in the frontend (with `moments`) instead of the browser timezone. Thereby, the datetime instances are always shown with their exact date and time. For example: `2020-01-10 23:30:00 EST` is rendered as `2020-01-10 23:30:00`; `2020-01-10 23:30:00 PT` is rendered as `2020-01-10 23:30:00`; `2020-01-10 23:30:00 UTC` is rendered as `2020-01-10 23:30:00`; ... - [x] This is a visible (user-facing) change ### Example: ```python import streamlit as st from datetime import time period = st.slider( "Period:", min_value=time(0, 0), max_value=time(23, 45), value=(time(10, 00), time(23, 45)), ) st.write(period) ``` **Revised:** ![image](https://user-images.githubusercontent.com/2852129/152066518-039035ad-0ac6-4604-a0b6-6fac56b83466.png) **Current:** ![image](https://user-images.githubusercontent.com/2852129/152066438-d6c2ea5b-006f-4b36-b553-95d6cb9ab48c.png) ## ๐Ÿงช Testing Done - [ ] Screenshots included - [x] Added/Updated unit tests - [ ] Added/Updated e2e tests ## ๐ŸŒ References _Does this depend on other work, documents, or tickets?_ - **Issue**: Closes #3402 --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2022-02-01T23:07:44Z
Datetime slider showing incorrect values on the browser ### Summary When I run my streamlit app at my localhost and the cloud, I see different st.slider time range values. To be precise the range values printed on the browser seem to be shifted. I suspect this shift is due to some time zone change. ### Code snippet: ``` from datetime import time import streamlist as st period = st.sidebar.slider("Period:", min_value=time(0,0), max_value=time(23, 45), value=(time(10, 00), time(23, 45))) ``` The code above shows a time period of 00:00 - 23:45 at my localhost, and 19:00 - 18:45 on the cloud with the same time length. ### Debug info - Streamlit version: 0.82.0 ### Suggestion I think st.slider should have a timezone keyword and in default should return the datetime information in users timezone rather than the server's timezone.
Hey @tdincer ! Thanks for the report! We have had a couple people mention our support for timezones is lacking. I have grouped this in with other issues. Most similar is #1346. We're going to get this in front of our product team to see how we can best integrate this with our future plans.
[ { "body": "### Summary\r\n\r\nWhen I run my streamlit app at my localhost and the cloud, I see different st.slider time range values. To be precise the range values printed on the browser seem to be shifted. I suspect this shift is due to some time zone change.\r\n\r\n### Code snippet:\r\n\r\n```\r\nfrom datetime import time\r\nimport streamlist as st\r\n\r\nperiod = st.sidebar.slider(\"Period:\", min_value=time(0,0), max_value=time(23, 45), value=(time(10, 00), time(23, 45)))\r\n```\r\n\r\nThe code above shows a time period of 00:00 - 23:45 at my localhost, and 19:00 - 18:45 on the cloud with the same time length.\r\n\r\n### Debug info\r\n\r\n- Streamlit version: 0.82.0\r\n\r\n### Suggestion\r\n\r\nI think st.slider should have a timezone keyword and in default should return the datetime information in users timezone rather than the server's timezone.", "number": 3402, "title": "Datetime slider showing incorrect values on the browser" } ]
fc0f126375344e3a1560745ab540bedf38940f81
{ "head_commit": "83d68a3792e53aa3f498ce1a8c7ff135042786b4", "head_commit_message": "Update micros values in tests", "patch_to_review": "diff --git a/frontend/src/components/widgets/Slider/Slider.tsx b/frontend/src/components/widgets/Slider/Slider.tsx\nindex 89e4007b0e88..bfe0d223c44a 100644\n--- a/frontend/src/components/widgets/Slider/Slider.tsx\n+++ b/frontend/src/components/widgets/Slider/Slider.tsx\n@@ -185,7 +185,10 @@ class Slider extends React.PureComponent<Props, State> {\n const { format, options } = this.props.element\n if (this.isDateTimeType()) {\n // Python datetime uses microseconds, but JS & Moment uses milliseconds\n- return moment(value / 1000).format(format)\n+ // The timestamp is always set to the UTC timezone, even so, the actual timezone\n+ // for this timestamp in the backend could be different.\n+ // However, the frontend component does not need to know about the actual timezone.\n+ return moment.utc(value / 1000).format(format)\n }\n \n if (options.length > 0) {\ndiff --git a/lib/streamlit/elements/slider.py b/lib/streamlit/elements/slider.py\nindex 344f0d39fec3..9f4afe0ee14c 100644\n--- a/lib/streamlit/elements/slider.py\n+++ b/lib/streamlit/elements/slider.py\n@@ -405,8 +405,11 @@ def _delta_to_micros(delta):\n UTC_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)\n \n def _datetime_to_micros(dt):\n- # If dt is naive, Python converts from local time\n- utc_dt = dt.astimezone(timezone.utc)\n+ # The frontend is not aware of timezones and only expects a UTC-based timestamp (in microseconds).\n+ # Since we want to show the date/time exactly as it is in the given datetime object,\n+ # we just set the tzinfo to UTC and do not do any timezone conversions.\n+ # Only the backend knows about original timezone and will replace the UTC timestamp in the deserialization.\n+ utc_dt = dt.replace(tzinfo=timezone.utc)\n return _delta_to_micros(utc_dt - UTC_EPOCH)\n \n # Restore times/datetimes to original timezone (dates are always naive)\n@@ -418,8 +421,9 @@ def _datetime_to_micros(dt):\n \n def _micros_to_datetime(micros):\n utc_dt = UTC_EPOCH + timedelta(microseconds=micros)\n- # Convert from utc back to original time (local time if naive)\n- return utc_dt.astimezone(orig_tz).replace(tzinfo=orig_tz)\n+ # Add the original timezone. No conversation is required here,\n+ # since in the serialization, we also just replace the timestamp with UTC.\n+ return utc_dt.replace(tzinfo=orig_tz)\n \n value = list(map(_datetime_to_micros, value))\n min_value = _datetime_to_micros(min_value)\ndiff --git a/lib/tests/streamlit/slider_test.py b/lib/tests/streamlit/slider_test.py\nindex 4218128da36f..282d4732c126 100644\n--- a/lib/tests/streamlit/slider_test.py\n+++ b/lib/tests/streamlit/slider_test.py\n@@ -55,10 +55,10 @@ def test_just_disabled(self):\n AWARE_TIME = time(12, 00, tzinfo=PST)\n AWARE_TIME_END = time(21, 00, tzinfo=PST)\n # datetimes are serialized in proto as micros since epoch\n- AWARE_DT_MICROS = 1577865600000000\n- AWARE_DT_END_MICROS = 1578211200000000\n- AWARE_TIME_MICROS = 946756800000000\n- AWARE_TIME_END_MICROS = 946789200000000\n+ AWARE_DT_MICROS = 1577836800000000\n+ AWARE_DT_END_MICROS = 1578182400000000\n+ AWARE_TIME_MICROS = 946728000000000\n+ AWARE_TIME_END_MICROS = 946760400000000\n \n @parameterized.expand(\n [\n" }
[ { "diff_hunk": "@@ -418,8 +421,9 @@ def _datetime_to_micros(dt):\n \n def _micros_to_datetime(micros):\n utc_dt = UTC_EPOCH + timedelta(microseconds=micros)\n- # Convert from utc back to original time (local time if naive)\n- return utc_dt.astimezone(orig_tz).replace(tzinfo=orig_tz)\n+ # Add the original timezone. No conversation is required here,", "line": null, "original_line": 424, "original_start_line": null, "path": "lib/streamlit/elements/slider.py", "start_line": null, "text": "@user1:\nnit: conversation -> conversion" } ]
a99126dd4bdd90ad6af3dafcad4a86fc97d6bc79
diff --git a/frontend/src/components/widgets/Slider/Slider.tsx b/frontend/src/components/widgets/Slider/Slider.tsx index 89e4007b0e88..bfe0d223c44a 100644 --- a/frontend/src/components/widgets/Slider/Slider.tsx +++ b/frontend/src/components/widgets/Slider/Slider.tsx @@ -185,7 +185,10 @@ class Slider extends React.PureComponent<Props, State> { const { format, options } = this.props.element if (this.isDateTimeType()) { // Python datetime uses microseconds, but JS & Moment uses milliseconds - return moment(value / 1000).format(format) + // The timestamp is always set to the UTC timezone, even so, the actual timezone + // for this timestamp in the backend could be different. + // However, the frontend component does not need to know about the actual timezone. + return moment.utc(value / 1000).format(format) } if (options.length > 0) { diff --git a/lib/streamlit/elements/slider.py b/lib/streamlit/elements/slider.py index 92637f4b57de..90dfc5819ae2 100644 --- a/lib/streamlit/elements/slider.py +++ b/lib/streamlit/elements/slider.py @@ -405,8 +405,11 @@ def _delta_to_micros(delta): UTC_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc) def _datetime_to_micros(dt): - # If dt is naive, Python converts from local time - utc_dt = dt.astimezone(timezone.utc) + # The frontend is not aware of timezones and only expects a UTC-based timestamp (in microseconds). + # Since we want to show the date/time exactly as it is in the given datetime object, + # we just set the tzinfo to UTC and do not do any timezone conversions. + # Only the backend knows about original timezone and will replace the UTC timestamp in the deserialization. + utc_dt = dt.replace(tzinfo=timezone.utc) return _delta_to_micros(utc_dt - UTC_EPOCH) # Restore times/datetimes to original timezone (dates are always naive) @@ -418,8 +421,9 @@ def _datetime_to_micros(dt): def _micros_to_datetime(micros): utc_dt = UTC_EPOCH + timedelta(microseconds=micros) - # Convert from utc back to original time (local time if naive) - return utc_dt.astimezone(orig_tz).replace(tzinfo=orig_tz) + # Add the original timezone. No conversion is required here, + # since in the serialization, we also just replace the timestamp with UTC. + return utc_dt.replace(tzinfo=orig_tz) value = list(map(_datetime_to_micros, value)) min_value = _datetime_to_micros(min_value) diff --git a/lib/tests/streamlit/slider_test.py b/lib/tests/streamlit/slider_test.py index 4218128da36f..282d4732c126 100644 --- a/lib/tests/streamlit/slider_test.py +++ b/lib/tests/streamlit/slider_test.py @@ -55,10 +55,10 @@ def test_just_disabled(self): AWARE_TIME = time(12, 00, tzinfo=PST) AWARE_TIME_END = time(21, 00, tzinfo=PST) # datetimes are serialized in proto as micros since epoch - AWARE_DT_MICROS = 1577865600000000 - AWARE_DT_END_MICROS = 1578211200000000 - AWARE_TIME_MICROS = 946756800000000 - AWARE_TIME_END_MICROS = 946789200000000 + AWARE_DT_MICROS = 1577836800000000 + AWARE_DT_END_MICROS = 1578182400000000 + AWARE_TIME_MICROS = 946728000000000 + AWARE_TIME_END_MICROS = 946760400000000 @parameterized.expand( [
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-3639@f3eec3c
streamlit/streamlit
Python
3,639
3638/add check script endpoint
**Issue:** https://github.com/streamlit/streamlit/issues/3638 **Description:** - add endpoint to verify if the script compiles and runs correctly - add config option to customise the above endpoint --- **Contribution License Agreement** By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2021-08-02T13:49:02Z
Health endpoint for checking that a script compiles and runs correctly ### Problem Currently there is no way to detect that the streamlit app is actually not running - if there is an error inside the client script (e.g. missing dependency, script error) the application still starts and the /healthz endpoint returns success. S4T requires better app monitoring and it would be helpful to detect the applications that cannot start because the above type of error. ### Solution A new endpoint will be provided which will return 200 HTTP status code if the script compiles and runs correctly. Alternatively a 503 HTTP status code is returned and the body will contain the error message. The endpoint by default will be `/check-script`. However, it can be changed by specifying the config option `server.checkScriptEndpoint`
[ { "body": "### Problem\r\n\r\nCurrently there is no way to detect that the streamlit app is actually not running - if there is an error inside the client script (e.g. missing dependency, script error) the application still starts and the /healthz endpoint returns success. \r\n\r\nS4T requires better app monitoring and it would be helpful to detect the applications that cannot start because the above type of error.\r\n\r\n### Solution\r\n\r\nA new endpoint will be provided which will return 200 HTTP status code if the script compiles and runs correctly. Alternatively a 503 HTTP status code is returned and the body will contain the error message.\r\n\r\nThe endpoint by default will be `/check-script`. However, it can be changed by specifying the config option `server.checkScriptEndpoint`\r\n", "number": 3638, "title": "Health endpoint for checking that a script compiles and runs correctly" } ]
188568206a95992b62272d8a5813b8c601dd5bf8
{ "head_commit": "f3eec3cd58d1b1cbcf6a2efa42f9ae7b299cf93c", "head_commit_message": "fix return type", "patch_to_review": "diff --git a/lib/streamlit/config.py b/lib/streamlit/config.py\nindex e66d0a8bd3db..18df80f236f7 100644\n--- a/lib/streamlit/config.py\n+++ b/lib/streamlit/config.py\n@@ -589,6 +589,17 @@ def _server_port() -> int:\n return 8501\n \n \n+_create_option(\n+ \"server.checkScriptEndpoint\",\n+ description=\"\"\"\n+ The endpoint used for checking if a script loads succefully. On success,\n+ the endpoint will return a 200 HTTP status code. On failure, the endpoint\n+ will return a 503 HTTP status code.\n+ \"\"\",\n+ default_val=\"check-script\",\n+ type_=str,\n+)\n+\n _create_option(\n \"server.baseUrlPath\",\n description=\"\"\"\ndiff --git a/lib/streamlit/server/routes.py b/lib/streamlit/server/routes.py\nindex 44e83e514ecb..753ab2a5cf73 100644\n--- a/lib/streamlit/server/routes.py\n+++ b/lib/streamlit/server/routes.py\n@@ -171,7 +171,8 @@ def initialize(self, callback):\n self._callback = callback\n \n def get(self):\n- if self._callback():\n+ ok, msg = self._callback()\n+ if ok:\n self.write(\"ok\")\n self.set_status(200)\n \n@@ -186,7 +187,7 @@ def get(self):\n else:\n # 503 = SERVICE_UNAVAILABLE\n self.set_status(503)\n- self.write(\"unavailable\")\n+ self.write(msg)\n \n \n class MetricsHandler(_SpecialRequestHandler):\ndiff --git a/lib/streamlit/server/server.py b/lib/streamlit/server/server.py\nindex 2300e44b1eed..2339b120fdb1 100644\n--- a/lib/streamlit/server/server.py\n+++ b/lib/streamlit/server/server.py\n@@ -18,10 +18,11 @@\n import socket\n import sys\n import errno\n+import types\n import traceback\n import click\n from enum import Enum\n-from typing import Any, Dict, Optional, TYPE_CHECKING\n+from typing import Any, Dict, Optional, TYPE_CHECKING, Tuple\n \n import tornado.concurrent\n import tornado.gen\n@@ -32,6 +33,8 @@\n \n from streamlit import config\n from streamlit import file_util\n+from streamlit import magic\n+from streamlit import source_util\n from streamlit import util\n from streamlit.config_option import ConfigOption\n from streamlit.forward_msg_cache import ForwardMsgCache\n@@ -325,6 +328,8 @@ def _create_app(self):\n \n \"\"\"\n base = config.get_option(\"server.baseUrlPath\")\n+ check_script_endpoint = config.get_option(\"server.checkScriptEndpoint\")\n+\n routes = [\n (\n make_url_path_regex(base, \"stream\"),\n@@ -336,6 +341,11 @@ def _create_app(self):\n HealthHandler,\n dict(callback=lambda: self.is_ready_for_browser_connection),\n ),\n+ (\n+ make_url_path_regex(base, check_script_endpoint),\n+ HealthHandler,\n+ dict(callback=lambda: self.is_script_loading),\n+ ),\n (make_url_path_regex(base, \"debugz\"), DebugHandler, dict(server=self)),\n (make_url_path_regex(base, \"metrics\"), MetricsHandler),\n (\n@@ -396,8 +406,38 @@ def _set_state(self, new_state):\n self._state = new_state\n \n @property\n- def is_ready_for_browser_connection(self):\n- return self._state not in (State.INITIAL, State.STOPPING, State.STOPPED)\n+ def is_ready_for_browser_connection(self) -> Tuple[bool, str]:\n+ if self._state not in (State.INITIAL, State.STOPPING, State.STOPPED):\n+ return True, \"ok\"\n+\n+ return False, \"unavailable\"\n+\n+ @property\n+ def is_script_loading(self) -> Tuple[bool, str]:\n+ try:\n+ with source_util.open_python_file(self.script_path) as f:\n+ filebody = f.read()\n+\n+ if config.get_option(\"runner.magicEnabled\"):\n+ filebody = magic.add_magic(filebody, self.script_path)\n+\n+ code = compile(\n+ filebody,\n+ self.script_path,\n+ mode=\"exec\",\n+ flags=0,\n+ dont_inherit=1,\n+ optimize=-1,\n+ )\n+\n+ module = types.ModuleType(\"__main__\")\n+ sys.modules[\"__main__\"] = module\n+ module.__dict__[\"__file__\"] = self.script_path\n+ exec(code, module.__dict__)\n+ except BaseException as e:\n+ return False, \"Error: %s\" % str(e)\n+\n+ return True, \"ok\"\n \n @property\n def browser_is_connected(self):\ndiff --git a/lib/tests/streamlit/config_test.py b/lib/tests/streamlit/config_test.py\nindex 4fc3ffc12ad0..94cfea1b4bbb 100644\n--- a/lib/tests/streamlit/config_test.py\n+++ b/lib/tests/streamlit/config_test.py\n@@ -329,6 +329,7 @@ def test_config_option_keys(self):\n \"server.baseUrlPath\",\n \"server.enableCORS\",\n \"server.cookieSecret\",\n+ \"server.checkScriptEndpoint\",\n \"server.enableWebsocketCompression\",\n \"server.enableXsrfProtection\",\n \"server.fileWatcherType\",\ndiff --git a/lib/tests/streamlit/server_test.py b/lib/tests/streamlit/server_test.py\nindex bd8e188f0419..1d7dea598e03 100644\n--- a/lib/tests/streamlit/server_test.py\n+++ b/lib/tests/streamlit/server_test.py\n@@ -17,6 +17,7 @@\n from unittest import mock\n from unittest.mock import MagicMock, patch\n import unittest\n+import tempfile\n \n import pytest\n import tornado.testing\n@@ -36,6 +37,7 @@\n from streamlit.elements import legacy_data_frame as data_frame\n from streamlit.proto.ForwardMsg_pb2 import ForwardMsg\n from streamlit.server.server import State\n+from streamlit.server.server import Server\n from streamlit.server.server import start_listening\n from streamlit.server.server import RetriesExceeded\n from streamlit.server.routes import DebugHandler\n@@ -422,7 +424,7 @@ def setUp(self):\n self._is_healthy = True\n \n def is_healthy(self):\n- return self._is_healthy\n+ return self._is_healthy, \"ok\"\n \n def get_app(self):\n return tornado.web.Application(\n@@ -593,3 +595,35 @@ def test_message_cache(self):\n # Cache misses\n self.assertEqual(404, self.fetch(\"/message\").code)\n self.assertEqual(404, self.fetch(\"/message?id=non_existent\").code)\n+\n+\n+class ScriptCheckTest(tornado.testing.AsyncTestCase):\n+ def test_invalid_script(self):\n+ self._check_script_loading(\n+ \"import streamlit as st\\n\\nst.deprecatedWrite('test')\",\n+ False,\n+ \"Error: module 'streamlit' has no attribute 'deprecatedWrite'\",\n+ )\n+\n+ def test_valid_script(self):\n+ self._check_script_loading(\n+ \"import streamlit as st\\n\\nst.write('test')\", True, \"ok\"\n+ )\n+\n+ def _check_script_loading(self, script, expected_loads, expected_msg):\n+ fd, path = tempfile.mkstemp()\n+ server = Server(self.io_loop, path, \"test command line\")\n+ try:\n+ with os.fdopen(fd, \"w\") as tmp:\n+ # do stuff with temp file\n+ tmp.write(script)\n+\n+ server._on_stopped = mock.MagicMock() # type: ignore[assignment]\n+\n+ ok, msg = server.is_script_loading\n+ self.assertEqual(expected_loads, ok)\n+ self.assertEqual(expected_msg, msg)\n+ finally:\n+ server.stop()\n+ Server._singleton = None\n+ os.remove(path)\n" }
[ { "diff_hunk": "@@ -396,8 +406,38 @@ def _set_state(self, new_state):\n self._state = new_state\n \n @property\n- def is_ready_for_browser_connection(self):\n- return self._state not in (State.INITIAL, State.STOPPING, State.STOPPED)\n+ def is_ready_for_browser_connection(self) -> Tuple[bool, str]:\n+ if self._state not in (State.INITIAL, State.STOPPING, State.STOPPED):\n+ return True, \"ok\"\n+\n+ return False, \"unavailable\"\n+\n+ @property\n+ def is_script_loading(self) -> Tuple[bool, str]:\n+ try:\n+ with source_util.open_python_file(self.script_path) as f:\n+ filebody = f.read()\n+\n+ if config.get_option(\"runner.magicEnabled\"):\n+ filebody = magic.add_magic(filebody, self.script_path)\n+\n+ code = compile(\n+ filebody,\n+ self.script_path,\n+ mode=\"exec\",\n+ flags=0,\n+ dont_inherit=1,\n+ optimize=-1,\n+ )\n+\n+ module = types.ModuleType(\"__main__\")\n+ sys.modules[\"__main__\"] = module\n+ module.__dict__[\"__file__\"] = self.script_path\n+ exec(code, module.__dict__)", "line": null, "original_line": 436, "original_start_line": null, "path": "lib/streamlit/server/server.py", "start_line": null, "text": "@user1:\nI'm a bit concerned about what might happen when actually trying to run the script here.\r\n\r\nBecause running a streamlit script requires a decent amount of machinery to be in place, we use an internal global variable `_is_running_with_streamlit` to ensure that a script is essentially a big no-op when a user does something like running it directly (that is, runs something like `python3 my_script.py`).\r\n\r\nThis flag gets set to true from within `cli.py`. What could be strange about running a script from here is that the flag may be set when the script is run from here, but in this context, I don't believe most of the machinery required to actually run many of the `st.*` commands will be available.\r\n\r\nI'm not exactly sure what'll happen when running the script in `exec`, so it would be useful to check the value of `st._is_running_with_streamlit` from within the script when it's run via this endpoint. If the flag is set to True, then it's possible that we'll see some weird/undefined behavior here.\n\n@user2:\nI suspect the more-correct thing to do here is to actually kick of a `ScriptRunner` execution of the script, rather than just `exec`'ing it \"raw\". That way the script is actually run \"within Streamlit\" (and if the definition of \"within Streamlit\" changes in ScriptRunner, it won't need to also change here).\r\n\r\nThe downside is that using ScriptRunner is a bigger pain than what you've already done (because it requires some inputs, and also it functions asynchronously on another thread that it creates). But we could definitely help with that integration!" }, { "diff_hunk": "@@ -171,7 +171,8 @@ def initialize(self, callback):\n self._callback = callback\n \n def get(self):\n- if self._callback():\n+ ok, msg = self._callback()\n+ if ok:\n self.write(\"ok\")", "line": null, "original_line": 176, "original_start_line": null, "path": "lib/streamlit/server/routes.py", "start_line": null, "text": "@user1:\nnit: should change this to `self.write(msg)` to match the 503 case below" }, { "diff_hunk": "@@ -396,8 +406,38 @@ def _set_state(self, new_state):\n self._state = new_state\n \n @property\n- def is_ready_for_browser_connection(self):\n- return self._state not in (State.INITIAL, State.STOPPING, State.STOPPED)\n+ def is_ready_for_browser_connection(self) -> Tuple[bool, str]:\n+ if self._state not in (State.INITIAL, State.STOPPING, State.STOPPED):\n+ return True, \"ok\"\n+\n+ return False, \"unavailable\"\n+\n+ @property\n+ def is_script_loading(self) -> Tuple[bool, str]:\n+ try:\n+ with source_util.open_python_file(self.script_path) as f:\n+ filebody = f.read()\n+\n+ if config.get_option(\"runner.magicEnabled\"):\n+ filebody = magic.add_magic(filebody, self.script_path)\n+\n+ code = compile(\n+ filebody,\n+ self.script_path,\n+ mode=\"exec\",\n+ flags=0,\n+ dont_inherit=1,\n+ optimize=-1,\n+ )\n+\n+ module = types.ModuleType(\"__main__\")\n+ sys.modules[\"__main__\"] = module\n+ module.__dict__[\"__file__\"] = self.script_path\n+ exec(code, module.__dict__)\n+ except BaseException as e:\n+ return False, \"Error: %s\" % str(e)", "line": null, "original_line": 438, "original_start_line": null, "path": "lib/streamlit/server/server.py", "start_line": null, "text": "@user1:\nNit: use string interpolation: `return False, f\"Error: {str(e)}\"` (we don't do this consistently thru the codebase because we originally targeted old Pythons, but now that we're 3.6+, we should be for new code.)" }, { "diff_hunk": "@@ -396,8 +406,38 @@ def _set_state(self, new_state):\n self._state = new_state\n \n @property\n- def is_ready_for_browser_connection(self):\n- return self._state not in (State.INITIAL, State.STOPPING, State.STOPPED)\n+ def is_ready_for_browser_connection(self) -> Tuple[bool, str]:\n+ if self._state not in (State.INITIAL, State.STOPPING, State.STOPPED):\n+ return True, \"ok\"\n+\n+ return False, \"unavailable\"\n+\n+ @property\n+ def is_script_loading(self) -> Tuple[bool, str]:", "line": null, "original_line": 416, "original_start_line": null, "path": "lib/streamlit/server/server.py", "start_line": null, "text": "@user1:\nA few nits:\r\n\r\n- Feels like maybe this shouldn't be a property, since it does an (unbounded!) amount of computation, and doesn't cache the result.\r\n- The name is confusing to me. What about something like: `does_script_execute_without_error()` or something?\r\n- Could use a docstring, since it's doing a non-obvious thing:\r\n\r\n```\r\n\"\"\"Load and execute the app's script to verify it runs without an error.\r\n\r\nReturns\r\n-------\r\n[True, \"ok\"] if the script completes without error, or [False, err_msg] if the script raises an exception.\r\n\"\"\"\r\n```" } ]
fbe6566f37c39d73e732bcf565a2872dec32a143
diff --git a/lib/streamlit/config.py b/lib/streamlit/config.py index e66d0a8bd3db..750b5d52937e 100644 --- a/lib/streamlit/config.py +++ b/lib/streamlit/config.py @@ -589,6 +589,21 @@ def _server_port() -> int: return 8501 +_create_option( + "server.scriptHealthCheckEnabled", + visibility="hidden", + description=""" + Flag for enabling the script health check endpoint. It used for checking if + a script loads successfully. On success, the endpoint will return a 200 + HTTP status code. On failure, the endpoint will return a 503 HTTP status code. + + Note: This is an experimental Streamlit internal API. The API is subject + to change anytime so this should be used at your own risk + """, + default_val=False, + type_=bool, +) + _create_option( "server.baseUrlPath", description=""" diff --git a/lib/streamlit/script_runner.py b/lib/streamlit/script_runner.py index 5a0a8c3f4a33..f908f3e3a9f1 100644 --- a/lib/streamlit/script_runner.py +++ b/lib/streamlit/script_runner.py @@ -29,7 +29,10 @@ from streamlit.report_thread import ReportThread, ReportContext from streamlit.report_thread import get_report_ctx from streamlit.script_request_queue import ScriptRequest -from streamlit.state.session_state import SessionState +from streamlit.state.session_state import ( + SessionState, + SCRIPT_RUN_WITHOUT_ERRORS_KEY, +) from streamlit.logger import get_logger from streamlit.proto.ClientState_pb2 import ClientState @@ -299,6 +302,7 @@ def _run_script(self, rerun_data): except BaseException as e: # We got a compile error. Send an error event and bail immediately. LOGGER.debug("Fatal script error: %s" % e) + self._session_state[SCRIPT_RUN_WITHOUT_ERRORS_KEY] = False self.on_event.send( ScriptRunnerEvent.SCRIPT_STOPPED_WITH_COMPILE_ERROR, exception=e ) @@ -348,7 +352,7 @@ def _run_script(self, rerun_data): ctx.on_script_start() exec(code, module.__dict__) - + self._session_state[SCRIPT_RUN_WITHOUT_ERRORS_KEY] = True except RerunException as e: rerun_with_data = e.rerun_data @@ -356,6 +360,7 @@ def _run_script(self, rerun_data): pass except BaseException as e: + self._session_state[SCRIPT_RUN_WITHOUT_ERRORS_KEY] = False handle_uncaught_app_exception(e) finally: diff --git a/lib/streamlit/server/routes.py b/lib/streamlit/server/routes.py index 44e83e514ecb..dcde1feef454 100644 --- a/lib/streamlit/server/routes.py +++ b/lib/streamlit/server/routes.py @@ -170,9 +170,10 @@ def initialize(self, callback): """ self._callback = callback - def get(self): - if self._callback(): - self.write("ok") + async def get(self): + ok, msg = await self._callback() + if ok: + self.write(msg) self.set_status(200) # Tornado will set the _xsrf cookie automatically for the page on @@ -186,7 +187,7 @@ def get(self): else: # 503 = SERVICE_UNAVAILABLE self.set_status(503) - self.write("unavailable") + self.write(msg) class MetricsHandler(_SpecialRequestHandler): diff --git a/lib/streamlit/server/server.py b/lib/streamlit/server/server.py index 2300e44b1eed..e20a3c3cd5e1 100644 --- a/lib/streamlit/server/server.py +++ b/lib/streamlit/server/server.py @@ -18,10 +18,11 @@ import socket import sys import errno +import time import traceback import click from enum import Enum -from typing import Any, Dict, Optional, TYPE_CHECKING +from typing import Any, Dict, Optional, TYPE_CHECKING, Tuple import tornado.concurrent import tornado.gen @@ -48,6 +49,8 @@ UploadFileRequestHandler, UPLOAD_FILE_ROUTE, ) + +from streamlit.state.session_state import SCRIPT_RUN_WITHOUT_ERRORS_KEY from streamlit.server.routes import AddSlashHandler from streamlit.server.routes import AssetsFileHandler from streamlit.server.routes import DebugHandler @@ -94,6 +97,10 @@ UNIX_SOCKET_PREFIX = "unix://" +# Wait for the script run result for 60s and if no result is available give up +SCRIPT_RUN_CHECK_TIMEOUT = 60 + + class SessionInfo(object): """Type stored in our _session_info_by_id dict. @@ -325,6 +332,7 @@ def _create_app(self): """ base = config.get_option("server.baseUrlPath") + routes = [ ( make_url_path_regex(base, "stream"), @@ -367,6 +375,17 @@ def _create_app(self): ), ] + if config.get_option("server.scriptHealthCheckEnabled"): + routes.extend( + [ + ( + make_url_path_regex(base, "script-health-check"), + HealthHandler, + dict(callback=lambda: self.does_script_run_without_error()), + ) + ] + ) + if config.get_option("global.developmentMode"): LOGGER.debug("Serving static content from the Node dev server") else: @@ -396,8 +415,46 @@ def _set_state(self, new_state): self._state = new_state @property - def is_ready_for_browser_connection(self): - return self._state not in (State.INITIAL, State.STOPPING, State.STOPPED) + async def is_ready_for_browser_connection(self) -> Tuple[bool, str]: + if self._state not in (State.INITIAL, State.STOPPING, State.STOPPED): + return True, "ok" + + return False, "unavailable" + + async def does_script_run_without_error(self) -> Tuple[bool, str]: + """Load and execute the app's script to verify it runs without an error. + + Returns + ------- + (True, "ok") if the script completes without error, or (False, err_msg) + if the script raises an exception. + """ + session = ReportSession( + ioloop=self._ioloop, + script_path=self._script_path, + command_line=self._command_line, + uploaded_file_manager=self._uploaded_file_mgr, + ) + + try: + session.request_rerun(None) + + now = time.perf_counter() + while ( + SCRIPT_RUN_WITHOUT_ERRORS_KEY not in session.session_state + and (time.perf_counter() - now) < SCRIPT_RUN_CHECK_TIMEOUT + ): + await tornado.gen.sleep(0.1) + + if SCRIPT_RUN_WITHOUT_ERRORS_KEY not in session.session_state: + return False, "timeout" + + ok = session.session_state[SCRIPT_RUN_WITHOUT_ERRORS_KEY] + msg = "ok" if ok else "error" + + return ok, msg + finally: + session.shutdown() @property def browser_is_connected(self): diff --git a/lib/streamlit/state/session_state.py b/lib/streamlit/state/session_state.py index e69239c23382..256fc712cff7 100644 --- a/lib/streamlit/state/session_state.py +++ b/lib/streamlit/state/session_state.py @@ -47,6 +47,11 @@ GENERATED_WIDGET_KEY_PREFIX = "$$GENERATED_WIDGET_KEY" +STREAMLIT_INTERNAL_KEY_PREFIX = "$$STREAMLIT_INTERNAL_KEY" +SCRIPT_RUN_WITHOUT_ERRORS_KEY = ( + f"{STREAMLIT_INTERNAL_KEY_PREFIX}_SCRIPT_RUN_WITHOUT_ERRORS" +) + @attr.s(auto_attribs=True, slots=True, frozen=True) class Serialized: @@ -304,7 +309,10 @@ def filtered_state(self) -> Dict[str, Any]: return { k: v for k, v in self._merged_state.items() - if not k.startswith(GENERATED_WIDGET_KEY_PREFIX) + if ( + not k.startswith(GENERATED_WIDGET_KEY_PREFIX) + and not k.startswith(STREAMLIT_INTERNAL_KEY_PREFIX) + ) } def is_new_state_value(self, key: str) -> bool: diff --git a/lib/tests/streamlit/config_test.py b/lib/tests/streamlit/config_test.py index 4fc3ffc12ad0..343de74a567d 100644 --- a/lib/tests/streamlit/config_test.py +++ b/lib/tests/streamlit/config_test.py @@ -329,6 +329,7 @@ def test_config_option_keys(self): "server.baseUrlPath", "server.enableCORS", "server.cookieSecret", + "server.scriptHealthCheckEnabled", "server.enableWebsocketCompression", "server.enableXsrfProtection", "server.fileWatcherType", diff --git a/lib/tests/streamlit/server_test.py b/lib/tests/streamlit/server_test.py index bd8e188f0419..0ff51fa312c5 100644 --- a/lib/tests/streamlit/server_test.py +++ b/lib/tests/streamlit/server_test.py @@ -14,9 +14,11 @@ """Server.py unit tests""" import os +import shutil from unittest import mock from unittest.mock import MagicMock, patch import unittest +import tempfile import pytest import tornado.testing @@ -28,7 +30,6 @@ import streamlit.server.server from streamlit import config, RootContainer from streamlit.cursor import make_delta_path -from streamlit.report_session import ReportSession from streamlit.uploaded_file_manager import UploadedFileRec from streamlit.server.server import MAX_PORT_SEARCH_RETRIES from streamlit.forward_msg_cache import ForwardMsgCache @@ -36,6 +37,7 @@ from streamlit.elements import legacy_data_frame as data_frame from streamlit.proto.ForwardMsg_pb2 import ForwardMsg from streamlit.server.server import State +from streamlit.server.server import Server from streamlit.server.server import start_listening from streamlit.server.server import RetriesExceeded from streamlit.server.routes import DebugHandler @@ -45,6 +47,7 @@ from streamlit.server.server_util import is_cacheable_msg from streamlit.server.server_util import is_url_from_allowed_origins from streamlit.server.server_util import serialize_forward_msg +from streamlit.watcher import event_based_file_watcher from tests.server_test_case import ServerTestCase from streamlit.logger import get_logger @@ -421,8 +424,8 @@ def setUp(self): super(HealthHandlerTest, self).setUp() self._is_healthy = True - def is_healthy(self): - return self._is_healthy + async def is_healthy(self): + return self._is_healthy, "ok" def get_app(self): return tornado.web.Application( @@ -593,3 +596,112 @@ def test_message_cache(self): # Cache misses self.assertEqual(404, self.fetch("/message").code) self.assertEqual(404, self.fetch("/message?id=non_existent").code) + + +class ScriptCheckTest(tornado.testing.AsyncTestCase): + def setUp(self) -> None: + super().setUp() + + self._home = tempfile.mkdtemp() + self._old_home = os.environ["HOME"] + os.environ["HOME"] = self._home + + self._fd, self._path = tempfile.mkstemp() + self._server = Server(self.io_loop, self._path, "test command line") + + def tearDown(self) -> None: + self._server.stop() + Server._singleton = None + + if event_based_file_watcher._MultiFileWatcher._singleton is not None: + event_based_file_watcher._MultiFileWatcher.get_singleton().close() + event_based_file_watcher._MultiFileWatcher._singleton = None + + os.environ["HOME"] = self._old_home + os.remove(self._path) + shutil.rmtree(self._home) + + super().tearDown() + + @tornado.testing.gen_test(timeout=30) + async def test_invalid_script(self): + await self._check_script_loading( + "import streamlit as st\n\nst.deprecatedWrite('test')", + False, + "error", + ) + + @tornado.testing.gen_test(timeout=30) + async def test_valid_script(self): + await self._check_script_loading( + "import streamlit as st\n\nst.write('test')", True, "ok" + ) + + @tornado.testing.gen_test(timeout=30) + async def test_timeout_script(self): + try: + streamlit.server.server.SCRIPT_RUN_CHECK_TIMEOUT = 0.1 + await self._check_script_loading( + "import time\n\ntime.sleep(5)", False, "timeout" + ) + finally: + streamlit.server.server.SCRIPT_RUN_CHECK_TIMEOUT = 60 + + async def _check_script_loading(self, script, expected_loads, expected_msg): + with os.fdopen(self._fd, "w") as tmp: + tmp.write(script) + + ok, msg = await self._server.does_script_run_without_error() + event_based_file_watcher._MultiFileWatcher.get_singleton().close() + event_based_file_watcher._MultiFileWatcher._singleton = None + self.assertEqual(expected_loads, ok) + self.assertEqual(expected_msg, msg) + + +class ScriptCheckEndpointExistsTest(tornado.testing.AsyncHTTPTestCase): + async def does_script_run_without_error(self): + return True, "test_message" + + def setUp(self): + self._server = Server(None, None, "test command line") + self._server.does_script_run_without_error = self.does_script_run_without_error + self._old_config = config.get_option("server.scriptHealthCheckEnabled") + config._set_option("server.scriptHealthCheckEnabled", True, "test") + super().setUp() + + def tearDown(self): + config._set_option("server.scriptHealthCheckEnabled", self._old_config, "test") + Server._singleton = None + super().tearDown() + + def get_app(self): + return self._server._create_app() + + def test_endpoint(self): + response = self.fetch("/script-health-check") + self.assertEqual(200, response.code) + self.assertEqual(b"test_message", response.body) + + +class ScriptCheckEndpointDoesNotExistTest(tornado.testing.AsyncHTTPTestCase): + async def does_script_run_without_error(self): + self.fail("Should not be called") + + def setUp(self): + self._server = Server(None, None, "test command line") + self._server.does_script_run_without_error = self.does_script_run_without_error + self._old_config = config.get_option("server.scriptHealthCheckEnabled") + config._set_option("server.scriptHealthCheckEnabled", False, "test") + super().setUp() + + def tearDown(self): + config._set_option("server.scriptHealthCheckEnabled", self._old_config, "test") + Server._singleton = None + super().tearDown() + + def get_app(self): + return self._server._create_app() + + def test_endpoint(self): + response = self.fetch("/script-health-check") + self.assertEqual(404, response.code)
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
streamlit__streamlit-3550@eaf6981
streamlit/streamlit
Python
3,550
Allow set_page_config after callbacks
fixes #3410 We only allow set_page_config to be called once per script run, and we do not allow any other st calls to execute prior to it. Callbacks changed things, and st calls are now allowed to run before set_page_config. This change ensures the following: - callbacks can call st commands without throwing an exception for `set_page_config` - `set_page_config` remains the first st command allowed when the script runs - In any case, we prevent `set_page_config` from being called twice.
2021-07-14T18:42:08Z
StreamlitAPIException : set_page_config() can only be called once per app, and must be called as the first Streamlit command in your script. I am facing this issue when trying to set page config in a multi-page app. However, I have an idea of setting the default layout to wide which will solve my issue but how to do so?
I was facing a similar issue, Below solved the issue is my case: in your `main.py` file, add the page config to the state object. ```python import streamlit as st from SessionState import _get_state state = _get_state() state.page_config = st.set_page_config( page_title="BPJV SI Database Manager test", layout="wide", initial_sidebar_state="expanded", ) # rest of the code state.sync() ``` for state object see [this](https://gist.github.com/tvst/036da038ab3e999a64497f42de966a92) gist i am facing this issue when use streamlit official state (0.84) for multi page Hey @afblaze94 and @muhammadAgfian96 ! Thanks for reaching out to us. I am not sure I understand the problem. Are you asking to be able to set different configs based on the page? I have the same issue. Actually, it is because my callbacks are executed at the beginning of the script and so, st_page_config is no longer the first Streamlit instruction. As described, st_page_config must be the very first st instruction but it's not always possible when callbacks contain st codes. Totally understand. I have put this in front of our Product team to take a look.
[ { "body": "I am facing this issue when trying to set page config in a multi-page app. However, I have an idea of setting the default layout to wide which will solve my issue but how to do so?\r\n", "number": 3410, "title": "StreamlitAPIException : set_page_config() can only be called once per app, and must be called as the first Streamlit command in your script." } ]
b817390cda935f5e3c0410c3c842fd6db764b939
{ "head_commit": "eaf6981118b8bb485e7d1dbfe3e8515326494792", "head_commit_message": "combine conditions", "patch_to_review": "diff --git a/e2e/scripts/st_set_page_config.py b/e2e/scripts/st_set_page_config.py\nindex e7c992df9681..c08122f76c9a 100644\n--- a/e2e/scripts/st_set_page_config.py\n+++ b/e2e/scripts/st_set_page_config.py\n@@ -22,3 +22,41 @@\n )\n st.sidebar.button(\"Sidebar!\")\n st.markdown(\"Main!\")\n+\n+\n+def show_balloons():\n+ st.balloons()\n+\n+\n+st.button(\"Balloons\", on_click=show_balloons)\n+\n+\n+def double_set_page_config():\n+ st.set_page_config(\n+ page_title=\"Change 1\",\n+ page_icon=\":shark:\",\n+ layout=\"wide\",\n+ initial_sidebar_state=\"collapsed\",\n+ )\n+\n+ st.set_page_config(\n+ page_title=\"Change 2\",\n+ page_icon=\":shark:\",\n+ layout=\"wide\",\n+ initial_sidebar_state=\"collapsed\",\n+ )\n+\n+\n+st.button(\"Double Set Page Config\", on_click=double_set_page_config)\n+\n+\n+def single_set_page_config():\n+ st.set_page_config(\n+ page_title=\"Change 3\",\n+ page_icon=\":shark:\",\n+ layout=\"wide\",\n+ initial_sidebar_state=\"collapsed\",\n+ )\n+\n+\n+st.button(\"Single Set Page Config\", on_click=single_set_page_config)\ndiff --git a/e2e/specs/st_set_page_config.spec.js b/e2e/specs/st_set_page_config.spec.js\nindex 0d1e775bccbe..f948ab4b7571 100644\n--- a/e2e/specs/st_set_page_config.spec.js\n+++ b/e2e/specs/st_set_page_config.spec.js\n@@ -51,4 +51,47 @@ describe(\"st.set_page_config\", () => {\n \"wide-mode\"\n );\n });\n+\n+ describe(\"double-setting set_page_config\", () => {\n+ beforeEach(() => {\n+ // Rerun the script to ensure a fresh slate.\n+ // This is done by typing r on the page\n+ cy.get(\"body\").type(\"r\");\n+ // Ensure the rerun completes\n+ cy.wait(1000);\n+ });\n+\n+ it(\"should not display an error when st command is used in a callback\", () => {\n+ cy.get(\".stButton button\")\n+ .eq(1)\n+ .click();\n+\n+ cy.get(\".stException\").should(\"not.exist\");\n+ cy.title().should(\"eq\", \"Heya, world?\");\n+ });\n+\n+ it(\"Should display an error when st.set_page_config is called multiple times in a callback\", () => {\n+ cy.get(\".stButton button\")\n+ .eq(2)\n+ .click();\n+\n+ cy.get(\".stException\")\n+ .contains(\"set_page_config() can only be called once per app\")\n+ .should(\"exist\");\n+ // Ensure that the first set_page_config worked\n+ cy.title().should(\"eq\", \"Change 1\");\n+ });\n+\n+ it(\"Should display an error when st.set_page_config is called multiple times in a callback\", () => {\n+ cy.get(\".stButton button\")\n+ .eq(3)\n+ .click();\n+\n+ cy.get(\".stException\")\n+ .contains(\"set_page_config() can only be called once per app\")\n+ .should(\"exist\");\n+ // Ensure that the first set_page_config worked\n+ cy.title().should(\"eq\", \"Change 3\");\n+ });\n+ });\n });\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png\nindex e8a72c1db00f..8bfaff6eddd6 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png differ\ndiff --git a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png\nindex 12267b69a0ee..402e75a7904c 100644\nBinary files a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png and b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png differ\ndiff --git a/lib/streamlit/report_thread.py b/lib/streamlit/report_thread.py\nindex 1f3e65d86b48..66d679c6c3b8 100644\n--- a/lib/streamlit/report_thread.py\n+++ b/lib/streamlit/report_thread.py\n@@ -73,6 +73,7 @@ def __init__(\n self.uploaded_file_mgr = uploaded_file_mgr\n # set_page_config is allowed at most once, as the very first st.command\n self._set_page_config_allowed = True\n+ self._has_script_started = False\n # Stack of DGs used for the with block. The current one is at the end.\n self.dg_stack: List[\"streamlit.delta_generator.DeltaGenerator\"] = []\n \n@@ -86,6 +87,10 @@ def reset(self, query_string: str = \"\") -> None:\n self.query_string = query_string\n # Permit set_page_config when the ReportContext is reused on a rerun\n self._set_page_config_allowed = True\n+ self._has_script_started = False\n+\n+ def on_script_start(self) -> None:\n+ self._has_script_started = True\n \n def enqueue(self, msg: ForwardMsg) -> None:\n if msg.HasField(\"page_config_changed\") and not self._set_page_config_allowed:\n@@ -96,7 +101,12 @@ def enqueue(self, msg: ForwardMsg) -> None:\n + \"(https://docs.streamlit.io/en/stable/api.html#streamlit.set_page_config).\"\n )\n \n- if msg.HasField(\"delta\") or msg.HasField(\"page_config_changed\"):\n+ # We want to disallow set_page config if one of the following occurs:\n+ # - set_page_config was called on this message\n+ # - The script has already started and a different st call occurs (a delta)\n+ if msg.HasField(\"page_config_changed\") or (\n+ msg.HasField(\"delta\") and self._has_script_started\n+ ):\n self._set_page_config_allowed = False\n \n self._enqueue(msg)\ndiff --git a/lib/streamlit/script_runner.py b/lib/streamlit/script_runner.py\nindex 11b4409c6ce6..5a0a8c3f4a33 100644\n--- a/lib/streamlit/script_runner.py\n+++ b/lib/streamlit/script_runner.py\n@@ -346,6 +346,7 @@ def _run_script(self, rerun_data):\n \n self._session_state.call_callbacks()\n \n+ ctx.on_script_start()\n exec(code, module.__dict__)\n \n except RerunException as e:\ndiff --git a/lib/tests/streamlit/report_context_test.py b/lib/tests/streamlit/report_context_test.py\nindex 840a7a7a6fbe..43b112b1b57d 100644\n--- a/lib/tests/streamlit/report_context_test.py\n+++ b/lib/tests/streamlit/report_context_test.py\n@@ -42,7 +42,8 @@ def test_set_page_config_immutable(self):\n ctx.enqueue(msg)\n \n def test_set_page_config_first(self):\n- \"\"\"st.set_page_config must be called before other st commands\"\"\"\n+ \"\"\"st.set_page_config must be called before other st commands\n+ when the script has been marked as started\"\"\"\n \n fake_enqueue = lambda msg: None\n ctx = ReportContext(\n@@ -53,6 +54,8 @@ def test_set_page_config_first(self):\n UploadedFileManager(),\n )\n \n+ ctx.on_script_start()\n+\n markdown_msg = ForwardMsg()\n markdown_msg.delta.new_element.markdown.body = \"foo\"\n \n@@ -63,6 +66,30 @@ def test_set_page_config_first(self):\n with self.assertRaises(StreamlitAPIException):\n ctx.enqueue(msg)\n \n+ def test_disallow_set_page_config_twice(self):\n+ \"\"\"st.set_page_config must be called before other st commands\n+ when the script has been marked as started\"\"\"\n+\n+ fake_enqueue = lambda msg: None\n+ ctx = ReportContext(\n+ \"TestSessionID\",\n+ fake_enqueue,\n+ \"\",\n+ SessionState(),\n+ UploadedFileManager(),\n+ )\n+\n+ ctx.on_script_start()\n+\n+ msg = ForwardMsg()\n+ msg.page_config_changed.title = \"foo\"\n+ ctx.enqueue(msg)\n+\n+ with self.assertRaises(StreamlitAPIException):\n+ same_msg = ForwardMsg()\n+ same_msg.page_config_changed.title = \"bar\"\n+ ctx.enqueue(same_msg)\n+\n def test_set_page_config_reset(self):\n \"\"\"st.set_page_config should be allowed after a rerun\"\"\"\n \n@@ -75,12 +102,15 @@ def test_set_page_config_reset(self):\n UploadedFileManager(),\n )\n \n+ ctx.on_script_start()\n+\n msg = ForwardMsg()\n msg.page_config_changed.title = \"foo\"\n \n ctx.enqueue(msg)\n ctx.reset()\n try:\n+ ctx.on_script_start()\n ctx.enqueue(msg)\n except StreamlitAPIException:\n self.fail(\"set_page_config should have succeeded after reset!\")\n" }
[ { "diff_hunk": "@@ -63,6 +66,30 @@ def test_set_page_config_first(self):\n with self.assertRaises(StreamlitAPIException):\n ctx.enqueue(msg)\n \n+ def test_disallow_set_page_config_twice(self):\n+ \"\"\"st.set_page_config must be called before other st commands", "line": null, "original_line": 70, "original_start_line": null, "path": "lib/tests/streamlit/report_context_test.py", "start_line": null, "text": "@user1:\nnit: comment seems to have been copied from the test above and doesn't quite apply here" } ]
04d7806e55aede5f39a38eb2faad3abb9214f055
diff --git a/e2e/scripts/st_set_page_config.py b/e2e/scripts/st_set_page_config.py index e7c992df9681..c08122f76c9a 100644 --- a/e2e/scripts/st_set_page_config.py +++ b/e2e/scripts/st_set_page_config.py @@ -22,3 +22,41 @@ ) st.sidebar.button("Sidebar!") st.markdown("Main!") + + +def show_balloons(): + st.balloons() + + +st.button("Balloons", on_click=show_balloons) + + +def double_set_page_config(): + st.set_page_config( + page_title="Change 1", + page_icon=":shark:", + layout="wide", + initial_sidebar_state="collapsed", + ) + + st.set_page_config( + page_title="Change 2", + page_icon=":shark:", + layout="wide", + initial_sidebar_state="collapsed", + ) + + +st.button("Double Set Page Config", on_click=double_set_page_config) + + +def single_set_page_config(): + st.set_page_config( + page_title="Change 3", + page_icon=":shark:", + layout="wide", + initial_sidebar_state="collapsed", + ) + + +st.button("Single Set Page Config", on_click=single_set_page_config) diff --git a/e2e/specs/st_set_page_config.spec.js b/e2e/specs/st_set_page_config.spec.js index 0d1e775bccbe..ad285737d93f 100644 --- a/e2e/specs/st_set_page_config.spec.js +++ b/e2e/specs/st_set_page_config.spec.js @@ -51,4 +51,47 @@ describe("st.set_page_config", () => { "wide-mode" ); }); + + describe("double-setting set_page_config", () => { + beforeEach(() => { + // Rerun the script to ensure a fresh slate. + // This is done by typing r on the page + cy.get("body").type("r"); + // Ensure the rerun completes + cy.wait(1000); + }); + + it("should not display an error when st.set_page_config is used after an st.* command in a callback", () => { + cy.get(".stButton button") + .eq(1) + .click(); + + cy.get(".stException").should("not.exist"); + cy.title().should("eq", "Heya, world?"); + }); + + it("should display an error when st.set_page_config is called multiple times in a callback", () => { + cy.get(".stButton button") + .eq(2) + .click(); + + cy.get(".stException") + .contains("set_page_config() can only be called once per app") + .should("exist"); + // Ensure that the first set_page_config worked + cy.title().should("eq", "Change 1"); + }); + + it("should display an error when st.set_page_config is called after being called in a callback", () => { + cy.get(".stButton button") + .eq(3) + .click(); + + cy.get(".stException") + .contains("set_page_config() can only be called once per app") + .should("exist"); + // Ensure that the first set_page_config worked + cy.title().should("eq", "Change 3"); + }); + }); }); diff --git a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png index e8a72c1db00f..8bfaff6eddd6 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png and b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode-dark.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png index 12267b69a0ee..402e75a7904c 100644 Binary files a/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png and b/frontend/cypress/snapshots/linux/2x/st_set_page_config.spec.js/wide-mode.snap.png differ diff --git a/lib/streamlit/report_thread.py b/lib/streamlit/report_thread.py index 1f3e65d86b48..66d679c6c3b8 100644 --- a/lib/streamlit/report_thread.py +++ b/lib/streamlit/report_thread.py @@ -73,6 +73,7 @@ def __init__( self.uploaded_file_mgr = uploaded_file_mgr # set_page_config is allowed at most once, as the very first st.command self._set_page_config_allowed = True + self._has_script_started = False # Stack of DGs used for the with block. The current one is at the end. self.dg_stack: List["streamlit.delta_generator.DeltaGenerator"] = [] @@ -86,6 +87,10 @@ def reset(self, query_string: str = "") -> None: self.query_string = query_string # Permit set_page_config when the ReportContext is reused on a rerun self._set_page_config_allowed = True + self._has_script_started = False + + def on_script_start(self) -> None: + self._has_script_started = True def enqueue(self, msg: ForwardMsg) -> None: if msg.HasField("page_config_changed") and not self._set_page_config_allowed: @@ -96,7 +101,12 @@ def enqueue(self, msg: ForwardMsg) -> None: + "(https://docs.streamlit.io/en/stable/api.html#streamlit.set_page_config)." ) - if msg.HasField("delta") or msg.HasField("page_config_changed"): + # We want to disallow set_page config if one of the following occurs: + # - set_page_config was called on this message + # - The script has already started and a different st call occurs (a delta) + if msg.HasField("page_config_changed") or ( + msg.HasField("delta") and self._has_script_started + ): self._set_page_config_allowed = False self._enqueue(msg) diff --git a/lib/streamlit/script_runner.py b/lib/streamlit/script_runner.py index 11b4409c6ce6..5a0a8c3f4a33 100644 --- a/lib/streamlit/script_runner.py +++ b/lib/streamlit/script_runner.py @@ -346,6 +346,7 @@ def _run_script(self, rerun_data): self._session_state.call_callbacks() + ctx.on_script_start() exec(code, module.__dict__) except RerunException as e: diff --git a/lib/tests/streamlit/report_context_test.py b/lib/tests/streamlit/report_context_test.py index 840a7a7a6fbe..4d708c521afb 100644 --- a/lib/tests/streamlit/report_context_test.py +++ b/lib/tests/streamlit/report_context_test.py @@ -42,7 +42,8 @@ def test_set_page_config_immutable(self): ctx.enqueue(msg) def test_set_page_config_first(self): - """st.set_page_config must be called before other st commands""" + """st.set_page_config must be called before other st commands + when the script has been marked as started""" fake_enqueue = lambda msg: None ctx = ReportContext( @@ -53,6 +54,8 @@ def test_set_page_config_first(self): UploadedFileManager(), ) + ctx.on_script_start() + markdown_msg = ForwardMsg() markdown_msg.delta.new_element.markdown.body = "foo" @@ -63,6 +66,29 @@ def test_set_page_config_first(self): with self.assertRaises(StreamlitAPIException): ctx.enqueue(msg) + def test_disallow_set_page_config_twice(self): + """st.set_page_config cannot be called twice""" + + fake_enqueue = lambda msg: None + ctx = ReportContext( + "TestSessionID", + fake_enqueue, + "", + SessionState(), + UploadedFileManager(), + ) + + ctx.on_script_start() + + msg = ForwardMsg() + msg.page_config_changed.title = "foo" + ctx.enqueue(msg) + + with self.assertRaises(StreamlitAPIException): + same_msg = ForwardMsg() + same_msg.page_config_changed.title = "bar" + ctx.enqueue(same_msg) + def test_set_page_config_reset(self): """st.set_page_config should be allowed after a rerun""" @@ -75,12 +101,15 @@ def test_set_page_config_reset(self): UploadedFileManager(), ) + ctx.on_script_start() + msg = ForwardMsg() msg.page_config_changed.title = "foo" ctx.enqueue(msg) ctx.reset() try: + ctx.on_script_start() ctx.enqueue(msg) except StreamlitAPIException: self.fail("set_page_config should have succeeded after reset!")
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
streamlit__streamlit-2317@69bfab7
streamlit/streamlit
Python
2,317
Send initialization data in the NewReport message
Removes `ForwardMsg.initialize`. All initialization data is now carried in every `ForwardMsg.new_report` message. This lets us remove the state from the server that stores whether a given client has already received the initialize message. This flag had a race condition - it was possible for the flag to be set to true, but the client not to actually have received the message, if a script used the `st.experimental_rerun` function towards the top of a script. - (Python) `report_session. _maybe_enqueue_initialize_message` (and its associated flag) is gone. - (frontend) `App.handleNewReport` now conditionally calls through to a new `App.handleOneTimeInitialization` function for the first new report message. There's logic that detects and errors if the initialize data unexpectedly changes from message to message. - (frontend) Several new `App.test.tsx` tests that make sure initialization/newReport handling is correct. Fixes #2226
2020-11-04T23:31:38Z
Script rerun event can cause `SessionInfo not initialized` error on the frontend If a script is rerun very quickly after being started, it's possible to trigger an error on the frontend ("Tried to use SessionInfo before it was initialized"). An easy repro is our `streamlit run e2e/scripts/st_experimental_rerun.py` test script. It _seems like_ what is happening is: - `ReportSession._on_scriptrunner_event`receives a `SCRIPT_STARTED` event - It enqueues two messages on its `self._report` object: "initialize" and "new_report" - _Before those messages are processed by the Report_, the rerun request is handled, `ReportSession` gets a _second_ `SCRIPT_STARTED` event, it clears the Report queue, and _does not re-enqueue_ the "initialize" event (because it's already been enqueued once for the session). Should we possibly always enqueue the initialize message whenever a run starts? It's a bit of extra overhead, but it's probably the right thing to do. Right now we're mistakenly assuming state on the client that doesn't always exist.
I don't know if we can send two Initialize messages in a row. I think a lot of our code expects Initialize to only be sent once per session. But if we can change this, that would be great! Another option: don't set the boolean `self._sent_initialize_message = True` until the message is _actually_ sent. But this would take a little refactoring too...
[ { "body": "If a script is rerun very quickly after being started, it's possible to trigger an error on the frontend (\"Tried to use SessionInfo before it was initialized\").\r\n\r\nAn easy repro is our `streamlit run e2e/scripts/st_experimental_rerun.py` test script.\r\n\r\nIt _seems like_ what is happening is:\r\n- `ReportSession._on_scriptrunner_event`receives a `SCRIPT_STARTED` event\r\n- It enqueues two messages on its `self._report` object: \"initialize\" and \"new_report\"\r\n- _Before those messages are processed by the Report_, the rerun request is handled, `ReportSession` gets a _second_ `SCRIPT_STARTED` event, it clears the Report queue, and _does not re-enqueue_ the \"initialize\" event (because it's already been enqueued once for the session).\r\n\r\nShould we possibly always enqueue the initialize message whenever a run starts? It's a bit of extra overhead, but it's probably the right thing to do. Right now we're mistakenly assuming state on the client that doesn't always exist.", "number": 2226, "title": "Script rerun event can cause `SessionInfo not initialized` error on the frontend" } ]
4996fe9f496288995337a3315d5869e2e7e7cb90
{ "head_commit": "69bfab700cba8acc0ca34b1bb5e0860dbded1ac7", "head_commit_message": "Don't error if SessionInfo data changes\n\nThis is totally legit - it happens when the Streamlit server is killed and restarted, and the client reconnects.\n\nTest SessionInfo.fromInitializeMessage", "patch_to_review": "diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx\nindex b2c55495dd53..c00f1a25d012 100644\n--- a/frontend/src/App.test.tsx\n+++ b/frontend/src/App.test.tsx\n@@ -17,7 +17,7 @@\n \n import React from \"react\"\n import { shallow, mount, ReactWrapper } from \"enzyme\"\n-import { ForwardMsg } from \"autogen/proto\"\n+import { ForwardMsg, NewReport } from \"autogen/proto\"\n import { IMenuItem } from \"hocs/withS4ACommunication/types\"\n import { MetricsManager } from \"./lib/MetricsManager\"\n import { getMetricsManagerForTest } from \"./lib/MetricsManagerTestUtils\"\n@@ -28,17 +28,10 @@ import MainMenu from \"./components/core/MainMenu\"\n \n const getProps = (extend?: Partial<Props>): Props => ({\n screenCast: {\n+ currentState: \"OFF\",\n toggleRecordAudio: jest.fn(),\n startRecording: jest.fn(),\n stopRecording: jest.fn(),\n- fileName: \"\",\n- recording: false,\n- recordAudio: false,\n- countdown: -1,\n- startAnimation: false,\n- showRecordedDialog: false,\n- showScreencastDialog: false,\n- showUnsupportedDialog: false,\n },\n s4aCommunication: {\n connect: jest.fn(),\n@@ -84,7 +77,8 @@ describe(\"App\", () => {\n })\n \n afterEach(() => {\n- SessionInfo.singleton = undefined\n+ const UnsafeSessionInfo = SessionInfo as any\n+ UnsafeSessionInfo.singleton = undefined\n })\n \n it(\"renders without crashing\", () => {\n@@ -93,7 +87,7 @@ describe(\"App\", () => {\n expect(wrapper.html()).not.toBeNull()\n })\n \n- it(\"should reload when streamlit server version changes\", () => {\n+ it(\"reloads when streamlit server version changes\", () => {\n const props = getProps()\n const wrapper = shallow(<App {...props} />)\n \n@@ -101,14 +95,16 @@ describe(\"App\", () => {\n \n const fwMessage = new ForwardMsg()\n \n- fwMessage.initialize = {\n- environmentInfo: {\n- streamlitVersion: \"svv\",\n+ fwMessage.newReport = {\n+ initialize: {\n+ environmentInfo: {\n+ streamlitVersion: \"svv\",\n+ },\n+ sessionId: \"sessionId\",\n+ userInfo: {},\n+ config: {},\n+ sessionState: {},\n },\n- sessionId: \"sessionId\",\n- userInfo: {},\n- config: {},\n- sessionState: {},\n }\n \n // @ts-ignore\n@@ -117,7 +113,7 @@ describe(\"App\", () => {\n expect(window.location.reload).toHaveBeenCalled()\n })\n \n- it(\"should start screencast recording when the MainMenu is clicked\", () => {\n+ it(\"starts screencast recording when the MainMenu is clicked\", () => {\n const props = getProps()\n const wrapper = shallow(<App {...props} />)\n \n@@ -135,7 +131,7 @@ describe(\"App\", () => {\n )\n })\n \n- it(\"should stop screencast when esc is pressed\", () => {\n+ it(\"stops screencast when esc is pressed\", () => {\n const props = getProps()\n const wrapper = shallow(<App {...props} />)\n \n@@ -145,7 +141,7 @@ describe(\"App\", () => {\n expect(props.screenCast.stopRecording).toBeCalled()\n })\n \n- it(\"should show s4aMenuItems\", () => {\n+ it(\"shows s4aMenuItems\", () => {\n const props = getProps({\n s4aCommunication: {\n connect: jest.fn(),\n@@ -167,3 +163,82 @@ describe(\"App\", () => {\n ])\n })\n })\n+\n+describe(\"App.handleNewReport\", () => {\n+ const NEW_REPORT = new NewReport({\n+ initialize: {\n+ userInfo: {\n+ installationId: \"installationId\",\n+ installationIdV1: \"installationIdV1\",\n+ installationIdV2: \"installationIdV2\",\n+ email: \"email\",\n+ },\n+ config: {\n+ sharingEnabled: false,\n+ gatherUsageStats: false,\n+ maxCachedMessageAge: 0,\n+ mapboxToken: \"mapboxToken\",\n+ allowRunOnSave: false,\n+ },\n+ environmentInfo: {\n+ streamlitVersion: \"streamlitVersion\",\n+ pythonVersion: \"pythonVersion\",\n+ },\n+ sessionState: {\n+ runOnSave: false,\n+ reportIsRunning: false,\n+ },\n+ sessionId: \"sessionId\",\n+ commandLine: \"commandLine\",\n+ },\n+ })\n+\n+ afterEach(() => {\n+ const UnsafeSessionInfo = SessionInfo as any\n+ UnsafeSessionInfo.singleton = undefined\n+ })\n+\n+ it(\"performs one-time initialization\", () => {\n+ const wrapper = shallow(<App {...getProps()} />)\n+ const app = wrapper.instance()\n+\n+ const oneTimeInitialization = jest.spyOn(\n+ app,\n+ // @ts-ignore\n+ \"handleOneTimeInitialization\"\n+ )\n+\n+ expect(SessionInfo.isSet()).toBe(false)\n+\n+ // @ts-ignore\n+ app.handleNewReport(NEW_REPORT)\n+\n+ expect(oneTimeInitialization).toHaveBeenCalledTimes(1)\n+ expect(SessionInfo.isSet()).toBe(true)\n+ })\n+\n+ it(\"performs one-time initialization only once\", () => {\n+ const wrapper = shallow(<App {...getProps()} />)\n+ const app = wrapper.instance()\n+\n+ const oneTimeInitialization = jest.spyOn(\n+ app,\n+ // @ts-ignore\n+ \"handleOneTimeInitialization\"\n+ )\n+\n+ expect(SessionInfo.isSet()).toBe(false)\n+\n+ // @ts-ignore\n+ app.handleNewReport(NEW_REPORT)\n+ // @ts-ignore\n+ app.handleNewReport(NEW_REPORT)\n+ // @ts-ignore\n+ app.handleNewReport(NEW_REPORT)\n+\n+ // Multiple NEW_REPORT messages should not result in one-time\n+ // initialization being performed more than once.\n+ expect(oneTimeInitialization).toHaveBeenCalledTimes(1)\n+ expect(SessionInfo.isSet()).toBe(true)\n+ })\n+})\ndiff --git a/frontend/src/App.tsx b/frontend/src/App.tsx\nindex d3a12e604ae8..9146c33e2bef 100644\n--- a/frontend/src/App.tsx\n+++ b/frontend/src/App.tsx\n@@ -54,6 +54,7 @@ import {\n SessionEvent,\n WidgetStates,\n SessionState,\n+ Config,\n } from \"autogen/proto\"\n \n import { RERUN_PROMPT_MODAL_DIALOG } from \"lib/baseconsts\"\n@@ -287,14 +288,12 @@ export class App extends PureComponent<Props, State> {\n \n try {\n dispatchProto(msgProto, \"type\", {\n- initialize: (initializeMsg: Initialize) =>\n- this.handleInitialize(initializeMsg),\n+ newReport: (newReportMsg: NewReport) =>\n+ this.handleNewReport(newReportMsg),\n sessionStateChanged: (msg: SessionState) =>\n this.handleSessionStateChanged(msg),\n sessionEvent: (evtMsg: SessionEvent) =>\n this.handleSessionEvent(evtMsg),\n- newReport: (newReportMsg: NewReport) =>\n- this.handleNewReport(newReportMsg),\n delta: (deltaMsg: Delta) =>\n this.handleDeltaMsg(\n deltaMsg,\n@@ -378,64 +377,6 @@ export class App extends PureComponent<Props, State> {\n })\n }\n \n- /**\n- * Handler for ForwardMsg.initialize messages\n- * @param initializeMsg an Initialize protobuf\n- */\n- handleInitialize = (initializeMsg: Initialize): void => {\n- const {\n- sessionId,\n- environmentInfo,\n- userInfo,\n- config,\n- sessionState,\n- } = initializeMsg\n-\n- if (\n- sessionId == null ||\n- !environmentInfo ||\n- !userInfo ||\n- !config ||\n- !sessionState\n- ) {\n- throw new Error(\"InitializeMsg is missing a required field\")\n- }\n-\n- if (App.hasStreamlitVersionChanged(initializeMsg)) {\n- window.location.reload()\n- return\n- }\n-\n- SessionInfo.current = new SessionInfo({\n- sessionId,\n- streamlitVersion: environmentInfo.streamlitVersion,\n- pythonVersion: environmentInfo.pythonVersion,\n- installationId: userInfo.installationId,\n- installationIdV1: userInfo.installationIdV1,\n- installationIdV2: userInfo.installationIdV2,\n- authorEmail: userInfo.email,\n- maxCachedMessageAge: config.maxCachedMessageAge,\n- commandLine: initializeMsg.commandLine,\n- userMapboxToken: config.mapboxToken,\n- })\n-\n- MetricsManager.current.initialize({\n- gatherUsageStats: Boolean(config.gatherUsageStats),\n- })\n-\n- MetricsManager.current.enqueue(\"createReport\", {\n- pythonVersion: SessionInfo.current.pythonVersion,\n- })\n-\n- this.setState({\n- sharingEnabled: Boolean(config.sharingEnabled),\n- allowRunOnSave: Boolean(config.allowRunOnSave),\n- })\n-\n- this.props.s4aCommunication.connect()\n- this.handleSessionStateChanged(sessionState)\n- }\n-\n /**\n * Handler for ForwardMsg.sessionStateChanged messages\n * @param stateChangeProto a SessionState protobuf\n@@ -530,9 +471,24 @@ export class App extends PureComponent<Props, State> {\n * @param newReportProto a NewReport protobuf\n */\n handleNewReport = (newReportProto: NewReport): void => {\n+ const initialize = newReportProto.initialize as Initialize\n+\n+ if (App.hasStreamlitVersionChanged(initialize)) {\n+ window.location.reload()\n+ return\n+ }\n+\n+ // First, handle initialization logic. Each NewReport message has\n+ // initialization data. If this is the _first_ time we're receiving\n+ // the NewReport message, we perform some one-time initialization.\n+ if (!SessionInfo.isSet()) {\n+ // We're not initialized. Perform one-time initialization.\n+ this.handleOneTimeInitialization(initialize)\n+ }\n+\n const { reportHash } = this.state\n const {\n- id: reportId,\n+ reportId,\n name: reportName,\n scriptPath,\n deployParams,\n@@ -561,6 +517,31 @@ export class App extends PureComponent<Props, State> {\n }\n }\n \n+ /**\n+ * Performs one-time initialization. This is called from `handleNewReport`.\n+ */\n+ handleOneTimeInitialization = (initialize: Initialize): void => {\n+ SessionInfo.current = SessionInfo.fromInitializeMessage(initialize)\n+\n+ const config = initialize.config as Config\n+\n+ MetricsManager.current.initialize({\n+ gatherUsageStats: config.gatherUsageStats,\n+ })\n+\n+ MetricsManager.current.enqueue(\"createReport\", {\n+ pythonVersion: SessionInfo.current.pythonVersion,\n+ })\n+\n+ this.setState({\n+ sharingEnabled: config.sharingEnabled,\n+ allowRunOnSave: config.allowRunOnSave,\n+ })\n+\n+ this.props.s4aCommunication.connect()\n+ this.handleSessionStateChanged(initialize.sessionState)\n+ }\n+\n /**\n * Handler for ForwardMsg.reportFinished messages\n * @param status the ReportFinishedStatus that the report finished with\ndiff --git a/frontend/src/lib/SessionInfo.test.ts b/frontend/src/lib/SessionInfo.test.ts\nindex 0aba31cd1293..fda973e1a4cd 100644\n--- a/frontend/src/lib/SessionInfo.test.ts\n+++ b/frontend/src/lib/SessionInfo.test.ts\n@@ -16,7 +16,47 @@\n */\n \n import { SessionInfo } from \"lib/SessionInfo\"\n+import { Initialize } from \"../autogen/proto\"\n \n test(\"Throws an error when used before initialization\", () => {\n expect(() => SessionInfo.current).toThrow()\n })\n+\n+test(\"Can be initialized from a protobuf\", () => {\n+ const MESSAGE = new Initialize({\n+ userInfo: {\n+ installationId: \"installationId\",\n+ installationIdV1: \"installationIdV1\",\n+ installationIdV2: \"installationIdV2\",\n+ email: \"email\",\n+ },\n+ config: {\n+ sharingEnabled: false,\n+ gatherUsageStats: false,\n+ maxCachedMessageAge: 31,\n+ mapboxToken: \"mapboxToken\",\n+ allowRunOnSave: false,\n+ },\n+ environmentInfo: {\n+ streamlitVersion: \"streamlitVersion\",\n+ pythonVersion: \"pythonVersion\",\n+ },\n+ sessionState: {\n+ runOnSave: false,\n+ reportIsRunning: false,\n+ },\n+ sessionId: \"sessionId\",\n+ commandLine: \"commandLine\",\n+ })\n+\n+ const si = SessionInfo.fromInitializeMessage(MESSAGE)\n+ expect(si.sessionId).toEqual(\"sessionId\")\n+ expect(si.streamlitVersion).toEqual(\"streamlitVersion\")\n+ expect(si.pythonVersion).toEqual(\"pythonVersion\")\n+ expect(si.installationId).toEqual(\"installationId\")\n+ expect(si.installationIdV1).toEqual(\"installationIdV1\")\n+ expect(si.installationIdV2).toEqual(\"installationIdV2\")\n+ expect(si.authorEmail).toEqual(\"email\")\n+ expect(si.maxCachedMessageAge).toEqual(31)\n+ expect(si.commandLine).toEqual(\"commandLine\")\n+})\ndiff --git a/frontend/src/lib/SessionInfo.ts b/frontend/src/lib/SessionInfo.ts\nindex 1e683421c88e..c5b8fb7a8482 100644\n--- a/frontend/src/lib/SessionInfo.ts\n+++ b/frontend/src/lib/SessionInfo.ts\n@@ -14,6 +14,12 @@\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n+import {\n+ Config,\n+ EnvironmentInfo,\n+ Initialize,\n+ UserInfo,\n+} from \"../autogen/proto\"\n \n export interface Args {\n sessionId: string\n@@ -84,7 +90,27 @@ export class SessionInfo {\n return this.current.commandLine === \"streamlit hello\"\n }\n \n- constructor({\n+ /** Create a SessionInfo from the relevant bits of an initialize message. */\n+ public static fromInitializeMessage(initialize: Initialize): SessionInfo {\n+ const environmentInfo = initialize.environmentInfo as EnvironmentInfo\n+ const userInfo = initialize.userInfo as UserInfo\n+ const config = initialize.config as Config\n+\n+ return new SessionInfo({\n+ sessionId: initialize.sessionId,\n+ streamlitVersion: environmentInfo.streamlitVersion,\n+ pythonVersion: environmentInfo.pythonVersion,\n+ installationId: userInfo.installationId,\n+ installationIdV1: userInfo.installationIdV1,\n+ installationIdV2: userInfo.installationIdV2,\n+ authorEmail: userInfo.email,\n+ maxCachedMessageAge: config.maxCachedMessageAge,\n+ commandLine: initialize.commandLine,\n+ userMapboxToken: config.mapboxToken,\n+ })\n+ }\n+\n+ public constructor({\n sessionId,\n streamlitVersion,\n pythonVersion,\ndiff --git a/lib/streamlit/report_session.py b/lib/streamlit/report_session.py\nindex 5fb1037b5da7..75fdf9112260 100644\n--- a/lib/streamlit/report_session.py\n+++ b/lib/streamlit/report_session.py\n@@ -96,7 +96,6 @@ def __init__(self, ioloop, script_path, command_line, uploaded_file_manager):\n self._local_sources_watcher = LocalSourcesWatcher(\n self._report, self._on_source_file_changed\n )\n- self._sent_initialize_message = False\n self._storage = None\n self._maybe_reuse_previous_run = False\n self._run_on_save = config.get_option(\"server.runOnSave\")\n@@ -264,7 +263,6 @@ def _on_scriptrunner_event(self, event, exception=None, client_state=None):\n self._ioloop.spawn_callback(self._save_running_report)\n \n self._clear_queue()\n- self._maybe_enqueue_initialize_message()\n self._enqueue_new_report_message()\n \n elif (\n@@ -348,15 +346,39 @@ def _enqueue_file_change_message(self):\n msg.session_event.report_changed_on_disk = True\n self.enqueue(msg)\n \n- def _maybe_enqueue_initialize_message(self):\n- if self._sent_initialize_message:\n- return\n+ def get_deploy_params(self):\n+ try:\n+ from streamlit.git_util import GitRepo\n \n- self._sent_initialize_message = True\n+ self._repo = GitRepo(self._report.script_path)\n+ return self._repo.get_repo_info()\n+ except:\n+ # Issues can arise based on the git structure\n+ # (e.g. if branch is in DETACHED HEAD state,\n+ # git is not installed, etc)\n+ # In this case, catch any errors\n+ return None\n \n+ def _enqueue_new_report_message(self):\n+ self._report.generate_new_id()\n msg = ForwardMsg()\n- imsg = msg.initialize\n+ msg.new_report.report_id = self._report.report_id\n+ msg.new_report.name = self._report.name\n+ msg.new_report.script_path = self._report.script_path\n \n+ # git deploy params\n+ deploy_params = self.get_deploy_params()\n+ if deploy_params is not None:\n+ repo, branch, module = deploy_params\n+ msg.new_report.deploy_params.repository = repo\n+ msg.new_report.deploy_params.branch = branch\n+ msg.new_report.deploy_params.module = module\n+\n+ # Immutable session data. We send this every time a new report is\n+ # started, to avoid having to track whether the client has already\n+ # received it. It does not change from run to run; it's up to the\n+ # to perform one-time initialization only once.\n+ imsg = msg.new_report.initialize\n imsg.config.sharing_enabled = config.get_option(\"global.sharingMode\") != \"off\"\n \n imsg.config.gather_usage_stats = config.get_option(\"browser.gatherUsageStats\")\n@@ -369,16 +391,6 @@ def _maybe_enqueue_initialize_message(self):\n \n imsg.config.allow_run_on_save = config.get_option(\"server.allowRunOnSave\")\n \n- LOGGER.debug(\n- \"New browser connection: \"\n- \"gather_usage_stats=%s, \"\n- \"sharing_enabled=%s, \"\n- \"max_cached_message_age=%s\",\n- imsg.config.gather_usage_stats,\n- imsg.config.sharing_enabled,\n- imsg.config.max_cached_message_age,\n- )\n-\n imsg.environment_info.streamlit_version = __version__\n imsg.environment_info.python_version = \".\".join(map(str, sys.version_info))\n \n@@ -400,34 +412,6 @@ def _maybe_enqueue_initialize_message(self):\n \n self.enqueue(msg)\n \n- def get_deploy_params(self):\n- try:\n- from streamlit.git_util import GitRepo\n-\n- self._repo = GitRepo(self._report.script_path)\n- return self._repo.get_repo_info()\n- except:\n- # Issues can arise based on the git structure\n- # (e.g. if branch is in DETACHED HEAD state,\n- # git is not installed, etc)\n- # In this case, catch any errors\n- return None\n-\n- def _enqueue_new_report_message(self):\n- self._report.generate_new_id()\n- msg = ForwardMsg()\n- msg.new_report.id = self._report.report_id\n- msg.new_report.name = self._report.name\n- msg.new_report.script_path = self._report.script_path\n-\n- deploy_params = self.get_deploy_params()\n- if deploy_params is not None:\n- repo, branch, module = deploy_params\n- msg.new_report.deploy_params.repository = repo\n- msg.new_report.deploy_params.branch = branch\n- msg.new_report.deploy_params.module = module\n- self.enqueue(msg)\n-\n def _enqueue_report_finished_message(self, status):\n \"\"\"Enqueue a report_finished ForwardMsg.\n \ndiff --git a/lib/tests/streamlit/report_queue_test.py b/lib/tests/streamlit/report_queue_test.py\nindex 97ba8c4868e0..91eafdb07c63 100644\n--- a/lib/tests/streamlit/report_queue_test.py\n+++ b/lib/tests/streamlit/report_queue_test.py\n@@ -27,9 +27,9 @@\n # For the messages below, we don't really care about their contents so much as\n # their general type.\n \n-INIT_MSG = ForwardMsg()\n-INIT_MSG.initialize.config.sharing_enabled = True\n-INIT_MSG.initialize.config.allow_run_on_save = True\n+NEW_REPORT_MSG = ForwardMsg()\n+NEW_REPORT_MSG.new_report.initialize.config.sharing_enabled = True\n+NEW_REPORT_MSG.new_report.initialize.config.allow_run_on_save = True\n \n TEXT_DELTA_MSG1 = ForwardMsg()\n TEXT_DELTA_MSG1.delta.new_element.text.body = \"text1\"\n@@ -57,20 +57,20 @@ def test_simple_enqueue(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n self.assertFalse(rq.is_empty())\n queue = rq.flush()\n self.assertTrue(rq.is_empty())\n self.assertEqual(len(queue), 1)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n- self.assertTrue(queue[0].initialize.config.allow_run_on_save)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.allow_run_on_save)\n \n def test_enqueue_two(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path(\n RootContainer.MAIN, (), 0\n@@ -79,7 +79,7 @@ def test_enqueue_two(self):\n \n queue = rq.flush()\n self.assertEqual(len(queue), 2)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n self.assertEqual(\n make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path\n )\n@@ -89,7 +89,7 @@ def test_enqueue_three(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path(\n RootContainer.MAIN, (), 0\n@@ -103,7 +103,7 @@ def test_enqueue_three(self):\n \n queue = rq.flush()\n self.assertEqual(len(queue), 3)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n self.assertEqual(\n make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path\n )\n@@ -117,7 +117,7 @@ def test_replace_element(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path(\n RootContainer.MAIN, (), 0\n@@ -131,7 +131,7 @@ def test_replace_element(self):\n \n queue = rq.flush()\n self.assertEqual(len(queue), 2)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n self.assertEqual(\n make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path\n )\n@@ -141,7 +141,7 @@ def test_simple_add_rows(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path(\n RootContainer.MAIN, (), 0\n@@ -156,7 +156,7 @@ def test_simple_add_rows(self):\n \n queue = rq.flush()\n self.assertEqual(len(queue), 3)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n self.assertEqual(\n make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path\n )\n@@ -173,7 +173,7 @@ def test_add_rows_rerun(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n # Simulate rerun\n for i in range(2):\n@@ -194,7 +194,7 @@ def test_add_rows_rerun(self):\n \n queue = rq.flush()\n self.assertEqual(len(queue), 3)\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n self.assertEqual(\n make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path\n )\n@@ -212,7 +212,7 @@ def test_multiple_containers(self):\n rq = ReportQueue()\n self.assertTrue(rq.is_empty())\n \n- rq.enqueue(INIT_MSG)\n+ rq.enqueue(NEW_REPORT_MSG)\n \n def enqueue_deltas(container: RootContainer, path: Tuple[int, ...]):\n # We deep-copy the protos because we mutate each one\n@@ -248,7 +248,7 @@ def assert_deltas(container: RootContainer, path: Tuple[int, ...], idx: int):\n \n queue = rq.flush()\n self.assertEqual(5, len(queue))\n- self.assertTrue(queue[0].initialize.config.sharing_enabled)\n+ self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled)\n \n assert_deltas(RootContainer.MAIN, (), 1)\n assert_deltas(RootContainer.SIDEBAR, (0, 0, 1), 3)\ndiff --git a/lib/tests/streamlit/report_test.py b/lib/tests/streamlit/report_test.py\nindex 0bd9197726a4..898722523f38 100644\n--- a/lib/tests/streamlit/report_test.py\n+++ b/lib/tests/streamlit/report_test.py\n@@ -27,9 +27,9 @@\n from streamlit.proto.StaticManifest_pb2 import StaticManifest\n from tests import testutil\n \n-INIT_MSG = ForwardMsg()\n-INIT_MSG.initialize.config.sharing_enabled = True\n-INIT_MSG.metadata.delta_path[:] = make_delta_path(RootContainer.MAIN, (), 0)\n+NEW_REPORT_MSG = ForwardMsg()\n+NEW_REPORT_MSG.new_report.initialize.config.sharing_enabled = True\n+NEW_REPORT_MSG.metadata.delta_path[:] = make_delta_path(RootContainer.MAIN, (), 0)\n \n TEXT_DELTA_MSG = ForwardMsg()\n TEXT_DELTA_MSG.delta.new_element.text.body = \"text1\"\n@@ -56,7 +56,7 @@ def _parse_msg(msg_string):\n class ReportTest(unittest.TestCase):\n def test_serialize_final_report(self):\n report = Report(\"/not/a/script.py\", \"\")\n- _enqueue(report, INIT_MSG)\n+ _enqueue(report, NEW_REPORT_MSG)\n _enqueue(report, TEXT_DELTA_MSG)\n _enqueue(report, EMPTY_DELTA_MSG)\n \n@@ -65,7 +65,7 @@ def test_serialize_final_report(self):\n # Validate our messages.\n messages = [_parse_msg(msg_string) for _, msg_string in files[:-1]]\n self.assertEqual(3, len(messages))\n- self.assertEqual(\"initialize\", messages[0].WhichOneof(\"type\"))\n+ self.assertEqual(\"new_report\", messages[0].WhichOneof(\"type\"))\n self.assertEqual(\"text1\", messages[1].delta.new_element.text.body)\n self.assertEqual(\"empty\", messages[2].delta.new_element.WhichOneof(\"type\"))\n \n@@ -82,7 +82,7 @@ def test_serialize_final_report(self):\n \n def test_serialize_running_report(self):\n report = Report(\"/not/a/script.py\", \"\")\n- _enqueue(report, INIT_MSG)\n+ _enqueue(report, NEW_REPORT_MSG)\n _enqueue(report, EMPTY_DELTA_MSG)\n _enqueue(report, TEXT_DELTA_MSG)\n _enqueue(report, EMPTY_DELTA_MSG)\ndiff --git a/proto/streamlit/proto/ForwardMsg.proto b/proto/streamlit/proto/ForwardMsg.proto\nindex 6e607cac94f5..76e0381a431a 100644\n--- a/proto/streamlit/proto/ForwardMsg.proto\n+++ b/proto/streamlit/proto/ForwardMsg.proto\n@@ -17,7 +17,6 @@\n syntax = \"proto3\";\n \n import \"streamlit/proto/Delta.proto\";\n-import \"streamlit/proto/Initialize.proto\";\n import \"streamlit/proto/NewReport.proto\";\n import \"streamlit/proto/PageConfig.proto\";\n import \"streamlit/proto/PageInfo.proto\";\n@@ -45,7 +44,6 @@ message ForwardMsg {\n oneof type {\n // Report lifecycle messages.\n \n- Initialize initialize = 3;\n NewReport new_report = 4;\n Delta delta = 5;\n PageInfo page_info_changed = 12;\ndiff --git a/proto/streamlit/proto/Initialize.proto b/proto/streamlit/proto/Initialize.proto\ndeleted file mode 100644\nindex d136599f1e8a..000000000000\n--- a/proto/streamlit/proto/Initialize.proto\n+++ /dev/null\n@@ -1,68 +0,0 @@\n-/**\n- * Copyright 2018-2020 Streamlit Inc.\n- *\n- * Licensed under the Apache License, Version 2.0 (the \"License\");\n- * you may not use this file except in compliance with the License.\n- * You may obtain a copy of the License at\n- *\n- * http://www.apache.org/licenses/LICENSE-2.0\n- *\n- * Unless required by applicable law or agreed to in writing, software\n- * distributed under the License is distributed on an \"AS IS\" BASIS,\n- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n- * See the License for the specific language governing permissions and\n- * limitations under the License.\n-*/\n-\n-syntax = \"proto3\";\n-\n-import \"streamlit/proto/SessionState.proto\";\n-\n-// This is the first message that is sent when the connection is opened.\n-// It contains streamlit configuration data, and the session state\n-// that existed at the time of connection.\n-message Initialize {\n- UserInfo user_info = 1;\n- Config config = 2;\n- EnvironmentInfo environment_info = 3;\n-\n- // The session state at the time the connection was established\n- SessionState session_state = 4;\n-\n- // the actual command line as a string\n- string command_line = 5;\n-\n- // The ReportSession.id for this connection's ReportSession.\n- // This is used to associate uploaded files with the client that uploaded\n- // them.\n- string session_id = 6;\n-}\n-\n-message Config {\n- // True if saving is properly configured.\n- bool sharing_enabled = 1;\n-\n- // See config option \"browser.gatherUsageStats\".\n- bool gather_usage_stats = 2;\n-\n- // See config option \"global.maxCachedMessageAge\".\n- int32 max_cached_message_age = 3;\n-\n- // See config option \"mapbox.token\".\n- string mapbox_token = 4;\n-\n- // See config option \"server.allowRunOnSave\"\n- bool allow_run_on_save = 5;\n-}\n-\n-message UserInfo {\n- string installation_id = 1;\n- string installation_id_v1 = 3;\n- string installation_id_v2 = 4;\n- string email = 2;\n-}\n-\n-message EnvironmentInfo {\n- string streamlit_version = 1;\n- string python_version = 2;\n-}\ndiff --git a/proto/streamlit/proto/NewReport.proto b/proto/streamlit/proto/NewReport.proto\nindex 61695a4a9ddb..2258148fc03b 100644\n--- a/proto/streamlit/proto/NewReport.proto\n+++ b/proto/streamlit/proto/NewReport.proto\n@@ -16,21 +16,85 @@\n \n syntax = \"proto3\";\n \n-// This is the first message that is sent when a new report is run\n+import \"streamlit/proto/SessionState.proto\";\n+\n+// This is the first message that is sent when a new report is run.\n message NewReport {\n+ // Initialization data. This data does *not* change from rerun to rerun,\n+ // but we send it every time so that we don't have to track whether the\n+ // client has already received it. The client is responsible for\n+ // performing initialization logic only once.\n+ Initialize initialize = 1;\n+\n // The report ID\n- string id = 1;\n+ string report_id = 2;\n \n // The basename of the script that launched this report. Example: \"foo.py\"\n- string name = 2;\n+ string name = 3;\n \n // The full path of the script that launched this report. Example:\n // \"/foo/bar/foo.py\"\n- string script_path = 3;\n+ string script_path = 4;\n \n DeployParams deploy_params = 5;\n }\n \n+// Contains Streamlit configuration data, and the session state that existed\n+// at the time the user connected.\n+message Initialize {\n+ UserInfo user_info = 1;\n+ Config config = 2;\n+ EnvironmentInfo environment_info = 3;\n+\n+ // The session state at the time the connection was established\n+ SessionState session_state = 4;\n+\n+ // the actual command line as a string\n+ string command_line = 5;\n+\n+ // The ReportSession.id for this connection's ReportSession.\n+ // This is used to associate uploaded files with the client that uploaded\n+ // them.\n+ string session_id = 6;\n+}\n+\n+// App configuration options, initialized mainly from the\n+// .streamlit/config.toml file. Does not change over the app's lifetime.\n+message Config {\n+ // True if saving is properly configured.\n+ bool sharing_enabled = 1;\n+\n+ // See config option \"browser.gatherUsageStats\".\n+ bool gather_usage_stats = 2;\n+\n+ // See config option \"global.maxCachedMessageAge\".\n+ int32 max_cached_message_age = 3;\n+\n+ // See config option \"mapbox.token\".\n+ string mapbox_token = 4;\n+\n+ // See config option \"server.allowRunOnSave\"\n+ bool allow_run_on_save = 5;\n+}\n+\n+// Data that identifies the Streamlit app creator.\n+// Does not change over the app's lifetime.\n+message UserInfo {\n+ string installation_id = 1;\n+ string installation_id_v1 = 3;\n+ string installation_id_v2 = 4;\n+ string email = 2;\n+}\n+\n+// Data that identifies the Streamlit app's environment.\n+// Does not change over the app lifetime.\n+message EnvironmentInfo {\n+ string streamlit_version = 1;\n+ string python_version = 2;\n+}\n+\n+// Data about the git repository this app is deployed from (if any).\n+// Does not change over the app's lifetime.\n message DeployParams {\n string repository = 1;\n string branch = 2;\n" }
[ { "diff_hunk": "@@ -14,6 +14,12 @@\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n+import {\n+ Config,\n+ EnvironmentInfo,\n+ Initialize,\n+ UserInfo,\n+} from \"../autogen/proto\"", "line": null, "original_line": 22, "original_start_line": null, "path": "frontend/src/lib/SessionInfo.ts", "start_line": null, "text": "@user1:\nSame here. No need for the `../`" }, { "diff_hunk": "@@ -16,7 +16,47 @@\n */\n \n import { SessionInfo } from \"lib/SessionInfo\"\n+import { Initialize } from \"../autogen/proto\"", "line": null, "original_line": 19, "original_start_line": null, "path": "frontend/src/lib/SessionInfo.test.ts", "start_line": null, "text": "@user1:\nNot a big deal, but this doesn't need to be relative, right?\r\n\r\n`import { Initialize } from \"autogen/proto\"`\r\n\r\nIt's not a big deal, but just what we do in other places." } ]
26228d02ffcfbd1531b310bf8e6974a10e40048a
diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx index b2c55495dd53..c00f1a25d012 100644 --- a/frontend/src/App.test.tsx +++ b/frontend/src/App.test.tsx @@ -17,7 +17,7 @@ import React from "react" import { shallow, mount, ReactWrapper } from "enzyme" -import { ForwardMsg } from "autogen/proto" +import { ForwardMsg, NewReport } from "autogen/proto" import { IMenuItem } from "hocs/withS4ACommunication/types" import { MetricsManager } from "./lib/MetricsManager" import { getMetricsManagerForTest } from "./lib/MetricsManagerTestUtils" @@ -28,17 +28,10 @@ import MainMenu from "./components/core/MainMenu" const getProps = (extend?: Partial<Props>): Props => ({ screenCast: { + currentState: "OFF", toggleRecordAudio: jest.fn(), startRecording: jest.fn(), stopRecording: jest.fn(), - fileName: "", - recording: false, - recordAudio: false, - countdown: -1, - startAnimation: false, - showRecordedDialog: false, - showScreencastDialog: false, - showUnsupportedDialog: false, }, s4aCommunication: { connect: jest.fn(), @@ -84,7 +77,8 @@ describe("App", () => { }) afterEach(() => { - SessionInfo.singleton = undefined + const UnsafeSessionInfo = SessionInfo as any + UnsafeSessionInfo.singleton = undefined }) it("renders without crashing", () => { @@ -93,7 +87,7 @@ describe("App", () => { expect(wrapper.html()).not.toBeNull() }) - it("should reload when streamlit server version changes", () => { + it("reloads when streamlit server version changes", () => { const props = getProps() const wrapper = shallow(<App {...props} />) @@ -101,14 +95,16 @@ describe("App", () => { const fwMessage = new ForwardMsg() - fwMessage.initialize = { - environmentInfo: { - streamlitVersion: "svv", + fwMessage.newReport = { + initialize: { + environmentInfo: { + streamlitVersion: "svv", + }, + sessionId: "sessionId", + userInfo: {}, + config: {}, + sessionState: {}, }, - sessionId: "sessionId", - userInfo: {}, - config: {}, - sessionState: {}, } // @ts-ignore @@ -117,7 +113,7 @@ describe("App", () => { expect(window.location.reload).toHaveBeenCalled() }) - it("should start screencast recording when the MainMenu is clicked", () => { + it("starts screencast recording when the MainMenu is clicked", () => { const props = getProps() const wrapper = shallow(<App {...props} />) @@ -135,7 +131,7 @@ describe("App", () => { ) }) - it("should stop screencast when esc is pressed", () => { + it("stops screencast when esc is pressed", () => { const props = getProps() const wrapper = shallow(<App {...props} />) @@ -145,7 +141,7 @@ describe("App", () => { expect(props.screenCast.stopRecording).toBeCalled() }) - it("should show s4aMenuItems", () => { + it("shows s4aMenuItems", () => { const props = getProps({ s4aCommunication: { connect: jest.fn(), @@ -167,3 +163,82 @@ describe("App", () => { ]) }) }) + +describe("App.handleNewReport", () => { + const NEW_REPORT = new NewReport({ + initialize: { + userInfo: { + installationId: "installationId", + installationIdV1: "installationIdV1", + installationIdV2: "installationIdV2", + email: "email", + }, + config: { + sharingEnabled: false, + gatherUsageStats: false, + maxCachedMessageAge: 0, + mapboxToken: "mapboxToken", + allowRunOnSave: false, + }, + environmentInfo: { + streamlitVersion: "streamlitVersion", + pythonVersion: "pythonVersion", + }, + sessionState: { + runOnSave: false, + reportIsRunning: false, + }, + sessionId: "sessionId", + commandLine: "commandLine", + }, + }) + + afterEach(() => { + const UnsafeSessionInfo = SessionInfo as any + UnsafeSessionInfo.singleton = undefined + }) + + it("performs one-time initialization", () => { + const wrapper = shallow(<App {...getProps()} />) + const app = wrapper.instance() + + const oneTimeInitialization = jest.spyOn( + app, + // @ts-ignore + "handleOneTimeInitialization" + ) + + expect(SessionInfo.isSet()).toBe(false) + + // @ts-ignore + app.handleNewReport(NEW_REPORT) + + expect(oneTimeInitialization).toHaveBeenCalledTimes(1) + expect(SessionInfo.isSet()).toBe(true) + }) + + it("performs one-time initialization only once", () => { + const wrapper = shallow(<App {...getProps()} />) + const app = wrapper.instance() + + const oneTimeInitialization = jest.spyOn( + app, + // @ts-ignore + "handleOneTimeInitialization" + ) + + expect(SessionInfo.isSet()).toBe(false) + + // @ts-ignore + app.handleNewReport(NEW_REPORT) + // @ts-ignore + app.handleNewReport(NEW_REPORT) + // @ts-ignore + app.handleNewReport(NEW_REPORT) + + // Multiple NEW_REPORT messages should not result in one-time + // initialization being performed more than once. + expect(oneTimeInitialization).toHaveBeenCalledTimes(1) + expect(SessionInfo.isSet()).toBe(true) + }) +}) diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index d3a12e604ae8..9146c33e2bef 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -54,6 +54,7 @@ import { SessionEvent, WidgetStates, SessionState, + Config, } from "autogen/proto" import { RERUN_PROMPT_MODAL_DIALOG } from "lib/baseconsts" @@ -287,14 +288,12 @@ export class App extends PureComponent<Props, State> { try { dispatchProto(msgProto, "type", { - initialize: (initializeMsg: Initialize) => - this.handleInitialize(initializeMsg), + newReport: (newReportMsg: NewReport) => + this.handleNewReport(newReportMsg), sessionStateChanged: (msg: SessionState) => this.handleSessionStateChanged(msg), sessionEvent: (evtMsg: SessionEvent) => this.handleSessionEvent(evtMsg), - newReport: (newReportMsg: NewReport) => - this.handleNewReport(newReportMsg), delta: (deltaMsg: Delta) => this.handleDeltaMsg( deltaMsg, @@ -378,64 +377,6 @@ export class App extends PureComponent<Props, State> { }) } - /** - * Handler for ForwardMsg.initialize messages - * @param initializeMsg an Initialize protobuf - */ - handleInitialize = (initializeMsg: Initialize): void => { - const { - sessionId, - environmentInfo, - userInfo, - config, - sessionState, - } = initializeMsg - - if ( - sessionId == null || - !environmentInfo || - !userInfo || - !config || - !sessionState - ) { - throw new Error("InitializeMsg is missing a required field") - } - - if (App.hasStreamlitVersionChanged(initializeMsg)) { - window.location.reload() - return - } - - SessionInfo.current = new SessionInfo({ - sessionId, - streamlitVersion: environmentInfo.streamlitVersion, - pythonVersion: environmentInfo.pythonVersion, - installationId: userInfo.installationId, - installationIdV1: userInfo.installationIdV1, - installationIdV2: userInfo.installationIdV2, - authorEmail: userInfo.email, - maxCachedMessageAge: config.maxCachedMessageAge, - commandLine: initializeMsg.commandLine, - userMapboxToken: config.mapboxToken, - }) - - MetricsManager.current.initialize({ - gatherUsageStats: Boolean(config.gatherUsageStats), - }) - - MetricsManager.current.enqueue("createReport", { - pythonVersion: SessionInfo.current.pythonVersion, - }) - - this.setState({ - sharingEnabled: Boolean(config.sharingEnabled), - allowRunOnSave: Boolean(config.allowRunOnSave), - }) - - this.props.s4aCommunication.connect() - this.handleSessionStateChanged(sessionState) - } - /** * Handler for ForwardMsg.sessionStateChanged messages * @param stateChangeProto a SessionState protobuf @@ -530,9 +471,24 @@ export class App extends PureComponent<Props, State> { * @param newReportProto a NewReport protobuf */ handleNewReport = (newReportProto: NewReport): void => { + const initialize = newReportProto.initialize as Initialize + + if (App.hasStreamlitVersionChanged(initialize)) { + window.location.reload() + return + } + + // First, handle initialization logic. Each NewReport message has + // initialization data. If this is the _first_ time we're receiving + // the NewReport message, we perform some one-time initialization. + if (!SessionInfo.isSet()) { + // We're not initialized. Perform one-time initialization. + this.handleOneTimeInitialization(initialize) + } + const { reportHash } = this.state const { - id: reportId, + reportId, name: reportName, scriptPath, deployParams, @@ -561,6 +517,31 @@ export class App extends PureComponent<Props, State> { } } + /** + * Performs one-time initialization. This is called from `handleNewReport`. + */ + handleOneTimeInitialization = (initialize: Initialize): void => { + SessionInfo.current = SessionInfo.fromInitializeMessage(initialize) + + const config = initialize.config as Config + + MetricsManager.current.initialize({ + gatherUsageStats: config.gatherUsageStats, + }) + + MetricsManager.current.enqueue("createReport", { + pythonVersion: SessionInfo.current.pythonVersion, + }) + + this.setState({ + sharingEnabled: config.sharingEnabled, + allowRunOnSave: config.allowRunOnSave, + }) + + this.props.s4aCommunication.connect() + this.handleSessionStateChanged(initialize.sessionState) + } + /** * Handler for ForwardMsg.reportFinished messages * @param status the ReportFinishedStatus that the report finished with diff --git a/frontend/src/lib/SessionEventDispatcher.ts b/frontend/src/lib/SessionEventDispatcher.ts index 5edf098ee5f5..516abde1e945 100644 --- a/frontend/src/lib/SessionEventDispatcher.ts +++ b/frontend/src/lib/SessionEventDispatcher.ts @@ -16,7 +16,7 @@ */ import { Signal } from "typed-signals" -import { SessionEvent } from "../autogen/proto" +import { SessionEvent } from "autogen/proto" /** Redispatches SessionEvent messages received from the server. */ export class SessionEventDispatcher { diff --git a/frontend/src/lib/SessionInfo.test.ts b/frontend/src/lib/SessionInfo.test.ts index 0aba31cd1293..4f304808032e 100644 --- a/frontend/src/lib/SessionInfo.test.ts +++ b/frontend/src/lib/SessionInfo.test.ts @@ -16,7 +16,47 @@ */ import { SessionInfo } from "lib/SessionInfo" +import { Initialize } from "autogen/proto" test("Throws an error when used before initialization", () => { expect(() => SessionInfo.current).toThrow() }) + +test("Can be initialized from a protobuf", () => { + const MESSAGE = new Initialize({ + userInfo: { + installationId: "installationId", + installationIdV1: "installationIdV1", + installationIdV2: "installationIdV2", + email: "email", + }, + config: { + sharingEnabled: false, + gatherUsageStats: false, + maxCachedMessageAge: 31, + mapboxToken: "mapboxToken", + allowRunOnSave: false, + }, + environmentInfo: { + streamlitVersion: "streamlitVersion", + pythonVersion: "pythonVersion", + }, + sessionState: { + runOnSave: false, + reportIsRunning: false, + }, + sessionId: "sessionId", + commandLine: "commandLine", + }) + + const si = SessionInfo.fromInitializeMessage(MESSAGE) + expect(si.sessionId).toEqual("sessionId") + expect(si.streamlitVersion).toEqual("streamlitVersion") + expect(si.pythonVersion).toEqual("pythonVersion") + expect(si.installationId).toEqual("installationId") + expect(si.installationIdV1).toEqual("installationIdV1") + expect(si.installationIdV2).toEqual("installationIdV2") + expect(si.authorEmail).toEqual("email") + expect(si.maxCachedMessageAge).toEqual(31) + expect(si.commandLine).toEqual("commandLine") +}) diff --git a/frontend/src/lib/SessionInfo.ts b/frontend/src/lib/SessionInfo.ts index 1e683421c88e..b9ff184e0cbb 100644 --- a/frontend/src/lib/SessionInfo.ts +++ b/frontend/src/lib/SessionInfo.ts @@ -14,18 +14,19 @@ * See the License for the specific language governing permissions and * limitations under the License. */ +import { Config, EnvironmentInfo, Initialize, UserInfo } from "autogen/proto" export interface Args { sessionId: string - streamlitVersion?: string | null - pythonVersion?: string | null - installationId?: string | null - installationIdV1?: string | null - installationIdV2?: string | null - authorEmail?: string | null - maxCachedMessageAge?: number | null - commandLine?: string | null - userMapboxToken?: string | null + streamlitVersion: string + pythonVersion: string + installationId: string + installationIdV1: string + installationIdV2: string + authorEmail: string + maxCachedMessageAge: number + commandLine: string + userMapboxToken: string } export class SessionInfo { @@ -84,7 +85,27 @@ export class SessionInfo { return this.current.commandLine === "streamlit hello" } - constructor({ + /** Create a SessionInfo from the relevant bits of an initialize message. */ + public static fromInitializeMessage(initialize: Initialize): SessionInfo { + const environmentInfo = initialize.environmentInfo as EnvironmentInfo + const userInfo = initialize.userInfo as UserInfo + const config = initialize.config as Config + + return new SessionInfo({ + sessionId: initialize.sessionId, + streamlitVersion: environmentInfo.streamlitVersion, + pythonVersion: environmentInfo.pythonVersion, + installationId: userInfo.installationId, + installationIdV1: userInfo.installationIdV1, + installationIdV2: userInfo.installationIdV2, + authorEmail: userInfo.email, + maxCachedMessageAge: config.maxCachedMessageAge, + commandLine: initialize.commandLine, + userMapboxToken: config.mapboxToken, + }) + } + + public constructor({ sessionId, streamlitVersion, pythonVersion, diff --git a/lib/streamlit/report_session.py b/lib/streamlit/report_session.py index 5fb1037b5da7..75fdf9112260 100644 --- a/lib/streamlit/report_session.py +++ b/lib/streamlit/report_session.py @@ -96,7 +96,6 @@ def __init__(self, ioloop, script_path, command_line, uploaded_file_manager): self._local_sources_watcher = LocalSourcesWatcher( self._report, self._on_source_file_changed ) - self._sent_initialize_message = False self._storage = None self._maybe_reuse_previous_run = False self._run_on_save = config.get_option("server.runOnSave") @@ -264,7 +263,6 @@ def _on_scriptrunner_event(self, event, exception=None, client_state=None): self._ioloop.spawn_callback(self._save_running_report) self._clear_queue() - self._maybe_enqueue_initialize_message() self._enqueue_new_report_message() elif ( @@ -348,15 +346,39 @@ def _enqueue_file_change_message(self): msg.session_event.report_changed_on_disk = True self.enqueue(msg) - def _maybe_enqueue_initialize_message(self): - if self._sent_initialize_message: - return + def get_deploy_params(self): + try: + from streamlit.git_util import GitRepo - self._sent_initialize_message = True + self._repo = GitRepo(self._report.script_path) + return self._repo.get_repo_info() + except: + # Issues can arise based on the git structure + # (e.g. if branch is in DETACHED HEAD state, + # git is not installed, etc) + # In this case, catch any errors + return None + def _enqueue_new_report_message(self): + self._report.generate_new_id() msg = ForwardMsg() - imsg = msg.initialize + msg.new_report.report_id = self._report.report_id + msg.new_report.name = self._report.name + msg.new_report.script_path = self._report.script_path + # git deploy params + deploy_params = self.get_deploy_params() + if deploy_params is not None: + repo, branch, module = deploy_params + msg.new_report.deploy_params.repository = repo + msg.new_report.deploy_params.branch = branch + msg.new_report.deploy_params.module = module + + # Immutable session data. We send this every time a new report is + # started, to avoid having to track whether the client has already + # received it. It does not change from run to run; it's up to the + # to perform one-time initialization only once. + imsg = msg.new_report.initialize imsg.config.sharing_enabled = config.get_option("global.sharingMode") != "off" imsg.config.gather_usage_stats = config.get_option("browser.gatherUsageStats") @@ -369,16 +391,6 @@ def _maybe_enqueue_initialize_message(self): imsg.config.allow_run_on_save = config.get_option("server.allowRunOnSave") - LOGGER.debug( - "New browser connection: " - "gather_usage_stats=%s, " - "sharing_enabled=%s, " - "max_cached_message_age=%s", - imsg.config.gather_usage_stats, - imsg.config.sharing_enabled, - imsg.config.max_cached_message_age, - ) - imsg.environment_info.streamlit_version = __version__ imsg.environment_info.python_version = ".".join(map(str, sys.version_info)) @@ -400,34 +412,6 @@ def _maybe_enqueue_initialize_message(self): self.enqueue(msg) - def get_deploy_params(self): - try: - from streamlit.git_util import GitRepo - - self._repo = GitRepo(self._report.script_path) - return self._repo.get_repo_info() - except: - # Issues can arise based on the git structure - # (e.g. if branch is in DETACHED HEAD state, - # git is not installed, etc) - # In this case, catch any errors - return None - - def _enqueue_new_report_message(self): - self._report.generate_new_id() - msg = ForwardMsg() - msg.new_report.id = self._report.report_id - msg.new_report.name = self._report.name - msg.new_report.script_path = self._report.script_path - - deploy_params = self.get_deploy_params() - if deploy_params is not None: - repo, branch, module = deploy_params - msg.new_report.deploy_params.repository = repo - msg.new_report.deploy_params.branch = branch - msg.new_report.deploy_params.module = module - self.enqueue(msg) - def _enqueue_report_finished_message(self, status): """Enqueue a report_finished ForwardMsg. diff --git a/lib/tests/streamlit/report_queue_test.py b/lib/tests/streamlit/report_queue_test.py index 97ba8c4868e0..91eafdb07c63 100644 --- a/lib/tests/streamlit/report_queue_test.py +++ b/lib/tests/streamlit/report_queue_test.py @@ -27,9 +27,9 @@ # For the messages below, we don't really care about their contents so much as # their general type. -INIT_MSG = ForwardMsg() -INIT_MSG.initialize.config.sharing_enabled = True -INIT_MSG.initialize.config.allow_run_on_save = True +NEW_REPORT_MSG = ForwardMsg() +NEW_REPORT_MSG.new_report.initialize.config.sharing_enabled = True +NEW_REPORT_MSG.new_report.initialize.config.allow_run_on_save = True TEXT_DELTA_MSG1 = ForwardMsg() TEXT_DELTA_MSG1.delta.new_element.text.body = "text1" @@ -57,20 +57,20 @@ def test_simple_enqueue(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) self.assertFalse(rq.is_empty()) queue = rq.flush() self.assertTrue(rq.is_empty()) self.assertEqual(len(queue), 1) - self.assertTrue(queue[0].initialize.config.sharing_enabled) - self.assertTrue(queue[0].initialize.config.allow_run_on_save) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.allow_run_on_save) def test_enqueue_two(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path( RootContainer.MAIN, (), 0 @@ -79,7 +79,7 @@ def test_enqueue_two(self): queue = rq.flush() self.assertEqual(len(queue), 2) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) self.assertEqual( make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path ) @@ -89,7 +89,7 @@ def test_enqueue_three(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path( RootContainer.MAIN, (), 0 @@ -103,7 +103,7 @@ def test_enqueue_three(self): queue = rq.flush() self.assertEqual(len(queue), 3) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) self.assertEqual( make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path ) @@ -117,7 +117,7 @@ def test_replace_element(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path( RootContainer.MAIN, (), 0 @@ -131,7 +131,7 @@ def test_replace_element(self): queue = rq.flush() self.assertEqual(len(queue), 2) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) self.assertEqual( make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path ) @@ -141,7 +141,7 @@ def test_simple_add_rows(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) TEXT_DELTA_MSG1.metadata.delta_path[:] = make_delta_path( RootContainer.MAIN, (), 0 @@ -156,7 +156,7 @@ def test_simple_add_rows(self): queue = rq.flush() self.assertEqual(len(queue), 3) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) self.assertEqual( make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path ) @@ -173,7 +173,7 @@ def test_add_rows_rerun(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) # Simulate rerun for i in range(2): @@ -194,7 +194,7 @@ def test_add_rows_rerun(self): queue = rq.flush() self.assertEqual(len(queue), 3) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) self.assertEqual( make_delta_path(RootContainer.MAIN, (), 0), queue[1].metadata.delta_path ) @@ -212,7 +212,7 @@ def test_multiple_containers(self): rq = ReportQueue() self.assertTrue(rq.is_empty()) - rq.enqueue(INIT_MSG) + rq.enqueue(NEW_REPORT_MSG) def enqueue_deltas(container: RootContainer, path: Tuple[int, ...]): # We deep-copy the protos because we mutate each one @@ -248,7 +248,7 @@ def assert_deltas(container: RootContainer, path: Tuple[int, ...], idx: int): queue = rq.flush() self.assertEqual(5, len(queue)) - self.assertTrue(queue[0].initialize.config.sharing_enabled) + self.assertTrue(queue[0].new_report.initialize.config.sharing_enabled) assert_deltas(RootContainer.MAIN, (), 1) assert_deltas(RootContainer.SIDEBAR, (0, 0, 1), 3) diff --git a/lib/tests/streamlit/report_test.py b/lib/tests/streamlit/report_test.py index 0bd9197726a4..898722523f38 100644 --- a/lib/tests/streamlit/report_test.py +++ b/lib/tests/streamlit/report_test.py @@ -27,9 +27,9 @@ from streamlit.proto.StaticManifest_pb2 import StaticManifest from tests import testutil -INIT_MSG = ForwardMsg() -INIT_MSG.initialize.config.sharing_enabled = True -INIT_MSG.metadata.delta_path[:] = make_delta_path(RootContainer.MAIN, (), 0) +NEW_REPORT_MSG = ForwardMsg() +NEW_REPORT_MSG.new_report.initialize.config.sharing_enabled = True +NEW_REPORT_MSG.metadata.delta_path[:] = make_delta_path(RootContainer.MAIN, (), 0) TEXT_DELTA_MSG = ForwardMsg() TEXT_DELTA_MSG.delta.new_element.text.body = "text1" @@ -56,7 +56,7 @@ def _parse_msg(msg_string): class ReportTest(unittest.TestCase): def test_serialize_final_report(self): report = Report("/not/a/script.py", "") - _enqueue(report, INIT_MSG) + _enqueue(report, NEW_REPORT_MSG) _enqueue(report, TEXT_DELTA_MSG) _enqueue(report, EMPTY_DELTA_MSG) @@ -65,7 +65,7 @@ def test_serialize_final_report(self): # Validate our messages. messages = [_parse_msg(msg_string) for _, msg_string in files[:-1]] self.assertEqual(3, len(messages)) - self.assertEqual("initialize", messages[0].WhichOneof("type")) + self.assertEqual("new_report", messages[0].WhichOneof("type")) self.assertEqual("text1", messages[1].delta.new_element.text.body) self.assertEqual("empty", messages[2].delta.new_element.WhichOneof("type")) @@ -82,7 +82,7 @@ def test_serialize_final_report(self): def test_serialize_running_report(self): report = Report("/not/a/script.py", "") - _enqueue(report, INIT_MSG) + _enqueue(report, NEW_REPORT_MSG) _enqueue(report, EMPTY_DELTA_MSG) _enqueue(report, TEXT_DELTA_MSG) _enqueue(report, EMPTY_DELTA_MSG) diff --git a/proto/streamlit/proto/ForwardMsg.proto b/proto/streamlit/proto/ForwardMsg.proto index 6e607cac94f5..76e0381a431a 100644 --- a/proto/streamlit/proto/ForwardMsg.proto +++ b/proto/streamlit/proto/ForwardMsg.proto @@ -17,7 +17,6 @@ syntax = "proto3"; import "streamlit/proto/Delta.proto"; -import "streamlit/proto/Initialize.proto"; import "streamlit/proto/NewReport.proto"; import "streamlit/proto/PageConfig.proto"; import "streamlit/proto/PageInfo.proto"; @@ -45,7 +44,6 @@ message ForwardMsg { oneof type { // Report lifecycle messages. - Initialize initialize = 3; NewReport new_report = 4; Delta delta = 5; PageInfo page_info_changed = 12; diff --git a/proto/streamlit/proto/Initialize.proto b/proto/streamlit/proto/Initialize.proto deleted file mode 100644 index d136599f1e8a..000000000000 --- a/proto/streamlit/proto/Initialize.proto +++ /dev/null @@ -1,68 +0,0 @@ -/** - * Copyright 2018-2020 Streamlit Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. -*/ - -syntax = "proto3"; - -import "streamlit/proto/SessionState.proto"; - -// This is the first message that is sent when the connection is opened. -// It contains streamlit configuration data, and the session state -// that existed at the time of connection. -message Initialize { - UserInfo user_info = 1; - Config config = 2; - EnvironmentInfo environment_info = 3; - - // The session state at the time the connection was established - SessionState session_state = 4; - - // the actual command line as a string - string command_line = 5; - - // The ReportSession.id for this connection's ReportSession. - // This is used to associate uploaded files with the client that uploaded - // them. - string session_id = 6; -} - -message Config { - // True if saving is properly configured. - bool sharing_enabled = 1; - - // See config option "browser.gatherUsageStats". - bool gather_usage_stats = 2; - - // See config option "global.maxCachedMessageAge". - int32 max_cached_message_age = 3; - - // See config option "mapbox.token". - string mapbox_token = 4; - - // See config option "server.allowRunOnSave" - bool allow_run_on_save = 5; -} - -message UserInfo { - string installation_id = 1; - string installation_id_v1 = 3; - string installation_id_v2 = 4; - string email = 2; -} - -message EnvironmentInfo { - string streamlit_version = 1; - string python_version = 2; -} diff --git a/proto/streamlit/proto/NewReport.proto b/proto/streamlit/proto/NewReport.proto index 61695a4a9ddb..2258148fc03b 100644 --- a/proto/streamlit/proto/NewReport.proto +++ b/proto/streamlit/proto/NewReport.proto @@ -16,21 +16,85 @@ syntax = "proto3"; -// This is the first message that is sent when a new report is run +import "streamlit/proto/SessionState.proto"; + +// This is the first message that is sent when a new report is run. message NewReport { + // Initialization data. This data does *not* change from rerun to rerun, + // but we send it every time so that we don't have to track whether the + // client has already received it. The client is responsible for + // performing initialization logic only once. + Initialize initialize = 1; + // The report ID - string id = 1; + string report_id = 2; // The basename of the script that launched this report. Example: "foo.py" - string name = 2; + string name = 3; // The full path of the script that launched this report. Example: // "/foo/bar/foo.py" - string script_path = 3; + string script_path = 4; DeployParams deploy_params = 5; } +// Contains Streamlit configuration data, and the session state that existed +// at the time the user connected. +message Initialize { + UserInfo user_info = 1; + Config config = 2; + EnvironmentInfo environment_info = 3; + + // The session state at the time the connection was established + SessionState session_state = 4; + + // the actual command line as a string + string command_line = 5; + + // The ReportSession.id for this connection's ReportSession. + // This is used to associate uploaded files with the client that uploaded + // them. + string session_id = 6; +} + +// App configuration options, initialized mainly from the +// .streamlit/config.toml file. Does not change over the app's lifetime. +message Config { + // True if saving is properly configured. + bool sharing_enabled = 1; + + // See config option "browser.gatherUsageStats". + bool gather_usage_stats = 2; + + // See config option "global.maxCachedMessageAge". + int32 max_cached_message_age = 3; + + // See config option "mapbox.token". + string mapbox_token = 4; + + // See config option "server.allowRunOnSave" + bool allow_run_on_save = 5; +} + +// Data that identifies the Streamlit app creator. +// Does not change over the app's lifetime. +message UserInfo { + string installation_id = 1; + string installation_id_v1 = 3; + string installation_id_v2 = 4; + string email = 2; +} + +// Data that identifies the Streamlit app's environment. +// Does not change over the app lifetime. +message EnvironmentInfo { + string streamlit_version = 1; + string python_version = 2; +} + +// Data about the git repository this app is deployed from (if any). +// Does not change over the app's lifetime. message DeployParams { string repository = 1; string branch = 2;
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
zulip__zulip-22988@5929b81
zulip/zulip
Python
22,988
delete_in_topic: Split up the deletion into batches.
Fixes https://github.com/zulip/zulip/issues/22821. As explained in the comment in the code: Topics can be large enough that this request will inevitably time out. In such a case, it's good for some progress to be accomplished, so that full deletion can be achieved by repeating the request. For that purpose, we delete messages in atomic batches, committing after each batch. The additional perk is that the ordering of messages should prevent some hypothetical deadlocks - ref https://github.com/zulip/zulip/issues/19054 --- Tested manually with print statements to verify messages are getting deleted in expected batches. `TopicDeleteTest` is a simple automated test that already exists for verifying this endpoint works.
2022-09-17T21:13:09Z
Unable to delete a topic Hi! I'm attemptimg to delete a large (over 50k messages) topic that contains alerts and logs quotations sent by elastalert. When deleting it from the web gui, the delete request gets stuck for about a minute and then i get 502 with no any entry in zulip logs. Messages from other topics and entire topics still can be deleted, only this one remains untouched. Is there any way to delete such large topics? Can i delete messages from the database directly?
Hello @zulip/server-message-view members, this issue was labeled with the "area: message-editing" label, so you may want to check it out! <!-- areaLabelAddition --> Yikes, 50k messages is a topic is a lot of log spam. You can likely delete it using `do_delete_messages` via a management command shell (https://zulip.readthedocs.io/en/latest/production/management-commands.html), which would avoid the time limit for requests that we have configured. The other option is to modify `/etc/zulip/uwsgi.conf` to dramatically increase the timeout (the setting is called `harikiri` in that file; and then you'll need use `scripts/restart-server` to restart the server) and see if that's good enough; might be easier. @timabbott Thanks for you response! I'm a bit confused, manage.py doesn't show such command: ``` zulip@f866a8398bf6:~/deployments/current$ ./manage.py help do_delete_messages Unknown command: 'do_delete_messages'. Did you mean restore_messages? ``` And yes, i'm running Zulip in docker, if it's relevant. @timabbott increased hahakiri to about 3 hours as you suggested but it still fails after the time is up, looks like it takes much more... Maybe you could point how can that `do_delete_messages` be used? Or if it is possible to delete messages from the database directly? To make it easier to address follow-up questions, I [started a thread in the development community](https://chat.zulip.org/#narrow/stream/31-production-help/topic/Unable.20to.20delete.20a.20topic.20.2322821/near/1432762). I said "management command shell" - I meant `manage.py shell`, which is documented on that page. `do_delete_messages` is a function you need to call there; it's a Python shell. If you need help scripting that, I recommend getting a support contract. In terms of a bug fix for Zulip, we'll likely just want to paginate the queries to do blocks of 1k messages in the topic as individual transactions. @mateuszmandera perhaps you can look at whether this is easy to do. I think it'd be a significant improvement even if it still times out, if it just manages to make clear progress before doing so, since that at least would offer a UI level workaround of doing it repeatedly. Eventually we'll need to move to the `deferred_work` queue processor.
[ { "body": "Hi! I'm attemptimg to delete a large (over 50k messages) topic that contains alerts and logs quotations sent by elastalert. When deleting it from the web gui, the delete request gets stuck for about a minute and then i get 502 with no any entry in zulip logs. Messages from other topics and entire topics still can be deleted, only this one remains untouched.\r\n\r\nIs there any way to delete such large topics? Can i delete messages from the database directly?", "number": 22821, "title": "Unable to delete a topic" } ]
09c6ee6468e302b5c9d09fb40e6955c1d928e408
{ "head_commit": "5929b8156dbe0cb8558565d34fdb7160056413cd", "head_commit_message": "delete_in_topic: Split up the deletion into batches.\n\nFixes #22821.\n\nAs explained in the comment in the code:\n\nTopics can be large enough that this request will inevitably time out.\nIn such a case, it's good for some progress to be accomplished, so that\nfull deletion can be achieved by repeating the request. For that purpose,\nwe delete messages in atomic batches, committing after each batch.\n\nThe additional perk is that the ordering of messages should prevent some\nhypothetical deadlocks - ref #19054", "patch_to_review": "diff --git a/templates/zerver/api/changelog.md b/templates/zerver/api/changelog.md\nindex 6e26a135e626d..ed50d848a848f 100644\n--- a/templates/zerver/api/changelog.md\n+++ b/templates/zerver/api/changelog.md\n@@ -20,6 +20,13 @@ format used by the Zulip server that they are interacting with.\n \n ## Changes in Zulip 6.0\n \n+**Feature level 147**\n+\n+* [`POST /streams/{stream_id}/delete_topic`](/api/delete-topic):\n+ Messages now are deleted in batches, starting from the newest, so\n+ that progress will be made even if the request times out because of\n+ an extremely large topic.\n+\n **Feature level 146**\n \n * [`POST /realm/profile_fields`](/api/create-custom-profile-field),\ndiff --git a/version.py b/version.py\nindex 97524b849f587..c6e4ee55e7fb3 100644\n--- a/version.py\n+++ b/version.py\n@@ -33,7 +33,7 @@\n # Changes should be accompanied by documentation explaining what the\n # new level means in templates/zerver/api/changelog.md, as well as\n # \"**Changes**\" entries in the endpoint's documentation in `zulip.yaml`.\n-API_FEATURE_LEVEL = 146\n+API_FEATURE_LEVEL = 147\n \n # Bump the minor PROVISION_VERSION to indicate that folks should provision\n # only when going from an old version of the code to a newer version. Bump\ndiff --git a/zerver/openapi/zulip.yaml b/zerver/openapi/zulip.yaml\nindex a2a651e1c659a..873733f56e110 100644\n--- a/zerver/openapi/zulip.yaml\n+++ b/zerver/openapi/zulip.yaml\n@@ -14232,6 +14232,12 @@ paths:\n Topics are a field on messages (not an independent\n data structure), so deleting all the messages in the topic\n deletes the topic from Zulip.\n+\n+ **Changes**: Before Zulip 6.0 (feature level 147), this\n+ request did a single atomic operation, which could time out\n+ for very large topics. It now deletes messages in batches,\n+ starting with the newest messages, so that progress will be\n+ made even if the request times out.\n parameters:\n - $ref: \"#/components/parameters/StreamIdInPath\"\n - name: topic_name\ndiff --git a/zerver/views/streams.py b/zerver/views/streams.py\nindex cb928e334358b..818c36001fd68 100644\n--- a/zerver/views/streams.py\n+++ b/zerver/views/streams.py\n@@ -55,6 +55,7 @@\n from zerver.lib.mention import MentionBackend, silent_mention_syntax_for_user\n from zerver.lib.request import REQ, has_request_variables\n from zerver.lib.response import json_success\n+from zerver.lib.retention import STREAM_MESSAGE_BATCH_SIZE as RETENTION_STREAM_MESSAGE_BATCH_SIZE\n from zerver.lib.retention import parse_message_retention_days\n from zerver.lib.streams import (\n StreamDict,\n@@ -848,7 +849,6 @@ def get_topics_backend(\n return json_success(request, data=dict(topics=result))\n \n \[email protected]\n @require_realm_admin\n @has_request_variables\n def delete_in_topic(\n@@ -857,7 +857,7 @@ def delete_in_topic(\n stream_id: int = REQ(converter=to_non_negative_int, path_only=True),\n topic_name: str = REQ(\"topic_name\"),\n ) -> HttpResponse:\n- (stream, sub) = access_stream_by_id(user_profile, stream_id)\n+ stream, ignored_sub = access_stream_by_id(user_profile, stream_id)\n \n messages = messages_for_topic(assert_is_not_none(stream.recipient_id), topic_name)\n if not stream.is_history_public_to_subscribers():\n@@ -867,9 +867,20 @@ def delete_in_topic(\n ).values_list(\"message_id\", flat=True)\n messages = messages.filter(id__in=deletable_message_ids)\n \n- messages = messages.select_for_update(of=(\"self\",))\n-\n- do_delete_messages(user_profile.realm, messages)\n+ # Topics can be large enough that this request will inevitably time out.\n+ # In such a case, it's good for some progress to be accomplished, so that\n+ # full deletion can be achieved by repeating the request. For that purpose,\n+ # we delete messages in atomic batches, committing after each batch.\n+ # TODO: Ideally this should be moved to the deferred_work queue.\n+ batch_size = RETENTION_STREAM_MESSAGE_BATCH_SIZE\n+ while True:\n+ with transaction.atomic(durable=True):\n+ messages_to_delete = messages.order_by(\"-id\")[0:batch_size].select_for_update(\n+ of=(\"self\",)\n+ )\n+ if not messages_to_delete:\n+ break\n+ do_delete_messages(user_profile.realm, messages_to_delete)\n \n return json_success(request)\n \n" }
[ { "diff_hunk": "@@ -1139,6 +1139,20 @@ export function delete_topic(stream_id, topic_name) {\n data: {\n topic_name,\n },\n+ success() {},\n+ error(xhr) {\n+ if (xhr.status === 502) {\n+ /* When trying to delete a very large topic, it's\n+ possible for the request to the server to\n+ time out after making some progress. Retry the\n+ request, so that the user can just do nothing and\n+ watch the topic slowly be deleted.\n+\n+ TODO: Show a nice loading indicator experience.\n+ */\n+ delete_topic(stream_id, topic_name);\n+ }\n+ },", "line": 1160, "original_line": 1160, "original_start_line": null, "path": "static/js/message_edit.js", "start_line": null, "text": "@user1:\nAn unlimited retry load on 502s is the kind of code we don't want to have exist because you can DoS yourself. I think given the very infrequent nature of calls to this code path, it's probably fine; but in an ideal world we'd limit this to 10 retries or something.\r\n\r\nIs it easy to add that?\n\n@author:\n@user1 Yeah, it crossed my mind but the retries just run into rate limits, but not sure if that's sufficient for us? I added some code to limit the retries in the frontend now though, it's awkward but we do something similar in `static/js/server_events.js` with `get_events_failures`.\r\n\r\nTest manually that it works in dev env:\r\n1. Failure case, by haaving the server return 502 infinitely without doing anything. The frontend stops at 10 tries indeed.\r\n2. Success, where the server returns 502 after processing a tiny batch. The app keeps retrying until the topic is deleted (as long as that's under 10 tries).\n\n@user1:\nI think you don't need a global variable, which is problematic because multiple of these requests can be in flight at the same time. Just pass `delete_topic_failures + 1` to `delete_topic` in the recursive call, and have the initial call assume 0 if the parameter isn't provided." } ]
10dbe6fcd851fde01fbbdccd8967bd15cd940da9
diff --git a/static/js/message_edit.js b/static/js/message_edit.js index e7be7edce6433..871ca38ed38b4 100644 --- a/static/js/message_edit.js +++ b/static/js/message_edit.js @@ -1133,12 +1133,31 @@ export function delete_message(msg_id) { }); } -export function delete_topic(stream_id, topic_name) { +export function delete_topic(stream_id, topic_name, failures = 0) { channel.post({ url: "/json/streams/" + stream_id + "/delete_topic", data: { topic_name, }, + success() {}, + error(xhr) { + if (failures >= 9) { + // Don't keep retrying indefinitely to avoid DoSing the server. + return; + } + if (xhr.status === 502) { + /* When trying to delete a very large topic, it's + possible for the request to the server to + time out after making some progress. Retry the + request, so that the user can just do nothing and + watch the topic slowly be deleted. + + TODO: Show a nice loading indicator experience. + */ + failures += 1; + delete_topic(stream_id, topic_name, failures); + } + }, }); } diff --git a/templates/zerver/api/changelog.md b/templates/zerver/api/changelog.md index 6e26a135e626d..ed50d848a848f 100644 --- a/templates/zerver/api/changelog.md +++ b/templates/zerver/api/changelog.md @@ -20,6 +20,13 @@ format used by the Zulip server that they are interacting with. ## Changes in Zulip 6.0 +**Feature level 147** + +* [`POST /streams/{stream_id}/delete_topic`](/api/delete-topic): + Messages now are deleted in batches, starting from the newest, so + that progress will be made even if the request times out because of + an extremely large topic. + **Feature level 146** * [`POST /realm/profile_fields`](/api/create-custom-profile-field), diff --git a/version.py b/version.py index 97524b849f587..c6e4ee55e7fb3 100644 --- a/version.py +++ b/version.py @@ -33,7 +33,7 @@ # Changes should be accompanied by documentation explaining what the # new level means in templates/zerver/api/changelog.md, as well as # "**Changes**" entries in the endpoint's documentation in `zulip.yaml`. -API_FEATURE_LEVEL = 146 +API_FEATURE_LEVEL = 147 # Bump the minor PROVISION_VERSION to indicate that folks should provision # only when going from an old version of the code to a newer version. Bump diff --git a/zerver/openapi/zulip.yaml b/zerver/openapi/zulip.yaml index a2a651e1c659a..873733f56e110 100644 --- a/zerver/openapi/zulip.yaml +++ b/zerver/openapi/zulip.yaml @@ -14232,6 +14232,12 @@ paths: Topics are a field on messages (not an independent data structure), so deleting all the messages in the topic deletes the topic from Zulip. + + **Changes**: Before Zulip 6.0 (feature level 147), this + request did a single atomic operation, which could time out + for very large topics. It now deletes messages in batches, + starting with the newest messages, so that progress will be + made even if the request times out. parameters: - $ref: "#/components/parameters/StreamIdInPath" - name: topic_name diff --git a/zerver/views/streams.py b/zerver/views/streams.py index cb928e334358b..818c36001fd68 100644 --- a/zerver/views/streams.py +++ b/zerver/views/streams.py @@ -55,6 +55,7 @@ from zerver.lib.mention import MentionBackend, silent_mention_syntax_for_user from zerver.lib.request import REQ, has_request_variables from zerver.lib.response import json_success +from zerver.lib.retention import STREAM_MESSAGE_BATCH_SIZE as RETENTION_STREAM_MESSAGE_BATCH_SIZE from zerver.lib.retention import parse_message_retention_days from zerver.lib.streams import ( StreamDict, @@ -848,7 +849,6 @@ def get_topics_backend( return json_success(request, data=dict(topics=result)) [email protected] @require_realm_admin @has_request_variables def delete_in_topic( @@ -857,7 +857,7 @@ def delete_in_topic( stream_id: int = REQ(converter=to_non_negative_int, path_only=True), topic_name: str = REQ("topic_name"), ) -> HttpResponse: - (stream, sub) = access_stream_by_id(user_profile, stream_id) + stream, ignored_sub = access_stream_by_id(user_profile, stream_id) messages = messages_for_topic(assert_is_not_none(stream.recipient_id), topic_name) if not stream.is_history_public_to_subscribers(): @@ -867,9 +867,20 @@ def delete_in_topic( ).values_list("message_id", flat=True) messages = messages.filter(id__in=deletable_message_ids) - messages = messages.select_for_update(of=("self",)) - - do_delete_messages(user_profile.realm, messages) + # Topics can be large enough that this request will inevitably time out. + # In such a case, it's good for some progress to be accomplished, so that + # full deletion can be achieved by repeating the request. For that purpose, + # we delete messages in atomic batches, committing after each batch. + # TODO: Ideally this should be moved to the deferred_work queue. + batch_size = RETENTION_STREAM_MESSAGE_BATCH_SIZE + while True: + with transaction.atomic(durable=True): + messages_to_delete = messages.order_by("-id")[0:batch_size].select_for_update( + of=("self",) + ) + if not messages_to_delete: + break + do_delete_messages(user_profile.realm, messages_to_delete) return json_success(request)
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
streamlit__streamlit-2060@f5d153f
streamlit/streamlit
Python
2,060
Adding experimental_rerun
**Issue:** Fixes https://github.com/streamlit/streamlit/issues/653 **Description:** Added `st.experimental_rerun` and `st.experimental_get_session_id`. Given that rerun needed access to the server I chose to make the relevant test be within the e2e testing suite. This e2e test undergoes a few reruns within a thread, and then a couple more just within the main streamlit instance.
2020-09-29T13:00:50Z
Add the ability to programatically re-run the streamlit script ### Problem I am building a small labeling too where I read in an example, the user will select labels from a list of check boxes and click submit in order to save the labels, when they submit a field in the data for it being labeled is added so in the next read it skips that example. The problem is that the submit button will cause a rerun where the labels are collected and saved but it doesn't cause another re-run that will cause the data to be reread (this will move the app on to the next question) ### Solution I think a solution would be a `st.rerun()` function that you can call to reexecute the script from the top. In my cause this would be called after my save and trigger a re-read. Basically it would do the same thing I am currently using a second `Next` button for but remove the need for user interaction.
Hey @blester125 We've been thinking a lot about different ways to solve issues like yours recently. We have a few really nice and clean solutions in mind but they will take a little time to implement. In the meantime, if I understand your app correctly, I think what you need can be implemented by persisting some data between reruns of your script, via a feature we call _Session State_. The prototype for Session State can be downloaded from [this Gist](https://gist.githubusercontent.com/tvst/036da038ab3e999a64497f42de966a92/raw/36e810364f34b25e270bd856cb25611775e691e3/SessionState.py). After you download it, you can create a labeling app this way: ```py import streamlit as st import SessionState s = SessionState.get(current_file_index = 0) # This is a function that loads your file, given the file index. f = get_file_to_label(s.current_file_index) # These are your labels. label1 = st.checkbox("foo") label2 = st.checkbox("bar") label2 = st.checkbox("baz") if st.button("submit"): # This is a function that saves labels, given a file index. save_labels(s.current_file_index, label1, label2, label3) s.current_file_index += 1 ``` That said, it's very possible I misunderstood your app and its requirements. So if you _really_ need to have some way to rerun apps you can actually do it today by delving into Streamlit's internals: ```py from streamlit.ScriptRunner import StopException, RerunException # do stuff if st.button(): # do other stuff raise RerunException() # This causes the app to rerun # even more stuff ``` You can also use `StopException` to stop the execution at any point. I'm confused by the behaviour on any button/checkbox click. I want to click on multiple checkboxes and then hit a submit button. When I try to implement your first example below I can never click on the submit button because as soon as I change the first checkbox the submit button disappears and returns me to my initial setup. This is a problem because I want to label multiple items at a time, but I can only ever do one. Adding in StopExceptions doesn't seem to help either ``` label1 = st.checkbox("Relevant",False) label2 = st.checkbox("Not relevant",False) if label1: labelled.loc[row,'relevance'] == 1 raise StopException() if label2: labelled.loc[row,'relevance'] == 0 raise StopException() if st.button('submit'): labelled = tm.addToTrainValidation(labelled.loc[[row]],train,valid,unlabelled) ``` Any suggestions? Duplicate of #166 I guess @tvst Thanks for providing a workaround while the permanent functionality is being worked out. However, by placing the RerunException as indicated, it seems that I get an error: ``` TypeError: __init__() missing 1 required positional argument: 'rerun_data' Traceback: File "c:\users\fabien~1.val\trial1~1\venv~1\lib\site-packages\streamlit\ScriptRunner.py", line 311, in _run_script exec(code, module.__dict__) File "C:\Users\Fab\trial 1\streamlit_gui.py", line 110, in <module> raise RerunException() ``` What data should I provide to this exception? Thanks > @tvst Thanks for providing a workaround while the permanent functionality is being worked out. > However, by placing the RerunException as indicated, it seems that I get an error: > > ``` > TypeError: __init__() missing 1 required positional argument: 'rerun_data' > Traceback: > File "c:\users\fabien~1.val\trial1~1\venv~1\lib\site-packages\streamlit\ScriptRunner.py", line 311, in _run_script > exec(code, module.__dict__) > File "C:\Users\Fab\trial 1\streamlit_gui.py", line 110, in <module> > raise RerunException() > ``` > > What data should I provide to this exception? > Thanks Oh, sorry, I just found your [gist](https://gist.github.com/tvst/ef477845ac86962fa4c92ec6a72bb5bd) that do not raise the error. (however, values of gui elements are reseted to default and do not match the UI it seems) This issue is already open for quite some time. Could someone on the team elaborate on the time scale on which this will be implemented if ever? Also the above mentioned [gist](https://gist.github.com/tvst/ef477845ac86962fa4c92ec6a72bb5bd) seems to be not working anymore in more recent versions (is working for me under 0.53.0, but not 0.56.0 with an error complaining about `_main_dg` not being a member of `session`. So probably some internal changes broke this, would be nice, if someone could update the gist accordingly. Thank you for this amazing project anyway, became quite fond of it. Hi @DavidS3141 , here's the updated gist, https://gist.github.com/tvst/036da038ab3e999a64497f42de966a92 Regarding the triage of this issue, will get back to you shortly! Sorry, I was not clear. I really need the rerun functionality, as the `SessionState` workaround is not viable for me. Maybe take another look at the gist I linked ([here](https://gist.github.com/tvst/ef477845ac86962fa4c92ec6a72bb5bd) again) to make sure what I mean. Thanks! Okay, I looked at your linked gist again and understood why you linked it, as it shows how to fix the `_main_dg` issue! I fixed the rerun logic accordingly and the resulting fork can be found [here](https://gist.github.com/DavidS3141/822310a2ed9f600362ac31ee49223f86). A neater fix seems to be the following: ```python import streamtlit as st def rerun(): raise st.ScriptRunner.RerunException(st.ScriptRequestQueue.RerunData(None)) ``` Worked this out from the comment written over at: ```python # Data attached to RERUN requests RerunData = namedtuple( "RerunData", [ # WidgetStates protobuf to run the script with. If this is None, the # widget_state from the most recent run of the script will be used instead. "widget_state" ], ) ``` Meaning, passing `None` to `RerunData` defaults to no change in state.
[ { "body": "### Problem\r\n\r\nI am building a small labeling too where I read in an example, the user will select labels from a list of check boxes and click submit in order to save the labels, when they submit a field in the data for it being labeled is added so in the next read it skips that example. The problem is that the submit button will cause a rerun where the labels are collected and saved but it doesn't cause another re-run that will cause the data to be reread (this will move the app on to the next question)\r\n\r\n### Solution\r\n\r\nI think a solution would be a `st.rerun()` function that you can call to reexecute the script from the top. In my cause this would be called after my save and trigger a re-read. Basically it would do the same thing I am currently using a second `Next` button for but remove the need for user interaction.", "number": 653, "title": "Add the ability to programatically re-run the streamlit script" } ]
8b8d031f7574740ee596a32a91bba6d7e925ab94
{ "head_commit": "f5d153f667e72f325b0551fa55c3701f75ba5c79", "head_commit_message": "ignore rerun within integration tests", "patch_to_review": "diff --git a/e2e/scripts/st_experimental_rerun.py b/e2e/scripts/st_experimental_rerun.py\nnew file mode 100644\nindex 000000000000..baf2719c10aa\n--- /dev/null\n+++ b/e2e/scripts/st_experimental_rerun.py\n@@ -0,0 +1,30 @@\n+# Copyright 2018-2020 Streamlit Inc.\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import streamlit as st\n+\n+\[email protected](allow_output_mutation=True)\n+def rerun_record():\n+ return [0]\n+\n+\n+count = rerun_record()\n+count[0] += 1\n+\n+if count[0] < 4:\n+ st.experimental_rerun()\n+\n+if count[0] >= 4:\n+ st.text(\"Being able to rerun a session is awesome!\")\ndiff --git a/e2e/specs/st_experimental_rerun.spec.ts b/e2e/specs/st_experimental_rerun.spec.ts\nnew file mode 100644\nindex 000000000000..9638d4553199\n--- /dev/null\n+++ b/e2e/specs/st_experimental_rerun.spec.ts\n@@ -0,0 +1,31 @@\n+/**\n+ * @license\n+ * Copyright 2018-2020 Streamlit Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+/// <reference types=\"cypress\" />\n+\n+describe(\"st.experimental_rerun\", () => {\n+ before(() => {\n+ cy.visit(\"http://localhost:3000/\");\n+ });\n+\n+ it(\"works within threads\", () => {\n+ cy.get(\".element-container .stText\").should(\n+ \"contain\",\n+ \"Being able to rerun a session is awesome!\"\n+ );\n+ });\n+});\ndiff --git a/lib/streamlit/__init__.py b/lib/streamlit/__init__.py\nindex d60c766f37ed..91d245ac145a 100644\n--- a/lib/streamlit/__init__.py\n+++ b/lib/streamlit/__init__.py\n@@ -76,6 +76,8 @@\n from streamlit.report_thread import add_report_ctx as _add_report_ctx\n from streamlit.report_thread import get_report_ctx as _get_report_ctx\n from streamlit.script_runner import StopException\n+from streamlit.script_runner import RerunException as _RerunException\n+from streamlit.script_request_queue import RerunData as _RerunData\n from streamlit.errors import StreamlitAPIException\n from streamlit.proto import BlockPath_pb2 as _BlockPath_pb2\n from streamlit.proto import ForwardMsg_pb2 as _ForwardMsg_pb2\n@@ -507,3 +509,12 @@ def stop():\n \n \"\"\"\n raise StopException()\n+\n+\n+def experimental_rerun():\n+ \"\"\"Reruns the streamlit application.\n+\n+ When run outside of Streamlit this will raise an Exception.\n+ \"\"\"\n+\n+ raise _RerunException(_RerunData(None))\ndiff --git a/scripts/run_bare_integration_tests.py b/scripts/run_bare_integration_tests.py\nindex abb0d95394e2..df267e7515ba 100755\n--- a/scripts/run_bare_integration_tests.py\n+++ b/scripts/run_bare_integration_tests.py\n@@ -36,6 +36,10 @@\n \n EXCLUDED_FILENAMES = set() # type: Set[str]\n \n+# st_experimental_rerun.py calls st.experimental_rerun which raises a\n+# RerunException when called within plain Python.\n+EXCLUDED_FILENAMES.add(\"st_experimental_rerun.py\")\n+\n # Since there is not DISPLAY set (and since Streamlit is not actually running\n # and fixing Matplotlib in these tests), we set the MPL backend to something\n # that doesn't require a display.\n" }
[ { "diff_hunk": "@@ -0,0 +1,31 @@\n+/**\n+ * @license\n+ * Copyright 2018-2020 Streamlit Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+/// <reference types=\"cypress\" />\n+\n+describe(\"st.experimental_rerun\", () => {\n+ before(() => {\n+ cy.visit(\"http://localhost:3000/\");\n+ });\n+\n+ it(\"works within threads\", () => {", "line": null, "original_line": 25, "original_start_line": null, "path": "e2e/specs/st_experimental_rerun.spec.ts", "start_line": null, "text": "@user1:\nI think this test is misnamed now, no?\n\n@author:\nYup :). Thanks." }, { "diff_hunk": "@@ -507,3 +509,12 @@ def stop():\n \n \"\"\"\n raise StopException()\n+\n+\n+def experimental_rerun():\n+ \"\"\"Reruns the streamlit application.\n+\n+ When run outside of Streamlit this will raise an Exception.\n+ \"\"\"", "line": null, "original_line": 518, "original_start_line": 515, "path": "lib/streamlit/__init__.py", "start_line": null, "text": "@user1:\nCan we change this to:\r\n\r\n```\r\n \"\"\"Rerun the script immediately.\r\n\r\n When `st.experimental_rerun` is called, the script is halted - no more\r\n statements will be run, and the script will be queued to re-run from the\r\n top.\r\n\r\n If this function is called outside of Streamlit, it will raise an\r\n Exception.\r\n \"\"\"\r\n```\n\n@author:\nCan do. Thanks :)." } ]
998fcfd982c0e9bb05503863a07158d50b9f24e8
diff --git a/e2e/scripts/st_experimental_rerun.py b/e2e/scripts/st_experimental_rerun.py new file mode 100644 index 000000000000..baf2719c10aa --- /dev/null +++ b/e2e/scripts/st_experimental_rerun.py @@ -0,0 +1,30 @@ +# Copyright 2018-2020 Streamlit Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import streamlit as st + + [email protected](allow_output_mutation=True) +def rerun_record(): + return [0] + + +count = rerun_record() +count[0] += 1 + +if count[0] < 4: + st.experimental_rerun() + +if count[0] >= 4: + st.text("Being able to rerun a session is awesome!") diff --git a/e2e/specs/st_experimental_rerun.spec.ts b/e2e/specs/st_experimental_rerun.spec.ts new file mode 100644 index 000000000000..5b8fdfa5b206 --- /dev/null +++ b/e2e/specs/st_experimental_rerun.spec.ts @@ -0,0 +1,31 @@ +/** + * @license + * Copyright 2018-2020 Streamlit Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/// <reference types="cypress" /> + +describe("st.experimental_rerun", () => { + before(() => { + cy.visit("http://localhost:3000/"); + }); + + it("restarts the session when invoked", () => { + cy.get(".element-container .stText").should( + "contain", + "Being able to rerun a session is awesome!" + ); + }); +}); diff --git a/lib/streamlit/__init__.py b/lib/streamlit/__init__.py index d60c766f37ed..cdcf7797bc92 100644 --- a/lib/streamlit/__init__.py +++ b/lib/streamlit/__init__.py @@ -76,6 +76,8 @@ from streamlit.report_thread import add_report_ctx as _add_report_ctx from streamlit.report_thread import get_report_ctx as _get_report_ctx from streamlit.script_runner import StopException +from streamlit.script_runner import RerunException as _RerunException +from streamlit.script_request_queue import RerunData as _RerunData from streamlit.errors import StreamlitAPIException from streamlit.proto import BlockPath_pb2 as _BlockPath_pb2 from streamlit.proto import ForwardMsg_pb2 as _ForwardMsg_pb2 @@ -507,3 +509,17 @@ def stop(): """ raise StopException() + + +def experimental_rerun(): + """Rerun the script immediately. + + When `st.experimental_rerun()` is called, the script is halted - no + more statements will be run, and the script will be queued to re-run + from the top. + + If this function is called outside of Streamlit, it will raise an + Exception. + """ + + raise _RerunException(_RerunData(None)) diff --git a/scripts/run_bare_integration_tests.py b/scripts/run_bare_integration_tests.py index abb0d95394e2..df267e7515ba 100755 --- a/scripts/run_bare_integration_tests.py +++ b/scripts/run_bare_integration_tests.py @@ -36,6 +36,10 @@ EXCLUDED_FILENAMES = set() # type: Set[str] +# st_experimental_rerun.py calls st.experimental_rerun which raises a +# RerunException when called within plain Python. +EXCLUDED_FILENAMES.add("st_experimental_rerun.py") + # Since there is not DISPLAY set (and since Streamlit is not actually running # and fixing Matplotlib in these tests), we set the MPL backend to something # that doesn't require a display.
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
zulip__zulip-22583@37ae377
zulip/zulip
Python
22,583
unread: Indicate which streams and topics have unread @-mentions.
<!-- Describe your pull request here.--> Fixes: #21637 <!-- If the PR makes UI changes, always include one or more still screenshots to demonstrate your changes. If it seems helpful, add a screen capture of the new functionality as well. Tooling tips: https://zulip.readthedocs.io/en/latest/tutorials/screenshot-and-gif-software.html --> **Screenshots and screen captures:** **Self-review checklist** <!-- Prior to submitting a PR, follow our step-by-step guide to review your own code: https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code --> <!-- Once you create the PR, check off all the steps below that you have completed. If any of these steps are not relevant or you have not completed, leave them unchecked.--> - [x] [Self-reviewed](https://zulip.readthedocs.io/en/latest/contributing/code-reviewing.html#how-to-review-code) the changes for clarity and maintainability (variable names, code reuse, readability, etc.). Communicate decisions, questions, and potential concerns. - [x] Explains differences from previous plans (e.g., issue description). - [x] Highlights technical choices and bugs encountered. - [x] Calls out remaining decisions and concerns. - [x] Automated tests verify logic where appropriate. Individual commits are ready for review (see [commit discipline](https://zulip.readthedocs.io/en/latest/contributing/version-control.html)). - [x] Each commit is a coherent idea. - [x] Commit message(s) explain reasoning and motivation for changes. Completed manual review and testing of the following: - [x] Visual appearance of the changes. - [x] Responsiveness and internationalization. - [x] Strings and tooltips. - [x] End-to-end functionality of buttons, interactions and flows. - [x] Corner cases, error conditions, and easily imagined bugs.
2022-07-25T16:04:47Z
Indicate which streams and topics have unread @-mentions When looking at streams and topics in the left sidebar, it would be helpful to know which ones have unread messages where the user has been @-mentioned. To address this, we should display an `@` to the left of the unread message count when there is at least one unread message with an @-mention in the stream or topic. Additional notes: * The same messages as we show in the @-mention tab should count. I.e. group mentions are included; silent mentions are excluded. * The @ should be displayed both in the regular left sidebar view and in the "more topics" view. * As a follow-up, we should plan to figure out a UI for showing this in Recent topics as well.
Hello @zulip/server-sidebars members, this issue was labeled with the "area: left-sidebar" label, so you may want to check it out! <!-- areaLabelAddition --> @zulipbot claim Hello @AnushaNathRoy, you have been unassigned from this issue because you have not updated this issue or any referenced pull requests for over 14 days. You can reclaim this issue or claim any other issue by commenting `@zulipbot claim` on that issue. Thanks for your contributions, and hope to see you again soon! Bumping up the priority, as this feature has been very nice on mobile! @zulipbot claim @jai2201 You have been unassigned from this issue because you have not made any updates for over 14 days. Please feel free to reclaim the issue if you decide to pick up again. Thanks! @zulipbot claim
[ { "body": "When looking at streams and topics in the left sidebar, it would be helpful to know which ones have unread messages where the user has been @-mentioned. To address this, we should display an `@` to the left of the unread message count when there is at least one unread message with an @-mention in the stream or topic.\r\n\r\nAdditional notes:\r\n* The same messages as we show in the @-mention tab should count. I.e. group mentions are included; silent mentions are excluded.\r\n* The @ should be displayed both in the regular left sidebar view and in the \"more topics\" view.\r\n* As a follow-up, we should plan to figure out a UI for showing this in Recent topics as well.", "number": 21637, "title": "Indicate which streams and topics have unread @-mentions" } ]
69685c2864428e40d0f1ddab49058f70e86bfb8c
{ "head_commit": "37ae377b954c207ebf8bc5ba53f648ada9e308c1", "head_commit_message": "bucketer: Store bucket_keys in reverse_lookup maps.\n\nThis makes it possible to fetch the bucket_key, not just the bucket\nitself, given an item_id.", "patch_to_review": "diff --git a/static/js/unread.js b/static/js/unread.js\nindex 8f58cbe13acd4..fbac66d479b46 100644\n--- a/static/js/unread.js\n+++ b/static/js/unread.js\n@@ -31,6 +31,7 @@ export const unread_mentions_counter = new Set();\n const unread_messages = new Set();\n \n class Bucketer {\n+ // Maps item_id => bucket_key for items present in a bucket.\n reverse_lookup = new Map();\n \n constructor(options) {\n@@ -58,12 +59,13 @@ class Bucketer {\n } else {\n bucket.add(item_id);\n }\n- this.reverse_lookup.set(item_id, bucket);\n+ this.reverse_lookup.set(item_id, bucket_key);\n }\n \n delete(item_id) {\n- const bucket = this.reverse_lookup.get(item_id);\n- if (bucket) {\n+ const bucket_key = this.reverse_lookup.get(item_id);\n+ if (bucket_key) {\n+ const bucket = this.get_bucket(bucket_key);\n bucket.delete(item_id);\n this.reverse_lookup.delete(item_id);\n }\n" }
[ { "diff_hunk": "@@ -563,6 +563,11 @@ div.overlay {\n color: hsl(0, 0%, 100%);\n }\n \n+.unread_mention_info:not(:empty) {", "line": 566, "original_line": 566, "original_start_line": null, "path": "static/styles/app_components.css", "start_line": null, "text": "@user1:\nI think a 2px `margin-left` here would fix the overflow issue; test-deploying that but you might want to play with it." } ]
71204abb5dbc43b96895194cc61e16115ed57de7
diff --git a/frontend_tests/node_tests/stream_list.js b/frontend_tests/node_tests/stream_list.js index b33b83baaacc9..905d4e4142605 100644 --- a/frontend_tests/node_tests/stream_list.js +++ b/frontend_tests/node_tests/stream_list.js @@ -14,7 +14,7 @@ page_params.realm_users = []; // We use this with override. let num_unread_for_stream; - +let stream_has_any_unread_mentions; const noop = () => {}; mock_esm("../../static/js/narrow_state", { @@ -30,6 +30,7 @@ const scroll_util = mock_esm("../../static/js/scroll_util", { mock_esm("../../static/js/ui", {get_scroll_element: ($element) => $element}); mock_esm("../../static/js/unread", { num_unread_for_stream: () => num_unread_for_stream, + stream_has_any_unread_mentions: () => stream_has_any_unread_mentions, }); const {Filter} = zrequire("../js/filter"); @@ -60,11 +61,13 @@ let inactive_subheader_flag = false; function create_devel_sidebar_row({mock_template}) { const $devel_count = $.create("devel-count"); const $subscription_block = $.create("devel-block"); + const $devel_unread_mention_info = $.create("devel-unread-mention-info"); const $sidebar_row = $("<devel-sidebar-row-stub>"); $sidebar_row.set_find_results(".subscription_block", $subscription_block); $subscription_block.set_find_results(".unread_count", $devel_count); + $subscription_block.set_find_results(".unread_mention_info", $devel_unread_mention_info); mock_template("stream_sidebar_row.hbs", false, (data) => { assert.equal(data.uri, "#narrow/stream/100-devel"); @@ -72,18 +75,22 @@ function create_devel_sidebar_row({mock_template}) { }); num_unread_for_stream = 42; + stream_has_any_unread_mentions = false; stream_list.create_sidebar_row(devel); assert.equal($devel_count.text(), "42"); + assert.equal($devel_unread_mention_info.text(), ""); } function create_social_sidebar_row({mock_template}) { const $social_count = $.create("social-count"); const $subscription_block = $.create("social-block"); + const $social_unread_mention_info = $.create("social-unread-mention-info"); const $sidebar_row = $("<social-sidebar-row-stub>"); $sidebar_row.set_find_results(".subscription_block", $subscription_block); $subscription_block.set_find_results(".unread_count", $social_count); + $subscription_block.set_find_results(".unread_mention_info", $social_unread_mention_info); mock_template("stream_sidebar_row.hbs", false, (data) => { assert.equal(data.uri, "#narrow/stream/200-social"); @@ -91,8 +98,10 @@ function create_social_sidebar_row({mock_template}) { }); num_unread_for_stream = 99; + stream_has_any_unread_mentions = true; stream_list.create_sidebar_row(social); assert.equal($social_count.text(), "99"); + assert.equal($social_unread_mention_info.text(), "@"); } function create_stream_subheader({mock_template}) { @@ -658,8 +667,10 @@ test_ui("rename_stream", ({mock_template}) => { const $subscription_block = $.create("development-block"); const $unread_count = $.create("development-count"); + const $unread_mention_info = $.create("development-unread-mention-info"); $li_stub.set_find_results(".subscription_block", $subscription_block); $subscription_block.set_find_results(".unread_count", $unread_count); + $subscription_block.set_find_results(".unread_mention_info", $unread_mention_info); stream_list.rename_stream(sub); assert.equal($unread_count.text(), "99"); diff --git a/frontend_tests/node_tests/topic_list_data.js b/frontend_tests/node_tests/topic_list_data.js index 409e7f95ca521..4968fba0e9b3d 100644 --- a/frontend_tests/node_tests/topic_list_data.js +++ b/frontend_tests/node_tests/topic_list_data.js @@ -50,6 +50,7 @@ test("get_list_info w/real stream_topic_history", ({override}) => { assert.deepEqual(empty_list_info, { items: [], + more_topics_have_unread_mention_messages: false, more_topics_unreads: 0, num_possible_topics: 0, }); @@ -77,9 +78,11 @@ test("get_list_info w/real stream_topic_history", ({override}) => { list_info = get_list_info(); assert.equal(list_info.items.length, 5); assert.equal(list_info.more_topics_unreads, 0); + assert.equal(list_info.more_topics_have_unread_mention_messages, false); assert.equal(list_info.num_possible_topics, 7); assert.deepEqual(list_info.items[0], { + contains_unread_mention: false, is_active_topic: true, is_muted: false, is_zero: true, @@ -91,6 +94,7 @@ test("get_list_info w/real stream_topic_history", ({override}) => { }); assert.deepEqual(list_info.items[1], { + contains_unread_mention: false, is_active_topic: false, is_muted: false, is_zero: true, @@ -108,6 +112,7 @@ test("get_list_info w/real stream_topic_history", ({override}) => { list_info = get_list_info(zoomed); assert.equal(list_info.items.length, 7); assert.equal(list_info.more_topics_unreads, 0); + assert.equal(list_info.more_topics_have_unread_mention_messages, false); assert.equal(list_info.num_possible_topics, 7); add_topic_message("After Brooklyn", 1008); @@ -117,6 +122,7 @@ test("get_list_info w/real stream_topic_history", ({override}) => { list_info = get_list_info(zoomed); assert.equal(list_info.items.length, 2); assert.equal(list_info.more_topics_unreads, 0); + assert.equal(list_info.more_topics_have_unread_mention_messages, false); assert.equal(list_info.num_possible_topics, 2); }); @@ -144,6 +150,20 @@ test("get_list_info unreads", ({override}) => { ); } + function add_unreads_with_mention(topic, count) { + unread.process_loaded_messages( + Array.from({length: count}, () => ({ + id: (message_id += 1), + stream_id: general.stream_id, + topic, + type: "stream", + unread: true, + mentioned: true, + mentioned_me_directly: true, + })), + ); + } + /* We have 15 topics, but we only show up to 8 topics, depending on how many have @@ -156,9 +176,18 @@ test("get_list_info unreads", ({override}) => { add_unreads("topic 8", 8); add_unreads("topic 9", 9); + /* + We added 9 unread messages in 'topic 9', + but now we would add a unread message + with `mention` for user, to test + `more_topics_have_unread_mention_messages`. + */ + add_unreads_with_mention("topic 9", 1); + list_info = get_list_info(); assert.equal(list_info.items.length, 7); assert.equal(list_info.more_topics_unreads, 0); + assert.equal(list_info.more_topics_have_unread_mention_messages, false); assert.equal(list_info.num_possible_topics, 15); assert.deepEqual( @@ -171,7 +200,8 @@ test("get_list_info unreads", ({override}) => { list_info = get_list_info(); assert.equal(list_info.items.length, 8); - assert.equal(list_info.more_topics_unreads, 9); + assert.equal(list_info.more_topics_unreads, 10); + assert.equal(list_info.more_topics_have_unread_mention_messages, true); assert.equal(list_info.num_possible_topics, 15); assert.deepEqual( @@ -190,7 +220,8 @@ test("get_list_info unreads", ({override}) => { list_info = get_list_info(); assert.equal(list_info.items.length, 8); - assert.equal(list_info.more_topics_unreads, 9 + 13); + assert.equal(list_info.more_topics_unreads, 10 + 13); + assert.equal(list_info.more_topics_have_unread_mention_messages, true); assert.equal(list_info.num_possible_topics, 15); assert.deepEqual( diff --git a/frontend_tests/node_tests/unread.js b/frontend_tests/node_tests/unread.js index 6056dfae2c27e..5d5110bb33405 100644 --- a/frontend_tests/node_tests/unread.js +++ b/frontend_tests/node_tests/unread.js @@ -582,6 +582,80 @@ test("mention updates", () => { test_counted(true); }); +test("stream_has_any_unread_mentions", () => { + const muted_stream_id = 401; + user_topics.add_muted_topic(401, "lunch"); + + const mention_me_message = { + id: 15, + type: "stream", + stream_id: 400, + topic: "lunch", + mentioned: true, + mentioned_me_directly: true, + unread: true, + }; + + const mention_all_message = { + id: 16, + type: "stream", + stream_id: 400, + topic: "lunch", + mentioned: true, + mentioned_me_directly: false, + unread: true, + }; + + // This message's stream_id should not be present in `streams_with_mentions`. + const muted_mention_all_message = { + id: 17, + type: "stream", + stream_id: muted_stream_id, + topic: "lunch", + mentioned: true, + mentioned_me_directly: false, + unread: true, + }; + + unread.process_loaded_messages([ + mention_me_message, + mention_all_message, + muted_mention_all_message, + ]); + + assert.equal(unread.stream_has_any_unread_mentions(400), true); + assert.equal(unread.stream_has_any_unread_mentions(muted_stream_id), false); +}); + +test("topics with unread mentions", () => { + const message_with_mention = { + id: 98, + type: "stream", + stream_id: 999, + topic: "topic with mention", + mentioned: true, + mentioned_me_directly: true, + unread: true, + }; + + const message_without_mention = { + id: 99, + type: "stream", + stream_id: 999, + topic: "topic without mention", + mentioned: false, + mentioned_me_directly: false, + unread: true, + }; + + unread.process_loaded_messages([message_with_mention, message_without_mention]); + assert.equal(unread.get_topics_with_unread_mentions(999).size, 1); + assert.deepEqual(unread.get_topics_with_unread_mentions(999), new Set(["topic with mention"])); + unread.mark_as_read(message_with_mention.id); + assert.equal(unread.get_topics_with_unread_mentions(999).size, 0); + assert.deepEqual(unread.get_topics_with_unread_mentions(999), new Set([])); +}); + test("starring", () => { // We don't need any setup here, because we just hard code // this to [] in the code. diff --git a/static/js/stream_list.js b/static/js/stream_list.js index e5bb182023801..056e7c347c70d 100644 --- a/static/js/stream_list.js +++ b/static/js/stream_list.js @@ -32,13 +32,17 @@ export let stream_cursor; let has_scrolled = false; -export function update_count_in_dom($stream_li, count) { +export function update_count_in_dom($stream_li, count, stream_has_any_unread_mention_messages) { // The subscription_block properly excludes the topic list, // and it also has sensitive margins related to whether the // count is there or not. const $subscription_block = $stream_li.find(".subscription_block"); ui_util.update_unread_count_in_dom($subscription_block, count); + ui_util.update_unread_mention_info_in_dom( + $subscription_block, + stream_has_any_unread_mention_messages, + ); if (count === 0) { $subscription_block.removeClass("stream-with-count"); @@ -335,7 +339,10 @@ class StreamSidebarRow { update_unread_count() { const count = unread.num_unread_for_stream(this.sub.stream_id); - update_count_in_dom(this.$list_item, count); + const stream_has_any_unread_mention_messages = unread.stream_has_any_unread_mentions( + this.sub.stream_id, + ); + update_count_in_dom(this.$list_item, count, stream_has_any_unread_mention_messages); } } @@ -374,7 +381,7 @@ export function redraw_stream_privacy(sub) { $div.html(html); } -function set_stream_unread_count(stream_id, count) { +function set_stream_unread_count(stream_id, count, stream_has_any_unread_mention_messages) { const $stream_li = get_stream_li(stream_id); if (!$stream_li) { // This can happen for legitimate reasons, but we warn @@ -382,7 +389,7 @@ function set_stream_unread_count(stream_id, count) { blueslip.warn("stream id no longer in sidebar: " + stream_id); return; } - update_count_in_dom($stream_li, count); + update_count_in_dom($stream_li, count, stream_has_any_unread_mention_messages); } export function update_streams_sidebar(force_rerender) { @@ -402,7 +409,9 @@ export function update_streams_sidebar(force_rerender) { export function update_dom_with_unread_counts(counts) { // counts.stream_count maps streams to counts for (const [stream_id, count] of counts.stream_count) { - set_stream_unread_count(stream_id, count); + const stream_has_any_unread_mention_messages = + counts.streams_with_mentions.includes(stream_id); + set_stream_unread_count(stream_id, count, stream_has_any_unread_mention_messages); } } diff --git a/static/js/topic_list.js b/static/js/topic_list.js index 6108db2599889..3701c6a4619b4 100644 --- a/static/js/topic_list.js +++ b/static/js/topic_list.js @@ -82,10 +82,11 @@ export function keyed_topic_li(conversation) { }; } -export function more_li(more_topics_unreads) { +export function more_li(more_topics_unreads, more_topics_have_unread_mention_messages) { const render = () => render_more_topics({ more_topics_unreads, + more_topics_have_unread_mention_messages, }); const eq = (other) => other.more_items && more_topics_unreads === other.more_topics_unreads; @@ -142,6 +143,8 @@ export class TopicListWidget { const num_possible_topics = list_info.num_possible_topics; const more_topics_unreads = list_info.more_topics_unreads; + const more_topics_have_unread_mention_messages = + list_info.more_topics_have_unread_mention_messages; const is_showing_all_possible_topics = list_info.items.length === num_possible_topics && @@ -154,7 +157,7 @@ export class TopicListWidget { if (spinner) { nodes.push(spinner_li()); } else if (!is_showing_all_possible_topics) { - nodes.push(more_li(more_topics_unreads)); + nodes.push(more_li(more_topics_unreads, more_topics_have_unread_mention_messages)); } else if (zoomed) { // In the zoomed topic view, we need to add the input // for filtering through list of topics. diff --git a/static/js/topic_list_data.js b/static/js/topic_list_data.js index 9647ff53e0070..a3ee1c2830bdb 100644 --- a/static/js/topic_list_data.js +++ b/static/js/topic_list_data.js @@ -14,6 +14,7 @@ const max_topics_with_unread = 8; export function get_list_info(stream_id, zoomed) { let topics_selected = 0; let more_topics_unreads = 0; + let more_topics_have_unread_mention_messages = false; let active_topic = narrow_state.topic(); @@ -29,12 +30,16 @@ export function get_list_info(stream_id, zoomed) { const items = []; + const topics_with_unread_mentions = unread.get_topics_with_unread_mentions(stream_id); + for (const [idx, topic_name] of topic_names.entries()) { const num_unread = unread.num_unread_for_topic(stream_id, topic_name); const is_active_topic = active_topic === topic_name.toLowerCase(); const is_topic_muted = user_topics.is_topic_muted(stream_id, topic_name); const [topic_resolved_prefix, topic_display_name] = resolved_topic.display_parts(topic_name); + // Important: Topics are lower-case in this set. + const contains_unread_mention = topics_with_unread_mentions.has(topic_name.toLowerCase()); if (!zoomed) { function should_show_topic(topics_selected) { @@ -85,6 +90,9 @@ export function get_list_info(stream_id, zoomed) { // stream-level counts, only counts messages // on unmuted topics. more_topics_unreads += num_unread; + if (contains_unread_mention) { + more_topics_have_unread_mention_messages = true; + } } continue; } @@ -102,6 +110,7 @@ export function get_list_info(stream_id, zoomed) { is_muted: is_topic_muted, is_active_topic, url: hash_util.by_stream_topic_url(stream_id, topic_name), + contains_unread_mention, }; items.push(topic_info); @@ -111,5 +120,6 @@ export function get_list_info(stream_id, zoomed) { items, num_possible_topics: topic_names.length, more_topics_unreads, + more_topics_have_unread_mention_messages, }; } diff --git a/static/js/ui_util.ts b/static/js/ui_util.ts index dad8d395309d0..fe2d43f592fca 100644 --- a/static/js/ui_util.ts +++ b/static/js/ui_util.ts @@ -49,6 +49,21 @@ export function update_unread_count_in_dom($unread_count_elem: JQuery, count: nu $unread_count_span.text(count); } +export function update_unread_mention_info_in_dom( + $unread_mention_info_elem: JQuery, + stream_has_any_unread_mention_messages: Boolean, +): void { + const $unread_mention_info_span = $unread_mention_info_elem.find(".unread_mention_info"); + if (!stream_has_any_unread_mention_messages) { + $unread_mention_info_span.hide(); + $unread_mention_info_span.text(""); + return; + } + + $unread_mention_info_span.show(); + $unread_mention_info_span.text("@"); +} + /** * Parse HTML and return a DocumentFragment. * diff --git a/static/js/unread.js b/static/js/unread.js index 8f58cbe13acd4..28f865149708c 100644 --- a/static/js/unread.js +++ b/static/js/unread.js @@ -31,6 +31,7 @@ export const unread_mentions_counter = new Set(); const unread_messages = new Set(); class Bucketer { + // Maps item_id => bucket_key for items present in a bucket. reverse_lookup = new Map(); constructor(options) { @@ -58,12 +59,13 @@ class Bucketer { } else { bucket.add(item_id); } - this.reverse_lookup.set(item_id, bucket); + this.reverse_lookup.set(item_id, bucket_key); } delete(item_id) { - const bucket = this.reverse_lookup.get(item_id); - if (bucket) { + const bucket_key = this.reverse_lookup.get(item_id); + if (bucket_key) { + const bucket = this.get_bucket(bucket_key); bucket.delete(item_id); this.reverse_lookup.delete(item_id); } @@ -373,6 +375,21 @@ class UnreadTopicCounter { return util.sorted_ids(ids); } + get_streams_with_unread_mentions() { + const streams_with_mentions = new Set(); + // Collect the set of streams containing at least one mention. + // We can do this efficiently, since unread_mentions_counter + // contains all unread message IDs, and we use stream_ids as + // bucket keys in our outer bucketer. + + for (const message_id of unread_mentions_counter) { + const stream_id = this.bucketer.reverse_lookup.get(message_id); + streams_with_mentions.add(stream_id); + } + + return streams_with_mentions; + } + topic_has_any_unread(stream_id, topic) { const per_stream_bucketer = this.bucketer.get_bucket(stream_id); @@ -387,6 +404,32 @@ class UnreadTopicCounter { return id_set.size !== 0; } + + get_topics_with_unread_mentions(stream_id) { + // Returns the set of lower cased topics with unread mentions + // in the given stream. + const result = new Set(); + const per_stream_bucketer = this.bucketer.get_bucket(stream_id); + + if (!per_stream_bucketer) { + return result; + } + + for (const message_id of unread_mentions_counter) { + // Because bucket keys in per_stream_bucketer are topics, + // we can just directly use reverse_lookup to find the + // topic in this stream containing a given unread message + // ID. If it's not in this stream, we'll get undefined. + const topic_match = per_stream_bucketer.reverse_lookup.get(message_id); + if (topic_match !== undefined) { + // Important: We lower-case topics here before adding them + // to this set, to support case-insensitive checks. + result.add(topic_match.toLowerCase()); + } + } + + return result; + } } const unread_topic_counter = new UnreadTopicCounter(); @@ -526,8 +569,10 @@ export function get_counts() { // This sets stream_count, topic_count, and home_unread_messages const topic_res = unread_topic_counter.get_counts(); + const streams_with_mentions = unread_topic_counter.get_streams_with_unread_mentions(); res.home_unread_messages = topic_res.stream_unread_messages; res.stream_count = topic_res.stream_count; + res.streams_with_mentions = Array.from(streams_with_mentions); const pm_res = unread_pm_counter.get_counts(); res.pm_count = pm_res.pm_dict; @@ -573,10 +618,19 @@ export function num_unread_for_topic(stream_id, topic_name) { return unread_topic_counter.get(stream_id, topic_name); } +export function stream_has_any_unread_mentions(stream_id) { + const streams_with_mentions = unread_topic_counter.get_streams_with_unread_mentions(); + return streams_with_mentions.has(stream_id); +} + export function topic_has_any_unread(stream_id, topic) { return unread_topic_counter.topic_has_any_unread(stream_id, topic); } +export function get_topics_with_unread_mentions(stream_id) { + return unread_topic_counter.get_topics_with_unread_mentions(stream_id); +} + export function num_unread_for_person(user_ids_string) { return unread_pm_counter.num_unread(user_ids_string); } diff --git a/static/styles/app_components.css b/static/styles/app_components.css index af133ab37ce8e..867d0633ecf1b 100644 --- a/static/styles/app_components.css +++ b/static/styles/app_components.css @@ -563,6 +563,12 @@ div.overlay { color: hsl(0, 0%, 100%); } +.unread_mention_info:not(:empty) { + margin-right: 5px; + margin-left: 2px; + opacity: 0.7; +} + /* Implement the web app's default-hidden convention for alert elements. Portico pages lack this CSS and thus show them by default. */ diff --git a/static/templates/more_topics.hbs b/static/templates/more_topics.hbs index d605f491dbbae..a0afa7a0ffae0 100644 --- a/static/templates/more_topics.hbs +++ b/static/templates/more_topics.hbs @@ -1,6 +1,11 @@ <li class="topic-list-item show-more-topics bottom_left_row {{#unless more_topics_unreads}}zero-topic-unreads{{/unless}}"> <span class='topic-box'> <a class="topic-name" tabindex="0">{{t "more topics" }}</a> + {{#if more_topics_have_unread_mention_messages}} + <span class="unread_mention_info"> + @ + </span> + {{/if}} <span class="unread_count {{#unless more_topics_unreads}}zero_count{{/unless}}"> {{more_topics_unreads}} </span> diff --git a/static/templates/stream_sidebar_row.hbs b/static/templates/stream_sidebar_row.hbs index 5565abdbe2de0..81dd50c0ac3b8 100644 --- a/static/templates/stream_sidebar_row.hbs +++ b/static/templates/stream_sidebar_row.hbs @@ -10,6 +10,7 @@ <a href="{{uri}}" title="{{name}}" class="stream-name">{{name}}</a> + <span class="unread_mention_info"></span> <span class="unread_count"></span> </div> <span class="stream-sidebar-menu-icon hidden-for-spectators"><i class="zulip-icon zulip-icon-ellipsis-v-solid" aria-hidden="true"></i></span> diff --git a/static/templates/topic_list_item.hbs b/static/templates/topic_list_item.hbs index ba2858f67a967..6f4c7cea3688b 100644 --- a/static/templates/topic_list_item.hbs +++ b/static/templates/topic_list_item.hbs @@ -6,6 +6,11 @@ <a href='{{url}}' class="topic-name" title="{{topic_name}}"> {{topic_display_name}} </a> + {{#if contains_unread_mention}} + <span class="unread_mention_info"> + @ + </span> + {{/if}} <span class="unread_count {{#if is_zero}}zero_count{{/if}}"> {{unread}} </span>
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
streamlit__streamlit-1423@305f351
streamlit/streamlit
Python
1,423
TextArea and TextInput max_chars
**Issue:** This PR fixes #613 **Description:** Adding max_chars arg to text area and text input --- **Contribution License Agreement** By submiting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2020-05-06T18:40:14Z
Specify max number of characters in text area ### Problem Right now, there is no limit to specify number of characters in text area. This is very risky and an overkill if users use it to process too large textual data. ### Solution It would be great to give users the option to specify the max number of characters allowed in text area. It should be up to the user to specify number of characters though.
How about a `max_chars` kwarg? Default `==None` meaning infinite. I think this should be fine. I hope the documentation will be updated accordingly once this is implemented. Thanks a million @treuille for creating such an amazing framework. Sorry to bother you guys. Any progress on this one? I am trying to make an app available that uses spaCy, but increased number of words in the text area basically kills it. I want to limit to something like 2000 characters for example. Hi @mzeidhassan we are prioritizing it on our backlog but in the meantime you could do something like this: ```python import streamlit as st text = st.text_area("Your text:") if len(text) > 10: st.error("You can't use more than 10 characters") else: st.success(text) ```
[ { "body": "### Problem\r\n\r\nRight now, there is no limit to specify number of characters in text area. This is very risky and an overkill if users use it to process too large textual data.\r\n\r\n### Solution\r\n\r\nIt would be great to give users the option to specify the max number of characters allowed in text area. It should be up to the user to specify number of characters though. \r\n", "number": 613, "title": "Specify max number of characters in text area" } ]
97e57f45738174a69ea14649a1355a2c1939478c
{ "head_commit": "305f3512c17e50d45f5e12f4c11e564683d7ba66", "head_commit_message": "e2e", "patch_to_review": "diff --git a/e2e/scripts/st_text_area.py b/e2e/scripts/st_text_area.py\nindex 665d302648ff..e0f0426933fa 100644\n--- a/e2e/scripts/st_text_area.py\n+++ b/e2e/scripts/st_text_area.py\n@@ -25,3 +25,6 @@\n \n i4 = st.text_area(\"text area 4\", None)\n st.write('value 4: \"', i4, '\"')\n+\n+i5 = st.text_area(\"text area 5\", max_chars=10)\n+st.write('value 5: \"', i5, '\"')\ndiff --git a/e2e/specs/st_text_area.spec.ts b/e2e/specs/st_text_area.spec.ts\nindex 652c01ef0e04..ee526b9e8fe0 100644\n--- a/e2e/specs/st_text_area.spec.ts\n+++ b/e2e/specs/st_text_area.spec.ts\n@@ -26,7 +26,7 @@ describe(\"st.text_area\", () => {\n });\n \n it(\"shows widget correctly\", () => {\n- cy.get(\".stTextArea\").should(\"have.length\", 4);\n+ cy.get(\".stTextArea\").should(\"have.length\", 5);\n \n cy.get(\".stTextArea\").each((el, idx) => {\n return cy.wrap(el).matchImageSnapshot(\"text_area\" + idx);\n@@ -39,21 +39,23 @@ describe(\"st.text_area\", () => {\n 'value 1: \" \"' +\n 'value 2: \" default text \"' +\n 'value 3: \" 1234 \"' +\n- 'value 4: \" None \"'\n+ 'value 4: \" None \"' +\n+ 'value 5: \" \"'\n );\n });\n \n it(\"sets value correctly when user types\", () => {\n cy.get(\".stTextArea textarea\")\n .first()\n- .type(\"test area{enter}\");\n+ .type(\"test area{ctrl}{enter}\");\n \n cy.get(\".stMarkdown\").should(\n \"have.text\",\n- 'value 1: \" \"' +\n+ 'value 1: \" test area \"' +\n 'value 2: \" default text \"' +\n 'value 3: \" 1234 \"' +\n- 'value 4: \" None \"'\n+ 'value 4: \" None \"' +\n+ 'value 5: \" \"'\n );\n });\n \n@@ -67,7 +69,8 @@ describe(\"st.text_area\", () => {\n 'value 1: \" test area \"' +\n 'value 2: \" default text \"' +\n 'value 3: \" 1234 \"' +\n- 'value 4: \" None \"'\n+ 'value 4: \" None \"' +\n+ 'value 5: \" \"'\n );\n });\n \n@@ -81,7 +84,8 @@ describe(\"st.text_area\", () => {\n 'value 1: \" test area \"' +\n 'value 2: \" default text \"' +\n 'value 3: \" 1234 \"' +\n- 'value 4: \" None \"'\n+ 'value 4: \" None \"' +\n+ 'value 5: \" \"'\n );\n });\n \n@@ -96,7 +100,24 @@ describe(\"st.text_area\", () => {\n 'value 1: \" test area \"' +\n 'value 2: \" default text \"' +\n 'value 3: \" 1234 \"' +\n- 'value 4: \" None \"'\n+ 'value 4: \" None \"' +\n+ 'value 5: \" \"'\n+ );\n+ });\n+\n+ it(\"sets value correctly with max_chars enabled\", () => {\n+ cy.get(\".stTextArea textarea\")\n+ .last()\n+ .type(\"test area! this shouldn't be returned\")\n+ .blur();\n+\n+ cy.get(\".stMarkdown\").should(\n+ \"have.text\",\n+ 'value 1: \" \"' +\n+ 'value 2: \" default text \"' +\n+ 'value 3: \" 1234 \"' +\n+ 'value 4: \" None \"' +\n+ 'value 5: \" test area! \"'\n );\n });\n });\ndiff --git a/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx b/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx\nnew file mode 100644\nindex 000000000000..c77b07fc84a7\n--- /dev/null\n+++ b/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx\n@@ -0,0 +1,84 @@\n+/**\n+ * @license\n+ * Copyright 2018-2020 Streamlit Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+import React from \"react\"\n+import { shallow } from \"enzyme\"\n+\n+import InputInstructions, { Props } from \"./InputInstructions\"\n+\n+const getProps = (props: Partial<Props> = {}): Props => ({\n+ dirty: true,\n+ value: \"asd\",\n+ ...props,\n+})\n+\n+describe(\"InputInstructions\", () => {\n+ const props = getProps()\n+ const wrapper = shallow(<InputInstructions {...props} />)\n+\n+ it(\"renders without crashing\", () => {\n+ expect(wrapper.text()).toBeDefined()\n+ })\n+\n+ it(\"should show Enter instructions\", () => {\n+ expect(wrapper.text()).toBe(\"Press Enter to apply\")\n+ })\n+\n+ describe(\"Multiline type\", () => {\n+ const props = getProps({\n+ type: \"multiline\",\n+ })\n+ const wrapper = shallow(<InputInstructions {...props} />)\n+\n+ it(\"should show Ctrl+Enter instructions\", () => {\n+ expect(wrapper.text()).toBe(\"Press Ctrl+Enter to apply\")\n+ })\n+\n+ it(\"show โŒ˜+Enter instructions\", () => {\n+ Object.defineProperty(navigator, \"platform\", {\n+ value: \"MacIntel\",\n+ writable: true,\n+ })\n+\n+ const props = getProps({\n+ type: \"multiline\",\n+ })\n+ const wrapper = shallow(<InputInstructions {...props} />)\n+\n+ expect(wrapper.text()).toBe(\"Press โŒ˜+Enter to apply\")\n+ })\n+\n+ it(\"should show instructions for max length\", () => {\n+ const props = getProps({\n+ type: \"multiline\",\n+ maxLength: 3,\n+ })\n+ const wrapper = shallow(<InputInstructions {...props} />)\n+\n+ expect(wrapper.text()).toBe(\"Press โŒ˜+Enter to applyโ€ข3/3\")\n+ })\n+ })\n+\n+ it(\"should show instructions for max length\", () => {\n+ const props = getProps({\n+ maxLength: 3,\n+ })\n+ const wrapper = shallow(<InputInstructions {...props} />)\n+\n+ expect(wrapper.text()).toBe(\"Press Enter to applyโ€ข3/3\")\n+ })\n+})\ndiff --git a/frontend/src/components/shared/InputInstructions/InputInstructions.tsx b/frontend/src/components/shared/InputInstructions/InputInstructions.tsx\nnew file mode 100644\nindex 000000000000..c247ea2db092\n--- /dev/null\n+++ b/frontend/src/components/shared/InputInstructions/InputInstructions.tsx\n@@ -0,0 +1,60 @@\n+import React, { ReactElement } from \"react\"\n+import { isFromMac } from \"lib/utils\"\n+import classNames from \"classnames\"\n+\n+import \"./style.scss\"\n+\n+export interface Props {\n+ dirty: boolean\n+ value: string\n+ maxLength?: number\n+ className?: string\n+ type?: \"multiline\" | \"single\"\n+}\n+\n+const InputInstructions = ({\n+ dirty,\n+ value,\n+ maxLength,\n+ className,\n+ type = \"single\",\n+}: Props): ReactElement => {\n+ const containerClassName = classNames(\"instructions\", className)\n+ let message\n+\n+ if (type === \"multiline\") {\n+ if (isFromMac()) {\n+ message = \"Press โŒ˜+Enter to apply\"\n+ }\n+\n+ if (!isFromMac()) {\n+ message = \"Press Ctrl+Enter to apply\"\n+ }\n+ } else {\n+ message = \"Press Enter to apply\"\n+ }\n+\n+ if (dirty && maxLength && value.length > 0) {\n+ return (\n+ <div className={containerClassName}>\n+ <span className=\"message\">{message}</span>\n+ <span className=\"separator\">โ€ข</span>\n+ <span\n+ className={classNames(\"counter\", {\n+ blink: value.length >= maxLength,\n+ })}\n+ >\n+ {value.length}/{maxLength}\n+ </span>\n+ </div>\n+ )\n+ }\n+\n+ if (dirty) {\n+ return <div className={containerClassName}>{message}</div>\n+ }\n+\n+ return <></>\n+}\n+\n+export default InputInstructions\ndiff --git a/frontend/src/components/shared/InputInstructions/style.scss b/frontend/src/components/shared/InputInstructions/style.scss\nnew file mode 100644\nindex 000000000000..3e890dc6c2af\n--- /dev/null\n+++ b/frontend/src/components/shared/InputInstructions/style.scss\n@@ -0,0 +1,22 @@\n+@import \"src/assets/css/variables\";\n+\n+.instructions {\n+ .separator {\n+ margin: 0 5px 0 5px;\n+ }\n+\n+ .counter {\n+ &.blink {\n+ color: $red;\n+ animation-name: blinker;\n+ animation-duration: 0.5s;\n+ animation-iteration-count: 5;\n+ }\n+ }\n+}\n+\n+@keyframes blinker {\n+ 50% {\n+ opacity: 0;\n+ }\n+}\ndiff --git a/frontend/src/components/widgets/NumberInput/NumberInput.scss b/frontend/src/components/widgets/NumberInput/NumberInput.scss\nindex 0bcbb356c9a4..07c439322950 100644\n--- a/frontend/src/components/widgets/NumberInput/NumberInput.scss\n+++ b/frontend/src/components/widgets/NumberInput/NumberInput.scss\n@@ -82,7 +82,7 @@\n }\n }\n \n- .instructions {\n+ .input-instructions {\n margin-right: 5px;\n right: $controls-width * 2;\n }\ndiff --git a/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx\nindex f5df1320c32c..ebb7d9a1ab87 100644\n--- a/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx\n+++ b/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx\n@@ -283,15 +283,4 @@ describe(\"NumberInput widget\", () => {\n expect(preventDefault).toHaveBeenCalled()\n })\n })\n-\n- it(\"should show a message when it's dirty\", () => {\n- const props = getIntProps()\n- const wrapper = shallow(<NumberInput {...props} />)\n-\n- wrapper.setState({\n- dirty: true,\n- })\n-\n- expect(wrapper.find(\"div.instructions\").length).toBe(1)\n- })\n })\ndiff --git a/frontend/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/src/components/widgets/NumberInput/NumberInput.tsx\nindex ff5c4990791f..3f005d770c1d 100644\n--- a/frontend/src/components/widgets/NumberInput/NumberInput.tsx\n+++ b/frontend/src/components/widgets/NumberInput/NumberInput.tsx\n@@ -15,15 +15,16 @@\n * limitations under the License.\n */\n \n-import { NumberInput as NumberInputProto } from \"autogen/proto\"\n import React from \"react\"\n import { sprintf } from \"sprintf-js\"\n-import { Input as UIInput } from \"baseui/input\"\n+import { logWarning } from \"lib/log\"\n import { Map as ImmutableMap } from \"immutable\"\n+import { NumberInput as NumberInputProto } from \"autogen/proto\"\n import { WidgetStateManager, Source } from \"lib/WidgetStateManager\"\n-import { logWarning } from \"lib/log\"\n \n import Icon from \"components/shared/Icon\"\n+import { Input as UIInput } from \"baseui/input\"\n+import InputInstructions from \"components/shared/InputInstructions/InputInstructions\"\n \n import \"./NumberInput.scss\"\n \n@@ -281,7 +282,11 @@ class NumberInput extends React.PureComponent<Props, State> {\n </button>\n </div>\n </div>\n- {dirty && <div className=\"instructions\">Press Enter to apply</div>}\n+ <InputInstructions\n+ dirty={dirty}\n+ value={formattedValue}\n+ className=\"input-instructions\"\n+ />\n </div>\n )\n }\ndiff --git a/frontend/src/components/widgets/TextArea/TextArea.test.tsx b/frontend/src/components/widgets/TextArea/TextArea.test.tsx\nindex 4474753670ba..5a60189a19bb 100644\n--- a/frontend/src/components/widgets/TextArea/TextArea.test.tsx\n+++ b/frontend/src/components/widgets/TextArea/TextArea.test.tsx\n@@ -84,19 +84,6 @@ describe(\"TextArea widget\", () => {\n expect(wrapper.find(UITextArea).prop(\"disabled\")).toBe(props.disabled)\n })\n \n- it(\"should show Ctrl+Enter instructions\", () => {\n- // @ts-ignore\n- wrapper.find(UITextArea).prop(\"onChange\")({\n- target: {\n- value: \"testing\",\n- },\n- } as React.ChangeEvent<HTMLTextAreaElement>)\n-\n- expect(wrapper.find(\"div.instructions\").text()).toBe(\n- \"Press Ctrl+Enter to apply\"\n- )\n- })\n-\n it(\"should set widget value on blur\", () => {\n const props = getProps()\n const wrapper = shallow(<TextArea {...props} />)\n@@ -157,6 +144,32 @@ describe(\"TextArea widget\", () => {\n expect(resize).toBe(\"vertical\")\n })\n \n+ it(\"should limit the length if max_chars is passed\", () => {\n+ const props = getProps({\n+ height: 500,\n+ maxChars: 10,\n+ })\n+ const wrapper = shallow(<TextArea {...props} />)\n+\n+ // @ts-ignore\n+ wrapper.find(UITextArea).prop(\"onChange\")({\n+ target: {\n+ value: \"0123456789\",\n+ },\n+ } as EventTarget)\n+\n+ expect(wrapper.find(UITextArea).prop(\"value\")).toBe(\"0123456789\")\n+\n+ // @ts-ignore\n+ wrapper.find(UITextArea).prop(\"onChange\")({\n+ target: {\n+ value: \"0123456789a\",\n+ },\n+ } as EventTarget)\n+\n+ expect(wrapper.find(UITextArea).prop(\"value\")).toBe(\"0123456789\")\n+ })\n+\n describe(\"On mac it should\", () => {\n Object.defineProperty(navigator, \"platform\", {\n value: \"MacIntel\",\n@@ -166,19 +179,6 @@ describe(\"TextArea widget\", () => {\n const props = getProps()\n const wrapper = shallow(<TextArea {...props} />)\n \n- it(\"show โŒ˜+Enter instructions\", () => {\n- // @ts-ignore\n- wrapper.find(UITextArea).prop(\"onChange\")({\n- target: {\n- value: \"testing\",\n- },\n- } as React.ChangeEvent<HTMLTextAreaElement>)\n-\n- expect(wrapper.find(\"div.instructions\").text()).toBe(\n- \"Press โŒ˜+Enter to apply\"\n- )\n- })\n-\n it(\"should set widget value when โŒ˜+enter is pressed\", () => {\n // @ts-ignore\n wrapper.find(UITextArea).prop(\"onChange\")({\ndiff --git a/frontend/src/components/widgets/TextArea/TextArea.tsx b/frontend/src/components/widgets/TextArea/TextArea.tsx\nindex 3649fd66bfa5..c034aae4019a 100644\n--- a/frontend/src/components/widgets/TextArea/TextArea.tsx\n+++ b/frontend/src/components/widgets/TextArea/TextArea.tsx\n@@ -20,6 +20,7 @@ import { Map as ImmutableMap } from \"immutable\"\n import { WidgetStateManager, Source } from \"lib/WidgetStateManager\"\n \n import { Textarea as UITextArea } from \"baseui/textarea\"\n+import InputInstructions from \"components/shared/InputInstructions/InputInstructions\"\n \n export interface Props {\n disabled: boolean\n@@ -51,8 +52,6 @@ class TextArea extends React.PureComponent<Props, State> {\n this.setWidgetValue({ fromUi: false })\n }\n \n- private isFromMac = /Mac/i.test(navigator.platform)\n-\n private setWidgetValue = (source: Source): void => {\n const widgetId: string = this.props.element.get(\"id\")\n this.props.widgetMgr.setStringValue(widgetId, this.state.value, source)\n@@ -66,10 +65,17 @@ class TextArea extends React.PureComponent<Props, State> {\n }\n \n private onChange = (e: React.ChangeEvent<HTMLTextAreaElement>): void => {\n- this.setState({\n- dirty: true,\n- value: e.target.value,\n- })\n+ const { value } = e.target\n+ const { element } = this.props\n+\n+ const maxChars = element.get(\"maxChars\")\n+\n+ if (!maxChars || value.length <= maxChars) {\n+ this.setState({\n+ dirty: true,\n+ value,\n+ })\n+ }\n }\n \n isEnterKeyPressed = (\n@@ -100,6 +106,7 @@ class TextArea extends React.PureComponent<Props, State> {\n const style = { width }\n const label = element.get(\"label\")\n const height = element.get(\"height\")\n+ const maxChars = element.get(\"maxChars\")\n \n return (\n <div className=\"Widget stTextArea\" style={style}>\n@@ -120,13 +127,12 @@ class TextArea extends React.PureComponent<Props, State> {\n },\n }}\n />\n- {dirty && !this.isFromMac && (\n- <div className=\"instructions\">Press Ctrl+Enter to apply</div>\n- )}\n-\n- {dirty && this.isFromMac && (\n- <div className=\"instructions\">Press โŒ˜+Enter to apply</div>\n- )}\n+ <InputInstructions\n+ dirty={dirty}\n+ value={value}\n+ maxLength={maxChars}\n+ type={\"multiline\"}\n+ />\n </div>\n )\n }\ndiff --git a/frontend/src/components/widgets/TextInput/TextInput.test.tsx b/frontend/src/components/widgets/TextInput/TextInput.test.tsx\nindex 9f75e46d2b8f..8d716af559e5 100644\n--- a/frontend/src/components/widgets/TextInput/TextInput.test.tsx\n+++ b/frontend/src/components/widgets/TextInput/TextInput.test.tsx\n@@ -89,19 +89,6 @@ describe(\"TextInput widget\", () => {\n expect(wrapper.find(UIInput).prop(\"disabled\")).toBe(props.disabled)\n })\n \n- it(\"should show Enter instructions\", () => {\n- // @ts-ignore\n- wrapper.find(UIInput).prop(\"onChange\")({\n- target: {\n- value: \"testing\",\n- },\n- } as React.ChangeEvent<HTMLTextAreaElement>)\n-\n- expect(wrapper.find(\"div.instructions\").text()).toBe(\n- \"Press Enter to apply\"\n- )\n- })\n-\n it(\"should set widget value on blur\", () => {\n const props = getProps()\n const wrapper = shallow(<TextInput {...props} />)\n@@ -161,4 +148,29 @@ describe(\"TextInput widget\", () => {\n \n expect(props.widgetMgr.setStringValue).toHaveBeenCalledTimes(1)\n })\n+\n+ it(\"should limit the length if max_chars is passed\", () => {\n+ const props = getProps({\n+ maxChars: 10,\n+ })\n+ const wrapper = shallow(<TextInput {...props} />)\n+\n+ // @ts-ignore\n+ wrapper.find(UIInput).prop(\"onChange\")({\n+ target: {\n+ value: \"0123456789\",\n+ },\n+ } as EventTarget)\n+\n+ expect(wrapper.find(UIInput).prop(\"value\")).toBe(\"0123456789\")\n+\n+ // @ts-ignore\n+ wrapper.find(UIInput).prop(\"onChange\")({\n+ target: {\n+ value: \"0123456789a\",\n+ },\n+ } as EventTarget)\n+\n+ expect(wrapper.find(UIInput).prop(\"value\")).toBe(\"0123456789\")\n+ })\n })\ndiff --git a/frontend/src/components/widgets/TextInput/TextInput.tsx b/frontend/src/components/widgets/TextInput/TextInput.tsx\nindex 5d7fef01db24..f81eef35afe6 100644\n--- a/frontend/src/components/widgets/TextInput/TextInput.tsx\n+++ b/frontend/src/components/widgets/TextInput/TextInput.tsx\n@@ -18,8 +18,9 @@\n import React from \"react\"\n import { Input as UIInput } from \"baseui/input\"\n import { Map as ImmutableMap } from \"immutable\"\n-import { WidgetStateManager, Source } from \"lib/WidgetStateManager\"\n import { TextInput as TextInputProto } from \"autogen/proto\"\n+import { WidgetStateManager, Source } from \"lib/WidgetStateManager\"\n+import InputInstructions from \"components/shared/InputInstructions/InputInstructions\"\n \n export interface Props {\n disabled: boolean\n@@ -64,10 +65,17 @@ class TextInput extends React.PureComponent<Props, State> {\n }\n \n private onChange = (e: React.ChangeEvent<HTMLInputElement>): void => {\n- this.setState({\n- dirty: true,\n- value: e.target.value,\n- })\n+ const { value } = e.target\n+ const { element } = this.props\n+\n+ const maxChars = element.get(\"maxChars\")\n+\n+ if (!maxChars || value.length <= maxChars) {\n+ this.setState({\n+ dirty: true,\n+ value,\n+ })\n+ }\n }\n \n private onKeyPress = (e: React.KeyboardEvent<HTMLInputElement>): void => {\n@@ -83,23 +91,25 @@ class TextInput extends React.PureComponent<Props, State> {\n }\n \n public render = (): React.ReactNode => {\n- const label: string = this.props.element.get(\"label\")\n- const style = { width: this.props.width }\n+ const { dirty, value } = this.state\n+ const { element, width, disabled } = this.props\n+\n+ const label: string = element.get(\"label\")\n+ const maxChars = element.get(\"maxChars\")\n+ const style = { width }\n \n return (\n <div className=\"Widget row-widget stTextInput\" style={style}>\n <label>{label}</label>\n <UIInput\n- value={this.state.value}\n+ value={value}\n onBlur={this.onBlur}\n onChange={this.onChange}\n onKeyPress={this.onKeyPress}\n- disabled={this.props.disabled}\n+ disabled={disabled}\n type={this.getTypeString()}\n />\n- {this.state.dirty ? (\n- <div className=\"instructions\">Press Enter to apply</div>\n- ) : null}\n+ <InputInstructions dirty={dirty} value={value} maxLength={maxChars} />\n </div>\n )\n }\ndiff --git a/frontend/src/lib/utils.ts b/frontend/src/lib/utils.ts\nindex 471b60f93fc9..102d4308f799 100644\n--- a/frontend/src/lib/utils.ts\n+++ b/frontend/src/lib/utils.ts\n@@ -118,3 +118,10 @@ export function flattenElements(\n export function timeout(ms: number): Promise<void> {\n return new Promise(resolve => setTimeout(resolve, ms))\n }\n+\n+/**\n+ * Tests if the app is running from a Mac\n+ */\n+export function isFromMac(): boolean {\n+ return /Mac/i.test(navigator.platform)\n+}\ndiff --git a/lib/streamlit/DeltaGenerator.py b/lib/streamlit/DeltaGenerator.py\nindex 59986ec5dc57..7ca6f482562f 100644\n--- a/lib/streamlit/DeltaGenerator.py\n+++ b/lib/streamlit/DeltaGenerator.py\n@@ -1723,7 +1723,7 @@ def _check_and_convert_to_indices(options, default_values):\n if not isinstance(default_values, list):\n # This if is done before others because calling if not x (done\n # right below) when x is of type pd.Series() or np.array() throws a\n- # ValueError exception. \n+ # ValueError exception.\n if is_type(default_values, \"numpy.ndarray\") or is_type(\n default_values, \"pandas.core.series.Series\"\n ):\n@@ -2214,7 +2214,7 @@ def beta_color_picker(self, element, label, value=None, key=None):\n return str(current_value)\n \n @_with_element\n- def text_input(self, element, label, value=\"\", key=None, type=\"default\"):\n+ def text_input(self, element, label, value=\"\", max_chars=None, key=None, type=\"default\"):\n \"\"\"Display a single-line text input widget.\n \n Parameters\n@@ -2224,6 +2224,8 @@ def text_input(self, element, label, value=\"\", key=None, type=\"default\"):\n value : any\n The text value of this widget when it first renders. This will be\n cast to str internally.\n+ max_chars : int or None\n+ Max number of characters allowed in text input.\n key : str\n An optional string to use as the unique key for the widget.\n If this is omitted, a key will be generated for the widget\n@@ -2247,6 +2249,10 @@ def text_input(self, element, label, value=\"\", key=None, type=\"default\"):\n \"\"\"\n element.text_input.label = label\n element.text_input.default = str(value)\n+\n+ if max_chars is not None:\n+ element.text_input.max_chars = max_chars\n+\n if type == \"default\":\n element.text_input.type = TextInput.DEFAULT\n elif type == \"password\":\n@@ -2262,7 +2268,7 @@ def text_input(self, element, label, value=\"\", key=None, type=\"default\"):\n return str(current_value)\n \n @_with_element\n- def text_area(self, element, label, value=\"\", height=None, key=None):\n+ def text_area(self, element, label, value=\"\", height=None, max_chars=None, key=None):\n \"\"\"Display a multi-line text input widget.\n \n Parameters\n@@ -2275,6 +2281,8 @@ def text_area(self, element, label, value=\"\", height=None, key=None):\n height : int or None\n Desired height of the UI element expressed in pixels. If None, a\n default height is used.\n+ max_chars : int or None\n+ Maximum number of characters allowed in text area.\n key : str\n An optional string to use as the unique key for the widget.\n If this is omitted, a key will be generated for the widget\n@@ -2301,9 +2309,12 @@ def text_area(self, element, label, value=\"\", height=None, key=None):\n element.text_area.label = label\n element.text_area.default = str(value)\n \n- if height is not None:\n+ if height is not None:\n element.text_area.height = height\n \n+ if max_chars is not None:\n+ element.text_area.max_chars = max_chars\n+\n ui_value = _get_widget_ui_value(\"text_area\", element, user_key=key)\n current_value = ui_value if ui_value is not None else value\n return str(current_value)\ndiff --git a/proto/streamlit/proto/TextArea.proto b/proto/streamlit/proto/TextArea.proto\nindex c3895a4406c0..488d0109cced 100644\n--- a/proto/streamlit/proto/TextArea.proto\n+++ b/proto/streamlit/proto/TextArea.proto\n@@ -21,4 +21,5 @@ message TextArea {\n string label = 2;\n string default = 3;\n uint32 height = 4;\n+ uint32 max_chars = 5;\n }\ndiff --git a/proto/streamlit/proto/TextInput.proto b/proto/streamlit/proto/TextInput.proto\nindex e1446b8b7dcc..4ad67dc0cf10 100644\n--- a/proto/streamlit/proto/TextInput.proto\n+++ b/proto/streamlit/proto/TextInput.proto\n@@ -29,4 +29,5 @@ message TextInput {\n string label = 2;\n string default = 3;\n Type type = 4;\n+ uint32 max_chars = 5;\n }\n" }
[ { "diff_hunk": "@@ -0,0 +1,60 @@\n+import React, { ReactElement } from \"react\"\n+import { isFromMac } from \"lib/utils\"\n+import classNames from \"classnames\"\n+\n+import \"./style.scss\"\n+\n+export interface Props {\n+ dirty: boolean\n+ value: string\n+ maxLength?: number\n+ className?: string\n+ type?: \"multiline\" | \"single\"\n+}\n+\n+const InputInstructions = ({\n+ dirty,\n+ value,\n+ maxLength,\n+ className,\n+ type = \"single\",\n+}: Props): ReactElement => {\n+ const containerClassName = classNames(\"instructions\", className)\n+ let message\n+\n+ if (type === \"multiline\") {\n+ if (isFromMac()) {\n+ message = \"Press โŒ˜+Enter to apply\"\n+ }\n+\n+ if (!isFromMac()) {\n+ message = \"Press Ctrl+Enter to apply\"\n+ }\n+ } else {\n+ message = \"Press Enter to apply\"\n+ }\n+\n+ if (dirty && maxLength && value.length > 0) {", "line": null, "original_line": 37, "original_start_line": null, "path": "frontend/src/components/shared/InputInstructions/InputInstructions.tsx", "start_line": null, "text": "@user1:\nI think the logic is a little off. We should show always the fraction \"x/y\" when there's a `maxLength`.\r\n\r\nSo the code should be more like:\r\n\r\n```\r\nmessages = []\r\n\r\nif (dirty) {\r\n messages.push(\r\n <span className=\"message\">{message}</span>\r\n )\r\n}\r\n\r\nif (maxLength != null) {\r\n messages.push(\r\n <span\r\n className={classNames(\"counter\", {\r\n blink: value.length >= maxLength,\r\n })}\r\n >\r\n {value.length}/{maxLength}\r\n </span>\r\n )\r\n}\r\n\r\nreturn (\r\n <div ...>\r\n { messages }\r\n </div>\r\n)\r\n```" }, { "diff_hunk": "@@ -0,0 +1,60 @@\n+import React, { ReactElement } from \"react\"\n+import { isFromMac } from \"lib/utils\"\n+import classNames from \"classnames\"\n+\n+import \"./style.scss\"\n+\n+export interface Props {\n+ dirty: boolean\n+ value: string\n+ maxLength?: number\n+ className?: string\n+ type?: \"multiline\" | \"single\"\n+}\n+\n+const InputInstructions = ({\n+ dirty,\n+ value,\n+ maxLength,\n+ className,\n+ type = \"single\",\n+}: Props): ReactElement => {\n+ const containerClassName = classNames(\"instructions\", className)\n+ let message\n+\n+ if (type === \"multiline\") {\n+ if (isFromMac()) {\n+ message = \"Press โŒ˜+Enter to apply\"\n+ }\n+\n+ if (!isFromMac()) {\n+ message = \"Press Ctrl+Enter to apply\"\n+ }\n+ } else {\n+ message = \"Press Enter to apply\"\n+ }\n+\n+ if (dirty && maxLength && value.length > 0) {\n+ return (\n+ <div className={containerClassName}>\n+ <span className=\"message\">{message}</span>\n+ <span className=\"separator\">โ€ข</span>", "line": null, "original_line": 41, "original_start_line": null, "path": "frontend/src/components/shared/InputInstructions/InputInstructions.tsx", "start_line": null, "text": "@user1:\nThis can be done more cleanly with CSS.\r\n\r\n```\r\n.message:not(:first)::before {\r\n content: \"โ€ข\";\r\n}\r\n```\n\n@author:\nIf I use this approach, the blink animation will affect the separator as well, is that ok for you?\n\n@author:\nSolved using color instead of opacity :)" }, { "diff_hunk": "@@ -0,0 +1,60 @@\n+import React, { ReactElement } from \"react\"\n+import { isFromMac } from \"lib/utils\"\n+import classNames from \"classnames\"\n+\n+import \"./style.scss\"\n+\n+export interface Props {\n+ dirty: boolean\n+ value: string\n+ maxLength?: number\n+ className?: string\n+ type?: \"multiline\" | \"single\"\n+}\n+\n+const InputInstructions = ({\n+ dirty,\n+ value,\n+ maxLength,\n+ className,\n+ type = \"single\",\n+}: Props): ReactElement => {\n+ const containerClassName = classNames(\"instructions\", className)\n+ let message\n+\n+ if (type === \"multiline\") {\n+ if (isFromMac()) {\n+ message = \"Press โŒ˜+Enter to apply\"\n+ }\n+\n+ if (!isFromMac()) {", "line": null, "original_line": 30, "original_start_line": null, "path": "frontend/src/components/shared/InputInstructions/InputInstructions.tsx", "start_line": null, "text": "@user1:\nNit: should be `else` instead" } ]
3dc4a4358c3775495ef7b35474dd1af23b3d0ac9
diff --git a/e2e/scripts/st_text_area.py b/e2e/scripts/st_text_area.py index 665d302648ff..e0f0426933fa 100644 --- a/e2e/scripts/st_text_area.py +++ b/e2e/scripts/st_text_area.py @@ -25,3 +25,6 @@ i4 = st.text_area("text area 4", None) st.write('value 4: "', i4, '"') + +i5 = st.text_area("text area 5", max_chars=10) +st.write('value 5: "', i5, '"') diff --git a/e2e/specs/st_text_area.spec.ts b/e2e/specs/st_text_area.spec.ts index 652c01ef0e04..ee526b9e8fe0 100644 --- a/e2e/specs/st_text_area.spec.ts +++ b/e2e/specs/st_text_area.spec.ts @@ -26,7 +26,7 @@ describe("st.text_area", () => { }); it("shows widget correctly", () => { - cy.get(".stTextArea").should("have.length", 4); + cy.get(".stTextArea").should("have.length", 5); cy.get(".stTextArea").each((el, idx) => { return cy.wrap(el).matchImageSnapshot("text_area" + idx); @@ -39,21 +39,23 @@ describe("st.text_area", () => { 'value 1: " "' + 'value 2: " default text "' + 'value 3: " 1234 "' + - 'value 4: " None "' + 'value 4: " None "' + + 'value 5: " "' ); }); it("sets value correctly when user types", () => { cy.get(".stTextArea textarea") .first() - .type("test area{enter}"); + .type("test area{ctrl}{enter}"); cy.get(".stMarkdown").should( "have.text", - 'value 1: " "' + + 'value 1: " test area "' + 'value 2: " default text "' + 'value 3: " 1234 "' + - 'value 4: " None "' + 'value 4: " None "' + + 'value 5: " "' ); }); @@ -67,7 +69,8 @@ describe("st.text_area", () => { 'value 1: " test area "' + 'value 2: " default text "' + 'value 3: " 1234 "' + - 'value 4: " None "' + 'value 4: " None "' + + 'value 5: " "' ); }); @@ -81,7 +84,8 @@ describe("st.text_area", () => { 'value 1: " test area "' + 'value 2: " default text "' + 'value 3: " 1234 "' + - 'value 4: " None "' + 'value 4: " None "' + + 'value 5: " "' ); }); @@ -96,7 +100,24 @@ describe("st.text_area", () => { 'value 1: " test area "' + 'value 2: " default text "' + 'value 3: " 1234 "' + - 'value 4: " None "' + 'value 4: " None "' + + 'value 5: " "' + ); + }); + + it("sets value correctly with max_chars enabled", () => { + cy.get(".stTextArea textarea") + .last() + .type("test area! this shouldn't be returned") + .blur(); + + cy.get(".stMarkdown").should( + "have.text", + 'value 1: " "' + + 'value 2: " default text "' + + 'value 3: " 1234 "' + + 'value 4: " None "' + + 'value 5: " test area! "' ); }); }); diff --git a/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx b/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx new file mode 100644 index 000000000000..b0f72a036dd6 --- /dev/null +++ b/frontend/src/components/shared/InputInstructions/InputInstructions.test.tsx @@ -0,0 +1,84 @@ +/** + * @license + * Copyright 2018-2020 Streamlit Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import React from "react" +import { shallow } from "enzyme" + +import InputInstructions, { Props } from "./InputInstructions" + +const getProps = (props: Partial<Props> = {}): Props => ({ + dirty: true, + value: "asd", + ...props, +}) + +describe("InputInstructions", () => { + const props = getProps() + const wrapper = shallow(<InputInstructions {...props} />) + + it("renders without crashing", () => { + expect(wrapper.text()).toBeDefined() + }) + + it("should show Enter instructions", () => { + expect(wrapper.text()).toBe("Press Enter to apply") + }) + + describe("Multiline type", () => { + const props = getProps({ + type: "multiline", + }) + const wrapper = shallow(<InputInstructions {...props} />) + + it("should show Ctrl+Enter instructions", () => { + expect(wrapper.text()).toBe("Press Ctrl+Enter to apply") + }) + + it("show โŒ˜+Enter instructions", () => { + Object.defineProperty(navigator, "platform", { + value: "MacIntel", + writable: true, + }) + + const props = getProps({ + type: "multiline", + }) + const wrapper = shallow(<InputInstructions {...props} />) + + expect(wrapper.text()).toBe("Press โŒ˜+Enter to apply") + }) + + it("should show instructions for max length", () => { + const props = getProps({ + type: "multiline", + maxLength: 3, + }) + const wrapper = shallow(<InputInstructions {...props} />) + + expect(wrapper.text()).toBe("Press โŒ˜+Enter to apply3/3") + }) + }) + + it("should show instructions for max length", () => { + const props = getProps({ + maxLength: 3, + }) + const wrapper = shallow(<InputInstructions {...props} />) + + expect(wrapper.text()).toBe("Press Enter to apply3/3") + }) +}) diff --git a/frontend/src/components/shared/InputInstructions/InputInstructions.tsx b/frontend/src/components/shared/InputInstructions/InputInstructions.tsx new file mode 100644 index 000000000000..dd062dc1c689 --- /dev/null +++ b/frontend/src/components/shared/InputInstructions/InputInstructions.tsx @@ -0,0 +1,65 @@ +import React, { ReactElement } from "react" +import { isFromMac } from "lib/utils" +import classNames from "classnames" + +import "./style.scss" + +export interface Props { + dirty: boolean + value: string + maxLength?: number + className?: string + type?: "multiline" | "single" +} + +const InputInstructions = ({ + dirty, + value, + maxLength, + className, + type = "single", +}: Props): ReactElement => { + const containerClassName = classNames("instructions", className) + const messages = [] + + if (dirty) { + if (type === "multiline") { + if (isFromMac()) { + messages.push( + <span key={0} className="message"> + Press โŒ˜+Enter to apply + </span> + ) + } else { + messages.push( + <span key={0} className="message"> + Press Ctrl+Enter to apply + </span> + ) + } + } else { + messages.push( + <span key={0} className="message"> + Press Enter to apply + </span> + ) + } + } + + if (maxLength) { + messages.push( + <span + key={1} + className={classNames("message", "counter", { + blink: dirty && value.length >= maxLength, + })} + > + {value.length}/{maxLength} + </span> + ) + } + + return <div className={containerClassName}>{messages}</div> +} + +export default InputInstructions diff --git a/frontend/src/components/shared/InputInstructions/style.scss b/frontend/src/components/shared/InputInstructions/style.scss new file mode 100644 index 000000000000..506605b9d94d --- /dev/null +++ b/frontend/src/components/shared/InputInstructions/style.scss @@ -0,0 +1,26 @@ +@import "src/assets/css/variables"; + +.instructions { + .message:not(:first-child)::before { + opacity: 1; + content: "โ€ข"; + animation: none; + color: $gray-600; + margin: 0 5px 0 5px; + } + + .counter { + &.blink { + color: $red; + animation-name: blinker; + animation-duration: 0.5s; + animation-iteration-count: 5; + } + } +} + +@keyframes blinker { + 50% { + color: rgba(0, 0, 0, 0); + } +} diff --git a/frontend/src/components/widgets/NumberInput/NumberInput.scss b/frontend/src/components/widgets/NumberInput/NumberInput.scss index 0bcbb356c9a4..07c439322950 100644 --- a/frontend/src/components/widgets/NumberInput/NumberInput.scss +++ b/frontend/src/components/widgets/NumberInput/NumberInput.scss @@ -82,7 +82,7 @@ } } - .instructions { + .input-instructions { margin-right: 5px; right: $controls-width * 2; } diff --git a/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx b/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx index f5df1320c32c..ebb7d9a1ab87 100644 --- a/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx +++ b/frontend/src/components/widgets/NumberInput/NumberInput.test.tsx @@ -283,15 +283,4 @@ describe("NumberInput widget", () => { expect(preventDefault).toHaveBeenCalled() }) }) - - it("should show a message when it's dirty", () => { - const props = getIntProps() - const wrapper = shallow(<NumberInput {...props} />) - - wrapper.setState({ - dirty: true, - }) - - expect(wrapper.find("div.instructions").length).toBe(1) - }) }) diff --git a/frontend/src/components/widgets/NumberInput/NumberInput.tsx b/frontend/src/components/widgets/NumberInput/NumberInput.tsx index ff5c4990791f..3f005d770c1d 100644 --- a/frontend/src/components/widgets/NumberInput/NumberInput.tsx +++ b/frontend/src/components/widgets/NumberInput/NumberInput.tsx @@ -15,15 +15,16 @@ * limitations under the License. */ -import { NumberInput as NumberInputProto } from "autogen/proto" import React from "react" import { sprintf } from "sprintf-js" -import { Input as UIInput } from "baseui/input" +import { logWarning } from "lib/log" import { Map as ImmutableMap } from "immutable" +import { NumberInput as NumberInputProto } from "autogen/proto" import { WidgetStateManager, Source } from "lib/WidgetStateManager" -import { logWarning } from "lib/log" import Icon from "components/shared/Icon" +import { Input as UIInput } from "baseui/input" +import InputInstructions from "components/shared/InputInstructions/InputInstructions" import "./NumberInput.scss" @@ -281,7 +282,11 @@ class NumberInput extends React.PureComponent<Props, State> { </button> </div> </div> - {dirty && <div className="instructions">Press Enter to apply</div>} + <InputInstructions + dirty={dirty} + value={formattedValue} + className="input-instructions" + /> </div> ) } diff --git a/frontend/src/components/widgets/TextArea/TextArea.test.tsx b/frontend/src/components/widgets/TextArea/TextArea.test.tsx index 4474753670ba..5a60189a19bb 100644 --- a/frontend/src/components/widgets/TextArea/TextArea.test.tsx +++ b/frontend/src/components/widgets/TextArea/TextArea.test.tsx @@ -84,19 +84,6 @@ describe("TextArea widget", () => { expect(wrapper.find(UITextArea).prop("disabled")).toBe(props.disabled) }) - it("should show Ctrl+Enter instructions", () => { - // @ts-ignore - wrapper.find(UITextArea).prop("onChange")({ - target: { - value: "testing", - }, - } as React.ChangeEvent<HTMLTextAreaElement>) - - expect(wrapper.find("div.instructions").text()).toBe( - "Press Ctrl+Enter to apply" - ) - }) - it("should set widget value on blur", () => { const props = getProps() const wrapper = shallow(<TextArea {...props} />) @@ -157,6 +144,32 @@ describe("TextArea widget", () => { expect(resize).toBe("vertical") }) + it("should limit the length if max_chars is passed", () => { + const props = getProps({ + height: 500, + maxChars: 10, + }) + const wrapper = shallow(<TextArea {...props} />) + + // @ts-ignore + wrapper.find(UITextArea).prop("onChange")({ + target: { + value: "0123456789", + }, + } as EventTarget) + + expect(wrapper.find(UITextArea).prop("value")).toBe("0123456789") + + // @ts-ignore + wrapper.find(UITextArea).prop("onChange")({ + target: { + value: "0123456789a", + }, + } as EventTarget) + + expect(wrapper.find(UITextArea).prop("value")).toBe("0123456789") + }) + describe("On mac it should", () => { Object.defineProperty(navigator, "platform", { value: "MacIntel", @@ -166,19 +179,6 @@ describe("TextArea widget", () => { const props = getProps() const wrapper = shallow(<TextArea {...props} />) - it("show โŒ˜+Enter instructions", () => { - // @ts-ignore - wrapper.find(UITextArea).prop("onChange")({ - target: { - value: "testing", - }, - } as React.ChangeEvent<HTMLTextAreaElement>) - - expect(wrapper.find("div.instructions").text()).toBe( - "Press โŒ˜+Enter to apply" - ) - }) - it("should set widget value when โŒ˜+enter is pressed", () => { // @ts-ignore wrapper.find(UITextArea).prop("onChange")({ diff --git a/frontend/src/components/widgets/TextArea/TextArea.tsx b/frontend/src/components/widgets/TextArea/TextArea.tsx index 3649fd66bfa5..c034aae4019a 100644 --- a/frontend/src/components/widgets/TextArea/TextArea.tsx +++ b/frontend/src/components/widgets/TextArea/TextArea.tsx @@ -20,6 +20,7 @@ import { Map as ImmutableMap } from "immutable" import { WidgetStateManager, Source } from "lib/WidgetStateManager" import { Textarea as UITextArea } from "baseui/textarea" +import InputInstructions from "components/shared/InputInstructions/InputInstructions" export interface Props { disabled: boolean @@ -51,8 +52,6 @@ class TextArea extends React.PureComponent<Props, State> { this.setWidgetValue({ fromUi: false }) } - private isFromMac = /Mac/i.test(navigator.platform) - private setWidgetValue = (source: Source): void => { const widgetId: string = this.props.element.get("id") this.props.widgetMgr.setStringValue(widgetId, this.state.value, source) @@ -66,10 +65,17 @@ class TextArea extends React.PureComponent<Props, State> { } private onChange = (e: React.ChangeEvent<HTMLTextAreaElement>): void => { - this.setState({ - dirty: true, - value: e.target.value, - }) + const { value } = e.target + const { element } = this.props + + const maxChars = element.get("maxChars") + + if (!maxChars || value.length <= maxChars) { + this.setState({ + dirty: true, + value, + }) + } } isEnterKeyPressed = ( @@ -100,6 +106,7 @@ class TextArea extends React.PureComponent<Props, State> { const style = { width } const label = element.get("label") const height = element.get("height") + const maxChars = element.get("maxChars") return ( <div className="Widget stTextArea" style={style}> @@ -120,13 +127,12 @@ class TextArea extends React.PureComponent<Props, State> { }, }} /> - {dirty && !this.isFromMac && ( - <div className="instructions">Press Ctrl+Enter to apply</div> - )} - - {dirty && this.isFromMac && ( - <div className="instructions">Press โŒ˜+Enter to apply</div> - )} + <InputInstructions + dirty={dirty} + value={value} + maxLength={maxChars} + type={"multiline"} + /> </div> ) } diff --git a/frontend/src/components/widgets/TextInput/TextInput.test.tsx b/frontend/src/components/widgets/TextInput/TextInput.test.tsx index 9f75e46d2b8f..8d716af559e5 100644 --- a/frontend/src/components/widgets/TextInput/TextInput.test.tsx +++ b/frontend/src/components/widgets/TextInput/TextInput.test.tsx @@ -89,19 +89,6 @@ describe("TextInput widget", () => { expect(wrapper.find(UIInput).prop("disabled")).toBe(props.disabled) }) - it("should show Enter instructions", () => { - // @ts-ignore - wrapper.find(UIInput).prop("onChange")({ - target: { - value: "testing", - }, - } as React.ChangeEvent<HTMLTextAreaElement>) - - expect(wrapper.find("div.instructions").text()).toBe( - "Press Enter to apply" - ) - }) - it("should set widget value on blur", () => { const props = getProps() const wrapper = shallow(<TextInput {...props} />) @@ -161,4 +148,29 @@ describe("TextInput widget", () => { expect(props.widgetMgr.setStringValue).toHaveBeenCalledTimes(1) }) + + it("should limit the length if max_chars is passed", () => { + const props = getProps({ + maxChars: 10, + }) + const wrapper = shallow(<TextInput {...props} />) + + // @ts-ignore + wrapper.find(UIInput).prop("onChange")({ + target: { + value: "0123456789", + }, + } as EventTarget) + + expect(wrapper.find(UIInput).prop("value")).toBe("0123456789") + + // @ts-ignore + wrapper.find(UIInput).prop("onChange")({ + target: { + value: "0123456789a", + }, + } as EventTarget) + + expect(wrapper.find(UIInput).prop("value")).toBe("0123456789") + }) }) diff --git a/frontend/src/components/widgets/TextInput/TextInput.tsx b/frontend/src/components/widgets/TextInput/TextInput.tsx index 5d7fef01db24..f81eef35afe6 100644 --- a/frontend/src/components/widgets/TextInput/TextInput.tsx +++ b/frontend/src/components/widgets/TextInput/TextInput.tsx @@ -18,8 +18,9 @@ import React from "react" import { Input as UIInput } from "baseui/input" import { Map as ImmutableMap } from "immutable" -import { WidgetStateManager, Source } from "lib/WidgetStateManager" import { TextInput as TextInputProto } from "autogen/proto" +import { WidgetStateManager, Source } from "lib/WidgetStateManager" +import InputInstructions from "components/shared/InputInstructions/InputInstructions" export interface Props { disabled: boolean @@ -64,10 +65,17 @@ class TextInput extends React.PureComponent<Props, State> { } private onChange = (e: React.ChangeEvent<HTMLInputElement>): void => { - this.setState({ - dirty: true, - value: e.target.value, - }) + const { value } = e.target + const { element } = this.props + + const maxChars = element.get("maxChars") + + if (!maxChars || value.length <= maxChars) { + this.setState({ + dirty: true, + value, + }) + } } private onKeyPress = (e: React.KeyboardEvent<HTMLInputElement>): void => { @@ -83,23 +91,25 @@ class TextInput extends React.PureComponent<Props, State> { } public render = (): React.ReactNode => { - const label: string = this.props.element.get("label") - const style = { width: this.props.width } + const { dirty, value } = this.state + const { element, width, disabled } = this.props + + const label: string = element.get("label") + const maxChars = element.get("maxChars") + const style = { width } return ( <div className="Widget row-widget stTextInput" style={style}> <label>{label}</label> <UIInput - value={this.state.value} + value={value} onBlur={this.onBlur} onChange={this.onChange} onKeyPress={this.onKeyPress} - disabled={this.props.disabled} + disabled={disabled} type={this.getTypeString()} /> - {this.state.dirty ? ( - <div className="instructions">Press Enter to apply</div> - ) : null} + <InputInstructions dirty={dirty} value={value} maxLength={maxChars} /> </div> ) } diff --git a/frontend/src/lib/utils.ts b/frontend/src/lib/utils.ts index 471b60f93fc9..102d4308f799 100644 --- a/frontend/src/lib/utils.ts +++ b/frontend/src/lib/utils.ts @@ -118,3 +118,10 @@ export function flattenElements( export function timeout(ms: number): Promise<void> { return new Promise(resolve => setTimeout(resolve, ms)) } + +/** + * Tests if the app is running from a Mac + */ +export function isFromMac(): boolean { + return /Mac/i.test(navigator.platform) +} diff --git a/lib/streamlit/DeltaGenerator.py b/lib/streamlit/DeltaGenerator.py index 59986ec5dc57..7ca6f482562f 100644 --- a/lib/streamlit/DeltaGenerator.py +++ b/lib/streamlit/DeltaGenerator.py @@ -1723,7 +1723,7 @@ def _check_and_convert_to_indices(options, default_values): if not isinstance(default_values, list): # This if is done before others because calling if not x (done # right below) when x is of type pd.Series() or np.array() throws a - # ValueError exception. + # ValueError exception. if is_type(default_values, "numpy.ndarray") or is_type( default_values, "pandas.core.series.Series" ): @@ -2214,7 +2214,7 @@ def beta_color_picker(self, element, label, value=None, key=None): return str(current_value) @_with_element - def text_input(self, element, label, value="", key=None, type="default"): + def text_input(self, element, label, value="", max_chars=None, key=None, type="default"): """Display a single-line text input widget. Parameters @@ -2224,6 +2224,8 @@ def text_input(self, element, label, value="", key=None, type="default"): value : any The text value of this widget when it first renders. This will be cast to str internally. + max_chars : int or None + Max number of characters allowed in text input. key : str An optional string to use as the unique key for the widget. If this is omitted, a key will be generated for the widget @@ -2247,6 +2249,10 @@ def text_input(self, element, label, value="", key=None, type="default"): """ element.text_input.label = label element.text_input.default = str(value) + + if max_chars is not None: + element.text_input.max_chars = max_chars + if type == "default": element.text_input.type = TextInput.DEFAULT elif type == "password": @@ -2262,7 +2268,7 @@ def text_input(self, element, label, value="", key=None, type="default"): return str(current_value) @_with_element - def text_area(self, element, label, value="", height=None, key=None): + def text_area(self, element, label, value="", height=None, max_chars=None, key=None): """Display a multi-line text input widget. Parameters @@ -2275,6 +2281,8 @@ def text_area(self, element, label, value="", height=None, key=None): height : int or None Desired height of the UI element expressed in pixels. If None, a default height is used. + max_chars : int or None + Maximum number of characters allowed in text area. key : str An optional string to use as the unique key for the widget. If this is omitted, a key will be generated for the widget @@ -2301,9 +2309,12 @@ def text_area(self, element, label, value="", height=None, key=None): element.text_area.label = label element.text_area.default = str(value) - if height is not None: + if height is not None: element.text_area.height = height + if max_chars is not None: + element.text_area.max_chars = max_chars + ui_value = _get_widget_ui_value("text_area", element, user_key=key) current_value = ui_value if ui_value is not None else value return str(current_value) diff --git a/proto/streamlit/proto/TextArea.proto b/proto/streamlit/proto/TextArea.proto index c3895a4406c0..488d0109cced 100644 --- a/proto/streamlit/proto/TextArea.proto +++ b/proto/streamlit/proto/TextArea.proto @@ -21,4 +21,5 @@ message TextArea { string label = 2; string default = 3; uint32 height = 4; + uint32 max_chars = 5; } diff --git a/proto/streamlit/proto/TextInput.proto b/proto/streamlit/proto/TextInput.proto index e1446b8b7dcc..4ad67dc0cf10 100644 --- a/proto/streamlit/proto/TextInput.proto +++ b/proto/streamlit/proto/TextInput.proto @@ -29,4 +29,5 @@ message TextInput { string label = 2; string default = 3; Type type = 4; + uint32 max_chars = 5; }
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
streamlit__streamlit-1295@fcbac4b
streamlit/streamlit
Python
1,295
Showing an error when it's not localhost and has no mapbox token
**Issue:** Fix #1136 #812 **Description:** As it is not possible to render a Mapbox without any token we are delimiting the use locally. - Prevent fetching token when it's not localhost or hello.py - Using axios instead of fetch - Added new step for cypress job in which we set the mapbox token - Unittest --- **Contribution License Agreement** By submiting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2020-04-02T23:34:00Z
Remove Streamlit Mapbox API key _Edit: apparently this is _not_ how Mapbox works. See Update section below._ If I understand correctly how Mapbox works, I think we don't need an API key by default since Mapbox throttles usage of their maps based on IP address (which is user-specific). Can we just remove the default API key? **And we'd need to allow users to enter their own API key, of course.** --- Update: DeckGL depends on Mapbox to show maps. Mapbox requires developers to have a token. Up to now, Streamlit hard-coded a token to make people's lives easier. The problem is that for the past few months we've started going over the free QPS limit of that token, and so we've been paying for everyone's usage of maps. So let's fix this! Proposal: - Whenever a users tries to use DeckGL and they didn't provide a token (this is done via the config file), we'll show an error explaining what they need to do. - We'll update our docs to make this abundantly clear. - To make the `streamlit hello` experience nice, the map in that app will continue to use our own paid token. But we'll take steps to make sure only that app can use that token, as much as possible.
## Changes: - When you use a DeckGL chart: - If running `streamlit hello` AND running from localhost: - The map is shown using our own token (which we pay for) - Else: - If the user has a token: - We show the map - Else: - We show an error telling the user to pass in a token - AND we don't show the map ## Discussion https://discuss.streamlit.io/t/showing-an-error-when-its-not-localhost-and-has-no-mapbox-token/2418
[ { "body": "_Edit: apparently this is _not_ how Mapbox works. See Update section below._\r\n\r\nIf I understand correctly how Mapbox works, I think we don't need an API key by default since Mapbox throttles usage of their maps based on IP address (which is user-specific).\r\n\r\nCan we just remove the default API key? **And we'd need to allow users to enter their own API key, of course.** \r\n\r\n---\r\n\r\nUpdate:\r\n\r\nDeckGL depends on Mapbox to show maps. Mapbox requires developers to have a token. Up to now, Streamlit hard-coded a token to make people's lives easier. The problem is that for the past few months we've started going over the free QPS limit of that token, and so we've been paying for everyone's usage of maps.\r\n\r\nSo let's fix this!\r\n\r\nProposal:\r\n- Whenever a users tries to use DeckGL and they didn't provide a token (this is done via the config file), we'll show an error explaining what they need to do.\r\n- We'll update our docs to make this abundantly clear.\r\n- To make the `streamlit hello` experience nice, the map in that app will continue to use our own paid token. But we'll take steps to make sure only that app can use that token, as much as possible.", "number": 1136, "title": "Remove Streamlit Mapbox API key" } ]
bc28208d86b95769314b67b112d06356f65edf2f
{ "head_commit": "fcbac4b8a1b41e8b09f4e293ecb5be91854094a2", "head_commit_message": "Update lib/streamlit/config.py\n\nCo-authored-by: Amey Deshpande <[email protected]>", "patch_to_review": "diff --git a/.circleci/config.yml b/.circleci/config.yml\nindex e2807b3a3b19..02d6fc93e26e 100644\n--- a/.circleci/config.yml\n+++ b/.circleci/config.yml\n@@ -481,12 +481,19 @@ jobs:\n - run:\n name: Install Cypress dependencies\n command: |\n- ${SUDO} apt-get install -y xvfb libgtk2.0-0 libnotify-dev libgconf-2-4 libnss3 libxss1 libasound2\n+ ${SUDO} apt-get install -y xvfb libgtk2.0-0 libnotify-dev libgconf-2-4 libnss3 libxss1 libasound2 jq curl\n \n - run:\n- name: Init credentials\n+ name: Init config\n command: |\n mkdir ~/.streamlit\n+ MAPBOX_TOKEN=$(curl -sS https://data.streamlit.io/tokens.json | jq -r '.mapbox')\n+ echo '[mapbox]' > ~/.streamlit/config.toml\n+ echo 'token = \"'$MAPBOX_TOKEN'\"' >> ~/.streamlit/config.toml\n+\n+ - run:\n+ name: Init credentials\n+ command: |\n echo '[general]' > ~/.streamlit/credentials.toml\n echo 'email = \"[email protected]\"' >> ~/.streamlit/credentials.toml\n \ndiff --git a/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts b/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts\nindex 91d3c80cffe9..9d8dc00279b9 100644\n--- a/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts\n+++ b/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts\n@@ -15,11 +15,15 @@\n * limitations under the License.\n */\n \n-import { MapboxToken, TOKENS_URL } from \"hocs/withMapboxToken/MapboxToken\"\n+import axios from \"axios\"\n import { SessionInfo } from \"lib/SessionInfo\"\n-import fetchMock from \"fetch-mock\"\n+import AxiosMockAdapter from \"axios-mock-adapter\"\n+import { MapboxToken, TOKENS_URL } from \"hocs/withMapboxToken/MapboxToken\"\n \n-function setSessionInfoWithMapboxToken(userMapboxToken: string): void {\n+function setSessionInfo(\n+ userMapboxToken: string,\n+ commandLine = \"streamlit hello\"\n+): void {\n SessionInfo.current = new SessionInfo({\n sessionId: \"mockSessionId\",\n streamlitVersion: \"sv\",\n@@ -27,31 +31,36 @@ function setSessionInfoWithMapboxToken(userMapboxToken: string): void {\n installationId: \"iid\",\n authorEmail: \"ae\",\n maxCachedMessageAge: 2,\n- commandLine: \"cl\",\n- userMapboxToken: userMapboxToken,\n+ commandLine,\n+ userMapboxToken,\n })\n }\n \n describe(\"MapboxToken\", () => {\n+ let axiosMock: AxiosMockAdapter\n+\n beforeEach(() => {\n- setSessionInfoWithMapboxToken(\"\")\n+ window.location.hostname = \"localhost\"\n+ axiosMock = new AxiosMockAdapter(axios)\n+ setSessionInfo(\"\")\n })\n \n afterEach(() => {\n- SessionInfo[\"singleton\"] = undefined\n+ axiosMock.restore()\n MapboxToken[\"token\"] = undefined\n- fetchMock.restore()\n+ SessionInfo[\"singleton\"] = undefined\n })\n \n test(\"Returns cached token if defined\", async () => {\n MapboxToken[\"token\"] = \"cached\"\n+\n await expect(MapboxToken.get()).resolves.toEqual(\"cached\")\n })\n \n test(\"Returns userMapboxToken if non-empty\", async () => {\n const userToken = \"nonEmptyToken\"\n \n- setSessionInfoWithMapboxToken(userToken)\n+ setSessionInfo(userToken)\n await expect(MapboxToken.get()).resolves.toEqual(userToken)\n \n // The token should also be cached.\n@@ -61,7 +70,8 @@ describe(\"MapboxToken\", () => {\n test(\"Fetches remote token if userMapboxToken is empty\", async () => {\n const remoteToken = \"remoteMapboxToken\"\n \n- fetchMock.get(TOKENS_URL, { mapbox: remoteToken })\n+ axiosMock.onGet(TOKENS_URL).reply(200, { \"mapbox-localhost\": remoteToken })\n+\n await expect(MapboxToken.get()).resolves.toEqual(remoteToken)\n \n // The token should also be cached.\n@@ -69,7 +79,7 @@ describe(\"MapboxToken\", () => {\n })\n \n test(\"Errors if remote token is missing\", async () => {\n- fetchMock.get(TOKENS_URL, { ohNo: \"noTokenHere\" })\n+ axiosMock.onGet(TOKENS_URL).replyOnce(200, { ohNo: \"noTokenHere\" })\n \n await expect(MapboxToken.get()).rejects.toEqual(\n new Error(`Missing token \"mapbox\" (${TOKENS_URL})`)\n@@ -78,13 +88,26 @@ describe(\"MapboxToken\", () => {\n // No cached token after failure.\n expect(MapboxToken[\"token\"]).toBeUndefined()\n \n- fetchMock.restore()\n- fetchMock.get(TOKENS_URL, 404)\n+ axiosMock.onGet(TOKENS_URL).replyOnce(404, {})\n await expect(MapboxToken.get()).rejects.toEqual(\n- new Error(`Bad status: 404 (${TOKENS_URL})`)\n+ new Error(`Request failed with status code 404 (${TOKENS_URL})`)\n )\n \n // No cached token after failure.\n expect(MapboxToken[\"token\"]).toBeUndefined()\n })\n+\n+ it(\"Errors if not localhost and missing token\", async () => {\n+ delete window.location\n+ window.location = { hostname: \"http://streamlit.io\" } as Location\n+ setSessionInfo(\"\")\n+\n+ await expect(MapboxToken.get()).rejects.toMatchSnapshot()\n+ })\n+\n+ it(\"Errors if not hello.py and missing token\", async () => {\n+ setSessionInfo(\"\", \"streamlit run example.py\")\n+\n+ await expect(MapboxToken.get()).rejects.toMatchSnapshot()\n+ })\n })\ndiff --git a/frontend/src/hocs/withMapboxToken/MapboxToken.ts b/frontend/src/hocs/withMapboxToken/MapboxToken.ts\nindex 39604cb26eb0..3cfd6ce5e31a 100644\n--- a/frontend/src/hocs/withMapboxToken/MapboxToken.ts\n+++ b/frontend/src/hocs/withMapboxToken/MapboxToken.ts\n@@ -15,6 +15,7 @@\n * limitations under the License.\n */\n \n+import axios from \"axios\"\n import { SessionInfo } from \"lib/SessionInfo\"\n \n /**\n@@ -25,6 +26,12 @@ export const TOKENS_URL = \"https://data.streamlit.io/tokens.json\"\n export class MapboxToken {\n private static token?: string\n \n+ private static isItRunningLocal = (): boolean => {\n+ const { hostname } = window.location\n+\n+ return hostname === \"localhost\" || hostname === \"127.0.0.1\"\n+ }\n+\n /**\n * Expose a singleton MapboxToken:\n * - If the user specified a token in their streamlit config, return it.\n@@ -34,11 +41,29 @@ export class MapboxToken {\n * only be fetched once per session.)\n */\n public static async get(): Promise<string> {\n- if (MapboxToken.token == null) {\n+ if (!MapboxToken.token) {\n if (SessionInfo.current.userMapboxToken !== \"\") {\n MapboxToken.token = SessionInfo.current.userMapboxToken\n } else {\n- MapboxToken.token = await this.fetchToken(TOKENS_URL, \"mapbox\")\n+ const { commandLine } = SessionInfo.current\n+\n+ if (\n+ this.isItRunningLocal() &&\n+ commandLine.toLowerCase() === \"streamlit hello\"\n+ ) {\n+ MapboxToken.token = await this.fetchToken(TOKENS_URL, \"mapbox\")\n+ } else {\n+ throw new Error(\n+ `\n+ To use this you'll need a Mapbox access token. Please add it to your config.\n+ \n+ To get a token for yourself, create an account at\n+ <a href=\"https://mapbox.com\">https://mapbox.com</a>. It's free (for moderate usage levels)! See\n+ <a href=\"https://docs.streamlit.io/cli.html#view-all-config-options\">our documentation</a> for more\n+ info on how to set config options.\n+ `\n+ )\n+ }\n }\n }\n \n@@ -49,26 +74,17 @@ export class MapboxToken {\n url: string,\n tokenName: string\n ): Promise<string> {\n- let rsp: Response\n try {\n- rsp = await fetch(url)\n- } catch (e) {\n- // Fetch error messages are abysmal, and give virtually no useful\n- // context. Catch errors and append the offending URL to their messages\n- // to make them a bit more useful.\n- throw new Error(`${e.message} (${url})`)\n- }\n+ const response = await axios.get(url)\n+ const { \"mapbox-localhost\": token } = response.data\n \n- if (!rsp.ok) {\n- throw new Error(`Bad status: ${rsp.status} (${url})`)\n- }\n+ if (token == null || token === \"\") {\n+ throw new Error(`Missing token \"${tokenName}\"`)\n+ }\n \n- const json = await rsp.json()\n- const token = json[tokenName]\n- if (token == null || token === \"\") {\n- throw new Error(`Missing token \"${tokenName}\" (${url})`)\n+ return token\n+ } catch (e) {\n+ throw new Error(`${e.message} (${url})`)\n }\n-\n- return token\n }\n }\ndiff --git a/frontend/src/hocs/withMapboxToken/__snapshots__/MapboxToken.test.ts.snap b/frontend/src/hocs/withMapboxToken/__snapshots__/MapboxToken.test.ts.snap\nnew file mode 100644\nindex 000000000000..a8d7d44f8567\n--- /dev/null\n+++ b/frontend/src/hocs/withMapboxToken/__snapshots__/MapboxToken.test.ts.snap\n@@ -0,0 +1,23 @@\n+// Jest Snapshot v1, https://goo.gl/fbAQLP\n+\n+exports[`MapboxToken Errors if not hello.py and missing token 1`] = `\n+[Error: \n+ To use this you'll need a Mapbox access token. Please add it to your config.\n+ \n+ To get a token for yourself, create an account at\n+ <a href=\"https://mapbox.com\">https://mapbox.com</a>. It's free (for moderate usage levels)! See\n+ <a href=\"https://docs.streamlit.io/cli.html#view-all-config-options\">our documentation</a> for more\n+ info on how to set config options.\n+ ]\n+`;\n+\n+exports[`MapboxToken Errors if not localhost and missing token 1`] = `\n+[Error: \n+ To use this you'll need a Mapbox access token. Please add it to your config.\n+ \n+ To get a token for yourself, create an account at\n+ <a href=\"https://mapbox.com\">https://mapbox.com</a>. It's free (for moderate usage levels)! See\n+ <a href=\"https://docs.streamlit.io/cli.html#view-all-config-options\">our documentation</a> for more\n+ info on how to set config options.\n+ ]\n+`;\ndiff --git a/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx b/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx\nindex cfacee473cd7..4de6f7015bd5 100644\n--- a/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx\n+++ b/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx\n@@ -35,7 +35,7 @@ function waitOneTick(): Promise<void> {\n }\n \n describe(\"withMapboxToken\", () => {\n- function getProps(): any {\n+ function getProps(): object {\n return { label: \"label\" }\n }\n \ndiff --git a/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx b/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx\nindex d7b28bc79d09..fef3b602a9cc 100644\n--- a/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx\n+++ b/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx\n@@ -29,6 +29,7 @@ interface Props {\n interface State {\n mapboxToken?: string\n mapboxTokenError?: Error\n+ isFetching: boolean\n }\n \n /**\n@@ -47,50 +48,64 @@ function withMapboxToken(\n super(props)\n \n this.state = {\n+ isFetching: true,\n mapboxToken: undefined,\n mapboxTokenError: undefined,\n }\n+\n this.initMapboxToken()\n }\n \n /**\n * Fetch our MapboxToken.\n */\n- private initMapboxToken = (): void => {\n- MapboxToken.get()\n- .then(token => this.setState({ mapboxToken: token }))\n- .catch(error => this.setState({ mapboxTokenError: error }))\n+ private initMapboxToken = async (): Promise<void> => {\n+ try {\n+ const mapboxToken = await MapboxToken.get()\n+\n+ this.setState({\n+ mapboxToken,\n+ isFetching: false,\n+ })\n+ } catch (error) {\n+ this.setState({\n+ mapboxTokenError: error,\n+ isFetching: false,\n+ })\n+ }\n }\n \n public render = (): JSX.Element => {\n+ const { mapboxToken, mapboxTokenError, isFetching } = this.state\n+ const { width } = this.props\n+\n // We got an error when fetching our mapbox token: show the error.\n- if (this.state.mapboxTokenError != null) {\n+ if (mapboxTokenError) {\n+ const { message } = mapboxTokenError\n+ const messageWithHTML = (\n+ <div dangerouslySetInnerHTML={{ __html: message }} />\n+ )\n return (\n <ErrorElement\n- width={this.props.width}\n+ width={width}\n name=\"Error fetching Mapbox token\"\n- message={this.state.mapboxTokenError.message}\n+ message={messageWithHTML}\n />\n )\n }\n \n // If our mapboxToken hasn't been retrieved yet, show a loading alert.\n- if (this.state.mapboxToken === undefined) {\n+ if (isFetching) {\n return (\n <Alert\n element={makeElementWithInfoText(\"Loading...\").get(\"alert\")}\n- width={this.props.width}\n+ width={width}\n />\n )\n }\n \n // We have the mapbox token. Pass it through to our component.\n- return (\n- <WrappedComponent\n- mapboxToken={this.state.mapboxToken}\n- {...this.props}\n- />\n- )\n+ return <WrappedComponent mapboxToken={mapboxToken} {...this.props} />\n }\n }\n \ndiff --git a/lib/streamlit/config.py b/lib/streamlit/config.py\nindex b0eff7c60a6e..457a9fdf1382 100644\n--- a/lib/streamlit/config.py\n+++ b/lib/streamlit/config.py\n@@ -537,11 +537,9 @@ def _browser_server_port():\n _create_option(\n \"mapbox.token\",\n description=\"\"\"Configure Streamlit to use a custom Mapbox\n- token for elements like st.deck_gl_chart and st.map. If you\n- don't do this you'll be using Streamlit's own token,\n- which has limitations and is not guaranteed to always work.\n+ token for elements like st.deck_gl_chart and st.map.\n To get a token for yourself, create an account at\n- https://mapbox.com. It's free! (for moderate usage levels)\"\"\",\n+ https://mapbox.com. It's free (for moderate usage levels)!\"\"\",\n default_val=\"\",\n )\n \n" }
[ { "diff_hunk": "@@ -47,50 +48,64 @@ function withMapboxToken(\n super(props)\n \n this.state = {\n+ isFetching: true,\n mapboxToken: undefined,\n mapboxTokenError: undefined,\n }\n+\n this.initMapboxToken()\n }\n \n /**\n * Fetch our MapboxToken.\n */\n- private initMapboxToken = (): void => {\n- MapboxToken.get()\n- .then(token => this.setState({ mapboxToken: token }))\n- .catch(error => this.setState({ mapboxTokenError: error }))\n+ private initMapboxToken = async (): Promise<void> => {\n+ try {\n+ const mapboxToken = await MapboxToken.get()\n+\n+ this.setState({\n+ mapboxToken,\n+ isFetching: false,\n+ })\n+ } catch (error) {\n+ this.setState({\n+ mapboxTokenError: error,\n+ isFetching: false,\n+ })\n+ }\n }\n \n public render = (): JSX.Element => {\n+ const { mapboxToken, mapboxTokenError, isFetching } = this.state\n+ const { width } = this.props\n+\n // We got an error when fetching our mapbox token: show the error.\n- if (this.state.mapboxTokenError != null) {\n+ if (mapboxTokenError) {\n+ const { message } = mapboxTokenError\n+ const messageWithHTML = (\n+ <div dangerouslySetInnerHTML={{ __html: message }} />", "line": null, "original_line": 86, "original_start_line": null, "path": "frontend/src/hocs/withMapboxToken/withMapboxToken.tsx", "start_line": null, "text": "@user1:\nLet's not do `dangerouslySetInnerHTML`. This opens a possible security hole.\r\n\r\nWe could just throw a well-known error class, then render the error using JSX in here instead:\r\n\r\n```\r\n// In MapboxToken.ts\r\nclass MapboxFetchError extends Error {}\r\n(...)\r\nthrow new MapboxFetchError()\r\n\r\n// In withMapboxToken.tsx\r\nif (mapboxTokenError instanceof MapboxFetchError) {\r\n messageWithHTML = (\r\n <div>Any <strong>JSX</strong> you want</div>\r\n )\r\n} else {\r\n messageWithHTML = (\r\n <div>Unexpected error: { mapboxTokenError.message }</div>\r\n )\r\n}\r\n```" } ]
9dc51c13e5045bb606208607c925ae39e5872339
diff --git a/.circleci/config.yml b/.circleci/config.yml index bc14981c8d6f..af5b55d8066f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -482,12 +482,19 @@ jobs: - run: name: Install Cypress dependencies command: | - ${SUDO} apt-get install -y xvfb libgtk2.0-0 libnotify-dev libgconf-2-4 libnss3 libxss1 libasound2 + ${SUDO} apt-get install -y xvfb libgtk2.0-0 libnotify-dev libgconf-2-4 libnss3 libxss1 libasound2 jq curl - run: - name: Init credentials + name: Init config command: | mkdir ~/.streamlit + MAPBOX_TOKEN=$(curl -sS https://data.streamlit.io/tokens.json | jq -r '.["mapbox-localhost"]') + echo '[mapbox]' > ~/.streamlit/config.toml + echo 'token = "'$MAPBOX_TOKEN'"' >> ~/.streamlit/config.toml + + - run: + name: Init credentials + command: | echo '[general]' > ~/.streamlit/credentials.toml echo 'email = "[email protected]"' >> ~/.streamlit/credentials.toml diff --git a/e2e/Dockerfile b/e2e/Dockerfile index 3299bfe1c0a0..8b150a84c0b2 100644 --- a/e2e/Dockerfile +++ b/e2e/Dockerfile @@ -78,4 +78,9 @@ RUN mkdir $HOME/.streamlit \ && echo '[general]' > $HOME/.streamlit/credentials.toml \ && echo 'email = "[email protected]"' >> $HOME/.streamlit/credentials.toml +# register mapbox token +RUN MAPBOX_TOKEN=$(curl -sS https://data.streamlit.io/tokens.json | jq -r '.["mapbox-localhost"]') \ + && echo '[mapbox]' > ~/.streamlit/config.toml \ + && echo 'token = "'$MAPBOX_TOKEN'"' >> ~/.streamlit/config.toml + CMD /bin/bash diff --git a/frontend/src/components/elements/DeckGlChart/DeckGlChart.tsx b/frontend/src/components/elements/DeckGlChart/DeckGlChart.tsx index dcc10685030d..d60fed08186d 100644 --- a/frontend/src/components/elements/DeckGlChart/DeckGlChart.tsx +++ b/frontend/src/components/elements/DeckGlChart/DeckGlChart.tsx @@ -475,4 +475,6 @@ function parseGetters(type: any, spec: any): void { }) } -export default withMapboxToken(withFullScreenWrapper(DeckGlChart)) +export default withMapboxToken("st.deck_gl_chart")( + withFullScreenWrapper(DeckGlChart) +) diff --git a/frontend/src/components/elements/DeckGlJsonChart/DeckGlJsonChart.tsx b/frontend/src/components/elements/DeckGlJsonChart/DeckGlJsonChart.tsx index 5125df3b7256..b6f433b5f789 100644 --- a/frontend/src/components/elements/DeckGlJsonChart/DeckGlJsonChart.tsx +++ b/frontend/src/components/elements/DeckGlJsonChart/DeckGlJsonChart.tsx @@ -21,8 +21,8 @@ import Immutable from "immutable" import { StaticMap } from "react-map-gl" import * as layers from "@deck.gl/layers" import { JSONConverter } from "@deck.gl/json" -import * as aggregationLayers from "@deck.gl/aggregation-layers" import * as geoLayers from "@deck.gl/geo-layers" +import * as aggregationLayers from "@deck.gl/aggregation-layers" import { CSVLoader } from "@loaders.gl/csv" import { registerLoaders } from "@loaders.gl/core" @@ -182,4 +182,6 @@ export class DeckGlJsonChart extends PureComponent<PropsWithHeight, State> { } } -export default withMapboxToken(withFullScreenWrapper(DeckGlJsonChart)) +export default withMapboxToken("st.pydeck_chart")( + withFullScreenWrapper(DeckGlJsonChart) +) diff --git a/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts b/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts index 91d3c80cffe9..274f42e71b84 100644 --- a/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts +++ b/frontend/src/hocs/withMapboxToken/MapboxToken.test.ts @@ -15,11 +15,15 @@ * limitations under the License. */ -import { MapboxToken, TOKENS_URL } from "hocs/withMapboxToken/MapboxToken" +import axios from "axios" import { SessionInfo } from "lib/SessionInfo" -import fetchMock from "fetch-mock" +import AxiosMockAdapter from "axios-mock-adapter" +import { MapboxToken, TOKENS_URL } from "hocs/withMapboxToken/MapboxToken" -function setSessionInfoWithMapboxToken(userMapboxToken: string): void { +function setSessionInfo( + userMapboxToken = "", + commandLine = "streamlit hello" +): void { SessionInfo.current = new SessionInfo({ sessionId: "mockSessionId", streamlitVersion: "sv", @@ -27,31 +31,38 @@ function setSessionInfoWithMapboxToken(userMapboxToken: string): void { installationId: "iid", authorEmail: "ae", maxCachedMessageAge: 2, - commandLine: "cl", - userMapboxToken: userMapboxToken, + commandLine, + userMapboxToken, }) } describe("MapboxToken", () => { + let axiosMock: AxiosMockAdapter + beforeEach(() => { - setSessionInfoWithMapboxToken("") + window.location.hostname = "localhost" + axiosMock = new AxiosMockAdapter(axios) + setSessionInfo("") }) afterEach(() => { - SessionInfo["singleton"] = undefined + axiosMock.restore() MapboxToken["token"] = undefined - fetchMock.restore() + MapboxToken["commandLine"] = undefined + SessionInfo["singleton"] = undefined }) test("Returns cached token if defined", async () => { MapboxToken["token"] = "cached" + MapboxToken["commandLine"] = "streamlit hello" + await expect(MapboxToken.get()).resolves.toEqual("cached") }) test("Returns userMapboxToken if non-empty", async () => { const userToken = "nonEmptyToken" - setSessionInfoWithMapboxToken(userToken) + setSessionInfo(userToken) await expect(MapboxToken.get()).resolves.toEqual(userToken) // The token should also be cached. @@ -61,7 +72,8 @@ describe("MapboxToken", () => { test("Fetches remote token if userMapboxToken is empty", async () => { const remoteToken = "remoteMapboxToken" - fetchMock.get(TOKENS_URL, { mapbox: remoteToken }) + axiosMock.onGet(TOKENS_URL).reply(200, { "mapbox-localhost": remoteToken }) + await expect(MapboxToken.get()).resolves.toEqual(remoteToken) // The token should also be cached. @@ -69,7 +81,7 @@ describe("MapboxToken", () => { }) test("Errors if remote token is missing", async () => { - fetchMock.get(TOKENS_URL, { ohNo: "noTokenHere" }) + axiosMock.onGet(TOKENS_URL).replyOnce(200, { ohNo: "noTokenHere" }) await expect(MapboxToken.get()).rejects.toEqual( new Error(`Missing token "mapbox" (${TOKENS_URL})`) @@ -78,13 +90,40 @@ describe("MapboxToken", () => { // No cached token after failure. expect(MapboxToken["token"]).toBeUndefined() - fetchMock.restore() - fetchMock.get(TOKENS_URL, 404) + axiosMock.onGet(TOKENS_URL).replyOnce(404, {}) await expect(MapboxToken.get()).rejects.toEqual( - new Error(`Bad status: 404 (${TOKENS_URL})`) + new Error(`Request failed with status code 404 (${TOKENS_URL})`) ) // No cached token after failure. expect(MapboxToken["token"]).toBeUndefined() }) + + it("Errors if not localhost and missing token", async () => { + delete window.location + window.location = { hostname: "http://streamlit.io" } as Location + setSessionInfo("") + + await expect(MapboxToken.get()).rejects.toThrow("No Mapbox token provided") + }) + + it("Errors if not hello.py and missing token", async () => { + setSessionInfo("", "streamlit run example.py") + + await expect(MapboxToken.get()).rejects.toThrow("No Mapbox token provided") + }) + + it("Should reload token if command line has changed", async () => { + setSessionInfo() + + const remoteToken = "remoteMapboxToken" + + axiosMock.onGet(TOKENS_URL).reply(200, { "mapbox-localhost": remoteToken }) + + await expect(MapboxToken.get()).resolves.toEqual(remoteToken) + + setSessionInfo("password", "streamlit run test.py") + + await expect(MapboxToken.get()).resolves.toEqual("password") + }) }) diff --git a/frontend/src/hocs/withMapboxToken/MapboxToken.ts b/frontend/src/hocs/withMapboxToken/MapboxToken.ts index 39604cb26eb0..764045fbebd2 100644 --- a/frontend/src/hocs/withMapboxToken/MapboxToken.ts +++ b/frontend/src/hocs/withMapboxToken/MapboxToken.ts @@ -15,8 +15,12 @@ * limitations under the License. */ +import axios from "axios" import { SessionInfo } from "lib/SessionInfo" +export class MapboxTokenNotProvidedError extends Error {} +export class MapboxTokenFetchingError extends Error {} + /** * A remote file that stores user-visible tokens. */ @@ -24,6 +28,13 @@ export const TOKENS_URL = "https://data.streamlit.io/tokens.json" export class MapboxToken { private static token?: string + private static commandLine?: string + + private static isItRunningLocal = (): boolean => { + const { hostname } = window.location + + return hostname === "localhost" || hostname === "127.0.0.1" + } /** * Expose a singleton MapboxToken: @@ -34,12 +45,23 @@ export class MapboxToken { * only be fetched once per session.) */ public static async get(): Promise<string> { - if (MapboxToken.token == null) { - if (SessionInfo.current.userMapboxToken !== "") { - MapboxToken.token = SessionInfo.current.userMapboxToken + const { commandLine, userMapboxToken } = SessionInfo.current + + if ( + !MapboxToken.token || + MapboxToken.commandLine !== commandLine.toLowerCase() + ) { + if (userMapboxToken !== "") { + MapboxToken.token = userMapboxToken } else { - MapboxToken.token = await this.fetchToken(TOKENS_URL, "mapbox") + if (this.isItRunningLocal() && SessionInfo.isHello) { + MapboxToken.token = await this.fetchToken(TOKENS_URL, "mapbox") + } else { + throw new MapboxTokenNotProvidedError("No Mapbox token provided") + } } + + MapboxToken.commandLine = commandLine.toLowerCase() } return MapboxToken.token @@ -49,26 +71,17 @@ export class MapboxToken { url: string, tokenName: string ): Promise<string> { - let rsp: Response try { - rsp = await fetch(url) - } catch (e) { - // Fetch error messages are abysmal, and give virtually no useful - // context. Catch errors and append the offending URL to their messages - // to make them a bit more useful. - throw new Error(`${e.message} (${url})`) - } + const response = await axios.get(url) + const { "mapbox-localhost": token } = response.data - if (!rsp.ok) { - throw new Error(`Bad status: ${rsp.status} (${url})`) - } + if (token == null || token === "") { + throw new Error(`Missing token "${tokenName}"`) + } - const json = await rsp.json() - const token = json[tokenName] - if (token == null || token === "") { - throw new Error(`Missing token "${tokenName}" (${url})`) + return token + } catch (e) { + throw new MapboxTokenFetchingError(`${e.message} (${url})`) } - - return token } } diff --git a/frontend/src/hocs/withMapboxToken/MapboxTokenError.tsx b/frontend/src/hocs/withMapboxToken/MapboxTokenError.tsx new file mode 100644 index 000000000000..20246f5d66ac --- /dev/null +++ b/frontend/src/hocs/withMapboxToken/MapboxTokenError.tsx @@ -0,0 +1,88 @@ +import React, { ReactElement } from "react" +import ErrorElement from "components/shared/ErrorElement" +import { + MapboxTokenFetchingError, + MapboxTokenNotProvidedError, +} from "hocs/withMapboxToken/MapboxToken" + +interface Props { + error: Error | MapboxTokenFetchingError | MapboxTokenNotProvidedError + deltaType: string + width: number +} + +const MapboxTokenError = ({ + error, + width, + deltaType, +}: Props): ReactElement => { + if (error instanceof MapboxTokenNotProvidedError) { + return ( + <ErrorElement + width={width} + name="No Mapbox token provided" + message={ + <> + <p> + To use <code>st.{deltaType}</code> or <code>st.map</code> you + need to set up a Mapbox access token. + </p> + + <p> + To get a token, create an account at{" "} + <a href="https://mapbox.com">https://mapbox.com</a>. It's free + for moderate usage levels! + </p> + + <p> + Once you have a token, just set it using the Streamlit config + option <code>mapbox.token</code> and don't forget to restart your + Streamlit server at this point if it's still running, then reload + this tab. + </p> + + <p> + See{" "} + <a href="https://docs.streamlit.io/cli.html#view-all-config-options"> + our documentation + </a>{" "} + for more info on how to set config options. + </p> + </> + } + /> + ) + } + + if (error instanceof MapboxTokenFetchingError) { + return ( + <ErrorElement + width={width} + name="Error fetching Streamlit Mapbox token" + message={ + <> + <p>This app requires an internet connection.</p> + <p>Please check your connection and try again.</p> + <p> + If you think this is a bug, please file bug report{" "} + <a href="https://github.com/streamlit/streamlit/issues/new/choose"> + here + </a> + . + </p> + </> + } + /> + ) + } + + return ( + <ErrorElement + width={width} + name="Error fetching Streamlit Mapbox token" + message={error.message} + /> + ) +} + +export default MapboxTokenError diff --git a/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx b/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx index cfacee473cd7..9e4c6c6e531d 100644 --- a/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx +++ b/frontend/src/hocs/withMapboxToken/withMapboxToken.test.tsx @@ -18,6 +18,7 @@ import React from "react" import { shallow } from "enzyme" import { MapboxToken } from "./MapboxToken" +import { SessionInfo } from "lib/SessionInfo" import withMapboxToken from "./withMapboxToken" @@ -35,23 +36,41 @@ function waitOneTick(): Promise<void> { } describe("withMapboxToken", () => { - function getProps(): any { + const token = "mockToken" + const commandLine = "streamlit run test.py" + + function getProps(): object { return { label: "label" } } + beforeAll(() => { + SessionInfo.current = new SessionInfo({ + sessionId: "mockSessionId", + streamlitVersion: "sv", + pythonVersion: "pv", + installationId: "iid", + authorEmail: "ae", + maxCachedMessageAge: 2, + commandLine, + userMapboxToken: token, + }) + }) + // Install a mock token in our token fetcher so that we don't hit // the network. beforeEach(() => { - MapboxToken["token"] = "mockToken" + MapboxToken["token"] = token + MapboxToken["commandLine"] = commandLine }) afterEach(() => { MapboxToken["token"] = undefined + MapboxToken["commandLine"] = undefined }) it("renders without crashing", async () => { const props = getProps() - const WrappedComponent = withMapboxToken(TestComponent) + const WrappedComponent = withMapboxToken("st.test")(TestComponent) const wrapper = shallow(<WrappedComponent {...props} />) expect(wrapper.html()).not.toBeNull() @@ -59,7 +78,7 @@ describe("withMapboxToken", () => { it("passes mapboxToken to wrapped component", async () => { const props = getProps() - const WrappedComponent = withMapboxToken(TestComponent) + const WrappedComponent = withMapboxToken("st.test")(TestComponent) const wrapper = shallow(<WrappedComponent {...props} />) // Wait one tick for our MapboxToken promise to resolve diff --git a/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx b/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx index d7b28bc79d09..77dbb3696d61 100644 --- a/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx +++ b/frontend/src/hocs/withMapboxToken/withMapboxToken.tsx @@ -16,11 +16,12 @@ */ import React, { ComponentType, PureComponent } from "react" +import { makeElementWithInfoText } from "lib/utils" import hoistNonReactStatics from "hoist-non-react-statics" import { MapboxToken } from "hocs/withMapboxToken/MapboxToken" -import ErrorElement from "components/shared/ErrorElement" + +import MapboxTokenError from "./MapboxTokenError" import Alert from "components/elements/Alert" -import { makeElementWithInfoText } from "lib/utils" interface Props { width: number @@ -29,16 +30,20 @@ interface Props { interface State { mapboxToken?: string mapboxTokenError?: Error + isFetching: boolean } /** * A higher-order component that fetches our mapbox token and passes * it through to the wrapped component. If the token fetch fails, an error * will be rendered in place of the wrapped component. + * + * @param {string} deltaType In case of an exception we show an error with this */ -function withMapboxToken( + +const withMapboxToken = (deltaType: string) => ( WrappedComponent: ComponentType<any> -): ComponentType<any> { +): ComponentType<any> => { class WithMapboxToken extends PureComponent<Props, State> { public static readonly displayName = `withMapboxToken(${WrappedComponent.displayName || WrappedComponent.name})` @@ -47,50 +52,60 @@ function withMapboxToken( super(props) this.state = { + isFetching: true, mapboxToken: undefined, mapboxTokenError: undefined, } + this.initMapboxToken() } /** * Fetch our MapboxToken. */ - private initMapboxToken = (): void => { - MapboxToken.get() - .then(token => this.setState({ mapboxToken: token })) - .catch(error => this.setState({ mapboxTokenError: error })) + private initMapboxToken = async (): Promise<void> => { + try { + const mapboxToken = await MapboxToken.get() + + this.setState({ + mapboxToken, + isFetching: false, + }) + } catch (error) { + this.setState({ + mapboxTokenError: error, + isFetching: false, + }) + } } public render = (): JSX.Element => { + const { mapboxToken, mapboxTokenError, isFetching } = this.state + const { width } = this.props + // We got an error when fetching our mapbox token: show the error. - if (this.state.mapboxTokenError != null) { + if (mapboxTokenError) { return ( - <ErrorElement - width={this.props.width} - name="Error fetching Mapbox token" - message={this.state.mapboxTokenError.message} + <MapboxTokenError + width={width} + error={mapboxTokenError} + deltaType={deltaType} /> ) } // If our mapboxToken hasn't been retrieved yet, show a loading alert. - if (this.state.mapboxToken === undefined) { + if (isFetching) { return ( <Alert element={makeElementWithInfoText("Loading...").get("alert")} - width={this.props.width} + width={width} /> ) } // We have the mapbox token. Pass it through to our component. - return ( - <WrappedComponent - mapboxToken={this.state.mapboxToken} - {...this.props} - /> - ) + return <WrappedComponent mapboxToken={mapboxToken} {...this.props} /> } } diff --git a/frontend/src/lib/SessionInfo.ts b/frontend/src/lib/SessionInfo.ts index 2a6ec0bbef9b..bfeec4fa965a 100644 --- a/frontend/src/lib/SessionInfo.ts +++ b/frontend/src/lib/SessionInfo.ts @@ -68,6 +68,10 @@ export class SessionInfo { return SessionInfo.singleton != null } + public static get isHello(): boolean { + return this.current.commandLine === "streamlit hello" + } + constructor({ sessionId, streamlitVersion, diff --git a/lib/streamlit/config.py b/lib/streamlit/config.py index b0eff7c60a6e..457a9fdf1382 100644 --- a/lib/streamlit/config.py +++ b/lib/streamlit/config.py @@ -537,11 +537,9 @@ def _browser_server_port(): _create_option( "mapbox.token", description="""Configure Streamlit to use a custom Mapbox - token for elements like st.deck_gl_chart and st.map. If you - don't do this you'll be using Streamlit's own token, - which has limitations and is not guaranteed to always work. + token for elements like st.deck_gl_chart and st.map. To get a token for yourself, create an account at - https://mapbox.com. It's free! (for moderate usage levels)""", + https://mapbox.com. It's free (for moderate usage levels)!""", default_val="", )
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
zulip__zulip-20379@c389759
zulip/zulip
Python
20,379
compose_actions: Fix misplaced caret position when qouting.
When replying with a quote, the cursor moves back to the beginning once the quote resolves and disrupts ongoing typing. Fixes #20071.
2021-11-27T16:40:49Z
Cursor jumps after quoted message loads When replying with a quote, the cursor moves back to the beginning once the quote resolves, disrupting ongoing typing. Steps to reproduce: - Select a message. - Make sure the text box isn't currently open (press esc if necessary). - In quick succession, press > and then immediately start typing a reply. - For a short time, the text box will include a note saying Quoting in square brackets, but you can keep typing while that box is present. - After a moment, the note Quoting gets replaced by the quote. **Expected**: Cursor position should not change when the quote finishes resolving. **Actual**: When the quote appears, the editing cursor moves back to the beginning of the subsequent line, disrupting the user's current typing. Thanks @joshtriplett for the report!
Hello @zulip/server-compose members, this issue was labeled with the "area: compose" label, so you may want to check it out! <!-- areaLabelAddition --> We certainly have logic that's intended to prevent. ``` compose_ui.insert_syntax_and_focus("[Quotingโ€ฆ]\n", textarea); function replace_content(message) { // Final message looks like: // @_**Iago|5** [said](link to message): // ```quote // message content // ``` let content = $t( {defaultMessage: "{username} [said]({link_to_message}):"}, { username: `@_**${message.sender_full_name}|${message.sender_id}**`, link_to_message: `${hash_util.by_conversation_and_time_uri(message)}`, }, ); content += "\n"; const fence = fenced_code.get_unused_fence(message.raw_content); content += `${fence}quote\n${message.raw_content}\n${fence}`; compose_ui.replace_syntax("[Quotingโ€ฆ]", content, textarea); compose_ui.autosize_textarea($("#compose-textarea")); // Update textarea caret to point to the new line after quoted message. textarea.caret(prev_caret + content.length + 1); } if (message && message.raw_content) { replace_content(message); return; } channel.get({ url: "/json/messages/" + message_id, idempotent: true, success(data) { message.raw_content = data.raw_content; replace_content(message); }, }); ``` @amanagr can you do a quick investigation into this one? It might be the case that we can switch to using the newer libraries that support "Undo" you added for the new compose box buttons rather than debugging why this isn't working.
[ { "body": "When replying with a quote, the cursor moves back to the beginning once the quote resolves, disrupting ongoing typing.\r\n\r\nSteps to reproduce:\r\n\r\n- Select a message.\r\n- Make sure the text box isn't currently open (press esc if necessary).\r\n- In quick succession, press > and then immediately start typing a reply.\r\n- For a short time, the text box will include a note saying Quoting in square brackets, but you can keep typing while that box is present.\r\n- After a moment, the note Quoting gets replaced by the quote. \r\n\r\n**Expected**: Cursor position should not change when the quote finishes resolving.\r\n**Actual**: When the quote appears, the editing cursor moves back to the beginning of the subsequent line, disrupting the user's current typing.\r\n\r\nThanks @joshtriplett for the report!\r\n", "number": 20071, "title": "Cursor jumps after quoted message loads" } ]
089fdc4d9453790bf5e64fec40dc72ab0e8e3e29
{ "head_commit": "c389759568d55a5dae60d8bd3beb8c080264cc77", "head_commit_message": "compose: Fix cursor position loss in quote-and-reply.\n\nIf you used \"Quote and reply\" to start composing a message, and\nstarted typing before receiving the original message body from the\nserver, we ended up resetting your cursor to the start of the line\nafter the quote for two reasons:\n\n* We were incorrectly fetching the pre_cursor for our replacement\n operation before doing the server fetch, which meant we ignored any\n editing done while waiting for the server to respond.\n\n* Worse, we actually fetched the original cursor position before\n inserting the \"[Quoting...]\" placeholder text. So we were guaranteed\n to have at least some amount of error in the cursor position.\n\nFixes #20379.", "patch_to_review": "diff --git a/static/js/compose_actions.js b/static/js/compose_actions.js\nindex 7bbea45264432..e44c35fa69572 100644\n--- a/static/js/compose_actions.js\n+++ b/static/js/compose_actions.js\n@@ -482,8 +482,6 @@ export function quote_and_reply(opts) {\n respond_to_message(opts);\n }\n \n- const prev_caret = textarea.caret();\n-\n compose_ui.insert_syntax_and_focus(\"[Quotingโ€ฆ]\\n\", textarea);\n \n function replace_content(message) {\n@@ -492,6 +490,7 @@ export function quote_and_reply(opts) {\n // ```quote\n // message content\n // ```\n+ const prev_caret = textarea.caret();\n let content = $t(\n {defaultMessage: \"{username} [said]({link_to_message}):\"},\n {\n" }
[ { "diff_hunk": "@@ -505,7 +506,12 @@ export function quote_and_reply(opts) {\n compose_ui.replace_syntax(\"[Quotingโ€ฆ]\", content, textarea);\n compose_ui.autosize_textarea($(\"#compose-textarea\"));\n // Update textarea caret to point to the new line after quoted message.\n- textarea.caret(prev_caret + content.length + 1);\n+ if (composebox_was_open) {\n+ // If composebox was closed, `textbox replace`, by default, places\n+ // cursor at end of content. So, we don't need to restore\n+ // cursor position.\n+ textarea.caret(prev_caret + content.length + 1);\n+ }", "line": 524, "original_line": 524, "original_start_line": null, "path": "static/js/compose_actions.js", "start_line": null, "text": "@author:\nThe logic here is correct if you think about `open compose quote insertion`. For closed compose, we actually need `content = textbox.val()` instead of just quoted text, which is what `textbox replace` does by default, so we don't need to meddle with cursor position here.\r\n\r\nDoing what you are saying is not solving anything." } ]
d3772acb283fd41c6455d87bc1d984d74889f999
diff --git a/frontend_tests/node_tests/compose_actions.js b/frontend_tests/node_tests/compose_actions.js index 2ba4982e4e6f6..b01b45a05da8c 100644 --- a/frontend_tests/node_tests/compose_actions.js +++ b/frontend_tests/node_tests/compose_actions.js @@ -338,7 +338,7 @@ test("quote_and_reply", ({override}) => { let expected_replacement; let replaced; override(compose_ui, "replace_syntax", (syntax, replacement) => { - assert.equal(syntax, "[Quotingโ€ฆ]"); + assert.equal(syntax, "translated: [Quotingโ€ฆ]"); assert.equal(replacement, expected_replacement); replaced = true; }); @@ -361,7 +361,7 @@ test("quote_and_reply", ({override}) => { override(message_lists.current, "selected_id", () => 100); override(compose_ui, "insert_syntax_and_focus", (syntax) => { - assert.equal(syntax, "[Quotingโ€ฆ]\n"); + assert.equal(syntax, "translated: [Quotingโ€ฆ]\n"); }); const opts = { diff --git a/frontend_tests/node_tests/compose_ui.js b/frontend_tests/node_tests/compose_ui.js index f5e1e560bc12c..b04b19fdf4563 100644 --- a/frontend_tests/node_tests/compose_ui.js +++ b/frontend_tests/node_tests/compose_ui.js @@ -162,9 +162,9 @@ run_test("smart_insert", () => { textbox = make_textbox(""); textbox.caret(0); textbox.trigger("blur"); - compose_ui.smart_insert(textbox, "[Quotingโ€ฆ]\n"); - assert.equal(textbox.insert_text, "[Quotingโ€ฆ]\n"); - assert.equal(textbox.val(), "[Quotingโ€ฆ]\n"); + compose_ui.smart_insert(textbox, "translated: [Quotingโ€ฆ]\n"); + assert.equal(textbox.insert_text, "translated: [Quotingโ€ฆ]\n"); + assert.equal(textbox.val(), "translated: [Quotingโ€ฆ]\n"); assert.ok(textbox.focused); textbox = make_textbox("abc"); @@ -342,7 +342,7 @@ run_test("quote_and_reply", ({override}) => { set_compose_content_with_caret("hello %there"); // "%" is used to encode/display position of focus before change compose_actions.quote_and_reply(); - assert.equal(get_compose_content_with_caret(), "hello \n[Quotingโ€ฆ]\n%there"); + assert.equal(get_compose_content_with_caret(), "hello \ntranslated: [Quotingโ€ฆ]\n%there"); success_function({ raw_content: "Testing caret position", @@ -358,7 +358,7 @@ run_test("quote_and_reply", ({override}) => { // add a newline before the quoted message. set_compose_content_with_caret("%hello there"); compose_actions.quote_and_reply(); - assert.equal(get_compose_content_with_caret(), "[Quotingโ€ฆ]\n%hello there"); + assert.equal(get_compose_content_with_caret(), "translated: [Quotingโ€ฆ]\n%hello there"); success_function({ raw_content: "Testing with caret initially positioned at 0.", @@ -381,7 +381,7 @@ run_test("quote_and_reply", ({override}) => { // quoting a message, the quoted message should be placed // at the beginning of compose-box. compose_actions.quote_and_reply(); - assert.equal(get_compose_content_with_caret(), "[Quotingโ€ฆ]\n%"); + assert.equal(get_compose_content_with_caret(), "translated: [Quotingโ€ฆ]\n%"); success_function({ raw_content: "Testing with compose-box closed initially.", @@ -399,7 +399,7 @@ run_test("quote_and_reply", ({override}) => { // message should start from the beginning of compose-box. set_compose_content_with_caret(" \n\n \n %"); compose_actions.quote_and_reply(); - assert.equal(get_compose_content_with_caret(), "[Quotingโ€ฆ]\n%"); + assert.equal(get_compose_content_with_caret(), "translated: [Quotingโ€ฆ]\n%"); success_function({ raw_content: "Testing with compose-box containing whitespaces and newlines only.", diff --git a/static/js/compose_actions.js b/static/js/compose_actions.js index 7bbea45264432..5559132b25d55 100644 --- a/static/js/compose_actions.js +++ b/static/js/compose_actions.js @@ -463,6 +463,7 @@ export function quote_and_reply(opts) { const textarea = $("#compose-textarea"); const message_id = message_lists.current.selected_id(); const message = message_lists.current.selected_message(); + const quoting_placeholder = $t({defaultMessage: "[Quotingโ€ฆ]"}); if (compose_state.has_message_content()) { // The user already started typing a message, @@ -482,9 +483,7 @@ export function quote_and_reply(opts) { respond_to_message(opts); } - const prev_caret = textarea.caret(); - - compose_ui.insert_syntax_and_focus("[Quotingโ€ฆ]\n", textarea); + compose_ui.insert_syntax_and_focus(quoting_placeholder + "\n", textarea); function replace_content(message) { // Final message looks like: @@ -492,6 +491,7 @@ export function quote_and_reply(opts) { // ```quote // message content // ``` + const prev_caret = textarea.caret(); let content = $t( {defaultMessage: "{username} [said]({link_to_message}):"}, { @@ -502,10 +502,26 @@ export function quote_and_reply(opts) { content += "\n"; const fence = fenced_code.get_unused_fence(message.raw_content); content += `${fence}quote\n${message.raw_content}\n${fence}`; - compose_ui.replace_syntax("[Quotingโ€ฆ]", content, textarea); + + const placeholder_offset = $(textarea).val().indexOf(quoting_placeholder); + compose_ui.replace_syntax(quoting_placeholder, content, textarea); compose_ui.autosize_textarea($("#compose-textarea")); - // Update textarea caret to point to the new line after quoted message. - textarea.caret(prev_caret + content.length + 1); + + // When replacing content in a textarea, we need to move the + // cursor to preserve its logical position if and only if the + // content we just added was before the current cursor + // position. If we do, we need to move it by the increase in + // the length of the content before the placeholder. + if (prev_caret >= placeholder_offset + quoting_placeholder.length) { + textarea.caret(prev_caret + content.length - quoting_placeholder.length); + } else if (prev_caret > placeholder_offset) { + /* In the rare case that our cursor was inside the + * placeholder, we treat that as though the cursor was + * just after the placeholder. */ + textarea.caret(placeholder_offset + content.length + 1); + } else { + textarea.caret(prev_caret); + } } if (message && message.raw_content) {
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-874@44afc26
streamlit/streamlit
Python
874
Auto-reload page if Streamlit server version โ‰  frontend version
**Issue:** This PR fixes https://github.com/streamlit/streamlit/issues/536 **Description:** Reloading frontend if Streamlit server version changes --- **Contribution License Agreement** By submiting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2019-12-19T18:42:06Z
Auto-reload page if Streamlit server version โ‰  frontend version When users upgrade Streamlit, they sometimes have a browser tab running the frontend for the previous version. If we changed the Streamlit protos in between versions, this will break that tab in weird ways. Thankfully, we already send the server version to the frontend every time the websocket connects. So the frontend could just check that the version is correct, and autoreload the browser tab if there's a mismatch.
[ { "body": "When users upgrade Streamlit, they sometimes have a browser tab running the frontend for the previous version. If we changed the Streamlit protos in between versions, this will break that tab in weird ways.\r\n\r\nThankfully, we already send the server version to the frontend every time the websocket connects. So the frontend could just check that the version is correct, and autoreload the browser tab if there's a mismatch. ", "number": 536, "title": "Auto-reload page if Streamlit server version โ‰  frontend version" } ]
b5276f49bbe9e81f65caae4fc6a6f310cd3332f9
{ "head_commit": "44afc268709645401597340b4ada9a82278106cb", "head_commit_message": "wrong test name", "patch_to_review": "diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx\nindex 5e6634aff4b1..c93e50c1292b 100644\n--- a/frontend/src/App.test.tsx\n+++ b/frontend/src/App.test.tsx\n@@ -16,12 +16,22 @@\n */\n \n import React from \"react\"\n-import ReactDOM from \"react-dom\"\n-import { SessionInfo, Args as SessionInfoArgs } from \"./lib/SessionInfo\"\n+import { mount, CommonWrapper } from \"enzyme\"\n+import { ForwardMsg, Initialize } from \"autogen/proto\"\n import { MetricsManager } from \"./lib/MetricsManager\"\n import { getMetricsManagerForTest } from \"./lib/MetricsManagerTestUtils\"\n+import { SessionInfo, Args as SessionInfoArgs } from \"./lib/SessionInfo\"\n+\n import App from \"./App\"\n \n+const getWrapper = (): CommonWrapper => {\n+ const mountPoint = document.createElement(\"div\")\n+ mountPoint.setAttribute(\"id\", \"ConnectionStatus\")\n+ document.body.appendChild(mountPoint)\n+\n+ return mount(<App />, { attachTo: mountPoint })\n+}\n+\n describe(\"App\", () => {\n beforeEach(() => {\n SessionInfo.current = new SessionInfo({\n@@ -39,10 +49,28 @@ describe(\"App\", () => {\n })\n \n it(\"renders without crashing\", () => {\n- const mountPoint = document.createElement(\"div\")\n- mountPoint.setAttribute(\"id\", \"ConnectionStatus\")\n- document.body.appendChild(mountPoint)\n- ReactDOM.render(<App />, mountPoint)\n- ReactDOM.unmountComponentAtNode(mountPoint)\n+ const wrapper = getWrapper()\n+\n+ expect(wrapper.html()).not.toBeNull()\n+ })\n+\n+ it(\"should reload when streamlit server version changes\", () => {\n+ const wrapper = getWrapper()\n+\n+ window.location.reload = jest.fn()\n+\n+ const fwMessage = new ForwardMsg()\n+ const initMessage = new Initialize()\n+\n+ initMessage.environmentInfo = {\n+ streamlitVersion: \"svv\",\n+ }\n+\n+ fwMessage.initialize = initMessage\n+\n+ // @ts-ignore\n+ wrapper.instance().handleMessage(fwMessage)\n+\n+ expect(window.location.reload).toHaveBeenCalled()\n })\n })\ndiff --git a/frontend/src/App.tsx b/frontend/src/App.tsx\nindex b2d365497299..3540f2bdb9a0 100644\n--- a/frontend/src/App.tsx\n+++ b/frontend/src/App.tsx\n@@ -185,6 +185,30 @@ class App extends PureComponent<Props, State> {\n MetricsManager.current.enqueue(\"viewReport\")\n }\n \n+ showError(title: string, errorNode: ReactNode): void {\n+ logError(errorNode)\n+ const newDialog: DialogProps = {\n+ type: DialogType.WARNING,\n+ title: title,\n+ msg: errorNode,\n+ onClose: () => {},\n+ }\n+ this.openDialog(newDialog)\n+ }\n+\n+ isServerVersionUpdated(initializeMsg: Initialize): boolean {\n+ if (SessionInfo.isSet()) {\n+ const { streamlitVersion: currentStreamlitVersion } = SessionInfo.current\n+ const {\n+ streamlitVersion: newStreamlitVersion,\n+ } = initializeMsg.environmentInfo\n+\n+ return currentStreamlitVersion === newStreamlitVersion\n+ }\n+\n+ return true\n+ }\n+\n /**\n * Called by ConnectionManager when our connection state changes\n */\n@@ -204,17 +228,6 @@ class App extends PureComponent<Props, State> {\n }\n }\n \n- showError(title: string, errorNode: ReactNode): void {\n- logError(errorNode)\n- const newDialog: DialogProps = {\n- type: DialogType.WARNING,\n- title: title,\n- msg: errorNode,\n- onClose: () => {},\n- }\n- this.openDialog(newDialog)\n- }\n-\n /**\n * Callback when we get a message from the server.\n */\n@@ -278,6 +291,12 @@ class App extends PureComponent<Props, State> {\n */\n \n handleInitialize(initializeMsg: Initialize): void {\n+ if (!this.isServerVersionUpdated(initializeMsg)) {\n+ window.location.reload()\n+\n+ return\n+ }\n+\n SessionInfo.current = new SessionInfo({\n streamlitVersion: initializeMsg.environmentInfo.streamlitVersion,\n pythonVersion: initializeMsg.environmentInfo.pythonVersion,\n@@ -302,6 +321,8 @@ class App extends PureComponent<Props, State> {\n \n const initialState = initializeMsg.sessionState\n this.handleSessionStateChanged(initialState)\n+\n+ return\n }\n \n /**\ndiff --git a/frontend/src/components/core/Block/Block.tsx b/frontend/src/components/core/Block/Block.tsx\nindex d400c0809a0b..d4c77a39f2a5 100644\n--- a/frontend/src/components/core/Block/Block.tsx\n+++ b/frontend/src/components/core/Block/Block.tsx\n@@ -184,8 +184,8 @@ class Block extends PureComponent<Props> {\n const className = Block.getClassNames(isStale, isEmpty)\n \n return (\n- <Maybe enable={enable}>\n- <div key={index} className={className} style={{ width }}>\n+ <Maybe enable={enable} key={index}>\n+ <div className={className} style={{ width }}>\n <ErrorBoundary width={width}>\n <Suspense\n fallback={\ndiff --git a/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx b/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx\nindex e1fa88395256..2873454ad620 100644\n--- a/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx\n+++ b/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx\n@@ -35,7 +35,7 @@ const getProps = (props: object = {}): ReportElementProps => ({\n ...props,\n })\n \n-describe(\"Button widget\", () => {\n+describe(\"withFullScreenWrapper HOC\", () => {\n it(\"renders without crashing\", () => {\n const props = getProps()\n const WithHoc = withFullScreenWrapper(testComponent)\n" }
[ { "diff_hunk": "@@ -185,6 +185,30 @@ class App extends PureComponent<Props, State> {\n MetricsManager.current.enqueue(\"viewReport\")\n }\n \n+ showError(title: string, errorNode: ReactNode): void {\n+ logError(errorNode)\n+ const newDialog: DialogProps = {\n+ type: DialogType.WARNING,\n+ title: title,\n+ msg: errorNode,\n+ onClose: () => {},\n+ }\n+ this.openDialog(newDialog)\n+ }\n+\n+ isServerVersionUpdated(initializeMsg: Initialize): boolean {", "line": null, "original_line": 199, "original_start_line": null, "path": "frontend/src/App.tsx", "start_line": null, "text": "@user1:\nPlease, canyou add docstring to this func and maybe check the name? It's hard to understand :)" } ]
7af002b5140f626ad8f091db8a33971feaad8bfa
diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx index 06bbac2254e2..11463056eebe 100644 --- a/frontend/src/App.test.tsx +++ b/frontend/src/App.test.tsx @@ -16,12 +16,22 @@ */ import React from "react" -import ReactDOM from "react-dom" -import { SessionInfo, Args as SessionInfoArgs } from "./lib/SessionInfo" +import { mount, CommonWrapper } from "enzyme" +import { ForwardMsg } from "autogen/proto" import { MetricsManager } from "./lib/MetricsManager" import { getMetricsManagerForTest } from "./lib/MetricsManagerTestUtils" +import { SessionInfo, Args as SessionInfoArgs } from "./lib/SessionInfo" + import App from "./App" +const getWrapper = (): CommonWrapper => { + const mountPoint = document.createElement("div") + mountPoint.setAttribute("id", "ConnectionStatus") + document.body.appendChild(mountPoint) + + return mount(<App />, { attachTo: mountPoint }) +} + describe("App", () => { beforeEach(() => { SessionInfo.current = new SessionInfo({ @@ -41,10 +51,30 @@ describe("App", () => { }) it("renders without crashing", () => { - const mountPoint = document.createElement("div") - mountPoint.setAttribute("id", "ConnectionStatus") - document.body.appendChild(mountPoint) - ReactDOM.render(<App />, mountPoint) - ReactDOM.unmountComponentAtNode(mountPoint) + const wrapper = getWrapper() + + expect(wrapper.html()).not.toBeNull() + }) + + it("should reload when streamlit server version changes", () => { + const wrapper = getWrapper() + + window.location.reload = jest.fn() + + const fwMessage = new ForwardMsg() + + fwMessage.initialize = { + environmentInfo: { + streamlitVersion: "svv", + }, + userInfo: {}, + config: {}, + sessionState: {}, + } + + // @ts-ignore + wrapper.instance().handleMessage(fwMessage) + + expect(window.location.reload).toHaveBeenCalled() }) }) diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 6c97e104f376..2c69656f014b 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -185,6 +185,33 @@ class App extends PureComponent<Props, State> { MetricsManager.current.enqueue("viewReport") } + showError(title: string, errorNode: ReactNode): void { + logError(errorNode) + const newDialog: DialogProps = { + type: DialogType.WARNING, + title: title, + msg: errorNode, + onClose: () => {}, + } + this.openDialog(newDialog) + } + + /** + * Checks if the code version from the backend is different than the frontend + */ + hasStreamlitVersionChanged(initializeMsg: Initialize): boolean { + if (SessionInfo.isSet()) { + const { streamlitVersion: currentStreamlitVersion } = SessionInfo.current + const { environmentInfo } = initializeMsg + + if (environmentInfo) { + return currentStreamlitVersion !== environmentInfo.streamlitVersion + } + } + + return false + } + /** * Called by ConnectionManager when our connection state changes */ @@ -204,17 +231,6 @@ class App extends PureComponent<Props, State> { } } - showError(title: string, errorNode: ReactNode): void { - logError(errorNode) - const newDialog: DialogProps = { - type: DialogType.WARNING, - title: title, - msg: errorNode, - onClose: () => {}, - } - this.openDialog(newDialog) - } - /** * Callback when we get a message from the server. */ @@ -284,6 +300,12 @@ class App extends PureComponent<Props, State> { throw new Error("InitializeMsg is missing a required field") } + if (this.hasStreamlitVersionChanged(initializeMsg)) { + window.location.reload() + + return + } + SessionInfo.current = new SessionInfo({ streamlitVersion: environmentInfo.streamlitVersion, pythonVersion: environmentInfo.pythonVersion, diff --git a/frontend/src/components/core/Block/Block.tsx b/frontend/src/components/core/Block/Block.tsx index 78f81ded3af4..a7c9caa1a816 100644 --- a/frontend/src/components/core/Block/Block.tsx +++ b/frontend/src/components/core/Block/Block.tsx @@ -184,8 +184,8 @@ class Block extends PureComponent<Props> { const className = Block.getClassNames(isStale, isEmpty) return ( - <Maybe enable={enable}> - <div key={index} className={className} style={{ width }}> + <Maybe enable={enable} key={index}> + <div className={className} style={{ width }}> <ErrorBoundary width={width}> <Suspense fallback={ diff --git a/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx b/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx index e1fa88395256..2873454ad620 100644 --- a/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx +++ b/frontend/src/hocs/withFullScreenWrapper/withFullScreenWrapper.test.tsx @@ -35,7 +35,7 @@ const getProps = (props: object = {}): ReportElementProps => ({ ...props, }) -describe("Button widget", () => { +describe("withFullScreenWrapper HOC", () => { it("renders without crashing", () => { const props = getProps() const WithHoc = withFullScreenWrapper(testComponent)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-655@4d5cbc9
streamlit/streamlit
Python
655
Copy-to-clipboard button is showing at wrong place
**Issue:** This PR fixes #581 **Description:** Added a div that contains both elements --- **Contribution License Agreement** By submiting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
2019-11-08T19:25:38Z
Copy-to-clipboard button is showing at wrong place Steps to repro: st.markdown(""" Some text. Some text. Some text. Some text. Some text. Some text. Some text. Some text. Some text. Some text. Some text. Some text. ``` Some code ``` """) The button should be next to the code box.
[ { "body": "Steps to repro:\r\n\r\n st.markdown(\"\"\"\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n Some text.\r\n\r\n ```\r\n Some code\r\n ```\r\n \"\"\")\r\n\r\n\r\nThe button should be next to the code box.", "number": 581, "title": "Copy-to-clipboard button is showing at wrong place" } ]
8166563234323a086cd5045dee6eb88f05ce39bf
{ "head_commit": "4d5cbc931e52b4ae1568f4cd1769e735e651c139", "head_commit_message": "Fixing button alignment inside alerts and margins", "patch_to_review": "diff --git a/frontend/src/components/elements/CodeBlock/CodeBlock.scss b/frontend/src/components/elements/CodeBlock/CodeBlock.scss\nindex dbdff1935672..c70f42d712b0 100644\n--- a/frontend/src/components/elements/CodeBlock/CodeBlock.scss\n+++ b/frontend/src/components/elements/CodeBlock/CodeBlock.scss\n@@ -21,6 +21,10 @@\n See https://prismjs.com/download.html#themes=prism&languages=markup+css+clike+javascript\n */\n \n+div.code-block {\n+ margin: 0 0 1rem 0;\n+}\n+\n .token.comment,\n .token.prolog,\n .token.doctype,\ndiff --git a/frontend/src/components/elements/CodeBlock/CodeBlock.tsx b/frontend/src/components/elements/CodeBlock/CodeBlock.tsx\nindex d192933418b8..2336b36b1455 100644\n--- a/frontend/src/components/elements/CodeBlock/CodeBlock.tsx\n+++ b/frontend/src/components/elements/CodeBlock/CodeBlock.tsx\n@@ -45,12 +45,12 @@ class CodeBlock extends React.PureComponent<Props> {\n public render(): React.ReactNode {\n if (this.props.language == null) {\n return (\n- <>\n+ <div className=\"code-block\">\n <CopyButton text={this.props.value} />\n- <pre className={\"code-block\"}>\n+ <pre>\n <code>{this.props.value}</code>\n </pre>\n- </>\n+ </div>\n )\n }\n \n@@ -68,15 +68,15 @@ class CodeBlock extends React.PureComponent<Props> {\n : \"\"\n const languageClassName = `language-${this.props.language}`\n return (\n- <>\n+ <div className=\"code-block\">\n <CopyButton text={this.props.value} />\n- <pre className={\"code-block\"}>\n+ <pre>\n <code\n className={languageClassName}\n dangerouslySetInnerHTML={{ __html: safeHtml }}\n />\n </pre>\n- </>\n+ </div>\n )\n }\n }\ndiff --git a/frontend/src/components/elements/Text/Text.scss b/frontend/src/components/elements/Text/Text.scss\nindex 07fe9e2d6dea..bab629120001 100644\n--- a/frontend/src/components/elements/Text/Text.scss\n+++ b/frontend/src/components/elements/Text/Text.scss\n@@ -89,5 +89,12 @@\n color: inherit;\n text-decoration: underline;\n }\n+\n+ div.code-block {\n+ button.overlayBtn {\n+ top: 0;\n+ right: 0;\n+ }\n+ }\n }\n }\n" }
[ { "diff_hunk": "@@ -21,6 +21,10 @@\n See https://prismjs.com/download.html#themes=prism&languages=markup+css+clike+javascript\n */\n \n+div.code-block {\n+ margin: 0 0 1rem 0;\n+}", "line": 35, "original_line": 26, "original_start_line": null, "path": "frontend/src/components/elements/CodeBlock/CodeBlock.scss", "start_line": null, "text": "@user1:\nWhy is this needed?\n\n@author:\nthe snapshot was failing due to this, because the div has no margin" }, { "diff_hunk": "@@ -45,12 +45,12 @@ class CodeBlock extends React.PureComponent<Props> {\n public render(): React.ReactNode {\n if (this.props.language == null) {\n return (\n- <>\n+ <div className=\"code-block\">", "line": null, "original_line": 48, "original_start_line": null, "path": "frontend/src/components/elements/CodeBlock/CodeBlock.tsx", "start_line": null, "text": "@user1:\nCan you change this to `stCodeBlock`? This is the pattern we use everywhere else" } ]
d823f64bdb5b706e5e38559f8f6b0e2e3527dced
diff --git a/frontend/src/App.scss b/frontend/src/App.scss index 53b44d8e891e..258e8428cdf1 100644 --- a/frontend/src/App.scss +++ b/frontend/src/App.scss @@ -15,6 +15,7 @@ */ @import "src/assets/css/variables"; +@import "src/assets/css/mixins"; body.embedded { overflow: hidden; @@ -50,9 +51,10 @@ code { } pre { + @include element-margin-bottom; + background: $gray-faint; border-radius: $border-radius; - margin: 0 0 1rem 0; padding: 1rem; code { diff --git a/frontend/src/assets/css/mixins.scss b/frontend/src/assets/css/mixins.scss new file mode 100644 index 000000000000..58e117553ff2 --- /dev/null +++ b/frontend/src/assets/css/mixins.scss @@ -0,0 +1,3 @@ +@mixin element-margin-bottom { + margin: 0 0 1rem 0; +} diff --git a/frontend/src/assets/css/write.scss b/frontend/src/assets/css/write.scss index 07fe9e2d6dea..ebcd8e6291fb 100644 --- a/frontend/src/assets/css/write.scss +++ b/frontend/src/assets/css/write.scss @@ -70,7 +70,7 @@ .markdown-text-container { margin: 0; - & :last-child { + :last-child { margin-bottom: 0; } } @@ -89,5 +89,12 @@ color: inherit; text-decoration: underline; } + + div.stCodeBlock { + button.overlayBtn { + top: 0; + right: 0; + } + } } } diff --git a/frontend/src/components/core/ReportView/ReportView.scss b/frontend/src/components/core/ReportView/ReportView.scss index 4da3cd9c908d..28e9ab1446f8 100644 --- a/frontend/src/components/core/ReportView/ReportView.scss +++ b/frontend/src/components/core/ReportView/ReportView.scss @@ -15,6 +15,7 @@ */ @import "src/assets/css/variables"; +@import "src/assets/css/mixins"; .reportview-container { display: flex; @@ -87,9 +88,10 @@ } .element-container { + @include element-margin-bottom; + display: flex; flex-direction: column; - margin-bottom: 1rem; // Allows to have absolutely-positioned nodes inside report elements, like // floating buttons. position: relative; @@ -196,9 +198,10 @@ ul, ol, dl { - font-size: 1rem; - margin: 0 0 1em 0; + @include element-margin-bottom; + padding: 0; + font-size: 1rem; font-weight: 400; } diff --git a/frontend/src/components/elements/CodeBlock/CodeBlock.scss b/frontend/src/components/elements/CodeBlock/CodeBlock.scss index dbdff1935672..7e7c093f51f7 100644 --- a/frontend/src/components/elements/CodeBlock/CodeBlock.scss +++ b/frontend/src/components/elements/CodeBlock/CodeBlock.scss @@ -21,6 +21,19 @@ See https://prismjs.com/download.html#themes=prism&languages=markup+css+clike+javascript */ +@import "src/assets/css/mixins"; + +.stCodeBlock { + //code blocks can be used used inside an Alert + @include element-margin-bottom; + + position: relative; + + pre { + margin: 0; + } +} + .token.comment, .token.prolog, .token.doctype, diff --git a/frontend/src/components/elements/CodeBlock/CodeBlock.tsx b/frontend/src/components/elements/CodeBlock/CodeBlock.tsx index d192933418b8..c7455a836486 100644 --- a/frontend/src/components/elements/CodeBlock/CodeBlock.tsx +++ b/frontend/src/components/elements/CodeBlock/CodeBlock.tsx @@ -45,12 +45,12 @@ class CodeBlock extends React.PureComponent<Props> { public render(): React.ReactNode { if (this.props.language == null) { return ( - <> + <div className="stCodeBlock"> <CopyButton text={this.props.value} /> - <pre className={"code-block"}> + <pre> <code>{this.props.value}</code> </pre> - </> + </div> ) } @@ -68,15 +68,15 @@ class CodeBlock extends React.PureComponent<Props> { : "" const languageClassName = `language-${this.props.language}` return ( - <> + <div className="stCodeBlock"> <CopyButton text={this.props.value} /> - <pre className={"code-block"}> + <pre> <code className={languageClassName} dangerouslySetInnerHTML={{ __html: safeHtml }} /> </pre> - </> + </div> ) } } diff --git a/frontend/src/components/elements/CodeBlock/CopyButton.scss b/frontend/src/components/elements/CodeBlock/CopyButton.scss deleted file mode 100644 index 026cb5f1d29c..000000000000 --- a/frontend/src/components/elements/CodeBlock/CopyButton.scss +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2018-2019 Streamlit Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -@import "src/assets/css/variables"; - -.element-container { - .code-block { - position: relative; - } -} diff --git a/frontend/src/components/elements/CodeBlock/CopyButton.tsx b/frontend/src/components/elements/CodeBlock/CopyButton.tsx index f68674e2f343..3b18cabc71d6 100644 --- a/frontend/src/components/elements/CodeBlock/CopyButton.tsx +++ b/frontend/src/components/elements/CodeBlock/CopyButton.tsx @@ -18,7 +18,6 @@ import Clipboard from "clipboard" import React, { PureComponent } from "react" import { Copy as CopyIcon } from "react-feather" -import "./CopyButton.scss" interface Props { text: string
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
streamlit__streamlit-188@8f4b5f0
streamlit/streamlit
Python
188
LocalSourcesWatcher: blacklist venv and hidden folders
LocalSourcesWatcher blacklists conda folders (anaconda2/3, miniconda2/3) and any folder whose name starts with a `.`. For simplicity, the blacklist syntax now supports Unix glob patterns. (Also, there are a few unrelated tests that I cleaned up while I was fixing the new tests that were written for this PR.) Fixes #129
2019-09-25T19:26:11Z
Blacklist common virtualenv folders by default We should blacklist the folders below from being watched by Streamlit. This would fix the issue where some people hit the inotify watch limit when running Streamlit from a weird working directory. * */.virtualenv * */.venv * */anaconda3 * */anaconda2 * */miniconda3 * */miniconda2 * Actually let's blacklist every hidden folder: `.*` See also the config option `server.folderWatchBlacklist`. For this fix, you can probably use the same mechanism this config option uses.
[ { "body": "We should blacklist the folders below from being watched by Streamlit. This would fix the issue where some people hit the inotify watch limit when running Streamlit from a weird working directory.\r\n\r\n* */.virtualenv\r\n* */.venv\r\n* */anaconda3\r\n* */anaconda2\r\n* */miniconda3\r\n* */miniconda2\r\n* Actually let's blacklist every hidden folder: `.*`\r\n\r\n\r\nSee also the config option `server.folderWatchBlacklist`. For this fix, you can probably use the same mechanism this config option uses.", "number": 129, "title": "Blacklist common virtualenv folders by default" } ]
404987da8199097b2cbbdf40fc97f05d1b95dc85
{ "head_commit": "8f4b5f097c187ff60225a77725a53ce2abb74487", "head_commit_message": "Tweak", "patch_to_review": "diff --git a/lib/streamlit/watcher/LocalSourcesWatcher.py b/lib/streamlit/watcher/LocalSourcesWatcher.py\nindex 33080a28d7cf..236c9773b4dc 100644\n--- a/lib/streamlit/watcher/LocalSourcesWatcher.py\n+++ b/lib/streamlit/watcher/LocalSourcesWatcher.py\n@@ -13,6 +13,7 @@\n # See the License for the specific language governing permissions and\n # limitations under the License.\n \n+import fnmatch\n import os\n import sys\n import collections\n@@ -28,6 +29,7 @@\n from streamlit import util\n \n from streamlit.logger import get_logger\n+\n LOGGER = get_logger(__name__)\n \n try:\n@@ -39,15 +41,18 @@\n # Fallback that doesn't use watchdog.\n from streamlit.watcher.PollingFileWatcher import PollingFileWatcher as FileWatcher\n \n- if not config.get_option('global.disableWatchdogWarning'):\n- msg = '\\n $ xcode-select --install' if util.is_darwin() else ''\n+ if not config.get_option(\"global.disableWatchdogWarning\"):\n+ msg = \"\\n $ xcode-select --install\" if util.is_darwin() else \"\"\n \n- LOGGER.warning(\"\"\"\n+ LOGGER.warning(\n+ \"\"\"\n For better performance, install the Watchdog module:\n %s\n $ pip install watchdog\n \n- \"\"\" % msg)\n+ \"\"\"\n+ % msg\n+ )\n \n \n WatchedModule = collections.namedtuple(\"WatchedModule\", [\"watcher\", \"module_name\"])\n@@ -61,6 +66,11 @@ def __init__(self, report, on_file_changed):\n \n self._folder_blacklist = config.get_option(\"server.folderWatchBlacklist\")\n \n+ # Blacklist some additional folders, using glob syntax.\n+ self._folder_blacklist.extend(\n+ [\"**/.*\", \"**/anaconda2\", \"**/anaconda3\", \"**/miniconda2\", \"**/miniconda3\"]\n+ )\n+\n # A dict of filepath -> WatchedModule.\n self._watched_modules = {}\n \n@@ -140,12 +150,12 @@ def update_watched_modules(self):\n # .origin is 'built-in'.\n continue\n \n- folder_is_blacklisted = any(\n+ is_in_blacklisted_folder = any(\n _file_is_in_folder(filepath, blacklisted_folder)\n for blacklisted_folder in self._folder_blacklist\n )\n \n- if folder_is_blacklisted:\n+ if is_in_blacklisted_folder:\n continue\n \n file_is_new = filepath not in self._watched_modules\n@@ -175,7 +185,10 @@ def update_watched_modules(self):\n self._deregister_watcher(filepath)\n \n \n-def _file_is_in_folder(filepath, folderpath):\n- # Assumes filepath is an absolute path, as a teeny tiny optimization.\n- folderpath = os.path.abspath(folderpath) + \"/\"\n- return filepath.startswith(folderpath)\n+def _file_is_in_folder(filepath, folderpath_glob):\n+ # Strip trailing slash if it exists\n+ if folderpath_glob.endswith(\"/\"):\n+ folderpath_glob = folderpath_glob[:-1]\n+\n+ file_dir = os.path.dirname(filepath)\n+ return fnmatch.fnmatch(file_dir, folderpath_glob)\ndiff --git a/lib/tests/streamlit/delta_generator_test.py b/lib/tests/streamlit/delta_generator_test.py\nindex f3c53593f820..f17b426b99b0 100644\n--- a/lib/tests/streamlit/delta_generator_test.py\n+++ b/lib/tests/streamlit/delta_generator_test.py\n@@ -150,9 +150,9 @@ def test_wraps_with_cleaned_sig(self):\n wrapped = wrapped_function.keywords.get(\"wrapped\")\n \n # Check meta data.\n- self.assertEqual(wrapped.__module__, \"delta_generator_test\")\n- self.assertEqual(wrapped.__name__, \"fake_text\")\n- self.assertEqual(wrapped.__doc__, \"Fake text delta generator.\")\n+ self.assertEqual(\"delta_generator_test\", wrapped.__module__)\n+ self.assertEqual(\"fake_text\", wrapped.__name__)\n+ self.assertEqual(\"Fake text delta generator.\", wrapped.__doc__)\n \n # Verify original signature\n sig = signature(FakeDeltaGenerator.fake_text)\ndiff --git a/lib/tests/streamlit/help_test.py b/lib/tests/streamlit/help_test.py\nindex 40ca5910d9b1..510c4a94909e 100644\n--- a/lib/tests/streamlit/help_test.py\n+++ b/lib/tests/streamlit/help_test.py\n@@ -43,14 +43,14 @@ def my_func(some_param, another_param=123):\n st.help(my_func)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"my_func\")\n- self.assertEqual(ds.module, \"help_test\")\n+ self.assertEqual(\"my_func\", ds.name)\n+ self.assertEqual(\"help_test\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n- self.assertEqual(ds.signature, \"(some_param, another_param=123)\")\n- self.assertEqual(ds.doc_string, \"This is the doc\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n+ self.assertEqual(\"(some_param, another_param=123)\", ds.signature)\n+ self.assertEqual(\"This is the doc\", ds.doc_string)\n \n def test_basic_func_without_doc(self):\n \"\"\"Test basic function without docstring.\"\"\"\n@@ -61,14 +61,14 @@ def my_func(some_param, another_param=123):\n st.help(my_func)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"my_func\")\n- self.assertEqual(ds.module, \"help_test\")\n+ self.assertEqual(\"my_func\", ds.name)\n+ self.assertEqual(\"help_test\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n- self.assertEqual(ds.signature, \"(some_param, another_param=123)\")\n- self.assertEqual(ds.doc_string, \"No docs available.\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n+ self.assertEqual(\"(some_param, another_param=123)\", ds.signature)\n+ self.assertEqual(\"No docs available.\", ds.doc_string)\n \n def test_deltagenerator_func(self):\n \"\"\"Test Streamlit DeltaGenerator function.\"\"\"\n@@ -76,14 +76,14 @@ def test_deltagenerator_func(self):\n st.help(st.audio)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"audio\")\n- self.assertEqual(ds.module, \"streamlit\")\n+ self.assertEqual(\"audio\", ds.name)\n+ self.assertEqual(\"streamlit\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n- self.assertEqual(ds.signature, \"(data, format=u'audio/wav')\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n+ self.assertEqual(\"(data, format=u'audio/wav')\", ds.signature)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n- self.assertEqual(ds.signature, \"(data, format='audio/wav')\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n+ self.assertEqual(\"(data, format='audio/wav')\", ds.signature)\n self.assertTrue(ds.doc_string.startswith(\"Display an audio player\"))\n \n def test_unwrapped_deltagenerator_func(self):\n@@ -91,13 +91,13 @@ def test_unwrapped_deltagenerator_func(self):\n st.help(st.dataframe)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"dataframe\")\n- self.assertEqual(ds.module, \"streamlit\")\n+ self.assertEqual(\"dataframe\", ds.name)\n+ self.assertEqual(\"streamlit\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n- self.assertEqual(ds.signature, \"(data=None, width=None, height=None)\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n+ self.assertEqual(\"(data=None, width=None, height=None)\", ds.signature)\n self.assertTrue(ds.doc_string.startswith(\"Display a dataframe\"))\n \n def test_st_cache(self):\n@@ -105,12 +105,12 @@ def test_st_cache(self):\n st.help(st.cache)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"cache\")\n- self.assertEqual(ds.module, \"streamlit\")\n+ self.assertEqual(\"cache\", ds.name)\n+ self.assertEqual(\"streamlit\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n self.assertEqual(\n ds.signature,\n (\"(func=None, persist=False, \" \"ignore_hash=False, show_spinner=True)\"),\n@@ -122,13 +122,13 @@ def test_st_write(self):\n st.help(st.write)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"write\")\n- self.assertEqual(ds.module, \"streamlit\")\n+ self.assertEqual(\"write\", ds.name)\n+ self.assertEqual(\"streamlit\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'function'>\")\n+ self.assertEqual(\"<type 'function'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'function'>\")\n- self.assertEqual(ds.signature, \"(*args, **kwargs)\")\n+ self.assertEqual(\"<class 'function'>\", ds.type)\n+ self.assertEqual(\"(*args, **kwargs)\", ds.signature)\n self.assertTrue(ds.doc_string.startswith(\"Write arguments to the\"))\n \n def test_builtin_func(self):\n@@ -136,14 +136,14 @@ def test_builtin_func(self):\n st.help(dir)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"dir\")\n+ self.assertEqual(\"dir\", ds.name)\n if is_python_2:\n- self.assertEqual(ds.module, \"__builtin__\")\n- self.assertEqual(ds.type, \"<type 'builtin_function_or_method'>\")\n+ self.assertEqual(\"__builtin__\", ds.module)\n+ self.assertEqual(\"<type 'builtin_function_or_method'>\", ds.type)\n else:\n- self.assertEqual(ds.module, \"builtins\")\n- self.assertEqual(ds.type, \"<class 'builtin_function_or_method'>\")\n- self.assertEqual(ds.signature, \"\")\n+ self.assertEqual(\"builtins\", ds.module)\n+ self.assertEqual(\"<class 'builtin_function_or_method'>\", ds.type)\n+ self.assertEqual(\"\", ds.signature)\n self.assertTrue(len(ds.doc_string) > 0)\n \n def test_builtin_obj(self):\n@@ -151,13 +151,13 @@ def test_builtin_obj(self):\n st.help(123)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"\")\n- self.assertEqual(ds.module, \"\")\n+ self.assertEqual(\"\", ds.name)\n+ self.assertEqual(\"\", ds.module)\n if is_python_2:\n- self.assertEqual(ds.type, \"<type 'int'>\")\n+ self.assertEqual(\"<type 'int'>\", ds.type)\n else:\n- self.assertEqual(ds.type, \"<class 'int'>\")\n- self.assertEqual(ds.signature, \"\")\n+ self.assertEqual(\"<class 'int'>\", ds.type)\n+ self.assertEqual(\"\", ds.signature)\n self.assertTrue(len(ds.doc_string) > 0)\n \n def test_doc_defined_for_type(self):\n@@ -171,7 +171,7 @@ def test_doc_defined_for_type(self):\n st.help(array)\n \n ds = self.get_delta_from_queue().new_element.doc_string\n- self.assertEqual(ds.name, \"\")\n+ self.assertEqual(\"\", ds.name)\n self.assertTrue(\"ndarray\" in ds.doc_string)\n \n def test_doc_type_is_type(self):\n@@ -185,6 +185,6 @@ class MyClass(object):\n \n ds = self.get_delta_from_queue().new_element.doc_string\n self.assertEqual(type(MyClass), type)\n- self.assertEqual(ds.name, \"MyClass\")\n- self.assertEqual(ds.module, \"help_test\")\n- self.assertEqual(ds.doc_string, \"No docs available.\")\n+ self.assertEqual(\"MyClass\", ds.name)\n+ self.assertEqual(\"help_test\", ds.module)\n+ self.assertEqual(\"No docs available.\", ds.doc_string)\ndiff --git a/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py b/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py\nindex 9f35772a5298..28a7f9f793f2 100644\n--- a/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py\n+++ b/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py\n@@ -24,20 +24,42 @@\n from streamlit import config\n from streamlit.Report import Report\n from streamlit.watcher import LocalSourcesWatcher\n+from streamlit.watcher.LocalSourcesWatcher import _file_is_in_folder\n \n \n class FileIsInFolderTest(unittest.TestCase):\n def test_file_in_folder(self):\n+ # Test with and without trailing slash\n ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"/a/b/c/\")\n self.assertTrue(ret)\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"/a/b/c\")\n+ self.assertTrue(ret)\n \n def test_file_not_in_folder(self):\n+ # Test with and without trailing slash\n ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"/d/e/f/\")\n self.assertFalse(ret)\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"/d/e/f\")\n+ self.assertFalse(ret)\n \n def test_rel_file_not_in_folder(self):\n+ # Test with and without trailing slash\n ret = LocalSourcesWatcher._file_is_in_folder(\"foo.py\", \"/d/e/f/\")\n self.assertFalse(ret)\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"foo.py\", \"/d/e/f\")\n+ self.assertFalse(ret)\n+\n+ def test_file_in_folder_glob(self):\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"**/c\")\n+ self.assertTrue(ret)\n+\n+ def test_file_not_in_folder_glob(self):\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"/a/b/c/foo.py\", \"**/f\")\n+ self.assertFalse(ret)\n+\n+ def test_rel_file_not_in_folder_glob(self):\n+ ret = LocalSourcesWatcher._file_is_in_folder(\"foo.py\", \"**/f\")\n+ self.assertFalse(ret)\n \n \n if sys.version_info[0] == 2:\n@@ -51,7 +73,7 @@ def test_rel_file_not_in_folder(self):\n \n REPORT_PATH = os.path.join(os.path.dirname(__file__), \"test_data/not_a_real_script.py\")\n REPORT = Report(REPORT_PATH, [])\n-CALLBACK = lambda x: x\n+NOOP_CALLBACK = lambda x: x\n \n DUMMY_MODULE_1_FILE = os.path.abspath(DUMMY_MODULE_1.__file__)\n DUMMY_MODULE_2_FILE = os.path.abspath(DUMMY_MODULE_2.__file__)\n@@ -76,7 +98,7 @@ def setUp(self):\n \n @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n def test_just_script(self, fob):\n- lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK)\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n \n fob.assert_called_once()\n args = fob.call_args.args\n@@ -94,7 +116,7 @@ def test_just_script(self, fob):\n \n @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n def test_script_and_2_modules_at_once(self, fob):\n- lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK)\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n \n fob.assert_called_once()\n \n@@ -126,7 +148,7 @@ def test_script_and_2_modules_at_once(self, fob):\n \n @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n def test_script_and_2_modules_in_series(self, fob):\n- lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK)\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n \n fob.assert_called_once()\n \n@@ -160,7 +182,7 @@ def test_script_and_2_modules_in_series(self, fob):\n \n @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n def test_misbehaved_module(self, fob):\n- lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK)\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n \n fob.assert_called_once()\n \n@@ -171,14 +193,15 @@ def test_misbehaved_module(self, fob):\n fob.assert_called_once() # Just __init__.py\n \n @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n- def test_blacklist(self, fob):\n+ def test_config_blacklist(self, fob):\n+ \"\"\"Test server.folderWatchBlacklist\"\"\"\n prev_blacklist = config.get_option(\"server.folderWatchBlacklist\")\n \n config.set_option(\n \"server.folderWatchBlacklist\", [os.path.dirname(DUMMY_MODULE_1.__file__)]\n )\n \n- lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK)\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n \n fob.assert_called_once()\n \n@@ -192,6 +215,35 @@ def test_blacklist(self, fob):\n # Reset the config object.\n config.set_option(\"server.folderWatchBlacklist\", prev_blacklist)\n \n+ @patch(\"streamlit.watcher.LocalSourcesWatcher.FileWatcher\")\n+ def test_auto_blacklist(self, _):\n+ prev_blacklist = config.get_option(\"server.folderWatchBlacklist\")\n+ config.set_option(\"server.folderWatchBlacklist\", [])\n+\n+ lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK)\n+\n+ def is_blacklisted(filepath):\n+ return any(\n+ _file_is_in_folder(filepath, blacklisted_folder)\n+ for blacklisted_folder in lso._folder_blacklist\n+ )\n+\n+ # miniconda, anaconda, and .*/ folders should be blacklisted\n+ self.assertTrue(is_blacklisted(\"/foo/miniconda2/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/miniconda3/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/anaconda2/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/anaconda3/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/.virtualenv/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/.venv/script.py\"))\n+ self.assertTrue(is_blacklisted(\"/foo/.random_hidden_folder/script.py\"))\n+\n+ # Ensure we're not accidentally blacklisting things we shouldn't be\n+ self.assertFalse(is_blacklisted(\"/foo/not_blacklisted/script.py\"))\n+ self.assertFalse(is_blacklisted(\"/foo/not_blacklisted/.hidden_script.py\"))\n+\n+ # Reset the config object.\n+ config.set_option(\"server.folderWatchBlacklist\", prev_blacklist)\n+\n \n def sort_args_list(args_list):\n return sorted(args_list, key=lambda args: args[0])\n" }
[ { "diff_hunk": "@@ -61,6 +66,11 @@ def __init__(self, report, on_file_changed):\n \n self._folder_blacklist = config.get_option(\"server.folderWatchBlacklist\")\n \n+ # Blacklist some additional folders, using glob syntax.\n+ self._folder_blacklist.extend(\n+ [\"**/.*\", \"**/anaconda2\", \"**/anaconda3\", \"**/miniconda2\", \"**/miniconda3\"]", "line": null, "original_line": 71, "original_start_line": null, "path": "lib/streamlit/watcher/LocalSourcesWatcher.py", "start_line": null, "text": "@user1:\nMove this list to a top-level constant, `DEFAULT_FOLDER_BLACKLIST`\n\n@user1:\nOk, since we're releasing tomorrow I just fixed this :)" } ]
2bfd39840b560e4061b71186ef2b12aab75cfb21
diff --git a/lib/streamlit/watcher/LocalSourcesWatcher.py b/lib/streamlit/watcher/LocalSourcesWatcher.py index 33080a28d7cf..0d391626e051 100644 --- a/lib/streamlit/watcher/LocalSourcesWatcher.py +++ b/lib/streamlit/watcher/LocalSourcesWatcher.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import fnmatch import os import sys import collections @@ -28,6 +29,7 @@ from streamlit import util from streamlit.logger import get_logger + LOGGER = get_logger(__name__) try: @@ -39,15 +41,24 @@ # Fallback that doesn't use watchdog. from streamlit.watcher.PollingFileWatcher import PollingFileWatcher as FileWatcher - if not config.get_option('global.disableWatchdogWarning'): - msg = '\n $ xcode-select --install' if util.is_darwin() else '' + if not config.get_option("global.disableWatchdogWarning"): + msg = "\n $ xcode-select --install" if util.is_darwin() else "" - LOGGER.warning(""" + LOGGER.warning( + """ For better performance, install the Watchdog module: %s $ pip install watchdog - """ % msg) + """ + % msg + ) + + +# Streamlit never watches files in the folders below. +DEFAULT_FOLDER_BLACKLIST = [ + "**/.*", "**/anaconda2", "**/anaconda3", "**/miniconda2", "**/miniconda3" +] WatchedModule = collections.namedtuple("WatchedModule", ["watcher", "module_name"]) @@ -61,6 +72,9 @@ def __init__(self, report, on_file_changed): self._folder_blacklist = config.get_option("server.folderWatchBlacklist") + # Blacklist some additional folders, using glob syntax. + self._folder_blacklist.extend(DEFAULT_FOLDER_BLACKLIST) + # A dict of filepath -> WatchedModule. self._watched_modules = {} @@ -140,12 +154,12 @@ def update_watched_modules(self): # .origin is 'built-in'. continue - folder_is_blacklisted = any( + is_in_blacklisted_folder = any( _file_is_in_folder(filepath, blacklisted_folder) for blacklisted_folder in self._folder_blacklist ) - if folder_is_blacklisted: + if is_in_blacklisted_folder: continue file_is_new = filepath not in self._watched_modules @@ -175,7 +189,10 @@ def update_watched_modules(self): self._deregister_watcher(filepath) -def _file_is_in_folder(filepath, folderpath): - # Assumes filepath is an absolute path, as a teeny tiny optimization. - folderpath = os.path.abspath(folderpath) + "/" - return filepath.startswith(folderpath) +def _file_is_in_folder(filepath, folderpath_glob): + # Strip trailing slash if it exists + if folderpath_glob.endswith("/"): + folderpath_glob = folderpath_glob[:-1] + + file_dir = os.path.dirname(filepath) + return fnmatch.fnmatch(file_dir, folderpath_glob) diff --git a/lib/tests/streamlit/delta_generator_test.py b/lib/tests/streamlit/delta_generator_test.py index f3c53593f820..f17b426b99b0 100644 --- a/lib/tests/streamlit/delta_generator_test.py +++ b/lib/tests/streamlit/delta_generator_test.py @@ -150,9 +150,9 @@ def test_wraps_with_cleaned_sig(self): wrapped = wrapped_function.keywords.get("wrapped") # Check meta data. - self.assertEqual(wrapped.__module__, "delta_generator_test") - self.assertEqual(wrapped.__name__, "fake_text") - self.assertEqual(wrapped.__doc__, "Fake text delta generator.") + self.assertEqual("delta_generator_test", wrapped.__module__) + self.assertEqual("fake_text", wrapped.__name__) + self.assertEqual("Fake text delta generator.", wrapped.__doc__) # Verify original signature sig = signature(FakeDeltaGenerator.fake_text) diff --git a/lib/tests/streamlit/help_test.py b/lib/tests/streamlit/help_test.py index 40ca5910d9b1..510c4a94909e 100644 --- a/lib/tests/streamlit/help_test.py +++ b/lib/tests/streamlit/help_test.py @@ -43,14 +43,14 @@ def my_func(some_param, another_param=123): st.help(my_func) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "my_func") - self.assertEqual(ds.module, "help_test") + self.assertEqual("my_func", ds.name) + self.assertEqual("help_test", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") + self.assertEqual("<type 'function'>", ds.type) else: - self.assertEqual(ds.type, "<class 'function'>") - self.assertEqual(ds.signature, "(some_param, another_param=123)") - self.assertEqual(ds.doc_string, "This is the doc") + self.assertEqual("<class 'function'>", ds.type) + self.assertEqual("(some_param, another_param=123)", ds.signature) + self.assertEqual("This is the doc", ds.doc_string) def test_basic_func_without_doc(self): """Test basic function without docstring.""" @@ -61,14 +61,14 @@ def my_func(some_param, another_param=123): st.help(my_func) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "my_func") - self.assertEqual(ds.module, "help_test") + self.assertEqual("my_func", ds.name) + self.assertEqual("help_test", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") + self.assertEqual("<type 'function'>", ds.type) else: - self.assertEqual(ds.type, "<class 'function'>") - self.assertEqual(ds.signature, "(some_param, another_param=123)") - self.assertEqual(ds.doc_string, "No docs available.") + self.assertEqual("<class 'function'>", ds.type) + self.assertEqual("(some_param, another_param=123)", ds.signature) + self.assertEqual("No docs available.", ds.doc_string) def test_deltagenerator_func(self): """Test Streamlit DeltaGenerator function.""" @@ -76,14 +76,14 @@ def test_deltagenerator_func(self): st.help(st.audio) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "audio") - self.assertEqual(ds.module, "streamlit") + self.assertEqual("audio", ds.name) + self.assertEqual("streamlit", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") - self.assertEqual(ds.signature, "(data, format=u'audio/wav')") + self.assertEqual("<type 'function'>", ds.type) + self.assertEqual("(data, format=u'audio/wav')", ds.signature) else: - self.assertEqual(ds.type, "<class 'function'>") - self.assertEqual(ds.signature, "(data, format='audio/wav')") + self.assertEqual("<class 'function'>", ds.type) + self.assertEqual("(data, format='audio/wav')", ds.signature) self.assertTrue(ds.doc_string.startswith("Display an audio player")) def test_unwrapped_deltagenerator_func(self): @@ -91,13 +91,13 @@ def test_unwrapped_deltagenerator_func(self): st.help(st.dataframe) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "dataframe") - self.assertEqual(ds.module, "streamlit") + self.assertEqual("dataframe", ds.name) + self.assertEqual("streamlit", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") + self.assertEqual("<type 'function'>", ds.type) else: - self.assertEqual(ds.type, "<class 'function'>") - self.assertEqual(ds.signature, "(data=None, width=None, height=None)") + self.assertEqual("<class 'function'>", ds.type) + self.assertEqual("(data=None, width=None, height=None)", ds.signature) self.assertTrue(ds.doc_string.startswith("Display a dataframe")) def test_st_cache(self): @@ -105,12 +105,12 @@ def test_st_cache(self): st.help(st.cache) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "cache") - self.assertEqual(ds.module, "streamlit") + self.assertEqual("cache", ds.name) + self.assertEqual("streamlit", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") + self.assertEqual("<type 'function'>", ds.type) else: - self.assertEqual(ds.type, "<class 'function'>") + self.assertEqual("<class 'function'>", ds.type) self.assertEqual( ds.signature, ("(func=None, persist=False, " "ignore_hash=False, show_spinner=True)"), @@ -122,13 +122,13 @@ def test_st_write(self): st.help(st.write) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "write") - self.assertEqual(ds.module, "streamlit") + self.assertEqual("write", ds.name) + self.assertEqual("streamlit", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'function'>") + self.assertEqual("<type 'function'>", ds.type) else: - self.assertEqual(ds.type, "<class 'function'>") - self.assertEqual(ds.signature, "(*args, **kwargs)") + self.assertEqual("<class 'function'>", ds.type) + self.assertEqual("(*args, **kwargs)", ds.signature) self.assertTrue(ds.doc_string.startswith("Write arguments to the")) def test_builtin_func(self): @@ -136,14 +136,14 @@ def test_builtin_func(self): st.help(dir) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "dir") + self.assertEqual("dir", ds.name) if is_python_2: - self.assertEqual(ds.module, "__builtin__") - self.assertEqual(ds.type, "<type 'builtin_function_or_method'>") + self.assertEqual("__builtin__", ds.module) + self.assertEqual("<type 'builtin_function_or_method'>", ds.type) else: - self.assertEqual(ds.module, "builtins") - self.assertEqual(ds.type, "<class 'builtin_function_or_method'>") - self.assertEqual(ds.signature, "") + self.assertEqual("builtins", ds.module) + self.assertEqual("<class 'builtin_function_or_method'>", ds.type) + self.assertEqual("", ds.signature) self.assertTrue(len(ds.doc_string) > 0) def test_builtin_obj(self): @@ -151,13 +151,13 @@ def test_builtin_obj(self): st.help(123) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "") - self.assertEqual(ds.module, "") + self.assertEqual("", ds.name) + self.assertEqual("", ds.module) if is_python_2: - self.assertEqual(ds.type, "<type 'int'>") + self.assertEqual("<type 'int'>", ds.type) else: - self.assertEqual(ds.type, "<class 'int'>") - self.assertEqual(ds.signature, "") + self.assertEqual("<class 'int'>", ds.type) + self.assertEqual("", ds.signature) self.assertTrue(len(ds.doc_string) > 0) def test_doc_defined_for_type(self): @@ -171,7 +171,7 @@ def test_doc_defined_for_type(self): st.help(array) ds = self.get_delta_from_queue().new_element.doc_string - self.assertEqual(ds.name, "") + self.assertEqual("", ds.name) self.assertTrue("ndarray" in ds.doc_string) def test_doc_type_is_type(self): @@ -185,6 +185,6 @@ class MyClass(object): ds = self.get_delta_from_queue().new_element.doc_string self.assertEqual(type(MyClass), type) - self.assertEqual(ds.name, "MyClass") - self.assertEqual(ds.module, "help_test") - self.assertEqual(ds.doc_string, "No docs available.") + self.assertEqual("MyClass", ds.name) + self.assertEqual("help_test", ds.module) + self.assertEqual("No docs available.", ds.doc_string) diff --git a/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py b/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py index 56df9615e115..efbc85a1b58e 100644 --- a/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py +++ b/lib/tests/streamlit/watcher/LocalSourcesWatcher_test.py @@ -24,20 +24,42 @@ from streamlit import config from streamlit.Report import Report from streamlit.watcher import LocalSourcesWatcher +from streamlit.watcher.LocalSourcesWatcher import _file_is_in_folder class FileIsInFolderTest(unittest.TestCase): def test_file_in_folder(self): + # Test with and without trailing slash ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "/a/b/c/") self.assertTrue(ret) + ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "/a/b/c") + self.assertTrue(ret) def test_file_not_in_folder(self): + # Test with and without trailing slash ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "/d/e/f/") self.assertFalse(ret) + ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "/d/e/f") + self.assertFalse(ret) def test_rel_file_not_in_folder(self): + # Test with and without trailing slash ret = LocalSourcesWatcher._file_is_in_folder("foo.py", "/d/e/f/") self.assertFalse(ret) + ret = LocalSourcesWatcher._file_is_in_folder("foo.py", "/d/e/f") + self.assertFalse(ret) + + def test_file_in_folder_glob(self): + ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "**/c") + self.assertTrue(ret) + + def test_file_not_in_folder_glob(self): + ret = LocalSourcesWatcher._file_is_in_folder("/a/b/c/foo.py", "**/f") + self.assertFalse(ret) + + def test_rel_file_not_in_folder_glob(self): + ret = LocalSourcesWatcher._file_is_in_folder("foo.py", "**/f") + self.assertFalse(ret) if sys.version_info[0] == 2: @@ -51,7 +73,7 @@ def test_rel_file_not_in_folder(self): REPORT_PATH = os.path.join(os.path.dirname(__file__), "test_data/not_a_real_script.py") REPORT = Report(REPORT_PATH, "test command line") -CALLBACK = lambda x: x +NOOP_CALLBACK = lambda x: x DUMMY_MODULE_1_FILE = os.path.abspath(DUMMY_MODULE_1.__file__) DUMMY_MODULE_2_FILE = os.path.abspath(DUMMY_MODULE_2.__file__) @@ -76,7 +98,7 @@ def setUp(self): @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") def test_just_script(self, fob): - lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK) + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) fob.assert_called_once() args = fob.call_args.args @@ -94,7 +116,7 @@ def test_just_script(self, fob): @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") def test_script_and_2_modules_at_once(self, fob): - lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK) + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) fob.assert_called_once() @@ -126,7 +148,7 @@ def test_script_and_2_modules_at_once(self, fob): @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") def test_script_and_2_modules_in_series(self, fob): - lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK) + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) fob.assert_called_once() @@ -160,7 +182,7 @@ def test_script_and_2_modules_in_series(self, fob): @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") def test_misbehaved_module(self, fob): - lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK) + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) fob.assert_called_once() @@ -171,14 +193,15 @@ def test_misbehaved_module(self, fob): fob.assert_called_once() # Just __init__.py @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") - def test_blacklist(self, fob): + def test_config_blacklist(self, fob): + """Test server.folderWatchBlacklist""" prev_blacklist = config.get_option("server.folderWatchBlacklist") config.set_option( "server.folderWatchBlacklist", [os.path.dirname(DUMMY_MODULE_1.__file__)] ) - lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, CALLBACK) + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) fob.assert_called_once() @@ -192,6 +215,35 @@ def test_blacklist(self, fob): # Reset the config object. config.set_option("server.folderWatchBlacklist", prev_blacklist) + @patch("streamlit.watcher.LocalSourcesWatcher.FileWatcher") + def test_auto_blacklist(self, _): + prev_blacklist = config.get_option("server.folderWatchBlacklist") + config.set_option("server.folderWatchBlacklist", []) + + lso = LocalSourcesWatcher.LocalSourcesWatcher(REPORT, NOOP_CALLBACK) + + def is_blacklisted(filepath): + return any( + _file_is_in_folder(filepath, blacklisted_folder) + for blacklisted_folder in lso._folder_blacklist + ) + + # miniconda, anaconda, and .*/ folders should be blacklisted + self.assertTrue(is_blacklisted("/foo/miniconda2/script.py")) + self.assertTrue(is_blacklisted("/foo/miniconda3/script.py")) + self.assertTrue(is_blacklisted("/foo/anaconda2/script.py")) + self.assertTrue(is_blacklisted("/foo/anaconda3/script.py")) + self.assertTrue(is_blacklisted("/foo/.virtualenv/script.py")) + self.assertTrue(is_blacklisted("/foo/.venv/script.py")) + self.assertTrue(is_blacklisted("/foo/.random_hidden_folder/script.py")) + + # Ensure we're not accidentally blacklisting things we shouldn't be + self.assertFalse(is_blacklisted("/foo/not_blacklisted/script.py")) + self.assertFalse(is_blacklisted("/foo/not_blacklisted/.hidden_script.py")) + + # Reset the config object. + config.set_option("server.folderWatchBlacklist", prev_blacklist) + def sort_args_list(args_list): return sorted(args_list, key=lambda args: args[0])
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Code Refactoring / Architectural Improvement" }
streamlit__streamlit-621@e264960
streamlit/streamlit
Python
621
Display area/bar/line chart time values in UTC, rather than browser-local time
By default, vega-lite displays time data in the browser's local time zone, regardless of which time zone the data specifies. (See https://vega.github.io/vega-lite/docs/timeunit.html#output for more details.) This leads to unexpected results - see #485 for an example. With this PR, for generated altair charts (`st.area`, `bar`, `line`), we now explicitly set the X and Y axes' scale to `type="utc"` if they contain `datetime.date` values. This is meaningful only for date/time values, but it means that time data will be shown in UTC, rather the user's local time zone. Fixes #485
2019-11-04T23:57:26Z
Passing a date in index to st.line_chart defaults to UTC time zone # Summary This may not be a Streamlit bug - it may be a Python issue where I need to declare a datetime. When I pass a date through as the index to st.line_chart my graph is then shifted 7 hours back in time from my intended time (e.g. I would want to show value 50 on the graph for midnight on 2019-08-10 and it shows as 5pm for 2019-08-09). I think what is happening is somehow it's doing a UTC offset by default. Would like to # Steps to reproduce df = pd.DataFrame({ 'index': [date(2019, 8, 9), date(2019, 8, 10), date(2019, 8, 11), date(2019, 8, 12)], 'numbers': [10, 50, 30, 40] }) df st.line_chart(df.set_index('index')) ## Expected behavior: I would expect it to plot in the date that I passed through but it's converting it for some reason. Also happens when I use pd.to_datetime() on a dataset. ## Actual behavior: Shifts it back in time by 7 hours. ## Is this a regression? No # Debug info Streamlit, version 0.47.4 Python 3.7.2
By default, Vegalite outputs time data in local time: https://vega.github.io/vega-lite/docs/timeunit.html#output We can fix this by setting `scale: {type: "utc"}` in the chart spec: ```python chart = ( getattr(alt.Chart(data), "mark_" + chart_type)() .encode( alt.X("index", title="", scale=alt.Scale(type="utc")), # <-- set "scale" to "utc" alt.Y("value", title=""), alt.Color("variable", title="", type="nominal"), alt.Tooltip(["index", "value", "variable"]), opacity=opacity, ) .interactive() ) ``` (We'd only want to do this for temporal data, obviously.) But I'm not familiar with altair/vega-lite, so I'm not sure if this is the right solution. Maybe `st.line_chart` and friends take a new `datetime_display` param that defaults to "local" but can be set to "utc"?
[ { "body": "# Summary\r\nThis may not be a Streamlit bug - it may be a Python issue where I need to declare a datetime. When I pass a date through as the index to st.line_chart my graph is then shifted 7 hours back in time from my intended time (e.g. I would want to show value 50 on the graph for midnight on 2019-08-10 and it shows as 5pm for 2019-08-09). I think what is happening is somehow it's doing a UTC offset by default. Would like to\r\n\r\n# Steps to reproduce\r\ndf = pd.DataFrame({\r\n 'index': [date(2019, 8, 9), date(2019, 8, 10), date(2019, 8, 11), date(2019, 8, 12)],\r\n 'numbers': [10, 50, 30, 40]\r\n})\r\n\r\ndf\r\n\r\nst.line_chart(df.set_index('index'))\r\n\r\n\r\n\r\n## Expected behavior:\r\nI would expect it to plot in the date that I passed through but it's converting it for some reason. Also happens when I use pd.to_datetime() on a dataset. \r\n\r\n## Actual behavior:\r\nShifts it back in time by 7 hours.\r\n\r\n## Is this a regression?\r\nNo\r\n\r\n# Debug info\r\nStreamlit, version 0.47.4\r\nPython 3.7.2", "number": 485, "title": "Passing a date in index to st.line_chart defaults to UTC time zone" } ]
15967f076ec5a56b6072241b56731d1b0c66ab78
{ "head_commit": "e264960550e47ebc31d75bdf78d32a14eea4f4df", "head_commit_message": "chart_utc_time snapshot tests", "patch_to_review": "diff --git a/e2e/scripts/chart_utc_time.py b/e2e/scripts/chart_utc_time.py\nnew file mode 100644\nindex 000000000000..b024c12c1e20\n--- /dev/null\n+++ b/e2e/scripts/chart_utc_time.py\n@@ -0,0 +1,27 @@\n+from datetime import date\n+\n+import pandas as pd\n+import streamlit as st\n+\n+df = pd.DataFrame(\n+ {\n+ \"index\": [\n+ date(2019, 8, 9),\n+ date(2019, 8, 10),\n+ date(2019, 8, 11),\n+ date(2019, 8, 12),\n+ ],\n+ \"numbers\": [10, 50, 30, 40],\n+ }\n+)\n+\n+df.set_index(\"index\", inplace=True)\n+\n+# st.area/bar/line_chart all use Altair/Vega-Lite under the hood.\n+# By default, Vega-Lite displays time values in the browser's local\n+# time zone. In `altair.generate_chart`, we explicitly set the time\n+# display to UTC, so that our results are consistent. This test verifies\n+# that change!\n+st.area_chart(df)\n+st.bar_chart(df)\n+st.line_chart(df)\ndiff --git a/e2e/specs/chart_utc_time.ts b/e2e/specs/chart_utc_time.ts\nnew file mode 100644\nindex 000000000000..72cf1e003899\n--- /dev/null\n+++ b/e2e/specs/chart_utc_time.ts\n@@ -0,0 +1,35 @@\n+/**\n+ * @license\n+ * Copyright 2018-2019 Streamlit Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+/// <reference types=\"cypress\" />\n+\n+describe(\"st.area, bar, and line charts\", () => {\n+ before(() => {\n+ cy.visit(\"http://localhost:3000/\");\n+\n+ // Make the ribbon decoration line disappear\n+ cy.get(\".decoration\").invoke(\"css\", \"display\", \"none\");\n+ });\n+\n+ it(\"display times in UTC\", () => {\n+ cy.get(\".element-container .stVegaLiteChart\")\n+ .find(\"canvas\")\n+ .each((el, i) => {\n+ return cy.get(el).matchImageSnapshot(`chartUTCTime-${i}`);\n+ });\n+ });\n+});\ndiff --git a/lib/streamlit/elements/altair.py b/lib/streamlit/elements/altair.py\nindex c2534538f67e..ae8b0a79b6c5 100644\n--- a/lib/streamlit/elements/altair.py\n+++ b/lib/streamlit/elements/altair.py\n@@ -34,7 +34,6 @@ def generate_chart(chart_type, data):\n if not isinstance(data, pd.DataFrame):\n data = convert_anything_to_df(data)\n \n- n_cols = len(data.columns)\n data = pd.melt(data.reset_index(), id_vars=[\"index\"])\n \n if chart_type == \"area\":\n@@ -45,8 +44,14 @@ def generate_chart(chart_type, data):\n chart = (\n getattr(alt.Chart(data), \"mark_\" + chart_type)()\n .encode(\n- alt.X(\"index\", title=\"\"),\n- alt.Y(\"value\", title=\"\"),\n+ # Set the X and Y axes' scale to `type=\"utc\"`. This is meaningful\n+ # only for date/time values, but it means that time data will be\n+ # shown in UTC, rather the user's local time zone. (By default,\n+ # vega-lite displays time data in the browser's local time zone,\n+ # regardless of which time zone the data specifies:\n+ # https://vega.github.io/vega-lite/docs/timeunit.html#output).\n+ alt.X(\"index\", title=\"\", scale=alt.Scale(type=\"utc\")),\n+ alt.Y(\"value\", title=\"\", scale=alt.Scale(type=\"utc\")),\n alt.Color(\"variable\", title=\"\", type=\"nominal\"),\n alt.Tooltip([\"index\", \"value\", \"variable\"]),\n opacity=opacity,\n" }
[ { "diff_hunk": "@@ -45,8 +44,14 @@ def generate_chart(chart_type, data):\n chart = (\n getattr(alt.Chart(data), \"mark_\" + chart_type)()\n .encode(\n- alt.X(\"index\", title=\"\"),\n- alt.Y(\"value\", title=\"\"),\n+ # Set the X and Y axes' scale to `type=\"utc\"`. This is meaningful\n+ # only for date/time values, but it means that time data will be\n+ # shown in UTC, rather the user's local time zone. (By default,\n+ # vega-lite displays time data in the browser's local time zone,\n+ # regardless of which time zone the data specifies:\n+ # https://vega.github.io/vega-lite/docs/timeunit.html#output).\n+ alt.X(\"index\", title=\"\", scale=alt.Scale(type=\"utc\")),", "line": null, "original_line": 53, "original_start_line": null, "path": "lib/streamlit/elements/altair.py", "start_line": null, "text": "@author:\nWe should set the X, Y scales only if the data is time-based" } ]
4bf273f15c65f937a8cad07e216422da5fe97363
diff --git a/e2e/scripts/chart_utc_time.py b/e2e/scripts/chart_utc_time.py new file mode 100644 index 000000000000..b024c12c1e20 --- /dev/null +++ b/e2e/scripts/chart_utc_time.py @@ -0,0 +1,27 @@ +from datetime import date + +import pandas as pd +import streamlit as st + +df = pd.DataFrame( + { + "index": [ + date(2019, 8, 9), + date(2019, 8, 10), + date(2019, 8, 11), + date(2019, 8, 12), + ], + "numbers": [10, 50, 30, 40], + } +) + +df.set_index("index", inplace=True) + +# st.area/bar/line_chart all use Altair/Vega-Lite under the hood. +# By default, Vega-Lite displays time values in the browser's local +# time zone. In `altair.generate_chart`, we explicitly set the time +# display to UTC, so that our results are consistent. This test verifies +# that change! +st.area_chart(df) +st.bar_chart(df) +st.line_chart(df) diff --git a/e2e/specs/chart_utc_time.ts b/e2e/specs/chart_utc_time.ts new file mode 100644 index 000000000000..72cf1e003899 --- /dev/null +++ b/e2e/specs/chart_utc_time.ts @@ -0,0 +1,35 @@ +/** + * @license + * Copyright 2018-2019 Streamlit Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/// <reference types="cypress" /> + +describe("st.area, bar, and line charts", () => { + before(() => { + cy.visit("http://localhost:3000/"); + + // Make the ribbon decoration line disappear + cy.get(".decoration").invoke("css", "display", "none"); + }); + + it("display times in UTC", () => { + cy.get(".element-container .stVegaLiteChart") + .find("canvas") + .each((el, i) => { + return cy.get(el).matchImageSnapshot(`chartUTCTime-${i}`); + }); + }); +}); diff --git a/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-0.snap.png b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-0.snap.png new file mode 100644 index 000000000000..86d9497346b9 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-0.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-1.snap.png b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-1.snap.png new file mode 100644 index 000000000000..bf8b02abcf79 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-1.snap.png differ diff --git a/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-2.snap.png b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-2.snap.png new file mode 100644 index 000000000000..b2694edeafb2 Binary files /dev/null and b/frontend/cypress/snapshots/linux/2x/chart_utc_time.ts/chartUTCTime-2.snap.png differ diff --git a/lib/streamlit/elements/altair.py b/lib/streamlit/elements/altair.py index 8ce0f62263ca..f13ce39fcbb7 100644 --- a/lib/streamlit/elements/altair.py +++ b/lib/streamlit/elements/altair.py @@ -18,6 +18,7 @@ # Python 2/3 compatibility from __future__ import absolute_import +from datetime import date from streamlit.elements.data_frame_proto import convert_anything_to_df import streamlit.elements.vega_lite as vega_lite @@ -25,6 +26,29 @@ import pandas as pd +def _is_date_column(df, name): + """True if the column with the given name stores datetime.date values. + + This function just checks the first value in the given column, so + it's meaningful only for columns whose values all share the same type. + + Parameters + ---------- + df : pd.DataFrame + name : str + The column name + + Returns + ------- + bool + + """ + column = df[name] + if column.size == 0: + return False + return isinstance(column[0], date) + + def generate_chart(chart_type, data): if data is None: # Use an empty-ish dict because if we use None the x axis labels rotate @@ -34,12 +58,10 @@ def generate_chart(chart_type, data): if not isinstance(data, pd.DataFrame): data = convert_anything_to_df(data) - n_cols = len(data.columns) - index_name = data.index.name if index_name is None: - index_name = "index" - + index_name = "index" + data = pd.melt(data.reset_index(), id_vars=[index_name]) if chart_type == "area": @@ -47,11 +69,21 @@ def generate_chart(chart_type, data): else: opacity = {"value": 1.0} + # Set the X and Y axes' scale to "utc" if they contain date values. + # This causes time data to be displayed in UTC, rather the user's local + # time zone. (By default, vega-lite displays time data in the browser's + # local time zone, regardless of which time zone the data specifies: + # https://vega.github.io/vega-lite/docs/timeunit.html#output). + x_scale = ( + alt.Scale(type="utc") if _is_date_column(data, index_name) else alt.Undefined + ) + y_scale = alt.Scale(type="utc") if _is_date_column(data, "value") else alt.Undefined + chart = ( getattr(alt.Chart(data), "mark_" + chart_type)() .encode( - alt.X(index_name, title=""), - alt.Y("value", title=""), + alt.X(index_name, title="", scale=x_scale), + alt.Y("value", title="", scale=y_scale), alt.Color("variable", title="", type="nominal"), alt.Tooltip([index_name, "value", "variable"]), opacity=opacity, diff --git a/lib/tests/streamlit/altair_test.py b/lib/tests/streamlit/altair_test.py index eddd092139ed..be4bc0be8f70 100644 --- a/lib/tests/streamlit/altair_test.py +++ b/lib/tests/streamlit/altair_test.py @@ -14,18 +14,24 @@ # limitations under the License. """st.altair_chart unit test.""" +from datetime import date +from functools import reduce import altair as alt import json import pandas as pd +from streamlit.elements import altair from tests import testutil import streamlit as st -df1 = pd.DataFrame([["A", "B", "C", "D"], [28, 55, 43, 91]], index=["a", "b"]).T - -c1 = alt.Chart(df1).mark_bar().encode(x="a", y="b") +def _deep_get(dictionary, *keys): + return reduce( + lambda d, key: d.get(key, None) if isinstance(d, dict) else None, + keys, + dictionary, + ) class AltairTest(testutil.DeltaGeneratorTestCase): @@ -33,6 +39,10 @@ class AltairTest(testutil.DeltaGeneratorTestCase): def test_altair_chart(self): """Test that it can be called with no args.""" + df1 = pd.DataFrame([["A", "B", "C", "D"], [28, 55, 43, 91]], index=["a", "b"]).T + + c1 = alt.Chart(df1).mark_bar().encode(x="a", y="b") + st.altair_chart(c1) c = self.get_delta_from_queue().new_element.vega_lite_chart @@ -51,3 +61,23 @@ def test_altair_chart(self): self.assertEqual(spec_dict["mark"], "bar") self.assertTrue("config" in spec_dict) self.assertTrue("encoding" in spec_dict) + + def test_date_column_utc_scale(self): + """Test that columns with date values have UTC time scale""" + df = pd.DataFrame( + {"index": [date(2019, 8, 9), date(2019, 8, 10)], "numbers": [1, 10]} + ).set_index("index") + + chart = altair.generate_chart("line", df) + st.altair_chart(chart) + c = self.get_delta_from_queue().new_element.vega_lite_chart + spec_dict = json.loads(c.spec) + + # The x axis should have scale="utc", because it uses date values. + x_scale = _deep_get(spec_dict, "encoding", "x", "scale", "type") + self.assertEqual(x_scale, "utc") + + # The y axis should _not_ have scale="utc", because it doesn't + # use date values. + y_scale = _deep_get(spec_dict, "encoding", "y", "scale", "type") + self.assertNotEqual(y_scale, "utc")
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
streamlit__streamlit-118@bafecab
streamlit/streamlit
Python
118
Support hashing dataframes with unhashable objects. Gracefully fโ€ฆ
Fixes #111. Merge #117 first.
2019-09-16T22:16:57Z
Lists inside of DataFrames are unhashable and break st.cache # Summary A `DataFrame` which contains a list is unhashable and therefore breaks `st.cache`. ## Steps to reproduce Run this code ```python import streamlit as st import pandas as pd @st.cache def return_a_list(n): return pd.DataFrame({'n': [n], 'result': [list(range(n))]}) st.text(return_a_list(10)) ``` # Behavior ## Actual behavior We get an error with the following stack trace ``` TypeError: unhashable type: 'list' File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/ScriptRunner.py", line 317, in _run_script exec(code, module.__dict__) File "/Users/adrien/Desktop/streamlit/streamlit-staging/test_list_bug.py", line 9, in <module> st.text(return_a_list(10)) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py", line 412, in wrapped_func key, return_value, persist, ignore_hash, args_mutated) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py", line 307, in _write_to_cache _write_to_mem_cache(key, value, ignore_hash, args_mutated) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py", line 237, in _write_to_mem_cache hash=None if ignore_hash else get_hash(value), File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py", line 89, in get_hash hasher.update(f, context) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py", line 159, in update self._update(self.hasher, obj, context) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py", line 190, in _update b = self.to_bytes(obj, context) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py", line 179, in to_bytes b = self._to_bytes(obj, context) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py", line 230, in _to_bytes return pd.util.hash_pandas_object(obj).sum() File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py", line 115, in hash_pandas_object h = _combine_hash_arrays(hashes, num_items) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py", line 41, in _combine_hash_arrays for i, a in enumerate(arrays): File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py", line 104, in <genexpr> hashes = (hash_array(series.values) for _, series in obj.iteritems()) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py", line 286, in hash_array codes, categories = factorize(vals, sort=False) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/util/_decorators.py", line 178, in wrapper return func(*args, **kwargs) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/algorithms.py", line 630, in factorize na_value=na_value) File "/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/algorithms.py", line 476, in _factorize_array na_value=na_value) File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_labels ``` ## Expected behavior This should work, just as if `return_a_list` weren't decorated with `@st.cache`. ## Is this a regression? Not sure. # Debug info ``` $ streamlit version && python --version && pyenv --version && sw_vers && "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" --version Streamlit, version 0.45.0 Python 3.6.3 pyenv 1.2.3 ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G95 Google Chrome 76.0.3809.132 ```
This issue was first observed here: https://discuss.streamlit.io/t/using-caching-with-api-calls-and-messy-dataframes/75 This issue boils down to ```py pd.util.hash_pandas_object(pd.DataFrame({'a': [[1,2,3]]})) ```
[ { "body": "# Summary\r\n\r\nA `DataFrame` which contains a list is unhashable and therefore breaks `st.cache`.\r\n\r\n## Steps to reproduce\r\n\r\nRun this code\r\n```python\r\nimport streamlit as st\r\nimport pandas as pd\r\n\r\[email protected]\r\ndef return_a_list(n):\r\n return pd.DataFrame({'n': [n], 'result': [list(range(n))]})\r\n\r\n\r\nst.text(return_a_list(10))\r\n```\r\n\r\n# Behavior\r\n\r\n## Actual behavior\r\n\r\nWe get an error with the following stack trace\r\n```\r\nTypeError: unhashable type: 'list'\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/ScriptRunner.py\", line 317, in _run_script exec(code, module.__dict__)\r\nFile \"/Users/adrien/Desktop/streamlit/streamlit-staging/test_list_bug.py\", line 9, in <module> st.text(return_a_list(10))\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py\", line 412, in wrapped_func key, return_value, persist, ignore_hash, args_mutated)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py\", line 307, in _write_to_cache _write_to_mem_cache(key, value, ignore_hash, args_mutated)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/caching.py\", line 237, in _write_to_mem_cache hash=None if ignore_hash else get_hash(value),\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py\", line 89, in get_hash hasher.update(f, context)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py\", line 159, in update self._update(self.hasher, obj, context)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py\", line 190, in _update b = self.to_bytes(obj, context)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py\", line 179, in to_bytes b = self._to_bytes(obj, context)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/streamlit/hashing.py\", line 230, in _to_bytes return pd.util.hash_pandas_object(obj).sum()\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py\", line 115, in hash_pandas_object h = _combine_hash_arrays(hashes, num_items)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py\", line 41, in _combine_hash_arrays for i, a in enumerate(arrays):\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py\", line 104, in <genexpr> hashes = (hash_array(series.values) for _, series in obj.iteritems())\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/util/hashing.py\", line 286, in hash_array codes, categories = factorize(vals, sort=False)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/util/_decorators.py\", line 178, in wrapper return func(*args, **kwargs)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/algorithms.py\", line 630, in factorize na_value=na_value)\r\nFile \"/Users/adrien/.pyenv/versions/3.6.3/envs/streamlit-staging/lib/python3.6/site-packages/pandas/core/algorithms.py\", line 476, in _factorize_array na_value=na_value)\r\nFile \"pandas/_libs/hashtable_class_helper.pxi\", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_labels\r\n```\r\n\r\n## Expected behavior\r\n\r\nThis should work, just as if `return_a_list` weren't decorated with `@st.cache`.\r\n\r\n## Is this a regression?\r\n\r\nNot sure.\r\n\r\n# Debug info\r\n```\r\n$ streamlit version && python --version && pyenv --version && sw_vers && \"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome\" --version\r\nStreamlit, version 0.45.0\r\nPython 3.6.3\r\npyenv 1.2.3\r\nProductName:\tMac OS X\r\nProductVersion:\t10.14.6\r\nBuildVersion:\t18G95\r\nGoogle Chrome 76.0.3809.132 \r\n```\r\n\r\n", "number": 111, "title": "Lists inside of DataFrames are unhashable and break st.cache" } ]
ee4c4fad1e77f1e2205a7fa82b8d3c253ca92823
{ "head_commit": "bafecab21d80ae723216ce0f79eebe170bae23e5", "head_commit_message": "Support hashing dataframes with unhashable objects. Gracefully fail if Streamlit fails to hash.", "patch_to_review": "diff --git a/lib/streamlit/hashing.py b/lib/streamlit/hashing.py\nindex 26a5156905c0..dd3ce26e8550 100644\n--- a/lib/streamlit/hashing.py\n+++ b/lib/streamlit/hashing.py\n@@ -202,121 +202,128 @@ def _to_bytes(self, obj, context):\n Python's built in `hash` does not produce consistent results across\n runs.\"\"\"\n \n- if _is_magicmock(obj):\n- # MagicMock can result in objects that appear to be infinitely\n- # deep, so we don't try to hash them at all.\n- return self.to_bytes(id(obj))\n- elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n- return obj\n- elif isinstance(obj, string_types):\n- return obj.encode()\n- elif isinstance(obj, float):\n- return self.to_bytes(hash(obj))\n- elif isinstance(obj, int):\n- return _int_to_bytes(obj)\n- elif isinstance(obj, list) or isinstance(obj, tuple):\n- h = hashlib.new(self.name)\n- # add type to distingush x from [x]\n- self._update(h, type(obj).__name__.encode() + b\":\")\n- for e in obj:\n- self._update(h, e, context)\n- return h.digest()\n- elif obj is None:\n- # Special string since hashes change between sessions.\n- # We don't use Python's `hash` since hashes are not consistent\n- # across runs.\n- return b\"none:\"\n- elif obj is True:\n- return b\"bool:1\"\n- elif obj is False:\n- return b\"bool:0\"\n- elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n- import pandas as pd\n-\n- if len(obj) >= PANDAS_ROWS_LARGE:\n- obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n- return pd.util.hash_pandas_object(obj).sum()\n- elif util.is_type(obj, \"numpy.ndarray\"):\n- h = hashlib.new(self.name)\n- self._update(h, obj.shape)\n-\n- if obj.size >= NP_SIZE_LARGE:\n- import numpy as np\n-\n- state = np.random.RandomState(0)\n- obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n-\n- self._update(h, obj.tobytes())\n- return h.digest()\n- elif inspect.isbuiltin(obj):\n- return self.to_bytes(obj.__name__)\n- elif hasattr(obj, \"name\") and (\n- isinstance(obj, io.IOBase) or os.path.exists(obj.name)\n- ):\n- # Hash files as name + last modification date + offset.\n- h = hashlib.new(self.name)\n- self._update(h, obj.name)\n- self._update(h, os.path.getmtime(obj.name))\n- self._update(h, obj.tell())\n- return h.digest()\n- elif inspect.isroutine(obj):\n- if hasattr(obj, \"__wrapped__\"):\n- # Ignore the wrapper of wrapped functions.\n- return self.to_bytes(obj.__wrapped__)\n-\n- if obj.__module__.startswith(\"streamlit\"):\n- # Ignore streamlit modules even if they are in the CWD\n- # (e.g. during development).\n- return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n-\n- h = hashlib.new(self.name)\n- # TODO: This may be too restrictive for libraries in development.\n- if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n- context = _get_context(obj)\n- if obj.__defaults__:\n- self._update(h, obj.__defaults__, context)\n- h.update(self._code_to_bytes(obj.__code__, context))\n- else:\n- # Don't hash code that is not in the current working directory.\n- self._update(h, obj.__module__)\n- self._update(h, obj.__name__)\n- return h.digest()\n- elif inspect.iscode(obj):\n- return self._code_to_bytes(obj, context)\n- elif inspect.ismodule(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # so the current warning is quite annoying...\n- # st.warning(('Streamlit does not support hashing modules. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name for internal modules.\n- return self.to_bytes(obj.__name__)\n- elif inspect.isclass(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # (e.g. in every \"except\" statement) so the current warning is\n- # quite annoying...\n- # st.warning(('Streamlit does not support hashing classes. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name of classes.\n- return self.to_bytes(obj.__name__)\n- elif isinstance(obj, functools.partial):\n- # The return value of functools.partial is not a plain function:\n- # it's a callable object that remembers the original function plus\n- # the values you pickled into it. So here we need to special-case it.\n- h = hashlib.new(self.name)\n- self._update(h, obj.args)\n- self._update(h, obj.func)\n- self._update(h, obj.keywords)\n- return h.digest()\n- else:\n- try:\n- # As a last resort, we pickle the object to hash it.\n- return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n- except Exception:\n+ try:\n+ if _is_magicmock(obj):\n+ # MagicMock can result in objects that appear to be infinitely\n+ # deep, so we don't try to hash them at all.\n+ return self.to_bytes(id(obj))\n+ elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n+ return obj\n+ elif isinstance(obj, string_types):\n+ return obj.encode()\n+ elif isinstance(obj, float):\n+ return self.to_bytes(hash(obj))\n+ elif isinstance(obj, int):\n+ return _int_to_bytes(obj)\n+ elif isinstance(obj, list) or isinstance(obj, tuple):\n+ h = hashlib.new(self.name)\n+ # add type to distingush x from [x]\n+ self._update(h, type(obj).__name__.encode() + b\":\")\n+ for e in obj:\n+ self._update(h, e, context)\n+ return h.digest()\n+ elif obj is None:\n+ # Special string since hashes change between sessions.\n+ # We don't use Python's `hash` since hashes are not consistent\n+ # across runs.\n+ return b\"none:\"\n+ elif obj is True:\n+ return b\"bool:1\"\n+ elif obj is False:\n+ return b\"bool:0\"\n+ elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n+ import pandas as pd\n+\n+ if len(obj) >= PANDAS_ROWS_LARGE:\n+ obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n+ try:\n+ return pd.util.hash_pandas_object(obj).sum()\n+ except TypeError:\n+ # Use pickle if pandas cannot hash the object for example if\n+ # it contains unhashable objects.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ elif util.is_type(obj, \"numpy.ndarray\"):\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.shape)\n+\n+ if obj.size >= NP_SIZE_LARGE:\n+ import numpy as np\n+\n+ state = np.random.RandomState(0)\n+ obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n+\n+ self._update(h, obj.tobytes())\n+ return h.digest()\n+ elif inspect.isbuiltin(obj):\n+ return self.to_bytes(obj.__name__)\n+ elif hasattr(obj, \"name\") and (\n+ isinstance(obj, io.IOBase) or\n+ (isinstance(obj.name, string_types) and os.path.exists(obj.name))\n+ ):\n+ # Hash files as name + last modification date + offset.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.name)\n+ self._update(h, os.path.getmtime(obj.name))\n+ self._update(h, obj.tell())\n+ return h.digest()\n+ elif inspect.isroutine(obj):\n+ if hasattr(obj, \"__wrapped__\"):\n+ # Ignore the wrapper of wrapped functions.\n+ return self.to_bytes(obj.__wrapped__)\n+\n+ if obj.__module__.startswith(\"streamlit\"):\n+ # Ignore streamlit modules even if they are in the CWD\n+ # (e.g. during development).\n+ return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n+\n+ h = hashlib.new(self.name)\n+ # TODO: This may be too restrictive for libraries in development.\n+ if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n+ context = _get_context(obj)\n+ if obj.__defaults__:\n+ self._update(h, obj.__defaults__, context)\n+ h.update(self._code_to_bytes(obj.__code__, context))\n+ else:\n+ # Don't hash code that is not in the current working directory.\n+ self._update(h, obj.__module__)\n+ self._update(h, obj.__name__)\n+ return h.digest()\n+ elif inspect.iscode(obj):\n+ return self._code_to_bytes(obj, context)\n+ elif inspect.ismodule(obj):\n+ # TODO: Figure out how to best show this kind of warning to the\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # so the current warning is quite annoying...\n+ # st.warning(('Streamlit does not support hashing modules. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name for internal modules.\n+ return self.to_bytes(obj.__name__)\n+ elif inspect.isclass(obj):\n # TODO: Figure out how to best show this kind of warning to the\n- # user.\n- st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # (e.g. in every \"except\" statement) so the current warning is\n+ # quite annoying...\n+ # st.warning(('Streamlit does not support hashing classes. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name of classes.\n+ return self.to_bytes(obj.__name__)\n+ elif isinstance(obj, functools.partial):\n+ # The return value of functools.partial is not a plain function:\n+ # it's a callable object that remembers the original function plus\n+ # the values you pickled into it. So here we need to special-case it.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.args)\n+ self._update(h, obj.func)\n+ self._update(h, obj.keywords)\n+ return h.digest()\n+ else:\n+ try:\n+ # As a last resort, we pickle the object to hash it.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ except:\n+ st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ except:\n+ st.warning(\"Streamlit failed to hash an object of type %s.\" % type(obj))\n \n def _code_to_bytes(self, code, context):\n h = hashlib.new(self.name)\n" }
[ { "diff_hunk": "@@ -202,121 +202,128 @@ def _to_bytes(self, obj, context):\n Python's built in `hash` does not produce consistent results across\n runs.\"\"\"\n \n- if _is_magicmock(obj):\n- # MagicMock can result in objects that appear to be infinitely\n- # deep, so we don't try to hash them at all.\n- return self.to_bytes(id(obj))\n- elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n- return obj\n- elif isinstance(obj, string_types):\n- return obj.encode()\n- elif isinstance(obj, float):\n- return self.to_bytes(hash(obj))\n- elif isinstance(obj, int):\n- return _int_to_bytes(obj)\n- elif isinstance(obj, list) or isinstance(obj, tuple):\n- h = hashlib.new(self.name)\n- # add type to distingush x from [x]\n- self._update(h, type(obj).__name__.encode() + b\":\")\n- for e in obj:\n- self._update(h, e, context)\n- return h.digest()\n- elif obj is None:\n- # Special string since hashes change between sessions.\n- # We don't use Python's `hash` since hashes are not consistent\n- # across runs.\n- return b\"none:\"\n- elif obj is True:\n- return b\"bool:1\"\n- elif obj is False:\n- return b\"bool:0\"\n- elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n- import pandas as pd\n-\n- if len(obj) >= PANDAS_ROWS_LARGE:\n- obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n- return pd.util.hash_pandas_object(obj).sum()\n- elif util.is_type(obj, \"numpy.ndarray\"):\n- h = hashlib.new(self.name)\n- self._update(h, obj.shape)\n-\n- if obj.size >= NP_SIZE_LARGE:\n- import numpy as np\n-\n- state = np.random.RandomState(0)\n- obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n-\n- self._update(h, obj.tobytes())\n- return h.digest()\n- elif inspect.isbuiltin(obj):\n- return self.to_bytes(obj.__name__)\n- elif hasattr(obj, \"name\") and (\n- isinstance(obj, io.IOBase) or os.path.exists(obj.name)\n- ):\n- # Hash files as name + last modification date + offset.\n- h = hashlib.new(self.name)\n- self._update(h, obj.name)\n- self._update(h, os.path.getmtime(obj.name))\n- self._update(h, obj.tell())\n- return h.digest()\n- elif inspect.isroutine(obj):\n- if hasattr(obj, \"__wrapped__\"):\n- # Ignore the wrapper of wrapped functions.\n- return self.to_bytes(obj.__wrapped__)\n-\n- if obj.__module__.startswith(\"streamlit\"):\n- # Ignore streamlit modules even if they are in the CWD\n- # (e.g. during development).\n- return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n-\n- h = hashlib.new(self.name)\n- # TODO: This may be too restrictive for libraries in development.\n- if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n- context = _get_context(obj)\n- if obj.__defaults__:\n- self._update(h, obj.__defaults__, context)\n- h.update(self._code_to_bytes(obj.__code__, context))\n- else:\n- # Don't hash code that is not in the current working directory.\n- self._update(h, obj.__module__)\n- self._update(h, obj.__name__)\n- return h.digest()\n- elif inspect.iscode(obj):\n- return self._code_to_bytes(obj, context)\n- elif inspect.ismodule(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # so the current warning is quite annoying...\n- # st.warning(('Streamlit does not support hashing modules. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name for internal modules.\n- return self.to_bytes(obj.__name__)\n- elif inspect.isclass(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # (e.g. in every \"except\" statement) so the current warning is\n- # quite annoying...\n- # st.warning(('Streamlit does not support hashing classes. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name of classes.\n- return self.to_bytes(obj.__name__)\n- elif isinstance(obj, functools.partial):\n- # The return value of functools.partial is not a plain function:\n- # it's a callable object that remembers the original function plus\n- # the values you pickled into it. So here we need to special-case it.\n- h = hashlib.new(self.name)\n- self._update(h, obj.args)\n- self._update(h, obj.func)\n- self._update(h, obj.keywords)\n- return h.digest()\n- else:\n- try:\n- # As a last resort, we pickle the object to hash it.\n- return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n- except Exception:\n+ try:\n+ if _is_magicmock(obj):\n+ # MagicMock can result in objects that appear to be infinitely\n+ # deep, so we don't try to hash them at all.\n+ return self.to_bytes(id(obj))\n+ elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n+ return obj\n+ elif isinstance(obj, string_types):\n+ return obj.encode()\n+ elif isinstance(obj, float):\n+ return self.to_bytes(hash(obj))\n+ elif isinstance(obj, int):\n+ return _int_to_bytes(obj)\n+ elif isinstance(obj, list) or isinstance(obj, tuple):\n+ h = hashlib.new(self.name)\n+ # add type to distingush x from [x]\n+ self._update(h, type(obj).__name__.encode() + b\":\")\n+ for e in obj:\n+ self._update(h, e, context)\n+ return h.digest()\n+ elif obj is None:\n+ # Special string since hashes change between sessions.\n+ # We don't use Python's `hash` since hashes are not consistent\n+ # across runs.\n+ return b\"none:\"\n+ elif obj is True:\n+ return b\"bool:1\"\n+ elif obj is False:\n+ return b\"bool:0\"\n+ elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n+ import pandas as pd\n+\n+ if len(obj) >= PANDAS_ROWS_LARGE:\n+ obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n+ try:\n+ return pd.util.hash_pandas_object(obj).sum()\n+ except TypeError:\n+ # Use pickle if pandas cannot hash the object for example if\n+ # it contains unhashable objects.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ elif util.is_type(obj, \"numpy.ndarray\"):\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.shape)\n+\n+ if obj.size >= NP_SIZE_LARGE:\n+ import numpy as np\n+\n+ state = np.random.RandomState(0)\n+ obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n+\n+ self._update(h, obj.tobytes())\n+ return h.digest()\n+ elif inspect.isbuiltin(obj):\n+ return self.to_bytes(obj.__name__)\n+ elif hasattr(obj, \"name\") and (\n+ isinstance(obj, io.IOBase) or\n+ (isinstance(obj.name, string_types) and os.path.exists(obj.name))\n+ ):\n+ # Hash files as name + last modification date + offset.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.name)\n+ self._update(h, os.path.getmtime(obj.name))\n+ self._update(h, obj.tell())\n+ return h.digest()\n+ elif inspect.isroutine(obj):\n+ if hasattr(obj, \"__wrapped__\"):\n+ # Ignore the wrapper of wrapped functions.\n+ return self.to_bytes(obj.__wrapped__)\n+\n+ if obj.__module__.startswith(\"streamlit\"):\n+ # Ignore streamlit modules even if they are in the CWD\n+ # (e.g. during development).\n+ return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n+\n+ h = hashlib.new(self.name)\n+ # TODO: This may be too restrictive for libraries in development.\n+ if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n+ context = _get_context(obj)\n+ if obj.__defaults__:\n+ self._update(h, obj.__defaults__, context)\n+ h.update(self._code_to_bytes(obj.__code__, context))\n+ else:\n+ # Don't hash code that is not in the current working directory.\n+ self._update(h, obj.__module__)\n+ self._update(h, obj.__name__)\n+ return h.digest()\n+ elif inspect.iscode(obj):\n+ return self._code_to_bytes(obj, context)\n+ elif inspect.ismodule(obj):\n+ # TODO: Figure out how to best show this kind of warning to the\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # so the current warning is quite annoying...\n+ # st.warning(('Streamlit does not support hashing modules. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name for internal modules.\n+ return self.to_bytes(obj.__name__)\n+ elif inspect.isclass(obj):\n # TODO: Figure out how to best show this kind of warning to the\n- # user.\n- st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # (e.g. in every \"except\" statement) so the current warning is\n+ # quite annoying...\n+ # st.warning(('Streamlit does not support hashing classes. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name of classes.\n+ return self.to_bytes(obj.__name__)\n+ elif isinstance(obj, functools.partial):\n+ # The return value of functools.partial is not a plain function:\n+ # it's a callable object that remembers the original function plus\n+ # the values you pickled into it. So here we need to special-case it.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.args)\n+ self._update(h, obj.func)\n+ self._update(h, obj.keywords)\n+ return h.digest()\n+ else:\n+ try:\n+ # As a last resort, we pickle the object to hash it.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ except:\n+ st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))", "line": null, "original_line": 324, "original_start_line": null, "path": "lib/streamlit/hashing.py", "start_line": null, "text": "@user1:\nAlso: can we make this warning (and the one below) mention `st.cache` somewhere?\r\n\r\nFor example:\r\n\r\n> Streamlit cannot hash objects of type %s.\r\n> \r\n> **More information:** to prevent unexpected behavior, Streamlit tries to detect mutations in cached objects so it can alert the user if needed. However, this check is not supported for a small class of objects, including %s.\r\n>\r\n> Please file a bug [here](https://github.com/streamlit/streamlit/issues/new/choose).\r\n>\r\n> To stop this warning from showing in the meantime, try one of the following:\r\n> * **Preferred:** modify your code to avoid using this type of object.\r\n> * Or add the argument `ignore_cache=True` to the `st.cache` decorator.\n\n@author:\nDone" }, { "diff_hunk": "@@ -202,121 +202,128 @@ def _to_bytes(self, obj, context):\n Python's built in `hash` does not produce consistent results across\n runs.\"\"\"\n \n- if _is_magicmock(obj):\n- # MagicMock can result in objects that appear to be infinitely\n- # deep, so we don't try to hash them at all.\n- return self.to_bytes(id(obj))\n- elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n- return obj\n- elif isinstance(obj, string_types):\n- return obj.encode()\n- elif isinstance(obj, float):\n- return self.to_bytes(hash(obj))\n- elif isinstance(obj, int):\n- return _int_to_bytes(obj)\n- elif isinstance(obj, list) or isinstance(obj, tuple):\n- h = hashlib.new(self.name)\n- # add type to distingush x from [x]\n- self._update(h, type(obj).__name__.encode() + b\":\")\n- for e in obj:\n- self._update(h, e, context)\n- return h.digest()\n- elif obj is None:\n- # Special string since hashes change between sessions.\n- # We don't use Python's `hash` since hashes are not consistent\n- # across runs.\n- return b\"none:\"\n- elif obj is True:\n- return b\"bool:1\"\n- elif obj is False:\n- return b\"bool:0\"\n- elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n- import pandas as pd\n-\n- if len(obj) >= PANDAS_ROWS_LARGE:\n- obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n- return pd.util.hash_pandas_object(obj).sum()\n- elif util.is_type(obj, \"numpy.ndarray\"):\n- h = hashlib.new(self.name)\n- self._update(h, obj.shape)\n-\n- if obj.size >= NP_SIZE_LARGE:\n- import numpy as np\n-\n- state = np.random.RandomState(0)\n- obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n-\n- self._update(h, obj.tobytes())\n- return h.digest()\n- elif inspect.isbuiltin(obj):\n- return self.to_bytes(obj.__name__)\n- elif hasattr(obj, \"name\") and (\n- isinstance(obj, io.IOBase) or os.path.exists(obj.name)\n- ):\n- # Hash files as name + last modification date + offset.\n- h = hashlib.new(self.name)\n- self._update(h, obj.name)\n- self._update(h, os.path.getmtime(obj.name))\n- self._update(h, obj.tell())\n- return h.digest()\n- elif inspect.isroutine(obj):\n- if hasattr(obj, \"__wrapped__\"):\n- # Ignore the wrapper of wrapped functions.\n- return self.to_bytes(obj.__wrapped__)\n-\n- if obj.__module__.startswith(\"streamlit\"):\n- # Ignore streamlit modules even if they are in the CWD\n- # (e.g. during development).\n- return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n-\n- h = hashlib.new(self.name)\n- # TODO: This may be too restrictive for libraries in development.\n- if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n- context = _get_context(obj)\n- if obj.__defaults__:\n- self._update(h, obj.__defaults__, context)\n- h.update(self._code_to_bytes(obj.__code__, context))\n- else:\n- # Don't hash code that is not in the current working directory.\n- self._update(h, obj.__module__)\n- self._update(h, obj.__name__)\n- return h.digest()\n- elif inspect.iscode(obj):\n- return self._code_to_bytes(obj, context)\n- elif inspect.ismodule(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # so the current warning is quite annoying...\n- # st.warning(('Streamlit does not support hashing modules. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name for internal modules.\n- return self.to_bytes(obj.__name__)\n- elif inspect.isclass(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # (e.g. in every \"except\" statement) so the current warning is\n- # quite annoying...\n- # st.warning(('Streamlit does not support hashing classes. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name of classes.\n- return self.to_bytes(obj.__name__)\n- elif isinstance(obj, functools.partial):\n- # The return value of functools.partial is not a plain function:\n- # it's a callable object that remembers the original function plus\n- # the values you pickled into it. So here we need to special-case it.\n- h = hashlib.new(self.name)\n- self._update(h, obj.args)\n- self._update(h, obj.func)\n- self._update(h, obj.keywords)\n- return h.digest()\n- else:\n- try:\n- # As a last resort, we pickle the object to hash it.\n- return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n- except Exception:\n+ try:\n+ if _is_magicmock(obj):\n+ # MagicMock can result in objects that appear to be infinitely\n+ # deep, so we don't try to hash them at all.\n+ return self.to_bytes(id(obj))\n+ elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n+ return obj\n+ elif isinstance(obj, string_types):\n+ return obj.encode()\n+ elif isinstance(obj, float):\n+ return self.to_bytes(hash(obj))\n+ elif isinstance(obj, int):\n+ return _int_to_bytes(obj)\n+ elif isinstance(obj, list) or isinstance(obj, tuple):\n+ h = hashlib.new(self.name)\n+ # add type to distingush x from [x]\n+ self._update(h, type(obj).__name__.encode() + b\":\")\n+ for e in obj:\n+ self._update(h, e, context)\n+ return h.digest()\n+ elif obj is None:\n+ # Special string since hashes change between sessions.\n+ # We don't use Python's `hash` since hashes are not consistent\n+ # across runs.\n+ return b\"none:\"\n+ elif obj is True:\n+ return b\"bool:1\"\n+ elif obj is False:\n+ return b\"bool:0\"\n+ elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n+ import pandas as pd\n+\n+ if len(obj) >= PANDAS_ROWS_LARGE:\n+ obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n+ try:\n+ return pd.util.hash_pandas_object(obj).sum()\n+ except TypeError:\n+ # Use pickle if pandas cannot hash the object for example if\n+ # it contains unhashable objects.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ elif util.is_type(obj, \"numpy.ndarray\"):\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.shape)\n+\n+ if obj.size >= NP_SIZE_LARGE:\n+ import numpy as np\n+\n+ state = np.random.RandomState(0)\n+ obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n+\n+ self._update(h, obj.tobytes())\n+ return h.digest()\n+ elif inspect.isbuiltin(obj):\n+ return self.to_bytes(obj.__name__)\n+ elif hasattr(obj, \"name\") and (\n+ isinstance(obj, io.IOBase) or\n+ (isinstance(obj.name, string_types) and os.path.exists(obj.name))\n+ ):\n+ # Hash files as name + last modification date + offset.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.name)\n+ self._update(h, os.path.getmtime(obj.name))\n+ self._update(h, obj.tell())\n+ return h.digest()\n+ elif inspect.isroutine(obj):\n+ if hasattr(obj, \"__wrapped__\"):\n+ # Ignore the wrapper of wrapped functions.\n+ return self.to_bytes(obj.__wrapped__)\n+\n+ if obj.__module__.startswith(\"streamlit\"):\n+ # Ignore streamlit modules even if they are in the CWD\n+ # (e.g. during development).\n+ return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n+\n+ h = hashlib.new(self.name)\n+ # TODO: This may be too restrictive for libraries in development.\n+ if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n+ context = _get_context(obj)\n+ if obj.__defaults__:\n+ self._update(h, obj.__defaults__, context)\n+ h.update(self._code_to_bytes(obj.__code__, context))\n+ else:\n+ # Don't hash code that is not in the current working directory.\n+ self._update(h, obj.__module__)\n+ self._update(h, obj.__name__)\n+ return h.digest()\n+ elif inspect.iscode(obj):\n+ return self._code_to_bytes(obj, context)\n+ elif inspect.ismodule(obj):\n+ # TODO: Figure out how to best show this kind of warning to the\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # so the current warning is quite annoying...\n+ # st.warning(('Streamlit does not support hashing modules. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name for internal modules.\n+ return self.to_bytes(obj.__name__)\n+ elif inspect.isclass(obj):\n # TODO: Figure out how to best show this kind of warning to the\n- # user.\n- st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # (e.g. in every \"except\" statement) so the current warning is\n+ # quite annoying...\n+ # st.warning(('Streamlit does not support hashing classes. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name of classes.\n+ return self.to_bytes(obj.__name__)\n+ elif isinstance(obj, functools.partial):\n+ # The return value of functools.partial is not a plain function:\n+ # it's a callable object that remembers the original function plus\n+ # the values you pickled into it. So here we need to special-case it.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.args)\n+ self._update(h, obj.func)\n+ self._update(h, obj.keywords)\n+ return h.digest()\n+ else:\n+ try:\n+ # As a last resort, we pickle the object to hash it.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ except:\n+ st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ except:\n+ st.warning(\"Streamlit failed to hash an object of type %s.\" % type(obj))", "line": null, "original_line": 326, "original_start_line": null, "path": "lib/streamlit/hashing.py", "start_line": null, "text": "@user1:\nSame as above:\r\n\r\n> Error while hashing object of type %s.\r\n> \r\n> **More information:** to prevent unexpected behavior, Streamlit tries to detect mutations in cached objects so it can alert the user if needed. However, something went wrong while performing this check.\r\n>\r\n> Please file a bug [here](https://github.com/streamlit/streamlit/issues/new/choose).\r\n>\r\n> To stop this warning from showing in the meantime, try one of the following:\r\n> * **Preferred:** modify your code to avoid using this type of object.\r\n> * Or add the argument `ignore_cache=True` to the `st.cache` decorator.\n\n@author:\nDone" }, { "diff_hunk": "@@ -202,121 +202,128 @@ def _to_bytes(self, obj, context):\n Python's built in `hash` does not produce consistent results across\n runs.\"\"\"\n \n- if _is_magicmock(obj):\n- # MagicMock can result in objects that appear to be infinitely\n- # deep, so we don't try to hash them at all.\n- return self.to_bytes(id(obj))\n- elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n- return obj\n- elif isinstance(obj, string_types):\n- return obj.encode()\n- elif isinstance(obj, float):\n- return self.to_bytes(hash(obj))\n- elif isinstance(obj, int):\n- return _int_to_bytes(obj)\n- elif isinstance(obj, list) or isinstance(obj, tuple):\n- h = hashlib.new(self.name)\n- # add type to distingush x from [x]\n- self._update(h, type(obj).__name__.encode() + b\":\")\n- for e in obj:\n- self._update(h, e, context)\n- return h.digest()\n- elif obj is None:\n- # Special string since hashes change between sessions.\n- # We don't use Python's `hash` since hashes are not consistent\n- # across runs.\n- return b\"none:\"\n- elif obj is True:\n- return b\"bool:1\"\n- elif obj is False:\n- return b\"bool:0\"\n- elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n- import pandas as pd\n-\n- if len(obj) >= PANDAS_ROWS_LARGE:\n- obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n- return pd.util.hash_pandas_object(obj).sum()\n- elif util.is_type(obj, \"numpy.ndarray\"):\n- h = hashlib.new(self.name)\n- self._update(h, obj.shape)\n-\n- if obj.size >= NP_SIZE_LARGE:\n- import numpy as np\n-\n- state = np.random.RandomState(0)\n- obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n-\n- self._update(h, obj.tobytes())\n- return h.digest()\n- elif inspect.isbuiltin(obj):\n- return self.to_bytes(obj.__name__)\n- elif hasattr(obj, \"name\") and (\n- isinstance(obj, io.IOBase) or os.path.exists(obj.name)\n- ):\n- # Hash files as name + last modification date + offset.\n- h = hashlib.new(self.name)\n- self._update(h, obj.name)\n- self._update(h, os.path.getmtime(obj.name))\n- self._update(h, obj.tell())\n- return h.digest()\n- elif inspect.isroutine(obj):\n- if hasattr(obj, \"__wrapped__\"):\n- # Ignore the wrapper of wrapped functions.\n- return self.to_bytes(obj.__wrapped__)\n-\n- if obj.__module__.startswith(\"streamlit\"):\n- # Ignore streamlit modules even if they are in the CWD\n- # (e.g. during development).\n- return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n-\n- h = hashlib.new(self.name)\n- # TODO: This may be too restrictive for libraries in development.\n- if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n- context = _get_context(obj)\n- if obj.__defaults__:\n- self._update(h, obj.__defaults__, context)\n- h.update(self._code_to_bytes(obj.__code__, context))\n- else:\n- # Don't hash code that is not in the current working directory.\n- self._update(h, obj.__module__)\n- self._update(h, obj.__name__)\n- return h.digest()\n- elif inspect.iscode(obj):\n- return self._code_to_bytes(obj, context)\n- elif inspect.ismodule(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # so the current warning is quite annoying...\n- # st.warning(('Streamlit does not support hashing modules. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name for internal modules.\n- return self.to_bytes(obj.__name__)\n- elif inspect.isclass(obj):\n- # TODO: Figure out how to best show this kind of warning to the\n- # user. In the meantime, show nothing. This scenario is too common,\n- # (e.g. in every \"except\" statement) so the current warning is\n- # quite annoying...\n- # st.warning(('Streamlit does not support hashing classes. '\n- # 'We did not hash `%s`.') % obj.__name__)\n- # TODO: Hash more than just the name of classes.\n- return self.to_bytes(obj.__name__)\n- elif isinstance(obj, functools.partial):\n- # The return value of functools.partial is not a plain function:\n- # it's a callable object that remembers the original function plus\n- # the values you pickled into it. So here we need to special-case it.\n- h = hashlib.new(self.name)\n- self._update(h, obj.args)\n- self._update(h, obj.func)\n- self._update(h, obj.keywords)\n- return h.digest()\n- else:\n- try:\n- # As a last resort, we pickle the object to hash it.\n- return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n- except Exception:\n+ try:\n+ if _is_magicmock(obj):\n+ # MagicMock can result in objects that appear to be infinitely\n+ # deep, so we don't try to hash them at all.\n+ return self.to_bytes(id(obj))\n+ elif isinstance(obj, bytes) or isinstance(obj, bytearray):\n+ return obj\n+ elif isinstance(obj, string_types):\n+ return obj.encode()\n+ elif isinstance(obj, float):\n+ return self.to_bytes(hash(obj))\n+ elif isinstance(obj, int):\n+ return _int_to_bytes(obj)\n+ elif isinstance(obj, list) or isinstance(obj, tuple):\n+ h = hashlib.new(self.name)\n+ # add type to distingush x from [x]\n+ self._update(h, type(obj).__name__.encode() + b\":\")\n+ for e in obj:\n+ self._update(h, e, context)\n+ return h.digest()\n+ elif obj is None:\n+ # Special string since hashes change between sessions.\n+ # We don't use Python's `hash` since hashes are not consistent\n+ # across runs.\n+ return b\"none:\"\n+ elif obj is True:\n+ return b\"bool:1\"\n+ elif obj is False:\n+ return b\"bool:0\"\n+ elif util.is_type(obj, \"pandas.core.frame.DataFrame\"):\n+ import pandas as pd\n+\n+ if len(obj) >= PANDAS_ROWS_LARGE:\n+ obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0)\n+ try:\n+ return pd.util.hash_pandas_object(obj).sum()\n+ except TypeError:\n+ # Use pickle if pandas cannot hash the object for example if\n+ # it contains unhashable objects.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ elif util.is_type(obj, \"numpy.ndarray\"):\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.shape)\n+\n+ if obj.size >= NP_SIZE_LARGE:\n+ import numpy as np\n+\n+ state = np.random.RandomState(0)\n+ obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE)\n+\n+ self._update(h, obj.tobytes())\n+ return h.digest()\n+ elif inspect.isbuiltin(obj):\n+ return self.to_bytes(obj.__name__)\n+ elif hasattr(obj, \"name\") and (\n+ isinstance(obj, io.IOBase) or\n+ (isinstance(obj.name, string_types) and os.path.exists(obj.name))\n+ ):\n+ # Hash files as name + last modification date + offset.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.name)\n+ self._update(h, os.path.getmtime(obj.name))\n+ self._update(h, obj.tell())\n+ return h.digest()\n+ elif inspect.isroutine(obj):\n+ if hasattr(obj, \"__wrapped__\"):\n+ # Ignore the wrapper of wrapped functions.\n+ return self.to_bytes(obj.__wrapped__)\n+\n+ if obj.__module__.startswith(\"streamlit\"):\n+ # Ignore streamlit modules even if they are in the CWD\n+ # (e.g. during development).\n+ return self.to_bytes(\"%s.%s\" % (obj.__module__, obj.__name__))\n+\n+ h = hashlib.new(self.name)\n+ # TODO: This may be too restrictive for libraries in development.\n+ if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()):\n+ context = _get_context(obj)\n+ if obj.__defaults__:\n+ self._update(h, obj.__defaults__, context)\n+ h.update(self._code_to_bytes(obj.__code__, context))\n+ else:\n+ # Don't hash code that is not in the current working directory.\n+ self._update(h, obj.__module__)\n+ self._update(h, obj.__name__)\n+ return h.digest()\n+ elif inspect.iscode(obj):\n+ return self._code_to_bytes(obj, context)\n+ elif inspect.ismodule(obj):\n+ # TODO: Figure out how to best show this kind of warning to the\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # so the current warning is quite annoying...\n+ # st.warning(('Streamlit does not support hashing modules. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name for internal modules.\n+ return self.to_bytes(obj.__name__)\n+ elif inspect.isclass(obj):\n # TODO: Figure out how to best show this kind of warning to the\n- # user.\n- st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))\n+ # user. In the meantime, show nothing. This scenario is too common,\n+ # (e.g. in every \"except\" statement) so the current warning is\n+ # quite annoying...\n+ # st.warning(('Streamlit does not support hashing classes. '\n+ # 'We did not hash `%s`.') % obj.__name__)\n+ # TODO: Hash more than just the name of classes.\n+ return self.to_bytes(obj.__name__)\n+ elif isinstance(obj, functools.partial):\n+ # The return value of functools.partial is not a plain function:\n+ # it's a callable object that remembers the original function plus\n+ # the values you pickled into it. So here we need to special-case it.\n+ h = hashlib.new(self.name)\n+ self._update(h, obj.args)\n+ self._update(h, obj.func)\n+ self._update(h, obj.keywords)\n+ return h.digest()\n+ else:\n+ try:\n+ # As a last resort, we pickle the object to hash it.\n+ return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)\n+ except:\n+ st.warning(\"Streamlit cannot hash an object of type %s.\" % type(obj))", "line": null, "original_line": 324, "original_start_line": null, "path": "lib/streamlit/hashing.py", "start_line": null, "text": "@user1:\n\"an object\" โ†’ \"objects\"\n\n@author:\nI don't think that correct. We may be able to hash objects of some type in general but not a specific one." } ]
63ab7ed0bf2066a086d0c551c8bbad1e4e2b2da0
diff --git a/lib/streamlit/hashing.py b/lib/streamlit/hashing.py index 6776124cb673..9bebab6cb765 100644 --- a/lib/streamlit/hashing.py +++ b/lib/streamlit/hashing.py @@ -142,6 +142,17 @@ def is_simple(obj): return None +def _hashing_error_message(start): + return ( + start, + "\n\n**More information:** to prevent unexpected behavior, Streamlit tries to detect mutations in cached objects so it can alert the user if needed. However, something went wrong while performing this check.\n\n" + "Please [file a bug](https://github.com/streamlit/streamlit/issues/new/choose).\n\n" + "To stop this warning from showing in the meantime, try one of the following:\n" + "* **Preferred:** modify your code to avoid using this type of object.\n" + "* Or add the argument `ignore_cache=True` to the `st.cache` decorator.", + ) + + class CodeHasher: """A hasher that can hash code objects including dependencies.""" @@ -202,125 +213,138 @@ def _to_bytes(self, obj, context): Python's built in `hash` does not produce consistent results across runs.""" - if _is_magicmock(obj): - # MagicMock can result in objects that appear to be infinitely - # deep, so we don't try to hash them at all. - return self.to_bytes(id(obj)) - elif isinstance(obj, bytes) or isinstance(obj, bytearray): - return obj - elif isinstance(obj, string_types): - return obj.encode() - elif isinstance(obj, float): - return self.to_bytes(hash(obj)) - elif isinstance(obj, int): - return _int_to_bytes(obj) - elif isinstance(obj, list) or isinstance(obj, tuple): - h = hashlib.new(self.name) - # add type to distingush x from [x] - self._update(h, type(obj).__name__.encode() + b":") - for e in obj: - self._update(h, e, context) - return h.digest() - elif obj is None: - # Special string since hashes change between sessions. - # We don't use Python's `hash` since hashes are not consistent - # across runs. - return b"none:" - elif obj is True: - return b"bool:1" - elif obj is False: - return b"bool:0" - elif util.is_type(obj, "pandas.core.frame.Series"): - import pandas as pd - return pd.util.hash_pandas_object(obj).sum() - elif util.is_type(obj, "pandas.core.frame.DataFrame"): - import pandas as pd - - if len(obj) >= PANDAS_ROWS_LARGE: - obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0) - return pd.util.hash_pandas_object(obj).sum() - elif util.is_type(obj, "numpy.ndarray"): - h = hashlib.new(self.name) - self._update(h, obj.shape) - - if obj.size >= NP_SIZE_LARGE: - import numpy as np - - state = np.random.RandomState(0) - obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE) - - self._update(h, obj.tobytes()) - return h.digest() - elif inspect.isbuiltin(obj): - return self.to_bytes(obj.__name__) - elif hasattr(obj, "name") and ( - isinstance(obj, io.IOBase) or - (isinstance(obj.name, string_types) and os.path.exists(obj.name)) - ): - # Hash files as name + last modification date + offset. - h = hashlib.new(self.name) - self._update(h, obj.name) - self._update(h, os.path.getmtime(obj.name)) - self._update(h, obj.tell()) - return h.digest() - elif inspect.isroutine(obj): - if hasattr(obj, "__wrapped__"): - # Ignore the wrapper of wrapped functions. - return self.to_bytes(obj.__wrapped__) - - if obj.__module__.startswith("streamlit"): - # Ignore streamlit modules even if they are in the CWD - # (e.g. during development). - return self.to_bytes("%s.%s" % (obj.__module__, obj.__name__)) - - h = hashlib.new(self.name) - # TODO: This may be too restrictive for libraries in development. - if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()): - context = _get_context(obj) - if obj.__defaults__: - self._update(h, obj.__defaults__, context) - h.update(self._code_to_bytes(obj.__code__, context)) - else: - # Don't hash code that is not in the current working directory. - self._update(h, obj.__module__) - self._update(h, obj.__name__) - return h.digest() - elif inspect.iscode(obj): - return self._code_to_bytes(obj, context) - elif inspect.ismodule(obj): - # TODO: Figure out how to best show this kind of warning to the - # user. In the meantime, show nothing. This scenario is too common, - # so the current warning is quite annoying... - # st.warning(('Streamlit does not support hashing modules. ' - # 'We did not hash `%s`.') % obj.__name__) - # TODO: Hash more than just the name for internal modules. - return self.to_bytes(obj.__name__) - elif inspect.isclass(obj): - # TODO: Figure out how to best show this kind of warning to the - # user. In the meantime, show nothing. This scenario is too common, - # (e.g. in every "except" statement) so the current warning is - # quite annoying... - # st.warning(('Streamlit does not support hashing classes. ' - # 'We did not hash `%s`.') % obj.__name__) - # TODO: Hash more than just the name of classes. - return self.to_bytes(obj.__name__) - elif isinstance(obj, functools.partial): - # The return value of functools.partial is not a plain function: - # it's a callable object that remembers the original function plus - # the values you pickled into it. So here we need to special-case it. - h = hashlib.new(self.name) - self._update(h, obj.args) - self._update(h, obj.func) - self._update(h, obj.keywords) - return h.digest() - else: - try: - # As a last resort, we pickle the object to hash it. - return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL) - except Exception: + try: + if _is_magicmock(obj): + # MagicMock can result in objects that appear to be infinitely + # deep, so we don't try to hash them at all. + return self.to_bytes(id(obj)) + elif isinstance(obj, bytes) or isinstance(obj, bytearray): + return obj + elif isinstance(obj, string_types): + return obj.encode() + elif isinstance(obj, float): + return self.to_bytes(hash(obj)) + elif isinstance(obj, int): + return _int_to_bytes(obj) + elif isinstance(obj, list) or isinstance(obj, tuple): + h = hashlib.new(self.name) + # add type to distingush x from [x] + self._update(h, type(obj).__name__.encode() + b":") + for e in obj: + self._update(h, e, context) + return h.digest() + elif obj is None: + # Special string since hashes change between sessions. + # We don't use Python's `hash` since hashes are not consistent + # across runs. + return b"none:" + elif obj is True: + return b"bool:1" + elif obj is False: + return b"bool:0" + elif util.is_type(obj, "pandas.core.frame.DataFrame") or util.is_type( + obj, "pandas.core.series.Series" + ): + import pandas as pd + + if len(obj) >= PANDAS_ROWS_LARGE: + obj = obj.sample(n=PANDAS_SAMPLE_SIZE, random_state=0) + try: + return pd.util.hash_pandas_object(obj).sum() + except TypeError: + # Use pickle if pandas cannot hash the object for example if + # it contains unhashable objects. + return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL) + elif util.is_type(obj, "numpy.ndarray"): + h = hashlib.new(self.name) + self._update(h, obj.shape) + + if obj.size >= NP_SIZE_LARGE: + import numpy as np + + state = np.random.RandomState(0) + obj = state.choice(obj.flat, size=NP_SAMPLE_SIZE) + + self._update(h, obj.tobytes()) + return h.digest() + elif inspect.isbuiltin(obj): + return self.to_bytes(obj.__name__) + elif hasattr(obj, "name") and ( + isinstance(obj, io.IOBase) + or (isinstance(obj.name, string_types) and os.path.exists(obj.name)) + ): + # Hash files as name + last modification date + offset. + h = hashlib.new(self.name) + self._update(h, obj.name) + self._update(h, os.path.getmtime(obj.name)) + self._update(h, obj.tell()) + return h.digest() + elif inspect.isroutine(obj): + if hasattr(obj, "__wrapped__"): + # Ignore the wrapper of wrapped functions. + return self.to_bytes(obj.__wrapped__) + + if obj.__module__.startswith("streamlit"): + # Ignore streamlit modules even if they are in the CWD + # (e.g. during development). + return self.to_bytes("%s.%s" % (obj.__module__, obj.__name__)) + + h = hashlib.new(self.name) + # TODO: This may be too restrictive for libraries in development. + if os.path.abspath(obj.__code__.co_filename).startswith(os.getcwd()): + context = _get_context(obj) + if obj.__defaults__: + self._update(h, obj.__defaults__, context) + h.update(self._code_to_bytes(obj.__code__, context)) + else: + # Don't hash code that is not in the current working directory. + self._update(h, obj.__module__) + self._update(h, obj.__name__) + return h.digest() + elif inspect.iscode(obj): + return self._code_to_bytes(obj, context) + elif inspect.ismodule(obj): + # TODO: Figure out how to best show this kind of warning to the + # user. In the meantime, show nothing. This scenario is too common, + # so the current warning is quite annoying... + # st.warning(('Streamlit does not support hashing modules. ' + # 'We did not hash `%s`.') % obj.__name__) + # TODO: Hash more than just the name for internal modules. + return self.to_bytes(obj.__name__) + elif inspect.isclass(obj): # TODO: Figure out how to best show this kind of warning to the - # user. - st.warning("Streamlit cannot hash an object of type %s." % type(obj)) + # user. In the meantime, show nothing. This scenario is too common, + # (e.g. in every "except" statement) so the current warning is + # quite annoying... + # st.warning(('Streamlit does not support hashing classes. ' + # 'We did not hash `%s`.') % obj.__name__) + # TODO: Hash more than just the name of classes. + return self.to_bytes(obj.__name__) + elif isinstance(obj, functools.partial): + # The return value of functools.partial is not a plain function: + # it's a callable object that remembers the original function plus + # the values you pickled into it. So here we need to special-case it. + h = hashlib.new(self.name) + self._update(h, obj.args) + self._update(h, obj.func) + self._update(h, obj.keywords) + return h.digest() + else: + try: + # As a last resort, we pickle the object to hash it. + return pickle.dumps(obj, pickle.HIGHEST_PROTOCOL) + except: + st.warning( + _hashing_error_message( + "Streamlit cannot hash an object of type %s." % type(obj) + ) + ) + except: + st.warning( + _hashing_error_message( + "Streamlit failed to hash an object of type %s." % type(obj) + ) + ) def _code_to_bytes(self, code, context): h = hashlib.new(self.name) diff --git a/lib/tests/streamlit/hashing_test.py b/lib/tests/streamlit/hashing_test.py index 11a7d80c93b0..960b8e4db054 100644 --- a/lib/tests/streamlit/hashing_test.py +++ b/lib/tests/streamlit/hashing_test.py @@ -27,7 +27,7 @@ from mock import MagicMock import streamlit as st -from streamlit.hashing import NP_SIZE_LARGE, get_hash +from streamlit.hashing import NP_SIZE_LARGE, PANDAS_ROWS_LARGE, get_hash class HashTest(unittest.TestCase): @@ -77,6 +77,11 @@ def test_pandas_dataframe(self): self.assertEqual(get_hash(df1), get_hash(df3)) self.assertNotEqual(get_hash(df1), get_hash(df2)) + df4 = pd.DataFrame(np.zeros((PANDAS_ROWS_LARGE, 4)), columns=list("ABCD")) + df5 = pd.DataFrame(np.zeros((PANDAS_ROWS_LARGE, 4)), columns=list("ABCD")) + + self.assertEqual(get_hash(df4), get_hash(df5)) + def test_pandas_series(self): series1 = pd.Series([1, 2]) series2 = pd.Series([1, 3]) @@ -85,6 +90,11 @@ def test_pandas_series(self): self.assertEqual(get_hash(series1), get_hash(series3)) self.assertNotEqual(get_hash(series1), get_hash(series2)) + series4 = pd.Series(range(PANDAS_ROWS_LARGE)) + series5 = pd.Series(range(PANDAS_ROWS_LARGE)) + + self.assertEqual(get_hash(series4), get_hash(series5)) + def test_numpy(self): np1 = np.zeros(10) np2 = np.zeros(11)
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
streamlit__streamlit-85@a221f38
streamlit/streamlit
Python
85
Email, metrics prompt
- Nicer `streamlit activate` prompt and text from @tvst - Remove the unused "activation code" stuff from Credentials. - Credentials: email is now optional. If the user doesn't enter one, it will be stored as the empty string. The client will omit the "authoremail" trait from the Segment.io identifier if the user didn't enter an email. Fixes #63
2019-09-10T22:29:13Z
On first use, ask nicely for email/metrics When the user first runs a `streamlit foo` command on the CLI, we currently show a message asking for the user's email: ``` Enter your email for access to our beta ``` and then show a welcome message: ``` Welcome to Streamlit! Get started by typing: $ streamlit hello ``` This was fine for our beta, but for a public launch we should: 1) Ask more nicely for the email, explaining why we need it :) 2) Allow people to _not_ enter an email if they don't want to 3) Tell people we log high-level analytics, and let them know how to turn them off. (And as a follow up, we should make it easier to turn this on/off in the future) ## Proposal Replace the first message with: ``` ๐Ÿ‘‹ Welcome to Streamlit! [in green] If you are one of our development partners or are interested in getting personal technical support, please enter your email address below. Otherwise, you may leave the field blank. Email: ``` and the second with: ``` Telemetry: as an open source project, we use collect summary statistics and metadata to understand how people are using Streamlit. If you'd like to opt out, add the following to ~/.streamlit/config.toml, creating that file if necessary: [browser] gatherUsageStats = false ``` (leave the first and last lines in this block blank, so it doesn't touch any text we print above or below it.)
[ { "body": "When the user first runs a `streamlit foo` command on the CLI, we currently show a message asking for the user's email:\r\n\r\n```\r\n Enter your email for access to our beta\r\n```\r\n\r\nand then show a welcome message:\r\n```\r\n Welcome to Streamlit!\r\n\r\n Get started by typing:\r\n $ streamlit hello\r\n```\r\n\r\nThis was fine for our beta, but for a public launch we should:\r\n1) Ask more nicely for the email, explaining why we need it :)\r\n2) Allow people to _not_ enter an email if they don't want to\r\n3) Tell people we log high-level analytics, and let them know how to turn them off. (And as a follow up, we should make it easier to turn this on/off in the future)\r\n\r\n## Proposal\r\n\r\nReplace the first message with:\r\n```\r\n ๐Ÿ‘‹ Welcome to Streamlit! [in green]\r\n\r\n If you are one of our development partners or are interested in\r\n getting personal technical support, please enter your email address\r\n below. Otherwise, you may leave the field blank.\r\n\r\n Email:\r\n```\r\n\r\nand the second with:\r\n```\r\n\r\n Telemetry: as an open source project, we use collect summary\r\n statistics and metadata to understand how people are using Streamlit.\r\n\r\n If you'd like to opt out, add the following to ~/.streamlit/config.toml,\r\n creating that file if necessary:\r\n\r\n [browser]\r\n gatherUsageStats = false\r\n\r\n```\r\n(leave the first and last lines in this block blank, so it doesn't touch any text we print above or below it.)", "number": 63, "title": "On first use, ask nicely for email/metrics" } ]
93cf9393877c3122b569b74a4f35f0dea01d26cd
{ "head_commit": "a221f38bc74ab9c87ddacca3741870b54892464f", "head_commit_message": "Damn. Python 2.7 doesn't have textwrap.indent", "patch_to_review": "diff --git a/frontend/src/lib/MetricsManager.ts b/frontend/src/lib/MetricsManager.ts\nindex 4f599166eb42..7b05b36e62f4 100644\n--- a/frontend/src/lib/MetricsManager.ts\n+++ b/frontend/src/lib/MetricsManager.ts\n@@ -34,8 +34,6 @@ interface DeltaCounter {\n \n type Event = [string, object]\n \n-type SendFunction = (evName: string, evData: object) => void\n-\n export class MetricsManager {\n private initialized = false\n \n@@ -72,9 +70,12 @@ export class MetricsManager {\n this.actuallySendMetrics = gatherUsageStats\n \n if (this.actuallySendMetrics || IS_SHARED_REPORT) {\n- this.identify(SessionInfo.current.installationId, {\n- authoremail: SessionInfo.current.authorEmail,\n- })\n+ // Only record the user's email if they entered a non-empty one.\n+ const userTraits: any = {}\n+ if (SessionInfo.current.authorEmail !== \"\") {\n+ userTraits[\"authoremail\"] = SessionInfo.current.authorEmail\n+ }\n+ this.identify(SessionInfo.current.installationId, userTraits)\n this.sendPendingEvents()\n }\n \ndiff --git a/lib/streamlit/credentials.py b/lib/streamlit/credentials.py\nindex e025d521c606..0fcd624fd37a 100644\n--- a/lib/streamlit/credentials.py\n+++ b/lib/streamlit/credentials.py\n@@ -14,26 +14,23 @@\n # limitations under the License.\n \n \"\"\"Manage the user's Streamlit credentials.\"\"\"\n-from collections import namedtuple\n-import hashlib\n-import hmac\n+\n import os\n import sys\n import textwrap\n+from collections import namedtuple\n \n import click\n-import base58\n import toml\n \n-from streamlit.logger import get_logger\n from streamlit import util\n+from streamlit.logger import get_logger\n \n LOGGER = get_logger(__name__)\n \n Activation = namedtuple('Activation', [\n- 'code', # str: the user's activation code, sent via email.\n 'email', # str : the user's email.\n- 'is_valid', # boolean : whether the code+email combination is valid.\n+ 'is_valid', # boolean : whether the email is valid.\n ])\n \n # For python 2.7\n@@ -43,6 +40,32 @@\n FileNotFoundError = IOError\n \n \n+EMAIL_PROMPT = '''\n+ ๐Ÿ‘‹ %(welcome)s\n+ \n+ If you are one of our development partners or are interested in\n+ getting personal technical support, please enter your email address\n+ below. Otherwise, you may leave the field blank.\n+ \n+ Email''' % {'welcome': click.style('Welcome to Streamlit!', fg='green')}\n+\n+TELEMETRY_TEXT = '''\n+ Telemetry: as an open source project, we use collect summary\n+ statistics and metadata to understand how people are using Streamlit.\n+ \n+ If you'd like to opt out, add the following to ~/.streamlit/config.toml,\n+ creating that file if necessary:\n+ \n+ [browser]\n+ gatherUsageStats = false\n+'''\n+\n+INSTRUCTIONS_TEXT = '''\n+ Get started by typing:\n+ $ %(hello)s\n+''' % {'hello': click.style('streamlit hello', bold=True)}\n+\n+\n class Credentials(object):\n \"\"\"Credentials class.\"\"\"\n _singleton = None\n@@ -75,8 +98,7 @@ def load(self, auto_resolve=False):\n try:\n with open(self._conf_file, 'r') as f:\n data = toml.load(f).get('general')\n- self.activation = _verify_code(\n- data['email'], getattr(data, 'code', None))\n+ self.activation = _verify_email(data.get('email'))\n except FileNotFoundError:\n if auto_resolve:\n return self.activate(show_instructions=not auto_resolve)\n@@ -102,7 +124,7 @@ def check_activated(self, auto_resolve=False):\n _exit(str(e))\n \n if not self.activation.is_valid:\n- _exit('Activation code/email not valid.')\n+ _exit('Activation email not valid.')\n \n @classmethod\n def reset(cls):\n@@ -122,7 +144,6 @@ def save(self):\n \"\"\"Save to toml file.\"\"\"\n data = {\n 'email': self.activation.email,\n- 'code': self.activation.code,\n }\n with open(self._conf_file, 'w') as f:\n toml.dump({'general': data}, f)\n@@ -149,90 +170,49 @@ def activate(self, show_instructions=True):\n activated = False\n \n while not activated:\n- code = None #code = _get_data('Enter your invite code')\n- email = _get_data('Enter your email for access to our beta')\n \n- self.activation = _verify_code(email, code)\n+ email = click.prompt(\n+ text=EMAIL_PROMPT,\n+ default='', show_default=False)\n+\n+ self.activation = _verify_email(email)\n if self.activation.is_valid:\n self.save()\n- click.secho('')\n- click.secho(' Welcome to Streamlit!', fg='green')\n- click.secho('')\n+ click.secho(TELEMETRY_TEXT)\n if show_instructions:\n- click.secho(' Get started by typing:')\n- click.secho(' $ ', nl=False)\n- click.secho('streamlit hello', bold=True)\n- click.secho('')\n+ click.secho(INSTRUCTIONS_TEXT)\n activated = True\n else: # pragma: nocover\n LOGGER.error('Please try again.')\n \n \n-def _generate_code(secret, email):\n- \"\"\"Generate code for activation.\n+def _verify_email(email):\n+ \"\"\"Verify the user's email address.\n \n- This is here so streamlit developers can create activation codes if\n- needed that are not in the spreadsheet.\n- \"\"\"\n- secret = secret.encode('utf-8')\n- email = email.encode('utf-8')\n+ The email can either be an empty string (if the user chooses not to enter\n+ it), or a string with a single '@' somewhere in it.\n \n- salt = hmac.new(secret, email, hashlib.sha512).hexdigest()[0:4]\n- salt_encoded = salt.encode('utf-8')\n- hash = hmac.new(salt_encoded, email, hashlib.md5).hexdigest()[0:4]\n+ Parameters\n+ ----------\n+ email : str\n \n- code = base58.b58encode(salt + hash)\n- return code.decode('utf-8')\n+ Returns\n+ -------\n+ Activation\n+ An Activation object. Its 'is_valid' property will be True only if\n+ the email was validated.\n \n+ \"\"\"\n+ email = email.strip()\n \n-def _verify_code(email, code):\n- \"\"\"Verify activation code with email.\"\"\"\n- # Bypass code verification for now.\n- if email.count('@') != 1:\n+ if len(email) > 0 and email.count('@') != 1:\n LOGGER.error('That doesn\\'t look like an email :(')\n- return Activation(None, None, None)\n+ return Activation(None, False)\n \n- if code == None:\n- return Activation(None, email, True)\n-\n- # Python2/3 Madness\n- email_encoded = email\n- code_encoded = code\n- if sys.version_info >= (3, 0):\n- email_encoded = email.encode('utf-8')\n- else:\n- code_encoded = code.encode('utf-8') # pragma: nocover\n-\n- try:\n- decoded = base58.b58decode(code_encoded)\n-\n- salt, hash = decoded[0:4], decoded[4:8]\n-\n- calculated_hash = hmac.new(salt, email_encoded,\n- hashlib.md5).hexdigest()\n-\n- if hash.decode('utf-8') == calculated_hash[0:4]:\n- return Activation(code, email, True)\n- else:\n- return Activation(code, email, False)\n-\n- except Exception as e:\n- LOGGER.error('Unable to verify code: %s', e)\n- return Activation(None, None, None)\n+ return Activation(email, True)\n \n \n def _exit(message): # pragma: nocover\n \"\"\"Exit program with error.\"\"\"\n LOGGER.error(message)\n sys.exit(-1)\n-\n-\n-def _get_data(msg):\n- \"\"\"Utility to get data from console.\"\"\"\n- data = None\n- while not data:\n- if sys.version_info >= (3, 0):\n- data = input('%s: ' % msg)\n- else: # pragma: nocover\n- data = raw_input('%s: ' % msg) # noqa: F821\n- return data\ndiff --git a/lib/tests/streamlit/credentials_test.py b/lib/tests/streamlit/credentials_test.py\nindex 4fa2e197aa8e..80133559ca3a 100644\n--- a/lib/tests/streamlit/credentials_test.py\n+++ b/lib/tests/streamlit/credentials_test.py\n@@ -18,18 +18,21 @@\n import sys\n import textwrap\n import unittest\n-import pytest\n \n-from mock import call, mock_open, patch, MagicMock\n+import pytest\n+from mock import MagicMock\n+from mock import call\n+from mock import mock_open\n+from mock import patch\n \n-from streamlit.credentials import Activation, Credentials, _generate_code, _verify_code, _get_data\n+from streamlit.credentials import Activation\n+from streamlit.credentials import Credentials\n+from streamlit.credentials import _verify_email\n \n-if sys.version_info >= (3, 0):\n- INPUT = 'streamlit.credentials.input'\n-else:\n- INPUT = 'streamlit.credentials.raw_input'\n+if sys.version_info < (3, 0):\n FileNotFoundError = IOError\n \n+PROMPT = 'streamlit.credentials.click.prompt'\n \n mock_get_path = MagicMock(\n return_value='/mock/home/folder/.streamlit/credentials.toml')\n@@ -76,21 +79,32 @@ def test_Credentials_load(self):\n \"\"\"Test Credentials.load().\"\"\"\n data = textwrap.dedent('''\n [general]\n- code = \"ARzVsqhSB5i\"\n email = \"[email protected]\"\n ''').strip()\n m = mock_open(read_data=data)\n- with patch('streamlit.credentials.open', m, create=True) as m:\n+ with patch('streamlit.credentials.open', m, create=True):\n c = Credentials.get_current()\n c.load()\n- self.assertEqual(c.activation.email, '[email protected]')\n- self.assertEqual(c.activation.code, None)\n+ self.assertEqual('[email protected]', c.activation.email)\n+\n+ @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path)\n+ def test_Credentials_load_empty(self):\n+ \"\"\"Test Credentials.load() with empty email\"\"\"\n+ data = textwrap.dedent('''\n+ [general]\n+ email = \"\"\n+ ''').strip()\n+ m = mock_open(read_data=data)\n+ with patch('streamlit.credentials.open', m, create=True):\n+ c = Credentials.get_current()\n+ c.load()\n+ self.assertEqual('', c.activation.email)\n \n @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path)\n def test_Credentials_load_twice(self):\n \"\"\"Test Credentials.load() called twice.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', True)\n+ c.activation = Activation('some_email', True)\n with patch('streamlit.credentials.LOGGER') as p:\n c.load()\n p.error.assert_called_once_with(\n@@ -132,7 +146,7 @@ def test_Credentials_load_permission_denied(self):\n def test_Credentials_check_activated_already_loaded(self):\n \"\"\"Test Credentials.check_activated() already loaded.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', True)\n+ c.activation = Activation('some_email', True)\n with patch('streamlit.credentials._exit') as p:\n c.check_activated()\n p.assert_not_called()\n@@ -141,16 +155,16 @@ def test_Credentials_check_activated_already_loaded(self):\n def test_Credentials_check_activated_false(self):\n \"\"\"Test Credentials.check_activated() not activated.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', False)\n+ c.activation = Activation('some_email', False)\n with patch('streamlit.credentials._exit') as p:\n c.check_activated()\n- p.assert_called_once_with('Activation code/email not valid.')\n+ p.assert_called_once_with('Activation email not valid.')\n \n @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path)\n def test_Credentials_check_activated_error(self):\n \"\"\"Test Credentials.check_activated() has an error.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', True)\n+ c.activation = Activation('some_email', True)\n with patch.object(c, 'load', side_effect=Exception(\n 'Some error')), patch('streamlit.credentials._exit') as p:\n c.check_activated()\n@@ -160,16 +174,14 @@ def test_Credentials_check_activated_error(self):\n def test_Credentials_save(self):\n \"\"\"Test Credentials.save().\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_code', 'some_email', True)\n+ c.activation = Activation('some_email', True)\n truth = textwrap.dedent('''\n [general]\n email = \"some_email\"\n- code = \"some_code\"\n ''').lstrip()\n \n truth2 = textwrap.dedent('''\n [general]\n- code = \"some_code\"\n email = \"some_email\"\n ''').lstrip()\n \n@@ -185,7 +197,7 @@ def test_Credentials_save(self):\n def test_Credentials_activate_already_activated(self):\n \"\"\"Test Credentials.activate() already activated.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', True)\n+ c.activation = Activation('some_email', True)\n with patch('streamlit.credentials.LOGGER') as p:\n with pytest.raises(SystemExit):\n c.activate()\n@@ -197,7 +209,7 @@ def test_Credentials_activate_already_activated(self):\n def test_Credentials_activate_already_activated_not_valid(self):\n \"\"\"Test Credentials.activate() already activated but not valid.\"\"\"\n c = Credentials.get_current()\n- c.activation = Activation('some_email', 'some_code', False)\n+ c.activation = Activation('some_email', False)\n with patch('streamlit.credentials.LOGGER') as p:\n with pytest.raises(SystemExit):\n c.activate()\n@@ -211,14 +223,14 @@ def test_Credentials_activate(self):\n \"\"\"Test Credentials.activate()\"\"\"\n c = Credentials.get_current()\n c.activation = None\n- with patch.object(\n- c, 'load',\n- side_effect=RuntimeError('Some error')), patch.object(\n- c, 'save') as s, patch(INPUT) as p:\n- p.side_effect = ['ARzVsqhSB5i', '[email protected]']\n+\n+ with patch.object(c, 'load', side_effect=RuntimeError('Some error')), \\\n+ patch.object(c, 'save') as patched_save, \\\n+ patch(PROMPT) as patched_prompt:\n+\n+ patched_prompt.side_effect = ['[email protected]']\n c.activate()\n- s.assert_called_once()\n- self.assertEqual(c.activation.code, None)\n+ patched_save.assert_called_once()\n self.assertEqual(c.activation.email, '[email protected]')\n self.assertEqual(c.activation.is_valid, True)\n \n@@ -233,9 +245,9 @@ def test_Credentials_reset(self):\n @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path)\n def test_Credentials_reset_error(self):\n \"\"\"Test Credentials.reset() with error.\"\"\"\n- with patch('streamlit.credentials.os.remove',\n- side_effect=OSError('some error')), patch(\n- 'streamlit.credentials.LOGGER') as p:\n+ with patch('streamlit.credentials.os.remove', side_effect=OSError('some error')), \\\n+ patch('streamlit.credentials.LOGGER') as p:\n+\n Credentials.reset()\n p.error.assert_called_once_with(\n 'Error removing credentials file: some error')\n@@ -244,32 +256,8 @@ def test_Credentials_reset_error(self):\n class CredentialsModulesTest(unittest.TestCase):\n \"\"\"Credentials Module Unittest class.\"\"\"\n \n- def test__generate_code(self):\n- \"\"\"Test _generate_code.\"\"\"\n- code = _generate_code('testing', '[email protected]')\n- self.assertEqual('ARzVsqhSB5i', code)\n-\n- def test__verify_code(self):\n- \"\"\"Test _generate_code.\"\"\"\n- ret = _verify_code('[email protected]', 'ARzVsqhSB5i')\n- self.assertTrue(ret.is_valid)\n-\n- def test__verify_code_wrong_code(self):\n- \"\"\"Test credentials._verify_code with code from another user.\"\"\"\n- ret = _verify_code('[email protected]', 'ARxJtdP43GU')\n- self.assertFalse(ret.is_valid)\n-\n- def test__verify_code_bad_code(self):\n- \"\"\"Test credentials._verify_code with invalid base58 code.\"\"\"\n- ret = _verify_code('[email protected]', '****')\n- self.assertFalse(ret.is_valid)\n-\n- def test_get_data(self):\n- \"\"\"Test get_data.\"\"\"\n- with patch(INPUT) as p:\n- p.return_value = 'my data'\n- data = _get_data('some message')\n-\n- self.assertEqual(1, p.call_count)\n- self.assertEqual('some message: ', p.call_args[0][0])\n- self.assertEqual('my data', data)\n+ def test_verify_email(self):\n+ \"\"\"Test _verify_email.\"\"\"\n+ self.assertTrue(_verify_email('[email protected]').is_valid)\n+ self.assertTrue(_verify_email('').is_valid)\n+ self.assertFalse(_verify_email('missing_at_sign').is_valid)\n" }
[ { "diff_hunk": "@@ -43,6 +40,32 @@\n FileNotFoundError = IOError\n \n \n+EMAIL_PROMPT = '''\n+ ๐Ÿ‘‹ %(welcome)s\n+ \n+ If you are one of our development partners or are interested in\n+ getting personal technical support, please enter your email address\n+ below. Otherwise, you may leave the field blank.\n+ \n+ Email''' % {'welcome': click.style('Welcome to Streamlit!', fg='green')}\n+\n+TELEMETRY_TEXT = '''\n+ Telemetry: as an open source project, we use collect summary", "line": null, "original_line": 53, "original_start_line": null, "path": "lib/streamlit/credentials.py", "start_line": null, "text": "@user1:\nwe use collect?\n\n@author:\nWhoops - thanks" } ]
fafaafea667ee928d1eb8ad3f545ac7df434ae0c
diff --git a/frontend/src/lib/MetricsManager.ts b/frontend/src/lib/MetricsManager.ts index 4f599166eb42..7b05b36e62f4 100644 --- a/frontend/src/lib/MetricsManager.ts +++ b/frontend/src/lib/MetricsManager.ts @@ -34,8 +34,6 @@ interface DeltaCounter { type Event = [string, object] -type SendFunction = (evName: string, evData: object) => void - export class MetricsManager { private initialized = false @@ -72,9 +70,12 @@ export class MetricsManager { this.actuallySendMetrics = gatherUsageStats if (this.actuallySendMetrics || IS_SHARED_REPORT) { - this.identify(SessionInfo.current.installationId, { - authoremail: SessionInfo.current.authorEmail, - }) + // Only record the user's email if they entered a non-empty one. + const userTraits: any = {} + if (SessionInfo.current.authorEmail !== "") { + userTraits["authoremail"] = SessionInfo.current.authorEmail + } + this.identify(SessionInfo.current.installationId, userTraits) this.sendPendingEvents() } diff --git a/lib/streamlit/credentials.py b/lib/streamlit/credentials.py index e025d521c606..c859a3824020 100644 --- a/lib/streamlit/credentials.py +++ b/lib/streamlit/credentials.py @@ -14,26 +14,23 @@ # limitations under the License. """Manage the user's Streamlit credentials.""" -from collections import namedtuple -import hashlib -import hmac + import os import sys import textwrap +from collections import namedtuple import click -import base58 import toml -from streamlit.logger import get_logger from streamlit import util +from streamlit.logger import get_logger LOGGER = get_logger(__name__) Activation = namedtuple('Activation', [ - 'code', # str: the user's activation code, sent via email. 'email', # str : the user's email. - 'is_valid', # boolean : whether the code+email combination is valid. + 'is_valid', # boolean : whether the email is valid. ]) # For python 2.7 @@ -43,6 +40,32 @@ FileNotFoundError = IOError +EMAIL_PROMPT = ''' + ๐Ÿ‘‹ %(welcome)s + + If you are one of our development partners or are interested in + getting personal technical support, please enter your email address + below. Otherwise, you may leave the field blank. + + Email''' % {'welcome': click.style('Welcome to Streamlit!', fg='green')} + +TELEMETRY_TEXT = ''' + Telemetry: as an open source project, we collect summary statistics + and metadata to understand how people are using Streamlit. + + If you'd like to opt out, add the following to ~/.streamlit/config.toml, + creating that file if necessary: + + [browser] + gatherUsageStats = false +''' + +INSTRUCTIONS_TEXT = ''' + Get started by typing: + $ %(hello)s +''' % {'hello': click.style('streamlit hello', bold=True)} + + class Credentials(object): """Credentials class.""" _singleton = None @@ -75,8 +98,7 @@ def load(self, auto_resolve=False): try: with open(self._conf_file, 'r') as f: data = toml.load(f).get('general') - self.activation = _verify_code( - data['email'], getattr(data, 'code', None)) + self.activation = _verify_email(data.get('email')) except FileNotFoundError: if auto_resolve: return self.activate(show_instructions=not auto_resolve) @@ -102,7 +124,7 @@ def check_activated(self, auto_resolve=False): _exit(str(e)) if not self.activation.is_valid: - _exit('Activation code/email not valid.') + _exit('Activation email not valid.') @classmethod def reset(cls): @@ -122,7 +144,6 @@ def save(self): """Save to toml file.""" data = { 'email': self.activation.email, - 'code': self.activation.code, } with open(self._conf_file, 'w') as f: toml.dump({'general': data}, f) @@ -149,90 +170,49 @@ def activate(self, show_instructions=True): activated = False while not activated: - code = None #code = _get_data('Enter your invite code') - email = _get_data('Enter your email for access to our beta') - self.activation = _verify_code(email, code) + email = click.prompt( + text=EMAIL_PROMPT, + default='', show_default=False) + + self.activation = _verify_email(email) if self.activation.is_valid: self.save() - click.secho('') - click.secho(' Welcome to Streamlit!', fg='green') - click.secho('') + click.secho(TELEMETRY_TEXT) if show_instructions: - click.secho(' Get started by typing:') - click.secho(' $ ', nl=False) - click.secho('streamlit hello', bold=True) - click.secho('') + click.secho(INSTRUCTIONS_TEXT) activated = True else: # pragma: nocover LOGGER.error('Please try again.') -def _generate_code(secret, email): - """Generate code for activation. +def _verify_email(email): + """Verify the user's email address. - This is here so streamlit developers can create activation codes if - needed that are not in the spreadsheet. - """ - secret = secret.encode('utf-8') - email = email.encode('utf-8') + The email can either be an empty string (if the user chooses not to enter + it), or a string with a single '@' somewhere in it. - salt = hmac.new(secret, email, hashlib.sha512).hexdigest()[0:4] - salt_encoded = salt.encode('utf-8') - hash = hmac.new(salt_encoded, email, hashlib.md5).hexdigest()[0:4] + Parameters + ---------- + email : str - code = base58.b58encode(salt + hash) - return code.decode('utf-8') + Returns + ------- + Activation + An Activation object. Its 'is_valid' property will be True only if + the email was validated. + """ + email = email.strip() -def _verify_code(email, code): - """Verify activation code with email.""" - # Bypass code verification for now. - if email.count('@') != 1: + if len(email) > 0 and email.count('@') != 1: LOGGER.error('That doesn\'t look like an email :(') - return Activation(None, None, None) + return Activation(None, False) - if code == None: - return Activation(None, email, True) - - # Python2/3 Madness - email_encoded = email - code_encoded = code - if sys.version_info >= (3, 0): - email_encoded = email.encode('utf-8') - else: - code_encoded = code.encode('utf-8') # pragma: nocover - - try: - decoded = base58.b58decode(code_encoded) - - salt, hash = decoded[0:4], decoded[4:8] - - calculated_hash = hmac.new(salt, email_encoded, - hashlib.md5).hexdigest() - - if hash.decode('utf-8') == calculated_hash[0:4]: - return Activation(code, email, True) - else: - return Activation(code, email, False) - - except Exception as e: - LOGGER.error('Unable to verify code: %s', e) - return Activation(None, None, None) + return Activation(email, True) def _exit(message): # pragma: nocover """Exit program with error.""" LOGGER.error(message) sys.exit(-1) - - -def _get_data(msg): - """Utility to get data from console.""" - data = None - while not data: - if sys.version_info >= (3, 0): - data = input('%s: ' % msg) - else: # pragma: nocover - data = raw_input('%s: ' % msg) # noqa: F821 - return data diff --git a/lib/tests/streamlit/credentials_test.py b/lib/tests/streamlit/credentials_test.py index 4fa2e197aa8e..80133559ca3a 100644 --- a/lib/tests/streamlit/credentials_test.py +++ b/lib/tests/streamlit/credentials_test.py @@ -18,18 +18,21 @@ import sys import textwrap import unittest -import pytest -from mock import call, mock_open, patch, MagicMock +import pytest +from mock import MagicMock +from mock import call +from mock import mock_open +from mock import patch -from streamlit.credentials import Activation, Credentials, _generate_code, _verify_code, _get_data +from streamlit.credentials import Activation +from streamlit.credentials import Credentials +from streamlit.credentials import _verify_email -if sys.version_info >= (3, 0): - INPUT = 'streamlit.credentials.input' -else: - INPUT = 'streamlit.credentials.raw_input' +if sys.version_info < (3, 0): FileNotFoundError = IOError +PROMPT = 'streamlit.credentials.click.prompt' mock_get_path = MagicMock( return_value='/mock/home/folder/.streamlit/credentials.toml') @@ -76,21 +79,32 @@ def test_Credentials_load(self): """Test Credentials.load().""" data = textwrap.dedent(''' [general] - code = "ARzVsqhSB5i" email = "[email protected]" ''').strip() m = mock_open(read_data=data) - with patch('streamlit.credentials.open', m, create=True) as m: + with patch('streamlit.credentials.open', m, create=True): c = Credentials.get_current() c.load() - self.assertEqual(c.activation.email, '[email protected]') - self.assertEqual(c.activation.code, None) + self.assertEqual('[email protected]', c.activation.email) + + @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path) + def test_Credentials_load_empty(self): + """Test Credentials.load() with empty email""" + data = textwrap.dedent(''' + [general] + email = "" + ''').strip() + m = mock_open(read_data=data) + with patch('streamlit.credentials.open', m, create=True): + c = Credentials.get_current() + c.load() + self.assertEqual('', c.activation.email) @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path) def test_Credentials_load_twice(self): """Test Credentials.load() called twice.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', True) + c.activation = Activation('some_email', True) with patch('streamlit.credentials.LOGGER') as p: c.load() p.error.assert_called_once_with( @@ -132,7 +146,7 @@ def test_Credentials_load_permission_denied(self): def test_Credentials_check_activated_already_loaded(self): """Test Credentials.check_activated() already loaded.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', True) + c.activation = Activation('some_email', True) with patch('streamlit.credentials._exit') as p: c.check_activated() p.assert_not_called() @@ -141,16 +155,16 @@ def test_Credentials_check_activated_already_loaded(self): def test_Credentials_check_activated_false(self): """Test Credentials.check_activated() not activated.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', False) + c.activation = Activation('some_email', False) with patch('streamlit.credentials._exit') as p: c.check_activated() - p.assert_called_once_with('Activation code/email not valid.') + p.assert_called_once_with('Activation email not valid.') @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path) def test_Credentials_check_activated_error(self): """Test Credentials.check_activated() has an error.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', True) + c.activation = Activation('some_email', True) with patch.object(c, 'load', side_effect=Exception( 'Some error')), patch('streamlit.credentials._exit') as p: c.check_activated() @@ -160,16 +174,14 @@ def test_Credentials_check_activated_error(self): def test_Credentials_save(self): """Test Credentials.save().""" c = Credentials.get_current() - c.activation = Activation('some_code', 'some_email', True) + c.activation = Activation('some_email', True) truth = textwrap.dedent(''' [general] email = "some_email" - code = "some_code" ''').lstrip() truth2 = textwrap.dedent(''' [general] - code = "some_code" email = "some_email" ''').lstrip() @@ -185,7 +197,7 @@ def test_Credentials_save(self): def test_Credentials_activate_already_activated(self): """Test Credentials.activate() already activated.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', True) + c.activation = Activation('some_email', True) with patch('streamlit.credentials.LOGGER') as p: with pytest.raises(SystemExit): c.activate() @@ -197,7 +209,7 @@ def test_Credentials_activate_already_activated(self): def test_Credentials_activate_already_activated_not_valid(self): """Test Credentials.activate() already activated but not valid.""" c = Credentials.get_current() - c.activation = Activation('some_email', 'some_code', False) + c.activation = Activation('some_email', False) with patch('streamlit.credentials.LOGGER') as p: with pytest.raises(SystemExit): c.activate() @@ -211,14 +223,14 @@ def test_Credentials_activate(self): """Test Credentials.activate()""" c = Credentials.get_current() c.activation = None - with patch.object( - c, 'load', - side_effect=RuntimeError('Some error')), patch.object( - c, 'save') as s, patch(INPUT) as p: - p.side_effect = ['ARzVsqhSB5i', '[email protected]'] + + with patch.object(c, 'load', side_effect=RuntimeError('Some error')), \ + patch.object(c, 'save') as patched_save, \ + patch(PROMPT) as patched_prompt: + + patched_prompt.side_effect = ['[email protected]'] c.activate() - s.assert_called_once() - self.assertEqual(c.activation.code, None) + patched_save.assert_called_once() self.assertEqual(c.activation.email, '[email protected]') self.assertEqual(c.activation.is_valid, True) @@ -233,9 +245,9 @@ def test_Credentials_reset(self): @patch('streamlit.credentials.util.get_streamlit_file_path', mock_get_path) def test_Credentials_reset_error(self): """Test Credentials.reset() with error.""" - with patch('streamlit.credentials.os.remove', - side_effect=OSError('some error')), patch( - 'streamlit.credentials.LOGGER') as p: + with patch('streamlit.credentials.os.remove', side_effect=OSError('some error')), \ + patch('streamlit.credentials.LOGGER') as p: + Credentials.reset() p.error.assert_called_once_with( 'Error removing credentials file: some error') @@ -244,32 +256,8 @@ def test_Credentials_reset_error(self): class CredentialsModulesTest(unittest.TestCase): """Credentials Module Unittest class.""" - def test__generate_code(self): - """Test _generate_code.""" - code = _generate_code('testing', '[email protected]') - self.assertEqual('ARzVsqhSB5i', code) - - def test__verify_code(self): - """Test _generate_code.""" - ret = _verify_code('[email protected]', 'ARzVsqhSB5i') - self.assertTrue(ret.is_valid) - - def test__verify_code_wrong_code(self): - """Test credentials._verify_code with code from another user.""" - ret = _verify_code('[email protected]', 'ARxJtdP43GU') - self.assertFalse(ret.is_valid) - - def test__verify_code_bad_code(self): - """Test credentials._verify_code with invalid base58 code.""" - ret = _verify_code('[email protected]', '****') - self.assertFalse(ret.is_valid) - - def test_get_data(self): - """Test get_data.""" - with patch(INPUT) as p: - p.return_value = 'my data' - data = _get_data('some message') - - self.assertEqual(1, p.call_count) - self.assertEqual('some message: ', p.call_args[0][0]) - self.assertEqual('my data', data) + def test_verify_email(self): + """Test _verify_email.""" + self.assertTrue(_verify_email('[email protected]').is_valid) + self.assertTrue(_verify_email('').is_valid) + self.assertFalse(_verify_email('missing_at_sign').is_valid)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
zulip__zulip-18217@3c5b3da
zulip/zulip
Python
18,217
hotkey: Disable left arrow hotkey when view has unreads.
On pressing left arrow key while in narrow stream to edit the last message, if the user has unread messages sent in topics other than the one where the user has sent its last message (considering the topics for same stream) and the unread messages were sent before the user message, the hotkey unexpectedly marks the messages as read. Hence we disable the hotkey for such a case. Fixes #17737. <!-- What's this PR for? (Just a link to an issue is fine.) --> **Testing plan:** <!-- How have you tested? --> **GIFs or screenshots:** <!-- If a UI change. See: https://zulip.readthedocs.io/en/latest/tutorials/screenshot-and-gif-software.html --> <!-- Also be sure to make clear, coherent commits: https://zulip.readthedocs.io/en/latest/contributing/version-control.html -->
2021-04-20T09:48:52Z
Edit last message keyboard shortcut (โ†) may unexpectedly mark many messages read If you write a message in a narrowed view, then go back to an unnarrowed view, there may now be many unread messages between your new current position and the message you just wrote. If you then accidentally press `โ†`, all of those messages will be marked read unexpectedly, causing you to lose your place while catching up. We should either * disable the `โ†` shortcut if there are unread messages between your current position and the message to be edited, or * disable the `โ†` shortcut if youโ€™ve switched views since writing the last message.
Hello @zulip/server-message-view members, this issue was labeled with the "area: message-editing" label, so you may want to check it out! <!-- areaLabelAddition --> Hello @andersk I wanna work on this issue I think this option makes sense to me, probably with a notice: > disable the โ† shortcut if there are unread messages between your current position and the message to be edited, or @zulipbot claim Hello @ligmitz, you claimed this issue to work on it, but this issue and any referenced pull requests haven't been updated for 10 days. Are you still working on this issue? If so, please update this issue by leaving a comment on this issue to let me know that you're still working on it. Otherwise, I'll automatically remove you from this issue in 4 days. If you've decided to work on something else, simply comment `@zulipbot abandon` so that someone else can claim it and continue from where you left off. Thank you for your valuable contributions to Zulip! <!-- inactiveWarning -->
[ { "body": "If you write a message in a narrowed view, then go back to an unnarrowed view, there may now be many unread messages between your new current position and the message you just wrote. If you then accidentally press `โ†`, all of those messages will be marked read unexpectedly, causing you to lose your place while catching up.\r\n\r\nWe should either\r\n* disable the `โ†` shortcut if there are unread messages between your current position and the message to be edited, or\r\n* disable the `โ†` shortcut if youโ€™ve switched views since writing the last message.", "number": 17737, "title": "Edit last message keyboard shortcut (โ†) may unexpectedly mark many messages read" } ]
a8abcf5210deb1894147077a19545686418198d1
{ "head_commit": "3c5b3da20e5364aac51c581328aafa1616fe16fc", "head_commit_message": "message_edit: Remove incorrect `from_scroll` value.\n\nSince it is important that the message being edited is visible,\nit is safe to ignore `from_scroll` here which is also an\nunlikely possibility here.", "patch_to_review": "diff --git a/web/src/message_edit.ts b/web/src/message_edit.ts\nindex 01a1d0ef49e25..fe78b813cee19 100644\n--- a/web/src/message_edit.ts\n+++ b/web/src/message_edit.ts\n@@ -1272,7 +1272,7 @@ export function edit_last_sent_message(): void {\n return;\n }\n \n- message_lists.current.select_id(msg.id, {then_scroll: true, from_scroll: true});\n+ message_lists.current.select_id(msg.id, {then_scroll: true});\n \n const $msg_row = message_lists.current.get_row(msg.id);\n if (!$msg_row) {\n" }
[ { "diff_hunk": "@@ -1252,6 +1252,30 @@ export function maybe_show_edit($row: JQuery, id: number): void {\n }\n }\n \n+function warn_user_about_unread_msgs(last_sent_msg_id: number): void {\n+ feedback_widget.show({\n+ populate(container) {\n+ container.html(\n+ $t({\n+ defaultMessage:\n+ \"Editing the last message you sent will mark all older messages in this view as read.\",\n+ }),\n+ );\n+ },\n+ on_undo() {\n+ // Select the message we want to edit to mark messages between it and the\n+ // current selected id as read.\n+ message_lists.current?.select_id(last_sent_msg_id, {\n+ then_scroll: true,\n+ from_scroll: true,", "line": null, "original_line": 1270, "original_start_line": null, "path": "web/src/message_edit.ts", "start_line": null, "text": "@user1:\nI was trying to preserve the `select_id` parameters passed below, but yeah I see it not required even in the line after this, so I removed `from_scroll` from both the places." } ]
8af72a8a906f7601f0e9d95421a2a266c68a1df8
diff --git a/web/src/message_edit.ts b/web/src/message_edit.ts index 01a1d0ef49e25..9da77778a1574 100644 --- a/web/src/message_edit.ts +++ b/web/src/message_edit.ts @@ -6,6 +6,7 @@ import {z} from "zod"; import * as resolved_topic from "../shared/src/resolved_topic.ts"; import render_wildcard_mention_not_allowed_error from "../templates/compose_banner/wildcard_mention_not_allowed_error.hbs"; import render_delete_message_modal from "../templates/confirm_dialog/confirm_delete_message.hbs"; +import render_confirm_edit_messages from "../templates/confirm_dialog/confirm_edit_messages.hbs"; import render_confirm_merge_topics_with_rename from "../templates/confirm_dialog/confirm_merge_topics_with_rename.hbs"; import render_confirm_moving_messages_modal from "../templates/confirm_dialog/confirm_moving_messages.hbs"; import render_intro_resolve_topic_modal from "../templates/confirm_dialog/intro_resolve_topic.hbs"; @@ -1252,33 +1253,71 @@ export function maybe_show_edit($row: JQuery, id: number): void { } } +function warn_user_about_unread_msgs(last_sent_msg_id: number, num_unread: number): void { + confirm_dialog.launch({ + html_heading: $t({defaultMessage: "Edit last sent message"}), + html_body: render_confirm_edit_messages({ + num_unread, + }), + on_click() { + // Select the message we want to edit to mark messages between it and the + // current selected id as read. + message_lists.current?.select_id(last_sent_msg_id, { + then_scroll: true, + }); + edit_last_sent_message(); + }, + }); +} + export function edit_last_sent_message(): void { if (message_lists.current === undefined) { return; } - const msg = message_lists.current.get_last_message_sent_by_me(); + const last_sent_msg = message_lists.current.get_last_message_sent_by_me(); - if (!msg) { + if (!last_sent_msg) { return; } - if (!msg.id) { + if (!last_sent_msg.id) { blueslip.error("Message has invalid id in edit_last_sent_message."); return; } - if (!is_content_editable(msg, 5)) { + if (!is_content_editable(last_sent_msg, 5)) { return; } - message_lists.current.select_id(msg.id, {then_scroll: true, from_scroll: true}); + const current_selected_msg = message_store.get(message_lists.current.selected_id()); + if ( + current_selected_msg && + current_selected_msg.id < last_sent_msg.id && + message_lists.current.can_mark_messages_read() + ) { + // If there are any unread messages between the selected message and the + // message we want to edit, we don't edit the last sent message to avoid + // marking messages as read unintentionally. + let num_unread = 0; + for (const msg of message_lists.current.all_messages()) { + if (current_selected_msg.id < msg.id && msg.id < last_sent_msg.id && msg.unread) { + num_unread += 1; + } + } + if (num_unread > 0) { + warn_user_about_unread_msgs(last_sent_msg.id, num_unread); + return; + } + } + + message_lists.current.select_id(last_sent_msg.id, {then_scroll: true}); - const $msg_row = message_lists.current.get_row(msg.id); + const $msg_row = message_lists.current.get_row(last_sent_msg.id); if (!$msg_row) { // This should never happen, since we got the message above // from message_lists.current. - blueslip.error("Could not find row for id", {msg_id: msg.id}); + blueslip.error("Could not find row for id", {msg_id: last_sent_msg.id}); return; } diff --git a/web/templates/confirm_dialog/confirm_edit_messages.hbs b/web/templates/confirm_dialog/confirm_edit_messages.hbs new file mode 100644 index 0000000000000..7cc6a83984192 --- /dev/null +++ b/web/templates/confirm_dialog/confirm_edit_messages.hbs @@ -0,0 +1,5 @@ +<p> + {{#tr}} + Scrolling to the last message you sent will mark <b>{num_unread}</b> unread messages as read. Would you like to scroll to that message and edit it? + {{/tr}} +</p>
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
ultralytics__ultralytics-7136@bf9a991
ultralytics/ultralytics
Python
7,136
`ultralytics 8.0.236` dataset semantic & SQL search API
copilot:all ## ๐Ÿ› ๏ธ PR Summary <sub>Made with โค๏ธ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub> ### ๐ŸŒŸ Summary Introducing expanded dataset exploration features for Ultralytics with an API and GUI support. ### ๐Ÿ“Š Key Changes - Added `explorer` dependency with vector search, SQL queries, and GUI elements. - New `test_explorer.py` with tests for dataset exploration functionalities. - `Explorer` class in `__init__.py` for the easy creation of dataset explorations. - Updated `cfg/__init__.py` with an entry point for the dataset explorer. - Implemented `ExplorerDataset` within `explorer.py` for loading images without alteration. - Enabled semantic dataset search with the addition of `plot_similar` and `get_similar` methods. - Introduced similarity index computation with the `similarity_index` method. - Integrated GUI demo inside `dash.py`, providing interactive dataset exploration. - Incorporated contribution to plotting within `plotting.py`, enabling visualizations of dataset insights. ### ๐ŸŽฏ Purpose & Impact - **Dataset Insight**: Offers intuitive data analysis, helping users understand CV datasets better. - **Efficiency**: Save time on data analysis thanks to seamless SQL, vector similarity, and semantic searches. - **Enhanced UX**: Users can interactively explore datasets via included GUI, improving the overall experience. - **Visualization Tools**: Rich visual representation of data made possible, aiding in pattern recognition and decision-making.
2023-12-22T20:02:38Z
Add new feature - dataset explorer ## Summary Dataset explorer and utilities for Ultralytics ## Features ### Embedding - Allows user to calculate embeddings as a top level operation in YOLO models. - Start with detection/segmentation backbones for now, pose, etc. are not that relevant for embeddings. <p><details> <summary>Example</summary> ```py from ultralytics import yolo model = yolo() embeddings = model.embed(inputs) ``` </details> ### Data explorer dashboard - Similarity search - Single image - Multiple images - SQL query/filtering <p><details> <summary>Example</summary> ```sh # CLI example yolo explorer {DATASET_NAME} ``` ```py from ultralytics import Explorer exp = Explorer(โ€œcoco128.yamlโ€) exp.dashboard() ``` </details> ### Advanced features - Search across multiple (selected) datasets - Delete redundant data or enrich primary dataset from secondary data sources - Upload to HUB and export new dataset <p><details> <summary>Example</summary> ```sh # CLI example yolo explorer {PRIMARY_DATASET} {SECONDARY_DATA_1} {SECONDARY_DATA_2} โ€ฆ ``` ```py from ultralytics import Explorer exp_primary = Explorer(โ€œcoco128.yamlโ€) # Core dataset to clean or add to with images from other sources exp_1 = Explorer(โ€œVOC.yamlโ€) exp_2 = Explorer(โ€œcoco.yamlโ€) exp.dashboard(sources=[exp_1, exp_2]) ``` </details> ## Tasks - [ ] Create dashboard - [ ] use Ultralytics branding - [ ] Create utility functions/classes - [ ] ... - [ ] write unit test - [ ] create Ultralytics Documentation
@AyushExel I added the information you shared to this issue and cleaned it up some. I also tried to include sub-tasks, but wasn't certain on what else would be needed. Please reference this issue with your PR and any important commits. PR #6971 opened to add this feature Cool. Advanced features are out of scope for now. Let's only plan on getting these before the anniversary: * Embedding as a top level model feature * Explorer pythonic API * Explorer Dashboard
[ { "body": "## Summary\nDataset explorer and utilities for Ultralytics\n\n## Features\n\n### Embedding\n\n- Allows user to calculate embeddings as a top level operation in YOLO models.\n- Start with detection/segmentation backbones for now, pose, etc. are not that relevant for embeddings.\n\n<p><details>\n<summary>Example</summary>\n\n```py\nfrom ultralytics import yolo\n\nmodel = yolo()\nembeddings = model.embed(inputs)\n```\n</details>\n\n### Data explorer dashboard\n - Similarity search\n - Single image\n - Multiple images\n - SQL query/filtering\n\n <p><details>\n <summary>Example</summary>\n\n ```sh\n # CLI example\n yolo explorer {DATASET_NAME}\n ```\n ```py\n from ultralytics import Explorer\n \n exp = Explorer(โ€œcoco128.yamlโ€)\n exp.dashboard()\n ```\n </details>\n\n### Advanced features\n\n - Search across multiple (selected) datasets\n - Delete redundant data or enrich primary dataset from secondary data sources\n - Upload to HUB and export new dataset\n\n <p><details>\n <summary>Example</summary>\n\n ```sh\n # CLI example\n yolo explorer {PRIMARY_DATASET} {SECONDARY_DATA_1} {SECONDARY_DATA_2} โ€ฆ\n ```\n ```py\n from ultralytics import Explorer\n\n exp_primary = Explorer(โ€œcoco128.yamlโ€) # Core dataset to clean or add to with images from other sources\n \n exp_1 = Explorer(โ€œVOC.yamlโ€)\n exp_2 = Explorer(โ€œcoco.yamlโ€) \n \n exp.dashboard(sources=[exp_1, exp_2])\n ```\n </details>\n\n## Tasks\n- [ ] Create dashboard\n - [ ] use Ultralytics branding\n- [ ] Create utility functions/classes\n- [ ] ...\n- [ ] write unit test\n- [ ] create Ultralytics Documentation", "number": 6932, "title": "Add new feature - dataset explorer" } ]
40a5c0abe70731187b1a5d15809dcce00e479b35
{ "head_commit": "bf9a991f3d0dc6c43fee5a4fecad2c5b5f81f1cb", "head_commit_message": "Merge remote-tracking branch 'refs/remotes/origin/sem_search' into sem_search", "patch_to_review": "diff --git a/ultralytics/data/explorer/__init__.py b/ultralytics/data/explorer/__init__.py\nnew file mode 100644\nindex 00000000000..e69de29bb2d\ndiff --git a/ultralytics/data/explorer/explorer.py b/ultralytics/data/explorer/explorer.py\nnew file mode 100644\nindex 00000000000..0e80e2b0cb5\n--- /dev/null\n+++ b/ultralytics/data/explorer/explorer.py\n@@ -0,0 +1,158 @@\n+from pathlib import Path\n+from typing import List\n+\n+import cv2\n+import numpy as np\n+import pyarrow as pa\n+import torch\n+from tqdm import tqdm\n+\n+from ultralytics import YOLO\n+from ultralytics.data.augment import Format\n+from ultralytics.data.dataset import YOLODataset\n+from ultralytics.data.utils import check_det_dataset\n+from ultralytics.utils import logger\n+from ultralytics.utils.checks import check_requirements\n+\n+from .utils import sanitize_batch\n+\n+check_requirements('lancedb')\n+import lancedb\n+\n+\n+class ExplorerDataset(YOLODataset):\n+\n+ def __init__(self, *args, data=None, **kwargs):\n+ super().__init__(*args, data=data, **kwargs)\n+\n+ # NOTE: Load the image directly without any resize operations.\n+ def load_image(self, i):\n+ \"\"\"Loads 1 image from dataset index 'i', returns (im, resized hw).\"\"\"\n+ im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]\n+ if im is None: # not cached in RAM\n+ if fn.exists(): # load npy\n+ im = np.load(fn)\n+ else: # read image\n+ im = cv2.imread(f) # BGR\n+ if im is None:\n+ raise FileNotFoundError(f'Image Not Found {f}')\n+ h0, w0 = im.shape[:2] # orig hw\n+ return im, (h0, w0), im.shape[:2]\n+\n+ return self.ims[i], self.im_hw0[i], self.im_hw[i]\n+\n+ def build_transforms(self, hyp=None):\n+ transforms = Format(\n+ bbox_format='xyxy',\n+ normalize=False,\n+ return_mask=self.use_segments,\n+ return_keypoint=self.use_keypoints,\n+ batch_idx=True,\n+ mask_ratio=hyp.mask_ratio,\n+ mask_overlap=hyp.overlap_mask,\n+ )\n+ return transforms\n+\n+\n+class Explorer:\n+\n+ def __init__(self, data='coco128.yaml', model='yolov8n.pt', uri='~/ultralytics/explorer') -> None:\n+ self.connection = lancedb.connect(uri)\n+ self.table_name = Path(data).name\n+ self.model = YOLO(model)\n+ self.data = data # None\n+ self.choice_set = None\n+\n+ self.table = None\n+\n+ def create_embeddings_table(self, force=False, split='train', verbose=False):\n+ if (self.table is not None and not force):\n+ logger.info('Table already exists. Reusing it. Pass force=True to overwrite it.')\n+ return\n+ if self.table_name in self.connection.table_names():\n+ logger.info(f'Table {self.table_name} already exists. Reusing it. Pass force=True to overwrite it.')\n+ self.table = self.connection.open_table(self.table_name)\n+ return\n+ if self.data is None:\n+ raise ValueError('Data must be provided to create embeddings table')\n+\n+ data_info = check_det_dataset(self.data)\n+ if split not in data_info:\n+ raise ValueError(\n+ f'Split {split} is not found in the dataset. Available keys in the dataset are {list(data_info.keys())}'\n+ )\n+\n+ choice_set = data_info[split]\n+ choice_set = choice_set if isinstance(choice_set, list) else [choice_set]\n+ self.choice_set = choice_set\n+\n+ dataset = ExplorerDataset(img_path=choice_set, data=data_info, augment=False, cache=False)\n+\n+ # Create the table schema\n+ schema = pa.schema([\n+ pa.field('im_file', pa.string()),\n+ pa.field('labels', pa.list_(pa.string())),\n+ pa.field('bboxes', pa.list_(pa.list_(pa.float32()))),\n+ pa.field('cls', pa.list_(pa.int32())),\n+ pa.field('vector', pa.list_(pa.float32(),\n+ self.model.embed(dataset[0]['im_file'])[0].shape[0])), ])\n+ table = self.connection.create_table(self.table_name, schema=schema, mode='overwrite')\n+ table.add(\n+ self._yeild_batches(dataset,\n+ data_info,\n+ self.model,\n+ exclude_keys=['img', 'ratio_pad', 'resized_shape', 'ori_shape', 'batch_idx']))\n+\n+ self.table = table\n+\n+ @staticmethod\n+ def _yeild_batches(dataset, data_info, model, exclude_keys: List):\n+ # Implement Batching\n+ for i in tqdm(range(len(dataset))):\n+ batch = dataset[i]\n+ for k in exclude_keys:\n+ batch.pop(k, None)\n+ batch = sanitize_batch(batch, data_info)\n+ batch['vector'] = model.embed(batch['im_file'], verbose=False)[0].detach().tolist()\n+ yield [batch]\n+\n+ def query(self, img, limit=25):\n+ \"\"\"\n+ Query the table for similar images. Accepts a single image or a list of images.\n+\n+ Args:\n+ img (str or list): Path to the image or a list of paths to the images.\n+ limit (int): Number of results to return.\n+\n+ Returns:\n+ An arrow table containing the results.\n+ \"\"\"\n+ if self.table is None:\n+ raise ValueError('Table is not created. Please create the table first.')\n+ if isinstance(img, str):\n+ img = [img]\n+ elif isinstance(img, list):\n+ pass\n+ else:\n+ raise ValueError(f'img must be a string or a list of strings. Got {type(img)}')\n+ embeds = self.model.embed(img)\n+ # Get avg if multiple images are passed (len > 1)\n+ embeds = torch.mean(torch.stack(embeds))\n+\n+ query = self.table.query(embeds).limit(limit).to_arrow()\n+ return query\n+\n+ def sql_query(self, query):\n+ \"\"\"\n+ Run a SQL-Like query on the table. Utilizes LanceDB predicate pushdown.\n+\n+ Args:\n+ query (str): SQL query to run.\n+\n+ Returns:\n+ An arrow table containing the results.\n+ \"\"\"\n+ if self.table is None:\n+ raise ValueError('Table is not created. Please create the table first.')\n+\n+ return self.table.to_lance.to_table(filter=query).to_arrow()\ndiff --git a/ultralytics/data/explorer/gui/__init__.py b/ultralytics/data/explorer/gui/__init__.py\nnew file mode 100644\nindex 00000000000..e69de29bb2d\ndiff --git a/ultralytics/data/explorer/utils.py b/ultralytics/data/explorer/utils.py\nnew file mode 100644\nindex 00000000000..209dae743e1\n--- /dev/null\n+++ b/ultralytics/data/explorer/utils.py\n@@ -0,0 +1,7 @@\n+def sanitize_batch(batch, dataset_info):\n+ batch['cls'] = batch['cls'].flatten().int().tolist()\n+ box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1])\n+ batch['bboxes'] = [box for box, _ in box_cls_pair]\n+ batch['cls'] = [cls for _, cls in box_cls_pair]\n+ batch['labels'] = [dataset_info['names'][i] for i in batch['cls']]\n+ return batch\n" }
[ { "diff_hunk": "@@ -0,0 +1,158 @@\n+from pathlib import Path\n+from typing import List\n+\n+import cv2\n+import numpy as np\n+import pyarrow as pa\n+import torch\n+from tqdm import tqdm\n+\n+from ultralytics import YOLO\n+from ultralytics.data.augment import Format\n+from ultralytics.data.dataset import YOLODataset\n+from ultralytics.data.utils import check_det_dataset\n+from ultralytics.utils import logger\n+from ultralytics.utils.checks import check_requirements\n+\n+from .utils import sanitize_batch\n+\n+check_requirements('lancedb')\n+import lancedb\n+\n+\n+class ExplorerDataset(YOLODataset):\n+\n+ def __init__(self, *args, data=None, **kwargs):\n+ super().__init__(*args, data=data, **kwargs)\n+\n+ # NOTE: Load the image directly without any resize operations.\n+ def load_image(self, i):\n+ \"\"\"Loads 1 image from dataset index 'i', returns (im, resized hw).\"\"\"\n+ im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]\n+ if im is None: # not cached in RAM\n+ if fn.exists(): # load npy\n+ im = np.load(fn)\n+ else: # read image\n+ im = cv2.imread(f) # BGR\n+ if im is None:\n+ raise FileNotFoundError(f'Image Not Found {f}')\n+ h0, w0 = im.shape[:2] # orig hw\n+ return im, (h0, w0), im.shape[:2]\n+\n+ return self.ims[i], self.im_hw0[i], self.im_hw[i]\n+\n+ def build_transforms(self, hyp=None):\n+ transforms = Format(\n+ bbox_format='xyxy',\n+ normalize=False,\n+ return_mask=self.use_segments,\n+ return_keypoint=self.use_keypoints,\n+ batch_idx=True,\n+ mask_ratio=hyp.mask_ratio,\n+ mask_overlap=hyp.overlap_mask,\n+ )\n+ return transforms\n+\n+\n+class Explorer:\n+\n+ def __init__(self, data='coco128.yaml', model='yolov8n.pt', uri='~/ultralytics/explorer') -> None:\n+ self.connection = lancedb.connect(uri)\n+ self.table_name = Path(data).name\n+ self.model = YOLO(model)\n+ self.data = data # None\n+ self.choice_set = None\n+\n+ self.table = None\n+\n+ def create_embeddings_table(self, force=False, split='train', verbose=False):\n+ if (self.table is not None and not force):\n+ logger.info('Table already exists. Reusing it. Pass force=True to overwrite it.')\n+ return\n+ if self.table_name in self.connection.table_names():\n+ logger.info(f'Table {self.table_name} already exists. Reusing it. Pass force=True to overwrite it.')\n+ self.table = self.connection.open_table(self.table_name)\n+ return\n+ if self.data is None:\n+ raise ValueError('Data must be provided to create embeddings table')\n+\n+ data_info = check_det_dataset(self.data)\n+ if split not in data_info:\n+ raise ValueError(\n+ f'Split {split} is not found in the dataset. Available keys in the dataset are {list(data_info.keys())}'\n+ )\n+\n+ choice_set = data_info[split]\n+ choice_set = choice_set if isinstance(choice_set, list) else [choice_set]\n+ self.choice_set = choice_set\n+\n+ dataset = ExplorerDataset(img_path=choice_set, data=data_info, augment=False, cache=False)\n+\n+ # Create the table schema\n+ schema = pa.schema([\n+ pa.field('im_file', pa.string()),\n+ pa.field('labels', pa.list_(pa.string())),\n+ pa.field('bboxes', pa.list_(pa.list_(pa.float32()))),\n+ pa.field('cls', pa.list_(pa.int32())),\n+ pa.field('vector', pa.list_(pa.float32(),\n+ self.model.embed(dataset[0]['im_file'])[0].shape[0])), ])\n+ table = self.connection.create_table(self.table_name, schema=schema, mode='overwrite')\n+ table.add(\n+ self._yeild_batches(dataset,\n+ data_info,\n+ self.model,\n+ exclude_keys=['img', 'ratio_pad', 'resized_shape', 'ori_shape', 'batch_idx']))\n+\n+ self.table = table\n+\n+ @staticmethod\n+ def _yeild_batches(dataset, data_info, model, exclude_keys: List):", "line": null, "original_line": 109, "original_start_line": null, "path": "ultralytics/data/explorer/explorer.py", "start_line": null, "text": "@user1:\nIs there a typo? Did you mean `_yield_batches`?\n\n@author:\nYes. This is still is WIP" } ]
c0a8fcf6b0f87cbeeb98e8d3ea01685b181e7695
diff --git a/docs/ar/models/yolov5.md b/docs/ar/models/yolov5.md index 32481a3f151..a013f04bfcd 100644 --- a/docs/ar/models/yolov5.md +++ b/docs/ar/models/yolov5.md @@ -59,7 +59,7 @@ keywords: YOLOv5uุŒ ูƒุดู ุงู„ูƒุงุฆู†ุงุชุŒ ุงู„ู†ู…ุงุฐุฌ ุงู„ู…ุฏุฑุจุฉ ู…ุณ ```python from ultralytics import YOLO -#ู‚ู… ุจุชุญู…ูŠู„ ู†ู…ูˆุฐุฌ YOLOv5n ุงู„ู…ุฏุฑุจ ู…ุณุจู‚ู‹ุง ุนู„ู‰ ู…ุฌู…ูˆุนุฉ ุจูŠุงู†ุงุช COCO +# ู‚ู… ุจุชุญู…ูŠู„ ู†ู…ูˆุฐุฌ YOLOv5n ุงู„ู…ุฏุฑุจ ู…ุณุจู‚ู‹ุง ุนู„ู‰ ู…ุฌู…ูˆุนุฉ ุจูŠุงู†ุงุช COCO model = YOLO('yolov5n.pt') # ู‚ู… ุจุนุฑุถ ู…ุนู„ูˆู…ุงุช ุงู„ู†ู…ูˆุฐุฌ (ุงุฎุชูŠุงุฑูŠ) diff --git a/docs/en/datasets/explorer/api.md b/docs/en/datasets/explorer/api.md new file mode 100644 index 00000000000..612067f6271 --- /dev/null +++ b/docs/en/datasets/explorer/api.md @@ -0,0 +1,297 @@ +--- +comments: true +description: Explore and analyze CV datasets with Ultralytics Explorer API, offering SQL, vector similarity, and semantic searches for efficient dataset insights. +keywords: Ultralytics Explorer API, Dataset Exploration, SQL Queries, Vector Similarity Search, Semantic Search, Embeddings Table, Image Similarity, Python API for Datasets, CV Dataset Analysis, LanceDB Integration +--- + +# Ultralytics Explorer API + +## Introduction + +The Explorer API is a Python API for exploring your datasets. It supports filtering and searching your dataset using SQL queries, vector similarity search and semantic search. + +## Installation + +Explorer depends on external libraries for some of its functionality. These are automatically installed on usage. To manually install these dependencies, use the following command: + +```bash +pip install ultralytics[explorer] +``` + +## Usage + +```python +from ultralytics import Explorer + +# Create an Explorer object +explorer = Explorer(data='coco128.yaml', model='yolov8n.pt') + +# Create embeddings for your dataset +explorer.create_embeddings_table() + +# Search for similar images to a given image/images +dataframe = explorer.get_similar(img='path/to/image.jpg') + +# Or search for similar images to a given index/indices +dataframe = explorer.get_similar()(idx=0) +``` + +## 1. Similarity Search + +Similarity search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. +One the embeddings table is built, you can get run semantic search in any of the following ways: + +- On a given index / list of indices in the dataset like - `exp.get_similar(idx=[1,10], limit=10)` +- On any image/ list of images not in the dataset - `exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10)` +- + +In case of multiple inputs, the aggregate of their embeddings is used. + +You get a pandas dataframe with the `limit` number of most similar data points to the input, along with their distance in the embedding space. You can use this dataset to perform further filtering + +!!! Example "Semantic Search" + + === "Using Images" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + similar = exp.get_similar(img='https://ultralytics.com/images/bus.jpg', limit=10) + print(similar.head()) + + # Search using multiple indices + similar = exp.get_similar( + img=['https://ultralytics.com/images/bus.jpg', + 'https://ultralytics.com/images/bus.jpg'], + limit=10 + ) + print(similar.head()) + ``` + + === "Using Dataset Indices" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + similar = exp.get_similar(idx=1, limit=10) + print(similar.head()) + + # Search using multiple indices + similar = exp.get_similar(idx=[1,10], limit=10) + print(similar.head()) + ``` + +### Plotting Similar Images + +You can also plot the similar images using the `plot_similar` method. This method takes the same arguments as `get_similar` and plots the similar images in a grid. + +!!! Example "Plotting Similar Images" + + === "Using Images" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + plt = exp.plot_similar(img='https://ultralytics.com/images/bus.jpg', limit=10) + plt.show() + ``` + + === "Using Dataset Indices" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + plt = exp.plot_similar(idx=1, limit=10) + plt.show() + ``` + +## 2. SQL Querying + +You can run SQL queries on your dataset using the `sql_query` method. This method takes a SQL query as input and returns a pandas dataframe with the results. + +!!! Example "SQL Query" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'") + print(df.head()) + ``` + +### Plotting SQL Query Results + +You can also plot the results of a SQL query using the `plot_sql_query` method. This method takes the same arguments as `sql_query` and plots the results in a grid. + +!!! Example "Plotting SQL Query Results" + + ```python + from ultralytics import Explorer + + # create an Explorer object + exp = Explorer(data='coco128.yaml', model='yolov8n.pt') + exp.create_embeddings_table() + + df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'") + print(df.head()) + ``` + +## 3. Working with embeddings Table (Advanced) + +You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table` + +!!! Tip "Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre- and post-filters, etc." + + ```python + from ultralytics import Explorer + + exp = Explorer() + exp.create_embeddings_table() + table = exp.table + ``` + +Here are some examples of what you can do with the table: + +### Get raw Embeddings + +!!! Example + + ```python + from ultralytics import Explorer + + exp = Explorer() + exp.create_embeddings_table() + table = exp.table + + embeddings = table.to_pandas()["vector"] + print(embeddings) + ``` + +### Advanced Querying with pre and post filters + +!!! Example + + ```python + from ultralytics import Explorer + + exp = Explorer(model="yolov8n.pt") + exp.create_embeddings_table() + table = exp.table + + # Dummy embedding + embedding = [i for i in range(256)] + rs = table.search(embedding).metric("cosine").where("").limit(10) + ``` + +### Create Vector Index + +When using large datasets, you can also create a dedicated vector index for faster querying. This is done using the `create_index` method on LanceDB table. + +```python + table.create_index(num_partitions=..., num_sub_vectors=...) +``` + +Find more details on the type vector indices available and parameters [here](https://lancedb.github.io/lancedb/ann_indexes/#types-of-index) +In the future, we will add support for creating vector indices directly from Explorer API. + +## 4. Embeddings Applications + +You can use the embeddings table to perform a variety of exploratory analysis. Here are some examples: + +### Similarity Index + +Explorer comes with a `similarity_index` operation: + +* It tries to estimate how similar each data point is with the rest of the dataset. +* It does that by counting how many image embeddings lie closer than `max_dist` to the current image in the generated embedding space, considering `top_k` similar images at a time. + +It returns a pandas dataframe with the following columns: + +* `idx`: Index of the image in the dataset +* `im_file`: Path to the image file +* `count`: Number of images in the dataset that are closer than `max_dist` to the current image +* `sim_im_files`: List of paths to the `count` similar images + +!!! Tip + + For a given dataset, model, `max_dist` & `top_k` the similarity index once generated will be reused. In case, your dataset has changed, or you simply need to regenerate the similarity index, you can pass `force=True`. + +!!! Example "Similarity Index" + + ```python + from ultralytics import Explorer + + exp = Explorer() + exp.create_embeddings_table() + + sim_idx = exp.similarity_index() + ``` + +You can use similarity index to build custom conditions to filter out the dataset. For example, you can filter out images that are not similar to any other image in the dataset using the following code: + +```python +import numpy as np + +sim_count = np.array(sim_idx["count"]) +sim_idx['im_file'][sim_count > 30] +``` + +### Visualize Embedding Space + +You can also visualize the embedding space using the plotting tool of your choice. For example here is a simple example using matplotlib: + +```python +import numpy as np +from sklearn.decomposition import PCA +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +# Reduce dimensions using PCA to 3 components for visualization in 3D +pca = PCA(n_components=3) +reduced_data = pca.fit_transform(embeddings) + +# Create a 3D scatter plot using Matplotlib Axes3D +fig = plt.figure(figsize=(8, 6)) +ax = fig.add_subplot(111, projection='3d') + +# Scatter plot +ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5) +ax.set_title('3D Scatter Plot of Reduced 256-Dimensional Data (PCA)') +ax.set_xlabel('Component 1') +ax.set_ylabel('Component 2') +ax.set_zlabel('Component 3') + +plt.show() +``` + +Start creating your own CV dataset exploration reports using the Explorer API. For inspiration, check out the + +# Apps Built Using Ultralytics Explorer + +Try our GUI Demo based on Explorer API + +# Coming Soon + +- [ ] Merge specific labels from datasets. Example - Import all `person` labels from COCO and `car` labels from Cityscapes +- [ ] Remove images that have a higher similarity index than the given threshold +- [ ] Automatically persist new datasets after merging/removing entries +- [ ] Advanced Dataset Visualizations diff --git a/docs/en/datasets/explorer/dash.md b/docs/en/datasets/explorer/dash.md new file mode 100644 index 00000000000..e69de29bb2d diff --git a/docs/en/datasets/explorer/explorer.ipynb b/docs/en/datasets/explorer/explorer.ipynb new file mode 100644 index 00000000000..5e5b7b02443 --- /dev/null +++ b/docs/en/datasets/explorer/explorer.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aa923c26-81c8-4565-9277-1cb686e3702e", + "metadata": {}, + "source": [ + "# VOC Exploration Example \n", + "<div align=\"center\">\n", + "\n", + " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n", + " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", + "\n", + " [ไธญๆ–‡](https://docs.ultralytics.com/zh/) | [ํ•œ๊ตญ์–ด](https://docs.ultralytics.com/ko/) | [ๆ—ฅๆœฌ่ชž](https://docs.ultralytics.com/ja/) | [ะ ัƒััะบะธะน](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Franรงais](https://docs.ultralytics.com/fr/) | [Espaรฑol](https://docs.ultralytics.com/es/) | [Portuguรชs](https://docs.ultralytics.com/pt/) | [เคนเคฟเคจเฅเคฆเฅ€](https://docs.ultralytics.com/hi/) | [ุงู„ุนุฑุจูŠุฉ](https://docs.ultralytics.com/ar/)\n", + "\n", + " <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n", + " <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n", + " <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n", + "\n", + "Welcome to the Ultralytics Explorer API notebook! This notebook serves as the starting point for exploring the various resources available to help you get started with using Ultralytics to explore your datasets using with the power of semantic search. You can utilities out of the box that allow you to examine specific types of labels using vector search or even SQL queries.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of Ultralytics. Please browse the Explorer <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n", + "\n", + "Try `yolo explorer` powered by Exlorer API\n", + "\n", + "Simply `pip install ultralytics` and run `yolo explorer` in your terminal to run custom queries and semantic search on your datasets right inside your browser!\n", + "\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "id": "2454d9ba-9db4-4b37-98e8-201ba285c92f", + "metadata": {}, + "source": [ + "## Setup\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "433f3a4d-a914-42cb-b0b6-be84a84e5e41", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install ultralytics\n", + "ultralytics.checks()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae602549-3419-4909-9f82-35cba515483f", + "metadata": {}, + "outputs": [], + "source": [ + "from ultralytics import Explorer" + ] + }, + { + "cell_type": "markdown", + "id": "d8c06350-be8e-45cf-b3a6-b5017bbd943c", + "metadata": {}, + "source": [ + "# Similarity search\n", + "Utilize the power of vector similarity search to find the similar data points in your dataset along with their distance in the embedding space. Simply create an embeddings table for the given dataset-model pair. It is only needed once and it is reused automatically.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "334619da-6deb-4b32-9fe0-74e0a79cee20", + "metadata": {}, + "outputs": [], + "source": [ + "exp = Explorer(\"VOC.yaml\", model=\"yolov8n.pt\")\n", + "exp.create_embeddings_table()" + ] + }, + { + "cell_type": "markdown", + "id": "b6c5e42d-bc7e-4b4c-bde0-643072a2165d", + "metadata": {}, + "source": [ + "One the embeddings table is built, you can get run semantic search in any of the following ways:\n", + "- On a given index / list of indices in the dataset like - `exp.get_similar(idx=[1,10], limit=10)`\n", + "- On any image/ list of images not in the dataset - `exp.get_similar(img=[\"path/to/img1\", \"path/to/img2\"], limit=10)`\n", + "In case of multiple inputs, the aggregade of their embeddings is used.\n", + "\n", + "You get a pandas dataframe with the `limit` number of most similar data points to the input, along with their distance in the embedding space. You can use this dataset to perform further filtering\n", + "<img width=\"1120\" alt=\"Screenshot 2024-01-06 at 9 45 42โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/7742ac57-e22a-4cea-a0f9-2b2a257483c5\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b485f05b-d92d-42bc-8da7-5e361667b341", + "metadata": {}, + "outputs": [], + "source": [ + "similar = exp.get_similar(idx=1, limit=10)\n", + "similar.head()" + ] + }, + { + "cell_type": "markdown", + "id": "acf4b489-2161-4176-a1fe-d1d067d8083d", + "metadata": {}, + "source": [ + "You can use the also plot the similar samples directly using the `plot_similar` util\n", + "<img width=\"689\" alt=\"Screenshot 2024-01-06 at 9 46 48โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/70e1a4c4-6c67-4664-b77a-ad27b1fba8f8\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dbfe7d0-8613-4529-adb6-6e0632d7cce7", + "metadata": {}, + "outputs": [], + "source": [ + "exp.plot_similar(idx=6500, limit=20)\n", + "#exp.plot_similar(idx=[100,101], limit=10) # Can also pass list of idxs or imgs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "260e09bf-4960-4089-a676-cb0e76ff3c0d", + "metadata": {}, + "outputs": [], + "source": [ + "exp.plot_similar(img=\"https://ultralytics.com/images/bus.jpg\", limit=10, labels=False) # Can also pass any external images\n" + ] + }, + { + "cell_type": "markdown", + "id": "faa0b7a7-6318-40e4-b0f4-45a8113bdc3a", + "metadata": {}, + "source": [ + "<p>\n", + "<img width=\"766\" alt=\"Screenshot 2024-01-06 at 10 05 10โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/faa9c544-d96b-4528-a2ea-95c5d8856744\">\n", + "\n", + "</p>" + ] + }, + { + "cell_type": "markdown", + "id": "35315ae6-d827-40e4-8813-279f97a83b34", + "metadata": {}, + "source": [ + "## 2. Run SQL queries on your Dataset!\n", + "Sometimes you might want to investigate a certain type of entries in your dataset. For this Explorer allows you to execute SQL queries.\n", + "It accepts either of the formats:\n", + "- Queries beginning with \"WHERE\" will automatically select all columns. This can be thought of as a short-hand query\n", + "- You can also write full queries where you can specify which columns to select\n", + "\n", + "This can be used to investigate model performance and specific data points. For example:\n", + "- let's say your model struggles on images that have humans and dogs. You can write a query like this to select the points that have at least 2 humans AND at least one dog.\n", + "\n", + "You can combine SQL query and semantic search to filter down to specific type of results\n", + "<img width=\"994\" alt=\"Screenshot 2024-01-06 at 9 47 30โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/92bc3178-c151-4cd5-8007-c76178deb113\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cd1072f-3100-4331-a0e3-4e2f6b1005bf", + "metadata": {}, + "outputs": [], + "source": [ + "table = exp.sql_query(\"WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10\")\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "debf8a00-c9f6-448b-bd3b-454cf62f39ab", + "metadata": {}, + "source": [ + "Just like similarity search, you also get a util to directly plot the sql queries using `exp.plot_sql_query`\n", + "<img width=\"771\" alt=\"Screenshot 2024-01-06 at 9 48 08โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/332f5acd-3a4e-462d-a281-5d5effd1886e\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18b977e7-d048-4b22-b8c4-084a03b04f23", + "metadata": {}, + "outputs": [], + "source": [ + "exp.plot_sql_query(\"WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10\", labels=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f26804c5-840b-4fd1-987f-e362f29e3e06", + "metadata": {}, + "source": [ + "## 3. Working with embeddings Table (Advanced)\n", + "Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre and post filters, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea69260a-3407-40c9-9f42-8b34a6e6af7a", + "metadata": {}, + "outputs": [], + "source": [ + "table = exp.table\n", + "table.schema" + ] + }, + { + "cell_type": "markdown", + "id": "238db292-8610-40b3-9af7-dfd6be174892", + "metadata": {}, + "source": [ + "### Run raw queries\n", + "Vector Search finds the nearest vectors from the database. In a recommendation system or search engine, you can find similar products from the one you searched. In LLM and other AI applications, each data point can be presented by the embeddings generated from some models, it returns the most relevant features.\n", + "\n", + "A search in high-dimensional vector space, is to find K-Nearest-Neighbors (KNN) of the query vector.\n", + "\n", + "Metric\n", + "In LanceDB, a Metric is the way to describe the distance between a pair of vectors. Currently, it supports the following metrics:\n", + "- L2\n", + "- Cosine\n", + "- Dot\n", + "Explorer's similarity search uses L2 by default. You can run queries on tables directly, or use the lance format to build custom utilities to manage datasets. More details on available LanceDB table ops in the [docs](https://lancedb.github.io/lancedb/)\n", + "\n", + "<img width=\"1015\" alt=\"Screenshot 2024-01-06 at 9 48 35โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/a2ccdaf3-8877-4f70-bf47-8a9bd2bb20c0\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d74430fe-5aee-45a1-8863-3f2c31338792", + "metadata": {}, + "outputs": [], + "source": [ + "dummy_img_embedding = [i for i in range(256)] \n", + "table.search(dummy_img_embedding).limit(5).to_pandas()" + ] + }, + { + "cell_type": "markdown", + "id": "587486b4-0d19-4214-b994-f032fb2e8eb5", + "metadata": {}, + "source": [ + "### Inter-conversion to popular data formats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb2876ea-999b-4eba-96bc-c196ba02c41c", + "metadata": {}, + "outputs": [], + "source": [ + "df = table.to_pandas()\n", + "pa_table = table.to_arrow()\n" + ] + }, + { + "cell_type": "markdown", + "id": "42659d63-ad76-49d6-8dfc-78d77278db72", + "metadata": {}, + "source": [ + "### Work with Embeddings\n", + "You can access the raw embedding from lancedb Table and analyse it. The image embeddings are stored in column `vector`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66d69e9b-046e-41c8-80d7-c0ee40be3bca", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "embeddings = table.to_pandas()[\"vector\"].tolist()\n", + "embeddings = np.array(embeddings)" + ] + }, + { + "cell_type": "markdown", + "id": "e8df0a49-9596-4399-954b-b8ae1fd7a602", + "metadata": {}, + "source": [ + "### Scatterplot\n", + "One of the preliminary steps in analysing embeddings is by plotting them in 2D space via dimensionality reduction. Let's try an example\n", + "\n", + "<img width=\"646\" alt=\"Screenshot 2024-01-06 at 9 48 58โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/9e1da25c-face-4426-abc0-2f64a4e4952c\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9a150e8-8092-41b3-82f8-2247f8187fc8", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install scikit-learn --q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "196079c3-45a9-4325-81ab-af79a881e37a", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from sklearn.decomposition import PCA\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "# Reduce dimensions using PCA to 3 components for visualization in 3D\n", + "pca = PCA(n_components=3)\n", + "reduced_data = pca.fit_transform(embeddings)\n", + "\n", + "# Create a 3D scatter plot using Matplotlib's Axes3D\n", + "fig = plt.figure(figsize=(8, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "# Scatter plot\n", + "ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5)\n", + "ax.set_title('3D Scatter Plot of Reduced 256-Dimensional Data (PCA)')\n", + "ax.set_xlabel('Component 1')\n", + "ax.set_ylabel('Component 2')\n", + "ax.set_zlabel('Component 3')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1c843c23-e3f2-490e-8d6c-212fa038a149", + "metadata": {}, + "source": [ + "## 4. Similarity Index\n", + "Here's a simple example of an operation powered by the embeddings table. Explorer comes with a `similarity_index` operation-\n", + "* It tries to estimate how similar each data point is with the rest of the dataset.\n", + "* It does that by counting how many image embeddings lie closer than `max_dist` to the current image in the generated embedding space, considering `top_k` similar images at a time.\n", + "\n", + "For a given dataset, model, `max_dist` & `top_k` the similarity index once generated will be reused. In case, your dataset has changed, or you simply need to regenerate the similarity index, you can pass `force=True`.\n", + "Similar to vector and SQL search, this also comes with a util to directly plot it. Let's look at the plot first\n", + "<img width=\"633\" alt=\"Screenshot 2024-01-06 at 9 49 36โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/96a9d984-4a72-4784-ace1-428676ee2bdd\">\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953c2a5f-1b61-4acf-a8e4-ed08547dbafc", + "metadata": {}, + "outputs": [], + "source": [ + "exp.plot_similarity_index(max_dist=0.2, top_k=0.01)" + ] + }, + { + "cell_type": "markdown", + "id": "28228a9a-b727-45b5-8ca7-8db662c0b937", + "metadata": {}, + "source": [ + "Now let's look at the output of the operation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4161aaa-20e6-4df0-8e87-d2293ee0530a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "sim_idx = exp.similarity_index(max_dist=0.2, top_k=0.01, force=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b01d5b1a-9adb-4c3c-a873-217c71527c8d", + "metadata": {}, + "outputs": [], + "source": [ + "sim_idx" + ] + }, + { + "cell_type": "markdown", + "id": "22b28e54-4fbb-400e-ad8c-7068cbba11c4", + "metadata": {}, + "source": [ + "Let's create a query to see what data points have similarity count of more than 30 and plot images similar to them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58d2557b-d401-43cf-937d-4f554c7bc808", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "sim_count = np.array(sim_idx[\"count\"])\n", + "sim_idx['im_file'][sim_count > 30]" + ] + }, + { + "cell_type": "markdown", + "id": "a5ec8d76-271a-41ab-ac74-cf8c0084ba5e", + "metadata": {}, + "source": [ + "You should see something like this\n", + "<img width=\"897\" alt=\"Screenshot 2024-01-06 at 9 50 48โ€ฏPM\" src=\"https://github.com/AyushExel/assets/assets/15766192/5d3f0e35-2ad4-4a67-8df7-3a4c17867b72\">\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a7b2ee3-9f35-48a2-9c38-38379516f4d2", + "metadata": {}, + "outputs": [], + "source": [ + "exp.plot_similar(idx=[7146, 14035]) # Using avg embeddings of 2 images" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/en/datasets/explorer/index.md b/docs/en/datasets/explorer/index.md new file mode 100644 index 00000000000..ebfe189a652 --- /dev/null +++ b/docs/en/datasets/explorer/index.md @@ -0,0 +1,31 @@ +--- +comments: true +description: Discover the Ultralytics Explorer, a versatile tool and Python API for CV dataset exploration, enabling semantic search, SQL queries, and vector similarity searches. +keywords: Ultralytics Explorer, CV Dataset Tools, Semantic Search, SQL Dataset Queries, Vector Similarity, Python API, GUI Explorer, Dataset Analysis, YOLO Explorer, Data Insights +--- + +# Ultralytics Explorer + +Ultralytics Explorer is a tool for exploring CV datasets using semantic search, SQL queries and vector similarity search. It is also a Python API for accessing the same functionality. + +### Installation of optional dependencies + +Explorer depends on external libraries for some of its functionality. These are automatically installed on usage. To manually install these dependencies, use the following command: + +```bash +pip install ultralytics[explorer] +``` + +## GUI Explorer Usage + +The GUI demo runs in your browser allowing you to create embeddings for your dataset and search for similar images, run SQL queries and perform semantic search. It can be run using the following command: + +```bash +yolo explorer +``` + +### Explorer API + +This is a Python API for Exploring your datasets. It also powers the GUI Explorer. You can use this to create your own exploratory notebooks or scripts to get insights into your datasets. + +Learn more about the Explorer API [here](api.md). diff --git a/docs/en/datasets/index.md b/docs/en/datasets/index.md index 697bc67c21b..6a37c7a4543 100644 --- a/docs/en/datasets/index.md +++ b/docs/en/datasets/index.md @@ -8,6 +8,13 @@ keywords: computer vision, datasets, Ultralytics, YOLO, object detection, instan Ultralytics provides support for various datasets to facilitate computer vision tasks such as detection, instance segmentation, pose estimation, classification, and multi-object tracking. Below is a list of the main Ultralytics datasets, followed by a summary of each computer vision task and the respective datasets. +## ๐ŸŒŸ New: Ultralytics Explorer ๐ŸŒŸ + +Create embeddings for your dataset, search for similar images, run SQL queries and perform semantic search. You can get started with our GUI app or build your own using the API. Learn more [here](explorer/index.md). + +- Try the [GUI Demo](explorer/index.md) +- Learn more about the [Explorer API](explorer/index.md) + ## [Detection Datasets](detect/index.md) Bounding box object detection is a computer vision technique that involves detecting and localizing objects in an image by drawing a bounding box around each object. diff --git a/docs/en/guides/distance-calculation.md b/docs/en/guides/distance-calculation.md index b19d3976f38..6342706dc0b 100644 --- a/docs/en/guides/distance-calculation.md +++ b/docs/en/guides/distance-calculation.md @@ -65,7 +65,6 @@ Measuring the gap between two objects is known as distance calculation within a - Mouse Right Click will delete all drawn points - Mouse Left Click can be used to draw points - ### Optional Arguments `set_args` | Name | Type | Default | Description | diff --git a/docs/en/guides/heatmaps.md b/docs/en/guides/heatmaps.md index d4c74b7aa92..63b3dfc8388 100644 --- a/docs/en/guides/heatmaps.md +++ b/docs/en/guides/heatmaps.md @@ -25,7 +25,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult - **Intuitive Data Distribution Visualization:** Heatmaps simplify the comprehension of data concentration and distribution, converting complex datasets into easy-to-understand visual formats. - **Efficient Pattern Detection:** By visualizing data in heatmap format, it becomes easier to spot trends, clusters, and outliers, facilitating quicker analysis and insights. -- **Enhanced Spatial Analysis and Decision Making:** Heatmaps are instrumental in illustrating spatial relationships, aiding in decision-making processes in sectors such as business intelligence, environmental studies, and urban planning. +- **Enhanced Spatial Analysis and Decision-Making:** Heatmaps are instrumental in illustrating spatial relationships, aiding in decision-making processes in sectors such as business intelligence, environmental studies, and urban planning. ## Real World Applications @@ -34,12 +34,11 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult | ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) | | Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap | +!!! tip "Heatmap Configuration" -???+ tip "Heatmap Configuration" - `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0). - `decay_factor`: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0). - !!! Example "Heatmaps using Ultralytics YOLOv8 Example" === "Heatmap" diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md index dcc4756d39b..b4d2b91ec78 100644 --- a/docs/en/guides/object-counting.md +++ b/docs/en/guides/object-counting.md @@ -167,7 +167,6 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly ### Optional Arguments `set_args` - | Name | Type | Default | Description | |---------------------|-------------|----------------------------|-----------------------------------------------| | view_img | `bool` | `False` | Display frames with counts | diff --git a/docs/en/guides/speed-estimation.md b/docs/en/guides/speed-estimation.md index a30f89f4f06..12f1a1f9f1a 100644 --- a/docs/en/guides/speed-estimation.md +++ b/docs/en/guides/speed-estimation.md @@ -73,17 +73,16 @@ Speed estimation is the process of calculating the rate of movement of an object Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed. - ### Optional Arguments `set_args` -| Name | Type | Default | Description | -|---------------------|-------------|----------------------------|---------------------------------------------------| -| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area | -| names | `dict` | `None` | Classes names | -| view_img | `bool` | `False` | Display frames with counts | -| line_thickness | `int` | `2` | Increase bounding boxes thickness | -| region_thickness | `int` | `5` | Thickness for object counter region or line | -| spdl_dist_thresh | `int` | `10` | Euclidean Distance threshold for speed check line | +| Name | Type | Default | Description | +|------------------|--------|----------------------------|---------------------------------------------------| +| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area | +| names | `dict` | `None` | Classes names | +| view_img | `bool` | `False` | Display frames with counts | +| line_thickness | `int` | `2` | Increase bounding boxes thickness | +| region_thickness | `int` | `5` | Thickness for object counter region or line | +| spdl_dist_thresh | `int` | `10` | Euclidean Distance threshold for speed check line | ### Arguments `model.track` diff --git a/docs/en/index.md b/docs/en/index.md index 4f4b0d63cc9..60bc38b7739 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -39,12 +39,17 @@ Introducing [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/u Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned machine learning practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects +# ๐ŸŒŸ New: Ultralytics Explorer ๐ŸŒŸ + +Create embeddings for your dataset, search for similar images, run SQL queries and perform semantic search. You can get started with our GUI app or build your own using the API. Learn more [here](datasets/explorer/index.md). + ## Where to Start - **Install** `ultralytics` with pip and get up and running in minutes &nbsp; [:material-clock-fast: Get Started](quickstart.md){ .md-button } - **Predict** new images and videos with YOLOv8 &nbsp; [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button } - **Train** a new YOLOv8 model on your own custom dataset &nbsp; [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button } -- **Explore** YOLOv8 tasks like segment, classify, pose and track &nbsp; [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button } +- **Tasks** YOLOv8 tasks like segment, classify, pose and track &nbsp; [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button } +- **Explore** datasets with advanced semantic and SQL search &nbsp; [:material-magnify-expand: Run Explorer](datasets/explorer/index.md){ .md-button } <p align="center"> <br> diff --git a/docs/en/integrations/comet.md b/docs/en/integrations/comet.md index 1aa25d32c4b..eea84aee3bf 100644 --- a/docs/en/integrations/comet.md +++ b/docs/en/integrations/comet.md @@ -133,6 +133,7 @@ You can control the number of image predictions that Comet ML logs during your e ```python import os + os.environ["COMET_MAX_IMAGE_PREDICTIONS"] = "200" ``` @@ -142,6 +143,7 @@ Comet ML allows you to specify how often batches of image predictions are logged ```python import os + os.environ['COMET_EVAL_BATCH_LOGGING_INTERVAL'] = "4" ``` @@ -151,6 +153,7 @@ In some cases, you may not want to log the confusion matrix from your validation ```python import os + os.environ["COMET_EVAL_LOG_CONFUSION_MATRIX"] = "false" ``` @@ -160,6 +163,7 @@ If you find yourself in a situation where internet access is limited, Comet ML p ```python import os + os.environ["COMET_MODE"] = "offline" ``` diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md index 6606f57337d..2dd78d3a9e2 100644 --- a/docs/en/models/yolov8.md +++ b/docs/en/models/yolov8.md @@ -38,11 +38,11 @@ Each variant of the YOLOv8 series is optimized for its respective task, ensuring | Model | Filenames | Task | Inference | Validation | Training | Export | |-------------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------| -| YOLOv8 | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt` | [Detection](../tasks/detect.md) | โœ… | โœ… | โœ… | โœ… | -| YOLOv8-seg | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt` | [Instance Segmentation](../tasks/segment.md) | โœ… | โœ… | โœ… | โœ… | -| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md) | โœ… | โœ… | โœ… | โœ… | -| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | โœ… | โœ… | โœ… | โœ… | -| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | โœ… | โœ… | โœ… | โœ… | +| YOLOv8 | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt` | [Detection](../tasks/detect.md) | โœ… | โœ… | โœ… | โœ… | +| YOLOv8-seg | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt` | [Instance Segmentation](../tasks/segment.md) | โœ… | โœ… | โœ… | โœ… | +| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md) | โœ… | โœ… | โœ… | โœ… | +| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | โœ… | โœ… | โœ… | โœ… | +| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | โœ… | โœ… | โœ… | โœ… | This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in computer vision. diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md index cc6ed72c68f..af662009772 100644 --- a/docs/en/tasks/obb.md +++ b/docs/en/tasks/obb.md @@ -35,6 +35,7 @@ YOLOv8 pretrained Obb models are shown here, which are pretrained on the [DOTAv1 | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024 | <++> | <++> | <++> | 69.5 | 676.7 | <!-- TODO: should we report multi-scale results only as they're better or both multi-scale and single-scale. --> + - **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0` - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) @@ -76,7 +77,7 @@ Train YOLOv8n-obb on the dota128.yaml dataset for 100 epochs at image size 640. ### Dataset format -yolo obb dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md).. +OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md). ## Val @@ -164,18 +165,18 @@ Available YOLOv8-obb export formats are in the table below. You can predict or v | Format | `format` Argument | Model | Metadata | Arguments | |--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | โœ… | - | -| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-obb.torchscript` | โœ… | `imgsz`, `optimize` | -| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-obb.onnx` | โœ… | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | -| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-obb_openvino_model/` | โœ… | `imgsz`, `half` | -| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-obb.engine` | โœ… | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | -| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-obb.mlpackage` | โœ… | `imgsz`, `half`, `int8`, `nms` | -| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-obb_saved_model/` | โœ… | `imgsz`, `keras` | -| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-obb.pb` | โŒ | `imgsz` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-obb.tflite` | โœ… | `imgsz`, `half`, `int8` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | โœ… | `imgsz` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-obb_web_model/` | โœ… | `imgsz`, `half`, `int8` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-obb_paddle_model/` | โœ… | `imgsz` | -| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-obb_ncnn_model/` | โœ… | `imgsz`, `half` | +| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | โœ… | - | +| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-obb.torchscript` | โœ… | `imgsz`, `optimize` | +| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-obb.onnx` | โœ… | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | +| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-obb_openvino_model/` | โœ… | `imgsz`, `half` | +| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-obb.engine` | โœ… | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | +| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-obb.mlpackage` | โœ… | `imgsz`, `half`, `int8`, `nms` | +| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-obb_saved_model/` | โœ… | `imgsz`, `keras` | +| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-obb.pb` | โŒ | `imgsz` | +| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-obb.tflite` | โœ… | `imgsz`, `half`, `int8` | +| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | โœ… | `imgsz` | +| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-obb_web_model/` | โœ… | `imgsz`, `half`, `int8` | +| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-obb_paddle_model/` | โœ… | `imgsz` | +| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-obb_ncnn_model/` | โœ… | `imgsz`, `half` | See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md index 544dd38de8d..760d9b56dd9 100644 --- a/docs/en/usage/cfg.md +++ b/docs/en/usage/cfg.md @@ -224,23 +224,23 @@ Export settings for YOLO models encompass configurations and options related to Augmentation settings for YOLO models refer to the various transformations and modifications applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the augmentation process include the size and composition of the original dataset and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to ensure that the augmented dataset is diverse and representative enough to train a high-performing model. -| Key | Value | Description | -|-----------------|-----------------|--------------------------------------------------------------------------------| -| `hsv_h` | `0.015` | image HSV-Hue augmentation (fraction) | -| `hsv_s` | `0.7` | image HSV-Saturation augmentation (fraction) | -| `hsv_v` | `0.4` | image HSV-Value augmentation (fraction) | -| `degrees` | `0.0` | image rotation (+/- deg) | -| `translate` | `0.1` | image translation (+/- fraction) | -| `scale` | `0.5` | image scale (+/- gain) | -| `shear` | `0.0` | image shear (+/- deg) | -| `perspective` | `0.0` | image perspective (+/- fraction), range 0-0.001 | -| `flipud` | `0.0` | image flip up-down (probability) | -| `fliplr` | `0.5` | image flip left-right (probability) | -| `mosaic` | `1.0` | image mosaic (probability) | -| `mixup` | `0.0` | image mixup (probability) | -| `copy_paste` | `0.0` | segment copy-paste (probability) | -| `auto_augment` | `'randaugment'` | auto augmentation policy for classification (randaugment, autoaugment, augmix) | -| `erasing` | `0.4` | probability o random erasing during classification training (0-1) training | +| Key | Value | Description | +|----------------|-----------------|--------------------------------------------------------------------------------| +| `hsv_h` | `0.015` | image HSV-Hue augmentation (fraction) | +| `hsv_s` | `0.7` | image HSV-Saturation augmentation (fraction) | +| `hsv_v` | `0.4` | image HSV-Value augmentation (fraction) | +| `degrees` | `0.0` | image rotation (+/- deg) | +| `translate` | `0.1` | image translation (+/- fraction) | +| `scale` | `0.5` | image scale (+/- gain) | +| `shear` | `0.0` | image shear (+/- deg) | +| `perspective` | `0.0` | image perspective (+/- fraction), range 0-0.001 | +| `flipud` | `0.0` | image flip up-down (probability) | +| `fliplr` | `0.5` | image flip left-right (probability) | +| `mosaic` | `1.0` | image mosaic (probability) | +| `mixup` | `0.0` | image mixup (probability) | +| `copy_paste` | `0.0` | segment copy-paste (probability) | +| `auto_augment` | `'randaugment'` | auto augmentation policy for classification (randaugment, autoaugment, augmix) | +| `erasing` | `0.4` | probability o random erasing during classification training (0-1) training | ## Logging, checkpoints, plotting and file management diff --git a/docs/hi/models/rtdetr.md b/docs/hi/models/rtdetr.md index 383d780e5ba..addb8fcf095 100644 --- a/docs/hi/models/rtdetr.md +++ b/docs/hi/models/rtdetr.md @@ -1,7 +1,6 @@ --- comments: true -description: - Baidu เค•เฅ‡ RT-DETR เค•เคพ เค…เคตเคฒเฅ‹เค•เคจ เค•เคฐเฅ‡เค‚: เคตเคฟเคœเคผเคจ เคŸเฅเคฐเคพเค‚เคธเคซเฅ‰เคฐเฅเคฎเคฐ เค•เฅ‡ เคฆเฅเคตเคพเคฐเคพ เคธเค‚เคšเคพเคฒเคฟเคค, เค‰เคจเฅเคจเคค เค”เคฐ เค…เคจเฅเค•เฅ‚เคฒเคจเคฏเฅ‹เค—เฅเคฏ เคตเคพเคธเฅเคคเคตเคฟเค• เคธเคฎเคฏ เค‘เคฌเฅเคœเฅ‡เค•เฅเคŸ เคกเคฟเคŸเฅ‡เค•เฅเคŸเคฐ, เคœเคฟเคธเคฎเฅ‡เค‚ เคคเฅˆเคฏเคพเคฐ เคฎเฅ‰เคกเคฒ เคถเคพเคฎเคฟเคฒ เคนเฅˆเค‚เฅค +description: Baidu เค•เฅ‡ RT-DETR เค•เคพ เค…เคตเคฒเฅ‹เค•เคจ เค•เคฐเฅ‡เค‚: เคตเคฟเคœเคผเคจ เคŸเฅเคฐเคพเค‚เคธเคซเฅ‰เคฐเฅเคฎเคฐ เค•เฅ‡ เคฆเฅเคตเคพเคฐเคพ เคธเค‚เคšเคพเคฒเคฟเคค, เค‰เคจเฅเคจเคค เค”เคฐ เค…เคจเฅเค•เฅ‚เคฒเคจเคฏเฅ‹เค—เฅเคฏ เคตเคพเคธเฅเคคเคตเคฟเค• เคธเคฎเคฏ เค‘เคฌเฅเคœเฅ‡เค•เฅเคŸ เคกเคฟเคŸเฅ‡เค•เฅเคŸเคฐ, เคœเคฟเคธเคฎเฅ‡เค‚ เคคเฅˆเคฏเคพเคฐ เคฎเฅ‰เคกเคฒ เคถเคพเคฎเคฟเคฒ เคนเฅˆเค‚เฅค keywords: RT-DETR, Baidu, เคตเคฟเคœเคผเคจ เคŸเฅเคฐเคพเค‚เคธเคซเฅ‰เคฐเฅเคฎเคฐเฅเคธ, เค‘เคฌเฅเคœเฅ‡เค•เฅเคŸ เคกเคฟเคŸเฅ‡เค•เฅเคถเคจ, เคตเคพเคธเฅเคคเคตเคฟเค• เคธเคฎเคฏ เคชเฅเคฐเคฆเคฐเฅเคถเคจ, CUDA, TensorRT, IoU-เคœเคพเค—เคฐเฅ‚เค• เค•เฅเคตเฅ‡เคฐเฅ€ เคšเคฏเคจ, Ultralytics, เคชเคพเคฏเคฅเคจ เคเคชเฅ€เค†เคˆ, PaddlePaddle --- diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index b65ed08250f..9a7751e6d56 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -65,8 +65,7 @@ theme: # Customization copyright: <a href="https://ultralytics.com" target="_blank">ยฉ 2023 Ultralytics Inc.</a> All rights reserved. -extra: - # version: +extra: # version: # provider: mike # version drop-down menu robots: robots.txt analytics: @@ -220,6 +219,11 @@ nav: - RT-DETR (Realtime Detection Transformer): models/rtdetr.md - Datasets: - datasets/index.md + - Explorer: + - datasets/explorer/index.md + - Explorer API: datasets/explorer/api.md + - GUI Dashboard Demo: datasets/explorer/dash.md + - VOC Exploration Example: datasets/explorer/explorer.ipynb - Detection: - datasets/detect/index.md - Argoverse: datasets/detect/argoverse.md @@ -341,137 +345,137 @@ nav: - Inference API: hub/inference_api.md - Reference: - - cfg: - - __init__: reference/cfg/__init__.md - - data: - - annotator: reference/data/annotator.md - - augment: reference/data/augment.md - - base: reference/data/base.md - - build: reference/data/build.md - - converter: reference/data/converter.md - - dataset: reference/data/dataset.md - - loaders: reference/data/loaders.md - - split_dota: reference/data/split_dota.md - - utils: reference/data/utils.md - - engine: - - exporter: reference/engine/exporter.md - - model: reference/engine/model.md - - predictor: reference/engine/predictor.md - - results: reference/engine/results.md - - trainer: reference/engine/trainer.md - - tuner: reference/engine/tuner.md - - validator: reference/engine/validator.md - - hub: - - __init__: reference/hub/__init__.md - - auth: reference/hub/auth.md - - session: reference/hub/session.md - - utils: reference/hub/utils.md - - models: - - fastsam: - - model: reference/models/fastsam/model.md - - predict: reference/models/fastsam/predict.md - - prompt: reference/models/fastsam/prompt.md - - utils: reference/models/fastsam/utils.md - - val: reference/models/fastsam/val.md - - nas: - - model: reference/models/nas/model.md - - predict: reference/models/nas/predict.md - - val: reference/models/nas/val.md - - rtdetr: - - model: reference/models/rtdetr/model.md - - predict: reference/models/rtdetr/predict.md - - train: reference/models/rtdetr/train.md - - val: reference/models/rtdetr/val.md - - sam: - - amg: reference/models/sam/amg.md - - build: reference/models/sam/build.md - - model: reference/models/sam/model.md - - modules: - - decoders: reference/models/sam/modules/decoders.md - - encoders: reference/models/sam/modules/encoders.md - - sam: reference/models/sam/modules/sam.md - - tiny_encoder: reference/models/sam/modules/tiny_encoder.md - - transformer: reference/models/sam/modules/transformer.md - - predict: reference/models/sam/predict.md + - cfg: + - __init__: reference/cfg/__init__.md + - data: + - annotator: reference/data/annotator.md + - augment: reference/data/augment.md + - base: reference/data/base.md + - build: reference/data/build.md + - converter: reference/data/converter.md + - dataset: reference/data/dataset.md + - loaders: reference/data/loaders.md + - split_dota: reference/data/split_dota.md + - utils: reference/data/utils.md + - engine: + - exporter: reference/engine/exporter.md + - model: reference/engine/model.md + - predictor: reference/engine/predictor.md + - results: reference/engine/results.md + - trainer: reference/engine/trainer.md + - tuner: reference/engine/tuner.md + - validator: reference/engine/validator.md + - hub: + - __init__: reference/hub/__init__.md + - auth: reference/hub/auth.md + - session: reference/hub/session.md + - utils: reference/hub/utils.md + - models: + - fastsam: + - model: reference/models/fastsam/model.md + - predict: reference/models/fastsam/predict.md + - prompt: reference/models/fastsam/prompt.md + - utils: reference/models/fastsam/utils.md + - val: reference/models/fastsam/val.md + - nas: + - model: reference/models/nas/model.md + - predict: reference/models/nas/predict.md + - val: reference/models/nas/val.md + - rtdetr: + - model: reference/models/rtdetr/model.md + - predict: reference/models/rtdetr/predict.md + - train: reference/models/rtdetr/train.md + - val: reference/models/rtdetr/val.md + - sam: + - amg: reference/models/sam/amg.md + - build: reference/models/sam/build.md + - model: reference/models/sam/model.md + - modules: + - decoders: reference/models/sam/modules/decoders.md + - encoders: reference/models/sam/modules/encoders.md + - sam: reference/models/sam/modules/sam.md + - tiny_encoder: reference/models/sam/modules/tiny_encoder.md + - transformer: reference/models/sam/modules/transformer.md + - predict: reference/models/sam/predict.md + - utils: + - loss: reference/models/utils/loss.md + - ops: reference/models/utils/ops.md + - yolo: + - classify: + - predict: reference/models/yolo/classify/predict.md + - train: reference/models/yolo/classify/train.md + - val: reference/models/yolo/classify/val.md + - detect: + - predict: reference/models/yolo/detect/predict.md + - train: reference/models/yolo/detect/train.md + - val: reference/models/yolo/detect/val.md + - model: reference/models/yolo/model.md + - obb: + - predict: reference/models/yolo/obb/predict.md + - train: reference/models/yolo/obb/train.md + - val: reference/models/yolo/obb/val.md + - pose: + - predict: reference/models/yolo/pose/predict.md + - train: reference/models/yolo/pose/train.md + - val: reference/models/yolo/pose/val.md + - segment: + - predict: reference/models/yolo/segment/predict.md + - train: reference/models/yolo/segment/train.md + - val: reference/models/yolo/segment/val.md + - nn: + - autobackend: reference/nn/autobackend.md + - modules: + - block: reference/nn/modules/block.md + - conv: reference/nn/modules/conv.md + - head: reference/nn/modules/head.md + - transformer: reference/nn/modules/transformer.md + - utils: reference/nn/modules/utils.md + - tasks: reference/nn/tasks.md + - solutions: + - ai_gym: reference/solutions/ai_gym.md + - heatmap: reference/solutions/heatmap.md + - object_counter: reference/solutions/object_counter.md + - speed_estimation: reference/solutions/speed_estimation.md + - distance_calculation: reference/solutions/distance_calculation.md + - trackers: + - basetrack: reference/trackers/basetrack.md + - bot_sort: reference/trackers/bot_sort.md + - byte_tracker: reference/trackers/byte_tracker.md + - track: reference/trackers/track.md + - utils: + - gmc: reference/trackers/utils/gmc.md + - kalman_filter: reference/trackers/utils/kalman_filter.md + - matching: reference/trackers/utils/matching.md - utils: - - loss: reference/models/utils/loss.md - - ops: reference/models/utils/ops.md - - yolo: - - classify: - - predict: reference/models/yolo/classify/predict.md - - train: reference/models/yolo/classify/train.md - - val: reference/models/yolo/classify/val.md - - detect: - - predict: reference/models/yolo/detect/predict.md - - train: reference/models/yolo/detect/train.md - - val: reference/models/yolo/detect/val.md - - model: reference/models/yolo/model.md - - obb: - - predict: reference/models/yolo/obb/predict.md - - train: reference/models/yolo/obb/train.md - - val: reference/models/yolo/obb/val.md - - pose: - - predict: reference/models/yolo/pose/predict.md - - train: reference/models/yolo/pose/train.md - - val: reference/models/yolo/pose/val.md - - segment: - - predict: reference/models/yolo/segment/predict.md - - train: reference/models/yolo/segment/train.md - - val: reference/models/yolo/segment/val.md - - nn: - - autobackend: reference/nn/autobackend.md - - modules: - - block: reference/nn/modules/block.md - - conv: reference/nn/modules/conv.md - - head: reference/nn/modules/head.md - - transformer: reference/nn/modules/transformer.md - - utils: reference/nn/modules/utils.md - - tasks: reference/nn/tasks.md - - solutions: - - ai_gym: reference/solutions/ai_gym.md - - heatmap: reference/solutions/heatmap.md - - object_counter: reference/solutions/object_counter.md - - speed_estimation: reference/solutions/speed_estimation.md - - distance_calculation: reference/solutions/distance_calculation.md - - trackers: - - basetrack: reference/trackers/basetrack.md - - bot_sort: reference/trackers/bot_sort.md - - byte_tracker: reference/trackers/byte_tracker.md - - track: reference/trackers/track.md - - utils: - - gmc: reference/trackers/utils/gmc.md - - kalman_filter: reference/trackers/utils/kalman_filter.md - - matching: reference/trackers/utils/matching.md - - utils: - - __init__: reference/utils/__init__.md - - autobatch: reference/utils/autobatch.md - - benchmarks: reference/utils/benchmarks.md - - callbacks: - - base: reference/utils/callbacks/base.md - - clearml: reference/utils/callbacks/clearml.md - - comet: reference/utils/callbacks/comet.md - - dvc: reference/utils/callbacks/dvc.md - - hub: reference/utils/callbacks/hub.md - - mlflow: reference/utils/callbacks/mlflow.md - - neptune: reference/utils/callbacks/neptune.md - - raytune: reference/utils/callbacks/raytune.md - - tensorboard: reference/utils/callbacks/tensorboard.md - - wb: reference/utils/callbacks/wb.md - - checks: reference/utils/checks.md - - dist: reference/utils/dist.md - - downloads: reference/utils/downloads.md - - errors: reference/utils/errors.md - - files: reference/utils/files.md - - instance: reference/utils/instance.md - - loss: reference/utils/loss.md - - metrics: reference/utils/metrics.md - - ops: reference/utils/ops.md - - patches: reference/utils/patches.md - - plotting: reference/utils/plotting.md - - tal: reference/utils/tal.md - - torch_utils: reference/utils/torch_utils.md - - triton: reference/utils/triton.md - - tuner: reference/utils/tuner.md + - __init__: reference/utils/__init__.md + - autobatch: reference/utils/autobatch.md + - benchmarks: reference/utils/benchmarks.md + - callbacks: + - base: reference/utils/callbacks/base.md + - clearml: reference/utils/callbacks/clearml.md + - comet: reference/utils/callbacks/comet.md + - dvc: reference/utils/callbacks/dvc.md + - hub: reference/utils/callbacks/hub.md + - mlflow: reference/utils/callbacks/mlflow.md + - neptune: reference/utils/callbacks/neptune.md + - raytune: reference/utils/callbacks/raytune.md + - tensorboard: reference/utils/callbacks/tensorboard.md + - wb: reference/utils/callbacks/wb.md + - checks: reference/utils/checks.md + - dist: reference/utils/dist.md + - downloads: reference/utils/downloads.md + - errors: reference/utils/errors.md + - files: reference/utils/files.md + - instance: reference/utils/instance.md + - loss: reference/utils/loss.md + - metrics: reference/utils/metrics.md + - ops: reference/utils/ops.md + - patches: reference/utils/patches.md + - plotting: reference/utils/plotting.md + - tal: reference/utils/tal.md + - torch_utils: reference/utils/torch_utils.md + - triton: reference/utils/triton.md + - tuner: reference/utils/tuner.md - Help: - Help: help/index.md @@ -503,6 +507,7 @@ plugins: add_image: True add_share_buttons: True default_image: https://github.com/ultralytics/ultralytics/assets/26833433/6d09221c-c52a-4234-9a5d-b862e93c6529 + - mkdocs-jupyter - redirects: redirect_maps: callbacks.md: usage/callbacks.md diff --git a/pyproject.toml b/pyproject.toml index 4229d1d685f..9dced7e8fd2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -91,6 +91,7 @@ dev = [ "coverage[toml]", "mkdocs-material", "mkdocstrings[python]", + "mkdocs-jupyter", # for notebooks "mkdocs-redirects", # for 301 redirects "mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors ] @@ -103,6 +104,13 @@ export = [ "jaxlib<=0.4.21", # tensorflowjs bug https://github.com/google/jax/issues/18978 "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow ] + +explorer = [ + "lancedb", # vector search + "duckdb", # SQL queries, supports lancedb tables + "streamlit", # visualizing with GUI +] + # tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) # tflite-support # for TFLite model metadata # scikit-learn==0.19.2 # CoreML quantization diff --git a/tests/test_explorer.py b/tests/test_explorer.py new file mode 100644 index 00000000000..eedaf816897 --- /dev/null +++ b/tests/test_explorer.py @@ -0,0 +1,50 @@ +from ultralytics import Explorer + + +def test_similarity(): + exp = Explorer() + exp.create_embeddings_table() + similar = exp.get_similar(idx=1) + assert len(similar) == 25 + similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg') + assert len(similar) == 25 + similar = exp.get_similar(idx=[1, 2], limit=10) + assert len(similar) == 10 + sim_idx = exp.similarity_index() + assert len(sim_idx) > 0 + sql = exp.sql_query("WHERE labels LIKE '%person%'") + len(sql) > 0 + + +def test_det(): + exp = Explorer(data='coco8.yaml', model='yolov8n.pt') + exp.create_embeddings_table(force=True) + assert len(exp.table.head()['bboxes']) > 0 + similar = exp.get_similar(idx=[1, 2], limit=10) + assert len(similar) > 0 + # This is a loose test, just checks errors not correctness + similar = exp.plot_similar(idx=[1, 2], limit=10) + assert similar is not None + similar.show() + + +def test_seg(): + exp = Explorer(data='coco8-seg.yaml', model='yolov8n-seg.pt') + exp.create_embeddings_table(force=True) + assert len(exp.table.head()['masks']) > 0 + similar = exp.get_similar(idx=[1, 2], limit=10) + assert len(similar) > 0 + similar = exp.plot_similar(idx=[1, 2], limit=10) + assert similar is not None + similar.show() + + +def test_pose(): + exp = Explorer(data='coco8-pose.yaml', model='yolov8n-pose.pt') + exp.create_embeddings_table(force=True) + assert len(exp.table.head()['keypoints']) > 0 + similar = exp.get_similar(idx=[1, 2], limit=10) + assert len(similar) > 0 + similar = exp.plot_similar(idx=[1, 2], limit=10) + assert similar is not None + similar.show() diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 56709178946..73a8840a860 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,7 +1,8 @@ # Ultralytics YOLO ๐Ÿš€, AGPL-3.0 license -__version__ = '8.0.235' +__version__ = '8.0.236' +from ultralytics.data.explorer.explorer import Explorer from ultralytics.models import RTDETR, SAM, YOLO from ultralytics.models.fastsam import FastSAM from ultralytics.models.nas import NAS @@ -9,4 +10,4 @@ from ultralytics.utils.checks import check_yolo as checks from ultralytics.utils.downloads import download -__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings' +__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings', 'Explorer' diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py index 04984e27407..2488aa6b02d 100644 --- a/ultralytics/cfg/__init__.py +++ b/ultralytics/cfg/__init__.py @@ -2,6 +2,7 @@ import contextlib import shutil +import subprocess import sys from pathlib import Path from types import SimpleNamespace @@ -56,6 +57,9 @@ 4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required) yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128 + 6. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API + yolo explorer + 5. Run special commands: yolo help yolo checks @@ -297,6 +301,12 @@ def handle_yolo_settings(args: List[str]) -> None: LOGGER.warning(f"WARNING โš ๏ธ settings error: '{e}'. Please see {url} for help.") +def handle_explorer(): + """Open the Ultralytics Explorer GUI.""" + checks.check_requirements('streamlit') + subprocess.run(['streamlit', 'run', ROOT / 'data/explorer/gui/dash.py', '--server.maxMessageSize', '2048']) + + def parse_key_value_pair(pair): """Parse one 'key=value' pair and return key and value.""" k, v = pair.split('=', 1) # split on first '=' sign @@ -348,7 +358,8 @@ def entrypoint(debug=''): 'cfg': lambda: yaml_print(DEFAULT_CFG_PATH), 'hub': lambda: handle_yolo_hub(args[1:]), 'login': lambda: handle_yolo_hub(args), - 'copy-cfg': copy_default_cfg} + 'copy-cfg': copy_default_cfg, + 'explorer': lambda: handle_explorer()} full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special} # Define common misuses of special commands, i.e. -h, -help, --help diff --git a/ultralytics/data/explorer/__init__.py b/ultralytics/data/explorer/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/ultralytics/data/explorer/explorer.py b/ultralytics/data/explorer/explorer.py new file mode 100644 index 00000000000..ed99525e601 --- /dev/null +++ b/ultralytics/data/explorer/explorer.py @@ -0,0 +1,403 @@ +from io import BytesIO +from pathlib import Path +from typing import List + +import cv2 +import numpy as np +import torch +from matplotlib import pyplot as plt +from PIL import Image +from tqdm import tqdm + +from ultralytics.data.augment import Format +from ultralytics.data.dataset import YOLODataset +from ultralytics.data.utils import check_det_dataset +from ultralytics.models.yolo.model import YOLO +from ultralytics.utils import LOGGER, checks + +from .utils import get_sim_index_schema, get_table_schema, plot_similar_images, sanitize_batch + + +class ExplorerDataset(YOLODataset): + + def __init__(self, *args, data=None, **kwargs): + super().__init__(*args, data=data, **kwargs) + + # NOTE: Load the image directly without any resize operations. + def load_image(self, i): + """Loads 1 image from dataset index 'i', returns (im, resized hw).""" + im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i] + if im is None: # not cached in RAM + if fn.exists(): # load npy + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + if im is None: + raise FileNotFoundError(f'Image Not Found {f}') + h0, w0 = im.shape[:2] # orig hw + return im, (h0, w0), im.shape[:2] + + return self.ims[i], self.im_hw0[i], self.im_hw[i] + + def build_transforms(self, hyp=None): + transforms = Format( + bbox_format='xyxy', + normalize=False, + return_mask=self.use_segments, + return_keypoint=self.use_keypoints, + batch_idx=True, + mask_ratio=hyp.mask_ratio, + mask_overlap=hyp.overlap_mask, + ) + return transforms + + +class Explorer: + + def __init__(self, data='coco128.yaml', model='yolov8n.pt', uri='~/ultralytics/explorer') -> None: + checks.check_requirements(['lancedb', 'duckdb']) + import lancedb + + self.connection = lancedb.connect(uri) + self.table_name = Path(data).name.lower() + '_' + model.lower() + self.sim_idx_base_name = f'{self.table_name}_sim_idx'.lower( + ) # Use this name and append thres and top_k to reuse the table + self.model = YOLO(model) + self.data = data # None + self.choice_set = None + + self.table = None + self.progress = 0 + + def create_embeddings_table(self, force=False, split='train'): + """ + Create LanceDB table containing the embeddings of the images in the dataset. The table will be reused if it + already exists. Pass force=True to overwrite the existing table. + + Args: + force (bool): Whether to overwrite the existing table or not. Defaults to False. + split (str): Split of the dataset to use. Defaults to 'train'. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + ``` + """ + if self.table is not None and not force: + LOGGER.info('Table already exists. Reusing it. Pass force=True to overwrite it.') + return + if self.table_name in self.connection.table_names() and not force: + LOGGER.info(f'Table {self.table_name} already exists. Reusing it. Pass force=True to overwrite it.') + self.table = self.connection.open_table(self.table_name) + self.progress = 1 + return + if self.data is None: + raise ValueError('Data must be provided to create embeddings table') + + data_info = check_det_dataset(self.data) + if split not in data_info: + raise ValueError( + f'Split {split} is not found in the dataset. Available keys in the dataset are {list(data_info.keys())}' + ) + + choice_set = data_info[split] + choice_set = choice_set if isinstance(choice_set, list) else [choice_set] + self.choice_set = choice_set + dataset = ExplorerDataset(img_path=choice_set, data=data_info, augment=False, cache=False, task=self.model.task) + + # Create the table schema + batch = dataset[0] + vector_size = self.model.embed(batch['im_file'], verbose=False)[0].shape[0] + Schema = get_table_schema(vector_size) + table = self.connection.create_table(self.table_name, schema=Schema, mode='overwrite') + table.add( + self._yield_batches(dataset, + data_info, + self.model, + exclude_keys=['img', 'ratio_pad', 'resized_shape', 'ori_shape', 'batch_idx'])) + + self.table = table + + def _yield_batches(self, dataset, data_info, model, exclude_keys: List): + # Implement Batching + for i in tqdm(range(len(dataset))): + self.progress = float(i + 1) / len(dataset) + batch = dataset[i] + for k in exclude_keys: + batch.pop(k, None) + batch = sanitize_batch(batch, data_info) + batch['vector'] = model.embed(batch['im_file'], verbose=False)[0].detach().tolist() + yield [batch] + + def query(self, imgs=None, limit=25): + """ + Query the table for similar images. Accepts a single image or a list of images. + + Args: + imgs (str or list): Path to the image or a list of paths to the images. + limit (int): Number of results to return. + + Returns: + An arrow table containing the results. Supports converting to: + - pandas dataframe: `result.to_pandas()` + - dict of lists: `result.to_pydict()` + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + similar = exp.query(img='https://ultralytics.com/images/zidane.jpg') + ``` + """ + if self.table is None: + raise ValueError('Table is not created. Please create the table first.') + if isinstance(imgs, str): + imgs = [imgs] + elif isinstance(imgs, list): + pass + else: + raise ValueError(f'img must be a string or a list of strings. Got {type(imgs)}') + embeds = self.model.embed(imgs) + # Get avg if multiple images are passed (len > 1) + embeds = torch.mean(torch.stack(embeds), 0).cpu().numpy() if len(embeds) > 1 else embeds[0].cpu().numpy() + query = self.table.search(embeds).limit(limit).to_arrow() + return query + + def sql_query(self, query, return_type='pandas'): + """ + Run a SQL-Like query on the table. Utilizes LanceDB predicate pushdown. + + Args: + query (str): SQL query to run. + return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'. + + Returns: + An arrow table containing the results. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + query = 'SELECT * FROM table WHERE labels LIKE "%person%"' + result = exp.sql_query(query) + ``` + """ + import duckdb + + if self.table is None: + raise ValueError('Table is not created. Please create the table first.') + + # Note: using filter pushdown would be a better long term solution. Temporarily using duckdb for this. + table = self.table.to_arrow() # noqa + if not query.startswith('SELECT') and not query.startswith('WHERE'): + raise ValueError( + 'Query must start with SELECT or WHERE. You can either pass the entire query or just the WHERE clause.') + if query.startswith('WHERE'): + query = f"SELECT * FROM 'table' {query}" + LOGGER.info(f'Running query: {query}') + + rs = duckdb.sql(query) + if return_type == 'pandas': + return rs.df() + elif return_type == 'arrow': + return rs.arrow() + + def plot_sql_query(self, query, labels=True): + """ + Plot the results of a SQL-Like query on the table. + Args: + query (str): SQL query to run. + labels (bool): Whether to plot the labels or not. + + Returns: + PIL Image containing the plot. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + query = 'SELECT * FROM table WHERE labels LIKE "%person%"' + result = exp.plot_sql_query(query) + ``` + """ + result = self.sql_query(query, return_type='arrow') + img = plot_similar_images(result, plot_labels=labels) + img = Image.fromarray(img) + return img + + def get_similar(self, img=None, idx=None, limit=25, return_type='pandas'): + """ + Query the table for similar images. Accepts a single image or a list of images. + + Args: + img (str or list): Path to the image or a list of paths to the images. + idx (int or list): Index of the image in the table or a list of indexes. + limit (int): Number of results to return. Defaults to 25. + return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'. + + Returns: + A table or pandas dataframe containing the results. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg') + ``` + """ + img = self._check_imgs_or_idxs(img, idx) + similar = self.query(img, limit=limit) + + if return_type == 'pandas': + return similar.to_pandas() + elif return_type == 'arrow': + return similar + + def plot_similar(self, img=None, idx=None, limit=25, labels=True): + """ + Plot the similar images. Accepts images or indexes. + + Args: + img (str or list): Path to the image or a list of paths to the images. + idx (int or list): Index of the image in the table or a list of indexes. + labels (bool): Whether to plot the labels or not. + limit (int): Number of results to return. Defaults to 25. + + Returns: + PIL Image containing the plot. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + similar = exp.plot_similar(img='https://ultralytics.com/images/zidane.jpg') + ``` + """ + similar = self.get_similar(img, idx, limit, return_type='arrow') + img = plot_similar_images(similar, plot_labels=labels) + img = Image.fromarray(img) + return img + + def similarity_index(self, max_dist=0.2, top_k=None, force=False): + """ + Calculate the similarity index of all the images in the table. Here, the index will contain the data points that + are max_dist or closer to the image in the embedding space at a given index. + + Args: + max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2. + top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running + vector search. Defaults to 0.01. + force (bool): Whether to overwrite the existing similarity index or not. Defaults to True. + + Returns: + A pandas dataframe containing the similarity index. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + sim_idx = exp.similarity_index() + ``` + """ + if self.table is None: + raise ValueError('Table is not created. Please create the table first.') + sim_idx_table_name = f'{self.sim_idx_base_name}_thres_{max_dist}_top_{top_k}'.lower() + if sim_idx_table_name in self.connection.table_names() and not force: + LOGGER.info('Similarity matrix already exists. Reusing it. Pass force=True to overwrite it.') + return self.connection.open_table(sim_idx_table_name).to_pandas() + + if top_k and not (1.0 >= top_k >= 0.0): + raise ValueError(f'top_k must be between 0.0 and 1.0. Got {top_k}') + if max_dist < 0.0: + raise ValueError(f'max_dist must be greater than 0. Got {max_dist}') + + top_k = int(top_k * len(self.table)) if top_k else len(self.table) + top_k = max(top_k, 1) + features = self.table.to_lance().to_table(columns=['vector', 'im_file']).to_pydict() + im_files = features['im_file'] + embeddings = features['vector'] + + sim_table = self.connection.create_table(sim_idx_table_name, schema=get_sim_index_schema(), mode='overwrite') + + def _yield_sim_idx(): + for i in tqdm(range(len(embeddings))): + sim_idx = self.table.search(embeddings[i]).limit(top_k).to_pandas().query(f'_distance <= {max_dist}') + yield [{ + 'idx': i, + 'im_file': im_files[i], + 'count': len(sim_idx), + 'sim_im_files': sim_idx['im_file'].tolist()}] + + sim_table.add(_yield_sim_idx()) + self.sim_index = sim_table + + return sim_table.to_pandas() + + def plot_similarity_index(self, max_dist=0.2, top_k=None, force=False): + """ + Plot the similarity index of all the images in the table. Here, the index will contain the data points that are + max_dist or closer to the image in the embedding space at a given index. + + Args: + max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2. + top_k (float): Percentage of closest data points to consider when counting. Used to apply limit when + running vector search. Defaults to 0.01. + force (bool): Whether to overwrite the existing similarity index or not. Defaults to True. + + Returns: + PIL Image containing the plot. + + Example: + ```python + exp = Explorer() + exp.create_embeddings_table() + exp.plot_similarity_index() + ``` + """ + sim_idx = self.similarity_index(max_dist=max_dist, top_k=top_k, force=force) + sim_count = sim_idx['count'].tolist() + sim_count = np.array(sim_count) + + indices = np.arange(len(sim_count)) + + # Create the bar plot + plt.bar(indices, sim_count) + + # Customize the plot (optional) + plt.xlabel('data idx') + plt.ylabel('Count') + plt.title('Similarity Count') + buffer = BytesIO() + plt.savefig(buffer, format='png') + buffer.seek(0) + + # Use Pillow to open the image from the buffer + image = Image.open(buffer) + return image + + def _check_imgs_or_idxs(self, img, idx): + if img is None and idx is None: + raise ValueError('Either img or idx must be provided.') + if img is not None and idx is not None: + raise ValueError('Only one of img or idx must be provided.') + if idx is not None: + idx = idx if isinstance(idx, list) else [idx] + img = self.table.to_lance().take(idx, columns=['im_file']).to_pydict()['im_file'] + + img = img if isinstance(img, list) else [img] + return img + + def visualize(self, result): + """ + Visualize the results of a query. + + Args: + result (arrow table): Arrow table containing the results of a query. + """ + # TODO: + pass + + def generate_report(self, result): + """Generate a report of the dataset.""" + pass diff --git a/ultralytics/data/explorer/gui/__init__.py b/ultralytics/data/explorer/gui/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/ultralytics/data/explorer/gui/dash.py b/ultralytics/data/explorer/gui/dash.py new file mode 100644 index 00000000000..1de184406c0 --- /dev/null +++ b/ultralytics/data/explorer/gui/dash.py @@ -0,0 +1,178 @@ +import time +from threading import Thread + +from ultralytics import Explorer +from ultralytics.utils import ROOT +from ultralytics.utils.checks import check_requirements + +check_requirements('streamlit') +check_requirements('streamlit-select>=0.2') +import streamlit as st +from streamlit_select import image_select + + +def _get_explorer(): + exp = Explorer(data=st.session_state.get('dataset'), model=st.session_state.get('model')) + thread = Thread(target=exp.create_embeddings_table, + kwargs={'force': st.session_state.get('force_recreate_embeddings')}) + thread.start() + progress_bar = st.progress(0, text='Creating embeddings table...') + while exp.progress < 1: + time.sleep(0.1) + progress_bar.progress(exp.progress, text=f'Progress: {exp.progress * 100}%') + thread.join() + st.session_state['explorer'] = exp + progress_bar.empty() + + +def init_explorer_form(): + datasets = ROOT / 'cfg' / 'datasets' + ds = [d.name for d in datasets.glob('*.yaml')] + models = [ + 'yolov8n.pt', 'yolov8s.pt', 'yolov8m.pt', 'yolov8l.pt', 'yolov8x.pt', 'yolov8n-seg.pt', 'yolov8s-seg.pt', + 'yolov8m-seg.pt', 'yolov8l-seg.pt', 'yolov8x-seg.pt', 'yolov8n-pose.pt', 'yolov8s-pose.pt', 'yolov8m-pose.pt', + 'yolov8l-pose.pt', 'yolov8x-pose.pt'] + with st.form(key='explorer_init_form'): + col1, col2 = st.columns(2) + with col1: + dataset = st.selectbox('Select dataset', ds, key='dataset', index=ds.index('coco128.yaml')) + with col2: + model = st.selectbox('Select model', models, key='model') + st.checkbox('Force recreate embeddings', key='force_recreate_embeddings') + + st.form_submit_button('Explore', on_click=_get_explorer) + + +def query_form(): + with st.form('query_form'): + col1, col2 = st.columns([0.8, 0.2]) + with col1: + query = st.text_input('Query', '', label_visibility='collapsed', key='query') + with col2: + st.form_submit_button('Query', on_click=run_sql_query) + + +def find_similar_imgs(imgs): + exp = st.session_state['explorer'] + similar = exp.get_similar(img=imgs, limit=st.session_state.get('limit'), return_type='arrow') + paths = similar.to_pydict()['im_file'] + st.session_state['imgs'] = paths + + +def similarity_form(selected_imgs): + st.write('Similarity Search') + with st.form('similarity_form'): + subcol1, subcol2 = st.columns([1, 1]) + with subcol1: + limit = st.number_input('limit', + min_value=None, + max_value=None, + value=25, + label_visibility='collapsed', + key='limit') + + with subcol2: + disabled = not len(selected_imgs) + st.write('Selected: ', len(selected_imgs)) + st.form_submit_button( + 'Search', + disabled=disabled, + on_click=find_similar_imgs, + args=(selected_imgs, ), + ) + if disabled: + st.error('Select at least one image to search.') + + +# def persist_reset_form(): +# with st.form("persist_reset"): +# col1, col2 = st.columns([1, 1]) +# with col1: +# st.form_submit_button("Reset", on_click=reset) +# +# with col2: +# st.form_submit_button("Persist", on_click=update_state, args=("PERSISTING", True)) + + +def run_sql_query(): + query = st.session_state.get('query') + if query.rstrip().lstrip(): + exp = st.session_state['explorer'] + res = exp.sql_query(query, return_type='arrow') + st.session_state['imgs'] = res.to_pydict()['im_file'] + + +def reset_explorer(): + st.session_state['explorer'] = None + st.session_state['imgs'] = None + + +def utralytics_explorer_docs_callback(): + with st.container(border=True): + st.image('https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg', + width=100) + st.markdown( + "<p>This demo is built using Ultralytics Explorer API. Visit <a href=''>API docs</a> to try examples & learn more</p>", + unsafe_allow_html=True, + help=None) + st.link_button('Ultrlaytics Explorer API', 'https://docs.ultralytics.com/') + + +def layout(): + st.set_page_config(layout='wide', initial_sidebar_state='collapsed') + st.markdown("<h1 style='text-align: center;'>Ultralytics Explorer Demo</h1>", unsafe_allow_html=True) + + if st.session_state.get('explorer') is None: + init_explorer_form() + return + + st.button(':arrow_backward: Select Dataset', on_click=reset_explorer) + exp = st.session_state.get('explorer') + col1, col2 = st.columns([0.75, 0.25], gap='small') + + imgs = st.session_state.get('imgs') or exp.table.to_lance().to_table(columns=['im_file']).to_pydict()['im_file'] + total_imgs = len(imgs) + with col1: + subcol1, subcol2, subcol3, subcol4, subcol5 = st.columns(5) + with subcol1: + st.write('Max Images Displayed:') + with subcol2: + num = st.number_input('Max Images Displayed', + min_value=0, + max_value=total_imgs, + value=min(500, total_imgs), + key='num_imgs_displayed', + label_visibility='collapsed') + with subcol3: + st.write('Start Index:') + with subcol4: + start_idx = st.number_input('Start Index', + min_value=0, + max_value=total_imgs, + value=0, + key='start_index', + label_visibility='collapsed') + with subcol5: + reset = st.button('Reset', use_container_width=False, key='reset') + if reset: + st.session_state['imgs'] = None + st.experimental_rerun() + + query_form() + if total_imgs: + imgs_displayed = imgs[start_idx:start_idx + num] + selected_imgs = image_select( + f'Total samples: {total_imgs}', + images=imgs_displayed, + use_container_width=False, + # indices=[i for i in range(num)] if select_all else None, + ) + + with col2: + similarity_form(selected_imgs) + # display_labels = st.checkbox("Labels", value=False, key="display_labels") + utralytics_explorer_docs_callback() + + +if __name__ == '__main__': + layout() diff --git a/ultralytics/data/explorer/utils.py b/ultralytics/data/explorer/utils.py new file mode 100644 index 00000000000..a9aa563fd55 --- /dev/null +++ b/ultralytics/data/explorer/utils.py @@ -0,0 +1,103 @@ +from pathlib import Path +from typing import List + +import cv2 +import numpy as np + +from ultralytics.data.augment import LetterBox +from ultralytics.utils.ops import xyxy2xywh +from ultralytics.utils.plotting import plot_images + + +def get_table_schema(vector_size): + from lancedb.pydantic import LanceModel, Vector + + class Schema(LanceModel): + im_file: str + labels: List[str] + cls: List[int] + bboxes: List[List[float]] + masks: List[List[List[int]]] + keypoints: List[List[List[float]]] + vector: Vector(vector_size) + + return Schema + + +def get_sim_index_schema(): + from lancedb.pydantic import LanceModel + + class Schema(LanceModel): + idx: int + im_file: str + count: int + sim_im_files: List[str] + + return Schema + + +def sanitize_batch(batch, dataset_info): + batch['cls'] = batch['cls'].flatten().int().tolist() + box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1]) + batch['bboxes'] = [box for box, _ in box_cls_pair] + batch['cls'] = [cls for _, cls in box_cls_pair] + batch['labels'] = [dataset_info['names'][i] for i in batch['cls']] + batch['masks'] = batch['masks'].tolist() if 'masks' in batch else [[[]]] + batch['keypoints'] = batch['keypoints'].tolist() if 'keypoints' in batch else [[[]]] + + return batch + + +def plot_similar_images(similar_set, plot_labels=True): + """ + Plot images from the similar set. + + Args: + similar_set (list): Pyarrow table containing the similar data points + plot_labels (bool): Whether to plot labels or not + """ + similar_set = similar_set.to_pydict() + empty_masks = [[[]]] + empty_boxes = [[]] + images = similar_set.get('im_file', []) + bboxes = similar_set.get('bboxes', []) if similar_set.get('bboxes') is not empty_boxes else [] + masks = similar_set.get('masks') if similar_set.get('masks')[0] != empty_masks else [] + kpts = similar_set.get('keypoints') if similar_set.get('keypoints')[0] != empty_masks else [] + cls = similar_set.get('cls', []) + + plot_size = 640 + imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], [] + for i, imf in enumerate(images): + im = cv2.imread(imf) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + h, w = im.shape[:2] + r = min(plot_size / h, plot_size / w) + imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1)) + if plot_labels: + if len(bboxes) > i and len(bboxes[i]) > 0: + box = np.array(bboxes[i], dtype=np.float32) + box[:, [0, 2]] *= r + box[:, [1, 3]] *= r + plot_boxes.append(box) + if len(masks) > i and len(masks[i]) > 0: + mask = np.array(masks[i], dtype=np.uint8)[0] + plot_masks.append(LetterBox(plot_size, center=False)(image=mask)) + if len(kpts) > i and kpts[i] is not None: + kpt = np.array(kpts[i], dtype=np.float32) + kpt[:, :, :2] *= r + plot_kpts.append(kpt) + batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i) + imgs = np.stack(imgs, axis=0) + masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8) + kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32) + boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32) + batch_idx = np.concatenate(batch_idx, axis=0) + cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0) + + fname = 'temp_exp_grid.jpg' + plot_images(imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, fname=fname, + max_subplots=len(images)).join() + img = cv2.imread(fname, cv2.IMREAD_COLOR) + img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + Path(fname).unlink() + return img_rgb diff --git a/ultralytics/utils/plotting.py b/ultralytics/utils/plotting.py index aaab72a1828..7d46ecf1e6d 100644 --- a/ultralytics/utils/plotting.py +++ b/ultralytics/utils/plotting.py @@ -579,7 +579,8 @@ def plot_images(images, paths=None, fname='images.jpg', names=None, - on_plot=None): + on_plot=None, + max_subplots=16): """Plot image grid with labels.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() @@ -595,7 +596,7 @@ def plot_images(images, batch_idx = batch_idx.cpu().numpy() max_size = 1920 # max image size - max_subplots = 16 # max image subplots, i.e. 4x4 + max_subplots = max_subplots # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs ** 0.5) # number of subplots (square) @@ -685,7 +686,7 @@ def plot_images(images, image_masks = np.where(image_masks == index, 1.0, 0.0) im = np.asarray(annotator.im).copy() - for j, box in enumerate(boxes.T.tolist()): + for j in range(len(image_masks)): if labels or conf[j] > 0.25: # 0.25 conf thresh color = colors(classes[j]) mh, mw = image_masks[j].shape
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
zulip__zulip-7419@15d758e
zulip/zulip
Python
7,419
org settings: Add UI for managing user groups.
Fixes #165. # To-Do - [x] Add framework for page - [x] Add content and design to page - [x] Render current user groups data dynamically - [x] Add user group creation functionality (initialized with current user inside) - [x] Add user group deletion functionality - [x] Add save user group state functionality to group members - [x] Add save user group state functionality to group data (name + description) - [x] Add edit user group members functionality - [x] Add edit user group data functionality - [x] Add compose typeahead - [x] Add Node tests for `user_groups.js` - [x] Add placeholder for empty input pills - [ ] Add Node tests for `settings_user_groups.js` # Updates ## 01/05/18 * Updated UI with suggestions from Aditya * Fixed minor bugs (empty name OR description showing save button, duplicate user groups after editing group data, edit members button on new line) * Added i18n for text strings * Added placeholders for name, description, and empty input pills ![screenshot at jan 05 12-00-49](https://user-images.githubusercontent.com/15116870/34626068-18868284-f210-11e7-8d12-b284643a38fe.png) ## 01/03/18 * Updated UI with suggestions from Brock ![](https://chat.zulip.org/user_uploads/2/cd/8UEUqc4kOhtR3d6d1UyPyaF-/Screenshot-at-Jan-03-10-08-37.png) ## 12/29/17 Various UI updates from feedback (tests failing due to Casper flake) [![https://gyazo.com/53573303b8bf6ccea748a4889d0ef5fa](https://i.gyazo.com/53573303b8bf6ccea748a4889d0ef5fa.gif)](https://gyazo.com/53573303b8bf6ccea748a4889d0ef5fa) * Name and description have borders around them (and bolded outlines when focused) * Delete icon moved to name and description line * Prevent save icon from showing while name or description fields are empty * Prevent enter key from triggering line breaks * Duplicate edited groups bug should be fixed ## 12/19/17 * Fully covered `user_groups.js` * Added typeahead to user group member inputs [![https://gyazo.com/8e9d8678deacb4cd3a779580f0f965a1](https://i.gyazo.com/8e9d8678deacb4cd3a779580f0f965a1.gif)](https://gyazo.com/8e9d8678deacb4cd3a779580f0f965a1) ## 12/18/17 * Add functionality for changing group name/description [![https://gyazo.com/aeed3e7abd3e293eddc6eab58988168c](https://i.gyazo.com/aeed3e7abd3e293eddc6eab58988168c.gif)](https://gyazo.com/aeed3e7abd3e293eddc6eab58988168c) * Add save group data state functionality; button only appears if group data is changed * Add functionality for adding/removing users from groups * Add save group members state functionality; button only appears if group members are changed (button is hidden if there are no users in the group) [![https://gyazo.com/c29841abd27294a44fed962e1c91de1f](https://i.gyazo.com/c29841abd27294a44fed962e1c91de1f.gif)](https://gyazo.com/c29841abd27294a44fed962e1c91de1f) * Reject invalid inputs to prevent invalid pills from being created * Modify inputs to only accept user emails, not full names (non-unique) ## 12/11/17 * Initialize created user groups with current user already inserted * Various UI updates ## 12/06/17 * Added group deletion functionality * Added UI for deleting groups ![screenshot at dec 06 11-02-33](https://user-images.githubusercontent.com/15116870/33680002-1fe359ec-da75-11e7-97e5-ed7893e38a5c.png) * Implemented user group creation * Updated page styling to include group name and description ![screenshot at dec 06 09-37-36](https://user-images.githubusercontent.com/15116870/33675987-4d28790c-da69-11e7-81a0-4bba96270a18.png) ## 11/16/17 * Added page content with design suggestions from Brock * User group data is now dynamically rendered ![screenshot at nov 16 21-23-33](https://user-images.githubusercontent.com/15116870/32930582-6cb243f2-cb14-11e7-8302-949cd3cc0c03.png) * Initial commit: Add framework for User Groups page
2017-11-16T22:03:58Z
Add user-definable groups [summarizing a Zulip discussion] Add a way for users (or, at the very least, administrators) to define their own groups, such that someone can write `@group1` to notify everyone in `group1`. Design complication: what should happen when `group1` is (say) 100 members, but someone says `@group1` in a stream to which only 30 of those members are subscribed? Should the sender get a prompt asking whether the other 70 people should be subscribed to the stream?
why not use a stream instead? whoever subscribed to it is in the group... @ritschwumm It could be helpful to tag a specific group in a discussion that's open to a wider audience. For example, you might want to tag the support team in a discussion among all company employees. As a workaround, you can simulate that today using the "alert words" feature -- just have all the members of a team all add `@group1` to their list of alert words. Hello @umairwaheed, you claimed this issue to work on it, but this issue and any referenced pull requests haven't been updated for 10 days. Are you still working on this issue? If so, please update this issue by leaving a comment on this issue to let me know that you're still working on it. Otherwise, I'll automatically remove you from this issue in 4 days. If you've decided to work on something else, simply comment `@zulipbot abandon` so that someone else can claim it and continue from where you left off. Thank you for your valuable contributions to Zulip! I think from the point of view of the backend, we need to finish https://github.com/zulip/zulip/issues/7381. On the frontend side, we need to create the UI to manage groups. @synicalsyntax is working on the UI, so if you can finish #7381 we should be in great shape on this :) Sure, will do. Hello @umairwaheed, you claimed this issue to work on it, but this issue and any referenced pull requests haven't been updated for 10 days. Are you still working on this issue? If so, please update this issue by leaving a comment on this issue to let me know that you're still working on it. Otherwise, I'll automatically remove you from this issue in 4 days. If you've decided to work on something else, simply comment `@zulipbot abandon` so that someone else can claim it and continue from where you left off. Thank you for your valuable contributions to Zulip! <!-- inactiveWarning --> My work on this is done. I think @synicalsyntax is now working on the frontend. @zulipbot abandon.
[ { "body": "[summarizing a Zulip discussion]\n\nAdd a way for users (or, at the very least, administrators) to define their own groups, such that someone can write `@group1` to notify everyone in `group1`.\n\nDesign complication: what should happen when `group1` is (say) 100 members, but someone says `@group1` in a stream to which only 30 of those members are subscribed? Should the sender get a prompt asking whether the other 70 people should be subscribed to the stream?\n", "number": 165, "title": "Add user-definable groups" } ]
5bbc46762f52ccb18a0fc8837bce65a28602f97c
{ "head_commit": "15d758e2e105503283ae9508e1980ade6447219b", "head_commit_message": "org settings: Add user group deletion functionality.", "patch_to_review": "diff --git a/.eslintrc.json b/.eslintrc.json\nindex 182309f2fa5f2..31a3542bf707f 100644\n--- a/.eslintrc.json\n+++ b/.eslintrc.json\n@@ -56,6 +56,7 @@\n \"settings_streams\": false,\n \"settings_filters\": false,\n \"settings_invites\": false,\n+ \"settings_user_groups\": false,\n \"settings\": false,\n \"resize\": false,\n \"loading\": false,\ndiff --git a/frontend_tests/node_tests/templates.js b/frontend_tests/node_tests/templates.js\nindex 1150e71df1993..61fc5cac37a2e 100644\n--- a/frontend_tests/node_tests/templates.js\n+++ b/frontend_tests/node_tests/templates.js\n@@ -283,6 +283,31 @@ function render(template_name, args) {\n global.write_handlebars_output(\"admin_tab\", html);\n }());\n \n+(function admin_user_group_list() {\n+ var args = {\n+ user_group: {\n+ id: \"9\",\n+ name: \"uranohoshi\",\n+ description: \"Students at Uranohoshi Academy\",\n+ },\n+ };\n+\n+ var html = '';\n+ html += '<div id=\"user-groups\">';\n+ html += render('admin_user_group_list', args);\n+ html += '</div>';\n+\n+ global.write_handlebars_output('admin_user_group_list', html);\n+\n+ var group_id = $(html).find('.user-group:first').prop('id');\n+ var group_name = $(html).find('.user-group:first .pill-container').attr('data-group-pills');\n+ var group_description = $(html).find('.user-group:first h4').text();\n+\n+ assert.equal(group_id, '9');\n+ assert.equal(group_name, 'uranohoshi');\n+ assert.equal(group_description, 'uranohoshi โ€” Students at Uranohoshi Academy');\n+}());\n+\n (function admin_user_list() {\n var html = '<table>';\n var users = ['alice', 'bob', 'carl'];\ndiff --git a/static/js/admin_sections.js b/static/js/admin_sections.js\nindex 856e627a58e72..240e332682ed7 100644\n--- a/static/js/admin_sections.js\n+++ b/static/js/admin_sections.js\n@@ -32,6 +32,9 @@ exports.load_admin_section = function (name) {\n case 'invites-list-admin':\n section = 'invites';\n break;\n+ case 'user-groups-admin':\n+ section = 'user-groups';\n+ break;\n default:\n blueslip.error('Unknown admin id ' + name);\n return;\n@@ -62,6 +65,9 @@ exports.load_admin_section = function (name) {\n case 'invites':\n settings_invites.set_up();\n break;\n+ case 'user-groups':\n+ settings_user_groups.set_up();\n+ break;\n default:\n blueslip.error('programming error for section ' + section);\n return;\n@@ -78,6 +84,7 @@ exports.reset_sections = function () {\n settings_streams.reset();\n settings_filters.reset();\n settings_invites.reset();\n+ settings_user_groups.reset();\n };\n \n return exports;\ndiff --git a/static/js/server_events_dispatch.js b/static/js/server_events_dispatch.js\nindex 779a82b01b36f..332743cd316de 100644\n--- a/static/js/server_events_dispatch.js\n+++ b/static/js/server_events_dispatch.js\n@@ -359,6 +359,13 @@ exports.dispatch_normal_event = function dispatch_normal_event(event) {\n ui.remove_message(msg_id);\n break;\n \n+ case 'user_group':\n+ if (event.op === 'add') {\n+ user_groups.add(event.group);\n+ settings_user_groups.reload();\n+ }\n+ break;\n+\n }\n };\n \ndiff --git a/static/js/settings.js b/static/js/settings.js\nindex a42143d216fee..02dd8c46d7935 100644\n--- a/static/js/settings.js\n+++ b/static/js/settings.js\n@@ -84,6 +84,7 @@ function _setup_page() {\n \"default-streams-list\": i18n.t(\"Default streams\"),\n \"filter-settings\": i18n.t(\"Filter settings\"),\n \"invites-list-admin\": i18n.t(\"Invitations\"),\n+ \"user-groups-admin\": i18n.t(\"User groups\"),\n };\n }\n \ndiff --git a/static/js/settings_user_groups.js b/static/js/settings_user_groups.js\nnew file mode 100644\nindex 0000000000000..a02690e0b6613\n--- /dev/null\n+++ b/static/js/settings_user_groups.js\n@@ -0,0 +1,115 @@\n+var settings_user_groups = (function () {\n+\n+var exports = {};\n+\n+var meta = {\n+ loaded: false,\n+};\n+\n+exports.reset = function () {\n+ meta.loaded = false;\n+};\n+\n+exports.reload = function () {\n+ var user_groups_section = $('#user-groups').expectOne();\n+ user_groups_section.html('');\n+ exports.populate_user_groups();\n+};\n+\n+exports.populate_user_groups = function () {\n+ if (!meta.loaded) {\n+ return;\n+ }\n+\n+ var user_groups_section = $('#user-groups').expectOne();\n+ var user_groups_array = user_groups.get_realm_user_groups();\n+\n+ _.each(user_groups_array, function (data) {\n+ user_groups_section.append(templates.render('admin_user_group_list', {\n+ user_group: {\n+ name: data.name,\n+ id: data.id,\n+ description: data.description,\n+ },\n+ }));\n+\n+ var pill_container = $('.pill-container[data-group-pills=\"' + data.name + '\"]');\n+ var pills = input_pill(pill_container);\n+ data.members.forEach(function (user_id) {\n+ var user = people.get_person_from_user_id(user_id);\n+\n+ if (user) {\n+ pills.pill.append(user.full_name, user_id);\n+ } else {\n+ blueslip.warn('Unknown user ID ' + user_id + ' in members of user group ' + data.name);\n+ }\n+ });\n+ });\n+\n+};\n+\n+exports.set_up = function () {\n+ meta.loaded = true;\n+\n+ exports.populate_user_groups();\n+\n+ $(\".organization\").on(\"submit\", \"form.admin-user-group-form\", function (e) {\n+ e.preventDefault();\n+ e.stopPropagation();\n+\n+ var user_group_status = $('#admin-user-group-status');\n+\n+ var group = {\n+ members: JSON.stringify([people.my_current_user_id()]),\n+ };\n+ _.each($(this).serializeArray(), function (obj) {\n+ if (obj.value.trim() === \"\") {\n+ return;\n+ }\n+ group[obj.name] = obj.value;\n+ });\n+\n+ channel.post({\n+ url: \"/json/user_groups/create\",\n+ data: group,\n+ success: function () {\n+ user_group_status.hide();\n+ ui_report.success(i18n.t(\"User group added!\"), user_group_status);\n+ $(\"form.admin-user-group-form input[type='text']\").val(\"\");\n+ },\n+ error: function (xhr) {\n+ user_group_status.hide();\n+ var errors = JSON.parse(xhr.responseText).msg;\n+ xhr.responseText = JSON.stringify({msg: errors});\n+ ui_report.error(i18n.t(\"Failed\"), xhr, user_group_status);\n+ },\n+ });\n+ });\n+\n+ $('#user-groups').on('click', '.delete', function () {\n+ var group_id = $(this).parent().attr('id');\n+ var user_group = user_groups.get_user_group_from_id(group_id);\n+ var btn = $(this);\n+\n+ channel.del({\n+ url: \"/json/user_groups/\" + group_id,\n+ data: {\n+ id: group_id,\n+ },\n+ success: function () {\n+ user_groups.remove(user_group);\n+ settings_user_groups.reload();\n+ },\n+ error: function () {\n+ btn.text(i18n.t(\"Failed!\"));\n+ },\n+ });\n+ });\n+};\n+\n+return exports;\n+}());\n+\n+if (typeof module !== 'undefined') {\n+ module.exports = settings_user_groups;\n+}\ndiff --git a/static/js/user_groups.js b/static/js/user_groups.js\nindex e23c1bbc2f994..21d762300cc41 100644\n--- a/static/js/user_groups.js\n+++ b/static/js/user_groups.js\n@@ -15,11 +15,16 @@ exports.init = function () {\n // WE INITIALIZE DATA STRUCTURES HERE!\n exports.init();\n \n-exports.add = function add(user_group) {\n+exports.add = function (user_group) {\n user_group_name_dict.set(user_group.name, user_group);\n user_group_by_id_dict.set(user_group.id, user_group);\n };\n \n+exports.remove = function (user_group) {\n+ user_group_name_dict.del(user_group.name);\n+ user_group_by_id_dict.del(user_group.id);\n+};\n+\n exports.get_user_group_from_id = function (group_id) {\n if (!user_group_by_id_dict.has(group_id)) {\n blueslip.error('Unknown group_id in get_user_group_from_id: ' + group_id);\ndiff --git a/static/styles/settings.css b/static/styles/settings.css\nindex 523f581a89ce0..ca1f28788e54a 100644\n--- a/static/styles/settings.css\n+++ b/static/styles/settings.css\n@@ -477,15 +477,18 @@ input[type=checkbox].inline-block {\n max-width: 100%;\n }\n \n-.add-new-emoji-box {\n+.add-new-emoji-box,\n+.add-new-user-group-box {\n margin-bottom: 20px;\n }\n \n-.add-new-emoji-box .new-emoji-form {\n+.add-new-emoji-box .new-emoji-form,\n+.add-new-user-group-box .new-user-group-form {\n margin: 10px 0px;\n }\n \n .add-new-emoji-box input[type=text],\n+.add-new-user-group-box input[type=text],\n .add-new-default-stream-box input[type=text] {\n padding: 6px;\n }\n@@ -820,6 +823,10 @@ input[type=checkbox].inline-block {\n margin-right: 5px;\n }\n \n+#user-groups .user-group h4 > span {\n+ font-weight: initial;\n+}\n+\n /* -- new settings overlay -- */\n #settings_page {\n height: 95vh;\ndiff --git a/static/templates/admin_tab.handlebars b/static/templates/admin_tab.handlebars\nindex ae2ce2a4b9b7c..8d3274f4cfa78 100644\n--- a/static/templates/admin_tab.handlebars\n+++ b/static/templates/admin_tab.handlebars\n@@ -27,3 +27,5 @@\n {{ partial \"realm-filter-settings-admin\" }}\n \n {{ partial \"invites-list-admin\" }}\n+\n+{{ partial \"user-groups-admin\" }}\ndiff --git a/static/templates/admin_user_group_list.handlebars b/static/templates/admin_user_group_list.handlebars\nnew file mode 100644\nindex 0000000000000..0671f08335f52\n--- /dev/null\n+++ b/static/templates/admin_user_group_list.handlebars\n@@ -0,0 +1,11 @@\n+{{#with user_group}}\n+<div class=\"user-group\" id=\"{{id}}\">\n+ <h4>{{name}}<span> โ€” {{description}}</span></h4>\n+ <div class=\"pill-container\" data-group-pills=\"{{name}}\">\n+ <div class=\"input\" contenteditable=\"true\"></div>\n+ </div>\n+ <button class=\"button rounded small delete btn-danger\">\n+ <i class=\"icon-vector-trash\" aria-hidden=\"true\"></i>\n+ </button>\n+</div>\n+{{/with}}\ndiff --git a/static/templates/user-groups-admin.handlebars b/static/templates/user-groups-admin.handlebars\nnew file mode 100644\nindex 0000000000000..670cc7b22c8f4\n--- /dev/null\n+++ b/static/templates/user-groups-admin.handlebars\n@@ -0,0 +1,25 @@\n+<div id=\"user-groups-admin\" class=\"settings-section\" data-name=\"user-groups-admin\">\n+ <div class=\"tip\">Anyone in this organization can add user groups.</div>\n+ <form class=\"form-horizontal admin-user-group-form\">\n+ <div class=\"add-new-user-group-box grey-box\">\n+ <div class=\"new-user-group-form\">\n+ <div class=\"settings-section-title new-user-group-section-title no-padding\">{{t \"Add a new user group\" }}</div>\n+ <div class=\"alert\" id=\"admin-user-group-status\"></div>\n+ <div class=\"inline-block\">\n+ <label for=\"user_group_name\">{{t \"Name\" }}</label>\n+ <input type=\"text\" name=\"name\" id=\"user_group_name\" placeholder=\"hamletcharacters\" />\n+ </div>\n+ <div class=\"inline-block\">\n+ <label for=\"user_group_description\">{{t \"Description\" }}</label>\n+ <input type=\"text\" name=\"description\" id=\"user_group_description\" placeholder=\"{{t 'Characters of Hamlet' }}\" />\n+ </div>\n+ <button type=\"submit\" class=\"button rounded sea-green\">\n+ {{t 'Submit' }}\n+ </button>\n+ </div>\n+ </div>\n+ </form>\n+\n+ <p>{{#tr this}}Add members of your organization to mentionable user groups.{{/tr}}</p>\n+ <div id=\"user-groups\"></div>\n+</div>\ndiff --git a/templates/zerver/settings_overlay.html b/templates/zerver/settings_overlay.html\nindex 7bff54ad03d2b..b2758b3d94843 100644\n--- a/templates/zerver/settings_overlay.html\n+++ b/templates/zerver/settings_overlay.html\n@@ -60,6 +60,10 @@ <h1>{{ _('Settings') }}<span class=\"section\"></span></h1>\n <i class=\"icon icon-vector-smile\"></i>\n <div class=\"text\">{{ _('Custom emoji') }}</div>\n </li>\n+ <li class=\"admin\" tabindex=\"0\" data-section=\"user-groups-admin\">\n+ <i class=\"icon icon-vector-group\"></i>\n+ <div class=\"text\">{{ _('User groups') }}</div>\n+ </li>\n <li class=\"admin\" tabindex=\"0\" data-section=\"auth-methods\">\n <i class=\"icon icon-vector-lock\"></i>\n <div class=\"text\">{{ _('Authentication methods') }}</div>\ndiff --git a/tools/lib/capitalization.py b/tools/lib/capitalization.py\nindex fb38fab3438fd..d4aee800923da 100644\n--- a/tools/lib/capitalization.py\n+++ b/tools/lib/capitalization.py\n@@ -20,6 +20,7 @@\n r\"Dropbox\",\n r\"GitHub\",\n r\"Google\",\n+ r\"Hamlet\",\n r\"HTTP\",\n r\"ID\",\n r\"IDs\",\ndiff --git a/tools/linter_lib/custom_check.py b/tools/linter_lib/custom_check.py\nindex 1cdd6e1cb1f1b..88212db794c88 100644\n--- a/tools/linter_lib/custom_check.py\n+++ b/tools/linter_lib/custom_check.py\n@@ -503,7 +503,9 @@ def build_custom_checkers(by_lang):\n 'exclude_line': [('templates/zerver/register.html', 'placeholder=\"acme\"'),\n ('templates/zerver/register.html', 'placeholder=\"Acme or Aฮบฮผฮฎ\"'),\n ('static/templates/settings/realm-domains-modal.handlebars',\n- '<td><input type=\"text\" class=\"new-realm-domain\" placeholder=\"acme.com\"></input></td>')],\n+ '<td><input type=\"text\" class=\"new-realm-domain\" placeholder=\"acme.com\"></input></td>'),\n+ (\"static/templates/user-groups-admin.handlebars\",\n+ '<input type=\"text\" name=\"name\" id=\"user_group_name\" placeholder=\"hamletcharacters\" />')],\n 'exclude': set([\"static/templates/settings/emoji-settings-admin.handlebars\",\n \"static/templates/settings/realm-filter-settings-admin.handlebars\",\n \"static/templates/settings/bot-settings.handlebars\",\ndiff --git a/zproject/settings.py b/zproject/settings.py\nindex 87167742d0603..612fa7b75260f 100644\n--- a/zproject/settings.py\n+++ b/zproject/settings.py\n@@ -1079,6 +1079,7 @@ def get_secret(key: str) -> None:\n 'js/settings_streams.js',\n 'js/settings_filters.js',\n 'js/settings_invites.js',\n+ 'js/settings_user_groups.js',\n 'js/settings.js',\n 'js/admin_sections.js',\n 'js/admin.js',\n" }
[ { "diff_hunk": "@@ -4,5 +4,8 @@\n <div class=\"pill-container\" data-group-pills=\"{{name}}\">\n <div class=\"input\" contenteditable=\"true\"></div>\n </div>\n+ <button class=\"button rounded small delete btn-danger\">\n+ <i class=\"icon-vector-trash\" aria-hidden=\"true\"></i>", "line": null, "original_line": 8, "original_start_line": null, "path": "static/templates/admin_user_group_list.handlebars", "start_line": null, "text": "@user1:\nCan you please change the `class=\"icon-vector-trash\"` to `class=\"fa fa-trash\"`." } ]
b36c0c8072fa5a3aa4b500ab23618d01229555aa
diff --git a/.eslintrc.json b/.eslintrc.json index 182309f2fa5f2..31a3542bf707f 100644 --- a/.eslintrc.json +++ b/.eslintrc.json @@ -56,6 +56,7 @@ "settings_streams": false, "settings_filters": false, "settings_invites": false, + "settings_user_groups": false, "settings": false, "resize": false, "loading": false, diff --git a/frontend_tests/node_tests/templates.js b/frontend_tests/node_tests/templates.js index 1150e71df1993..765dab032c5de 100644 --- a/frontend_tests/node_tests/templates.js +++ b/frontend_tests/node_tests/templates.js @@ -283,6 +283,31 @@ function render(template_name, args) { global.write_handlebars_output("admin_tab", html); }()); +(function admin_user_group_list() { + var args = { + user_group: { + id: "9", + name: "uranohoshi", + description: "Students at Uranohoshi Academy", + }, + }; + + var html = ''; + html += '<div id="user-groups">'; + html += render('admin_user_group_list', args); + html += '</div>'; + + global.write_handlebars_output('admin_user_group_list', html); + + var group_id = $(html).find('.user-group:first').prop('id'); + var group_name = $(html).find('.user-group:first .pill-container').attr('data-group-pills'); + var group_description = $(html).find('.user-group:first h4').text().trim().replace(/\s+/g, ' '); + + assert.equal(group_id, '9'); + assert.equal(group_name, 'uranohoshi'); + assert.equal(group_description, 'uranohoshi โ€” Students at Uranohoshi Academy'); +}()); + (function admin_user_list() { var html = '<table>'; var users = ['alice', 'bob', 'carl']; diff --git a/frontend_tests/node_tests/user_groups.js b/frontend_tests/node_tests/user_groups.js index 0fbbafbb45527..fa83ea4c0a23f 100644 --- a/frontend_tests/node_tests/user_groups.js +++ b/frontend_tests/node_tests/user_groups.js @@ -1,8 +1,18 @@ set_global('blueslip', {}); +set_global('page_params', {}); zrequire('user_groups'); (function test_user_groups() { + var students = { + name: 'Students', + id: 0, + }; + global.page_params.realm_user_groups = [students]; + + user_groups.initialize(); + assert.equal(user_groups.get_user_group_from_id(students.id), students); + var admins = { name: 'Admins', id: 1, @@ -11,14 +21,22 @@ zrequire('user_groups'); name: 'Everyone', id: 2, }; + user_groups.add(admins); assert.equal(user_groups.get_user_group_from_id(admins.id), admins); + var called = false; global.blueslip.error = function (msg) { assert.equal(msg, "Unknown group_id in get_user_group_from_id: " + all.id); called = true; }; + assert.equal(user_groups.get_user_group_from_id(all.id), undefined); assert(called); -}()); + user_groups.remove(students); + global.blueslip.error = function (msg) { + assert.equal(msg, "Unknown group_id in get_user_group_from_id: " + students.id); + }; + assert.equal(user_groups.get_user_group_from_id(students.id), undefined); +}()); diff --git a/static/js/admin_sections.js b/static/js/admin_sections.js index 856e627a58e72..240e332682ed7 100644 --- a/static/js/admin_sections.js +++ b/static/js/admin_sections.js @@ -32,6 +32,9 @@ exports.load_admin_section = function (name) { case 'invites-list-admin': section = 'invites'; break; + case 'user-groups-admin': + section = 'user-groups'; + break; default: blueslip.error('Unknown admin id ' + name); return; @@ -62,6 +65,9 @@ exports.load_admin_section = function (name) { case 'invites': settings_invites.set_up(); break; + case 'user-groups': + settings_user_groups.set_up(); + break; default: blueslip.error('programming error for section ' + section); return; @@ -78,6 +84,7 @@ exports.reset_sections = function () { settings_streams.reset(); settings_filters.reset(); settings_invites.reset(); + settings_user_groups.reset(); }; return exports; diff --git a/static/js/server_events_dispatch.js b/static/js/server_events_dispatch.js index 779a82b01b36f..332743cd316de 100644 --- a/static/js/server_events_dispatch.js +++ b/static/js/server_events_dispatch.js @@ -359,6 +359,13 @@ exports.dispatch_normal_event = function dispatch_normal_event(event) { ui.remove_message(msg_id); break; + case 'user_group': + if (event.op === 'add') { + user_groups.add(event.group); + settings_user_groups.reload(); + } + break; + } }; diff --git a/static/js/settings.js b/static/js/settings.js index a42143d216fee..02dd8c46d7935 100644 --- a/static/js/settings.js +++ b/static/js/settings.js @@ -84,6 +84,7 @@ function _setup_page() { "default-streams-list": i18n.t("Default streams"), "filter-settings": i18n.t("Filter settings"), "invites-list-admin": i18n.t("Invitations"), + "user-groups-admin": i18n.t("User groups"), }; } diff --git a/static/js/settings_user_groups.js b/static/js/settings_user_groups.js new file mode 100644 index 0000000000000..68427de2c8190 --- /dev/null +++ b/static/js/settings_user_groups.js @@ -0,0 +1,268 @@ +var settings_user_groups = (function () { + +var exports = {}; + +var meta = { + loaded: false, +}; + +exports.reset = function () { + meta.loaded = false; +}; + +exports.reload = function () { + var user_groups_section = $('#user-groups').expectOne(); + user_groups_section.html(''); + exports.populate_user_groups(); +}; + +exports.populate_user_groups = function () { + if (!meta.loaded) { + return; + } + + var user_groups_section = $('#user-groups').expectOne(); + var user_groups_array = user_groups.get_realm_user_groups(); + + _.each(user_groups_array, function (data) { + user_groups_section.append(templates.render('admin_user_group_list', { + user_group: { + name: data.name, + id: data.id, + description: data.description, + }, + })); + + var pill_container = $('.pill-container[data-group-pills="' + data.name + '"]'); + var pills = input_pill(pill_container); + + data.members.forEach(function (user_id) { + var user = people.get_person_from_user_id(user_id); + + if (user) { + pills.pill.append(user.full_name, user_id); + } else { + blueslip.warn('Unknown user ID ' + user_id + ' in members of user group ' + data.name); + } + }); + + function update_save_state(draft_group) { + var original_group = user_groups.get_user_group_from_id(data.id).members; + var same_groups = _.isEqual(_.sortBy(draft_group), _.sortBy(original_group)); + var save_changes = pill_container.siblings('.save-member-changes'); + var save_hidden = save_changes.css('display') === 'none'; + + if ((!draft_group.length || same_groups) && !save_hidden) { + save_changes.fadeOut(); + } else if (!same_groups && draft_group.length && save_hidden) { + save_changes.css({display: 'inline-block', opacity: '0'}).fadeTo(400, 1); + } + } + + var input = pill_container.children('.input'); + + input.typeahead({ + items: 5, + fixed: true, + dropup: true, + source: people.get_realm_persons, + highlighter: function (item) { + return typeahead_helper.render_person(item); + }, + matcher: function (item) { + if (pills.keys().includes(item.user_id)) { + return false; + } + + var person = people.get_person_from_user_id(item.user_id); + var query = this.query.toLowerCase(); + + return (person.email.toLowerCase().indexOf(query) !== -1 + || person.full_name.toLowerCase().indexOf(query) !== -1); + }, + sorter: function (matches) { + return typeahead_helper.sort_recipientbox_typeahead( + this.query, matches, ""); + }, + updater: function (user) { + pills.pill.append(user.full_name, user.user_id); + input.text(''); + update_save_state(pills.keys()); + }, + stopAdvance: true, + }); + + pills.onPillCreate(function (value, reject) { + var person = people.get_by_email(value); + var draft_group = pills.keys(); + + if (!person || draft_group.includes(person.user_id)) { + return reject(); + } + + draft_group.push(person.user_id); + update_save_state(draft_group); + + return { key: person.user_id, value: person.full_name }; + }); + + pills.onPillRemove(function () { + update_save_state(pills.keys()); + }); + + $('#user-groups #' + data.id).on('click', '.save-member-changes', function () { + var draft_group = pills.keys(); + var group_data = user_groups.get_user_group_from_id(data.id); + var original_group = group_data.members; + var added = _.difference(draft_group, original_group); + var removed = _.difference(original_group, draft_group); + var btn = $(this); + + channel.post({ + url: "/json/user_groups/" + data.id + '/members', + data: { + add: JSON.stringify(added), + delete: JSON.stringify(removed), + }, + success: function () { + original_group = _.reject(original_group, function (e) { + return removed.includes(e); + }); + original_group = original_group.concat(added); + group_data.members = original_group; + user_groups.remove(group_data); + user_groups.add(group_data); + btn.text(i18n.t("Saved!")).delay(200).fadeOut(function () { + $(this).html('<i class="fa fa-check" aria-hidden="true"></i>'); + }); + }, + error: function () { + btn.text(i18n.t("Failed!")); + }, + }); + }); + }); +}; + +exports.set_up = function () { + meta.loaded = true; + + exports.populate_user_groups(); + + $(".organization").on("submit", "form.admin-user-group-form", function (e) { + e.preventDefault(); + e.stopPropagation(); + + var user_group_status = $('#admin-user-group-status'); + + var group = { + members: JSON.stringify([people.my_current_user_id()]), + }; + _.each($(this).serializeArray(), function (obj) { + if (obj.value.trim() === "") { + return; + } + group[obj.name] = obj.value; + }); + + channel.post({ + url: "/json/user_groups/create", + data: group, + success: function () { + user_group_status.hide(); + ui_report.success(i18n.t("User group added!"), user_group_status); + $("form.admin-user-group-form input[type='text']").val(""); + }, + error: function (xhr) { + user_group_status.hide(); + var errors = JSON.parse(xhr.responseText).msg; + xhr.responseText = JSON.stringify({msg: errors}); + ui_report.error(i18n.t("Failed"), xhr, user_group_status); + }, + }); + }); + + $('#user-groups').on('click', '.delete', function () { + var group_id = $(this).parents('.user-group').attr('id'); + var user_group = user_groups.get_user_group_from_id(group_id); + var btn = $(this); + + channel.del({ + url: "/json/user_groups/" + group_id, + data: { + id: group_id, + }, + success: function () { + user_groups.remove(user_group); + settings_user_groups.reload(); + }, + error: function () { + btn.text(i18n.t("Failed!")); + }, + }); + }); + + $('#user-groups').on('keypress', '.user-group h4 > span', function (e) { + if (e.which === 13) { + e.preventDefault(); + } + }); + + $('#user-groups').on('input', '.user-group h4 > span', function () { + var element = this.className; + var current_text = $(this).text(); + var sibling_element = $(this).siblings('span').first().attr('class'); + var sibling_text = $(this).siblings('span').first().text(); + var group_id = $(this).parents('.user-group').attr('id'); + var user_group = user_groups.get_user_group_from_id(group_id); + var saved_text = user_group[element]; + var saved_sibling_text = user_group[sibling_element]; + + var has_changes = saved_text !== current_text || saved_sibling_text !== sibling_text; + var save_changes = $(this).siblings('.save-group-changes'); + var save_hidden = save_changes.css('display') === 'none'; + var has_content = current_text.trim() !== '' && sibling_text.trim() !== ''; + + if (has_changes && save_hidden && has_content) { + save_changes.css({display: 'inline', opacity: '0'}).fadeTo(400, 1); + } else if ((!has_changes || !has_content) && !save_hidden) { + save_changes.fadeOut(); + } + }); + + $('#user-groups').on('click', '.save-group-changes', function () { + var group_id = $(this).parents('.user-group').attr('id'); + var group_data = user_groups.get_user_group_from_id(group_id); + + var description = $(this).siblings('.description').text().trim(); + var name = $(this).siblings('.name').text().trim(); + var btn = $(this); + + channel.patch({ + url: "/json/user_groups/" + group_id, + data: { + name: name, + description: description, + }, + success: function () { + user_groups.remove(group_data); + group_data.description = description; + group_data.name = name; + user_groups.add(group_data); + btn.text(i18n.t("Saved!")).delay(200).fadeOut(function () { + $(this).html('<i class="fa fa-check" aria-hidden="true"></i>'); + }); + }, + error: function () { + btn.text(i18n.t("Failed!")); + }, + }); + }); +}; + +return exports; +}()); + +if (typeof module !== 'undefined') { + module.exports = settings_user_groups; +} diff --git a/static/js/user_groups.js b/static/js/user_groups.js index e23c1bbc2f994..21d762300cc41 100644 --- a/static/js/user_groups.js +++ b/static/js/user_groups.js @@ -15,11 +15,16 @@ exports.init = function () { // WE INITIALIZE DATA STRUCTURES HERE! exports.init(); -exports.add = function add(user_group) { +exports.add = function (user_group) { user_group_name_dict.set(user_group.name, user_group); user_group_by_id_dict.set(user_group.id, user_group); }; +exports.remove = function (user_group) { + user_group_name_dict.del(user_group.name); + user_group_by_id_dict.del(user_group.id); +}; + exports.get_user_group_from_id = function (group_id) { if (!user_group_by_id_dict.has(group_id)) { blueslip.error('Unknown group_id in get_user_group_from_id: ' + group_id); diff --git a/static/styles/settings.css b/static/styles/settings.css index 523f581a89ce0..a4bbd402fd2d6 100644 --- a/static/styles/settings.css +++ b/static/styles/settings.css @@ -477,15 +477,18 @@ input[type=checkbox].inline-block { max-width: 100%; } -.add-new-emoji-box { +.add-new-emoji-box, +.add-new-user-group-box { margin-bottom: 20px; } -.add-new-emoji-box .new-emoji-form { +.add-new-emoji-box .new-emoji-form, +.add-new-user-group-box .new-user-group-form { margin: 10px 0px; } .add-new-emoji-box input[type=text], +.add-new-user-group-box input[type=text], .add-new-default-stream-box input[type=text] { padding: 6px; } @@ -820,6 +823,59 @@ input[type=checkbox].inline-block { margin-right: 5px; } +#user-groups .user-group { + margin-bottom: 20px; + background-color: hsl(0, 0%, 100%); + padding: 10px; + border-radius: 5px; +} + +#user-groups .user-group h4 { + font-weight: normal; + margin: 0px; +} + +#user-groups .user-group p { + font-weight: bold; + line-height: 2; + margin: 0px; +} + +#user-groups .user-group h4 > .name { + font-weight: bold; +} + +#user-groups .user-group span[contenteditable] { + display: inline-block; +} + +#user-groups .user-group span[contenteditable]:empty::before { + opacity: 0.5; + display: inline-block; + content: attr(data-placeholder); +} + +#user-groups .user-group span[contenteditable]:focus, +#user-groups .user-group span[contenteditable]:hover { + border-bottom: 1px solid hsl(0, 0%, 80%); + outline: none; +} + +#user-groups .user-group .pill-container .input[contenteditable]:empty::after { + content: attr(data-placeholder); + opacity: 0.5; +} + +#user-groups .user-group h4 > .button { + vertical-align: 1px; +} + +#user-groups .save-group-changes, +#user-groups .save-member-changes { + display: none; + opacity: 0; +} + /* -- new settings overlay -- */ #settings_page { height: 95vh; diff --git a/static/templates/admin_tab.handlebars b/static/templates/admin_tab.handlebars index ae2ce2a4b9b7c..8d3274f4cfa78 100644 --- a/static/templates/admin_tab.handlebars +++ b/static/templates/admin_tab.handlebars @@ -27,3 +27,5 @@ {{ partial "realm-filter-settings-admin" }} {{ partial "invites-list-admin" }} + +{{ partial "user-groups-admin" }} diff --git a/static/templates/admin_user_group_list.handlebars b/static/templates/admin_user_group_list.handlebars new file mode 100644 index 0000000000000..cd44fa6f2c2af --- /dev/null +++ b/static/templates/admin_user_group_list.handlebars @@ -0,0 +1,24 @@ +{{#with user_group}} +<div class="user-group" id="{{id}}"> + <h4> + <span class="name" data-placeholder="{{t 'Name' }}" contenteditable="true" spellcheck="false">{{name}}</span> + โ€” + <span class="description" data-placeholder="{{t 'Description' }}" contenteditable="true">{{description}}</span> + <button class="button rounded small save-group-changes sea-green"> + <i class="fa fa-check" aria-hidden="true"></i> + </button> + <button class="button rounded small delete btn-danger"> + <i class="fa fa-trash-o" aria-hidden="true"></i> + </button> + </h4> + <p> + {{t 'Subscribers' }} + <div class="pill-container" data-group-pills="{{name}}"> + <div class="input" contenteditable="true" data-placeholder="{{t 'Add member...' }}"></div> + </div> + <button class="button rounded small save-member-changes sea-green"> + <i class="fa fa-check" aria-hidden="true"></i> + </button> + </p> +</div> +{{/with}} diff --git a/static/templates/user-groups-admin.handlebars b/static/templates/user-groups-admin.handlebars new file mode 100644 index 0000000000000..670cc7b22c8f4 --- /dev/null +++ b/static/templates/user-groups-admin.handlebars @@ -0,0 +1,25 @@ +<div id="user-groups-admin" class="settings-section" data-name="user-groups-admin"> + <div class="tip">Anyone in this organization can add user groups.</div> + <form class="form-horizontal admin-user-group-form"> + <div class="add-new-user-group-box grey-box"> + <div class="new-user-group-form"> + <div class="settings-section-title new-user-group-section-title no-padding">{{t "Add a new user group" }}</div> + <div class="alert" id="admin-user-group-status"></div> + <div class="inline-block"> + <label for="user_group_name">{{t "Name" }}</label> + <input type="text" name="name" id="user_group_name" placeholder="hamletcharacters" /> + </div> + <div class="inline-block"> + <label for="user_group_description">{{t "Description" }}</label> + <input type="text" name="description" id="user_group_description" placeholder="{{t 'Characters of Hamlet' }}" /> + </div> + <button type="submit" class="button rounded sea-green"> + {{t 'Submit' }} + </button> + </div> + </div> + </form> + + <p>{{#tr this}}Add members of your organization to mentionable user groups.{{/tr}}</p> + <div id="user-groups"></div> +</div> diff --git a/templates/zerver/settings_overlay.html b/templates/zerver/settings_overlay.html index 7bff54ad03d2b..b2758b3d94843 100644 --- a/templates/zerver/settings_overlay.html +++ b/templates/zerver/settings_overlay.html @@ -60,6 +60,10 @@ <h1>{{ _('Settings') }}<span class="section"></span></h1> <i class="icon icon-vector-smile"></i> <div class="text">{{ _('Custom emoji') }}</div> </li> + <li class="admin" tabindex="0" data-section="user-groups-admin"> + <i class="icon icon-vector-group"></i> + <div class="text">{{ _('User groups') }}</div> + </li> <li class="admin" tabindex="0" data-section="auth-methods"> <i class="icon icon-vector-lock"></i> <div class="text">{{ _('Authentication methods') }}</div> diff --git a/tools/lib/capitalization.py b/tools/lib/capitalization.py index fb38fab3438fd..d4aee800923da 100644 --- a/tools/lib/capitalization.py +++ b/tools/lib/capitalization.py @@ -20,6 +20,7 @@ r"Dropbox", r"GitHub", r"Google", + r"Hamlet", r"HTTP", r"ID", r"IDs", diff --git a/tools/linter_lib/custom_check.py b/tools/linter_lib/custom_check.py index 1cdd6e1cb1f1b..88212db794c88 100644 --- a/tools/linter_lib/custom_check.py +++ b/tools/linter_lib/custom_check.py @@ -503,7 +503,9 @@ def build_custom_checkers(by_lang): 'exclude_line': [('templates/zerver/register.html', 'placeholder="acme"'), ('templates/zerver/register.html', 'placeholder="Acme or Aฮบฮผฮฎ"'), ('static/templates/settings/realm-domains-modal.handlebars', - '<td><input type="text" class="new-realm-domain" placeholder="acme.com"></input></td>')], + '<td><input type="text" class="new-realm-domain" placeholder="acme.com"></input></td>'), + ("static/templates/user-groups-admin.handlebars", + '<input type="text" name="name" id="user_group_name" placeholder="hamletcharacters" />')], 'exclude': set(["static/templates/settings/emoji-settings-admin.handlebars", "static/templates/settings/realm-filter-settings-admin.handlebars", "static/templates/settings/bot-settings.handlebars", diff --git a/tools/test-js-with-node b/tools/test-js-with-node index f81b6de9c7bf8..0a3be24d798b7 100755 --- a/tools/test-js-with-node +++ b/tools/test-js-with-node @@ -59,6 +59,7 @@ enforce_fully_covered = { 'static/js/typing_status.js', 'static/js/unread.js', 'static/js/user_events.js', + 'static/js/user_groups.js', 'static/js/util.js', } diff --git a/zproject/settings.py b/zproject/settings.py index 87167742d0603..612fa7b75260f 100644 --- a/zproject/settings.py +++ b/zproject/settings.py @@ -1079,6 +1079,7 @@ def get_secret(key: str) -> None: 'js/settings_streams.js', 'js/settings_filters.js', 'js/settings_invites.js', + 'js/settings_user_groups.js', 'js/settings.js', 'js/admin_sections.js', 'js/admin.js',
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
ultralytics__ultralytics-4546@3acf009
ultralytics/ultralytics
Python
4,546
`ultralytics 8.0.233` improve Classify train augmentations
<!-- copilot:all --> ### <samp>๐Ÿค– Generated by Copilot at 24f5c97</samp> ### Summary ๐Ÿงช๐Ÿ› ๏ธ๐ŸŒˆ <!-- 1. ๐Ÿงช for adding a new test function 2. ๐Ÿ› ๏ธ for adding new configuration options and modifying existing functions 3. ๐ŸŒˆ for adding support for new classification transforms and improving image processing --> This pull request improves the classification augmentations and transforms for the `ultralytics` package. It adds support for different torchvision versions and new configuration options for auto augmentation, random erasing and color jitter. It also updates the existing classes and functions that use the classification transforms and adds a new test function. The files affected are `ultralytics/data/augment.py`, `ultralytics/data/dataset.py`, `ultralytics/engine/predictor.py`, `ultralytics/models/yolo/classify/predict.py`, `ultralytics/utils/torch_utils.py`, `tests/test_python.py` and `ultralytics/cfg/default.yaml`. > _`torchvision` augments_ > _classification transforms_ > _better in the fall_ ### Walkthrough * Add and modify classification transforms for training and evaluation ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528L17-R25), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528L798-R807), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528R816-R978), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528L815-R995), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9fa2bf7f1273283680da3daeb8bae8e20cf8d477f838575796d2bf3b415bc534L39-R39), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9fa2bf7f1273283680da3daeb8bae8e20cf8d477f838575796d2bf3b415bc534L210-R210), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9bd07d929cb8522458c879bd81a82d9af468ffbc215e762f39b2a50a601758d7R18), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9bd07d929cb8522458c879bd81a82d9af468ffbc215e762f39b2a50a601758d7R30-R32)) - Import `torchvision` and define constants for checking torchvision version in `torch_utils.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9bd07d929cb8522458c879bd81a82d9af468ffbc215e762f39b2a50a601758d7R18), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9bd07d929cb8522458c879bd81a82d9af468ffbc215e762f39b2a50a601758d7R30-R32)) - Rename `classify_transforms` to `classify_transforms_train` and add more parameters and functionality in `augment.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528L798-R807), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528L815-R995)) - Add `classify_transforms_eval` to create evaluation transforms in `augment.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-8945a175b39a1efcabd4d2fb09b583d2f25409f05038ca9a0f1165e39ee48528R816-R978)) - Import and use the new transform functions in `predictor.py` and `dataset.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9fa2bf7f1273283680da3daeb8bae8e20cf8d477f838575796d2bf3b415bc534L39-R39), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-9fa2bf7f1273283680da3daeb8bae8e20cf8d477f838575796d2bf3b415bc534L210-R210), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L11-R17), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L226-R246)) * Add and modify configuration options for classification augmentations in `default.yaml` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-732d61cb4969eff986cec58058000b2949ea3b429a97ca5a1045897e754145c5R110-R112)) - Pass the arguments from the `args` object to the transform functions in `dataset.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L226-R246)) * Add and modify tests for classification transforms in `test_python.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-51deb1ccb3a8b618784df2e2abfbc3b056436f1db72d1896d5102f9906978492R446-R487)) * Modify the `ClassificationDataset` and `ClassificationPredictor` classes to handle the new and legacy transforms ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L236-R253), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L251-R273), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-663b5aaaf7a04b1a7fe40326af039d2baf50df166e9738332eacbae9271ec55fL3-R5), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-663b5aaaf7a04b1a7fe40326af039d2baf50df166e9738332eacbae9271ec55fL31-R44)) - Use `DEFAULT_MEAN` and `DEFAULT_STD` instead of hard-coded values in `dataset.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L236-R253)) - Convert the NumPy array to a PIL image before applying the torch transforms in `dataset.py` and `predict.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-0834dc62a5ebc93f43947ef49530b3e973013f844888646e5fb9962bfefecd90L251-R273), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-663b5aaaf7a04b1a7fe40326af039d2baf50df166e9738332eacbae9271ec55fL3-R5), [link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-663b5aaaf7a04b1a7fe40326af039d2baf50df166e9738332eacbae9271ec55fL31-R44)) - Check and store the legacy transform name in `predict.py` ([link](https://github.com/ultralytics/ultralytics/pull/4546/files?diff=unified&w=0#diff-663b5aaaf7a04b1a7fe40326af039d2baf50df166e9738332eacbae9271ec55fL31-R44)) ## ๐Ÿ› ๏ธ PR Summary <sub>Made with โค๏ธ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub> ### ๐ŸŒŸ Summary Enhanced classification model performance with improved augmentation settings. ### ๐Ÿ“Š Key Changes - ๐Ÿ”ง Updated accuracies for YOLOv8 classification models in documentation files. - ๐ŸŽจ Added new image augmentations with random erasing and auto augmentation policies like `randaugment`, `augmix`, and `autoaugment`. - โœ… Improved and added new tests for classification augmentation transformations. - ๐Ÿ“ˆ Updated the default configuration to include new augmentation settings. ### ๐ŸŽฏ Purpose & Impact - ๐ŸŽฏ The purpose is to improve model accuracies and provide more robust training through advanced augmentation techniques. - ๐Ÿ’ฅ This could lead to better-performing models and more variability in training, potentially improving the model's generalization capabilities. Users can now experiment with different augmentation settings tailored to their specific needs for more effective training customizations.
2023-08-24T11:22:03Z
Classification augmentations ## Summary Execute research and testing on new/additional augmentations to use for Classification task. ## Subtasks - [x] research new/additional augmentations for image classification - [x] identified new augmentations to test with - [x] execute new augmentations - [x] collect and analyze new augmentation performance impacts - [x] decide which augmentations to include - [x] implement new augmentations into deployment code - [ ] release new iteration of classification task with additional/new augmentations
[ { "body": "## Summary\nExecute research and testing on new/additional augmentations to use for Classification task.\n\n## Subtasks\n- [x] research new/additional augmentations for image classification\n- [x] identified new augmentations to test with\n- [x] execute new augmentations\n- [x] collect and analyze new augmentation performance impacts\n- [x] decide which augmentations to include\n- [x] implement new augmentations into deployment code\n- [ ] release new iteration of classification task with additional/new augmentations", "number": 6917, "title": "Classification augmentations" } ]
6218b820723b9e8647c582589c536c63c1f473f3
{ "head_commit": "3acf0097d6dbbc9f7289cb1a90aa30049a6f4c83", "head_commit_message": "add eval and no_aug train augmentations ofr cls", "patch_to_review": "diff --git a/ultralytics/cfg/default.yaml b/ultralytics/cfg/default.yaml\nindex cdeb95953d6..1d73d0280e6 100644\n--- a/ultralytics/cfg/default.yaml\n+++ b/ultralytics/cfg/default.yaml\n@@ -107,6 +107,7 @@ fliplr: 0.5 # (float) image flip left-right (probability)\n mosaic: 1.0 # (float) image mosaic (probability)\n mixup: 0.0 # (float) image mixup (probability)\n copy_paste: 0.0 # (float) segment copy-paste (probability)\n+auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)\n \n # Custom config.yaml ---------------------------------------------------------------------------------------------------\n cfg: # (str, optional) for overriding defaults.yaml\ndiff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py\nindex 68160d4477a..8e88ab1a169 100644\n--- a/ultralytics/data/augment.py\n+++ b/ultralytics/data/augment.py\n@@ -17,6 +17,10 @@\n \n from .utils import polygons2masks, polygons2masks_overlap\n \n+DEFAULT_MEAN = (0.0, 0.0, 0.0)\n+DEFAULT_STD = (1.0, 1.0, 1.0)\n+DEFAULT_CROP_PERCENTAGE = 0.875\n+\n \n # TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic\n class BaseTransform:\n@@ -792,15 +796,132 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):\n RandomFlip(direction='horizontal', p=hyp.fliplr, flip_idx=flip_idx)]) # transforms\n \n \n-# Classification augmentations -----------------------------------------------------------------------------------------\n-def classify_transforms(size=224, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD\n+# Classification augmentations eval -----------------------------------------------------------------------------------------\n+def classify_transforms_eval(\n+ size=224,\n+ mean=DEFAULT_MEAN,\n+ std=DEFAULT_STD,\n+ interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,\n+ crop_percentage: float = DEFAULT_CROP_PERCENTAGE,\n+):\n+\n+ if isinstance(size, (tuple, list)):\n+ assert len(size) == 2\n+ scale_size = tuple([math.floor(x / crop_percentage) for x in size])\n+ else:\n+ scale_size = math.floor(size / crop_percentage)\n+ scale_size = (scale_size, scale_size)\n+\n+ # aspect ratio is preserved, crops center within image, no borders are added, image is lost\n+ if scale_size[0] == scale_size[1]:\n+ # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)\n+ tfl = [T.Resize(scale_size[0], interpolation=interpolation)]\n+ else:\n+ # resize shortest edge to matching target dim for non-square target\n+ tfl = [T.Resize(scale_size)]\n+ tfl += [T.CenterCrop(size)]\n+\n+ tfl += [T.ToTensor(), T.Normalize(\n+ mean=torch.tensor(mean),\n+ std=torch.tensor(std),\n+ )]\n+\n+ return T.Compose(tfl)\n+\n+\n+def classify_transforms_noaug_train(\n+ img_size=224,\n+ interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,\n+ mean=DEFAULT_MEAN,\n+ std=DEFAULT_STD,\n+):\n+ tfl = [T.Resize(img_size, interpolation=interpolation), T.CenterCrop(img_size)]\n+ tfl += [T.ToTensor(), T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std))]\n+ return T.Compose(tfl)\n+\n+\n+# Classification augmentations train ---------------------------------------------------------------------------------------\n+def classify_transforms_train(\n+ size=224,\n+ mean=DEFAULT_MEAN,\n+ std=DEFAULT_STD,\n+ scale=None,\n+ ratio=None,\n+ hflip=0.5,\n+ vflip=0.0,\n+ auto_augment=None,\n+ color_jitter=0.4,\n+ force_color_jitter=False,\n+ re_prob=0.,\n+ interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,\n+):\n+ \"\"\"\n+ Classification augmentation with torchvision.\n+ Inspired by timm/data/transforms_factory.py\n+\n+ Args:\n+ size (int): image size\n+ scale (tuple): scale range of the image. default is (0.08, 1.0)\n+ ratio (tuple): aspect ratio range of the image. default is (3./4., 4./3.)\n+ mean (tuple): mean values of RGB channels\n+ std (tuple): std values of RGB channels\n+ hflip (float): probability of horizontal flip\n+ vflip (float): probability of vertical flip\n+ auto_augment (str): auto augmentation policy. can be 'randaugment', 'augmix', 'autoaugment' or None.\n+ color_jitter (float or tuple): color jitter factor or tuple of 3 values for brightness, contrast, saturation\n+ force_color_jitter (bool): force to apply color jitter even if auto augment is enabled\n+ re_prob (float): probability of random erasing\n+ interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR.\n+\n+ Returns:\n+ T.Compose: torchvision transforms\n+ \"\"\"\n # Transforms to apply if albumentations not installed\n if not isinstance(size, int):\n raise TypeError(f'classify_transforms() size {size} must be integer, not (list, tuple)')\n- if any(mean) or any(std):\n- return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(mean, std, inplace=True)])\n- else:\n- return T.Compose([CenterCrop(size), ToTensor()])\n+ scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range\n+ ratio = tuple(ratio or (3. / 4., 4. / 3.)) # default imagenet ratio range\n+ primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]\n+ if hflip > 0.:\n+ primary_tfl += [T.RandomHorizontalFlip(p=hflip)]\n+ if vflip > 0.:\n+ primary_tfl += [T.RandomVerticalFlip(p=vflip)]\n+\n+ secondary_tfl = []\n+ disable_color_jitter = False\n+ if auto_augment:\n+ assert isinstance(auto_augment, str)\n+ # color jitter is typically disabled if AA/RA on,\n+ # this allows override without breaking old hparm cfgs\n+ disable_color_jitter = not force_color_jitter\n+\n+ if auto_augment == 'randaugment':\n+ secondary_tfl += [T.RandAugment(ainterpolation=interpolation)]\n+ elif auto_augment == 'augmix':\n+ secondary_tfl += [T.AugMix(interpolation=interpolation)]\n+ elif auto_augment == 'autoaugment':\n+ secondary_tfl += [T.AutoAugment(interpolation=interpolation)]\n+ else:\n+ raise ValueError(f'Invalid auto_augment policy: {auto_augment}. Should be one of \"randaugment\", '\n+ f'\"augmix\", \"autoaugment\" or None')\n+\n+ if color_jitter is not None and not disable_color_jitter:\n+ # color jitter is enabled when not using AA or when forced\n+ if isinstance(color_jitter, (list, tuple)):\n+ # color jitter should be a 3-tuple/list if spec brightness/contrast/saturation\n+ # or 4 if also augmenting hue\n+ assert len(color_jitter) in (3, 4)\n+ else:\n+ # if it's a scalar, duplicate for brightness, contrast, and saturation, no hue\n+ color_jitter = (float(color_jitter), ) * 3\n+ secondary_tfl += [T.ColorJitter(*color_jitter)]\n+\n+ final_tfl = []\n+ final_tfl += [T.ToTensor(), T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std))]\n+ if re_prob > 0.:\n+ final_tfl.append(T.RandomErasing(p=re_prob, inplace=True))\n+\n+ return T.Compose(primary_tfl + secondary_tfl + final_tfl)\n \n \n def hsv2colorjitter(h, s, v):\n@@ -809,17 +930,17 @@ def hsv2colorjitter(h, s, v):\n \n \n def classify_albumentations(\n- augment=True,\n- size=224,\n- scale=(0.08, 1.0),\n- hflip=0.5,\n- vflip=0.0,\n- hsv_h=0.015, # image HSV-Hue augmentation (fraction)\n- hsv_s=0.7, # image HSV-Saturation augmentation (fraction)\n- hsv_v=0.4, # image HSV-Value augmentation (fraction)\n- mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN\n- std=(1.0, 1.0, 1.0), # IMAGENET_STD\n- auto_aug=False,\n+ augment=True,\n+ size=224,\n+ scale=(0.08, 1.0),\n+ hflip=0.5,\n+ vflip=0.0,\n+ hsv_h=0.015, # image HSV-Hue augmentation (fraction)\n+ hsv_s=0.7, # image HSV-Saturation augmentation (fraction)\n+ hsv_v=0.4, # image HSV-Value augmentation (fraction)\n+ mean=DEFAULT_MEAN, # IMAGENET_MEAN\n+ std=DEFAULT_STD, # IMAGENET_STD\n+ auto_aug=False,\n ):\n \"\"\"YOLOv8 classification Albumentations (optional, only used if package is installed).\"\"\"\n prefix = colorstr('albumentations: ')\ndiff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py\nindex cf3a7ce9dff..5ef1d445e4e 100644\n--- a/ultralytics/data/dataset.py\n+++ b/ultralytics/data/dataset.py\n@@ -8,11 +8,13 @@\n import numpy as np\n import torch\n import torchvision\n+from PIL import Image\n from tqdm import tqdm\n \n from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, colorstr, is_dir_writeable\n \n-from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms\n+from .augment import (DEFAULT_MEAN, DEFAULT_STD, Compose, Format, Instances, LetterBox, classify_albumentations,\n+ classify_transforms_eval, classify_transforms_train, v8_transforms)\n from .base import BaseDataset\n from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label\n \n@@ -225,7 +227,22 @@ def __init__(self, root, args, augment=False, cache=False, prefix=''):\n self.cache_disk = cache == 'disk'\n self.samples = self.verify_images() # filter out bad images\n self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im\n- self.torch_transforms = classify_transforms(args.imgsz)\n+ scale = (1.0 - args.scale, 1.0), # (0.08, 1.0)\n+ self.torch_transforms = classify_transforms_train(\n+ size=args.imgsz,\n+ scale=scale,\n+ mean=DEFAULT_MEAN,\n+ std=DEFAULT_STD,\n+ hflip=args.fliplr,\n+ vflip=args.flipud,\n+ auto_augment=args.auto_augment) if augment else classify_transforms_eval(size=args.imgsz,\n+ scale=scale,\n+ mean=DEFAULT_MEAN,\n+ std=DEFAULT_STD,\n+ hflip=args.fliplr,\n+ vflip=args.flipud,\n+ auto_augment=args.auto_augment)\n+\n self.album_transforms = classify_albumentations(\n augment=augment,\n size=args.imgsz,\n@@ -235,8 +252,8 @@ def __init__(self, root, args, augment=False, cache=False, prefix=''):\n hsv_h=args.hsv_h, # HSV-Hue augmentation (fraction)\n hsv_s=args.hsv_s, # HSV-Saturation augmentation (fraction)\n hsv_v=args.hsv_v, # HSV-Value augmentation (fraction)\n- mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN\n- std=(1.0, 1.0, 1.0), # IMAGENET_STD\n+ mean=DEFAULT_MEAN, # IMAGENET_MEAN\n+ std=DEFAULT_STD, # IMAGENET_STD\n auto_aug=False) if augment else None\n \n def __getitem__(self, i):\n@@ -250,10 +267,12 @@ def __getitem__(self, i):\n im = np.load(fn)\n else: # read image\n im = cv2.imread(f) # BGR\n- if self.album_transforms:\n- sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']\n- else:\n- sample = self.torch_transforms(im)\n+ # if self.album_transforms:\n+ # sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']\n+ # else:\n+ # Convert NumPy array to PIL image\n+ im = Image.fromarray(im)\n+ sample = self.torch_transforms(im)\n return {'img': sample, 'cls': j}\n \n def __len__(self) -> int:\ndiff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py\nindex 77dfcdcfb16..eafc8e02c11 100644\n--- a/ultralytics/engine/predictor.py\n+++ b/ultralytics/engine/predictor.py\n@@ -36,7 +36,7 @@\n \n from ultralytics.cfg import get_cfg\n from ultralytics.data import load_inference_source\n-from ultralytics.data.augment import LetterBox, classify_transforms\n+from ultralytics.data.augment import LetterBox, classify_transforms_eval\n from ultralytics.nn.autobackend import AutoBackend\n from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops\n from ultralytics.utils.checks import check_imgsz, check_imshow\n@@ -207,7 +207,7 @@ def predict_cli(self, source=None, model=None):\n def setup_source(self, source):\n \"\"\"Sets up source and inference mode.\"\"\"\n self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size\n- self.transforms = getattr(self.model.model, 'transforms', classify_transforms(\n+ self.transforms = getattr(self.model.model, 'transforms', classify_transforms_eval(\n self.imgsz[0])) if self.args.task == 'classify' else None\n self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)\n self.source_type = self.dataset.source_type\n" }
[ { "diff_hunk": "@@ -225,7 +227,22 @@ def __init__(self, root, args, augment=False, cache=False, prefix=''):\n self.cache_disk = cache == 'disk'\n self.samples = self.verify_images() # filter out bad images\n self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im\n- self.torch_transforms = classify_transforms(args.imgsz)\n+ scale = (1.0 - args.scale, 1.0), # (0.08, 1.0)", "line": null, "original_line": 230, "original_start_line": null, "path": "ultralytics/data/dataset.py", "start_line": null, "text": "@author:\n```suggestion\r\n scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)\r\n```" } ]
14716613a3ae0da3ce2c6f67eea023a7df7e94d1
diff --git a/README.md b/README.md index fcc51b4fd70..e798a33ab52 100644 --- a/README.md +++ b/README.md @@ -181,11 +181,11 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 | | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | -| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | -| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | -| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | -| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | -| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | +| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 | +| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 | +| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 | +| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 | +| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 | - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0` - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` diff --git a/README.zh-CN.md b/README.zh-CN.md index 49f7f2bafd4..cfad61aab92 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -181,11 +181,11 @@ success = model.export(format="onnx") # ๅฐ†ๆจกๅž‹ๅฏผๅ‡บไธบ ONNX ๆ ผๅผ | ๆจกๅž‹ | ๅฐบๅฏธ<br><sup>(ๅƒ็ด ) | acc<br><sup>top1 | acc<br><sup>top5 | ้€Ÿๅบฆ<br><sup>CPU ONNX<br>(ms) | ้€Ÿๅบฆ<br><sup>A100 TensorRT<br>(ms) | ๅ‚ๆ•ฐ<br><sup>(M) | FLOPs<br><sup>(B) at 640 | | -------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | --------------------------- | -------------------------------- | -------------- | ------------------------ | -| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | -| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | -| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | -| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | -| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | +| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 | +| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 | +| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 | +| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 | +| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 | - **acc** ๅ€ผๆ˜ฏๆจกๅž‹ๅœจ [ImageNet](https://www.image-net.org/) ๆ•ฐๆฎ้›†้ชŒ่ฏ้›†ไธŠ็š„ๅ‡†็กฎ็Ž‡ใ€‚ <br>้€š่ฟ‡ `yolo val classify data=path/to/ImageNet device=0` ๅค็Žฐ - **้€Ÿๅบฆ** ๆ˜ฏไฝฟ็”จ [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) ๅฎžไพ‹ๅฏน ImageNet val ๅ›พๅƒ่ฟ›่กŒๅนณๅ‡่ฎก็ฎ—็š„ใ€‚ <br>้€š่ฟ‡ `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` ๅค็Žฐ diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md index fd70130288e..624bd286a45 100644 --- a/docs/en/models/yolov8.md +++ b/docs/en/models/yolov8.md @@ -91,11 +91,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 | | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | - | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | - | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | - | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | - | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | - | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | + | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 | + | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 | + | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 | + | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 | + | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 | === "Pose (COCO)" diff --git a/docs/en/tasks/classify.md b/docs/en/tasks/classify.md index fc5ec085f5c..dc74a68eb40 100644 --- a/docs/en/tasks/classify.md +++ b/docs/en/tasks/classify.md @@ -35,11 +35,11 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 | |----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------| -| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | -| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | -| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | -| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | -| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | +| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 | +| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 | +| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 | +| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 | +| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 | - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0` diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md index 2e822aeeb3e..544dd38de8d 100644 --- a/docs/en/usage/cfg.md +++ b/docs/en/usage/cfg.md @@ -224,21 +224,23 @@ Export settings for YOLO models encompass configurations and options related to Augmentation settings for YOLO models refer to the various transformations and modifications applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the augmentation process include the size and composition of the original dataset and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to ensure that the augmented dataset is diverse and representative enough to train a high-performing model. -| Key | Value | Description | -|---------------|---------|-------------------------------------------------| -| `hsv_h` | `0.015` | image HSV-Hue augmentation (fraction) | -| `hsv_s` | `0.7` | image HSV-Saturation augmentation (fraction) | -| `hsv_v` | `0.4` | image HSV-Value augmentation (fraction) | -| `degrees` | `0.0` | image rotation (+/- deg) | -| `translate` | `0.1` | image translation (+/- fraction) | -| `scale` | `0.5` | image scale (+/- gain) | -| `shear` | `0.0` | image shear (+/- deg) | -| `perspective` | `0.0` | image perspective (+/- fraction), range 0-0.001 | -| `flipud` | `0.0` | image flip up-down (probability) | -| `fliplr` | `0.5` | image flip left-right (probability) | -| `mosaic` | `1.0` | image mosaic (probability) | -| `mixup` | `0.0` | image mixup (probability) | -| `copy_paste` | `0.0` | segment copy-paste (probability) | +| Key | Value | Description | +|-----------------|-----------------|--------------------------------------------------------------------------------| +| `hsv_h` | `0.015` | image HSV-Hue augmentation (fraction) | +| `hsv_s` | `0.7` | image HSV-Saturation augmentation (fraction) | +| `hsv_v` | `0.4` | image HSV-Value augmentation (fraction) | +| `degrees` | `0.0` | image rotation (+/- deg) | +| `translate` | `0.1` | image translation (+/- fraction) | +| `scale` | `0.5` | image scale (+/- gain) | +| `shear` | `0.0` | image shear (+/- deg) | +| `perspective` | `0.0` | image perspective (+/- fraction), range 0-0.001 | +| `flipud` | `0.0` | image flip up-down (probability) | +| `fliplr` | `0.5` | image flip left-right (probability) | +| `mosaic` | `1.0` | image mosaic (probability) | +| `mixup` | `0.0` | image mixup (probability) | +| `copy_paste` | `0.0` | segment copy-paste (probability) | +| `auto_augment` | `'randaugment'` | auto augmentation policy for classification (randaugment, autoaugment, augmix) | +| `erasing` | `0.4` | probability o random erasing during classification training (0-1) training | ## Logging, checkpoints, plotting and file management diff --git a/tests/test_python.py b/tests/test_python.py index 710974d42d1..8032602ef4d 100644 --- a/tests/test_python.py +++ b/tests/test_python.py @@ -505,6 +505,48 @@ def test_hub(): smart_request('GET', 'http://github.com', progress=True) [email protected] +def image(): + return cv2.imread(str(SOURCE)) + + [email protected]( + 'auto_augment, erasing, force_color_jitter', + [ + (None, 0.0, False), + ('randaugment', 0.5, True), + ('augmix', 0.2, False), + ('autoaugment', 0.0, True), ], +) +def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter): + import torchvision.transforms as T + + from ultralytics.data.augment import classify_augmentations + + transform = classify_augmentations( + size=224, + mean=(0.5, 0.5, 0.5), + std=(0.5, 0.5, 0.5), + scale=(0.08, 1.0), + ratio=(3.0 / 4.0, 4.0 / 3.0), + hflip=0.5, + vflip=0.5, + auto_augment=auto_augment, + hsv_h=0.015, + hsv_s=0.4, + hsv_v=0.4, + force_color_jitter=force_color_jitter, + erasing=erasing, + interpolation=T.InterpolationMode.BILINEAR, + ) + + transformed_image = transform(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))) + + assert transformed_image.shape == (3, 224, 224) + assert torch.is_tensor(transformed_image) + assert transformed_image.dtype == torch.float32 + + @pytest.mark.slow @pytest.mark.skipif(not ONLINE, reason='environment is offline') def test_model_tune(): diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 7ad3a039e0c..ad5d2484b5b 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO ๐Ÿš€, AGPL-3.0 license -__version__ = '8.0.232' +__version__ = '8.0.233' from ultralytics.models import RTDETR, SAM, YOLO from ultralytics.models.fastsam import FastSAM diff --git a/ultralytics/cfg/default.yaml b/ultralytics/cfg/default.yaml index f6edad2343b..ead9735e6da 100644 --- a/ultralytics/cfg/default.yaml +++ b/ultralytics/cfg/default.yaml @@ -113,6 +113,9 @@ fliplr: 0.5 # (float) image flip left-right (probability) mosaic: 1.0 # (float) image mosaic (probability) mixup: 0.0 # (float) image mixup (probability) copy_paste: 0.0 # (float) segment copy-paste (probability) +auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix) +erasing: 0.4 # (float) probability of random erasing during classification training (0-1) +crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1) # Custom config.yaml --------------------------------------------------------------------------------------------------- cfg: # (str, optional) for overriding defaults.yaml diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py index cd6a5465e53..97eb5fdba65 100644 --- a/ultralytics/data/augment.py +++ b/ultralytics/data/augment.py @@ -14,9 +14,14 @@ from ultralytics.utils.instance import Instances from ultralytics.utils.metrics import bbox_ioa from ultralytics.utils.ops import segment2box +from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13 from .utils import polygons2masks, polygons2masks_overlap +DEFAULT_MEAN = (0.0, 0.0, 0.0) +DEFAULT_STD = (1.0, 1.0, 1.0) +DEFAULT_CROP_FTACTION = 1.0 + # TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic class BaseTransform: @@ -982,65 +987,144 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False): # Classification augmentations ----------------------------------------------------------------------------------------- -def classify_transforms(size=224, rect=False, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD - """Transforms to apply if albumentations not installed.""" +def classify_transforms( + size=224, + mean=DEFAULT_MEAN, + std=DEFAULT_STD, + interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, + crop_fraction: float = DEFAULT_CROP_FTACTION, +): + """ + Classification transforms for evaluation/inference. Inspired by timm/data/transforms_factory.py. + + Args: + size (int): image size + mean (tuple): mean values of RGB channels + std (tuple): std values of RGB channels + interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR. + crop_fraction (float): fraction of image to crop. default is 1.0. + + Returns: + T.Compose: torchvision transforms + """ + + if isinstance(size, (tuple, list)): + assert len(size) == 2 + scale_size = tuple([math.floor(x / crop_fraction) for x in size]) + else: + scale_size = math.floor(size / crop_fraction) + scale_size = (scale_size, scale_size) + + # aspect ratio is preserved, crops center within image, no borders are added, image is lost + if scale_size[0] == scale_size[1]: + # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg) + tfl = [T.Resize(scale_size[0], interpolation=interpolation)] + else: + # resize shortest edge to matching target dim for non-square target + tfl = [T.Resize(scale_size)] + tfl += [T.CenterCrop(size)] + + tfl += [T.ToTensor(), T.Normalize( + mean=torch.tensor(mean), + std=torch.tensor(std), + )] + + return T.Compose(tfl) + + +# Classification augmentations train --------------------------------------------------------------------------------------- +def classify_augmentations( + size=224, + mean=DEFAULT_MEAN, + std=DEFAULT_STD, + scale=None, + ratio=None, + hflip=0.5, + vflip=0.0, + auto_augment=None, + hsv_h=0.015, # image HSV-Hue augmentation (fraction) + hsv_s=0.4, # image HSV-Saturation augmentation (fraction) + hsv_v=0.4, # image HSV-Value augmentation (fraction) + force_color_jitter=False, + erasing=0., + interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, +): + """ + Classification transforms with augmentation for training. Inspired by timm/data/transforms_factory.py. + + Args: + size (int): image size + scale (tuple): scale range of the image. default is (0.08, 1.0) + ratio (tuple): aspect ratio range of the image. default is (3./4., 4./3.) + mean (tuple): mean values of RGB channels + std (tuple): std values of RGB channels + hflip (float): probability of horizontal flip + vflip (float): probability of vertical flip + auto_augment (str): auto augmentation policy. can be 'randaugment', 'augmix', 'autoaugment' or None. + hsv_h (float): image HSV-Hue augmentation (fraction) + hsv_s (float): image HSV-Saturation augmentation (fraction) + hsv_v (float): image HSV-Value augmentation (fraction) + contrast (float): image contrast augmentation (fraction) + force_color_jitter (bool): force to apply color jitter even if auto augment is enabled + erasing (float): probability of random erasing + interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR. + + Returns: + T.Compose: torchvision transforms + """ + # Transforms to apply if albumentations not installed if not isinstance(size, int): raise TypeError(f'classify_transforms() size {size} must be integer, not (list, tuple)') - transforms = [ClassifyLetterBox(size, auto=True) if rect else CenterCrop(size), ToTensor()] - if any(mean) or any(std): - transforms.append(T.Normalize(mean, std, inplace=True)) - return T.Compose(transforms) - - -def hsv2colorjitter(h, s, v): - """Map HSV (hue, saturation, value) jitter into ColorJitter values (brightness, contrast, saturation, hue)""" - return v, v, s, h - - -def classify_albumentations( - augment=True, - size=224, - scale=(0.08, 1.0), - hflip=0.5, - vflip=0.0, - hsv_h=0.015, # image HSV-Hue augmentation (fraction) - hsv_s=0.7, # image HSV-Saturation augmentation (fraction) - hsv_v=0.4, # image HSV-Value augmentation (fraction) - mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN - std=(1.0, 1.0, 1.0), # IMAGENET_STD - auto_aug=False, -): - """YOLOv8 classification Albumentations (optional, only used if package is installed).""" - prefix = colorstr('albumentations: ') - try: - import albumentations as A - from albumentations.pytorch import ToTensorV2 - - check_version(A.__version__, '1.0.3', hard=True) # version requirement - if augment: # Resize and crop - T = [A.RandomResizedCrop(height=size, width=size, scale=scale)] - if auto_aug: - # TODO: implement AugMix, AutoAug & RandAug in albumentations - LOGGER.info(f'{prefix}auto augmentations are currently not supported') + scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range + ratio = tuple(ratio or (3. / 4., 4. / 3.)) # default imagenet ratio range + primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)] + if hflip > 0.: + primary_tfl += [T.RandomHorizontalFlip(p=hflip)] + if vflip > 0.: + primary_tfl += [T.RandomVerticalFlip(p=vflip)] + + secondary_tfl = [] + disable_color_jitter = False + if auto_augment: + assert isinstance(auto_augment, str) + # color jitter is typically disabled if AA/RA on, + # this allows override without breaking old hparm cfgs + disable_color_jitter = not force_color_jitter + + if auto_augment == 'randaugment': + if TORCHVISION_0_11: + secondary_tfl += [T.RandAugment(interpolation=interpolation)] else: - if hflip > 0: - T += [A.HorizontalFlip(p=hflip)] - if vflip > 0: - T += [A.VerticalFlip(p=vflip)] - if any((hsv_h, hsv_s, hsv_v)): - T += [A.ColorJitter(*hsv2colorjitter(hsv_h, hsv_s, hsv_v))] # brightness, contrast, saturation, hue - else: # Use fixed crop for eval set (reproducibility) - T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] - T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) - return A.Compose(T) - - except ImportError: # package not installed, skip - pass - except Exception as e: - LOGGER.info(f'{prefix}{e}') + LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.') + + elif auto_augment == 'augmix': + if TORCHVISION_0_13: + secondary_tfl += [T.AugMix(interpolation=interpolation)] + else: + LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.') + + elif auto_augment == 'autoaugment': + if TORCHVISION_0_10: + secondary_tfl += [T.AutoAugment(interpolation=interpolation)] + else: + LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.') + + else: + raise ValueError(f'Invalid auto_augment policy: {auto_augment}. Should be one of "randaugment", ' + f'"augmix", "autoaugment" or None') + + if not disable_color_jitter: + secondary_tfl += [T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h)] + + final_tfl = [ + T.ToTensor(), + T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)), + T.RandomErasing(p=erasing, inplace=True)] + + return T.Compose(primary_tfl + secondary_tfl + final_tfl) +# NOTE: keep this class for backward compatibility class ClassifyLetterBox: """ YOLOv8 LetterBox class for image preprocessing, designed to be part of a transformation pipeline, e.g., @@ -1091,6 +1175,7 @@ def __call__(self, im): return im_out +# NOTE: keep this class for backward compatibility class CenterCrop: """YOLOv8 CenterCrop class for image preprocessing, designed to be part of a transformation pipeline, e.g., T.Compose([CenterCrop(size), ToTensor()]). @@ -1117,6 +1202,7 @@ def __call__(self, im): return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) +# NOTE: keep this class for backward compatibility class ToTensor: """YOLOv8 ToTensor class for image preprocessing, i.e., T.Compose([LetterBox(size), ToTensor()]).""" diff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py index 068311efc86..538a1ff2aa4 100644 --- a/ultralytics/data/dataset.py +++ b/ultralytics/data/dataset.py @@ -8,10 +8,11 @@ import numpy as np import torch import torchvision +from PIL import Image from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable -from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms +from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, classify_transforms, v8_transforms from .base import BaseDataset from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label @@ -225,19 +226,17 @@ def __init__(self, root, args, augment=False, cache=False, prefix=''): self.cache_disk = cache == 'disk' self.samples = self.verify_images() # filter out bad images self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im - self.torch_transforms = classify_transforms(args.imgsz, rect=args.rect) - self.album_transforms = classify_albumentations( - augment=augment, - size=args.imgsz, - scale=(1.0 - args.scale, 1.0), # (0.08, 1.0) - hflip=args.fliplr, - vflip=args.flipud, - hsv_h=args.hsv_h, # HSV-Hue augmentation (fraction) - hsv_s=args.hsv_s, # HSV-Saturation augmentation (fraction) - hsv_v=args.hsv_v, # HSV-Value augmentation (fraction) - mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN - std=(1.0, 1.0, 1.0), # IMAGENET_STD - auto_aug=False) if augment else None + scale = (1.0 - args.scale, 1.0) # (0.08, 1.0) + self.torch_transforms = classify_augmentations(size=args.imgsz, + scale=scale, + hflip=args.fliplr, + vflip=args.flipud, + erasing=args.erasing, + auto_augment=args.auto_augment, + hsv_h=args.hsv_h, + hsv_s=args.hsv_s, + hsv_v=args.hsv_v) if augment else classify_transforms( + size=args.imgsz, crop_fraction=args.crop_fraction) def __getitem__(self, i): """Returns subset of data and targets corresponding to given indices.""" @@ -250,10 +249,9 @@ def __getitem__(self, i): im = np.load(fn) else: # read image im = cv2.imread(f) # BGR - if self.album_transforms: - sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] - else: - sample = self.torch_transforms(im) + # Convert NumPy array to PIL image + im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)) + sample = self.torch_transforms(im) return {'img': sample, 'cls': j} def __len__(self) -> int: diff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py index cda59944170..f78de2fa8ff 100644 --- a/ultralytics/engine/predictor.py +++ b/ultralytics/engine/predictor.py @@ -210,8 +210,9 @@ def predict_cli(self, source=None, model=None): def setup_source(self, source): """Sets up source and inference mode.""" self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size - self.transforms = getattr(self.model.model, 'transforms', classify_transforms( - self.imgsz[0])) if self.args.task == 'classify' else None + self.transforms = getattr( + self.model.model, 'transforms', classify_transforms( + self.imgsz[0], crop_fraction=self.args.crop_fraction)) if self.args.task == 'classify' else None self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride, diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py index ca463b67f45..9047d8df396 100644 --- a/ultralytics/models/yolo/classify/predict.py +++ b/ultralytics/models/yolo/classify/predict.py @@ -1,6 +1,8 @@ # Ultralytics YOLO ๐Ÿš€, AGPL-3.0 license +import cv2 import torch +from PIL import Image from ultralytics.engine.predictor import BasePredictor from ultralytics.engine.results import Results @@ -29,11 +31,18 @@ def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): """Initializes ClassificationPredictor setting the task to 'classify'.""" super().__init__(cfg, overrides, _callbacks) self.args.task = 'classify' + self._legacy_transform_name = 'ultralytics.yolo.data.augment.ToTensor' def preprocess(self, img): """Converts input image to model-compatible data type.""" if not isinstance(img, torch.Tensor): - img = torch.stack([self.transforms(im) for im in img], dim=0) + is_legacy_transform = any(self._legacy_transform_name in str(transform) + for transform in self.transforms.transforms) + if is_legacy_transform: # to handle legacy transforms + img = torch.stack([self.transforms(im) for im in img], dim=0) + else: + img = torch.stack([self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], + dim=0) img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 diff --git a/ultralytics/utils/torch_utils.py b/ultralytics/utils/torch_utils.py index 3a17a9acac8..e8051571e2c 100644 --- a/ultralytics/utils/torch_utils.py +++ b/ultralytics/utils/torch_utils.py @@ -15,6 +15,7 @@ import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F +import torchvision from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, __version__ from ultralytics.utils.checks import check_version @@ -26,6 +27,9 @@ TORCH_1_9 = check_version(torch.__version__, '1.9.0') TORCH_2_0 = check_version(torch.__version__, '2.0.0') +TORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0') +TORCHVISION_0_11 = check_version(torchvision.__version__, '0.11.0') +TORCHVISION_0_13 = check_version(torchvision.__version__, '0.13.0') @contextmanager
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-18208@ea012f4
vllm-project/vllm
Python
18,208
Allow users to pass arbitrary JSON keys from CLI
Closes #17640 Supersede #17842 Allows users to pass individual JSON keys via `FlexibleArgumentParser` in the format `--json-arg.key1.key2 value` i.e. `--hf-overrides.key1 val1 --hf-overrides.key2.key3 val2` will result in the following args: ```console INFO 05-15 17:09:16 [cli_args.py:297] non-default args: {'enforce_eager': True, 'hf_overrides': {'key1': 'val1', 'key2': {'key3': 'val2'}}, 'gpu_memory_utilization': 0.45} ```
2025-05-15T15:11:49Z
[Feature]: provide a way to configure rope-scaling that isn't inline JSON ### ๐Ÿš€ The feature, motivation and pitch Having to use literal JSON strings as a command argument is breaking a lot automation and scripts out there. I propose it's addressed so that all configuration can be done via simple key-value pairs. ### Alternatives Instead of configuring it as follows: ``` vllm server [...] --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' ``` I suggest something like: ``` vllm server [...] --rope-scaling "1" \ --rope-type "yarn" \ --rope-factor "4.0" \ --original-max-position-embeddings "32768"
@DarkLight1337 What do you think, If you agree, I can make changes. I think this isn't necessary, you can simply pass a YAML config file to `vllm serve`. @DarkLight1337 passing a yaml to `vllm serve` isn't a feasible solution for many situations. Most environments we work with can only be configured via ENV vars and command line arguments. Could you elaborate on what is the issue with sending JSON in the command line arguments? Since different rope methods have different parameters, I worry that supporting such an interface becomes less maintainable over time. cc @hmellor @DarkLight1337 * Most automation instrumentation out there has been built on the "contract" that command line parameters are primitives (strings or numbers). Many tools that do the remote provisioning do URLencoding or similar transport-safe encoding before sending into a container manifest or some form of container provisioning. You cannot make the assumption the user has access to the command line and can simply paste the literal json blob like one can do in one's local computer or via ssh. * using a yaml or json config for `vllm serve` is impracticable. If you manage dozens (or hundreds) of different inference backends, it's not reasonable to create and having to maintain dozens or hundreds of docker containers because each backend/model has its own params and because at deploy time the provider may not allow downloading or uploading of arbitrary files. ๐Ÿ˜ฌ https://github.com/vllm-project/vllm/blob/f62cad6431e2bce91c033c74e20835c8e0c9b288/vllm/config.py#L453-L468 Since these are parameters from the Hugging Face `config.json` would it be possible for you to change their values at the source rather than patching at the end in vLLM? @hmellor that would only work for models we control/own. But wouldn't work for ~dozens of base models we host/use. I am more open to supporting this via nested namespaces (like in `jsonargparse`). Using your example, we could have: ``` vllm serve [...] \ --hf-overrides.rope_scaling.rope_type yarn \ --hf-overrides.rope_scaling.factor 4.0 \ --hf-overrides.rope_scaling.original_max_position_embeddings 32768 ``` That way we don't have to worry about defining the inner configs at the top level. @DarkLight1337 that's a good compromise. Would be greatly helpful.
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nHaving to use literal JSON strings as a command argument is breaking a lot automation and scripts out there. I propose it's addressed so that all configuration can be done via simple key-value pairs.\n\n### Alternatives\n\nInstead of configuring it as follows:\n```\nvllm server [...] --rope-scaling '{\"rope_type\":\"yarn\",\"factor\":4.0,\"original_max_position_embeddings\":32768}' \n```\n\nI suggest something like:\n```\nvllm server [...] --rope-scaling \"1\" \\\n--rope-type \"yarn\" \\\n--rope-factor \"4.0\" \\\n--original-max-position-embeddings \"32768\" \n\n", "number": 17640, "title": "[Feature]: provide a way to configure rope-scaling that isn't inline JSON" } ]
01c22335baa03dbbc3ae662c76a089ff1c5b5742
{ "head_commit": "ea012f4666edceffb3743f9fe6ebcd91a43fe26f", "head_commit_message": "Add comments\n\nSigned-off-by: Harry Mellor <[email protected]>", "patch_to_review": "diff --git a/tests/test_utils.py b/tests/test_utils.py\nindex ea7db0a79c86..d760cdceb8ca 100644\n--- a/tests/test_utils.py\n+++ b/tests/test_utils.py\n@@ -3,6 +3,7 @@\n \n import asyncio\n import hashlib\n+import json\n import pickle\n import socket\n from collections.abc import AsyncIterator\n@@ -138,6 +139,7 @@ def parser():\n parser.add_argument('--model-name')\n parser.add_argument('--batch-size', type=int)\n parser.add_argument('--enable-feature', action='store_true')\n+ parser.add_argument('--hf-overrides', type=json.loads)\n return parser\n \n \n@@ -251,6 +253,17 @@ def test_no_model_tag(parser_with_config, cli_config_file):\n parser_with_config.parse_args(['serve', '--config', cli_config_file])\n \n \n+def test_dict_args(parser):\n+ args = [\"--hf-overrides.key1\", \"val1\", \"--hf-overrides.key2.key3\", \"val2\"]\n+ parsed_args = parser.parse_args(args)\n+ assert parsed_args.hf_overrides == {\n+ \"key1\": \"val1\",\n+ \"key2\": {\n+ \"key3\": \"val2\"\n+ }\n+ }\n+\n+\n # yapf: enable\n @pytest.mark.parametrize(\n \"callable,kw_name,requires_kw_only,allow_var_kwargs,is_supported\",\ndiff --git a/vllm/utils.py b/vllm/utils.py\nindex edfbb8c9481e..ab3ffe81d0ea 100644\n--- a/vllm/utils.py\n+++ b/vllm/utils.py\n@@ -15,6 +15,7 @@\n import importlib.util\n import inspect\n import ipaddress\n+import json\n import multiprocessing\n import os\n import pickle\n@@ -1419,6 +1420,35 @@ def parse_args( # type: ignore[override]\n else:\n processed_args.append(arg)\n \n+ def create_nested_dict(keys: list[str], value: str):\n+ \"\"\"Creates a nested dictionary from a list of keys and a value.\n+\n+ For example, `keys = [\"a\", \"b\", \"c\"]` and `value = 1` will create:\n+ `{\"a\": {\"b\": {\"c\": 1}}}`\n+ \"\"\"\n+ nested_dict: Any = value\n+ for key in reversed(keys):\n+ nested_dict = {key: nested_dict}\n+ return nested_dict\n+\n+ dict_args: dict[str, dict] = defaultdict(dict)\n+ # Loop in reverse because we are modifying the list\n+ for i, processed_arg in reversed(list(enumerate(processed_args))):\n+ if processed_arg.startswith(\"--\") and \".\" in processed_arg:\n+ if \"=\" in processed_arg:\n+ processed_arg, value = processed_arg.split(\"=\", 1)\n+ else:\n+ value = processed_args[i + 1]\n+ del processed_args[i + 1]\n+ key, *keys = processed_arg.split(\".\")\n+ # Merge all values with the same key into a single dict\n+ dict_args[key].update(create_nested_dict(keys, value))\n+ del processed_args[i]\n+ # Add the dict args back as if they were originally passed as JSON\n+ for dict_arg, dict_value in dict_args.items():\n+ processed_args.append(dict_arg)\n+ processed_args.append(json.dumps(dict_value))\n+\n return super().parse_args(processed_args, namespace)\n \n def check_port(self, value):\n" }
[ { "diff_hunk": "@@ -1419,6 +1420,35 @@ def parse_args( # type: ignore[override]\n else:\n processed_args.append(arg)\n \n+ def create_nested_dict(keys: list[str], value: str):\n+ \"\"\"Creates a nested dictionary from a list of keys and a value.\n+\n+ For example, `keys = [\"a\", \"b\", \"c\"]` and `value = 1` will create:\n+ `{\"a\": {\"b\": {\"c\": 1}}}`\n+ \"\"\"\n+ nested_dict: Any = value\n+ for key in reversed(keys):\n+ nested_dict = {key: nested_dict}\n+ return nested_dict\n+\n+ dict_args: dict[str, dict] = defaultdict(dict)\n+ # Loop in reverse because we are modifying the list\n+ for i, processed_arg in reversed(list(enumerate(processed_args))):\n+ if processed_arg.startswith(\"--\") and \".\" in processed_arg:\n+ if \"=\" in processed_arg:\n+ processed_arg, value = processed_arg.split(\"=\", 1)\n+ else:\n+ value = processed_args[i + 1]\n+ del processed_args[i + 1]\n+ key, *keys = processed_arg.split(\".\")\n+ # Merge all values with the same key into a single dict\n+ dict_args[key].update(create_nested_dict(keys, value))", "line": null, "original_line": 1445, "original_start_line": null, "path": "vllm/utils.py", "start_line": null, "text": "@user1:\nI think this would fail in a case like\r\n\r\n```\r\n--hf-overrides.key1.key2 a --hf-overrides.key1.key3 b\r\n```\r\n\r\nBecause `{key2: a}` is replaced with `{key3: b}` for the key `key1` instead of being merged together.\n\n@author:\nGood point, I'll change this to recursively update" } ]
680a78d9b3423049701dcdc9f6abbfa0c1b3d38f
diff --git a/tests/engine/test_arg_utils.py b/tests/engine/test_arg_utils.py index ce8873d58d4d..05d9cfc7ab74 100644 --- a/tests/engine/test_arg_utils.py +++ b/tests/engine/test_arg_utils.py @@ -181,8 +181,8 @@ def test_get_kwargs(): # literals of literals should have merged choices assert kwargs["literal_literal"]["choices"] == [1, 2] # dict should have json tip in help - json_tip = "\n\nShould be a valid JSON string." - assert kwargs["json_tip"]["help"].endswith(json_tip) + json_tip = "Should either be a valid JSON string or JSON keys" + assert json_tip in kwargs["json_tip"]["help"] # nested config should should construct the nested config assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2) # from_cli configs should be constructed with the correct method diff --git a/tests/test_utils.py b/tests/test_utils.py index ea7db0a79c86..0b88d05efeaa 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -3,6 +3,7 @@ import asyncio import hashlib +import json import pickle import socket from collections.abc import AsyncIterator @@ -138,6 +139,7 @@ def parser(): parser.add_argument('--model-name') parser.add_argument('--batch-size', type=int) parser.add_argument('--enable-feature', action='store_true') + parser.add_argument('--hf-overrides', type=json.loads) return parser @@ -251,6 +253,29 @@ def test_no_model_tag(parser_with_config, cli_config_file): parser_with_config.parse_args(['serve', '--config', cli_config_file]) +def test_dict_args(parser): + args = [ + "--model-name=something.something", + "--hf-overrides.key1", + "val1", + "--hf-overrides.key2.key3", + "val2", + "--hf-overrides.key2.key4", + "val3", + "--hf-overrides.key5=val4", + ] + parsed_args = parser.parse_args(args) + assert parsed_args.model_name == "something.something" + assert parsed_args.hf_overrides == { + "key1": "val1", + "key2": { + "key3": "val2", + "key4": "val3", + }, + "key5": "val4", + } + + # yapf: enable @pytest.mark.parametrize( "callable,kw_name,requires_kw_only,allow_var_kwargs,is_supported", diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 240142a1c5d1..bf4a000af03d 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -183,7 +183,11 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]: kwargs[name] = {"default": default, "help": help} # Set other kwargs based on the type hints - json_tip = "\n\nShould be a valid JSON string." + json_tip = """\n\nShould either be a valid JSON string or JSON keys + passed individually. For example, the following sets of arguments are + equivalent:\n\n + - `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`\n + - `--json-arg.key1 value1 --json-arg.key2.key3 value2`\n\n""" if dataclass_cls is not None: dataclass_init = lambda x, f=dataclass_cls: f(**json.loads(x)) # Special case for configs with a from_cli method diff --git a/vllm/utils.py b/vllm/utils.py index edfbb8c9481e..0cd90c130d3e 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -15,6 +15,7 @@ import importlib.util import inspect import ipaddress +import json import multiprocessing import os import pickle @@ -1419,6 +1420,51 @@ def parse_args( # type: ignore[override] else: processed_args.append(arg) + def create_nested_dict(keys: list[str], value: str): + """Creates a nested dictionary from a list of keys and a value. + + For example, `keys = ["a", "b", "c"]` and `value = 1` will create: + `{"a": {"b": {"c": 1}}}` + """ + nested_dict: Any = value + for key in reversed(keys): + nested_dict = {key: nested_dict} + return nested_dict + + def recursive_dict_update(original: dict, update: dict): + """Recursively updates a dictionary with another dictionary.""" + for k, v in update.items(): + if isinstance(v, dict) and isinstance(original.get(k), dict): + recursive_dict_update(original[k], v) + else: + original[k] = v + + delete = set() + dict_args: dict[str, dict] = defaultdict(dict) + for i, processed_arg in enumerate(processed_args): + if processed_arg.startswith("--") and "." in processed_arg: + if "=" in processed_arg: + processed_arg, value = processed_arg.split("=", 1) + if "." not in processed_arg: + # False positive, . was only in the value + continue + else: + value = processed_args[i + 1] + delete.add(i + 1) + key, *keys = processed_arg.split(".") + # Merge all values with the same key into a single dict + arg_dict = create_nested_dict(keys, value) + recursive_dict_update(dict_args[key], arg_dict) + delete.add(i) + # Filter out the dict args we set to None + processed_args = [ + a for i, a in enumerate(processed_args) if i not in delete + ] + # Add the dict args back as if they were originally passed as JSON + for dict_arg, dict_value in dict_args.items(): + processed_args.append(dict_arg) + processed_args.append(json.dumps(dict_value)) + return super().parse_args(processed_args, namespace) def check_port(self, value):
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-16291@3bda5d1
vllm-project/vllm
Python
16,291
[Hardware] add platform-specific request validation api
This PR adds a generic `validate_request` api to the platform interface. This allows platforms to implement any runtime checks on each request to ensure that all the requested features are supported before scheduling it. There is already one existing check in this category, `supports_structured_output`, and I'd like to avoid a proliferation of more platform apis for individual features like this. Currently, the spyre plugin needs to implement some extra validation around the shape of inputs, as we have more constraints on valid prompt lengths and max token requests. This new api would let us do that without needing to hack around rejecting requests from the scheduler. FIX https://github.com/vllm-project/vllm-spyre/issues/77 <!--- pyml disable-next-line no-emphasis-as-heading -->
2025-04-08T22:04:36Z
Add platform api for request validation to reject requests that don't fit warmup shapes From talking to @njhill, there's already precedent for doing hardware-specific request validation with the `supports_structured_output` platform api. For spyre we need a way to reject requests that don't fit a warmup shape, so we should propose a more general api to validate requests up front. Maybe something like: ``` def validate_request( cls, prompt: PromptType, params: Union[SamplingParams, PoolingParams], lora_request: Optional[LoRARequest] = None, ) -> None: """Raises if this request is unsupported on this platform""" ```
[ { "body": "From talking to @njhill, there's already precedent for doing hardware-specific request validation with the `supports_structured_output` platform api. For spyre we need a way to reject requests that don't fit a warmup shape, so we should propose a more general api to validate requests up front. Maybe something like:\n\n```\ndef validate_request(\n cls,\n prompt: PromptType,\n params: Union[SamplingParams, PoolingParams],\n lora_request: Optional[LoRARequest] = None,\n) -> None:\n \"\"\"Raises if this request is unsupported on this platform\"\"\"\n```\n", "number": 77, "title": "Add platform api for request validation to reject requests that don't fit warmup shapes" } ]
db104221845e44e0b85336a54e44eb8850b8a5bc
{ "head_commit": "3bda5d15f6c86629d861791232713c8d911cd013", "head_commit_message": ":sparkles: add platform-specific request validation\n\nSigned-off-by: Joe Runde <[email protected]>", "patch_to_review": "diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py\nindex 2bb543bd73f7..a6425a7ce895 100644\n--- a/vllm/platforms/interface.py\n+++ b/vllm/platforms/interface.py\n@@ -1,5 +1,4 @@\n # SPDX-License-Identifier: Apache-2.0\n-\n import enum\n import platform\n import random\n@@ -9,10 +8,14 @@\n import numpy as np\n import torch\n \n+from vllm.inputs import PromptType\n from vllm.logger import init_logger\n \n if TYPE_CHECKING:\n from vllm.config import ModelConfig, VllmConfig\n+ from vllm.lora.request import LoRARequest\n+ from vllm.pooling_params import PoolingParams\n+ from vllm.sampling_params import SamplingParams\n from vllm.utils import FlexibleArgumentParser\n else:\n ModelConfig = None\n@@ -393,6 +396,15 @@ def use_custom_allreduce(cls) -> bool:\n \"\"\"\n return False\n \n+ @classmethod\n+ def validate_request(\n+ cls,\n+ prompt: PromptType,\n+ params: Union[SamplingParams, PoolingParams],\n+ lora_request: Optional[LoRARequest] = None,\n+ ) -> None:\n+ \"\"\"Raises if this request is unsupported on this platform\"\"\"\n+\n \n class UnspecifiedPlatform(Platform):\n _enum = PlatformEnum.UNSPECIFIED\ndiff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py\nindex 403edddfcbee..35b436419f41 100644\n--- a/vllm/v1/engine/processor.py\n+++ b/vllm/v1/engine/processor.py\n@@ -183,6 +183,12 @@ def process_inputs(\n # TODO(woosuk): Support pooling models.\n # TODO(woosuk): Support encoder-decoder models.\n \n+ from vllm.platforms import current_platform\n+ current_platform.validate_request(\n+ prompt=prompt,\n+ params=params,\n+ lora_request=lora_request,\n+ )\n self._validate_lora(lora_request)\n self._validate_params(params)\n if priority != 0:\n" }
[ { "diff_hunk": "@@ -393,6 +396,15 @@ def use_custom_allreduce(cls) -> bool:\n \"\"\"\n return False\n \n+ @classmethod\n+ def validate_request(\n+ cls,\n+ prompt: PromptType,\n+ params: Union[SamplingParams, PoolingParams],\n+ lora_request: Optional[LoRARequest] = None,", "line": null, "original_line": 404, "original_start_line": null, "path": "vllm/platforms/interface.py", "start_line": null, "text": "@user1:\nDo we need to include lora_request here? Wouldn't a platform either support lora or not, and if not isn't this something that could be checked at startup time?\n\n@author:\nAh, yeah that's true. I was thinking there might be a case where something about the adapter needs validation but I think you're right that anything about supporting lora would be checked either at boot time, or at adapter load time" } ]
6ba2638d96f7c820e861ddab810b03206d7f49eb
diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py index cfd7bc2a4057..67466bdb9807 100644 --- a/vllm/platforms/cpu.py +++ b/vllm/platforms/cpu.py @@ -180,7 +180,3 @@ def get_device_communicator_cls(cls) -> str: Get device specific communicator class for distributed communication. """ return "vllm.distributed.device_communicators.cpu_communicator.CpuCommunicator" # noqa - - @classmethod - def supports_structured_output(cls) -> bool: - return True diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 053cf74ebceb..0576022be448 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -308,10 +308,6 @@ def supports_fp8(cls) -> bool: def supports_v1(cls, model_config: ModelConfig) -> bool: return True - @classmethod - def supports_structured_output(cls) -> bool: - return True - @classmethod def use_custom_allreduce(cls) -> bool: return True diff --git a/vllm/platforms/hpu.py b/vllm/platforms/hpu.py index f011f14029a3..4c842b525110 100644 --- a/vllm/platforms/hpu.py +++ b/vllm/platforms/hpu.py @@ -92,7 +92,3 @@ def get_punica_wrapper(cls) -> str: @classmethod def get_device_communicator_cls(cls) -> str: return "vllm.distributed.device_communicators.hpu_communicator.HpuCommunicator" # noqa - - @classmethod - def supports_structured_output(cls) -> bool: - return True diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index 2bb543bd73f7..9799d4cb2ea7 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -1,5 +1,4 @@ # SPDX-License-Identifier: Apache-2.0 - import enum import platform import random @@ -9,14 +8,21 @@ import numpy as np import torch +from vllm.inputs import PromptType from vllm.logger import init_logger if TYPE_CHECKING: from vllm.config import ModelConfig, VllmConfig + from vllm.lora.request import LoRARequest + from vllm.pooling_params import PoolingParams + from vllm.sampling_params import SamplingParams from vllm.utils import FlexibleArgumentParser else: ModelConfig = None VllmConfig = None + LoRARequest = None + PoolingParams = None + SamplingParams = None FlexibleArgumentParser = None logger = init_logger(__name__) @@ -379,13 +385,6 @@ def supports_v1(cls, model_config: ModelConfig) -> bool: """ return False - @classmethod - def supports_structured_output(cls) -> bool: - """ - Returns whether the current platform can support structured output. - """ - return False - @classmethod def use_custom_allreduce(cls) -> bool: """ @@ -393,6 +392,14 @@ def use_custom_allreduce(cls) -> bool: """ return False + @classmethod + def validate_request( + cls, + prompt: PromptType, + params: Union[SamplingParams, PoolingParams], + ) -> None: + """Raises if this request is unsupported on this platform""" + class UnspecifiedPlatform(Platform): _enum = PlatformEnum.UNSPECIFIED diff --git a/vllm/platforms/neuron.py b/vllm/platforms/neuron.py index 93657881cbdd..c1f426e5b880 100644 --- a/vllm/platforms/neuron.py +++ b/vllm/platforms/neuron.py @@ -67,7 +67,3 @@ def get_device_communicator_cls(cls) -> str: @classmethod def use_all_gather(cls) -> bool: return True - - @classmethod - def supports_structured_output(cls) -> bool: - return True diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index a2fbf416ecf2..d18b7c26f7ec 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -303,10 +303,6 @@ def supports_v1(cls, model_config: ModelConfig) -> bool: # V1 support on AMD gpus is experimental return True - @classmethod - def supports_structured_output(cls) -> bool: - return True - @classmethod def use_custom_allreduce(cls) -> bool: # We only enable custom allreduce for MI300 series diff --git a/vllm/platforms/tpu.py b/vllm/platforms/tpu.py index eeadb4a71e5e..d5848424b332 100644 --- a/vllm/platforms/tpu.py +++ b/vllm/platforms/tpu.py @@ -1,19 +1,26 @@ # SPDX-License-Identifier: Apache-2.0 -from typing import TYPE_CHECKING, Optional +from typing import TYPE_CHECKING, Optional, Union import torch import vllm.envs as envs +from vllm.inputs import PromptType from vllm.logger import init_logger from .interface import Platform, PlatformEnum, _Backend if TYPE_CHECKING: from vllm.config import ModelConfig, VllmConfig + from vllm.lora.request import LoRARequest + from vllm.pooling_params import PoolingParams + from vllm.sampling_params import SamplingParams else: ModelConfig = None VllmConfig = None + LoRARequest = None + PoolingParams = None + SamplingParams = None logger = init_logger(__name__) @@ -135,6 +142,13 @@ def supports_v1(cls, model_config: ModelConfig) -> bool: return True @classmethod - def supports_structured_output(cls) -> bool: - # Structured output is not supported on TPU. - return False + def validate_request( + cls, + prompt: PromptType, + params: Union[SamplingParams, PoolingParams], + ) -> None: + """Raises if this request is unsupported on this platform""" + if isinstance(params, + SamplingParams) and params.guided_decoding is not None: + raise ValueError("Structured output is not supported on " + f"{cls.device_name}.") diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py index c4bd639384a4..225e756cd7ce 100644 --- a/vllm/platforms/xpu.py +++ b/vllm/platforms/xpu.py @@ -140,7 +140,3 @@ def device_support_bf16(cls) -> bool: @classmethod def get_device_communicator_cls(cls) -> str: return "vllm.distributed.device_communicators.xpu_communicator.XpuCommunicator" # noqa - - @classmethod - def supports_structured_output(cls) -> bool: - return True diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py index 403edddfcbee..6c1a54bca9f8 100644 --- a/vllm/v1/engine/processor.py +++ b/vllm/v1/engine/processor.py @@ -137,11 +137,6 @@ def _validate_structured_output(self, params: SamplingParams) -> None: else: params.guided_decoding.backend = engine_level_backend - from vllm.platforms import current_platform - if not current_platform.supports_structured_output(): - raise ValueError("Structured output is not supported on " - f"{current_platform.device_name}.") - # Request content validation if engine_level_backend.startswith("xgrammar"): # xgrammar with no fallback @@ -183,6 +178,11 @@ def process_inputs( # TODO(woosuk): Support pooling models. # TODO(woosuk): Support encoder-decoder models. + from vllm.platforms import current_platform + current_platform.validate_request( + prompt=prompt, + params=params, + ) self._validate_lora(lora_request) self._validate_params(params) if priority != 0:
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-15417@838b01e
vllm-project/vllm
Python
15,417
[bugfix] add supports_v1 platform interface
This PR re-allows platform plugins to run on V1. I'm assuming that all out-of-tree platforms are responsible for all of their own validation at startup to ensure the provided configuration will work, so vllm shouldn't disallow them from running. Our plugin currently doesn't boot with v0.8.1 (and presumably won't with the upcoming v0.8.2), this little fix allows it to start again. FIX https://github.com/vllm-project/vllm-spyre/issues/38
2025-03-24T21:14:10Z
Plugin won't boot with vllm v0.8.1 Trying to boot with vllm 0.8.1 with `VLLM_SPYRE_DYNAMO_BACKEND=eager` and `VLLM_USE_V1=1` gives the fun error: ``` INFO 03-19 22:31:45 [config.py:2595] Downcasting torch.float32 to torch.float16. INFO 03-19 22:31:49 [config.py:583] This model supports multiple tasks: {'score', 'reward', 'classify', 'embed', 'generate'}. Defaulting to 'generate'. Traceback (most recent call last): File "/home/senuser/my-vllm/bin/vllm", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/cli/main.py", line 75, in main args.dispatch_function(args) File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/cli/serve.py", line 33, in cmd uvloop.run(run_server(args)) File "/home/senuser/my-vllm/lib64/python3.11/site-packages/uvloop/__init__.py", line 105, in run return runner.run(wrapper()) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/home/senuser/my-vllm/lib64/python3.11/site-packages/uvloop/__init__.py", line 61, in wrapper return await main ^^^^^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 1012, in run_server async with build_async_engine_client(args) as engine_client: File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 141, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 161, in build_async_engine_client_from_engine_args vllm_config = engine_args.create_engine_config(usage_context=usage_context) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py", line 1215, in create_engine_config if try_v1 and self._is_v1_supported_oracle(model_config): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py", line 1584, in _is_v1_supported_oracle _raise_or_fallback( File "/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py", line 1758, in _raise_or_fallback raise NotImplementedError( NotImplementedError: VLLM_USE_V1=1 is not supported with device type=cpu. ```
[ { "body": "Trying to boot with vllm 0.8.1 with `VLLM_SPYRE_DYNAMO_BACKEND=eager` and `VLLM_USE_V1=1` gives the fun error:\n\n```\nINFO 03-19 22:31:45 [config.py:2595] Downcasting torch.float32 to torch.float16.\nINFO 03-19 22:31:49 [config.py:583] This model supports multiple tasks: {'score', 'reward', 'classify', 'embed', 'generate'}. Defaulting to 'generate'.\nTraceback (most recent call last):\n File \"/home/senuser/my-vllm/bin/vllm\", line 8, in <module>\n sys.exit(main())\n ^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/cli/main.py\", line 75, in main\n args.dispatch_function(args)\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/cli/serve.py\", line 33, in cmd\n uvloop.run(run_server(args))\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/uvloop/__init__.py\", line 105, in run\n return runner.run(wrapper())\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib64/python3.11/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"uvloop/loop.pyx\", line 1518, in uvloop.loop.Loop.run_until_complete\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/uvloop/__init__.py\", line 61, in wrapper\n return await main\n ^^^^^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py\", line 1012, in run_server\n async with build_async_engine_client(args) as engine_client:\n File \"/usr/lib64/python3.11/contextlib.py\", line 210, in __aenter__\n return await anext(self.gen)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py\", line 141, in build_async_engine_client\n async with build_async_engine_client_from_engine_args(\n File \"/usr/lib64/python3.11/contextlib.py\", line 210, in __aenter__\n return await anext(self.gen)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py\", line 161, in build_async_engine_client_from_engine_args\n vllm_config = engine_args.create_engine_config(usage_context=usage_context)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py\", line 1215, in create_engine_config\n if try_v1 and self._is_v1_supported_oracle(model_config):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py\", line 1584, in _is_v1_supported_oracle\n _raise_or_fallback(\n File \"/home/senuser/my-vllm/lib64/python3.11/site-packages/vllm/engine/arg_utils.py\", line 1758, in _raise_or_fallback\n raise NotImplementedError(\nNotImplementedError: VLLM_USE_V1=1 is not supported with device type=cpu.\n```\n\n", "number": 38, "title": "Plugin won't boot with vllm v0.8.1" } ]
3eb08ed9b1114fc16eaa2386b2e7ad14eae87073
{ "head_commit": "838b01e17d8a075f99cda3b839989d2a49992b18", "head_commit_message": ":bug: re-allow OOT platforms on V1\n\nSigned-off-by: Joe Runde <[email protected]>", "patch_to_review": "diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py\nindex 38a47a846df7..8d96c836fbdc 100644\n--- a/vllm/engine/arg_utils.py\n+++ b/vllm/engine/arg_utils.py\n@@ -1670,8 +1670,10 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:\n return False\n \n # No support for device type other than CUDA, AMD (experiemntal) or\n- # TPU (experimental) so far.\n- if not (current_platform.is_cuda_alike() or current_platform.is_tpu()):\n+ # TPU (experimental) so far. Out-of-tree device support plugins can\n+ # maintain their own v1 compatibility checks.\n+ if not (current_platform.is_cuda_alike() or current_platform.is_tpu()\n+ or current_platform.is_out_of_tree()):\n _raise_or_fallback(\n feature_name=f\"device type={current_platform.device_type}\",\n recommend_to_remove=False)\n" }
[ { "diff_hunk": "@@ -1670,8 +1670,10 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:\n return False\n \n # No support for device type other than CUDA, AMD (experiemntal) or\n- # TPU (experimental) so far.\n- if not (current_platform.is_cuda_alike() or current_platform.is_tpu()):\n+ # TPU (experimental) so far. Out-of-tree device support plugins can\n+ # maintain their own v1 compatibility checks.\n+ if not (current_platform.is_cuda_alike() or current_platform.is_tpu()\n+ or current_platform.is_out_of_tree()):", "line": null, "original_line": 1676, "original_start_line": 1673, "path": "vllm/engine/arg_utils.py", "start_line": null, "text": "@user1:\nMaybe we should add a `supports_v1()` to `Platform` (defaulting to false) instead of this, so we don't break fallbacks for other out-of-tree platforms\n\n@author:\nYeah I'd be fine with that as well. I didn't do that here at first because I didn't want to push this centralized bit of logic back out to the different platforms in-tree, but that's not a huge deal.\r\n\r\n> so we don't break fallbacks\r\n\r\nYeah... I get that we don't want to turn on V1 where it won't work, but this explicitly denies it where in our case it does work! ๐Ÿ˜… It should be the responsibility of the plugin to determine compatibility, so I'm fine with either this or a `platform.supports_v1()` flag. I'l leave some time for more comments and check back on this tomorrow morning\n\n@user2:\nI have a comment here https://github.com/vllm-project/vllm/issues/15263#issuecomment-2745034522\r\n\r\nwe should add `platform.supports_v1()`\n\n@user2:\nin addition, to give oot platforms the same ability of falling back, we should pass the config object to the `supports_v1` function.\n\n@user3:\n> in addition, to give oot platforms the same ability of falling back, we should pass the config object to the `supports_v1` function.\r\n\r\nAgree ๐Ÿ‘ \n\n@author:\nCool, on it ๐Ÿ‘" } ]
aaf21cd9d07d32ec682c8104bbd31a41d7239493
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 38a47a846df7..69a164bbc6a8 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1669,9 +1669,8 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool: _raise_or_fallback(feature_name=name, recommend_to_remove=True) return False - # No support for device type other than CUDA, AMD (experiemntal) or - # TPU (experimental) so far. - if not (current_platform.is_cuda_alike() or current_platform.is_tpu()): + # Platforms must decide if they can support v1 for this model + if not current_platform.supports_v1(model_config=model_config): _raise_or_fallback( feature_name=f"device type={current_platform.device_type}", recommend_to_remove=False) diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index bb77318092fc..ca8a2d2640ec 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -20,8 +20,9 @@ from .interface import DeviceCapability, Platform, PlatformEnum, _Backend if TYPE_CHECKING: - from vllm.config import VllmConfig + from vllm.config import ModelConfig, VllmConfig else: + ModelConfig = None VllmConfig = None logger = init_logger(__name__) @@ -303,6 +304,10 @@ def get_device_communicator_cls(cls) -> str: def supports_fp8(cls) -> bool: return cls.has_device_capability(89) + @classmethod + def supports_v1(cls, model_config: ModelConfig) -> bool: + return True + # NVML utils # Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`, diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index 9981deee39b7..36db70681a19 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -12,9 +12,10 @@ from vllm.logger import init_logger if TYPE_CHECKING: - from vllm.config import VllmConfig + from vllm.config import ModelConfig, VllmConfig from vllm.utils import FlexibleArgumentParser else: + ModelConfig = None VllmConfig = None FlexibleArgumentParser = None @@ -371,6 +372,13 @@ def use_all_gather(cls) -> bool: or parallel_config.distributed_executor_backend == "external_launcher") + @classmethod + def supports_v1(cls, model_config: ModelConfig) -> bool: + """Returns whether the current platform can support v1 for the supplied + model configuration. + """ + return False + class UnspecifiedPlatform(Platform): _enum = PlatformEnum.UNSPECIFIED diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index ee708f5961df..d196e24ac7ac 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -12,8 +12,9 @@ from .interface import DeviceCapability, Platform, PlatformEnum, _Backend if TYPE_CHECKING: - from vllm.config import VllmConfig + from vllm.config import ModelConfig, VllmConfig else: + ModelConfig = None VllmConfig = None logger = init_logger(__name__) @@ -249,3 +250,8 @@ def fp8_dtype(cls) -> torch.dtype: return torch.float8_e4m3fnuz else: return torch.float8_e4m3fn + + @classmethod + def supports_v1(cls, model_config: ModelConfig) -> bool: + # V1 support on AMD gpus is experimental + return True diff --git a/vllm/platforms/tpu.py b/vllm/platforms/tpu.py index 073d46c25d57..43d3044cb93e 100644 --- a/vllm/platforms/tpu.py +++ b/vllm/platforms/tpu.py @@ -10,8 +10,9 @@ from .interface import Platform, PlatformEnum, _Backend if TYPE_CHECKING: - from vllm.config import VllmConfig + from vllm.config import ModelConfig, VllmConfig else: + ModelConfig = None VllmConfig = None logger = init_logger(__name__) @@ -127,3 +128,8 @@ def get_device_communicator_cls(cls) -> str: @classmethod def use_all_gather(cls) -> bool: return True + + @classmethod + def supports_v1(cls, model_config: ModelConfig) -> bool: + # V1 support on TPU is experimental + return True
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
vllm-project__vllm-10353@3456673
vllm-project/vllm
Python
10,353
[Core] Interface for accessing model from `VllmRunner`
This PR adds a new method `LLM.apply_model` to apply a function to the model in each worker without having to access `llm.llm_engine.model_executor.driver_worker.model_runner.model`. FIX #4084
2024-11-15T05:51:54Z
[Usage]: How do we add model hooks? ### Your current environment ```text PyTorch version: 2.1.0+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.4.0-172-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe Nvidia driver version: 535.154.05 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: AuthenticAMD Model name: AMD EPYC 7643 48-Core Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 48 Socket(s): 2 Stepping: 1 Frequency boost: enabled CPU max MHz: 2300.0000 CPU min MHz: 1500.0000 BogoMIPS: 4600.19 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca Virtualization: AMD-V L1d cache: 3 MiB (96 instances) L1i cache: 3 MiB (96 instances) L2 cache: 48 MiB (96 instances) L3 cache: 512 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-47,96-143 NUMA node1 CPU(s): 48-95,144-191 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.24.1 [pip3] torch==2.1.0+cu118 [pip3] torchaudio==2.1.0+cu118 [pip3] torchvision==0.16.0+cu118 [pip3] triton==2.1.0 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-47,96-143 0 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ``` ### How would you like to use vllm I want to add model hooks to a vllm model. I found a way to the model with: ```python def add_hooks_to_vllm_model(self): for name, param in self.model_instance.engine.model_executor.driver_worker.model_runner.model.named_modules(): if isinstance(param, MixtralDecoderLayer): # we need to create a list of all the layers we want to catch hook = CaptureHook() param.register_forward_hook(hook) self.hooks.append(hook) ``` But these seems to only capture information for the first token prediction rather than all of them. Is there a better way of using model hooks within the library?
I have the same question. Have you solved this? If you set enforce_eager to be True, then it will add call the hooks everytime. But I couldn't find a cleaner way of accessing model hooks. This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you! Did you find an efficient implementation of this for all tokens?
[ { "body": "### Your current environment\n\n```text\r\nPyTorch version: 2.1.0+cu118\r\nIs debug build: False\r\nCUDA used to build PyTorch: 11.8\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: Could not collect\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-5.4.0-172-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 11.8.89\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe\r\nNvidia driver version: 535.154.05\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 48 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 192\r\nOn-line CPU(s) list: 0-191\r\nVendor ID: AuthenticAMD\r\nModel name: AMD EPYC 7643 48-Core Processor\r\nCPU family: 25\r\nModel: 1\r\nThread(s) per core: 2\r\nCore(s) per socket: 48\r\nSocket(s): 2\r\nStepping: 1\r\nFrequency boost: enabled\r\nCPU max MHz: 2300.0000\r\nCPU min MHz: 1500.0000\r\nBogoMIPS: 4600.19\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca\r\nVirtualization: AMD-V\r\nL1d cache: 3 MiB (96 instances)\r\nL1i cache: 3 MiB (96 instances)\r\nL2 cache: 48 MiB (96 instances)\r\nL3 cache: 512 MiB (16 instances)\r\nNUMA node(s): 2\r\nNUMA node0 CPU(s): 0-47,96-143\r\nNUMA node1 CPU(s): 48-95,144-191\r\nVulnerability Gather data sampling: Not affected\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Not affected\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.24.1\r\n[pip3] torch==2.1.0+cu118\r\n[pip3] torchaudio==2.1.0+cu118\r\n[pip3] torchvision==0.16.0+cu118\r\n[pip3] triton==2.1.0\r\n[conda] Could not collectROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: N/A\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0 CPU Affinity NUMA Affinity GPU NUMA ID\r\nGPU0 X 0-47,96-143 0 N/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n```\r\n\n\n### How would you like to use vllm\n\nI want to add model hooks to a vllm model. \r\n\r\nI found a way to the model with:\r\n\r\n```python\r\n def add_hooks_to_vllm_model(self):\r\n for name, param in self.model_instance.engine.model_executor.driver_worker.model_runner.model.named_modules():\r\n if isinstance(param, MixtralDecoderLayer): # we need to create a list of all the layers we want to catch\r\n hook = CaptureHook()\r\n param.register_forward_hook(hook)\r\n self.hooks.append(hook)\r\n```\r\n\r\nBut these seems to only capture information for the first token prediction rather than all of them. \r\n\r\nIs there a better way of using model hooks within the library? \r\n", "number": 4084, "title": "[Usage]: How do we add model hooks? " } ]
81763c58a01eda9205f3750177358acc79613e65
{ "head_commit": "3456673828daa7944eb8fbf8cc8e3673b30457ea", "head_commit_message": "Make available to LLM\n\nSigned-off-by: DarkLight1337 <[email protected]>", "patch_to_review": "diff --git a/tests/conftest.py b/tests/conftest.py\nindex 95af4ac1eb17..279c1bf9a377 100644\n--- a/tests/conftest.py\n+++ b/tests/conftest.py\n@@ -244,6 +244,7 @@ def video_assets() -> _VideoAssets:\n \n \n _T = TypeVar(\"_T\", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict)\n+_R = TypeVar(\"_R\")\n \n \n class HfRunner:\n@@ -930,6 +931,10 @@ def score(\n req_outputs = self.model.score(text_1, text_2)\n return [req_output.outputs.score for req_output in req_outputs]\n \n+ def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:\n+ executor = self.model.llm_engine.model_executor\n+ return executor.apply_model(func)\n+\n def __enter__(self):\n return self\n \ndiff --git a/tests/engine/test_custom_executor.py b/tests/engine/test_custom_executor.py\nindex fdfcd4f4c9d5..0e33f3662da8 100644\n--- a/tests/engine/test_custom_executor.py\n+++ b/tests/engine/test_custom_executor.py\n@@ -51,7 +51,9 @@ def test_custom_executor(model, tmp_path):\n assert not os.path.exists(\".marker\")\n \n engine_args = EngineArgs(\n- model=model, distributed_executor_backend=CustomUniExecutor)\n+ model=model,\n+ distributed_executor_backend=CustomUniExecutor,\n+ )\n engine = LLMEngine.from_engine_args(engine_args)\n sampling_params = SamplingParams(max_tokens=1)\n \ndiff --git a/tests/model_executor/test_model_load_with_params.py b/tests/model_executor/test_model_load_with_params.py\nindex 0609fd96825e..9c1f784c1c93 100644\n--- a/tests/model_executor/test_model_load_with_params.py\n+++ b/tests/model_executor/test_model_load_with_params.py\n@@ -25,13 +25,12 @@ def test_model_loading_with_params(vllm_runner):\n with vllm_runner(model_name=MODEL_NAME,\n revision=REVISION,\n dtype=\"float16\",\n- max_model_len=MAX_MODEL_LEN) as model:\n- output = model.encode(\"Write a short story about a robot that\"\n- \" dreams for the first time.\\n\")\n+ max_model_len=MAX_MODEL_LEN) as vllm_model:\n+ output = vllm_model.encode(\"Write a short story about a robot that\"\n+ \" dreams for the first time.\\n\")\n \n- model_config = model.model.llm_engine.model_config\n-\n- model_tokenizer = model.model.llm_engine.tokenizer\n+ model_config = vllm_model.model.llm_engine.model_config\n+ model_tokenizer = vllm_model.model.llm_engine.tokenizer\n \n # asserts on the bert model config file\n assert model_config.encoder_config[\"max_seq_length\"] == 512\n@@ -46,11 +45,13 @@ def test_model_loading_with_params(vllm_runner):\n assert model_tokenizer.tokenizer_config[\"do_lower_case\"]\n assert model_tokenizer.tokenizer.model_max_length == 512\n \n- model = model.model.llm_engine.model_executor\\\n- .driver_worker.model_runner.model\n- assert isinstance(model, BertEmbeddingModel)\n- assert model._pooler.pooling_type == PoolingType.CLS\n- assert model._pooler.normalize\n+ def check_model(model):\n+ assert isinstance(model, BertEmbeddingModel)\n+ assert model._pooler.pooling_type == PoolingType.CLS\n+ assert model._pooler.normalize\n+\n+ vllm_model.apply_model(check_model)\n+\n # assert output\n assert output\n \n@@ -64,13 +65,12 @@ def test_roberta_model_loading_with_params(vllm_runner):\n with vllm_runner(model_name=MODEL_NAME_ROBERTA,\n revision=REVISION_ROBERTA,\n dtype=\"float16\",\n- max_model_len=MAX_MODEL_LEN) as model:\n- output = model.encode(\"Write a short story about a robot that\"\n- \" dreams for the first time.\\n\")\n+ max_model_len=MAX_MODEL_LEN) as vllm_model:\n+ output = vllm_model.encode(\"Write a short story about a robot that\"\n+ \" dreams for the first time.\\n\")\n \n- model_config = model.model.llm_engine.model_config\n-\n- model_tokenizer = model.model.llm_engine.tokenizer\n+ model_config = vllm_model.model.llm_engine.model_config\n+ model_tokenizer = vllm_model.model.llm_engine.tokenizer\n \n # asserts on the bert model config file\n assert model_config.encoder_config[\"max_seq_length\"] == 512\n@@ -84,11 +84,12 @@ def test_roberta_model_loading_with_params(vllm_runner):\n assert model_tokenizer.tokenizer_id == \"intfloat/multilingual-e5-large\"\n assert not model_tokenizer.tokenizer_config[\"do_lower_case\"]\n \n- model = model.model.llm_engine.model_executor\\\n- .driver_worker.model_runner.model\n- assert isinstance(model, RobertaEmbeddingModel)\n- assert model._pooler.pooling_type == PoolingType.MEAN\n- assert model._pooler.normalize\n+ def check_model(model):\n+ assert isinstance(model, RobertaEmbeddingModel)\n+ assert model._pooler.pooling_type == PoolingType.MEAN\n+ assert model._pooler.normalize\n+\n+ vllm_model.apply_model(check_model)\n \n # assert output\n assert output\n@@ -103,17 +104,18 @@ def test_facebook_roberta_model_loading_with_params(vllm_runner):\n model_name = \"FacebookAI/roberta-base\"\n with vllm_runner(model_name=model_name,\n dtype=\"float16\",\n- max_model_len=MAX_MODEL_LEN) as model:\n- output = model.encode(\"Write a short story about a robot that\"\n- \" dreams for the first time.\\n\")\n+ max_model_len=MAX_MODEL_LEN) as vllm_model:\n+ output = vllm_model.encode(\"Write a short story about a robot that\"\n+ \" dreams for the first time.\\n\")\n \n- model_tokenizer = model.model.llm_engine.tokenizer\n+ model_tokenizer = vllm_model.model.llm_engine.tokenizer\n assert model_tokenizer.tokenizer_id == model_name\n \n- model = model.model.llm_engine.model_executor\\\n- .driver_worker.model_runner.model\n- assert not hasattr(model, \"lm_head\")\n- assert isinstance(model, RobertaEmbeddingModel)\n- assert isinstance(model._pooler, CLSPool)\n+ def check_model(model):\n+ assert isinstance(model, RobertaEmbeddingModel)\n+ assert not hasattr(model, \"lm_head\")\n+ assert isinstance(model._pooler, CLSPool)\n+\n+ vllm_model.apply_model(check_model)\n \n assert output\ndiff --git a/tests/models/decoder_only/language/test_jamba.py b/tests/models/decoder_only/language/test_jamba.py\nindex 057b04349e8b..120508677e27 100644\n--- a/tests/models/decoder_only/language/test_jamba.py\n+++ b/tests/models/decoder_only/language/test_jamba.py\n@@ -35,8 +35,7 @@ def test_models(\n vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)\n \n for i in range(len(example_prompts)):\n hf_output_ids, hf_output_str = hf_outputs[i]\ndiff --git a/tests/models/decoder_only/language/test_mamba.py b/tests/models/decoder_only/language/test_mamba.py\nindex 06739e8f0225..060d5594de51 100644\n--- a/tests/models/decoder_only/language/test_mamba.py\n+++ b/tests/models/decoder_only/language/test_mamba.py\n@@ -53,8 +53,7 @@ def test_models(\n vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)\n \n for i in range(len(example_prompts)):\n hf_output_ids, hf_output_str = hf_outputs[i]\ndiff --git a/tests/models/decoder_only/language/test_models.py b/tests/models/decoder_only/language/test_models.py\nindex 4e110366a09f..f067576bbb60 100644\n--- a/tests/models/decoder_only/language/test_models.py\n+++ b/tests/models/decoder_only/language/test_models.py\n@@ -75,8 +75,7 @@ def test_models(\n example_prompts, max_tokens, num_logprobs)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)\n \n check_logprobs_close(\n outputs_0_lst=hf_outputs,\ndiff --git a/tests/models/decoder_only/vision_language/test_qwen2_vl.py b/tests/models/decoder_only/vision_language/test_qwen2_vl.py\nindex 2fd22f0cc88e..5a485f3d8174 100644\n--- a/tests/models/decoder_only/vision_language/test_qwen2_vl.py\n+++ b/tests/models/decoder_only/vision_language/test_qwen2_vl.py\n@@ -5,7 +5,6 @@\n import torch\n from PIL import Image\n \n-from vllm.entrypoints.llm import LLM\n from vllm.multimodal.image import rescale_image_size\n from vllm.multimodal.video import rescale_video_size, sample_frames_from_video\n \n@@ -69,7 +68,7 @@ class Qwen2VLPromptVideoEmbeddingInput(TypedDict):\n \n def batch_make_image_embeddings(\n image_batches: List[Union[Image.Image, List[Image.Image]]], processor,\n- llm: LLM) -> List[Qwen2VLPromptImageEmbeddingInput]:\n+ llm: VllmRunner) -> List[Qwen2VLPromptImageEmbeddingInput]:\n \"\"\"batched image embeddings for Qwen2-VL\n \n This will infer all images' embeddings in a single batch, \n@@ -106,16 +105,18 @@ def batch_make_image_embeddings(\n image_grid_thw = preprocess_result[\"image_grid_thw\"]\n \n # pixel values to embeddings & grid_thws\n- with torch.no_grad():\n- visual = llm.llm_engine.model_executor.driver_worker. \\\n- model_runner.model.visual\n+ def get_image_embeds(model):\n+ with torch.no_grad():\n+ visual = model.visual\n \n- pixel_values_on_device = pixel_values.to(visual.device,\n- dtype=visual.dtype)\n- image_grid_thw_on_device = image_grid_thw.to(visual.device,\n- dtype=torch.int64)\n- image_embeds = visual(pixel_values_on_device,\n- grid_thw=image_grid_thw_on_device)\n+ pixel_values_on_device = pixel_values.to(visual.device,\n+ dtype=visual.dtype)\n+ image_grid_thw_on_device = image_grid_thw.to(visual.device,\n+ dtype=torch.int64)\n+ return visual(pixel_values_on_device,\n+ grid_thw=image_grid_thw_on_device)\n+\n+ image_embeds = torch.concat(llm.apply_model(get_image_embeds))\n \n # split into original batches\n result: List[Qwen2VLPromptImageEmbeddingInput] = []\n@@ -150,7 +151,7 @@ def batch_make_image_embeddings(\n \n def batch_make_video_embeddings(\n video_batches: PromptVideoInput, processor,\n- llm: LLM) -> List[Qwen2VLPromptVideoEmbeddingInput]:\n+ llm: VllmRunner) -> List[Qwen2VLPromptVideoEmbeddingInput]:\n \"\"\"batched video embeddings for Qwen2-VL\n \n A NDArray represents a single video's all frames.\n@@ -187,16 +188,18 @@ def batch_make_video_embeddings(\n video_grid_thw = preprocess_result[\"video_grid_thw\"]\n \n # pixel values to embeddings & grid_thws\n- with torch.no_grad():\n- visual = llm.llm_engine.model_executor.driver_worker.\\\n- model_runner.model.visual\n+ def get_image_embeds(model):\n+ with torch.no_grad():\n+ visual = model.visual\n+\n+ pixel_values_on_device = pixel_values.to(visual.device,\n+ dtype=visual.dtype)\n+ video_grid_thw_on_device = video_grid_thw.to(visual.device,\n+ dtype=torch.int64)\n+ return visual(pixel_values_on_device,\n+ grid_thw=video_grid_thw_on_device)\n \n- pixel_values_on_device = pixel_values.to(visual.device,\n- dtype=visual.dtype)\n- video_grid_thw_on_device = video_grid_thw.to(visual.device,\n- dtype=torch.int64)\n- video_embeds = visual(pixel_values_on_device,\n- grid_thw=video_grid_thw_on_device)\n+ video_embeds = torch.concat(llm.apply_model(get_image_embeds))\n \n # split into original batches\n result: List[Qwen2VLPromptVideoEmbeddingInput] = []\n@@ -278,9 +281,9 @@ def run_embedding_input_test(\n max_tokens,\n num_logprobs=num_logprobs,\n images=batch_make_image_embeddings(\n- images, processor, vllm_model.model) if images else None,\n+ images, processor, vllm_model) if images else None,\n videos=batch_make_video_embeddings(\n- videos, processor, vllm_model.model) if videos else None)\n+ videos, processor, vllm_model) if videos else None)\n for prompts, images, videos in inputs\n ]\n \ndiff --git a/tests/models/embedding/language/test_cls_models.py b/tests/models/embedding/language/test_cls_models.py\nindex 6673a9fc22f6..1a6385f5e9c7 100644\n--- a/tests/models/embedding/language/test_cls_models.py\n+++ b/tests/models/embedding/language/test_cls_models.py\n@@ -26,8 +26,7 @@ def test_classification_models(\n vllm_outputs = vllm_model.classify(example_prompts)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)\n \n with hf_runner(model,\n dtype=dtype,\ndiff --git a/tests/models/embedding/language/test_embedding.py b/tests/models/embedding/language/test_embedding.py\nindex 04ab4dd7371a..bd633ac939f9 100644\n--- a/tests/models/embedding/language/test_embedding.py\n+++ b/tests/models/embedding/language/test_embedding.py\n@@ -63,8 +63,7 @@ def test_models(\n vllm_outputs = vllm_model.encode(example_prompts)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)\n \n check_embeddings_close(\n embeddings_0_lst=hf_outputs,\ndiff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py\nindex 92436889ecff..0cd86cef0a47 100644\n--- a/tests/quantization/test_compressed_tensors.py\n+++ b/tests/quantization/test_compressed_tensors.py\n@@ -30,50 +30,55 @@\n def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args):\n model_path, strategy, quant_type, shape_0, is_symmetric = model_args\n with vllm_runner(model_path, enforce_eager=True) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n-\n- qkv_proj = layer.self_attn.qkv_proj\n- o_proj = layer.self_attn.o_proj\n- gate_up_proj = layer.mlp.gate_up_proj\n- down_proj = layer.mlp.down_proj\n-\n- # assert zp for symmetric and asymmetric cases\n- def zp_valid(zp: Optional[torch.Tensor]):\n- if is_symmetric:\n- return zp is None\n-\n- return zp is not None and zp.dtype is torch.int32\n-\n- assert zp_valid(qkv_proj.input_zero_point)\n- assert zp_valid(o_proj.input_zero_point)\n- assert zp_valid(gate_up_proj.input_zero_point)\n- assert zp_valid(down_proj.input_zero_point)\n-\n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(o_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(gate_up_proj.quant_method,\n- CompressedTensorsLinearMethod)\n- assert isinstance(down_proj.quant_method,\n- CompressedTensorsLinearMethod)\n- assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8)\n-\n- assert qkv_proj.scheme.strategy == strategy\n- assert qkv_proj.scheme.is_static_input_scheme\n- expected_type = torch.int8\n-\n- assert qkv_proj.weight.dtype is expected_type\n- assert o_proj.weight.dtype is expected_type\n- assert gate_up_proj.weight.dtype is expected_type\n-\n- if qkv_proj.scheme.strategy == \"tensor\":\n- # Make sure it is a channelwise buffer\n- # After running process_weights_after_loading\n- assert len(qkv_proj.weight_scale.shape) == 2\n- assert qkv_proj.weight_scale.shape[0] == shape_0\n- assert qkv_proj.weight_scale.shape[1] == 1\n- assert qkv_proj.weight_scale.dtype is torch.float32\n- assert qkv_proj.input_scale.dtype is torch.float32\n+\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n+ o_proj = layer.self_attn.o_proj\n+ gate_up_proj = layer.mlp.gate_up_proj\n+ down_proj = layer.mlp.down_proj\n+\n+ # assert zp for symmetric and asymmetric cases\n+ def zp_valid(zp: Optional[torch.Tensor]):\n+ if is_symmetric:\n+ return zp is None\n+\n+ return zp is not None and zp.dtype is torch.int32\n+\n+ assert zp_valid(qkv_proj.input_zero_point)\n+ assert zp_valid(o_proj.input_zero_point)\n+ assert zp_valid(gate_up_proj.input_zero_point)\n+ assert zp_valid(down_proj.input_zero_point)\n+\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(o_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(gate_up_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(down_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8)\n+\n+ assert qkv_proj.scheme.strategy == strategy\n+ assert qkv_proj.scheme.is_static_input_scheme\n+ expected_type = torch.int8\n+\n+ assert qkv_proj.weight.dtype is expected_type\n+ assert o_proj.weight.dtype is expected_type\n+ assert gate_up_proj.weight.dtype is expected_type\n+\n+ if qkv_proj.scheme.strategy == \"tensor\":\n+ # Make sure it is a channelwise buffer\n+ # After running process_weights_after_loading\n+ assert len(qkv_proj.weight_scale.shape) == 2\n+ assert qkv_proj.weight_scale.shape[0] == shape_0\n+ assert qkv_proj.weight_scale.shape[1] == 1\n+ assert qkv_proj.weight_scale.dtype is torch.float32\n+ assert qkv_proj.input_scale.dtype is torch.float32\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy([\"Hello my name is\"], max_tokens=20)\n assert output\n@@ -129,16 +134,20 @@ def test_compressed_tensors_no_enforce_eager(vllm_runner):\n def test_compressed_tensors_w8a8_dynamic_per_token(vllm_runner, model_args):\n model_path, strategy = model_args\n with vllm_runner(model_path, dtype=torch.float16) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n+\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8)\n+ assert not qkv_proj.scheme.is_static_input_scheme\n+ assert qkv_proj.scheme.strategy == strategy\n+ assert qkv_proj.weight.dtype is torch.int8\n \n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8)\n- assert not qkv_proj.scheme.is_static_input_scheme\n- assert qkv_proj.scheme.strategy == strategy\n- assert qkv_proj.weight.dtype is torch.int8\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy([\"Hello my name is\"], max_tokens=20)\n assert output\n@@ -152,19 +161,24 @@ def test_compressed_tensors_w8a8_dynamic_per_token(vllm_runner, model_args):\n def test_compressed_tensors_wNa16(vllm_runner, wNa16_args):\n model, strategy, group, pack_factor = wNa16_args\n with vllm_runner(model) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16)\n+ def check_model(model):\n+ layer = model.model.layers[0]\n \n- assert qkv_proj.scheme.strategy == strategy\n- assert qkv_proj.scheme.group_size == (-1 if group is None else group)\n+ qkv_proj = layer.self_attn.qkv_proj\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16)\n \n- assert qkv_proj.weight_packed.dtype is torch.int32\n- assert qkv_proj.weight_scale.dtype is torch.float16\n- assert qkv_proj.scheme.pack_factor == pack_factor\n+ assert qkv_proj.scheme.strategy == strategy\n+ assert qkv_proj.scheme.group_size == (-1\n+ if group is None else group)\n+\n+ assert qkv_proj.weight_packed.dtype is torch.int32\n+ assert qkv_proj.weight_scale.dtype is torch.float16\n+ assert qkv_proj.scheme.pack_factor == pack_factor\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n assert output\n@@ -173,14 +187,18 @@ def test_compressed_tensors_wNa16(vllm_runner, wNa16_args):\n def test_compressed_tensors_w4a16_marlin24(vllm_runner):\n model_path = \"nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t\"\n with vllm_runner(model_path) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n \n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(qkv_proj.scheme, CompressedTensorsW4A16Sparse24)\n- assert qkv_proj.weight_packed.dtype is torch.int32\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(qkv_proj.scheme, CompressedTensorsW4A16Sparse24)\n+ assert qkv_proj.weight_packed.dtype is torch.int32\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n assert output\n@@ -189,23 +207,27 @@ def test_compressed_tensors_w4a16_marlin24(vllm_runner):\n def test_compressed_tensors_fp8(vllm_runner):\n model_path = \"nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test\"\n with vllm_runner(model_path) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n+ def check_model(model):\n+ layer = model.model.layers[0]\n \n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(\n- qkv_proj.scheme,\n- (CompressedTensorsW8A8Fp8, CompressedTensorsW8A16Fp8))\n+ qkv_proj = layer.self_attn.qkv_proj\n \n- assert qkv_proj.input_scale.dtype is torch.float32\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(\n+ qkv_proj.scheme,\n+ (CompressedTensorsW8A8Fp8, CompressedTensorsW8A16Fp8))\n \n- if isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8):\n- assert len(qkv_proj.input_scale.shape) == 0\n- assert qkv_proj.weight.dtype is torch.float8_e4m3fn\n- assert qkv_proj.weight_scale.dtype is torch.float32\n- assert len(qkv_proj.weight_scale.shape) == 0\n+ assert qkv_proj.input_scale.dtype is torch.float32\n+\n+ if isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8):\n+ assert len(qkv_proj.input_scale.shape) == 0\n+ assert qkv_proj.weight.dtype is torch.float8_e4m3fn\n+ assert qkv_proj.weight_scale.dtype is torch.float32\n+ assert len(qkv_proj.weight_scale.shape) == 0\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n assert output\n@@ -248,12 +270,15 @@ def _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy):\n def test_compressed_tensors_2of4_quant_fp8(vllm_runner, args_2of4):\n model, weight_strategy, input_strategy = args_2of4\n with vllm_runner(model) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n- assert qkv_proj.scheme.weights_dtype == torch.float8_e4m3fn\n- _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy)\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n+ assert qkv_proj.scheme.weights_dtype == torch.float8_e4m3fn\n+ _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy)\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n print(output)\n@@ -273,12 +298,15 @@ def test_compressed_tensors_2of4_quant_fp8(vllm_runner, args_2of4):\n def test_compressed_tensors_2of4_quant_int8(vllm_runner, args_2of4):\n model, weight_strategy, input_strategy = args_2of4\n with vllm_runner(model) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n- assert qkv_proj.scheme.weights_dtype == torch.int8\n- _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy)\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n+ assert qkv_proj.scheme.weights_dtype == torch.int8\n+ _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy)\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n print(output)\n@@ -293,20 +321,24 @@ def test_compressed_tensors_2of4_quant_int8(vllm_runner, args_2of4):\n def test_compressed_tensors_2of4_sparse(vllm_runner, args_2of4):\n model = args_2of4\n with vllm_runner(model) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n-\n- qkv_proj = layer.self_attn.qkv_proj\n- assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)\n- assert isinstance(qkv_proj.scheme, CompressedTensors24)\n-\n- assert qkv_proj.scheme.weight_quant is None\n- assert qkv_proj.scheme.input_quant is None\n- assert not qkv_proj.scheme.quantized\n- assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map\n- sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map # noqa: E501\n- assert sparsity_map.get(\"Linear\").format == \"dense\"\n- assert sparsity_map.get(\"Linear\").sparsity_structure == \"2:4\"\n+\n+ def check_model(model):\n+ layer = model.model.layers[0]\n+\n+ qkv_proj = layer.self_attn.qkv_proj\n+ assert isinstance(qkv_proj.quant_method,\n+ CompressedTensorsLinearMethod)\n+ assert isinstance(qkv_proj.scheme, CompressedTensors24)\n+\n+ assert qkv_proj.scheme.weight_quant is None\n+ assert qkv_proj.scheme.input_quant is None\n+ assert not qkv_proj.scheme.quantized\n+ assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map\n+ sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map # noqa: E501\n+ assert sparsity_map.get(\"Linear\").format == \"dense\"\n+ assert sparsity_map.get(\"Linear\").sparsity_structure == \"2:4\"\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n print(output)\ndiff --git a/tests/quantization/test_fp8.py b/tests/quantization/test_fp8.py\nindex a0c1d7e24c50..4bff73474629 100644\n--- a/tests/quantization/test_fp8.py\n+++ b/tests/quantization/test_fp8.py\n@@ -49,13 +49,17 @@ def test_model_load_and_run(vllm_runner, model_id: str, force_marlin: bool,\n def test_kv_cache_model_load_and_run(vllm_runner, model_id: str):\n with vllm_runner(model_id, kv_cache_dtype=\"fp8\") as llm:\n \n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- attn = model.model.layers[0].self_attn.attn\n- assert isinstance(attn.quant_method, Fp8KVCacheMethod)\n- # NOTE: it is valid for scales to be 1.0 (default value), but we know\n- # these checkpoints have scales < 1.0\n- assert 0.0 < attn._k_scale < 1.0\n- assert 0.0 < attn._v_scale < 1.0\n+ def check_model(model):\n+ attn = model.model.layers[0].self_attn.attn\n+\n+ assert isinstance(attn.quant_method, Fp8KVCacheMethod)\n+\n+ # NOTE: it is valid for scales to be 1.0 (default value), but\n+ # we know these checkpoints have scales < 1.0\n+ assert 0.0 < attn._k_scale < 1.0\n+ assert 0.0 < attn._v_scale < 1.0\n+\n+ llm.apply_model(check_model)\n \n # note: this does not test accuracy, just that we can run through\n # see lm-eval tests for accuracy\n@@ -77,22 +81,24 @@ def test_load_fp16_model(vllm_runner, kv_cache_dtype: str, force_marlin: bool,\n quantization=\"fp8\",\n kv_cache_dtype=kv_cache_dtype) as llm:\n \n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- fc1 = model.model.decoder.layers[0].fc1\n- assert isinstance(fc1.quant_method, Fp8LinearMethod)\n- if kv_cache_dtype == \"fp8\":\n- attn = model.model.decoder.layers[0].self_attn.attn\n- assert isinstance(attn.quant_method, Fp8KVCacheMethod)\n- assert attn._k_scale == 1.0\n- assert attn._v_scale == 1.0\n-\n- if current_platform.has_device_capability(89) and not force_marlin:\n- # For GPUs with hardware support, we keep weights in fp8\n- assert fc1.weight.dtype == torch.float8_e4m3fn\n- else:\n- # For GPUs without hardware support, we pack the fp8 weights\n- # for weight-only quantization using Marlin kernels\n- assert fc1.weight.dtype == torch.int32\n+ def check_model(model):\n+ fc1 = model.model.decoder.layers[0].fc1\n+ assert isinstance(fc1.quant_method, Fp8LinearMethod)\n+ if kv_cache_dtype == \"fp8\":\n+ attn = model.model.decoder.layers[0].self_attn.attn\n+ assert isinstance(attn.quant_method, Fp8KVCacheMethod)\n+ assert attn._k_scale == 1.0\n+ assert attn._v_scale == 1.0\n+\n+ if current_platform.has_device_capability(89) and not force_marlin:\n+ # For GPUs with hardware support, we keep weights in fp8\n+ assert fc1.weight.dtype == torch.float8_e4m3fn\n+ else:\n+ # For GPUs without hardware support, we pack the fp8 weights\n+ # for weight-only quantization using Marlin kernels\n+ assert fc1.weight.dtype == torch.int32\n+\n+ llm.apply_model(check_model)\n \n \n @pytest.mark.skipif(not is_quant_method_supported(\"fp8\"),\ndiff --git a/tests/quantization/test_lm_head.py b/tests/quantization/test_lm_head.py\nindex ad526a406510..fa2d9645ea47 100644\n--- a/tests/quantization/test_lm_head.py\n+++ b/tests/quantization/test_lm_head.py\n@@ -28,20 +28,23 @@ def test_lm_head(\n model_lm_head_quant: Tuple[str, bool],\n ) -> None:\n model, lm_head_quantized = model_lm_head_quant\n- vllm_model = vllm_runner(model, dtype=torch.float16, max_model_len=2048)\n-\n- lm_head_layer = (vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model.lm_head)\n-\n- if lm_head_quantized:\n- assert isinstance(\n- lm_head_layer.linear_method,\n- (GPTQLinearMethod, GPTQMarlinLinearMethod, MarlinLinearMethod))\n- else:\n- assert isinstance(lm_head_layer.linear_method,\n- UnquantizedEmbeddingMethod)\n-\n- print(\n- vllm_model.generate_greedy(prompts=[\"Hello my name is\"],\n- max_tokens=10)[0][1])\n- del vllm_model\n+\n+ with vllm_runner(model, dtype=torch.float16,\n+ max_model_len=2048) as vllm_model:\n+\n+ def check_model(model):\n+ lm_head_layer = model.lm_head\n+\n+ if lm_head_quantized:\n+ assert isinstance(lm_head_layer.linear_method,\n+ (GPTQLinearMethod, GPTQMarlinLinearMethod,\n+ MarlinLinearMethod))\n+ else:\n+ assert isinstance(lm_head_layer.linear_method,\n+ UnquantizedEmbeddingMethod)\n+\n+ vllm_model.apply_model(check_model)\n+\n+ print(\n+ vllm_model.generate_greedy(prompts=[\"Hello my name is\"],\n+ max_tokens=10)[0][1])\ndiff --git a/tests/quantization/test_quark.py b/tests/quantization/test_quark.py\nindex 27493a682b74..11382ad708fa 100644\n--- a/tests/quantization/test_quark.py\n+++ b/tests/quantization/test_quark.py\n@@ -12,19 +12,22 @@\n def test_quark_fp8(vllm_runner):\n model_path = \"amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test\"\n with vllm_runner(model_path) as llm:\n- model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n- layer = model.model.layers[0]\n \n- qkv_proj = layer.self_attn.qkv_proj\n+ def check_model(model):\n+ layer = model.model.layers[0]\n \n- assert isinstance(qkv_proj.quant_method, QuarkLinearMethod)\n- assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8)\n+ qkv_proj = layer.self_attn.qkv_proj\n \n- if isinstance(qkv_proj.scheme, QuarkW8A8Fp8):\n- assert len(qkv_proj.input_scale.shape) == 0\n- assert qkv_proj.weight.dtype is torch.float8_e4m3fn\n- #assert qkv_proj.weight.dtype is torch.float8_e4m3fnuz\n- assert len(qkv_proj.weight_scale.shape) == 0\n+ assert isinstance(qkv_proj.quant_method, QuarkLinearMethod)\n+ assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8)\n+\n+ if isinstance(qkv_proj.scheme, QuarkW8A8Fp8):\n+ assert len(qkv_proj.input_scale.shape) == 0\n+ assert qkv_proj.weight.dtype is torch.float8_e4m3fn\n+ #assert qkv_proj.weight.dtype is torch.float8_e4m3fnuz\n+ assert len(qkv_proj.weight_scale.shape) == 0\n+\n+ llm.apply_model(check_model)\n \n output = llm.generate_greedy(\"Hello my name is\", max_tokens=20)\n assert output\ndiff --git a/tests/tensorizer_loader/test_tensorizer.py b/tests/tensorizer_loader/test_tensorizer.py\nindex bf409d2d97aa..6e7eec1c6ab3 100644\n--- a/tests/tensorizer_loader/test_tensorizer.py\n+++ b/tests/tensorizer_loader/test_tensorizer.py\n@@ -3,6 +3,7 @@\n import os\n import pathlib\n import subprocess\n+from functools import partial\n from unittest.mock import MagicMock, patch\n \n import openai\n@@ -24,7 +25,6 @@\n # yapf: enable\n from vllm.utils import PlaceholderModule, import_from_path\n \n-from ..conftest import VllmRunner\n from ..utils import VLLM_PATH, RemoteOpenAIServer\n from .conftest import retry_until_skip\n \n@@ -58,16 +58,6 @@ def is_curl_installed():\n return False\n \n \n-def get_torch_model(vllm_runner: VllmRunner):\n- return vllm_runner \\\n- .model \\\n- .llm_engine \\\n- .model_executor \\\n- .driver_worker \\\n- .model_runner \\\n- .model\n-\n-\n def write_keyfile(keyfile_path: str):\n encryption_params = EncryptionParams.random()\n pathlib.Path(keyfile_path).parent.mkdir(parents=True, exist_ok=True)\n@@ -121,8 +111,10 @@ def test_deserialized_encrypted_vllm_model_has_same_outputs(\n \n config_for_serializing = TensorizerConfig(tensorizer_uri=model_path,\n encryption_keyfile=key_path)\n- serialize_vllm_model(get_torch_model(vllm_model),\n- config_for_serializing)\n+\n+ vllm_model.apply_model(\n+ partial(serialize_vllm_model,\n+ tensorizer_config=config_for_serializing))\n \n config_for_deserializing = TensorizerConfig(tensorizer_uri=model_path,\n encryption_keyfile=key_path)\n@@ -175,8 +167,10 @@ def test_vllm_model_can_load_with_lora(vllm_runner, tmp_path):\n with vllm_runner(model_ref, ) as vllm_model:\n model_path = tmp_path / (model_ref + \".tensors\")\n \n- serialize_vllm_model(get_torch_model(vllm_model),\n- TensorizerConfig(tensorizer_uri=model_path))\n+ vllm_model.apply_model(\n+ partial(\n+ serialize_vllm_model,\n+ tensorizer_config=TensorizerConfig(tensorizer_uri=model_path)))\n \n with vllm_runner(\n model_ref,\n@@ -215,8 +209,10 @@ def test_openai_apiserver_with_tensorizer(vllm_runner, tmp_path):\n with vllm_runner(model_ref, ) as vllm_model:\n model_path = tmp_path / (model_ref + \".tensors\")\n \n- serialize_vllm_model(get_torch_model(vllm_model),\n- TensorizerConfig(tensorizer_uri=model_path))\n+ vllm_model.apply_model(\n+ partial(\n+ serialize_vllm_model,\n+ tensorizer_config=TensorizerConfig(tensorizer_uri=model_path)))\n \n model_loader_extra_config = {\n \"tensorizer_uri\": str(model_path),\n@@ -337,7 +333,9 @@ def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path):\n \n with vllm_runner(model_ref) as vllm_model:\n outputs = vllm_model.generate(prompts, sampling_params)\n- serialize_vllm_model(get_torch_model(vllm_model), config)\n+\n+ vllm_model.apply_model(\n+ partial(serialize_vllm_model, tensorizer_config=config))\n \n assert is_vllm_tensorized(config)\n \ndiff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py\nindex 88c21f9a6d31..5d19ce03d5b5 100644\n--- a/vllm/engine/llm_engine.py\n+++ b/vllm/engine/llm_engine.py\n@@ -5,10 +5,10 @@\n from contextlib import contextmanager\n from dataclasses import dataclass\n from functools import partial\n-from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,\n- Iterable, List, Mapping, NamedTuple, Optional)\n+from typing import (TYPE_CHECKING, Callable, ClassVar, Deque, Dict, Iterable,\n+ List, Mapping, NamedTuple, Optional)\n from typing import Sequence as GenericSequence\n-from typing import Set, Tuple, Type, Union, cast, overload\n+from typing import Set, Type, Union, cast, overload\n \n import torch\n from typing_extensions import TypeVar, deprecated\n@@ -1816,17 +1816,6 @@ def start_profile(self) -> None:\n def stop_profile(self) -> None:\n self.model_executor.stop_profile()\n \n- def collective_rpc(self,\n- method: Union[str, Callable],\n- timeout: Optional[float] = None,\n- args: Tuple = (),\n- kwargs: Optional[Dict] = None) -> List[Any]:\n- \"\"\"\n- See LLM.collective_rpc for more details.\n- \"\"\"\n- return self.model_executor.collective_rpc(method, timeout, args,\n- kwargs)\n-\n def check_health(self) -> None:\n if self.tokenizer:\n self.tokenizer.check_health()\ndiff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py\nindex 0cfe6be9ac76..ce046f86a48e 100644\n--- a/vllm/entrypoints/llm.py\n+++ b/vllm/entrypoints/llm.py\n@@ -2,9 +2,10 @@\n import warnings\n from contextlib import contextmanager\n from typing import (Any, Callable, ClassVar, Dict, List, Optional, Sequence,\n- Tuple, Type, Union, cast, overload)\n+ Tuple, Type, TypeVar, Union, cast, overload)\n \n import cloudpickle\n+import torch.nn as nn\n from tqdm import tqdm\n from typing_extensions import deprecated\n \n@@ -42,6 +43,8 @@\n \n logger = init_logger(__name__)\n \n+_R = TypeVar(\"_R\")\n+\n \n class LLM:\n \"\"\"An LLM for generating texts from given prompts and sampling parameters.\n@@ -464,25 +467,42 @@ def generate(\n return self.engine_class.validate_outputs(outputs, RequestOutput)\n \n def collective_rpc(self,\n- method: Union[str, Callable],\n+ method: Union[str, Callable[..., _R]],\n timeout: Optional[float] = None,\n args: Tuple = (),\n- kwargs: Optional[Dict] = None) -> List[Any]:\n+ kwargs: Optional[Dict[str, Any]] = None) -> List[_R]:\n+ \"\"\"\n+ Execute an RPC call on all workers.\n+\n+ Args:\n+ method: Name of the worker method to execute, or a callable that\n+ is serialized and sent to all workers to execute.\n+\n+ If the method is a callable, it should accept an additional\n+ `self` argument, in addition to the arguments passed in `args`\n+ and `kwargs`. The `self` argument will be the worker object.\n+ timeout: Maximum time in seconds to wait for execution. Raises a\n+ :exc:`TimeoutError` on timeout. `None` means wait indefinitely.\n+ args: Positional arguments to pass to the worker method.\n+ kwargs: Keyword arguments to pass to the worker method.\n+\n+ Returns:\n+ List of results from each worker.\n+ \n+ Note:\n+ It is recommended to use this API to only pass control messages,\n+ and set up data-plane communication to pass data.\n+ \"\"\"\n+ executor = self.llm_engine.model_executor\n+ return executor.collective_rpc(method, timeout, args, kwargs)\n+\n+ def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:\n \"\"\"\n- Run a method on all workers, with homogeneous arguments.\n- The main extension point for the LLM entrypoint.\n- Users can provide custom worker class through `worker_cls`\n- argument, and implement new methods in the worker class.\n- Then, users can call the new methods through this API.\n- It is recommended to use this API to only pass control messages,\n- and set up data-plane communication to pass data.\n- The method can also be a callable, which will be serialized\n- and sent to all workers to execute.\n- If the method is a callable, it should accept an additional\n- `self` argument, in addition to the arguments passed in `args`\n- and `kwargs`. The `self` argument will be the worker object.\n+ Run a function directly on the model inside each worker,\n+ returning the result for each of them.\n \"\"\"\n- return self.llm_engine.collective_rpc(method, timeout, args, kwargs)\n+ executor = self.llm_engine.model_executor\n+ return executor.apply_model(func)\n \n def beam_search(\n self,\ndiff --git a/vllm/executor/executor_base.py b/vllm/executor/executor_base.py\nindex e5952b388c54..93909c1c1104 100644\n--- a/vllm/executor/executor_base.py\n+++ b/vllm/executor/executor_base.py\n@@ -1,7 +1,9 @@\n import asyncio\n from abc import ABC, abstractmethod\n from typing import (Any, Awaitable, Callable, Dict, List, Optional, Set, Tuple,\n- Union)\n+ TypeVar, Union)\n+\n+import torch.nn as nn\n \n from vllm.config import VllmConfig\n from vllm.logger import init_logger\n@@ -11,9 +13,12 @@\n from vllm.prompt_adapter.request import PromptAdapterRequest\n from vllm.sequence import ExecuteModelRequest, PoolerOutput\n from vllm.utils import make_async\n+from vllm.worker.worker_base import WorkerBase\n \n logger = init_logger(__name__)\n \n+_R = TypeVar(\"_R\")\n+\n \n class ExecutorBase(ABC):\n \"\"\"Base class for all executors.\n@@ -44,22 +49,34 @@ def __init__(\n \n @abstractmethod\n def _init_executor(self) -> None:\n- pass\n+ raise NotImplementedError\n \n @abstractmethod\n def collective_rpc(self,\n- method: Union[str, Callable],\n+ method: Union[str, Callable[..., _R]],\n timeout: Optional[float] = None,\n args: Tuple = (),\n- kwargs: Optional[Dict] = None) -> List[Any]:\n+ kwargs: Optional[Dict[str, Any]] = None) -> List[_R]:\n \"\"\"\n- The main interface of the executor to run a method on all workers,\n- with homogeneous arguments.\n- If the args are heterogeneous, then we can pack them into a list,\n- and unpack them in the method of every worker, because every worker\n- knows their own rank.\n+ Execute an RPC call on all workers.\n+\n+ Args:\n+ method: Name of the worker method to execute, or a callable that\n+ is serialized and sent to all workers to execute.\n+\n+ If the method is a callable, it should accept an additional\n+ `self` argument, in addition to the arguments passed in `args`\n+ and `kwargs`. The `self` argument will be the worker object.\n+ timeout: Maximum time in seconds to wait for execution. Raises a\n+ :exc:`TimeoutError` on timeout. `None` means wait indefinitely.\n+ args: Positional arguments to pass to the worker method.\n+ kwargs: Keyword arguments to pass to the worker method.\n+ \n+ Note:\n+ It is recommended to use this API to only pass control messages,\n+ and set up data-plane communication to pass data.\n \"\"\"\n- pass\n+ raise NotImplementedError\n \n def determine_num_available_blocks(self) -> Tuple[int, int]:\n \"\"\"Determine the number of available blocks for the GPU KV cache and\n@@ -97,6 +114,17 @@ def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:\n self.collective_rpc(\"initialize_cache\",\n args=(num_gpu_blocks, num_cpu_blocks))\n \n+ def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:\n+ \"\"\"\n+ Run a function directly on the model inside each worker,\n+ returning the result for each of them.\n+ \"\"\"\n+\n+ def rpc_func(worker: WorkerBase) -> _R:\n+ return func(worker.get_model())\n+\n+ return self.collective_rpc(rpc_func)\n+\n def execute_model(\n self, execute_model_req: ExecuteModelRequest\n ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]:\ndiff --git a/vllm/executor/mp_distributed_executor.py b/vllm/executor/mp_distributed_executor.py\nindex a80b0ee8b312..78c86321d861 100644\n--- a/vllm/executor/mp_distributed_executor.py\n+++ b/vllm/executor/mp_distributed_executor.py\n@@ -148,7 +148,7 @@ def _run_workers(\n async_run_tensor_parallel_workers_only: bool = False,\n max_concurrent_workers: Optional[int] = None,\n **kwargs,\n- ) -> Any:\n+ ) -> List[Any]:\n \"\"\"Runs the given method on all workers.\n \n Args:\ndiff --git a/vllm/model_executor/model_loader/tensorizer.py b/vllm/model_executor/model_loader/tensorizer.py\nindex fbd4937112e1..5b4757072353 100644\n--- a/vllm/model_executor/model_loader/tensorizer.py\n+++ b/vllm/model_executor/model_loader/tensorizer.py\n@@ -459,16 +459,7 @@ def tensorize_vllm_model(engine_args: EngineArgs,\n stream.write(encryption_params.key)\n \n engine = LLMEngine.from_engine_args(engine_args)\n- if tensorizer_config._is_sharded:\n- # if the engine is a distributed engine (for tensor parallel) then each\n- # worker shard needs to serialize its part of the model.\n- engine.model_executor._run_workers(\n- \"save_tensorized_model\",\n- tensorizer_config=tensorizer_config,\n- )\n- else:\n- # with a single worker, we can get to the underlying model directly\n- serialize_vllm_model(\n- engine.model_executor.driver_worker.model_runner.model,\n- tensorizer_config,\n- )\n+ engine.model_executor.collective_rpc(\n+ \"save_tensorized_model\",\n+ kwargs=dict(tensorizer_config=tensorizer_config),\n+ )\ndiff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py\nindex 540d118d65ec..0d66ede3d907 100644\n--- a/vllm/spec_decode/spec_decode_worker.py\n+++ b/vllm/spec_decode/spec_decode_worker.py\n@@ -4,6 +4,7 @@\n from typing import Any, Dict, List, Optional, Set, Tuple, Type\n \n import torch\n+import torch.nn as nn\n \n from vllm.config import ParallelConfig, SpeculativeConfig, VllmConfig\n from vllm.distributed.communication_op import broadcast_tensor_dict\n@@ -403,6 +404,9 @@ def initialize_cache(self, num_gpu_blocks: int,\n self.proposer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks,\n num_cpu_blocks=num_cpu_blocks)\n \n+ def get_model(self) -> nn.Module:\n+ return self.scorer_worker.get_model()\n+\n @torch.inference_mode()\n def execute_model(\n self,\ndiff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py\nindex 93026029ad13..f6cf35da0106 100644\n--- a/vllm/v1/executor/multiproc_executor.py\n+++ b/vllm/v1/executor/multiproc_executor.py\n@@ -94,22 +94,12 @@ def collective_rpc(self,\n timeout: Optional[float] = None,\n args: Tuple = (),\n kwargs: Optional[Dict] = None) -> List[Any]:\n- \"\"\"\n- Execute an RPC call on workers.\n- \n- Args:\n- method: Name of the worker method to execute\n- timeout: Maximum time in seconds to wait for execution. Rases a\n- TimeoutError on timeout. None means wait indefinitely.\n- args: Positional arguments to pass to the worker method\n- kwargs: Keyword arguments to pass to the worker method\n-\n- Returns:\n- List of results from each worker\n- \"\"\"\n start_time = time.monotonic()\n kwargs = kwargs or {}\n \n+ # NOTE: If the args are heterogeneous, then we pack them into a list,\n+ # and unpack them in the method of every worker, because every worker\n+ # knows their own rank.\n try:\n if isinstance(method, str):\n send_method = method\ndiff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py\nindex 87a1cd7f9e62..2350074c23a5 100644\n--- a/vllm/v1/worker/gpu_model_runner.py\n+++ b/vllm/v1/worker/gpu_model_runner.py\n@@ -689,6 +689,9 @@ def _gather_encoder_outputs(\n encoder_outputs.append(encoder_output[start_idx:end_idx])\n return encoder_outputs\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n @torch.inference_mode()\n def execute_model(\n self,\ndiff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py\nindex 4fb4197f1822..0929e64d58f1 100644\n--- a/vllm/v1/worker/gpu_worker.py\n+++ b/vllm/v1/worker/gpu_worker.py\n@@ -5,6 +5,7 @@\n \n import torch\n import torch.distributed\n+import torch.nn as nn\n \n import vllm.envs as envs\n from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig\n@@ -176,6 +177,9 @@ def compile_or_warm_up_model(self) -> None:\n # the model initialization and profiling.\n set_random_seed(self.model_config.seed)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model_runner.get_model()\n+\n @torch.inference_mode()\n def execute_model(\n self,\ndiff --git a/vllm/worker/cpu_model_runner.py b/vllm/worker/cpu_model_runner.py\nindex 303d9a15e9c3..abbf6450ab7f 100644\n--- a/vllm/worker/cpu_model_runner.py\n+++ b/vllm/worker/cpu_model_runner.py\n@@ -509,6 +509,9 @@ def load_model(self) -> None:\n )\n self.model = self.lora_manager.create_lora_manager(self.model)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def _prepare_model_input_tensors(\n self,\n seq_group_metadata_list: List[SequenceGroupMetadata],\ndiff --git a/vllm/worker/hpu_model_runner.py b/vllm/worker/hpu_model_runner.py\nindex 260ffaf27f9a..4c8f69e44939 100644\n--- a/vllm/worker/hpu_model_runner.py\n+++ b/vllm/worker/hpu_model_runner.py\n@@ -21,6 +21,7 @@\n import habana_frameworks.torch as htorch\n import habana_frameworks.torch.internal.bridge_config as bc\n import torch\n+import torch.nn as nn\n from vllm_hpu_extension.ops import LoraMask as LoraMask\n from vllm_hpu_extension.profiler import (HabanaHighLevelProfiler,\n HabanaMemoryProfiler, format_bytes)\n@@ -676,6 +677,9 @@ def load_model(self) -> None:\n msg = f\"Loading model weights took in total {m.get_summary_string()}\"\n logger.info(msg)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def _use_graphs(self, batch_size, seq_len, is_prompt):\n if self.enforce_eager:\n return False\ndiff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py\nindex ae8b7f97c827..cb2ff0c934da 100644\n--- a/vllm/worker/model_runner.py\n+++ b/vllm/worker/model_runner.py\n@@ -1176,6 +1176,9 @@ def load_model(self) -> None:\n fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,\n backend=backend)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def save_sharded_state(\n self,\n path: str,\ndiff --git a/vllm/worker/model_runner_base.py b/vllm/worker/model_runner_base.py\nindex c7abad7e0258..443bd3346210 100644\n--- a/vllm/worker/model_runner_base.py\n+++ b/vllm/worker/model_runner_base.py\n@@ -7,6 +7,7 @@\n Optional, Type, TypeVar)\n \n import torch\n+import torch.nn as nn\n from torch import is_tensor\n \n from vllm.config import VllmConfig\n@@ -264,6 +265,10 @@ def prepare_model_input(\n \"\"\"\n raise NotImplementedError\n \n+ @abstractmethod\n+ def get_model(self) -> nn.Module:\n+ raise NotImplementedError\n+\n def execute_model(\n self,\n model_input: T,\ndiff --git a/vllm/worker/neuron_model_runner.py b/vllm/worker/neuron_model_runner.py\nindex a35f5467e1a1..596c26eac28b 100644\n--- a/vllm/worker/neuron_model_runner.py\n+++ b/vllm/worker/neuron_model_runner.py\n@@ -113,6 +113,9 @@ def load_model(self) -> None:\n raise NotImplementedError(\n \"Supports only Transformer-NeuronX based models.\")\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def _prepare_prompt(\n self,\n seq_group_metadata_list: List[SequenceGroupMetadata],\ndiff --git a/vllm/worker/openvino_model_runner.py b/vllm/worker/openvino_model_runner.py\nindex a38b5a4e6e8d..9d0a759ca2f2 100644\n--- a/vllm/worker/openvino_model_runner.py\n+++ b/vllm/worker/openvino_model_runner.py\n@@ -84,6 +84,9 @@ def load_model(self) -> None:\n kv_cache_dtype=self.kv_cache_dtype,\n ov_core=self.ov_core)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def _prepare_model_input(\n self,\n seq_group_metadata_list: List[SequenceGroupMetadata],\ndiff --git a/vllm/worker/openvino_worker.py b/vllm/worker/openvino_worker.py\nindex 50a155d22c66..f5b46cde3969 100644\n--- a/vllm/worker/openvino_worker.py\n+++ b/vllm/worker/openvino_worker.py\n@@ -4,6 +4,7 @@\n import openvino as ov\n import torch\n import torch.distributed\n+import torch.nn as nn\n \n import vllm.envs as envs\n from vllm.attention import get_attn_backend\n@@ -362,6 +363,9 @@ def cache_copy(\n ) -> None:\n self.cache_engine.copy(blocks_to_copy) # type: ignore\n \n+ def get_model(self) -> nn.Module:\n+ return self.model_runner.get_model()\n+\n @torch.inference_mode()\n def execute_model(\n self,\ndiff --git a/vllm/worker/tpu_model_runner.py b/vllm/worker/tpu_model_runner.py\nindex 52c577bccab9..6a123d355a3d 100644\n--- a/vllm/worker/tpu_model_runner.py\n+++ b/vllm/worker/tpu_model_runner.py\n@@ -158,6 +158,9 @@ def load_model(self) -> None:\n fullgraph=True,\n dynamic=False)\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n def _dummy_run(\n self,\n batch_size: int,\ndiff --git a/vllm/worker/worker_base.py b/vllm/worker/worker_base.py\nindex fb9919f7a7b6..eead25a91218 100644\n--- a/vllm/worker/worker_base.py\n+++ b/vllm/worker/worker_base.py\n@@ -6,6 +6,7 @@\n \n import cloudpickle\n import torch\n+import torch.nn as nn\n \n from vllm.config import ObservabilityConfig, VllmConfig\n from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group\n@@ -90,6 +91,11 @@ def start_worker_execution_loop(self) -> None:\n if output is None:\n return None\n \n+ @abstractmethod\n+ def get_model(self) -> nn.Module:\n+ raise NotImplementedError\n+\n+ @abstractmethod\n def execute_model(\n self,\n execute_model_req: Optional[ExecuteModelRequest] = None\n@@ -363,6 +369,9 @@ def prepare_input(\n else:\n return self._get_worker_input_from_broadcast()\n \n+ def get_model(self) -> nn.Module:\n+ return self.model_runner.get_model()\n+\n def execute_model(\n self,\n execute_model_req: Optional[ExecuteModelRequest] = None,\ndiff --git a/vllm/worker/xpu_model_runner.py b/vllm/worker/xpu_model_runner.py\nindex 82b8f22a5af3..25a2fea1e8ea 100644\n--- a/vllm/worker/xpu_model_runner.py\n+++ b/vllm/worker/xpu_model_runner.py\n@@ -416,6 +416,9 @@ def load_model(self) -> None:\n logger.info(\"Loading model weights took %.4f GB\",\n self.model_memory_usage / float(2**30))\n \n+ def get_model(self) -> nn.Module:\n+ return self.model\n+\n @property\n def vocab_size(self) -> int:\n return self.model_config.get_vocab_size()\n" }
[ { "diff_hunk": "@@ -35,8 +35,7 @@ def test_models(\n vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)\n # This test is for verifying whether the model's extra_repr\n # can be printed correctly.\n- print(vllm_model.model.llm_engine.model_executor.driver_worker.\n- model_runner.model)\n+ vllm_model.apply_model(print)", "line": null, "original_line": 38, "original_start_line": null, "path": "tests/models/decoder_only/language/test_jamba.py", "start_line": null, "text": "@user1:\nwriting it out?\r\n\r\n```python\r\ndef print_model(model):\r\n print(model)\r\n\r\nvllm_model.apply_model(print_model)\r\n```\n\n@author:\nI think it's straightforward enough to inline this, but okay." } ]
2e966f0a7f3ace56ca967e4c34f66a7cf3f8ecfb
diff --git a/tests/conftest.py b/tests/conftest.py index 95af4ac1eb17..279c1bf9a377 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -244,6 +244,7 @@ def video_assets() -> _VideoAssets: _T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict) +_R = TypeVar("_R") class HfRunner: @@ -930,6 +931,10 @@ def score( req_outputs = self.model.score(text_1, text_2) return [req_output.outputs.score for req_output in req_outputs] + def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]: + executor = self.model.llm_engine.model_executor + return executor.apply_model(func) + def __enter__(self): return self diff --git a/tests/engine/test_custom_executor.py b/tests/engine/test_custom_executor.py index fdfcd4f4c9d5..0e33f3662da8 100644 --- a/tests/engine/test_custom_executor.py +++ b/tests/engine/test_custom_executor.py @@ -51,7 +51,9 @@ def test_custom_executor(model, tmp_path): assert not os.path.exists(".marker") engine_args = EngineArgs( - model=model, distributed_executor_backend=CustomUniExecutor) + model=model, + distributed_executor_backend=CustomUniExecutor, + ) engine = LLMEngine.from_engine_args(engine_args) sampling_params = SamplingParams(max_tokens=1) diff --git a/tests/model_executor/test_model_load_with_params.py b/tests/model_executor/test_model_load_with_params.py index 0609fd96825e..9c1f784c1c93 100644 --- a/tests/model_executor/test_model_load_with_params.py +++ b/tests/model_executor/test_model_load_with_params.py @@ -25,13 +25,12 @@ def test_model_loading_with_params(vllm_runner): with vllm_runner(model_name=MODEL_NAME, revision=REVISION, dtype="float16", - max_model_len=MAX_MODEL_LEN) as model: - output = model.encode("Write a short story about a robot that" - " dreams for the first time.\n") + max_model_len=MAX_MODEL_LEN) as vllm_model: + output = vllm_model.encode("Write a short story about a robot that" + " dreams for the first time.\n") - model_config = model.model.llm_engine.model_config - - model_tokenizer = model.model.llm_engine.tokenizer + model_config = vllm_model.model.llm_engine.model_config + model_tokenizer = vllm_model.model.llm_engine.tokenizer # asserts on the bert model config file assert model_config.encoder_config["max_seq_length"] == 512 @@ -46,11 +45,13 @@ def test_model_loading_with_params(vllm_runner): assert model_tokenizer.tokenizer_config["do_lower_case"] assert model_tokenizer.tokenizer.model_max_length == 512 - model = model.model.llm_engine.model_executor\ - .driver_worker.model_runner.model - assert isinstance(model, BertEmbeddingModel) - assert model._pooler.pooling_type == PoolingType.CLS - assert model._pooler.normalize + def check_model(model): + assert isinstance(model, BertEmbeddingModel) + assert model._pooler.pooling_type == PoolingType.CLS + assert model._pooler.normalize + + vllm_model.apply_model(check_model) + # assert output assert output @@ -64,13 +65,12 @@ def test_roberta_model_loading_with_params(vllm_runner): with vllm_runner(model_name=MODEL_NAME_ROBERTA, revision=REVISION_ROBERTA, dtype="float16", - max_model_len=MAX_MODEL_LEN) as model: - output = model.encode("Write a short story about a robot that" - " dreams for the first time.\n") + max_model_len=MAX_MODEL_LEN) as vllm_model: + output = vllm_model.encode("Write a short story about a robot that" + " dreams for the first time.\n") - model_config = model.model.llm_engine.model_config - - model_tokenizer = model.model.llm_engine.tokenizer + model_config = vllm_model.model.llm_engine.model_config + model_tokenizer = vllm_model.model.llm_engine.tokenizer # asserts on the bert model config file assert model_config.encoder_config["max_seq_length"] == 512 @@ -84,11 +84,12 @@ def test_roberta_model_loading_with_params(vllm_runner): assert model_tokenizer.tokenizer_id == "intfloat/multilingual-e5-large" assert not model_tokenizer.tokenizer_config["do_lower_case"] - model = model.model.llm_engine.model_executor\ - .driver_worker.model_runner.model - assert isinstance(model, RobertaEmbeddingModel) - assert model._pooler.pooling_type == PoolingType.MEAN - assert model._pooler.normalize + def check_model(model): + assert isinstance(model, RobertaEmbeddingModel) + assert model._pooler.pooling_type == PoolingType.MEAN + assert model._pooler.normalize + + vllm_model.apply_model(check_model) # assert output assert output @@ -103,17 +104,18 @@ def test_facebook_roberta_model_loading_with_params(vllm_runner): model_name = "FacebookAI/roberta-base" with vllm_runner(model_name=model_name, dtype="float16", - max_model_len=MAX_MODEL_LEN) as model: - output = model.encode("Write a short story about a robot that" - " dreams for the first time.\n") + max_model_len=MAX_MODEL_LEN) as vllm_model: + output = vllm_model.encode("Write a short story about a robot that" + " dreams for the first time.\n") - model_tokenizer = model.model.llm_engine.tokenizer + model_tokenizer = vllm_model.model.llm_engine.tokenizer assert model_tokenizer.tokenizer_id == model_name - model = model.model.llm_engine.model_executor\ - .driver_worker.model_runner.model - assert not hasattr(model, "lm_head") - assert isinstance(model, RobertaEmbeddingModel) - assert isinstance(model._pooler, CLSPool) + def check_model(model): + assert isinstance(model, RobertaEmbeddingModel) + assert not hasattr(model, "lm_head") + assert isinstance(model._pooler, CLSPool) + + vllm_model.apply_model(check_model) assert output diff --git a/tests/models/decoder_only/language/test_jamba.py b/tests/models/decoder_only/language/test_jamba.py index 057b04349e8b..2e06b10fbb82 100644 --- a/tests/models/decoder_only/language/test_jamba.py +++ b/tests/models/decoder_only/language/test_jamba.py @@ -33,10 +33,13 @@ def test_models( with vllm_runner(model, dtype=dtype) as vllm_model: vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) + # This test is for verifying whether the model's extra_repr # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) + def print_model(model): + print(model) + + vllm_model.apply_model(print_model) for i in range(len(example_prompts)): hf_output_ids, hf_output_str = hf_outputs[i] diff --git a/tests/models/decoder_only/language/test_mamba.py b/tests/models/decoder_only/language/test_mamba.py index 06739e8f0225..1ad4f5aae8f5 100644 --- a/tests/models/decoder_only/language/test_mamba.py +++ b/tests/models/decoder_only/language/test_mamba.py @@ -51,10 +51,13 @@ def test_models( with vllm_runner(model, dtype=dtype) as vllm_model: vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) + # This test is for verifying whether the model's extra_repr # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) + def print_model(model): + print(model) + + vllm_model.apply_model(print_model) for i in range(len(example_prompts)): hf_output_ids, hf_output_str = hf_outputs[i] diff --git a/tests/models/decoder_only/language/test_models.py b/tests/models/decoder_only/language/test_models.py index 4e110366a09f..c7efa4edbbc0 100644 --- a/tests/models/decoder_only/language/test_models.py +++ b/tests/models/decoder_only/language/test_models.py @@ -73,10 +73,13 @@ def test_models( with vllm_runner(model, dtype=dtype) as vllm_model: vllm_outputs = vllm_model.generate_greedy_logprobs( example_prompts, max_tokens, num_logprobs) + # This test is for verifying whether the model's extra_repr # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) + def print_model(model): + print(model) + + vllm_model.apply_model(print_model) check_logprobs_close( outputs_0_lst=hf_outputs, diff --git a/tests/models/decoder_only/vision_language/test_qwen2_vl.py b/tests/models/decoder_only/vision_language/test_qwen2_vl.py index 2fd22f0cc88e..5a485f3d8174 100644 --- a/tests/models/decoder_only/vision_language/test_qwen2_vl.py +++ b/tests/models/decoder_only/vision_language/test_qwen2_vl.py @@ -5,7 +5,6 @@ import torch from PIL import Image -from vllm.entrypoints.llm import LLM from vllm.multimodal.image import rescale_image_size from vllm.multimodal.video import rescale_video_size, sample_frames_from_video @@ -69,7 +68,7 @@ class Qwen2VLPromptVideoEmbeddingInput(TypedDict): def batch_make_image_embeddings( image_batches: List[Union[Image.Image, List[Image.Image]]], processor, - llm: LLM) -> List[Qwen2VLPromptImageEmbeddingInput]: + llm: VllmRunner) -> List[Qwen2VLPromptImageEmbeddingInput]: """batched image embeddings for Qwen2-VL This will infer all images' embeddings in a single batch, @@ -106,16 +105,18 @@ def batch_make_image_embeddings( image_grid_thw = preprocess_result["image_grid_thw"] # pixel values to embeddings & grid_thws - with torch.no_grad(): - visual = llm.llm_engine.model_executor.driver_worker. \ - model_runner.model.visual + def get_image_embeds(model): + with torch.no_grad(): + visual = model.visual - pixel_values_on_device = pixel_values.to(visual.device, - dtype=visual.dtype) - image_grid_thw_on_device = image_grid_thw.to(visual.device, - dtype=torch.int64) - image_embeds = visual(pixel_values_on_device, - grid_thw=image_grid_thw_on_device) + pixel_values_on_device = pixel_values.to(visual.device, + dtype=visual.dtype) + image_grid_thw_on_device = image_grid_thw.to(visual.device, + dtype=torch.int64) + return visual(pixel_values_on_device, + grid_thw=image_grid_thw_on_device) + + image_embeds = torch.concat(llm.apply_model(get_image_embeds)) # split into original batches result: List[Qwen2VLPromptImageEmbeddingInput] = [] @@ -150,7 +151,7 @@ def batch_make_image_embeddings( def batch_make_video_embeddings( video_batches: PromptVideoInput, processor, - llm: LLM) -> List[Qwen2VLPromptVideoEmbeddingInput]: + llm: VllmRunner) -> List[Qwen2VLPromptVideoEmbeddingInput]: """batched video embeddings for Qwen2-VL A NDArray represents a single video's all frames. @@ -187,16 +188,18 @@ def batch_make_video_embeddings( video_grid_thw = preprocess_result["video_grid_thw"] # pixel values to embeddings & grid_thws - with torch.no_grad(): - visual = llm.llm_engine.model_executor.driver_worker.\ - model_runner.model.visual + def get_image_embeds(model): + with torch.no_grad(): + visual = model.visual + + pixel_values_on_device = pixel_values.to(visual.device, + dtype=visual.dtype) + video_grid_thw_on_device = video_grid_thw.to(visual.device, + dtype=torch.int64) + return visual(pixel_values_on_device, + grid_thw=video_grid_thw_on_device) - pixel_values_on_device = pixel_values.to(visual.device, - dtype=visual.dtype) - video_grid_thw_on_device = video_grid_thw.to(visual.device, - dtype=torch.int64) - video_embeds = visual(pixel_values_on_device, - grid_thw=video_grid_thw_on_device) + video_embeds = torch.concat(llm.apply_model(get_image_embeds)) # split into original batches result: List[Qwen2VLPromptVideoEmbeddingInput] = [] @@ -278,9 +281,9 @@ def run_embedding_input_test( max_tokens, num_logprobs=num_logprobs, images=batch_make_image_embeddings( - images, processor, vllm_model.model) if images else None, + images, processor, vllm_model) if images else None, videos=batch_make_video_embeddings( - videos, processor, vllm_model.model) if videos else None) + videos, processor, vllm_model) if videos else None) for prompts, images, videos in inputs ] diff --git a/tests/models/embedding/language/test_cls_models.py b/tests/models/embedding/language/test_cls_models.py index 6673a9fc22f6..0cbe4afe96c0 100644 --- a/tests/models/embedding/language/test_cls_models.py +++ b/tests/models/embedding/language/test_cls_models.py @@ -24,10 +24,13 @@ def test_classification_models( ) -> None: with vllm_runner(model, dtype=dtype) as vllm_model: vllm_outputs = vllm_model.classify(example_prompts) + # This test is for verifying whether the model's extra_repr # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) + def print_model(model): + print(model) + + vllm_model.apply_model(print_model) with hf_runner(model, dtype=dtype, diff --git a/tests/models/embedding/language/test_embedding.py b/tests/models/embedding/language/test_embedding.py index 04ab4dd7371a..5976c9e7add0 100644 --- a/tests/models/embedding/language/test_embedding.py +++ b/tests/models/embedding/language/test_embedding.py @@ -61,10 +61,13 @@ def test_models( max_model_len=None, **vllm_extra_kwargs) as vllm_model: vllm_outputs = vllm_model.encode(example_prompts) + # This test is for verifying whether the model's extra_repr # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) + def print_model(model): + print(model) + + vllm_model.apply_model(print_model) check_embeddings_close( embeddings_0_lst=hf_outputs, diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index 92436889ecff..0cd86cef0a47 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -30,50 +30,55 @@ def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args): model_path, strategy, quant_type, shape_0, is_symmetric = model_args with vllm_runner(model_path, enforce_eager=True) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - - qkv_proj = layer.self_attn.qkv_proj - o_proj = layer.self_attn.o_proj - gate_up_proj = layer.mlp.gate_up_proj - down_proj = layer.mlp.down_proj - - # assert zp for symmetric and asymmetric cases - def zp_valid(zp: Optional[torch.Tensor]): - if is_symmetric: - return zp is None - - return zp is not None and zp.dtype is torch.int32 - - assert zp_valid(qkv_proj.input_zero_point) - assert zp_valid(o_proj.input_zero_point) - assert zp_valid(gate_up_proj.input_zero_point) - assert zp_valid(down_proj.input_zero_point) - - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(o_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(gate_up_proj.quant_method, - CompressedTensorsLinearMethod) - assert isinstance(down_proj.quant_method, - CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) - - assert qkv_proj.scheme.strategy == strategy - assert qkv_proj.scheme.is_static_input_scheme - expected_type = torch.int8 - - assert qkv_proj.weight.dtype is expected_type - assert o_proj.weight.dtype is expected_type - assert gate_up_proj.weight.dtype is expected_type - - if qkv_proj.scheme.strategy == "tensor": - # Make sure it is a channelwise buffer - # After running process_weights_after_loading - assert len(qkv_proj.weight_scale.shape) == 2 - assert qkv_proj.weight_scale.shape[0] == shape_0 - assert qkv_proj.weight_scale.shape[1] == 1 - assert qkv_proj.weight_scale.dtype is torch.float32 - assert qkv_proj.input_scale.dtype is torch.float32 + + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + o_proj = layer.self_attn.o_proj + gate_up_proj = layer.mlp.gate_up_proj + down_proj = layer.mlp.down_proj + + # assert zp for symmetric and asymmetric cases + def zp_valid(zp: Optional[torch.Tensor]): + if is_symmetric: + return zp is None + + return zp is not None and zp.dtype is torch.int32 + + assert zp_valid(qkv_proj.input_zero_point) + assert zp_valid(o_proj.input_zero_point) + assert zp_valid(gate_up_proj.input_zero_point) + assert zp_valid(down_proj.input_zero_point) + + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(o_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(gate_up_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(down_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) + + assert qkv_proj.scheme.strategy == strategy + assert qkv_proj.scheme.is_static_input_scheme + expected_type = torch.int8 + + assert qkv_proj.weight.dtype is expected_type + assert o_proj.weight.dtype is expected_type + assert gate_up_proj.weight.dtype is expected_type + + if qkv_proj.scheme.strategy == "tensor": + # Make sure it is a channelwise buffer + # After running process_weights_after_loading + assert len(qkv_proj.weight_scale.shape) == 2 + assert qkv_proj.weight_scale.shape[0] == shape_0 + assert qkv_proj.weight_scale.shape[1] == 1 + assert qkv_proj.weight_scale.dtype is torch.float32 + assert qkv_proj.input_scale.dtype is torch.float32 + + llm.apply_model(check_model) output = llm.generate_greedy(["Hello my name is"], max_tokens=20) assert output @@ -129,16 +134,20 @@ def test_compressed_tensors_no_enforce_eager(vllm_runner): def test_compressed_tensors_w8a8_dynamic_per_token(vllm_runner, model_args): model_path, strategy = model_args with vllm_runner(model_path, dtype=torch.float16) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) + assert not qkv_proj.scheme.is_static_input_scheme + assert qkv_proj.scheme.strategy == strategy + assert qkv_proj.weight.dtype is torch.int8 - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) - assert not qkv_proj.scheme.is_static_input_scheme - assert qkv_proj.scheme.strategy == strategy - assert qkv_proj.weight.dtype is torch.int8 + llm.apply_model(check_model) output = llm.generate_greedy(["Hello my name is"], max_tokens=20) assert output @@ -152,19 +161,24 @@ def test_compressed_tensors_w8a8_dynamic_per_token(vllm_runner, model_args): def test_compressed_tensors_wNa16(vllm_runner, wNa16_args): model, strategy, group, pack_factor = wNa16_args with vllm_runner(model) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16) + def check_model(model): + layer = model.model.layers[0] - assert qkv_proj.scheme.strategy == strategy - assert qkv_proj.scheme.group_size == (-1 if group is None else group) + qkv_proj = layer.self_attn.qkv_proj + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16) - assert qkv_proj.weight_packed.dtype is torch.int32 - assert qkv_proj.weight_scale.dtype is torch.float16 - assert qkv_proj.scheme.pack_factor == pack_factor + assert qkv_proj.scheme.strategy == strategy + assert qkv_proj.scheme.group_size == (-1 + if group is None else group) + + assert qkv_proj.weight_packed.dtype is torch.int32 + assert qkv_proj.weight_scale.dtype is torch.float16 + assert qkv_proj.scheme.pack_factor == pack_factor + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) assert output @@ -173,14 +187,18 @@ def test_compressed_tensors_wNa16(vllm_runner, wNa16_args): def test_compressed_tensors_w4a16_marlin24(vllm_runner): model_path = "nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t" with vllm_runner(model_path) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW4A16Sparse24) - assert qkv_proj.weight_packed.dtype is torch.int32 + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsW4A16Sparse24) + assert qkv_proj.weight_packed.dtype is torch.int32 + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) assert output @@ -189,23 +207,27 @@ def test_compressed_tensors_w4a16_marlin24(vllm_runner): def test_compressed_tensors_fp8(vllm_runner): model_path = "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" with vllm_runner(model_path) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj + def check_model(model): + layer = model.model.layers[0] - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance( - qkv_proj.scheme, - (CompressedTensorsW8A8Fp8, CompressedTensorsW8A16Fp8)) + qkv_proj = layer.self_attn.qkv_proj - assert qkv_proj.input_scale.dtype is torch.float32 + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance( + qkv_proj.scheme, + (CompressedTensorsW8A8Fp8, CompressedTensorsW8A16Fp8)) - if isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8): - assert len(qkv_proj.input_scale.shape) == 0 - assert qkv_proj.weight.dtype is torch.float8_e4m3fn - assert qkv_proj.weight_scale.dtype is torch.float32 - assert len(qkv_proj.weight_scale.shape) == 0 + assert qkv_proj.input_scale.dtype is torch.float32 + + if isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8): + assert len(qkv_proj.input_scale.shape) == 0 + assert qkv_proj.weight.dtype is torch.float8_e4m3fn + assert qkv_proj.weight_scale.dtype is torch.float32 + assert len(qkv_proj.weight_scale.shape) == 0 + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) assert output @@ -248,12 +270,15 @@ def _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy): def test_compressed_tensors_2of4_quant_fp8(vllm_runner, args_2of4): model, weight_strategy, input_strategy = args_2of4 with vllm_runner(model) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj - assert qkv_proj.scheme.weights_dtype == torch.float8_e4m3fn - _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy) + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + assert qkv_proj.scheme.weights_dtype == torch.float8_e4m3fn + _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy) + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) print(output) @@ -273,12 +298,15 @@ def test_compressed_tensors_2of4_quant_fp8(vllm_runner, args_2of4): def test_compressed_tensors_2of4_quant_int8(vllm_runner, args_2of4): model, weight_strategy, input_strategy = args_2of4 with vllm_runner(model) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj - assert qkv_proj.scheme.weights_dtype == torch.int8 - _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy) + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + assert qkv_proj.scheme.weights_dtype == torch.int8 + _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy) + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) print(output) @@ -293,20 +321,24 @@ def test_compressed_tensors_2of4_quant_int8(vllm_runner, args_2of4): def test_compressed_tensors_2of4_sparse(vllm_runner, args_2of4): model = args_2of4 with vllm_runner(model) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - - qkv_proj = layer.self_attn.qkv_proj - assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensors24) - - assert qkv_proj.scheme.weight_quant is None - assert qkv_proj.scheme.input_quant is None - assert not qkv_proj.scheme.quantized - assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map - sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map # noqa: E501 - assert sparsity_map.get("Linear").format == "dense" - assert sparsity_map.get("Linear").sparsity_structure == "2:4" + + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensors24) + + assert qkv_proj.scheme.weight_quant is None + assert qkv_proj.scheme.input_quant is None + assert not qkv_proj.scheme.quantized + assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map + sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map # noqa: E501 + assert sparsity_map.get("Linear").format == "dense" + assert sparsity_map.get("Linear").sparsity_structure == "2:4" + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) print(output) diff --git a/tests/quantization/test_fp8.py b/tests/quantization/test_fp8.py index a0c1d7e24c50..4bff73474629 100644 --- a/tests/quantization/test_fp8.py +++ b/tests/quantization/test_fp8.py @@ -49,13 +49,17 @@ def test_model_load_and_run(vllm_runner, model_id: str, force_marlin: bool, def test_kv_cache_model_load_and_run(vllm_runner, model_id: str): with vllm_runner(model_id, kv_cache_dtype="fp8") as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - attn = model.model.layers[0].self_attn.attn - assert isinstance(attn.quant_method, Fp8KVCacheMethod) - # NOTE: it is valid for scales to be 1.0 (default value), but we know - # these checkpoints have scales < 1.0 - assert 0.0 < attn._k_scale < 1.0 - assert 0.0 < attn._v_scale < 1.0 + def check_model(model): + attn = model.model.layers[0].self_attn.attn + + assert isinstance(attn.quant_method, Fp8KVCacheMethod) + + # NOTE: it is valid for scales to be 1.0 (default value), but + # we know these checkpoints have scales < 1.0 + assert 0.0 < attn._k_scale < 1.0 + assert 0.0 < attn._v_scale < 1.0 + + llm.apply_model(check_model) # note: this does not test accuracy, just that we can run through # see lm-eval tests for accuracy @@ -77,22 +81,24 @@ def test_load_fp16_model(vllm_runner, kv_cache_dtype: str, force_marlin: bool, quantization="fp8", kv_cache_dtype=kv_cache_dtype) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - fc1 = model.model.decoder.layers[0].fc1 - assert isinstance(fc1.quant_method, Fp8LinearMethod) - if kv_cache_dtype == "fp8": - attn = model.model.decoder.layers[0].self_attn.attn - assert isinstance(attn.quant_method, Fp8KVCacheMethod) - assert attn._k_scale == 1.0 - assert attn._v_scale == 1.0 - - if current_platform.has_device_capability(89) and not force_marlin: - # For GPUs with hardware support, we keep weights in fp8 - assert fc1.weight.dtype == torch.float8_e4m3fn - else: - # For GPUs without hardware support, we pack the fp8 weights - # for weight-only quantization using Marlin kernels - assert fc1.weight.dtype == torch.int32 + def check_model(model): + fc1 = model.model.decoder.layers[0].fc1 + assert isinstance(fc1.quant_method, Fp8LinearMethod) + if kv_cache_dtype == "fp8": + attn = model.model.decoder.layers[0].self_attn.attn + assert isinstance(attn.quant_method, Fp8KVCacheMethod) + assert attn._k_scale == 1.0 + assert attn._v_scale == 1.0 + + if current_platform.has_device_capability(89) and not force_marlin: + # For GPUs with hardware support, we keep weights in fp8 + assert fc1.weight.dtype == torch.float8_e4m3fn + else: + # For GPUs without hardware support, we pack the fp8 weights + # for weight-only quantization using Marlin kernels + assert fc1.weight.dtype == torch.int32 + + llm.apply_model(check_model) @pytest.mark.skipif(not is_quant_method_supported("fp8"), diff --git a/tests/quantization/test_lm_head.py b/tests/quantization/test_lm_head.py index ad526a406510..fa2d9645ea47 100644 --- a/tests/quantization/test_lm_head.py +++ b/tests/quantization/test_lm_head.py @@ -28,20 +28,23 @@ def test_lm_head( model_lm_head_quant: Tuple[str, bool], ) -> None: model, lm_head_quantized = model_lm_head_quant - vllm_model = vllm_runner(model, dtype=torch.float16, max_model_len=2048) - - lm_head_layer = (vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model.lm_head) - - if lm_head_quantized: - assert isinstance( - lm_head_layer.linear_method, - (GPTQLinearMethod, GPTQMarlinLinearMethod, MarlinLinearMethod)) - else: - assert isinstance(lm_head_layer.linear_method, - UnquantizedEmbeddingMethod) - - print( - vllm_model.generate_greedy(prompts=["Hello my name is"], - max_tokens=10)[0][1]) - del vllm_model + + with vllm_runner(model, dtype=torch.float16, + max_model_len=2048) as vllm_model: + + def check_model(model): + lm_head_layer = model.lm_head + + if lm_head_quantized: + assert isinstance(lm_head_layer.linear_method, + (GPTQLinearMethod, GPTQMarlinLinearMethod, + MarlinLinearMethod)) + else: + assert isinstance(lm_head_layer.linear_method, + UnquantizedEmbeddingMethod) + + vllm_model.apply_model(check_model) + + print( + vllm_model.generate_greedy(prompts=["Hello my name is"], + max_tokens=10)[0][1]) diff --git a/tests/quantization/test_quark.py b/tests/quantization/test_quark.py index 27493a682b74..11382ad708fa 100644 --- a/tests/quantization/test_quark.py +++ b/tests/quantization/test_quark.py @@ -12,19 +12,22 @@ def test_quark_fp8(vllm_runner): model_path = "amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test" with vllm_runner(model_path) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - layer = model.model.layers[0] - qkv_proj = layer.self_attn.qkv_proj + def check_model(model): + layer = model.model.layers[0] - assert isinstance(qkv_proj.quant_method, QuarkLinearMethod) - assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8) + qkv_proj = layer.self_attn.qkv_proj - if isinstance(qkv_proj.scheme, QuarkW8A8Fp8): - assert len(qkv_proj.input_scale.shape) == 0 - assert qkv_proj.weight.dtype is torch.float8_e4m3fn - #assert qkv_proj.weight.dtype is torch.float8_e4m3fnuz - assert len(qkv_proj.weight_scale.shape) == 0 + assert isinstance(qkv_proj.quant_method, QuarkLinearMethod) + assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8) + + if isinstance(qkv_proj.scheme, QuarkW8A8Fp8): + assert len(qkv_proj.input_scale.shape) == 0 + assert qkv_proj.weight.dtype is torch.float8_e4m3fn + #assert qkv_proj.weight.dtype is torch.float8_e4m3fnuz + assert len(qkv_proj.weight_scale.shape) == 0 + + llm.apply_model(check_model) output = llm.generate_greedy("Hello my name is", max_tokens=20) assert output diff --git a/tests/tensorizer_loader/test_tensorizer.py b/tests/tensorizer_loader/test_tensorizer.py index bf409d2d97aa..6e7eec1c6ab3 100644 --- a/tests/tensorizer_loader/test_tensorizer.py +++ b/tests/tensorizer_loader/test_tensorizer.py @@ -3,6 +3,7 @@ import os import pathlib import subprocess +from functools import partial from unittest.mock import MagicMock, patch import openai @@ -24,7 +25,6 @@ # yapf: enable from vllm.utils import PlaceholderModule, import_from_path -from ..conftest import VllmRunner from ..utils import VLLM_PATH, RemoteOpenAIServer from .conftest import retry_until_skip @@ -58,16 +58,6 @@ def is_curl_installed(): return False -def get_torch_model(vllm_runner: VllmRunner): - return vllm_runner \ - .model \ - .llm_engine \ - .model_executor \ - .driver_worker \ - .model_runner \ - .model - - def write_keyfile(keyfile_path: str): encryption_params = EncryptionParams.random() pathlib.Path(keyfile_path).parent.mkdir(parents=True, exist_ok=True) @@ -121,8 +111,10 @@ def test_deserialized_encrypted_vllm_model_has_same_outputs( config_for_serializing = TensorizerConfig(tensorizer_uri=model_path, encryption_keyfile=key_path) - serialize_vllm_model(get_torch_model(vllm_model), - config_for_serializing) + + vllm_model.apply_model( + partial(serialize_vllm_model, + tensorizer_config=config_for_serializing)) config_for_deserializing = TensorizerConfig(tensorizer_uri=model_path, encryption_keyfile=key_path) @@ -175,8 +167,10 @@ def test_vllm_model_can_load_with_lora(vllm_runner, tmp_path): with vllm_runner(model_ref, ) as vllm_model: model_path = tmp_path / (model_ref + ".tensors") - serialize_vllm_model(get_torch_model(vllm_model), - TensorizerConfig(tensorizer_uri=model_path)) + vllm_model.apply_model( + partial( + serialize_vllm_model, + tensorizer_config=TensorizerConfig(tensorizer_uri=model_path))) with vllm_runner( model_ref, @@ -215,8 +209,10 @@ def test_openai_apiserver_with_tensorizer(vllm_runner, tmp_path): with vllm_runner(model_ref, ) as vllm_model: model_path = tmp_path / (model_ref + ".tensors") - serialize_vllm_model(get_torch_model(vllm_model), - TensorizerConfig(tensorizer_uri=model_path)) + vllm_model.apply_model( + partial( + serialize_vllm_model, + tensorizer_config=TensorizerConfig(tensorizer_uri=model_path))) model_loader_extra_config = { "tensorizer_uri": str(model_path), @@ -337,7 +333,9 @@ def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path): with vllm_runner(model_ref) as vllm_model: outputs = vllm_model.generate(prompts, sampling_params) - serialize_vllm_model(get_torch_model(vllm_model), config) + + vllm_model.apply_model( + partial(serialize_vllm_model, tensorizer_config=config)) assert is_vllm_tensorized(config) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 88c21f9a6d31..5d19ce03d5b5 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -5,10 +5,10 @@ from contextlib import contextmanager from dataclasses import dataclass from functools import partial -from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict, - Iterable, List, Mapping, NamedTuple, Optional) +from typing import (TYPE_CHECKING, Callable, ClassVar, Deque, Dict, Iterable, + List, Mapping, NamedTuple, Optional) from typing import Sequence as GenericSequence -from typing import Set, Tuple, Type, Union, cast, overload +from typing import Set, Type, Union, cast, overload import torch from typing_extensions import TypeVar, deprecated @@ -1816,17 +1816,6 @@ def start_profile(self) -> None: def stop_profile(self) -> None: self.model_executor.stop_profile() - def collective_rpc(self, - method: Union[str, Callable], - timeout: Optional[float] = None, - args: Tuple = (), - kwargs: Optional[Dict] = None) -> List[Any]: - """ - See LLM.collective_rpc for more details. - """ - return self.model_executor.collective_rpc(method, timeout, args, - kwargs) - def check_health(self) -> None: if self.tokenizer: self.tokenizer.check_health() diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 0cfe6be9ac76..27386daa4bbc 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -5,8 +5,9 @@ Tuple, Type, Union, cast, overload) import cloudpickle +import torch.nn as nn from tqdm import tqdm -from typing_extensions import deprecated +from typing_extensions import TypeVar, deprecated from vllm import envs from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput, @@ -42,6 +43,8 @@ logger = init_logger(__name__) +_R = TypeVar("_R", default=Any) + class LLM: """An LLM for generating texts from given prompts and sampling parameters. @@ -464,25 +467,42 @@ def generate( return self.engine_class.validate_outputs(outputs, RequestOutput) def collective_rpc(self, - method: Union[str, Callable], + method: Union[str, Callable[..., _R]], timeout: Optional[float] = None, args: Tuple = (), - kwargs: Optional[Dict] = None) -> List[Any]: + kwargs: Optional[Dict[str, Any]] = None) -> List[_R]: + """ + Execute an RPC call on all workers. + + Args: + method: Name of the worker method to execute, or a callable that + is serialized and sent to all workers to execute. + + If the method is a callable, it should accept an additional + `self` argument, in addition to the arguments passed in `args` + and `kwargs`. The `self` argument will be the worker object. + timeout: Maximum time in seconds to wait for execution. Raises a + :exc:`TimeoutError` on timeout. `None` means wait indefinitely. + args: Positional arguments to pass to the worker method. + kwargs: Keyword arguments to pass to the worker method. + + Returns: + A list containing the results from each worker. + + Note: + It is recommended to use this API to only pass control messages, + and set up data-plane communication to pass data. + """ + executor = self.llm_engine.model_executor + return executor.collective_rpc(method, timeout, args, kwargs) + + def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]: """ - Run a method on all workers, with homogeneous arguments. - The main extension point for the LLM entrypoint. - Users can provide custom worker class through `worker_cls` - argument, and implement new methods in the worker class. - Then, users can call the new methods through this API. - It is recommended to use this API to only pass control messages, - and set up data-plane communication to pass data. - The method can also be a callable, which will be serialized - and sent to all workers to execute. - If the method is a callable, it should accept an additional - `self` argument, in addition to the arguments passed in `args` - and `kwargs`. The `self` argument will be the worker object. + Run a function directly on the model inside each worker, + returning the result for each of them. """ - return self.llm_engine.collective_rpc(method, timeout, args, kwargs) + executor = self.llm_engine.model_executor + return executor.apply_model(func) def beam_search( self, diff --git a/vllm/executor/executor_base.py b/vllm/executor/executor_base.py index e5952b388c54..859e105f15d9 100644 --- a/vllm/executor/executor_base.py +++ b/vllm/executor/executor_base.py @@ -3,6 +3,9 @@ from typing import (Any, Awaitable, Callable, Dict, List, Optional, Set, Tuple, Union) +import torch.nn as nn +from typing_extensions import TypeVar + from vllm.config import VllmConfig from vllm.logger import init_logger from vllm.lora.request import LoRARequest @@ -11,9 +14,12 @@ from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sequence import ExecuteModelRequest, PoolerOutput from vllm.utils import make_async +from vllm.worker.worker_base import WorkerBase logger = init_logger(__name__) +_R = TypeVar("_R", default=Any) + class ExecutorBase(ABC): """Base class for all executors. @@ -44,22 +50,37 @@ def __init__( @abstractmethod def _init_executor(self) -> None: - pass + raise NotImplementedError @abstractmethod def collective_rpc(self, - method: Union[str, Callable], + method: Union[str, Callable[..., _R]], timeout: Optional[float] = None, args: Tuple = (), - kwargs: Optional[Dict] = None) -> List[Any]: + kwargs: Optional[Dict[str, Any]] = None) -> List[_R]: """ - The main interface of the executor to run a method on all workers, - with homogeneous arguments. - If the args are heterogeneous, then we can pack them into a list, - and unpack them in the method of every worker, because every worker - knows their own rank. + Execute an RPC call on all workers. + + Args: + method: Name of the worker method to execute, or a callable that + is serialized and sent to all workers to execute. + + If the method is a callable, it should accept an additional + `self` argument, in addition to the arguments passed in `args` + and `kwargs`. The `self` argument will be the worker object. + timeout: Maximum time in seconds to wait for execution. Raises a + :exc:`TimeoutError` on timeout. `None` means wait indefinitely. + args: Positional arguments to pass to the worker method. + kwargs: Keyword arguments to pass to the worker method. + + Returns: + A list containing the results from each worker. + + Note: + It is recommended to use this API to only pass control messages, + and set up data-plane communication to pass data. """ - pass + raise NotImplementedError def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available blocks for the GPU KV cache and @@ -97,6 +118,17 @@ def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None: self.collective_rpc("initialize_cache", args=(num_gpu_blocks, num_cpu_blocks)) + def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]: + """ + Run a function directly on the model inside each worker, + returning the result for each of them. + """ + + def rpc_func(worker: WorkerBase) -> _R: + return func(worker.get_model()) + + return self.collective_rpc(rpc_func) + def execute_model( self, execute_model_req: ExecuteModelRequest ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]: diff --git a/vllm/executor/mp_distributed_executor.py b/vllm/executor/mp_distributed_executor.py index a80b0ee8b312..78c86321d861 100644 --- a/vllm/executor/mp_distributed_executor.py +++ b/vllm/executor/mp_distributed_executor.py @@ -148,7 +148,7 @@ def _run_workers( async_run_tensor_parallel_workers_only: bool = False, max_concurrent_workers: Optional[int] = None, **kwargs, - ) -> Any: + ) -> List[Any]: """Runs the given method on all workers. Args: diff --git a/vllm/model_executor/model_loader/tensorizer.py b/vllm/model_executor/model_loader/tensorizer.py index fbd4937112e1..5b4757072353 100644 --- a/vllm/model_executor/model_loader/tensorizer.py +++ b/vllm/model_executor/model_loader/tensorizer.py @@ -459,16 +459,7 @@ def tensorize_vllm_model(engine_args: EngineArgs, stream.write(encryption_params.key) engine = LLMEngine.from_engine_args(engine_args) - if tensorizer_config._is_sharded: - # if the engine is a distributed engine (for tensor parallel) then each - # worker shard needs to serialize its part of the model. - engine.model_executor._run_workers( - "save_tensorized_model", - tensorizer_config=tensorizer_config, - ) - else: - # with a single worker, we can get to the underlying model directly - serialize_vllm_model( - engine.model_executor.driver_worker.model_runner.model, - tensorizer_config, - ) + engine.model_executor.collective_rpc( + "save_tensorized_model", + kwargs=dict(tensorizer_config=tensorizer_config), + ) diff --git a/vllm/spec_decode/ngram_worker.py b/vllm/spec_decode/ngram_worker.py index bb6b99135580..e906b1789cde 100644 --- a/vllm/spec_decode/ngram_worker.py +++ b/vllm/spec_decode/ngram_worker.py @@ -2,6 +2,7 @@ from typing import List, Optional, Set, Tuple import torch +import torch.nn as nn from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest @@ -10,6 +11,10 @@ from vllm.spec_decode.top1_proposer import Top1Proposer +class _DummyModel(nn.Module): + pass + + class NGramWorker(NonLLMProposerWorkerBase): """NGramWorker provides a light drafter without need for model. @@ -36,7 +41,6 @@ def set_ngram_window_size(self, ngram_prompt_lookup_min: int, def init_device(self): self.device = torch.device(f"{self.device_type}:{self.local_rank}") - self.load_model = lambda *args, **kwargs: None # Current NGramWorker only supports Top1Proposer self._proposer = Top1Proposer( @@ -45,6 +49,12 @@ def init_device(self): vocab_size=self.vocab_size, ) + def load_model(self) -> None: + pass # Dummy + + def get_model(self) -> nn.Module: + return _DummyModel() + def sampler_output( self, execute_model_req: ExecuteModelRequest, diff --git a/vllm/spec_decode/smaller_tp_proposer_worker.py b/vllm/spec_decode/smaller_tp_proposer_worker.py index 8896b7dbc6b8..c6ff5e52f938 100644 --- a/vllm/spec_decode/smaller_tp_proposer_worker.py +++ b/vllm/spec_decode/smaller_tp_proposer_worker.py @@ -1,6 +1,7 @@ from typing import List, Optional, Set, Tuple import torch +import torch.nn as nn from vllm.distributed.parallel_state import (get_tp_group, init_model_parallel_group, @@ -15,6 +16,10 @@ logger = init_logger(__name__) +class _DummyModel(nn.Module): + pass + + class SmallerTpProposerWorker(ProposerWorkerBase): """Class which allows a speculative draft model to run with smaller tensor parallel degree than target model. @@ -139,6 +144,13 @@ def get_spec_proposals( return self._worker.get_spec_proposals( execute_model_req, seq_ids_with_bonus_token_in_last_step) + def get_model(self) -> nn.Module: + if self._is_dummy: + return _DummyModel() + + with self._patch_tensor_parallel_group(): + return self._worker.get_model() + def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None diff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py index 540d118d65ec..0d66ede3d907 100644 --- a/vllm/spec_decode/spec_decode_worker.py +++ b/vllm/spec_decode/spec_decode_worker.py @@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional, Set, Tuple, Type import torch +import torch.nn as nn from vllm.config import ParallelConfig, SpeculativeConfig, VllmConfig from vllm.distributed.communication_op import broadcast_tensor_dict @@ -403,6 +404,9 @@ def initialize_cache(self, num_gpu_blocks: int, self.proposer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) + def get_model(self) -> nn.Module: + return self.scorer_worker.get_model() + @torch.inference_mode() def execute_model( self, diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py index 93026029ad13..f6cf35da0106 100644 --- a/vllm/v1/executor/multiproc_executor.py +++ b/vllm/v1/executor/multiproc_executor.py @@ -94,22 +94,12 @@ def collective_rpc(self, timeout: Optional[float] = None, args: Tuple = (), kwargs: Optional[Dict] = None) -> List[Any]: - """ - Execute an RPC call on workers. - - Args: - method: Name of the worker method to execute - timeout: Maximum time in seconds to wait for execution. Rases a - TimeoutError on timeout. None means wait indefinitely. - args: Positional arguments to pass to the worker method - kwargs: Keyword arguments to pass to the worker method - - Returns: - List of results from each worker - """ start_time = time.monotonic() kwargs = kwargs or {} + # NOTE: If the args are heterogeneous, then we pack them into a list, + # and unpack them in the method of every worker, because every worker + # knows their own rank. try: if isinstance(method, str): send_method = method diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 87a1cd7f9e62..2350074c23a5 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -689,6 +689,9 @@ def _gather_encoder_outputs( encoder_outputs.append(encoder_output[start_idx:end_idx]) return encoder_outputs + def get_model(self) -> nn.Module: + return self.model + @torch.inference_mode() def execute_model( self, diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index 4fb4197f1822..0929e64d58f1 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -5,6 +5,7 @@ import torch import torch.distributed +import torch.nn as nn import vllm.envs as envs from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig @@ -176,6 +177,9 @@ def compile_or_warm_up_model(self) -> None: # the model initialization and profiling. set_random_seed(self.model_config.seed) + def get_model(self) -> nn.Module: + return self.model_runner.get_model() + @torch.inference_mode() def execute_model( self, diff --git a/vllm/worker/cpu_model_runner.py b/vllm/worker/cpu_model_runner.py index 303d9a15e9c3..abbf6450ab7f 100644 --- a/vllm/worker/cpu_model_runner.py +++ b/vllm/worker/cpu_model_runner.py @@ -509,6 +509,9 @@ def load_model(self) -> None: ) self.model = self.lora_manager.create_lora_manager(self.model) + def get_model(self) -> nn.Module: + return self.model + def _prepare_model_input_tensors( self, seq_group_metadata_list: List[SequenceGroupMetadata], diff --git a/vllm/worker/hpu_model_runner.py b/vllm/worker/hpu_model_runner.py index 260ffaf27f9a..4c8f69e44939 100644 --- a/vllm/worker/hpu_model_runner.py +++ b/vllm/worker/hpu_model_runner.py @@ -21,6 +21,7 @@ import habana_frameworks.torch as htorch import habana_frameworks.torch.internal.bridge_config as bc import torch +import torch.nn as nn from vllm_hpu_extension.ops import LoraMask as LoraMask from vllm_hpu_extension.profiler import (HabanaHighLevelProfiler, HabanaMemoryProfiler, format_bytes) @@ -676,6 +677,9 @@ def load_model(self) -> None: msg = f"Loading model weights took in total {m.get_summary_string()}" logger.info(msg) + def get_model(self) -> nn.Module: + return self.model + def _use_graphs(self, batch_size, seq_len, is_prompt): if self.enforce_eager: return False diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index ae8b7f97c827..cb2ff0c934da 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -1176,6 +1176,9 @@ def load_model(self) -> None: fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, backend=backend) + def get_model(self) -> nn.Module: + return self.model + def save_sharded_state( self, path: str, diff --git a/vllm/worker/model_runner_base.py b/vllm/worker/model_runner_base.py index c7abad7e0258..acfd6d0b03f6 100644 --- a/vllm/worker/model_runner_base.py +++ b/vllm/worker/model_runner_base.py @@ -7,6 +7,7 @@ Optional, Type, TypeVar) import torch +import torch.nn as nn from torch import is_tensor from vllm.config import VllmConfig @@ -264,6 +265,10 @@ def prepare_model_input( """ raise NotImplementedError + @abstractmethod + def get_model(self) -> nn.Module: + raise NotImplementedError + def execute_model( self, model_input: T, @@ -297,9 +302,9 @@ class ModelRunnerWrapperBase: def __init__( self, - moderl_runner: ModelRunnerBase, + model_runner: ModelRunnerBase, ) -> None: - self.model_runner: ModelRunnerBase = moderl_runner + self.model_runner: ModelRunnerBase = model_runner def __getattr__(self, attr): return getattr(self.model_runner, attr) diff --git a/vllm/worker/neuron_model_runner.py b/vllm/worker/neuron_model_runner.py index a35f5467e1a1..596c26eac28b 100644 --- a/vllm/worker/neuron_model_runner.py +++ b/vllm/worker/neuron_model_runner.py @@ -113,6 +113,9 @@ def load_model(self) -> None: raise NotImplementedError( "Supports only Transformer-NeuronX based models.") + def get_model(self) -> nn.Module: + return self.model + def _prepare_prompt( self, seq_group_metadata_list: List[SequenceGroupMetadata], diff --git a/vllm/worker/openvino_model_runner.py b/vllm/worker/openvino_model_runner.py index a38b5a4e6e8d..9d0a759ca2f2 100644 --- a/vllm/worker/openvino_model_runner.py +++ b/vllm/worker/openvino_model_runner.py @@ -84,6 +84,9 @@ def load_model(self) -> None: kv_cache_dtype=self.kv_cache_dtype, ov_core=self.ov_core) + def get_model(self) -> nn.Module: + return self.model + def _prepare_model_input( self, seq_group_metadata_list: List[SequenceGroupMetadata], diff --git a/vllm/worker/openvino_worker.py b/vllm/worker/openvino_worker.py index 50a155d22c66..f5b46cde3969 100644 --- a/vllm/worker/openvino_worker.py +++ b/vllm/worker/openvino_worker.py @@ -4,6 +4,7 @@ import openvino as ov import torch import torch.distributed +import torch.nn as nn import vllm.envs as envs from vllm.attention import get_attn_backend @@ -362,6 +363,9 @@ def cache_copy( ) -> None: self.cache_engine.copy(blocks_to_copy) # type: ignore + def get_model(self) -> nn.Module: + return self.model_runner.get_model() + @torch.inference_mode() def execute_model( self, diff --git a/vllm/worker/tpu_model_runner.py b/vllm/worker/tpu_model_runner.py index 52c577bccab9..f5c7bc955a67 100644 --- a/vllm/worker/tpu_model_runner.py +++ b/vllm/worker/tpu_model_runner.py @@ -158,6 +158,9 @@ def load_model(self) -> None: fullgraph=True, dynamic=False) + def get_model(self) -> nn.Module: + return self.model.model + def _dummy_run( self, batch_size: int, diff --git a/vllm/worker/worker_base.py b/vllm/worker/worker_base.py index fb9919f7a7b6..1104eceef72a 100644 --- a/vllm/worker/worker_base.py +++ b/vllm/worker/worker_base.py @@ -6,6 +6,7 @@ import cloudpickle import torch +import torch.nn as nn from vllm.config import ObservabilityConfig, VllmConfig from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group @@ -90,6 +91,11 @@ def start_worker_execution_loop(self) -> None: if output is None: return None + @abstractmethod + def get_model(self) -> nn.Module: + raise NotImplementedError + + @abstractmethod def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None @@ -147,6 +153,9 @@ def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: self.worker.initialize_cache(num_gpu_blocks, num_cpu_blocks) + def get_model(self) -> nn.Module: + return self.worker.get_model() + def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None @@ -363,6 +372,9 @@ def prepare_input( else: return self._get_worker_input_from_broadcast() + def get_model(self) -> nn.Module: + return self.model_runner.get_model() + def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None, diff --git a/vllm/worker/xpu_model_runner.py b/vllm/worker/xpu_model_runner.py index 82b8f22a5af3..25a2fea1e8ea 100644 --- a/vllm/worker/xpu_model_runner.py +++ b/vllm/worker/xpu_model_runner.py @@ -416,6 +416,9 @@ def load_model(self) -> None: logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) + def get_model(self) -> nn.Module: + return self.model + @property def vocab_size(self) -> int: return self.model_config.get_vocab_size()
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-18557@2ab78b1
vllm-project/vllm
Python
18,557
[Platform] Add custom default max tokens
FIX: https://github.com/vllm-project/vllm-spyre/issues/148 Currently the default max tokens for the completions API is set to `max_model_len - prompt_len`. The changes in this PR make so that when a platform needs to use a different value for `default_max_tokens` it can be altered simply by overriding the `maybe_update_max_tokens` method in the class `Plataform`. When it is not needed it returns the current default. Edit: typo in commit message: `class Plataform` is meant to be `class Platform`.
2025-05-22T17:11:45Z
Incorrect default max_completion_tokens being set ### Changes Trying to run vLLM with `VLLM_SPYRE_WARMUP_NEW_TOKENS` set to X number always results in error: ``` No applicable warmup shape exists for combination of prompt length (Z tokens) and maximum number of output tokens to be generated (Y tokens) ``` Where `Z` is input token count and `Y` is `Z + VLLM_SPYRE_WARMUP_NEW_TOKENS`
To add some more context: This is observed when using the Chat API while not setting `max_completions_token` or `max_tokens` in the request body. Turns out that the OpenAI frontend code is where the default value for `max_completions_token`/`max_tokens` is set. For the completions API, it defaults to 16 ([REF](https://github.com/vllm-project/vllm/blob/a944f8ede7361a5233e112a575ff77c4aaa268a5/vllm/entrypoints/openai/protocol.py#L747)), so is typically overwritten in the incoming request. For the chat API, it defaults to `max_model_len - prompt_len` ([REF](https://github.com/vllm-project/vllm/blob/a944f8ede7361a5233e112a575ff77c4aaa268a5/vllm/entrypoints/openai/serving_chat.py#L213-L214)). The setting of the default max tokens happens before the execution gets to any vllm-spyre code ๐Ÿค” The code in `platform.py` overrides `max_model_len` to be the sum of the max prompt size and generation length [REF](https://github.com/vllm-project/vllm-spyre/blob/main/vllm_spyre/platform.py#L83-L88), so the only way that leaving max tokens unset works is if the incoming request has tokens equal to the largest max prompt size. Since the default value for generation tokens is set in the OpenAI frontend code, the only fix seems to involve altering the vllm source code: If a new method is implemented in the class `Plataform` in `vllm/platforms/interface.py`: ```python def maybe_update_default_max_tokens(self, prompt_len: int, default_max_tokens: int) -> int: return default_max_tokens ``` It can be override in `vllm_spyre/platform.py`: ```python @classmethod def maybe_update_default_max_tokens(self, prompt_len: int, default_max_tokens: int) -> int: max_new_tokens = 1 for shape in self._warmup_shapes: if prompt_len <= shape['prompt_length']: max_new_tokens = max(max_new_tokens, shape['new_tokens']) return max_new_tokens ``` Now, in `vllm/entrypoints/openai/serving_chat.py`, in the class `OpenAIServingChat`, instead of using: ```python default_max_tokens = self.max_model_len - len(engine_prompt["prompt_token_ids"]) ``` use: ```python default_max_tokens = current_platform.maybe_update_default_max_tokens( prompt_len=prompt_len, default_max_tokens=self.max_model_len - len(engine_prompt["prompt_token_ids"]) ) ``` This way, any plataform can override this method and use a different default if needed, without altering the vllm default value.
[ { "body": "### Changes\n\nTrying to run vLLM with `VLLM_SPYRE_WARMUP_NEW_TOKENS` set to X number always results in error:\n\n```\nNo applicable warmup shape exists for combination of prompt length (Z tokens) and maximum number of output tokens to be generated (Y tokens)\n```\n\nWhere `Z` is input token count and `Y` is `Z + VLLM_SPYRE_WARMUP_NEW_TOKENS`\n\n", "number": 148, "title": "Incorrect default max_completion_tokens being set" } ]
536fd330036b0406786c847f68e4f67cba06f421
{ "head_commit": "2ab78b136b3daf6c661ba87ba79b7e2b07c78dcc", "head_commit_message": "trigger ci\n\nSigned-off-by: Gabriel Marinho <[email protected]>", "patch_to_review": "diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py\nindex 3b5281962b2d..d9d112730150 100644\n--- a/vllm/entrypoints/openai/protocol.py\n+++ b/vllm/entrypoints/openai/protocol.py\n@@ -229,7 +229,6 @@ class ChatCompletionRequest(OpenAIBaseModel):\n logit_bias: Optional[dict[str, float]] = None\n logprobs: Optional[bool] = False\n top_logprobs: Optional[int] = 0\n- # TODO(#9845): remove max_tokens when field is removed from OpenAI API\n max_tokens: Optional[int] = Field(\n default=None,\n deprecated=\n@@ -433,23 +432,10 @@ class ChatCompletionRequest(OpenAIBaseModel):\n }\n \n def to_beam_search_params(\n- self,\n- default_max_tokens: int,\n- default_sampling_params: Optional[dict] = None\n- ) -> BeamSearchParams:\n- # TODO(#9845): remove max_tokens when field is removed from OpenAI API\n- max_tokens = self.max_completion_tokens or self.max_tokens\n+ self, max_tokens: int,\n+ default_sampling_params: dict) -> BeamSearchParams:\n \n- if default_sampling_params is None:\n- default_sampling_params = {}\n n = self.n if self.n is not None else 1\n-\n- # Use minimum of context window, user request & server limit.\n- max_tokens = min(\n- val for val in (default_max_tokens, max_tokens,\n- default_sampling_params.get(\"max_tokens\", None))\n- if val is not None)\n-\n if (temperature := self.temperature) is None:\n temperature = default_sampling_params.get(\n \"temperature\", self._DEFAULT_SAMPLING_PARAMS[\"temperature\"])\n@@ -465,21 +451,10 @@ def to_beam_search_params(\n \n def to_sampling_params(\n self,\n- default_max_tokens: int,\n+ max_tokens: int,\n logits_processor_pattern: Optional[str],\n- default_sampling_params: Optional[dict] = None,\n+ default_sampling_params: dict,\n ) -> SamplingParams:\n- # TODO(#9845): remove max_tokens when field is removed from OpenAI API\n- max_tokens = self.max_completion_tokens or self.max_tokens\n-\n- if default_sampling_params is None:\n- default_sampling_params = {}\n-\n- # Use minimum of context window, user request & server limit.\n- max_tokens = min(\n- val for val in (default_max_tokens, max_tokens,\n- default_sampling_params.get(\"max_tokens\", None))\n- if val is not None)\n \n # Default parameters\n if (repetition_penalty := self.repetition_penalty) is None:\n@@ -900,22 +875,15 @@ class CompletionRequest(OpenAIBaseModel):\n }\n \n def to_beam_search_params(\n- self,\n- default_max_tokens: int,\n- default_sampling_params: Optional[dict] = None\n+ self,\n+ max_tokens: int,\n+ default_sampling_params: Optional[dict] = None,\n ) -> BeamSearchParams:\n- max_tokens = self.max_tokens\n \n if default_sampling_params is None:\n default_sampling_params = {}\n n = self.n if self.n is not None else 1\n \n- # Use minimum of context window, user request & server limit.\n- max_tokens = min(\n- val for val in (default_max_tokens, max_tokens,\n- default_sampling_params.get(\"max_tokens\", None))\n- if val is not None)\n-\n if (temperature := self.temperature) is None:\n temperature = default_sampling_params.get(\"temperature\", 1.0)\n \n@@ -930,21 +898,14 @@ def to_beam_search_params(\n \n def to_sampling_params(\n self,\n- default_max_tokens: int,\n+ max_tokens: int,\n logits_processor_pattern: Optional[str],\n default_sampling_params: Optional[dict] = None,\n ) -> SamplingParams:\n- max_tokens = self.max_tokens\n \n if default_sampling_params is None:\n default_sampling_params = {}\n \n- # Use minimum of context window, user request & server limit.\n- max_tokens = min(\n- val for val in (default_max_tokens, max_tokens,\n- default_sampling_params.get(\"max_tokens\", None))\n- if val is not None)\n-\n # Default parameters\n if (repetition_penalty := self.repetition_penalty) is None:\n repetition_penalty = default_sampling_params.get(\n@@ -1816,7 +1777,7 @@ def to_sampling_params(\n self,\n default_max_tokens: int,\n default_sampling_params: Optional[dict] = None) -> SamplingParams:\n- # TODO(#9845): remove max_tokens when field is removed from OpenAI API\n+\n max_tokens = default_max_tokens\n \n if default_sampling_params is None:\n@@ -2032,7 +1993,7 @@ def to_sampling_params(\n self,\n default_max_tokens: int,\n default_sampling_params: Optional[dict] = None) -> SamplingParams:\n- # TODO(#9845): remove max_tokens when field is removed from OpenAI API\n+\n max_tokens = default_max_tokens\n \n if default_sampling_params is None:\ndiff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py\nindex 10aced83b60b..64bf7d9e34f8 100644\n--- a/vllm/entrypoints/openai/serving_chat.py\n+++ b/vllm/entrypoints/openai/serving_chat.py\n@@ -34,6 +34,7 @@\n from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager\n from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (\n MistralToolCall)\n+from vllm.entrypoints.utils import get_max_tokens\n from vllm.logger import init_logger\n from vllm.outputs import CompletionOutput, RequestOutput\n from vllm.reasoning import ReasoningParser, ReasoningParserManager\n@@ -215,15 +216,22 @@ async def create_chat_completion(\n try:\n for i, engine_prompt in enumerate(engine_prompts):\n sampling_params: Union[SamplingParams, BeamSearchParams]\n- default_max_tokens = self.max_model_len - len(\n- engine_prompt[\"prompt_token_ids\"])\n+\n+ if self.default_sampling_params is None:\n+ self.default_sampling_params = {}\n+\n+ max_tokens = get_max_tokens(\n+ max_model_len=self.max_model_len,\n+ request=request,\n+ input_length=len(engine_prompt[\"prompt_token_ids\"]),\n+ default_sampling_params=self.default_sampling_params)\n+\n if request.use_beam_search:\n sampling_params = request.to_beam_search_params(\n- default_max_tokens, self.default_sampling_params)\n+ max_tokens, self.default_sampling_params)\n else:\n sampling_params = request.to_sampling_params(\n- default_max_tokens,\n- self.model_config.logits_processor_pattern,\n+ max_tokens, self.model_config.logits_processor_pattern,\n self.default_sampling_params)\n \n self._log_inputs(request_id,\ndiff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py\nindex a19fde8d70a8..52c7b88cbf32 100644\n--- a/vllm/entrypoints/openai/serving_completion.py\n+++ b/vllm/entrypoints/openai/serving_completion.py\n@@ -30,6 +30,7 @@\n clamp_prompt_logprobs,\n is_text_tokens_prompt)\n from vllm.entrypoints.openai.serving_models import OpenAIServingModels\n+from vllm.entrypoints.utils import get_max_tokens\n from vllm.inputs.data import (EmbedsPrompt, TokensPrompt, is_embeds_prompt,\n is_tokens_prompt)\n from vllm.logger import init_logger\n@@ -157,15 +158,22 @@ async def create_completion(\n input_length = len(engine_prompt[\"prompt_token_ids\"])\n else:\n assert_never(engine_prompt)\n- default_max_tokens = self.max_model_len - input_length\n+\n+ if self.default_sampling_params is None:\n+ self.default_sampling_params = {}\n+\n+ max_tokens = get_max_tokens(\n+ max_model_len=self.max_model_len,\n+ request=request,\n+ input_length=input_length,\n+ default_sampling_params=self.default_sampling_params)\n \n if request.use_beam_search:\n sampling_params = request.to_beam_search_params(\n- default_max_tokens, self.default_sampling_params)\n+ max_tokens, self.default_sampling_params)\n else:\n sampling_params = request.to_sampling_params(\n- default_max_tokens,\n- self.model_config.logits_processor_pattern,\n+ max_tokens, self.model_config.logits_processor_pattern,\n self.default_sampling_params)\n \n request_id_item = f\"{request_id}-{i}\"\ndiff --git a/vllm/entrypoints/utils.py b/vllm/entrypoints/utils.py\nindex 16ba2b4531ac..582396391edc 100644\n--- a/vllm/entrypoints/utils.py\n+++ b/vllm/entrypoints/utils.py\n@@ -4,13 +4,17 @@\n import asyncio\n import functools\n import os\n-from typing import Any, Optional\n+import sys\n+from typing import Any, Optional, Union\n \n from fastapi import Request\n from fastapi.responses import JSONResponse, StreamingResponse\n from starlette.background import BackgroundTask, BackgroundTasks\n \n+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,\n+ CompletionRequest)\n from vllm.logger import init_logger\n+from vllm.platforms import current_platform\n \n logger = init_logger(__name__)\n \n@@ -175,7 +179,6 @@ def _validate_truncation_size(\n \n \n def show_filtered_argument_or_group_from_help(parser, subcommand_name):\n- import sys\n \n # Only handle --help=<keyword> for the current subcommand.\n # Since subparser_init() runs for all subcommands during CLI setup,\n@@ -231,3 +234,17 @@ def show_filtered_argument_or_group_from_help(parser, subcommand_name):\n print(f\"\\nNo group or parameter matching '{search_keyword}'\")\n print(\"Tip: use `--help=listgroup` to view all groups.\")\n sys.exit(1)\n+\n+\n+def get_max_tokens(max_model_len: int, request: Union[ChatCompletionRequest,\n+ CompletionRequest],\n+ input_length: int, default_sampling_params: dict) -> int:\n+\n+ max_tokens = getattr(request, \"max_completion_tokens\", request.max_tokens)\n+ default_max_tokens = max_model_len - input_length\n+ max_output_tokens = current_platform.get_max_output_tokens(input_length)\n+\n+ return min(val\n+ for val in (default_max_tokens, max_tokens, max_output_tokens,\n+ default_sampling_params.get(\"max_tokens\"))\n+ if val is not None)\ndiff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py\nindex 0f08bf986333..567d5cbf503f 100644\n--- a/vllm/platforms/interface.py\n+++ b/vllm/platforms/interface.py\n@@ -4,6 +4,7 @@\n import os\n import platform\n import random\n+import sys\n from datetime import timedelta\n from platform import uname\n from typing import TYPE_CHECKING, NamedTuple, Optional, Union\n@@ -164,6 +165,9 @@ def is_neuron(self) -> bool:\n def is_out_of_tree(self) -> bool:\n return self._enum == PlatformEnum.OOT\n \n+ def get_max_output_tokens(self, prompt_len: int) -> int:\n+ return sys.maxsize\n+\n def is_cuda_alike(self) -> bool:\n \"\"\"Stateless version of [torch.cuda.is_available][].\"\"\"\n return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM)\n" }
[ { "diff_hunk": "@@ -231,3 +234,17 @@ def show_filtered_argument_or_group_from_help(parser, subcommand_name):\n print(f\"\\nNo group or parameter matching '{search_keyword}'\")\n print(\"Tip: use `--help=listgroup` to view all groups.\")\n sys.exit(1)\n+\n+\n+def get_max_tokens(max_model_len: int, request: Union[ChatCompletionRequest,\n+ CompletionRequest],\n+ input_length: int, default_sampling_params: dict) -> int:\n+\n+ max_tokens = getattr(request, \"max_completion_tokens\", request.max_tokens)", "line": null, "original_line": 243, "original_start_line": null, "path": "vllm/entrypoints/utils.py", "start_line": null, "text": "@user1:\nThis will fix the test error:\r\n```suggestion\r\nmax_tokens = getattr(request, \"max_completion_tokens\", None) or request.max_tokens\r\n```\r\nIf the attribute exists but is None, getattr will return None and not the default." } ]
5f5310ad512fba7c9d4eafd89675d556b143fbd9
diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 3df11db33384..93d9c588d8d2 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -229,7 +229,6 @@ class ChatCompletionRequest(OpenAIBaseModel): logit_bias: Optional[dict[str, float]] = None logprobs: Optional[bool] = False top_logprobs: Optional[int] = 0 - # TODO(#9845): remove max_tokens when field is removed from OpenAI API max_tokens: Optional[int] = Field( default=None, deprecated= @@ -433,23 +432,10 @@ class ChatCompletionRequest(OpenAIBaseModel): } def to_beam_search_params( - self, - default_max_tokens: int, - default_sampling_params: Optional[dict] = None - ) -> BeamSearchParams: - # TODO(#9845): remove max_tokens when field is removed from OpenAI API - max_tokens = self.max_completion_tokens or self.max_tokens + self, max_tokens: int, + default_sampling_params: dict) -> BeamSearchParams: - if default_sampling_params is None: - default_sampling_params = {} n = self.n if self.n is not None else 1 - - # Use minimum of context window, user request & server limit. - max_tokens = min( - val for val in (default_max_tokens, max_tokens, - default_sampling_params.get("max_tokens", None)) - if val is not None) - if (temperature := self.temperature) is None: temperature = default_sampling_params.get( "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"]) @@ -465,21 +451,10 @@ def to_beam_search_params( def to_sampling_params( self, - default_max_tokens: int, + max_tokens: int, logits_processor_pattern: Optional[str], - default_sampling_params: Optional[dict] = None, + default_sampling_params: dict, ) -> SamplingParams: - # TODO(#9845): remove max_tokens when field is removed from OpenAI API - max_tokens = self.max_completion_tokens or self.max_tokens - - if default_sampling_params is None: - default_sampling_params = {} - - # Use minimum of context window, user request & server limit. - max_tokens = min( - val for val in (default_max_tokens, max_tokens, - default_sampling_params.get("max_tokens", None)) - if val is not None) # Default parameters if (repetition_penalty := self.repetition_penalty) is None: @@ -898,22 +873,15 @@ class CompletionRequest(OpenAIBaseModel): } def to_beam_search_params( - self, - default_max_tokens: int, - default_sampling_params: Optional[dict] = None + self, + max_tokens: int, + default_sampling_params: Optional[dict] = None, ) -> BeamSearchParams: - max_tokens = self.max_tokens if default_sampling_params is None: default_sampling_params = {} n = self.n if self.n is not None else 1 - # Use minimum of context window, user request & server limit. - max_tokens = min( - val for val in (default_max_tokens, max_tokens, - default_sampling_params.get("max_tokens", None)) - if val is not None) - if (temperature := self.temperature) is None: temperature = default_sampling_params.get("temperature", 1.0) @@ -928,21 +896,14 @@ def to_beam_search_params( def to_sampling_params( self, - default_max_tokens: int, + max_tokens: int, logits_processor_pattern: Optional[str], default_sampling_params: Optional[dict] = None, ) -> SamplingParams: - max_tokens = self.max_tokens if default_sampling_params is None: default_sampling_params = {} - # Use minimum of context window, user request & server limit. - max_tokens = min( - val for val in (default_max_tokens, max_tokens, - default_sampling_params.get("max_tokens", None)) - if val is not None) - # Default parameters if (repetition_penalty := self.repetition_penalty) is None: repetition_penalty = default_sampling_params.get( @@ -1813,7 +1774,7 @@ def to_sampling_params( self, default_max_tokens: int, default_sampling_params: Optional[dict] = None) -> SamplingParams: - # TODO(#9845): remove max_tokens when field is removed from OpenAI API + max_tokens = default_max_tokens if default_sampling_params is None: @@ -2029,7 +1990,7 @@ def to_sampling_params( self, default_max_tokens: int, default_sampling_params: Optional[dict] = None) -> SamplingParams: - # TODO(#9845): remove max_tokens when field is removed from OpenAI API + max_tokens = default_max_tokens if default_sampling_params is None: diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 299ade4e4d7d..a802fbc3865f 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -34,6 +34,7 @@ from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import ( MistralToolCall) +from vllm.entrypoints.utils import get_max_tokens from vllm.logger import init_logger from vllm.outputs import CompletionOutput, RequestOutput from vllm.reasoning import ReasoningParser, ReasoningParserManager @@ -233,15 +234,22 @@ async def create_chat_completion( try: for i, engine_prompt in enumerate(engine_prompts): sampling_params: Union[SamplingParams, BeamSearchParams] - default_max_tokens = self.max_model_len - len( - engine_prompt["prompt_token_ids"]) + + if self.default_sampling_params is None: + self.default_sampling_params = {} + + max_tokens = get_max_tokens( + max_model_len=self.max_model_len, + request=request, + input_length=len(engine_prompt["prompt_token_ids"]), + default_sampling_params=self.default_sampling_params) + if request.use_beam_search: sampling_params = request.to_beam_search_params( - default_max_tokens, self.default_sampling_params) + max_tokens, self.default_sampling_params) else: sampling_params = request.to_sampling_params( - default_max_tokens, - self.model_config.logits_processor_pattern, + max_tokens, self.model_config.logits_processor_pattern, self.default_sampling_params) self._log_inputs(request_id, diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index 8171b491aafc..6c9c29b71445 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -33,6 +33,7 @@ is_text_tokens_prompt) # yapf: enable from vllm.entrypoints.openai.serving_models import OpenAIServingModels +from vllm.entrypoints.utils import get_max_tokens from vllm.inputs.data import (EmbedsPrompt, TokensPrompt, is_embeds_prompt, is_tokens_prompt) from vllm.logger import init_logger @@ -160,15 +161,22 @@ async def create_completion( input_length = len(engine_prompt["prompt_token_ids"]) else: assert_never(engine_prompt) - default_max_tokens = self.max_model_len - input_length + + if self.default_sampling_params is None: + self.default_sampling_params = {} + + max_tokens = get_max_tokens( + max_model_len=self.max_model_len, + request=request, + input_length=input_length, + default_sampling_params=self.default_sampling_params) if request.use_beam_search: sampling_params = request.to_beam_search_params( - default_max_tokens, self.default_sampling_params) + max_tokens, self.default_sampling_params) else: sampling_params = request.to_sampling_params( - default_max_tokens, - self.model_config.logits_processor_pattern, + max_tokens, self.model_config.logits_processor_pattern, self.default_sampling_params) request_id_item = f"{request_id}-{i}" diff --git a/vllm/entrypoints/utils.py b/vllm/entrypoints/utils.py index 5b085e5b7947..423b99dbe565 100644 --- a/vllm/entrypoints/utils.py +++ b/vllm/entrypoints/utils.py @@ -5,13 +5,17 @@ import asyncio import functools import os -from typing import Any, Optional +import sys +from typing import Any, Optional, Union from fastapi import Request from fastapi.responses import JSONResponse, StreamingResponse from starlette.background import BackgroundTask, BackgroundTasks +from vllm.entrypoints.openai.protocol import (ChatCompletionRequest, + CompletionRequest) from vllm.logger import init_logger +from vllm.platforms import current_platform logger = init_logger(__name__) @@ -181,7 +185,6 @@ def _validate_truncation_size( def show_filtered_argument_or_group_from_help(parser: argparse.ArgumentParser, subcommand_name: list[str]): - import sys # Only handle --help=<keyword> for the current subcommand. # Since subparser_init() runs for all subcommands during CLI setup, @@ -242,3 +245,18 @@ def show_filtered_argument_or_group_from_help(parser: argparse.ArgumentParser, print(f"\nNo group or parameter matching '{search_keyword}'") print("Tip: use `--help=listgroup` to view all groups.") sys.exit(1) + + +def get_max_tokens(max_model_len: int, request: Union[ChatCompletionRequest, + CompletionRequest], + input_length: int, default_sampling_params: dict) -> int: + + max_tokens = getattr(request, "max_completion_tokens", + None) or request.max_tokens + default_max_tokens = max_model_len - input_length + max_output_tokens = current_platform.get_max_output_tokens(input_length) + + return min(val + for val in (default_max_tokens, max_tokens, max_output_tokens, + default_sampling_params.get("max_tokens")) + if val is not None) diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index 0f08bf986333..567d5cbf503f 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -4,6 +4,7 @@ import os import platform import random +import sys from datetime import timedelta from platform import uname from typing import TYPE_CHECKING, NamedTuple, Optional, Union @@ -164,6 +165,9 @@ def is_neuron(self) -> bool: def is_out_of_tree(self) -> bool: return self._enum == PlatformEnum.OOT + def get_max_output_tokens(self, prompt_len: int) -> int: + return sys.maxsize + def is_cuda_alike(self) -> bool: """Stateless version of [torch.cuda.is_available][].""" return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-9396@3d6f0bc
vllm-project/vllm
Python
9,396
[Model][Bugfix] Add FATReLU activation and support for openbmb/MiniCPM-S-1B-sft
Add FATReLU ([Kurtz et al., 2020](https://proceedings.mlr.press/v119/kurtz20a/kurtz20a.pdf)) and support for [openbmb/MiniCPM-S-1B-sft](https://huggingface.co/openbmb/MiniCPM-S-1B-sft) FIX #7678 (*link existing issues this PR will resolve*) **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Adding or changing kernels</h3> <p>Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.</p> <ul> <li>Make sure custom ops are registered following PyTorch guidelines: <a href="https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial">Custom C++ and CUDA Operators</a> and <a href="https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU">The Custom Operators Manual</a></li> <li>Custom operations that return <code>Tensors</code> require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.</li> <li>Use <a href="https://pytorch.org/docs/stable/library.html#torch.library.opcheck"><code>torch.libary.opcheck()</code></a> to test the function registration and meta-function for any registered ops. See <code>tests/kernels</code> for examples.</li> <li>When changing the C++ signature of an existing op, the schema must be updated to reflect the changes.</li> <li>If a new custom type is needed, see the following document: <a href="https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA">Custom Class Support in PT2</a>. </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-10-16T00:49:34Z
[Feature]: Please Support FATRelu ### ๐Ÿš€ The feature, motivation and pitch #### When I tried to deploy `openbmb/ProSparse-MiniCPM-1B-sft`, it raised an error: ``` File "/home/xxxxxx/.conda/envs/vllm_env/lib/python3.10/site-packages/vllm/model_executor/models/minicpm.py", line 290, in __init__ self.mlp = MiniCPMMLP( File "/home/xxxxxx/.conda/envs/vllm_env/lib/python3.10/site-packages/vllm/model_executor/models/minicpm.py", line 166, in __init__ raise ValueError(f"Unsupported activation: {hidden_act}. " ValueError: Unsupported activation: fatrelu. Only silu is supported for now. ``` #### Here is the scripts: ``` #!/bin/bash set -e source ~/.bashrc conda activate vllm_env port=${1:-"8010"} devices=${2:-"6,7"} model_name=${3:-"LOCAL_PATH/ProSparse-MiniCPM-1B-sft"} export CUDA_VISIBLE_DEVICES="$devices" vllm serve ${model_name} --port $port --trust-remote-code ``` #### Packages: -torch2.4.0 -vllm0.5.4 ### Alternatives _No response_ ### Additional context _No response_
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\r\n\r\n#### When I tried to deploy `openbmb/ProSparse-MiniCPM-1B-sft`, it raised an error:\r\n\r\n```\r\n File \"/home/xxxxxx/.conda/envs/vllm_env/lib/python3.10/site-packages/vllm/model_executor/models/minicpm.py\", line 290, in __init__\r\n self.mlp = MiniCPMMLP(\r\n File \"/home/xxxxxx/.conda/envs/vllm_env/lib/python3.10/site-packages/vllm/model_executor/models/minicpm.py\", line 166, in __init__\r\n raise ValueError(f\"Unsupported activation: {hidden_act}. \"\r\nValueError: Unsupported activation: fatrelu. Only silu is supported for now.\r\n```\r\n\r\n#### Here is the scripts:\r\n```\r\n#!/bin/bash\r\n\r\nset -e\r\n\r\nsource ~/.bashrc\r\nconda activate vllm_env\r\n\r\nport=${1:-\"8010\"}\r\ndevices=${2:-\"6,7\"}\r\nmodel_name=${3:-\"LOCAL_PATH/ProSparse-MiniCPM-1B-sft\"}\r\nexport CUDA_VISIBLE_DEVICES=\"$devices\"\r\n\r\nvllm serve ${model_name} --port $port --trust-remote-code\r\n```\r\n\r\n#### Packages:\r\n-torch2.4.0\r\n-vllm0.5.4\r\n\r\n### Alternatives\r\n\r\n_No response_\r\n\r\n### Additional context\r\n\r\n_No response_", "number": 7678, "title": "[Feature]: Please Support FATRelu" } ]
717a5f82cda6dd6a52be6504179adaa64bbdc67a
{ "head_commit": "3d6f0bc694da216d693214a121d534113a3fd3e0", "head_commit_message": "Remove an extra space in supported_models.rst", "patch_to_review": "diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst\nindex 102842b0a188..544f6d680d5a 100644\n--- a/docs/source/models/supported_models.rst\n+++ b/docs/source/models/supported_models.rst\n@@ -159,7 +159,7 @@ Text Generation\n -\n * - :code:`MiniCPMForCausalLM`\n - MiniCPM\n- - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, etc.\n+ - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc.\n - โœ…๏ธŽ\n - โœ…๏ธŽ\n * - :code:`MiniCPM3ForCausalLM`\ndiff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py\nindex 43056786d35c..81252e69c8c8 100644\n--- a/vllm/model_executor/layers/activation.py\n+++ b/vllm/model_executor/layers/activation.py\n@@ -13,6 +13,30 @@\n from vllm.model_executor.utils import set_weight_attrs\n \n \n+class FatreluAndMul(CustomOp):\n+ \"\"\"An activation function for FATReLU.\n+ \n+ The function computes x -> FATReLU(x[:d]) * x[d:] where\n+ d = x.shape[-1] // 2.\n+ This is used in openbmb/MiniCPM-S-1B-sft.\n+\n+ Shapes:\n+ x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)\n+ return: (num_tokens, d) or (batch_size, seq_len, d)\n+ \"\"\"\n+\n+ def __init__(self, threshold: float = 0.):\n+ super().__init__()\n+ self.threshold = threshold\n+\n+ def forward(self, x: torch.Tensor) -> torch.Tensor:\n+ d = x.shape[-1] // 2\n+ x1 = x[..., :d]\n+ x2 = x[..., d:]\n+ x1 = F.threshold(x1, self.threshold, 0.0)\n+ return x1 * x2\n+\n+\n class SiluAndMul(CustomOp):\n \"\"\"An activation function for SwiGLU.\n \ndiff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py\nindex 41c2877194bb..decd90b682a1 100644\n--- a/vllm/model_executor/models/minicpm.py\n+++ b/vllm/model_executor/models/minicpm.py\n@@ -33,7 +33,7 @@\n from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,\n get_tensor_model_parallel_world_size,\n tensor_model_parallel_all_reduce)\n-from vllm.model_executor.layers.activation import SiluAndMul\n+from vllm.model_executor.layers.activation import FatreluAndMul, SiluAndMul\n from vllm.model_executor.layers.fused_moe import fused_moe\n from vllm.model_executor.layers.layernorm import RMSNorm\n from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,\n@@ -152,6 +152,7 @@ def __init__(\n hidden_size: int,\n intermediate_size: int,\n hidden_act: str,\n+ hidden_act_param: float,\n quant_config: Optional[QuantizationConfig] = None,\n ) -> None:\n super().__init__()\n@@ -163,10 +164,13 @@ def __init__(\n hidden_size,\n bias=False,\n quant_config=quant_config)\n- if hidden_act != \"silu\":\n+ if hidden_act == \"silu\":\n+ self.act_fn = SiluAndMul()\n+ elif hidden_act == \"fatrelu\":\n+ self.act_fn = FatreluAndMul(threshold=hidden_act_param)\n+ else:\n raise ValueError(f\"Unsupported activation: {hidden_act}. \"\n- \"Only silu is supported for now.\")\n- self.act_fn = SiluAndMul()\n+ \"Only silu and fatrelu are supported for now.\")\n \n def forward(self, x):\n gate_up, _ = self.gate_up_proj(x)\n@@ -304,6 +308,7 @@ def _init_ffn_block(self):\n hidden_size=self.hidden_size,\n intermediate_size=self.config.intermediate_size,\n hidden_act=self.config.hidden_act,\n+ hidden_act_param=getattr(self.config, \"hidden_act_param\", 0.),\n quant_config=self.quant_config,\n )\n else:\n" }
[ { "diff_hunk": "@@ -13,6 +13,30 @@\n from vllm.model_executor.utils import set_weight_attrs\n \n \n+class FatreluAndMul(CustomOp):\n+ \"\"\"An activation function for FATReLU.\n+ \n+ The function computes x -> FATReLU(x[:d]) * x[d:] where\n+ d = x.shape[-1] // 2.\n+ This is used in openbmb/MiniCPM-S-1B-sft.\n+\n+ Shapes:\n+ x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)\n+ return: (num_tokens, d) or (batch_size, seq_len, d)\n+ \"\"\"\n+\n+ def __init__(self, threshold: float = 0.):\n+ super().__init__()\n+ self.threshold = threshold\n+\n+ def forward(self, x: torch.Tensor) -> torch.Tensor:", "line": null, "original_line": 32, "original_start_line": null, "path": "vllm/model_executor/layers/activation.py", "start_line": null, "text": "@user1:\nWe should override `forward_native` instead of `forward` here.\n\n@author:\nDone. I also overrode forward_cuda to forward_native, similar to the ReLUSquaredActivation, so that we at least have cuda support." } ]
695256b9e8a4840bc3505c905f6965e32ea00142
diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 102842b0a188..544f6d680d5a 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -159,7 +159,7 @@ Text Generation - * - :code:`MiniCPMForCausalLM` - MiniCPM - - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, etc. + - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc. - โœ…๏ธŽ - โœ…๏ธŽ * - :code:`MiniCPM3ForCausalLM` diff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py index 43056786d35c..f2ea53cad9f2 100644 --- a/vllm/model_executor/layers/activation.py +++ b/vllm/model_executor/layers/activation.py @@ -13,6 +13,33 @@ from vllm.model_executor.utils import set_weight_attrs +class FatreluAndMul(CustomOp): + """An activation function for FATReLU. + + The function computes x -> FATReLU(x[:d]) * x[d:] where + d = x.shape[-1] // 2. + This is used in openbmb/MiniCPM-S-1B-sft. + + Shapes: + x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d) + return: (num_tokens, d) or (batch_size, seq_len, d) + """ + + def __init__(self, threshold: float = 0.): + super().__init__() + self.threshold = threshold + + def forward_native(self, x: torch.Tensor) -> torch.Tensor: + d = x.shape[-1] // 2 + x1 = x[..., :d] + x2 = x[..., d:] + x1 = F.threshold(x1, self.threshold, 0.0) + return x1 * x2 + + def forward_cuda(self, x: torch.Tensor) -> torch.Tensor: + return self.forward_native(x) + + class SiluAndMul(CustomOp): """An activation function for SwiGLU. diff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py index 41c2877194bb..decd90b682a1 100644 --- a/vllm/model_executor/models/minicpm.py +++ b/vllm/model_executor/models/minicpm.py @@ -33,7 +33,7 @@ from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) -from vllm.model_executor.layers.activation import SiluAndMul +from vllm.model_executor.layers.activation import FatreluAndMul, SiluAndMul from vllm.model_executor.layers.fused_moe import fused_moe from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, @@ -152,6 +152,7 @@ def __init__( hidden_size: int, intermediate_size: int, hidden_act: str, + hidden_act_param: float, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() @@ -163,10 +164,13 @@ def __init__( hidden_size, bias=False, quant_config=quant_config) - if hidden_act != "silu": + if hidden_act == "silu": + self.act_fn = SiluAndMul() + elif hidden_act == "fatrelu": + self.act_fn = FatreluAndMul(threshold=hidden_act_param) + else: raise ValueError(f"Unsupported activation: {hidden_act}. " - "Only silu is supported for now.") - self.act_fn = SiluAndMul() + "Only silu and fatrelu are supported for now.") def forward(self, x): gate_up, _ = self.gate_up_proj(x) @@ -304,6 +308,7 @@ def _init_ffn_block(self): hidden_size=self.hidden_size, intermediate_size=self.config.intermediate_size, hidden_act=self.config.hidden_act, + hidden_act_param=getattr(self.config, "hidden_act_param", 0.), quant_config=self.quant_config, ) else:
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
Textualize__textual-5795@2648f82
Textualize/textual
Python
5,795
Refresh layout of option lists on add.
Ensures options added to an empty list after mount are displayed. Fixes #5794. Attached screenshot of added snapshot test with fix reverted. <img width="1495" alt="Screenshot 2025-05-10 at 11 52 38โ€ฏPM" src="https://github.com/user-attachments/assets/f4241756-0c91-4bc5-9e69-fb8b989dccf0" />
2025-05-11T07:31:56Z
Options added to an empty (or cleared) OptionList after mount are not displayed. OptionList._update_lines() exits early if the scroll region has zero height, which can be the case when called from add_options() after mount. Fix is to refresh the layout before _update_lines(). Filing issue to attach a PR to.
We found the following entries in the [FAQ](https://textual.textualize.io/FAQ/) which you may find helpful: - [How do I center a widget in a screen?](https://textual.textualize.io/FAQ/#how-do-i-center-a-widget-in-a-screen) - [How do I pass arguments to an app?](https://textual.textualize.io/FAQ/#how-do-i-pass-arguments-to-an-app) Feel free to close this issue if you found an answer in the FAQ. Otherwise, please give us a little time to review. This is an automated reply, generated by [FAQtory](https://github.com/willmcgugan/faqtory)
[ { "body": "OptionList._update_lines() exits early if the scroll region has zero height, which can be the case when called from add_options() after mount.\n\nFix is to refresh the layout before _update_lines().\n\nFiling issue to attach a PR to.", "number": 5794, "title": "Options added to an empty (or cleared) OptionList after mount are not displayed." } ]
d50b353e55de9ff6364a20cc49fc2458d2b1808e
{ "head_commit": "2648f82fdf1925ad4195ac218b008469d4d8ab1e", "head_commit_message": "Refresh layout of option lists on add.\n\nEnsures options added to an empty list after mount are displayed.", "patch_to_review": "diff --git a/src/textual/widgets/_option_list.py b/src/textual/widgets/_option_list.py\nindex 00909e3d9d..3cd4bdd489 100644\n--- a/src/textual/widgets/_option_list.py\n+++ b/src/textual/widgets/_option_list.py\n@@ -373,6 +373,7 @@ def add_options(self, new_options: Iterable[OptionListContent]) -> Self:\n self._id_to_option[option._id] = option\n add_option(option)\n if self.is_mounted:\n+ self.refresh(layout=True)\n self._update_lines()\n return self\n \ndiff --git a/tests/snapshot_tests/__snapshots__/test_snapshots/test_option_list_build.svg b/tests/snapshot_tests/__snapshots__/test_snapshots/test_option_list_build.svg\nindex ac27972501..9378efcf58 100644\n--- a/tests/snapshot_tests/__snapshots__/test_snapshots/test_option_list_build.svg\n+++ b/tests/snapshot_tests/__snapshots__/test_snapshots/test_option_list_build.svg\n@@ -19,138 +19,138 @@\n font-weight: 700;\n }\n \n- .terminal-242230253-matrix {\n+ .terminal-matrix {\n font-family: Fira Code, monospace;\n font-size: 20px;\n line-height: 24.4px;\n font-variant-east-asian: full-width;\n }\n \n- .terminal-242230253-title {\n+ .terminal-title {\n font-size: 18px;\n font-weight: bold;\n font-family: arial;\n }\n \n- .terminal-242230253-r1 { fill: #121212 }\n-.terminal-242230253-r2 { fill: #0178d4 }\n-.terminal-242230253-r3 { fill: #191919 }\n-.terminal-242230253-r4 { fill: #c5c8c6 }\n-.terminal-242230253-r5 { fill: #ddedf9;font-weight: bold }\n-.terminal-242230253-r6 { fill: #e0e0e0 }\n-.terminal-242230253-r7 { fill: #424242 }\n-.terminal-242230253-r8 { fill: #3b3b3b }\n-.terminal-242230253-r9 { fill: #f4005f }\n+ .terminal-r1 { fill: #121212 }\n+.terminal-r2 { fill: #0178d4 }\n+.terminal-r3 { fill: #191919 }\n+.terminal-r4 { fill: #c5c8c6 }\n+.terminal-r5 { fill: 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clip-path=\"url(#terminal-line-0)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-r2\" x=\"231.8\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Ž</text><text class=\"terminal-r1\" x=\"244\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Š</text><text class=\"terminal-r3\" x=\"256.2\" y=\"20\" textLength=\"219.6\" clip-path=\"url(#terminal-line-0)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-r3\" x=\"475.8\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Ž</text><text class=\"terminal-r1\" x=\"488\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Š</text><text class=\"terminal-r3\" x=\"500.2\" y=\"20\" textLength=\"219.6\" clip-path=\"url(#terminal-line-0)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-r3\" x=\"719.8\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Ž</text><text class=\"terminal-r1\" x=\"732\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Š</text><text class=\"terminal-r3\" x=\"744.2\" y=\"20\" textLength=\"219.6\" clip-path=\"url(#terminal-line-0)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-r3\" x=\"963.8\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">โ–Ž</text><text class=\"terminal-r4\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-line-0)\">\n+</text><text class=\"terminal-r1\" x=\"0\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Š</text><text class=\"terminal-r5\" x=\"24.4\" y=\"44.4\" textLength=\"36.6\" clip-path=\"url(#terminal-line-1)\">One</text><text class=\"terminal-r2\" x=\"231.8\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Ž</text><text class=\"terminal-r1\" x=\"244\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Š</text><text class=\"terminal-r6\" x=\"268.4\" y=\"44.4\" textLength=\"36.6\" clip-path=\"url(#terminal-line-1)\">One</text><text class=\"terminal-r3\" x=\"475.8\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Ž</text><text class=\"terminal-r1\" x=\"488\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Š</text><text class=\"terminal-r6\" x=\"512.4\" y=\"44.4\" textLength=\"36.6\" clip-path=\"url(#terminal-line-1)\">One</text><text class=\"terminal-r3\" x=\"719.8\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Ž</text><text class=\"terminal-r1\" x=\"732\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Š</text><text class=\"terminal-r6\" x=\"756.4\" y=\"44.4\" textLength=\"36.6\" clip-path=\"url(#terminal-line-1)\">One</text><text class=\"terminal-r3\" x=\"963.8\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">โ–Ž</text><text class=\"terminal-r4\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-1)\">\n+</text><text class=\"terminal-r1\" x=\"0\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Š</text><text class=\"terminal-r6\" x=\"24.4\" y=\"68.8\" textLength=\"36.6\" clip-path=\"url(#terminal-line-2)\">Two</text><text class=\"terminal-r2\" x=\"231.8\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Ž</text><text class=\"terminal-r1\" x=\"244\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Š</text><text class=\"terminal-r6\" x=\"268.4\" y=\"68.8\" textLength=\"36.6\" clip-path=\"url(#terminal-line-2)\">Two</text><text class=\"terminal-r3\" x=\"475.8\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Ž</text><text class=\"terminal-r1\" x=\"488\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Š</text><text class=\"terminal-r6\" x=\"512.4\" y=\"68.8\" textLength=\"36.6\" clip-path=\"url(#terminal-line-2)\">Two</text><text class=\"terminal-r3\" x=\"719.8\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Ž</text><text class=\"terminal-r1\" x=\"732\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Š</text><text class=\"terminal-r6\" x=\"756.4\" y=\"68.8\" textLength=\"36.6\" clip-path=\"url(#terminal-line-2)\">Two</text><text class=\"terminal-r3\" x=\"963.8\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">โ–Ž</text><text class=\"terminal-r4\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-2)\">\n+</text><text class=\"terminal-r1\" x=\"0\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-line-3)\">โ–Š</text><text class=\"terminal-r7\" x=\"24.4\" y=\"93.2\" textLength=\"195.2\" clip-path=\"url(#terminal-line-3)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-r2\" x=\"231.8\" y=\"93.2\" textLength=\"12.2\" 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y=\"93.2\" textLength=\"195.2\" clip-path=\"url(#terminal-line-3)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-r3\" x=\"963.8\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-line-3)\">โ–Ž</text><text class=\"terminal-r4\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-line-3)\">\n+</text><text class=\"terminal-r1\" x=\"0\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-line-4)\">โ–Š</text><text class=\"terminal-r9\" x=\"24.4\" y=\"117.6\" textLength=\"61\" clip-path=\"url(#terminal-line-4)\">Three</text><text class=\"terminal-r2\" x=\"231.8\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-line-4)\">โ–Ž</text><text class=\"terminal-r1\" x=\"244\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-line-4)\">โ–Š</text><text class=\"terminal-r9\" x=\"268.4\" y=\"117.6\" textLength=\"61\" clip-path=\"url(#terminal-line-4)\">Three</text><text class=\"terminal-r3\" x=\"475.8\" y=\"117.6\" 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clip-path=\"url(#terminal-line-16)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-17)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-line-18)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-line-19)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-line-20)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-line-21)\">\n+</text><text class=\"terminal-r4\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-line-22)\">\n </text>\n </g>\n </g>\ndiff --git a/tests/snapshot_tests/snapshot_apps/option_list.py b/tests/snapshot_tests/snapshot_apps/option_list.py\nindex 36e4691dd5..580a3c8cf1 100644\n--- a/tests/snapshot_tests/snapshot_apps/option_list.py\n+++ b/tests/snapshot_tests/snapshot_apps/option_list.py\n@@ -10,6 +10,8 @@\n \n class OptionListApp(App[None]):\n \n+ BINDINGS = [(\"a\", \"add\", \"add\")]\n+\n def compose( self ) -> ComposeResult:\n with Horizontal():\n yield OptionList(\n@@ -20,6 +22,7 @@ def compose( self ) -> ComposeResult:\n )\n yield OptionList(id=\"later-individual\")\n yield OptionList(id=\"later-at-once\")\n+ yield OptionList(id=\"after-mount\")\n \n def on_mount(self) -> None:\n options: list[None | str | Text | Option] = [\n@@ -41,5 +44,15 @@ def on_mount(self) -> None:\n ])\n option_list.highlighted = 0\n \n+ def action_add(self):\n+ option_list = self.query_one(\"#after-mount\", OptionList)\n+ option_list.add_options([\n+ \"One\",\n+ Option(\"Two\"),\n+ None,\n+ Text.from_markup(\"[red]Three[/]\"),\n+ ])\n+ option_list.highlighted = 0\n+\n if __name__ == \"__main__\":\n OptionListApp().run()\ndiff --git a/tests/snapshot_tests/test_snapshots.py b/tests/snapshot_tests/test_snapshots.py\nindex 07f046d1e2..7f95c17f89 100644\n--- a/tests/snapshot_tests/test_snapshots.py\n+++ b/tests/snapshot_tests/test_snapshots.py\n@@ -505,7 +505,7 @@ def test_option_list_tables(snap_compare):\n \n \n def test_option_list_build(snap_compare):\n- assert snap_compare(SNAPSHOT_APPS_DIR / \"option_list.py\")\n+ assert snap_compare(SNAPSHOT_APPS_DIR / \"option_list.py\", press=[\"a\"])\n \n \n def test_option_list_replace_prompt_from_single_line_to_single_line(snap_compare):\n" }
[ { "diff_hunk": "@@ -373,6 +373,7 @@ def add_options(self, new_options: Iterable[OptionListContent]) -> Self:\n self._id_to_option[option._id] = option\n add_option(option)\n if self.is_mounted:\n+ self.refresh(layout=True)", "line": null, "original_line": 376, "original_start_line": null, "path": "src/textual/widgets/_option_list.py", "start_line": null, "text": "@user1:\nTry `layout=self.styles.auto_dimensions` to avoid the layout if the widget wouldn't change dimensions.\n\n@author:\nDone. Thanks for the suggestion." } ]
1cd868f791aaee2768c2d5122d205c370985ba56
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class="terminal-r6" x="512.4" y="68.8" textLength="36.6" clip-path="url(#terminal-line-2)">Two</text><text class="terminal-r3" x="719.8" y="68.8" textLength="12.2" clip-path="url(#terminal-line-2)">โ–Ž</text><text class="terminal-r1" x="732" y="68.8" textLength="12.2" clip-path="url(#terminal-line-2)">โ–Š</text><text class="terminal-r6" x="756.4" y="68.8" textLength="36.6" clip-path="url(#terminal-line-2)">Two</text><text class="terminal-r3" x="963.8" y="68.8" textLength="12.2" clip-path="url(#terminal-line-2)">โ–Ž</text><text class="terminal-r4" x="976" y="68.8" textLength="12.2" clip-path="url(#terminal-line-2)"> +</text><text class="terminal-r1" x="0" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Š</text><text class="terminal-r7" x="24.4" y="93.2" textLength="195.2" clip-path="url(#terminal-line-3)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-r2" x="231.8" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Ž</text><text class="terminal-r1" x="244" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Š</text><text class="terminal-r8" x="268.4" y="93.2" textLength="195.2" clip-path="url(#terminal-line-3)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-r3" x="475.8" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Ž</text><text class="terminal-r1" x="488" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Š</text><text class="terminal-r8" x="512.4" y="93.2" textLength="195.2" clip-path="url(#terminal-line-3)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-r3" x="719.8" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Ž</text><text class="terminal-r1" x="732" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Š</text><text class="terminal-r8" x="756.4" y="93.2" textLength="195.2" clip-path="url(#terminal-line-3)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-r3" x="963.8" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)">โ–Ž</text><text class="terminal-r4" x="976" y="93.2" textLength="12.2" clip-path="url(#terminal-line-3)"> +</text><text class="terminal-r1" x="0" y="117.6" textLength="12.2" clip-path="url(#terminal-line-4)">โ–Š</text><text class="terminal-r9" x="24.4" y="117.6" textLength="61" clip-path="url(#terminal-line-4)">Three</text><text class="terminal-r2" x="231.8" y="117.6" textLength="12.2" clip-path="url(#terminal-line-4)">โ–Ž</text><text class="terminal-r1" x="244" y="117.6" textLength="12.2" clip-path="url(#terminal-line-4)">โ–Š</text><text class="terminal-r9" x="268.4" y="117.6" textLength="61" clip-path="url(#terminal-line-4)">Three</text><text class="terminal-r3" x="475.8" y="117.6" textLength="12.2" clip-path="url(#terminal-line-4)">โ–Ž</text><text class="terminal-r1" x="488" y="117.6" textLength="12.2" clip-path="url(#terminal-line-4)">โ–Š</text><text class="terminal-r9" x="512.4" y="117.6" 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</g> diff --git a/tests/snapshot_tests/snapshot_apps/option_list.py b/tests/snapshot_tests/snapshot_apps/option_list.py index 36e4691dd5..580a3c8cf1 100644 --- a/tests/snapshot_tests/snapshot_apps/option_list.py +++ b/tests/snapshot_tests/snapshot_apps/option_list.py @@ -10,6 +10,8 @@ class OptionListApp(App[None]): + BINDINGS = [("a", "add", "add")] + def compose( self ) -> ComposeResult: with Horizontal(): yield OptionList( @@ -20,6 +22,7 @@ def compose( self ) -> ComposeResult: ) yield OptionList(id="later-individual") yield OptionList(id="later-at-once") + yield OptionList(id="after-mount") def on_mount(self) -> None: options: list[None | str | Text | Option] = [ @@ -41,5 +44,15 @@ def on_mount(self) -> None: ]) option_list.highlighted = 0 + def action_add(self): + option_list = self.query_one("#after-mount", OptionList) + option_list.add_options([ + "One", + Option("Two"), + None, + Text.from_markup("[red]Three[/]"), + ]) + option_list.highlighted = 0 + if __name__ == "__main__": OptionListApp().run() diff --git a/tests/snapshot_tests/test_snapshots.py b/tests/snapshot_tests/test_snapshots.py index 07f046d1e2..7f95c17f89 100644 --- a/tests/snapshot_tests/test_snapshots.py +++ b/tests/snapshot_tests/test_snapshots.py @@ -505,7 +505,7 @@ def test_option_list_tables(snap_compare): def test_option_list_build(snap_compare): - assert snap_compare(SNAPSHOT_APPS_DIR / "option_list.py") + assert snap_compare(SNAPSHOT_APPS_DIR / "option_list.py", press=["a"]) def test_option_list_replace_prompt_from_single_line_to_single_line(snap_compare):
{ "difficulty": "medium", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
vllm-project__vllm-9846@d598a7c
vllm-project/vllm
Python
9,846
[CI/Build] Add Model Tests for Qwen2-VL
- Adds tests for comparing the HF / vLLM outputs for Qwen2 VL for single image / multi-image / video using the default size factors - Moves the `runner_mm_key` from the test `VLMTestInfo` -> `CustomTestOptions` and sets `runner_mm_key` for `images` for single/multi image / embedding tests, and `videos` for video tests The tests added here work without issue and don't run into the long context issues that seem to be floating around for qwen2-vl models - we can add another custom inputs case for qwen2-vl for that case as we investigate. Also, split up the VLM tests into standard and extended subsets. FIX #7439 **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Adding or changing kernels</h3> <p>Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.</p> <ul> <li>Make sure custom ops are registered following PyTorch guidelines: <a href="https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial">Custom C++ and CUDA Operators</a> and <a href="https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU">The Custom Operators Manual</a></li> <li>Custom operations that return <code>Tensors</code> require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.</li> <li>Use <a href="https://pytorch.org/docs/stable/library.html#torch.library.opcheck"><code>torch.libary.opcheck()</code></a> to test the function registration and meta-function for any registered ops. See <code>tests/kernels</code> for examples.</li> <li>When changing the C++ signature of an existing op, the schema must be updated to reflect the changes.</li> <li>If a new custom type is needed, see the following document: <a href="https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA">Custom Class Support in PT2</a>. </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-10-30T17:41:33Z
[Feature]: CI - Split up "Models Test" and "Vision Language Models Test" ### ๐Ÿš€ The feature, motivation and pitch Takes 1 hour+ on CI compared to others, which take <~30 min. Thus, ends up being a bottleneck So, should be split up similar to kernels CC: @khluu
root for this! This sounds great! However, we should still avoid regressions. I suggest testing a representative subset per PR and perform full testing once in a while. Any ideas on which models we should always test? A more concrete plan: Let's add a pytest marker `core_model` to explicitly tag model tests to be tested in each PR. By default, unmarked model tests will only be tested daily. Separately, we can organize the models tests as follows: - `models/embedding/language` - `models/encoder_decoder/language` - `models/decoder_only/language` - `models/decoder_only/vision` - `models/decoder_only/audio` (once #7615 is merged) The first three are currently included in Models test while the fourth one is currently in VLM test. They are split apart further so that model PRs can easily run only the tests that are relevant to their model. (By the way, the models directory in main vLLM is pretty cluttered... perhaps we can also organize the model files as above?) In the normal CI, we can add the pytest flag `-m core_model` so that only the tagged tests are run. The full CI (run daily) omits this flag to run all tests. Regarding which model tests to mark as core_model: - We should select one model per family of architectures. - For multi-modal models, we should consider one model with multi-modal encoder defined by Transformers, and another one that is defined by ourselves. - There is no need to consider LoRA, TP/PP and quantization behavior since their basic implementations are covered by other tests already. The full CI should be able to catch any regressions of those features for individual models. Hmm, it sounds unreliable to ask users to run specified model tests... in pytorch for instance it's mandatory to run quite comprehensive test suite in every PR I think that changing this behaviour will potentially introduce uncaught bugs. > Separately, we can organize the models tests as follows: > > models/embedding/language > models/encoder_decoder/language > models/decoder_only/language > models/decoder_only/vision > models/decoder_only/audio (once https://github.com/vllm-project/vllm/pull/7615 is merged) I think that first step of splitting up like this is a good idea. After some offline discussion, we have decided to **cut down on the number of models to regularly test in CI**. This is due to the following reasons: - Most of the model tests aim to test the correctness of the model implementation. However, there is no need to repeatedly test this unless the model file itself is updated. Instead, we can cover the code paths in model loader, layers, executor and runner (which are shared between models) using a relatively small number of tests. - Some of the model PRs are from the same organization who originally developed the model. In this case, we trust the authors to implement their own model correctly. - With the growing number of models supported by vLLM, it becomes increasingly expensive to test them all in the long run. For encoder-decoder and embedding models, we will preserve all tests since there are only a few tests involved. For multimodal models, @ywang96 and I have decided to only test the following models regularly in CI: - LLaVA w/ our implementation of `CLIPEncoder` - PaliGemma w/ our implementation of `SiglipEncoder` - Ultravox w/ HuggingFace's implementation of `WhisperEncoder` - (A model that supports video input, once it's implemented in vLLM. Qwen2-VL will add M-ROPE so we should test that) Meanwhile, @simon-mo will help narrow down the list of decoder-only language models to test. **Note that this does not remove the need to test new models.** New model PRs should still implement tests so we can verify their implementation, as well as re-run those tests whenever a future PR updates related code. Until @khluu figures out how to create a separate test item for each model without clogging up the web UI, these tests will remain excluded from CI; instead, we can run these tests locally as necessary. Closing as completed by #9488. VLM tests will be further split up after #9372.
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\r\n\r\nTakes 1 hour+ on CI compared to others, which take <~30 min. Thus, ends up being a bottleneck\r\n\r\nSo, should be split up similar to kernels\r\n\r\nCC: @khluu \r\n", "number": 7439, "title": "[Feature]: CI - Split up \"Models Test\" and \"Vision Language Models Test\"" } ]
5608e611c2116cc17c6808b2ae1ecb4a3e263493
{ "head_commit": "d598a7c0cd50bddfd3f04f826575b89fa5175129", "head_commit_message": "Separate core / extended mm tests\n\nSigned-off-by: Alex-Brooks <[email protected]>", "patch_to_review": "diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml\nindex 32eed1a77171..49639b59c7c5 100644\n--- a/.buildkite/test-pipeline.yaml\n+++ b/.buildkite/test-pipeline.yaml\n@@ -330,18 +330,28 @@ steps:\n commands:\n - pytest -v -s models/decoder_only/language --ignore=models/decoder_only/language/test_models.py --ignore=models/decoder_only/language/test_big_models.py\n \n-- label: Decoder-only Multi-Modal Models Test # 1h31min\n+- label: Decoder-only Multi-Modal Models Test (Standard)\n #mirror_hardwares: [amd]\n source_file_dependencies:\n - vllm/\n - tests/models/decoder_only/audio_language\n - tests/models/decoder_only/vision_language\n commands:\n- - pytest -v -s models/decoder_only/audio_language\n+ - pytest -v -s models/decoder_only/audio_language -m core_mm_model_test\n+ - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m core_mm_model_test\n+\n+- label: Decoder-only Multi-Modal Models Test (Extended)\n+ #mirror_hardwares: [amd]\n+ source_file_dependencies:\n+ - vllm/\n+ - tests/models/decoder_only/audio_language\n+ - tests/models/decoder_only/vision_language\n+ commands:\n+ - pytest -v -s models/decoder_only/audio_language -m 'not core_mm_model_test'\n # HACK - run phi3v tests separately to sidestep this transformers bug\n # https://github.com/huggingface/transformers/issues/34307\n - pytest -v -s models/decoder_only/vision_language/test_phi3v.py\n- - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language\n+ - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_mm_model_test'\n \n - label: Other Models Test # 6min\n #mirror_hardwares: [amd]\ndiff --git a/pyproject.toml b/pyproject.toml\nindex e78f5652f486..e6571a449b73 100644\n--- a/pyproject.toml\n+++ b/pyproject.toml\n@@ -97,4 +97,5 @@ markers = [\n \"skip_global_cleanup\",\n \"core_model: run this model test in each PR instead of just daily\",\n \"distributed_2_gpus: run this test only in distributed tests for 2 GPUs\",\n+ \"core_mm_model_test: run this vlm test in the core decoder only mm tests\",\n ]\ndiff --git a/tests/models/decoder_only/audio_language/test_ultravox.py b/tests/models/decoder_only/audio_language/test_ultravox.py\nindex ad6c2d854d1f..ab45bb8d2997 100644\n--- a/tests/models/decoder_only/audio_language/test_ultravox.py\n+++ b/tests/models/decoder_only/audio_language/test_ultravox.py\n@@ -158,6 +158,7 @@ def run_multi_audio_test(\n assert all(tokens for tokens, *_ in vllm_outputs)\n \n \[email protected]_mm_model_test\n @pytest.mark.parametrize(\"dtype\", [\"half\"])\n @pytest.mark.parametrize(\"max_tokens\", [128])\n @pytest.mark.parametrize(\"num_logprobs\", [5])\n@@ -178,6 +179,7 @@ def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,\n )\n \n \[email protected]_mm_model_test\n @pytest.mark.parametrize(\"dtype\", [\"half\"])\n @pytest.mark.parametrize(\"max_tokens\", [128])\n @pytest.mark.parametrize(\"num_logprobs\", [5])\ndiff --git a/tests/models/decoder_only/vision_language/test_models.py b/tests/models/decoder_only/vision_language/test_models.py\nindex 9370527e3cd5..ffc42e729fcf 100644\n--- a/tests/models/decoder_only/vision_language/test_models.py\n+++ b/tests/models/decoder_only/vision_language/test_models.py\n@@ -75,6 +75,62 @@\n # this is a good idea for checking your command first, since tests are slow.\n \n VLM_TEST_SETTINGS = {\n+ #### Core tests to always run in the CI\n+ \"llava\": VLMTestInfo(\n+ models=[\"llava-hf/llava-1.5-7b-hf\"],\n+ test_type=(\n+ VLMTestType.EMBEDDING,\n+ VLMTestType.IMAGE,\n+ VLMTestType.CUSTOM_INPUTS\n+ ),\n+ prompt_formatter=lambda img_prompt: f\"USER: {img_prompt}\\nASSISTANT:\",\n+ convert_assets_to_embeddings=model_utils.get_llava_embeddings,\n+ max_model_len=4096,\n+ auto_cls=AutoModelForVision2Seq,\n+ vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,\n+ custom_test_opts=[CustomTestOptions(\n+ inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(\n+ formatter=lambda img_prompt: f\"USER: {img_prompt}\\nASSISTANT:\"\n+ ),\n+ limit_mm_per_prompt={\"image\": 4},\n+ )],\n+ marks=[pytest.mark.core_mm_model_test],\n+ ),\n+ \"paligemma\": VLMTestInfo(\n+ models=[\"google/paligemma-3b-mix-224\"],\n+ test_type=VLMTestType.IMAGE,\n+ prompt_formatter=identity,\n+ img_idx_to_prompt = lambda idx: \"\",\n+ # Paligemma uses its own sample prompts because the default one fails\n+ single_image_prompts=IMAGE_ASSETS.prompts({\n+ \"stop_sign\": \"caption es\",\n+ \"cherry_blossom\": \"What is in the picture?\",\n+ }),\n+ auto_cls=AutoModelForVision2Seq,\n+ postprocess_inputs=model_utils.get_key_type_post_processor(\n+ \"pixel_values\"\n+ ),\n+ vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,\n+ dtype=\"half\" if current_platform.is_rocm() else (\"half\", \"float\"),\n+ marks=[pytest.mark.core_mm_model_test],\n+ ),\n+ \"qwen2vl\": VLMTestInfo(\n+ models=[\"Qwen/Qwen2-VL-2B-Instruct\"],\n+ test_type=(\n+ VLMTestType.IMAGE,\n+ VLMTestType.MULTI_IMAGE,\n+ VLMTestType.VIDEO\n+ ),\n+ prompt_formatter=lambda img_prompt: f\"<|im_start|>User\\n{img_prompt}<|im_end|>\\n<|im_start|>assistant\\n\", # noqa: E501\n+ img_idx_to_prompt=lambda idx: \"<|vision_start|><|image_pad|><|vision_end|>\", # noqa: E501\n+ video_idx_to_prompt=lambda idx: \"<|vision_start|><|video_pad|><|vision_end|>\", # noqa: E501\n+ max_model_len=8192,\n+ max_num_seqs=2,\n+ auto_cls=AutoModelForVision2Seq,\n+ vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,\n+ marks=[pytest.mark.core_mm_model_test],\n+ ),\n+ #### Extended model tests\n \"blip2\": VLMTestInfo(\n models=[\"Salesforce/blip2-opt-2.7b\"],\n test_type=VLMTestType.IMAGE,\n@@ -151,25 +207,6 @@\n use_tokenizer_eos=True,\n patch_hf_runner=model_utils.internvl_patch_hf_runner,\n ),\n- \"llava\": VLMTestInfo(\n- models=[\"llava-hf/llava-1.5-7b-hf\"],\n- test_type=(\n- VLMTestType.EMBEDDING,\n- VLMTestType.IMAGE,\n- VLMTestType.CUSTOM_INPUTS\n- ),\n- prompt_formatter=lambda img_prompt: f\"USER: {img_prompt}\\nASSISTANT:\",\n- convert_assets_to_embeddings=model_utils.get_llava_embeddings,\n- max_model_len=4096,\n- auto_cls=AutoModelForVision2Seq,\n- vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,\n- custom_test_opts=[CustomTestOptions(\n- inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs(\n- formatter=lambda img_prompt: f\"USER: {img_prompt}\\nASSISTANT:\"\n- ),\n- limit_mm_per_prompt={\"image\": 4},\n- )],\n- ),\n \"llava_next\": VLMTestInfo(\n models=[\"llava-hf/llava-v1.6-mistral-7b-hf\"],\n test_type=(VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS),\n@@ -200,12 +237,12 @@\n vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,\n # Llava-one-vision tests fixed sizes & the default size factors\n image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))],\n- runner_mm_key=\"videos\",\n custom_test_opts=[CustomTestOptions(\n inputs=custom_inputs.multi_video_multi_aspect_ratio_inputs(\n formatter=lambda vid_prompt: f\"<|im_start|>user\\n{vid_prompt}<|im_end|>\\n<|im_start|>assistant\\n\", # noqa: E501\n ),\n limit_mm_per_prompt={\"video\": 4},\n+ runner_mm_key=\"videos\",\n )],\n ),\n # FIXME\n@@ -218,9 +255,11 @@\n auto_cls=AutoModelForVision2Seq,\n vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output,\n image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))],\n- runner_mm_key=\"videos\",\n marks=[\n- pytest.mark.skip(reason=\"LLava next video tests currently fail.\")\n+ pytest.mark.skipif(\n+ transformers.__version__.startswith(\"4.46\"),\n+ reason=\"Model broken with changes in transformers 4.46\"\n+ )\n ],\n ),\n \"minicpmv\": VLMTestInfo(\n@@ -234,23 +273,6 @@\n postprocess_inputs=model_utils.wrap_inputs_post_processor,\n hf_output_post_proc=model_utils.minicmpv_trunc_hf_output,\n ),\n- \"paligemma\": VLMTestInfo(\n- models=[\"google/paligemma-3b-mix-224\"],\n- test_type=VLMTestType.IMAGE,\n- prompt_formatter=identity,\n- img_idx_to_prompt = lambda idx: \"\",\n- # Paligemma uses its own sample prompts because the default one fails\n- single_image_prompts=IMAGE_ASSETS.prompts({\n- \"stop_sign\": \"caption es\",\n- \"cherry_blossom\": \"What is in the picture?\",\n- }),\n- auto_cls=AutoModelForVision2Seq,\n- postprocess_inputs=model_utils.get_key_type_post_processor(\n- \"pixel_values\"\n- ),\n- vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,\n- dtype=\"half\" if current_platform.is_rocm() else (\"half\", \"float\"),\n- ),\n # Tests for phi3v currently live in another file because of a bug in\n # transformers. Once this issue is fixed, we can enable them here instead.\n # https://github.com/huggingface/transformers/issues/34307\ndiff --git a/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py b/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py\nindex 6856e8df81a1..e925934db0e7 100644\n--- a/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py\n+++ b/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py\n@@ -56,6 +56,17 @@ def qwen_vllm_to_hf_output(\n return output_ids, hf_output_str, out_logprobs\n \n \n+def qwen2_vllm_to_hf_output(\n+ vllm_output: RunnerOutput,\n+ model: str) -> Tuple[List[int], str, Optional[SampleLogprobs]]:\n+ \"\"\"Sanitize vllm output [qwen2 models] to be comparable with hf output.\"\"\"\n+ output_ids, output_str, out_logprobs = vllm_output\n+\n+ hf_output_str = output_str + \"<|im_end|>\"\n+\n+ return output_ids, hf_output_str, out_logprobs\n+\n+\n def llava_image_vllm_to_hf_output(vllm_output: RunnerOutput,\n model: str) -> RunnerOutput:\n config = AutoConfig.from_pretrained(model)\ndiff --git a/tests/models/decoder_only/vision_language/vlm_utils/runners.py b/tests/models/decoder_only/vision_language/vlm_utils/runners.py\nindex 5a3f9e820dad..2d3b39fe3594 100644\n--- a/tests/models/decoder_only/vision_language/vlm_utils/runners.py\n+++ b/tests/models/decoder_only/vision_language/vlm_utils/runners.py\n@@ -29,6 +29,7 @@ def run_single_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,\n num_logprobs=test_case.num_logprobs,\n limit_mm_per_prompt={\"image\": 1},\n distributed_executor_backend=test_case.distributed_executor_backend,\n+ runner_mm_key=\"images\",\n **model_test_info.get_non_parametrized_runner_kwargs())\n \n \n@@ -51,6 +52,7 @@ def run_multi_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,\n num_logprobs=test_case.num_logprobs,\n limit_mm_per_prompt={\"image\": len(image_assets)},\n distributed_executor_backend=test_case.distributed_executor_backend,\n+ runner_mm_key=\"images\",\n **model_test_info.get_non_parametrized_runner_kwargs())\n \n \n@@ -74,6 +76,7 @@ def run_embedding_test(*, model_test_info: VLMTestInfo,\n limit_mm_per_prompt={\"image\": 1},\n vllm_embeddings=vllm_embeddings,\n distributed_executor_backend=test_case.distributed_executor_backend,\n+ runner_mm_key=\"images\",\n **model_test_info.get_non_parametrized_runner_kwargs())\n \n \n@@ -101,6 +104,7 @@ def run_video_test(\n num_logprobs=test_case.num_logprobs,\n limit_mm_per_prompt={\"video\": len(video_assets)},\n distributed_executor_backend=test_case.distributed_executor_backend,\n+ runner_mm_key=\"videos\",\n **model_test_info.get_non_parametrized_runner_kwargs())\n \n \n@@ -115,7 +119,11 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo,\n \n inputs = test_case.custom_test_opts.inputs\n limit_mm_per_prompt = test_case.custom_test_opts.limit_mm_per_prompt\n- assert inputs is not None and limit_mm_per_prompt is not None\n+ runner_mm_key = test_case.custom_test_opts.runner_mm_key\n+ # Inputs, limit_mm_per_prompt, and runner_mm_key should all be set\n+ assert inputs is not None\n+ assert limit_mm_per_prompt is not None\n+ assert runner_mm_key is not None\n \n core.run_test(\n hf_runner=hf_runner,\n@@ -127,4 +135,5 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo,\n num_logprobs=test_case.num_logprobs,\n limit_mm_per_prompt=limit_mm_per_prompt,\n distributed_executor_backend=test_case.distributed_executor_backend,\n+ runner_mm_key=runner_mm_key,\n **model_test_info.get_non_parametrized_runner_kwargs())\ndiff --git a/tests/models/decoder_only/vision_language/vlm_utils/types.py b/tests/models/decoder_only/vision_language/vlm_utils/types.py\nindex 4d18d53af30f..fd18c7c8346f 100644\n--- a/tests/models/decoder_only/vision_language/vlm_utils/types.py\n+++ b/tests/models/decoder_only/vision_language/vlm_utils/types.py\n@@ -52,6 +52,8 @@ class SizeType(Enum):\n class CustomTestOptions(NamedTuple):\n inputs: List[Tuple[List[str], List[Union[List[Image], Image]]]]\n limit_mm_per_prompt: Dict[str, int]\n+ # kwarg to pass multimodal data in as to vllm/hf runner instances.\n+ runner_mm_key: str = \"images\"\n \n \n class ImageSizeWrapper(NamedTuple):\n@@ -141,9 +143,6 @@ class VLMTestInfo(NamedTuple):\n Callable[[PosixPath, str, Union[List[ImageAsset], _ImageAssets]],\n str]] = None # noqa: E501\n \n- # kwarg to pass multimodal data in as to vllm/hf runner instances\n- runner_mm_key: str = \"images\"\n-\n # Allows configuring a test to run with custom inputs\n custom_test_opts: Optional[List[CustomTestOptions]] = None\n \n@@ -168,7 +167,6 @@ def get_non_parametrized_runner_kwargs(self):\n \"get_stop_token_ids\": self.get_stop_token_ids,\n \"model_kwargs\": self.model_kwargs,\n \"patch_hf_runner\": self.patch_hf_runner,\n- \"runner_mm_key\": self.runner_mm_key,\n }\n \n \ndiff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py\nindex a8d0ac4fc160..9fab5898a06b 100644\n--- a/tests/models/embedding/vision_language/test_llava_next.py\n+++ b/tests/models/embedding/vision_language/test_llava_next.py\n@@ -2,6 +2,7 @@\n \n import pytest\n import torch.nn.functional as F\n+import transformers\n from transformers import AutoModelForVision2Seq\n \n from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner\n@@ -85,8 +86,8 @@ def _run_test(\n )\n \n \n-# FIXME\[email protected](reason=\"LLava next embedding tests currently fail\")\[email protected](transformers.__version__.startswith(\"4.46\"),\n+ reason=\"Model broken with changes in transformers 4.46\")\n @pytest.mark.parametrize(\"model\", MODELS)\n @pytest.mark.parametrize(\"dtype\", [\"half\"])\n def test_models_text(\n" }
[ { "diff_hunk": "@@ -97,4 +97,5 @@ markers = [\n \"skip_global_cleanup\",\n \"core_model: run this model test in each PR instead of just daily\",\n \"distributed_2_gpus: run this test only in distributed tests for 2 GPUs\",\n+ \"core_mm_model_test: run this vlm test in the core decoder only mm tests\",", "line": null, "original_line": 100, "original_start_line": null, "path": "pyproject.toml", "start_line": null, "text": "@user1:\nLet's use the `core_model` label. I reserved it for this use case long ago.\n\n@author:\nSounds good!" } ]
df000e757947374c2f65f95dde64c02de72f5bd3
diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 32eed1a77171..9444dc43ea97 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -9,6 +9,7 @@ # label(str): the name of the test. emoji allowed. # fast_check(bool): whether to run this on each commit on fastcheck pipeline. # fast_check_only(bool): run this test on fastcheck pipeline only +# nightly(bool): run this test in nightly pipeline only # optional(bool): never run this test by default (i.e. need to unblock manually) # command(str): the single command to run for tests. incompatible with commands. # commands(list): the list of commands to run for test. incompatbile with command. @@ -330,18 +331,28 @@ steps: commands: - pytest -v -s models/decoder_only/language --ignore=models/decoder_only/language/test_models.py --ignore=models/decoder_only/language/test_big_models.py -- label: Decoder-only Multi-Modal Models Test # 1h31min +- label: Decoder-only Multi-Modal Models Test (Standard) #mirror_hardwares: [amd] source_file_dependencies: - vllm/ - tests/models/decoder_only/audio_language - tests/models/decoder_only/vision_language commands: - - pytest -v -s models/decoder_only/audio_language + - pytest -v -s models/decoder_only/audio_language -m core_model + - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m core_model + +- label: Decoder-only Multi-Modal Models Test (Extended) + nightly: true + source_file_dependencies: + - vllm/ + - tests/models/decoder_only/audio_language + - tests/models/decoder_only/vision_language + commands: + - pytest -v -s models/decoder_only/audio_language -m 'not core_model' # HACK - run phi3v tests separately to sidestep this transformers bug # https://github.com/huggingface/transformers/issues/34307 - pytest -v -s models/decoder_only/vision_language/test_phi3v.py - - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language + - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model' - label: Other Models Test # 6min #mirror_hardwares: [amd] diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 83d2548a506e..60cdb186331f 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -262,10 +262,9 @@ def run_qwen2_vl(question: str, modality: str): model_name = "Qwen/Qwen2-VL-7B-Instruct" - # Tested on L40 llm = LLM( model=model_name, - max_model_len=8192, + max_model_len=4096, max_num_seqs=5, # Note - mm_processor_kwargs can also be passed to generate/chat calls mm_processor_kwargs={ diff --git a/tests/models/decoder_only/audio_language/test_ultravox.py b/tests/models/decoder_only/audio_language/test_ultravox.py index ad6c2d854d1f..b9089e75ffab 100644 --- a/tests/models/decoder_only/audio_language/test_ultravox.py +++ b/tests/models/decoder_only/audio_language/test_ultravox.py @@ -158,6 +158,7 @@ def run_multi_audio_test( assert all(tokens for tokens, *_ in vllm_outputs) [email protected]_model @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [128]) @pytest.mark.parametrize("num_logprobs", [5]) @@ -178,6 +179,7 @@ def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int, ) [email protected]_model @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [128]) @pytest.mark.parametrize("num_logprobs", [5]) diff --git a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py index 5c90e7f7a267..c23fbedf0c6a 100644 --- a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py +++ b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py @@ -17,7 +17,7 @@ # Fixtures lazy import to avoid initializing CUDA during test collection -# NOTE: Qwen2vl supports multiple input modalities, so it registers multiple +# NOTE: Qwen2VL supports multiple input modalities, so it registers multiple # input mappers. @pytest.fixture() def image_input_mapper_for_qwen2_vl(): diff --git a/tests/models/decoder_only/vision_language/test_models.py b/tests/models/decoder_only/vision_language/test_models.py index 9370527e3cd5..d738647c91b6 100644 --- a/tests/models/decoder_only/vision_language/test_models.py +++ b/tests/models/decoder_only/vision_language/test_models.py @@ -75,6 +75,63 @@ # this is a good idea for checking your command first, since tests are slow. VLM_TEST_SETTINGS = { + #### Core tests to always run in the CI + "llava": VLMTestInfo( + models=["llava-hf/llava-1.5-7b-hf"], + test_type=( + VLMTestType.EMBEDDING, + VLMTestType.IMAGE, + VLMTestType.CUSTOM_INPUTS + ), + prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", + convert_assets_to_embeddings=model_utils.get_llava_embeddings, + max_model_len=4096, + auto_cls=AutoModelForVision2Seq, + vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, + custom_test_opts=[CustomTestOptions( + inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs( + formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:" + ), + limit_mm_per_prompt={"image": 4}, + )], + marks=[pytest.mark.core_model], + ), + "paligemma": VLMTestInfo( + models=["google/paligemma-3b-mix-224"], + test_type=VLMTestType.IMAGE, + prompt_formatter=identity, + img_idx_to_prompt = lambda idx: "", + # Paligemma uses its own sample prompts because the default one fails + single_image_prompts=IMAGE_ASSETS.prompts({ + "stop_sign": "caption es", + "cherry_blossom": "What is in the picture?", + }), + auto_cls=AutoModelForVision2Seq, + postprocess_inputs=model_utils.get_key_type_post_processor( + "pixel_values" + ), + vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output, + dtype="half" if current_platform.is_rocm() else ("half", "float"), + marks=[pytest.mark.core_model], + ), + "qwen2_vl": VLMTestInfo( + models=["Qwen/Qwen2-VL-2B-Instruct"], + test_type=( + VLMTestType.IMAGE, + VLMTestType.MULTI_IMAGE, + VLMTestType.VIDEO + ), + prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501 + img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>", # noqa: E501 + video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>", # noqa: E501 + max_model_len=4096, + max_num_seqs=2, + auto_cls=AutoModelForVision2Seq, + vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output, + marks=[pytest.mark.core_model], + image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)], + ), + #### Extended model tests "blip2": VLMTestInfo( models=["Salesforce/blip2-opt-2.7b"], test_type=VLMTestType.IMAGE, @@ -151,25 +208,6 @@ use_tokenizer_eos=True, patch_hf_runner=model_utils.internvl_patch_hf_runner, ), - "llava": VLMTestInfo( - models=["llava-hf/llava-1.5-7b-hf"], - test_type=( - VLMTestType.EMBEDDING, - VLMTestType.IMAGE, - VLMTestType.CUSTOM_INPUTS - ), - prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", - convert_assets_to_embeddings=model_utils.get_llava_embeddings, - max_model_len=4096, - auto_cls=AutoModelForVision2Seq, - vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, - custom_test_opts=[CustomTestOptions( - inputs=custom_inputs.multi_image_multi_aspect_ratio_inputs( - formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:" - ), - limit_mm_per_prompt={"image": 4}, - )], - ), "llava_next": VLMTestInfo( models=["llava-hf/llava-v1.6-mistral-7b-hf"], test_type=(VLMTestType.IMAGE, VLMTestType.CUSTOM_INPUTS), @@ -200,12 +238,12 @@ vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output, # Llava-one-vision tests fixed sizes & the default size factors image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))], - runner_mm_key="videos", custom_test_opts=[CustomTestOptions( inputs=custom_inputs.multi_video_multi_aspect_ratio_inputs( formatter=lambda vid_prompt: f"<|im_start|>user\n{vid_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501 ), limit_mm_per_prompt={"video": 4}, + runner_mm_key="videos", )], ), # FIXME @@ -218,9 +256,11 @@ auto_cls=AutoModelForVision2Seq, vllm_output_post_proc=model_utils.llava_video_vllm_to_hf_output, image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))], - runner_mm_key="videos", marks=[ - pytest.mark.skip(reason="LLava next video tests currently fail.") + pytest.mark.skipif( + transformers.__version__.startswith("4.46"), + reason="Model broken with changes in transformers 4.46" + ) ], ), "minicpmv": VLMTestInfo( @@ -234,23 +274,6 @@ postprocess_inputs=model_utils.wrap_inputs_post_processor, hf_output_post_proc=model_utils.minicmpv_trunc_hf_output, ), - "paligemma": VLMTestInfo( - models=["google/paligemma-3b-mix-224"], - test_type=VLMTestType.IMAGE, - prompt_formatter=identity, - img_idx_to_prompt = lambda idx: "", - # Paligemma uses its own sample prompts because the default one fails - single_image_prompts=IMAGE_ASSETS.prompts({ - "stop_sign": "caption es", - "cherry_blossom": "What is in the picture?", - }), - auto_cls=AutoModelForVision2Seq, - postprocess_inputs=model_utils.get_key_type_post_processor( - "pixel_values" - ), - vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output, - dtype="half" if current_platform.is_rocm() else ("half", "float"), - ), # Tests for phi3v currently live in another file because of a bug in # transformers. Once this issue is fixed, we can enable them here instead. # https://github.com/huggingface/transformers/issues/34307 diff --git a/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py b/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py index 6856e8df81a1..e925934db0e7 100644 --- a/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py +++ b/tests/models/decoder_only/vision_language/vlm_utils/model_utils.py @@ -56,6 +56,17 @@ def qwen_vllm_to_hf_output( return output_ids, hf_output_str, out_logprobs +def qwen2_vllm_to_hf_output( + vllm_output: RunnerOutput, + model: str) -> Tuple[List[int], str, Optional[SampleLogprobs]]: + """Sanitize vllm output [qwen2 models] to be comparable with hf output.""" + output_ids, output_str, out_logprobs = vllm_output + + hf_output_str = output_str + "<|im_end|>" + + return output_ids, hf_output_str, out_logprobs + + def llava_image_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOutput: config = AutoConfig.from_pretrained(model) diff --git a/tests/models/decoder_only/vision_language/vlm_utils/runners.py b/tests/models/decoder_only/vision_language/vlm_utils/runners.py index 5a3f9e820dad..2d3b39fe3594 100644 --- a/tests/models/decoder_only/vision_language/vlm_utils/runners.py +++ b/tests/models/decoder_only/vision_language/vlm_utils/runners.py @@ -29,6 +29,7 @@ def run_single_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo, num_logprobs=test_case.num_logprobs, limit_mm_per_prompt={"image": 1}, distributed_executor_backend=test_case.distributed_executor_backend, + runner_mm_key="images", **model_test_info.get_non_parametrized_runner_kwargs()) @@ -51,6 +52,7 @@ def run_multi_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo, num_logprobs=test_case.num_logprobs, limit_mm_per_prompt={"image": len(image_assets)}, distributed_executor_backend=test_case.distributed_executor_backend, + runner_mm_key="images", **model_test_info.get_non_parametrized_runner_kwargs()) @@ -74,6 +76,7 @@ def run_embedding_test(*, model_test_info: VLMTestInfo, limit_mm_per_prompt={"image": 1}, vllm_embeddings=vllm_embeddings, distributed_executor_backend=test_case.distributed_executor_backend, + runner_mm_key="images", **model_test_info.get_non_parametrized_runner_kwargs()) @@ -101,6 +104,7 @@ def run_video_test( num_logprobs=test_case.num_logprobs, limit_mm_per_prompt={"video": len(video_assets)}, distributed_executor_backend=test_case.distributed_executor_backend, + runner_mm_key="videos", **model_test_info.get_non_parametrized_runner_kwargs()) @@ -115,7 +119,11 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo, inputs = test_case.custom_test_opts.inputs limit_mm_per_prompt = test_case.custom_test_opts.limit_mm_per_prompt - assert inputs is not None and limit_mm_per_prompt is not None + runner_mm_key = test_case.custom_test_opts.runner_mm_key + # Inputs, limit_mm_per_prompt, and runner_mm_key should all be set + assert inputs is not None + assert limit_mm_per_prompt is not None + assert runner_mm_key is not None core.run_test( hf_runner=hf_runner, @@ -127,4 +135,5 @@ def run_custom_inputs_test(*, model_test_info: VLMTestInfo, num_logprobs=test_case.num_logprobs, limit_mm_per_prompt=limit_mm_per_prompt, distributed_executor_backend=test_case.distributed_executor_backend, + runner_mm_key=runner_mm_key, **model_test_info.get_non_parametrized_runner_kwargs()) diff --git a/tests/models/decoder_only/vision_language/vlm_utils/types.py b/tests/models/decoder_only/vision_language/vlm_utils/types.py index 4d18d53af30f..fd18c7c8346f 100644 --- a/tests/models/decoder_only/vision_language/vlm_utils/types.py +++ b/tests/models/decoder_only/vision_language/vlm_utils/types.py @@ -52,6 +52,8 @@ class SizeType(Enum): class CustomTestOptions(NamedTuple): inputs: List[Tuple[List[str], List[Union[List[Image], Image]]]] limit_mm_per_prompt: Dict[str, int] + # kwarg to pass multimodal data in as to vllm/hf runner instances. + runner_mm_key: str = "images" class ImageSizeWrapper(NamedTuple): @@ -141,9 +143,6 @@ class VLMTestInfo(NamedTuple): Callable[[PosixPath, str, Union[List[ImageAsset], _ImageAssets]], str]] = None # noqa: E501 - # kwarg to pass multimodal data in as to vllm/hf runner instances - runner_mm_key: str = "images" - # Allows configuring a test to run with custom inputs custom_test_opts: Optional[List[CustomTestOptions]] = None @@ -168,7 +167,6 @@ def get_non_parametrized_runner_kwargs(self): "get_stop_token_ids": self.get_stop_token_ids, "model_kwargs": self.model_kwargs, "patch_hf_runner": self.patch_hf_runner, - "runner_mm_key": self.runner_mm_key, } diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py index a8d0ac4fc160..9fab5898a06b 100644 --- a/tests/models/embedding/vision_language/test_llava_next.py +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -2,6 +2,7 @@ import pytest import torch.nn.functional as F +import transformers from transformers import AutoModelForVision2Seq from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner @@ -85,8 +86,8 @@ def _run_test( ) -# FIXME [email protected](reason="LLava next embedding tests currently fail") [email protected](transformers.__version__.startswith("4.46"), + reason="Model broken with changes in transformers 4.46") @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["half"]) def test_models_text(
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Test Suite / CI Enhancements" }
vllm-project__vllm-9242@2719fac
vllm-project/vllm
Python
9,242
[Model] Add GLM-4v support and meet vllm==0.6.2
Overview --- This PR support the [glm-4v-9b](https://github.com/THUDM/GLM-4) multimodal model while maintaining compatibility with `chatglm`. This PR was inspired and reused some code here [#5358](https://github.com/vllm-project/vllm/pull/5358) This PR is updated from [#8663](https://github.com/vllm-project/vllm/pull/8663) FIX #5417 FIX #6097 Changes --- 1. Add `vision_config` for `ChatGLMConfig` 2. Add glm4 vision encoder in `vllm/model_executor/models/glm4_vision_encoder.py`. 3. Add optional `vision` module for `ChatGLMModel`, making `ChatGLMForCausalLM` multimodal capable. 4. Added support for receiving and processing `image_embeds` parameters 5. Added the weight loading logic 6. Add pytest 7. Add example Development Environment --- ```Plain Text vllm==0.6.2.post2+cu123 vllm-flash-attn==2.6.1 transformers==4.44.2 torch==2.4.0 torchvision==0.19.0 cuda==12.3 python==3.10 ``` **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Adding or changing kernels</h3> <p>Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.</p> <ul> <li>Make sure custom ops are registered following PyTorch guidelines: <a href="https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial">Custom C++ and CUDA Operators</a> and <a href="https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU">The Custom Operators Manual</a></li> <li>Custom operations that return <code>Tensors</code> require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.</li> <li>Use <a href="https://pytorch.org/docs/stable/library.html#torch.library.opcheck"><code>torch.libary.opcheck()</code></a> to test the function registration and meta-function for any registered ops. See <code>tests/kernels</code> for examples.</li> <li>When changing the C++ signature of an existing op, the schema must be updated to reflect the changes.</li> <li>If a new custom type is needed, see the following document: <a href="https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA">Custom Class Support in PT2</a>. </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-10-10T10:04:33Z
[Bug]: vllm deployment of GLM-4V reports KeyError: 'transformer.vision.transformer.layers.45.mlp.fc2.weight' ### Your current environment ```shell Collecting environment information... PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.29.2 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.4.0-94-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB GPU 1: NVIDIA A100-SXM4-80GB GPU 2: NVIDIA A100-SXM4-80GB GPU 3: NVIDIA A100-SXM4-80GB GPU 4: NVIDIA A100-SXM4-80GB GPU 5: NVIDIA A100-SXM4-80GB GPU 6: NVIDIA A100-SXM4-80GB GPU 7: NVIDIA A100-SXM4-80GB Nvidia driver version: 535.104.05 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 43 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: AuthenticAMD Model name: AMD EPYC 7742 64-Core Processor CPU family: 23 Model: 49 Thread(s) per core: 1 Core(s) per socket: 64 Socket(s): 2 Stepping: 0 Frequency boost: enabled CPU max MHz: 2250.0000 CPU min MHz: 1500.0000 BogoMIPS: 4499.81 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca Virtualization: AMD-V L1d cache: 4 MiB (128 instances) L1i cache: 4 MiB (128 instances) L2 cache: 64 MiB (128 instances) L3 cache: 512 MiB (32 instances) NUMA node(s): 8 NUMA node0 CPU(s): 0-15 NUMA node1 CPU(s): 16-31 NUMA node2 CPU(s): 32-47 NUMA node3 CPU(s): 48-63 NUMA node4 CPU(s): 64-79 NUMA node5 CPU(s): 80-95 NUMA node6 CPU(s): 96-111 NUMA node7 CPU(s): 112-127 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Full AMD retpoline, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] torch==2.3.0 [pip3] torchvision==0.18.1 [pip3] transformers==4.40.0 [pip3] triton==2.3.0 [pip3] vllm-nccl-cu12==2.18.1.0.4.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.3 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS 48-63 3 N/A GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS 48-63 3 N/A GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 16-31 1 N/A GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 16-31 1 N/A GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS SYS SYS SYS SYS SYS PXB PXB 112-127 7 N/A GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS SYS SYS SYS SYS SYS PXB PXB 112-127 7 N/A GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS 80-95 5 N/A GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS 80-95 5 N/A NIC0 SYS SYS PXB PXB SYS SYS SYS SYS X PIX SYS SYS SYS SYS SYS SYS SYS SYS NIC1 SYS SYS PXB PXB SYS SYS SYS SYS PIX X SYS SYS SYS SYS SYS SYS SYS SYS NIC2 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS X PXB SYS SYS SYS SYS SYS SYS NIC3 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS PXB X SYS SYS SYS SYS SYS SYS NIC4 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS X PIX SYS SYS SYS SYS NIC5 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS PIX X SYS SYS SYS SYS NIC6 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS X PXB SYS SYS NIC7 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS PXB X SYS SYS NIC8 SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS X PXB NIC9 SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS PXB X Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks NIC Legend: NIC0: mlx5_0 NIC1: mlx5_1 NIC2: mlx5_2 NIC3: mlx5_3 NIC4: mlx5_4 NIC5: mlx5_5 NIC6: mlx5_6 NIC7: mlx5_7 NIC8: mlx5_8 NIC9: mlx5_9 ``` ### ๐Ÿ› Describe the bug 1. The command executed when starting CLM4-V using vllm ```shell CUDA_VISIBLE_DEVICES=0,3 python3 -m vllm.entrypoints.openai.api_server --model=/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b --served-model-name=glm-4v-9b --device=cuda --port=8000 --host=0.0.0.0 --tensor-parallel-size=1 --dtype=auto --trust-remote-code ``` 2. errors info ```shell INFO 06-11 08:11:33 llm_engine.py:161] Initializing an LLM engine (v0.4.3) with config: model='/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b', speculative_config=None, tokenizer='/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=glm-4v-9b) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. WARNING 06-11 08:11:34 tokenizer.py:126] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead. [rank0]: Traceback (most recent call last): [rank0]: File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main [rank0]: return _run_code(code, main_globals, None, [rank0]: File "/usr/lib/python3.10/runpy.py", line 86, in _run_code [rank0]: exec(code, run_globals) [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 186, in <module> [rank0]: engine = AsyncLLMEngine.from_engine_args( [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 386, in from_engine_args [rank0]: engine = cls( [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 340, in __init__ [rank0]: self.engine = self._init_engine(*args, **kwargs) [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 462, in _init_engine [rank0]: return engine_class(*args, **kwargs) [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 222, in __init__ [rank0]: self.model_executor = executor_class( [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 41, in __init__ [rank0]: self._init_executor() [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 24, in _init_executor [rank0]: self.driver_worker.load_model() [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 121, in load_model [rank0]: self.model_runner.load_model() [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 134, in load_model [rank0]: self.model = get_model( [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model [rank0]: return loader.load_model(model_config=model_config, [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/loader.py", line 243, in load_model [rank0]: model.load_weights( [rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/chatglm.py", line 392, in load_weights [rank0]: param = params_dict[name] [rank0]: KeyError: 'transformer.vision.transformer.layers.45.mlp.fc2.weight' ``` [Feature]: when to support GLM-4V? ### ๐Ÿš€ The feature, motivation and pitch Hey guys, I want to know is there a plan to support GLM-4v VLM model? ### Alternatives _No response_ ### Additional context _No response_
GLM-4V is not supported yet. You can track its progress in #5358. FYI: https://github.com/vllm-project/vllm/pull/5358 thanks
[ { "body": "### Your current environment\r\n\r\n```shell\r\nCollecting environment information...\r\nPyTorch version: 2.3.0+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.29.2\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-5.4.0-94-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: \r\nGPU 0: NVIDIA A100-SXM4-80GB\r\nGPU 1: NVIDIA A100-SXM4-80GB\r\nGPU 2: NVIDIA A100-SXM4-80GB\r\nGPU 3: NVIDIA A100-SXM4-80GB\r\nGPU 4: NVIDIA A100-SXM4-80GB\r\nGPU 5: NVIDIA A100-SXM4-80GB\r\nGPU 6: NVIDIA A100-SXM4-80GB\r\nGPU 7: NVIDIA A100-SXM4-80GB\r\n\r\nNvidia driver version: 535.104.05\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 43 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 128\r\nOn-line CPU(s) list: 0-127\r\nVendor ID: AuthenticAMD\r\nModel name: AMD EPYC 7742 64-Core Processor\r\nCPU family: 23\r\nModel: 49\r\nThread(s) per core: 1\r\nCore(s) per socket: 64\r\nSocket(s): 2\r\nStepping: 0\r\nFrequency boost: enabled\r\nCPU max MHz: 2250.0000\r\nCPU min MHz: 1500.0000\r\nBogoMIPS: 4499.81\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca\r\nVirtualization: AMD-V\r\nL1d cache: 4 MiB (128 instances)\r\nL1i cache: 4 MiB (128 instances)\r\nL2 cache: 64 MiB (128 instances)\r\nL3 cache: 512 MiB (32 instances)\r\nNUMA node(s): 8\r\nNUMA node0 CPU(s): 0-15\r\nNUMA node1 CPU(s): 16-31\r\nNUMA node2 CPU(s): 32-47\r\nNUMA node3 CPU(s): 48-63\r\nNUMA node4 CPU(s): 64-79\r\nNUMA node5 CPU(s): 80-95\r\nNUMA node6 CPU(s): 96-111\r\nNUMA node7 CPU(s): 112-127\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Full AMD retpoline, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.26.4\r\n[pip3] nvidia-nccl-cu12==2.20.5\r\n[pip3] torch==2.3.0\r\n[pip3] torchvision==0.18.1\r\n[pip3] transformers==4.40.0\r\n[pip3] triton==2.3.0\r\n[pip3] vllm-nccl-cu12==2.18.1.0.4.0\r\n[conda] Could not collect\r\nROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.4.3\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 CPU Affinity NUMA Affinity GPU NUMA ID\r\nGPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS 48-63 3 N/A\r\nGPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS 48-63 3 N/A\r\nGPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 16-31 1 N/A\r\nGPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 16-31 1 N/A\r\nGPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS SYS SYS SYS SYS SYS PXB PXB 112-127 7 N/A\r\nGPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS SYS SYS SYS SYS SYS PXB PXB 112-127 7 N/A\r\nGPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS 80-95 5 N/A\r\nGPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS 80-95 5 N/A\r\nNIC0 SYS SYS PXB PXB SYS SYS SYS SYS X PIX SYS SYS SYS SYS SYS SYS SYS SYS\r\nNIC1 SYS SYS PXB PXB SYS SYS SYS SYS PIX X SYS SYS SYS SYS SYS SYS SYS SYS\r\nNIC2 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS X PXB SYS SYS SYS SYS SYS SYS\r\nNIC3 PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS PXB X SYS SYS SYS SYS SYS SYS\r\nNIC4 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS X PIX SYS SYS SYS SYS\r\nNIC5 SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS PIX X SYS SYS SYS SYS\r\nNIC6 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS X PXB SYS SYS\r\nNIC7 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS PXB X SYS SYS\r\nNIC8 SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS X PXB\r\nNIC9 SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS PXB X \r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n\r\nNIC Legend:\r\n\r\n NIC0: mlx5_0\r\n NIC1: mlx5_1\r\n NIC2: mlx5_2\r\n NIC3: mlx5_3\r\n NIC4: mlx5_4\r\n NIC5: mlx5_5\r\n NIC6: mlx5_6\r\n NIC7: mlx5_7\r\n NIC8: mlx5_8\r\n NIC9: mlx5_9\r\n```\r\n\r\n\r\n### ๐Ÿ› Describe the bug\r\n\r\n1. The command executed when starting CLM4-V using vllm\r\n```shell\r\nCUDA_VISIBLE_DEVICES=0,3 python3 -m vllm.entrypoints.openai.api_server --model=/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b --served-model-name=glm-4v-9b --device=cuda --port=8000 --host=0.0.0.0 --tensor-parallel-size=1 --dtype=auto --trust-remote-code\r\n```\r\n2. errors info\r\n```shell\r\nINFO 06-11 08:11:33 llm_engine.py:161] Initializing an LLM engine (v0.4.3) with config: model='/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b', speculative_config=None, tokenizer='/data/lush-dev/liwei/code/gpt/models/huggingface/glm-4v-9b', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=glm-4v-9b)\r\nSpecial tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\r\nWARNING 06-11 08:11:34 tokenizer.py:126] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead.\r\n[rank0]: Traceback (most recent call last):\r\n[rank0]: File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n[rank0]: return _run_code(code, main_globals, None,\r\n[rank0]: File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\r\n[rank0]: exec(code, run_globals)\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py\", line 186, in <module>\r\n[rank0]: engine = AsyncLLMEngine.from_engine_args(\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 386, in from_engine_args\r\n[rank0]: engine = cls(\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 340, in __init__\r\n[rank0]: self.engine = self._init_engine(*args, **kwargs)\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 462, in _init_engine\r\n[rank0]: return engine_class(*args, **kwargs)\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py\", line 222, in __init__\r\n[rank0]: self.model_executor = executor_class(\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py\", line 41, in __init__\r\n[rank0]: self._init_executor()\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py\", line 24, in _init_executor\r\n[rank0]: self.driver_worker.load_model()\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py\", line 121, in load_model\r\n[rank0]: self.model_runner.load_model()\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py\", line 134, in load_model\r\n[rank0]: self.model = get_model(\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/__init__.py\", line 21, in get_model\r\n[rank0]: return loader.load_model(model_config=model_config,\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/loader.py\", line 243, in load_model\r\n[rank0]: model.load_weights(\r\n[rank0]: File \"/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/chatglm.py\", line 392, in load_weights\r\n[rank0]: param = params_dict[name]\r\n[rank0]: KeyError: 'transformer.vision.transformer.layers.45.mlp.fc2.weight'\r\n```", "number": 5417, "title": "[Bug]: vllm deployment of GLM-4V reports KeyError: 'transformer.vision.transformer.layers.45.mlp.fc2.weight'" }, { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nHey guys, I want to know is there a plan to support GLM-4v VLM model?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_", "number": 6097, "title": "[Feature]: when to support GLM-4V?" } ]
cbc2ef55292b2af6ff742095c030e8425124c005
{ "head_commit": "2719fac48cc83bc3fcf000a2be0077e3fdc6dde7", "head_commit_message": "Add GLM-4v support and meet vllm==0.6.2", "patch_to_review": "diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py\nindex 5dd539c3d5ee..7361deac2ea2 100644\n--- a/examples/offline_inference_vision_language.py\n+++ b/examples/offline_inference_vision_language.py\n@@ -300,6 +300,20 @@ def run_mllama(question: str, modality: str):\n return llm, prompt, stop_token_ids\n \n \n+# GLM-4v\n+def run_glm4v(question):\n+ model_name = \"THUDM/glm-4v-9b\"\n+\n+ llm = LLM(model=model_name,\n+ tensor_parallel_size=1,\n+ max_model_len=8192,\n+ trust_remote_code=True,\n+ enforce_eager=True)\n+ prompt = question\n+ stop_token_ids = [151329, 151336, 151338]\n+ return llm, prompt, stop_token_ids\n+\n+\n model_example_map = {\n \"llava\": run_llava,\n \"llava-next\": run_llava_next,\n@@ -316,6 +330,7 @@ def run_mllama(question: str, modality: str):\n \"qwen_vl\": run_qwen_vl,\n \"qwen2_vl\": run_qwen2_vl,\n \"mllama\": run_mllama,\n+ \"glm4v\": run_glm4v,\n }\n \n \ndiff --git a/tests/models/decoder_only/vision_language/test_glm4.py b/tests/models/decoder_only/vision_language/test_glm4.py\nnew file mode 100644\nindex 000000000000..196b5647f6c0\n--- /dev/null\n+++ b/tests/models/decoder_only/vision_language/test_glm4.py\n@@ -0,0 +1,107 @@\n+# tests/models/decoder_only/vision_language/test_glm4v.py\n+import pytest\n+from typing import List, Optional, Tuple, Type\n+from vllm.multimodal.utils import rescale_image_size\n+from ....conftest import (IMAGE_ASSETS, HfRunner, \n+ PromptImageInput, VllmRunner)\n+from ...utils import check_logprobs_close\n+\n+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({\n+ \"stop_sign\":\n+ \"What's the content of the image?\",\n+ \"cherry_blossom\":\n+ \"What is the season?\",\n+})\n+\n+models = [\"THUDM/glm-4v-9b\"]\n+target_dtype = \"bfloat16\"\n+\n+def run_test(\n+ hf_runner: Type[HfRunner],\n+ vllm_runner: Type[VllmRunner],\n+ inputs: List[Tuple[List[str], PromptImageInput]],\n+ model: str,\n+ *,\n+ dtype: str,\n+ max_tokens: int,\n+ num_logprobs: int,\n+ mm_limit: int,\n+ tensor_parallel_size: int,\n+ distributed_executor_backend: Optional[str] = None,\n+):\n+ # max_model_len should be greater than image_feature_size\n+ with vllm_runner(\n+ model,\n+ max_model_len=4096,\n+ max_num_seqs=1,\n+ dtype=dtype,\n+ limit_mm_per_prompt={\"image\": mm_limit},\n+ tensor_parallel_size=tensor_parallel_size,\n+ distributed_executor_backend=distributed_executor_backend,\n+ enforce_eager=True) as vllm_model:\n+ stop_token_ids = [151329, 151336, 151338]\n+ vllm_outputs_per_image = [\n+ vllm_model.generate_greedy_logprobs(prompts,\n+ max_tokens,\n+ num_logprobs=num_logprobs,\n+ images=images,\n+ stop_token_ids=stop_token_ids)\n+ for prompts, images in inputs\n+ ]\n+ with hf_runner(model, dtype=dtype) as hf_model:\n+ hf_model.model.get_output_embeddings = lambda: \\\n+ hf_model.model.transformer.output_layer\n+ hf_outputs_per_image = [\n+ hf_model.generate_greedy_logprobs_limit(prompts,\n+ max_tokens,\n+ num_logprobs=num_logprobs,\n+ images=images,\n+ )\n+ for prompts, images in inputs\n+ ]\n+\n+ for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,\n+ vllm_outputs_per_image):\n+ check_logprobs_close(\n+ outputs_0_lst=hf_outputs,\n+ outputs_1_lst=vllm_outputs,\n+ name_0=\"hf\",\n+ name_1=\"vllm\",\n+ )\n+\[email protected](\"model\", models)\[email protected](\n+ \"size_factors\",\n+ [\n+ # No image\n+ [],\n+ # Single-scale\n+ [1.0],\n+ # Single-scale, batched\n+ [1.0, 1.0, 1.0],\n+ # Multi-scale\n+ [0.25, 0.5, 1.0],\n+ ],\n+)\[email protected](\"dtype\", [target_dtype])\[email protected](\"max_tokens\", [128])\[email protected](\"num_logprobs\", [5])\n+def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,\n+ dtype: str, max_tokens: int, num_logprobs: int) -> None:\n+ images = [asset.pil_image for asset in image_assets]\n+\n+ inputs_per_image = [(\n+ [prompt for _ in size_factors],\n+ [rescale_image_size(image, factor) for factor in size_factors],\n+ ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]\n+ run_test(\n+ hf_runner,\n+ vllm_runner,\n+ inputs_per_image,\n+ model,\n+ dtype=dtype,\n+ max_tokens=max_tokens,\n+ num_logprobs=num_logprobs,\n+ mm_limit=1,\n+ tensor_parallel_size=1,\n+ )\ndiff --git a/vllm/model_executor/models/chatglm.py b/vllm/model_executor/models/chatglm.py\nindex 879795c0d595..d9f86e689e77 100644\n--- a/vllm/model_executor/models/chatglm.py\n+++ b/vllm/model_executor/models/chatglm.py\n@@ -1,42 +1,228 @@\n # coding=utf-8\n # Adapted from\n-# https://github.com/THUDM/ChatGLM2-6B\n+# https://github.com/THUDM/GLM-4\n \"\"\"Inference-only ChatGLM model compatible with THUDM weights.\"\"\"\n-from typing import Iterable, List, Optional, Tuple, Union\n+from argparse import Namespace\n+from array import array\n+from typing import Dict, Iterable, List, Mapping, Optional, Tuple, TypedDict\n \n import torch\n+from PIL import Image\n from torch import nn\n from torch.nn import LayerNorm\n \n from vllm.attention import Attention, AttentionMetadata\n-from vllm.config import CacheConfig, LoRAConfig\n-from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size\n+from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig\n+from vllm.distributed import get_tensor_model_parallel_world_size\n+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs\n+from vllm.logger import init_logger\n from vllm.model_executor.layers.activation import SiluAndMul\n from vllm.model_executor.layers.layernorm import RMSNorm\n from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,\n QKVParallelLinear,\n RowParallelLinear)\n from vllm.model_executor.layers.logits_processor import LogitsProcessor\n-from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.layers.quantization.base_config import (\n+ QuantizationConfig)\n from vllm.model_executor.layers.rotary_embedding import get_rope\n from vllm.model_executor.layers.sampler import Sampler, SamplerOutput\n from vllm.model_executor.layers.vocab_parallel_embedding import (\n ParallelLMHead, VocabParallelEmbedding)\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n+from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n-from vllm.sequence import IntermediateTensors\n+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict\n+from vllm.multimodal.base import MultiModalData\n+from vllm.multimodal.utils import cached_get_tokenizer\n+from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,\n+ SequenceData)\n from vllm.transformers_utils.configs import ChatGLMConfig\n \n-from .interfaces import SupportsLoRA, SupportsPP\n-from .utils import (is_pp_missing_parameter,\n- make_empty_intermediate_tensors_factory, make_layers)\n+from .interfaces import SupportsLoRA, SupportsMultiModal\n+\n+logger = init_logger(__name__)\n+\n+\n+def calculate_image_placeholder(vision_config):\n+ return (vision_config[\"image_size\"] // vision_config[\"patch_size\"] // 2)**2\n+\n+\n+def mm_input_mapper_for_glmv(\n+ ctx: InputContext,\n+ data: MultiModalData[object],\n+) -> Dict:\n+ model_config = ctx.model_config\n+ tokenizer = cached_get_tokenizer(model_config.tokenizer,\n+ trust_remote_code=True)\n+ if tokenizer is None:\n+ raise RuntimeError(\"No HuggingFace processor is available \"\n+ \"to process the image object\")\n+ try:\n+ raw_batch_data = tokenizer.apply_chat_template(\n+ conversation=[{\n+ \"role\": \"user\",\n+ \"image\": data\n+ }],\n+ add_generation_prompt=True,\n+ tokenize=True,\n+ return_tensors=\"pt\",\n+ return_dict=True).data\n+ except Exception:\n+ logger.error(\"Failed to process image (%s)\", data)\n+ raise\n+ pixel_values = raw_batch_data['images']\n+\n+ return {'pixel_values': pixel_values}\n+\n+\n+def merge_glm_vision_embeddings(\n+ input_ids: torch.Tensor,\n+ inputs_embeds: torch.Tensor,\n+ vision_embeddings: torch.Tensor,\n+ boi_token_id: int,\n+ eoi_token_id: int,\n+) -> torch.Tensor:\n+\n+ boi_positions = (input_ids == boi_token_id).nonzero(as_tuple=True)[0]\n+ eoi_positions = (input_ids == eoi_token_id).nonzero(as_tuple=True)[0]\n+\n+ mask = torch.zeros_like(input_ids, dtype=torch.bool)\n+\n+ for boi_pos, eoi_pos in zip(boi_positions, eoi_positions):\n+ assert boi_pos < eoi_pos\n+ mask[boi_pos:eoi_pos + 1] = True\n+ inputs_embeds[mask] = vision_embeddings.view(-1,\n+ vision_embeddings.shape[-1])\n+ return inputs_embeds\n+\n+\n+class GLMImagePixelInputs(TypedDict):\n+ pixel_values: torch.Tensor\n+ \"\"\"Shape: `(batch_size, num_channels, height, width)`\"\"\"\n+\n+\n+def get_max_glmv_image_tokens(ctx: InputContext):\n+ hf_config = ctx.get_hf_config(ChatGLMConfig)\n+\n+ vision_config = getattr(hf_config, 'vision_config', None)\n+ if vision_config is None:\n+ return 1\n+ elif isinstance(vision_config, dict):\n+ return calculate_image_placeholder(vision_config)\n+\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+\n+def dummy_data_for_glmv(\n+ ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]\n+) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:\n+ hf_config = ctx.get_hf_config(ChatGLMConfig)\n+ vision_config = getattr(hf_config, 'vision_config', None)\n+\n+ if vision_config is None:\n+ token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * seq_len)\n+ seq_data = SequenceData(token_ids)\n+ return seq_data, None\n+ elif isinstance(vision_config, dict):\n+ image_size = vision_config[\"image_size\"]\n+ image_placeholder_length = calculate_image_placeholder(vision_config)\n+ token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [hf_config.boi_token_id] +\n+ [0] * image_placeholder_length +\n+ [hf_config.eoi_token_id])\n+ token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,\n+ [0] * (seq_len - image_placeholder_length - 2))\n+ seq_data = SequenceData(token_ids)\n+\n+ mm_data = {\n+ \"image\": Image.new(\"RGB\", (image_size, image_size), color=0)\n+ }\n+\n+ return seq_data, mm_data\n+\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+\n+def find_all_positions(input_ids: List[int], target: int) -> List[int]:\n+ return [index for index, value in enumerate(input_ids) if value == target]\n+\n+\n+def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs):\n+ hf_config = ctx.get_hf_config(ChatGLMConfig)\n+ vision_config = getattr(hf_config, 'vision_config', None)\n+\n+ if vision_config is None:\n+ return llm_inputs\n+ elif isinstance(vision_config, dict):\n+ image_placeholder_length = calculate_image_placeholder(vision_config)\n+ else:\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+ input_ids = llm_inputs.get(\"prompt_token_ids\")\n+ position_ids = llm_inputs.get(\"position_ids\")\n+ tokenizer = cached_get_tokenizer(\n+ ctx.model_config.model,\n+ trust_remote_code=ctx.model_config.trust_remote_code)\n+\n+ try:\n+ raw_batch_data = tokenizer.apply_chat_template(\n+ conversation=[{\n+ \"role\": \"user\",\n+ \"image\": llm_inputs['multi_modal_data'][\"image\"],\n+ \"content\": llm_inputs['prompt']\n+ }],\n+ add_generation_prompt=True,\n+ tokenize=True,\n+ return_tensors=\"pt\",\n+ return_dict=True).data\n+ except Exception:\n+ logger.error(\"Failed to process content (%s)\", llm_inputs['prompt'])\n+ raise\n+ input_ids = raw_batch_data['input_ids'][0].tolist()\n+\n+ if position_ids is None:\n+ position_ids = list(range(len(input_ids)))\n+ boi_token_id = hf_config.boi_token_id\n+ eoi_token_id = hf_config.eoi_token_id\n+ boi_positions = find_all_positions(input_ids, boi_token_id)\n+ eoi_positions = find_all_positions(input_ids, eoi_token_id)\n+\n+ assert len(boi_positions) == len(eoi_positions)\n+\n+ new_input_ids = []\n+ new_position_ids = []\n+ final_processed_position = 0\n+ final_processed_position = 0\n+\n+ for boi_position, eoi_position in zip(boi_positions, eoi_positions):\n+ assert boi_position < eoi_position\n+ new_input_ids.extend(input_ids[final_processed_position:boi_position +\n+ 1])\n+ new_position_ids.extend(\n+ list(range(final_processed_position, boi_position + 1)))\n+ new_input_ids.extend([input_ids[boi_position + 1]] *\n+ image_placeholder_length)\n+ new_position_ids.extend([boi_position + 1] * image_placeholder_length)\n+ final_processed_position = eoi_position\n+\n+ new_input_ids.extend(input_ids[final_processed_position:])\n+ new_position_ids.extend(\n+ list(range(final_processed_position, len(input_ids))))\n+\n+ assert len(new_input_ids) == len(new_position_ids)\n+\n+ llm_inputs[\"prompt_token_ids\"] = new_input_ids\n+ llm_inputs[\"position_ids\"] = new_position_ids\n+ return llm_inputs\n \n \n class GLMAttention(nn.Module):\n \n def __init__(\n self,\n- config: ChatGLMConfig,\n+ config,\n cache_config: Optional[CacheConfig] = None,\n quant_config: Optional[QuantizationConfig] = None,\n ):\n@@ -127,7 +313,7 @@ class GLMMLP(nn.Module):\n \n def __init__(\n self,\n- config: ChatGLMConfig,\n+ config,\n quant_config: Optional[QuantizationConfig] = None,\n ):\n super().__init__()\n@@ -170,7 +356,7 @@ class GLMBlock(nn.Module):\n \n def __init__(\n self,\n- config: ChatGLMConfig,\n+ config,\n cache_config: Optional[CacheConfig] = None,\n quant_config: Optional[QuantizationConfig] = None,\n ):\n@@ -241,10 +427,9 @@ class GLMTransformer(nn.Module):\n \n def __init__(\n self,\n- config: ChatGLMConfig,\n+ config,\n cache_config: Optional[CacheConfig] = None,\n quant_config: Optional[QuantizationConfig] = None,\n- prefix: str = \"\",\n ):\n super().__init__()\n self.post_layer_norm = config.post_layer_norm\n@@ -253,11 +438,10 @@ def __init__(\n self.num_layers = config.num_layers\n \n # Transformer layers.\n- self.start_layer, self.end_layer, self.layers = make_layers(\n- self.num_layers,\n- lambda prefix: GLMBlock(config, cache_config, quant_config),\n- prefix=f\"{prefix}.layers\",\n- )\n+ self.layers = nn.ModuleList([\n+ GLMBlock(config, cache_config, quant_config)\n+ for i in range(self.num_layers)\n+ ])\n \n if self.post_layer_norm:\n layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm\n@@ -272,16 +456,16 @@ def forward(\n kv_caches: List[torch.Tensor],\n attn_metadata: AttentionMetadata,\n ) -> torch.Tensor:\n- for i in range(self.start_layer, self.end_layer):\n+ for i in range(self.num_layers):\n layer = self.layers[i]\n hidden_states = layer(\n hidden_states=hidden_states,\n position_ids=position_ids,\n- kv_cache=kv_caches[i - self.start_layer],\n+ kv_cache=kv_caches[i],\n attn_metadata=attn_metadata,\n )\n # Final layer norm.\n- if get_pp_group().is_last_rank and self.post_layer_norm:\n+ if self.post_layer_norm:\n hidden_states = self.final_layernorm(hidden_states)\n \n return hidden_states\n@@ -291,14 +475,17 @@ class ChatGLMModel(nn.Module):\n \n def __init__(\n self,\n- config: ChatGLMConfig,\n+ config,\n cache_config: Optional[CacheConfig] = None,\n quant_config: Optional[QuantizationConfig] = None,\n ):\n super().__init__()\n \n+ self.config = config\n+\n self.embedding = VocabParallelEmbedding(config.padded_vocab_size,\n- config.hidden_size)\n+ config.hidden_size,\n+ quant_config=quant_config)\n \n self.num_layers = config.num_layers\n self.multi_query_group_num = config.multi_query_group_num\n@@ -308,37 +495,73 @@ def __init__(\n self.output_layer = ParallelLMHead(config.padded_vocab_size,\n config.hidden_size,\n quant_config=quant_config)\n- self.make_empty_intermediate_tensors = (\n- make_empty_intermediate_tensors_factory([\"hidden_states\"],\n- config.hidden_size))\n+\n+ vision_config_flag = getattr(config, 'vision_config', None)\n+ if vision_config_flag is not None:\n+ self.vision_config = Namespace(**config.vision_config)\n+ self.vision = EVA2CLIPModel(self.config, quant_config)\n+ else:\n+ self.vision = None\n+\n+ def _parse_and_validate_image_input(\n+ self, **kwargs: object) -> GLMImagePixelInputs:\n+\n+ pixel_values = kwargs.pop(\"pixel_values\", None)\n+ if pixel_values is not None and self.vision is not None:\n+ if isinstance(pixel_values, torch.Tensor):\n+ if pixel_values.ndim > 2:\n+ pixel_values = torch.concat(list(pixel_values))\n+ elif isinstance(pixel_values, list):\n+ return torch.concat(pixel_values)\n+ else:\n+ raise TypeError(\"\"\"pixel_values must be a torch.Tensor \n+ or a list of torch.Tensor\n+ \"\"\")\n+ return GLMImagePixelInputs(pixel_values=pixel_values)\n \n def forward(\n self,\n input_ids: torch.Tensor,\n- position_ids: torch.Tensor,\n+ positions: torch.Tensor,\n kv_caches: List[torch.Tensor],\n attn_metadata: AttentionMetadata,\n- intermediate_tensors: Optional[IntermediateTensors],\n- ) -> Union[torch.Tensor, IntermediateTensors]:\n- if get_pp_group().is_first_rank:\n- inputs_embeds = self.embedding(input_ids)\n- else:\n- inputs_embeds = intermediate_tensors[\"hidden_states\"]\n+ intermediate_tensors: Optional[IntermediateTensors] = None,\n+ **kwargs: object,\n+ ) -> torch.Tensor:\n+\n+ inputs_embeds = self.embedding(input_ids)\n+ image_input = self._parse_and_validate_image_input(**kwargs)\n+\n+ if image_input[\"pixel_values\"] is not None:\n+ pixel_values = image_input[\"pixel_values\"].to(\n+ dtype=inputs_embeds.dtype)\n+ image_embeds = self.vision(pixel_values)\n+\n+ boi_token_id = self.config.boi_token_id\n+ eoi_token_id = self.config.eoi_token_id\n+\n+ inputs_embeds = merge_glm_vision_embeddings(\n+ input_ids=input_ids,\n+ inputs_embeds=inputs_embeds,\n+ vision_embeddings=image_embeds,\n+ boi_token_id=boi_token_id,\n+ eoi_token_id=eoi_token_id)\n \n # Run encoder.\n hidden_states = self.encoder(\n hidden_states=inputs_embeds,\n- position_ids=position_ids,\n+ position_ids=positions,\n kv_caches=kv_caches,\n attn_metadata=attn_metadata,\n )\n-\n- if not get_pp_group().is_last_rank:\n- return IntermediateTensors({\"hidden_states\": hidden_states})\n return hidden_states\n \n \n-class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):\n+@MULTIMODAL_REGISTRY.register_image_input_mapper(mm_input_mapper_for_glmv)\n+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)\n+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)\n+@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)\n+class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):\n packed_modules_mapping = {\n \"query_key_value\": [\"query_key_value\"],\n \"dense_h_to_4h\": [\"dense_h_to_4h\"]\n@@ -356,6 +579,7 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):\n def __init__(\n self,\n config: ChatGLMConfig,\n+ multimodal_config: MultiModalConfig,\n cache_config: Optional[CacheConfig] = None,\n quant_config: Optional[QuantizationConfig] = None,\n lora_config: Optional[LoRAConfig] = None,\n@@ -364,6 +588,7 @@ def __init__(\n \n self.config = config\n self.lora_config = lora_config\n+ self.multimodal_config = multimodal_config\n \n self.quant_config = quant_config\n self.max_position_embeddings = getattr(config, \"max_sequence_length\",\n@@ -375,19 +600,16 @@ def __init__(\n self.lm_head = self.transformer.output_layer\n self.logits_processor = LogitsProcessor(config.padded_vocab_size)\n self.sampler = Sampler()\n- self.make_empty_intermediate_tensors = (\n- self.transformer.make_empty_intermediate_tensors)\n \n- def forward(\n- self,\n- input_ids: torch.Tensor,\n- positions: torch.Tensor,\n- kv_caches: List[torch.Tensor],\n- attn_metadata: AttentionMetadata,\n- intermediate_tensors: Optional[IntermediateTensors] = None,\n- ) -> Union[torch.Tensor, IntermediateTensors]:\n+ def forward(self,\n+ input_ids: torch.Tensor,\n+ positions: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ intermediate_tensors: Optional[IntermediateTensors] = None,\n+ **kwargs) -> torch.Tensor:\n hidden_states = self.transformer(input_ids, positions, kv_caches,\n- attn_metadata, intermediate_tensors)\n+ attn_metadata, **kwargs)\n return hidden_states\n \n def compute_logits(\n@@ -408,8 +630,24 @@ def sample(\n return next_tokens\n \n def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n+ # Merge two ColumnParallelLinear into one MergedColumnParallelLinear\n+ merged_weights_dict: Dict[str, Dict[str, Optional[torch.Tensor]]] = {\n+ \"transformer.vision.linear_proj.merged_proj.weight\": {\n+ \"transformer.vision.linear_proj.gate_proj.weight\": None,\n+ \"transformer.vision.linear_proj.dense_h_to_4h.weight\": None,\n+ }\n+ }\n+\n params_dict = dict(self.named_parameters(remove_duplicate=False))\n for name, loaded_weight in weights:\n+ is_weight_to_be_merge = False\n+ for _, merged_weight_dict in merged_weights_dict.items():\n+ if name in merged_weight_dict:\n+ assert merged_weight_dict[name] is None\n+ merged_weight_dict[name] = loaded_weight\n+ is_weight_to_be_merge = True\n+ if is_weight_to_be_merge:\n+ continue\n if \"rotary_pos_emb.inv_freq\" in name:\n continue\n if \"word_embeddings\" in name:\n@@ -417,9 +655,16 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n # Skip loading extra bias for GPTQ models.\n if name.endswith(\".bias\") and name not in params_dict:\n continue\n- if is_pp_missing_parameter(name, self):\n- continue\n param = params_dict[name]\n weight_loader = getattr(param, \"weight_loader\",\n default_weight_loader)\n weight_loader(param, loaded_weight)\n+\n+ for combined_name, merged_weight_dict in merged_weights_dict.items():\n+ if combined_name in params_dict:\n+ param = params_dict[combined_name]\n+ combined_weight = torch.cat(list(merged_weight_dict.values()),\n+ dim=0)\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, combined_weight)\ndiff --git a/vllm/model_executor/models/glm4_vision_encoder.py b/vllm/model_executor/models/glm4_vision_encoder.py\nnew file mode 100644\nindex 000000000000..3213a8b29a10\n--- /dev/null\n+++ b/vllm/model_executor/models/glm4_vision_encoder.py\n@@ -0,0 +1,298 @@\n+# coding=utf-8\n+# Adapted from\n+# https://github.com/THUDM/GLM-4\n+\"\"\"Inference-only GLM-4v model visual encoder compatible with THUDM weights.\"\"\"\n+from argparse import Namespace\n+from typing import Optional\n+\n+import torch\n+from torch import nn\n+from torch.nn import LayerNorm\n+\n+from vllm.distributed import get_tensor_model_parallel_world_size\n+from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn\n+from vllm.model_executor.layers.linear import (ColumnParallelLinear,\n+ MergedColumnParallelLinear,\n+ QKVParallelLinear,\n+ ReplicatedLinear,\n+ RowParallelLinear)\n+from vllm.model_executor.layers.quantization.base_config import (\n+ QuantizationConfig)\n+\n+\n+class PatchEmbedding(nn.Module):\n+\n+ def __init__(self, config):\n+ super().__init__()\n+ self.proj = nn.Conv2d(config.in_channels,\n+ config.hidden_size,\n+ kernel_size=config.patch_size,\n+ stride=config.patch_size)\n+ self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))\n+ self.position_embedding = nn.Embedding(config.num_positions,\n+ config.hidden_size)\n+\n+ def forward(self, images: torch.Tensor) -> torch.Tensor:\n+ \"\"\"\n+ Parameters:\n+ images : torch.Tensor\n+ Input image tensor with shape (B, C, H, W)\n+\n+ Returns:\n+ torch.Tensor\n+ Transformed tensor with shape (B, L, D)\n+ \"\"\"\n+ images = images.to(self.proj.weight.device)\n+ x = self.proj(images)\n+ x = x.flatten(2).transpose(1, 2)\n+ cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)\n+ x = torch.cat((cls_token, x), dim=1)\n+ x += self.position_embedding.weight.unsqueeze(0)\n+ return x\n+\n+\n+class Attention(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.hidden_size = config.hidden_size\n+ self.tp_size = get_tensor_model_parallel_world_size()\n+ self.num_heads_per_rank = config.num_heads // self.tp_size\n+ self.head_dim = config.hidden_size // config.num_heads\n+ self.scale = self.head_dim**-0.5\n+\n+ self.query_key_value = QKVParallelLinear(\n+ config.hidden_size,\n+ self.head_dim,\n+ config.num_heads,\n+ quant_config=quant_config,\n+ )\n+ self.dense = RowParallelLinear(\n+ config.hidden_size,\n+ config.hidden_size,\n+ quant_config=quant_config,\n+ )\n+\n+ self.output_dropout = torch.nn.Dropout(config.dropout_prob)\n+\n+ def forward(self, x: torch.Tensor) -> torch.Tensor:\n+ B, L, _ = x.shape\n+ qkv, _ = self.query_key_value(x) # B, L, 3 * H * D\n+ q, k, v = qkv.chunk(3, dim=-1)\n+ q = q.reshape(B, L, self.num_heads_per_rank,\n+ self.head_dim).permute(0, 2, 1, 3) # B, H, L, D\n+ k = k.reshape(B, L, self.num_heads_per_rank,\n+ self.head_dim).permute(0, 2, 1, 3) # B, H, L, D\n+ v = v.reshape(B, L, self.num_heads_per_rank,\n+ self.head_dim).permute(0, 2, 1, 3) # B, H, L, D\n+\n+ out = torch.nn.functional.scaled_dot_product_attention(q,\n+ k,\n+ v,\n+ attn_mask=None,\n+ dropout_p=0.,\n+ is_causal=False)\n+\n+ output, _ = self.dense(out.transpose(1, 2).view(B, L, -1))\n+ output = self.output_dropout(output)\n+ return output\n+\n+\n+class MLP(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.activation_fn = get_act_fn(config.hidden_act)\n+ self.fc1 = ColumnParallelLinear(\n+ config.hidden_size,\n+ config.intermediate_size,\n+ quant_config=quant_config,\n+ )\n+ self.fc2 = RowParallelLinear(\n+ config.intermediate_size,\n+ config.hidden_size,\n+ quant_config=quant_config,\n+ )\n+\n+ def forward(self, x: torch.Tensor) -> torch.Tensor:\n+ x, _ = self.fc1(x)\n+ x = self.activation_fn(x)\n+ x, _ = self.fc2(x)\n+ return x\n+\n+\n+class TransformerLayer(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.input_layernorm = LayerNorm(config.hidden_size,\n+ eps=config.layer_norm_eps)\n+ self.attention = Attention(config, quant_config=quant_config)\n+ self.mlp = MLP(config, quant_config=quant_config)\n+ self.post_attention_layernorm = LayerNorm(config.hidden_size,\n+ eps=config.layer_norm_eps)\n+\n+ def forward(self, hidden_states):\n+ attention_input = hidden_states\n+ attention_output = self.input_layernorm(\n+ self.attention(attention_input))\n+ hidden_states = attention_input + attention_output\n+ mlp_input = hidden_states\n+ mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))\n+ output = mlp_input + mlp_output\n+ return output\n+\n+\n+class Transformer(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.layers = nn.ModuleList([\n+ TransformerLayer(config, quant_config=quant_config)\n+ for _ in range(config.num_hidden_layers)\n+ ])\n+\n+ def forward(self, hidden_states):\n+ for layer_module in self.layers:\n+ hidden_states = layer_module(hidden_states)\n+ return hidden_states\n+\n+\n+class GLU(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ in_features,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ \"\"\"\n+ The original implementation is the same as:\n+ ```python\n+ self.dense_h_to_4h = ColumnParallelLinear(\n+ config.hidden_size,\n+ config.ffn_hidden_size,\n+ bias=False,\n+ quant_config=quant_config\n+ )\n+\n+ self.gate_proj = ColumnParallelLinear(\n+ config.hidden_size,\n+ config.ffn_hidden_size,\n+ bias=False,\n+ quant_config=quant_config\n+ )\n+ ```\n+ ```\n+ gate_proj_output, _ = self.gate_proj(x)\n+ dense_h_to_4h_output, _ = self.dense_h_to_4h(x)\n+ x = torch.cat([gate_proj_output, dense_h_to_4h_output], dim=-1)\n+ ```\n+\n+ We merge two ColumnParallelLinear into one MergedColumnParallelLinear:\n+ ```\n+ self.merged_proj = MergedColumnParallelLinear(\n+ config.hidden_size,\n+ [config.ffn_hidden_size] * 2,\n+ bias=False,\n+ quant_config=quant_config\n+ )\n+ ```\n+ ```\n+ x, _ = self.merged_proj(x)\n+ ```\n+ \"\"\"\n+ super().__init__()\n+ self.linear_proj = ReplicatedLinear(in_features,\n+ config.hidden_size,\n+ bias=False,\n+ quant_config=quant_config)\n+ self.norm1 = nn.LayerNorm(config.hidden_size)\n+ self.act1 = nn.GELU()\n+ self.act2 = SiluAndMul()\n+\n+ self.merged_proj = MergedColumnParallelLinear(\n+ config.hidden_size, [config.ffn_hidden_size] * 2,\n+ bias=False,\n+ quant_config=quant_config)\n+\n+ self.dense_4h_to_h = RowParallelLinear(config.ffn_hidden_size,\n+ config.hidden_size,\n+ bias=False,\n+ quant_config=quant_config)\n+\n+ def forward(self, x):\n+ x, _ = self.linear_proj(x)\n+ x = self.act1(self.norm1(x))\n+ x, _ = self.merged_proj(x)\n+ x = self.act2(x)\n+ x, _ = self.dense_4h_to_h(x)\n+ return x\n+\n+\n+class EVA2CLIPModel(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ vision_config = Namespace(**config.vision_config)\n+ self.patch_embedding = PatchEmbedding(vision_config)\n+ self.transformer = Transformer(vision_config,\n+ quant_config=quant_config)\n+ self.linear_proj = GLU(config,\n+ in_features=config.hidden_size,\n+ quant_config=quant_config)\n+ self.conv = nn.Conv2d(in_channels=vision_config.hidden_size,\n+ out_channels=config.hidden_size,\n+ kernel_size=2,\n+ stride=2)\n+ self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))\n+ self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))\n+ self.scaling_factor = vision_config.scaling_factor\n+\n+ def forward(self, images: torch.Tensor) -> torch.Tensor:\n+ \"\"\"\n+ Parameters:\n+ images : torch.Tensor\n+ Input image tensor with shape (B, C, H, W)\n+\n+ Returns:\n+ torch.Tensor\n+ Transformed tensor with shape (B, L, D)\n+ \"\"\"\n+ x = self.patch_embedding(images)\n+ x = self.transformer(x)\n+ x = x[:, 1:]\n+\n+ b, s, h = x.shape\n+ grid_size = int(s**0.5)\n+ x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)\n+ x = self.conv(x)\n+\n+ x = x.flatten(2).transpose(1, 2)\n+ x = self.linear_proj(x)\n+ boi = self.boi.expand(x.shape[0], -1, -1)\n+ eoi = self.eoi.expand(x.shape[0], -1, -1)\n+ x = torch.cat((boi, x, eoi), dim=1)\n+ x = x / self.scaling_factor\n+ return x\ndiff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py\nindex f1d484521acb..9def82949452 100644\n--- a/vllm/model_executor/models/registry.py\n+++ b/vllm/model_executor/models/registry.py\n@@ -91,6 +91,7 @@\n # [Decoder-only]\n \"Blip2ForConditionalGeneration\": (\"blip2\", \"Blip2ForConditionalGeneration\"),\n \"ChameleonForConditionalGeneration\": (\"chameleon\", \"ChameleonForConditionalGeneration\"), # noqa: E501\n+ \"ChatGLMModel\": (\"chatglm\", \"ChatGLMForCausalLM\"),\n \"FuyuForCausalLM\": (\"fuyu\", \"FuyuForCausalLM\"),\n \"InternVLChatModel\": (\"internvl\", \"InternVLChatModel\"),\n \"LlavaForConditionalGeneration\": (\"llava\", \"LlavaForConditionalGeneration\"),\n" }
[ { "diff_hunk": "@@ -300,6 +300,20 @@ def run_mllama(question: str, modality: str):\n return llm, prompt, stop_token_ids\n \n \n+# GLM-4v\n+def run_glm4v(question):", "line": null, "original_line": 304, "original_start_line": null, "path": "examples/offline_inference_vision_language.py", "start_line": null, "text": "@user1:\n```suggestion\r\ndef run_glm4v(question: str, modality: str):\r\n assert modality == \"image\"\r\n```" }, { "diff_hunk": "@@ -1,42 +1,228 @@\n # coding=utf-8\n # Adapted from\n-# https://github.com/THUDM/ChatGLM2-6B\n+# https://github.com/THUDM/GLM-4\n \"\"\"Inference-only ChatGLM model compatible with THUDM weights.\"\"\"\n-from typing import Iterable, List, Optional, Tuple, Union\n+from argparse import Namespace\n+from array import array\n+from typing import Dict, Iterable, List, Mapping, Optional, Tuple, TypedDict\n \n import torch\n+from PIL import Image\n from torch import nn\n from torch.nn import LayerNorm\n \n from vllm.attention import Attention, AttentionMetadata\n-from vllm.config import CacheConfig, LoRAConfig\n-from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size\n+from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig\n+from vllm.distributed import get_tensor_model_parallel_world_size\n+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs\n+from vllm.logger import init_logger\n from vllm.model_executor.layers.activation import SiluAndMul\n from vllm.model_executor.layers.layernorm import RMSNorm\n from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,\n QKVParallelLinear,\n RowParallelLinear)\n from vllm.model_executor.layers.logits_processor import LogitsProcessor\n-from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.layers.quantization.base_config import (\n+ QuantizationConfig)\n from vllm.model_executor.layers.rotary_embedding import get_rope\n from vllm.model_executor.layers.sampler import Sampler, SamplerOutput\n from vllm.model_executor.layers.vocab_parallel_embedding import (\n ParallelLMHead, VocabParallelEmbedding)\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n+from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n-from vllm.sequence import IntermediateTensors\n+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict\n+from vllm.multimodal.base import MultiModalData\n+from vllm.multimodal.utils import cached_get_tokenizer\n+from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,\n+ SequenceData)\n from vllm.transformers_utils.configs import ChatGLMConfig\n \n-from .interfaces import SupportsLoRA, SupportsPP\n-from .utils import (is_pp_missing_parameter,\n- make_empty_intermediate_tensors_factory, make_layers)\n+from .interfaces import SupportsLoRA, SupportsMultiModal\n+\n+logger = init_logger(__name__)\n+\n+\n+def calculate_image_placeholder(vision_config):\n+ return (vision_config[\"image_size\"] // vision_config[\"patch_size\"] // 2)**2\n+\n+\n+def mm_input_mapper_for_glmv(\n+ ctx: InputContext,\n+ data: MultiModalData[object],\n+) -> Dict:\n+ model_config = ctx.model_config\n+ tokenizer = cached_get_tokenizer(model_config.tokenizer,\n+ trust_remote_code=True)\n+ if tokenizer is None:\n+ raise RuntimeError(\"No HuggingFace processor is available \"\n+ \"to process the image object\")\n+ try:\n+ raw_batch_data = tokenizer.apply_chat_template(\n+ conversation=[{\n+ \"role\": \"user\",\n+ \"image\": data\n+ }],\n+ add_generation_prompt=True,\n+ tokenize=True,\n+ return_tensors=\"pt\",\n+ return_dict=True).data\n+ except Exception:\n+ logger.error(\"Failed to process image (%s)\", data)\n+ raise\n+ pixel_values = raw_batch_data['images']\n+\n+ return {'pixel_values': pixel_values}", "line": null, "original_line": 75, "original_start_line": null, "path": "vllm/model_executor/models/chatglm.py", "start_line": null, "text": "@user1:\n```suggestion\r\n return MultiModalInputs({'pixel_values': pixel_values})\r\n```" } ]
0008b8f64a8edb4eae4e80f8449753f8840a3c1c
diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index ec64a82de84d..3090e649976c 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -346,6 +346,12 @@ Text Generation - :code:`adept/fuyu-8b` etc. - - โœ…๏ธŽ + * - :code:`ChatGLMModel` + - GLM-4V + - Image + - :code:`THUDM/glm-4v-9b` etc. + - + - โœ…๏ธŽ * - :code:`InternVLChatModel` - InternVL2 - Image\ :sup:`E+` diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 5dd539c3d5ee..8d6818e7dfd3 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -300,6 +300,21 @@ def run_mllama(question: str, modality: str): return llm, prompt, stop_token_ids +# GLM-4v +def run_glm4v(question: str, modality: str): + assert modality == "image" + model_name = "THUDM/glm-4v-9b" + + llm = LLM(model=model_name, + max_model_len=2048, + max_num_seqs=2, + trust_remote_code=True, + enforce_eager=True) + prompt = question + stop_token_ids = [151329, 151336, 151338] + return llm, prompt, stop_token_ids + + model_example_map = { "llava": run_llava, "llava-next": run_llava_next, @@ -316,6 +331,7 @@ def run_mllama(question: str, modality: str): "qwen_vl": run_qwen_vl, "qwen2_vl": run_qwen2_vl, "mllama": run_mllama, + "glm4v": run_glm4v, } diff --git a/tests/models/decoder_only/vision_language/test_glm4.py b/tests/models/decoder_only/vision_language/test_glm4.py new file mode 100644 index 000000000000..47922a57f680 --- /dev/null +++ b/tests/models/decoder_only/vision_language/test_glm4.py @@ -0,0 +1,133 @@ +from typing import List, Optional, Tuple, Type + +import pytest + +from vllm.multimodal.utils import rescale_image_size +from vllm.transformers_utils.tokenizer import patch_padding_side + +from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner +from ....utils import large_gpu_test +from ...utils import check_logprobs_close + +HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ + "stop_sign": + "What's the content of the image?", + "cherry_blossom": + "What is the season?", +}) + +models = ["THUDM/glm-4v-9b"] +target_dtype = "bfloat16" + + +def run_test( + hf_runner: Type[HfRunner], + vllm_runner: Type[VllmRunner], + inputs: List[Tuple[List[str], PromptImageInput]], + model: str, + *, + dtype: str, + max_tokens: int, + num_logprobs: int, + mm_limit: int, + tensor_parallel_size: int, + distributed_executor_backend: Optional[str] = None, +): + # max_model_len should be greater than image_feature_size + with vllm_runner(model, + max_model_len=2048, + max_num_seqs=2, + dtype=dtype, + limit_mm_per_prompt={"image": mm_limit}, + tensor_parallel_size=tensor_parallel_size, + distributed_executor_backend=distributed_executor_backend, + enforce_eager=True) as vllm_model: + stop_token_ids = [151329, 151336, 151338] + vllm_outputs_per_image = [ + vllm_model.generate_greedy_logprobs(prompts, + max_tokens, + num_logprobs=num_logprobs, + images=images, + stop_token_ids=stop_token_ids) + for prompts, images in inputs + ] + + with hf_runner(model, dtype=dtype) as hf_model: + hf_processor = hf_model.processor + patch_padding_side(hf_processor) + + def processor(*args, text="", images=None, **kwargs): + if images is None: + return hf_processor(*args, **kwargs) + + return hf_processor.apply_chat_template( + [{ + "role": "user", + "image": images, + "content": text + }], + add_generation_prompt=True, + tokenize=True, + return_dict=True, + **kwargs, + ) + + hf_model.processor = processor + hf_model.model.get_output_embeddings = lambda: \ + hf_model.model.transformer.output_layer + hf_outputs_per_image = [ + hf_model.generate_greedy_logprobs_limit( + prompts, + max_tokens, + num_logprobs=num_logprobs, + images=images, + ) for prompts, images in inputs + ] + + for hf_outputs, vllm_outputs in zip(hf_outputs_per_image, + vllm_outputs_per_image): + check_logprobs_close( + outputs_0_lst=hf_outputs, + outputs_1_lst=vllm_outputs, + name_0="hf", + name_1="vllm", + ) + + +@large_gpu_test(min_gb=48) [email protected]("model", models) [email protected]( + "size_factors", + [ + # No image + [], + # Single-scale + [1.0], + # Single-scale, batched + [1.0, 1.0, 1.0], + # Multi-scale + [0.25, 0.5, 1.0], + ], +) [email protected]("dtype", [target_dtype]) [email protected]("max_tokens", [128]) [email protected]("num_logprobs", [5]) +def test_models(hf_runner, vllm_runner, image_assets, model, size_factors, + dtype: str, max_tokens: int, num_logprobs: int) -> None: + images = [asset.pil_image for asset in image_assets] + + inputs_per_image = [( + [prompt for _ in size_factors], + [rescale_image_size(image, factor) for factor in size_factors], + ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)] + run_test( + hf_runner, + vllm_runner, + inputs_per_image, + model, + dtype=dtype, + max_tokens=max_tokens, + num_logprobs=num_logprobs, + mm_limit=1, + tensor_parallel_size=1, + ) diff --git a/vllm/model_executor/models/chatglm.py b/vllm/model_executor/models/chatglm.py index 879795c0d595..f26c9f950dd3 100644 --- a/vllm/model_executor/models/chatglm.py +++ b/vllm/model_executor/models/chatglm.py @@ -1,42 +1,229 @@ # coding=utf-8 # Adapted from -# https://github.com/THUDM/ChatGLM2-6B +# https://github.com/THUDM/GLM-4 """Inference-only ChatGLM model compatible with THUDM weights.""" -from typing import Iterable, List, Optional, Tuple, Union +from argparse import Namespace +from array import array +from typing import Dict, Iterable, List, Mapping, Optional, Tuple, TypedDict import torch +from PIL import Image from torch import nn from torch.nn import LayerNorm from vllm.attention import Attention, AttentionMetadata -from vllm.config import CacheConfig, LoRAConfig -from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size +from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig +from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs +from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel from vllm.model_executor.sampling_metadata import SamplingMetadata -from vllm.sequence import IntermediateTensors +from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict, + MultiModalInputs) +from vllm.multimodal.base import MultiModalData +from vllm.multimodal.utils import cached_get_tokenizer +from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors, + SequenceData) from vllm.transformers_utils.configs import ChatGLMConfig -from .interfaces import SupportsLoRA, SupportsPP -from .utils import (is_pp_missing_parameter, - make_empty_intermediate_tensors_factory, make_layers) +from .interfaces import SupportsLoRA, SupportsMultiModal + +logger = init_logger(__name__) + + +def calculate_image_placeholder(vision_config): + return (vision_config["image_size"] // vision_config["patch_size"] // 2)**2 + + +def mm_input_mapper_for_glmv( + ctx: InputContext, + data: MultiModalData[object], +) -> Dict: + model_config = ctx.model_config + tokenizer = cached_get_tokenizer(model_config.tokenizer, + trust_remote_code=True) + if tokenizer is None: + raise RuntimeError("No HuggingFace processor is available " + "to process the image object") + try: + raw_batch_data = tokenizer.apply_chat_template( + conversation=[{ + "role": "user", + "image": data + }], + add_generation_prompt=True, + tokenize=True, + return_tensors="pt", + return_dict=True).data + except Exception: + logger.error("Failed to process image (%s)", data) + raise + pixel_values = raw_batch_data['images'] + + return MultiModalInputs({'pixel_values': pixel_values}) + + +def merge_glm_vision_embeddings( + input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, + vision_embeddings: torch.Tensor, + boi_token_id: int, + eoi_token_id: int, +) -> torch.Tensor: + + boi_positions = (input_ids == boi_token_id).nonzero(as_tuple=True)[0] + eoi_positions = (input_ids == eoi_token_id).nonzero(as_tuple=True)[0] + + mask = torch.zeros_like(input_ids, dtype=torch.bool) + + for boi_pos, eoi_pos in zip(boi_positions, eoi_positions): + assert boi_pos < eoi_pos + mask[boi_pos:eoi_pos + 1] = True + inputs_embeds[mask] = vision_embeddings.view(-1, + vision_embeddings.shape[-1]) + return inputs_embeds + + +class GLMImagePixelInputs(TypedDict): + pixel_values: torch.Tensor + """Shape: `(batch_size, num_channels, height, width)`""" + + +def get_max_glmv_image_tokens(ctx: InputContext): + hf_config = ctx.get_hf_config(ChatGLMConfig) + + vision_config = getattr(hf_config, 'vision_config', None) + if vision_config is None: + return 1 + elif isinstance(vision_config, dict): + return calculate_image_placeholder(vision_config) + + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + +def dummy_data_for_glmv( + ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int] +) -> Tuple[SequenceData, Optional[MultiModalDataDict]]: + hf_config = ctx.get_hf_config(ChatGLMConfig) + vision_config = getattr(hf_config, 'vision_config', None) + + if vision_config is None: + token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * seq_len) + seq_data = SequenceData(token_ids) + return seq_data, None + elif isinstance(vision_config, dict): + image_size = vision_config["image_size"] + image_placeholder_length = calculate_image_placeholder(vision_config) + token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [hf_config.boi_token_id] + + [0] * image_placeholder_length + + [hf_config.eoi_token_id]) + token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE, + [0] * (seq_len - image_placeholder_length - 2)) + seq_data = SequenceData(token_ids) + + mm_data = { + "image": Image.new("RGB", (image_size, image_size), color=0) + } + + return seq_data, mm_data + + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + +def find_all_positions(input_ids: List[int], target: int) -> List[int]: + return [index for index, value in enumerate(input_ids) if value == target] + + +def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs): + hf_config = ctx.get_hf_config(ChatGLMConfig) + vision_config = getattr(hf_config, 'vision_config', None) + + if vision_config is None: + return llm_inputs + elif isinstance(vision_config, dict): + image_placeholder_length = calculate_image_placeholder(vision_config) + else: + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + input_ids = llm_inputs.get("prompt_token_ids") + position_ids = llm_inputs.get("position_ids") + tokenizer = cached_get_tokenizer( + ctx.model_config.model, + trust_remote_code=ctx.model_config.trust_remote_code) + + try: + raw_batch_data = tokenizer.apply_chat_template( + conversation=[{ + "role": "user", + "image": llm_inputs['multi_modal_data']["image"], + "content": llm_inputs['prompt'] + }], + add_generation_prompt=True, + tokenize=True, + return_tensors="pt", + return_dict=True).data + except Exception: + logger.error("Failed to process content (%s)", llm_inputs['prompt']) + raise + input_ids = raw_batch_data['input_ids'][0].tolist() + + if position_ids is None: + position_ids = list(range(len(input_ids))) + boi_token_id = hf_config.boi_token_id + eoi_token_id = hf_config.eoi_token_id + boi_positions = find_all_positions(input_ids, boi_token_id) + eoi_positions = find_all_positions(input_ids, eoi_token_id) + + assert len(boi_positions) == len(eoi_positions) + + new_input_ids = [] + new_position_ids = [] + final_processed_position = 0 + final_processed_position = 0 + + for boi_position, eoi_position in zip(boi_positions, eoi_positions): + assert boi_position < eoi_position + new_input_ids.extend(input_ids[final_processed_position:boi_position + + 1]) + new_position_ids.extend( + list(range(final_processed_position, boi_position + 1))) + new_input_ids.extend([input_ids[boi_position + 1]] * + image_placeholder_length) + new_position_ids.extend([boi_position + 1] * image_placeholder_length) + final_processed_position = eoi_position + + new_input_ids.extend(input_ids[final_processed_position:]) + new_position_ids.extend( + list(range(final_processed_position, len(input_ids)))) + + assert len(new_input_ids) == len(new_position_ids) + + llm_inputs["prompt_token_ids"] = new_input_ids + llm_inputs["position_ids"] = new_position_ids + return llm_inputs class GLMAttention(nn.Module): def __init__( self, - config: ChatGLMConfig, + config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): @@ -127,7 +314,7 @@ class GLMMLP(nn.Module): def __init__( self, - config: ChatGLMConfig, + config, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() @@ -170,7 +357,7 @@ class GLMBlock(nn.Module): def __init__( self, - config: ChatGLMConfig, + config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): @@ -241,10 +428,9 @@ class GLMTransformer(nn.Module): def __init__( self, - config: ChatGLMConfig, + config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", ): super().__init__() self.post_layer_norm = config.post_layer_norm @@ -253,11 +439,10 @@ def __init__( self.num_layers = config.num_layers # Transformer layers. - self.start_layer, self.end_layer, self.layers = make_layers( - self.num_layers, - lambda prefix: GLMBlock(config, cache_config, quant_config), - prefix=f"{prefix}.layers", - ) + self.layers = nn.ModuleList([ + GLMBlock(config, cache_config, quant_config) + for i in range(self.num_layers) + ]) if self.post_layer_norm: layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm @@ -272,16 +457,16 @@ def forward( kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: - for i in range(self.start_layer, self.end_layer): + for i in range(self.num_layers): layer = self.layers[i] hidden_states = layer( hidden_states=hidden_states, position_ids=position_ids, - kv_cache=kv_caches[i - self.start_layer], + kv_cache=kv_caches[i], attn_metadata=attn_metadata, ) # Final layer norm. - if get_pp_group().is_last_rank and self.post_layer_norm: + if self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states @@ -291,14 +476,17 @@ class ChatGLMModel(nn.Module): def __init__( self, - config: ChatGLMConfig, + config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() + self.config = config + self.embedding = VocabParallelEmbedding(config.padded_vocab_size, - config.hidden_size) + config.hidden_size, + quant_config=quant_config) self.num_layers = config.num_layers self.multi_query_group_num = config.multi_query_group_num @@ -308,37 +496,73 @@ def __init__( self.output_layer = ParallelLMHead(config.padded_vocab_size, config.hidden_size, quant_config=quant_config) - self.make_empty_intermediate_tensors = ( - make_empty_intermediate_tensors_factory(["hidden_states"], - config.hidden_size)) + + vision_config_flag = getattr(config, 'vision_config', None) + if vision_config_flag is not None: + self.vision_config = Namespace(**config.vision_config) + self.vision = EVA2CLIPModel(self.config, quant_config) + else: + self.vision = None + + def _parse_and_validate_image_input( + self, **kwargs: object) -> GLMImagePixelInputs: + + pixel_values = kwargs.pop("pixel_values", None) + if pixel_values is not None and self.vision is not None: + if isinstance(pixel_values, torch.Tensor): + if pixel_values.ndim > 2: + pixel_values = torch.concat(list(pixel_values)) + elif isinstance(pixel_values, list): + return torch.concat(pixel_values) + else: + raise TypeError("""pixel_values must be a torch.Tensor + or a list of torch.Tensor + """) + return GLMImagePixelInputs(pixel_values=pixel_values) def forward( self, input_ids: torch.Tensor, - position_ids: torch.Tensor, + positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, - intermediate_tensors: Optional[IntermediateTensors], - ) -> Union[torch.Tensor, IntermediateTensors]: - if get_pp_group().is_first_rank: - inputs_embeds = self.embedding(input_ids) - else: - inputs_embeds = intermediate_tensors["hidden_states"] + intermediate_tensors: Optional[IntermediateTensors] = None, + **kwargs: object, + ) -> torch.Tensor: + + inputs_embeds = self.embedding(input_ids) + image_input = self._parse_and_validate_image_input(**kwargs) + + if image_input["pixel_values"] is not None: + pixel_values = image_input["pixel_values"].to( + dtype=inputs_embeds.dtype) + image_embeds = self.vision(pixel_values) + + boi_token_id = self.config.boi_token_id + eoi_token_id = self.config.eoi_token_id + + inputs_embeds = merge_glm_vision_embeddings( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + vision_embeddings=image_embeds, + boi_token_id=boi_token_id, + eoi_token_id=eoi_token_id) # Run encoder. hidden_states = self.encoder( hidden_states=inputs_embeds, - position_ids=position_ids, + position_ids=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, ) - - if not get_pp_group().is_last_rank: - return IntermediateTensors({"hidden_states": hidden_states}) return hidden_states -class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP): +@MULTIMODAL_REGISTRY.register_image_input_mapper(mm_input_mapper_for_glmv) +@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens) +@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv) +@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv) +class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal): packed_modules_mapping = { "query_key_value": ["query_key_value"], "dense_h_to_4h": ["dense_h_to_4h"] @@ -356,6 +580,7 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP): def __init__( self, config: ChatGLMConfig, + multimodal_config: MultiModalConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, lora_config: Optional[LoRAConfig] = None, @@ -364,6 +589,7 @@ def __init__( self.config = config self.lora_config = lora_config + self.multimodal_config = multimodal_config self.quant_config = quant_config self.max_position_embeddings = getattr(config, "max_sequence_length", @@ -375,19 +601,16 @@ def __init__( self.lm_head = self.transformer.output_layer self.logits_processor = LogitsProcessor(config.padded_vocab_size) self.sampler = Sampler() - self.make_empty_intermediate_tensors = ( - self.transformer.make_empty_intermediate_tensors) - def forward( - self, - input_ids: torch.Tensor, - positions: torch.Tensor, - kv_caches: List[torch.Tensor], - attn_metadata: AttentionMetadata, - intermediate_tensors: Optional[IntermediateTensors] = None, - ) -> Union[torch.Tensor, IntermediateTensors]: + def forward(self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + **kwargs) -> torch.Tensor: hidden_states = self.transformer(input_ids, positions, kv_caches, - attn_metadata, intermediate_tensors) + attn_metadata, **kwargs) return hidden_states def compute_logits( @@ -408,8 +631,24 @@ def sample( return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + # Merge two ColumnParallelLinear into one MergedColumnParallelLinear + merged_weights_dict: Dict[str, Dict[str, Optional[torch.Tensor]]] = { + "transformer.vision.linear_proj.merged_proj.weight": { + "transformer.vision.linear_proj.gate_proj.weight": None, + "transformer.vision.linear_proj.dense_h_to_4h.weight": None, + } + } + params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: + is_weight_to_be_merge = False + for _, merged_weight_dict in merged_weights_dict.items(): + if name in merged_weight_dict: + assert merged_weight_dict[name] is None + merged_weight_dict[name] = loaded_weight + is_weight_to_be_merge = True + if is_weight_to_be_merge: + continue if "rotary_pos_emb.inv_freq" in name: continue if "word_embeddings" in name: @@ -417,9 +656,16 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue - if is_pp_missing_parameter(name, self): - continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) + + for combined_name, merged_weight_dict in merged_weights_dict.items(): + if combined_name in params_dict: + param = params_dict[combined_name] + combined_weight = torch.cat(list(merged_weight_dict.values()), + dim=0) + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, combined_weight) diff --git a/vllm/model_executor/models/glm4_vision_encoder.py b/vllm/model_executor/models/glm4_vision_encoder.py new file mode 100644 index 000000000000..3213a8b29a10 --- /dev/null +++ b/vllm/model_executor/models/glm4_vision_encoder.py @@ -0,0 +1,298 @@ +# coding=utf-8 +# Adapted from +# https://github.com/THUDM/GLM-4 +"""Inference-only GLM-4v model visual encoder compatible with THUDM weights.""" +from argparse import Namespace +from typing import Optional + +import torch +from torch import nn +from torch.nn import LayerNorm + +from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn +from vllm.model_executor.layers.linear import (ColumnParallelLinear, + MergedColumnParallelLinear, + QKVParallelLinear, + ReplicatedLinear, + RowParallelLinear) +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) + + +class PatchEmbedding(nn.Module): + + def __init__(self, config): + super().__init__() + self.proj = nn.Conv2d(config.in_channels, + config.hidden_size, + kernel_size=config.patch_size, + stride=config.patch_size) + self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size)) + self.position_embedding = nn.Embedding(config.num_positions, + config.hidden_size) + + def forward(self, images: torch.Tensor) -> torch.Tensor: + """ + Parameters: + images : torch.Tensor + Input image tensor with shape (B, C, H, W) + + Returns: + torch.Tensor + Transformed tensor with shape (B, L, D) + """ + images = images.to(self.proj.weight.device) + x = self.proj(images) + x = x.flatten(2).transpose(1, 2) + cls_token = self.cls_embedding.expand(x.shape[0], -1, -1) + x = torch.cat((cls_token, x), dim=1) + x += self.position_embedding.weight.unsqueeze(0) + return x + + +class Attention(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.hidden_size = config.hidden_size + self.tp_size = get_tensor_model_parallel_world_size() + self.num_heads_per_rank = config.num_heads // self.tp_size + self.head_dim = config.hidden_size // config.num_heads + self.scale = self.head_dim**-0.5 + + self.query_key_value = QKVParallelLinear( + config.hidden_size, + self.head_dim, + config.num_heads, + quant_config=quant_config, + ) + self.dense = RowParallelLinear( + config.hidden_size, + config.hidden_size, + quant_config=quant_config, + ) + + self.output_dropout = torch.nn.Dropout(config.dropout_prob) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, L, _ = x.shape + qkv, _ = self.query_key_value(x) # B, L, 3 * H * D + q, k, v = qkv.chunk(3, dim=-1) + q = q.reshape(B, L, self.num_heads_per_rank, + self.head_dim).permute(0, 2, 1, 3) # B, H, L, D + k = k.reshape(B, L, self.num_heads_per_rank, + self.head_dim).permute(0, 2, 1, 3) # B, H, L, D + v = v.reshape(B, L, self.num_heads_per_rank, + self.head_dim).permute(0, 2, 1, 3) # B, H, L, D + + out = torch.nn.functional.scaled_dot_product_attention(q, + k, + v, + attn_mask=None, + dropout_p=0., + is_causal=False) + + output, _ = self.dense(out.transpose(1, 2).view(B, L, -1)) + output = self.output_dropout(output) + return output + + +class MLP(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.activation_fn = get_act_fn(config.hidden_act) + self.fc1 = ColumnParallelLinear( + config.hidden_size, + config.intermediate_size, + quant_config=quant_config, + ) + self.fc2 = RowParallelLinear( + config.intermediate_size, + config.hidden_size, + quant_config=quant_config, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x, _ = self.fc1(x) + x = self.activation_fn(x) + x, _ = self.fc2(x) + return x + + +class TransformerLayer(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.input_layernorm = LayerNorm(config.hidden_size, + eps=config.layer_norm_eps) + self.attention = Attention(config, quant_config=quant_config) + self.mlp = MLP(config, quant_config=quant_config) + self.post_attention_layernorm = LayerNorm(config.hidden_size, + eps=config.layer_norm_eps) + + def forward(self, hidden_states): + attention_input = hidden_states + attention_output = self.input_layernorm( + self.attention(attention_input)) + hidden_states = attention_input + attention_output + mlp_input = hidden_states + mlp_output = self.post_attention_layernorm(self.mlp(mlp_input)) + output = mlp_input + mlp_output + return output + + +class Transformer(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.layers = nn.ModuleList([ + TransformerLayer(config, quant_config=quant_config) + for _ in range(config.num_hidden_layers) + ]) + + def forward(self, hidden_states): + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + return hidden_states + + +class GLU(nn.Module): + + def __init__( + self, + config, + in_features, + quant_config: Optional[QuantizationConfig] = None, + ): + """ + The original implementation is the same as: + ```python + self.dense_h_to_4h = ColumnParallelLinear( + config.hidden_size, + config.ffn_hidden_size, + bias=False, + quant_config=quant_config + ) + + self.gate_proj = ColumnParallelLinear( + config.hidden_size, + config.ffn_hidden_size, + bias=False, + quant_config=quant_config + ) + ``` + ``` + gate_proj_output, _ = self.gate_proj(x) + dense_h_to_4h_output, _ = self.dense_h_to_4h(x) + x = torch.cat([gate_proj_output, dense_h_to_4h_output], dim=-1) + ``` + + We merge two ColumnParallelLinear into one MergedColumnParallelLinear: + ``` + self.merged_proj = MergedColumnParallelLinear( + config.hidden_size, + [config.ffn_hidden_size] * 2, + bias=False, + quant_config=quant_config + ) + ``` + ``` + x, _ = self.merged_proj(x) + ``` + """ + super().__init__() + self.linear_proj = ReplicatedLinear(in_features, + config.hidden_size, + bias=False, + quant_config=quant_config) + self.norm1 = nn.LayerNorm(config.hidden_size) + self.act1 = nn.GELU() + self.act2 = SiluAndMul() + + self.merged_proj = MergedColumnParallelLinear( + config.hidden_size, [config.ffn_hidden_size] * 2, + bias=False, + quant_config=quant_config) + + self.dense_4h_to_h = RowParallelLinear(config.ffn_hidden_size, + config.hidden_size, + bias=False, + quant_config=quant_config) + + def forward(self, x): + x, _ = self.linear_proj(x) + x = self.act1(self.norm1(x)) + x, _ = self.merged_proj(x) + x = self.act2(x) + x, _ = self.dense_4h_to_h(x) + return x + + +class EVA2CLIPModel(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + vision_config = Namespace(**config.vision_config) + self.patch_embedding = PatchEmbedding(vision_config) + self.transformer = Transformer(vision_config, + quant_config=quant_config) + self.linear_proj = GLU(config, + in_features=config.hidden_size, + quant_config=quant_config) + self.conv = nn.Conv2d(in_channels=vision_config.hidden_size, + out_channels=config.hidden_size, + kernel_size=2, + stride=2) + self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.scaling_factor = vision_config.scaling_factor + + def forward(self, images: torch.Tensor) -> torch.Tensor: + """ + Parameters: + images : torch.Tensor + Input image tensor with shape (B, C, H, W) + + Returns: + torch.Tensor + Transformed tensor with shape (B, L, D) + """ + x = self.patch_embedding(images) + x = self.transformer(x) + x = x[:, 1:] + + b, s, h = x.shape + grid_size = int(s**0.5) + x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2) + x = self.conv(x) + + x = x.flatten(2).transpose(1, 2) + x = self.linear_proj(x) + boi = self.boi.expand(x.shape[0], -1, -1) + eoi = self.eoi.expand(x.shape[0], -1, -1) + x = torch.cat((boi, x, eoi), dim=1) + x = x / self.scaling_factor + return x diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index f1d484521acb..b42a71ac90c5 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -26,8 +26,7 @@ "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), # baichuan-7b "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), # baichuan-13b "BloomForCausalLM": ("bloom", "BloomForCausalLM"), - "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"), - "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"), + # ChatGLMModel supports multimodal "CohereForCausalLM": ("commandr", "CohereForCausalLM"), "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"), "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"), @@ -68,6 +67,7 @@ "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"), "Phi3SmallForCausalLM": ("phi3_small", "Phi3SmallForCausalLM"), "PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"), + # QWenLMHeadModel supports multimodal "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"), "Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"), "RWForCausalLM": ("falcon", "FalconForCausalLM"), @@ -91,6 +91,8 @@ # [Decoder-only] "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"), "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"), # noqa: E501 + "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"), + "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"), "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"), "InternVLChatModel": ("internvl", "InternVLChatModel"), "LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"), diff --git a/vllm/transformers_utils/tokenizer.py b/vllm/transformers_utils/tokenizer.py index 85c339df4a76..94af2388d79d 100644 --- a/vllm/transformers_utils/tokenizer.py +++ b/vllm/transformers_utils/tokenizer.py @@ -59,6 +59,26 @@ def __len__(self): return tokenizer +def patch_padding_side(tokenizer: PreTrainedTokenizer) -> None: + """Patch _pad method to accept `padding_side` for older tokenizers.""" + orig_pad = tokenizer._pad + + def _pad( + self: PreTrainedTokenizer, + *args, + padding_side: Optional[str] = None, + **kwargs, + ): + if padding_side is not None and padding_side != self.padding_side: + msg = ("`padding_side` argument is not supported by " + f"{type(tokenizer).__name__} and will be ignored.") + warnings.warn(msg, stacklevel=2) + + return orig_pad(*args, **kwargs) + + tokenizer._pad = MethodType(_pad, tokenizer) + + def get_tokenizer( tokenizer_name: Union[str, Path], *args, @@ -143,24 +163,7 @@ def get_tokenizer( if type(tokenizer).__name__ in ("ChatGLMTokenizer", "ChatGLM4Tokenizer"): assert isinstance(tokenizer, PreTrainedTokenizer) - orig_pad = tokenizer._pad - - # Patch _pad method to accept `padding_side` - def _pad( - self: PreTrainedTokenizer, - *args, - padding_side: Optional[str] = None, - **kwargs, - ): - if (padding_side is not None - and padding_side != self.padding_side): - msg = ("`padding_side` argument is not supported by " - "ChatGLMTokenizer and will be ignored.") - warnings.warn(msg, stacklevel=2) - - return orig_pad(*args, **kwargs) - - tokenizer._pad = MethodType(_pad, tokenizer) + patch_padding_side(tokenizer) if not isinstance(tokenizer, PreTrainedTokenizerFast): logger.warning(
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7746@85ee9ff
vllm-project/vllm
Python
7,746
[BugFix] Fix server crash on empty prompt
Fixes https://github.com/vllm-project/vllm/issues/7632 To reproduce, start the server with `python -m vllm.entrypoints.openai.api_server --model gpt2` and send an empty prompt: ``` $ curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{ "model": "gpt2", "prompt": [""], "max_tokens": 20, "temperature": 0 }' Internal Server Error ``` On the server side this log will show and the server will be dead: ``` INFO 08-21 14:49:19 logger.py:36] Received request cmpl-470c7a46582b4554b7926ce4559b0337-0: prompt: '', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=20, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [], lora_request: None, prompt_adapter_request: None. INFO 08-21 14:49:19 async_llm_engine.py:208] Added request cmpl-470c7a46582b4554b7926ce4559b0337-0. ERROR 08-21 14:49:19 async_llm_engine.py:65] Engine background task failed ERROR 08-21 14:49:19 async_llm_engine.py:65] Traceback (most recent call last): ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion ERROR 08-21 14:49:19 async_llm_engine.py:65] return_value = task.result() ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 930, in run_engine_loop ERROR 08-21 14:49:19 async_llm_engine.py:65] result = task.result() ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 873, in engine_step ERROR 08-21 14:49:19 async_llm_engine.py:65] request_outputs = await self.engine.step_async(virtual_engine) ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 301, in step_async ERROR 08-21 14:49:19 async_llm_engine.py:65] virtual_engine].schedule() ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1039, in schedule ERROR 08-21 14:49:19 async_llm_engine.py:65] scheduler_outputs = self._schedule() ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1013, in _schedule ERROR 08-21 14:49:19 async_llm_engine.py:65] return self._schedule_default() ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 857, in _schedule_default ERROR 08-21 14:49:19 async_llm_engine.py:65] prefills = self._schedule_prefills(budget, ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 752, in _schedule_prefills ERROR 08-21 14:49:19 async_llm_engine.py:65] num_new_tokens = self._get_num_new_tokens(seq_group, ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1349, in _get_num_new_tokens ERROR 08-21 14:49:19 async_llm_engine.py:65] assert num_new_tokens > 0 ERROR 08-21 14:49:19 async_llm_engine.py:65] ^^^^^^^^^^^^^^^^^^ ERROR 08-21 14:49:19 async_llm_engine.py:65] AssertionError Exception in callback functools.partial(<function _log_task_completion at 0x7f1abf494220>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f1aa38d0050>>) handle: <Handle functools.partial(<function _log_task_completion at 0x7f1abf494220>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f1aa38d0050>>)> Traceback (most recent call last): File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion return_value = task.result() ^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 930, in run_engine_loop result = task.result() ^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 873, in engine_step request_outputs = await self.engine.step_async(virtual_engine) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 301, in step_async virtual_engine].schedule() ^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1039, in schedule scheduler_outputs = self._schedule() ^^^^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1013, in _schedule return self._schedule_default() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 857, in _schedule_default prefills = self._schedule_prefills(budget, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 752, in _schedule_prefills num_new_tokens = self._get_num_new_tokens(seq_group, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/core/scheduler.py", line 1349, in _get_num_new_tokens assert num_new_tokens > 0 ^^^^^^^^^^^^^^^^^^ AssertionError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "uvloop/cbhandles.pyx", line 63, in uvloop.loop.Handle._run File "/home/mbayser/IBMProjects/FoundationModels/inference/vllm/vllm/engine/async_llm_engine.py", line 67, in _log_task_completion raise AsyncEngineDeadError( ``` After the fix, an error 400 is returned: ``` $ curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{ "model": "gpt2", "prompt": [""], "max_tokens": 20, "temperature": 0 }' {"object":"error","message":"Empty prompt","type":"BadRequestError","param":null,"code":400}```
2024-08-21T17:56:35Z
[Bug]: assert num_new_tokens > 0 crashes entire worker instead of just failing single API call ### Your current environment vllm docker 0.5.4 ``` docker pull vllm/vllm-openai:latest docker stop danube3_mig ; docker remove danube3_mig docker run -d --restart=always \ --runtime=nvidia \ --gpus '"device=MIG-a6dbed35-9d05-58da-a0b5-23ae5bf8427e"' \ --shm-size=10.24gb \ -p 5004:5004 \ -e NCCL_IGNORE_DISABLED_P2P=1 \ -e HUGGING_FACE_HUB_TOKEN=$HUGGING_FACE_HUB_TOKEN \ -e VLLM_NCCL_SO_PATH=/usr/local/lib/python3.10/dist-packages/nvidia/nccl/lib/libnccl.so.2 \ -v /etc/passwd:/etc/passwd:ro \ -v /etc/group:/etc/group:ro \ -u `id -u`:`id -g` \ -v "${HOME}"/.cache:$HOME/.cache/ -v "${HOME}"/.config:$HOME/.config/ -v "${HOME}"/.triton:$HOME/.triton/ \ --network host \ --name danube3_mig \ vllm/vllm-openai:latest \ --port=5004 \ --host=0.0.0.0 \ --model=h2oai/h2o-danube3-4b-chat \ --seed 1234 \ --trust-remote-code \ --tensor-parallel-size=1 \ --max-model-len=8192 \ --gpu-memory-utilization=0.99 \ --max-num-batched-tokens=131072 --max-log-len=100 \ --use-v2-block-manager \ --num-speculative-tokens=5 \ --ngram-prompt-lookup-max=4 \ --enable-prefix-caching \ --speculative-model="[ngram]" \ --download-dir=$HOME/.cache/huggingface/hub &>> logs.vllm_server.danube3_migb.txt ``` Unsure if has to do with speculative, seems just like prompt='' causes it. ### ๐Ÿ› Describe the bug ``` INFO: 172.16.0.199:21756 - "GET /health HTTP/1.1" 200 OK INFO 08-18 00:51:02 logger.py:36] Received request cmpl-14b87b97d9a8481d8963a0a1652b217b-0: prompt: '', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.3, top_p=1.> INFO: 172.16.0.199:21766 - "POST /v1/completions HTTP/1.1" 200 OK INFO 08-18 00:51:02 async_llm_engine.py:174] Added request cmpl-14b87b97d9a8481d8963a0a1652b217b-0. ERROR 08-18 00:51:02 async_llm_engine.py:57] Engine background task failed ERROR 08-18 00:51:02 async_llm_engine.py:57] Traceback (most recent call last): ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 47, in _log_task_completion ERROR 08-18 00:51:02 async_llm_engine.py:57] return_value = task.result() ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 642, in run_engine_loop ERROR 08-18 00:51:02 async_llm_engine.py:57] result = task.result() ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 585, in engine_step ERROR 08-18 00:51:02 async_llm_engine.py:57] request_outputs = await self.engine.step_async(virtual_engine) ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 239, in step_async ERROR 08-18 00:51:02 async_llm_engine.py:57] virtual_engine].schedule() ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 950, in schedule ERROR 08-18 00:51:02 async_llm_engine.py:57] scheduler_outputs = self._schedule() ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 925, in _schedule ERROR 08-18 00:51:02 async_llm_engine.py:57] return self._schedule_default() ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 785, in _schedule_default ERROR 08-18 00:51:02 async_llm_engine.py:57] prefills = self._schedule_prefills(budget, ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 683, in _schedule_prefills ERROR 08-18 00:51:02 async_llm_engine.py:57] num_new_tokens = self._get_num_new_tokens(seq_group, ERROR 08-18 00:51:02 async_llm_engine.py:57] File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 1206, in _get_num_new_tokens ERROR 08-18 00:51:02 async_llm_engine.py:57] assert num_new_tokens > 0 ERROR 08-18 00:51:02 async_llm_engine.py:57] AssertionError Exception in callback _log_task_completion(error_callback=<bound method...72047c646d70>>)(<Task finishe...ertionError()>) at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:37 handle: <Handle _log_task_completion(error_callback=<bound method...72047c646d70>>)(<Task finishe...ertionError()>) at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:37> Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 47, in _log_task_completion return_value = task.result() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 642, in run_engine_loop result = task.result() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 585, in engine_step request_outputs = await self.engine.step_async(virtual_engine) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 239, in step_async virtual_engine].schedule() File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 950, in schedule scheduler_outputs = self._schedule() File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 925, in _schedule return self._schedule_default() File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 785, in _schedule_default prefills = self._schedule_prefills(budget, INFO 08-18 00:51:02 async_llm_engine.py:181] Aborted request cmpl-14b87b97d9a8481d8963a0a1652b217b-0. File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 683, in _schedule_prefills num_new_tokens = self._get_num_new_tokens(seq_group, File "/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py", line 1206, in _get_num_new_tokens assert num_new_tokens > 0 AssertionError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 59, in _log_task_completion raise AsyncEngineDeadError( vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for theactual cause. ERROR: Exception in ASGI application Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 265, in __call__ await wrap(partial(self.listen_for_disconnect, receive)) File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 261, in wrap await func() File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 238, in listen_for_disconnect message = await receive() File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 54, in wrapped_receive msg = await self.receive() File "/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py", line 553, in receive await self.message_event.wait() File "/usr/lib/python3.10/asyncio/locks.py", line 214, in wait await fut asyncio.exceptions.CancelledError: Cancelled by cancel scope 720537665120 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 192, in __call__ await response(scope, wrapped_receive, send) File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 258, in __call__ async with anyio.create_task_group() as task_group: File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 680, in __aexit__ raise BaseExceptionGroup( exceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/starlette/_utils.py", line 87, in collapse_excgroups yield File "/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py", line 190, in __call__ async with anyio.create_task_group() as task_group: File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 680, in __aexit__ raise BaseExceptionGroup( exceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception) During handling of the above exception, another exception occurred: Traceback (most recent call last): ```
To be clear, the bug is at least that the entire vllm engine is taken down by prompt=''
[ { "body": "### Your current environment\r\n\r\nvllm docker 0.5.4\r\n\r\n```\r\ndocker pull vllm/vllm-openai:latest\r\ndocker stop danube3_mig ; docker remove danube3_mig\r\ndocker run -d --restart=always \\\r\n --runtime=nvidia \\\r\n --gpus '\"device=MIG-a6dbed35-9d05-58da-a0b5-23ae5bf8427e\"' \\\r\n --shm-size=10.24gb \\\r\n -p 5004:5004 \\\r\n -e NCCL_IGNORE_DISABLED_P2P=1 \\\r\n -e HUGGING_FACE_HUB_TOKEN=$HUGGING_FACE_HUB_TOKEN \\\r\n -e VLLM_NCCL_SO_PATH=/usr/local/lib/python3.10/dist-packages/nvidia/nccl/lib/libnccl.so.2 \\\r\n -v /etc/passwd:/etc/passwd:ro \\\r\n -v /etc/group:/etc/group:ro \\\r\n -u `id -u`:`id -g` \\\r\n -v \"${HOME}\"/.cache:$HOME/.cache/ -v \"${HOME}\"/.config:$HOME/.config/ -v \"${HOME}\"/.triton:$HOME/.triton/ \\\r\n --network host \\\r\n --name danube3_mig \\\r\n vllm/vllm-openai:latest \\\r\n --port=5004 \\\r\n --host=0.0.0.0 \\\r\n --model=h2oai/h2o-danube3-4b-chat \\\r\n --seed 1234 \\\r\n --trust-remote-code \\\r\n --tensor-parallel-size=1 \\\r\n --max-model-len=8192 \\\r\n --gpu-memory-utilization=0.99 \\\r\n --max-num-batched-tokens=131072 --max-log-len=100 \\\r\n --use-v2-block-manager \\\r\n --num-speculative-tokens=5 \\\r\n --ngram-prompt-lookup-max=4 \\\r\n --enable-prefix-caching \\\r\n --speculative-model=\"[ngram]\" \\\r\n --download-dir=$HOME/.cache/huggingface/hub &>> logs.vllm_server.danube3_migb.txt\r\n```\r\n\r\nUnsure if has to do with speculative, seems just like prompt='' causes it.\r\n\r\n### ๐Ÿ› Describe the bug\r\n\r\n```\r\nINFO: 172.16.0.199:21756 - \"GET /health HTTP/1.1\" 200 OK\r\nINFO 08-18 00:51:02 logger.py:36] Received request cmpl-14b87b97d9a8481d8963a0a1652b217b-0: prompt: '', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.3, top_p=1.>\r\nINFO: 172.16.0.199:21766 - \"POST /v1/completions HTTP/1.1\" 200 OK\r\nINFO 08-18 00:51:02 async_llm_engine.py:174] Added request cmpl-14b87b97d9a8481d8963a0a1652b217b-0.\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] Engine background task failed\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] Traceback (most recent call last):\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 47, in _log_task_completion\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] return_value = task.result()\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 642, in run_engine_loop\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] result = task.result()\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 585, in engine_step\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] request_outputs = await self.engine.step_async(virtual_engine)\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 239, in step_async\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] virtual_engine].schedule()\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 950, in schedule\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] scheduler_outputs = self._schedule()\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 925, in _schedule\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] return self._schedule_default()\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 785, in _schedule_default\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] prefills = self._schedule_prefills(budget,\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 683, in _schedule_prefills\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] num_new_tokens = self._get_num_new_tokens(seq_group,\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 1206, in _get_num_new_tokens\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] assert num_new_tokens > 0\r\nERROR 08-18 00:51:02 async_llm_engine.py:57] AssertionError\r\nException in callback _log_task_completion(error_callback=<bound method...72047c646d70>>)(<Task finishe...ertionError()>) at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:37\r\nhandle: <Handle _log_task_completion(error_callback=<bound method...72047c646d70>>)(<Task finishe...ertionError()>) at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:37>\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 47, in _log_task_completion\r\n return_value = task.result()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 642, in run_engine_loop\r\n result = task.result()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 585, in engine_step\r\n request_outputs = await self.engine.step_async(virtual_engine)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 239, in step_async\r\n virtual_engine].schedule()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 950, in schedule\r\n scheduler_outputs = self._schedule()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 925, in _schedule\r\n return self._schedule_default()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 785, in _schedule_default\r\n prefills = self._schedule_prefills(budget,\r\nINFO 08-18 00:51:02 async_llm_engine.py:181] Aborted request cmpl-14b87b97d9a8481d8963a0a1652b217b-0.\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 683, in _schedule_prefills\r\n num_new_tokens = self._get_num_new_tokens(seq_group,\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/core/scheduler.py\", line 1206, in _get_num_new_tokens\r\n assert num_new_tokens > 0\r\nAssertionError\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\r\n self._context.run(self._callback, *self._args)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 59, in _log_task_completion\r\n raise AsyncEngineDeadError(\r\nvllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for theactual cause.\r\nERROR: Exception in ASGI application\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 265, in __call__\r\n await wrap(partial(self.listen_for_disconnect, receive))\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 261, in wrap\r\n await func()\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 238, in listen_for_disconnect\r\n message = await receive()\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py\", line 54, in wrapped_receive\r\n msg = await self.receive()\r\n File \"/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py\", line 553, in receive\r\n await self.message_event.wait()\r\n File \"/usr/lib/python3.10/asyncio/locks.py\", line 214, in wait\r\n await fut\r\nasyncio.exceptions.CancelledError: Cancelled by cancel scope 720537665120\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py\", line 192, in __call__\r\n await response(scope, wrapped_receive, send)\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 258, in __call__\r\n async with anyio.create_task_group() as task_group:\r\n File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 680, in __aexit__\r\n raise BaseExceptionGroup(\r\nexceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/_utils.py\", line 87, in collapse_excgroups\r\n yield\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/middleware/base.py\", line 190, in __call__\r\n async with anyio.create_task_group() as task_group:\r\n File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 680, in __aexit__\r\n raise BaseExceptionGroup(\r\nexceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n\r\n```", "number": 7632, "title": "[Bug]: assert num_new_tokens > 0 crashes entire worker instead of just failing single API call" } ]
15310b5101963818b76f1821e93887cb22f0aea6
{ "head_commit": "85ee9ff94094eb318794736e57fb54397e656589", "head_commit_message": "move test to another file due to conflicting fixtures\n\nSigned-off-by: Max de Bayser <[email protected]>", "patch_to_review": "diff --git a/tests/entrypoints/llm/test_generate.py b/tests/entrypoints/llm/test_generate.py\nindex c426e9b4ee8..9df11df98aa 100644\n--- a/tests/entrypoints/llm/test_generate.py\n+++ b/tests/entrypoints/llm/test_generate.py\n@@ -142,6 +142,12 @@ def test_multiple_sampling_params(llm: LLM):\n assert len(PROMPTS) == len(outputs)\n \n \n+def test_empty_prompt():\n+ llm = LLM(model=\"gpt2\")\n+ with pytest.raises(ValueError, match='Empty prompt'):\n+ llm.generate([\"\"])\n+\n+\n def test_chat():\n \n llm = LLM(model=\"meta-llama/Meta-Llama-3-8B-Instruct\")\ndiff --git a/tests/entrypoints/openai/test_prompt_validation.py b/tests/entrypoints/openai/test_prompt_validation.py\nnew file mode 100644\nindex 00000000000..a63c693c21c\n--- /dev/null\n+++ b/tests/entrypoints/openai/test_prompt_validation.py\n@@ -0,0 +1,22 @@\n+# imports for guided decoding tests\n+import re\n+\n+import openai\n+import pytest\n+\n+from ...utils import RemoteOpenAIServer\n+\n+\[email protected]\n+async def test_empty_prompt():\n+ model_name = \"gpt2\"\n+ server_args = [\"--disable-frontend-multiprocessing\", \"--enforce-eager\"]\n+ with RemoteOpenAIServer(model_name, server_args) as remote_server:\n+ client = remote_server.get_async_client()\n+\n+ with pytest.raises(openai.BadRequestError,\n+ match=re.compile('.+Empty prompt.+')):\n+ await client.completions.create(model=model_name,\n+ prompt=\"\",\n+ max_tokens=5,\n+ temperature=0.0)\n\\ No newline at end of file\ndiff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py\nindex 8812b853c06..d77fb96b943 100644\n--- a/vllm/engine/async_llm_engine.py\n+++ b/vllm/engine/async_llm_engine.py\n@@ -581,6 +581,7 @@ async def add_request_async(\n lora_request=lora_request,\n prompt_adapter_request=prompt_adapter_request,\n )\n+ self._validate_model_inputs(processed_inputs)\n \n self._add_processed_request(\n request_id=request_id,\ndiff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py\nindex f72902c3721..fd9445eaff0 100644\n--- a/vllm/engine/llm_engine.py\n+++ b/vllm/engine/llm_engine.py\n@@ -900,10 +900,8 @@ def _build_decoder_only_llm_inputs(\n prompt_adapter_request: Optional[PromptAdapterRequest],\n ) -> LLMInputs:\n prompt, prompt_token_ids, multi_modal_data = prompt_comps\n-\n prompt_token_ids = self._apply_prompt_adapter(\n prompt_token_ids, prompt_adapter_request=prompt_adapter_request)\n-\n return LLMInputs(prompt_token_ids=prompt_token_ids,\n prompt=prompt,\n multi_modal_data=multi_modal_data)\n@@ -1036,6 +1034,7 @@ def add_request(\n lora_request=lora_request,\n prompt_adapter_request=prompt_adapter_request,\n )\n+ self._validate_model_inputs(processed_inputs)\n \n self._add_processed_request(\n request_id=request_id,\n@@ -1647,3 +1646,14 @@ def is_encoder_decoder_model(self):\n \n def is_embedding_model(self):\n return self.model_config.is_embedding_model\n+\n+ def _validate_model_inputs(self, inputs: Union[LLMInputs,\n+ EncoderDecoderLLMInputs]):\n+ if self.is_encoder_decoder_model():\n+ if \"encoder_prompt_token_ids\" not in inputs or\\\n+ len(inputs[\"encoder_prompt_token_ids\"]) == 0:\n+ raise ValueError(\"Empty prompt\")\n+ else:\n+ if \"prompt_token_ids\" not in inputs or len(\n+ inputs[\"prompt_token_ids\"]) == 0:\n+ raise ValueError(\"Empty prompt\")\n" }
[ { "diff_hunk": "@@ -0,0 +1,22 @@\n+# imports for guided decoding tests\n+import re\n+\n+import openai\n+import pytest\n+\n+from ...utils import RemoteOpenAIServer\n+\n+\[email protected]\n+async def test_empty_prompt():\n+ model_name = \"gpt2\"\n+ server_args = [\"--disable-frontend-multiprocessing\", \"--enforce-eager\"]", "line": null, "original_line": 13, "original_start_line": null, "path": "tests/entrypoints/openai/test_prompt_validation.py", "start_line": null, "text": "@user1:\nDo you need to disable the front end multiprocessing? It would be nice to test through that too\n\n@author:\nNo, it was just to reduce the number of moving pieces. But I've enabled it again." } ]
68610cbe870be02cd640fbaaf5df83c4b658bb54
diff --git a/tests/entrypoints/llm/test_prompt_validation.py b/tests/entrypoints/llm/test_prompt_validation.py new file mode 100644 index 000000000000..565dfa01346c --- /dev/null +++ b/tests/entrypoints/llm/test_prompt_validation.py @@ -0,0 +1,9 @@ +import pytest + +from vllm import LLM + + +def test_empty_prompt(): + llm = LLM(model="gpt2") + with pytest.raises(ValueError, match='Prompt cannot be empty'): + llm.generate([""]) diff --git a/tests/entrypoints/openai/test_prompt_validation.py b/tests/entrypoints/openai/test_prompt_validation.py new file mode 100644 index 000000000000..0a573a0066d3 --- /dev/null +++ b/tests/entrypoints/openai/test_prompt_validation.py @@ -0,0 +1,22 @@ +# imports for guided decoding tests +import re + +import openai +import pytest + +from ...utils import RemoteOpenAIServer + + [email protected] +async def test_empty_prompt(): + model_name = "gpt2" + server_args = ["--enforce-eager"] + with RemoteOpenAIServer(model_name, server_args) as remote_server: + client = remote_server.get_async_client() + + with pytest.raises(openai.BadRequestError, + match=re.compile('.+Prompt cannot be empty.+')): + await client.completions.create(model=model_name, + prompt="", + max_tokens=5, + temperature=0.0) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index f72902c37218..8c98b64181d0 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -591,6 +591,7 @@ def _add_processed_request( prompt_adapter_request: Optional[PromptAdapterRequest], trace_headers: Optional[Mapping[str, str]] = None, ) -> None: + self._validate_model_inputs(processed_inputs) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) @@ -1647,3 +1648,10 @@ def is_encoder_decoder_model(self): def is_embedding_model(self): return self.model_config.is_embedding_model + + def _validate_model_inputs(self, inputs: Union[LLMInputs, + EncoderDecoderLLMInputs]): + prompt_key = "encoder_prompt_token_ids" \ + if self.is_encoder_decoder_model() else "prompt_token_ids" + if not inputs.get(prompt_key): + raise ValueError("Prompt cannot be empty")
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7710@4f278b7
vllm-project/vllm
Python
7,710
[Model] Fix Phi-3.5-vision-instruct 'num_crops' issue
This PR changes phi3v to load `num_crops` config from image processor config. FIX #7718 **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-08-20T23:19:38Z
[Bug]: Error loading microsoft/Phi-3.5-vision-instruct ### Your current environment vllm version: `Version: 0.5.4` ### ๐Ÿ› Describe the bug Repro command: ``` vllm serve microsoft/Phi-3.5-vision-instruct --trust-remote-code --max-model-len 4096 ``` Error: ``` vllm serve microsoft/Phi-3.5-vision-instruct --trust-remote-code --max-model-len 4096 INFO 08-21 04:43:37 api_server.py:339] vLLM API server version 0.5.4 INFO 08-21 04:43:37 api_server.py:340] args: Namespace(model_tag='microsoft/Phi-3.5-vision-instruct', host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, model='microsoft/Phi-3.5-vision-instruct', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=4096, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None, dispatch_function=<function serve at 0x7206f7951750>) WARNING 08-21 04:43:37 config.py:1454] Casting torch.bfloat16 to torch.float16. INFO 08-21 04:43:38 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='microsoft/Phi-3.5-vision-instruct', speculative_config=None, tokenizer='microsoft/Phi-3.5-vision-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=microsoft/Phi-3.5-vision-instruct, use_v2_block_manager=False, enable_prefix_caching=False) INFO 08-21 04:43:38 selector.py:170] Cannot use FlashAttention-2 backend due to sliding window. INFO 08-21 04:43:38 selector.py:54] Using XFormers backend. /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_fwd") /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_bwd") INFO 08-21 04:43:39 model_runner.py:720] Starting to load model microsoft/Phi-3.5-vision-instruct... INFO 08-21 04:43:39 selector.py:170] Cannot use FlashAttention-2 backend due to sliding window. INFO 08-21 04:43:39 selector.py:54] Using XFormers backend. INFO 08-21 04:43:40 weight_utils.py:225] Using model weights format ['*.safetensors'] Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s] Loading safetensors checkpoint shards: 50% Completed | 1/2 [00:00<00:00, 1.84it/s] Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00, 1.29it/s] Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00, 1.35it/s] INFO 08-21 04:43:42 model_runner.py:732] Loading model weights took 7.7498 GB /usr/local/lib/python3.10/dist-packages/transformers/models/auto/image_processing_auto.py:513: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead warnings.warn( Process Process-1: Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 217, in run_rpc_server server = AsyncEngineRPCServer(async_engine_args, usage_context, port) File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 25, in __init__ self.engine = AsyncLLMEngine.from_engine_args(async_engine_args, File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 471, in from_engine_args engine = cls( File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 381, in __init__ self.engine = self._init_engine(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 552, in _init_engine return engine_class(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 263, in __init__ self._initialize_kv_caches() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 362, in _initialize_kv_caches self.model_executor.determine_num_available_blocks()) File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 94, in determine_num_available_blocks return self.driver_worker.determine_num_available_blocks() File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 179, in determine_num_available_blocks self.model_runner.profile_run() File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 940, in profile_run self.execute_model(model_input, kv_caches, intermediate_tensors) File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1363, in execute_model hidden_or_intermediate_states = model_executable( File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/phi3v.py", line 532, in forward inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/utils.py", line 29, in merge_vision_embeddings raise ValueError( ValueError: Attempted to assign 1 x 781 = 781 image tokens to 2653 placeholders ```
Can you check out #7710 and see if it fixes your issue?
[ { "body": "### Your current environment\n\nvllm version: `Version: 0.5.4`\n\n### ๐Ÿ› Describe the bug\n\nRepro command:\r\n```\r\nvllm serve microsoft/Phi-3.5-vision-instruct --trust-remote-code --max-model-len 4096\r\n```\r\n\r\nError:\r\n```\r\nvllm serve microsoft/Phi-3.5-vision-instruct --trust-remote-code --max-model-len 4096\r\nINFO 08-21 04:43:37 api_server.py:339] vLLM API server version 0.5.4\r\nINFO 08-21 04:43:37 api_server.py:340] args: Namespace(model_tag='microsoft/Phi-3.5-vision-instruct', host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, model='microsoft/Phi-3.5-vision-instruct', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=4096, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None, dispatch_function=<function serve at 0x7206f7951750>)\r\nWARNING 08-21 04:43:37 config.py:1454] Casting torch.bfloat16 to torch.float16.\r\nINFO 08-21 04:43:38 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='microsoft/Phi-3.5-vision-instruct', speculative_config=None, tokenizer='microsoft/Phi-3.5-vision-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=microsoft/Phi-3.5-vision-instruct, use_v2_block_manager=False, enable_prefix_caching=False)\r\nINFO 08-21 04:43:38 selector.py:170] Cannot use FlashAttention-2 backend due to sliding window.\r\nINFO 08-21 04:43:38 selector.py:54] Using XFormers backend.\r\n/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.\r\n @torch.library.impl_abstract(\"xformers_flash::flash_fwd\")\r\n/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.\r\n @torch.library.impl_abstract(\"xformers_flash::flash_bwd\")\r\nINFO 08-21 04:43:39 model_runner.py:720] Starting to load model microsoft/Phi-3.5-vision-instruct...\r\nINFO 08-21 04:43:39 selector.py:170] Cannot use FlashAttention-2 backend due to sliding window.\r\nINFO 08-21 04:43:39 selector.py:54] Using XFormers backend.\r\nINFO 08-21 04:43:40 weight_utils.py:225] Using model weights format ['*.safetensors']\r\nLoading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]\r\nLoading safetensors checkpoint shards: 50% Completed | 1/2 [00:00<00:00, 1.84it/s]\r\nLoading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00, 1.29it/s]\r\nLoading safetensors checkpoint shards: 100% Completed | 2/2 [00:01<00:00, 1.35it/s]\r\n\r\nINFO 08-21 04:43:42 model_runner.py:732] Loading model weights took 7.7498 GB\r\n/usr/local/lib/python3.10/dist-packages/transformers/models/auto/image_processing_auto.py:513: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead\r\n warnings.warn(\r\nProcess Process-1:\r\nTraceback (most recent call last):\r\n File \"/usr/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\r\n self.run()\r\n File \"/usr/lib/python3.10/multiprocessing/process.py\", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py\", line 217, in run_rpc_server\r\n server = AsyncEngineRPCServer(async_engine_args, usage_context, port)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py\", line 25, in __init__\r\n self.engine = AsyncLLMEngine.from_engine_args(async_engine_args,\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 471, in from_engine_args\r\n engine = cls(\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 381, in __init__\r\n self.engine = self._init_engine(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 552, in _init_engine\r\n return engine_class(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py\", line 263, in __init__\r\n self._initialize_kv_caches()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py\", line 362, in _initialize_kv_caches\r\n self.model_executor.determine_num_available_blocks())\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py\", line 94, in determine_num_available_blocks\r\n return self.driver_worker.determine_num_available_blocks()\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py\", line 179, in determine_num_available_blocks\r\n self.model_runner.profile_run()\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py\", line 940, in profile_run\r\n self.execute_model(model_input, kv_caches, intermediate_tensors)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py\", line 1363, in execute_model\r\n hidden_or_intermediate_states = model_executable(\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\r\n return self._call_impl(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/phi3v.py\", line 532, in forward\r\n inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/utils.py\", line 29, in merge_vision_embeddings\r\n raise ValueError(\r\nValueError: Attempted to assign 1 x 781 = 781 image tokens to 2653 placeholders\r\n```", "number": 7718, "title": "[Bug]: Error loading microsoft/Phi-3.5-vision-instruct" } ]
53328d7536b3e8ea4863e351b09e4284f5601cae
{ "head_commit": "4f278b7e28aeae55c3a38830e0bb4e0ba8ba8007", "head_commit_message": "Address comments", "patch_to_review": "diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst\nindex c761d1b32cd..286d8e15bfb 100644\n--- a/docs/source/models/supported_models.rst\n+++ b/docs/source/models/supported_models.rst\n@@ -227,7 +227,7 @@ Multimodal Language Models\n * - :code:`Phi3VForCausalLM`\n - Phi-3-Vision\n - Image\n- - :code:`microsoft/Phi-3-vision-128k-instruct`, etc.\n+ - :code:`microsoft/Phi-3-vision-128k-instruct`, :code:`microsoft/Phi-3.5-vision-instruct` etc.\n -\n * - :code:`MiniCPMV`\n - MiniCPM-V\ndiff --git a/tests/models/test_phi3v.py b/tests/models/test_phi3v.py\nindex ccfc98a3259..197e63b1b1e 100644\n--- a/tests/models/test_phi3v.py\n+++ b/tests/models/test_phi3v.py\n@@ -21,7 +21,7 @@\n \"<|user|>\\n<|image_1|>\\nWhat is the season?<|end|>\\n<|assistant|>\\n\",\n })\n \n-models = [\"microsoft/Phi-3-vision-128k-instruct\"]\n+models = [\"microsoft/Phi-3.5-vision-instruct\"]\n \n \n def vllm_to_hf_output(vllm_output: Tuple[List[int], str,\ndiff --git a/vllm/config.py b/vllm/config.py\nindex 0d5d098bc88..e02c8ba380c 100644\n--- a/vllm/config.py\n+++ b/vllm/config.py\n@@ -13,7 +13,9 @@\n from vllm.model_executor.models import ModelRegistry\n from vllm.platforms import current_platform\n from vllm.tracing import is_otel_available, otel_import_error_traceback\n-from vllm.transformers_utils.config import get_config, get_hf_text_config\n+from vllm.transformers_utils.config import (get_config,\n+ get_hf_image_processor_config,\n+ get_hf_text_config)\n from vllm.utils import (STR_NOT_IMPL_ENC_DEC_CUDAGRAPH, GiB_bytes,\n cuda_device_count_stateless, get_cpu_memory, is_cpu,\n is_hip, is_neuron, is_openvino, is_xpu,\n@@ -166,6 +168,8 @@ def __init__(\n self.hf_config = get_config(self.model, trust_remote_code, revision,\n code_revision, rope_scaling, rope_theta)\n self.hf_text_config = get_hf_text_config(self.hf_config)\n+ self.hf_image_processor_config = get_hf_image_processor_config(\n+ self.model, revision)\n self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)\n \n # Choose a default enforce_eager value if the user did not specify\ndiff --git a/vllm/inputs/registry.py b/vllm/inputs/registry.py\nindex deb66f0b0cb..7b64941ff16 100644\n--- a/vllm/inputs/registry.py\n+++ b/vllm/inputs/registry.py\n@@ -55,6 +55,13 @@ def get_hf_config(self, hf_config_type: Type[C] = PretrainedConfig) -> C:\n \n return hf_config\n \n+ def get_hf_image_processor_config(self) -> Dict:\n+ \"\"\"\n+ Get the HuggingFace image processor configuration of the model.\n+ \"\"\"\n+\n+ return self.model_config.hf_image_processor_config\n+\n \n N = TypeVar(\"N\", bound=Type[nn.Module])\n \ndiff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py\nindex 328f4e6fa82..6b4aac843be 100644\n--- a/vllm/model_executor/models/phi3v.py\n+++ b/vllm/model_executor/models/phi3v.py\n@@ -15,7 +15,7 @@\n # limitations under the License.\n import re\n from functools import lru_cache\n-from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,\n+from typing import (Dict, Iterable, List, Literal, Mapping, Optional, Tuple,\n TypedDict, Union)\n \n import numpy as np\n@@ -324,12 +324,12 @@ def _calc_hd_transform_size(*, width: int, height: int, hd_num: int = 16):\n \n # Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L181\n def get_phi3v_image_feature_size(\n- hf_config: PretrainedConfig,\n+ hf_config: Dict,\n *,\n input_height: int,\n input_width: int,\n ) -> int:\n- num_crops = getattr(hf_config, \"num_crops\", 16)\n+ num_crops = hf_config.get(\"num_crops\", 16)\n new_width, new_height = _calc_hd_transform_size(width=input_width,\n height=input_height,\n hd_num=num_crops)\n@@ -341,7 +341,7 @@ def get_phi3v_image_feature_size(\n def get_max_phi3v_image_tokens(ctx: InputContext):\n \n return get_phi3v_image_feature_size(\n- ctx.get_hf_config(),\n+ ctx.get_hf_image_processor_config(),\n input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,\n input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,\n )\n@@ -395,7 +395,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs):\n return llm_inputs\n \n model_config = ctx.model_config\n- hf_config = ctx.get_hf_config()\n+ hf_config = ctx.get_hf_image_processor_config()\n \n image_data = multi_modal_data[\"image\"]\n if isinstance(image_data, Image.Image):\ndiff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py\nindex 5f04b39ef52..9b7ae5a332b 100644\n--- a/vllm/transformers_utils/config.py\n+++ b/vllm/transformers_utils/config.py\n@@ -3,6 +3,8 @@\n from typing import Dict, Optional, Type, Union\n \n from transformers import GenerationConfig, PretrainedConfig\n+from transformers.models.auto.image_processing_auto import (\n+ get_image_processor_config)\n from transformers.models.auto.modeling_auto import (\n MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)\n \n@@ -97,6 +99,14 @@ def get_config(\n return config\n \n \n+def get_hf_image_processor_config(\n+ model: Union[str, Path],\n+ revision: Optional[str] = None,\n+ **kwargs,\n+) -> Dict:\n+ return get_image_processor_config(model, revision=revision, **kwargs)\n+\n+\n def get_hf_text_config(config: PretrainedConfig):\n \"\"\"Get the \"sub\" config relevant to llm for multi modal models.\n No op for pure text models.\n" }
[ { "diff_hunk": "@@ -97,6 +99,14 @@ def get_config(\n return config\n \n \n+def get_hf_image_processor_config(\n+ model: Union[str, Path],\n+ revision: Optional[str] = None,\n+ **kwargs,\n+) -> Dict:", "line": null, "original_line": 106, "original_start_line": null, "path": "vllm/transformers_utils/config.py", "start_line": null, "text": "@user1:\n```suggestion\r\n) -> Dict[str, Any]:\r\n```\r\n\r\nNeed to also update the import to include `Any`\n\n@author:\nDone." } ]
17ccce8c568c2b1d7349fbaec727ceff89df4d84
diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index c761d1b32cd9..356ebf9f6bee 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -225,9 +225,9 @@ Multimodal Language Models - :code:`google/paligemma-3b-pt-224`, :code:`google/paligemma-3b-mix-224`, etc. - * - :code:`Phi3VForCausalLM` - - Phi-3-Vision + - Phi-3-Vision, Phi-3.5-Vision - Image - - :code:`microsoft/Phi-3-vision-128k-instruct`, etc. + - :code:`microsoft/Phi-3-vision-128k-instruct`, :code:`microsoft/Phi-3.5-vision-instruct` etc. - * - :code:`MiniCPMV` - MiniCPM-V diff --git a/tests/models/test_phi3v.py b/tests/models/test_phi3v.py index ccfc98a32598..197e63b1b1e5 100644 --- a/tests/models/test_phi3v.py +++ b/tests/models/test_phi3v.py @@ -21,7 +21,7 @@ "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n", }) -models = ["microsoft/Phi-3-vision-128k-instruct"] +models = ["microsoft/Phi-3.5-vision-instruct"] def vllm_to_hf_output(vllm_output: Tuple[List[int], str, diff --git a/vllm/config.py b/vllm/config.py index 0d5d098bc885..e02c8ba380c0 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -13,7 +13,9 @@ from vllm.model_executor.models import ModelRegistry from vllm.platforms import current_platform from vllm.tracing import is_otel_available, otel_import_error_traceback -from vllm.transformers_utils.config import get_config, get_hf_text_config +from vllm.transformers_utils.config import (get_config, + get_hf_image_processor_config, + get_hf_text_config) from vllm.utils import (STR_NOT_IMPL_ENC_DEC_CUDAGRAPH, GiB_bytes, cuda_device_count_stateless, get_cpu_memory, is_cpu, is_hip, is_neuron, is_openvino, is_xpu, @@ -166,6 +168,8 @@ def __init__( self.hf_config = get_config(self.model, trust_remote_code, revision, code_revision, rope_scaling, rope_theta) self.hf_text_config = get_hf_text_config(self.hf_config) + self.hf_image_processor_config = get_hf_image_processor_config( + self.model, revision) self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype) # Choose a default enforce_eager value if the user did not specify diff --git a/vllm/inputs/registry.py b/vllm/inputs/registry.py index deb66f0b0cb3..ae6c6c05d9f7 100644 --- a/vllm/inputs/registry.py +++ b/vllm/inputs/registry.py @@ -2,8 +2,8 @@ from array import array from collections import UserDict from dataclasses import dataclass -from typing import (TYPE_CHECKING, Callable, Dict, Mapping, Optional, Protocol, - Tuple, Type) +from typing import (TYPE_CHECKING, Any, Callable, Dict, Mapping, Optional, + Protocol, Tuple, Type) from torch import nn from transformers import PretrainedConfig @@ -55,6 +55,13 @@ def get_hf_config(self, hf_config_type: Type[C] = PretrainedConfig) -> C: return hf_config + def get_hf_image_processor_config(self) -> Dict[str, Any]: + """ + Get the HuggingFace image processor configuration of the model. + """ + + return self.model_config.hf_image_processor_config + N = TypeVar("N", bound=Type[nn.Module]) diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index 328f4e6fa827..6b5175090d47 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -15,8 +15,8 @@ # limitations under the License. import re from functools import lru_cache -from typing import (Iterable, List, Literal, Mapping, Optional, Tuple, - TypedDict, Union) +from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, + Tuple, TypedDict, Union) import numpy as np import torch @@ -324,12 +324,12 @@ def _calc_hd_transform_size(*, width: int, height: int, hd_num: int = 16): # Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L181 def get_phi3v_image_feature_size( - hf_config: PretrainedConfig, + hf_config: Dict[str, Any], *, input_height: int, input_width: int, ) -> int: - num_crops = getattr(hf_config, "num_crops", 16) + num_crops = hf_config.get("num_crops", 16) new_width, new_height = _calc_hd_transform_size(width=input_width, height=input_height, hd_num=num_crops) @@ -341,7 +341,7 @@ def get_phi3v_image_feature_size( def get_max_phi3v_image_tokens(ctx: InputContext): return get_phi3v_image_feature_size( - ctx.get_hf_config(), + ctx.get_hf_image_processor_config(), input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH, ) @@ -395,7 +395,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs): return llm_inputs model_config = ctx.model_config - hf_config = ctx.get_hf_config() + hf_config = ctx.get_hf_image_processor_config() image_data = multi_modal_data["image"] if isinstance(image_data, Image.Image): diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 5f04b39ef524..1a57df05089c 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -1,8 +1,10 @@ import contextlib from pathlib import Path -from typing import Dict, Optional, Type, Union +from typing import Any, Dict, Optional, Type, Union from transformers import GenerationConfig, PretrainedConfig +from transformers.models.auto.image_processing_auto import ( + get_image_processor_config) from transformers.models.auto.modeling_auto import ( MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) @@ -97,6 +99,17 @@ def get_config( return config +def get_hf_image_processor_config( + model: Union[str, Path], + revision: Optional[str] = None, + **kwargs, +) -> Dict[str, Any]: + # Separate model folder from file path for GGUF models + if Path(model).is_file() and Path(model).suffix == ".gguf": + model = Path(model).parent + return get_image_processor_config(model, revision=revision, **kwargs) + + def get_hf_text_config(config: PretrainedConfig): """Get the "sub" config relevant to llm for multi modal models. No op for pure text models.
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7453@a8e0511
vllm-project/vllm
Python
7,453
[Feature]: Add OpenAI server prompt_logprobs support #6508
This commit adds a prompt_logprobs option in the extra body field of the chat completions API. When set to true, it will return the log probabilities of the decoded input tokens. This option was not included in the streaming API. This decision was made since streaming is meant for real time feedback with reduced latency, it doesn't make much sense to include the same prompt log probabilities every single time. This can be included if that is also deemed to be useful. Currently, the server will report an error if stream and prompt_logprobs are both enabled. The return value in the chat completions API was modeled after the prompt_logprobs return value during offline inference to reduce coding complexity if switching between online/offline. It was possible to get the prompt_logprobs earlier if echo and top_logprobs were enabled. This behavior was kept the same to not break any existing configurations. FIX #6508
2024-08-13T01:41:59Z
[Feature]: Add OpenAI server `prompt_logprobs` support ### ๐Ÿš€ The feature, motivation and pitch As noted in documentation OpenAI API don't support outputing only one token But it's a very strong advantage over commertial models Been able to get logits for prompt tokens ### Alternatives _No response_ ### Additional context _No response_
Hey I'm doing the same thing, actually, you can try `echo=True` `logprobs=1` and this should return the prompt logprobabilities. **You have to disable prompt caching**. You may have to use `max_tokens=1` as well. Let me know if it works, and what parameters you use in the end. @gabrielhuang Thanks, it's working With vllm 0.5.2 (0.4.0post1 not working) ``` completion = client.completions.create( model=model_name, prompt=prompt, echo=True, logprobs=1, max_tokens=1, ) completion.choices[0].logprobs.tokens completion.choices[0].logprobs.token_logprobs ``` Great workaround to switch to completions api Reopening this as the workaround is not really ideal to solve this problem. It would be better to add an option to explicitly return the logprobs of the input prompt. @DarkLight1337 According to [the latest doc](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) and [sampling parameters](https://docs.vllm.ai/en/latest/dev/sampling_params.html) 1. The _chat_ API allows "echo", "logprobs", "max_tokens" as (extra) parameters, but actually **it is not work** for Vision LLM (e.g., I tried on served ```OpenGVLab/InternVL2-4B```, which only returns output logprobs, not input.) The following code could reproduce this problem. ``` from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="token-abc123", ) # sampling_params = SamplingParams(temperature=0.2, max_tokens=64, prompt_logprobs= 1, stop= ['<|eot_id|>']) completion = client.chat.completions.create( model="OpenGVLab/InternVL2-8B", messages= [ { "role": "system", "content": "You are chatting with a language model." } ], extra_body={ "stop": ['<|eot_id|>'], "echo": True, "max_tokens": 1, "logprobs": 1, } ) ``` 2. The [the latest doc](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) show that _completions_ API **does not** allow "echo" as extra parameters, **but actually it allows**, as demonstrated by this issue, and the completions API can provide input prompt logprobs. However, the _completions_ API seems only accept string as the input prompt, then how do we extend this to allow images? > 1. The _chat_ API allows "echo", "logprobs", "max_tokens" as (extra) parameters, but actually **it is not work** for Vision LLM (e.g., I tried on served ```OpenGVLab/InternVL2-4B```, which only returns output logprobs, not input.) The following code could reproduce this problem. There is currently no way to explicitly return logprobs for the input prompt in online inference, which is why I called the above solution a workaround (and reopened this issue). It would be great if this can be enabled similar to the offline `SamplingParams`. > 2. The [the latest doc](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) show that _completions_ API **does not** allow "echo" as extra parameters, **but actually it allows**, as demonstrated by this issue, and the completions API can provide input prompt logprobs. However, the _completions_ API seems only accept string as the input prompt, then how do we extend this to allow images? The Completions API was never made for image input. Since it is now considered legacy by OpenAI, we should focus on adding this feature to Chat Completions API instead. > There is currently no way to explicitly return logprobs for the input prompt in online inference, which is why I called the above solution a workaround (and reopened this issue). It would be great if this can be enabled similar to the offline `SamplingParams`. > Thanks for your patience. It would be great to see an option to return the logprobs of the input prompt (with image) in online inference. Could I take this issue since it has been reopened? It is unclear if anyone is working on it, so apologies if someone already is. @cjfcsjt @DarkLight1337 > Could I take this issue since it has been reopened? It is unclear if anyone is working on it, so apologies if someone already is. @cjfcsjt @DarkLight1337 Not that I'm aware of. Thanks for helping out! Great, thanks! Will get right on it @DarkLight1337 > Hey I'm doing the same thing, actually, you can try `echo=True` `logprobs=1` and this should return the prompt logprobabilities. **You have to disable prompt caching**. You may have to use `max_tokens=1` as well. Let me know if it works, and what parameters you use in the end. Hi @gabrielhuang , Why should I disable prompt caching? if the prompt changed by even one character, the engine would only utilise the kv up to the block before that, so I thought I could enable prefix caching.
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nAs noted in documentation OpenAI API don't support outputing only one token\r\nBut it's a very strong advantage over commertial models\r\nBeen able to get logits for prompt tokens\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_", "number": 6508, "title": "[Feature]: Add OpenAI server `prompt_logprobs` support" } ]
21313e09e3f9448817016290da20d0db1adf3664
{ "head_commit": "a8e0511cde889381cb481171c67829162ef0ef38", "head_commit_message": "[Feature]: Add OpenAI server prompt_logprobs support #6508\n\nThis commit adds a prompt_logprobs option in the extra body field of the\nchat completions API. When set to a value higher than 0, the response\nwill return the log probabilities of the decoded input tokens.\n\nThe same option has been included for the completions API. Note that the\nprompt_logprobs will be included for every prompt that the completions\nrequest contains. This is why the prompt_logprompts in the completions\nresponse in nested further than in the chat completions response.\n\nThis option was not included in the streaming API. This decision was made\nsince streaming is meant for real time feedback with reduced latency, it\ndoesn't make much sense to include the same prompt log probabilities every\nsingle time. This can be included if that is also deemed to be useful.\n\nCurrently, the server will report an error if stream is enabled and\nprompt_logprobs is set to a value higher than 0.\n\nThe return value in the chat completions API was modeled after the\nprompt_logprobs return value during offline inference to reduce coding\ncomplexity if switching between online/offline.\n\nIt was possible to get the prompt_logprobs earlier if echo and\ntop_logprobs were enabled. This behavior was kept the same to not break\nany existing configurations.\n\nFIX #6508", "patch_to_review": "diff --git a/tests/entrypoints/openai/test_completion.py b/tests/entrypoints/openai/test_completion.py\nindex 05f66723173..4d0c6d73518 100644\n--- a/tests/entrypoints/openai/test_completion.py\n+++ b/tests/entrypoints/openai/test_completion.py\n@@ -3,7 +3,7 @@\n import re\n import shutil\n from tempfile import TemporaryDirectory\n-from typing import List\n+from typing import Dict, List\n \n import jsonschema\n import openai # use the official client for correctness check\n@@ -130,6 +130,7 @@ async def test_single_completion(client: openai.AsyncOpenAI, model_name: str,\n temperature=0.0,\n )\n assert len(completion.choices[0].text) >= 1\n+ assert completion.choices[0].prompt_logprobs is None\n \n \n @pytest.mark.asyncio\n@@ -267,6 +268,128 @@ async def test_too_many_completion_logprobs(client: openai.AsyncOpenAI,\n assert len(completion.choices[0].text) >= 0\n \n \[email protected]\[email protected](\n+ \"model_name, prompt_logprobs\",\n+ [(MODEL_NAME, 1), (MODEL_NAME, 0), (MODEL_NAME, -1), (MODEL_NAME, None)],\n+)\n+async def test_prompt_logprobs_chat(client: openai.AsyncOpenAI,\n+ model_name: str, prompt_logprobs: int):\n+ params: Dict = {\n+ \"messages\": [{\n+ \"role\": \"system\",\n+ \"content\": \"You are a helpful assistant.\"\n+ }, {\n+ \"role\": \"user\",\n+ \"content\": \"Who won the world series in 2020?\"\n+ }, {\n+ \"role\":\n+ \"assistant\",\n+ \"content\":\n+ \"The Los Angeles Dodgers won the World Series in 2020.\"\n+ }, {\n+ \"role\": \"user\",\n+ \"content\": \"Where was it played?\"\n+ }],\n+ \"model\":\n+ model_name\n+ }\n+\n+ if prompt_logprobs is not None:\n+ params[\"extra_body\"] = {\"prompt_logprobs\": prompt_logprobs}\n+\n+ if prompt_logprobs and prompt_logprobs < 0:\n+ with pytest.raises(BadRequestError) as err_info:\n+ await client.chat.completions.create(**params)\n+ expected_err_string = (\n+ \"Error code: 400 - {'object': 'error', 'message': \"\n+ \"'Prompt_logprobs set to invalid negative value: -1',\"\n+ \" 'type': 'BadRequestError', 'param': None, 'code': 400}\")\n+ assert str(err_info.value) == expected_err_string\n+ else:\n+ completion = await client.chat.completions.create(**params)\n+ if prompt_logprobs and prompt_logprobs > 0:\n+ assert completion.prompt_logprobs is not None\n+ assert len(completion.prompt_logprobs) > 0\n+ else:\n+ assert completion.prompt_logprobs is None\n+\n+\[email protected]\[email protected](\n+ \"model_name\",\n+ [MODEL_NAME],\n+)\n+async def test_more_than_one_prompt_logprobs_chat(client: openai.AsyncOpenAI,\n+ model_name: str):\n+ params: Dict = {\n+ \"messages\": [{\n+ \"role\": \"system\",\n+ \"content\": \"You are a helpful assistant.\"\n+ }, {\n+ \"role\": \"user\",\n+ \"content\": \"Who won the world series in 2020?\"\n+ }, {\n+ \"role\":\n+ \"assistant\",\n+ \"content\":\n+ \"The Los Angeles Dodgers won the World Series in 2020.\"\n+ }, {\n+ \"role\": \"user\",\n+ \"content\": \"Where was it played?\"\n+ }],\n+ \"model\":\n+ model_name,\n+ \"extra_body\": {\n+ \"prompt_logprobs\": 1\n+ }\n+ }\n+\n+ completion_1 = await client.chat.completions.create(**params)\n+\n+ params[\"extra_body\"] = {\"prompt_logprobs\": 2}\n+ completion_2 = await client.chat.completions.create(**params)\n+\n+ assert len(completion_1.prompt_logprobs[3]) == 1\n+ assert len(completion_2.prompt_logprobs[3]) == 2\n+\n+\[email protected]\[email protected](\"model_name, prompt_logprobs\", [(MODEL_NAME, -1),\n+ (MODEL_NAME, 0),\n+ (MODEL_NAME, 1),\n+ (MODEL_NAME, None)])\n+async def test_prompt_logprobs_completion(client: openai.AsyncOpenAI,\n+ model_name: str,\n+ prompt_logprobs: int):\n+ params: Dict = {\n+ \"prompt\": [\"A robot may not injure another robot\", \"My name is\"],\n+ \"model\": model_name,\n+ }\n+ if prompt_logprobs is not None:\n+ params[\"extra_body\"] = {\"prompt_logprobs\": prompt_logprobs}\n+\n+ if prompt_logprobs and prompt_logprobs < 0:\n+ with pytest.raises(BadRequestError) as err_info:\n+ await client.completions.create(**params)\n+ expected_err_string = (\n+ \"Error code: 400 - {'object': 'error', 'message': \"\n+ \"'Prompt_logprobs set to invalid negative value: -1',\"\n+ \" 'type': 'BadRequestError', 'param': None, 'code': 400}\")\n+ assert str(err_info.value) == expected_err_string\n+ else:\n+ completion = await client.completions.create(**params)\n+ if prompt_logprobs and prompt_logprobs > 0:\n+ assert completion.choices[0].prompt_logprobs is not None\n+ assert len(completion.choices[0].prompt_logprobs) > 0\n+\n+ assert completion.choices[1].prompt_logprobs is not None\n+ assert len(completion.choices[1].prompt_logprobs) > 0\n+\n+ else:\n+ assert completion.choices[0].prompt_logprobs is None\n+\n+\n @pytest.mark.asyncio\n @pytest.mark.parametrize(\n \"model_name\",\ndiff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py\nindex 7da3002b283..aef42e9425e 100644\n--- a/vllm/entrypoints/openai/protocol.py\n+++ b/vllm/entrypoints/openai/protocol.py\n@@ -13,6 +13,7 @@\n from vllm.entrypoints.openai.logits_processors import get_logits_processors\n from vllm.pooling_params import PoolingParams\n from vllm.sampling_params import LogitsProcessor, SamplingParams\n+from vllm.sequence import Logprob\n from vllm.utils import random_uuid\n \n # torch is mocked during docs generation,\n@@ -152,6 +153,7 @@ class ChatCompletionRequest(OpenAIBaseModel):\n skip_special_tokens: bool = True\n spaces_between_special_tokens: bool = True\n truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None\n+ prompt_logprobs: Optional[int] = None\n # doc: end-chat-completion-sampling-params\n \n # doc: begin-chat-completion-extra-params\n@@ -263,7 +265,8 @@ def to_sampling_params(\n stop=self.stop,\n stop_token_ids=self.stop_token_ids,\n logprobs=self.top_logprobs if self.logprobs else None,\n- prompt_logprobs=self.top_logprobs if self.echo else None,\n+ prompt_logprobs=self.prompt_logprobs if self.prompt_logprobs else\n+ (self.top_logprobs if self.echo else None),\n ignore_eos=self.ignore_eos,\n max_tokens=max_tokens,\n min_tokens=self.min_tokens,\n@@ -368,6 +371,7 @@ class CompletionRequest(OpenAIBaseModel):\n spaces_between_special_tokens: bool = True\n truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None\n allowed_token_ids: Optional[List[int]] = None\n+ prompt_logprobs: Optional[int] = None\n # doc: end-completion-sampling-params\n \n # doc: begin-completion-extra-params\n@@ -454,7 +458,8 @@ def to_sampling_params(\n min_tokens=self.min_tokens,\n use_beam_search=self.use_beam_search,\n early_stopping=self.early_stopping,\n- prompt_logprobs=self.logprobs if self.echo else None,\n+ prompt_logprobs=self.prompt_logprobs\n+ if self.prompt_logprobs else self.logprobs if self.echo else None,\n skip_special_tokens=self.skip_special_tokens,\n spaces_between_special_tokens=self.spaces_between_special_tokens,\n include_stop_str_in_output=self.include_stop_str_in_output,\n@@ -532,6 +537,7 @@ class CompletionResponseChoice(OpenAIBaseModel):\n \"to stop, None if the completion finished for some other reason \"\n \"including encountering the EOS token\"),\n )\n+ prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None\n \n \n class CompletionResponse(OpenAIBaseModel):\n@@ -627,6 +633,7 @@ class ChatCompletionResponse(OpenAIBaseModel):\n model: str\n choices: List[ChatCompletionResponseChoice]\n usage: UsageInfo\n+ prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None\n \n \n class DeltaMessage(OpenAIBaseModel):\ndiff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py\nindex 2167b967b14..8732fa204c7 100644\n--- a/vllm/entrypoints/openai/serving_chat.py\n+++ b/vllm/entrypoints/openai/serving_chat.py\n@@ -83,6 +83,16 @@ async def create_chat_completion(\n if error_check_ret is not None:\n return error_check_ret\n \n+ if request.prompt_logprobs is not None:\n+ if request.stream and request.prompt_logprobs > 0:\n+ return self.create_error_response(\n+ \"Prompt_logprobs are not available when stream is enabled\")\n+\n+ if request.prompt_logprobs < 0:\n+ return self.create_error_response(\n+ f\"Prompt_logprobs set to invalid \"\n+ f\"negative value: {request.prompt_logprobs}\")\n+\n try:\n (\n lora_request,\n@@ -506,7 +516,7 @@ async def chat_completion_full_generator(\n model=model_name,\n choices=choices,\n usage=usage,\n- )\n+ prompt_logprobs=final_res.prompt_logprobs)\n \n return response\n \ndiff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py\nindex f4c91ce0468..6362f2accca 100644\n--- a/vllm/entrypoints/openai/serving_completion.py\n+++ b/vllm/entrypoints/openai/serving_completion.py\n@@ -84,6 +84,15 @@ async def create_completion(self, request: CompletionRequest,\n request_id = f\"cmpl-{random_uuid()}\"\n created_time = int(time.time())\n \n+ if request.prompt_logprobs is not None:\n+ if request.stream and request.prompt_logprobs > 0:\n+ return self.create_error_response(\n+ \"Prompt_logprobs are not available when stream is enabled\")\n+ elif request.prompt_logprobs < 0:\n+ return self.create_error_response(\n+ f\"Prompt_logprobs set to invalid negative \"\n+ f\"value: {request.prompt_logprobs}\")\n+\n # Schedule the request and get the result generator.\n generators: List[AsyncGenerator[RequestOutput, None]] = []\n try:\n@@ -377,7 +386,7 @@ def request_output_to_completion_response(\n logprobs=logprobs,\n finish_reason=output.finish_reason,\n stop_reason=output.stop_reason,\n- )\n+ prompt_logprobs=final_res.prompt_logprobs)\n choices.append(choice_data)\n \n num_prompt_tokens += len(prompt_token_ids)\n" }
[ { "diff_hunk": "@@ -506,7 +516,7 @@ async def chat_completion_full_generator(\n model=model_name,\n choices=choices,\n usage=usage,\n- )\n+ prompt_logprobs=final_res.prompt_logprobs)", "line": null, "original_line": 519, "original_start_line": null, "path": "vllm/entrypoints/openai/serving_chat.py", "start_line": null, "text": "@user1:\n```suggestion\r\n prompt_logprobs=final_res.prompt_logprobs,\r\n )\r\n```" }, { "diff_hunk": "@@ -377,7 +386,7 @@ def request_output_to_completion_response(\n logprobs=logprobs,\n finish_reason=output.finish_reason,\n stop_reason=output.stop_reason,\n- )\n+ prompt_logprobs=final_res.prompt_logprobs)", "line": null, "original_line": 389, "original_start_line": null, "path": "vllm/entrypoints/openai/serving_completion.py", "start_line": null, "text": "@user1:\n```suggestion\r\n prompt_logprobs=final_res.prompt_logprobs,\r\n )\r\n```" } ]
6fa0b168925aa080c41bcffe6fc3629606465138
diff --git a/tests/entrypoints/openai/test_completion.py b/tests/entrypoints/openai/test_completion.py index 05f667231738..4d0c6d73518d 100644 --- a/tests/entrypoints/openai/test_completion.py +++ b/tests/entrypoints/openai/test_completion.py @@ -3,7 +3,7 @@ import re import shutil from tempfile import TemporaryDirectory -from typing import List +from typing import Dict, List import jsonschema import openai # use the official client for correctness check @@ -130,6 +130,7 @@ async def test_single_completion(client: openai.AsyncOpenAI, model_name: str, temperature=0.0, ) assert len(completion.choices[0].text) >= 1 + assert completion.choices[0].prompt_logprobs is None @pytest.mark.asyncio @@ -267,6 +268,128 @@ async def test_too_many_completion_logprobs(client: openai.AsyncOpenAI, assert len(completion.choices[0].text) >= 0 [email protected] [email protected]( + "model_name, prompt_logprobs", + [(MODEL_NAME, 1), (MODEL_NAME, 0), (MODEL_NAME, -1), (MODEL_NAME, None)], +) +async def test_prompt_logprobs_chat(client: openai.AsyncOpenAI, + model_name: str, prompt_logprobs: int): + params: Dict = { + "messages": [{ + "role": "system", + "content": "You are a helpful assistant." + }, { + "role": "user", + "content": "Who won the world series in 2020?" + }, { + "role": + "assistant", + "content": + "The Los Angeles Dodgers won the World Series in 2020." + }, { + "role": "user", + "content": "Where was it played?" + }], + "model": + model_name + } + + if prompt_logprobs is not None: + params["extra_body"] = {"prompt_logprobs": prompt_logprobs} + + if prompt_logprobs and prompt_logprobs < 0: + with pytest.raises(BadRequestError) as err_info: + await client.chat.completions.create(**params) + expected_err_string = ( + "Error code: 400 - {'object': 'error', 'message': " + "'Prompt_logprobs set to invalid negative value: -1'," + " 'type': 'BadRequestError', 'param': None, 'code': 400}") + assert str(err_info.value) == expected_err_string + else: + completion = await client.chat.completions.create(**params) + if prompt_logprobs and prompt_logprobs > 0: + assert completion.prompt_logprobs is not None + assert len(completion.prompt_logprobs) > 0 + else: + assert completion.prompt_logprobs is None + + [email protected] [email protected]( + "model_name", + [MODEL_NAME], +) +async def test_more_than_one_prompt_logprobs_chat(client: openai.AsyncOpenAI, + model_name: str): + params: Dict = { + "messages": [{ + "role": "system", + "content": "You are a helpful assistant." + }, { + "role": "user", + "content": "Who won the world series in 2020?" + }, { + "role": + "assistant", + "content": + "The Los Angeles Dodgers won the World Series in 2020." + }, { + "role": "user", + "content": "Where was it played?" + }], + "model": + model_name, + "extra_body": { + "prompt_logprobs": 1 + } + } + + completion_1 = await client.chat.completions.create(**params) + + params["extra_body"] = {"prompt_logprobs": 2} + completion_2 = await client.chat.completions.create(**params) + + assert len(completion_1.prompt_logprobs[3]) == 1 + assert len(completion_2.prompt_logprobs[3]) == 2 + + [email protected] [email protected]("model_name, prompt_logprobs", [(MODEL_NAME, -1), + (MODEL_NAME, 0), + (MODEL_NAME, 1), + (MODEL_NAME, None)]) +async def test_prompt_logprobs_completion(client: openai.AsyncOpenAI, + model_name: str, + prompt_logprobs: int): + params: Dict = { + "prompt": ["A robot may not injure another robot", "My name is"], + "model": model_name, + } + if prompt_logprobs is not None: + params["extra_body"] = {"prompt_logprobs": prompt_logprobs} + + if prompt_logprobs and prompt_logprobs < 0: + with pytest.raises(BadRequestError) as err_info: + await client.completions.create(**params) + expected_err_string = ( + "Error code: 400 - {'object': 'error', 'message': " + "'Prompt_logprobs set to invalid negative value: -1'," + " 'type': 'BadRequestError', 'param': None, 'code': 400}") + assert str(err_info.value) == expected_err_string + else: + completion = await client.completions.create(**params) + if prompt_logprobs and prompt_logprobs > 0: + assert completion.choices[0].prompt_logprobs is not None + assert len(completion.choices[0].prompt_logprobs) > 0 + + assert completion.choices[1].prompt_logprobs is not None + assert len(completion.choices[1].prompt_logprobs) > 0 + + else: + assert completion.choices[0].prompt_logprobs is None + + @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 7da3002b283f..aef42e9425ef 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -13,6 +13,7 @@ from vllm.entrypoints.openai.logits_processors import get_logits_processors from vllm.pooling_params import PoolingParams from vllm.sampling_params import LogitsProcessor, SamplingParams +from vllm.sequence import Logprob from vllm.utils import random_uuid # torch is mocked during docs generation, @@ -152,6 +153,7 @@ class ChatCompletionRequest(OpenAIBaseModel): skip_special_tokens: bool = True spaces_between_special_tokens: bool = True truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None + prompt_logprobs: Optional[int] = None # doc: end-chat-completion-sampling-params # doc: begin-chat-completion-extra-params @@ -263,7 +265,8 @@ def to_sampling_params( stop=self.stop, stop_token_ids=self.stop_token_ids, logprobs=self.top_logprobs if self.logprobs else None, - prompt_logprobs=self.top_logprobs if self.echo else None, + prompt_logprobs=self.prompt_logprobs if self.prompt_logprobs else + (self.top_logprobs if self.echo else None), ignore_eos=self.ignore_eos, max_tokens=max_tokens, min_tokens=self.min_tokens, @@ -368,6 +371,7 @@ class CompletionRequest(OpenAIBaseModel): spaces_between_special_tokens: bool = True truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None allowed_token_ids: Optional[List[int]] = None + prompt_logprobs: Optional[int] = None # doc: end-completion-sampling-params # doc: begin-completion-extra-params @@ -454,7 +458,8 @@ def to_sampling_params( min_tokens=self.min_tokens, use_beam_search=self.use_beam_search, early_stopping=self.early_stopping, - prompt_logprobs=self.logprobs if self.echo else None, + prompt_logprobs=self.prompt_logprobs + if self.prompt_logprobs else self.logprobs if self.echo else None, skip_special_tokens=self.skip_special_tokens, spaces_between_special_tokens=self.spaces_between_special_tokens, include_stop_str_in_output=self.include_stop_str_in_output, @@ -532,6 +537,7 @@ class CompletionResponseChoice(OpenAIBaseModel): "to stop, None if the completion finished for some other reason " "including encountering the EOS token"), ) + prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None class CompletionResponse(OpenAIBaseModel): @@ -627,6 +633,7 @@ class ChatCompletionResponse(OpenAIBaseModel): model: str choices: List[ChatCompletionResponseChoice] usage: UsageInfo + prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None class DeltaMessage(OpenAIBaseModel): diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 2167b967b14b..08209d44d207 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -83,6 +83,16 @@ async def create_chat_completion( if error_check_ret is not None: return error_check_ret + if request.prompt_logprobs is not None: + if request.stream and request.prompt_logprobs > 0: + return self.create_error_response( + "Prompt_logprobs are not available when stream is enabled") + + if request.prompt_logprobs < 0: + return self.create_error_response( + f"Prompt_logprobs set to invalid " + f"negative value: {request.prompt_logprobs}") + try: ( lora_request, @@ -506,6 +516,7 @@ async def chat_completion_full_generator( model=model_name, choices=choices, usage=usage, + prompt_logprobs=final_res.prompt_logprobs, ) return response diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index f4c91ce04684..24206b59cf5e 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -84,6 +84,15 @@ async def create_completion(self, request: CompletionRequest, request_id = f"cmpl-{random_uuid()}" created_time = int(time.time()) + if request.prompt_logprobs is not None: + if request.stream and request.prompt_logprobs > 0: + return self.create_error_response( + "Prompt_logprobs are not available when stream is enabled") + elif request.prompt_logprobs < 0: + return self.create_error_response( + f"Prompt_logprobs set to invalid negative " + f"value: {request.prompt_logprobs}") + # Schedule the request and get the result generator. generators: List[AsyncGenerator[RequestOutput, None]] = [] try: @@ -377,6 +386,7 @@ def request_output_to_completion_response( logprobs=logprobs, finish_reason=output.finish_reason, stop_reason=output.stop_reason, + prompt_logprobs=final_res.prompt_logprobs, ) choices.append(choice_data)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-7446@986a0e9
vllm-project/vllm
Python
7,446
[Frontend][Core] Add plumbing to support audio language models
This adds infrastructure to support multi-modal audio language models. - `MultiModalDataBuiltins` defines audio as a tuple of a `numpy.ndarray` and the sample rate. (`librosa` is used to parse the audio.) - `DEFAULT_PLUGINS` now includes an `AudioPlugin` - The OpenAI API frontend tentatively supports `"audio_url"` content parts, albeit with the same restrictions that currently apply to `"image_url"` parts (e.g. only one multi-modal chunk per prompt). - A number of methods/types that were only nominally vision-specific are renamed to be more generically multi-modal. For example, `SupportsVision` is now `SupportsMultiModal`. This PR does not include any such audio models, but does include a test that exercises the OpenAI frontend and audio pipeline (see `tests/entrypoints/openai/test_audio.py`). FIX #7335 **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-08-12T23:34:21Z
[Feature]: support voice llm like cosyvoice ### ๐Ÿš€ The feature, motivation and pitch As we can see that vllm already support many vision language models. Do we have a plan to support voice models like cosyVoice? ### Alternatives _No response_ ### Additional context _No response_
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nAs we can see that vllm already support many vision language models. Do we have a plan to support voice models like cosyVoice?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_", "number": 7335, "title": "[Feature]: support voice llm like cosyvoice" } ]
774cd1d3bf7890c6abae6c7ace798c4a376b2b20
{ "head_commit": "986a0e9c2bde95da88144c558dca3b3e28552d93", "head_commit_message": "Fix lint", "patch_to_review": "diff --git a/docs/source/models/enabling_multimodal_inputs.rst b/docs/source/models/enabling_multimodal_inputs.rst\nindex 20be920b5f6..dc76f921d5b 100644\n--- a/docs/source/models/enabling_multimodal_inputs.rst\n+++ b/docs/source/models/enabling_multimodal_inputs.rst\n@@ -15,14 +15,14 @@ This document walks you through the steps to extend a vLLM model so that it acce\n It is assumed that you have already implemented the model in vLLM according to :ref:`these steps <adding_a_new_model>`.\n Further update the model as follows:\n \n-- Implement the :class:`~vllm.model_executor.models.interfaces.SupportsVision` interface.\n+- Implement the :class:`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.\n \n .. code-block:: diff\n \n- + from vllm.model_executor.models.interfaces import SupportsVision\n+ + from vllm.model_executor.models.interfaces import SupportsMultiModal\n \n - class YourModelForImage2Seq(nn.Module):\n- + class YourModelForImage2Seq(nn.Module, SupportsVision):\n+ + class YourModelForImage2Seq(nn.Module, SupportsMultiModal):\n \n .. note::\n The model class does not have to be named :code:`*ForCausalLM`.\n@@ -51,11 +51,11 @@ This decorator accepts a function that maps multi-modal inputs to the keyword ar\n \n .. code-block:: diff\n \n- from vllm.model_executor.models.interfaces import SupportsVision\n+ from vllm.model_executor.models.interfaces import SupportsMultiModal\n + from vllm.multimodal import MULTIMODAL_REGISTRY\n \n + @MULTIMODAL_REGISTRY.register_image_input_mapper()\n- class YourModelForImage2Seq(nn.Module, SupportsVision):\n+ class YourModelForImage2Seq(nn.Module, SupportsMultiModal):\n \n A default mapper is available for each modality in the core vLLM library. This input mapper will be used if you do not provide your own function.\n \n@@ -72,13 +72,13 @@ and register it via :meth:`INPUT_REGISTRY.register_dummy_data <vllm.inputs.regis\n .. code-block:: diff\n \n from vllm.inputs import INPUT_REGISTRY\n- from vllm.model_executor.models.interfaces import SupportsVision\n+ from vllm.model_executor.models.interfaces import SupportsMultiModal\n from vllm.multimodal import MULTIMODAL_REGISTRY\n \n @MULTIMODAL_REGISTRY.register_image_input_mapper()\n + @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>)\n @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>)\n- class YourModelForImage2Seq(nn.Module, SupportsVision):\n+ class YourModelForImage2Seq(nn.Module, SupportsMultiModal):\n \n Here are some examples:\n \n@@ -98,13 +98,13 @@ In such cases, you can define your own dummy data by registering a factory metho\n .. code-block:: diff\n \n from vllm.inputs import INPUT_REGISTRY\n- from vllm.model_executor.models.interfaces import SupportsVision\n+ from vllm.model_executor.models.interfaces import SupportsMultiModal\n from vllm.multimodal import MULTIMODAL_REGISTRY\n \n @MULTIMODAL_REGISTRY.register_image_input_mapper()\n @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>)\n + @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>)\n- class YourModelForImage2Seq(nn.Module, SupportsVision):\n+ class YourModelForImage2Seq(nn.Module, SupportsMultiModal):\n \n .. note::\n The dummy data should have the maximum possible number of multi-modal tokens, as described in the previous step.\n@@ -128,14 +128,14 @@ You can register input processors via :meth:`INPUT_REGISTRY.register_input_proce\n .. code-block:: diff\n \n from vllm.inputs import INPUT_REGISTRY\n- from vllm.model_executor.models.interfaces import SupportsVision\n+ from vllm.model_executor.models.interfaces import SupportsMultiModal\n from vllm.multimodal import MULTIMODAL_REGISTRY\n \n @MULTIMODAL_REGISTRY.register_image_input_mapper()\n @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>)\n @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>)\n + @INPUT_REGISTRY.register_input_processor(<your_input_processor>)\n- class YourModelForImage2Seq(nn.Module, SupportsVision):\n+ class YourModelForImage2Seq(nn.Module, SupportsMultiModal):\n \n A common use case of input processors is inserting placeholder tokens to leverage the vLLM framework for attention mask generation.\n Here are some examples:\ndiff --git a/docs/source/models/vlm.rst b/docs/source/models/vlm.rst\nindex 236e37b51d4..3ff534633a1 100644\n--- a/docs/source/models/vlm.rst\n+++ b/docs/source/models/vlm.rst\n@@ -150,7 +150,7 @@ A full code example can be found in `examples/openai_vision_api_client.py <https\n \n .. code-block:: shell\n \n- export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>\n+ export VLLM_MULTIMODAL_FETCH_TIMEOUT=<timeout>\n \n .. note::\n There is no need to format the prompt in the API request since it will be handled by the server.\ndiff --git a/requirements-common.txt b/requirements-common.txt\nindex 5078a65f80f..b2ea47815e8 100644\n--- a/requirements-common.txt\n+++ b/requirements-common.txt\n@@ -22,4 +22,5 @@ outlines >= 0.0.43, < 0.1 # Requires torch >= 2.1.0\n typing_extensions >= 4.10\n filelock >= 3.10.4 # filelock starts to support `mode` argument from 3.10.4\n pyzmq\n+librosa # Required for audio processing\n gguf == 0.9.1\ndiff --git a/tests/entrypoints/openai/test_audio.py b/tests/entrypoints/openai/test_audio.py\nnew file mode 100644\nindex 00000000000..3c2c652fd31\n--- /dev/null\n+++ b/tests/entrypoints/openai/test_audio.py\n@@ -0,0 +1,351 @@\n+import math\n+import sys\n+import time\n+from typing import Dict, List, Optional, Tuple, Union, cast\n+from unittest.mock import patch\n+\n+import librosa\n+import numpy as np\n+import openai\n+import pytest\n+import requests\n+import torch\n+\n+from vllm import ModelRegistry\n+from vllm.config import MultiModalConfig\n+from vllm.inputs import INPUT_REGISTRY\n+from vllm.inputs.data import LLMInputs\n+from vllm.inputs.registry import InputContext\n+from vllm.model_executor.models.interfaces import SupportsMultiModal\n+from vllm.model_executor.models.opt import OPTForCausalLM\n+from vllm.multimodal import MULTIMODAL_REGISTRY\n+from vllm.multimodal.base import MultiModalInputs\n+from vllm.multimodal.image import (cached_get_tokenizer,\n+ repeat_and_pad_image_tokens)\n+from vllm.multimodal.utils import encode_audio_base64, fetch_audio\n+from vllm.utils import get_open_port\n+\n+from ...utils import VLLM_PATH\n+\n+chatml_jinja_path = VLLM_PATH / \"examples/template_chatml.jinja\"\n+assert chatml_jinja_path.exists()\n+\n+MODEL_NAME = \"facebook/opt-125m\"\n+TEST_AUDIO_URLS = [\n+ \"https://upload.wikimedia.org/wikipedia/en/b/bf/Dave_Niehaus_Winning_Call_1995_AL_Division_Series.ogg\",\n+]\n+\n+\n+def server_function(port):\n+\n+ def fake_input_mapper(ctx: InputContext, data: object):\n+ assert isinstance(data, tuple)\n+ (audio, sr) = cast(Tuple[np.ndarray, Union[float, int]], data)\n+\n+ # Resample it to 1 sample per second\n+ audio = librosa.resample(audio, orig_sr=sr, target_sr=1)\n+ return MultiModalInputs({\"processed_audio\": torch.from_numpy(audio)})\n+\n+ def fake_input_processor(ctx: InputContext, llm_inputs: LLMInputs):\n+ multi_modal_data = llm_inputs.get(\"multi_modal_data\")\n+ if multi_modal_data is None or \"audio\" not in multi_modal_data:\n+ return llm_inputs\n+\n+ audio, sr = multi_modal_data.get(\"audio\")\n+ audio_duration = math.ceil(len(audio) / sr)\n+\n+ new_prompt, new_token_ids = repeat_and_pad_image_tokens(\n+ cached_get_tokenizer(ctx.model_config.tokenizer),\n+ llm_inputs.get(\"prompt\"),\n+ llm_inputs[\"prompt_token_ids\"],\n+ image_token_id=62, # \"_\"\n+ repeat_count=audio_duration)\n+\n+ return LLMInputs(prompt_token_ids=new_token_ids,\n+ prompt=new_prompt,\n+ multi_modal_data=multi_modal_data)\n+\n+ @MULTIMODAL_REGISTRY.register_input_mapper(\"audio\", fake_input_mapper)\n+ @MULTIMODAL_REGISTRY.register_max_multimodal_tokens(\n+ \"audio\", lambda *_, **__: 100)\n+ @INPUT_REGISTRY.register_input_processor(fake_input_processor)\n+ class FakeAudioModel(OPTForCausalLM, SupportsMultiModal):\n+\n+ def __init__(self, *args, multimodal_config: MultiModalConfig,\n+ **kwargs):\n+ assert multimodal_config is not None\n+ super().__init__(*args, **kwargs)\n+\n+ def forward(\n+ self,\n+ *args,\n+ processed_audio: Optional[torch.Tensor] = None,\n+ **kwargs,\n+ ) -> torch.Tensor:\n+ return super().forward(*args, **kwargs)\n+\n+ ModelRegistry.register_model(\"OPTForCausalLM\", FakeAudioModel)\n+\n+ with patch(\"vllm.entrypoints.chat_utils._mm_token_str\",\n+ lambda *_, **__: \"_\"):\n+ sys.argv = [\"placeholder.py\"] + \\\n+ (f\"--model {MODEL_NAME} --gpu-memory-utilization 0.10 \"\n+ \"--dtype bfloat16 --enforce-eager --api-key token-abc123 \"\n+ f\"--port {port} --chat-template {chatml_jinja_path} \"\n+ \"--disable-frontend-multiprocessing\").split()\n+ import runpy\n+ runpy.run_module('vllm.entrypoints.openai.api_server',\n+ run_name='__main__')\n+\n+\[email protected](scope=\"module\")\n+def client():\n+ port = get_open_port()\n+ ctx = torch.multiprocessing.get_context(\"spawn\")\n+ server = ctx.Process(target=server_function, args=(port, ))\n+ server.start()\n+ MAX_SERVER_START_WAIT_S = 60\n+ client = openai.AsyncOpenAI(\n+ base_url=f\"http://localhost:{port}/v1\",\n+ api_key=\"token-abc123\",\n+ )\n+ # run health check\n+ health_url = f\"http://localhost:{port}/health\"\n+ start = time.time()\n+ while True:\n+ try:\n+ if requests.get(health_url).status_code == 200:\n+ break\n+ except Exception as err:\n+ result = server.exitcode\n+ if result is not None:\n+ raise RuntimeError(\"Server exited unexpectedly.\") from err\n+\n+ time.sleep(0.5)\n+ if time.time() - start > MAX_SERVER_START_WAIT_S:\n+ raise RuntimeError(\"Server failed to start in time.\") from err\n+\n+ try:\n+ yield client\n+ finally:\n+ server.kill()\n+\n+\[email protected](scope=\"session\")\n+def base64_encoded_audio() -> Dict[str, str]:\n+ return {\n+ audio_url: encode_audio_base64(*fetch_audio(audio_url))\n+ for audio_url in TEST_AUDIO_URLS\n+ }\n+\n+\[email protected]\[email protected](\"model_name\", [MODEL_NAME])\[email protected](\"audio_url\", TEST_AUDIO_URLS)\n+async def test_single_chat_session_audio(client: openai.AsyncOpenAI,\n+ model_name: str, audio_url: str):\n+ messages = [{\n+ \"role\":\n+ \"user\",\n+ \"content\": [\n+ {\n+ \"type\": \"audio_url\",\n+ \"audio_url\": {\n+ \"url\": audio_url\n+ }\n+ },\n+ {\n+ \"type\": \"text\",\n+ \"text\": \"What's happening in this audio?\"\n+ },\n+ ],\n+ }]\n+\n+ # test single completion\n+ chat_completion = await client.chat.completions.create(model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ logprobs=True,\n+ top_logprobs=5)\n+ assert len(chat_completion.choices) == 1\n+\n+ choice = chat_completion.choices[0]\n+ assert choice.finish_reason == \"length\"\n+ assert chat_completion.usage == openai.types.CompletionUsage(\n+ completion_tokens=10, prompt_tokens=36, total_tokens=46)\n+\n+ message = choice.message\n+ message = chat_completion.choices[0].message\n+ assert message.content is not None and len(message.content) >= 10\n+ assert message.role == \"assistant\"\n+ messages.append({\"role\": \"assistant\", \"content\": message.content})\n+\n+ # test multi-turn dialogue\n+ messages.append({\"role\": \"user\", \"content\": \"express your result in json\"})\n+ chat_completion = await client.chat.completions.create(\n+ model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ )\n+ message = chat_completion.choices[0].message\n+ assert message.content is not None and len(message.content) >= 0\n+\n+\[email protected]\[email protected](\"model_name\", [MODEL_NAME])\[email protected](\"audio_url\", TEST_AUDIO_URLS)\n+async def test_single_chat_session_audio_base64encoded(\n+ client: openai.AsyncOpenAI, model_name: str, audio_url: str,\n+ base64_encoded_audio: Dict[str, str]):\n+\n+ messages = [{\n+ \"role\":\n+ \"user\",\n+ \"content\": [\n+ {\n+ \"type\": \"audio_url\",\n+ \"audio_url\": {\n+ \"url\":\n+ f\"data:audio/wav;base64,{base64_encoded_audio[audio_url]}\"\n+ }\n+ },\n+ {\n+ \"type\": \"text\",\n+ \"text\": \"What's happening in this audio?\"\n+ },\n+ ],\n+ }]\n+\n+ # test single completion\n+ chat_completion = await client.chat.completions.create(model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ logprobs=True,\n+ top_logprobs=5)\n+ assert len(chat_completion.choices) == 1\n+\n+ choice = chat_completion.choices[0]\n+ assert choice.finish_reason == \"length\"\n+ assert chat_completion.usage == openai.types.CompletionUsage(\n+ completion_tokens=10, prompt_tokens=36, total_tokens=46)\n+\n+ message = choice.message\n+ message = chat_completion.choices[0].message\n+ assert message.content is not None and len(message.content) >= 10\n+ assert message.role == \"assistant\"\n+ messages.append({\"role\": \"assistant\", \"content\": message.content})\n+\n+ # test multi-turn dialogue\n+ messages.append({\"role\": \"user\", \"content\": \"express your result in json\"})\n+ chat_completion = await client.chat.completions.create(\n+ model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ )\n+ message = chat_completion.choices[0].message\n+ assert message.content is not None and len(message.content) >= 0\n+\n+\[email protected]\[email protected](\"model_name\", [MODEL_NAME])\[email protected](\"audio_url\", TEST_AUDIO_URLS)\n+async def test_chat_streaming_audio(client: openai.AsyncOpenAI,\n+ model_name: str, audio_url: str):\n+ messages = [{\n+ \"role\":\n+ \"user\",\n+ \"content\": [\n+ {\n+ \"type\": \"audio_url\",\n+ \"audio_url\": {\n+ \"url\": audio_url\n+ }\n+ },\n+ {\n+ \"type\": \"text\",\n+ \"text\": \"What's happening in this audio?\"\n+ },\n+ ],\n+ }]\n+\n+ # test single completion\n+ chat_completion = await client.chat.completions.create(\n+ model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ temperature=0.0,\n+ )\n+ output = chat_completion.choices[0].message.content\n+ stop_reason = chat_completion.choices[0].finish_reason\n+\n+ # test streaming\n+ stream = await client.chat.completions.create(\n+ model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ temperature=0.0,\n+ stream=True,\n+ )\n+ chunks: List[str] = []\n+ finish_reason_count = 0\n+ async for chunk in stream:\n+ delta = chunk.choices[0].delta\n+ if delta.role:\n+ assert delta.role == \"assistant\"\n+ if delta.content:\n+ chunks.append(delta.content)\n+ if chunk.choices[0].finish_reason is not None:\n+ finish_reason_count += 1\n+ # finish reason should only return in last block\n+ assert finish_reason_count == 1\n+ assert chunk.choices[0].finish_reason == stop_reason\n+ assert delta.content\n+ assert \"\".join(chunks) == output\n+\n+\[email protected]\[email protected](\"model_name\", [MODEL_NAME])\[email protected](\"audio_url\", TEST_AUDIO_URLS)\n+async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str,\n+ audio_url: str):\n+\n+ messages = [{\n+ \"role\":\n+ \"user\",\n+ \"content\": [\n+ {\n+ \"type\": \"audio_url\",\n+ \"audio_url\": {\n+ \"url\": audio_url\n+ }\n+ },\n+ {\n+ \"type\": \"audio_url\",\n+ \"audio_url\": {\n+ \"url\": audio_url\n+ }\n+ },\n+ {\n+ \"type\": \"text\",\n+ \"text\": \"What's happening in this audio?\"\n+ },\n+ ],\n+ }]\n+\n+ with pytest.raises(openai.BadRequestError): # test multi-audio input\n+ await client.chat.completions.create(\n+ model=model_name,\n+ messages=messages,\n+ max_tokens=10,\n+ temperature=0.0,\n+ )\n+\n+ # the server should still work afterwards\n+ completion = await client.completions.create(\n+ model=model_name,\n+ prompt=[0, 0, 0, 0, 0],\n+ max_tokens=5,\n+ temperature=0.0,\n+ )\n+ completion = completion.choices[0].text\n+ assert completion is not None and len(completion) >= 0\ndiff --git a/vllm/assets/base.py b/vllm/assets/base.py\nindex f97e8c218f6..3e4ea4970bf 100644\n--- a/vllm/assets/base.py\n+++ b/vllm/assets/base.py\n@@ -4,7 +4,7 @@\n \n import vllm.envs as envs\n from vllm.connections import global_http_connection\n-from vllm.envs import VLLM_IMAGE_FETCH_TIMEOUT\n+from vllm.envs import VLLM_MULTIMODAL_FETCH_TIMEOUT\n \n vLLM_S3_BUCKET_URL = \"https://vllm-public-assets.s3.us-west-2.amazonaws.com\"\n \n@@ -34,6 +34,6 @@ def get_vllm_public_assets(filename: str,\n global_http_connection.download_file(\n f\"{vLLM_S3_BUCKET_URL}/{filename}\",\n asset_path,\n- timeout=VLLM_IMAGE_FETCH_TIMEOUT)\n+ timeout=VLLM_MULTIMODAL_FETCH_TIMEOUT)\n \n return asset_path\ndiff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py\nindex 1197c70d88a..19622b444ba 100644\n--- a/vllm/entrypoints/chat_utils.py\n+++ b/vllm/entrypoints/chat_utils.py\n@@ -2,7 +2,8 @@\n from dataclasses import dataclass\n from functools import lru_cache\n from pathlib import Path\n-from typing import Any, Awaitable, Iterable, List, Optional, Tuple, Union, cast\n+from typing import (Any, Awaitable, Iterable, List, Literal, Optional, Tuple,\n+ Union, cast)\n \n # yapf conflicts with isort for this block\n # yapf: disable\n@@ -21,12 +22,25 @@\n from vllm.config import ModelConfig\n from vllm.logger import init_logger\n from vllm.multimodal import MultiModalDataDict\n-from vllm.multimodal.utils import async_get_and_parse_image\n+from vllm.multimodal.utils import (async_get_and_parse_audio,\n+ async_get_and_parse_image)\n from vllm.transformers_utils.tokenizer import AnyTokenizer\n \n logger = init_logger(__name__)\n \n \n+class AudioURL(TypedDict, total=False):\n+ url: Required[str]\n+ \"\"\"Either a URL of the audio or the base64 encoded image data.\"\"\"\n+\n+\n+class ChatCompletionContentPartAudioParam(TypedDict, total=False):\n+ audio_url: Required[AudioURL]\n+\n+ type: Required[Literal[\"audio_url\"]]\n+ \"\"\"The type of the content part.\"\"\"\n+\n+\n class CustomChatCompletionContentPartParam(TypedDict, total=False):\n __pydantic_config__ = ConfigDict(extra=\"allow\") # type: ignore\n \n@@ -35,6 +49,7 @@ class CustomChatCompletionContentPartParam(TypedDict, total=False):\n \n \n ChatCompletionContentPartParam = Union[OpenAIChatCompletionContentPartParam,\n+ ChatCompletionContentPartAudioParam,\n CustomChatCompletionContentPartParam]\n \n \n@@ -97,34 +112,41 @@ def load_chat_template(\n \n \n @lru_cache(maxsize=None)\n-def _image_token_str(model_config: ModelConfig,\n- tokenizer: PreTrainedTokenizer) -> Optional[str]:\n+def _mm_token_str(model_config: ModelConfig, tokenizer: PreTrainedTokenizer,\n+ modality: Literal[\"image\", \"audio\"]) -> Optional[str]:\n # TODO: Let user specify how to insert image tokens into prompt\n # (similar to chat template)\n- model_type = model_config.hf_config.model_type\n- if model_type == \"phi3_v\":\n- # Workaround since this token is not defined in the tokenizer\n- return \"<|image_1|>\"\n- if model_type == \"minicpmv\":\n- return \"(<image>./</image>)\"\n- if model_type in (\"blip-2\", \"chatglm\", \"fuyu\", \"paligemma\"):\n- # These models do not use image tokens in the prompt\n- return None\n- if model_type.startswith(\"llava\"):\n- return tokenizer.decode(model_config.hf_config.image_token_index)\n- if model_type in (\"chameleon\", \"internvl_chat\"):\n- return \"<image>\"\n- raise TypeError(f\"Unknown model type: {model_type}\")\n-\n-\n-# TODO: Let user specify how to insert image tokens into prompt\n+ if modality == \"image\":\n+ model_type = model_config.hf_config.model_type\n+ if model_type == \"phi3_v\":\n+ # Workaround since this token is not defined in the tokenizer\n+ return \"<|image_1|>\"\n+ if model_type == \"minicpmv\":\n+ return \"(<image>./</image>)\"\n+ if model_type in (\"blip-2\", \"chatglm\", \"fuyu\", \"paligemma\"):\n+ # These models do not use image tokens in the prompt\n+ return None\n+ if model_type.startswith(\"llava\"):\n+ return tokenizer.decode(model_config.hf_config.image_token_index)\n+ if model_type in (\"chameleon\", \"internvl_chat\"):\n+ return \"<image>\"\n+\n+ raise TypeError(f\"Unknown model type: {model_type}\")\n+ elif modality == \"audio\":\n+ raise TypeError(\"No audio models are supported yet.\")\n+ else:\n+ raise TypeError(f\"Unknown modality: {modality}\")\n+\n+\n+# TODO: Let user specify how to insert multimodal tokens into prompt\n # (similar to chat template)\n-def _get_full_image_text_prompt(image_token_str: str, text_prompt: str) -> str:\n- \"\"\"Combine image and text prompts for vision language model\"\"\"\n+def _get_full_multimodal_text_prompt(placeholder_token_str: str,\n+ text_prompt: str) -> str:\n+ \"\"\"Combine multimodal prompts for a multimodal language model\"\"\"\n \n # NOTE: For now we assume all model architectures use the same\n- # image + text prompt format. This may change in the future.\n- return f\"{image_token_str}\\n{text_prompt}\"\n+ # placeholder + text prompt format. This may change in the future.\n+ return f\"{placeholder_token_str}\\n{text_prompt}\"\n \n \n def _parse_chat_message_content_parts(\n@@ -135,6 +157,7 @@ def _parse_chat_message_content_parts(\n ) -> ChatMessageParseResult:\n texts: List[str] = []\n mm_futures: List[Awaitable[MultiModalDataDict]] = []\n+ modality: Literal[\"image\", \"audio\"] = \"image\"\n \n for part in parts:\n part_type = part[\"type\"]\n@@ -142,9 +165,10 @@ def _parse_chat_message_content_parts(\n text = cast(ChatCompletionContentPartTextParam, part)[\"text\"]\n texts.append(text)\n elif part_type == \"image_url\":\n+ modality = \"image\"\n if len(mm_futures) > 0:\n raise NotImplementedError(\n- \"Multiple 'image_url' input is currently not supported.\")\n+ \"Multiple multimodal inputs is currently not supported.\")\n \n image_url = cast(ChatCompletionContentPartImageParam,\n part)[\"image_url\"]\n@@ -156,21 +180,32 @@ def _parse_chat_message_content_parts(\n \n image_future = async_get_and_parse_image(image_url[\"url\"])\n mm_futures.append(image_future)\n+ elif part_type == \"audio_url\":\n+ modality = \"audio\"\n+ if len(mm_futures) > 0:\n+ raise NotImplementedError(\n+ \"Multiple multimodal inputs is currently not supported.\")\n+\n+ audio_url = cast(ChatCompletionContentPartAudioParam,\n+ part)[\"audio_url\"]\n+ audio_future = async_get_and_parse_audio(audio_url[\"url\"])\n+ mm_futures.append(audio_future)\n else:\n raise NotImplementedError(f\"Unknown part type: {part_type}\")\n \n text_prompt = \"\\n\".join(texts)\n \n if mm_futures:\n- image_token_str = _image_token_str(model_config, tokenizer)\n- if image_token_str is not None:\n- if image_token_str in text_prompt:\n+ placeholder_token_str = _mm_token_str(model_config, tokenizer,\n+ modality)\n+ if placeholder_token_str is not None:\n+ if placeholder_token_str in text_prompt:\n logger.warning(\n- \"Detected image token string in the text prompt. \"\n+ \"Detected multi-modal token string in the text prompt. \"\n \"Skipping prompt formatting.\")\n else:\n- text_prompt = _get_full_image_text_prompt(\n- image_token_str=image_token_str,\n+ text_prompt = _get_full_multimodal_text_prompt(\n+ placeholder_token_str=placeholder_token_str,\n text_prompt=text_prompt,\n )\n \ndiff --git a/vllm/envs.py b/vllm/envs.py\nindex 26d0c33707f..e409ce09b0e 100644\n--- a/vllm/envs.py\n+++ b/vllm/envs.py\n@@ -43,7 +43,7 @@\n VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL: bool = True\n VLLM_WORKER_MULTIPROC_METHOD: str = \"fork\"\n VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, \"assets\")\n- VLLM_IMAGE_FETCH_TIMEOUT: int = 5\n+ VLLM_MULTIMODAL_FETCH_TIMEOUT: int = 5\n VLLM_TARGET_DEVICE: str = \"cuda\"\n MAX_JOBS: Optional[str] = None\n NVCC_THREADS: Optional[str] = None\n@@ -316,10 +316,10 @@ def get_default_config_root():\n os.path.join(get_default_cache_root(), \"vllm\", \"assets\"),\n )),\n \n- # Timeout for fetching images when serving multimodal models\n+ # Timeout for fetching images or audio when serving multimodal models\n # Default is 5 seconds\n- \"VLLM_IMAGE_FETCH_TIMEOUT\":\n- lambda: int(os.getenv(\"VLLM_IMAGE_FETCH_TIMEOUT\", \"5\")),\n+ \"VLLM_MULTIMODAL_FETCH_TIMEOUT\":\n+ lambda: int(os.getenv(\"VLLM_MULTIMODAL_FETCH_TIMEOUT\", \"5\")),\n \n # Path to the XLA persistent cache directory.\n # Only used for XLA devices such as TPUs.\ndiff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py\nindex ba9c8af88f8..a2181db38e0 100644\n--- a/vllm/model_executor/model_loader/loader.py\n+++ b/vllm/model_executor/model_loader/loader.py\n@@ -38,7 +38,7 @@\n safetensors_weights_iterator)\n from vllm.model_executor.models.interfaces import (has_inner_state,\n supports_lora,\n- supports_vision)\n+ supports_multimodal)\n from vllm.model_executor.utils import set_weight_attrs\n from vllm.platforms import current_platform\n from vllm.utils import is_pin_memory_available, is_tpu\n@@ -131,7 +131,7 @@ def _get_model_initialization_kwargs(\n \"be added in the future. If this is important to you, \"\n \"please open an issue on github.\")\n \n- if supports_vision(model_class):\n+ if supports_multimodal(model_class):\n if multimodal_config is None:\n raise ValueError(\"Provide vision related configurations \"\n \"through LLM entrypoint or engine arguments.\")\ndiff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py\nindex 084cbf35533..82a893e1aba 100644\n--- a/vllm/model_executor/models/blip2.py\n+++ b/vllm/model_executor/models/blip2.py\n@@ -20,8 +20,8 @@\n \n from .blip import (BlipVisionModel, dummy_image_for_blip,\n get_max_blip_image_tokens)\n-from .interfaces import SupportsVision\n-from .utils import merge_vision_embeddings\n+from .interfaces import SupportsMultiModal\n+from .utils import merge_multimodal_embeddings\n \n _KEYS_TO_MODIFY_MAPPING = {\n \"language_model.lm_head\": \"lm_head\",\n@@ -457,7 +457,7 @@ def input_processor_for_blip2(ctx: InputContext, llm_inputs: LLMInputs):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_blip2_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_blip2)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_blip2)\n-class Blip2ForConditionalGeneration(nn.Module, SupportsVision):\n+class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: Blip2Config,\n@@ -621,9 +621,9 @@ def forward(\n vision_embeddings = self._process_image_input(image_input)\n inputs_embeds = self.language_model.get_input_embeddings(input_ids)\n \n- inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n- vision_embeddings,\n- BLIP2_IMAGE_TOKEN_ID)\n+ inputs_embeds = merge_multimodal_embeddings(\n+ input_ids, inputs_embeds, vision_embeddings,\n+ BLIP2_IMAGE_TOKEN_ID)\n \n input_ids = None\n else:\ndiff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py\nindex 10a82207d90..22d861f6e85 100644\n--- a/vllm/model_executor/models/chameleon.py\n+++ b/vllm/model_executor/models/chameleon.py\n@@ -33,7 +33,7 @@\n from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData\n from vllm.utils import print_warning_once\n \n-from .interfaces import SupportsVision\n+from .interfaces import SupportsMultiModal\n \n logger = init_logger(__name__)\n \n@@ -877,7 +877,7 @@ def forward(\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_chameleon_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_chameleon)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_chameleon)\n-class ChameleonForConditionalGeneration(nn.Module, SupportsVision):\n+class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal):\n \n def __init__(\n self,\ndiff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py\nindex bb49349e795..c08b3ae3afe 100644\n--- a/vllm/model_executor/models/fuyu.py\n+++ b/vllm/model_executor/models/fuyu.py\n@@ -40,8 +40,8 @@\n cached_get_tokenizer)\n from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData\n \n-from .interfaces import SupportsVision\n-from .utils import merge_vision_embeddings\n+from .interfaces import SupportsMultiModal\n+from .utils import merge_multimodal_embeddings\n \n logger = init_logger(__name__)\n \n@@ -209,7 +209,7 @@ def input_mapper_for_fuyu(ctx: InputContext, data: object):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_fuyu_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_fuyu)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_fuyu)\n-class FuyuForCausalLM(nn.Module, SupportsVision):\n+class FuyuForCausalLM(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: FuyuConfig,\n@@ -271,9 +271,9 @@ def forward(\n if image_input is not None:\n vision_embeddings = self._process_image_input(image_input)\n inputs_embeds = self.language_model.model.embed_tokens(input_ids)\n- inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n- vision_embeddings,\n- self.image_token_id)\n+ inputs_embeds = merge_multimodal_embeddings(\n+ input_ids, inputs_embeds, vision_embeddings,\n+ self.image_token_id)\n \n else:\n inputs_embeds = None\ndiff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py\nindex db0d6b429d6..2f323ea552c 100644\n--- a/vllm/model_executor/models/interfaces.py\n+++ b/vllm/model_executor/models/interfaces.py\n@@ -10,12 +10,15 @@\n \n \n @runtime_checkable\n-class SupportsVision(Protocol):\n- \"\"\"The interface required for all vision language models (VLMs).\"\"\"\n+class SupportsMultiModal(Protocol):\n+ \"\"\"\n+ The interface required for all multimodal (vision or audio) language\n+ models.\n+ \"\"\"\n \n- supports_vision: ClassVar[Literal[True]] = True\n+ supports_multimodal: ClassVar[Literal[True]] = True\n \"\"\"\n- A flag that indicates this model supports vision inputs.\n+ A flag that indicates this model supports multimodal inputs.\n \n Note:\n There is no need to redefine this flag if this class is in the\n@@ -29,30 +32,31 @@ def __init__(self, *, multimodal_config: MultiModalConfig) -> None:\n # We can't use runtime_checkable with ClassVar for issubclass checks\n # so we need to treat the class as an instance and use isinstance instead\n @runtime_checkable\n-class _SupportsVisionType(Protocol):\n- supports_vision: Literal[True]\n+class _SupportsMultiModalType(Protocol):\n+ supports_multimodal: Literal[True]\n \n def __call__(self, *, multimodal_config: MultiModalConfig) -> None:\n ...\n \n \n @overload\n-def supports_vision(model: Type[object]) -> TypeIs[Type[SupportsVision]]:\n+def supports_multimodal(\n+ model: Type[object]) -> TypeIs[Type[SupportsMultiModal]]:\n ...\n \n \n @overload\n-def supports_vision(model: object) -> TypeIs[SupportsVision]:\n+def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]:\n ...\n \n \n-def supports_vision(\n+def supports_multimodal(\n model: Union[Type[object], object],\n-) -> Union[TypeIs[Type[SupportsVision]], TypeIs[SupportsVision]]:\n+) -> Union[TypeIs[Type[SupportsMultiModal]], TypeIs[SupportsMultiModal]]:\n if isinstance(model, type):\n- return isinstance(model, _SupportsVisionType)\n+ return isinstance(model, _SupportsMultiModalType)\n \n- return isinstance(model, SupportsVision)\n+ return isinstance(model, SupportsMultiModal)\n \n \n @runtime_checkable\ndiff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py\nindex 26c02d46a18..d87305a6773 100644\n--- a/vllm/model_executor/models/internvl.py\n+++ b/vllm/model_executor/models/internvl.py\n@@ -27,9 +27,9 @@\n \n from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,\n get_clip_num_patches)\n-from .interfaces import SupportsVision\n+from .interfaces import SupportsMultiModal\n from .utils import (filter_weights, init_vllm_registered_model,\n- merge_vision_embeddings)\n+ merge_multimodal_embeddings)\n \n IMG_START = '<img>'\n IMG_END = '</img>'\n@@ -292,7 +292,7 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_internvl_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_internvl)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_internvl)\n-class InternVLChatModel(nn.Module, SupportsVision):\n+class InternVLChatModel(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: PretrainedConfig,\n@@ -450,10 +450,10 @@ def forward(\n if image_input is not None:\n inputs_embeds = self.language_model.model.get_input_embeddings(\n input_ids)\n- vision_embeddings = self._process_image_input(image_input)\n- inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n- vision_embeddings,\n- self.img_context_token_id)\n+ vit_embeds = self.extract_feature(image_input[\"data\"])\n+ inputs_embeds = merge_multimodal_embeddings(\n+ input_ids, inputs_embeds, vit_embeds,\n+ self.img_context_token_id)\n input_ids = None\n else:\n inputs_embeds = None\ndiff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py\nindex 0ff68943b51..f95f3ce76cf 100644\n--- a/vllm/model_executor/models/llava.py\n+++ b/vllm/model_executor/models/llava.py\n@@ -19,12 +19,12 @@\n from .clip import (CLIPVisionModel, dummy_image_for_clip,\n dummy_seq_data_for_clip, get_max_clip_image_tokens,\n input_processor_for_clip)\n-from .interfaces import SupportsVision\n+from .interfaces import SupportsMultiModal\n from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n dummy_seq_data_for_siglip, get_max_siglip_image_tokens,\n input_processor_for_siglip)\n from .utils import (filter_weights, init_vllm_registered_model,\n- merge_vision_embeddings)\n+ merge_multimodal_embeddings)\n \n \n class LlavaImagePixelInputs(TypedDict):\n@@ -181,7 +181,7 @@ def _init_vision_tower(hf_config: LlavaConfig):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_llava)\n-class LlavaForConditionalGeneration(nn.Module, SupportsVision):\n+class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: LlavaConfig,\n@@ -338,7 +338,7 @@ def forward(\n inputs_embeds = self.language_model.model.get_input_embeddings(\n input_ids)\n \n- inputs_embeds = merge_vision_embeddings(\n+ inputs_embeds = merge_multimodal_embeddings(\n input_ids, inputs_embeds, vision_embeddings,\n self.config.image_token_index)\n \ndiff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py\nindex d94af966162..6a481e62872 100644\n--- a/vllm/model_executor/models/llava_next.py\n+++ b/vllm/model_executor/models/llava_next.py\n@@ -23,13 +23,13 @@\n from .clip import (CLIPVisionModel, dummy_image_for_clip,\n dummy_seq_data_for_clip, get_clip_image_feature_size,\n get_clip_patch_grid_length, input_processor_for_clip)\n-from .interfaces import SupportsVision\n+from .interfaces import SupportsMultiModal\n from .llava import LlavaMultiModalProjector\n from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n dummy_seq_data_for_siglip, get_siglip_image_feature_size,\n get_siglip_patch_grid_length, input_processor_for_siglip)\n from .utils import (filter_weights, init_vllm_registered_model,\n- merge_vision_embeddings)\n+ merge_multimodal_embeddings)\n \n logger = init_logger(__name__)\n \n@@ -275,7 +275,7 @@ def _init_vision_tower(hf_config: LlavaNextConfig):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_next_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next)\n-class LlavaNextForConditionalGeneration(nn.Module, SupportsVision):\n+class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: LlavaNextConfig,\n@@ -571,7 +571,7 @@ def forward(\n inputs_embeds = self.language_model.model.get_input_embeddings(\n input_ids)\n \n- inputs_embeds = merge_vision_embeddings(\n+ inputs_embeds = merge_multimodal_embeddings(\n input_ids, inputs_embeds, vision_embeddings,\n self.config.image_token_index)\n \ndiff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py\nindex fc962434cab..703f6e58248 100644\n--- a/vllm/model_executor/models/minicpmv.py\n+++ b/vllm/model_executor/models/minicpmv.py\n@@ -48,7 +48,7 @@\n from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead\n from vllm.model_executor.model_loader.utils import set_default_torch_dtype\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n-from vllm.model_executor.models.interfaces import SupportsVision\n+from vllm.model_executor.models.interfaces import SupportsMultiModal\n from vllm.model_executor.models.llama import LlamaModel\n from vllm.model_executor.models.minicpm import MiniCPMModel\n from vllm.model_executor.models.qwen2 import Qwen2Model\n@@ -479,7 +479,7 @@ def get_placeholder(image_size: Tuple[int, int], num_image: int):\n return llm_inputs\n \n \n-class MiniCPMVBaseModel(nn.Module, SupportsVision):\n+class MiniCPMVBaseModel(nn.Module, SupportsMultiModal):\n \"\"\"\n The abstract class of MiniCPMV can only be inherited, but cannot be\n instantiated.\ndiff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py\nindex c6d59db643b..ed88326cf61 100644\n--- a/vllm/model_executor/models/paligemma.py\n+++ b/vllm/model_executor/models/paligemma.py\n@@ -19,10 +19,10 @@\n from vllm.multimodal.image import cached_get_tokenizer\n from vllm.sequence import IntermediateTensors, SamplerOutput\n \n-from .interfaces import SupportsVision\n+from .interfaces import SupportsMultiModal\n from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n dummy_seq_data_for_siglip, get_max_siglip_image_tokens)\n-from .utils import merge_vision_embeddings\n+from .utils import merge_multimodal_embeddings\n \n logger = init_logger(__name__)\n \n@@ -130,7 +130,7 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor:\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_paligemma_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma)\n-class PaliGemmaForConditionalGeneration(nn.Module, SupportsVision):\n+class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: PaliGemmaConfig,\n@@ -244,7 +244,7 @@ def forward(self,\n \n inputs_embeds = self.language_model.get_input_embeddings(input_ids)\n \n- inputs_embeds = merge_vision_embeddings(\n+ inputs_embeds = merge_multimodal_embeddings(\n input_ids, inputs_embeds, vision_embeddings,\n self.config.image_token_index)\n \ndiff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py\nindex 51d3a75ea6f..6bbe4f977ad 100644\n--- a/vllm/model_executor/models/phi3v.py\n+++ b/vllm/model_executor/models/phi3v.py\n@@ -42,8 +42,8 @@\n \n from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,\n input_processor_for_clip)\n-from .interfaces import SupportsVision\n-from .utils import merge_vision_embeddings\n+from .interfaces import SupportsMultiModal\n+from .utils import merge_multimodal_embeddings\n \n logger = init_logger(__name__)\n \n@@ -453,7 +453,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs):\n @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens)\n @INPUT_REGISTRY.register_dummy_data(dummy_data_for_phi3v)\n @INPUT_REGISTRY.register_input_processor(input_processor_for_phi3v)\n-class Phi3VForCausalLM(nn.Module, SupportsVision):\n+class Phi3VForCausalLM(nn.Module, SupportsMultiModal):\n \n def __init__(self,\n config: PretrainedConfig,\n@@ -568,9 +568,9 @@ def forward(self,\n if image_input is not None:\n vision_embeddings = self._process_image_input(image_input)\n inputs_embeds = self.model.get_input_embeddings(input_ids)\n- inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n- vision_embeddings,\n- self.image_token_id)\n+ inputs_embeds = merge_multimodal_embeddings(\n+ input_ids, inputs_embeds, vision_embeddings,\n+ self.image_token_id)\n input_ids = None\n else:\n inputs_embeds = None\ndiff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py\nindex d1bb030c6c9..91b414b1fd9 100644\n--- a/vllm/model_executor/models/utils.py\n+++ b/vllm/model_executor/models/utils.py\n@@ -54,41 +54,42 @@ def init_vllm_registered_model(\n )\n \n \n-def merge_vision_embeddings(input_ids: torch.Tensor,\n- inputs_embeds: torch.Tensor,\n- vision_embeddings: BatchedTensors,\n- image_token_id: int) -> torch.Tensor:\n+def merge_multimodal_embeddings(input_ids: torch.Tensor,\n+ inputs_embeds: torch.Tensor,\n+ multimodal_embeddings: BatchedTensors,\n+ placeholder_token_id: int) -> torch.Tensor:\n \"\"\"\n- Merge ``vision_embeddings`` into ``inputs_embeds`` by overwriting the\n- positions in ``inputs_embeds`` corresponding to placeholder image tokens in\n+ Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the\n+ positions in ``inputs_embeds`` corresponding to placeholder tokens in\n ``input_ids``.\n \n Note:\n This updates ``inputs_embeds`` in place.\n \"\"\"\n- mask = (input_ids == image_token_id)\n+ mask = (input_ids == placeholder_token_id)\n num_expected_tokens = mask.sum()\n \n- if isinstance(vision_embeddings, torch.Tensor):\n- batch_size, batch_tokens, *_, embed_dim = vision_embeddings.shape\n+ if isinstance(multimodal_embeddings, torch.Tensor):\n+ batch_size, batch_tokens, *_, embed_dim = multimodal_embeddings.shape\n total_tokens = batch_size * batch_tokens\n if num_expected_tokens != total_tokens:\n expr = f\"{batch_size} x {batch_tokens}\"\n raise ValueError(\n f\"Attempted to assign {expr} = {total_tokens} \"\n- f\"image tokens to {num_expected_tokens} placeholders\")\n+ f\"multimodal tokens to {num_expected_tokens} placeholders\")\n \n- inputs_embeds[mask] = vision_embeddings.view(total_tokens, embed_dim)\n+ inputs_embeds[mask] = multimodal_embeddings.view(\n+ total_tokens, embed_dim)\n else:\n- size_per_batch = [t.shape[0] for t in vision_embeddings]\n+ size_per_batch = [t.shape[0] for t in multimodal_embeddings]\n total_tokens = sum(size_per_batch)\n if num_expected_tokens != total_tokens:\n expr = ' + '.join(map(str, size_per_batch))\n raise ValueError(\n f\"Attempted to assign {expr} = {total_tokens} \"\n- f\"image tokens to {num_expected_tokens} placeholders\")\n+ f\"multimodal tokens to {num_expected_tokens} placeholders\")\n \n- inputs_embeds[mask] = torch.cat(vision_embeddings)\n+ inputs_embeds[mask] = torch.cat(multimodal_embeddings)\n \n return inputs_embeds\n \ndiff --git a/vllm/multimodal/audio.py b/vllm/multimodal/audio.py\nnew file mode 100644\nindex 00000000000..b4bf4b4541d\n--- /dev/null\n+++ b/vllm/multimodal/audio.py\n@@ -0,0 +1,17 @@\n+from vllm.inputs.registry import InputContext\n+from vllm.multimodal.base import MultiModalInputs, MultiModalPlugin\n+\n+\n+class AudioPlugin(MultiModalPlugin):\n+ \"\"\"Plugin for audio data.\"\"\"\n+\n+ def get_data_key(self) -> str:\n+ return \"audio\"\n+\n+ def _default_input_mapper(self, ctx: InputContext,\n+ data: object) -> MultiModalInputs:\n+ raise NotImplementedError(\"There is no default audio input mapper\")\n+\n+ def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:\n+ raise NotImplementedError(\n+ \"There is no default maximum multimodal tokens\")\ndiff --git a/vllm/multimodal/base.py b/vllm/multimodal/base.py\nindex aefb5f438c5..7717d77198a 100644\n--- a/vllm/multimodal/base.py\n+++ b/vllm/multimodal/base.py\n@@ -3,8 +3,9 @@\n from collections import UserDict, defaultdict\n from typing import Any, Callable, Dict, List, Optional\n from typing import Sequence as GenericSequence\n-from typing import Type, TypedDict, TypeVar, Union, cast\n+from typing import Tuple, Type, TypedDict, TypeVar, Union, cast\n \n+import numpy as np\n import torch\n import torch.types\n from PIL import Image\n@@ -121,6 +122,9 @@ class MultiModalDataBuiltins(TypedDict, total=False):\n image: Image.Image\n \"\"\"The input image.\"\"\"\n \n+ audio: Tuple[np.ndarray, Union[int, float]]\n+ \"\"\"The input audio and its sampling rate.\"\"\"\n+\n \n MultiModalDataDict = Union[MultiModalDataBuiltins, Dict[str, Any]]\n \"\"\"\ndiff --git a/vllm/multimodal/registry.py b/vllm/multimodal/registry.py\nindex d8e1b68178a..19c26123c2d 100644\n--- a/vllm/multimodal/registry.py\n+++ b/vllm/multimodal/registry.py\n@@ -6,6 +6,7 @@\n from vllm.config import ModelConfig\n from vllm.logger import init_logger\n \n+from .audio import AudioPlugin\n from .base import (MultiModalDataDict, MultiModalInputMapper, MultiModalInputs,\n MultiModalPlugin, MultiModalTokensCalc)\n from .image import ImagePlugin\n@@ -19,7 +20,7 @@ class MultiModalRegistry:\n :class:`~vllm.multimodal.MultiModalPlugin` for each modality.\n \"\"\"\n \n- DEFAULT_PLUGINS = (ImagePlugin(), )\n+ DEFAULT_PLUGINS = (ImagePlugin(), AudioPlugin())\n \n def __init__(\n self,\ndiff --git a/vllm/multimodal/utils.py b/vllm/multimodal/utils.py\nindex 8f7e613cdf9..c657916e7b9 100644\n--- a/vllm/multimodal/utils.py\n+++ b/vllm/multimodal/utils.py\n@@ -1,11 +1,14 @@\n import base64\n from io import BytesIO\n-from typing import Union\n+from typing import Tuple, Union\n \n+import librosa\n+import numpy as np\n+import soundfile\n from PIL import Image\n \n from vllm.connections import global_http_connection\n-from vllm.envs import VLLM_IMAGE_FETCH_TIMEOUT\n+from vllm.envs import VLLM_MULTIMODAL_FETCH_TIMEOUT\n from vllm.multimodal.base import MultiModalDataDict\n \n \n@@ -29,7 +32,7 @@ def fetch_image(image_url: str, *, image_mode: str = \"RGB\") -> Image.Image:\n \"\"\"\n if image_url.startswith('http'):\n image_raw = global_http_connection.get_bytes(\n- image_url, timeout=VLLM_IMAGE_FETCH_TIMEOUT)\n+ image_url, timeout=VLLM_MULTIMODAL_FETCH_TIMEOUT)\n image = _load_image_from_bytes(image_raw)\n \n elif image_url.startswith('data:image'):\n@@ -51,7 +54,7 @@ async def async_fetch_image(image_url: str,\n \"\"\"\n if image_url.startswith('http'):\n image_raw = await global_http_connection.async_get_bytes(\n- image_url, timeout=VLLM_IMAGE_FETCH_TIMEOUT)\n+ image_url, timeout=VLLM_MULTIMODAL_FETCH_TIMEOUT)\n image = _load_image_from_bytes(image_raw)\n \n elif image_url.startswith('data:image'):\n@@ -63,11 +66,62 @@ async def async_fetch_image(image_url: str,\n return image.convert(image_mode)\n \n \n+def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:\n+ \"\"\"\n+ Load audio from a URL.\n+ \"\"\"\n+ if audio_url.startswith(\"http\"):\n+ audio_bytes = global_http_connection.get_bytes(\n+ audio_url, timeout=VLLM_MULTIMODAL_FETCH_TIMEOUT)\n+ elif audio_url.startswith(\"data:audio\"):\n+ _, audio_base64 = audio_url.split(\",\", 1)\n+ audio_bytes = base64.b64decode(audio_base64)\n+ else:\n+ raise ValueError(\"Invalid 'audio_url': A valid 'audio_url' must start \"\n+ \"with either 'data:audio' or 'http'.\")\n+\n+ return librosa.load(BytesIO(audio_bytes), sr=None)\n+\n+\n+async def async_fetch_audio(\n+ audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:\n+ \"\"\"\n+ Asynchronously fetch audio from a URL.\n+ \"\"\"\n+ if audio_url.startswith(\"http\"):\n+ audio_bytes = await global_http_connection.async_get_bytes(\n+ audio_url, timeout=VLLM_MULTIMODAL_FETCH_TIMEOUT)\n+ elif audio_url.startswith(\"data:audio\"):\n+ _, audio_base64 = audio_url.split(\",\", 1)\n+ audio_bytes = base64.b64decode(audio_base64)\n+ else:\n+ raise ValueError(\"Invalid 'audio_url': A valid 'audio_url' must start \"\n+ \"with either 'data:audio' or 'http'.\")\n+\n+ return librosa.load(BytesIO(audio_bytes), sr=None)\n+\n+\n+async def async_get_and_parse_audio(audio_url: str) -> MultiModalDataDict:\n+ audio, sr = await async_fetch_audio(audio_url)\n+ return {\"audio\": (audio, sr)}\n+\n+\n async def async_get_and_parse_image(image_url: str) -> MultiModalDataDict:\n image = await async_fetch_image(image_url)\n return {\"image\": image}\n \n \n+def encode_audio_base64(\n+ audio: np.ndarray,\n+ sampling_rate: int,\n+) -> str:\n+ \"\"\"Encode audio as base64.\"\"\"\n+ buffered = BytesIO()\n+ soundfile.write(buffered, audio, sampling_rate, format=\"WAV\")\n+\n+ return base64.b64encode(buffered.getvalue()).decode('utf-8')\n+\n+\n def encode_image_base64(\n image: Image.Image,\n *,\ndiff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py\nindex cfbbb6698cd..a4ce1b512dd 100644\n--- a/vllm/worker/model_runner.py\n+++ b/vllm/worker/model_runner.py\n@@ -40,7 +40,7 @@\n from vllm.model_executor.model_loader import get_model\n from vllm.model_executor.model_loader.tensorizer import TensorizerConfig\n from vllm.model_executor.models.interfaces import (supports_lora,\n- supports_vision)\n+ supports_multimodal)\n from vllm.model_executor.models.utils import set_cpu_offload_max_bytes\n from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,\n MultiModalInputs)\n@@ -900,9 +900,9 @@ def load_model(self) -> None:\n \n if self.lora_config:\n assert supports_lora(self.model), \"Model does not support LoRA\"\n- assert not supports_vision(\n+ assert not supports_multimodal(\n self.model\n- ), \"To be tested: vision language model with LoRA settings.\"\n+ ), \"To be tested: multimodal language model with LoRA settings.\"\n \n self.lora_manager = LRUCacheWorkerLoRAManager(\n self.scheduler_config.max_num_seqs,\n@@ -1054,7 +1054,7 @@ def profile_run(self) -> None:\n # of images processed.\n model_config = self.model_config\n \n- if supports_vision(self.model):\n+ if supports_multimodal(self.model):\n max_mm_tokens = MULTIMODAL_REGISTRY \\\n .get_max_multimodal_tokens(model_config)\n max_num_seqs_orig = max_num_seqs\ndiff --git a/vllm/worker/xpu_model_runner.py b/vllm/worker/xpu_model_runner.py\nindex 112e494fade..a1e1c1bef63 100644\n--- a/vllm/worker/xpu_model_runner.py\n+++ b/vllm/worker/xpu_model_runner.py\n@@ -12,7 +12,7 @@\n from vllm.inputs import INPUT_REGISTRY\n from vllm.logger import init_logger\n from vllm.model_executor.model_loader import get_model\n-from vllm.model_executor.models.interfaces import supports_vision\n+from vllm.model_executor.models.interfaces import supports_multimodal\n from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,\n MultiModalInputs)\n from vllm.sampling_params import SamplingParams\n@@ -165,7 +165,7 @@ def profile_run(self) -> None:\n # of images processed.\n model_config = self.model_config\n \n- if supports_vision(self.model):\n+ if supports_multimodal(self.model):\n max_mm_tokens = MULTIMODAL_REGISTRY \\\n .get_max_multimodal_tokens(model_config)\n max_num_seqs_orig = max_num_seqs\n" }
[ { "diff_hunk": "@@ -150,7 +150,7 @@ A full code example can be found in `examples/openai_vision_api_client.py <https\n \n .. code-block:: shell\n \n- export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>\n+ export VLLM_MULTIMODAL_FETCH_TIMEOUT=<timeout>", "line": null, "original_line": 153, "original_start_line": null, "path": "docs/source/models/vlm.rst", "start_line": null, "text": "@user1:\nGiven that different types of data may have different sizes (think image vs video), it may be better to have a separate timeout for each modality.\n\n@author:\nAh, good thought. I split it out accordingly." }, { "diff_hunk": "@@ -21,12 +22,25 @@\n from vllm.config import ModelConfig\n from vllm.logger import init_logger\n from vllm.multimodal import MultiModalDataDict\n-from vllm.multimodal.utils import async_get_and_parse_image\n+from vllm.multimodal.utils import (async_get_and_parse_audio,\n+ async_get_and_parse_image)\n from vllm.transformers_utils.tokenizer import AnyTokenizer\n \n logger = init_logger(__name__)\n \n \n+class AudioURL(TypedDict, total=False):\n+ url: Required[str]\n+ \"\"\"Either a URL of the audio or the base64 encoded image data.\"\"\"", "line": null, "original_line": 34, "original_start_line": null, "path": "vllm/entrypoints/chat_utils.py", "start_line": null, "text": "@user1:\n```suggestion\r\n \"\"\"Either a URL of the audio or the base64 encoded audio data.\"\"\"\r\n```" }, { "diff_hunk": "@@ -450,10 +450,10 @@ def forward(\n if image_input is not None:\n inputs_embeds = self.language_model.model.get_input_embeddings(\n input_ids)\n- vision_embeddings = self._process_image_input(image_input)\n- inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n- vision_embeddings,\n- self.img_context_token_id)\n+ vit_embeds = self.extract_feature(image_input[\"data\"])", "line": null, "original_line": 453, "original_start_line": null, "path": "vllm/model_executor/models/internvl.py", "start_line": null, "text": "@user1:\nIs this change intended?\n\n@author:\nNo, sorry! Bad merge, good catch :)" } ]
4eb851da054d7fc2dc62c8ce7baa2c5eb30b6e97
diff --git a/docs/source/conf.py b/docs/source/conf.py index ded6742ea2e5..d24ed0320782 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -112,6 +112,8 @@ def setup(app): "tensorizer", "pynvml", "outlines", + "librosa", + "soundfile", "gguf", "lark", ] diff --git a/docs/source/models/enabling_multimodal_inputs.rst b/docs/source/models/enabling_multimodal_inputs.rst index 20be920b5f69..dc76f921d5b0 100644 --- a/docs/source/models/enabling_multimodal_inputs.rst +++ b/docs/source/models/enabling_multimodal_inputs.rst @@ -15,14 +15,14 @@ This document walks you through the steps to extend a vLLM model so that it acce It is assumed that you have already implemented the model in vLLM according to :ref:`these steps <adding_a_new_model>`. Further update the model as follows: -- Implement the :class:`~vllm.model_executor.models.interfaces.SupportsVision` interface. +- Implement the :class:`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface. .. code-block:: diff - + from vllm.model_executor.models.interfaces import SupportsVision + + from vllm.model_executor.models.interfaces import SupportsMultiModal - class YourModelForImage2Seq(nn.Module): - + class YourModelForImage2Seq(nn.Module, SupportsVision): + + class YourModelForImage2Seq(nn.Module, SupportsMultiModal): .. note:: The model class does not have to be named :code:`*ForCausalLM`. @@ -51,11 +51,11 @@ This decorator accepts a function that maps multi-modal inputs to the keyword ar .. code-block:: diff - from vllm.model_executor.models.interfaces import SupportsVision + from vllm.model_executor.models.interfaces import SupportsMultiModal + from vllm.multimodal import MULTIMODAL_REGISTRY + @MULTIMODAL_REGISTRY.register_image_input_mapper() - class YourModelForImage2Seq(nn.Module, SupportsVision): + class YourModelForImage2Seq(nn.Module, SupportsMultiModal): A default mapper is available for each modality in the core vLLM library. This input mapper will be used if you do not provide your own function. @@ -72,13 +72,13 @@ and register it via :meth:`INPUT_REGISTRY.register_dummy_data <vllm.inputs.regis .. code-block:: diff from vllm.inputs import INPUT_REGISTRY - from vllm.model_executor.models.interfaces import SupportsVision + from vllm.model_executor.models.interfaces import SupportsMultiModal from vllm.multimodal import MULTIMODAL_REGISTRY @MULTIMODAL_REGISTRY.register_image_input_mapper() + @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>) @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>) - class YourModelForImage2Seq(nn.Module, SupportsVision): + class YourModelForImage2Seq(nn.Module, SupportsMultiModal): Here are some examples: @@ -98,13 +98,13 @@ In such cases, you can define your own dummy data by registering a factory metho .. code-block:: diff from vllm.inputs import INPUT_REGISTRY - from vllm.model_executor.models.interfaces import SupportsVision + from vllm.model_executor.models.interfaces import SupportsMultiModal from vllm.multimodal import MULTIMODAL_REGISTRY @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>) + @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>) - class YourModelForImage2Seq(nn.Module, SupportsVision): + class YourModelForImage2Seq(nn.Module, SupportsMultiModal): .. note:: The dummy data should have the maximum possible number of multi-modal tokens, as described in the previous step. @@ -128,14 +128,14 @@ You can register input processors via :meth:`INPUT_REGISTRY.register_input_proce .. code-block:: diff from vllm.inputs import INPUT_REGISTRY - from vllm.model_executor.models.interfaces import SupportsVision + from vllm.model_executor.models.interfaces import SupportsMultiModal from vllm.multimodal import MULTIMODAL_REGISTRY @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(<your_calculation>) @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>) + @INPUT_REGISTRY.register_input_processor(<your_input_processor>) - class YourModelForImage2Seq(nn.Module, SupportsVision): + class YourModelForImage2Seq(nn.Module, SupportsMultiModal): A common use case of input processors is inserting placeholder tokens to leverage the vLLM framework for attention mask generation. Here are some examples: diff --git a/requirements-common.txt b/requirements-common.txt index 5078a65f80f1..ff5848867ed7 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -22,4 +22,6 @@ outlines >= 0.0.43, < 0.1 # Requires torch >= 2.1.0 typing_extensions >= 4.10 filelock >= 3.10.4 # filelock starts to support `mode` argument from 3.10.4 pyzmq +librosa # Required for audio processing +soundfile # Required for audio processing gguf == 0.9.1 diff --git a/tests/entrypoints/openai/test_audio.py b/tests/entrypoints/openai/test_audio.py new file mode 100644 index 000000000000..3c2c652fd317 --- /dev/null +++ b/tests/entrypoints/openai/test_audio.py @@ -0,0 +1,351 @@ +import math +import sys +import time +from typing import Dict, List, Optional, Tuple, Union, cast +from unittest.mock import patch + +import librosa +import numpy as np +import openai +import pytest +import requests +import torch + +from vllm import ModelRegistry +from vllm.config import MultiModalConfig +from vllm.inputs import INPUT_REGISTRY +from vllm.inputs.data import LLMInputs +from vllm.inputs.registry import InputContext +from vllm.model_executor.models.interfaces import SupportsMultiModal +from vllm.model_executor.models.opt import OPTForCausalLM +from vllm.multimodal import MULTIMODAL_REGISTRY +from vllm.multimodal.base import MultiModalInputs +from vllm.multimodal.image import (cached_get_tokenizer, + repeat_and_pad_image_tokens) +from vllm.multimodal.utils import encode_audio_base64, fetch_audio +from vllm.utils import get_open_port + +from ...utils import VLLM_PATH + +chatml_jinja_path = VLLM_PATH / "examples/template_chatml.jinja" +assert chatml_jinja_path.exists() + +MODEL_NAME = "facebook/opt-125m" +TEST_AUDIO_URLS = [ + "https://upload.wikimedia.org/wikipedia/en/b/bf/Dave_Niehaus_Winning_Call_1995_AL_Division_Series.ogg", +] + + +def server_function(port): + + def fake_input_mapper(ctx: InputContext, data: object): + assert isinstance(data, tuple) + (audio, sr) = cast(Tuple[np.ndarray, Union[float, int]], data) + + # Resample it to 1 sample per second + audio = librosa.resample(audio, orig_sr=sr, target_sr=1) + return MultiModalInputs({"processed_audio": torch.from_numpy(audio)}) + + def fake_input_processor(ctx: InputContext, llm_inputs: LLMInputs): + multi_modal_data = llm_inputs.get("multi_modal_data") + if multi_modal_data is None or "audio" not in multi_modal_data: + return llm_inputs + + audio, sr = multi_modal_data.get("audio") + audio_duration = math.ceil(len(audio) / sr) + + new_prompt, new_token_ids = repeat_and_pad_image_tokens( + cached_get_tokenizer(ctx.model_config.tokenizer), + llm_inputs.get("prompt"), + llm_inputs["prompt_token_ids"], + image_token_id=62, # "_" + repeat_count=audio_duration) + + return LLMInputs(prompt_token_ids=new_token_ids, + prompt=new_prompt, + multi_modal_data=multi_modal_data) + + @MULTIMODAL_REGISTRY.register_input_mapper("audio", fake_input_mapper) + @MULTIMODAL_REGISTRY.register_max_multimodal_tokens( + "audio", lambda *_, **__: 100) + @INPUT_REGISTRY.register_input_processor(fake_input_processor) + class FakeAudioModel(OPTForCausalLM, SupportsMultiModal): + + def __init__(self, *args, multimodal_config: MultiModalConfig, + **kwargs): + assert multimodal_config is not None + super().__init__(*args, **kwargs) + + def forward( + self, + *args, + processed_audio: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + return super().forward(*args, **kwargs) + + ModelRegistry.register_model("OPTForCausalLM", FakeAudioModel) + + with patch("vllm.entrypoints.chat_utils._mm_token_str", + lambda *_, **__: "_"): + sys.argv = ["placeholder.py"] + \ + (f"--model {MODEL_NAME} --gpu-memory-utilization 0.10 " + "--dtype bfloat16 --enforce-eager --api-key token-abc123 " + f"--port {port} --chat-template {chatml_jinja_path} " + "--disable-frontend-multiprocessing").split() + import runpy + runpy.run_module('vllm.entrypoints.openai.api_server', + run_name='__main__') + + [email protected](scope="module") +def client(): + port = get_open_port() + ctx = torch.multiprocessing.get_context("spawn") + server = ctx.Process(target=server_function, args=(port, )) + server.start() + MAX_SERVER_START_WAIT_S = 60 + client = openai.AsyncOpenAI( + base_url=f"http://localhost:{port}/v1", + api_key="token-abc123", + ) + # run health check + health_url = f"http://localhost:{port}/health" + start = time.time() + while True: + try: + if requests.get(health_url).status_code == 200: + break + except Exception as err: + result = server.exitcode + if result is not None: + raise RuntimeError("Server exited unexpectedly.") from err + + time.sleep(0.5) + if time.time() - start > MAX_SERVER_START_WAIT_S: + raise RuntimeError("Server failed to start in time.") from err + + try: + yield client + finally: + server.kill() + + [email protected](scope="session") +def base64_encoded_audio() -> Dict[str, str]: + return { + audio_url: encode_audio_base64(*fetch_audio(audio_url)) + for audio_url in TEST_AUDIO_URLS + } + + [email protected] [email protected]("model_name", [MODEL_NAME]) [email protected]("audio_url", TEST_AUDIO_URLS) +async def test_single_chat_session_audio(client: openai.AsyncOpenAI, + model_name: str, audio_url: str): + messages = [{ + "role": + "user", + "content": [ + { + "type": "audio_url", + "audio_url": { + "url": audio_url + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + # test single completion + chat_completion = await client.chat.completions.create(model=model_name, + messages=messages, + max_tokens=10, + logprobs=True, + top_logprobs=5) + assert len(chat_completion.choices) == 1 + + choice = chat_completion.choices[0] + assert choice.finish_reason == "length" + assert chat_completion.usage == openai.types.CompletionUsage( + completion_tokens=10, prompt_tokens=36, total_tokens=46) + + message = choice.message + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 10 + assert message.role == "assistant" + messages.append({"role": "assistant", "content": message.content}) + + # test multi-turn dialogue + messages.append({"role": "user", "content": "express your result in json"}) + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_tokens=10, + ) + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 0 + + [email protected] [email protected]("model_name", [MODEL_NAME]) [email protected]("audio_url", TEST_AUDIO_URLS) +async def test_single_chat_session_audio_base64encoded( + client: openai.AsyncOpenAI, model_name: str, audio_url: str, + base64_encoded_audio: Dict[str, str]): + + messages = [{ + "role": + "user", + "content": [ + { + "type": "audio_url", + "audio_url": { + "url": + f"data:audio/wav;base64,{base64_encoded_audio[audio_url]}" + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + # test single completion + chat_completion = await client.chat.completions.create(model=model_name, + messages=messages, + max_tokens=10, + logprobs=True, + top_logprobs=5) + assert len(chat_completion.choices) == 1 + + choice = chat_completion.choices[0] + assert choice.finish_reason == "length" + assert chat_completion.usage == openai.types.CompletionUsage( + completion_tokens=10, prompt_tokens=36, total_tokens=46) + + message = choice.message + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 10 + assert message.role == "assistant" + messages.append({"role": "assistant", "content": message.content}) + + # test multi-turn dialogue + messages.append({"role": "user", "content": "express your result in json"}) + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_tokens=10, + ) + message = chat_completion.choices[0].message + assert message.content is not None and len(message.content) >= 0 + + [email protected] [email protected]("model_name", [MODEL_NAME]) [email protected]("audio_url", TEST_AUDIO_URLS) +async def test_chat_streaming_audio(client: openai.AsyncOpenAI, + model_name: str, audio_url: str): + messages = [{ + "role": + "user", + "content": [ + { + "type": "audio_url", + "audio_url": { + "url": audio_url + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + # test single completion + chat_completion = await client.chat.completions.create( + model=model_name, + messages=messages, + max_tokens=10, + temperature=0.0, + ) + output = chat_completion.choices[0].message.content + stop_reason = chat_completion.choices[0].finish_reason + + # test streaming + stream = await client.chat.completions.create( + model=model_name, + messages=messages, + max_tokens=10, + temperature=0.0, + stream=True, + ) + chunks: List[str] = [] + finish_reason_count = 0 + async for chunk in stream: + delta = chunk.choices[0].delta + if delta.role: + assert delta.role == "assistant" + if delta.content: + chunks.append(delta.content) + if chunk.choices[0].finish_reason is not None: + finish_reason_count += 1 + # finish reason should only return in last block + assert finish_reason_count == 1 + assert chunk.choices[0].finish_reason == stop_reason + assert delta.content + assert "".join(chunks) == output + + [email protected] [email protected]("model_name", [MODEL_NAME]) [email protected]("audio_url", TEST_AUDIO_URLS) +async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str, + audio_url: str): + + messages = [{ + "role": + "user", + "content": [ + { + "type": "audio_url", + "audio_url": { + "url": audio_url + } + }, + { + "type": "audio_url", + "audio_url": { + "url": audio_url + } + }, + { + "type": "text", + "text": "What's happening in this audio?" + }, + ], + }] + + with pytest.raises(openai.BadRequestError): # test multi-audio input + await client.chat.completions.create( + model=model_name, + messages=messages, + max_tokens=10, + temperature=0.0, + ) + + # the server should still work afterwards + completion = await client.completions.create( + model=model_name, + prompt=[0, 0, 0, 0, 0], + max_tokens=5, + temperature=0.0, + ) + completion = completion.choices[0].text + assert completion is not None and len(completion) >= 0 diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index 1197c70d88ae..4a0b0f879e8e 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -2,7 +2,8 @@ from dataclasses import dataclass from functools import lru_cache from pathlib import Path -from typing import Any, Awaitable, Iterable, List, Optional, Tuple, Union, cast +from typing import (Any, Awaitable, Iterable, List, Literal, Optional, Tuple, + Union, cast) # yapf conflicts with isort for this block # yapf: disable @@ -21,12 +22,27 @@ from vllm.config import ModelConfig from vllm.logger import init_logger from vllm.multimodal import MultiModalDataDict -from vllm.multimodal.utils import async_get_and_parse_image +from vllm.multimodal.utils import (async_get_and_parse_audio, + async_get_and_parse_image) from vllm.transformers_utils.tokenizer import AnyTokenizer logger = init_logger(__name__) +class AudioURL(TypedDict, total=False): + url: Required[str] + """ + Either a URL of the audio or a data URL with base64 encoded audio data. + """ + + +class ChatCompletionContentPartAudioParam(TypedDict, total=False): + audio_url: Required[AudioURL] + + type: Required[Literal["audio_url"]] + """The type of the content part.""" + + class CustomChatCompletionContentPartParam(TypedDict, total=False): __pydantic_config__ = ConfigDict(extra="allow") # type: ignore @@ -35,6 +51,7 @@ class CustomChatCompletionContentPartParam(TypedDict, total=False): ChatCompletionContentPartParam = Union[OpenAIChatCompletionContentPartParam, + ChatCompletionContentPartAudioParam, CustomChatCompletionContentPartParam] @@ -97,34 +114,41 @@ def load_chat_template( @lru_cache(maxsize=None) -def _image_token_str(model_config: ModelConfig, - tokenizer: PreTrainedTokenizer) -> Optional[str]: +def _mm_token_str(model_config: ModelConfig, tokenizer: PreTrainedTokenizer, + modality: Literal["image", "audio"]) -> Optional[str]: # TODO: Let user specify how to insert image tokens into prompt # (similar to chat template) - model_type = model_config.hf_config.model_type - if model_type == "phi3_v": - # Workaround since this token is not defined in the tokenizer - return "<|image_1|>" - if model_type == "minicpmv": - return "(<image>./</image>)" - if model_type in ("blip-2", "chatglm", "fuyu", "paligemma"): - # These models do not use image tokens in the prompt - return None - if model_type.startswith("llava"): - return tokenizer.decode(model_config.hf_config.image_token_index) - if model_type in ("chameleon", "internvl_chat"): - return "<image>" - raise TypeError(f"Unknown model type: {model_type}") - - -# TODO: Let user specify how to insert image tokens into prompt + if modality == "image": + model_type = model_config.hf_config.model_type + if model_type == "phi3_v": + # Workaround since this token is not defined in the tokenizer + return "<|image_1|>" + if model_type == "minicpmv": + return "(<image>./</image>)" + if model_type in ("blip-2", "chatglm", "fuyu", "paligemma"): + # These models do not use image tokens in the prompt + return None + if model_type.startswith("llava"): + return tokenizer.decode(model_config.hf_config.image_token_index) + if model_type in ("chameleon", "internvl_chat"): + return "<image>" + + raise TypeError(f"Unknown model type: {model_type}") + elif modality == "audio": + raise TypeError("No audio models are supported yet.") + else: + raise TypeError(f"Unknown modality: {modality}") + + +# TODO: Let user specify how to insert multimodal tokens into prompt # (similar to chat template) -def _get_full_image_text_prompt(image_token_str: str, text_prompt: str) -> str: - """Combine image and text prompts for vision language model""" +def _get_full_multimodal_text_prompt(placeholder_token_str: str, + text_prompt: str) -> str: + """Combine multimodal prompts for a multimodal language model""" # NOTE: For now we assume all model architectures use the same - # image + text prompt format. This may change in the future. - return f"{image_token_str}\n{text_prompt}" + # placeholder + text prompt format. This may change in the future. + return f"{placeholder_token_str}\n{text_prompt}" def _parse_chat_message_content_parts( @@ -135,6 +159,7 @@ def _parse_chat_message_content_parts( ) -> ChatMessageParseResult: texts: List[str] = [] mm_futures: List[Awaitable[MultiModalDataDict]] = [] + modality: Literal["image", "audio"] = "image" for part in parts: part_type = part["type"] @@ -142,9 +167,10 @@ def _parse_chat_message_content_parts( text = cast(ChatCompletionContentPartTextParam, part)["text"] texts.append(text) elif part_type == "image_url": + modality = "image" if len(mm_futures) > 0: raise NotImplementedError( - "Multiple 'image_url' input is currently not supported.") + "Multiple multimodal inputs is currently not supported.") image_url = cast(ChatCompletionContentPartImageParam, part)["image_url"] @@ -156,21 +182,32 @@ def _parse_chat_message_content_parts( image_future = async_get_and_parse_image(image_url["url"]) mm_futures.append(image_future) + elif part_type == "audio_url": + modality = "audio" + if len(mm_futures) > 0: + raise NotImplementedError( + "Multiple multimodal inputs is currently not supported.") + + audio_url = cast(ChatCompletionContentPartAudioParam, + part)["audio_url"] + audio_future = async_get_and_parse_audio(audio_url["url"]) + mm_futures.append(audio_future) else: raise NotImplementedError(f"Unknown part type: {part_type}") text_prompt = "\n".join(texts) if mm_futures: - image_token_str = _image_token_str(model_config, tokenizer) - if image_token_str is not None: - if image_token_str in text_prompt: + placeholder_token_str = _mm_token_str(model_config, tokenizer, + modality) + if placeholder_token_str is not None: + if placeholder_token_str in text_prompt: logger.warning( - "Detected image token string in the text prompt. " + "Detected multi-modal token string in the text prompt. " "Skipping prompt formatting.") else: - text_prompt = _get_full_image_text_prompt( - image_token_str=image_token_str, + text_prompt = _get_full_multimodal_text_prompt( + placeholder_token_str=placeholder_token_str, text_prompt=text_prompt, ) diff --git a/vllm/envs.py b/vllm/envs.py index 26d0c33707fe..ca8ec96d07aa 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -44,6 +44,7 @@ VLLM_WORKER_MULTIPROC_METHOD: str = "fork" VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets") VLLM_IMAGE_FETCH_TIMEOUT: int = 5 + VLLM_AUDIO_FETCH_TIMEOUT: int = 5 VLLM_TARGET_DEVICE: str = "cuda" MAX_JOBS: Optional[str] = None NVCC_THREADS: Optional[str] = None @@ -321,6 +322,11 @@ def get_default_config_root(): "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")), + # Timeout for fetching audio when serving multimodal models + # Default is 5 seconds + "VLLM_AUDIO_FETCH_TIMEOUT": + lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "5")), + # Path to the XLA persistent cache directory. # Only used for XLA devices such as TPUs. "VLLM_XLA_CACHE_PATH": diff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py index ba9c8af88f86..a2181db38e04 100644 --- a/vllm/model_executor/model_loader/loader.py +++ b/vllm/model_executor/model_loader/loader.py @@ -38,7 +38,7 @@ safetensors_weights_iterator) from vllm.model_executor.models.interfaces import (has_inner_state, supports_lora, - supports_vision) + supports_multimodal) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.utils import is_pin_memory_available, is_tpu @@ -131,7 +131,7 @@ def _get_model_initialization_kwargs( "be added in the future. If this is important to you, " "please open an issue on github.") - if supports_vision(model_class): + if supports_multimodal(model_class): if multimodal_config is None: raise ValueError("Provide vision related configurations " "through LLM entrypoint or engine arguments.") diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index 084cbf35533b..82a893e1aba7 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -20,8 +20,8 @@ from .blip import (BlipVisionModel, dummy_image_for_blip, get_max_blip_image_tokens) -from .interfaces import SupportsVision -from .utils import merge_vision_embeddings +from .interfaces import SupportsMultiModal +from .utils import merge_multimodal_embeddings _KEYS_TO_MODIFY_MAPPING = { "language_model.lm_head": "lm_head", @@ -457,7 +457,7 @@ def input_processor_for_blip2(ctx: InputContext, llm_inputs: LLMInputs): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_blip2_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_blip2) @INPUT_REGISTRY.register_input_processor(input_processor_for_blip2) -class Blip2ForConditionalGeneration(nn.Module, SupportsVision): +class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__(self, config: Blip2Config, @@ -621,9 +621,9 @@ def forward( vision_embeddings = self._process_image_input(image_input) inputs_embeds = self.language_model.get_input_embeddings(input_ids) - inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, - vision_embeddings, - BLIP2_IMAGE_TOKEN_ID) + inputs_embeds = merge_multimodal_embeddings( + input_ids, inputs_embeds, vision_embeddings, + BLIP2_IMAGE_TOKEN_ID) input_ids = None else: diff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py index 10a82207d90e..22d861f6e85f 100644 --- a/vllm/model_executor/models/chameleon.py +++ b/vllm/model_executor/models/chameleon.py @@ -33,7 +33,7 @@ from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData from vllm.utils import print_warning_once -from .interfaces import SupportsVision +from .interfaces import SupportsMultiModal logger = init_logger(__name__) @@ -877,7 +877,7 @@ def forward( @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_chameleon_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_chameleon) @INPUT_REGISTRY.register_input_processor(input_processor_for_chameleon) -class ChameleonForConditionalGeneration(nn.Module, SupportsVision): +class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__( self, diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py index bb49349e7954..c08b3ae3afe8 100644 --- a/vllm/model_executor/models/fuyu.py +++ b/vllm/model_executor/models/fuyu.py @@ -40,8 +40,8 @@ cached_get_tokenizer) from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData -from .interfaces import SupportsVision -from .utils import merge_vision_embeddings +from .interfaces import SupportsMultiModal +from .utils import merge_multimodal_embeddings logger = init_logger(__name__) @@ -209,7 +209,7 @@ def input_mapper_for_fuyu(ctx: InputContext, data: object): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_fuyu_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_fuyu) @INPUT_REGISTRY.register_input_processor(input_processor_for_fuyu) -class FuyuForCausalLM(nn.Module, SupportsVision): +class FuyuForCausalLM(nn.Module, SupportsMultiModal): def __init__(self, config: FuyuConfig, @@ -271,9 +271,9 @@ def forward( if image_input is not None: vision_embeddings = self._process_image_input(image_input) inputs_embeds = self.language_model.model.embed_tokens(input_ids) - inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, - vision_embeddings, - self.image_token_id) + inputs_embeds = merge_multimodal_embeddings( + input_ids, inputs_embeds, vision_embeddings, + self.image_token_id) else: inputs_embeds = None diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py index db0d6b429d64..2f323ea552cc 100644 --- a/vllm/model_executor/models/interfaces.py +++ b/vllm/model_executor/models/interfaces.py @@ -10,12 +10,15 @@ @runtime_checkable -class SupportsVision(Protocol): - """The interface required for all vision language models (VLMs).""" +class SupportsMultiModal(Protocol): + """ + The interface required for all multimodal (vision or audio) language + models. + """ - supports_vision: ClassVar[Literal[True]] = True + supports_multimodal: ClassVar[Literal[True]] = True """ - A flag that indicates this model supports vision inputs. + A flag that indicates this model supports multimodal inputs. Note: There is no need to redefine this flag if this class is in the @@ -29,30 +32,31 @@ def __init__(self, *, multimodal_config: MultiModalConfig) -> None: # We can't use runtime_checkable with ClassVar for issubclass checks # so we need to treat the class as an instance and use isinstance instead @runtime_checkable -class _SupportsVisionType(Protocol): - supports_vision: Literal[True] +class _SupportsMultiModalType(Protocol): + supports_multimodal: Literal[True] def __call__(self, *, multimodal_config: MultiModalConfig) -> None: ... @overload -def supports_vision(model: Type[object]) -> TypeIs[Type[SupportsVision]]: +def supports_multimodal( + model: Type[object]) -> TypeIs[Type[SupportsMultiModal]]: ... @overload -def supports_vision(model: object) -> TypeIs[SupportsVision]: +def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]: ... -def supports_vision( +def supports_multimodal( model: Union[Type[object], object], -) -> Union[TypeIs[Type[SupportsVision]], TypeIs[SupportsVision]]: +) -> Union[TypeIs[Type[SupportsMultiModal]], TypeIs[SupportsMultiModal]]: if isinstance(model, type): - return isinstance(model, _SupportsVisionType) + return isinstance(model, _SupportsMultiModalType) - return isinstance(model, SupportsVision) + return isinstance(model, SupportsMultiModal) @runtime_checkable diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index 26c02d46a18e..406b89017a10 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -27,9 +27,9 @@ from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip, get_clip_num_patches) -from .interfaces import SupportsVision +from .interfaces import SupportsMultiModal from .utils import (filter_weights, init_vllm_registered_model, - merge_vision_embeddings) + merge_multimodal_embeddings) IMG_START = '<img>' IMG_END = '</img>' @@ -292,7 +292,7 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_internvl_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_internvl) @INPUT_REGISTRY.register_input_processor(input_processor_for_internvl) -class InternVLChatModel(nn.Module, SupportsVision): +class InternVLChatModel(nn.Module, SupportsMultiModal): def __init__(self, config: PretrainedConfig, @@ -451,9 +451,9 @@ def forward( inputs_embeds = self.language_model.model.get_input_embeddings( input_ids) vision_embeddings = self._process_image_input(image_input) - inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, - vision_embeddings, - self.img_context_token_id) + inputs_embeds = merge_multimodal_embeddings( + input_ids, inputs_embeds, vision_embeddings, + self.img_context_token_id) input_ids = None else: inputs_embeds = None diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 0ff68943b510..f95f3ce76cfc 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -19,12 +19,12 @@ from .clip import (CLIPVisionModel, dummy_image_for_clip, dummy_seq_data_for_clip, get_max_clip_image_tokens, input_processor_for_clip) -from .interfaces import SupportsVision +from .interfaces import SupportsMultiModal from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_max_siglip_image_tokens, input_processor_for_siglip) from .utils import (filter_weights, init_vllm_registered_model, - merge_vision_embeddings) + merge_multimodal_embeddings) class LlavaImagePixelInputs(TypedDict): @@ -181,7 +181,7 @@ def _init_vision_tower(hf_config: LlavaConfig): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava) @INPUT_REGISTRY.register_input_processor(input_processor_for_llava) -class LlavaForConditionalGeneration(nn.Module, SupportsVision): +class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__(self, config: LlavaConfig, @@ -338,7 +338,7 @@ def forward( inputs_embeds = self.language_model.model.get_input_embeddings( input_ids) - inputs_embeds = merge_vision_embeddings( + inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, vision_embeddings, self.config.image_token_index) diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index d94af966162f..6a481e628724 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -23,13 +23,13 @@ from .clip import (CLIPVisionModel, dummy_image_for_clip, dummy_seq_data_for_clip, get_clip_image_feature_size, get_clip_patch_grid_length, input_processor_for_clip) -from .interfaces import SupportsVision +from .interfaces import SupportsMultiModal from .llava import LlavaMultiModalProjector from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_siglip_image_feature_size, get_siglip_patch_grid_length, input_processor_for_siglip) from .utils import (filter_weights, init_vllm_registered_model, - merge_vision_embeddings) + merge_multimodal_embeddings) logger = init_logger(__name__) @@ -275,7 +275,7 @@ def _init_vision_tower(hf_config: LlavaNextConfig): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_next_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next) @INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next) -class LlavaNextForConditionalGeneration(nn.Module, SupportsVision): +class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__(self, config: LlavaNextConfig, @@ -571,7 +571,7 @@ def forward( inputs_embeds = self.language_model.model.get_input_embeddings( input_ids) - inputs_embeds = merge_vision_embeddings( + inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, vision_embeddings, self.config.image_token_index) diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py index fc962434cab0..703f6e58248c 100644 --- a/vllm/model_executor/models/minicpmv.py +++ b/vllm/model_executor/models/minicpmv.py @@ -48,7 +48,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.interfaces import SupportsVision +from vllm.model_executor.models.interfaces import SupportsMultiModal from vllm.model_executor.models.llama import LlamaModel from vllm.model_executor.models.minicpm import MiniCPMModel from vllm.model_executor.models.qwen2 import Qwen2Model @@ -479,7 +479,7 @@ def get_placeholder(image_size: Tuple[int, int], num_image: int): return llm_inputs -class MiniCPMVBaseModel(nn.Module, SupportsVision): +class MiniCPMVBaseModel(nn.Module, SupportsMultiModal): """ The abstract class of MiniCPMV can only be inherited, but cannot be instantiated. diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index c6d59db643bb..ed88326cf61b 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -19,10 +19,10 @@ from vllm.multimodal.image import cached_get_tokenizer from vllm.sequence import IntermediateTensors, SamplerOutput -from .interfaces import SupportsVision +from .interfaces import SupportsMultiModal from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_max_siglip_image_tokens) -from .utils import merge_vision_embeddings +from .utils import merge_multimodal_embeddings logger = init_logger(__name__) @@ -130,7 +130,7 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor: @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_paligemma_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma) @INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma) -class PaliGemmaForConditionalGeneration(nn.Module, SupportsVision): +class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__(self, config: PaliGemmaConfig, @@ -244,7 +244,7 @@ def forward(self, inputs_embeds = self.language_model.get_input_embeddings(input_ids) - inputs_embeds = merge_vision_embeddings( + inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, vision_embeddings, self.config.image_token_index) diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index 51d3a75ea6ff..6bbe4f977adc 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -42,8 +42,8 @@ from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip, input_processor_for_clip) -from .interfaces import SupportsVision -from .utils import merge_vision_embeddings +from .interfaces import SupportsMultiModal +from .utils import merge_multimodal_embeddings logger = init_logger(__name__) @@ -453,7 +453,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_phi3v) @INPUT_REGISTRY.register_input_processor(input_processor_for_phi3v) -class Phi3VForCausalLM(nn.Module, SupportsVision): +class Phi3VForCausalLM(nn.Module, SupportsMultiModal): def __init__(self, config: PretrainedConfig, @@ -568,9 +568,9 @@ def forward(self, if image_input is not None: vision_embeddings = self._process_image_input(image_input) inputs_embeds = self.model.get_input_embeddings(input_ids) - inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, - vision_embeddings, - self.image_token_id) + inputs_embeds = merge_multimodal_embeddings( + input_ids, inputs_embeds, vision_embeddings, + self.image_token_id) input_ids = None else: inputs_embeds = None diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index d1bb030c6c90..91b414b1fd91 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -54,41 +54,42 @@ def init_vllm_registered_model( ) -def merge_vision_embeddings(input_ids: torch.Tensor, - inputs_embeds: torch.Tensor, - vision_embeddings: BatchedTensors, - image_token_id: int) -> torch.Tensor: +def merge_multimodal_embeddings(input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, + multimodal_embeddings: BatchedTensors, + placeholder_token_id: int) -> torch.Tensor: """ - Merge ``vision_embeddings`` into ``inputs_embeds`` by overwriting the - positions in ``inputs_embeds`` corresponding to placeholder image tokens in + Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the + positions in ``inputs_embeds`` corresponding to placeholder tokens in ``input_ids``. Note: This updates ``inputs_embeds`` in place. """ - mask = (input_ids == image_token_id) + mask = (input_ids == placeholder_token_id) num_expected_tokens = mask.sum() - if isinstance(vision_embeddings, torch.Tensor): - batch_size, batch_tokens, *_, embed_dim = vision_embeddings.shape + if isinstance(multimodal_embeddings, torch.Tensor): + batch_size, batch_tokens, *_, embed_dim = multimodal_embeddings.shape total_tokens = batch_size * batch_tokens if num_expected_tokens != total_tokens: expr = f"{batch_size} x {batch_tokens}" raise ValueError( f"Attempted to assign {expr} = {total_tokens} " - f"image tokens to {num_expected_tokens} placeholders") + f"multimodal tokens to {num_expected_tokens} placeholders") - inputs_embeds[mask] = vision_embeddings.view(total_tokens, embed_dim) + inputs_embeds[mask] = multimodal_embeddings.view( + total_tokens, embed_dim) else: - size_per_batch = [t.shape[0] for t in vision_embeddings] + size_per_batch = [t.shape[0] for t in multimodal_embeddings] total_tokens = sum(size_per_batch) if num_expected_tokens != total_tokens: expr = ' + '.join(map(str, size_per_batch)) raise ValueError( f"Attempted to assign {expr} = {total_tokens} " - f"image tokens to {num_expected_tokens} placeholders") + f"multimodal tokens to {num_expected_tokens} placeholders") - inputs_embeds[mask] = torch.cat(vision_embeddings) + inputs_embeds[mask] = torch.cat(multimodal_embeddings) return inputs_embeds diff --git a/vllm/multimodal/audio.py b/vllm/multimodal/audio.py new file mode 100644 index 000000000000..b4bf4b4541db --- /dev/null +++ b/vllm/multimodal/audio.py @@ -0,0 +1,17 @@ +from vllm.inputs.registry import InputContext +from vllm.multimodal.base import MultiModalInputs, MultiModalPlugin + + +class AudioPlugin(MultiModalPlugin): + """Plugin for audio data.""" + + def get_data_key(self) -> str: + return "audio" + + def _default_input_mapper(self, ctx: InputContext, + data: object) -> MultiModalInputs: + raise NotImplementedError("There is no default audio input mapper") + + def _default_max_multimodal_tokens(self, ctx: InputContext) -> int: + raise NotImplementedError( + "There is no default maximum multimodal tokens") diff --git a/vllm/multimodal/base.py b/vllm/multimodal/base.py index aefb5f438c5a..7717d77198a1 100644 --- a/vllm/multimodal/base.py +++ b/vllm/multimodal/base.py @@ -3,8 +3,9 @@ from collections import UserDict, defaultdict from typing import Any, Callable, Dict, List, Optional from typing import Sequence as GenericSequence -from typing import Type, TypedDict, TypeVar, Union, cast +from typing import Tuple, Type, TypedDict, TypeVar, Union, cast +import numpy as np import torch import torch.types from PIL import Image @@ -121,6 +122,9 @@ class MultiModalDataBuiltins(TypedDict, total=False): image: Image.Image """The input image.""" + audio: Tuple[np.ndarray, Union[int, float]] + """The input audio and its sampling rate.""" + MultiModalDataDict = Union[MultiModalDataBuiltins, Dict[str, Any]] """ diff --git a/vllm/multimodal/registry.py b/vllm/multimodal/registry.py index d8e1b68178ac..19c26123c2df 100644 --- a/vllm/multimodal/registry.py +++ b/vllm/multimodal/registry.py @@ -6,6 +6,7 @@ from vllm.config import ModelConfig from vllm.logger import init_logger +from .audio import AudioPlugin from .base import (MultiModalDataDict, MultiModalInputMapper, MultiModalInputs, MultiModalPlugin, MultiModalTokensCalc) from .image import ImagePlugin @@ -19,7 +20,7 @@ class MultiModalRegistry: :class:`~vllm.multimodal.MultiModalPlugin` for each modality. """ - DEFAULT_PLUGINS = (ImagePlugin(), ) + DEFAULT_PLUGINS = (ImagePlugin(), AudioPlugin()) def __init__( self, diff --git a/vllm/multimodal/utils.py b/vllm/multimodal/utils.py index 8f7e613cdf90..d1e624cdb8ac 100644 --- a/vllm/multimodal/utils.py +++ b/vllm/multimodal/utils.py @@ -1,11 +1,14 @@ import base64 from io import BytesIO -from typing import Union +from typing import Tuple, Union +import librosa +import numpy as np +import soundfile from PIL import Image from vllm.connections import global_http_connection -from vllm.envs import VLLM_IMAGE_FETCH_TIMEOUT +from vllm.envs import VLLM_AUDIO_FETCH_TIMEOUT, VLLM_IMAGE_FETCH_TIMEOUT from vllm.multimodal.base import MultiModalDataDict @@ -63,11 +66,62 @@ async def async_fetch_image(image_url: str, return image.convert(image_mode) +def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]: + """ + Load audio from a URL. + """ + if audio_url.startswith("http"): + audio_bytes = global_http_connection.get_bytes( + audio_url, timeout=VLLM_AUDIO_FETCH_TIMEOUT) + elif audio_url.startswith("data:audio"): + _, audio_base64 = audio_url.split(",", 1) + audio_bytes = base64.b64decode(audio_base64) + else: + raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start " + "with either 'data:audio' or 'http'.") + + return librosa.load(BytesIO(audio_bytes), sr=None) + + +async def async_fetch_audio( + audio_url: str) -> Tuple[np.ndarray, Union[int, float]]: + """ + Asynchronously fetch audio from a URL. + """ + if audio_url.startswith("http"): + audio_bytes = await global_http_connection.async_get_bytes( + audio_url, timeout=VLLM_AUDIO_FETCH_TIMEOUT) + elif audio_url.startswith("data:audio"): + _, audio_base64 = audio_url.split(",", 1) + audio_bytes = base64.b64decode(audio_base64) + else: + raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start " + "with either 'data:audio' or 'http'.") + + return librosa.load(BytesIO(audio_bytes), sr=None) + + +async def async_get_and_parse_audio(audio_url: str) -> MultiModalDataDict: + audio, sr = await async_fetch_audio(audio_url) + return {"audio": (audio, sr)} + + async def async_get_and_parse_image(image_url: str) -> MultiModalDataDict: image = await async_fetch_image(image_url) return {"image": image} +def encode_audio_base64( + audio: np.ndarray, + sampling_rate: int, +) -> str: + """Encode audio as base64.""" + buffered = BytesIO() + soundfile.write(buffered, audio, sampling_rate, format="WAV") + + return base64.b64encode(buffered.getvalue()).decode('utf-8') + + def encode_image_base64( image: Image.Image, *, diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index cfbbb6698cd8..a4ce1b512dd0 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -40,7 +40,7 @@ from vllm.model_executor.model_loader import get_model from vllm.model_executor.model_loader.tensorizer import TensorizerConfig from vllm.model_executor.models.interfaces import (supports_lora, - supports_vision) + supports_multimodal) from vllm.model_executor.models.utils import set_cpu_offload_max_bytes from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs, MultiModalInputs) @@ -900,9 +900,9 @@ def load_model(self) -> None: if self.lora_config: assert supports_lora(self.model), "Model does not support LoRA" - assert not supports_vision( + assert not supports_multimodal( self.model - ), "To be tested: vision language model with LoRA settings." + ), "To be tested: multimodal language model with LoRA settings." self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, @@ -1054,7 +1054,7 @@ def profile_run(self) -> None: # of images processed. model_config = self.model_config - if supports_vision(self.model): + if supports_multimodal(self.model): max_mm_tokens = MULTIMODAL_REGISTRY \ .get_max_multimodal_tokens(model_config) max_num_seqs_orig = max_num_seqs diff --git a/vllm/worker/xpu_model_runner.py b/vllm/worker/xpu_model_runner.py index 112e494faded..a1e1c1bef633 100644 --- a/vllm/worker/xpu_model_runner.py +++ b/vllm/worker/xpu_model_runner.py @@ -12,7 +12,7 @@ from vllm.inputs import INPUT_REGISTRY from vllm.logger import init_logger from vllm.model_executor.model_loader import get_model -from vllm.model_executor.models.interfaces import supports_vision +from vllm.model_executor.models.interfaces import supports_multimodal from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs, MultiModalInputs) from vllm.sampling_params import SamplingParams @@ -165,7 +165,7 @@ def profile_run(self) -> None: # of images processed. model_config = self.model_config - if supports_vision(self.model): + if supports_multimodal(self.model): max_mm_tokens = MULTIMODAL_REGISTRY \ .get_max_multimodal_tokens(model_config) max_num_seqs_orig = max_num_seqs
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
vllm-project__vllm-7562@5b734fb
vllm-project/vllm
Python
7,562
[Bugfix] neuron: enable tensor parallelism
FILL IN THE PR DESCRIPTION HERE FIX #6269 Following #7175, this enables tensor parallelism on neuron with vLLM > 0.5.0. The `block-size` choices are also reverted back to the previous values and vLLM will take the `max-model-len` as the `block-size` on neuron devices.
2024-08-15T17:29:40Z
f[Bug]: TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker ### Your current environment ```text The output of `python collect_env.py` ``` Collecting environment information... WARNING 07-09 19:46:51 _custom_ops.py:14] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'") PyTorch version: 2.1.2+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.30.0 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-1031-aws-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R13 Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 1 BogoMIPS: 5299.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 8 MiB (16 instances) L3 cache: 64 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-7,16-23 NUMA node1 CPU(s): 8-15,24-31 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.25.2 [pip3] nvidia-nccl-cu12==2.18.1 [pip3] torch==2.1.2 [pip3] torch-neuronx==2.1.2.2.2.0 [pip3] torch-xla==2.1.3 [pip3] torchvision==0.16.2 [pip3] transformers==4.42.3 [pip3] transformers-neuronx==0.11.351 [pip3] triton==2.1.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: (0, 'instance-type: inf2.8xlarge\ninstance-id: i-0d1255be16fb43afc\n+--------+--------+--------+---------+\n| NEURON | NEURON | NEURON | PCI |\n| DEVICE | CORES | MEMORY | BDF |\n+--------+--------+--------+---------+\n| 0 | 2 | 32 GB | 00:1f.0 |\n+--------+--------+--------+---------+', '') vLLM Version: 0.5.1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: Could not collect ### ๐Ÿ› Describe the bug Offline Batched Inference Example from your Quickstart page, running on Neuron inf2 (AWS inf2.8xlarge) returns: "TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker". The only change from your example is the example code is adding device="neuron" argument to LLM. This code should reproduce the error: ------------------------------------------ from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="facebook/opt-125m",device="neuron") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
I still get this error with the latest https://github.com/vllm-project/vllm/pull/6313 merge. ``` Traceback (most recent call last): File "/home/ubuntu/vllm/simple_test.py", line 11, in <module> llm = LLM(model="facebook/opt-125m",device="neuron") File "/home/ubuntu/vllm/vllm/entrypoints/llm.py", line 150, in __init__ self.llm_engine = LLMEngine.from_engine_args( File "/home/ubuntu/vllm/vllm/engine/llm_engine.py", line 421, in from_engine_args engine = cls( File "/home/ubuntu/vllm/vllm/engine/llm_engine.py", line 249, in __init__ self.model_executor = executor_class( File "/home/ubuntu/vllm/vllm/executor/executor_base.py", line 46, in __init__ self._init_executor() File "/home/ubuntu/vllm/vllm/executor/neuron_executor.py", line 21, in _init_executor self._init_worker() File "/home/ubuntu/vllm/vllm/executor/neuron_executor.py", line 26, in _init_worker self.driver_worker = NeuronWorker( TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker ``` @liangfu Could you please take a look at @jianyinglangaws's comment? Saw that the patch to #6313 was merged to main, so I did a git pull origin main to update to the latest. I can confirm that the behavior seen in #6269 is still present, namely: TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker I posted this info on #6313 as well because it was speculated that #6313 would fix #6269. I am beginning to wonder about the status of support for the Neuron architecture in vLLM. Is it vLLM's intention to drop support for Neuron? I only ask this because the vLLM Documentation suggests that at least in V 0.3 vLLM did support neuron. Now, of course, those quickstart examples do not work. Any information regarding the direction vLLM is headed relative to Neuron would be appreciated. One extra question: can you recommend a previous known-working version of vLLM that we can pull that would work on Neuron and can you provide a tag for that? I tried to clone and install vLLM v0.3.0 but the requirements for that include pkg_resources which has been deprecated. The vllm 0.5.0 works for me with the latest Neuron SDK 2.19.0 and transformers-neuronx. > The vllm 0.5.0 works for me with the latest Neuron SDK 2.19.0 and transformers-neuronx. That's very interesting to know. So AFAIK, the DLAMI instance one can get by default from AWS in 2.18.2. How are you accessing 2.19.0? How do I install/upgrade to it? The default Neuron DLAMI (Deep Learning AMI Neuron (Ubuntu 22.04) 20240703) was updated to 2.19.0 last week. I see, thank you - I will try to use a new instance. I installed vllm on DLAMI 2.19.0 following these procedures (steps 2 and 3) VERBATIM : https://docs.vllm.ai/en/latest/getting_started/neuron-installation.html. The example does not work with the same error with the Quickstart example. So I guess we need to understand how your working instance on 2.19.0 is differing from my broken? Can you provide your collect_env.py output? Or do you want me to provide mine? > The vllm 0.5.0 works for me with the latest Neuron SDK 2.19.0 and transformers-neuronx.
[ { "body": "### Your current environment\n\n```text\r\nThe output of `python collect_env.py`\r\n```\r\nCollecting environment information...\r\nWARNING 07-09 19:46:51 _custom_ops.py:14] Failed to import from vllm._C with ModuleNotFoundError(\"No module named 'vllm._C'\")\r\nPyTorch version: 2.1.2+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.4 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.30.0\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-1031-aws-x86_64-with-glibc2.35\r\nIs CUDA available: False\r\nCUDA runtime version: No CUDA\r\nCUDA_MODULE_LOADING set to: N/A\r\nGPU models and configuration: No CUDA\r\nNvidia driver version: No CUDA\r\ncuDNN version: No CUDA\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 48 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 32\r\nOn-line CPU(s) list: 0-31\r\nVendor ID: AuthenticAMD\r\nModel name: AMD EPYC 7R13 Processor\r\nCPU family: 25\r\nModel: 1\r\nThread(s) per core: 2\r\nCore(s) per socket: 16\r\nSocket(s): 1\r\nStepping: 1\r\nBogoMIPS: 5299.99\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\r\nHypervisor vendor: KVM\r\nVirtualization type: full\r\nL1d cache: 512 KiB (16 instances)\r\nL1i cache: 512 KiB (16 instances)\r\nL2 cache: 8 MiB (16 instances)\r\nL3 cache: 64 MiB (2 instances)\r\nNUMA node(s): 2\r\nNUMA node0 CPU(s): 0-7,16-23\r\nNUMA node1 CPU(s): 8-15,24-31\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Not affected\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.25.2\r\n[pip3] nvidia-nccl-cu12==2.18.1\r\n[pip3] torch==2.1.2\r\n[pip3] torch-neuronx==2.1.2.2.2.0\r\n[pip3] torch-xla==2.1.3\r\n[pip3] torchvision==0.16.2\r\n[pip3] transformers==4.42.3\r\n[pip3] transformers-neuronx==0.11.351\r\n[pip3] triton==2.1.0\r\n[conda] Could not collect\r\nROCM Version: Could not collect\r\nNeuron SDK Version: (0, 'instance-type: inf2.8xlarge\\ninstance-id: i-0d1255be16fb43afc\\n+--------+--------+--------+---------+\\n| NEURON | NEURON | NEURON | PCI |\\n| DEVICE | CORES | MEMORY | BDF |\\n+--------+--------+--------+---------+\\n| 0 | 2 | 32 GB | 00:1f.0 |\\n+--------+--------+--------+---------+', '')\r\nvLLM Version: 0.5.1\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nCould not collect\r\n\n\n### ๐Ÿ› Describe the bug\n\nOffline Batched Inference Example from your Quickstart page, running on Neuron inf2 (AWS inf2.8xlarge) returns: \"TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker\". The only change from your example is the example code is adding device=\"neuron\" argument to LLM. \r\n\r\nThis code should reproduce the error:\r\n------------------------------------------\r\nfrom vllm import LLM, SamplingParams\r\n\r\nprompts = [\r\n \"Hello, my name is\",\r\n \"The president of the United States is\",\r\n \"The capital of France is\",\r\n \"The future of AI is\",\r\n]\r\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\r\n\r\nllm = LLM(model=\"facebook/opt-125m\",device=\"neuron\")\r\noutputs = llm.generate(prompts, sampling_params)\r\n\r\n# Print the outputs.\r\nfor output in outputs:\r\n prompt = output.prompt\r\n generated_text = output.outputs[0].text\r\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\r\n", "number": 6269, "title": "f[Bug]: TypeError: Can't instantiate abstract class NeuronWorker with abstract method execute_worker" } ]
2deb029d115dadd012ce5ea70487a207cb025493
{ "head_commit": "5b734fb7edfdf3f8a836a3ddee81eba506230fdd", "head_commit_message": "block_size should equal max_model_len on neuron, not max_num_seqs\n\nSigned-off-by: omrishiv <[email protected]>", "patch_to_review": "diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py\nindex 48d01fcfd8f..5a490863631 100644\n--- a/vllm/engine/arg_utils.py\n+++ b/vllm/engine/arg_utils.py\n@@ -316,9 +316,10 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:\n parser.add_argument('--block-size',\n type=int,\n default=EngineArgs.block_size,\n- choices=[8, 16, 32, 128, 256, 512, 1024, 2048],\n+ choices=[8, 16, 32],\n help='Token block size for contiguous chunks of '\n- 'tokens.')\n+ 'tokens. This is ignored on neuron devices and '\n+ 'set to max-model-len')\n \n parser.add_argument('--enable-prefix-caching',\n action='store_true',\n@@ -780,7 +781,8 @@ def create_engine_config(self, ) -> EngineConfig:\n served_model_name=self.served_model_name,\n multimodal_config=multimodal_config)\n cache_config = CacheConfig(\n- block_size=self.block_size,\n+ block_size=self.block_size if self.device != \"neuron\" else\n+ self.max_model_len, # neuron needs block_size = max_model_len\n gpu_memory_utilization=self.gpu_memory_utilization,\n swap_space=self.swap_space,\n cache_dtype=self.kv_cache_dtype,\ndiff --git a/vllm/executor/neuron_executor.py b/vllm/executor/neuron_executor.py\nindex b45d5d86b54..02627de3e0b 100644\n--- a/vllm/executor/neuron_executor.py\n+++ b/vllm/executor/neuron_executor.py\n@@ -4,7 +4,8 @@\n from vllm.logger import init_logger\n from vllm.lora.request import LoRARequest\n from vllm.sequence import ExecuteModelRequest, SamplerOutput\n-from vllm.utils import make_async\n+from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,\n+ make_async)\n \n logger = init_logger(__name__)\n \n@@ -24,14 +25,17 @@ def _init_executor(self) -> None:\n \n def _init_worker(self):\n from vllm.worker.neuron_worker import NeuronWorker\n-\n+ distributed_init_method = get_distributed_init_method(\n+ get_ip(), get_open_port())\n self.driver_worker = NeuronWorker(\n- self.model_config,\n- self.parallel_config,\n- self.scheduler_config,\n- self.device_config,\n- self.cache_config,\n- )\n+ model_config=self.model_config,\n+ parallel_config=self.parallel_config,\n+ scheduler_config=self.scheduler_config,\n+ device_config=self.device_config,\n+ cache_config=self.cache_config,\n+ local_rank=0,\n+ rank=0,\n+ distributed_init_method=distributed_init_method)\n self.driver_worker.init_device()\n self.driver_worker.load_model()\n \ndiff --git a/vllm/worker/neuron_worker.py b/vllm/worker/neuron_worker.py\nindex 3b0ded36ca1..9b4367d761b 100644\n--- a/vllm/worker/neuron_worker.py\n+++ b/vllm/worker/neuron_worker.py\n@@ -1,4 +1,5 @@\n \"\"\"A Neuron worker class.\"\"\"\n+import os\n from typing import List, Optional, Tuple\n \n import torch\n@@ -6,6 +7,8 @@\n \n from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,\n ParallelConfig, SchedulerConfig)\n+from vllm.distributed import (ensure_model_parallel_initialized,\n+ init_distributed_environment)\n from vllm.model_executor import set_random_seed\n from vllm.sequence import ExecuteModelRequest\n from vllm.worker.neuron_model_runner import NeuronModelRunner\n@@ -24,12 +27,18 @@ def __init__(\n scheduler_config: SchedulerConfig,\n device_config: DeviceConfig,\n cache_config: CacheConfig,\n+ local_rank: int,\n+ rank: int,\n+ distributed_init_method: str,\n ) -> None:\n self.model_config = model_config\n self.parallel_config = parallel_config\n self.scheduler_config = scheduler_config\n self.device_config = device_config\n self.cache_config = cache_config\n+ self.local_rank = local_rank\n+ self.rank = rank\n+ self.distributed_init_method = distributed_init_method\n if self.model_config.trust_remote_code:\n # note: lazy import to avoid importing torch before initializing\n from vllm.utils import init_cached_hf_modules\n@@ -40,6 +49,9 @@ def __init__(\n self.is_driver_worker = True\n \n def init_device(self) -> None:\n+ os.environ[\"PJRT_DEVICE\"] = \"NEURON\"\n+ self.init_distributed_environment()\n+\n # Set random seed.\n set_random_seed(self.model_config.seed)\n \n@@ -98,3 +110,20 @@ def get_cache_block_size_bytes(self) -> int:\n This is required for speculative decoding; it is not yet implemented.\n \"\"\"\n raise NotImplementedError\n+\n+ def init_distributed_environment(self):\n+ \"\"\"Neuron uses transformers-neuronx for tensor parallelism.\n+\n+ vLLM still needs the environment inited when TP/PP > 1\n+ \"\"\"\n+ init_distributed_environment(\n+ world_size=1,\n+ rank=self.rank,\n+ local_rank=self.local_rank,\n+ distributed_init_method=self.distributed_init_method,\n+ backend=\"gloo\",\n+ )\n+ ensure_model_parallel_initialized(\n+ 1,\n+ 1,\n+ )\n" }
[ { "diff_hunk": "@@ -40,6 +49,9 @@ def __init__(\n self.is_driver_worker = True\n \n def init_device(self) -> None:\n+ os.environ[\"PJRT_DEVICE\"] = \"NEURON\"", "line": null, "original_line": 52, "original_start_line": null, "path": "vllm/worker/neuron_worker.py", "start_line": null, "text": "@user1:\nHelp take this out?" } ]
0ed98d74c14820868acb35f8467cd3f206872dbc
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 987c1be3d5ad..d759ce04d75e 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -317,9 +317,10 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, - choices=[8, 16, 32, 128, 256, 512, 1024, 2048], + choices=[8, 16, 32], help='Token block size for contiguous chunks of ' - 'tokens.') + 'tokens. This is ignored on neuron devices and ' + 'set to max-model-len') parser.add_argument('--enable-prefix-caching', action='store_true', @@ -793,7 +794,8 @@ def create_engine_config(self) -> EngineConfig: limit_mm_per_prompt=self.limit_mm_per_prompt, ) cache_config = CacheConfig( - block_size=self.block_size, + block_size=self.block_size if self.device != "neuron" else + self.max_model_len, # neuron needs block_size = max_model_len gpu_memory_utilization=self.gpu_memory_utilization, swap_space=self.swap_space, cache_dtype=self.kv_cache_dtype, diff --git a/vllm/executor/neuron_executor.py b/vllm/executor/neuron_executor.py index b45d5d86b54f..02627de3e0be 100644 --- a/vllm/executor/neuron_executor.py +++ b/vllm/executor/neuron_executor.py @@ -4,7 +4,8 @@ from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.sequence import ExecuteModelRequest, SamplerOutput -from vllm.utils import make_async +from vllm.utils import (get_distributed_init_method, get_ip, get_open_port, + make_async) logger = init_logger(__name__) @@ -24,14 +25,17 @@ def _init_executor(self) -> None: def _init_worker(self): from vllm.worker.neuron_worker import NeuronWorker - + distributed_init_method = get_distributed_init_method( + get_ip(), get_open_port()) self.driver_worker = NeuronWorker( - self.model_config, - self.parallel_config, - self.scheduler_config, - self.device_config, - self.cache_config, - ) + model_config=self.model_config, + parallel_config=self.parallel_config, + scheduler_config=self.scheduler_config, + device_config=self.device_config, + cache_config=self.cache_config, + local_rank=0, + rank=0, + distributed_init_method=distributed_init_method) self.driver_worker.init_device() self.driver_worker.load_model() diff --git a/vllm/worker/neuron_worker.py b/vllm/worker/neuron_worker.py index 3b0ded36ca1b..fff14d6402b4 100644 --- a/vllm/worker/neuron_worker.py +++ b/vllm/worker/neuron_worker.py @@ -6,6 +6,8 @@ from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig) +from vllm.distributed import (ensure_model_parallel_initialized, + init_distributed_environment) from vllm.model_executor import set_random_seed from vllm.sequence import ExecuteModelRequest from vllm.worker.neuron_model_runner import NeuronModelRunner @@ -24,12 +26,18 @@ def __init__( scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, + local_rank: int, + rank: int, + distributed_init_method: str, ) -> None: self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.cache_config = cache_config + self.local_rank = local_rank + self.rank = rank + self.distributed_init_method = distributed_init_method if self.model_config.trust_remote_code: # note: lazy import to avoid importing torch before initializing from vllm.utils import init_cached_hf_modules @@ -40,6 +48,8 @@ def __init__( self.is_driver_worker = True def init_device(self) -> None: + self.init_distributed_environment() + # Set random seed. set_random_seed(self.model_config.seed) @@ -98,3 +108,20 @@ def get_cache_block_size_bytes(self) -> int: This is required for speculative decoding; it is not yet implemented. """ raise NotImplementedError + + def init_distributed_environment(self): + """Neuron uses transformers-neuronx for tensor parallelism. + + vLLM still needs the environment inited when TP/PP > 1 + """ + init_distributed_environment( + world_size=1, + rank=self.rank, + local_rank=self.local_rank, + distributed_init_method=self.distributed_init_method, + backend="gloo", + ) + ensure_model_parallel_initialized( + 1, + 1, + )
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-6954@68ada2d
vllm-project/vllm
Python
6,954
[Bugfix] Set SamplingParams.max_tokens for OpenAI requests if not provided by user
FIX #6707 vllm/engine/output_processor/multi_step.py requires `SamplingParams.max_tokens` to be set. AFAICS, all the requests without `max_tokens` (the default) will fail if spec_decoding is turned on now. The bug is introduced in #4028 in which `_validate_prompt_and_tokenize()` is [moved](https://github.com/vllm-project/vllm/pull/4028/files#diff-f3135631994e5e8f63fff36f5fb493f404a7c253c004183613a007548156e558L122) behind `request.to_sampling_params()`. `_validate_prompt_and_tokenize()` which calls `_validate_input()` will rewrite the request and set `max_tokens`. In this PR, the `max_tokens` is set explicitly outside `_tokenize_prompt_input`. I will create another PR to allow multi step output processor accept requests without `max_tokens`. **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. 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2024-07-30T17:36:02Z
[Bug]: Engine crashes when max_tokens undefined ### Your current environment ```text PyTorch version: 2.3.1+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.30.1 Libc version: glibc-2.31 Python version: 3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB Nvidia driver version: 525.105.17 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 43 bits physical, 48 bits virtual CPU(s): 256 On-line CPU(s) list: 0-255 Thread(s) per core: 2 Core(s) per socket: 64 Socket(s): 2 NUMA node(s): 8 Vendor ID: AuthenticAMD CPU family: 23 Model: 49 Model name: AMD EPYC 7742 64-Core Processor Stepping: 0 Frequency boost: enabled CPU MHz: 3391.595 CPU max MHz: 2250.0000 CPU min MHz: 1500.0000 BogoMIPS: 4491.45 Virtualization: AMD-V L1d cache: 4 MiB L1i cache: 4 MiB L2 cache: 64 MiB L3 cache: 512 MiB NUMA node0 CPU(s): 0-15,128-143 NUMA node1 CPU(s): 16-31,144-159 NUMA node2 CPU(s): 32-47,160-175 NUMA node3 CPU(s): 48-63,176-191 NUMA node4 CPU(s): 64-79,192-207 NUMA node5 CPU(s): 80-95,208-223 NUMA node6 CPU(s): 96-111,224-239 NUMA node7 CPU(s): 112-127,240-255 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es Versions of relevant libraries: [pip3] flashinfer==0.0.9+cu121torch2.3 [pip3] numpy==1.26.4 [pip3] torch==2.3.1 [pip3] torchvision==0.18.1 [pip3] triton==2.3.1 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.3.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 CPU Affinity NUMA Affinity GPU0 X PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 48-63,176-191 3 NIC0 PXB X PXB SYS SYS SYS SYS SYS SYS SYS SYS NIC1 PXB PXB X SYS SYS SYS SYS SYS SYS SYS SYS NIC2 SYS SYS SYS X PXB SYS SYS SYS SYS SYS SYS NIC3 SYS SYS SYS PXB X SYS SYS SYS SYS SYS SYS NIC4 SYS SYS SYS SYS SYS X PXB SYS SYS SYS SYS NIC5 SYS SYS SYS SYS SYS PXB X SYS SYS SYS SYS NIC6 SYS SYS SYS SYS SYS SYS SYS X PXB SYS SYS NIC7 SYS SYS SYS SYS SYS SYS SYS PXB X SYS SYS NIC8 SYS SYS SYS SYS SYS SYS SYS SYS SYS X PIX NIC9 SYS SYS SYS SYS SYS SYS SYS SYS SYS PIX X Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks NIC Legend: NIC0: mlx5_0 NIC1: mlx5_1 NIC2: mlx5_2 NIC3: mlx5_3 NIC4: mlx5_4 NIC5: mlx5_5 NIC6: mlx5_6 NIC7: mlx5_7 NIC8: mlx5_8 NIC9: mlx5_9 ``` ### ๐Ÿ› Describe the bug I am running models with the openai endpoint with the following command: ```bash sudo docker run --rm --shm-size=10.24gb --gpus '"device=1"' -p 5006:5006 -v /dgxdata/aiml/:/home/ndurkee --entrypoint /bin/bash arti.bsf.ball.com/docker-group/vllm/vllm-openai:latest -c "python3 -m vllm.entrypoints.openai.api_server --model /home/ndurkee/Llama-3-8B-Instruct -tp 1 --gpu-memory-utilization 0.90 --dtype auto --distributed-executor-backend mp --port 5006 --served-model-name /home/ndurkee/Qwen2-7B-Instruct-GPTQ-Int8 --max-log-len 10 --enable-prefix-caching --speculative-model '[ngram]' --num-speculative-tokens 3 --ngram-prompt-lookup-max 3 --use-v2-block-manager --max-model-len 4000" ``` In short, I am trying to run llama3(and 3.1) using the openai endpoint on 1 gpu. I call the model with the following command. ```python stream = clients[model].chat.completions.create( model=model_names[model], # Model name to use messages=prompt, # Chat history temperature=0.5, # Temperature for text generation stream=True, # Stream response extra_body={ 'repetition_penalty':1, "stop_token_ids": [128009] }) ``` This gives the following error. ```text INFO 07-23 22:06:46 logger.py:36] Received request chat-afd24089e29d4255b532cb1981bf669d: prompt: '<|begin_of', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.5, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[128009], include_stop_str_in_output=False, ignore_eos=False, max_tokens=None, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [128000, 128006, 882, 128007, 271, 80024, 3545, 31892, 2703, 284], lora_request: None, prompt_adapter_request: None. INFO: 10.72.7.76:47614 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 07-23 22:06:46 async_llm_engine.py:173] Added request chat-afd24089e29d4255b532cb1981bf669d. ERROR 07-23 22:06:46 async_llm_engine.py:56] Engine background task failed ERROR 07-23 22:06:46 async_llm_engine.py:56] Traceback (most recent call last): ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 46, in _log_task_completion ERROR 07-23 22:06:46 async_llm_engine.py:56] return_value = task.result() ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 637, in run_engine_loop ERROR 07-23 22:06:46 async_llm_engine.py:56] result = task.result() ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 580, in engine_step ERROR 07-23 22:06:46 async_llm_engine.py:56] request_outputs = await self.engine.step_async(virtual_engine) ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 258, in step_async ERROR 07-23 22:06:46 async_llm_engine.py:56] request_outputs = self._process_model_outputs( ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 823, in _process_model_outputs ERROR 07-23 22:06:46 async_llm_engine.py:56] self.output_processor.process_outputs(seq_group, outputs) ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py", line 90, in process_outputs ERROR 07-23 22:06:46 async_llm_engine.py:56] self._process_seq_outputs(seq, valid_samples, ERROR 07-23 22:06:46 async_llm_engine.py:56] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py", line 100, in _process_seq_outputs ERROR 07-23 22:06:46 async_llm_engine.py:56] remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() + ERROR 07-23 22:06:46 async_llm_engine.py:56] TypeError: unsupported operand type(s) for -: 'NoneType' and 'int' INFO 07-23 22:06:46 async_llm_engine.py:180] Aborted request chat-afd24089e29d4255b532cb1981bf669d. ERROR:asyncio:Exception in callback functools.partial(<function _log_task_completion at 0x7f3882e248b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f386a7e7f70>>) handle: <Handle functools.partial(<function _log_task_completion at 0x7f3882e248b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f386a7e7f70>>)> Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 46, in _log_task_completion return_value = task.result() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 637, in run_engine_loop result = task.result() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 580, in engine_step request_outputs = await self.engine.step_async(virtual_engine) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 258, in step_async request_outputs = self._process_model_outputs( File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 823, in _process_model_outputs self.output_processor.process_outputs(seq_group, outputs) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py", line 90, in process_outputs self._process_seq_outputs(seq, valid_samples, File "/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py", line 100, in _process_seq_outputs remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() + TypeError: unsupported operand type(s) for -: 'NoneType' and 'int' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "uvloop/cbhandles.pyx", line 63, in uvloop.loop.Handle._run File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 58, in _log_task_completion raise AsyncEngineDeadError( vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for theactual cause. ERROR: Exception in ASGI application Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 265, in __call__ await wrap(partial(self.listen_for_disconnect, receive)) File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 261, in wrap await func() File "/usr/local/lib/python3.10/dist-packages/starlette/responses.py", line 238, in listen_for_disconnect message = await receive() File "/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py", line 553, in receive await self.message_event.wait() File "/usr/lib/python3.10/asyncio/locks.py", line 214, in wait await fut asyncio.exceptions.CancelledError: Cancelled by cancel scope 7f338811c880 ``` Note that when `max_tokens` is not defined, it defaults to `None` which crashes the code. Not only does it crash the current query, it crashes the entire instance and future calls with `max_tokens` fail as well. The code previously worked on 0.5.2 so this stems from a change between 0.5.2 and 0.5.3.post1. Note that I tried llama3 and 3.1 and both had this issue.
Add "max_tokens":xxx can solve this problem temporarily
[ { "body": "### Your current environment\n\n```text\r\nPyTorch version: 2.3.1+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 20.04.6 LTS (x86_64)\r\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.30.1\r\nLibc version: glibc-2.31\r\n\r\nPython version: 3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime)\r\nPython platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.31\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB\r\nNvidia driver version: 525.105.17\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nByte Order: Little Endian\r\nAddress sizes: 43 bits physical, 48 bits virtual\r\nCPU(s): 256\r\nOn-line CPU(s) list: 0-255\r\nThread(s) per core: 2\r\nCore(s) per socket: 64\r\nSocket(s): 2\r\nNUMA node(s): 8\r\nVendor ID: AuthenticAMD\r\nCPU family: 23\r\nModel: 49\r\nModel name: AMD EPYC 7742 64-Core Processor\r\nStepping: 0\r\nFrequency boost: enabled\r\nCPU MHz: 3391.595\r\nCPU max MHz: 2250.0000\r\nCPU min MHz: 1500.0000\r\nBogoMIPS: 4491.45\r\nVirtualization: AMD-V\r\nL1d cache: 4 MiB\r\nL1i cache: 4 MiB\r\nL2 cache: 64 MiB\r\nL3 cache: 512 MiB\r\nNUMA node0 CPU(s): 0-15,128-143\r\nNUMA node1 CPU(s): 16-31,144-159\r\nNUMA node2 CPU(s): 32-47,160-175\r\nNUMA node3 CPU(s): 48-63,176-191\r\nNUMA node4 CPU(s): 64-79,192-207\r\nNUMA node5 CPU(s): 80-95,208-223\r\nNUMA node6 CPU(s): 96-111,224-239\r\nNUMA node7 CPU(s): 112-127,240-255\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Not affected\r\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es\r\n\r\nVersions of relevant libraries:\r\n[pip3] flashinfer==0.0.9+cu121torch2.3\r\n[pip3] numpy==1.26.4\r\n[pip3] torch==2.3.1\r\n[pip3] torchvision==0.18.1\r\n[pip3] triton==2.3.1\r\n[conda] Could not collectROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.5.3.post1\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 CPU Affinity NUMA Affinity\r\nGPU0 X PXB PXB SYS SYS SYS SYS SYS SYS SYS SYS 48-63,176-191 3\r\nNIC0 PXB X PXB SYS SYS SYS SYS SYS SYS SYS SYS\r\nNIC1 PXB PXB X SYS SYS SYS SYS SYS SYS SYS SYS\r\nNIC2 SYS SYS SYS X PXB SYS SYS SYS SYS SYS SYS\r\nNIC3 SYS SYS SYS PXB X SYS SYS SYS SYS SYS SYS\r\nNIC4 SYS SYS SYS SYS SYS X PXB SYS SYS SYS SYS\r\nNIC5 SYS SYS SYS SYS SYS PXB X SYS SYS SYS SYS\r\nNIC6 SYS SYS SYS SYS SYS SYS SYS X PXB SYS SYS\r\nNIC7 SYS SYS SYS SYS SYS SYS SYS PXB X SYS SYS\r\nNIC8 SYS SYS SYS SYS SYS SYS SYS SYS SYS X PIX\r\nNIC9 SYS SYS SYS SYS SYS SYS SYS SYS SYS PIX X\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n\r\nNIC Legend:\r\n\r\n NIC0: mlx5_0\r\n NIC1: mlx5_1\r\n NIC2: mlx5_2\r\n NIC3: mlx5_3\r\n NIC4: mlx5_4\r\n NIC5: mlx5_5\r\n NIC6: mlx5_6\r\n NIC7: mlx5_7\r\n NIC8: mlx5_8\r\n NIC9: mlx5_9\r\n```\r\n\r\n\n\n### ๐Ÿ› Describe the bug\n\nI am running models with the openai endpoint with the following command:\r\n\r\n```bash\r\nsudo docker run --rm --shm-size=10.24gb --gpus '\"device=1\"' -p 5006:5006 -v /dgxdata/aiml/:/home/ndurkee --entrypoint /bin/bash arti.bsf.ball.com/docker-group/vllm/vllm-openai:latest -c \"python3 -m vllm.entrypoints.openai.api_server --model /home/ndurkee/Llama-3-8B-Instruct -tp 1 --gpu-memory-utilization 0.90 --dtype auto --distributed-executor-backend mp --port 5006 --served-model-name /home/ndurkee/Qwen2-7B-Instruct-GPTQ-Int8 --max-log-len 10 --enable-prefix-caching --speculative-model '[ngram]' --num-speculative-tokens 3 --ngram-prompt-lookup-max 3 --use-v2-block-manager --max-model-len 4000\"\r\n```\r\n\r\nIn short, I am trying to run llama3(and 3.1) using the openai endpoint on 1 gpu. I call the model with the following command.\r\n\r\n```python\r\n stream = clients[model].chat.completions.create(\r\n model=model_names[model], # Model name to use\r\n messages=prompt, # Chat history\r\n temperature=0.5, # Temperature for text generation\r\n stream=True, # Stream response\r\n extra_body={\r\n 'repetition_penalty':1,\r\n \"stop_token_ids\": [128009]\r\n })\r\n```\r\n\r\nThis gives the following error.\r\n\r\n```text\r\nINFO 07-23 22:06:46 logger.py:36] Received request chat-afd24089e29d4255b532cb1981bf669d: prompt: '<|begin_of', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.5, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[128009], include_stop_str_in_output=False, ignore_eos=False, max_tokens=None, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [128000, 128006, 882, 128007, 271, 80024, 3545, 31892, 2703, 284], lora_request: None, prompt_adapter_request: None.\r\nINFO: 10.72.7.76:47614 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\r\nINFO 07-23 22:06:46 async_llm_engine.py:173] Added request chat-afd24089e29d4255b532cb1981bf669d.\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] Engine background task failed\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] Traceback (most recent call last):\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 46, in _log_task_completion\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] return_value = task.result()\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 637, in run_engine_loop\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] result = task.result()\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 580, in engine_step\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] request_outputs = await self.engine.step_async(virtual_engine)\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 258, in step_async\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] request_outputs = self._process_model_outputs(\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py\", line 823, in _process_model_outputs\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] self.output_processor.process_outputs(seq_group, outputs)\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py\", line 90, in process_outputs\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] self._process_seq_outputs(seq, valid_samples,\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py\", line 100, in _process_seq_outputs\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() +\r\nERROR 07-23 22:06:46 async_llm_engine.py:56] TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'\r\nINFO 07-23 22:06:46 async_llm_engine.py:180] Aborted request chat-afd24089e29d4255b532cb1981bf669d.\r\nERROR:asyncio:Exception in callback functools.partial(<function _log_task_completion at 0x7f3882e248b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f386a7e7f70>>)\r\nhandle: <Handle functools.partial(<function _log_task_completion at 0x7f3882e248b0>, error_callback=<bound method AsyncLLMEngine._error_callback of <vllm.engine.async_llm_engine.AsyncLLMEngine object at 0x7f386a7e7f70>>)>\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 46, in _log_task_completion\r\n return_value = task.result()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 637, in run_engine_loop\r\n result = task.result()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 580, in engine_step\r\n request_outputs = await self.engine.step_async(virtual_engine)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 258, in step_async\r\n request_outputs = self._process_model_outputs(\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py\", line 823, in _process_model_outputs\r\n self.output_processor.process_outputs(seq_group, outputs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py\", line 90, in process_outputs\r\n self._process_seq_outputs(seq, valid_samples,\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/output_processor/multi_step.py\", line 100, in _process_seq_outputs\r\n remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() +\r\nTypeError: unsupported operand type(s) for -: 'NoneType' and 'int'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"uvloop/cbhandles.pyx\", line 63, in uvloop.loop.Handle._run\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 58, in _log_task_completion\r\n raise AsyncEngineDeadError(\r\nvllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for theactual cause.\r\nERROR: Exception in ASGI application\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 265, in __call__\r\n await wrap(partial(self.listen_for_disconnect, receive))\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 261, in wrap\r\n await func()\r\n File \"/usr/local/lib/python3.10/dist-packages/starlette/responses.py\", line 238, in listen_for_disconnect\r\n message = await receive()\r\n File \"/usr/local/lib/python3.10/dist-packages/uvicorn/protocols/http/httptools_impl.py\", line 553, in receive\r\n await self.message_event.wait()\r\n File \"/usr/lib/python3.10/asyncio/locks.py\", line 214, in wait\r\n await fut\r\nasyncio.exceptions.CancelledError: Cancelled by cancel scope 7f338811c880\r\n```\r\n\r\nNote that when `max_tokens` is not defined, it defaults to `None` which crashes the code. Not only does it crash the current query, it crashes the entire instance and future calls with `max_tokens` fail as well. The code previously worked on 0.5.2 so this stems from a change between 0.5.2 and 0.5.3.post1.\r\n\r\nNote that I tried llama3 and 3.1 and both had this issue.", "number": 6707, "title": "[Bug]: Engine crashes when max_tokens undefined" } ]
6ca8031e7130f810ce7248286754a60044f65c73
{ "head_commit": "68ada2df94c4c832c43cf87d76dad6bc990c2957", "head_commit_message": "cleanup", "patch_to_review": "diff --git a/tests/entrypoints/openai/test_serving_chat.py b/tests/entrypoints/openai/test_serving_chat.py\nindex 464465494b7..168ba7ba888 100644\n--- a/tests/entrypoints/openai/test_serving_chat.py\n+++ b/tests/entrypoints/openai/test_serving_chat.py\n@@ -1,7 +1,12 @@\n import asyncio\n+from contextlib import suppress\n from dataclasses import dataclass\n+from unittest.mock import MagicMock\n \n+from vllm.engine.async_llm_engine import AsyncLLMEngine\n+from vllm.entrypoints.openai.protocol import ChatCompletionRequest\n from vllm.entrypoints.openai.serving_chat import OpenAIServingChat\n+from vllm.transformers_utils.tokenizer import get_tokenizer\n \n MODEL_NAME = \"openai-community/gpt2\"\n CHAT_TEMPLATE = \"Dummy chat template for testing {}\"\n@@ -42,3 +47,37 @@ async def _async_serving_chat_init():\n def test_async_serving_chat_init():\n serving_completion = asyncio.run(_async_serving_chat_init())\n assert serving_completion.chat_template == CHAT_TEMPLATE\n+\n+\n+def test_serving_chat_should_set_correct_max_tokens():\n+ mock_engine = MagicMock(spec=AsyncLLMEngine)\n+ mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)\n+\n+ serving_chat = OpenAIServingChat(mock_engine,\n+ MockModelConfig(),\n+ served_model_names=[MODEL_NAME],\n+ response_role=\"assistant\",\n+ chat_template=CHAT_TEMPLATE,\n+ lora_modules=None,\n+ prompt_adapters=None,\n+ request_logger=None)\n+ req = ChatCompletionRequest(\n+ model=MODEL_NAME,\n+ messages=[{\n+ \"role\": \"user\",\n+ \"content\": \"what is 1+1?\"\n+ }],\n+ guided_decoding_backend=\"outlines\",\n+ )\n+\n+ with suppress(Exception):\n+ asyncio.run(serving_chat.create_chat_completion(req))\n+\n+ # AsyncLLMEngine.generate(inputs, sampling_params, ...)\n+ assert mock_engine.generate.call_args.args[1].max_tokens == 93\n+\n+ req.max_tokens = 10\n+ with suppress(Exception):\n+ asyncio.run(serving_chat.create_chat_completion(req))\n+\n+ assert mock_engine.generate.call_args.args[1].max_tokens == 10\ndiff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py\nindex 205860aa8e7..8b60b7a70b1 100644\n--- a/vllm/entrypoints/openai/protocol.py\n+++ b/vllm/entrypoints/openai/protocol.py\n@@ -11,7 +11,7 @@\n from vllm.entrypoints.chat_utils import ChatCompletionMessageParam\n from vllm.entrypoints.openai.logits_processors import get_logits_processors\n from vllm.pooling_params import PoolingParams\n-from vllm.sampling_params import SamplingParams\n+from vllm.sampling_params import LogitsProcessor, SamplingParams\n from vllm.utils import random_uuid\n \n \n@@ -215,15 +215,18 @@ class ChatCompletionRequest(OpenAIBaseModel):\n \n # doc: end-chat-completion-extra-params\n \n- def to_sampling_params(self,\n- tokenizer: PreTrainedTokenizer) -> SamplingParams:\n+ def to_sampling_params(\n+ self, tokenizer: PreTrainedTokenizer,\n+ guided_decode_logits_processor: Optional[LogitsProcessor]\n+ ) -> SamplingParams:\n # We now allow logprobs being true without top_logrobs.\n-\n logits_processors = get_logits_processors(\n logit_bias=self.logit_bias,\n allowed_token_ids=None,\n tokenizer=tokenizer,\n )\n+ if guided_decode_logits_processor:\n+ logits_processors.append(guided_decode_logits_processor)\n \n return SamplingParams(\n n=self.n,\n@@ -395,7 +398,10 @@ class CompletionRequest(OpenAIBaseModel):\n \n # doc: end-completion-extra-params\n \n- def to_sampling_params(self, tokenizer: PreTrainedTokenizer):\n+ def to_sampling_params(\n+ self, tokenizer: PreTrainedTokenizer,\n+ guided_decode_logits_processor: Optional[LogitsProcessor]\n+ ) -> SamplingParams:\n echo_without_generation = self.echo and self.max_tokens == 0\n \n logits_processors = get_logits_processors(\n@@ -403,6 +409,8 @@ def to_sampling_params(self, tokenizer: PreTrainedTokenizer):\n allowed_token_ids=self.allowed_token_ids,\n tokenizer=tokenizer,\n )\n+ if guided_decode_logits_processor:\n+ logits_processors.append(guided_decode_logits_processor)\n \n return SamplingParams(\n n=self.n,\ndiff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py\nindex 01843930bf1..1dee798858a 100644\n--- a/vllm/entrypoints/openai/serving_chat.py\n+++ b/vllm/entrypoints/openai/serving_chat.py\n@@ -25,8 +25,6 @@\n PromptAdapterPath)\n from vllm.inputs import PromptInputs\n from vllm.logger import init_logger\n-from vllm.model_executor.guided_decoding import (\n- get_guided_decoding_logits_processor)\n from vllm.multimodal import MultiModalDataDict\n from vllm.outputs import RequestOutput\n from vllm.sequence import Logprob\n@@ -134,28 +132,23 @@ async def create_chat_completion(\n \n request_id = f\"chat-{random_uuid()}\"\n try:\n- sampling_params = request.to_sampling_params(tokenizer)\n- decoding_config = await self.engine.get_decoding_config()\n- guided_decoding_backend = request.guided_decoding_backend \\\n- or decoding_config.guided_decoding_backend\n guided_decode_logits_processor = (\n- await\n- get_guided_decoding_logits_processor(guided_decoding_backend,\n- request, tokenizer))\n- if guided_decode_logits_processor:\n- if sampling_params.logits_processors is None:\n- sampling_params.logits_processors = []\n- sampling_params.logits_processors.append(\n- guided_decode_logits_processor)\n+ await self._guided_decode_logits_processor(request, tokenizer))\n \n prompt_inputs = self._tokenize_prompt_input(\n request,\n tokenizer,\n prompt,\n- truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,\n+ truncate_prompt_tokens=request.truncate_prompt_tokens,\n add_special_tokens=request.add_special_tokens,\n )\n \n+ sampling_params = request.to_sampling_params(\n+ tokenizer, guided_decode_logits_processor)\n+ if sampling_params.max_tokens is None:\n+ sampling_params.max_tokens = \\\n+ self.max_model_len - len(prompt_inputs[\"prompt_token_ids\"])\n+\n self._log_inputs(request_id,\n prompt_inputs,\n params=sampling_params,\ndiff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py\nindex 85483527916..51de2f18265 100644\n--- a/vllm/entrypoints/openai/serving_completion.py\n+++ b/vllm/entrypoints/openai/serving_completion.py\n@@ -24,8 +24,6 @@\n OpenAIServing,\n PromptAdapterPath)\n from vllm.logger import init_logger\n-from vllm.model_executor.guided_decoding import (\n- get_guided_decoding_logits_processor)\n from vllm.outputs import RequestOutput\n from vllm.sequence import Logprob\n from vllm.tracing import (contains_trace_headers, extract_trace_headers,\n@@ -95,31 +93,24 @@ async def create_completion(self, request: CompletionRequest,\n \n tokenizer = await self.engine.get_tokenizer(lora_request)\n \n- sampling_params = request.to_sampling_params(tokenizer)\n- decoding_config = await self.engine.get_decoding_config()\n- guided_decoding_backend = request.guided_decoding_backend \\\n- or decoding_config.guided_decoding_backend\n- guided_decode_logit_processor = (\n- await\n- get_guided_decoding_logits_processor(guided_decoding_backend,\n- request, tokenizer))\n- if guided_decode_logit_processor is not None:\n- if sampling_params.logits_processors is None:\n- sampling_params.logits_processors = []\n- sampling_params.logits_processors.append(\n- guided_decode_logit_processor)\n-\n+ guided_decode_logits_processor = (\n+ await self._guided_decode_logits_processor(request, tokenizer))\n prompts = list(\n self._tokenize_prompt_input_or_inputs(\n request,\n tokenizer,\n request.prompt,\n- truncate_prompt_tokens=sampling_params.\n- truncate_prompt_tokens,\n+ truncate_prompt_tokens=request.truncate_prompt_tokens,\n add_special_tokens=request.add_special_tokens,\n ))\n \n for i, prompt_inputs in enumerate(prompts):\n+ sampling_params = request.to_sampling_params(\n+ tokenizer, guided_decode_logits_processor)\n+ if sampling_params.max_tokens is None:\n+ sampling_params.max_tokens = self.max_model_len - \\\n+ len(prompt_inputs[\"prompt_token_ids\"])\n+\n request_id_item = f\"{request_id}-{i}\"\n \n self._log_inputs(request_id_item,\ndiff --git a/vllm/entrypoints/openai/serving_engine.py b/vllm/entrypoints/openai/serving_engine.py\nindex 321c9ac2c1d..a9992f12c60 100644\n--- a/vllm/entrypoints/openai/serving_engine.py\n+++ b/vllm/entrypoints/openai/serving_engine.py\n@@ -26,9 +26,11 @@\n from vllm.inputs import parse_and_batch_prompt\n from vllm.logger import init_logger\n from vllm.lora.request import LoRARequest\n+from vllm.model_executor.guided_decoding import (\n+ get_guided_decoding_logits_processor)\n from vllm.pooling_params import PoolingParams\n from vllm.prompt_adapter.request import PromptAdapterRequest\n-from vllm.sampling_params import SamplingParams\n+from vllm.sampling_params import LogitsProcessor, SamplingParams\n from vllm.sequence import Logprob\n \n logger = init_logger(__name__)\n@@ -152,6 +154,15 @@ def create_streaming_error_response(\n })\n return json_str\n \n+ async def _guided_decode_logits_processor(\n+ self, request: Union[ChatCompletionRequest, CompletionRequest],\n+ tokenizer: AnyTokenizer) -> Optional[LogitsProcessor]:\n+ decoding_config = await self.engine.get_decoding_config()\n+ guided_decoding_backend = request.guided_decoding_backend \\\n+ or decoding_config.guided_decoding_backend\n+ return await get_guided_decoding_logits_processor(\n+ guided_decoding_backend, request, tokenizer)\n+\n async def _check_model(\n self,\n request: AnyRequest,\n@@ -256,9 +267,7 @@ def _validate_input(\n f\"{self.max_model_len} tokens. However, you requested \"\n f\"{token_num} tokens in the messages, \"\n f\"Please reduce the length of the messages.\")\n- request.max_tokens = self.max_model_len - token_num\n-\n- if token_num + request.max_tokens > self.max_model_len:\n+ elif token_num + request.max_tokens > self.max_model_len:\n raise ValueError(\n f\"This model's maximum context length is \"\n f\"{self.max_model_len} tokens. However, you requested \"\n" }
[ { "diff_hunk": "@@ -134,28 +132,23 @@ async def create_chat_completion(\n \n request_id = f\"chat-{random_uuid()}\"\n try:\n- sampling_params = request.to_sampling_params(tokenizer)\n- decoding_config = await self.engine.get_decoding_config()\n- guided_decoding_backend = request.guided_decoding_backend \\\n- or decoding_config.guided_decoding_backend\n guided_decode_logits_processor = (\n- await\n- get_guided_decoding_logits_processor(guided_decoding_backend,\n- request, tokenizer))\n- if guided_decode_logits_processor:\n- if sampling_params.logits_processors is None:\n- sampling_params.logits_processors = []\n- sampling_params.logits_processors.append(\n- guided_decode_logits_processor)\n+ await self._guided_decode_logits_processor(request, tokenizer))\n \n prompt_inputs = self._tokenize_prompt_input(\n request,\n tokenizer,\n prompt,\n- truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,\n+ truncate_prompt_tokens=request.truncate_prompt_tokens,\n add_special_tokens=request.add_special_tokens,\n )\n \n+ sampling_params = request.to_sampling_params(\n+ tokenizer, guided_decode_logits_processor)\n+ if sampling_params.max_tokens is None:\n+ sampling_params.max_tokens = \\\n+ self.max_model_len - len(prompt_inputs[\"prompt_token_ids\"])", "line": null, "original_line": 150, "original_start_line": 148, "path": "vllm/entrypoints/openai/serving_chat.py", "start_line": null, "text": "@user1:\nShould we move this logic into `request.to_sampling_params` as well?\n\n@author:\nDone" } ]
5069fb70d675dd93ecc487bd7101fb77d16c16c4
diff --git a/tests/entrypoints/openai/test_serving_chat.py b/tests/entrypoints/openai/test_serving_chat.py index 464465494b71..168ba7ba888e 100644 --- a/tests/entrypoints/openai/test_serving_chat.py +++ b/tests/entrypoints/openai/test_serving_chat.py @@ -1,7 +1,12 @@ import asyncio +from contextlib import suppress from dataclasses import dataclass +from unittest.mock import MagicMock +from vllm.engine.async_llm_engine import AsyncLLMEngine +from vllm.entrypoints.openai.protocol import ChatCompletionRequest from vllm.entrypoints.openai.serving_chat import OpenAIServingChat +from vllm.transformers_utils.tokenizer import get_tokenizer MODEL_NAME = "openai-community/gpt2" CHAT_TEMPLATE = "Dummy chat template for testing {}" @@ -42,3 +47,37 @@ async def _async_serving_chat_init(): def test_async_serving_chat_init(): serving_completion = asyncio.run(_async_serving_chat_init()) assert serving_completion.chat_template == CHAT_TEMPLATE + + +def test_serving_chat_should_set_correct_max_tokens(): + mock_engine = MagicMock(spec=AsyncLLMEngine) + mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME) + + serving_chat = OpenAIServingChat(mock_engine, + MockModelConfig(), + served_model_names=[MODEL_NAME], + response_role="assistant", + chat_template=CHAT_TEMPLATE, + lora_modules=None, + prompt_adapters=None, + request_logger=None) + req = ChatCompletionRequest( + model=MODEL_NAME, + messages=[{ + "role": "user", + "content": "what is 1+1?" + }], + guided_decoding_backend="outlines", + ) + + with suppress(Exception): + asyncio.run(serving_chat.create_chat_completion(req)) + + # AsyncLLMEngine.generate(inputs, sampling_params, ...) + assert mock_engine.generate.call_args.args[1].max_tokens == 93 + + req.max_tokens = 10 + with suppress(Exception): + asyncio.run(serving_chat.create_chat_completion(req)) + + assert mock_engine.generate.call_args.args[1].max_tokens == 10 diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 205860aa8e72..3b35ae1ebd70 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -11,7 +11,7 @@ from vllm.entrypoints.chat_utils import ChatCompletionMessageParam from vllm.entrypoints.openai.logits_processors import get_logits_processors from vllm.pooling_params import PoolingParams -from vllm.sampling_params import SamplingParams +from vllm.sampling_params import LogitsProcessor, SamplingParams from vllm.utils import random_uuid @@ -215,15 +215,22 @@ class ChatCompletionRequest(OpenAIBaseModel): # doc: end-chat-completion-extra-params - def to_sampling_params(self, - tokenizer: PreTrainedTokenizer) -> SamplingParams: - # We now allow logprobs being true without top_logrobs. + def to_sampling_params( + self, tokenizer: PreTrainedTokenizer, + guided_decode_logits_processor: Optional[LogitsProcessor], + default_max_tokens: int) -> SamplingParams: + max_tokens = self.max_tokens + if max_tokens is None: + max_tokens = default_max_tokens + # We now allow logprobs being true without top_logrobs. logits_processors = get_logits_processors( logit_bias=self.logit_bias, allowed_token_ids=None, tokenizer=tokenizer, ) + if guided_decode_logits_processor: + logits_processors.append(guided_decode_logits_processor) return SamplingParams( n=self.n, @@ -241,7 +248,7 @@ def to_sampling_params(self, logprobs=self.top_logprobs if self.logprobs else None, prompt_logprobs=self.top_logprobs if self.echo else None, ignore_eos=self.ignore_eos, - max_tokens=self.max_tokens, + max_tokens=max_tokens, min_tokens=self.min_tokens, use_beam_search=self.use_beam_search, early_stopping=self.early_stopping, @@ -395,7 +402,14 @@ class CompletionRequest(OpenAIBaseModel): # doc: end-completion-extra-params - def to_sampling_params(self, tokenizer: PreTrainedTokenizer): + def to_sampling_params( + self, tokenizer: PreTrainedTokenizer, + guided_decode_logits_processor: Optional[LogitsProcessor], + default_max_tokens: int) -> SamplingParams: + max_tokens = self.max_tokens + if max_tokens is None: + max_tokens = default_max_tokens + echo_without_generation = self.echo and self.max_tokens == 0 logits_processors = get_logits_processors( @@ -403,6 +417,8 @@ def to_sampling_params(self, tokenizer: PreTrainedTokenizer): allowed_token_ids=self.allowed_token_ids, tokenizer=tokenizer, ) + if guided_decode_logits_processor: + logits_processors.append(guided_decode_logits_processor) return SamplingParams( n=self.n, @@ -419,7 +435,7 @@ def to_sampling_params(self, tokenizer: PreTrainedTokenizer): stop_token_ids=self.stop_token_ids, logprobs=self.logprobs, ignore_eos=self.ignore_eos, - max_tokens=self.max_tokens if not echo_without_generation else 1, + max_tokens=max_tokens if not echo_without_generation else 1, min_tokens=self.min_tokens, use_beam_search=self.use_beam_search, early_stopping=self.early_stopping, diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 01843930bf11..c832cf2a24b5 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -25,8 +25,6 @@ PromptAdapterPath) from vllm.inputs import PromptInputs from vllm.logger import init_logger -from vllm.model_executor.guided_decoding import ( - get_guided_decoding_logits_processor) from vllm.multimodal import MultiModalDataDict from vllm.outputs import RequestOutput from vllm.sequence import Logprob @@ -134,28 +132,23 @@ async def create_chat_completion( request_id = f"chat-{random_uuid()}" try: - sampling_params = request.to_sampling_params(tokenizer) - decoding_config = await self.engine.get_decoding_config() - guided_decoding_backend = request.guided_decoding_backend \ - or decoding_config.guided_decoding_backend guided_decode_logits_processor = ( - await - get_guided_decoding_logits_processor(guided_decoding_backend, - request, tokenizer)) - if guided_decode_logits_processor: - if sampling_params.logits_processors is None: - sampling_params.logits_processors = [] - sampling_params.logits_processors.append( - guided_decode_logits_processor) + await self._guided_decode_logits_processor(request, tokenizer)) prompt_inputs = self._tokenize_prompt_input( request, tokenizer, prompt, - truncate_prompt_tokens=sampling_params.truncate_prompt_tokens, + truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, ) + sampling_params = request.to_sampling_params( + tokenizer, + guided_decode_logits_processor, + default_max_tokens=self.max_model_len - + len(prompt_inputs["prompt_token_ids"])) + self._log_inputs(request_id, prompt_inputs, params=sampling_params, diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index 854835279168..7765c5903f34 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -24,8 +24,6 @@ OpenAIServing, PromptAdapterPath) from vllm.logger import init_logger -from vllm.model_executor.guided_decoding import ( - get_guided_decoding_logits_processor) from vllm.outputs import RequestOutput from vllm.sequence import Logprob from vllm.tracing import (contains_trace_headers, extract_trace_headers, @@ -95,31 +93,24 @@ async def create_completion(self, request: CompletionRequest, tokenizer = await self.engine.get_tokenizer(lora_request) - sampling_params = request.to_sampling_params(tokenizer) - decoding_config = await self.engine.get_decoding_config() - guided_decoding_backend = request.guided_decoding_backend \ - or decoding_config.guided_decoding_backend - guided_decode_logit_processor = ( - await - get_guided_decoding_logits_processor(guided_decoding_backend, - request, tokenizer)) - if guided_decode_logit_processor is not None: - if sampling_params.logits_processors is None: - sampling_params.logits_processors = [] - sampling_params.logits_processors.append( - guided_decode_logit_processor) - + guided_decode_logits_processor = ( + await self._guided_decode_logits_processor(request, tokenizer)) prompts = list( self._tokenize_prompt_input_or_inputs( request, tokenizer, request.prompt, - truncate_prompt_tokens=sampling_params. - truncate_prompt_tokens, + truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, )) for i, prompt_inputs in enumerate(prompts): + sampling_params = request.to_sampling_params( + tokenizer, + guided_decode_logits_processor, + default_max_tokens=self.max_model_len - + len(prompt_inputs["prompt_token_ids"])) + request_id_item = f"{request_id}-{i}" self._log_inputs(request_id_item, diff --git a/vllm/entrypoints/openai/serving_engine.py b/vllm/entrypoints/openai/serving_engine.py index 321c9ac2c1d5..a9992f12c604 100644 --- a/vllm/entrypoints/openai/serving_engine.py +++ b/vllm/entrypoints/openai/serving_engine.py @@ -26,9 +26,11 @@ from vllm.inputs import parse_and_batch_prompt from vllm.logger import init_logger from vllm.lora.request import LoRARequest +from vllm.model_executor.guided_decoding import ( + get_guided_decoding_logits_processor) from vllm.pooling_params import PoolingParams from vllm.prompt_adapter.request import PromptAdapterRequest -from vllm.sampling_params import SamplingParams +from vllm.sampling_params import LogitsProcessor, SamplingParams from vllm.sequence import Logprob logger = init_logger(__name__) @@ -152,6 +154,15 @@ def create_streaming_error_response( }) return json_str + async def _guided_decode_logits_processor( + self, request: Union[ChatCompletionRequest, CompletionRequest], + tokenizer: AnyTokenizer) -> Optional[LogitsProcessor]: + decoding_config = await self.engine.get_decoding_config() + guided_decoding_backend = request.guided_decoding_backend \ + or decoding_config.guided_decoding_backend + return await get_guided_decoding_logits_processor( + guided_decoding_backend, request, tokenizer) + async def _check_model( self, request: AnyRequest, @@ -256,9 +267,7 @@ def _validate_input( f"{self.max_model_len} tokens. However, you requested " f"{token_num} tokens in the messages, " f"Please reduce the length of the messages.") - request.max_tokens = self.max_model_len - token_num - - if token_num + request.max_tokens > self.max_model_len: + elif token_num + request.max_tokens > self.max_model_len: raise ValueError( f"This model's maximum context length is " f"{self.max_model_len} tokens. However, you requested "
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7129@e78542f
vllm-project/vllm
Python
7,129
[Bugfix] Specify device when loading LoRA and embedding tensors
[Bugfix] Assign device when loading LoRA modules from file Fixes [#3374](https://github.com/vllm-project/vllm/issues/3374). Note โ€”ย existing PR to address this issue got stale; so bumping with some light updates. This PR addresses the issue of CUDA device mismatch when loading LoRA modules from files. It fixes the `Attempting to deserialize object on CUDA device X but torch.cuda.device_count() is Y` error by explicitly specifying the device during tensor loading. Changes: - Add `map_location="device"` when loading LoRA tensors from .bin files - Add `map_location="device"` when loading new embeddings from .bin files
2024-08-04T20:00:06Z
lora load failed ``` Exception in callback functools.partial(<function _raise_exception_on_finish at 0x7f9123d01870>, request_tracker=<vllm.engine.async_llm_engine.RequestTracker object at 0x7f91197ea980>) handle: <Handle functools.partial(<function _raise_exception_on_finish at 0x7f9123d01870>, request_tracker=<vllm.engine.async_llm_engine.RequestTracker object at 0x7f91197ea980>)> Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 29, in _raise_exception_on_finish task.result() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 414, in run_engine_loop has_requests_in_progress = await self.engine_step() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 393, in engine_step request_outputs = await self.engine.step_async() File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 189, in step_async all_outputs = await self._run_workers_async( File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 276, in _run_workers_async all_outputs = await asyncio.gather(*coros) File "/usr/lib/python3.10/asyncio/tasks.py", line 650, in _wrap_awaitable return (yield from awaitable.__await__()) ray.exceptions.RayTaskError(RuntimeError): ray::RayWorkerVllm.execute_method() (pid=59396, ip=10.192.82.3, actor_id=81f1bd9d1e1f68a1ddb5a26301000000, repr=<vllm.engine.ray_utils.RayWorkerVll m object at 0x7f3fe6e189a0>) File "/usr/local/lib/python3.10/dist-packages/vllm/lora/models.py", line 212, in from_local_checkpoint tensors = torch.load(lora_bin_file_path) File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1014, in load return _load(opened_zipfile, File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1422, in _load result = unpickler.load() File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1392, in persistent_load typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location)) File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 1366, in load_tensor wrap_storage=restore_location(storage, location), File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 381, in default_restore_location result = fn(storage, location) File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 274, in _cuda_deserialize device = validate_cuda_device(location) File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 265, in validate_cuda_device raise RuntimeError('Attempting to deserialize object on CUDA device ' RuntimeError: Attempting to deserialize object on CUDA device 5 but torch.cuda.device_count() is 2. Please use torch.load with map_location to map your storages to an existing device. The above exception was the direct cause of the following exception: ray::RayWorkerVllm.execute_method() (pid=59396, ip=10.192.82.3, actor_id=81f1bd9d1e1f68a1ddb5a26301000000, repr=<vllm.engine.ray_utils.RayWorkerVllm object at 0x7f3fe6e189a0>) File "/usr/local/lib/python3.10/dist-packages/vllm/engine/ray_utils.py", line 37, in execute_method return executor(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 223, in execute_model output = self.model_runner.execute_model(seq_group_metadata_list, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 574, in execute_model self.set_active_loras(lora_requests, lora_mapping) File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 660, in set_active_loras self.lora_manager.set_active_loras(lora_requests, lora_mapping) File "/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py", line 112, in set_active_loras self._apply_loras(lora_requests) File "/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py", line 226, in _apply_loras self.add_lora(lora) File "/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py", line 233, in add_lora lora = self._load_lora(lora_request) File "/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py", line 151, in _load_lora raise RuntimeError( RuntimeError: Loading lora /root/.cache/huggingface/hub/models--shibing624--ziya-llama-13b-medical-lora/snapshots/eadf08120f701bbdf0abaa4b1f2bca6cc1b23c1d/ failed ```
Similar error (Jul 29) here: ```shell File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:195](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=194), in LRUCacheWorkerLoRAManager._apply_adapters(self, lora_requests) 190 raise RuntimeError( 191 f"Number of requested LoRAs ({len(loras_map)}) is greater " 192 "than the number of GPU LoRA slots " 193 f"({self._adapter_manager.lora_slots}).") 194 for lora in loras_map.values(): --> 195 self.add_adapter(lora) File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:204](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=203), in LRUCacheWorkerLoRAManager.add_adapter(self, lora_request) 201 assert isinstance(self._adapter_manager, 202 LRUCacheLoRAModelManager) 203 self._adapter_manager.remove_oldest_adapter() --> 204 lora = self._load_adapter(lora_request) 205 loaded = self._adapter_manager.add_adapter(lora) 206 else: 207 # If the lora is already loaded, just touch it to 208 # update its position in the caches File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:107](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=106), in WorkerLoRAManager._load_adapter(self, lora_request) 94 lora = self._lora_model_cls.from_local_checkpoint( 95 lora_path, 96 expected_lora_modules, (...) 104 embedding_padding_modules=self.embedding_padding_modules, 105 ) 106 except Exception as e: --> 107 raise RuntimeError(f"Loading lora {lora_path} failed") from e 108 if lora.rank > self.lora_config.max_lora_rank: 109 raise ValueError( 110 f"LoRA rank {lora.rank} is greater than max_lora_rank " 111 f"{self.lora_config.max_lora_rank}.") RuntimeError: Loading lora /root/.cache/huggingface/hub/models--Ksgk-fy--IGR-Adaptor-Meta-Llama-3-8B-Instruct-1/snapshots/37a69f049c3d0233bc77e9c8ac481d909cd6699b failed ``` > Similar error (Jul 29) here: > > ```shell > File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:195](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=194), in LRUCacheWorkerLoRAManager._apply_adapters(self, lora_requests) > 190 raise RuntimeError( > 191 f"Number of requested LoRAs ({len(loras_map)}) is greater " > 192 "than the number of GPU LoRA slots " > 193 f"({self._adapter_manager.lora_slots}).") > 194 for lora in loras_map.values(): > --> 195 self.add_adapter(lora) > > File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:204](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=203), in LRUCacheWorkerLoRAManager.add_adapter(self, lora_request) > 201 assert isinstance(self._adapter_manager, > 202 LRUCacheLoRAModelManager) > 203 self._adapter_manager.remove_oldest_adapter() > --> 204 lora = self._load_adapter(lora_request) > 205 loaded = self._adapter_manager.add_adapter(lora) > 206 else: > 207 # If the lora is already loaded, just touch it to > 208 # update its position in the caches > > File [/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py:107](https://566mynctadei05-8888.proxy.runpod.net/lab/tree/workspace/graph-llm/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py#line=106), in WorkerLoRAManager._load_adapter(self, lora_request) > 94 lora = self._lora_model_cls.from_local_checkpoint( > 95 lora_path, > 96 expected_lora_modules, > (...) > 104 embedding_padding_modules=self.embedding_padding_modules, > 105 ) > 106 except Exception as e: > --> 107 raise RuntimeError(f"Loading lora {lora_path} failed") from e > 108 if lora.rank > self.lora_config.max_lora_rank: > 109 raise ValueError( > 110 f"LoRA rank {lora.rank} is greater than max_lora_rank " > 111 f"{self.lora_config.max_lora_rank}.") > > RuntimeError: Loading lora /root/.cache/huggingface/hub/models--Ksgk-fy--IGR-Adaptor-Meta-Llama-3-8B-Instruct-1/snapshots/37a69f049c3d0233bc77e9c8ac481d909cd6699b failed > ``` it's fixed. see the related pr upstairs.
[ { "body": "```\r\nException in callback functools.partial(<function _raise_exception_on_finish at 0x7f9123d01870>, request_tracker=<vllm.engine.async_llm_engine.RequestTracker object at 0x7f91197ea980>)\r\nhandle: <Handle functools.partial(<function _raise_exception_on_finish at 0x7f9123d01870>, request_tracker=<vllm.engine.async_llm_engine.RequestTracker object at 0x7f91197ea980>)>\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 29, in _raise_exception_on_finish\r\n task.result()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 414, in run_engine_loop\r\n has_requests_in_progress = await self.engine_step()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 393, in engine_step\r\n request_outputs = await self.engine.step_async()\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 189, in step_async\r\n all_outputs = await self._run_workers_async(\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py\", line 276, in _run_workers_async\r\n all_outputs = await asyncio.gather(*coros)\r\n File \"/usr/lib/python3.10/asyncio/tasks.py\", line 650, in _wrap_awaitable\r\n return (yield from awaitable.__await__())\r\nray.exceptions.RayTaskError(RuntimeError): ray::RayWorkerVllm.execute_method() (pid=59396, ip=10.192.82.3, actor_id=81f1bd9d1e1f68a1ddb5a26301000000, repr=<vllm.engine.ray_utils.RayWorkerVll\r\nm object at 0x7f3fe6e189a0>)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/lora/models.py\", line 212, in from_local_checkpoint\r\n tensors = torch.load(lora_bin_file_path)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 1014, in load\r\n return _load(opened_zipfile,\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 1422, in _load\r\n result = unpickler.load()\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 1392, in persistent_load\r\n typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 1366, in load_tensor\r\n wrap_storage=restore_location(storage, location),\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 381, in default_restore_location\r\n result = fn(storage, location)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 274, in _cuda_deserialize\r\n device = validate_cuda_device(location)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 265, in validate_cuda_device\r\n raise RuntimeError('Attempting to deserialize object on CUDA device '\r\nRuntimeError: Attempting to deserialize object on CUDA device 5 but torch.cuda.device_count() is 2. Please use torch.load with map_location to map your storages to an existing device.\r\nThe above exception was the direct cause of the following exception:\r\n\r\nray::RayWorkerVllm.execute_method() (pid=59396, ip=10.192.82.3, actor_id=81f1bd9d1e1f68a1ddb5a26301000000, repr=<vllm.engine.ray_utils.RayWorkerVllm object at 0x7f3fe6e189a0>)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/engine/ray_utils.py\", line 37, in execute_method\r\n return executor(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py\", line 223, in execute_model\r\n output = self.model_runner.execute_model(seq_group_metadata_list,\r\n File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py\", line 574, in execute_model\r\n self.set_active_loras(lora_requests, lora_mapping)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py\", line 660, in set_active_loras\r\n self.lora_manager.set_active_loras(lora_requests, lora_mapping)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py\", line 112, in set_active_loras\r\n self._apply_loras(lora_requests)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py\", line 226, in _apply_loras\r\n self.add_lora(lora)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py\", line 233, in add_lora\r\n lora = self._load_lora(lora_request)\r\n File \"/usr/local/lib/python3.10/dist-packages/vllm/lora/worker_manager.py\", line 151, in _load_lora\r\n raise RuntimeError(\r\nRuntimeError: Loading lora /root/.cache/huggingface/hub/models--shibing624--ziya-llama-13b-medical-lora/snapshots/eadf08120f701bbdf0abaa4b1f2bca6cc1b23c1d/ failed\r\n```", "number": 3374, "title": "lora load failed" } ]
16a1cc9bb2b4bba82d78f329e5a89b44a5523ac8
{ "head_commit": "e78542f97feeb95f3b0643338c05fe8455c13def", "head_commit_message": "fix: Specify device when loading LoRA and embedding tensors\n\n- Add map_location=\"device\" when loading LoRA tensors from .bin files\n- Add map_location=\"device\" when loading new embeddings from .bin files", "patch_to_review": "diff --git a/vllm/lora/models.py b/vllm/lora/models.py\nindex 017a1002bb9..2ce46204ab9 100644\n--- a/vllm/lora/models.py\n+++ b/vllm/lora/models.py\n@@ -248,7 +248,7 @@ def from_local_checkpoint(\n f\" target modules in {expected_lora_modules}\"\n f\" but received {unexpected_modules}.\"\n f\" Please verify that the loaded LoRA module is correct\")\n- tensors = torch.load(lora_bin_file_path)\n+ tensors = torch.load(lora_bin_file_path, map_location=\"device\")\n else:\n raise ValueError(f\"{lora_dir} doesn't contain tensors\")\n \n@@ -257,7 +257,8 @@ def from_local_checkpoint(\n embeddings = safetensors.torch.load_file(\n new_embeddings_tensor_path)\n elif os.path.isfile(new_embeddings_bin_file_path):\n- embeddings = torch.load(new_embeddings_bin_file_path)\n+ embeddings = torch.load(new_embeddings_bin_file_path,\n+ map_location=\"device\")\n \n rank = config[\"r\"]\n lora_alpha = config[\"lora_alpha\"]\n" }
[ { "diff_hunk": "@@ -248,7 +248,7 @@ def from_local_checkpoint(\n f\" target modules in {expected_lora_modules}\"\n f\" but received {unexpected_modules}.\"\n f\" Please verify that the loaded LoRA module is correct\")\n- tensors = torch.load(lora_bin_file_path)\n+ tensors = torch.load(lora_bin_file_path, map_location=\"device\")", "line": null, "original_line": 251, "original_start_line": null, "path": "vllm/lora/models.py", "start_line": null, "text": "@user1:\ndid you test it? I don't think `\"device\"` works. this is a string." } ]
274c3a23c3d46b1d246c5dc4ab29d71f8f9ebbcd
diff --git a/vllm/lora/models.py b/vllm/lora/models.py index 017a1002bb9a..279477562a94 100644 --- a/vllm/lora/models.py +++ b/vllm/lora/models.py @@ -248,7 +248,7 @@ def from_local_checkpoint( f" target modules in {expected_lora_modules}" f" but received {unexpected_modules}." f" Please verify that the loaded LoRA module is correct") - tensors = torch.load(lora_bin_file_path) + tensors = torch.load(lora_bin_file_path, map_location=device) else: raise ValueError(f"{lora_dir} doesn't contain tensors") @@ -257,7 +257,8 @@ def from_local_checkpoint( embeddings = safetensors.torch.load_file( new_embeddings_tensor_path) elif os.path.isfile(new_embeddings_bin_file_path): - embeddings = torch.load(new_embeddings_bin_file_path) + embeddings = torch.load(new_embeddings_bin_file_path, + map_location=device) rank = config["r"] lora_alpha = config["lora_alpha"]
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7217@a296788
vllm-project/vllm
Python
7,217
[BugFix] Fix frontend multiprocessing hang
Now that the OpenAI server has [optional multiprocessing](https://github.com/vllm-project/vllm/pull/6883), there is a hang that can happen if the backend dies during initialization and never replies to the `IS_SERVER_READY` message sent in `async_engine_client.setup()` . This PR add an optional timeout to `_send_one_way_rpc_request` so that during the initialization we can check periodically if the server process is still running while we wait for the reply to the `IS_SERVER_READY` message. FIX #7213 FYI @njhill @robertgshaw2-neuralmagic
2024-08-06T19:50:37Z
[Bug]: With frontend multiprocessing the openai server hangs if there is an initialization error ### Your current environment The environment is not relevant for this issue. ### ๐Ÿ› Describe the bug With frontend multiprocessing, if there is an error in the initialization if the LLMEngine, the server prints the stack trace and then hangs and has to be killed. The expected behavior is that the server would exit, ideally with a non-zero status. Reproducing this error is pretty simple. Just add `raise Exception("foo")` at the first line of `AsyncLLEngine.from_engine_args()`. Passing `--disable-frontend-multiprocessing` makes this problem go away.
This happens with version `1f26efb`.
[ { "body": "### Your current environment\n\nThe environment is not relevant for this issue.\r\n\n\n### ๐Ÿ› Describe the bug\n\nWith frontend multiprocessing, if there is an error in the initialization if the LLMEngine, the server prints the stack trace and then hangs and has to be killed. The expected behavior is that the server would exit, ideally with a non-zero status.\r\n\r\nReproducing this error is pretty simple. Just add `raise Exception(\"foo\")` at the first line of `AsyncLLEngine.from_engine_args()`. \r\n\r\nPassing `--disable-frontend-multiprocessing` makes this problem go away.", "number": 7213, "title": "[Bug]: With frontend multiprocessing the openai server hangs if there is an initialization error" } ]
564985729abc267af281de11f737cfb29b5c0abb
{ "head_commit": "a296788eb3f9de0d9e0507f399366e4861a5f118", "head_commit_message": "Merge branch 'vllm-project:main' into fix_multiprocess_hang", "patch_to_review": "diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py\nindex 88f0bd4ee4d..9d69002edd1 100644\n--- a/vllm/entrypoints/openai/api_server.py\n+++ b/vllm/entrypoints/openai/api_server.py\n@@ -116,7 +116,14 @@ async def build_async_engine_client(args) -> AsyncIterator[AsyncEngineClient]:\n \n # Build RPCClient, which conforms to AsyncEngineClient Protocol.\n async_engine_client = AsyncEngineRPCClient(port)\n- await async_engine_client.setup()\n+\n+ while True:\n+ try:\n+ await async_engine_client.setup()\n+ break\n+ except TimeoutError as e:\n+ if not rpc_server_process.is_alive():\n+ raise RuntimeError(\"Server crashed\") from e\n \n try:\n yield async_engine_client\ndiff --git a/vllm/entrypoints/openai/rpc/client.py b/vllm/entrypoints/openai/rpc/client.py\nindex 45bf88b5bf5..49f55608766 100644\n--- a/vllm/entrypoints/openai/rpc/client.py\n+++ b/vllm/entrypoints/openai/rpc/client.py\n@@ -18,6 +18,9 @@\n from vllm.sampling_params import SamplingParams\n from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs\n \n+# Time to wait before checking it the server process is alive.\n+SERVER_START_TIMEOUT = 1000\n+\n \n class AsyncEngineRPCClient:\n \n@@ -85,14 +88,19 @@ async def _send_get_data_rpc_request(self, request: RPCUtilityRequest,\n \n return data\n \n- async def _send_one_way_rpc_request(self, request: RPC_REQUEST_TYPE,\n- error_message: str):\n+ async def _send_one_way_rpc_request(self,\n+ request: RPC_REQUEST_TYPE,\n+ error_message: str,\n+ timeout: int = 0):\n \"\"\"Send one-way RPC request to trigger an action.\"\"\"\n with self.socket() as socket:\n # Ping RPC Server with request.\n await socket.send(cloudpickle.dumps(request))\n \n # Await acknowledgement from RPCServer.\n+ if timeout > 0 and await socket.poll(timeout) == 0:\n+ raise TimeoutError(f\"server didn't reply within {timeout} ms\")\n+\n response = cloudpickle.loads(await socket.recv())\n \n if not isinstance(response, str) or response != VLLM_RPC_SUCCESS_STR:\n@@ -117,7 +125,8 @@ async def wait_for_server(self):\n \n await self._send_one_way_rpc_request(\n request=RPCUtilityRequest.IS_SERVER_READY,\n- error_message=\"Unable to start RPC Server.\")\n+ error_message=\"Unable to start RPC Server.\",\n+ timeout=SERVER_START_TIMEOUT)\n \n async def _get_model_config_rpc(self) -> ModelConfig:\n \"\"\"Get the ModelConfig object from the RPC Server\"\"\"\n" }
[ { "diff_hunk": "@@ -85,14 +88,19 @@ async def _send_get_data_rpc_request(self, request: RPCUtilityRequest,\n \n return data\n \n- async def _send_one_way_rpc_request(self, request: RPC_REQUEST_TYPE,\n- error_message: str):\n+ async def _send_one_way_rpc_request(self,\n+ request: RPC_REQUEST_TYPE,\n+ error_message: str,\n+ timeout: int = 0):\n \"\"\"Send one-way RPC request to trigger an action.\"\"\"\n with self.socket() as socket:\n # Ping RPC Server with request.\n await socket.send(cloudpickle.dumps(request))\n \n # Await acknowledgement from RPCServer.\n+ if timeout > 0 and await socket.poll(timeout) == 0:", "line": null, "original_line": 101, "original_start_line": null, "path": "vllm/entrypoints/openai/rpc/client.py", "start_line": null, "text": "@user1:\n```suggestion\r\n if timeout is not None and await socket.poll(timeout=timeout) == 0:\r\n```" }, { "diff_hunk": "@@ -116,7 +116,14 @@ async def build_async_engine_client(args) -> AsyncIterator[AsyncEngineClient]:\n \n # Build RPCClient, which conforms to AsyncEngineClient Protocol.\n async_engine_client = AsyncEngineRPCClient(port)\n- await async_engine_client.setup()\n+\n+ while True:\n+ try:\n+ await async_engine_client.setup()\n+ break\n+ except TimeoutError as e:\n+ if not rpc_server_process.is_alive():\n+ raise RuntimeError(\"Server crashed\") from e", "line": null, "original_line": 126, "original_start_line": 120, "path": "vllm/entrypoints/openai/api_server.py", "start_line": null, "text": "@user1:\nWould be best to move this within the `try` block below I think\n\n@author:\nRight, so that the clean up code in the finally section is called. Just moving the code into the try block caused another \"hang\" because it turns out that `context.destroy()` by default waits for 30 seconds if there are undelivered messages, which is the case if the server process crashed. So I added `linger=0` to the socket.close() calls to prevent that. I think this makes sense because in all uses of `self.socket()` the client sends a request and waits for the answer, so in all these cases we know that the server has seen the message." }, { "diff_hunk": "@@ -85,14 +88,19 @@ async def _send_get_data_rpc_request(self, request: RPCUtilityRequest,\n \n return data\n \n- async def _send_one_way_rpc_request(self, request: RPC_REQUEST_TYPE,\n- error_message: str):\n+ async def _send_one_way_rpc_request(self,\n+ request: RPC_REQUEST_TYPE,\n+ error_message: str,\n+ timeout: int = 0):", "line": null, "original_line": 94, "original_start_line": null, "path": "vllm/entrypoints/openai/rpc/client.py", "start_line": null, "text": "@user1:\nI think default here should be `None` rather than 0. Could you include `Optional[int]` type\n\n@author:\nOh yeah, C++ programmer habits..." } ]
a2f26392fa73e9e45a84c2ceefb671d9a4ea4d74
diff --git a/tests/entrypoints/openai/test_mp_crash.py b/tests/entrypoints/openai/test_mp_crash.py new file mode 100644 index 000000000000..7dc595a7be35 --- /dev/null +++ b/tests/entrypoints/openai/test_mp_crash.py @@ -0,0 +1,35 @@ +from typing import Any + +import pytest + +from vllm.engine.async_llm_engine import AsyncLLMEngine +from vllm.entrypoints.openai.api_server import build_async_engine_client +from vllm.entrypoints.openai.cli_args import make_arg_parser +from vllm.utils import FlexibleArgumentParser + + +def crashing_from_engine_args( + cls, + engine_args: Any = None, + start_engine_loop: Any = None, + usage_context: Any = None, + stat_loggers: Any = None, +) -> "AsyncLLMEngine": + raise Exception("foo") + + [email protected] +async def test_mp_crash_detection(monkeypatch): + + with pytest.raises(RuntimeError) as excinfo, monkeypatch.context() as m: + m.setattr(AsyncLLMEngine, "from_engine_args", + crashing_from_engine_args) + parser = FlexibleArgumentParser( + description="vLLM's remote OpenAI server.") + parser = make_arg_parser(parser) + args = parser.parse_args([]) + + async with build_async_engine_client(args): + pass + assert "The server process died before responding to the readiness probe"\ + in str(excinfo.value) diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index 48aa904d4721..d44604b12fb6 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -120,9 +120,18 @@ async def build_async_engine_client(args) -> AsyncIterator[AsyncEngineClient]: # Build RPCClient, which conforms to AsyncEngineClient Protocol. async_engine_client = AsyncEngineRPCClient(rpc_path) - await async_engine_client.setup() try: + while True: + try: + await async_engine_client.setup() + break + except TimeoutError as e: + if not rpc_server_process.is_alive(): + raise RuntimeError( + "The server process died before " + "responding to the readiness probe") from e + yield async_engine_client finally: # Ensure rpc server process was terminated diff --git a/vllm/entrypoints/openai/rpc/client.py b/vllm/entrypoints/openai/rpc/client.py index 8552c286eeee..d69b202e2d1b 100644 --- a/vllm/entrypoints/openai/rpc/client.py +++ b/vllm/entrypoints/openai/rpc/client.py @@ -18,6 +18,9 @@ from vllm.sampling_params import SamplingParams from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs +# Time to wait before checking it the server process is alive. +SERVER_START_TIMEOUT_MS = 1000 + class AsyncEngineRPCClient: @@ -61,7 +64,16 @@ def socket(self): socket.connect(self.rpc_path) yield socket finally: - socket.close() + # linger == 0 means discard unsent messages + # when the socket is closed. This is necessary + # because otherwise self.context.destroy() will + # wait for 30 seconds until unsent messages are + # received, which is impossible if the server + # crashed. In the absence of a server crash we + # always expect a response before closing the + # socket anyway. + # Reference: http://api.zeromq.org/4-2:zmq-setsockopt#toc24 + socket.close(linger=0) async def _send_get_data_rpc_request(self, request: RPCUtilityRequest, expected_type: Any, @@ -85,14 +97,19 @@ async def _send_get_data_rpc_request(self, request: RPCUtilityRequest, return data - async def _send_one_way_rpc_request(self, request: RPC_REQUEST_TYPE, - error_message: str): + async def _send_one_way_rpc_request(self, + request: RPC_REQUEST_TYPE, + error_message: str, + timeout: Optional[int] = None): """Send one-way RPC request to trigger an action.""" with self.socket() as socket: # Ping RPC Server with request. await socket.send(cloudpickle.dumps(request)) # Await acknowledgement from RPCServer. + if timeout is not None and await socket.poll(timeout=timeout) == 0: + raise TimeoutError(f"server didn't reply within {timeout} ms") + response = cloudpickle.loads(await socket.recv()) if not isinstance(response, str) or response != VLLM_RPC_SUCCESS_STR: @@ -117,7 +134,8 @@ async def wait_for_server(self): await self._send_one_way_rpc_request( request=RPCUtilityRequest.IS_SERVER_READY, - error_message="Unable to start RPC Server.") + error_message="Unable to start RPC Server.", + timeout=SERVER_START_TIMEOUT_MS) async def _get_model_config_rpc(self) -> ModelConfig: """Get the ModelConfig object from the RPC Server"""
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7153@7c45a52
vllm-project/vllm
Python
7,153
[Model] Support SigLIP encoder and alternative decoders for LLaVA models
Based on #7067, this PR partially addresses #7143 by enabling SigLIP to be used in LLaVA models. The same code also enables them to load other LLM decoders. This PR also fixes two hidden bugs in the calculation for the number of placeholder tokens: - For CLIP encoder, the feature size should be increased by 1 because the CLS token is prepended to the output. - For LLaVA models, the feature size should be reduced by 1 when `vision_feature_select_strategy="default"`. These two miscalculations cancelled out each other prior to this PR. They were discovered when trying to support SigLIP in #7149 because SigLIP encoder, unlike CLIP encoder, does not output the CLS token, resulting in an off-by-one error when filling in placeholders. FIX #7149
2024-08-05T09:11:39Z
[Bug]: The deployment function requires a divisible relationship, but the model structure does not meet this requirement. What should I do? ### ๐Ÿ› Describe the bug def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int: assert image_size % patch_size == 0 return image_size // patch_size <html> <body> <!--StartFragment--> "vision_config": { -- ย  | "hidden_act": "gelu_pytorch_tanh", ย  | "hidden_size": 1152, ย  | "image_size": 384, ย  | "intermediate_size": 4304, ย  | "layer_norm_eps": 1e-06, ย  | "model_type": "siglip_vision_model", ย  | "num_attention_heads": 16, ย  | "num_hidden_layers": 27, ย  | "patch_size": 14 <!--EndFragment--> </body> </html>
Which HF repo are you using? TIGER-Lab/Mantis-8B-siglip-llama3 Perhaps I should write a new Python script to support this model. It should only take a small modification since the architecture is the same as `LlavaForConditionalGeneration`. We just need to support loading the SigLIP model inside vLLM. > It should only take a small modification since the architecture is the same as `LlavaForConditionalGeneration`. We just need to support loading the SigLIP model inside vLLM. I have add the SigLIP to `LlavaForConditionalGeneration`, but the SigLIP need to get grid_length, which requires a divisible relationship. In Mantis, the `LlavaForConditionalGeneration` have been rewrite. I'm working on a PR to add SigLIP support to LLaVA architecture. > > It should only take a small modification since the architecture is the same as `LlavaForConditionalGeneration`. We just need to support loading the SigLIP model inside vLLM. > > I have add the SigLIP to `LlavaForConditionalGeneration`, but the SigLIP need to get grid_length, which requires a divisible relationship. In Mantis, the `LlavaForConditionalGeneration` have been rewrite. I think the model is still the same (since the HuggingFace repo didn't supply its own `modeling_` file), just that a different vision encoder is being loaded. Thank for your reply. [https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/modeling_llava.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/modeling_llava.py) This is the `modeling_` file. I will continue to find how to solve this problem. > Thank for your reply. [https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/modeling_llava.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/modeling_llava.py) This is the `modeling_` file. I will continue to find how to solve this problem. Oh, I didn't check the repo on GitHub. Thanks for pointing that out! I guess you can work on implementing it then. Can you try out #7153 and see if vLLM can run the model without modification? There is a chance this could work since that particular model uses `LlavaForConditionalGeneration`, not `MLlavaForConditionalGeneration`.
[ { "body": "\r\n\r\n### ๐Ÿ› Describe the bug\r\n\r\ndef get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:\r\n assert image_size % patch_size == 0\r\n return image_size // patch_size\r\n \r\n<html>\r\n<body>\r\n<!--StartFragment-->\r\n\"vision_config\": {\r\n--\r\nย  | \"hidden_act\": \"gelu_pytorch_tanh\",\r\nย  | \"hidden_size\": 1152,\r\nย  | \"image_size\": 384,\r\nย  | \"intermediate_size\": 4304,\r\nย  | \"layer_norm_eps\": 1e-06,\r\nย  | \"model_type\": \"siglip_vision_model\",\r\nย  | \"num_attention_heads\": 16,\r\nย  | \"num_hidden_layers\": 27,\r\nย  | \"patch_size\": 14\r\n\r\n<!--EndFragment-->\r\n</body>\r\n</html>", "number": 7149, "title": "[Bug]: The deployment function requires a divisible relationship, but the model structure does not meet this requirement. What should I do?" } ]
9118217f58c39040aa935b7c85250c7364ffa72d
{ "head_commit": "7c45a523cc43e99465b6f22f99a8f4e1efab27c0", "head_commit_message": "Auto-detect image ID to be robust against switching test models; add test code for Mantis model", "patch_to_review": "diff --git a/requirements-test.txt b/requirements-test.txt\nindex 5f3fd15c7ee..f9f6e8a84f8 100644\n--- a/requirements-test.txt\n+++ b/requirements-test.txt\n@@ -19,6 +19,7 @@ ray\n sentence-transformers # required for embedding\n compressed-tensors==0.4.0 # required for compressed-tensors\n timm # required for internvl test\n+git+https://github.com/TIGER-AI-Lab/Mantis.git # required for llava(mantis) test\n \n # Benchmarking\n aiohttp\ndiff --git a/tests/models/test_llava.py b/tests/models/test_llava.py\nindex 79ab58c364f..bd666dce7ab 100644\n--- a/tests/models/test_llava.py\n+++ b/tests/models/test_llava.py\n@@ -1,10 +1,11 @@\n from typing import List, Optional, Tuple, Type\n \n import pytest\n-from transformers import AutoTokenizer\n+from transformers import AutoConfig, AutoTokenizer\n \n from vllm.multimodal.utils import rescale_image_size\n from vllm.sequence import SampleLogprobs\n+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE\n \n from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets\n from .utils import check_logprobs_close\n@@ -18,9 +19,11 @@\n \"USER: <image>\\nWhat is the season?\\nASSISTANT:\",\n })\n \n-IMAGE_TOKEN_ID = 32000\n-\n-models = [\"llava-hf/llava-1.5-7b-hf\"]\n+models = [\n+ \"llava-hf/llava-1.5-7b-hf\",\n+ # TODO: Get this model to produce meaningful output in vLLM\n+ # \"TIGER-Lab/Mantis-8B-siglip-llama3\",\n+]\n \n \n def vllm_to_hf_output(vllm_output: Tuple[List[int], str,\n@@ -29,12 +32,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,\n \"\"\"Sanitize vllm output to be comparable with hf output.\"\"\"\n output_ids, output_str, out_logprobs = vllm_output\n \n+ config = AutoConfig.from_pretrained(model)\n+ image_token_id = config.image_token_index\n+\n tokenizer = AutoTokenizer.from_pretrained(model)\n eos_token_id = tokenizer.eos_token_id\n \n hf_output_ids = [\n token_id for idx, token_id in enumerate(output_ids)\n- if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID\n+ if token_id != image_token_id or output_ids[idx - 1] != image_token_id\n ]\n \n assert output_str[0] == \" \"\n@@ -67,6 +73,16 @@ def run_test(\n Note, the text input is also adjusted to abide by vllm contract.\n The text output is sanitized to be able to compare with hf.\n \"\"\"\n+ if model.startswith(\"TIGER-Lab/Mantis\"):\n+ from mantis.models.mllava import MLlavaProcessor\n+\n+ torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]\n+ mantis_processor = MLlavaProcessor.from_pretrained(\n+ model, torch_dtype=torch_dtype)\n+ assert isinstance(mantis_processor, MLlavaProcessor)\n+ else:\n+ mantis_processor = None\n+\n images = [asset.pil_image for asset in image_assets]\n \n inputs_per_image = [(\n@@ -94,6 +110,15 @@ def run_test(\n ]\n \n with hf_runner(model, dtype=dtype, is_vision_model=True) as hf_model:\n+ if mantis_processor is not None:\n+\n+ def process(*args, **kwargs):\n+ output = mantis_processor(*args, **kwargs)\n+ output[\"pixel_values\"] = output[\"pixel_values\"].to(torch_dtype)\n+ return output\n+\n+ hf_model.processor = process\n+\n hf_outputs_per_image = [\n hf_model.generate_greedy_logprobs_limit(prompts,\n max_tokens,\ndiff --git a/tests/models/test_llava_next.py b/tests/models/test_llava_next.py\nindex b6d72dee5c5..60c7fc33b72 100644\n--- a/tests/models/test_llava_next.py\n+++ b/tests/models/test_llava_next.py\n@@ -1,7 +1,7 @@\n from typing import List, Optional, Tuple, Type, overload\n \n import pytest\n-from transformers import AutoTokenizer\n+from transformers import AutoConfig, AutoTokenizer\n \n from vllm.multimodal.utils import rescale_image_size\n from vllm.sequence import SampleLogprobs\n@@ -23,8 +23,6 @@\n f\"{_PREFACE} USER: <image>\\nWhat is the season? ASSISTANT:\",\n })\n \n-IMAGE_TOKEN_ID = 32000\n-\n models = [\"llava-hf/llava-v1.6-vicuna-7b-hf\"]\n \n \n@@ -34,12 +32,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,\n \"\"\"Sanitize vllm output to be comparable with hf output.\"\"\"\n output_ids, output_str, out_logprobs = vllm_output\n \n+ config = AutoConfig.from_pretrained(model)\n+ image_token_id = config.image_token_index\n+\n tokenizer = AutoTokenizer.from_pretrained(model)\n eos_token_id = tokenizer.eos_token_id\n \n hf_output_ids = [\n token_id for idx, token_id in enumerate(output_ids)\n- if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID\n+ if token_id != image_token_id or output_ids[idx - 1] != image_token_id\n ]\n \n assert output_str[0] == \" \"\ndiff --git a/tests/models/test_paligemma.py b/tests/models/test_paligemma.py\nindex e1c39ee6fec..f3f682b1c2c 100644\n--- a/tests/models/test_paligemma.py\n+++ b/tests/models/test_paligemma.py\n@@ -2,7 +2,7 @@\n from typing import List, Optional, Tuple, Type\n \n import pytest\n-from transformers import AutoTokenizer\n+from transformers import AutoConfig, AutoTokenizer\n \n from vllm.multimodal.utils import rescale_image_size\n from vllm.sequence import SampleLogprobs\n@@ -20,8 +20,6 @@\n \"What is in the picture?\",\n })\n \n-IMAGE_TOKEN_ID = 257152\n-\n models = [\"google/paligemma-3b-mix-224\"]\n \n # ROCm Triton FA can run into compilation issues with these models due to,\n@@ -37,12 +35,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,\n \"\"\"Sanitize vllm output to be comparable with hf output.\"\"\"\n output_ids, output_str, out_logprobs = vllm_output\n \n+ config = AutoConfig.from_pretrained(model)\n+ image_token_id = config.image_token_index\n+\n tokenizer = AutoTokenizer.from_pretrained(model)\n eos_token_id = tokenizer.eos_token_id\n \n hf_output_ids = [\n token_id for idx, token_id in enumerate(output_ids)\n- if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID\n+ if token_id != image_token_id or output_ids[idx - 1] != image_token_id\n ]\n \n hf_output_str = output_str\ndiff --git a/tests/models/test_registry.py b/tests/models/test_registry.py\nindex 547ab10051f..b058e2755c2 100644\n--- a/tests/models/test_registry.py\n+++ b/tests/models/test_registry.py\n@@ -6,4 +6,4 @@\n @pytest.mark.parametrize(\"model_cls\", _MODELS)\n def test_registry_imports(model_cls):\n # Ensure all model classes can be imported successfully\n- ModelRegistry.load_model_cls(model_cls)\n+ ModelRegistry.resolve_model_cls([model_cls])\ndiff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py\nindex f72515e0148..6a0460f3d8e 100644\n--- a/vllm/model_executor/model_loader/loader.py\n+++ b/vllm/model_executor/model_loader/loader.py\n@@ -15,6 +15,7 @@\n import torch\n from huggingface_hub import HfApi, hf_hub_download\n from torch import nn\n+from transformers import PretrainedConfig\n \n from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoadFormat,\n LoRAConfig, ModelConfig, MultiModalConfig,\n@@ -140,6 +141,22 @@ def _get_model_initialization_kwargs(\n return extra_kwargs\n \n \n+def build_model(model_class: Type[nn.Module], hf_config: PretrainedConfig,\n+ cache_config: Optional[CacheConfig],\n+ quant_config: Optional[QuantizationConfig], *,\n+ lora_config: Optional[LoRAConfig],\n+ multimodal_config: Optional[MultiModalConfig],\n+ scheduler_config: Optional[SchedulerConfig]) -> nn.Module:\n+ extra_kwargs = _get_model_initialization_kwargs(model_class, lora_config,\n+ multimodal_config,\n+ scheduler_config)\n+\n+ return model_class(config=hf_config,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ **extra_kwargs)\n+\n+\n def _initialize_model(\n model_config: ModelConfig,\n load_config: LoadConfig,\n@@ -148,15 +165,17 @@ def _initialize_model(\n cache_config: CacheConfig,\n scheduler_config: Optional[SchedulerConfig] = None) -> nn.Module:\n \"\"\"Initialize a model with the given configurations.\"\"\"\n- model_class = get_model_architecture(model_config)[0]\n- quant_config = _get_quantization_config(model_config, load_config)\n-\n- return model_class(config=model_config.hf_config,\n- cache_config=cache_config,\n- quant_config=quant_config,\n- **_get_model_initialization_kwargs(\n- model_class, lora_config, multimodal_config,\n- scheduler_config))\n+ model_class, _ = get_model_architecture(model_config)\n+\n+ return build_model(\n+ model_class,\n+ model_config.hf_config,\n+ quant_config=_get_quantization_config(model_config, load_config),\n+ lora_config=lora_config,\n+ multimodal_config=multimodal_config,\n+ cache_config=cache_config,\n+ scheduler_config=scheduler_config,\n+ )\n \n \n class BaseModelLoader(ABC):\ndiff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py\nindex f7e0f56c1a4..331b859d2ad 100644\n--- a/vllm/model_executor/model_loader/utils.py\n+++ b/vllm/model_executor/model_loader/utils.py\n@@ -28,13 +28,7 @@ def get_model_architecture(\n and \"MixtralForCausalLM\" in architectures):\n architectures = [\"QuantMixtralForCausalLM\"]\n \n- for arch in architectures:\n- model_cls = ModelRegistry.load_model_cls(arch)\n- if model_cls is not None:\n- return (model_cls, arch)\n- raise ValueError(\n- f\"Model architectures {architectures} are not supported for now. \"\n- f\"Supported architectures: {ModelRegistry.get_supported_archs()}\")\n+ return ModelRegistry.resolve_model_cls(architectures)\n \n \n def get_architecture_class_name(model_config: ModelConfig) -> str:\ndiff --git a/vllm/model_executor/models/__init__.py b/vllm/model_executor/models/__init__.py\nindex 94c3cea98be..ebb77a802d5 100644\n--- a/vllm/model_executor/models/__init__.py\n+++ b/vllm/model_executor/models/__init__.py\n@@ -1,6 +1,6 @@\n import functools\n import importlib\n-from typing import Dict, List, Optional, Type\n+from typing import Dict, List, Optional, Tuple, Type\n \n import torch.nn as nn\n \n@@ -126,7 +126,7 @@ def _get_model(model_arch: str):\n return getattr(module, model_cls_name, None)\n \n @staticmethod\n- def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:\n+ def _try_load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:\n if model_arch in _OOT_MODELS:\n return _OOT_MODELS[model_arch]\n if model_arch not in _MODELS:\n@@ -143,6 +143,18 @@ def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:\n \n return ModelRegistry._get_model(model_arch)\n \n+ @staticmethod\n+ def resolve_model_cls(\n+ architectures: List[str]) -> Tuple[Type[nn.Module], str]:\n+ for arch in architectures:\n+ model_cls = ModelRegistry._try_load_model_cls(arch)\n+ if model_cls is not None:\n+ return (model_cls, arch)\n+\n+ raise ValueError(\n+ f\"Model architectures {architectures} are not supported for now. \"\n+ f\"Supported architectures: {ModelRegistry.get_supported_archs()}\")\n+\n @staticmethod\n def get_supported_archs() -> List[str]:\n return list(_MODELS.keys())\ndiff --git a/vllm/model_executor/models/clip.py b/vllm/model_executor/models/clip.py\nindex b4f628061f1..805ade39389 100644\n--- a/vllm/model_executor/models/clip.py\n+++ b/vllm/model_executor/models/clip.py\n@@ -1,6 +1,6 @@\n \"\"\"Minimal implementation of CLIPVisionModel intended to be only used \n within a vision language model.\"\"\"\n-from typing import Optional\n+from typing import Iterable, Optional, Tuple\n \n import torch\n import torch.nn as nn\n@@ -14,6 +14,7 @@\n from vllm.model_executor.layers.linear import (ColumnParallelLinear,\n RowParallelLinear)\n from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n from vllm.multimodal.image import (cached_get_tokenizer,\n repeat_and_pad_image_tokens)\n from vllm.sequence import SequenceData\n@@ -32,7 +33,7 @@ def get_clip_num_patches(*, image_size: int, patch_size: int) -> int:\n \n def get_clip_image_feature_size(hf_config: CLIPVisionConfig) -> int:\n return get_clip_num_patches(image_size=hf_config.image_size,\n- patch_size=hf_config.patch_size)\n+ patch_size=hf_config.patch_size) + 1\n \n \n def get_max_clip_image_tokens(hf_config: CLIPVisionConfig) -> int:\n@@ -291,3 +292,22 @@ def forward(self, pixel_values: Optional[torch.Tensor] = None):\n @property\n def device(self):\n return next(self.parameters()).device\n+\n+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n+ params_dict = dict(self.named_parameters())\n+ layer_count = len(self.vision_model.encoder.layers)\n+\n+ for name, loaded_weight in weights:\n+ # post_layernorm is not needed in CLIPVisionModel\n+ if \"vision_model.post_layernorm\" in name:\n+ continue\n+ # omit layers when num_hidden_layers_override is set\n+ if \"vision_model.encoder.layers.\" in name:\n+ layer_idx = int(name.split(\".\")[3])\n+ if layer_idx >= layer_count:\n+ continue\n+\n+ param = params_dict[name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\ndiff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py\nindex 47492512714..e380f07c593 100644\n--- a/vllm/model_executor/models/internvl.py\n+++ b/vllm/model_executor/models/internvl.py\n@@ -18,7 +18,6 @@\n from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs\n from vllm.model_executor.layers.quantization import QuantizationConfig\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n-from vllm.model_executor.models import ModelRegistry\n from vllm.model_executor.models.intern_vit import InternVisionModel\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n from vllm.multimodal import MULTIMODAL_REGISTRY\n@@ -29,7 +28,7 @@\n from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,\n get_clip_num_patches)\n from .interfaces import SupportsVision\n-from .utils import merge_vision_embeddings\n+from .utils import filter_weights, init_inner_model, merge_vision_embeddings\n \n IMG_START = '<img>'\n IMG_END = '</img>'\n@@ -283,10 +282,8 @@ def __init__(self,\n self.vision_model = InternVisionModel(\n config.vision_config, num_hidden_layers_override=num_hidden_layers)\n \n- llm_class = ModelRegistry.load_model_cls(\n- config.text_config.architectures[0])\n- self.language_model = llm_class(config.text_config, cache_config,\n- quant_config)\n+ self.language_model = init_inner_model(config.text_config,\n+ cache_config, quant_config)\n \n vit_hidden_size = config.vision_config.hidden_size\n llm_hidden_size = config.text_config.hidden_size\n@@ -415,24 +412,16 @@ def sample(\n ) -> Optional[SamplerOutput]:\n return self.language_model.sample(logits, sampling_metadata)\n \n- def _filter_weights(self, weights: Iterable[Tuple[str, torch.Tensor]],\n- prefix: str):\n- for name, loaded_weight in weights:\n- name = name.split(\".\")\n- if prefix == name.pop(0):\n- name = \".\".join(name)\n- yield name, loaded_weight\n-\n def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n # prepare weight iterators for components\n vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)\n \n # load vision encoder\n- vit_weights = self._filter_weights(vit_weights, \"vision_model\")\n+ vit_weights = filter_weights(vit_weights, \"vision_model\")\n self.vision_model.load_weights(vit_weights)\n \n # load mlp projector\n- mlp_weights = self._filter_weights(mlp_weights, \"mlp1\")\n+ mlp_weights = filter_weights(mlp_weights, \"mlp1\")\n mlp_params_dict = dict(self.mlp1.named_parameters())\n for name, loaded_weight in mlp_weights:\n param = mlp_params_dict[name]\n@@ -441,5 +430,5 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n weight_loader(param, loaded_weight)\n \n # load llm backbone\n- llm_weights = self._filter_weights(llm_weights, \"language_model\")\n+ llm_weights = filter_weights(llm_weights, \"language_model\")\n self.language_model.load_weights(llm_weights)\ndiff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py\nindex 4e7e6c47f0a..cf6a3fd75e0 100644\n--- a/vllm/model_executor/models/llava.py\n+++ b/vllm/model_executor/models/llava.py\n@@ -1,34 +1,29 @@\n-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict\n+import itertools\n+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union\n \n import torch\n import torch.nn as nn\n-from transformers import CLIPVisionConfig, LlavaConfig\n+from transformers import CLIPVisionConfig, LlavaConfig, SiglipVisionConfig\n \n from vllm.attention import AttentionMetadata\n from vllm.config import CacheConfig, MultiModalConfig\n from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs\n from vllm.model_executor.layers.activation import get_act_fn\n-from vllm.model_executor.layers.logits_processor import LogitsProcessor\n from vllm.model_executor.layers.quantization.base_config import (\n QuantizationConfig)\n-from vllm.model_executor.layers.sampler import Sampler\n-from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n-from vllm.model_executor.models.clip import CLIPVisionModel\n-from vllm.model_executor.models.llama import LlamaModel\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n from vllm.multimodal import MULTIMODAL_REGISTRY\n from vllm.sequence import IntermediateTensors, SamplerOutput\n \n-from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,\n- get_max_clip_image_tokens, input_processor_for_clip)\n+from .clip import (CLIPVisionModel, dummy_image_for_clip,\n+ dummy_seq_data_for_clip, get_max_clip_image_tokens,\n+ input_processor_for_clip)\n from .interfaces import SupportsVision\n-from .utils import merge_vision_embeddings\n-\n-_KEYS_TO_MODIFY_MAPPING = {\n- \"language_model.lm_head\": \"lm_head\",\n- \"language_model.model\": \"language_model\",\n-}\n+from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n+ dummy_seq_data_for_siglip, get_max_siglip_image_tokens,\n+ input_processor_for_siglip)\n+from .utils import filter_weights, init_inner_model, merge_vision_embeddings\n \n \n # TODO(xwjiang): Run benchmark and decide if TP.\n@@ -67,25 +62,48 @@ def get_max_llava_image_tokens(ctx: InputContext):\n vision_config = hf_config.vision_config\n \n if isinstance(vision_config, CLIPVisionConfig):\n- return get_max_clip_image_tokens(vision_config)\n-\n- msg = f\"Unsupported vision config: {type(vision_config)}\"\n- raise NotImplementedError(msg)\n+ num_image_tokens = get_max_clip_image_tokens(vision_config)\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ num_image_tokens = get_max_siglip_image_tokens(vision_config)\n+ else:\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+ strategy = hf_config.vision_feature_select_strategy\n+ if strategy == \"default\":\n+ return num_image_tokens - 1\n+ elif strategy == \"full\":\n+ return num_image_tokens\n+ else:\n+ raise ValueError(f\"Unexpected select feature strategy: {strategy}\")\n \n \n def dummy_data_for_llava(ctx: InputContext, seq_len: int):\n hf_config = ctx.get_hf_config(LlavaConfig)\n vision_config = hf_config.vision_config\n \n+ image_feature_size = get_max_llava_image_tokens(ctx)\n+\n if isinstance(vision_config, CLIPVisionConfig):\n seq_data = dummy_seq_data_for_clip(\n vision_config,\n seq_len,\n image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n )\n \n mm_data = dummy_image_for_clip(vision_config)\n return seq_data, mm_data\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ seq_data = dummy_seq_data_for_siglip(\n+ vision_config,\n+ seq_len,\n+ image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n+ )\n+\n+ mm_data = dummy_image_for_siglip(vision_config)\n+ return seq_data, mm_data\n \n msg = f\"Unsupported vision config: {type(vision_config)}\"\n raise NotImplementedError(msg)\n@@ -100,12 +118,49 @@ def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):\n hf_config = ctx.get_hf_config(LlavaConfig)\n vision_config = hf_config.vision_config\n \n+ image_feature_size = get_max_llava_image_tokens(ctx)\n+\n if isinstance(vision_config, CLIPVisionConfig):\n return input_processor_for_clip(\n model_config,\n vision_config,\n llm_inputs,\n image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n+ )\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ return input_processor_for_siglip(\n+ model_config,\n+ vision_config,\n+ llm_inputs,\n+ image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n+ )\n+\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+\n+def _init_vision_tower(hf_config: LlavaConfig):\n+ vision_config = hf_config.vision_config\n+\n+ # Initialize the vision tower only up to the required feature layer\n+ vision_feature_layer = hf_config.vision_feature_layer\n+ if vision_feature_layer < 0:\n+ num_hidden_layers = hf_config.vision_config.num_hidden_layers \\\n+ + vision_feature_layer + 1\n+ else:\n+ num_hidden_layers = vision_feature_layer + 1\n+\n+ if isinstance(vision_config, CLIPVisionConfig):\n+ return CLIPVisionModel(\n+ vision_config,\n+ num_hidden_layers_override=num_hidden_layers,\n+ )\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ return SiglipVisionModel(\n+ vision_config,\n+ num_hidden_layers_override=num_hidden_layers,\n )\n \n msg = f\"Unsupported vision config: {type(vision_config)}\"\n@@ -128,36 +183,15 @@ def __init__(self,\n self.config = config\n self.multimodal_config = multimodal_config\n \n- # Initialize the vision tower only up to the required feature layer\n- vision_feature_layer = config.vision_feature_layer\n- if vision_feature_layer < 0:\n- num_hidden_layers = config.vision_config.num_hidden_layers \\\n- + vision_feature_layer + 1\n- else:\n- num_hidden_layers = vision_feature_layer + 1\n-\n # TODO: Optionally initializes this for supporting embeddings.\n- self.vision_tower = CLIPVisionModel(\n- config.vision_config, num_hidden_layers_override=num_hidden_layers)\n+ self.vision_tower = _init_vision_tower(config)\n self.multi_modal_projector = LlavaMultiModalProjector(\n vision_hidden_size=config.vision_config.hidden_size,\n text_hidden_size=config.text_config.hidden_size,\n projector_hidden_act=config.projector_hidden_act)\n \n- self.quant_config = quant_config\n- self.language_model = LlamaModel(config.text_config, cache_config,\n- quant_config)\n- self.unpadded_vocab_size = config.text_config.vocab_size\n- self.lm_head = ParallelLMHead(\n- self.unpadded_vocab_size,\n- config.text_config.hidden_size,\n- org_num_embeddings=self.language_model.org_vocab_size,\n- quant_config=quant_config)\n- logit_scale = getattr(config, \"logit_scale\", 1.0)\n- self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,\n- config.text_config.vocab_size,\n- logit_scale)\n- self.sampler = Sampler()\n+ self.language_model = init_inner_model(config.text_config,\n+ cache_config, quant_config)\n \n def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:\n h = w = self.config.vision_config.image_size\n@@ -198,8 +232,11 @@ def _select_image_features(self, image_features: torch.Tensor, *,\n \n raise ValueError(f\"Unexpected select feature strategy: {strategy}\")\n \n- def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,\n- pixel_values: torch.Tensor) -> torch.Tensor:\n+ def _image_pixels_to_features(\n+ self,\n+ vision_tower: Union[CLIPVisionModel, SiglipVisionModel],\n+ pixel_values: torch.Tensor,\n+ ) -> torch.Tensor:\n \n # NOTE: we skip the step to select the vision feature layer since\n # this is already done inside the vision tower\n@@ -272,7 +309,8 @@ def forward(\n \n if image_input is not None:\n vision_embeddings = self._process_image_input(image_input)\n- inputs_embeds = self.language_model.get_input_embeddings(input_ids)\n+ inputs_embeds = self.language_model.model.get_input_embeddings(\n+ input_ids)\n \n inputs_embeds = merge_vision_embeddings(\n input_ids, inputs_embeds, vision_embeddings,\n@@ -282,68 +320,44 @@ def forward(\n else:\n inputs_embeds = None\n \n- hidden_states = self.language_model(input_ids,\n- positions,\n- kv_caches,\n- attn_metadata,\n- None,\n- inputs_embeds=inputs_embeds)\n+ hidden_states = self.language_model.model(input_ids,\n+ positions,\n+ kv_caches,\n+ attn_metadata,\n+ None,\n+ inputs_embeds=inputs_embeds)\n \n return hidden_states\n \n def compute_logits(self, hidden_states: torch.Tensor,\n sampling_metadata: SamplingMetadata) -> torch.Tensor:\n- logits = self.logits_processor(self.lm_head, hidden_states,\n- sampling_metadata)\n- return logits\n+ return self.language_model.compute_logits(hidden_states,\n+ sampling_metadata)\n \n def sample(\n self,\n logits: torch.Tensor,\n sampling_metadata: SamplingMetadata,\n ) -> Optional[SamplerOutput]:\n- next_tokens = self.sampler(logits, sampling_metadata)\n- return next_tokens\n+ return self.language_model.sample(logits, sampling_metadata)\n \n def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n- # only doing this for language model part for now.\n- stacked_params_mapping = [\n- # (param_name, shard_name, shard_id)\n- (\"qkv_proj\", \"q_proj\", \"q\"),\n- (\"qkv_proj\", \"k_proj\", \"k\"),\n- (\"qkv_proj\", \"v_proj\", \"v\"),\n- (\"gate_up_proj\", \"gate_proj\", 0),\n- (\"gate_up_proj\", \"up_proj\", 1),\n- ]\n- params_dict = dict(self.named_parameters())\n- for name, loaded_weight in weights:\n- if \"rotary_emb.inv_freq\" in name:\n- continue\n- # post_layernorm is not needed in CLIPVisionModel\n- if \"vision_model.post_layernorm\" in name:\n- continue\n- for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():\n- if key_to_modify in name:\n- name = name.replace(key_to_modify, new_key)\n- use_default_weight_loading = False\n- if \"vision\" in name:\n- if self.vision_tower is not None:\n- # We only do sharding for language model and\n- # not vision model for now.\n- use_default_weight_loading = True\n- else:\n- for (param_name, weight_name,\n- shard_id) in stacked_params_mapping:\n- if weight_name not in name:\n- continue\n- param = params_dict[name.replace(weight_name, param_name)]\n- weight_loader = param.weight_loader\n- weight_loader(param, loaded_weight, shard_id)\n- break\n- else:\n- use_default_weight_loading = True\n- if use_default_weight_loading and name in params_dict:\n- param = params_dict[name]\n- weight_loader = getattr(param, \"weight_loader\",\n- default_weight_loader)\n- weight_loader(param, loaded_weight)\n+ # prepare weight iterators for components\n+ vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)\n+\n+ # load vision encoder\n+ vit_weights = filter_weights(vit_weights, \"vision_tower\")\n+ self.vision_tower.load_weights(vit_weights)\n+\n+ # load mlp projector\n+ mlp_weights = filter_weights(mlp_weights, \"multi_modal_projector\")\n+ mlp_params_dict = dict(self.multi_modal_projector.named_parameters())\n+ for name, loaded_weight in mlp_weights:\n+ param = mlp_params_dict[name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\n+\n+ # load llm backbone\n+ llm_weights = filter_weights(llm_weights, \"language_model\")\n+ self.language_model.load_weights(llm_weights)\ndiff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py\nindex 4a67b9a583e..801889852de 100644\n--- a/vllm/model_executor/models/llava_next.py\n+++ b/vllm/model_executor/models/llava_next.py\n@@ -1,9 +1,10 @@\n+import itertools\n from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union\n \n import torch\n import torch.nn as nn\n from PIL import Image\n-from transformers import CLIPVisionConfig, LlavaNextConfig\n+from transformers import CLIPVisionConfig, LlavaNextConfig, SiglipVisionConfig\n from transformers.models.llava_next.modeling_llava_next import (\n get_anyres_image_grid_shape, unpad_image)\n from typing_extensions import NotRequired\n@@ -12,23 +13,22 @@\n from vllm.config import CacheConfig, MultiModalConfig\n from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs\n from vllm.logger import init_logger\n-from vllm.model_executor.layers.logits_processor import LogitsProcessor\n from vllm.model_executor.layers.quantization.base_config import (\n QuantizationConfig)\n-from vllm.model_executor.layers.sampler import Sampler\n-from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead\n from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n-from vllm.model_executor.models.clip import CLIPVisionModel\n-from vllm.model_executor.models.llama import LlamaModel\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n from vllm.multimodal import MULTIMODAL_REGISTRY\n from vllm.sequence import IntermediateTensors, SamplerOutput\n \n-from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,\n+from .clip import (CLIPVisionModel, dummy_image_for_clip,\n+ dummy_seq_data_for_clip, get_clip_image_feature_size,\n get_clip_patch_grid_length, input_processor_for_clip)\n from .interfaces import SupportsVision\n from .llava import LlavaMultiModalProjector\n-from .utils import merge_vision_embeddings\n+from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n+ dummy_seq_data_for_siglip, get_siglip_image_feature_size,\n+ get_siglip_patch_grid_length, input_processor_for_siglip)\n+from .utils import filter_weights, init_inner_model, merge_vision_embeddings\n \n logger = init_logger(__name__)\n \n@@ -104,30 +104,42 @@ def get_llava_next_image_feature_size(\n image_size=vision_config.image_size,\n patch_size=vision_config.patch_size,\n )\n- base_feature_size = num_patches * num_patches\n-\n- num_patch_height, num_patch_width = get_anyres_image_grid_shape(\n- image_size=(input_height, input_width),\n- grid_pinpoints=hf_config.image_grid_pinpoints,\n- patch_size=vision_config.image_size,\n+ base_feature_size = get_clip_image_feature_size(vision_config)\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ num_patches = get_siglip_patch_grid_length(\n+ image_size=vision_config.image_size,\n+ patch_size=vision_config.patch_size,\n )\n+ base_feature_size = get_siglip_image_feature_size(vision_config)\n+ else:\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+ strategy = hf_config.vision_feature_select_strategy\n+ if strategy == \"default\":\n+ base_feature_size -= 1\n+ elif strategy == \"full\":\n+ pass\n+ else:\n+ raise ValueError(f\"Unexpected select feature strategy: {strategy}\")\n \n- (\n- unpadded_feature_size,\n- newline_feature_size,\n- ) = _get_llava_next_num_unpadded_features(input_height, input_width,\n- num_patches,\n- num_patch_height,\n- num_patch_width)\n+ num_patch_height, num_patch_width = get_anyres_image_grid_shape(\n+ image_size=(input_height, input_width),\n+ grid_pinpoints=hf_config.image_grid_pinpoints,\n+ patch_size=vision_config.image_size,\n+ )\n \n- return unpadded_feature_size + newline_feature_size + base_feature_size\n+ (\n+ unpadded_feature_size,\n+ newline_feature_size,\n+ ) = _get_llava_next_num_unpadded_features(input_height, input_width,\n+ num_patches, num_patch_height,\n+ num_patch_width)\n \n- msg = f\"Unsupported vision config: {type(vision_config)}\"\n- raise NotImplementedError(msg)\n+ return unpadded_feature_size + newline_feature_size + base_feature_size\n \n \n def get_max_llava_next_image_tokens(ctx: InputContext):\n-\n return get_llava_next_image_feature_size(\n ctx.get_hf_config(LlavaNextConfig),\n input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,\n@@ -155,6 +167,21 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int):\n image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,\n )\n \n+ return seq_data, mm_data\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ seq_data = dummy_seq_data_for_siglip(\n+ vision_config,\n+ seq_len,\n+ image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n+ )\n+\n+ mm_data = dummy_image_for_siglip(\n+ vision_config,\n+ image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,\n+ image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,\n+ )\n+\n return seq_data, mm_data\n \n msg = f\"Unsupported vision config: {type(vision_config)}\"\n@@ -194,6 +221,40 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):\n image_token_id=hf_config.image_token_index,\n image_feature_size_override=image_feature_size,\n )\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ return input_processor_for_siglip(\n+ model_config,\n+ vision_config,\n+ llm_inputs,\n+ image_token_id=hf_config.image_token_index,\n+ image_feature_size_override=image_feature_size,\n+ )\n+\n+ msg = f\"Unsupported vision config: {type(vision_config)}\"\n+ raise NotImplementedError(msg)\n+\n+\n+def _init_vision_tower(hf_config: LlavaNextConfig):\n+ vision_config = hf_config.vision_config\n+\n+ # Initialize the vision tower only up to the required feature layer\n+ vision_feature_layer = hf_config.vision_feature_layer\n+ if vision_feature_layer < 0:\n+ num_hidden_layers = hf_config.vision_config.num_hidden_layers \\\n+ + vision_feature_layer + 1\n+ else:\n+ num_hidden_layers = vision_feature_layer + 1\n+\n+ if isinstance(vision_config, CLIPVisionConfig):\n+ return CLIPVisionModel(\n+ vision_config,\n+ num_hidden_layers_override=num_hidden_layers,\n+ )\n+ elif isinstance(vision_config, SiglipVisionConfig):\n+ return SiglipVisionModel(\n+ vision_config,\n+ num_hidden_layers_override=num_hidden_layers,\n+ )\n \n msg = f\"Unsupported vision config: {type(vision_config)}\"\n raise NotImplementedError(msg)\n@@ -215,36 +276,15 @@ def __init__(self,\n self.config = config\n self.multimodal_config = multimodal_config\n \n- # Initialize the vision tower only up to the required feature layer\n- vision_feature_layer = config.vision_feature_layer\n- if vision_feature_layer < 0:\n- num_hidden_layers = config.vision_config.num_hidden_layers \\\n- + vision_feature_layer + 1\n- else:\n- num_hidden_layers = vision_feature_layer + 1\n-\n # TODO: Optionally initializes this for supporting embeddings.\n- self.vision_tower = CLIPVisionModel(\n- config.vision_config, num_hidden_layers_override=num_hidden_layers)\n+ self.vision_tower = _init_vision_tower(config)\n self.multi_modal_projector = LlavaMultiModalProjector(\n vision_hidden_size=config.vision_config.hidden_size,\n text_hidden_size=config.text_config.hidden_size,\n projector_hidden_act=config.projector_hidden_act)\n \n- self.quant_config = quant_config\n- self.language_model = LlamaModel(config.text_config, cache_config,\n- quant_config)\n- self.unpadded_vocab_size = config.text_config.vocab_size\n- self.lm_head = ParallelLMHead(\n- self.unpadded_vocab_size,\n- config.text_config.hidden_size,\n- org_num_embeddings=self.language_model.org_vocab_size,\n- quant_config=quant_config)\n- logit_scale = getattr(config, \"logit_scale\", 1.0)\n- self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,\n- config.text_config.vocab_size,\n- logit_scale)\n- self.sampler = Sampler()\n+ self.language_model = init_inner_model(config.text_config,\n+ cache_config, quant_config)\n \n self.image_newline = nn.Parameter(\n torch.empty(config.text_config.hidden_size))\n@@ -310,8 +350,11 @@ def _select_image_features(self, image_features: torch.Tensor, *,\n \n raise ValueError(f\"Unexpected select feature strategy: {strategy}\")\n \n- def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,\n- pixel_values: torch.Tensor) -> torch.Tensor:\n+ def _image_pixels_to_features(\n+ self,\n+ vision_tower: Union[CLIPVisionModel, SiglipVisionModel],\n+ pixel_values: torch.Tensor,\n+ ) -> torch.Tensor:\n \n # NOTE: we skip the step to select the vision feature layer since\n # this is already done inside the vision tower\n@@ -496,7 +539,8 @@ def forward(\n \n if image_input is not None:\n vision_embeddings = self._process_image_input(image_input)\n- inputs_embeds = self.language_model.get_input_embeddings(input_ids)\n+ inputs_embeds = self.language_model.model.get_input_embeddings(\n+ input_ids)\n \n inputs_embeds = merge_vision_embeddings(\n input_ids, inputs_embeds, vision_embeddings,\n@@ -506,68 +550,54 @@ def forward(\n else:\n inputs_embeds = None\n \n- hidden_states = self.language_model(input_ids,\n- positions,\n- kv_caches,\n- attn_metadata,\n- None,\n- inputs_embeds=inputs_embeds)\n+ hidden_states = self.language_model.model(input_ids,\n+ positions,\n+ kv_caches,\n+ attn_metadata,\n+ None,\n+ inputs_embeds=inputs_embeds)\n \n return hidden_states\n \n def compute_logits(self, hidden_states: torch.Tensor,\n sampling_metadata: SamplingMetadata) -> torch.Tensor:\n- logits = self.logits_processor(self.lm_head, hidden_states,\n- sampling_metadata)\n- return logits\n+ return self.language_model.compute_logits(hidden_states,\n+ sampling_metadata)\n \n def sample(\n self,\n logits: torch.Tensor,\n sampling_metadata: SamplingMetadata,\n ) -> Optional[SamplerOutput]:\n- next_tokens = self.sampler(logits, sampling_metadata)\n- return next_tokens\n+ return self.language_model.sample(logits, sampling_metadata)\n \n def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n- # only doing this for language model part for now.\n- stacked_params_mapping = [\n- # (param_name, shard_name, shard_id)\n- (\"qkv_proj\", \"q_proj\", \"q\"),\n- (\"qkv_proj\", \"k_proj\", \"k\"),\n- (\"qkv_proj\", \"v_proj\", \"v\"),\n- (\"gate_up_proj\", \"gate_proj\", 0),\n- (\"gate_up_proj\", \"up_proj\", 1),\n- ]\n- params_dict = dict(self.named_parameters())\n- for name, loaded_weight in weights:\n- if \"rotary_emb.inv_freq\" in name:\n- continue\n- # post_layernorm is not needed in CLIPVisionModel\n- if \"vision_model.post_layernorm\" in name:\n- continue\n- for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():\n- if key_to_modify in name:\n- name = name.replace(key_to_modify, new_key)\n- use_default_weight_loading = False\n- if \"vision\" in name:\n- if self.vision_tower is not None:\n- # We only do sharding for language model and\n- # not vision model for now.\n- use_default_weight_loading = True\n- else:\n- for (param_name, weight_name,\n- shard_id) in stacked_params_mapping:\n- if weight_name not in name:\n- continue\n- param = params_dict[name.replace(weight_name, param_name)]\n- weight_loader = param.weight_loader\n- weight_loader(param, loaded_weight, shard_id)\n- break\n- else:\n- use_default_weight_loading = True\n- if use_default_weight_loading and name in params_dict:\n- param = params_dict[name]\n- weight_loader = getattr(param, \"weight_loader\",\n- default_weight_loader)\n- weight_loader(param, loaded_weight)\n+ # prepare weight iterators for components\n+ vit_weights, mlp_weights, newline_weights, llm_weights = itertools.tee(\n+ weights, 4)\n+\n+ # load vision encoder\n+ vit_weights = filter_weights(vit_weights, \"vision_tower\")\n+ self.vision_tower.load_weights(vit_weights)\n+\n+ # load mlp projector\n+ mlp_weights = filter_weights(mlp_weights, \"multi_modal_projector\")\n+ mlp_params_dict = dict(self.multi_modal_projector.named_parameters())\n+ for name, loaded_weight in mlp_weights:\n+ param = mlp_params_dict[name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\n+\n+ # load newline\n+ newline_weights = filter_weights(newline_weights, \"image_newline\")\n+ for name, loaded_weight in newline_weights:\n+ assert name == \"\"\n+ param = self.image_newline\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\n+\n+ # load llm backbone\n+ llm_weights = filter_weights(llm_weights, \"language_model\")\n+ self.language_model.load_weights(llm_weights)\ndiff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py\nindex 6faef45c9a6..5ba14f73394 100644\n--- a/vllm/model_executor/models/siglip.py\n+++ b/vllm/model_executor/models/siglip.py\n@@ -2,12 +2,12 @@\n within a vision language model.\"\"\"\n \n import math\n-from typing import Optional, Tuple\n+from typing import Iterable, Optional, Tuple\n \n import torch\n from PIL import Image\n from torch import nn\n-from transformers import SiglipConfig, SiglipVisionConfig\n+from transformers import SiglipVisionConfig\n from transformers.models.siglip.modeling_siglip import SiglipAttention\n from vllm_flash_attn import flash_attn_func\n from xformers.ops import memory_efficient_attention\n@@ -22,13 +22,15 @@\n from vllm.model_executor.layers.quantization import QuantizationConfig\n from vllm.model_executor.layers.vocab_parallel_embedding import (\n VocabParallelEmbedding)\n+from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n from vllm.multimodal.image import (cached_get_tokenizer,\n repeat_and_pad_image_tokens)\n from vllm.sequence import SequenceData\n \n \n def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:\n- assert image_size % patch_size == 0\n+ # Since interpolation is applied, the image size need not be divisible\n+ # assert image_size % patch_size == 0\n return image_size // patch_size\n \n \n@@ -454,7 +456,7 @@ class SiglipEncoderLayer(nn.Module):\n \n def __init__(\n self,\n- config: SiglipConfig,\n+ config: SiglipVisionConfig,\n quant_config: Optional[QuantizationConfig] = None,\n ):\n super().__init__()\n@@ -474,7 +476,7 @@ def __init__(\n def forward(\n self,\n hidden_states: torch.Tensor,\n- ) -> Tuple[torch.Tensor]:\n+ ) -> Tuple[torch.Tensor, None]:\n residual = hidden_states\n \n hidden_states = self.layer_norm1(hidden_states)\n@@ -493,22 +495,27 @@ class SiglipEncoder(nn.Module):\n \n def __init__(\n self,\n- config: SiglipConfig,\n+ config: SiglipVisionConfig,\n quant_config: Optional[QuantizationConfig] = None,\n+ num_hidden_layers_override: Optional[int] = None,\n ):\n super().__init__()\n self.config = config\n+\n+ if num_hidden_layers_override is None:\n+ num_hidden_layers = config.num_hidden_layers\n+ else:\n+ num_hidden_layers = num_hidden_layers_override\n+\n self.layers = nn.ModuleList([\n- SiglipEncoderLayer(\n- config,\n- quant_config=quant_config,\n- ) for _ in range(config.num_hidden_layers)\n+ SiglipEncoderLayer(config, quant_config=quant_config)\n+ for _ in range(num_hidden_layers)\n ])\n \n def forward(\n self,\n inputs_embeds: torch.Tensor,\n- ) -> Tuple:\n+ ) -> torch.Tensor:\n hidden_states = inputs_embeds\n for encoder_layer in self.layers:\n hidden_states, _ = encoder_layer(hidden_states)\n@@ -553,6 +560,7 @@ def __init__(\n self,\n config: SiglipVisionConfig,\n quant_config: Optional[QuantizationConfig] = None,\n+ num_hidden_layers_override: Optional[int] = None,\n ):\n super().__init__()\n self.config = config\n@@ -562,6 +570,7 @@ def __init__(\n self.encoder = SiglipEncoder(\n config,\n quant_config=quant_config,\n+ num_hidden_layers_override=num_hidden_layers_override,\n )\n self.post_layernorm = nn.LayerNorm(embed_dim,\n eps=config.layer_norm_eps)\n@@ -600,11 +609,13 @@ def __init__(\n self,\n config: SiglipVisionConfig,\n quant_config: Optional[QuantizationConfig] = None,\n+ num_hidden_layers_override: Optional[int] = None,\n ):\n super().__init__()\n self.vision_model = SiglipVisionTransformer(\n config,\n quant_config,\n+ num_hidden_layers_override=num_hidden_layers_override,\n )\n \n def get_input_embeddings(self) -> nn.Module:\n@@ -619,3 +630,19 @@ def forward(\n pixel_values=pixel_values,\n interpolate_pos_encoding=interpolate_pos_encoding,\n )\n+\n+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n+ params_dict = dict(self.named_parameters())\n+ layer_count = len(self.vision_model.encoder.layers)\n+\n+ for name, loaded_weight in weights:\n+ # omit layers when num_hidden_layers_override is set\n+ if \"vision_model.encoder.layers.\" in name:\n+ layer_idx = int(name.split(\".\")[3])\n+ if layer_idx >= layer_count:\n+ continue\n+\n+ param = params_dict[name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\ndiff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py\nindex 91b4a27814b..82f4892d825 100644\n--- a/vllm/model_executor/models/utils.py\n+++ b/vllm/model_executor/models/utils.py\n@@ -1,22 +1,70 @@\n-from typing import Dict, List, Protocol, Tuple\n+from typing import Dict, Iterable, List, Optional, Protocol, Tuple\n \n import torch\n+import torch.nn as nn\n from torch.func import functional_call\n+from transformers import PretrainedConfig\n \n+from vllm.config import (CacheConfig, LoRAConfig, MultiModalConfig,\n+ SchedulerConfig)\n+from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.model_loader.loader import build_model\n+from vllm.model_executor.models import ModelRegistry\n from vllm.multimodal import BatchedTensors\n from vllm.utils import is_pin_memory_available\n \n \n+def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str):\n+ \"\"\"\n+ Helper function to load weights for inner models.\n+\n+ See also:\n+ :ref:`init_inner_model`\n+ \"\"\"\n+ for name, loaded_weight in weights:\n+ name = name.split(\".\")\n+ if prefix == name.pop(0):\n+ name = \".\".join(name)\n+ yield name, loaded_weight\n+\n+\n+def init_inner_model(\n+ hf_config: PretrainedConfig,\n+ cache_config: Optional[CacheConfig],\n+ quant_config: Optional[QuantizationConfig],\n+ *,\n+ lora_config: Optional[LoRAConfig] = None,\n+ multimodal_config: Optional[MultiModalConfig] = None,\n+ scheduler_config: Optional[SchedulerConfig] = None,\n+) -> nn.Module:\n+ \"\"\"\n+ Helper function to initialize an inner vLLM model based on the arguments\n+ passed to the outer vLLM model.\n+ \"\"\"\n+ model_class, _ = ModelRegistry.resolve_model_cls(hf_config.architectures)\n+\n+ return build_model(\n+ model_class,\n+ hf_config,\n+ cache_config,\n+ quant_config,\n+ lora_config=lora_config,\n+ multimodal_config=multimodal_config,\n+ scheduler_config=scheduler_config,\n+ )\n+\n+\n def merge_vision_embeddings(input_ids: torch.Tensor,\n inputs_embeds: torch.Tensor,\n vision_embeddings: BatchedTensors,\n image_token_id: int) -> torch.Tensor:\n \"\"\"\n- Merge `vision_embeddings` into `inputs_embeds` by overwriting the positions\n- in `inputs_embeds` corresponding to placeholder image tokens in `input_ids`.\n+ Merge ``vision_embeddings`` into ``inputs_embeds`` by overwriting the\n+ positions in ``inputs_embeds`` corresponding to placeholder image tokens in\n+ ``input_ids``.\n \n Note:\n- This updates `inputs_embeds` in place.\n+ This updates ``inputs_embeds`` in place.\n \"\"\"\n mask = (input_ids == image_token_id)\n num_expected_tokens = mask.sum()\n" }
[ { "diff_hunk": "@@ -19,6 +19,7 @@ ray\n sentence-transformers # required for embedding\n compressed-tensors==0.4.0 # required for compressed-tensors\n timm # required for internvl test\n+git+https://github.com/TIGER-AI-Lab/Mantis.git # required for llava(mantis) test", "line": null, "original_line": 22, "original_start_line": null, "path": "requirements-test.txt", "start_line": null, "text": "@user1:\nIf we're only going to fix and test mantis correctness in a late PR, then let's remove this for now.\n\n@author:\nSure, I'll comment this out and add a TODO then." }, { "diff_hunk": "@@ -1,22 +1,70 @@\n-from typing import Dict, List, Protocol, Tuple\n+from typing import Dict, Iterable, List, Optional, Protocol, Tuple\n \n import torch\n+import torch.nn as nn\n from torch.func import functional_call\n+from transformers import PretrainedConfig\n \n+from vllm.config import (CacheConfig, LoRAConfig, MultiModalConfig,\n+ SchedulerConfig)\n+from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.model_loader.loader import build_model\n+from vllm.model_executor.models import ModelRegistry\n from vllm.multimodal import BatchedTensors\n from vllm.utils import is_pin_memory_available\n \n \n+def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str):\n+ \"\"\"\n+ Helper function to load weights for inner models.\n+\n+ See also:\n+ :ref:`init_inner_model`\n+ \"\"\"\n+ for name, loaded_weight in weights:\n+ name = name.split(\".\")\n+ if prefix == name.pop(0):\n+ name = \".\".join(name)\n+ yield name, loaded_weight\n+\n+\n+def init_inner_model(", "line": null, "original_line": 31, "original_start_line": null, "path": "vllm/model_executor/models/utils.py", "start_line": null, "text": "@user1:\nWhy don't we name this `init_language_model`? I think the word `inner` is pretty confusing for developers. Is there a case where we use this function to initialize a non-LM model?\n\n@author:\nThis can be used for any model registered in vLLM (not just LMs), since it leverages the existing logic for model initialization. The recipe introduced by #7067 can be applied to any inner model that implements weight loading logic, making this a potential way of implementing #7124.\n\n@user1:\nI think it's just a bit confusing when we have a `_init_vision_tower` and `init_inner_model` together, and perhaps we can think of a better name for this pattern other than `inner`. How about explicitly calling it out as `init_registered_model`?\n\n@author:\nHmm... I'm thinking of `init_vllm_model`. Would that work?\n\n@user1:\nLet's do `init_vllm_registered_model` - I know it's a bit wordy, but I think the purpose of this function will be a lot easier to tell from its name.\n\n@author:\nAlright, sounds good." } ]
5a9c179736e5065980f0f3d789e8944fe71da3bc
diff --git a/requirements-test.txt b/requirements-test.txt index 5f3fd15c7ee5..62d6cc49eade 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -20,6 +20,9 @@ sentence-transformers # required for embedding compressed-tensors==0.4.0 # required for compressed-tensors timm # required for internvl test +# TODO: Add this after fully implementing llava(mantis) +# git+https://github.com/TIGER-AI-Lab/Mantis.git # required for llava(mantis) test + # Benchmarking aiohttp diff --git a/tests/models/test_llava.py b/tests/models/test_llava.py index 79ab58c364f6..749d3353717e 100644 --- a/tests/models/test_llava.py +++ b/tests/models/test_llava.py @@ -1,10 +1,11 @@ from typing import List, Optional, Tuple, Type import pytest -from transformers import AutoTokenizer +from transformers import AutoConfig, AutoTokenizer from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets from .utils import check_logprobs_close @@ -18,9 +19,11 @@ "USER: <image>\nWhat is the season?\nASSISTANT:", }) -IMAGE_TOKEN_ID = 32000 - -models = ["llava-hf/llava-1.5-7b-hf"] +models = [ + "llava-hf/llava-1.5-7b-hf", + # TODO: Get this model to produce meaningful output in vLLM + # "TIGER-Lab/Mantis-8B-siglip-llama3", +] def vllm_to_hf_output(vllm_output: Tuple[List[int], str, @@ -29,12 +32,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str, """Sanitize vllm output to be comparable with hf output.""" output_ids, output_str, out_logprobs = vllm_output + config = AutoConfig.from_pretrained(model) + image_token_id = config.image_token_index + tokenizer = AutoTokenizer.from_pretrained(model) eos_token_id = tokenizer.eos_token_id hf_output_ids = [ token_id for idx, token_id in enumerate(output_ids) - if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID + if token_id != image_token_id or output_ids[idx - 1] != image_token_id ] assert output_str[0] == " " @@ -67,6 +73,17 @@ def run_test( Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. """ + # NOTE: For local use; this isn't tested in CI yet (see TODO above) + if model.startswith("TIGER-Lab/Mantis"): + from mantis.models.mllava import MLlavaProcessor + + torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype] + mantis_processor = MLlavaProcessor.from_pretrained( + model, torch_dtype=torch_dtype) + assert isinstance(mantis_processor, MLlavaProcessor) + else: + mantis_processor = None + images = [asset.pil_image for asset in image_assets] inputs_per_image = [( @@ -94,6 +111,15 @@ def run_test( ] with hf_runner(model, dtype=dtype, is_vision_model=True) as hf_model: + if mantis_processor is not None: + + def process(*args, **kwargs): + output = mantis_processor(*args, **kwargs) + output["pixel_values"] = output["pixel_values"].to(torch_dtype) + return output + + hf_model.processor = process + hf_outputs_per_image = [ hf_model.generate_greedy_logprobs_limit(prompts, max_tokens, diff --git a/tests/models/test_llava_next.py b/tests/models/test_llava_next.py index b6d72dee5c5b..60c7fc33b72f 100644 --- a/tests/models/test_llava_next.py +++ b/tests/models/test_llava_next.py @@ -1,7 +1,7 @@ from typing import List, Optional, Tuple, Type, overload import pytest -from transformers import AutoTokenizer +from transformers import AutoConfig, AutoTokenizer from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs @@ -23,8 +23,6 @@ f"{_PREFACE} USER: <image>\nWhat is the season? ASSISTANT:", }) -IMAGE_TOKEN_ID = 32000 - models = ["llava-hf/llava-v1.6-vicuna-7b-hf"] @@ -34,12 +32,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str, """Sanitize vllm output to be comparable with hf output.""" output_ids, output_str, out_logprobs = vllm_output + config = AutoConfig.from_pretrained(model) + image_token_id = config.image_token_index + tokenizer = AutoTokenizer.from_pretrained(model) eos_token_id = tokenizer.eos_token_id hf_output_ids = [ token_id for idx, token_id in enumerate(output_ids) - if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID + if token_id != image_token_id or output_ids[idx - 1] != image_token_id ] assert output_str[0] == " " diff --git a/tests/models/test_paligemma.py b/tests/models/test_paligemma.py index e1c39ee6fecb..f3f682b1c2cd 100644 --- a/tests/models/test_paligemma.py +++ b/tests/models/test_paligemma.py @@ -2,7 +2,7 @@ from typing import List, Optional, Tuple, Type import pytest -from transformers import AutoTokenizer +from transformers import AutoConfig, AutoTokenizer from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs @@ -20,8 +20,6 @@ "What is in the picture?", }) -IMAGE_TOKEN_ID = 257152 - models = ["google/paligemma-3b-mix-224"] # ROCm Triton FA can run into compilation issues with these models due to, @@ -37,12 +35,15 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str, """Sanitize vllm output to be comparable with hf output.""" output_ids, output_str, out_logprobs = vllm_output + config = AutoConfig.from_pretrained(model) + image_token_id = config.image_token_index + tokenizer = AutoTokenizer.from_pretrained(model) eos_token_id = tokenizer.eos_token_id hf_output_ids = [ token_id for idx, token_id in enumerate(output_ids) - if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID + if token_id != image_token_id or output_ids[idx - 1] != image_token_id ] hf_output_str = output_str diff --git a/tests/models/test_registry.py b/tests/models/test_registry.py index 547ab10051f1..b058e2755c24 100644 --- a/tests/models/test_registry.py +++ b/tests/models/test_registry.py @@ -6,4 +6,4 @@ @pytest.mark.parametrize("model_cls", _MODELS) def test_registry_imports(model_cls): # Ensure all model classes can be imported successfully - ModelRegistry.load_model_cls(model_cls) + ModelRegistry.resolve_model_cls([model_cls]) diff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py index a5c5cb87bc46..44c04c9ba8dd 100644 --- a/vllm/model_executor/model_loader/loader.py +++ b/vllm/model_executor/model_loader/loader.py @@ -16,7 +16,7 @@ import torch from huggingface_hub import HfApi, hf_hub_download from torch import nn -from transformers import AutoModelForCausalLM +from transformers import AutoModelForCausalLM, PretrainedConfig from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoadFormat, LoRAConfig, ModelConfig, MultiModalConfig, @@ -143,6 +143,22 @@ def _get_model_initialization_kwargs( return extra_kwargs +def build_model(model_class: Type[nn.Module], hf_config: PretrainedConfig, + cache_config: Optional[CacheConfig], + quant_config: Optional[QuantizationConfig], *, + lora_config: Optional[LoRAConfig], + multimodal_config: Optional[MultiModalConfig], + scheduler_config: Optional[SchedulerConfig]) -> nn.Module: + extra_kwargs = _get_model_initialization_kwargs(model_class, lora_config, + multimodal_config, + scheduler_config) + + return model_class(config=hf_config, + cache_config=cache_config, + quant_config=quant_config, + **extra_kwargs) + + def _initialize_model( model_config: ModelConfig, load_config: LoadConfig, @@ -151,15 +167,17 @@ def _initialize_model( cache_config: CacheConfig, scheduler_config: Optional[SchedulerConfig] = None) -> nn.Module: """Initialize a model with the given configurations.""" - model_class = get_model_architecture(model_config)[0] - quant_config = _get_quantization_config(model_config, load_config) - - return model_class(config=model_config.hf_config, - cache_config=cache_config, - quant_config=quant_config, - **_get_model_initialization_kwargs( - model_class, lora_config, multimodal_config, - scheduler_config)) + model_class, _ = get_model_architecture(model_config) + + return build_model( + model_class, + model_config.hf_config, + quant_config=_get_quantization_config(model_config, load_config), + lora_config=lora_config, + multimodal_config=multimodal_config, + cache_config=cache_config, + scheduler_config=scheduler_config, + ) class BaseModelLoader(ABC): diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py index f7e0f56c1a46..331b859d2ade 100644 --- a/vllm/model_executor/model_loader/utils.py +++ b/vllm/model_executor/model_loader/utils.py @@ -28,13 +28,7 @@ def get_model_architecture( and "MixtralForCausalLM" in architectures): architectures = ["QuantMixtralForCausalLM"] - for arch in architectures: - model_cls = ModelRegistry.load_model_cls(arch) - if model_cls is not None: - return (model_cls, arch) - raise ValueError( - f"Model architectures {architectures} are not supported for now. " - f"Supported architectures: {ModelRegistry.get_supported_archs()}") + return ModelRegistry.resolve_model_cls(architectures) def get_architecture_class_name(model_config: ModelConfig) -> str: diff --git a/vllm/model_executor/models/__init__.py b/vllm/model_executor/models/__init__.py index 94c3cea98be7..ebb77a802d5c 100644 --- a/vllm/model_executor/models/__init__.py +++ b/vllm/model_executor/models/__init__.py @@ -1,6 +1,6 @@ import functools import importlib -from typing import Dict, List, Optional, Type +from typing import Dict, List, Optional, Tuple, Type import torch.nn as nn @@ -126,7 +126,7 @@ def _get_model(model_arch: str): return getattr(module, model_cls_name, None) @staticmethod - def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]: + def _try_load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]: if model_arch in _OOT_MODELS: return _OOT_MODELS[model_arch] if model_arch not in _MODELS: @@ -143,6 +143,18 @@ def load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]: return ModelRegistry._get_model(model_arch) + @staticmethod + def resolve_model_cls( + architectures: List[str]) -> Tuple[Type[nn.Module], str]: + for arch in architectures: + model_cls = ModelRegistry._try_load_model_cls(arch) + if model_cls is not None: + return (model_cls, arch) + + raise ValueError( + f"Model architectures {architectures} are not supported for now. " + f"Supported architectures: {ModelRegistry.get_supported_archs()}") + @staticmethod def get_supported_archs() -> List[str]: return list(_MODELS.keys()) diff --git a/vllm/model_executor/models/clip.py b/vllm/model_executor/models/clip.py index b4f628061f19..805ade39389d 100644 --- a/vllm/model_executor/models/clip.py +++ b/vllm/model_executor/models/clip.py @@ -1,6 +1,6 @@ """Minimal implementation of CLIPVisionModel intended to be only used within a vision language model.""" -from typing import Optional +from typing import Iterable, Optional, Tuple import torch import torch.nn as nn @@ -14,6 +14,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.multimodal.image import (cached_get_tokenizer, repeat_and_pad_image_tokens) from vllm.sequence import SequenceData @@ -32,7 +33,7 @@ def get_clip_num_patches(*, image_size: int, patch_size: int) -> int: def get_clip_image_feature_size(hf_config: CLIPVisionConfig) -> int: return get_clip_num_patches(image_size=hf_config.image_size, - patch_size=hf_config.patch_size) + patch_size=hf_config.patch_size) + 1 def get_max_clip_image_tokens(hf_config: CLIPVisionConfig) -> int: @@ -291,3 +292,22 @@ def forward(self, pixel_values: Optional[torch.Tensor] = None): @property def device(self): return next(self.parameters()).device + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + params_dict = dict(self.named_parameters()) + layer_count = len(self.vision_model.encoder.layers) + + for name, loaded_weight in weights: + # post_layernorm is not needed in CLIPVisionModel + if "vision_model.post_layernorm" in name: + continue + # omit layers when num_hidden_layers_override is set + if "vision_model.encoder.layers." in name: + layer_idx = int(name.split(".")[3]) + if layer_idx >= layer_count: + continue + + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index 474925127148..8850fd7c6763 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -18,7 +18,6 @@ from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models import ModelRegistry from vllm.model_executor.models.intern_vit import InternVisionModel from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY @@ -29,7 +28,8 @@ from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip, get_clip_num_patches) from .interfaces import SupportsVision -from .utils import merge_vision_embeddings +from .utils import (filter_weights, init_vllm_registered_model, + merge_vision_embeddings) IMG_START = '<img>' IMG_END = '</img>' @@ -283,10 +283,8 @@ def __init__(self, self.vision_model = InternVisionModel( config.vision_config, num_hidden_layers_override=num_hidden_layers) - llm_class = ModelRegistry.load_model_cls( - config.text_config.architectures[0]) - self.language_model = llm_class(config.text_config, cache_config, - quant_config) + self.language_model = init_vllm_registered_model( + config.text_config, cache_config, quant_config) vit_hidden_size = config.vision_config.hidden_size llm_hidden_size = config.text_config.hidden_size @@ -415,24 +413,16 @@ def sample( ) -> Optional[SamplerOutput]: return self.language_model.sample(logits, sampling_metadata) - def _filter_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], - prefix: str): - for name, loaded_weight in weights: - name = name.split(".") - if prefix == name.pop(0): - name = ".".join(name) - yield name, loaded_weight - def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): # prepare weight iterators for components vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3) # load vision encoder - vit_weights = self._filter_weights(vit_weights, "vision_model") + vit_weights = filter_weights(vit_weights, "vision_model") self.vision_model.load_weights(vit_weights) # load mlp projector - mlp_weights = self._filter_weights(mlp_weights, "mlp1") + mlp_weights = filter_weights(mlp_weights, "mlp1") mlp_params_dict = dict(self.mlp1.named_parameters()) for name, loaded_weight in mlp_weights: param = mlp_params_dict[name] @@ -441,5 +431,5 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): weight_loader(param, loaded_weight) # load llm backbone - llm_weights = self._filter_weights(llm_weights, "language_model") + llm_weights = filter_weights(llm_weights, "language_model") self.language_model.load_weights(llm_weights) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 4e7e6c47f0a0..9a11bcc4c54c 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -1,34 +1,30 @@ -from typing import Iterable, List, Literal, Optional, Tuple, TypedDict +import itertools +from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union import torch import torch.nn as nn -from transformers import CLIPVisionConfig, LlavaConfig +from transformers import CLIPVisionConfig, LlavaConfig, SiglipVisionConfig from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs from vllm.model_executor.layers.activation import get_act_fn -from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) -from vllm.model_executor.layers.sampler import Sampler -from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.clip import CLIPVisionModel -from vllm.model_executor.models.llama import LlamaModel from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.sequence import IntermediateTensors, SamplerOutput -from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip, - get_max_clip_image_tokens, input_processor_for_clip) +from .clip import (CLIPVisionModel, dummy_image_for_clip, + dummy_seq_data_for_clip, get_max_clip_image_tokens, + input_processor_for_clip) from .interfaces import SupportsVision -from .utils import merge_vision_embeddings - -_KEYS_TO_MODIFY_MAPPING = { - "language_model.lm_head": "lm_head", - "language_model.model": "language_model", -} +from .siglip import (SiglipVisionModel, dummy_image_for_siglip, + dummy_seq_data_for_siglip, get_max_siglip_image_tokens, + input_processor_for_siglip) +from .utils import (filter_weights, init_vllm_registered_model, + merge_vision_embeddings) # TODO(xwjiang): Run benchmark and decide if TP. @@ -67,25 +63,48 @@ def get_max_llava_image_tokens(ctx: InputContext): vision_config = hf_config.vision_config if isinstance(vision_config, CLIPVisionConfig): - return get_max_clip_image_tokens(vision_config) - - msg = f"Unsupported vision config: {type(vision_config)}" - raise NotImplementedError(msg) + num_image_tokens = get_max_clip_image_tokens(vision_config) + elif isinstance(vision_config, SiglipVisionConfig): + num_image_tokens = get_max_siglip_image_tokens(vision_config) + else: + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + strategy = hf_config.vision_feature_select_strategy + if strategy == "default": + return num_image_tokens - 1 + elif strategy == "full": + return num_image_tokens + else: + raise ValueError(f"Unexpected select feature strategy: {strategy}") def dummy_data_for_llava(ctx: InputContext, seq_len: int): hf_config = ctx.get_hf_config(LlavaConfig) vision_config = hf_config.vision_config + image_feature_size = get_max_llava_image_tokens(ctx) + if isinstance(vision_config, CLIPVisionConfig): seq_data = dummy_seq_data_for_clip( vision_config, seq_len, image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, ) mm_data = dummy_image_for_clip(vision_config) return seq_data, mm_data + elif isinstance(vision_config, SiglipVisionConfig): + seq_data = dummy_seq_data_for_siglip( + vision_config, + seq_len, + image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + + mm_data = dummy_image_for_siglip(vision_config) + return seq_data, mm_data msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -100,12 +119,49 @@ def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs): hf_config = ctx.get_hf_config(LlavaConfig) vision_config = hf_config.vision_config + image_feature_size = get_max_llava_image_tokens(ctx) + if isinstance(vision_config, CLIPVisionConfig): return input_processor_for_clip( model_config, vision_config, llm_inputs, image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + elif isinstance(vision_config, SiglipVisionConfig): + return input_processor_for_siglip( + model_config, + vision_config, + llm_inputs, + image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + +def _init_vision_tower(hf_config: LlavaConfig): + vision_config = hf_config.vision_config + + # Initialize the vision tower only up to the required feature layer + vision_feature_layer = hf_config.vision_feature_layer + if vision_feature_layer < 0: + num_hidden_layers = hf_config.vision_config.num_hidden_layers \ + + vision_feature_layer + 1 + else: + num_hidden_layers = vision_feature_layer + 1 + + if isinstance(vision_config, CLIPVisionConfig): + return CLIPVisionModel( + vision_config, + num_hidden_layers_override=num_hidden_layers, + ) + elif isinstance(vision_config, SiglipVisionConfig): + return SiglipVisionModel( + vision_config, + num_hidden_layers_override=num_hidden_layers, ) msg = f"Unsupported vision config: {type(vision_config)}" @@ -128,36 +184,15 @@ def __init__(self, self.config = config self.multimodal_config = multimodal_config - # Initialize the vision tower only up to the required feature layer - vision_feature_layer = config.vision_feature_layer - if vision_feature_layer < 0: - num_hidden_layers = config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = CLIPVisionModel( - config.vision_config, num_hidden_layers_override=num_hidden_layers) + self.vision_tower = _init_vision_tower(config) self.multi_modal_projector = LlavaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, text_hidden_size=config.text_config.hidden_size, projector_hidden_act=config.projector_hidden_act) - self.quant_config = quant_config - self.language_model = LlamaModel(config.text_config, cache_config, - quant_config) - self.unpadded_vocab_size = config.text_config.vocab_size - self.lm_head = ParallelLMHead( - self.unpadded_vocab_size, - config.text_config.hidden_size, - org_num_embeddings=self.language_model.org_vocab_size, - quant_config=quant_config) - logit_scale = getattr(config, "logit_scale", 1.0) - self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, - config.text_config.vocab_size, - logit_scale) - self.sampler = Sampler() + self.language_model = init_vllm_registered_model( + config.text_config, cache_config, quant_config) def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: h = w = self.config.vision_config.image_size @@ -198,8 +233,11 @@ def _select_image_features(self, image_features: torch.Tensor, *, raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features(self, vision_tower: CLIPVisionModel, - pixel_values: torch.Tensor) -> torch.Tensor: + def _image_pixels_to_features( + self, + vision_tower: Union[CLIPVisionModel, SiglipVisionModel], + pixel_values: torch.Tensor, + ) -> torch.Tensor: # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower @@ -272,7 +310,8 @@ def forward( if image_input is not None: vision_embeddings = self._process_image_input(image_input) - inputs_embeds = self.language_model.get_input_embeddings(input_ids) + inputs_embeds = self.language_model.model.get_input_embeddings( + input_ids) inputs_embeds = merge_vision_embeddings( input_ids, inputs_embeds, vision_embeddings, @@ -282,68 +321,44 @@ def forward( else: inputs_embeds = None - hidden_states = self.language_model(input_ids, - positions, - kv_caches, - attn_metadata, - None, - inputs_embeds=inputs_embeds) + hidden_states = self.language_model.model(input_ids, + positions, + kv_caches, + attn_metadata, + None, + inputs_embeds=inputs_embeds) return hidden_states def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: - logits = self.logits_processor(self.lm_head, hidden_states, - sampling_metadata) - return logits + return self.language_model.compute_logits(hidden_states, + sampling_metadata) def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: - next_tokens = self.sampler(logits, sampling_metadata) - return next_tokens + return self.language_model.sample(logits, sampling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): - # only doing this for language model part for now. - stacked_params_mapping = [ - # (param_name, shard_name, shard_id) - ("qkv_proj", "q_proj", "q"), - ("qkv_proj", "k_proj", "k"), - ("qkv_proj", "v_proj", "v"), - ("gate_up_proj", "gate_proj", 0), - ("gate_up_proj", "up_proj", 1), - ] - params_dict = dict(self.named_parameters()) - for name, loaded_weight in weights: - if "rotary_emb.inv_freq" in name: - continue - # post_layernorm is not needed in CLIPVisionModel - if "vision_model.post_layernorm" in name: - continue - for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items(): - if key_to_modify in name: - name = name.replace(key_to_modify, new_key) - use_default_weight_loading = False - if "vision" in name: - if self.vision_tower is not None: - # We only do sharding for language model and - # not vision model for now. - use_default_weight_loading = True - else: - for (param_name, weight_name, - shard_id) in stacked_params_mapping: - if weight_name not in name: - continue - param = params_dict[name.replace(weight_name, param_name)] - weight_loader = param.weight_loader - weight_loader(param, loaded_weight, shard_id) - break - else: - use_default_weight_loading = True - if use_default_weight_loading and name in params_dict: - param = params_dict[name] - weight_loader = getattr(param, "weight_loader", - default_weight_loader) - weight_loader(param, loaded_weight) + # prepare weight iterators for components + vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3) + + # load vision encoder + vit_weights = filter_weights(vit_weights, "vision_tower") + self.vision_tower.load_weights(vit_weights) + + # load mlp projector + mlp_weights = filter_weights(mlp_weights, "multi_modal_projector") + mlp_params_dict = dict(self.multi_modal_projector.named_parameters()) + for name, loaded_weight in mlp_weights: + param = mlp_params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + + # load llm backbone + llm_weights = filter_weights(llm_weights, "language_model") + self.language_model.load_weights(llm_weights) diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 4a67b9a583ea..9abc480f60de 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -1,9 +1,10 @@ +import itertools from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union import torch import torch.nn as nn from PIL import Image -from transformers import CLIPVisionConfig, LlavaNextConfig +from transformers import CLIPVisionConfig, LlavaNextConfig, SiglipVisionConfig from transformers.models.llava_next.modeling_llava_next import ( get_anyres_image_grid_shape, unpad_image) from typing_extensions import NotRequired @@ -12,23 +13,23 @@ from vllm.config import CacheConfig, MultiModalConfig from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs from vllm.logger import init_logger -from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) -from vllm.model_executor.layers.sampler import Sampler -from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.clip import CLIPVisionModel -from vllm.model_executor.models.llama import LlamaModel from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.sequence import IntermediateTensors, SamplerOutput -from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip, +from .clip import (CLIPVisionModel, dummy_image_for_clip, + dummy_seq_data_for_clip, get_clip_image_feature_size, get_clip_patch_grid_length, input_processor_for_clip) from .interfaces import SupportsVision from .llava import LlavaMultiModalProjector -from .utils import merge_vision_embeddings +from .siglip import (SiglipVisionModel, dummy_image_for_siglip, + dummy_seq_data_for_siglip, get_siglip_image_feature_size, + get_siglip_patch_grid_length, input_processor_for_siglip) +from .utils import (filter_weights, init_vllm_registered_model, + merge_vision_embeddings) logger = init_logger(__name__) @@ -104,30 +105,42 @@ def get_llava_next_image_feature_size( image_size=vision_config.image_size, patch_size=vision_config.patch_size, ) - base_feature_size = num_patches * num_patches - - num_patch_height, num_patch_width = get_anyres_image_grid_shape( - image_size=(input_height, input_width), - grid_pinpoints=hf_config.image_grid_pinpoints, - patch_size=vision_config.image_size, + base_feature_size = get_clip_image_feature_size(vision_config) + elif isinstance(vision_config, SiglipVisionConfig): + num_patches = get_siglip_patch_grid_length( + image_size=vision_config.image_size, + patch_size=vision_config.patch_size, ) + base_feature_size = get_siglip_image_feature_size(vision_config) + else: + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + strategy = hf_config.vision_feature_select_strategy + if strategy == "default": + base_feature_size -= 1 + elif strategy == "full": + pass + else: + raise ValueError(f"Unexpected select feature strategy: {strategy}") - ( - unpadded_feature_size, - newline_feature_size, - ) = _get_llava_next_num_unpadded_features(input_height, input_width, - num_patches, - num_patch_height, - num_patch_width) + num_patch_height, num_patch_width = get_anyres_image_grid_shape( + image_size=(input_height, input_width), + grid_pinpoints=hf_config.image_grid_pinpoints, + patch_size=vision_config.image_size, + ) - return unpadded_feature_size + newline_feature_size + base_feature_size + ( + unpadded_feature_size, + newline_feature_size, + ) = _get_llava_next_num_unpadded_features(input_height, input_width, + num_patches, num_patch_height, + num_patch_width) - msg = f"Unsupported vision config: {type(vision_config)}" - raise NotImplementedError(msg) + return unpadded_feature_size + newline_feature_size + base_feature_size def get_max_llava_next_image_tokens(ctx: InputContext): - return get_llava_next_image_feature_size( ctx.get_hf_config(LlavaNextConfig), input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, @@ -155,6 +168,21 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int): image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, ) + return seq_data, mm_data + elif isinstance(vision_config, SiglipVisionConfig): + seq_data = dummy_seq_data_for_siglip( + vision_config, + seq_len, + image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + + mm_data = dummy_image_for_siglip( + vision_config, + image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH, + image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, + ) + return seq_data, mm_data msg = f"Unsupported vision config: {type(vision_config)}" @@ -194,6 +222,40 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs): image_token_id=hf_config.image_token_index, image_feature_size_override=image_feature_size, ) + elif isinstance(vision_config, SiglipVisionConfig): + return input_processor_for_siglip( + model_config, + vision_config, + llm_inputs, + image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + + msg = f"Unsupported vision config: {type(vision_config)}" + raise NotImplementedError(msg) + + +def _init_vision_tower(hf_config: LlavaNextConfig): + vision_config = hf_config.vision_config + + # Initialize the vision tower only up to the required feature layer + vision_feature_layer = hf_config.vision_feature_layer + if vision_feature_layer < 0: + num_hidden_layers = hf_config.vision_config.num_hidden_layers \ + + vision_feature_layer + 1 + else: + num_hidden_layers = vision_feature_layer + 1 + + if isinstance(vision_config, CLIPVisionConfig): + return CLIPVisionModel( + vision_config, + num_hidden_layers_override=num_hidden_layers, + ) + elif isinstance(vision_config, SiglipVisionConfig): + return SiglipVisionModel( + vision_config, + num_hidden_layers_override=num_hidden_layers, + ) msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -215,36 +277,15 @@ def __init__(self, self.config = config self.multimodal_config = multimodal_config - # Initialize the vision tower only up to the required feature layer - vision_feature_layer = config.vision_feature_layer - if vision_feature_layer < 0: - num_hidden_layers = config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = CLIPVisionModel( - config.vision_config, num_hidden_layers_override=num_hidden_layers) + self.vision_tower = _init_vision_tower(config) self.multi_modal_projector = LlavaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, text_hidden_size=config.text_config.hidden_size, projector_hidden_act=config.projector_hidden_act) - self.quant_config = quant_config - self.language_model = LlamaModel(config.text_config, cache_config, - quant_config) - self.unpadded_vocab_size = config.text_config.vocab_size - self.lm_head = ParallelLMHead( - self.unpadded_vocab_size, - config.text_config.hidden_size, - org_num_embeddings=self.language_model.org_vocab_size, - quant_config=quant_config) - logit_scale = getattr(config, "logit_scale", 1.0) - self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, - config.text_config.vocab_size, - logit_scale) - self.sampler = Sampler() + self.language_model = init_vllm_registered_model( + config.text_config, cache_config, quant_config) self.image_newline = nn.Parameter( torch.empty(config.text_config.hidden_size)) @@ -310,8 +351,11 @@ def _select_image_features(self, image_features: torch.Tensor, *, raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features(self, vision_tower: CLIPVisionModel, - pixel_values: torch.Tensor) -> torch.Tensor: + def _image_pixels_to_features( + self, + vision_tower: Union[CLIPVisionModel, SiglipVisionModel], + pixel_values: torch.Tensor, + ) -> torch.Tensor: # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower @@ -496,7 +540,8 @@ def forward( if image_input is not None: vision_embeddings = self._process_image_input(image_input) - inputs_embeds = self.language_model.get_input_embeddings(input_ids) + inputs_embeds = self.language_model.model.get_input_embeddings( + input_ids) inputs_embeds = merge_vision_embeddings( input_ids, inputs_embeds, vision_embeddings, @@ -506,68 +551,54 @@ def forward( else: inputs_embeds = None - hidden_states = self.language_model(input_ids, - positions, - kv_caches, - attn_metadata, - None, - inputs_embeds=inputs_embeds) + hidden_states = self.language_model.model(input_ids, + positions, + kv_caches, + attn_metadata, + None, + inputs_embeds=inputs_embeds) return hidden_states def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: - logits = self.logits_processor(self.lm_head, hidden_states, - sampling_metadata) - return logits + return self.language_model.compute_logits(hidden_states, + sampling_metadata) def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: - next_tokens = self.sampler(logits, sampling_metadata) - return next_tokens + return self.language_model.sample(logits, sampling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): - # only doing this for language model part for now. - stacked_params_mapping = [ - # (param_name, shard_name, shard_id) - ("qkv_proj", "q_proj", "q"), - ("qkv_proj", "k_proj", "k"), - ("qkv_proj", "v_proj", "v"), - ("gate_up_proj", "gate_proj", 0), - ("gate_up_proj", "up_proj", 1), - ] - params_dict = dict(self.named_parameters()) - for name, loaded_weight in weights: - if "rotary_emb.inv_freq" in name: - continue - # post_layernorm is not needed in CLIPVisionModel - if "vision_model.post_layernorm" in name: - continue - for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items(): - if key_to_modify in name: - name = name.replace(key_to_modify, new_key) - use_default_weight_loading = False - if "vision" in name: - if self.vision_tower is not None: - # We only do sharding for language model and - # not vision model for now. - use_default_weight_loading = True - else: - for (param_name, weight_name, - shard_id) in stacked_params_mapping: - if weight_name not in name: - continue - param = params_dict[name.replace(weight_name, param_name)] - weight_loader = param.weight_loader - weight_loader(param, loaded_weight, shard_id) - break - else: - use_default_weight_loading = True - if use_default_weight_loading and name in params_dict: - param = params_dict[name] - weight_loader = getattr(param, "weight_loader", - default_weight_loader) - weight_loader(param, loaded_weight) + # prepare weight iterators for components + vit_weights, mlp_weights, newline_weights, llm_weights = itertools.tee( + weights, 4) + + # load vision encoder + vit_weights = filter_weights(vit_weights, "vision_tower") + self.vision_tower.load_weights(vit_weights) + + # load mlp projector + mlp_weights = filter_weights(mlp_weights, "multi_modal_projector") + mlp_params_dict = dict(self.multi_modal_projector.named_parameters()) + for name, loaded_weight in mlp_weights: + param = mlp_params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + + # load newline + newline_weights = filter_weights(newline_weights, "image_newline") + for name, loaded_weight in newline_weights: + assert name == "" + param = self.image_newline + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + + # load llm backbone + llm_weights = filter_weights(llm_weights, "language_model") + self.language_model.load_weights(llm_weights) diff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py index 6faef45c9a6d..5ba14f73394f 100644 --- a/vllm/model_executor/models/siglip.py +++ b/vllm/model_executor/models/siglip.py @@ -2,12 +2,12 @@ within a vision language model.""" import math -from typing import Optional, Tuple +from typing import Iterable, Optional, Tuple import torch from PIL import Image from torch import nn -from transformers import SiglipConfig, SiglipVisionConfig +from transformers import SiglipVisionConfig from transformers.models.siglip.modeling_siglip import SiglipAttention from vllm_flash_attn import flash_attn_func from xformers.ops import memory_efficient_attention @@ -22,13 +22,15 @@ from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.multimodal.image import (cached_get_tokenizer, repeat_and_pad_image_tokens) from vllm.sequence import SequenceData def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int: - assert image_size % patch_size == 0 + # Since interpolation is applied, the image size need not be divisible + # assert image_size % patch_size == 0 return image_size // patch_size @@ -454,7 +456,7 @@ class SiglipEncoderLayer(nn.Module): def __init__( self, - config: SiglipConfig, + config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() @@ -474,7 +476,7 @@ def __init__( def forward( self, hidden_states: torch.Tensor, - ) -> Tuple[torch.Tensor]: + ) -> Tuple[torch.Tensor, None]: residual = hidden_states hidden_states = self.layer_norm1(hidden_states) @@ -493,22 +495,27 @@ class SiglipEncoder(nn.Module): def __init__( self, - config: SiglipConfig, + config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, + num_hidden_layers_override: Optional[int] = None, ): super().__init__() self.config = config + + if num_hidden_layers_override is None: + num_hidden_layers = config.num_hidden_layers + else: + num_hidden_layers = num_hidden_layers_override + self.layers = nn.ModuleList([ - SiglipEncoderLayer( - config, - quant_config=quant_config, - ) for _ in range(config.num_hidden_layers) + SiglipEncoderLayer(config, quant_config=quant_config) + for _ in range(num_hidden_layers) ]) def forward( self, inputs_embeds: torch.Tensor, - ) -> Tuple: + ) -> torch.Tensor: hidden_states = inputs_embeds for encoder_layer in self.layers: hidden_states, _ = encoder_layer(hidden_states) @@ -553,6 +560,7 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, + num_hidden_layers_override: Optional[int] = None, ): super().__init__() self.config = config @@ -562,6 +570,7 @@ def __init__( self.encoder = SiglipEncoder( config, quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers_override, ) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) @@ -600,11 +609,13 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, + num_hidden_layers_override: Optional[int] = None, ): super().__init__() self.vision_model = SiglipVisionTransformer( config, quant_config, + num_hidden_layers_override=num_hidden_layers_override, ) def get_input_embeddings(self) -> nn.Module: @@ -619,3 +630,19 @@ def forward( pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, ) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + params_dict = dict(self.named_parameters()) + layer_count = len(self.vision_model.encoder.layers) + + for name, loaded_weight in weights: + # omit layers when num_hidden_layers_override is set + if "vision_model.encoder.layers." in name: + layer_idx = int(name.split(".")[3]) + if layer_idx >= layer_count: + continue + + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index 91b4a27814bf..d1bb030c6c90 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -1,22 +1,70 @@ -from typing import Dict, List, Protocol, Tuple +from typing import Dict, Iterable, List, Optional, Protocol, Tuple import torch +import torch.nn as nn from torch.func import functional_call +from transformers import PretrainedConfig +from vllm.config import (CacheConfig, LoRAConfig, MultiModalConfig, + SchedulerConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.model_loader.loader import build_model +from vllm.model_executor.models import ModelRegistry from vllm.multimodal import BatchedTensors from vllm.utils import is_pin_memory_available +def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str): + """ + Helper function to load weights for inner vLLM models. + + See also: + :ref:`init_vllm_registered_model` + """ + for name, loaded_weight in weights: + name = name.split(".") + if prefix == name.pop(0): + name = ".".join(name) + yield name, loaded_weight + + +def init_vllm_registered_model( + hf_config: PretrainedConfig, + cache_config: Optional[CacheConfig], + quant_config: Optional[QuantizationConfig], + *, + lora_config: Optional[LoRAConfig] = None, + multimodal_config: Optional[MultiModalConfig] = None, + scheduler_config: Optional[SchedulerConfig] = None, +) -> nn.Module: + """ + Helper function to initialize an inner model registered to vLLM, + based on the arguments passed to the outer vLLM model. + """ + model_class, _ = ModelRegistry.resolve_model_cls(hf_config.architectures) + + return build_model( + model_class, + hf_config, + cache_config, + quant_config, + lora_config=lora_config, + multimodal_config=multimodal_config, + scheduler_config=scheduler_config, + ) + + def merge_vision_embeddings(input_ids: torch.Tensor, inputs_embeds: torch.Tensor, vision_embeddings: BatchedTensors, image_token_id: int) -> torch.Tensor: """ - Merge `vision_embeddings` into `inputs_embeds` by overwriting the positions - in `inputs_embeds` corresponding to placeholder image tokens in `input_ids`. + Merge ``vision_embeddings`` into ``inputs_embeds`` by overwriting the + positions in ``inputs_embeds`` corresponding to placeholder image tokens in + ``input_ids``. Note: - This updates `inputs_embeds` in place. + This updates ``inputs_embeds`` in place. """ mask = (input_ids == image_token_id) num_expected_tokens = mask.sum()
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-7208@47bf473
vllm-project/vllm
Python
7,208
[Core/Bugfix] Add FP8 K/V Scale and dtype conversion for prefix/prefill Triton Kernel
Fix the FP8 Triton kernel issue. Should enable FP8 KV Cache to be used with: 1. chunked prefill 2. prefix caching FIX https://github.com/vllm-project/vllm/issues/4381 https://github.com/vllm-project/vllm/issues/3880 https://github.com/vllm-project/vllm/issues/3156 https://github.com/vllm-project/vllm/issues/3880 TODO: - [x] Undo guards: https://github.com/vllm-project/vllm/pull/3903 - [x] Regression test: https://github.com/vllm-project/vllm/issues/4381#issuecomment-2271579989 Notes: 1. @comaniac mentions upcoming flashinfer support, but I think supporting it in Triton as fallback is good for users who want to avoid installing heavyweight dependency 2. Regarding correctness, you can see that this PR merely brings up triton kernel to parity with vLLM's custom torch CUDA paged attention kernels: https://github.com/vllm-project/vllm/blob/a3bbbfa1d8c2f30581d37c6f30429d648bbbf87c/csrc/attention/attention_kernels.cu#L287, https://github.com/vllm-project/vllm/blob/a3bbbfa1d8c2f30581d37c6f30429d648bbbf87c/csrc/attention/attention_kernels.cu#L417, where scaled_convert is defined as: https://github.com/vllm-project/vllm/blob/a3bbbfa1d8c2f30581d37c6f30429d648bbbf87c/csrc/quantization/fp8/nvidia/quant_utils.cuh#L299 --- Example Output (decode v.s. chunked prefill with FP8 KV Cache): ![image](https://github.com/user-attachments/assets/0bbe392d-cbff-43ac-970b-ae1707e7802a) Performance is similar to decode in low load case on max_chunk=16,max_sequence_len=512 ![image](https://github.com/user-attachments/assets/d513c9d6-5124-4e6a-a007-7a7fc39b1d4b) ![image](https://github.com/user-attachments/assets/c3366502-fac2-456b-bf8d-44b2cb98c451) I attribute the slightly lower perf to lack of perf tuning of the triton kernels <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. 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It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details> CC: @comaniac
2024-08-06T15:06:56Z
[Bug]: Chunked prefill doesn't seem to work when --kv-cache-dtype fp8 ### Your current environment H100 (but I believe it happens in any machine) ### ๐Ÿ› Describe the bug ``` --enable-chunked-prefill --num-max-batched-tokens 2048 --kv-cache-dtype "fp8" ``` Seems to be broken with some type incompatibility error.
Yep, can confirm. I think it's undocumented that using both together is not supported? I get this error on a dual 4090 machine: ``` 2024-06-03T14:15:05.332567820Z raise CompilationError(fn.src, node, repr(e)) from e 2024-06-03T14:15:05.332573240Z triton.compiler.errors.CompilationError: at 114:24: off_v = ( 2024-06-03T14:15:05.332578110Z bn[:, None] * stride_v_cache_bs + 2024-06-03T14:15:05.332588389Z cur_kv_head * stride_v_cache_h + 2024-06-03T14:15:05.332593288Z offs_d[None, :] * stride_v_cache_d + 2024-06-03T14:15:05.332598147Z (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) 2024-06-03T14:15:05.332602996Z k = tl.load(K_cache + off_k, 2024-06-03T14:15:05.332607825Z mask=dim_mask[:, None] & 2024-06-03T14:15:05.332612695Z ((start_n + offs_n[None, :]) < cur_batch_ctx_len), 2024-06-03T14:15:05.332617564Z other=0.0) # [D,N] 2024-06-03T14:15:05.332622383Z 2024-06-03T14:15:05.332627142Z qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N] 2024-06-03T14:15:05.332632532Z qk += tl.dot(q, k) 2024-06-03T14:15:05.332637411Z ^ 2024-06-03T14:15:05.332642260Z AssertionError('Both operands must be same type. First operand (fp16) and second operand (uint8)') ``` Some other engine args that I used, in case they're relevant: ``` --quantization gptq --dtype float16 --enforce-eager --tensor-parallel-size 2 ``` Let me make a PR to raise an error for now. cc @comaniac I believe you made this work before. Did you use kv cache dtype fp 8? It should work with xformers backend with paged attention, but I'm not sure if that works with GPTQ. Same issue here. I am using llama 3.1 8B which has a context length of 128k. Chunked prefill is automatically enabled for models over a certain sequence length (128k is over it) and I found that I had to set `--enable-chunked-prefill False` in order to use `--kv-cache-dtype fp8` That's not expected. I'll file a PR to automatically disable chunked prefill for now if fp8 kv-cache is enabled. I know it's super long but here's the full trace: <details><summary>the full very long trace</summary> ``` (constellate-vllm) (venv) constellate@1-ai-appserver-staging:/mnt/disk/AI/constellate-vllm$ python -m vllm.entrypoints.openai.api_server --dtype half --kv-cache-dtype fp8 --model meta-llama/Meta-Llama-3.1-8B-Instruct --chat-template examples/tool_chat_template_llama_3_1.jinja --enable-auto-tool-choice --tool-call-parser llama3.1 INFO 08-05 23:12:08 api_server.py:370] vLLM API server version 0.5.3.post1 INFO 08-05 23:12:08 api_server.py:371] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='examples/tool_chat_template_llama_3_1.jinja', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_auto_tool_choice=True, tool_call_parser='llama3.1', model='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='half', kv_cache_dtype='fp8', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None) WARNING 08-05 23:12:10 config.py:1439] Casting torch.bfloat16 to torch.float16. WARNING 08-05 23:12:11 config.py:1439] Casting torch.bfloat16 to torch.float16. INFO 08-05 23:12:11 config.py:483] Using fp8 data type to store kv cache. It reduces the GPU memory footprint and boosts the performance. Meanwhile, it may cause accuracy drop without a proper scaling factor WARNING 08-05 23:12:11 arg_utils.py:766] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False. INFO 08-05 23:12:11 config.py:819] Chunked prefill is enabled with max_num_batched_tokens=512. INFO 08-05 23:12:11 llm_engine.py:174] Initializing an LLM engine (v0.5.3.post1) with config: model='meta-llama/Meta-Llama-3.1-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3.1-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=fp8, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=meta-llama/Meta-Llama-3.1-8B-Instruct, use_v2_block_manager=False, enable_prefix_caching=False) INFO 08-05 23:12:11 selector.py:151] Cannot use FlashAttention-2 backend for Volta and Turing GPUs. INFO 08-05 23:12:11 selector.py:54] Using XFormers backend. /mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_fwd") /mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_bwd") INFO 08-05 23:12:12 model_runner.py:720] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct... INFO 08-05 23:12:13 selector.py:151] Cannot use FlashAttention-2 backend for Volta and Turing GPUs. INFO 08-05 23:12:13 selector.py:54] Using XFormers backend. INFO 08-05 23:12:13 weight_utils.py:225] Using model weights format ['*.safetensors'] Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00<?, ?it/s] Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:00<00:01, 2.84it/s] Loading safetensors checkpoint shards: 50% Completed | 2/4 [00:01<00:02, 1.01s/it] Loading safetensors checkpoint shards: 75% Completed | 3/4 [00:03<00:01, 1.26s/it] Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:05<00:00, 1.40s/it] Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:05<00:00, 1.25s/it] INFO 08-05 23:12:18 model_runner.py:732] Loading model weights took 14.9888 GB INFO 08-05 23:12:19 gpu_executor.py:102] # GPU blocks: 12723, # CPU blocks: 4096 INFO 08-05 23:12:22 model_runner.py:1024] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. INFO 08-05 23:12:22 model_runner.py:1028] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage. INFO 08-05 23:12:39 model_runner.py:1225] Graph capturing finished in 18 secs. INFO 08-05 23:12:40 chat_utils.py:53] Using supplied chat template: INFO 08-05 23:12:40 chat_utils.py:53] {{- bos_token }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if custom_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools = custom_tools %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not tools_in_user_message is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools_in_user_message = true %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not date_string is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set date_string = "26 Jul 2024" %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools = none %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {#- This block extracts the system message, so we can slot it into the right place. #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if messages[0]['role'] == 'system' %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set system_message = messages[0]['content']|trim %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set messages = messages[1:] %} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set system_message = "" %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {#- System message + builtin tools #} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|start_header_id|>system<|end_header_id|>\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined or tools is not none %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Environment: ipython\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {#- REMOVED BUILTIN TOOLS - NOT USED OR NEEDED FOR OPENAI COMPATIBILITY INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] #} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {{- "Cutting Knowledge Date: December 2023\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Today Date: " + date_string if date_string else '5 Aug 2024' + "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if tools is not none and not tools_in_user_message %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} INFO 08-05 23:12:40 chat_utils.py:53] {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Do not use variables.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for t in tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- t | tojson(indent=4) }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- system_message }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {#- Custom tools are passed in a user message with some extra guidance #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if tools_in_user_message and not tools is none %} INFO 08-05 23:12:40 chat_utils.py:53] {#- Extract the first user message so we can plug it in here #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if messages | length != 0 %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set first_user_message = messages[0]['content']|trim %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set messages = messages[1:] %} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Given the following functions, please respond with a JSON for a function call " }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "with its proper arguments that best answers the given prompt.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Do not use variables.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for t in tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- t | tojson(indent=4) }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {{- first_user_message + "<|eot_id|>"}} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {%- for message in messages %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- elif 'tool_calls' in message %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not message.tool_calls|length == 1 %} INFO 08-05 23:12:40 chat_utils.py:53] {{- raise_exception("This model only supports single tool-calls at once!") }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tool_call = message.tool_calls[0].function %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined and tool_call.name in builtin_tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|python_tag|>" + tool_call.name + ".call(" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for arg_name, arg_val in tool_call.arguments | items %} INFO 08-05 23:12:40 chat_utils.py:53] {{- arg_name + '="' + arg_val + '"' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not loop.last %} INFO 08-05 23:12:40 chat_utils.py:53] {{- ", " }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {{- ")" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- '{"name": "' + tool_call.name + '", ' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- '"parameters": ' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- tool_call.arguments | tojson }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "}" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {#- This means we're in ipython mode #} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eom_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- elif message.role == "tool" or message.role == "ipython" %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if message.content is mapping or message.content is iterable %} INFO 08-05 23:12:40 chat_utils.py:53] {{- message.content | tojson }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- message.content }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if add_generation_prompt %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 serving_chat.py:80] "Auto" tool choice has been enabled please note that while the parallel_tool_calls client option is preset for compatibility reasons, it will be ignored. WARNING 08-05 23:12:40 serving_embedding.py:171] embedding_mode is False. Embedding API will not work. INFO 08-05 23:12:40 chat_utils.py:53] Using supplied chat template: INFO 08-05 23:12:40 chat_utils.py:53] {{- bos_token }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if custom_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools = custom_tools %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not tools_in_user_message is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools_in_user_message = true %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not date_string is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set date_string = "26 Jul 2024" %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tools = none %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {#- This block extracts the system message, so we can slot it into the right place. #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if messages[0]['role'] == 'system' %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set system_message = messages[0]['content']|trim %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set messages = messages[1:] %} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set system_message = "" %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {#- System message + builtin tools #} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|start_header_id|>system<|end_header_id|>\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined or tools is not none %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Environment: ipython\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {#- REMOVED BUILTIN TOOLS - NOT USED OR NEEDED FOR OPENAI COMPATIBILITY INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] #} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {{- "Cutting Knowledge Date: December 2023\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Today Date: " + date_string if date_string else '5 Aug 2024' + "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if tools is not none and not tools_in_user_message %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} INFO 08-05 23:12:40 chat_utils.py:53] {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Do not use variables.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for t in tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- t | tojson(indent=4) }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- system_message }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {#- Custom tools are passed in a user message with some extra guidance #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if tools_in_user_message and not tools is none %} INFO 08-05 23:12:40 chat_utils.py:53] {#- Extract the first user message so we can plug it in here #} INFO 08-05 23:12:40 chat_utils.py:53] {%- if messages | length != 0 %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set first_user_message = messages[0]['content']|trim %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set messages = messages[1:] %} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Given the following functions, please respond with a JSON for a function call " }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "with its proper arguments that best answers the given prompt.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "Do not use variables.\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for t in tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- t | tojson(indent=4) }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {{- first_user_message + "<|eot_id|>"}} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] INFO 08-05 23:12:40 chat_utils.py:53] {%- for message in messages %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- elif 'tool_calls' in message %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not message.tool_calls|length == 1 %} INFO 08-05 23:12:40 chat_utils.py:53] {{- raise_exception("This model only supports single tool-calls at once!") }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- set tool_call = message.tool_calls[0].function %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined and tool_call.name in builtin_tools %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|python_tag|>" + tool_call.name + ".call(" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- for arg_name, arg_val in tool_call.arguments | items %} INFO 08-05 23:12:40 chat_utils.py:53] {{- arg_name + '="' + arg_val + '"' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if not loop.last %} INFO 08-05 23:12:40 chat_utils.py:53] {{- ", " }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {{- ")" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} INFO 08-05 23:12:40 chat_utils.py:53] {{- '{"name": "' + tool_call.name + '", ' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- '"parameters": ' }} INFO 08-05 23:12:40 chat_utils.py:53] {{- tool_call.arguments | tojson }} INFO 08-05 23:12:40 chat_utils.py:53] {{- "}" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if builtin_tools is defined %} INFO 08-05 23:12:40 chat_utils.py:53] {#- This means we're in ipython mode #} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eom_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- elif message.role == "tool" or message.role == "ipython" %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- if message.content is mapping or message.content is iterable %} INFO 08-05 23:12:40 chat_utils.py:53] {{- message.content | tojson }} INFO 08-05 23:12:40 chat_utils.py:53] {%- else %} INFO 08-05 23:12:40 chat_utils.py:53] {{- message.content }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {{- "<|eot_id|>" }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 chat_utils.py:53] {%- endfor %} INFO 08-05 23:12:40 chat_utils.py:53] {%- if add_generation_prompt %} INFO 08-05 23:12:40 chat_utils.py:53] {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} INFO 08-05 23:12:40 chat_utils.py:53] {%- endif %} INFO 08-05 23:12:40 api_server.py:346] Available routes are: INFO 08-05 23:12:40 api_server.py:351] Route: /openapi.json, Methods: HEAD, GET INFO 08-05 23:12:40 api_server.py:351] Route: /docs, Methods: HEAD, GET INFO 08-05 23:12:40 api_server.py:351] Route: /docs/oauth2-redirect, Methods: HEAD, GET INFO 08-05 23:12:40 api_server.py:351] Route: /redoc, Methods: HEAD, GET INFO 08-05 23:12:40 api_server.py:351] Route: /health, Methods: GET INFO 08-05 23:12:40 api_server.py:351] Route: /tokenize, Methods: POST INFO 08-05 23:12:40 api_server.py:351] Route: /detokenize, Methods: POST INFO 08-05 23:12:40 api_server.py:351] Route: /v1/models, Methods: GET INFO 08-05 23:12:40 api_server.py:351] Route: /version, Methods: GET INFO 08-05 23:12:40 api_server.py:351] Route: /v1/chat/completions, Methods: POST INFO 08-05 23:12:40 api_server.py:351] Route: /v1/completions, Methods: POST INFO 08-05 23:12:40 api_server.py:351] Route: /v1/embeddings, Methods: POST INFO: Started server process [3431585] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) INFO 08-05 23:12:43 serving_chat.py:149] Created full prompt INFO 08-05 23:12:43 serving_chat.py:150] <|begin_of_text|><|start_header_id|>system<|end_header_id|> INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] Environment: ipython INFO 08-05 23:12:43 serving_chat.py:150] Cutting Knowledge Date: December 2023 INFO 08-05 23:12:43 serving_chat.py:150] Today Date: 26 Jul 2024<|eot_id|><|start_header_id|>user<|end_header_id|> INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.Do not use variables. INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] { INFO 08-05 23:12:43 serving_chat.py:150] "type": "function", INFO 08-05 23:12:43 serving_chat.py:150] "function": { INFO 08-05 23:12:43 serving_chat.py:150] "name": "get_current_weather", INFO 08-05 23:12:43 serving_chat.py:150] "description": "Get the current weather in a given location", INFO 08-05 23:12:43 serving_chat.py:150] "parameters": { INFO 08-05 23:12:43 serving_chat.py:150] "type": "object", INFO 08-05 23:12:43 serving_chat.py:150] "properties": { INFO 08-05 23:12:43 serving_chat.py:150] "city": { INFO 08-05 23:12:43 serving_chat.py:150] "type": "string", INFO 08-05 23:12:43 serving_chat.py:150] "description": "The city to find the weather for, e.g. 'San Francisco'" INFO 08-05 23:12:43 serving_chat.py:150] }, INFO 08-05 23:12:43 serving_chat.py:150] "state": { INFO 08-05 23:12:43 serving_chat.py:150] "type": "string", INFO 08-05 23:12:43 serving_chat.py:150] "description": "the two-letter abbreviation for the state that the city is in, e.g. 'CA' which would mean 'California'" INFO 08-05 23:12:43 serving_chat.py:150] }, INFO 08-05 23:12:43 serving_chat.py:150] "unit": { INFO 08-05 23:12:43 serving_chat.py:150] "type": "string", INFO 08-05 23:12:43 serving_chat.py:150] "description": "The unit to fetch the temperature in", INFO 08-05 23:12:43 serving_chat.py:150] "enum": [ INFO 08-05 23:12:43 serving_chat.py:150] "celsius", INFO 08-05 23:12:43 serving_chat.py:150] "fahrenheit" INFO 08-05 23:12:43 serving_chat.py:150] ] INFO 08-05 23:12:43 serving_chat.py:150] } INFO 08-05 23:12:43 serving_chat.py:150] } INFO 08-05 23:12:43 serving_chat.py:150] } INFO 08-05 23:12:43 serving_chat.py:150] } INFO 08-05 23:12:43 serving_chat.py:150] } INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] Hi! How are you doing today?<|eot_id|><|start_header_id|>assistant<|end_header_id|> INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] I'm doing well! How can I help you?<|eot_id|><|start_header_id|>user<|end_header_id|> INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] Can you tell me what the weather will be in Dallas and San Francisco? I like fahrenheit.<|eot_id|><|start_header_id|>assistant<|end_header_id|> INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 serving_chat.py:150] INFO 08-05 23:12:43 logger.py:36] Received request chat-30088efd6e3645e2b07ea083bb9d7446: prompt: '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nEnvironment: ipython\nCutting Knowledge Date: December 2023\nToday Date: 26 Jul 2024<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nGiven the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\n\nRespond in the format {"name": function name, "parameters": dictionary of argument name and its value}.Do not use variables.\n\n{\n "type": "function",\n "function": {\n "name": "get_current_weather",\n "description": "Get the current weather in a given location",\n "parameters": {\n "type": "object",\n "properties": {\n "city": {\n "type": "string",\n "description": "The city to find the weather for, e.g. \'San Francisco\'"\n },\n "state": {\n "type": "string",\n "description": "the two-letter abbreviation for the state that the city is in, e.g. \'CA\' which would mean \'California\'"\n },\n "unit": {\n "type": "string",\n "description": "The unit to fetch the temperature in",\n "enum": [\n "celsius",\n "fahrenheit"\n ]\n }\n }\n }\n }\n}\n\nHi! How are you doing today?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI\'m doing well! How can I help you?<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCan you tell me what the weather will be in Dallas and San Francisco? I like fahrenheit.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=130751, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [128000, 128006, 9125, 128007, 271, 13013, 25, 6125, 27993, 198, 38766, 1303, 33025, 2696, 25, 6790, 220, 2366, 18, 198, 15724, 2696, 25, 220, 1627, 10263, 220, 2366, 19, 128009, 128006, 882, 128007, 271, 22818, 279, 2768, 5865, 11, 4587, 6013, 449, 264, 4823, 369, 264, 734, 1650, 449, 1202, 6300, 6105, 430, 1888, 11503, 279, 2728, 10137, 382, 66454, 304, 279, 3645, 5324, 609, 794, 734, 836, 11, 330, 14105, 794, 11240, 315, 5811, 836, 323, 1202, 907, 7966, 5519, 539, 1005, 7482, 382, 517, 262, 330, 1337, 794, 330, 1723, 761, 262, 330, 1723, 794, 341, 286, 330, 609, 794, 330, 456, 11327, 70464, 761, 286, 330, 4789, 794, 330, 1991, 279, 1510, 9282, 304, 264, 2728, 3813, 761, 286, 330, 14105, 794, 341, 310, 330, 1337, 794, 330, 1735, 761, 310, 330, 13495, 794, 341, 394, 330, 9103, 794, 341, 504, 330, 1337, 794, 330, 928, 761, 504, 330, 4789, 794, 330, 791, 3363, 311, 1505, 279, 9282, 369, 11, 384, 1326, 13, 364, 24661, 13175, 42265, 394, 1173, 394, 330, 2513, 794, 341, 504, 330, 1337, 794, 330, 928, 761, 504, 330, 4789, 794, 330, 1820, 1403, 80468, 72578, 369, 279, 1614, 430, 279, 3363, 374, 304, 11, 384, 1326, 13, 364, 5158, 6, 902, 1053, 3152, 364, 46510, 42265, 394, 1173, 394, 330, 3928, 794, 341, 504, 330, 1337, 794, 330, 928, 761, 504, 330, 4789, 794, 330, 791, 5089, 311, 7963, 279, 9499, 304, 761, 504, 330, 9195, 794, 2330, 667, 330, 66, 41347, 761, 667, 330, 69, 49010, 702, 504, 5243, 394, 457, 310, 457, 286, 457, 262, 457, 633, 13347, 0, 2650, 527, 499, 3815, 3432, 30, 128009, 128006, 78191, 128007, 271, 40, 2846, 3815, 1664, 0, 2650, 649, 358, 1520, 499, 30, 128009, 128006, 882, 128007, 271, 6854, 499, 3371, 757, 1148, 279, 9282, 690, 387, 304, 19051, 323, 5960, 13175, 30, 358, 1093, 282, 49010, 13, 128009, 128006, 78191, 128007, 271], lora_request: None, prompt_adapter_request: None. INFO 08-05 23:12:43 async_llm_engine.py:174] Added request chat-30088efd6e3645e2b07ea083bb9d7446. ERROR 08-05 23:12:45 async_llm_engine.py:57] Engine background task failed ERROR 08-05 23:12:45 async_llm_engine.py:57] Traceback (most recent call last): ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/core.py", line 35, in wrapper ERROR 08-05 23:12:45 async_llm_engine.py:57] return fn(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/core.py", line 1534, in dot ERROR 08-05 23:12:45 async_llm_engine.py:57] return semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype, _builder) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1355, in dot ERROR 08-05 23:12:45 async_llm_engine.py:57] assert_dtypes_valid(lhs.dtype, rhs.dtype, builder.options) ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1328, in assert_dtypes_valid ERROR 08-05 23:12:45 async_llm_engine.py:57] assert lhs_dtype == rhs_dtype, f"First input ({lhs_dtype}) and second input ({rhs_dtype}) must have the same dtype!" ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] AssertionError: First input (fp16) and second input (uint8) must have the same dtype! ERROR 08-05 23:12:45 async_llm_engine.py:57] ERROR 08-05 23:12:45 async_llm_engine.py:57] The above exception was the direct cause of the following exception: ERROR 08-05 23:12:45 async_llm_engine.py:57] ERROR 08-05 23:12:45 async_llm_engine.py:57] Traceback (most recent call last): ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 47, in _log_task_completion ERROR 08-05 23:12:45 async_llm_engine.py:57] return_value = task.result() ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 642, in run_engine_loop ERROR 08-05 23:12:45 async_llm_engine.py:57] result = task.result() ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 585, in engine_step ERROR 08-05 23:12:45 async_llm_engine.py:57] request_outputs = await self.engine.step_async(virtual_engine) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 254, in step_async ERROR 08-05 23:12:45 async_llm_engine.py:57] output = await self.model_executor.execute_model_async( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/executor/gpu_executor.py", line 159, in execute_model_async ERROR 08-05 23:12:45 async_llm_engine.py:57] output = await make_async(self.driver_worker.execute_model ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/usr/lib/python3.11/concurrent/futures/thread.py", line 58, in run ERROR 08-05 23:12:45 async_llm_engine.py:57] result = self.fn(*self.args, **self.kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/worker/worker_base.py", line 273, in execute_model ERROR 08-05 23:12:45 async_llm_engine.py:57] output = self.model_runner.execute_model( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context ERROR 08-05 23:12:45 async_llm_engine.py:57] return func(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/worker/model_runner.py", line 1363, in execute_model ERROR 08-05 23:12:45 async_llm_engine.py:57] hidden_or_intermediate_states = model_executable( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return self._call_impl(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return forward_call(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 422, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] model_output = self.model(input_ids, positions, kv_caches, ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return self._call_impl(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return forward_call(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 322, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] hidden_states, residual = layer( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return self._call_impl(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return forward_call(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 245, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] hidden_states = self.self_attn( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return self._call_impl(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return forward_call(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 175, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] attn_output = self.attn(q, k, v, kv_cache, attn_metadata) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return self._call_impl(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl ERROR 08-05 23:12:45 async_llm_engine.py:57] return forward_call(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/attention/layer.py", line 98, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] return self.impl.forward(query, ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/attention/backends/xformers.py", line 603, in forward ERROR 08-05 23:12:45 async_llm_engine.py:57] out = PagedAttention.forward_prefix( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/attention/ops/paged_attn.py", line 208, in forward_prefix ERROR 08-05 23:12:45 async_llm_engine.py:57] context_attention_fwd( ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context ERROR 08-05 23:12:45 async_llm_engine.py:57] return func(*args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/vllm/attention/ops/prefix_prefill.py", line 765, in context_attention_fwd ERROR 08-05 23:12:45 async_llm_engine.py:57] _fwd_kernel[grid]( ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 345, in <lambda> ERROR 08-05 23:12:45 async_llm_engine.py:57] return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 662, in run ERROR 08-05 23:12:45 async_llm_engine.py:57] kernel = self.compile( ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 276, in compile ERROR 08-05 23:12:45 async_llm_engine.py:57] module = src.make_ir(options, codegen_fns, context) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 113, in make_ir ERROR 08-05 23:12:45 async_llm_engine.py:57] return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 08-05 23:12:45 async_llm_engine.py:57] triton.compiler.errors.CompilationError: at 114:14: ERROR 08-05 23:12:45 async_llm_engine.py:57] off_v = ( ERROR 08-05 23:12:45 async_llm_engine.py:57] bn[:, None] * stride_v_cache_bs + ERROR 08-05 23:12:45 async_llm_engine.py:57] cur_kv_head * stride_v_cache_h + ERROR 08-05 23:12:45 async_llm_engine.py:57] offs_d[None, :] * stride_v_cache_d + ERROR 08-05 23:12:45 async_llm_engine.py:57] (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) ERROR 08-05 23:12:45 async_llm_engine.py:57] k = tl.load(K_cache + off_k, ERROR 08-05 23:12:45 async_llm_engine.py:57] mask=dim_mask[:, None] & ERROR 08-05 23:12:45 async_llm_engine.py:57] ((start_n + offs_n[None, :]) < cur_batch_ctx_len), ERROR 08-05 23:12:45 async_llm_engine.py:57] other=0.0) # [D,N] ERROR 08-05 23:12:45 async_llm_engine.py:57] ERROR 08-05 23:12:45 async_llm_engine.py:57] qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N] ERROR 08-05 23:12:45 async_llm_engine.py:57] qk += tl.dot(q, k) ERROR 08-05 23:12:45 async_llm_engine.py:57] ^ Exception in callback _log_task_completion(error_callback=<bound method...7f999479a510>>)(<Task finishe...de320>, None)>) at /mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py:37 handle: <Handle _log_task_completion(error_callback=<bound method...7f999479a510>>)(<Task finishe...de320>, None)>) at /mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py:37> Traceback (most recent call last): File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/core.py", line 35, in wrapper return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/core.py", line 1534, in dot return semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype, _builder) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1355, in dot assert_dtypes_valid(lhs.dtype, rhs.dtype, builder.options) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/language/semantic.py", line 1328, in assert_dtypes_valid assert lhs_dtype == rhs_dtype, f"First input ({lhs_dtype}) and second input ({rhs_dtype}) must have the same dtype!" ^^^^^^^^^^^^^^^^^^^^^^ AssertionError: First input (fp16) and second input (uint8) must have the same dtype! The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 47, in _log_task_completion return_value = task.result() ^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 642, in run_engine_loop result = task.result() ^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 585, in engine_step request_outputs = await self.engine.step_async(virtual_engine) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 254, in step_async output = await self.model_executor.execute_model_async( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/executor/gpu_executor.py", line 159, in execute_model_async output = await make_async(self.driver_worker.execute_model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/worker/worker_base.py", line 273, in execute_model output = self.model_runner.execute_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/worker/model_runner.py", line 1363, in execute_model hidden_or_intermediate_states = model_executable( ^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 422, in forward model_output = self.model(input_ids, positions, kv_caches, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 322, in forward hidden_states, residual = layer( ^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 245, in forward hidden_states = self.self_attn( ^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/model_executor/models/llama.py", line 175, in forward attn_output = self.attn(q, k, v, kv_cache, attn_metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/attention/layer.py", line 98, in forward return self.impl.forward(query, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/attention/backends/xformers.py", line 603, in forward out = PagedAttention.forward_prefix( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/attention/ops/paged_attn.py", line 208, in forward_prefix context_attention_fwd( File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/attention/ops/prefix_prefill.py", line 765, in context_attention_fwd _fwd_kernel[grid]( File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 345, in <lambda> return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 662, in run kernel = self.compile( ^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 276, in compile module = src.make_ir(options, codegen_fns, context) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 113, in make_ir return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ triton.compiler.errors.CompilationError: at 114:14: off_v = ( bn[:, None] * stride_v_cache_bs + cur_kv_head * stride_v_cache_h + offs_d[None, :] * stride_v_cache_d + (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) k = tl.load(K_cache + off_k, mask=dim_mask[:, None] & ((start_n + offs_n[None, :]) < cur_batch_ctx_len), other=0.0) # [D,N] qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N] qk += tl.dot(q, k) ^ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/lib/python3.11/asyncio/events.py", line 80, in _run self._context.run(self._callback, *self._args) File "/mnt/disk/AI/constellate-vllm/vllm/engine/async_llm_engine.py", line 59, in _log_task_completion raise AsyncEngineDeadError( vllm.engine.async_llm_engine.AsyncEngineDeadError: Task finished unexpectedly. This should never happen! Please open an issue on Github. See stack trace above for theactual cause. INFO 08-05 23:12:45 async_llm_engine.py:181] Aborted request chat-30088efd6e3645e2b07ea083bb9d7446. INFO: 10.3.10.164:65191 - "POST /v1/chat/completions HTTP/1.1" 500 Internal Server Error ERROR: Exception in ASGI application Traceback (most recent call last): File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/uvicorn/protocols/http/httptools_impl.py", line 399, in run_asgi result = await app( # type: ignore[func-returns-value] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 70, in __call__ return await self.app(scope, receive, send) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/fastapi/applications.py", line 1054, in __call__ await super().__call__(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/applications.py", line 123, in __call__ await self.middleware_stack(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/middleware/errors.py", line 186, in __call__ raise exc File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/middleware/errors.py", line 164, in __call__ await self.app(scope, receive, _send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/middleware/cors.py", line 85, in __call__ await self.app(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 65, in __call__ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app raise exc File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/routing.py", line 756, in __call__ await self.middleware_stack(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/routing.py", line 776, in app await route.handle(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/routing.py", line 297, in handle await self.app(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/routing.py", line 77, in app await wrap_app_handling_exceptions(app, request)(scope, receive, send) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app raise exc File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/starlette/routing.py", line 72, in app response = await func(request) ^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/fastapi/routing.py", line 278, in app raw_response = await run_endpoint_function( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/venv/lib/python3.11/site-packages/fastapi/routing.py", line 191, in run_endpoint_function return await dependant.call(**values) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/entrypoints/openai/api_server.py", line 191, in create_chat_completion generator = await openai_serving_chat.create_chat_completion( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/entrypoints/openai/serving_chat.py", line 241, in create_chat_completion generator = await self.chat_completion_full_generator( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/mnt/disk/AI/constellate-vllm/vllm/entrypoints/openai/serving_chat.py", line 569, in chat_completion_full_generator async for res in result_generator: File "/mnt/disk/AI/constellate-vllm/vllm/entrypoints/openai/rpc/client.py", line 216, in generate raise request_output triton.compiler.errors.CompilationError: at 114:14: off_v = ( bn[:, None] * stride_v_cache_bs + cur_kv_head * stride_v_cache_h + offs_d[None, :] * stride_v_cache_d + (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) k = tl.load(K_cache + off_k, mask=dim_mask[:, None] & ((start_n + offs_n[None, :]) < cur_batch_ctx_len), other=0.0) # [D,N] qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N] qk += tl.dot(q, k) ^ INFO 08-05 23:12:50 metrics.py:406] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO 08-05 23:13:00 metrics.py:406] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. ^CINFO 08-05 23:13:06 server.py:207] vLLM ZMQ RPC Server was interrupted. INFO 08-05 23:13:06 api_server.py:396] Gracefully stopping http server ``` </details (on a v100 tesla hence the fp16 instead of bf16) > I know it's super long but here's the full trace: Seems like triton kernel issue, looks fixable. Let me take a look. Also: https://github.com/vllm-project/vllm/blob/1f26efbb3a5e6dad0b98421dd697167c42a50629/vllm/attention/backends/xformers.py#L600 Also, is this comment still relevant? https://github.com/vllm-project/vllm/blob/1f26efbb3a5e6dad0b98421dd697167c42a50629/vllm/worker/model_runner.py#L765 Related: https://github.com/vllm-project/vllm/issues/3880, https://github.com/vllm-project/vllm/issues/3156, https://github.com/vllm-project/vllm/issues/3880 This PR is directly relevant as the solution: https://github.com/vllm-project/vllm/pull/3234 (although the PR is incomplete and incorrect) Additional context on FP8 KV Cache: https://github.com/vllm-project/vllm/issues/4532 with solution: https://github.com/vllm-project/vllm/pull/4893
[ { "body": "### Your current environment\r\n\r\nH100 (but I believe it happens in any machine)\r\n\r\n### ๐Ÿ› Describe the bug\r\n\r\n```\r\n--enable-chunked-prefill --num-max-batched-tokens 2048 --kv-cache-dtype \"fp8\"\r\n```\r\n\r\nSeems to be broken with some type incompatibility error. \r\n\r\n", "number": 4381, "title": "[Bug]: Chunked prefill doesn't seem to work when --kv-cache-dtype fp8" } ]
24154f8618aa5da695b18cbbd71704f59803ec6b
{ "head_commit": "47bf473676ae40b44cec49d5070bbc7bf3ca92ab", "head_commit_message": "fix dtype conversion only when mixed-precision dot not supported", "patch_to_review": "diff --git a/docs/source/quantization/fp8_e4m3_kvcache.rst b/docs/source/quantization/fp8_e4m3_kvcache.rst\nindex fd71c00b7bf..cc52d8f40af 100644\n--- a/docs/source/quantization/fp8_e4m3_kvcache.rst\n+++ b/docs/source/quantization/fp8_e4m3_kvcache.rst\n@@ -45,5 +45,3 @@ Here is an example of how to enable this feature:\n # output w/ scaling factors: England, the United Kingdom, and one of the world's leading financial,\n # output w/o scaling factors: England, located in the southeastern part of the country. It is known \n \n-Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type.\n-\ndiff --git a/docs/source/quantization/fp8_e5m2_kvcache.rst b/docs/source/quantization/fp8_e5m2_kvcache.rst\nindex 337252a00ae..9ae07bcd3b9 100644\n--- a/docs/source/quantization/fp8_e5m2_kvcache.rst\n+++ b/docs/source/quantization/fp8_e5m2_kvcache.rst\n@@ -32,5 +32,3 @@ Here is an example of how to enable this feature:\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\n \n \n-Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type.\n-\ndiff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py\nindex 26e9b8a93fb..868cceced40 100644\n--- a/vllm/attention/backends/rocm_flash_attn.py\n+++ b/vllm/attention/backends/rocm_flash_attn.py\n@@ -468,6 +468,8 @@ def forward(\n prefill_meta.max_query_len,\n self.alibi_slopes,\n self.sliding_window[0],\n+ k_scale,\n+ v_scale,\n )\n \n if decode_meta := attn_metadata.decode_metadata:\ndiff --git a/vllm/attention/backends/xformers.py b/vllm/attention/backends/xformers.py\nindex 24ba5fc7254..ebc11e4f80c 100644\n--- a/vllm/attention/backends/xformers.py\n+++ b/vllm/attention/backends/xformers.py\n@@ -613,6 +613,8 @@ def forward(\n prefill_meta.max_query_len,\n self.alibi_slopes,\n self.sliding_window,\n+ k_scale,\n+ v_scale,\n )\n assert output[:num_prefill_tokens].shape == out.shape\n output[:num_prefill_tokens] = out\ndiff --git a/vllm/attention/ops/paged_attn.py b/vllm/attention/ops/paged_attn.py\nindex e88963ade16..42a13212dca 100644\n--- a/vllm/attention/ops/paged_attn.py\n+++ b/vllm/attention/ops/paged_attn.py\n@@ -203,6 +203,8 @@ def forward_prefix(\n max_query_len: int,\n alibi_slopes: Optional[torch.Tensor],\n sliding_window: Optional[int],\n+ k_scale: float,\n+ v_scale: float,\n ) -> torch.Tensor:\n output = torch.empty_like(query)\n context_attention_fwd(\n@@ -218,6 +220,8 @@ def forward_prefix(\n seq_lens_tensor,\n context_lens,\n max_query_len,\n+ k_scale,\n+ v_scale,\n alibi_slopes,\n sliding_window,\n )\ndiff --git a/vllm/attention/ops/prefix_prefill.py b/vllm/attention/ops/prefix_prefill.py\nindex 4577d84db18..afdb2e6b0d6 100644\n--- a/vllm/attention/ops/prefix_prefill.py\n+++ b/vllm/attention/ops/prefix_prefill.py\n@@ -18,6 +18,8 @@ def _fwd_kernel(\n V_cache,\n B_Loc,\n sm_scale,\n+ k_scale,\n+ v_scale,\n B_Start_Loc,\n B_Seqlen,\n B_Ctxlen,\n@@ -117,10 +119,19 @@ def _fwd_kernel(\n cur_kv_head * stride_v_cache_h +\n offs_d[None, :] * stride_v_cache_d +\n (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)\n- k = tl.load(K_cache + off_k,\n- mask=dim_mask[:, None] &\n- ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n- other=0.0) # [D,N]\n+ k_cache = tl.load(\n+ K_cache + off_k,\n+ mask=dim_mask[:, None] &\n+ ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n+ other=0.0) # [D,N]\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if k_cache.dtype.is_fp8() or k_cache.dtype.is_uint8():\n+ k = k_cache.to(q.dtype)\n+ else:\n+ k = k_cache\n+ if k_scale != 1.0:\n+ k *= k_scale\n \n qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N]\n qk += tl.dot(q, k)\n@@ -166,7 +177,14 @@ def _fwd_kernel(\n ((start_n + offs_n[:, None]) < cur_batch_ctx_len),\n other=0.0) # [N,D]\n \n- p = p.to(v.dtype)\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if v_cache.dtype.is_fp8() or v_cache.dtype.is_uint8():\n+ v = v_cache.to(q.dtype)\n+ else:\n+ v = v_cache\n+ if v_scale != 1.0:\n+ v *= v_scale\n+\n acc += tl.dot(p, v)\n # # update m_i and l_i\n l_i = l_i_new\n@@ -192,6 +210,14 @@ def _fwd_kernel(\n ((start_n + offs_n[None, :]) < cur_batch_query_len),\n other=0.0)\n \n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if k_cache.dtype.is_fp8() or k_cache.dtype.is_uint8():\n+ k = k_cache.to(q.dtype)\n+ else:\n+ k = k_cache\n+ if k_scale != 1.0:\n+ k *= k_scale\n+\n qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)\n qk += tl.dot(q, k)\n qk *= sm_scale\n@@ -220,13 +246,20 @@ def _fwd_kernel(\n acc_scale = l_i / l_i_new * alpha\n acc = acc * acc_scale[:, None]\n # update acc\n- v = tl.load(v_ptrs +\n- (cur_batch_in_all_start_index + start_n) * stride_vbs,\n- mask=dim_mask[None, :] &\n- ((start_n + offs_n[:, None]) < cur_batch_query_len),\n- other=0.0)\n+ v_cache = tl.load(\n+ v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,\n+ mask=dim_mask[None, :] &\n+ ((start_n + offs_n[:, None]) < cur_batch_query_len),\n+ other=0.0)\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if v_cache.dtype.is_fp8() or v_cache.dtype.is_uint8():\n+ v = v_cache.to(q.dtype)\n+ else:\n+ v = v_cache\n+ if v_scale != 1.0:\n+ v *= v_scale\n \n- p = p.to(v.dtype)\n acc += tl.dot(p, v)\n # update m_i and l_i\n l_i = l_i_new\n@@ -336,7 +369,6 @@ def _fwd_kernel_flash_attn_v2(\n k = tl.load(K_cache + off_k,\n mask=(start_n + offs_n[None, :]) < cur_batch_ctx_len,\n other=0.0)\n-\n qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)\n qk += tl.dot(q, k)\n qk = tl.where((start_n + offs_n[None, :]) < cur_batch_ctx_len, qk,\n@@ -442,6 +474,8 @@ def _fwd_kernel_alibi(\n V_cache,\n B_Loc,\n sm_scale,\n+ k_scale,\n+ v_scale,\n B_Start_Loc,\n B_Seqlen,\n B_Ctxlen,\n@@ -537,10 +571,19 @@ def _fwd_kernel_alibi(\n cur_kv_head * stride_v_cache_h +\n offs_d[None, :] * stride_v_cache_d +\n (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)\n- k = tl.load(K_cache + off_k,\n- mask=dim_mask[:, None] &\n- ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n- other=0.0) # [D,N]\n+ k_cache = tl.load(\n+ K_cache + off_k,\n+ mask=dim_mask[:, None] &\n+ ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n+ other=0.0) # [D,N]\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if k_cache.dtype.is_fp8() or k_cache.dtype.is_uint8():\n+ k = k_cache.to(q.dtype)\n+ else:\n+ k = k_cache\n+ if k_scale != 1.0:\n+ k *= k_scale\n \n qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)\n qk += tl.dot(q, k)\n@@ -578,7 +621,14 @@ def _fwd_kernel_alibi(\n ((start_n + offs_n[:, None]) < cur_batch_ctx_len),\n other=0.0)\n \n- p = p.to(v.dtype)\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if v_cache.dtype.is_fp8() or v_cache.dtype.is_uint8():\n+ v = v_cache.to(q.dtype)\n+ else:\n+ v = v_cache\n+ if v_scale != 1.0:\n+ v *= v_scale\n+\n acc += tl.dot(p, v, allow_tf32=False)\n # update m_i and l_i\n l_i = l_i_new\n@@ -606,12 +656,20 @@ def _fwd_kernel_alibi(\n for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):\n start_n = tl.multiple_of(start_n, BLOCK_N)\n # -- compute qk ----\n- k = tl.load(k_ptrs +\n- (cur_batch_in_all_start_index + start_n) * stride_kbs,\n- mask=dim_mask[:, None] &\n- ((start_n + offs_n[None, :]) <\n- cur_batch_seq_len - cur_batch_ctx_len),\n- other=0.0)\n+ k_cache = tl.load(\n+ k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,\n+ mask=dim_mask[:, None] &\n+ ((start_n + offs_n[None, :]) <\n+ cur_batch_seq_len - cur_batch_ctx_len),\n+ other=0.0)\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if k_cache.dtype.is_fp8() or k_cache.dtype.is_uint8():\n+ k = k_cache.to(q.dtype)\n+ else:\n+ k = k_cache\n+ if k_scale != 1.0:\n+ k *= k_scale\n \n qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)\n qk += tl.dot(q, k, allow_tf32=False)\n@@ -644,14 +702,21 @@ def _fwd_kernel_alibi(\n # acc_scale = l_i / l_i_new * alpha\n acc = acc * acc_scale[:, None]\n # update acc\n- v = tl.load(v_ptrs +\n- (cur_batch_in_all_start_index + start_n) * stride_vbs,\n- mask=dim_mask[None, :] &\n- ((start_n + offs_n[:, None]) <\n- cur_batch_seq_len - cur_batch_ctx_len),\n- other=0.0)\n+ v_cache = tl.load(\n+ v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,\n+ mask=dim_mask[None, :] &\n+ ((start_n + offs_n[:, None]) <\n+ cur_batch_seq_len - cur_batch_ctx_len),\n+ other=0.0)\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if v_cache.dtype.is_fp8() or v_cache.dtype.is_uint8():\n+ v = v_cache.to(q.dtype)\n+ else:\n+ v = v_cache\n+ if v_scale != 1.0:\n+ v *= v_scale\n \n- p = p.to(v.dtype)\n acc += tl.dot(p, v, allow_tf32=False)\n # update m_i and l_i\n l_i = l_i_new\n@@ -682,6 +747,8 @@ def context_attention_fwd(q,\n b_seq_len,\n b_ctx_len,\n max_input_len,\n+ k_scale: float = 1.0,\n+ v_scale: float = 1.0,\n alibi_slopes=None,\n sliding_window=None):\n \n@@ -719,6 +786,8 @@ def context_attention_fwd(q,\n v_cache,\n b_loc,\n sm_scale,\n+ k_scale,\n+ v_scale,\n b_start_loc,\n b_seq_len,\n b_ctx_len,\n@@ -770,6 +839,8 @@ def context_attention_fwd(q,\n v_cache,\n b_loc,\n sm_scale,\n+ k_scale,\n+ v_scale,\n b_start_loc,\n b_seq_len,\n b_ctx_len,\ndiff --git a/vllm/config.py b/vllm/config.py\nindex bec0b63197e..fdd5100e7c9 100644\n--- a/vllm/config.py\n+++ b/vllm/config.py\n@@ -497,10 +497,6 @@ def _verify_prefix_caching(self) -> None:\n raise NotImplementedError(\n \"Prefix caching is not supported with sliding window. \"\n \"Run with --disable-sliding-window to use prefix caching.\")\n- if self.cache_dtype == \"fp8\":\n- raise NotImplementedError(\n- \"Prefix caching is not supported for fp8 cache_dtype. \"\n- \"Run with --kv-cache-dtype auto to use prefix caching.\")\n \n def verify_with_parallel_config(\n self,\n" }
[ { "diff_hunk": "@@ -117,10 +119,19 @@ def _fwd_kernel(\n cur_kv_head * stride_v_cache_h +\n offs_d[None, :] * stride_v_cache_d +\n (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)\n- k = tl.load(K_cache + off_k,\n- mask=dim_mask[:, None] &\n- ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n- other=0.0) # [D,N]\n+ k_cache = tl.load(\n+ K_cache + off_k,\n+ mask=dim_mask[:, None] &\n+ ((start_n + offs_n[None, :]) < cur_batch_ctx_len),\n+ other=0.0) # [D,N]\n+\n+ # Only convert if mixed tl.dot is unsupported (e.g. 8-bit types)\n+ if k_cache.dtype.is_fp8() or k_cache.dtype.is_uint8():\n+ k = k_cache.to(q.dtype)\n+ else:\n+ k = k_cache\n+ if k_scale != 1.0:\n+ k *= k_scale", "line": null, "original_line": 134, "original_start_line": 128, "path": "vllm/attention/ops/prefix_prefill.py", "start_line": null, "text": "@user1:\nDid you verify if this works? I got some issues when converting from FP8 to FP16 on the triton kernel before, but not sure if it has been fixed.\n\n@author:\nNot verified, I have no dev env setup unfortunately. Hopefully will set it up soon (today/tmr). For now will try and see.\n\n@author:\nActually, I think if the KV cache dtype for vLLM is in `uint8`. This code above is definitely wrong if this is the case.\r\n\r\nThis means we need to do `Tensor.view(dtype=fp8_dtype)` before passing into triton\n\n@user1:\nRight you need to explicitly use `view` to correct the type.\r\n\r\nFor now I'll remove the ready label to save CI resources. We should add the label back after the PR is verified locally and got reviewed.\n\n@author:\nOk, I will work on getting my local env set up properly. I used to have a laptop with NVIDIA gpu but now use macbook ๐Ÿ˜…" } ]
39e54cbc597992caa1e72890f0b626fa6ea1556b
diff --git a/docs/source/quantization/fp8_e4m3_kvcache.rst b/docs/source/quantization/fp8_e4m3_kvcache.rst index fd71c00b7bf8..cc52d8f40af8 100644 --- a/docs/source/quantization/fp8_e4m3_kvcache.rst +++ b/docs/source/quantization/fp8_e4m3_kvcache.rst @@ -45,5 +45,3 @@ Here is an example of how to enable this feature: # output w/ scaling factors: England, the United Kingdom, and one of the world's leading financial, # output w/o scaling factors: England, located in the southeastern part of the country. It is known -Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type. - diff --git a/docs/source/quantization/fp8_e5m2_kvcache.rst b/docs/source/quantization/fp8_e5m2_kvcache.rst index 337252a00aef..9ae07bcd3b99 100644 --- a/docs/source/quantization/fp8_e5m2_kvcache.rst +++ b/docs/source/quantization/fp8_e5m2_kvcache.rst @@ -32,5 +32,3 @@ Here is an example of how to enable this feature: print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") -Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type. - diff --git a/tests/basic_correctness/test_chunked_prefill.py b/tests/basic_correctness/test_chunked_prefill.py index 767e0628765b..fcc444842213 100644 --- a/tests/basic_correctness/test_chunked_prefill.py +++ b/tests/basic_correctness/test_chunked_prefill.py @@ -6,14 +6,27 @@ Run `pytest tests/models/test_chunked_prefill.py`. """ + import pytest -from ..models.utils import check_outputs_equal +from ..models.utils import check_logprobs_close, check_outputs_equal MODELS = [ "facebook/opt-125m", "meta-llama/Llama-2-7b-hf", ] +E5M2_KV_MODELS = [ + "facebook/opt-125m", + "meta-llama/Llama-2-7b-chat-hf", +] +E4M3_KV_MODELS = [ + "meta-llama/Llama-2-7b-chat-hf", "nm-testing/Qwen2-1.5B-Instruct-FP8-K-V", + "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme" +] +KV_CACHE_QUANTIZATION_PATHS = { + "meta-llama/Llama-2-7b-chat-hf": + "./tests/fp8_kv/llama2-7b-fp8-kv/kv_cache_scales.json" +} @pytest.mark.parametrize("model", MODELS) @@ -35,12 +48,12 @@ def test_models( enforce_eager: bool, tensor_parallel_size: int, ) -> None: - max_num_seqs = min(chunked_prefill_token_size, 256) - enable_chunked_prefill = False - max_num_batched_tokens = None - if chunked_prefill_token_size != -1: - enable_chunked_prefill = True - max_num_batched_tokens = chunked_prefill_token_size + """ + Checks exact match decode between huggingface model and vllm runner with + chunked prefill. + """ + max_num_seqs = chunked_prefill_token_size + max_num_batched_tokens = chunked_prefill_token_size with hf_runner(model, dtype=dtype) as hf_model: hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) @@ -49,7 +62,7 @@ def test_models( model, dtype=dtype, max_num_batched_tokens=max_num_batched_tokens, - enable_chunked_prefill=enable_chunked_prefill, + enable_chunked_prefill=True, tensor_parallel_size=tensor_parallel_size, enforce_eager=enforce_eager, max_num_seqs=max_num_seqs, @@ -62,3 +75,78 @@ def test_models( name_0="hf", name_1="vllm", ) + + [email protected]("kv_cache_dtype,model", + [("fp8_e5m2", m) + for m in E5M2_KV_MODELS] + [("fp8_e4m3", m) + for m in E4M3_KV_MODELS]) +# Due to low-precision numerical divergence, we only test logprob of 4 tokens [email protected]("max_tokens", [4]) [email protected]("chunked_prefill_token_size", [1, 4, 16]) [email protected]("enforce_eager", [False, True]) +# NOTE: Increasing this in this suite will fail CI because we currently cannot +# reset distributed env properly. Use a value > 1 just when you test. [email protected]("tensor_parallel_size", [1]) +def test_models_with_fp8_kv_cache( + vllm_runner, + example_prompts, + kv_cache_dtype: str, + model: str, + max_tokens: int, + chunked_prefill_token_size: int, + enforce_eager: bool, + tensor_parallel_size: int, +) -> None: + """ + Only checks log probs match between chunked-prefill and + non-chunked-prefill version of vLLM model runner. + + This test is used when there is discrepancy in kernels + / numerics (e.g. when using lower-precision types like FP8). + """ + NUM_LOG_PROBS = 8 + + if model == "facebook/opt-125m": + pytest.skip( + "#7378: CUDA illegal memory access (undiagnosed) facebook/opt-125m" + ) + + max_num_seqs = chunked_prefill_token_size + max_num_batched_tokens = chunked_prefill_token_size + + extra_kwargs = {} + if model in KV_CACHE_QUANTIZATION_PATHS: + extra_kwargs["quantization_param_path"] = KV_CACHE_QUANTIZATION_PATHS[ + model] + + with vllm_runner( + model, + tensor_parallel_size=tensor_parallel_size, + enforce_eager=enforce_eager, + max_num_seqs=max_num_seqs, + kv_cache_dtype=kv_cache_dtype, + **extra_kwargs, + ) as vllm_model: + no_chunked_prefill_outputs = vllm_model.generate_greedy_logprobs( + example_prompts, max_tokens, NUM_LOG_PROBS) + + with vllm_runner( + model, + max_num_batched_tokens=max_num_batched_tokens, + enable_chunked_prefill=True, + tensor_parallel_size=tensor_parallel_size, + enforce_eager=enforce_eager, + max_num_seqs=max_num_seqs, + kv_cache_dtype=kv_cache_dtype, + **extra_kwargs, + ) as vllm_model: + chunked_prefill_outputs = vllm_model.generate_greedy_logprobs( + example_prompts, max_tokens, NUM_LOG_PROBS) + + check_logprobs_close( + outputs_0_lst=no_chunked_prefill_outputs, + outputs_1_lst=chunked_prefill_outputs, + name_0="no_chunked_prefill", + name_1="chunked_prefill", + ) diff --git a/tests/kernels/test_prefix_prefill.py b/tests/kernels/test_prefix_prefill.py index 99fda8364dc0..60f9a4dc9f90 100644 --- a/tests/kernels/test_prefix_prefill.py +++ b/tests/kernels/test_prefix_prefill.py @@ -9,6 +9,7 @@ from vllm.attention.backends.xformers import _make_alibi_bias from vllm.attention.ops.prefix_prefill import context_attention_fwd +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE NUM_HEADS = [64] NUM_QUERIES_PER_KV = [1, 8, 64] @@ -18,12 +19,14 @@ f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2) ] SLIDING_WINDOW = [0, 16, 64, 128, 256, 512, 2048] +KV_CACHE_DTYPES = ["auto", "fp8", "fp8_e5m2"] @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV) @pytest.mark.parametrize("head_size", HEAD_SIZES) @pytest.mark.parametrize("dtype", DTYPES) [email protected]("kv_cache_dtype", KV_CACHE_DTYPES) @pytest.mark.parametrize("device", CUDA_DEVICES) @pytest.mark.parametrize("sliding_window", SLIDING_WINDOW) @torch.inference_mode() @@ -33,6 +36,7 @@ def test_contexted_kv_attention( head_size: int, sliding_window: int, dtype: torch.dtype, + kv_cache_dtype: str, device: str, ) -> None: random.seed(0) @@ -67,16 +71,20 @@ def test_contexted_kv_attention( kv.uniform_(-1e-3, 1e-3) key, value = kv.unbind(dim=1) + if kv_cache_dtype == "auto": + cache_dtype = dtype + else: + cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[kv_cache_dtype] k_cache = torch.zeros(cache_size, block_size, num_kv_heads, head_size, - dtype=dtype) + dtype=cache_dtype) v_cache = torch.zeros(cache_size, block_size, num_kv_heads, head_size, - dtype=dtype) + dtype=cache_dtype) k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype) v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype) values = torch.arange(0, cache_size, dtype=torch.long) @@ -132,6 +140,7 @@ def test_contexted_kv_attention( k, v, output, + kv_cache_dtype, k_cache, v_cache, block_table, @@ -146,6 +155,7 @@ def test_contexted_kv_attention( k, v, output, + kv_cache_dtype, k_cache, v_cache, block_table, @@ -208,13 +218,15 @@ def test_contexted_kv_attention( end_time = time.time() print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms") output_ref = output_ref.reshape(output.shape) - assert torch.allclose(output_ref, output, atol=1e-6, rtol=0) + atol = 1e-3 if "fp8" in kv_cache_dtype else 1e-6 + torch.testing.assert_close(output, output_ref, atol=atol, rtol=0) @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV) @pytest.mark.parametrize("head_size", HEAD_SIZES) @pytest.mark.parametrize("dtype", DTYPES) [email protected]("kv_cache_dtype", KV_CACHE_DTYPES) @pytest.mark.parametrize("device", CUDA_DEVICES) @torch.inference_mode() def test_contexted_kv_attention_alibi( @@ -222,6 +234,7 @@ def test_contexted_kv_attention_alibi( num_queries_per_kv: int, head_size: int, dtype: torch.dtype, + kv_cache_dtype: str, device: str, ) -> None: random.seed(0) @@ -282,17 +295,20 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype) kv.uniform_(-1e-3, 1e-3) key, value = kv.unbind(dim=1) - + if kv_cache_dtype == "auto": + cache_dtype = dtype + else: + cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[kv_cache_dtype] k_cache = torch.zeros(cache_size, block_size, num_kv_heads, head_size, - dtype=dtype) + dtype=cache_dtype) v_cache = torch.zeros(cache_size, block_size, num_kv_heads, head_size, - dtype=dtype) + dtype=cache_dtype) k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype) v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype) values = torch.arange(0, cache_size, dtype=torch.long) @@ -348,6 +364,7 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: k, v, output, + kv_cache_dtype, k_cache, v_cache, block_table, @@ -362,6 +379,7 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: k, v, output, + kv_cache_dtype, k_cache, v_cache, block_table, @@ -447,4 +465,5 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: torch.cuda.synchronize() end_time = time.time() print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms") - assert torch.allclose(output_ref, output, atol=1e-6, rtol=0) + atol = 1e-3 if "fp8" in kv_cache_dtype else 1e-6 + torch.testing.assert_close(output, output_ref, atol=atol, rtol=0) diff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py index 26e9b8a93fb9..e305679231d0 100644 --- a/vllm/attention/backends/rocm_flash_attn.py +++ b/vllm/attention/backends/rocm_flash_attn.py @@ -459,6 +459,7 @@ def forward( query, key, value, + self.kv_cache_dtype, key_cache, value_cache, prefill_meta.block_tables, @@ -468,6 +469,8 @@ def forward( prefill_meta.max_query_len, self.alibi_slopes, self.sliding_window[0], + k_scale, + v_scale, ) if decode_meta := attn_metadata.decode_metadata: diff --git a/vllm/attention/backends/xformers.py b/vllm/attention/backends/xformers.py index 24ba5fc72540..7e36509bff86 100644 --- a/vllm/attention/backends/xformers.py +++ b/vllm/attention/backends/xformers.py @@ -604,6 +604,7 @@ def forward( query, key, value, + self.kv_cache_dtype, key_cache, value_cache, prefill_meta.block_tables, @@ -613,6 +614,8 @@ def forward( prefill_meta.max_query_len, self.alibi_slopes, self.sliding_window, + k_scale, + v_scale, ) assert output[:num_prefill_tokens].shape == out.shape output[:num_prefill_tokens] = out diff --git a/vllm/attention/ops/ipex_attn.py b/vllm/attention/ops/ipex_attn.py index 81d308c4d4e2..6b270ffd5bc0 100644 --- a/vllm/attention/ops/ipex_attn.py +++ b/vllm/attention/ops/ipex_attn.py @@ -90,6 +90,7 @@ def forward_prefix( query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, + kv_cache_dtype: str, key_cache: torch.Tensor, value_cache: torch.Tensor, block_tables: torch.Tensor, diff --git a/vllm/attention/ops/paged_attn.py b/vllm/attention/ops/paged_attn.py index e88963ade16c..92023d5b75f5 100644 --- a/vllm/attention/ops/paged_attn.py +++ b/vllm/attention/ops/paged_attn.py @@ -194,6 +194,7 @@ def forward_prefix( query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, + kv_cache_dtype: str, key_cache: torch.Tensor, value_cache: torch.Tensor, block_tables: torch.Tensor, @@ -203,6 +204,8 @@ def forward_prefix( max_query_len: int, alibi_slopes: Optional[torch.Tensor], sliding_window: Optional[int], + k_scale: float, + v_scale: float, ) -> torch.Tensor: output = torch.empty_like(query) context_attention_fwd( @@ -210,6 +213,7 @@ def forward_prefix( key, value, output, + kv_cache_dtype, key_cache, value_cache, block_tables, @@ -218,6 +222,8 @@ def forward_prefix( seq_lens_tensor, context_lens, max_query_len, + k_scale, + v_scale, alibi_slopes, sliding_window, ) diff --git a/vllm/attention/ops/prefix_prefill.py b/vllm/attention/ops/prefix_prefill.py index 4577d84db18a..558b2f3eeac7 100644 --- a/vllm/attention/ops/prefix_prefill.py +++ b/vllm/attention/ops/prefix_prefill.py @@ -18,6 +18,8 @@ def _fwd_kernel( V_cache, B_Loc, sm_scale, + k_scale, + v_scale, B_Start_Loc, B_Seqlen, B_Ctxlen, @@ -117,10 +119,15 @@ def _fwd_kernel( cur_kv_head * stride_v_cache_h + offs_d[None, :] * stride_v_cache_d + (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) - k = tl.load(K_cache + off_k, - mask=dim_mask[:, None] & - ((start_n + offs_n[None, :]) < cur_batch_ctx_len), - other=0.0) # [D,N] + k_load = tl.load(K_cache + off_k, + mask=dim_mask[:, None] & + ((start_n + offs_n[None, :]) < cur_batch_ctx_len), + other=0.0) # [D,N] + + if k_load.dtype.is_fp8(): + k = (k_load.to(tl.float32) * k_scale).to(q.dtype) + else: + k = k_load qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) # [M,N] qk += tl.dot(q, k) @@ -161,12 +168,16 @@ def _fwd_kernel( acc_scale = l_i / l_i_new * alpha acc = acc * acc_scale[:, None] # update acc - v = tl.load(V_cache + off_v, - mask=dim_mask[None, :] & - ((start_n + offs_n[:, None]) < cur_batch_ctx_len), - other=0.0) # [N,D] - + v_load = tl.load(V_cache + off_v, + mask=dim_mask[None, :] & + ((start_n + offs_n[:, None]) < cur_batch_ctx_len), + other=0.0) # [N,D] + if v_load.dtype.is_fp8(): + v = (v_load.to(tl.float32) * v_scale).to(q.dtype) + else: + v = v_load p = p.to(v.dtype) + acc += tl.dot(p, v) # # update m_i and l_i l_i = l_i_new @@ -225,8 +236,8 @@ def _fwd_kernel( mask=dim_mask[None, :] & ((start_n + offs_n[:, None]) < cur_batch_query_len), other=0.0) - p = p.to(v.dtype) + acc += tl.dot(p, v) # update m_i and l_i l_i = l_i_new @@ -336,7 +347,6 @@ def _fwd_kernel_flash_attn_v2( k = tl.load(K_cache + off_k, mask=(start_n + offs_n[None, :]) < cur_batch_ctx_len, other=0.0) - qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) qk += tl.dot(q, k) qk = tl.where((start_n + offs_n[None, :]) < cur_batch_ctx_len, qk, @@ -442,6 +452,8 @@ def _fwd_kernel_alibi( V_cache, B_Loc, sm_scale, + k_scale, + v_scale, B_Start_Loc, B_Seqlen, B_Ctxlen, @@ -537,10 +549,15 @@ def _fwd_kernel_alibi( cur_kv_head * stride_v_cache_h + offs_d[None, :] * stride_v_cache_d + (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl) - k = tl.load(K_cache + off_k, - mask=dim_mask[:, None] & - ((start_n + offs_n[None, :]) < cur_batch_ctx_len), - other=0.0) # [D,N] + k_load = tl.load(K_cache + off_k, + mask=dim_mask[:, None] & + ((start_n + offs_n[None, :]) < cur_batch_ctx_len), + other=0.0) # [D,N] + + if k_load.dtype.is_fp8(): + k = (k_load.to(tl.float32) * k_scale).to(q.dtype) + else: + k = k_load qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) qk += tl.dot(q, k) @@ -573,12 +590,16 @@ def _fwd_kernel_alibi( # acc_scale = l_i / l_i_new * alpha acc = acc * acc_scale[:, None] # update acc - v = tl.load(V_cache + off_v, - mask=dim_mask[None, :] & - ((start_n + offs_n[:, None]) < cur_batch_ctx_len), - other=0.0) - + v_load = tl.load(V_cache + off_v, + mask=dim_mask[None, :] & + ((start_n + offs_n[:, None]) < cur_batch_ctx_len), + other=0.0) + if v_load.dtype.is_fp8(): + v = (v_load.to(tl.float32) * v_scale).to(q.dtype) + else: + v = v_load p = p.to(v.dtype) + acc += tl.dot(p, v, allow_tf32=False) # update m_i and l_i l_i = l_i_new @@ -650,8 +671,8 @@ def _fwd_kernel_alibi( ((start_n + offs_n[:, None]) < cur_batch_seq_len - cur_batch_ctx_len), other=0.0) - p = p.to(v.dtype) + acc += tl.dot(p, v, allow_tf32=False) # update m_i and l_i l_i = l_i_new @@ -675,6 +696,7 @@ def context_attention_fwd(q, k, v, o, + kv_cache_dtype: str, k_cache, v_cache, b_loc, @@ -682,17 +704,41 @@ def context_attention_fwd(q, b_seq_len, b_ctx_len, max_input_len, + k_scale: float = 1.0, + v_scale: float = 1.0, alibi_slopes=None, sliding_window=None): cap = current_platform.get_device_capability() BLOCK = 128 if cap[0] >= 8 else 64 + NUM_WARPS = 8 # need to reduce num. blocks when using fp32 # due to increased use of GPU shared memory if q.dtype is torch.float32: BLOCK = BLOCK // 2 + # Conversion of FP8 Tensor from uint8 storage to + # appropriate torch.dtype for interpretation by Triton + if "fp8" in kv_cache_dtype: + assert (k_cache.dtype == torch.uint8) + assert (v_cache.dtype == torch.uint8) + + if kv_cache_dtype in ("fp8", "fp8_e4m3"): + target_dtype = torch.float8_e4m3fn + elif kv_cache_dtype == "fp8_e5m2": + target_dtype = torch.float8_e5m2 + else: + raise ValueError("Unsupported FP8 dtype:", kv_cache_dtype) + + k_cache = k_cache.view(target_dtype) + v_cache = v_cache.view(target_dtype) + + if (k_cache.dtype == torch.uint8 + or v_cache.dtype == torch.uint8 and kv_cache_dtype == "auto"): + raise ValueError("kv_cache_dtype='auto' unsupported for\ + FP8 KV Cache prefill kernel") + # shape constraints Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1] assert Lq == Lk and Lk == Lv @@ -709,7 +755,6 @@ def context_attention_fwd(q, if sliding_window is None or sliding_window <= 0: sliding_window = 0 - num_warps = 8 if Lk <= 64 else 8 if alibi_slopes is not None: _fwd_kernel_alibi[grid]( q, @@ -719,6 +764,8 @@ def context_attention_fwd(q, v_cache, b_loc, sm_scale, + k_scale, + v_scale, b_start_loc, b_seq_len, b_ctx_len, @@ -757,7 +804,7 @@ def context_attention_fwd(q, BLOCK_DMODEL=Lk, BLOCK_DMODEL_PADDED=Lk_padded, BLOCK_N=BLOCK, - num_warps=num_warps, + num_warps=NUM_WARPS, num_stages=1, ) return @@ -770,6 +817,8 @@ def context_attention_fwd(q, v_cache, b_loc, sm_scale, + k_scale, + v_scale, b_start_loc, b_seq_len, b_ctx_len, @@ -807,7 +856,7 @@ def context_attention_fwd(q, BLOCK_DMODEL_PADDED=Lk_padded, BLOCK_N=BLOCK, SLIDING_WINDOW=sliding_window, - num_warps=num_warps, + num_warps=NUM_WARPS, num_stages=1, ) return diff --git a/vllm/config.py b/vllm/config.py index 4207466cfc5c..0ebe8110be55 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -546,10 +546,6 @@ def _verify_prefix_caching(self) -> None: raise NotImplementedError( "Prefix caching is not supported with sliding window. " "Run with --disable-sliding-window to use prefix caching.") - if self.cache_dtype == "fp8": - raise NotImplementedError( - "Prefix caching is not supported for fp8 cache_dtype. " - "Run with --kv-cache-dtype auto to use prefix caching.") def verify_with_parallel_config( self,
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-6951@82d6887
vllm-project/vllm
Python
6,951
[CI/Build] Update torch to 2.4
This PR updates pytorch to version 2.4. All dependencies that depend on pytorch have also been updated.
2024-07-30T15:04:56Z
[Feature]: when will support torch2.4 use it out of box? ### ๐Ÿš€ The feature, motivation and pitch [Feature]: when will support torch2.4 use it out of box? ### Alternatives [Feature]: when will support torch2.4 use it out of box? ### Additional context _No response_
We are working on update soon. Currently we require torch 2.4 wheels to be released for our xformers, flashinfer, and flash-attn dependencies. I believe flashinfer is the only hold-up at the moment.
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\n[Feature]: when will support torch2.4 use it out of box?\n\n### Alternatives\n\n[Feature]: when will support torch2.4 use it out of box?\n\n### Additional context\n\n_No response_", "number": 6944, "title": "[Feature]: when will support torch2.4 use it out of box?" } ]
c8a7e93273ff4338d6f89f8a63ff16426ac240b8
{ "head_commit": "82d68874437c42a315cf1a54f295dbd43fc4eaa1", "head_commit_message": "update torch to 2.4", "patch_to_review": "diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml\nindex be8807df0b0..166e94cf08c 100644\n--- a/.buildkite/test-pipeline.yaml\n+++ b/.buildkite/test-pipeline.yaml\n@@ -44,7 +44,7 @@ steps:\n fast_check: true\n commands:\n # This flashinfer installation will fail on AMD ROCm, so it is set as optional.\n- - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl || true\n+ - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl || true\n - pytest -v -s basic_correctness/test_basic_correctness.py\n - pytest -v -s basic_correctness/test_cpu_offload.py\n - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py\n@@ -158,14 +158,14 @@ steps:\n - label: Kernels Test %N\n #mirror_hardwares: [amd]\n commands:\n- - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl\n+ - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl\n - pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT\n parallelism: 4\n \n - label: Models Test\n #mirror_hardwares: [amd]\n commands:\n- - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl\n+ - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl\n - pytest -v -s models -m \\\"not vlm\\\"\n \n - label: Vision Language Models Test\n@@ -282,7 +282,7 @@ steps:\n - pytest -v -s distributed/test_custom_all_reduce.py\n - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py\n - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py\n- - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl\n+ - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl\n - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py\n - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=meta-llama/Meta-Llama-3-8B DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py\n - pytest -v -s -x lora/test_mixtral.py\ndiff --git a/Dockerfile b/Dockerfile\nindex b9a56e67e8d..87ccca20c1e 100644\n--- a/Dockerfile\n+++ b/Dockerfile\n@@ -194,7 +194,7 @@ RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamb\n python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir\n \n RUN --mount=type=cache,target=/root/.cache/pip \\\n- python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl\n+ python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl\n #################### vLLM installation IMAGE ####################\n \n \ndiff --git a/requirements-build.txt b/requirements-build.txt\nindex b05f38a0ed9..5e06e4366e4 100644\n--- a/requirements-build.txt\n+++ b/requirements-build.txt\n@@ -3,5 +3,5 @@ cmake>=3.21\n ninja\r\n packaging\r\n setuptools>=49.4.0\r\n-torch==2.3.1\r\n+torch==2.4\r\n wheel\r\ndiff --git a/requirements-cuda.txt b/requirements-cuda.txt\nindex 3eb91212e97..5f16ab527fd 100644\n--- a/requirements-cuda.txt\n+++ b/requirements-cuda.txt\n@@ -4,8 +4,8 @@\n # Dependencies for NVIDIA GPUs\n ray >= 2.9\n nvidia-ml-py # for pynvml package\n-torch == 2.3.1\n+torch == 2.4\n # These must be updated alongside torch\n-torchvision == 0.18.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version\n-xformers == 0.0.27 # Requires PyTorch 2.3.1\n-vllm-flash-attn == 2.5.9.post1 # Requires PyTorch 2.3.1\n+torchvision == 0.19 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version\n+xformers == 0.0.27.post2 # Requires PyTorch 2.4\n+vllm-flash-attn == 2.6.0 # Requires PyTorch 2.4\n" }
[ { "diff_hunk": "@@ -3,5 +3,5 @@ cmake>=3.21\n ninja\r\n packaging\r\n setuptools>=49.4.0\r\n-torch==2.3.1\r\n+torch==2.4\r", "line": null, "original_line": 6, "original_start_line": null, "path": "requirements-build.txt", "start_line": null, "text": "@user1:\nCan you please set this to 2.4.0?" } ]
90a47114c2fae1af63662294ffd72847b6c48d42
diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 9ec9ec12bfcf..573c3740f0bb 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -44,7 +44,7 @@ steps: fast_check: true commands: # This flashinfer installation will fail on AMD ROCm, so it is set as optional. - - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl || true + - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl || true - pytest -v -s basic_correctness/test_basic_correctness.py - pytest -v -s basic_correctness/test_cpu_offload.py - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py @@ -164,7 +164,7 @@ steps: - label: Models Test #mirror_hardwares: [amd] commands: - - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl + - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl - pytest -v -s models -m \"not vlm\" - label: Vision Language Models Test @@ -281,7 +281,7 @@ steps: - pytest -v -s distributed/test_custom_all_reduce.py - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py - - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl + - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=meta-llama/Meta-Llama-3-8B DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py - pytest -v -s -x lora/test_mixtral.py diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 15c2ec05b25d..607fda754bf2 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -49,7 +49,7 @@ jobs: matrix: os: ['ubuntu-20.04'] python-version: ['3.8', '3.9', '3.10', '3.11'] - pytorch-version: ['2.3.1'] # Must be the most recent version that meets requirements-cuda.txt. + pytorch-version: ['2.4.0'] # Must be the most recent version that meets requirements-cuda.txt. cuda-version: ['11.8', '12.1'] steps: diff --git a/CMakeLists.txt b/CMakeLists.txt index 0d599c547070..9c2cb360fca3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -32,7 +32,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx11 # requirements.txt files and should be kept consistent. The ROCm torch # versions are derived from Dockerfile.rocm # -set(TORCH_SUPPORTED_VERSION_CUDA "2.3.1") +set(TORCH_SUPPORTED_VERSION_CUDA "2.4.0") set(TORCH_SUPPORTED_VERSION_ROCM "2.5.0") # diff --git a/Dockerfile b/Dockerfile index db4453ab0efc..7294707046ab 100644 --- a/Dockerfile +++ b/Dockerfile @@ -192,7 +192,7 @@ RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamb python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir RUN --mount=type=cache,target=/root/.cache/pip \ - python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl + python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl #################### vLLM installation IMAGE #################### diff --git a/pyproject.toml b/pyproject.toml index b0d115a091c4..26d963aa5109 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ requires = [ "ninja", "packaging", "setuptools >= 49.4.0", - "torch == 2.3.1", + "torch == 2.4.0", "wheel", ] build-backend = "setuptools.build_meta" diff --git a/requirements-build.txt b/requirements-build.txt index b05f38a0ed91..d0f677fd344e 100644 --- a/requirements-build.txt +++ b/requirements-build.txt @@ -3,5 +3,5 @@ cmake>=3.21 ninja packaging setuptools>=49.4.0 -torch==2.3.1 +torch==2.4.0 wheel diff --git a/requirements-cuda.txt b/requirements-cuda.txt index 3eb91212e976..1f60d54083b4 100644 --- a/requirements-cuda.txt +++ b/requirements-cuda.txt @@ -4,8 +4,8 @@ # Dependencies for NVIDIA GPUs ray >= 2.9 nvidia-ml-py # for pynvml package -torch == 2.3.1 +torch == 2.4.0 # These must be updated alongside torch -torchvision == 0.18.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version -xformers == 0.0.27 # Requires PyTorch 2.3.1 -vllm-flash-attn == 2.5.9.post1 # Requires PyTorch 2.3.1 +torchvision == 0.19 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version +xformers == 0.0.27.post2 # Requires PyTorch 2.4.0 +vllm-flash-attn == 2.6.0 # Requires PyTorch 2.4.0 diff --git a/vllm/model_executor/layers/ops/sample.py b/vllm/model_executor/layers/ops/sample.py index bdb577da3172..fb88a05daf48 100644 --- a/vllm/model_executor/layers/ops/sample.py +++ b/vllm/model_executor/layers/ops/sample.py @@ -7,7 +7,7 @@ from vllm.model_executor.layers.ops.rand import seeded_uniform from vllm.triton_utils.sample import get_num_triton_sampler_splits -_EPS = 1e-6 +_EPS: tl.constexpr = 1e-6 def _multi_split_sample(
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-7445@450ae2a
vllm-project/vllm
Python
7,445
support bitsandbytes 8-bit and FP4 quantized models
This PR does the following: 1. support quantized bitsandbytes 8-bit models, such as meta-llama/Llama-Guard-3-8B-INT8 2. support quantized bitsandbytes 4-bit FP4 models, such as PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed 3. Add comments about enforcing eager-mode in bnb quantization, as I identified it is a bug in the underlying dependency package of bitsandbytes. FIX https://github.com/vllm-project/vllm/issues/6756
2024-08-12T23:23:37Z
[Bug]: Unable to run meta-llama/Llama-Guard-3-8B-INT8 ### Your current environment Latest Docker image, RTX 4090 ### ๐Ÿ› Describe the bug ``` docker run --gpus all vllm/vllm-openai:latest --model meta-llama/Llama-Guard-3-8B-INT8 ... [rank0]: raise ValueError(f"Cannot find any of {keys} in the model's " [rank0]: ValueError: Cannot find any of ['adapter_name_or_path'] in the model's quantization config. ```
@thesues @chenqianfzh It looks like this is an 8bit BNB model. Would it be easy to add support for these checkpoints as well? > @thesues @chenqianfzh It looks like this is an 8bit BNB model. Would it be easy to add support for these checkpoints as well? It won't be difficult. I will work on it with higher priority. seems version 0.5.4+cu124 is working with bnb 4bit model. but it says WARNING 08-06 06:27:07 config.py:254] bitsandbytes quantization is not fully optimized yet. The speed can be slower than non-quantized models. Will that be a easy fix/support too?
[ { "body": "### Your current environment\n\nLatest Docker image, RTX 4090\r\n\n\n### ๐Ÿ› Describe the bug\n\n```\r\ndocker run --gpus all vllm/vllm-openai:latest --model meta-llama/Llama-Guard-3-8B-INT8\r\n...\r\n[rank0]: raise ValueError(f\"Cannot find any of {keys} in the model's \"\r\n[rank0]: ValueError: Cannot find any of ['adapter_name_or_path'] in the model's quantization config.\r\n```", "number": 6756, "title": "[Bug]: Unable to run meta-llama/Llama-Guard-3-8B-INT8" } ]
029c71de11bc3bcf84a1b3cf9d91e79ab6949799
{ "head_commit": "450ae2ae997eb338459b6fdcc247878a606cc860", "head_commit_message": "support bitsandbytes 8-bit and FP4 quantized models", "patch_to_review": "diff --git a/tests/quantization/test_bitsandbytes.py b/tests/quantization/test_bitsandbytes.py\nindex b760e9ccb6b..f15db983744 100644\n--- a/tests/quantization/test_bitsandbytes.py\n+++ b/tests/quantization/test_bitsandbytes.py\n@@ -5,19 +5,27 @@\n import pytest\n import torch\n \n+from tests.conftest import VllmRunner\n from tests.quantization.utils import is_quant_method_supported\n from vllm import SamplingParams\n \n-models_to_test = [\n+models_4bit_to_test = [\n ('huggyllama/llama-7b', 'quantize model inflight'),\n- ('lllyasviel/omost-llama-3-8b-4bits', 'read pre-quantized model'),\n+ ('lllyasviel/omost-llama-3-8b-4bits',\n+ 'read pre-quantized 4-bit NF4 model'),\n+ ('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed',\n+ 'read pre-quantized 4-bit FP4 model'),\n+]\n+\n+models_8bit_to_test = [\n+ ('meta-llama/Llama-Guard-3-8B-INT8', 'read pre-quantized 8-bit model'),\n ]\n \n \n @pytest.mark.skipif(not is_quant_method_supported(\"bitsandbytes\"),\n reason='bitsandbytes is not supported on this GPU type.')\[email protected](\"model_name, description\", models_to_test)\n-def test_load_bnb_model(vllm_runner, model_name, description) -> None:\[email protected](\"model_name, description\", models_4bit_to_test)\n+def test_load_4bit_bnb_model(vllm_runner, model_name, description) -> None:\n with vllm_runner(model_name,\n quantization='bitsandbytes',\n load_format='bitsandbytes',\n@@ -25,62 +33,80 @@ def test_load_bnb_model(vllm_runner, model_name, description) -> None:\n model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n \n # check the weights in MLP & SelfAttention are quantized to torch.uint8\n- qweight = model.model.layers[0].mlp.gate_up_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].mlp.down_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].self_attn.o_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].self_attn.qkv_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- # some weights should not be quantized\n- weight = model.lm_head.weight\n- assert weight.dtype != torch.uint8, (\n- 'lm_head weight dtype should not be torch.uint8')\n-\n- weight = model.model.embed_tokens.weight\n- assert weight.dtype != torch.uint8, (\n- 'embed_tokens weight dtype should not be torch.uint8')\n-\n- weight = model.model.layers[0].input_layernorm.weight\n- assert weight.dtype != torch.uint8, (\n- 'input_layernorm weight dtype should not be torch.uint8')\n-\n- weight = model.model.layers[0].post_attention_layernorm.weight\n- assert weight.dtype != torch.uint8, (\n- 'input_layernorm weight dtype should not be torch.uint8')\n-\n- # check the output of the model is expected\n- sampling_params = SamplingParams(temperature=0.0,\n- logprobs=1,\n- prompt_logprobs=1,\n- max_tokens=8)\n-\n- prompts = ['That which does not kill us', 'To be or not to be,']\n- expected_outputs = [\n- 'That which does not kill us makes us stronger.',\n- 'To be or not to be, that is the question.'\n- ]\n- outputs = llm.generate(prompts, sampling_params=sampling_params)\n- assert len(outputs) == len(prompts)\n-\n- for index in range(len(outputs)):\n- # compare the first line of the output\n- actual_output = outputs[index][1][0].split('\\n', 1)[0]\n- expected_output = expected_outputs[index].split('\\n', 1)[0]\n-\n- assert len(actual_output) >= len(expected_output), (\n- f'Actual {actual_output} should be larger than or equal to '\n- f'expected {expected_output}')\n- actual_output = actual_output[:len(expected_output)]\n-\n- assert actual_output == expected_output, (\n- f'Expected: {expected_output}, but got: {actual_output}')\n+ validate_model_weight_type(model, torch.uint8)\n+\n+ validate_model_output(llm)\n+\n+\[email protected](not is_quant_method_supported(\"bitsandbytes\"),\n+ reason='bitsandbytes is not supported on this GPU type.')\[email protected](\"model_name, description\", models_8bit_to_test)\n+def test_load_8bit_bnb_model(vllm_runner, model_name, description) -> None:\n+ with vllm_runner(model_name,\n+ quantization='bitsandbytes',\n+ load_format='bitsandbytes',\n+ enforce_eager=True) as llm:\n+ model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n+\n+ # check the weights in MLP & SelfAttention are quantized to torch.int8\n+ validate_model_weight_type(model, torch.int8)\n+\n+ validate_model_output(llm)\n+\n+\n+def validate_model_weight_type(model, quantized_dtype=torch.uint8):\n+ # Check quantized weights\n+ quantized_layers = [('mlp.gate_up_proj.qweight',\n+ model.model.layers[0].mlp.gate_up_proj.qweight),\n+ ('mlp.down_proj.qweight',\n+ model.model.layers[0].mlp.down_proj.qweight),\n+ ('self_attn.o_proj.qweight',\n+ model.model.layers[0].self_attn.o_proj.qweight),\n+ ('self_attn.qkv_proj.qweight',\n+ model.model.layers[0].self_attn.qkv_proj.qweight)]\n+\n+ for name, qweight in quantized_layers:\n+ assert qweight.dtype == quantized_dtype, (\n+ f'Expected {name} dtype {quantized_dtype} but got {qweight.dtype}')\n+\n+ # Check non-quantized weights\n+ non_quantized_layers = [\n+ ('lm_head.weight', model.lm_head.weight),\n+ ('embed_tokens.weight', model.model.embed_tokens.weight),\n+ ('input_layernorm.weight',\n+ model.model.layers[0].input_layernorm.weight),\n+ ('post_attention_layernorm.weight',\n+ model.model.layers[0].post_attention_layernorm.weight)\n+ ]\n+\n+ for name, weight in non_quantized_layers:\n+ assert weight.dtype != quantized_dtype, (\n+ f'{name} dtype should not be {quantized_dtype}')\n+\n+\n+def validate_model_output(llm: VllmRunner):\n+ sampling_params = SamplingParams(temperature=0.0,\n+ logprobs=1,\n+ prompt_logprobs=1,\n+ max_tokens=8)\n+\n+ prompts = ['That which does not kill us', 'To be or not to be,']\n+ expected_outputs = [\n+ 'That which does not kill us makes us stronger.',\n+ 'To be or not to be, that is the question.'\n+ ]\n+ outputs = llm.generate(prompts, sampling_params=sampling_params)\n+ assert len(outputs) == len(prompts)\n+\n+ for index in range(len(outputs)):\n+ # compare the first line of the output\n+ actual_output = outputs[index][1][0].split('\\n', 1)[0]\n+ expected_output = expected_outputs[index].split('\\n', 1)[0]\n+\n+ assert len(actual_output) >= len(expected_output), (\n+ f'Actual {actual_output} should be larger than or equal to '\n+ f'expected {expected_output}')\n+ actual_output = actual_output[:len(expected_output)]\n+\n+ assert actual_output == expected_output, (\n+ f'Expected: {expected_output}, but got: {actual_output}')\ndiff --git a/vllm/config.py b/vllm/config.py\nindex c06f698fd52..06122578763 100644\n--- a/vllm/config.py\n+++ b/vllm/config.py\n@@ -326,6 +326,8 @@ def verify_with_parallel_config(\n raise ValueError(\n \"BitAndBytes quantization with TP or PP is not supported yet.\")\n \n+ # Remove the constraint after the bitsandbytes issue is fixed:\n+ # https://github.com/bitsandbytes-foundation/bitsandbytes/issues/1308\n if self.quantization == \"bitsandbytes\" and self.enforce_eager is False:\n logger.warning(\"CUDA graph is not supported on BitAndBytes yet, \"\n \"fallback to the eager mode.\")\ndiff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py\nindex e574062e463..85317be52a0 100644\n--- a/vllm/model_executor/layers/linear.py\n+++ b/vllm/model_executor/layers/linear.py\n@@ -31,9 +31,9 @@ def adjust_marlin_shard(param, shard_size, shard_offset):\n return shard_size * marlin_tile_size, shard_offset * marlin_tile_size\n \n \n-def adjust_bitsandbytes_shard(param: Parameter,\n- qkv_offsets: Dict[str, Tuple[int, int]],\n- loaded_shard_id: str) -> Tuple[int, int]:\n+def adjust_bitsandbytes_4bit_shard(param: Parameter,\n+ qkv_offsets: Dict[str, Tuple[int, int]],\n+ loaded_shard_id: str) -> Tuple[int, int]:\n \"\"\"Adjust the quantization offsets and sizes for BitsAndBytes sharding.\"\"\"\n \n total, _ = qkv_offsets[\"total\"]\n@@ -497,8 +497,9 @@ def weight_loader(self,\n shard_size, shard_offset = adjust_marlin_shard(\n param, shard_size, shard_offset)\n \n- use_bitsandbytes = getattr(param, \"use_bitsandbytes\", False)\n- if use_bitsandbytes:\n+ use_bitsandbytes_4bit = getattr(param, \"use_bitsandbytes_4bit\",\n+ False)\n+ if use_bitsandbytes_4bit:\n shard_size = loaded_weight.shape[output_dim]\n shard_offset = loaded_weight.shape[output_dim] * \\\n loaded_shard_id\n@@ -843,8 +844,9 @@ def weight_loader(self,\n shard_size, shard_offset = adjust_marlin_shard(\n param, shard_size, shard_offset)\n \n- use_bitsandbytes = getattr(param, \"use_bitsandbytes\", False)\n- if use_bitsandbytes:\n+ use_bitsandbytes_4bit = getattr(param, \"use_bitsandbytes_4bit\",\n+ False)\n+ if use_bitsandbytes_4bit:\n orig_qkv_offsets = {\n \"q\": (0, self.num_heads * self.head_size),\n \"k\": (self.num_heads * self.head_size,\n@@ -856,7 +858,7 @@ def weight_loader(self,\n ((self.num_heads + 2 * self.num_kv_heads) * self.head_size,\n 0)\n }\n- shard_size, shard_offset = adjust_bitsandbytes_shard(\n+ shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(\n param, orig_qkv_offsets, loaded_shard_id)\n \n if is_gguf_weight:\ndiff --git a/vllm/model_executor/layers/quantization/bitsandbytes.py b/vllm/model_executor/layers/quantization/bitsandbytes.py\nindex c143d1a8f2b..85eed04a6af 100644\n--- a/vllm/model_executor/layers/quantization/bitsandbytes.py\n+++ b/vllm/model_executor/layers/quantization/bitsandbytes.py\n@@ -1,7 +1,6 @@\n from typing import Any, Dict, List, Optional\n \n import torch\n-from torch.nn.parameter import Parameter\n \n from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,\n set_weight_attrs)\n@@ -15,8 +14,28 @@ class BitsAndBytesConfig(QuantizationConfig):\n Reference: https://arxiv.org/abs/2305.14314\n \"\"\"\n \n- def __init__(self, ) -> None:\n- pass\n+ def __init__(\n+ self,\n+ load_in_8bit: bool = False,\n+ load_in_4bit: bool = True,\n+ bnb_4bit_compute_dtype: str = \"float32\",\n+ bnb_4bit_quant_type: str = \"fp4\",\n+ bnb_4bit_use_double_quant: bool = False,\n+ llm_int8_enable_fp32_cpu_offload: bool = False,\n+ llm_int8_has_fp16_weight: bool = False,\n+ llm_int8_skip_modules: Optional[Any] = None,\n+ llm_int8_threshold: float = 0.0,\n+ ) -> None:\n+\n+ self.load_in_8bit = load_in_8bit\n+ self.load_in_4bit = load_in_4bit\n+ self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype\n+ self.bnb_4bit_quant_type = bnb_4bit_quant_type\n+ self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant\n+ self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload\n+ self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight\n+ self.llm_int8_skip_modules = llm_int8_skip_modules\n+ self.llm_int8_threshold = llm_int8_threshold\n \n def __repr__(self) -> str:\n return \"BitsAndBytesConfig\"\n@@ -41,7 +60,46 @@ def get_config_filenames() -> List[str]:\n \n @classmethod\n def from_config(cls, config: Dict[str, Any]) -> \"BitsAndBytesConfig\":\n- return cls()\n+\n+ def get_safe_value(config, keys, default_value=None):\n+ try:\n+ value = cls.get_from_keys(config, keys)\n+ return value if value is not None else default_value\n+ except ValueError:\n+ return default_value\n+\n+ load_in_8bit = get_safe_value(config, [\"load_in_8bit\"],\n+ default_value=False)\n+ load_in_4bit = get_safe_value(config, [\"load_in_4bit\"],\n+ default_value=True)\n+ bnb_4bit_compute_dtype = get_safe_value(config,\n+ [\"bnb_4bit_compute_dtype\"],\n+ default_value=\"float32\")\n+ bnb_4bit_quant_type = get_safe_value(config, [\"bnb_4bit_quant_type\"],\n+ default_value=\"fp4\")\n+ bnb_4bit_use_double_quant = get_safe_value(\n+ config, [\"bnb_4bit_use_double_quant\"], default_value=False)\n+ llm_int8_enable_fp32_cpu_offload = get_safe_value(\n+ config, [\"llm_int8_enable_fp32_cpu_offload\"], default_value=False)\n+ llm_int8_has_fp16_weight = get_safe_value(config,\n+ [\"llm_int8_has_fp16_weight\"],\n+ default_value=False)\n+ llm_int8_skip_modules = get_safe_value(config,\n+ [\"llm_int8_skip_modules\"],\n+ default_value=[])\n+ llm_int8_threshold = get_safe_value(config, [\"llm_int8_threshold\"],\n+ default_value=0.0)\n+\n+ return cls(\n+ load_in_8bit=load_in_8bit,\n+ load_in_4bit=load_in_4bit,\n+ bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,\n+ bnb_4bit_quant_type=bnb_4bit_quant_type,\n+ bnb_4bit_use_double_quant=bnb_4bit_use_double_quant,\n+ llm_int8_enable_fp32_cpu_offload=llm_int8_enable_fp32_cpu_offload,\n+ llm_int8_has_fp16_weight=llm_int8_has_fp16_weight,\n+ llm_int8_skip_modules=llm_int8_skip_modules,\n+ llm_int8_threshold=llm_int8_threshold)\n \n def get_quant_method(self, layer: torch.nn.Module,\n prefix: str) -> Optional[\"BitsAndBytesLinearMethod\"]:\n@@ -78,39 +136,57 @@ def create_weights(self, layer: torch.nn.Module,\n output_partition_sizes: List[int], input_size: int,\n output_size: int, params_dtype: torch.dtype,\n **extra_weight_attrs):\n- quant_ratio = 0\n- if params_dtype.is_floating_point:\n- quant_ratio = torch.finfo(params_dtype).bits // torch.iinfo(\n- torch.uint8).bits\n+ from bitsandbytes.nn import Int8Params\n+\n+ def calculate_quant_ratio(dtype):\n+ if dtype.is_floating_point:\n+ return torch.finfo(dtype).bits // torch.iinfo(torch.uint8).bits\n+ else:\n+ return torch.iinfo(dtype).bits // torch.iinfo(torch.uint8).bits\n+\n+ def create_qweight_for_8bit():\n+ qweight = Int8Params(\n+ data=torch.empty(sum(output_partition_sizes),\n+ input_size_per_partition,\n+ dtype=torch.int8),\n+ has_fp16_weights=self.quant_config.llm_int8_has_fp16_weight,\n+ requires_grad=False)\n+ set_weight_attrs(\n+ qweight, {\n+ \"input_dim\": 0,\n+ \"output_dim\": 0,\n+ \"pack_factor\": 1,\n+ \"use_bitsandbytes_8bit\": True\n+ })\n+ return qweight\n+\n+ def create_qweight_for_4bit():\n+ quant_ratio = calculate_quant_ratio(params_dtype)\n+\n+ total_size = input_size_per_partition * sum(output_partition_sizes)\n+ if total_size % quant_ratio != 0:\n+ raise ValueError(\n+ \"The input size is not aligned with the quantized \"\n+ \"weight shape.\")\n+\n+ qweight = torch.nn.Parameter(torch.empty(total_size // quant_ratio,\n+ 1,\n+ dtype=torch.uint8),\n+ requires_grad=False)\n+ set_weight_attrs(\n+ qweight, {\n+ \"input_dim\": 0,\n+ \"output_dim\": 0,\n+ \"pack_factor\": quant_ratio,\n+ \"use_bitsandbytes_4bit\": True\n+ })\n+ return qweight\n+\n+ if self.quant_config.load_in_8bit:\n+ qweight = create_qweight_for_8bit()\n else:\n- quant_ratio = torch.iinfo(params_dtype).bits // torch.iinfo(\n- torch.uint8).bits\n-\n- if input_size_per_partition * sum(\n- output_partition_sizes) % quant_ratio != 0:\n- raise ValueError(\n- \"The input size is not aligned with the quantized \"\n- \"weight shape. \")\n- qweight = Parameter(\n- torch.empty(\n- input_size_per_partition * sum(output_partition_sizes) //\n- quant_ratio,\n- 1,\n- dtype=torch.uint8,\n- ),\n- requires_grad=False,\n- )\n-\n- set_weight_attrs(\n- qweight,\n- {\n- \"input_dim\": 0,\n- # In bitsandbytes, a tensor of shape [n,m] is quantized to\n- #[n*m/pack_ratio, 1],so the output_dim is 0\n- \"output_dim\": 0,\n- \"pack_factor\": quant_ratio,\n- \"use_bitsandbytes\": True,\n- })\n+ qweight = create_qweight_for_4bit()\n+\n layer.register_parameter(\"qweight\", qweight)\n set_weight_attrs(qweight, extra_weight_attrs)\n \n@@ -119,6 +195,81 @@ def apply(self,\n x: torch.Tensor,\n bias: Optional[torch.Tensor] = None) -> torch.Tensor:\n \n+ if self.quant_config.load_in_8bit:\n+ return self._apply_8bit_weight(layer, x, bias)\n+ else:\n+ return self._apply_4bit_weight(layer, x, bias)\n+\n+ def _apply_8bit_weight(\n+ self,\n+ layer: torch.nn.Module,\n+ x: torch.Tensor,\n+ bias: Optional[torch.Tensor] = None) -> torch.Tensor:\n+\n+ # only load the bitsandbytes module when needed\n+ from bitsandbytes import MatmulLtState, matmul\n+\n+ original_type = x.dtype\n+ bf_x = x.to(torch.bfloat16)\n+\n+ qweight = layer.qweight\n+ offsets = qweight.bnb_shard_offsets\n+ quant_states = qweight.bnb_quant_state\n+ matmul_states = qweight.matmul_state\n+\n+ out_dim_0 = x.shape[0]\n+ out_dim_1 = sum(\n+ [quant_state[1].shape[0] for quant_state in quant_states.items()])\n+ out = torch.empty(out_dim_0,\n+ out_dim_1,\n+ dtype=torch.float16,\n+ device=x.device)\n+\n+ current_index = 0\n+ for i in range(len(quant_states)):\n+ output_size = quant_states[i].shape[0]\n+\n+ if matmul_states[i] is None:\n+ matmul_states[i] = MatmulLtState()\n+ matmul_states[i].CB = qweight[offsets[i]:offsets[i + 1]]\n+ matmul_states[i].SCB = quant_states[i]\n+ matmul_states[i].threshold = (\n+ self.quant_config.llm_int8_threshold)\n+ matmul_states[i].has_fp16_weights = (\n+ self.quant_config.llm_int8_has_fp16_weight)\n+ matmul_states[i].is_training = False\n+ if matmul_states[i].threshold > 0.0 and not matmul_states[\n+ i].has_fp16_weights:\n+ matmul_states[i].use_pool = True\n+\n+ new_x = bf_x.unsqueeze(0)\n+\n+ out[:, current_index:current_index + output_size] = matmul(\n+ new_x,\n+ qweight[offsets[i]:offsets[i + 1]],\n+ state=matmul_states[i])\n+\n+ current_index += output_size\n+\n+ if (not self.quant_config.llm_int8_has_fp16_weight\n+ and matmul_states[i].CB is not None\n+ and matmul_states[i].CxB is not None):\n+ del matmul_states[i].CB\n+ qweight[offsets[i]:offsets[i + 1]] = matmul_states[i].CxB\n+\n+ out = out.to(original_type)\n+\n+ if bias is not None:\n+ out += bias\n+\n+ return out\n+\n+ def _apply_4bit_weight(\n+ self,\n+ layer: torch.nn.Module,\n+ x: torch.Tensor,\n+ bias: Optional[torch.Tensor] = None) -> torch.Tensor:\n+\n # only load the bitsandbytes module when needed\n from bitsandbytes import matmul_4bit\n \ndiff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py\nindex ba9c8af88f8..a73c06fcece 100644\n--- a/vllm/model_executor/model_loader/loader.py\n+++ b/vllm/model_executor/model_loader/loader.py\n@@ -781,7 +781,11 @@ def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):\n return pt_weights_iterator(hf_weights_files)\n \n def _get_quantized_weights_iterator(\n- self, model_name_or_path: str, revision: Optional[str], pre_quant: bool\n+ self,\n+ model_name_or_path: str,\n+ revision: Optional[str],\n+ pre_quant: bool,\n+ load_8bit: bool,\n ) -> Tuple[Generator[Tuple[str, torch.Tensor], None, None], Dict[str,\n Any]]:\n \"\"\"Get an iterator to the model weights with bitsandbytes quantization,\n@@ -790,11 +794,9 @@ def _get_quantized_weights_iterator(\n # only load the bitsandbytes module when needed\n try:\n import bitsandbytes\n- from bitsandbytes.functional import QuantState\n if bitsandbytes.__version__ < \"0.42.0\":\n raise ImportError(\"bitsandbytes version is wrong. Please \"\n \"install bitsandbytes>=0.42.0.\")\n- from bitsandbytes.functional import quantize_4bit\n except ImportError as err:\n raise ImportError(\"Please install bitsandbytes>=0.42.0 via \"\n \"`pip install bitsandbytes>=0.42.0` to use \"\n@@ -803,80 +805,111 @@ def _get_quantized_weights_iterator(\n hf_weights_files, use_safetensors = self._prepare_weights(\n model_name_or_path, revision)\n \n- quant_state_dict = {}\n-\n- def quantized_checkpoint() -> Generator:\n- # First iterate over all quant state weights\n- weight_iterator = self._hf_weight_iter(hf_weights_files,\n- use_safetensors)\n- temp_state_dict = {}\n- for weight_name, weight_tensor in weight_iterator:\n- if weight_name.endswith(\".weight\"):\n- continue\n- # TODO: only nf4 quantization is supported for now\n- if weight_name.endswith(\".quant_state.bitsandbytes__fp4\"):\n- raise NotImplementedError(\n- \"Only bitsandbytes_nf4 quantization\"\n- f\"is supported for now. {weight_name} is fp4 quantized\"\n- )\n- temp_state_dict[weight_name] = weight_tensor\n+ quant_state_dict: Dict[str, Any] = {}\n \n- # Closure to parse quant_state for each prequant weight\n- def _parse_quant_state(param_name: str,\n- temp_state_dict: Dict) -> QuantState:\n- quant_state = {}\n- for k in temp_state_dict:\n- if param_name + \".\" in k:\n- quant_state[k] = temp_state_dict[k]\n- # bitsandbytes library requires\n- # weight.quant_state.bitsandbytes__nf4 in CPU\n- quant_state[param_name +\n- \".quant_state.bitsandbytes__nf4\"] = quant_state[\n- param_name +\n- \".quant_state.bitsandbytes__nf4\"].cpu().data\n- return QuantState.from_dict(quant_state, device=\"cuda\")\n-\n- # Second iterate over all prequant and normal weights\n- # pre quantized weights would have a quant_state\n- for weight_name, weight_tensor in self._hf_weight_iter(\n- hf_weights_files, use_safetensors):\n- # Filter out all weights whose suffix is not \".weight\"\n- if not weight_name.endswith(\".weight\"):\n- continue\n- if weight_name + \".quant_state.bitsandbytes__nf4\" \\\n- in temp_state_dict:\n- quant_state = _parse_quant_state(weight_name,\n- temp_state_dict)\n- weight_name = weight_name.replace(\".weight\", \".qweight\")\n- quant_state_dict[weight_name] = quant_state\n- yield weight_name.replace(\".weight\",\n- \".qweight\"), weight_tensor\n- else:\n- yield weight_name, weight_tensor\n-\n- def generator() -> Generator:\n- for weight_name, weight_tensor in self._hf_weight_iter(\n- hf_weights_files, use_safetensors):\n- if any(target_module in weight_name\n- for target_module in self.target_modules):\n- weight_name = weight_name.replace(\".weight\", \".qweight\")\n- # bitsandbytes requires data in GPU\n- loaded_weight = weight_tensor.cuda().data\n- with set_default_torch_dtype(torch.float32):\n- processed_weight, quant_state = quantize_4bit(\n- loaded_weight,\n- compress_statistics=True,\n- quant_type=\"nf4\")\n-\n- quant_state_dict[weight_name] = quant_state\n- else:\n- processed_weight = weight_tensor\n+ if pre_quant:\n+ if load_8bit:\n+ return self._quantized_8bit_generator(\n+ hf_weights_files, use_safetensors,\n+ quant_state_dict), quant_state_dict\n+ else:\n+ return self._quantized_4bit_generator(\n+ hf_weights_files, use_safetensors,\n+ quant_state_dict), quant_state_dict\n \n- yield weight_name, processed_weight\n+ return self._unquantized_generator(hf_weights_files, use_safetensors,\n+ quant_state_dict), quant_state_dict\n \n- if pre_quant:\n- return quantized_checkpoint(), quant_state_dict\n- return generator(), quant_state_dict\n+ def _quantized_8bit_generator(self, hf_weights_files, use_safetensors,\n+ quant_state_dict) -> Generator:\n+ for weight_name, weight_tensor in self._hf_weight_iter(\n+ hf_weights_files, use_safetensors):\n+ if not weight_name.lower().endswith(\".scb\"):\n+ continue\n+\n+ weight_key = weight_name.lower().replace(\".scb\", \".qweight\")\n+ quant_state_dict[weight_key] = weight_tensor\n+\n+ for weight_name, weight_tensor in self._hf_weight_iter(\n+ hf_weights_files, use_safetensors):\n+\n+ if not weight_name.endswith(\".weight\"):\n+ continue\n+\n+ qweight_name = weight_name.replace(\".weight\", \".qweight\")\n+ if qweight_name in quant_state_dict:\n+ set_weight_attrs(weight_tensor, {\"load_in_8bit\": True})\n+ yield qweight_name, weight_tensor\n+ else:\n+ yield weight_name, weight_tensor\n+\n+ def _quantized_4bit_generator(self, hf_weights_files, use_safetensors,\n+ quant_state_dict) -> Generator:\n+ from bitsandbytes.functional import QuantState\n+\n+ # First iterate over all quant state weights\n+ weight_iterator = self._hf_weight_iter(hf_weights_files,\n+ use_safetensors)\n+ temp_state_dict = {}\n+ for weight_name, weight_tensor in weight_iterator:\n+ if weight_name.endswith(\".weight\"):\n+ continue\n+ # bitsandbytes library requires\n+ # weight.quant_state.bitsandbytes__* in CPU\n+ if \"quant_state.bitsandbytes\" in weight_name:\n+ temp_state_dict[weight_name] = weight_tensor.cpu().data\n+ else:\n+ temp_state_dict[weight_name] = weight_tensor\n+\n+ # Closure to parse quant_state for each prequant weight\n+ def _parse_quant_state(param_name: str,\n+ temp_state_dict: Dict) -> QuantState:\n+ quant_state = {}\n+ for k in temp_state_dict:\n+ if param_name + \".\" in k:\n+ quant_state[k] = temp_state_dict[k]\n+\n+ return QuantState.from_dict(quant_state, device=\"cuda\")\n+\n+ # Second iterate over all prequant and normal weights\n+ # pre quantized weights would have a quant_state\n+ for weight_name, weight_tensor in self._hf_weight_iter(\n+ hf_weights_files, use_safetensors):\n+ # Filter out all weights whose suffix is not \".weight\"\n+ if not weight_name.endswith(\".weight\"):\n+ continue\n+ if (f\"{weight_name}.quant_state.bitsandbytes__nf4\" \\\n+ in temp_state_dict) or \\\n+ (f\"{weight_name}.quant_state.bitsandbytes__fp4\" \\\n+ in temp_state_dict):\n+ quant_state = _parse_quant_state(weight_name, temp_state_dict)\n+ weight_name = weight_name.replace(\".weight\", \".qweight\")\n+ quant_state_dict[weight_name] = quant_state\n+ yield weight_name.replace(\".weight\", \".qweight\"), weight_tensor\n+ else:\n+ yield weight_name, weight_tensor\n+\n+ def _unquantized_generator(self, hf_weights_files, use_safetensors,\n+ quant_state_dict) -> Generator:\n+ from bitsandbytes.functional import quantize_4bit\n+ for weight_name, weight_tensor in self._hf_weight_iter(\n+ hf_weights_files, use_safetensors):\n+ if any(target_module in weight_name\n+ for target_module in self.target_modules):\n+ weight_name = weight_name.replace(\".weight\", \".qweight\")\n+ # bitsandbytes requires data in GPU\n+ loaded_weight = weight_tensor.cuda().data\n+ with set_default_torch_dtype(torch.float32):\n+ processed_weight, quant_state = quantize_4bit(\n+ loaded_weight,\n+ compress_statistics=True,\n+ quant_type=\"nf4\")\n+\n+ quant_state_dict[weight_name] = quant_state\n+ else:\n+ processed_weight = weight_tensor\n+\n+ yield weight_name, processed_weight\n \n def _load_weights(self, model_config: ModelConfig,\n model: nn.Module) -> None:\n@@ -893,16 +926,26 @@ def _load_weights(self, model_config: ModelConfig,\n logger.info(\"Loading weights with BitsAndBytes quantization. \"\n \" May take a while ...\")\n \n- is_quantized_checkpoint = False\n quant_config = getattr(model_config.hf_config, \"quantization_config\",\n None)\n- if quant_config is not None and quant_config.get(\n- 'quant_method') == \"bitsandbytes\":\n- is_quantized_checkpoint = True\n+\n+ pre_quant = False\n+ if quant_config is not None:\n+ quant_method = quant_config.get('quant_method')\n+ if quant_method == \"bitsandbytes\":\n+ pre_quant = True\n+ else:\n+ raise ValueError(\n+ f\"BitsAndBytes loader does not support {quant_method} \"\n+ \"quantization\")\n+\n+ load_8bit = False\n+ if pre_quant:\n+ load_8bit = quant_config.get('load_in_8bit', False)\n \n qweight_iterator, quant_state_dict = \\\n self._get_quantized_weights_iterator(\n- model_config.model, model_config.revision, is_quantized_checkpoint)\n+ model_config.model, model_config.revision, pre_quant, load_8bit)\n \n model.load_weights(qweight_iterator)\n \n@@ -952,6 +995,10 @@ def _load_weights(self, model_config: ModelConfig,\n offsets = np.concatenate(([0], np.cumsum(num_elements)))\n set_weight_attrs(param, {\"bnb_shard_offsets\": offsets})\n \n+ if load_8bit:\n+ set_weight_attrs(\n+ param, {\"matmul_state\": [None] * len(quant_states)})\n+\n def load_model(self, *, model_config: ModelConfig,\n device_config: DeviceConfig,\n lora_config: Optional[LoRAConfig],\n" }
[ { "diff_hunk": "@@ -5,82 +5,108 @@\n import pytest\n import torch\n \n+from tests.conftest import VllmRunner\n from tests.quantization.utils import is_quant_method_supported\n from vllm import SamplingParams\n \n-models_to_test = [\n+models_4bit_to_test = [\n ('huggyllama/llama-7b', 'quantize model inflight'),\n- ('lllyasviel/omost-llama-3-8b-4bits', 'read pre-quantized model'),\n+ ('lllyasviel/omost-llama-3-8b-4bits',\n+ 'read pre-quantized 4-bit NF4 model'),\n+ ('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed',\n+ 'read pre-quantized 4-bit FP4 model'),\n+]\n+\n+models_8bit_to_test = [\n+ ('meta-llama/Llama-Guard-3-8B-INT8', 'read pre-quantized 8-bit model'),\n ]\n \n \n @pytest.mark.skipif(not is_quant_method_supported(\"bitsandbytes\"),\n reason='bitsandbytes is not supported on this GPU type.')\[email protected](\"model_name, description\", models_to_test)\n-def test_load_bnb_model(vllm_runner, model_name, description) -> None:\[email protected](\"model_name, description\", models_4bit_to_test)\n+def test_load_4bit_bnb_model(vllm_runner, model_name, description) -> None:\n with vllm_runner(model_name,\n quantization='bitsandbytes',\n load_format='bitsandbytes',\n enforce_eager=True) as llm:\n model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n \n # check the weights in MLP & SelfAttention are quantized to torch.uint8\n- qweight = model.model.layers[0].mlp.gate_up_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].mlp.down_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].self_attn.o_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- qweight = model.model.layers[0].self_attn.qkv_proj.qweight\n- assert qweight.dtype == torch.uint8, (\n- f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}')\n-\n- # some weights should not be quantized\n- weight = model.lm_head.weight\n- assert weight.dtype != torch.uint8, (\n- 'lm_head weight dtype should not be torch.uint8')\n-\n- weight = model.model.embed_tokens.weight\n- assert weight.dtype != torch.uint8, (\n- 'embed_tokens weight dtype should not be torch.uint8')\n-\n- weight = model.model.layers[0].input_layernorm.weight\n- assert weight.dtype != torch.uint8, (\n- 'input_layernorm weight dtype should not be torch.uint8')\n-\n- weight = model.model.layers[0].post_attention_layernorm.weight\n- assert weight.dtype != torch.uint8, (\n- 'input_layernorm weight dtype should not be torch.uint8')\n-\n- # check the output of the model is expected\n- sampling_params = SamplingParams(temperature=0.0,\n- logprobs=1,\n- prompt_logprobs=1,\n- max_tokens=8)\n-\n- prompts = ['That which does not kill us', 'To be or not to be,']\n- expected_outputs = [\n- 'That which does not kill us makes us stronger.',\n- 'To be or not to be, that is the question.'\n- ]\n- outputs = llm.generate(prompts, sampling_params=sampling_params)\n- assert len(outputs) == len(prompts)\n-\n- for index in range(len(outputs)):\n- # compare the first line of the output\n- actual_output = outputs[index][1][0].split('\\n', 1)[0]\n- expected_output = expected_outputs[index].split('\\n', 1)[0]\n-\n- assert len(actual_output) >= len(expected_output), (\n- f'Actual {actual_output} should be larger than or equal to '\n- f'expected {expected_output}')\n- actual_output = actual_output[:len(expected_output)]\n-\n- assert actual_output == expected_output, (\n- f'Expected: {expected_output}, but got: {actual_output}')\n+ validate_model_weight_type(model, torch.uint8)\n+\n+ validate_model_output(llm)\n+\n+\[email protected](not is_quant_method_supported(\"bitsandbytes\"),\n+ reason='bitsandbytes is not supported on this GPU type.')\[email protected](\"model_name, description\", models_8bit_to_test)\n+def test_load_8bit_bnb_model(vllm_runner, model_name, description) -> None:\n+ with vllm_runner(model_name,\n+ quantization='bitsandbytes',\n+ load_format='bitsandbytes',\n+ enforce_eager=True) as llm:\n+ model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501\n+\n+ # check the weights in MLP & SelfAttention are quantized to torch.int8\n+ validate_model_weight_type(model, torch.int8)\n+\n+ validate_model_output(llm)\n+\n+\n+def validate_model_weight_type(model, quantized_dtype=torch.uint8):\n+ # Check quantized weights\n+ quantized_layers = [('mlp.gate_up_proj.qweight',\n+ model.model.layers[0].mlp.gate_up_proj.qweight),\n+ ('mlp.down_proj.qweight',\n+ model.model.layers[0].mlp.down_proj.qweight),\n+ ('self_attn.o_proj.qweight',\n+ model.model.layers[0].self_attn.o_proj.qweight),\n+ ('self_attn.qkv_proj.qweight',\n+ model.model.layers[0].self_attn.qkv_proj.qweight)]\n+\n+ for name, qweight in quantized_layers:\n+ assert qweight.dtype == quantized_dtype, (\n+ f'Expected {name} dtype {quantized_dtype} but got {qweight.dtype}')\n+\n+ # Check non-quantized weights\n+ non_quantized_layers = [\n+ ('lm_head.weight', model.lm_head.weight),\n+ ('embed_tokens.weight', model.model.embed_tokens.weight),\n+ ('input_layernorm.weight',\n+ model.model.layers[0].input_layernorm.weight),\n+ ('post_attention_layernorm.weight',\n+ model.model.layers[0].post_attention_layernorm.weight)\n+ ]\n+\n+ for name, weight in non_quantized_layers:\n+ assert weight.dtype != quantized_dtype, (\n+ f'{name} dtype should not be {quantized_dtype}')\n+\n+\n+def validate_model_output(llm: VllmRunner):\n+ sampling_params = SamplingParams(temperature=0.0,\n+ logprobs=1,\n+ prompt_logprobs=1,\n+ max_tokens=8)\n+\n+ prompts = ['That which does not kill us', 'To be or not to be,']\n+ expected_outputs = [\n+ 'That which does not kill us makes us stronger.',\n+ 'To be or not to be, that is the question.'\n+ ]\n+ outputs = llm.generate(prompts, sampling_params=sampling_params)\n+ assert len(outputs) == len(prompts)\n+\n+ for index in range(len(outputs)):\n+ # compare the first line of the output\n+ actual_output = outputs[index][1][0].split('\\n', 1)[0]\n+ expected_output = expected_outputs[index].split('\\n', 1)[0]\n+\n+ assert len(actual_output) >= len(expected_output), (\n+ f'Actual {actual_output} should be larger than or equal to '\n+ f'expected {expected_output}')\n+ actual_output = actual_output[:len(expected_output)]\n+\n+ assert actual_output == expected_output, (\n+ f'Expected: {expected_output}, but got: {actual_output}')", "line": null, "original_line": 112, "original_start_line": 87, "path": "tests/quantization/test_bitsandbytes.py", "start_line": null, "text": "@user1:\nCould you change this to do a direct top-logprob greedy comparison between HF and vLLM? There are several references in the testing but here is one https://github.com/vllm-project/vllm/blob/9ba85bc1527928f30ec56961520d7e07ee385167/tests/models/test_mistral.py#L29-L35\n\n@author:\n> Could you change this to do a direct top-logprob greedy comparison between HF and vLLM? There are several references in the testing but here is one\r\n\r\nUpdated the code per comments, could you take a look? @user1" } ]
368ce90363ea9c64bcb5d5e9fa8d9f70443eefb1
diff --git a/tests/conftest.py b/tests/conftest.py index ae362b228d9d..c56a3059a17e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -205,8 +205,14 @@ class HfRunner: def wrap_device(self, input: _T) -> _T: if not is_cpu(): + # Check if the input is already on the GPU + if hasattr(input, 'device') and input.device.type == "cuda": + return input # Already on GPU, no need to move return input.to("cuda") else: + # Check if the input is already on the CPU + if hasattr(input, 'device') and input.device.type == "cpu": + return input # Already on CPU, no need to move return input.to("cpu") def __init__( diff --git a/tests/quantization/test_bitsandbytes.py b/tests/quantization/test_bitsandbytes.py index b760e9ccb6b7..3f0c6cbc051a 100644 --- a/tests/quantization/test_bitsandbytes.py +++ b/tests/quantization/test_bitsandbytes.py @@ -2,85 +2,115 @@ Run `pytest tests/quantization/test_bitsandbytes.py`. ''' + +import gc + import pytest import torch from tests.quantization.utils import is_quant_method_supported -from vllm import SamplingParams -models_to_test = [ +models_4bit_to_test = [ ('huggyllama/llama-7b', 'quantize model inflight'), - ('lllyasviel/omost-llama-3-8b-4bits', 'read pre-quantized model'), ] +models_pre_qaunt_4bit_to_test = [ + ('lllyasviel/omost-llama-3-8b-4bits', + 'read pre-quantized 4-bit NF4 model'), + ('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed', + 'read pre-quantized 4-bit FP4 model'), +] + +models_pre_quant_8bit_to_test = [ + ('meta-llama/Llama-Guard-3-8B-INT8', 'read pre-quantized 8-bit model'), +] + + [email protected](not is_quant_method_supported("bitsandbytes"), + reason='bitsandbytes is not supported on this GPU type.') [email protected]("model_name, description", models_4bit_to_test) +def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts, + model_name, description) -> None: + + hf_model_kwargs = {"load_in_4bit": True} + validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1], + model_name, hf_model_kwargs) + + [email protected](not is_quant_method_supported("bitsandbytes"), + reason='bitsandbytes is not supported on this GPU type.') [email protected]("model_name, description", + models_pre_qaunt_4bit_to_test) +def test_load_pre_quant_4bit_bnb_model(hf_runner, vllm_runner, example_prompts, + model_name, description) -> None: + + validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1], + model_name) + @pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"), reason='bitsandbytes is not supported on this GPU type.') [email protected]("model_name, description", models_to_test) -def test_load_bnb_model(vllm_runner, model_name, description) -> None: [email protected]("model_name, description", + models_pre_quant_8bit_to_test) +def test_load_8bit_bnb_model(hf_runner, vllm_runner, example_prompts, + model_name, description) -> None: + + validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1], + model_name) + + +def log_generated_texts(prompts, outputs, runner_name): + logged_texts = [] + for i, (_, generated_text) in enumerate(outputs): + log_entry = { + "prompt": prompts[i], + "runner_name": runner_name, + "generated_text": generated_text, + } + logged_texts.append(log_entry) + return logged_texts + + +def validate_generated_texts(hf_runner, + vllm_runner, + prompts, + model_name, + hf_model_kwargs=None): + + if hf_model_kwargs is None: + hf_model_kwargs = {} + + # Run with HF runner + with hf_runner(model_name, model_kwargs=hf_model_kwargs) as llm: + hf_outputs = llm.generate_greedy(prompts, 8) + hf_logs = log_generated_texts(prompts, hf_outputs, "HfRunner") + + # Clean up the GPU memory for the next test + torch.cuda.synchronize() + gc.collect() + torch.cuda.empty_cache() + + #Run with vLLM runner with vllm_runner(model_name, quantization='bitsandbytes', load_format='bitsandbytes', - enforce_eager=True) as llm: - model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 - - # check the weights in MLP & SelfAttention are quantized to torch.uint8 - qweight = model.model.layers[0].mlp.gate_up_proj.qweight - assert qweight.dtype == torch.uint8, ( - f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}') - - qweight = model.model.layers[0].mlp.down_proj.qweight - assert qweight.dtype == torch.uint8, ( - f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}') - - qweight = model.model.layers[0].self_attn.o_proj.qweight - assert qweight.dtype == torch.uint8, ( - f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}') - - qweight = model.model.layers[0].self_attn.qkv_proj.qweight - assert qweight.dtype == torch.uint8, ( - f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}') - - # some weights should not be quantized - weight = model.lm_head.weight - assert weight.dtype != torch.uint8, ( - 'lm_head weight dtype should not be torch.uint8') - - weight = model.model.embed_tokens.weight - assert weight.dtype != torch.uint8, ( - 'embed_tokens weight dtype should not be torch.uint8') - - weight = model.model.layers[0].input_layernorm.weight - assert weight.dtype != torch.uint8, ( - 'input_layernorm weight dtype should not be torch.uint8') - - weight = model.model.layers[0].post_attention_layernorm.weight - assert weight.dtype != torch.uint8, ( - 'input_layernorm weight dtype should not be torch.uint8') - - # check the output of the model is expected - sampling_params = SamplingParams(temperature=0.0, - logprobs=1, - prompt_logprobs=1, - max_tokens=8) - - prompts = ['That which does not kill us', 'To be or not to be,'] - expected_outputs = [ - 'That which does not kill us makes us stronger.', - 'To be or not to be, that is the question.' - ] - outputs = llm.generate(prompts, sampling_params=sampling_params) - assert len(outputs) == len(prompts) - - for index in range(len(outputs)): - # compare the first line of the output - actual_output = outputs[index][1][0].split('\n', 1)[0] - expected_output = expected_outputs[index].split('\n', 1)[0] - - assert len(actual_output) >= len(expected_output), ( - f'Actual {actual_output} should be larger than or equal to ' - f'expected {expected_output}') - actual_output = actual_output[:len(expected_output)] - - assert actual_output == expected_output, ( - f'Expected: {expected_output}, but got: {actual_output}') + enforce_eager=True, + gpu_memory_utilization=0.8) as llm: + vllm_outputs = llm.generate_greedy(prompts, 8) + vllm_logs = log_generated_texts(prompts, vllm_outputs, "VllmRunner") + + # Clean up the GPU memory for the next test + torch.cuda.synchronize() + gc.collect() + torch.cuda.empty_cache() + + # Compare the generated strings + for hf_log, vllm_log in zip(hf_logs, vllm_logs): + hf_str = hf_log["generated_text"] + vllm_str = vllm_log["generated_text"] + prompt = hf_log["prompt"] + assert hf_str == vllm_str, (f"Model: {model_name}" + f"Mismatch between HF and vLLM outputs:\n" + f"Prompt: {prompt}\n" + f"HF Output: '{hf_str}'\n" + f"vLLM Output: '{vllm_str}'") diff --git a/vllm/config.py b/vllm/config.py index 4cbdde5e113a..48187d57a29b 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -353,6 +353,8 @@ def verify_with_parallel_config( raise ValueError( "BitAndBytes quantization with TP or PP is not supported yet.") + # Remove the constraint after the bitsandbytes issue is fixed: + # https://github.com/bitsandbytes-foundation/bitsandbytes/issues/1308 if self.quantization == "bitsandbytes" and self.enforce_eager is False: logger.warning("CUDA graph is not supported on BitAndBytes yet, " "fallback to the eager mode.") diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py index e5b40a64abc4..e3956f847c54 100644 --- a/vllm/model_executor/layers/linear.py +++ b/vllm/model_executor/layers/linear.py @@ -35,9 +35,9 @@ def adjust_marlin_shard(param, shard_size, shard_offset): return shard_size * marlin_tile_size, shard_offset * marlin_tile_size -def adjust_bitsandbytes_shard(param: Parameter, - qkv_offsets: Dict[str, Tuple[int, int]], - loaded_shard_id: str) -> Tuple[int, int]: +def adjust_bitsandbytes_4bit_shard(param: Parameter, + qkv_offsets: Dict[str, Tuple[int, int]], + loaded_shard_id: str) -> Tuple[int, int]: """Adjust the quantization offsets and sizes for BitsAndBytes sharding.""" total, _ = qkv_offsets["total"] @@ -506,8 +506,9 @@ def weight_loader(self, shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset) - use_bitsandbytes = getattr(param, "use_bitsandbytes", False) - if use_bitsandbytes: + use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", + False) + if use_bitsandbytes_4bit: shard_size = loaded_weight.shape[output_dim] shard_offset = loaded_weight.shape[output_dim] * \ loaded_shard_id @@ -859,8 +860,9 @@ def weight_loader(self, shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset) - use_bitsandbytes = getattr(param, "use_bitsandbytes", False) - if use_bitsandbytes: + use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", + False) + if use_bitsandbytes_4bit: orig_qkv_offsets = { "q": (0, self.num_heads * self.head_size), "k": (self.num_heads * self.head_size, @@ -872,7 +874,7 @@ def weight_loader(self, ((self.num_heads + 2 * self.num_kv_heads) * self.head_size, 0) } - shard_size, shard_offset = adjust_bitsandbytes_shard( + shard_size, shard_offset = adjust_bitsandbytes_4bit_shard( param, orig_qkv_offsets, loaded_shard_id) if is_gguf_weight: diff --git a/vllm/model_executor/layers/quantization/bitsandbytes.py b/vllm/model_executor/layers/quantization/bitsandbytes.py index c143d1a8f2bc..66bc5395dbd7 100644 --- a/vllm/model_executor/layers/quantization/bitsandbytes.py +++ b/vllm/model_executor/layers/quantization/bitsandbytes.py @@ -1,7 +1,6 @@ from typing import Any, Dict, List, Optional import torch -from torch.nn.parameter import Parameter from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, set_weight_attrs) @@ -15,8 +14,28 @@ class BitsAndBytesConfig(QuantizationConfig): Reference: https://arxiv.org/abs/2305.14314 """ - def __init__(self, ) -> None: - pass + def __init__( + self, + load_in_8bit: bool = False, + load_in_4bit: bool = True, + bnb_4bit_compute_dtype: str = "float32", + bnb_4bit_quant_type: str = "fp4", + bnb_4bit_use_double_quant: bool = False, + llm_int8_enable_fp32_cpu_offload: bool = False, + llm_int8_has_fp16_weight: bool = False, + llm_int8_skip_modules: Optional[Any] = None, + llm_int8_threshold: float = 0.0, + ) -> None: + + self.load_in_8bit = load_in_8bit + self.load_in_4bit = load_in_4bit + self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype + self.bnb_4bit_quant_type = bnb_4bit_quant_type + self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant + self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload + self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight + self.llm_int8_skip_modules = llm_int8_skip_modules + self.llm_int8_threshold = llm_int8_threshold def __repr__(self) -> str: return "BitsAndBytesConfig" @@ -41,7 +60,46 @@ def get_config_filenames() -> List[str]: @classmethod def from_config(cls, config: Dict[str, Any]) -> "BitsAndBytesConfig": - return cls() + + def get_safe_value(config, keys, default_value=None): + try: + value = cls.get_from_keys(config, keys) + return value if value is not None else default_value + except ValueError: + return default_value + + load_in_8bit = get_safe_value(config, ["load_in_8bit"], + default_value=False) + load_in_4bit = get_safe_value(config, ["load_in_4bit"], + default_value=True) + bnb_4bit_compute_dtype = get_safe_value(config, + ["bnb_4bit_compute_dtype"], + default_value="float32") + bnb_4bit_quant_type = get_safe_value(config, ["bnb_4bit_quant_type"], + default_value="fp4") + bnb_4bit_use_double_quant = get_safe_value( + config, ["bnb_4bit_use_double_quant"], default_value=False) + llm_int8_enable_fp32_cpu_offload = get_safe_value( + config, ["llm_int8_enable_fp32_cpu_offload"], default_value=False) + llm_int8_has_fp16_weight = get_safe_value(config, + ["llm_int8_has_fp16_weight"], + default_value=False) + llm_int8_skip_modules = get_safe_value(config, + ["llm_int8_skip_modules"], + default_value=[]) + llm_int8_threshold = get_safe_value(config, ["llm_int8_threshold"], + default_value=0.0) + + return cls( + load_in_8bit=load_in_8bit, + load_in_4bit=load_in_4bit, + bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, + bnb_4bit_quant_type=bnb_4bit_quant_type, + bnb_4bit_use_double_quant=bnb_4bit_use_double_quant, + llm_int8_enable_fp32_cpu_offload=llm_int8_enable_fp32_cpu_offload, + llm_int8_has_fp16_weight=llm_int8_has_fp16_weight, + llm_int8_skip_modules=llm_int8_skip_modules, + llm_int8_threshold=llm_int8_threshold) def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["BitsAndBytesLinearMethod"]: @@ -78,39 +136,58 @@ def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs): - quant_ratio = 0 - if params_dtype.is_floating_point: - quant_ratio = torch.finfo(params_dtype).bits // torch.iinfo( - torch.uint8).bits + from bitsandbytes.nn import Int8Params + + def calculate_quant_ratio(dtype): + if dtype.is_floating_point: + return torch.finfo(dtype).bits // torch.iinfo(torch.uint8).bits + else: + return torch.iinfo(dtype).bits // torch.iinfo(torch.uint8).bits + + def create_qweight_for_8bit(): + qweight = Int8Params( + data=torch.empty(sum(output_partition_sizes), + input_size_per_partition, + dtype=torch.int8), + has_fp16_weights=self.quant_config.llm_int8_has_fp16_weight, + requires_grad=False) + set_weight_attrs( + qweight, { + "input_dim": 0, + "output_dim": 0, + "pack_factor": 1, + "use_bitsandbytes_8bit": True, + "generation": 0 + }) + return qweight + + def create_qweight_for_4bit(): + quant_ratio = calculate_quant_ratio(params_dtype) + + total_size = input_size_per_partition * sum(output_partition_sizes) + if total_size % quant_ratio != 0: + raise ValueError( + "The input size is not aligned with the quantized " + "weight shape.") + + qweight = torch.nn.Parameter(torch.empty(total_size // quant_ratio, + 1, + dtype=torch.uint8), + requires_grad=False) + set_weight_attrs( + qweight, { + "input_dim": 0, + "output_dim": 0, + "pack_factor": quant_ratio, + "use_bitsandbytes_4bit": True + }) + return qweight + + if self.quant_config.load_in_8bit: + qweight = create_qweight_for_8bit() else: - quant_ratio = torch.iinfo(params_dtype).bits // torch.iinfo( - torch.uint8).bits - - if input_size_per_partition * sum( - output_partition_sizes) % quant_ratio != 0: - raise ValueError( - "The input size is not aligned with the quantized " - "weight shape. ") - qweight = Parameter( - torch.empty( - input_size_per_partition * sum(output_partition_sizes) // - quant_ratio, - 1, - dtype=torch.uint8, - ), - requires_grad=False, - ) - - set_weight_attrs( - qweight, - { - "input_dim": 0, - # In bitsandbytes, a tensor of shape [n,m] is quantized to - #[n*m/pack_ratio, 1],so the output_dim is 0 - "output_dim": 0, - "pack_factor": quant_ratio, - "use_bitsandbytes": True, - }) + qweight = create_qweight_for_4bit() + layer.register_parameter("qweight", qweight) set_weight_attrs(qweight, extra_weight_attrs) @@ -119,6 +196,88 @@ def apply(self, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: + if self.quant_config.load_in_8bit: + return self._apply_8bit_weight(layer, x, bias) + else: + return self._apply_4bit_weight(layer, x, bias) + + def _apply_8bit_weight( + self, + layer: torch.nn.Module, + x: torch.Tensor, + bias: Optional[torch.Tensor] = None) -> torch.Tensor: + + # only load the bitsandbytes module when needed + from bitsandbytes import MatmulLtState, matmul + + original_type = x.dtype + bf_x = x.to(torch.bfloat16) + + qweight = layer.qweight + offsets = qweight.bnb_shard_offsets + quant_states = qweight.bnb_quant_state + matmul_states = qweight.matmul_state + generation = qweight.generation + + out_dim_0 = x.shape[0] + out_dim_1 = sum( + [quant_state[1].shape[0] for quant_state in quant_states.items()]) + out = torch.empty(out_dim_0, + out_dim_1, + dtype=torch.float16, + device=x.device) + + current_index = 0 + for i in range(len(quant_states)): + output_size = quant_states[i].shape[0] + + # in profile_run or the first generation of inference, + # create new matmul_states + if generation == 0 or generation == 1: + matmul_states[i] = MatmulLtState() + matmul_states[i].CB = qweight[offsets[i]:offsets[i + 1]] + matmul_states[i].SCB = quant_states[i] + matmul_states[i].threshold = ( + self.quant_config.llm_int8_threshold) + matmul_states[i].has_fp16_weights = ( + self.quant_config.llm_int8_has_fp16_weight) + matmul_states[i].is_training = False + if matmul_states[i].threshold > 0.0 and not matmul_states[ + i].has_fp16_weights: + matmul_states[i].use_pool = True + + new_x = bf_x.unsqueeze(0) + + out[:, current_index:current_index + output_size] = matmul( + new_x, + qweight[offsets[i]:offsets[i + 1]], + state=matmul_states[i]) + + current_index += output_size + + # only update the matmul_states if it is not profile_run + if (generation > 0 + and not self.quant_config.llm_int8_has_fp16_weight + and matmul_states[i].CB is not None + and matmul_states[i].CxB is not None): + del matmul_states[i].CB + qweight[offsets[i]:offsets[i + 1]] = matmul_states[i].CxB + + out = out.to(original_type) + + if bias is not None: + out += bias + + qweight.generation += 1 + + return out + + def _apply_4bit_weight( + self, + layer: torch.nn.Module, + x: torch.Tensor, + bias: Optional[torch.Tensor] = None) -> torch.Tensor: + # only load the bitsandbytes module when needed from bitsandbytes import matmul_4bit diff --git a/vllm/model_executor/model_loader/loader.py b/vllm/model_executor/model_loader/loader.py index 2f6cdbc6ce3e..553fa848489b 100644 --- a/vllm/model_executor/model_loader/loader.py +++ b/vllm/model_executor/model_loader/loader.py @@ -771,7 +771,11 @@ def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool): return pt_weights_iterator(hf_weights_files) def _get_quantized_weights_iterator( - self, model_name_or_path: str, revision: Optional[str], pre_quant: bool + self, + model_name_or_path: str, + revision: Optional[str], + pre_quant: bool, + load_8bit: bool, ) -> Tuple[Generator[Tuple[str, torch.Tensor], None, None], Dict[str, Any]]: """Get an iterator to the model weights with bitsandbytes quantization, @@ -780,11 +784,9 @@ def _get_quantized_weights_iterator( # only load the bitsandbytes module when needed try: import bitsandbytes - from bitsandbytes.functional import QuantState if bitsandbytes.__version__ < "0.42.0": raise ImportError("bitsandbytes version is wrong. Please " "install bitsandbytes>=0.42.0.") - from bitsandbytes.functional import quantize_4bit except ImportError as err: raise ImportError("Please install bitsandbytes>=0.42.0 via " "`pip install bitsandbytes>=0.42.0` to use " @@ -793,80 +795,111 @@ def _get_quantized_weights_iterator( hf_weights_files, use_safetensors = self._prepare_weights( model_name_or_path, revision) - quant_state_dict = {} - - def quantized_checkpoint() -> Generator: - # First iterate over all quant state weights - weight_iterator = self._hf_weight_iter(hf_weights_files, - use_safetensors) - temp_state_dict = {} - for weight_name, weight_tensor in weight_iterator: - if weight_name.endswith(".weight"): - continue - # TODO: only nf4 quantization is supported for now - if weight_name.endswith(".quant_state.bitsandbytes__fp4"): - raise NotImplementedError( - "Only bitsandbytes_nf4 quantization" - f"is supported for now. {weight_name} is fp4 quantized" - ) - temp_state_dict[weight_name] = weight_tensor + quant_state_dict: Dict[str, Any] = {} - # Closure to parse quant_state for each prequant weight - def _parse_quant_state(param_name: str, - temp_state_dict: Dict) -> QuantState: - quant_state = {} - for k in temp_state_dict: - if param_name + "." in k: - quant_state[k] = temp_state_dict[k] - # bitsandbytes library requires - # weight.quant_state.bitsandbytes__nf4 in CPU - quant_state[param_name + - ".quant_state.bitsandbytes__nf4"] = quant_state[ - param_name + - ".quant_state.bitsandbytes__nf4"].cpu().data - return QuantState.from_dict(quant_state, device="cuda") - - # Second iterate over all prequant and normal weights - # pre quantized weights would have a quant_state - for weight_name, weight_tensor in self._hf_weight_iter( - hf_weights_files, use_safetensors): - # Filter out all weights whose suffix is not ".weight" - if not weight_name.endswith(".weight"): - continue - if weight_name + ".quant_state.bitsandbytes__nf4" \ - in temp_state_dict: - quant_state = _parse_quant_state(weight_name, - temp_state_dict) - weight_name = weight_name.replace(".weight", ".qweight") - quant_state_dict[weight_name] = quant_state - yield weight_name.replace(".weight", - ".qweight"), weight_tensor - else: - yield weight_name, weight_tensor - - def generator() -> Generator: - for weight_name, weight_tensor in self._hf_weight_iter( - hf_weights_files, use_safetensors): - if any(target_module in weight_name - for target_module in self.target_modules): - weight_name = weight_name.replace(".weight", ".qweight") - # bitsandbytes requires data in GPU - loaded_weight = weight_tensor.cuda().data - with set_default_torch_dtype(torch.float32): - processed_weight, quant_state = quantize_4bit( - loaded_weight, - compress_statistics=True, - quant_type="nf4") - - quant_state_dict[weight_name] = quant_state - else: - processed_weight = weight_tensor + if pre_quant: + if load_8bit: + return self._quantized_8bit_generator( + hf_weights_files, use_safetensors, + quant_state_dict), quant_state_dict + else: + return self._quantized_4bit_generator( + hf_weights_files, use_safetensors, + quant_state_dict), quant_state_dict - yield weight_name, processed_weight + return self._unquantized_generator(hf_weights_files, use_safetensors, + quant_state_dict), quant_state_dict - if pre_quant: - return quantized_checkpoint(), quant_state_dict - return generator(), quant_state_dict + def _quantized_8bit_generator(self, hf_weights_files, use_safetensors, + quant_state_dict) -> Generator: + for weight_name, weight_tensor in self._hf_weight_iter( + hf_weights_files, use_safetensors): + if not weight_name.lower().endswith(".scb"): + continue + + weight_key = weight_name.lower().replace(".scb", ".qweight") + quant_state_dict[weight_key] = weight_tensor + + for weight_name, weight_tensor in self._hf_weight_iter( + hf_weights_files, use_safetensors): + + if not weight_name.endswith(".weight"): + continue + + qweight_name = weight_name.replace(".weight", ".qweight") + if qweight_name in quant_state_dict: + set_weight_attrs(weight_tensor, {"load_in_8bit": True}) + yield qweight_name, weight_tensor + else: + yield weight_name, weight_tensor + + def _quantized_4bit_generator(self, hf_weights_files, use_safetensors, + quant_state_dict) -> Generator: + from bitsandbytes.functional import QuantState + + # First iterate over all quant state weights + weight_iterator = self._hf_weight_iter(hf_weights_files, + use_safetensors) + temp_state_dict = {} + for weight_name, weight_tensor in weight_iterator: + if weight_name.endswith(".weight"): + continue + # bitsandbytes library requires + # weight.quant_state.bitsandbytes__* in CPU + if "quant_state.bitsandbytes" in weight_name: + temp_state_dict[weight_name] = weight_tensor.cpu().data + else: + temp_state_dict[weight_name] = weight_tensor + + # Closure to parse quant_state for each prequant weight + def _parse_quant_state(param_name: str, + temp_state_dict: Dict) -> QuantState: + quant_state = {} + for k in temp_state_dict: + if param_name + "." in k: + quant_state[k] = temp_state_dict[k] + + return QuantState.from_dict(quant_state, device="cuda") + + # Second iterate over all prequant and normal weights + # pre quantized weights would have a quant_state + for weight_name, weight_tensor in self._hf_weight_iter( + hf_weights_files, use_safetensors): + # Filter out all weights whose suffix is not ".weight" + if not weight_name.endswith(".weight"): + continue + if (f"{weight_name}.quant_state.bitsandbytes__nf4" \ + in temp_state_dict) or \ + (f"{weight_name}.quant_state.bitsandbytes__fp4" \ + in temp_state_dict): + quant_state = _parse_quant_state(weight_name, temp_state_dict) + weight_name = weight_name.replace(".weight", ".qweight") + quant_state_dict[weight_name] = quant_state + yield weight_name.replace(".weight", ".qweight"), weight_tensor + else: + yield weight_name, weight_tensor + + def _unquantized_generator(self, hf_weights_files, use_safetensors, + quant_state_dict) -> Generator: + from bitsandbytes.functional import quantize_4bit + for weight_name, weight_tensor in self._hf_weight_iter( + hf_weights_files, use_safetensors): + if any(target_module in weight_name + for target_module in self.target_modules): + weight_name = weight_name.replace(".weight", ".qweight") + # bitsandbytes requires data in GPU + loaded_weight = weight_tensor.cuda().data + with set_default_torch_dtype(torch.float32): + processed_weight, quant_state = quantize_4bit( + loaded_weight, + compress_statistics=True, + quant_type="nf4") + + quant_state_dict[weight_name] = quant_state + else: + processed_weight = weight_tensor + + yield weight_name, processed_weight def _load_weights(self, model_config: ModelConfig, model: nn.Module) -> None: @@ -883,16 +916,26 @@ def _load_weights(self, model_config: ModelConfig, logger.info("Loading weights with BitsAndBytes quantization. " " May take a while ...") - is_quantized_checkpoint = False quant_config = getattr(model_config.hf_config, "quantization_config", None) - if quant_config is not None and quant_config.get( - 'quant_method') == "bitsandbytes": - is_quantized_checkpoint = True + + pre_quant = False + if quant_config is not None: + quant_method = quant_config.get('quant_method') + if quant_method == "bitsandbytes": + pre_quant = True + else: + raise ValueError( + f"BitsAndBytes loader does not support {quant_method} " + "quantization") + + load_8bit = False + if pre_quant: + load_8bit = quant_config.get('load_in_8bit', False) qweight_iterator, quant_state_dict = \ self._get_quantized_weights_iterator( - model_config.model, model_config.revision, is_quantized_checkpoint) + model_config.model, model_config.revision, pre_quant, load_8bit) model.load_weights(qweight_iterator) @@ -942,6 +985,10 @@ def _load_weights(self, model_config: ModelConfig, offsets = np.concatenate(([0], np.cumsum(num_elements))) set_weight_attrs(param, {"bnb_shard_offsets": offsets}) + if load_8bit: + set_weight_attrs( + param, {"matmul_state": [None] * len(quant_states)}) + def load_model(self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig],
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-6942@7fdb13d
vllm-project/vllm
Python
6,942
[Model] SiglipVisionModel ported from transformers
This PR implemented SiglipVisionModel for VLMs. - Some of the pre-trained SiglipVisionModel cannot use vLLM's Attention layer. Therefore, I implemented alternative attention layers if vLLM's one is impossible. - I tried vllm_flash_attn backend which doesn't work properly with CUDA Error. Thus, only the basic attention mechanism is working properly for now. - Modified Paligemma to use implemented SiglipVisionModel. FIX #6941 FIX #7144
2024-07-30T11:26:22Z
[Feature]: SiglipVisionModel Support ### ๐Ÿš€ The feature, motivation and pitch Paligemma uses SiglipVisionModel from the transformers library, which can be ported to the vLLM. As CLIPVisionModel is supported for VLMs, the SiglipVisionModel can be supported too. ### Alternatives _No response_ ### Additional context _No response_ [Usage]: Can it support using the siglip model as the vision model in a multimodal model? ### Your current environment ```text The output of `python collect_env.py` ``` ### How would you like to use vllm I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
Great suggestion! This is already on our roadmap (https://github.com/vllm-project/vllm/issues/4194), we'll get to it eventually!
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nPaligemma uses SiglipVisionModel from the transformers library, which can be ported to the vLLM.\r\nAs CLIPVisionModel is supported for VLMs, the SiglipVisionModel can be supported too.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_", "number": 6941, "title": "[Feature]: SiglipVisionModel Support" }, { "body": "### Your current environment\n\n```text\r\nThe output of `python collect_env.py`\r\n```\r\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\r\n", "number": 7144, "title": "[Usage]: Can it support using the siglip model as the vision model in a multimodal model?" } ]
cc08fc7225616aeb6709a2e75e5ac47ace124985
{ "head_commit": "7fdb13d0a5a2e2b3d4c84d70e84ed91384275de3", "head_commit_message": "fix: remove unnecessary comments", "patch_to_review": "diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py\nindex 8a2bacbd96b..8553c3a504e 100644\n--- a/vllm/model_executor/models/paligemma.py\n+++ b/vllm/model_executor/models/paligemma.py\n@@ -1,9 +1,8 @@\n from typing import Iterable, List, Literal, Optional, Tuple, TypedDict\n \n import torch\n-from PIL import Image\n from torch import nn\n-from transformers import PaliGemmaConfig, SiglipVisionConfig, SiglipVisionModel\n+from transformers import PaliGemmaConfig\n \n from vllm.attention import AttentionMetadata\n from vllm.config import CacheConfig, MultiModalConfig\n@@ -19,9 +18,11 @@\n from vllm.model_executor.sampling_metadata import SamplingMetadata\n from vllm.multimodal import MULTIMODAL_REGISTRY\n from vllm.multimodal.image import cached_get_tokenizer\n-from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData\n+from vllm.sequence import IntermediateTensors, SamplerOutput\n \n from .interfaces import SupportsVision\n+from .siglip import (SiglipVisionModel, dummy_image_for_siglip,\n+ dummy_seq_data_for_siglip, get_max_siglip_image_tokens)\n from .utils import merge_vision_embeddings\n \n logger = init_logger(__name__)\n@@ -33,55 +34,22 @@\n \n def get_max_paligemma_image_tokens(ctx: InputContext):\n hf_config = ctx.get_hf_config(PaliGemmaConfig)\n- text_config = hf_config.text_config\n-\n- return text_config.num_image_tokens\n-\n-\n-def dummy_seq_data_for_paligemma(\n- hf_config: PaliGemmaConfig,\n- seq_len: int,\n- *,\n- image_token_id: int,\n- image_feature_size_override: Optional[int] = None,\n-):\n- if image_feature_size_override is None:\n- image_feature_size = hf_config.text_config.num_image_tokens\n- else:\n- image_feature_size = image_feature_size_override\n-\n- token_ids = [image_token_id] * image_feature_size\n- token_ids += [0] * (seq_len - image_feature_size)\n- return SequenceData(token_ids)\n-\n-\n-def dummy_image_for_paligemma(\n- hf_config: SiglipVisionConfig,\n- *,\n- image_width_override: Optional[int] = None,\n- image_height_override: Optional[int] = None,\n-):\n- width = height = hf_config.image_size\n- if image_width_override is not None:\n- width = image_width_override\n- if image_height_override is not None:\n- height = image_height_override\n+ vision_config = hf_config.vision_config\n \n- image = Image.new(\"RGB\", (width, height), color=0)\n- return {\"image\": image}\n+ return get_max_siglip_image_tokens(vision_config)\n \n \n def dummy_data_for_paligemma(ctx: InputContext, seq_len: int):\n hf_config = ctx.get_hf_config(PaliGemmaConfig)\n vision_config = hf_config.vision_config\n \n- seq_data = dummy_seq_data_for_paligemma(\n- hf_config,\n+ seq_data = dummy_seq_data_for_siglip(\n+ vision_config,\n seq_len,\n image_token_id=hf_config.image_token_index,\n )\n \n- mm_data = dummy_image_for_paligemma(vision_config)\n+ mm_data = dummy_image_for_siglip(vision_config)\n return seq_data, mm_data\n \n \n@@ -211,30 +179,52 @@ def _parse_and_validate_image_input(\n data=self._validate_pixel_values(pixel_values),\n )\n \n- def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,\n- pixel_values: torch.Tensor) -> torch.Tensor:\n+ def _image_pixels_to_features(\n+ self,\n+ vision_tower: SiglipVisionModel,\n+ pixel_values: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> torch.Tensor:\n \n target_dtype = vision_tower.get_input_embeddings().weight.dtype\n image_outputs = vision_tower(pixel_values.to(dtype=target_dtype),\n- output_hidden_states=True)\n+ kv_caches, attn_metadata)\n \n- selected_image_features = image_outputs.last_hidden_state\n+ selected_image_features = image_outputs[0]\n \n return selected_image_features\n \n def _process_image_pixels(\n- self, inputs: PaliGemmaImagePixelInputs) -> torch.Tensor:\n+ self,\n+ inputs: PaliGemmaImagePixelInputs,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> torch.Tensor:\n assert self.vision_tower is not None\n \n pixel_values = inputs[\"data\"]\n \n- return self._image_pixels_to_features(self.vision_tower, pixel_values)\n+ return self._image_pixels_to_features(\n+ self.vision_tower,\n+ pixel_values,\n+ kv_caches,\n+ attn_metadata,\n+ )\n \n def _process_image_input(\n- self, image_input: PaliGemmaImageInputs) -> torch.Tensor:\n+ self,\n+ image_input: PaliGemmaImageInputs,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> torch.Tensor:\n \n assert self.vision_tower is not None\n- image_features = self._process_image_pixels(image_input)\n+ image_features = self._process_image_pixels(\n+ image_input,\n+ kv_caches,\n+ attn_metadata,\n+ )\n \n return self.multi_modal_projector(image_features)\n \n@@ -249,7 +239,11 @@ def forward(self,\n parsed_image_input = self._parse_and_validate_image_input(**kwargs)\n \n if parsed_image_input is not None:\n- vision_embeddings = self._process_image_input(parsed_image_input)\n+ vision_embeddings = self._process_image_input(\n+ parsed_image_input,\n+ kv_caches,\n+ attn_metadata,\n+ )\n # https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa\n vision_embeddings = vision_embeddings * (self.config.hidden_size**\n -0.5)\n@@ -308,9 +302,23 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n use_default_weight_loading = False\n if \"vision\" in name:\n if self.vision_tower is not None:\n- # We only do sharding for language model and\n- # not vision model for now.\n- use_default_weight_loading = True\n+ for (param_name, shard_name,\n+ shard_id) in stacked_params_mapping:\n+ if shard_name not in name:\n+ continue\n+ name = name.replace(shard_name, param_name)\n+ # Skip loading extra bias for GPTQ models.\n+ if name.endswith(\".bias\") and name not in params_dict:\n+ continue\n+ param = params_dict[name]\n+ weight_loader = param.weight_loader\n+ weight_loader(param, loaded_weight, shard_id)\n+ break\n+ else:\n+ # Skip loading extra bias for GPTQ models.\n+ if name.endswith(\".bias\") and name not in params_dict:\n+ continue\n+ use_default_weight_loading = True\n else:\n for (param_name, shard_name,\n shard_id) in stacked_params_mapping:\ndiff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py\nnew file mode 100644\nindex 00000000000..c68eb245f15\n--- /dev/null\n+++ b/vllm/model_executor/models/siglip.py\n@@ -0,0 +1,780 @@\n+\"\"\"Implementation of SiglipVisionModel intended to be only used\n+within a vision language model.\"\"\"\n+\n+import math\n+from typing import Iterable, List, Optional, Tuple\n+\n+import torch\n+from PIL import Image\n+from torch import nn\n+from transformers import SiglipConfig, SiglipVisionConfig\n+from vllm_flash_attn import flash_attn_func\n+from xformers.ops import memory_efficient_attention\n+\n+from vllm.attention import Attention, AttentionMetadata\n+from vllm.config import CacheConfig, ModelConfig\n+from vllm.distributed import (get_tensor_model_parallel_rank,\n+ get_tensor_model_parallel_world_size)\n+from vllm.inputs import LLMInputs\n+from vllm.model_executor.layers.activation import get_act_fn\n+from vllm.model_executor.layers.linear import (ColumnParallelLinear,\n+ QKVParallelLinear,\n+ RowParallelLinear)\n+from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (\n+ get_compressed_tensors_cache_scale)\n+from vllm.model_executor.layers.vocab_parallel_embedding import (\n+ VocabParallelEmbedding)\n+from vllm.model_executor.model_loader.weight_utils import (\n+ default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)\n+from vllm.multimodal.image import (cached_get_tokenizer,\n+ repeat_and_pad_image_tokens)\n+from vllm.sequence import SequenceData\n+from vllm.utils import is_hip\n+\n+from .utils import is_pp_missing_parameter\n+\n+\n+def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:\n+ assert image_size % patch_size == 0\n+ return image_size // patch_size\n+\n+\n+def get_siglip_num_patches(*, image_size: int, patch_size: int) -> int:\n+ grid_length = get_siglip_patch_grid_length(image_size=image_size,\n+ patch_size=patch_size)\n+ return grid_length * grid_length\n+\n+\n+def get_siglip_image_feature_size(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_num_patches(image_size=hf_config.image_size,\n+ patch_size=hf_config.patch_size)\n+\n+\n+def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_image_feature_size(hf_config)\n+\n+\n+def dummy_seq_data_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ seq_len: int,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ token_ids = [image_token_id] * image_feature_size\n+ token_ids += [0] * (seq_len - image_feature_size)\n+ return SequenceData(token_ids)\n+\n+\n+def dummy_image_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ *,\n+ image_width_override: Optional[int] = None,\n+ image_height_override: Optional[int] = None,\n+):\n+ width = height = hf_config.image_size\n+ if image_width_override is not None:\n+ width = image_width_override\n+ if image_height_override is not None:\n+ height = image_height_override\n+\n+ image = Image.new(\"RGB\", (width, height), color=0)\n+ return {\"image\": image}\n+\n+\n+def input_processor_for_siglip(\n+ model_config: ModelConfig,\n+ hf_config: SiglipVisionConfig,\n+ llm_inputs: LLMInputs,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ multi_modal_data = llm_inputs.get(\"multi_modal_data\")\n+ if multi_modal_data is None or \"image\" not in multi_modal_data:\n+ return llm_inputs\n+\n+ tokenizer = cached_get_tokenizer(model_config.tokenizer)\n+\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ new_prompt, new_token_ids = repeat_and_pad_image_tokens(\n+ tokenizer,\n+ llm_inputs.get(\"prompt\"),\n+ llm_inputs[\"prompt_token_ids\"],\n+ image_token_id=image_token_id,\n+ repeat_count=image_feature_size,\n+ )\n+\n+ # NOTE: Create a defensive copy of the original inputs\n+ return LLMInputs(\n+ prompt_token_ids=new_token_ids,\n+ prompt=new_prompt,\n+ multi_modal_data=multi_modal_data,\n+ )\n+\n+\n+# Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa\n+class SiglipVisionEmbeddings(nn.Module):\n+\n+ def __init__(self, config: SiglipVisionConfig):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+ self.image_size = config.image_size\n+ self.patch_size = config.patch_size\n+\n+ self.patch_embedding = nn.Conv2d(\n+ in_channels=config.num_channels,\n+ out_channels=self.embed_dim,\n+ kernel_size=self.patch_size,\n+ stride=self.patch_size,\n+ padding=\"valid\",\n+ )\n+\n+ self.num_patches = (self.image_size // self.patch_size)**2\n+ self.num_positions = self.num_patches\n+ self.position_embedding = VocabParallelEmbedding(\n+ self.num_positions, self.embed_dim)\n+ self.register_buffer(\n+ \"position_ids\",\n+ torch.arange(self.num_positions, dtype=torch.int64).expand(\n+ (1, -1)),\n+ persistent=False,\n+ )\n+\n+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int,\n+ width: int) -> torch.Tensor:\n+ \"\"\"\n+ This method is an adapted method for SigLIP (due to SigLIP not having\n+ class embedding unlike other ViTs) that allows the model to interpolate\n+ the pre-trained position encodings such that it can be usable on higher\n+ resolution images.\n+\n+ Source:\n+ https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174\n+ \"\"\"\n+ position_embeddings = self.position_embedding.weight.unsqueeze(0)\n+ num_patches = embeddings.shape[1]\n+ num_positions = position_embeddings.shape[1]\n+ if num_patches == num_positions and height == width:\n+ return position_embeddings\n+\n+ dim = embeddings.shape[-1]\n+ height = height // self.patch_size\n+ width = width // self.patch_size\n+ # we add a small number to avoid floating point error\n+ # in the interpolation\n+ # see discussion at https://github.com/facebookresearch/dino/issues/8\n+ height, width = height + 0.1, width + 0.1\n+\n+ patch_pos_embed = position_embeddings.reshape(\n+ 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)),\n+ dim)\n+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)\n+ patch_pos_embed = nn.functional.interpolate(\n+ patch_pos_embed,\n+ scale_factor=(\n+ height / math.sqrt(num_positions),\n+ width / math.sqrt(num_positions),\n+ ),\n+ mode=\"bicubic\",\n+ align_corners=False,\n+ )\n+ if (int(height) != patch_pos_embed.shape[-2]\n+ or int(width) != patch_pos_embed.shape[-1]):\n+ raise ValueError(\"Width or height does not match with \"\n+ \"the interpolated position embeddings\")\n+\n+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)\n+ return patch_pos_embed\n+\n+ def forward(self,\n+ pixel_values: torch.FloatTensor,\n+ interpolate_pos_encoding=False) -> torch.Tensor:\n+ _, _, height, width = pixel_values.shape\n+ target_dtype = self.patch_embedding.weight.dtype\n+ patch_embeds = self.patch_embedding(pixel_values.to(\n+ dtype=target_dtype)) # shape = [*, width, grid, grid]\n+ embeddings = patch_embeds.flatten(2).transpose(1, 2)\n+\n+ if interpolate_pos_encoding:\n+ embeddings = embeddings + self.interpolate_pos_encoding(\n+ embeddings, height, width)\n+ else:\n+ embeddings = embeddings + self.position_embedding(\n+ self.position_ids)\n+ return embeddings\n+\n+\n+class SiglipAttention(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+\n+ tp_size = get_tensor_model_parallel_world_size()\n+ self.total_num_heads = config.num_attention_heads\n+ if self.total_num_heads % tp_size != 0:\n+ raise ValueError(\n+ f\"Number of attention heads ({self.total_num_heads}) \"\n+ \"must be divisible by the tensor model parallel size\"\n+ f\" ({tp_size}).\")\n+\n+ self.num_heads = self.total_num_heads // tp_size\n+ self.head_dim = self.embed_dim // self.total_num_heads\n+ if self.head_dim * self.total_num_heads != self.embed_dim:\n+ raise ValueError(f\"embed_dim must be divisible by num_heads (got \"\n+ \"`embed_dim`: {self.embed_dim} and `num_heads`:\"\n+ f\" {self.num_heads}).\")\n+ self.qkv_size = self.num_heads * self.head_dim\n+ self.scale = self.head_dim**-0.5\n+ self.dropout = config.attention_dropout\n+\n+ self.qkv_proj = QKVParallelLinear(\n+ hidden_size=self.embed_dim,\n+ head_size=self.head_dim,\n+ total_num_heads=self.total_num_heads,\n+ quant_config=quant_config,\n+ )\n+ self.out_proj = RowParallelLinear(\n+ input_size=self.embed_dim,\n+ output_size=self.embed_dim,\n+ quant_config=quant_config,\n+ )\n+\n+ try:\n+ self.attn = Attention(\n+ self.num_heads,\n+ self.head_dim,\n+ self.scale,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )\n+ self.attn_fn = self._vllm_attention_forward\n+ except Exception:\n+ # For some pretrained Siglip models, the backend is not supported\n+ # (e.g. google/siglip-so400m-patch14-384 has hidden_size=1152\n+ # with num_attention_heads=16, which is not supported)\n+ # If the backend is not supported, fall back to the default\n+ self.attn_fn = self._basic_attention_forward\n+\n+ def forward(\n+ self,\n+ hidden_states: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> torch.Tensor:\n+ \"\"\"Input shape: Batch x Time x Channel\"\"\"\n+ batch_size, q_len, _ = hidden_states.size()\n+\n+ qkv_states, _ = self.qkv_proj(hidden_states)\n+ query_states, key_states, value_states = qkv_states.split(\n+ [self.qkv_size] * 3, dim=-1)\n+\n+ attn_output = self.attn_fn(\n+ q=query_states,\n+ k=key_states,\n+ v=value_states,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ batch_size=batch_size,\n+ q_len=q_len,\n+ )\n+\n+ attn_output, _ = self.out_proj(attn_output)\n+ return attn_output\n+\n+ def _vllm_attention_forward(self, q, k, v, kv_caches, attn_metadata, *args,\n+ **kwargs):\n+ return self.attn(q, k, v, kv_caches, attn_metadata)\n+\n+ def _basic_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ k = k.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ v = v.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+\n+ k_v_seq_len = k.shape[-2]\n+ attn_weights = torch.matmul(q, k.transpose(2, 3)) * self.scale\n+\n+ if attn_weights.size() != (\n+ batch_size,\n+ self.num_heads,\n+ q_len,\n+ k_v_seq_len,\n+ ):\n+ raise ValueError(\n+ \"Attention weights should be of size \"\n+ f\"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is\"\n+ f\" {attn_weights.size()}\")\n+\n+ # upcast attention to fp32\n+ attn_weights = nn.functional.softmax(attn_weights,\n+ dim=-1,\n+ dtype=torch.float32).to(q.dtype)\n+ attn_weights = nn.functional.dropout(attn_weights,\n+ p=self.dropout,\n+ training=self.training)\n+ attn_output = torch.matmul(attn_weights, v)\n+\n+ if attn_output.size() != (\n+ batch_size,\n+ self.num_heads,\n+ q_len,\n+ self.head_dim,\n+ ):\n+ raise ValueError(\n+ \"`attn_output` should be of size \"\n+ f\"{(batch_size, self.num_heads, q_len, self.head_dim)}, but is\"\n+ f\" {attn_output.size()}\")\n+\n+ attn_output = attn_output.transpose(1, 2).contiguous()\n+ attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)\n+\n+ return attn_output\n+\n+\n+# TODO(ChristopherCho): flash_attn_func is not working properly.\n+# It constantly throws a CUDA error.\n+class SiglipFlashAttention2(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.attn_fn = self._flash_attention_forward\n+\n+ # Ported from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L449\n+ # and https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/modeling_flash_attention_utils.py#L133\n+ def _flash_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ \"\"\"Implements the multihead softmax attention.\n+ Arguments\n+ ---------\n+ q, k, v: The tensor containing the\n+ query, key, and value. (B, S, H, D)\n+ \"\"\"\n+\n+ q = q.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ k = k.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ v = v.view(batch_size, q_len, self.num_heads, self.head_dim)\n+\n+ attn_output = flash_attn_func(\n+ q,\n+ k,\n+ v,\n+ dropout_p=0.0,\n+ causal=False,\n+ )\n+\n+ attn_output = attn_output.reshape(batch_size, q_len,\n+ self.embed_dim).contiguous()\n+\n+ return attn_output\n+\n+\n+class SiglipSdpaAttention(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.is_causal = False\n+ self.attn_fn = self._sdpa_attention_forward\n+\n+ def _sdpa_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ k = k.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ v = v.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+\n+ attn_output = torch.nn.functional.scaled_dot_product_attention(\n+ q, k, v, dropout_p=0.0, is_causal=False, scale=self.scale)\n+\n+ attn_output = attn_output.transpose(1, 2).contiguous()\n+ attn_output = attn_output.view(batch_size, q_len, self.embed_dim)\n+\n+ return attn_output\n+\n+\n+class SiglipxFormersAttention(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.attn_fn = self._xformers_attention_forward\n+\n+ def _xformers_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ k = k.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ v = v.view(batch_size, q_len, self.num_heads, self.head_dim)\n+\n+ attn_output = memory_efficient_attention(q,\n+ k,\n+ v,\n+ p=0.0,\n+ scale=self.scale)\n+ attn_output = attn_output.reshape(batch_size, q_len,\n+ self.embed_dim).contiguous()\n+\n+ return attn_output\n+\n+\n+SIGLIP_ATTENTION_CLASSES = {\n+ \"eager\": SiglipAttention,\n+ \"flash_attention_2\": SiglipFlashAttention2,\n+ \"sdpa\": SiglipSdpaAttention,\n+ \"xformers\": SiglipxFormersAttention,\n+}\n+\n+\n+class SiglipMLP(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.activation_fn = get_act_fn(config.hidden_act)\n+\n+ # For quantization, we require the hidden size to be a multiple of 64\n+ quantizable = (config.hidden_size % 64 == 0\n+ and config.intermediate_size % 64 == 0)\n+ self.fc1 = ColumnParallelLinear(\n+ config.hidden_size,\n+ config.intermediate_size,\n+ quant_config=quant_config if quantizable else None,\n+ )\n+ self.fc2 = RowParallelLinear(\n+ config.intermediate_size,\n+ config.hidden_size,\n+ quant_config=quant_config if quantizable else None,\n+ )\n+\n+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n+ hidden_states, _ = self.fc1(hidden_states)\n+ hidden_states = self.activation_fn(hidden_states)\n+ hidden_states, _ = self.fc2(hidden_states)\n+ return hidden_states\n+\n+\n+class SiglipEncoderLayer(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config: SiglipConfig,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.embed_dim = config.hidden_size\n+\n+ self.self_attn = SIGLIP_ATTENTION_CLASSES[config._attn_implementation](\n+ config,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )\n+ self.layer_norm1 = nn.LayerNorm(self.embed_dim,\n+ eps=config.layer_norm_eps)\n+ self.mlp = SiglipMLP(\n+ config,\n+ quant_config=quant_config,\n+ )\n+ self.layer_norm2 = nn.LayerNorm(self.embed_dim,\n+ eps=config.layer_norm_eps)\n+\n+ def forward(\n+ self,\n+ hidden_states: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> Tuple[torch.FloatTensor]:\n+ \"\"\"\n+ Args:\n+ hidden_states (`torch.FloatTensor`):\n+ Input to the layer of shape `(batch, seq_len, embed_dim)`.\n+ attention_mask (`torch.FloatTensor`):\n+ Attention mask of shape `(batch, 1, q_len, k_v_seq_len)`\n+ where padding elements are indicated by very large negative\n+ values.\n+ output_attentions (`bool`, *optional*, defaults to `False`):\n+ Whether or not to return the attentions tensors of all attention\n+ layers. See `attentions` under returned tensors for more detail.\n+ \"\"\"\n+ residual = hidden_states\n+\n+ hidden_states = self.layer_norm1(hidden_states)\n+ hidden_states = self.self_attn(\n+ hidden_states=hidden_states,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ )\n+ hidden_states = residual + hidden_states\n+\n+ residual = hidden_states\n+ hidden_states = self.layer_norm2(hidden_states)\n+ hidden_states = self.mlp(hidden_states)\n+ hidden_states = residual + hidden_states\n+\n+ outputs = (hidden_states, )\n+\n+ return outputs\n+\n+\n+class SiglipEncoder(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config: SiglipConfig,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.layers = nn.ModuleList([\n+ SiglipEncoderLayer(\n+ config,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ ) for _ in range(config.num_hidden_layers)\n+ ])\n+\n+ def forward(\n+ self,\n+ inputs_embeds: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> Tuple:\n+ hidden_states = inputs_embeds\n+ for encoder_layer in self.layers:\n+ encoder_states = (hidden_states, )\n+ layer_outputs = encoder_layer(\n+ hidden_states=hidden_states,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ )\n+ hidden_states = layer_outputs[0]\n+\n+ encoder_states = encoder_states + (hidden_states, )\n+\n+ return (hidden_states, encoder_states)\n+\n+\n+class SiglipVisionTransformer(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config: SiglipVisionConfig,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ embed_dim = config.hidden_size\n+\n+ self.embeddings = SiglipVisionEmbeddings(config)\n+ self.encoder = SiglipEncoder(\n+ config,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )\n+ self.post_layernorm = nn.LayerNorm(embed_dim,\n+ eps=config.layer_norm_eps)\n+ self.use_head = (True if not hasattr(config, \"vision_use_head\") else\n+ config.vision_use_head)\n+ if self.use_head:\n+ self.head = SiglipMultiheadAttentionPoolingHead(\n+ config=config, quant_config=quant_config)\n+\n+ def forward(\n+ self,\n+ pixel_values,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ interpolate_pos_encoding: Optional[bool] = True,\n+ ) -> Tuple:\n+ r\"\"\"\n+ Returns:\n+\n+ \"\"\"\n+ hidden_states = self.embeddings(\n+ pixel_values,\n+ interpolate_pos_encoding=interpolate_pos_encoding,\n+ )\n+\n+ encoder_outputs = self.encoder(\n+ inputs_embeds=hidden_states,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ )\n+\n+ last_hidden_state = encoder_outputs[0]\n+ last_hidden_state = self.post_layernorm(last_hidden_state)\n+\n+ pooled_output = self.head(last_hidden_state) if self.use_head else None\n+\n+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n+\n+\n+class SiglipMultiheadAttentionPoolingHead(nn.Module):\n+ \"\"\"Multihead Attention Pooling.\"\"\"\n+\n+ def __init__(\n+ self,\n+ config: SiglipVisionConfig,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+\n+ self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))\n+ # TODO(ChristopherCho): Implement vLLM version of MultiheadAttention\n+ self.attention = torch.nn.MultiheadAttention(\n+ config.hidden_size, config.num_attention_heads, batch_first=True)\n+ self.layernorm = nn.LayerNorm(config.hidden_size,\n+ eps=config.layer_norm_eps)\n+ self.mlp = SiglipMLP(config=config, quant_config=quant_config)\n+\n+ def forward(self, hidden_state):\n+ batch_size = hidden_state.shape[0]\n+ probe = self.probe.repeat(batch_size, 1, 1)\n+\n+ hidden_state = self.attention(probe, hidden_state, hidden_state)[0]\n+\n+ residual = hidden_state\n+ hidden_state = self.layernorm(hidden_state)\n+ hidden_state = residual + self.mlp(hidden_state)\n+\n+ return hidden_state[:, 0]\n+\n+\n+class SiglipVisionModel(nn.Module):\n+ config_class = SiglipVisionConfig\n+ main_input_name = \"pixel_values\"\n+\n+ def __init__(\n+ self,\n+ config: SiglipVisionConfig,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.vision_model = SiglipVisionTransformer(\n+ config,\n+ cache_config,\n+ quant_config,\n+ )\n+\n+ def get_input_embeddings(self) -> nn.Module:\n+ return self.vision_model.embeddings.patch_embedding\n+\n+ def forward(\n+ self,\n+ pixel_values,\n+ kv_caches: List[torch.Tensor] = None,\n+ attn_metadata: AttentionMetadata = None,\n+ interpolate_pos_encoding: Optional[bool] = False,\n+ ) -> Tuple:\n+ return self.vision_model(\n+ pixel_values=pixel_values,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ interpolate_pos_encoding=interpolate_pos_encoding,\n+ )\n+\n+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n+ stacked_params_mapping = [\n+ # (param_name, shard_name, shard_id)\n+ (\".qkv_proj\", \".q_proj\", \"q\"),\n+ (\".qkv_proj\", \".k_proj\", \"k\"),\n+ (\".qkv_proj\", \".v_proj\", \"v\"),\n+ ]\n+ params_dict = dict(self.named_parameters())\n+ for name, loaded_weight in weights:\n+ if scale_name := get_compressed_tensors_cache_scale(name):\n+ # Loading kv cache scales for compressed-tensors quantization\n+ param = params_dict[scale_name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ loaded_weight = loaded_weight[0]\n+ weight_loader(param, loaded_weight)\n+ continue\n+ for (param_name, weight_name, shard_id) in stacked_params_mapping:\n+ if weight_name not in name:\n+ continue\n+ name = name.replace(weight_name, param_name)\n+ # Skip loading extra bias for GPTQ models.\n+ if name.endswith(\".bias\") and name not in params_dict:\n+ continue\n+\n+ if is_pp_missing_parameter(name, self):\n+ continue\n+\n+ param = params_dict[name]\n+ weight_loader = param.weight_loader\n+ weight_loader(param, loaded_weight, shard_id)\n+\n+ break\n+ else:\n+ # Skip loading extra bias for GPTQ models.\n+ if name.endswith(\".bias\") and name not in params_dict:\n+ continue\n+ # Remapping the name of FP8 kv-scale.\n+ name = maybe_remap_kv_scale_name(name, params_dict)\n+ if name is None:\n+ continue\n+\n+ if is_pp_missing_parameter(name, self):\n+ continue\n+\n+ param = params_dict[name]\n+ weight_loader = getattr(param, \"weight_loader\",\n+ default_weight_loader)\n+ weight_loader(param, loaded_weight)\n+\n+ # If this function is called, it should always initialize KV cache scale\n+ # factors (or else raise an exception). Thus, handled exceptions should\n+ # make sure to leave KV cache scale factors in a known good (dummy) state\n+ def load_kv_cache_scales(self, quantization_param_path: str) -> None:\n+ tp_size = get_tensor_model_parallel_world_size()\n+ tp_rank = get_tensor_model_parallel_rank()\n+ for layer_idx, scaling_factor in kv_cache_scales_loader(\n+ quantization_param_path,\n+ tp_rank,\n+ tp_size,\n+ self.config.num_hidden_layers,\n+ self.config.__class__.model_type,\n+ ):\n+ if not isinstance(self.model.layers[layer_idx], nn.Identity):\n+ layer_self_attn = self.model.layers[layer_idx].self_attn\n+\n+ if is_hip():\n+ # The scaling factor convention we are assuming is\n+ # quantized_value * scaling_factor ~= true_value\n+ # which is consistent with the practice of setting\n+ # scaling_factor = tensor_amax / FPtype_max\n+ scaling_factor *= 2\n+ if hasattr(layer_self_attn, \"kv_scale\"):\n+ layer_self_attn.attn._kv_scale = scaling_factor\n+ else:\n+ raise RuntimeError(\"Self attention has no KV cache scaling \"\n+ \"factor attribute!\")\n" }
[ { "diff_hunk": "@@ -0,0 +1,780 @@\n+\"\"\"Implementation of SiglipVisionModel intended to be only used\n+within a vision language model.\"\"\"\n+\n+import math\n+from typing import Iterable, List, Optional, Tuple\n+\n+import torch\n+from PIL import Image\n+from torch import nn\n+from transformers import SiglipConfig, SiglipVisionConfig\n+from vllm_flash_attn import flash_attn_func\n+from xformers.ops import memory_efficient_attention\n+\n+from vllm.attention import Attention, AttentionMetadata\n+from vllm.config import CacheConfig, ModelConfig\n+from vllm.distributed import (get_tensor_model_parallel_rank,\n+ get_tensor_model_parallel_world_size)\n+from vllm.inputs import LLMInputs\n+from vllm.model_executor.layers.activation import get_act_fn\n+from vllm.model_executor.layers.linear import (ColumnParallelLinear,\n+ QKVParallelLinear,\n+ RowParallelLinear)\n+from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (\n+ get_compressed_tensors_cache_scale)\n+from vllm.model_executor.layers.vocab_parallel_embedding import (\n+ VocabParallelEmbedding)\n+from vllm.model_executor.model_loader.weight_utils import (\n+ default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)\n+from vllm.multimodal.image import (cached_get_tokenizer,\n+ repeat_and_pad_image_tokens)\n+from vllm.sequence import SequenceData\n+from vllm.utils import is_hip\n+\n+from .utils import is_pp_missing_parameter\n+\n+\n+def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:\n+ assert image_size % patch_size == 0\n+ return image_size // patch_size\n+\n+\n+def get_siglip_num_patches(*, image_size: int, patch_size: int) -> int:\n+ grid_length = get_siglip_patch_grid_length(image_size=image_size,\n+ patch_size=patch_size)\n+ return grid_length * grid_length\n+\n+\n+def get_siglip_image_feature_size(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_num_patches(image_size=hf_config.image_size,\n+ patch_size=hf_config.patch_size)\n+\n+\n+def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_image_feature_size(hf_config)\n+\n+\n+def dummy_seq_data_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ seq_len: int,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ token_ids = [image_token_id] * image_feature_size\n+ token_ids += [0] * (seq_len - image_feature_size)\n+ return SequenceData(token_ids)\n+\n+\n+def dummy_image_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ *,\n+ image_width_override: Optional[int] = None,\n+ image_height_override: Optional[int] = None,\n+):\n+ width = height = hf_config.image_size\n+ if image_width_override is not None:\n+ width = image_width_override\n+ if image_height_override is not None:\n+ height = image_height_override\n+\n+ image = Image.new(\"RGB\", (width, height), color=0)\n+ return {\"image\": image}\n+\n+\n+def input_processor_for_siglip(\n+ model_config: ModelConfig,\n+ hf_config: SiglipVisionConfig,\n+ llm_inputs: LLMInputs,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ multi_modal_data = llm_inputs.get(\"multi_modal_data\")\n+ if multi_modal_data is None or \"image\" not in multi_modal_data:\n+ return llm_inputs\n+\n+ tokenizer = cached_get_tokenizer(model_config.tokenizer)\n+\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ new_prompt, new_token_ids = repeat_and_pad_image_tokens(\n+ tokenizer,\n+ llm_inputs.get(\"prompt\"),\n+ llm_inputs[\"prompt_token_ids\"],\n+ image_token_id=image_token_id,\n+ repeat_count=image_feature_size,\n+ )\n+\n+ # NOTE: Create a defensive copy of the original inputs\n+ return LLMInputs(\n+ prompt_token_ids=new_token_ids,\n+ prompt=new_prompt,\n+ multi_modal_data=multi_modal_data,\n+ )\n+\n+\n+# Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa\n+class SiglipVisionEmbeddings(nn.Module):\n+\n+ def __init__(self, config: SiglipVisionConfig):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+ self.image_size = config.image_size\n+ self.patch_size = config.patch_size\n+\n+ self.patch_embedding = nn.Conv2d(\n+ in_channels=config.num_channels,\n+ out_channels=self.embed_dim,\n+ kernel_size=self.patch_size,\n+ stride=self.patch_size,\n+ padding=\"valid\",\n+ )\n+\n+ self.num_patches = (self.image_size // self.patch_size)**2\n+ self.num_positions = self.num_patches\n+ self.position_embedding = VocabParallelEmbedding(\n+ self.num_positions, self.embed_dim)\n+ self.register_buffer(\n+ \"position_ids\",\n+ torch.arange(self.num_positions, dtype=torch.int64).expand(\n+ (1, -1)),\n+ persistent=False,\n+ )\n+\n+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int,\n+ width: int) -> torch.Tensor:\n+ \"\"\"\n+ This method is an adapted method for SigLIP (due to SigLIP not having\n+ class embedding unlike other ViTs) that allows the model to interpolate\n+ the pre-trained position encodings such that it can be usable on higher\n+ resolution images.\n+\n+ Source:\n+ https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174\n+ \"\"\"\n+ position_embeddings = self.position_embedding.weight.unsqueeze(0)\n+ num_patches = embeddings.shape[1]\n+ num_positions = position_embeddings.shape[1]\n+ if num_patches == num_positions and height == width:\n+ return position_embeddings\n+\n+ dim = embeddings.shape[-1]\n+ height = height // self.patch_size\n+ width = width // self.patch_size\n+ # we add a small number to avoid floating point error\n+ # in the interpolation\n+ # see discussion at https://github.com/facebookresearch/dino/issues/8\n+ height, width = height + 0.1, width + 0.1\n+\n+ patch_pos_embed = position_embeddings.reshape(\n+ 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)),\n+ dim)\n+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)\n+ patch_pos_embed = nn.functional.interpolate(\n+ patch_pos_embed,\n+ scale_factor=(\n+ height / math.sqrt(num_positions),\n+ width / math.sqrt(num_positions),\n+ ),\n+ mode=\"bicubic\",\n+ align_corners=False,\n+ )\n+ if (int(height) != patch_pos_embed.shape[-2]\n+ or int(width) != patch_pos_embed.shape[-1]):\n+ raise ValueError(\"Width or height does not match with \"\n+ \"the interpolated position embeddings\")\n+\n+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)\n+ return patch_pos_embed\n+\n+ def forward(self,\n+ pixel_values: torch.FloatTensor,\n+ interpolate_pos_encoding=False) -> torch.Tensor:\n+ _, _, height, width = pixel_values.shape\n+ target_dtype = self.patch_embedding.weight.dtype\n+ patch_embeds = self.patch_embedding(pixel_values.to(\n+ dtype=target_dtype)) # shape = [*, width, grid, grid]\n+ embeddings = patch_embeds.flatten(2).transpose(1, 2)\n+\n+ if interpolate_pos_encoding:\n+ embeddings = embeddings + self.interpolate_pos_encoding(\n+ embeddings, height, width)\n+ else:\n+ embeddings = embeddings + self.position_embedding(\n+ self.position_ids)\n+ return embeddings\n+\n+\n+class SiglipAttention(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+\n+ tp_size = get_tensor_model_parallel_world_size()\n+ self.total_num_heads = config.num_attention_heads\n+ if self.total_num_heads % tp_size != 0:\n+ raise ValueError(\n+ f\"Number of attention heads ({self.total_num_heads}) \"\n+ \"must be divisible by the tensor model parallel size\"\n+ f\" ({tp_size}).\")\n+\n+ self.num_heads = self.total_num_heads // tp_size\n+ self.head_dim = self.embed_dim // self.total_num_heads\n+ if self.head_dim * self.total_num_heads != self.embed_dim:\n+ raise ValueError(f\"embed_dim must be divisible by num_heads (got \"\n+ \"`embed_dim`: {self.embed_dim} and `num_heads`:\"\n+ f\" {self.num_heads}).\")\n+ self.qkv_size = self.num_heads * self.head_dim\n+ self.scale = self.head_dim**-0.5\n+ self.dropout = config.attention_dropout\n+\n+ self.qkv_proj = QKVParallelLinear(\n+ hidden_size=self.embed_dim,\n+ head_size=self.head_dim,\n+ total_num_heads=self.total_num_heads,\n+ quant_config=quant_config,\n+ )\n+ self.out_proj = RowParallelLinear(\n+ input_size=self.embed_dim,\n+ output_size=self.embed_dim,\n+ quant_config=quant_config,\n+ )\n+\n+ try:\n+ self.attn = Attention(\n+ self.num_heads,\n+ self.head_dim,\n+ self.scale,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )\n+ self.attn_fn = self._vllm_attention_forward\n+ except Exception:\n+ # For some pretrained Siglip models, the backend is not supported\n+ # (e.g. google/siglip-so400m-patch14-384 has hidden_size=1152\n+ # with num_attention_heads=16, which is not supported)\n+ # If the backend is not supported, fall back to the default\n+ self.attn_fn = self._basic_attention_forward\n+\n+ def forward(\n+ self,\n+ hidden_states: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> torch.Tensor:\n+ \"\"\"Input shape: Batch x Time x Channel\"\"\"\n+ batch_size, q_len, _ = hidden_states.size()\n+\n+ qkv_states, _ = self.qkv_proj(hidden_states)\n+ query_states, key_states, value_states = qkv_states.split(\n+ [self.qkv_size] * 3, dim=-1)\n+\n+ attn_output = self.attn_fn(\n+ q=query_states,\n+ k=key_states,\n+ v=value_states,\n+ kv_caches=kv_caches,\n+ attn_metadata=attn_metadata,\n+ batch_size=batch_size,\n+ q_len=q_len,\n+ )\n+\n+ attn_output, _ = self.out_proj(attn_output)\n+ return attn_output\n+\n+ def _vllm_attention_forward(self, q, k, v, kv_caches, attn_metadata, *args,\n+ **kwargs):\n+ return self.attn(q, k, v, kv_caches, attn_metadata)\n+\n+ def _basic_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ k = k.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ v = v.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+\n+ k_v_seq_len = k.shape[-2]\n+ attn_weights = torch.matmul(q, k.transpose(2, 3)) * self.scale\n+\n+ if attn_weights.size() != (\n+ batch_size,\n+ self.num_heads,\n+ q_len,\n+ k_v_seq_len,\n+ ):\n+ raise ValueError(\n+ \"Attention weights should be of size \"\n+ f\"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is\"\n+ f\" {attn_weights.size()}\")\n+\n+ # upcast attention to fp32\n+ attn_weights = nn.functional.softmax(attn_weights,\n+ dim=-1,\n+ dtype=torch.float32).to(q.dtype)\n+ attn_weights = nn.functional.dropout(attn_weights,\n+ p=self.dropout,\n+ training=self.training)\n+ attn_output = torch.matmul(attn_weights, v)\n+\n+ if attn_output.size() != (\n+ batch_size,\n+ self.num_heads,\n+ q_len,\n+ self.head_dim,\n+ ):\n+ raise ValueError(\n+ \"`attn_output` should be of size \"\n+ f\"{(batch_size, self.num_heads, q_len, self.head_dim)}, but is\"\n+ f\" {attn_output.size()}\")\n+\n+ attn_output = attn_output.transpose(1, 2).contiguous()\n+ attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)\n+\n+ return attn_output\n+\n+\n+# TODO(ChristopherCho): flash_attn_func is not working properly.\n+# It constantly throws a CUDA error.\n+class SiglipFlashAttention2(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.attn_fn = self._flash_attention_forward\n+\n+ # Ported from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L449\n+ # and https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/modeling_flash_attention_utils.py#L133\n+ def _flash_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ \"\"\"Implements the multihead softmax attention.\n+ Arguments\n+ ---------\n+ q, k, v: The tensor containing the\n+ query, key, and value. (B, S, H, D)\n+ \"\"\"\n+\n+ q = q.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ k = k.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ v = v.view(batch_size, q_len, self.num_heads, self.head_dim)\n+\n+ attn_output = flash_attn_func(\n+ q,\n+ k,\n+ v,\n+ dropout_p=0.0,\n+ causal=False,\n+ )\n+\n+ attn_output = attn_output.reshape(batch_size, q_len,\n+ self.embed_dim).contiguous()\n+\n+ return attn_output\n+\n+\n+class SiglipSdpaAttention(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.is_causal = False\n+ self.attn_fn = self._sdpa_attention_forward\n+\n+ def _sdpa_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ k = k.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+ v = v.view(batch_size, q_len, self.num_heads,\n+ self.head_dim).transpose(1, 2)\n+\n+ attn_output = torch.nn.functional.scaled_dot_product_attention(\n+ q, k, v, dropout_p=0.0, is_causal=False, scale=self.scale)\n+\n+ attn_output = attn_output.transpose(1, 2).contiguous()\n+ attn_output = attn_output.view(batch_size, q_len, self.embed_dim)\n+\n+ return attn_output\n+\n+\n+class SiglipxFormersAttention(SiglipAttention):\n+\n+ def __init__(self, *args, **kwargs):\n+ super().__init__(*args, **kwargs)\n+ self.attn_fn = self._xformers_attention_forward\n+\n+ def _xformers_attention_forward(self, q, k, v, batch_size, q_len, *args,\n+ **kwargs):\n+ q = q.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ k = k.view(batch_size, q_len, self.num_heads, self.head_dim)\n+ v = v.view(batch_size, q_len, self.num_heads, self.head_dim)\n+\n+ attn_output = memory_efficient_attention(q,\n+ k,\n+ v,\n+ p=0.0,\n+ scale=self.scale)\n+ attn_output = attn_output.reshape(batch_size, q_len,\n+ self.embed_dim).contiguous()\n+\n+ return attn_output\n+\n+\n+SIGLIP_ATTENTION_CLASSES = {\n+ \"eager\": SiglipAttention,\n+ \"flash_attention_2\": SiglipFlashAttention2,\n+ \"sdpa\": SiglipSdpaAttention,\n+ \"xformers\": SiglipxFormersAttention,\n+}\n+\n+\n+class SiglipMLP(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.activation_fn = get_act_fn(config.hidden_act)\n+\n+ # For quantization, we require the hidden size to be a multiple of 64\n+ quantizable = (config.hidden_size % 64 == 0\n+ and config.intermediate_size % 64 == 0)\n+ self.fc1 = ColumnParallelLinear(\n+ config.hidden_size,\n+ config.intermediate_size,\n+ quant_config=quant_config if quantizable else None,\n+ )\n+ self.fc2 = RowParallelLinear(\n+ config.intermediate_size,\n+ config.hidden_size,\n+ quant_config=quant_config if quantizable else None,\n+ )\n+\n+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n+ hidden_states, _ = self.fc1(hidden_states)\n+ hidden_states = self.activation_fn(hidden_states)\n+ hidden_states, _ = self.fc2(hidden_states)\n+ return hidden_states\n+\n+\n+class SiglipEncoderLayer(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config: SiglipConfig,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.embed_dim = config.hidden_size\n+\n+ self.self_attn = SIGLIP_ATTENTION_CLASSES[config._attn_implementation](\n+ config,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )\n+ self.layer_norm1 = nn.LayerNorm(self.embed_dim,\n+ eps=config.layer_norm_eps)\n+ self.mlp = SiglipMLP(\n+ config,\n+ quant_config=quant_config,\n+ )\n+ self.layer_norm2 = nn.LayerNorm(self.embed_dim,\n+ eps=config.layer_norm_eps)\n+\n+ def forward(\n+ self,\n+ hidden_states: torch.Tensor,\n+ kv_caches: List[torch.Tensor],\n+ attn_metadata: AttentionMetadata,\n+ ) -> Tuple[torch.FloatTensor]:\n+ \"\"\"\n+ Args:\n+ hidden_states (`torch.FloatTensor`):\n+ Input to the layer of shape `(batch, seq_len, embed_dim)`.\n+ attention_mask (`torch.FloatTensor`):\n+ Attention mask of shape `(batch, 1, q_len, k_v_seq_len)`\n+ where padding elements are indicated by very large negative\n+ values.\n+ output_attentions (`bool`, *optional*, defaults to `False`):\n+ Whether or not to return the attentions tensors of all attention\n+ layers. See `attentions` under returned tensors for more detail.\n+ \"\"\"", "line": null, "original_line": 523, "original_start_line": 512, "path": "vllm/model_executor/models/siglip.py", "start_line": null, "text": "@user1:\n```suggestion\r\n```\r\nPlease delete these - I believe you copied the function signature from transformers but forgot to remove them." }, { "diff_hunk": "@@ -308,9 +302,23 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):\n use_default_weight_loading = False\n if \"vision\" in name:\n if self.vision_tower is not None:\n- # We only do sharding for language model and\n- # not vision model for now.\n- use_default_weight_loading = True", "line": null, "original_line": 310, "original_start_line": 311, "path": "vllm/model_executor/models/paligemma.py", "start_line": null, "text": "@user1:\nPlease revert this for now - if we're going to apply TP on the vision tower, we should do it in another separate PR with `CLIPVisionModel` together. Ideally, we should not apply a infrastructure change to only one model.\n\n@author:\nReverted via dee55d0" }, { "diff_hunk": "@@ -0,0 +1,780 @@\n+\"\"\"Implementation of SiglipVisionModel intended to be only used\n+within a vision language model.\"\"\"\n+\n+import math\n+from typing import Iterable, List, Optional, Tuple\n+\n+import torch\n+from PIL import Image\n+from torch import nn\n+from transformers import SiglipConfig, SiglipVisionConfig\n+from vllm_flash_attn import flash_attn_func\n+from xformers.ops import memory_efficient_attention\n+\n+from vllm.attention import Attention, AttentionMetadata\n+from vllm.config import CacheConfig, ModelConfig\n+from vllm.distributed import (get_tensor_model_parallel_rank,\n+ get_tensor_model_parallel_world_size)\n+from vllm.inputs import LLMInputs\n+from vllm.model_executor.layers.activation import get_act_fn\n+from vllm.model_executor.layers.linear import (ColumnParallelLinear,\n+ QKVParallelLinear,\n+ RowParallelLinear)\n+from vllm.model_executor.layers.quantization import QuantizationConfig\n+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (\n+ get_compressed_tensors_cache_scale)\n+from vllm.model_executor.layers.vocab_parallel_embedding import (\n+ VocabParallelEmbedding)\n+from vllm.model_executor.model_loader.weight_utils import (\n+ default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)\n+from vllm.multimodal.image import (cached_get_tokenizer,\n+ repeat_and_pad_image_tokens)\n+from vllm.sequence import SequenceData\n+from vllm.utils import is_hip\n+\n+from .utils import is_pp_missing_parameter\n+\n+\n+def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:\n+ assert image_size % patch_size == 0\n+ return image_size // patch_size\n+\n+\n+def get_siglip_num_patches(*, image_size: int, patch_size: int) -> int:\n+ grid_length = get_siglip_patch_grid_length(image_size=image_size,\n+ patch_size=patch_size)\n+ return grid_length * grid_length\n+\n+\n+def get_siglip_image_feature_size(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_num_patches(image_size=hf_config.image_size,\n+ patch_size=hf_config.patch_size)\n+\n+\n+def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int:\n+ return get_siglip_image_feature_size(hf_config)\n+\n+\n+def dummy_seq_data_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ seq_len: int,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ token_ids = [image_token_id] * image_feature_size\n+ token_ids += [0] * (seq_len - image_feature_size)\n+ return SequenceData(token_ids)\n+\n+\n+def dummy_image_for_siglip(\n+ hf_config: SiglipVisionConfig,\n+ *,\n+ image_width_override: Optional[int] = None,\n+ image_height_override: Optional[int] = None,\n+):\n+ width = height = hf_config.image_size\n+ if image_width_override is not None:\n+ width = image_width_override\n+ if image_height_override is not None:\n+ height = image_height_override\n+\n+ image = Image.new(\"RGB\", (width, height), color=0)\n+ return {\"image\": image}\n+\n+\n+def input_processor_for_siglip(\n+ model_config: ModelConfig,\n+ hf_config: SiglipVisionConfig,\n+ llm_inputs: LLMInputs,\n+ *,\n+ image_token_id: int,\n+ image_feature_size_override: Optional[int] = None,\n+):\n+ multi_modal_data = llm_inputs.get(\"multi_modal_data\")\n+ if multi_modal_data is None or \"image\" not in multi_modal_data:\n+ return llm_inputs\n+\n+ tokenizer = cached_get_tokenizer(model_config.tokenizer)\n+\n+ if image_feature_size_override is None:\n+ image_feature_size = get_siglip_image_feature_size(hf_config)\n+ else:\n+ image_feature_size = image_feature_size_override\n+\n+ new_prompt, new_token_ids = repeat_and_pad_image_tokens(\n+ tokenizer,\n+ llm_inputs.get(\"prompt\"),\n+ llm_inputs[\"prompt_token_ids\"],\n+ image_token_id=image_token_id,\n+ repeat_count=image_feature_size,\n+ )\n+\n+ # NOTE: Create a defensive copy of the original inputs\n+ return LLMInputs(\n+ prompt_token_ids=new_token_ids,\n+ prompt=new_prompt,\n+ multi_modal_data=multi_modal_data,\n+ )\n+\n+\n+# Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa\n+class SiglipVisionEmbeddings(nn.Module):\n+\n+ def __init__(self, config: SiglipVisionConfig):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+ self.image_size = config.image_size\n+ self.patch_size = config.patch_size\n+\n+ self.patch_embedding = nn.Conv2d(\n+ in_channels=config.num_channels,\n+ out_channels=self.embed_dim,\n+ kernel_size=self.patch_size,\n+ stride=self.patch_size,\n+ padding=\"valid\",\n+ )\n+\n+ self.num_patches = (self.image_size // self.patch_size)**2\n+ self.num_positions = self.num_patches\n+ self.position_embedding = VocabParallelEmbedding(\n+ self.num_positions, self.embed_dim)\n+ self.register_buffer(\n+ \"position_ids\",\n+ torch.arange(self.num_positions, dtype=torch.int64).expand(\n+ (1, -1)),\n+ persistent=False,\n+ )\n+\n+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int,\n+ width: int) -> torch.Tensor:\n+ \"\"\"\n+ This method is an adapted method for SigLIP (due to SigLIP not having\n+ class embedding unlike other ViTs) that allows the model to interpolate\n+ the pre-trained position encodings such that it can be usable on higher\n+ resolution images.\n+\n+ Source:\n+ https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174\n+ \"\"\"\n+ position_embeddings = self.position_embedding.weight.unsqueeze(0)\n+ num_patches = embeddings.shape[1]\n+ num_positions = position_embeddings.shape[1]\n+ if num_patches == num_positions and height == width:\n+ return position_embeddings\n+\n+ dim = embeddings.shape[-1]\n+ height = height // self.patch_size\n+ width = width // self.patch_size\n+ # we add a small number to avoid floating point error\n+ # in the interpolation\n+ # see discussion at https://github.com/facebookresearch/dino/issues/8\n+ height, width = height + 0.1, width + 0.1\n+\n+ patch_pos_embed = position_embeddings.reshape(\n+ 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)),\n+ dim)\n+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)\n+ patch_pos_embed = nn.functional.interpolate(\n+ patch_pos_embed,\n+ scale_factor=(\n+ height / math.sqrt(num_positions),\n+ width / math.sqrt(num_positions),\n+ ),\n+ mode=\"bicubic\",\n+ align_corners=False,\n+ )\n+ if (int(height) != patch_pos_embed.shape[-2]\n+ or int(width) != patch_pos_embed.shape[-1]):\n+ raise ValueError(\"Width or height does not match with \"\n+ \"the interpolated position embeddings\")\n+\n+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)\n+ return patch_pos_embed\n+\n+ def forward(self,\n+ pixel_values: torch.FloatTensor,\n+ interpolate_pos_encoding=False) -> torch.Tensor:\n+ _, _, height, width = pixel_values.shape\n+ target_dtype = self.patch_embedding.weight.dtype\n+ patch_embeds = self.patch_embedding(pixel_values.to(\n+ dtype=target_dtype)) # shape = [*, width, grid, grid]\n+ embeddings = patch_embeds.flatten(2).transpose(1, 2)\n+\n+ if interpolate_pos_encoding:\n+ embeddings = embeddings + self.interpolate_pos_encoding(\n+ embeddings, height, width)\n+ else:\n+ embeddings = embeddings + self.position_embedding(\n+ self.position_ids)\n+ return embeddings\n+\n+\n+class SiglipAttention(nn.Module):\n+\n+ def __init__(\n+ self,\n+ config,\n+ cache_config: Optional[CacheConfig] = None,\n+ quant_config: Optional[QuantizationConfig] = None,\n+ ):\n+ super().__init__()\n+ self.config = config\n+ self.embed_dim = config.hidden_size\n+\n+ tp_size = get_tensor_model_parallel_world_size()\n+ self.total_num_heads = config.num_attention_heads\n+ if self.total_num_heads % tp_size != 0:\n+ raise ValueError(\n+ f\"Number of attention heads ({self.total_num_heads}) \"\n+ \"must be divisible by the tensor model parallel size\"\n+ f\" ({tp_size}).\")\n+\n+ self.num_heads = self.total_num_heads // tp_size\n+ self.head_dim = self.embed_dim // self.total_num_heads\n+ if self.head_dim * self.total_num_heads != self.embed_dim:\n+ raise ValueError(f\"embed_dim must be divisible by num_heads (got \"\n+ \"`embed_dim`: {self.embed_dim} and `num_heads`:\"\n+ f\" {self.num_heads}).\")\n+ self.qkv_size = self.num_heads * self.head_dim\n+ self.scale = self.head_dim**-0.5\n+ self.dropout = config.attention_dropout\n+\n+ self.qkv_proj = QKVParallelLinear(\n+ hidden_size=self.embed_dim,\n+ head_size=self.head_dim,\n+ total_num_heads=self.total_num_heads,\n+ quant_config=quant_config,\n+ )\n+ self.out_proj = RowParallelLinear(\n+ input_size=self.embed_dim,\n+ output_size=self.embed_dim,\n+ quant_config=quant_config,\n+ )\n+\n+ try:\n+ self.attn = Attention(\n+ self.num_heads,\n+ self.head_dim,\n+ self.scale,\n+ cache_config=cache_config,\n+ quant_config=quant_config,\n+ )", "line": null, "original_line": 268, "original_start_line": 262, "path": "vllm/model_executor/models/siglip.py", "start_line": null, "text": "@user1:\nCurrently for `ClipVisionModel`, we don't use the vLLM internal `Attention` since the ViT encoder only runs once at the prefill time per sequence, thus I don't think there's much value leveraging a KV cache for this.\r\n\r\nHave you seen a significant performance speedup using vLLM `Attention` compared to `transformers` Attention? If not, I think we'd rather just use the one from `transformers` for simplicity for now since this is not the major bottleneck in the whole inference pipeline.\n\n@author:\nIndeed, there weren't significant improvements by using vLLM Attention.\r\nI believe it is due to the reason that you mentioned. It does not leverage the advantages of using a KV cache.\r\nI removed the vLLM Attention part, but keep the log at [bb570c3](https://github.com/vllm-project/vllm/pull/6942/commits/bb570c319fba0441e3e5e2682ee579b89f3ac167) for the future." } ]
54faf5d36e2a8da7c270114d8ca5ba781cdf6481
diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 846246a2062a..ce9dc9e457c0 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -65,7 +65,8 @@ def run_phi3v(question): # PaliGemma def run_paligemma(question): - prompt = question + # PaliGemma has special prompt format for VQA + prompt = "caption en" llm = LLM(model="google/paligemma-3b-mix-224") return llm, prompt diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index fe91611cd30f..9ba53b8b59a2 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -1,9 +1,8 @@ from typing import Iterable, List, Literal, Optional, Tuple, TypedDict import torch -from PIL import Image from torch import nn -from transformers import PaliGemmaConfig, SiglipVisionConfig, SiglipVisionModel +from transformers import PaliGemmaConfig from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig @@ -18,9 +17,11 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.image import cached_get_tokenizer -from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData +from vllm.sequence import IntermediateTensors, SamplerOutput from .interfaces import SupportsVision +from .siglip import (SiglipVisionModel, dummy_image_for_siglip, + dummy_seq_data_for_siglip, get_max_siglip_image_tokens) from .utils import merge_vision_embeddings logger = init_logger(__name__) @@ -32,55 +33,22 @@ def get_max_paligemma_image_tokens(ctx: InputContext): hf_config = ctx.get_hf_config(PaliGemmaConfig) - text_config = hf_config.text_config - - return text_config.num_image_tokens - - -def dummy_seq_data_for_paligemma( - hf_config: PaliGemmaConfig, - seq_len: int, - *, - image_token_id: int, - image_feature_size_override: Optional[int] = None, -): - if image_feature_size_override is None: - image_feature_size = hf_config.text_config.num_image_tokens - else: - image_feature_size = image_feature_size_override - - token_ids = [image_token_id] * image_feature_size - token_ids += [0] * (seq_len - image_feature_size) - return SequenceData(token_ids) - - -def dummy_image_for_paligemma( - hf_config: SiglipVisionConfig, - *, - image_width_override: Optional[int] = None, - image_height_override: Optional[int] = None, -): - width = height = hf_config.image_size - if image_width_override is not None: - width = image_width_override - if image_height_override is not None: - height = image_height_override + vision_config = hf_config.vision_config - image = Image.new("RGB", (width, height), color=0) - return {"image": image} + return get_max_siglip_image_tokens(vision_config) def dummy_data_for_paligemma(ctx: InputContext, seq_len: int): hf_config = ctx.get_hf_config(PaliGemmaConfig) vision_config = hf_config.vision_config - seq_data = dummy_seq_data_for_paligemma( - hf_config, + seq_data = dummy_seq_data_for_siglip( + vision_config, seq_len, image_token_id=hf_config.image_token_index, ) - mm_data = dummy_image_for_paligemma(vision_config) + mm_data = dummy_image_for_siglip(vision_config) return seq_data, mm_data @@ -208,30 +176,37 @@ def _parse_and_validate_image_input( data=self._validate_pixel_values(pixel_values), ) - def _image_pixels_to_features(self, vision_tower: SiglipVisionModel, - pixel_values: torch.Tensor) -> torch.Tensor: + def _image_pixels_to_features( + self, + vision_tower: SiglipVisionModel, + pixel_values: torch.Tensor, + ) -> torch.Tensor: target_dtype = vision_tower.get_input_embeddings().weight.dtype - image_outputs = vision_tower(pixel_values.to(dtype=target_dtype), - output_hidden_states=True) - - selected_image_features = image_outputs.last_hidden_state + image_features = vision_tower(pixel_values.to(dtype=target_dtype)) - return selected_image_features + return image_features def _process_image_pixels( - self, inputs: PaliGemmaImagePixelInputs) -> torch.Tensor: + self, + inputs: PaliGemmaImagePixelInputs, + ) -> torch.Tensor: assert self.vision_tower is not None pixel_values = inputs["data"] - return self._image_pixels_to_features(self.vision_tower, pixel_values) + return self._image_pixels_to_features( + self.vision_tower, + pixel_values, + ) def _process_image_input( - self, image_input: PaliGemmaImageInputs) -> torch.Tensor: + self, + image_input: PaliGemmaImageInputs, + ) -> torch.Tensor: assert self.vision_tower is not None - image_features = self._process_image_pixels(image_input) + image_features = self._process_image_pixels(image_input, ) return self.multi_modal_projector(image_features) diff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py new file mode 100644 index 000000000000..6faef45c9a6d --- /dev/null +++ b/vllm/model_executor/models/siglip.py @@ -0,0 +1,621 @@ +"""Implementation of SiglipVisionModel intended to be only used +within a vision language model.""" + +import math +from typing import Optional, Tuple + +import torch +from PIL import Image +from torch import nn +from transformers import SiglipConfig, SiglipVisionConfig +from transformers.models.siglip.modeling_siglip import SiglipAttention +from vllm_flash_attn import flash_attn_func +from xformers.ops import memory_efficient_attention + +from vllm.config import ModelConfig +from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.inputs import LLMInputs +from vllm.model_executor.layers.activation import get_act_fn +from vllm.model_executor.layers.linear import (ColumnParallelLinear, + QKVParallelLinear, + RowParallelLinear) +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.vocab_parallel_embedding import ( + VocabParallelEmbedding) +from vllm.multimodal.image import (cached_get_tokenizer, + repeat_and_pad_image_tokens) +from vllm.sequence import SequenceData + + +def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int: + assert image_size % patch_size == 0 + return image_size // patch_size + + +def get_siglip_num_patches(*, image_size: int, patch_size: int) -> int: + grid_length = get_siglip_patch_grid_length(image_size=image_size, + patch_size=patch_size) + return grid_length * grid_length + + +def get_siglip_image_feature_size(hf_config: SiglipVisionConfig) -> int: + return get_siglip_num_patches(image_size=hf_config.image_size, + patch_size=hf_config.patch_size) + + +def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int: + return get_siglip_image_feature_size(hf_config) + + +def dummy_seq_data_for_siglip( + hf_config: SiglipVisionConfig, + seq_len: int, + *, + image_token_id: int, + image_feature_size_override: Optional[int] = None, +): + if image_feature_size_override is None: + image_feature_size = get_siglip_image_feature_size(hf_config) + else: + image_feature_size = image_feature_size_override + + token_ids = [image_token_id] * image_feature_size + token_ids += [0] * (seq_len - image_feature_size) + return SequenceData(token_ids) + + +def dummy_image_for_siglip( + hf_config: SiglipVisionConfig, + *, + image_width_override: Optional[int] = None, + image_height_override: Optional[int] = None, +): + width = height = hf_config.image_size + if image_width_override is not None: + width = image_width_override + if image_height_override is not None: + height = image_height_override + + image = Image.new("RGB", (width, height), color=0) + return {"image": image} + + +def input_processor_for_siglip( + model_config: ModelConfig, + hf_config: SiglipVisionConfig, + llm_inputs: LLMInputs, + *, + image_token_id: int, + image_feature_size_override: Optional[int] = None, +): + multi_modal_data = llm_inputs.get("multi_modal_data") + if multi_modal_data is None or "image" not in multi_modal_data: + return llm_inputs + + tokenizer = cached_get_tokenizer(model_config.tokenizer) + + if image_feature_size_override is None: + image_feature_size = get_siglip_image_feature_size(hf_config) + else: + image_feature_size = image_feature_size_override + + new_prompt, new_token_ids = repeat_and_pad_image_tokens( + tokenizer, + llm_inputs.get("prompt"), + llm_inputs["prompt_token_ids"], + image_token_id=image_token_id, + repeat_count=image_feature_size, + ) + + # NOTE: Create a defensive copy of the original inputs + return LLMInputs( + prompt_token_ids=new_token_ids, + prompt=new_prompt, + multi_modal_data=multi_modal_data, + ) + + +# Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa +class SiglipVisionEmbeddings(nn.Module): + + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches = (self.image_size // self.patch_size)**2 + self.num_positions = self.num_patches + self.position_embedding = VocabParallelEmbedding( + self.num_positions, self.embed_dim) + self.register_buffer( + "position_ids", + torch.arange(self.num_positions, dtype=torch.int64).expand( + (1, -1)), + persistent=False, + ) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, + width: int) -> torch.Tensor: + """ + This method is an adapted method for SigLIP (due to SigLIP not having + class embedding unlike other ViTs) that allows the model to interpolate + the pre-trained position encodings such that it can be usable on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + position_embeddings = self.position_embedding.weight.unsqueeze(0) + num_patches = embeddings.shape[1] + num_positions = position_embeddings.shape[1] + if num_patches == num_positions and height == width: + return position_embeddings + + dim = embeddings.shape[-1] + height = height // self.patch_size + width = width // self.patch_size + # we add a small number to avoid floating point error + # in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + height, width = height + 0.1, width + 0.1 + + patch_pos_embed = position_embeddings.reshape( + 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), + dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=( + height / math.sqrt(num_positions), + width / math.sqrt(num_positions), + ), + mode="bicubic", + align_corners=False, + ) + if (int(height) != patch_pos_embed.shape[-2] + or int(width) != patch_pos_embed.shape[-1]): + raise ValueError("Width or height does not match with " + "the interpolated position embeddings") + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + def forward(self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = False) -> torch.Tensor: + _, _, height, width = pixel_values.shape + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to( + dtype=target_dtype)) # shape = [*, width, grid, grid] + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding( + embeddings, height, width) + else: + embeddings = embeddings + self.position_embedding( + self.position_ids) + return embeddings + + +# NOTE: Not used - kept for later when we TP the ViT +# TODO(ChristopherCho): Implement TP version of Attention +class SiglipTPAttention(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = config.num_attention_heads + if self.total_num_heads % tp_size != 0: + raise ValueError( + f"Number of attention heads ({self.total_num_heads}) " + "must be divisible by the tensor model parallel size" + f" ({tp_size}).") + + self.num_heads = self.total_num_heads // tp_size + self.head_dim = self.embed_dim // self.total_num_heads + if self.head_dim * self.total_num_heads != self.embed_dim: + raise ValueError(f"embed_dim must be divisible by num_heads (got " + "`embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads}).") + self.qkv_size = self.num_heads * self.head_dim + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + + self.qkv_proj = QKVParallelLinear( + hidden_size=self.embed_dim, + head_size=self.head_dim, + total_num_heads=self.total_num_heads, + quant_config=quant_config, + ) + self.out_proj = RowParallelLinear( + input_size=self.embed_dim, + output_size=self.embed_dim, + quant_config=quant_config, + ) + + self.attn_fn = self._basic_attention_forward + + def forward( + self, + hidden_states: torch.Tensor, + ) -> torch.Tensor: + """Input shape: Batch x Time x Channel""" + batch_size, q_len, _ = hidden_states.size() + + qkv_states, _ = self.qkv_proj(hidden_states) + query_states, key_states, value_states = qkv_states.split( + [self.qkv_size] * 3, dim=-1) + + attn_output = self.attn_fn( + q=query_states, + k=key_states, + v=value_states, + batch_size=batch_size, + q_len=q_len, + ) + + attn_output, _ = self.out_proj(attn_output) + return attn_output + + def _basic_attention_forward(self, q, k, v, batch_size, q_len): + q = q.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + k = k.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + v = v.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + + k_v_seq_len = k.shape[-2] + attn_weights = torch.matmul(q, k.transpose(2, 3)) * self.scale + + if attn_weights.size() != ( + batch_size, + self.num_heads, + q_len, + k_v_seq_len, + ): + raise ValueError( + "Attention weights should be of size " + f"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" + f" {attn_weights.size()}") + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, + dim=-1, + dtype=torch.float32).to(q.dtype) + attn_weights = nn.functional.dropout(attn_weights, + p=self.dropout, + training=self.training) + attn_output = torch.matmul(attn_weights, v) + + if attn_output.size() != ( + batch_size, + self.num_heads, + q_len, + self.head_dim, + ): + raise ValueError( + "`attn_output` should be of size " + f"{(batch_size, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}") + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) + + return attn_output + + +# NOTE: Not used - kept for later when we TP the ViT +# TODO(ChristopherCho): flash_attn_func is not working properly. +# It constantly throws a CUDA error. +class SiglipFlashAttention2(SiglipTPAttention): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.attn_fn = self._flash_attention_forward + + # Ported from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L449 + # and https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/modeling_flash_attention_utils.py#L133 + def _flash_attention_forward(self, q, k, v, batch_size, q_len, *args, + **kwargs): + """Implements the multihead softmax attention. + Arguments + --------- + q, k, v: The tensor containing the + query, key, and value. (B, S, H, D) + """ + + q = q.view(batch_size, q_len, self.num_heads, self.head_dim) + k = k.view(batch_size, q_len, self.num_heads, self.head_dim) + v = v.view(batch_size, q_len, self.num_heads, self.head_dim) + + attn_output = flash_attn_func( + q, + k, + v, + dropout_p=self.dropout, + causal=False, + ) + + attn_output = attn_output.reshape(batch_size, q_len, + self.embed_dim).contiguous() + + return attn_output + + +# NOTE: Not used - kept for later when we TP the ViT +class SiglipSdpaAttention(SiglipTPAttention): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.is_causal = False + self.attn_fn = self._sdpa_attention_forward + + def _sdpa_attention_forward(self, q, k, v, batch_size, q_len): + q = q.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + k = k.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + v = v.view(batch_size, q_len, self.num_heads, + self.head_dim).transpose(1, 2) + + attn_output = torch.nn.functional.scaled_dot_product_attention( + q, k, v, dropout_p=self.dropout, is_causal=False, scale=self.scale) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(batch_size, q_len, self.embed_dim) + + return attn_output + + +# NOTE: Not used - kept for later when we TP the ViT +class SiglipxFormersAttention(SiglipTPAttention): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.attn_fn = self._xformers_attention_forward + + def _xformers_attention_forward(self, q, k, v, batch_size, q_len): + q = q.view(batch_size, q_len, self.num_heads, self.head_dim) + k = k.view(batch_size, q_len, self.num_heads, self.head_dim) + v = v.view(batch_size, q_len, self.num_heads, self.head_dim) + + attn_output = memory_efficient_attention(q, + k, + v, + p=0.0, + scale=self.scale) + attn_output = attn_output.reshape(batch_size, q_len, + self.embed_dim).contiguous() + + return attn_output + + +# NOTE: Not used - kept for later when we TP the ViT +SIGLIP_ATTENTION_CLASSES = { + "eager": SiglipTPAttention, + "flash_attention_2": SiglipFlashAttention2, + "sdpa": SiglipSdpaAttention, + "xformers": SiglipxFormersAttention, +} + + +class SiglipMLP(nn.Module): + + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.activation_fn = get_act_fn(config.hidden_act) + + # For quantization, we require the hidden size to be a multiple of 64 + quantizable = (config.hidden_size % 64 == 0 + and config.intermediate_size % 64 == 0) + self.fc1 = ColumnParallelLinear( + config.hidden_size, + config.intermediate_size, + quant_config=quant_config if quantizable else None, + ) + self.fc2 = RowParallelLinear( + config.intermediate_size, + config.hidden_size, + quant_config=quant_config if quantizable else None, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states, _ = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states, _ = self.fc2(hidden_states) + return hidden_states + + +class SiglipEncoderLayer(nn.Module): + + def __init__( + self, + config: SiglipConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.embed_dim = config.hidden_size + + # TODO(ChristopherCho): use TP'ed Attention block + self.self_attn = SiglipAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, + eps=config.layer_norm_eps) + self.mlp = SiglipMLP( + config, + quant_config=quant_config, + ) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, + eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + ) -> Tuple[torch.Tensor]: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn(hidden_states=hidden_states) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states, None + + +class SiglipEncoder(nn.Module): + + def __init__( + self, + config: SiglipConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.layers = nn.ModuleList([ + SiglipEncoderLayer( + config, + quant_config=quant_config, + ) for _ in range(config.num_hidden_layers) + ]) + + def forward( + self, + inputs_embeds: torch.Tensor, + ) -> Tuple: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states, _ = encoder_layer(hidden_states) + + return hidden_states + + +class SiglipMultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__( + self, + config: SiglipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + # TODO(ChristopherCho): Implement vLLM version of MultiheadAttention + self.attention = torch.nn.MultiheadAttention( + config.hidden_size, config.num_attention_heads, batch_first=True) + self.layernorm = nn.LayerNorm(config.hidden_size, + eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config=config, quant_config=quant_config) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention(probe, hidden_state, hidden_state)[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +class SiglipVisionTransformer(nn.Module): + + def __init__( + self, + config: SiglipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder( + config, + quant_config=quant_config, + ) + self.post_layernorm = nn.LayerNorm(embed_dim, + eps=config.layer_norm_eps) + self.use_head = (True if not hasattr(config, "vision_use_head") else + config.vision_use_head) + if self.use_head: + self.head = SiglipMultiheadAttentionPoolingHead( + config=config, quant_config=quant_config) + + def forward( + self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = True, + ) -> torch.Tensor: + hidden_states = self.embeddings( + pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + ) + + encoder_outputs = self.encoder(inputs_embeds=hidden_states) + + last_hidden_state = self.post_layernorm(encoder_outputs) + + # TODO: add this back when pooled_output is used in inference + # if self.use_head: + # pooled_output = self.head(last_hidden_state) + + return last_hidden_state + + +class SiglipVisionModel(nn.Module): + config_class = SiglipVisionConfig + main_input_name = "pixel_values" + + def __init__( + self, + config: SiglipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.vision_model = SiglipVisionTransformer( + config, + quant_config, + ) + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + def forward( + self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + return self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + )
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "New Feature Additions" }
Textualize__textual-4964@9e4f4db
Textualize/textual
Python
4,964
fix object leak
Fixes https://github.com/Textualize/textual/issues/4959 This turned out to be quite a rabbit hole. I fixed a class of problems where something was holding on to references to DOM nodes, and preventing garbage collection. One culprit was `count_parameters`. The `@lru_cache` was holding on to a reference chain. I refactored that method to cache in a different way. Caching or cache-like structures were also holding on to references. I've added clearing of these "caches" to make garbage collection more prompt. I've made some references weak references, to prevent potential reference cycles. But the largest part of this work was related to context vars. These weren't being managed well, and it was leaving references around to the app or widgets, which would prevent entire branches from being collected. The context var issue I fixed by writing a context var context manager, which resets the context var. DOMNodes and App have grown a `_context()` which sets and restores the active app and message pump. Incidentally, I think this is the cause of some of the weirdness in tests where tests work in a run but fail in isolation. Errors in managing the context vars could result in an app being shared across tests. Many of the tests had to be updated after the context var changes. For some reason the `pilot` fixture broke. I think because of the way that pytest runs async tests. I'm pretty sure these changes improve shutdown time. Although I haven't profiled.
2024-09-03T16:24:57Z
`clear_panes` and memory leak Hello! Thanks for this wonderful framework, having a blast with it. I seem to have a memory leak with my app. I suspect the problem to be caused by my usage of the `TabbedContent`'s method `clear_panes()`. I have a `TabbedContent` which i want to refresh every time a user provides a file input. The widget contains tabs which reference large tables. I was expecting `clear_panes()`ย to remove all references to the stale tabs, letting the stale tables to the garbage collector. This doesn't seem to be happening. I have reproduced this behavior with the following MRE: ```python from textual.app import App from textual.widgets import Header, TabbedContent, Footer, TabPane from textual import work class MyTabPane(TabPane): def __init__(self): super().__init__("table") self.df = [1] * 100_000_000 class Explorer(App): BINDINGS = [ ("ctrl+o", "add_table", "add table"), ] def __init__(self): super().__init__() def compose(self): yield Header(show_clock=False) yield TabbedContent() yield Footer() @work async def action_add_table(self): tabs = self.query_one(TabbedContent) tabs.loading = True await tabs.clear_panes() await tabs.add_pane(MyTabPane()) tabs.loading = False if __name__ == "__main__": app = Explorer() app.run() ``` Pressing `ctrl+o` repeatedly I can see an increase in the process's memory usage. I am on MacOS Ventura, iterm2. What can I do to solve this problem with my program? Thank you.
Thank you for your issue. Give us a little time to review it. PS. You might want to check the [FAQ](https://textual.textualize.io/FAQ/) if you haven't done so already. This is an automated reply, generated by [FAQtory](https://github.com/willmcgugan/faqtory) Will take a look later. In the meantime you could do `del self.df` in `on_unmount`
[ { "body": "Hello! Thanks for this wonderful framework, having a blast with it.\r\nI seem to have a memory leak with my app. I suspect the problem to be caused by my usage of the `TabbedContent`'s method `clear_panes()`. I have a `TabbedContent` which i want to refresh every time a user provides a file input. The widget contains tabs which reference large tables. I was expecting `clear_panes()`ย to remove all references to the stale tabs, letting the stale tables to the garbage collector. This doesn't seem to be happening. I have reproduced this behavior with the following MRE:\r\n\r\n```python\r\nfrom textual.app import App\r\nfrom textual.widgets import Header, TabbedContent, Footer, TabPane\r\nfrom textual import work\r\n\r\n\r\nclass MyTabPane(TabPane):\r\n def __init__(self):\r\n super().__init__(\"table\")\r\n self.df = [1] * 100_000_000\r\n\r\n\r\nclass Explorer(App):\r\n BINDINGS = [\r\n (\"ctrl+o\", \"add_table\", \"add table\"),\r\n ]\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n def compose(self):\r\n yield Header(show_clock=False)\r\n yield TabbedContent()\r\n yield Footer()\r\n\r\n @work\r\n async def action_add_table(self):\r\n tabs = self.query_one(TabbedContent)\r\n tabs.loading = True\r\n await tabs.clear_panes()\r\n await tabs.add_pane(MyTabPane())\r\n tabs.loading = False\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app = Explorer()\r\n app.run()\r\n```\r\n\r\nPressing `ctrl+o` repeatedly I can see an increase in the process's memory usage. I am on MacOS Ventura, iterm2.\r\nWhat can I do to solve this problem with my program?\r\nThank you.", "number": 4959, "title": "`clear_panes` and memory leak" } ]
ab4fc4b4890c2417cee1085c02a70e1af47cab61
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[\"pytest-cov\"]\n doc = [\"furo\", \"jaraco.packaging (>=9.3)\", \"jaraco.tidelift (>=1.4)\", \"rst.linker (>=1.9)\", \"sphinx (>=3.5)\", \"sphinx-lint\"]\n-test = [\"big-O\", \"importlib-resources\", \"jaraco.functools\", \"jaraco.itertools\", \"jaraco.test\", \"more-itertools\", \"pytest (>=6,!=8.1.*)\", \"pytest-checkdocs (>=2.4)\", \"pytest-cov\", \"pytest-enabler (>=2.2)\", \"pytest-ignore-flaky\", \"pytest-mypy\", \"pytest-ruff (>=0.2.1)\"]\n+enabler = [\"pytest-enabler (>=2.2)\"]\n+test = [\"big-O\", \"importlib-resources\", \"jaraco.functools\", \"jaraco.itertools\", \"jaraco.test\", \"more-itertools\", \"pytest (>=6,!=8.1.*)\", \"pytest-ignore-flaky\"]\n+type = [\"pytest-mypy\"]\n \n [extras]\n syntax = [\"tree-sitter\", \"tree-sitter-languages\"]\ndiff --git a/pyproject.toml b/pyproject.toml\nindex c704952ac4..71a51de9c9 100644\n--- a/pyproject.toml\n+++ b/pyproject.toml\n@@ -83,6 +83,7 @@ addopts = \"--strict-markers\"\n markers = [\n \"syntax: marks tests that require syntax highlighting (deselect with '-m \\\"not syntax\\\"')\",\n ]\n+asyncio_default_fixture_loop_scope = \"function\"\n \n [build-system]\n requires = [\"poetry-core>=1.2.0\"]\ndiff --git a/src/textual/_callback.py b/src/textual/_callback.py\nindex d2fd2403c2..ae808f80a3 100644\n--- a/src/textual/_callback.py\n+++ b/src/textual/_callback.py\n@@ -1,7 +1,7 @@\n from __future__ import annotations\n \n import asyncio\n-from functools import lru_cache, partial\n+from functools import partial\n from inspect import isawaitable, signature\n from typing import TYPE_CHECKING, Any, Callable\n \n@@ -16,16 +16,24 @@\n \n def count_parameters(func: Callable) -> int:\n \"\"\"Count the number of parameters in a callable\"\"\"\n+ try:\n+ return func._param_count\n+ except AttributeError:\n+ pass\n if isinstance(func, partial):\n- return _count_parameters(func.func) + len(func.args)\n- if hasattr(func, \"__self__\"):\n+ param_count = _count_parameters(func.func) - (\n+ len(func.args) + len(func.keywords)\n+ )\n+ elif hasattr(func, \"__self__\"):\n # Bound method\n func = func.__func__ # type: ignore\n- return _count_parameters(func) - 1\n- return _count_parameters(func)\n+ param_count = _count_parameters(func) - 1\n+ else:\n+ param_count = _count_parameters(func)\n+ func._param_count = param_count\n+ return param_count\n \n \n-@lru_cache(maxsize=2048)\n def _count_parameters(func: Callable) -> int:\n \"\"\"Count the number of parameters in a callable\"\"\"\n return len(signature(func).parameters)\ndiff --git a/src/textual/_compositor.py b/src/textual/_compositor.py\nindex f18417d001..0639945b5a 100644\n--- a/src/textual/_compositor.py\n+++ b/src/textual/_compositor.py\n@@ -309,6 +309,15 @@ def __init__(self) -> None:\n # Mapping of line numbers on to lists of widget and regions\n self._layers_visible: list[list[tuple[Widget, Region, Region]]] | None = None\n \n+ def clear(self) -> None:\n+ \"\"\"Remove all references to widgets (used when the screen closes).\"\"\"\n+ self._full_map.clear()\n+ self._visible_map = None\n+ self._layers = None\n+ self.widgets.clear()\n+ self._visible_widgets = None\n+ self._layers_visible = None\n+\n @classmethod\n def _regions_to_spans(\n cls, regions: Iterable[Region]\ndiff --git a/src/textual/_context.py b/src/textual/_context.py\nindex b1b20b4d29..039bb7b4ef 100644\n--- a/src/textual/_context.py\n+++ b/src/textual/_context.py\n@@ -1,7 +1,7 @@\n from __future__ import annotations\n \n from contextvars import ContextVar\n-from typing import TYPE_CHECKING, Callable\n+from typing import TYPE_CHECKING, Any, Callable\n \n if TYPE_CHECKING:\n from .app import App\n@@ -14,8 +14,9 @@ class NoActiveAppError(RuntimeError):\n \"\"\"Runtime error raised if we try to retrieve the active app when there is none.\"\"\"\n \n \n-active_app: ContextVar[\"App[object]\"] = ContextVar(\"active_app\")\n+active_app: ContextVar[\"App[Any]\"] = ContextVar(\"active_app\")\n active_message_pump: ContextVar[\"MessagePump\"] = ContextVar(\"active_message_pump\")\n+\n prevent_message_types_stack: ContextVar[list[set[type[Message]]]] = ContextVar(\n \"prevent_message_types_stack\"\n )\ndiff --git a/src/textual/_node_list.py b/src/textual/_node_list.py\nindex 52555f9d61..abae4c298f 100644\n--- a/src/textual/_node_list.py\n+++ b/src/textual/_node_list.py\n@@ -1,6 +1,7 @@\n from __future__ import annotations\n \n import sys\n+import weakref\n from operator import attrgetter\n from typing import TYPE_CHECKING, Any, Callable, Iterator, Sequence, overload\n \n@@ -26,7 +27,13 @@ class NodeList(Sequence[\"Widget\"]):\n \"\"\"\n \n def __init__(self, parent: DOMNode | None = None) -> None:\n- self._parent = parent\n+ \"\"\"Initialize a node list.\n+\n+ Args:\n+ parent: The parent node which holds a reference to this object, or `None` if\n+ there is no parent.\n+ \"\"\"\n+ self._parent = None if parent is None else weakref.ref(parent)\n # The nodes in the list\n self._nodes: list[Widget] = []\n self._nodes_set: set[Widget] = set()\n@@ -57,7 +64,9 @@ def __contains__(self, widget: object) -> bool:\n def updated(self) -> None:\n \"\"\"Mark the nodes as having been updated.\"\"\"\n self._updates += 1\n- node = self._parent\n+ node = None if self._parent is None else self._parent()\n+ if node is None:\n+ return\n while node is not None and (node := node._parent) is not None:\n node._nodes._updates += 1\n \ndiff --git a/src/textual/app.py b/src/textual/app.py\nindex b721507249..e01f6842cf 100644\n--- a/src/textual/app.py\n+++ b/src/textual/app.py\n@@ -809,6 +809,17 @@ def _end_batch(self) -> None:\n if not self._batch_count:\n self.check_idle()\n \n+ @contextmanager\n+ def _context(self) -> Generator[None, None, None]:\n+ \"\"\"Context manager to set ContextVars.\"\"\"\n+ app_reset_token = active_app.set(self)\n+ message_pump_reset_token = active_message_pump.set(self)\n+ try:\n+ yield\n+ finally:\n+ active_message_pump.reset(message_pump_reset_token)\n+ active_app.reset(app_reset_token)\n+\n def animate(\n self,\n attribute: str,\n@@ -1046,10 +1057,6 @@ def get_default_screen(self) -> Screen:\n \"\"\"\n return Screen(id=\"_default\")\n \n- def _set_active(self) -> None:\n- \"\"\"Set this app to be the currently active app.\"\"\"\n- active_app.set(self)\n-\n def compose(self) -> ComposeResult:\n \"\"\"Yield child widgets for a container.\n \n@@ -1355,8 +1362,8 @@ def call_from_thread(\n \n async def run_callback() -> CallThreadReturnType:\n \"\"\"Run the callback, set the result or error on the future.\"\"\"\n- self._set_active()\n- return await invoke(callback_with_args)\n+ with self._context():\n+ return await invoke(callback_with_args)\n \n # Post the message to the main loop\n future: Future[CallThreadReturnType] = asyncio.run_coroutine_threadsafe(\n@@ -1667,41 +1674,39 @@ async def run_app(app: App) -> None:\n app: App to run.\n \"\"\"\n \n- try:\n- if message_hook is not None:\n- message_hook_context_var.set(message_hook)\n- app._loop = asyncio.get_running_loop()\n- app._thread_id = threading.get_ident()\n- await app._process_messages(\n- ready_callback=on_app_ready,\n- headless=headless,\n- terminal_size=size,\n- )\n- finally:\n- app_ready_event.set()\n+ with app._context():\n+ try:\n+ if message_hook is not None:\n+ message_hook_context_var.set(message_hook)\n+ app._loop = asyncio.get_running_loop()\n+ app._thread_id = threading.get_ident()\n+ await app._process_messages(\n+ ready_callback=on_app_ready,\n+ headless=headless,\n+ terminal_size=size,\n+ )\n+ finally:\n+ app_ready_event.set()\n \n # Launch the app in the \"background\"\n- active_message_pump.set(app)\n+\n app_task = create_task(run_app(app), name=f\"run_test {app}\")\n \n # Wait until the app has performed all startup routines.\n await app_ready_event.wait()\n-\n- # Get the app in an active state.\n- app._set_active()\n-\n- # Context manager returns pilot object to manipulate the app\n- try:\n- pilot = Pilot(app)\n- await pilot._wait_for_screen()\n- yield pilot\n- finally:\n- # Shutdown the app cleanly\n- await app._shutdown()\n- await app_task\n- # Re-raise the exception which caused panic so test frameworks are aware\n- if self._exception:\n- raise self._exception\n+ with app._context():\n+ # Context manager returns pilot object to manipulate the app\n+ try:\n+ pilot = Pilot(app)\n+ await pilot._wait_for_screen()\n+ yield pilot\n+ finally:\n+ # Shutdown the app cleanly\n+ await app._shutdown()\n+ await app_task\n+ # Re-raise the exception which caused panic so test frameworks are aware\n+ if self._exception:\n+ raise self._exception\n \n async def run_async(\n self,\n@@ -1751,14 +1756,14 @@ async def app_ready() -> None:\n async def run_auto_pilot(\n auto_pilot: AutopilotCallbackType, pilot: Pilot\n ) -> None:\n- try:\n- await auto_pilot(pilot)\n- except Exception:\n- app.exit()\n- raise\n+ with self._context():\n+ try:\n+ await auto_pilot(pilot)\n+ except Exception:\n+ app.exit()\n+ raise\n \n pilot = Pilot(app)\n- active_message_pump.set(self)\n auto_pilot_task = create_task(\n run_auto_pilot(auto_pilot, pilot), name=repr(pilot)\n )\n@@ -1816,18 +1821,19 @@ async def run_app() -> None:\n \"\"\"Run the app.\"\"\"\n self._loop = asyncio.get_running_loop()\n self._thread_id = threading.get_ident()\n- try:\n- await self.run_async(\n- headless=headless,\n- inline=inline,\n- inline_no_clear=inline_no_clear,\n- mouse=mouse,\n- size=size,\n- auto_pilot=auto_pilot,\n- )\n- finally:\n- self._loop = None\n- self._thread_id = 0\n+ with self._context():\n+ try:\n+ await self.run_async(\n+ headless=headless,\n+ inline=inline,\n+ inline_no_clear=inline_no_clear,\n+ mouse=mouse,\n+ size=size,\n+ auto_pilot=auto_pilot,\n+ )\n+ finally:\n+ self._loop = None\n+ self._thread_id = 0\n \n if _ASYNCIO_GET_EVENT_LOOP_IS_DEPRECATED:\n # N.B. This doesn't work with Python<3.10, as we end up with 2 event loops:\n@@ -2680,6 +2686,17 @@ def _build_driver(\n )\n return driver\n \n+ async def _init_devtools(self):\n+ \"\"\"Initialize developer tools.\"\"\"\n+ if self.devtools is not None:\n+ from textual_dev.client import DevtoolsConnectionError\n+\n+ try:\n+ await self.devtools.connect()\n+ self.log.system(f\"Connected to devtools ( {self.devtools.url} )\")\n+ except DevtoolsConnectionError:\n+ self.log.system(f\"Couldn't connect to devtools ( {self.devtools.url} )\")\n+\n async def _process_messages(\n self,\n ready_callback: CallbackType | None = None,\n@@ -2690,54 +2707,49 @@ async def _process_messages(\n terminal_size: tuple[int, int] | None = None,\n message_hook: Callable[[Message], None] | None = None,\n ) -> None:\n- self._set_active()\n- active_message_pump.set(self)\n+ async def app_prelude() -> bool:\n+ \"\"\"Work required before running the app.\n \n- if self.devtools is not None:\n- from textual_dev.client import DevtoolsConnectionError\n+ Returns:\n+ `True` if the app should continue, or `False` if there was a problem starting.\n+ \"\"\"\n+ await self._init_devtools()\n+ self.log.system(\"---\")\n+ self.log.system(loop=asyncio.get_running_loop())\n+ self.log.system(features=self.features)\n+ if constants.LOG_FILE is not None:\n+ _log_path = os.path.abspath(constants.LOG_FILE)\n+ self.log.system(f\"Writing logs to {_log_path!r}\")\n \n try:\n- await self.devtools.connect()\n- self.log.system(f\"Connected to devtools ( {self.devtools.url} )\")\n- except DevtoolsConnectionError:\n- self.log.system(f\"Couldn't connect to devtools ( {self.devtools.url} )\")\n-\n- self.log.system(\"---\")\n-\n- self.log.system(loop=asyncio.get_running_loop())\n- self.log.system(features=self.features)\n- if constants.LOG_FILE is not None:\n- _log_path = os.path.abspath(constants.LOG_FILE)\n- self.log.system(f\"Writing logs to {_log_path!r}\")\n-\n- try:\n- if self.css_path:\n- self.stylesheet.read_all(self.css_path)\n- for read_from, css, tie_breaker, scope in self._get_default_css():\n- self.stylesheet.add_source(\n- css,\n- read_from=read_from,\n- is_default_css=True,\n- tie_breaker=tie_breaker,\n- scope=scope,\n- )\n- if self.CSS:\n- try:\n- app_path = inspect.getfile(self.__class__)\n- except (TypeError, OSError):\n- app_path = \"\"\n- read_from = (app_path, f\"{self.__class__.__name__}.CSS\")\n- self.stylesheet.add_source(\n- self.CSS, read_from=read_from, is_default_css=False\n- )\n- except Exception as error:\n- self._handle_exception(error)\n- self._print_error_renderables()\n- return\n+ if self.css_path:\n+ self.stylesheet.read_all(self.css_path)\n+ for read_from, css, tie_breaker, scope in self._get_default_css():\n+ self.stylesheet.add_source(\n+ css,\n+ read_from=read_from,\n+ is_default_css=True,\n+ tie_breaker=tie_breaker,\n+ scope=scope,\n+ )\n+ if self.CSS:\n+ try:\n+ app_path = inspect.getfile(self.__class__)\n+ except (TypeError, OSError):\n+ app_path = \"\"\n+ read_from = (app_path, f\"{self.__class__.__name__}.CSS\")\n+ self.stylesheet.add_source(\n+ self.CSS, read_from=read_from, is_default_css=False\n+ )\n+ except Exception as error:\n+ self._handle_exception(error)\n+ self._print_error_renderables()\n+ return False\n \n- if self.css_monitor:\n- self.set_interval(0.25, self.css_monitor, name=\"css monitor\")\n- self.log.system(\"STARTED\", self.css_monitor)\n+ if self.css_monitor:\n+ self.set_interval(0.25, self.css_monitor, name=\"css monitor\")\n+ self.log.system(\"STARTED\", self.css_monitor)\n+ return True\n \n async def run_process_messages():\n \"\"\"The main message loop, invoke below.\"\"\"\n@@ -2788,40 +2800,45 @@ async def invoke_ready_callback() -> None:\n finally:\n await Timer._stop_all(self._timers)\n \n- self._running = True\n- try:\n- load_event = events.Load()\n- await self._dispatch_message(load_event)\n+ with self._context():\n+ if not await app_prelude():\n+ return\n+ self._running = True\n+ try:\n+ load_event = events.Load()\n+ await self._dispatch_message(load_event)\n \n- driver = self._driver = self._build_driver(\n- headless=headless,\n- inline=inline,\n- mouse=mouse,\n- size=terminal_size,\n- )\n- self.log(driver=driver)\n+ driver = self._driver = self._build_driver(\n+ headless=headless,\n+ inline=inline,\n+ mouse=mouse,\n+ size=terminal_size,\n+ )\n+ self.log(driver=driver)\n \n- if not self._exit:\n- driver.start_application_mode()\n- try:\n- with redirect_stdout(self._capture_stdout):\n- with redirect_stderr(self._capture_stderr):\n- await run_process_messages()\n+ if not self._exit:\n+ driver.start_application_mode()\n+ try:\n+ with redirect_stdout(self._capture_stdout):\n+ with redirect_stderr(self._capture_stderr):\n+ await run_process_messages()\n \n- finally:\n- if self._driver.is_inline:\n- cursor_x, cursor_y = self._previous_cursor_position\n- self._driver.write(\n- Control.move(-cursor_x, -cursor_y + 1).segment.text\n- )\n- if inline_no_clear and not not self.app._exit_renderables:\n- console = Console()\n- console.print(self.screen._compositor)\n- console.print()\n+ finally:\n+ if hasattr(self, \"_watchers\"):\n+ self._watchers.clear()\n+ if self._driver.is_inline:\n+ cursor_x, cursor_y = self._previous_cursor_position\n+ self._driver.write(\n+ Control.move(-cursor_x, -cursor_y + 1).segment.text\n+ )\n+ if inline_no_clear and not not self.app._exit_renderables:\n+ console = Console()\n+ console.print(self.screen._compositor)\n+ console.print()\n \n- driver.stop_application_mode()\n- except Exception as error:\n- self._handle_exception(error)\n+ driver.stop_application_mode()\n+ except Exception as error:\n+ self._handle_exception(error)\n \n async def _pre_process(self) -> bool:\n \"\"\"Special case for the app, which doesn't need the functionality in MessagePump.\"\"\"\n@@ -3029,6 +3046,8 @@ async def _close_all(self) -> None:\n if stack_screen._running:\n await self._prune(stack_screen)\n stack.clear()\n+ self._installed_screens.clear()\n+ self._modes.clear()\n \n # Close any remaining nodes\n # Should be empty by now\n@@ -3045,12 +3064,13 @@ async def _shutdown(self) -> None:\n \n await self._close_all()\n await self._close_messages()\n-\n await self._dispatch_message(events.Unmount())\n \n if self._driver is not None:\n self._driver.close()\n \n+ self._nodes._clear()\n+\n if self.devtools is not None and self.devtools.is_connected:\n await self._disconnect_devtools()\n \ndiff --git a/src/textual/command.py b/src/textual/command.py\nindex 54bc5a4c73..55789e0bb5 100644\n--- a/src/textual/command.py\n+++ b/src/textual/command.py\n@@ -241,6 +241,7 @@ async def _wait_init(self) -> None:\n \"\"\"Wait for initialization.\"\"\"\n if self._init_task is not None:\n await self._init_task\n+ self._init_task = None\n \n async def startup(self) -> None:\n \"\"\"Called after the Provider is initialized, but before any calls to `search`.\"\"\"\ndiff --git a/src/textual/css/_styles_builder.py b/src/textual/css/_styles_builder.py\nindex 3107375a83..96cc8e2b9a 100644\n--- a/src/textual/css/_styles_builder.py\n+++ b/src/textual/css/_styles_builder.py\n@@ -1,7 +1,6 @@\n from __future__ import annotations\n \n-from functools import lru_cache\n-from typing import Iterable, NoReturn, Sequence, cast\n+from typing import Iterable, NoReturn, cast\n \n import rich.repr\n \n@@ -137,19 +136,6 @@ def add_declaration(self, declaration: Declaration) -> None:\n except Exception as error:\n self.error(declaration.name, declaration.token, str(error))\n \n- @lru_cache(maxsize=None)\n- def _get_processable_rule_names(self) -> Sequence[str]:\n- \"\"\"\n- Returns the list of CSS properties we can manage -\n- i.e. the ones for which we have a `process_[property name]` method\n-\n- Returns:\n- All the \"Python-ised\" CSS property names this class can handle.\n-\n- Example: (\"width\", \"background\", \"offset_x\", ...)\n- \"\"\"\n- return [attr[8:] for attr in dir(self) if attr.startswith(\"process_\")]\n-\n def _process_enum_multiple(\n self, name: str, tokens: list[Token], valid_values: set[str], count: int\n ) -> tuple[str, ...]:\n@@ -1154,4 +1140,7 @@ def _get_suggested_property_name_for_rule(self, rule_name: str) -> str | None:\n \n Example: returns \"background\" for rule_name \"bkgrund\", \"offset_x\" for \"ofset_x\"\n \"\"\"\n- return get_suggestion(rule_name, self._get_processable_rule_names())\n+ processable_rules_name = [\n+ attr[8:] for attr in dir(self) if attr.startswith(\"process_\")\n+ ]\n+ return get_suggestion(rule_name, processable_rules_name)\ndiff --git a/src/textual/css/styles.py b/src/textual/css/styles.py\nindex c2d14ea02b..8fe230bc75 100644\n--- a/src/textual/css/styles.py\n+++ b/src/textual/css/styles.py\n@@ -1,7 +1,7 @@\n from __future__ import annotations\n \n from dataclasses import dataclass, field\n-from functools import lru_cache, partial\n+from functools import partial\n from operator import attrgetter\n from typing import TYPE_CHECKING, Any, Callable, Iterable, cast\n \n@@ -554,7 +554,6 @@ def is_animatable(cls, rule: str) -> bool:\n return rule in cls.ANIMATABLE\n \n @classmethod\n- @lru_cache(maxsize=1024)\n def parse(\n cls, css: str, read_from: CSSLocation, *, node: DOMNode | None = None\n ) -> Styles:\ndiff --git a/src/textual/message_pump.py b/src/textual/message_pump.py\nindex 317fd0fcd3..2e0316bb3b 100644\n--- a/src/textual/message_pump.py\n+++ b/src/textual/message_pump.py\n@@ -229,7 +229,7 @@ def app(self) -> \"App[object]\":\n if node is None:\n raise NoActiveAppError()\n node = node._parent\n- active_app.set(node)\n+\n return node\n \n @property\n@@ -501,24 +501,27 @@ def _start_messages(self) -> None:\n \n async def _process_messages(self) -> None:\n self._running = True\n- active_message_pump.set(self)\n \n- if not await self._pre_process():\n- self._running = False\n- return\n+ with self._context():\n+ if not await self._pre_process():\n+ self._running = False\n+ return\n \n- try:\n- await self._process_messages_loop()\n- except CancelledError:\n- pass\n- finally:\n- self._running = False\n try:\n- if self._timers:\n- await Timer._stop_all(self._timers)\n- self._timers.clear()\n+ await self._process_messages_loop()\n+ except CancelledError:\n+ pass\n finally:\n- await self._message_loop_exit()\n+ self._running = False\n+ try:\n+ if self._timers:\n+ await Timer._stop_all(self._timers)\n+ self._timers.clear()\n+ if hasattr(self, \"_watchers\"):\n+ self._watchers.clear()\n+ finally:\n+ await self._message_loop_exit()\n+ self._task = None\n \n async def _message_loop_exit(self) -> None:\n \"\"\"Called when the message loop has completed.\"\"\"\n@@ -558,6 +561,15 @@ def _close_messages_no_wait(self) -> None:\n \"\"\"Request the message queue to immediately exit.\"\"\"\n self._message_queue.put_nowait(messages.CloseMessages())\n \n+ @contextmanager\n+ def _context(self) -> Generator[None, None, None]:\n+ \"\"\"Context manager to set ContextVars.\"\"\"\n+ reset_token = active_message_pump.set(self)\n+ try:\n+ yield\n+ finally:\n+ active_message_pump.reset(reset_token)\n+\n async def _on_close_messages(self, message: messages.CloseMessages) -> None:\n await self._close_messages()\n \ndiff --git a/src/textual/reactive.py b/src/textual/reactive.py\nindex 64b6e007ce..4fa8deb59d 100644\n--- a/src/textual/reactive.py\n+++ b/src/textual/reactive.py\n@@ -24,7 +24,6 @@\n \n from . import events\n from ._callback import count_parameters\n-from ._context import active_message_pump\n from ._types import (\n MessageTarget,\n WatchCallbackBothValuesType,\n@@ -82,8 +81,8 @@ def invoke_watcher(\n _rich_traceback_omit = True\n \n param_count = count_parameters(watch_function)\n- reset_token = active_message_pump.set(watcher_object)\n- try:\n+\n+ with watcher_object._context():\n if param_count == 2:\n watch_result = cast(WatchCallbackBothValuesType, watch_function)(\n old_value, value\n@@ -97,8 +96,6 @@ def invoke_watcher(\n watcher_object.call_next(\n partial(await_watcher, watcher_object, watch_result)\n )\n- finally:\n- active_message_pump.reset(reset_token)\n \n \n @rich.repr.auto\n@@ -203,7 +200,7 @@ def _reset_object(cls, obj: object) -> None:\n Args:\n obj: A reactive object.\n \"\"\"\n- getattr(obj, \"__watchers\", {}).clear()\n+ getattr(obj, \"_watchers\", {}).clear()\n getattr(obj, \"__computes\", []).clear()\n \n def __set_name__(self, owner: Type[MessageTarget], name: str) -> None:\n@@ -351,7 +348,7 @@ def _check_watchers(cls, obj: Reactable, name: str, old_value: Any) -> None:\n \n # Process \"global\" watchers\n watchers: list[tuple[Reactable, WatchCallbackType]]\n- watchers = getattr(obj, \"__watchers\", {}).get(name, [])\n+ watchers = getattr(obj, \"_watchers\", {}).get(name, [])\n # Remove any watchers for reactables that have since closed\n if watchers:\n watchers[:] = [\n@@ -466,10 +463,10 @@ def _watch(\n callback: A callable to call when the attribute changes.\n init: True to call watcher initialization.\n \"\"\"\n- if not hasattr(obj, \"__watchers\"):\n- setattr(obj, \"__watchers\", {})\n+ if not hasattr(obj, \"_watchers\"):\n+ setattr(obj, \"_watchers\", {})\n watchers: dict[str, list[tuple[Reactable, WatchCallbackType]]] = getattr(\n- obj, \"__watchers\"\n+ obj, \"_watchers\"\n )\n watcher_list = watchers.setdefault(attribute_name, [])\n if any(callback == callback_from_list for _, callback_from_list in watcher_list):\ndiff --git a/src/textual/screen.py b/src/textual/screen.py\nindex 7697e38f20..4ea8eaba26 100644\n--- a/src/textual/screen.py\n+++ b/src/textual/screen.py\n@@ -892,7 +892,8 @@ async def _on_idle(self, event: events.Idle) -> None:\n finally:\n if self._bindings_updated:\n self._bindings_updated = False\n- self.app.call_later(self.bindings_updated_signal.publish, self)\n+ if self.is_attached and not self.app._exit:\n+ self.app.call_later(self.bindings_updated_signal.publish, self)\n \n def _compositor_refresh(self) -> None:\n \"\"\"Perform a compositor refresh.\"\"\"\n@@ -978,11 +979,8 @@ async def _invoke_and_clear_callbacks(self) -> None:\n callbacks = self._callbacks[:]\n self._callbacks.clear()\n for callback, message_pump in callbacks:\n- reset_token = active_message_pump.set(message_pump)\n- try:\n+ with message_pump._context():\n await invoke(callback)\n- finally:\n- active_message_pump.reset(reset_token)\n \n def _invoke_later(self, callback: CallbackType, sender: MessagePump) -> None:\n \"\"\"Enqueue a callback to be invoked after the screen is repainted.\n@@ -1012,6 +1010,16 @@ def _push_result_callback(\n ResultCallback[Optional[ScreenResultType]](requester, callback, future)\n )\n \n+ async def _message_loop_exit(self) -> None:\n+ await super()._message_loop_exit()\n+ self._compositor.clear()\n+ self._dirty_widgets.clear()\n+ self._dirty_regions.clear()\n+ self._arrangement_cache.clear()\n+ self.screen_layout_refresh_signal.unsubscribe(self)\n+ self._nodes._clear()\n+ self._task = None\n+\n def _pop_result_callback(self) -> None:\n \"\"\"Remove the latest result callback from the stack.\"\"\"\n self._result_callbacks.pop()\ndiff --git a/src/textual/signal.py b/src/textual/signal.py\nindex 5960ea687d..2e9a946e90 100644\n--- a/src/textual/signal.py\n+++ b/src/textual/signal.py\n@@ -10,7 +10,7 @@\n from __future__ import annotations\n \n from typing import TYPE_CHECKING, Any, Awaitable, Callable, Generic, TypeVar, Union\n-from weakref import WeakKeyDictionary\n+from weakref import WeakKeyDictionary, ref\n \n import rich.repr\n \n@@ -41,17 +41,22 @@ def __init__(self, owner: DOMNode, name: str) -> None:\n owner: The owner of this signal.\n name: An identifier for debugging purposes.\n \"\"\"\n- self._owner = owner\n+ self._owner = ref(owner)\n self._name = name\n self._subscriptions: WeakKeyDictionary[\n MessagePump, list[SignalCallbackType]\n ] = WeakKeyDictionary()\n \n def __rich_repr__(self) -> rich.repr.Result:\n- yield \"owner\", self._owner\n+ yield \"owner\", self.owner\n yield \"name\", self._name\n yield \"subscriptions\", list(self._subscriptions.keys())\n \n+ @property\n+ def owner(self) -> DOMNode | None:\n+ \"\"\"The owner of this Signal, or `None` if there is no owner.\"\"\"\n+ return self._owner()\n+\n def subscribe(\n self,\n node: MessagePump,\n@@ -108,9 +113,12 @@ def publish(self, data: SignalT) -> None:\n \n \"\"\"\n # Don't publish if the DOM is not ready or shutting down\n- if not self._owner.is_attached or self._owner._pruning:\n+ owner = self.owner\n+ if owner is None:\n+ return\n+ if not owner.is_attached or owner._pruning:\n return\n- for ancestor_node in self._owner.ancestors_with_self:\n+ for ancestor_node in owner.ancestors_with_self:\n if not ancestor_node.is_running:\n return\n \ndiff --git a/src/textual/timer.py b/src/textual/timer.py\nindex d158962fd5..e593203ee3 100644\n--- a/src/textual/timer.py\n+++ b/src/textual/timer.py\n@@ -84,7 +84,7 @@ def _start(self) -> None:\n \"\"\"Start the timer.\"\"\"\n self._task = create_task(self._run_timer(), name=self.name)\n \n- def stop(self) -> Task:\n+ def stop(self) -> None:\n \"\"\"Stop the timer.\n \n Returns:\n@@ -92,15 +92,11 @@ def stop(self) -> Task:\n \n \"\"\"\n if self._task is None:\n-\n- async def noop() -> None:\n- \"\"\"A dummy task.\"\"\"\n-\n- return create_task(noop())\n+ return\n \n self._active.set()\n self._task.cancel()\n- return self._task\n+ self._task = None\n \n @classmethod\n async def _stop_all(cls, timers: Iterable[Timer]) -> None:\n@@ -123,6 +119,7 @@ async def stop_timer(timer: Timer) -> None:\n await timer._task\n except CancelledError:\n pass\n+ timer._task = None\n \n await gather(*[stop_timer(timer) for timer in list(timers)])\n \ndiff --git a/src/textual/widget.py b/src/textual/widget.py\nindex b71cc971ae..58305d0497 100644\n--- a/src/textual/widget.py\n+++ b/src/textual/widget.py\n@@ -49,7 +49,7 @@\n from ._animator import DEFAULT_EASING, Animatable, BoundAnimator, EasingFunction\n from ._arrange import DockArrangeResult, arrange\n from ._compose import compose\n-from ._context import NoActiveAppError, active_app\n+from ._context import NoActiveAppError\n from ._debug import get_caller_file_and_line\n from ._dispatch_key import dispatch_key\n from ._easing import DEFAULT_SCROLL_EASING\n@@ -1194,9 +1194,10 @@ async def recompose(self) -> None:\n return\n \n async with self.batch():\n- await self.query(\"*\").exclude(\".-textual-system\").remove()\n+ await self.query_children(\"*\").exclude(\".-textual-system\").remove()\n if self.is_attached:\n- await self.mount_all(compose(self))\n+ compose_nodes = compose(self)\n+ await self.mount_all(compose_nodes)\n \n def _post_register(self, app: App) -> None:\n \"\"\"Called when the instance is registered.\n@@ -1883,7 +1884,7 @@ def _console(self) -> Console:\n Returns:\n A Rich console object.\n \"\"\"\n- return active_app.get().console\n+ return self.app.console\n \n @property\n def _has_relative_children_width(self) -> bool:\n@@ -3676,6 +3677,11 @@ async def _message_loop_exit(self) -> None:\n parent._nodes._remove(self)\n self.app._registry.discard(self)\n self._detach()\n+ self._arrangement_cache.clear()\n+ self._nodes._clear()\n+ self._render_cache = _RenderCache(NULL_SIZE, [])\n+ self._component_styles.clear()\n+ self._query_one_cache.clear()\n \n async def _on_idle(self, event: events.Idle) -> None:\n \"\"\"Called when there are no more events on the queue.\ndiff --git a/src/textual/widgets/_footer.py b/src/textual/widgets/_footer.py\nindex 0d51f9f76e..98214a56f7 100644\n--- a/src/textual/widgets/_footer.py\n+++ b/src/textual/widgets/_footer.py\n@@ -227,15 +227,15 @@ def compose(self) -> ComposeResult:\n )\n break\n \n+ async def bindings_changed(self, screen: Screen) -> None:\n+ self._bindings_ready = True\n+ if not screen.app.app_focus:\n+ return\n+ if self.is_attached and screen is self.screen:\n+ await self.recompose()\n+\n def on_mount(self) -> None:\n- async def bindings_changed(screen: Screen) -> None:\n- self._bindings_ready = True\n- if not screen.app.app_focus:\n- return\n- if self.is_attached and screen is self.screen:\n- await self.recompose()\n-\n- self.screen.bindings_updated_signal.subscribe(self, bindings_changed)\n+ self.screen.bindings_updated_signal.subscribe(self, self.bindings_changed)\n \n def on_unmount(self) -> None:\n self.screen.bindings_updated_signal.unsubscribe(self)\ndiff --git a/src/textual/worker.py b/src/textual/worker.py\nindex 9ad60a64ac..c0b1cbbffd 100644\n--- a/src/textual/worker.py\n+++ b/src/textual/worker.py\n@@ -359,30 +359,30 @@ async def _run(self, app: App) -> None:\n Args:\n app: App instance.\n \"\"\"\n- app._set_active()\n- active_worker.set(self)\n+ with app._context():\n+ active_worker.set(self)\n \n- self.state = WorkerState.RUNNING\n- app.log.worker(self)\n- try:\n- self._result = await self.run()\n- except asyncio.CancelledError as error:\n- self.state = WorkerState.CANCELLED\n- self._error = error\n- app.log.worker(self)\n- except Exception as error:\n- self.state = WorkerState.ERROR\n- self._error = error\n- app.log.worker(self, \"failed\", repr(error))\n- from rich.traceback import Traceback\n-\n- app.log.worker(Traceback())\n- if self.exit_on_error:\n- worker_failed = WorkerFailed(self._error)\n- app._handle_exception(worker_failed)\n- else:\n- self.state = WorkerState.SUCCESS\n+ self.state = WorkerState.RUNNING\n app.log.worker(self)\n+ try:\n+ self._result = await self.run()\n+ except asyncio.CancelledError as error:\n+ self.state = WorkerState.CANCELLED\n+ self._error = error\n+ app.log.worker(self)\n+ except Exception as error:\n+ self.state = WorkerState.ERROR\n+ self._error = error\n+ app.log.worker(self, \"failed\", repr(error))\n+ from rich.traceback import Traceback\n+\n+ app.log.worker(Traceback())\n+ if self.exit_on_error:\n+ worker_failed = WorkerFailed(self._error)\n+ app._handle_exception(worker_failed)\n+ else:\n+ self.state = WorkerState.SUCCESS\n+ app.log.worker(self)\n \n def _start(\n self, app: App, done_callback: Callable[[Worker], None] | None = None\ndiff --git a/tests/layouts/test_horizontal.py b/tests/layouts/test_horizontal.py\nindex 05ce4c8905..62d766aecb 100644\n--- a/tests/layouts/test_horizontal.py\n+++ b/tests/layouts/test_horizontal.py\n@@ -19,11 +19,11 @@ def compose(self) -> ComposeResult:\n yield self.horizontal\n \n app = HorizontalAutoWidth()\n- async with app.run_test():\n- yield app\n+ yield app\n \n \n async def test_horizontal_get_content_width(app):\n- size = app.screen.size\n- width = app.horizontal.get_content_width(size, size)\n- assert width == 15\n+ async with app.run_test():\n+ size = app.screen.size\n+ width = app.horizontal.get_content_width(size, size)\n+ assert width == 15\ndiff --git a/tests/test_count_parameters.py b/tests/test_count_parameters.py\nnew file mode 100644\nindex 0000000000..be3adda3b8\n--- /dev/null\n+++ b/tests/test_count_parameters.py\n@@ -0,0 +1,57 @@\n+from functools import partial\n+\n+from textual._callback import count_parameters\n+\n+\n+def test_functions() -> None:\n+ \"\"\"Test count parameters of functions.\"\"\"\n+\n+ def foo(): ...\n+ def bar(a): ...\n+ def baz(a, b): ...\n+\n+ # repeat to allow for caching\n+ for _ in range(3):\n+ assert count_parameters(foo) == 0\n+ assert count_parameters(bar) == 1\n+ assert count_parameters(baz) == 2\n+\n+\n+def test_methods() -> None:\n+ \"\"\"Test count parameters of methods.\"\"\"\n+\n+ class Foo:\n+ def foo(self): ...\n+ def bar(self, a): ...\n+ def baz(self, a, b): ...\n+\n+ foo = Foo()\n+\n+ # repeat to allow for caching\n+ for _ in range(3):\n+ assert count_parameters(foo.foo) == 0\n+ assert count_parameters(foo.bar) == 1\n+ assert count_parameters(foo.baz) == 2\n+\n+\n+def test_partials() -> None:\n+ \"\"\"Test count parameters of partials.\"\"\"\n+\n+ class Foo:\n+ def method(self, a, b, c, d): ...\n+\n+ foo = Foo()\n+\n+ partial0 = partial(foo.method)\n+ partial1 = partial(foo.method, 10)\n+ partial2 = partial(foo.method, b=10, c=20)\n+\n+ for _ in range(3):\n+ assert count_parameters(partial0) == 4\n+ assert count_parameters(partial0) == 4\n+\n+ assert count_parameters(partial1) == 3\n+ assert count_parameters(partial1) == 3\n+\n+ assert count_parameters(partial2) == 2\n+ assert count_parameters(partial2) == 2\ndiff --git a/tests/test_focus.py b/tests/test_focus.py\nindex ee955e23e5..a23046bc94 100644\n--- a/tests/test_focus.py\n+++ b/tests/test_focus.py\n@@ -22,46 +22,47 @@ class ChildrenFocusableOnly(Widget, can_focus=False, can_focus_children=True):\n @pytest.fixture\n def screen() -> Screen:\n app = App()\n- app._set_active()\n- app.push_screen(Screen())\n \n- screen = app.screen\n+ with app._context():\n+ app.push_screen(Screen())\n \n- # The classes even/odd alternate along the focus chain.\n- # The classes in/out identify nested widgets.\n- screen._add_children(\n- Focusable(id=\"foo\", classes=\"a\"),\n- NonFocusable(id=\"bar\"),\n- Focusable(Focusable(id=\"Paul\", classes=\"c\"), id=\"container1\", classes=\"b\"),\n- NonFocusable(Focusable(id=\"Jessica\", classes=\"a\"), id=\"container2\"),\n- Focusable(id=\"baz\", classes=\"b\"),\n- ChildrenFocusableOnly(Focusable(id=\"child\", classes=\"c\")),\n- )\n+ screen = app.screen\n \n- return screen\n+ # The classes even/odd alternate along the focus chain.\n+ # The classes in/out identify nested widgets.\n+ screen._add_children(\n+ Focusable(id=\"foo\", classes=\"a\"),\n+ NonFocusable(id=\"bar\"),\n+ Focusable(Focusable(id=\"Paul\", classes=\"c\"), id=\"container1\", classes=\"b\"),\n+ NonFocusable(Focusable(id=\"Jessica\", classes=\"a\"), id=\"container2\"),\n+ Focusable(id=\"baz\", classes=\"b\"),\n+ ChildrenFocusableOnly(Focusable(id=\"child\", classes=\"c\")),\n+ )\n+\n+ return screen\n \n \n def test_focus_chain():\n app = App()\n- app._set_active()\n- app.push_screen(Screen())\n+ with app._context():\n+ app.push_screen(Screen())\n \n- screen = app.screen\n+ screen = app.screen\n \n- # Check empty focus chain\n- assert not screen.focus_chain\n+ # Check empty focus chain\n+ assert not screen.focus_chain\n \n- app.screen._add_children(\n- Focusable(id=\"foo\"),\n- NonFocusable(id=\"bar\"),\n- Focusable(Focusable(id=\"Paul\"), id=\"container1\"),\n- NonFocusable(Focusable(id=\"Jessica\"), id=\"container2\"),\n- Focusable(id=\"baz\"),\n- ChildrenFocusableOnly(Focusable(id=\"child\")),\n- )\n+ app.screen._add_children(\n+ Focusable(id=\"foo\"),\n+ NonFocusable(id=\"bar\"),\n+ Focusable(Focusable(id=\"Paul\"), id=\"container1\"),\n+ NonFocusable(Focusable(id=\"Jessica\"), id=\"container2\"),\n+ Focusable(id=\"baz\"),\n+ ChildrenFocusableOnly(Focusable(id=\"child\")),\n+ )\n \n- focus_chain = [widget.id for widget in screen.focus_chain]\n- assert focus_chain == [\"foo\", \"container1\", \"Paul\", \"baz\", \"child\"]\n+ focus_chain = [widget.id for widget in screen.focus_chain]\n+ assert focus_chain == [\"foo\", \"container1\", \"Paul\", \"baz\", \"child\"]\n \n \n def test_allow_focus():\n@@ -90,18 +91,19 @@ def allow_focus_children(self) -> bool:\n return False\n \n app = App()\n- app._set_active()\n- app.push_screen(Screen())\n \n- app.screen._add_children(\n- Focusable(id=\"foo\"),\n- NonFocusable(id=\"bar\"),\n- FocusableContainer(Button(\"egg\", id=\"egg\")),\n- NonFocusableContainer(Button(\"EGG\", id=\"qux\")),\n- )\n- assert [widget.id for widget in app.screen.focus_chain] == [\"foo\", \"egg\"]\n- assert focusable_allow_focus_called\n- assert non_focusable_allow_focus_called\n+ with app._context():\n+ app.push_screen(Screen())\n+\n+ app.screen._add_children(\n+ Focusable(id=\"foo\"),\n+ NonFocusable(id=\"bar\"),\n+ FocusableContainer(Button(\"egg\", id=\"egg\")),\n+ NonFocusableContainer(Button(\"EGG\", id=\"qux\")),\n+ )\n+ assert [widget.id for widget in app.screen.focus_chain] == [\"foo\", \"egg\"]\n+ assert focusable_allow_focus_called\n+ assert non_focusable_allow_focus_called\n \n \n def test_focus_next_and_previous(screen: Screen):\n@@ -188,47 +190,47 @@ def test_focus_next_and_previous_with_str_selector(screen: Screen):\n def test_focus_next_and_previous_with_type_selector_without_self():\n \"\"\"Test moving the focus with a selector that does not match the currently focused node.\"\"\"\n app = App()\n- app._set_active()\n- app.push_screen(Screen())\n-\n- screen = app.screen\n-\n- from textual.containers import Horizontal, VerticalScroll\n- from textual.widgets import Button, Input, Switch\n-\n- screen._add_children(\n- VerticalScroll(\n- Horizontal(\n- Input(id=\"w3\"),\n- Switch(id=\"w4\"),\n- Input(id=\"w5\"),\n- Button(id=\"w6\"),\n- Switch(id=\"w7\"),\n- id=\"w2\",\n- ),\n- Horizontal(\n- Button(id=\"w9\"),\n- Switch(id=\"w10\"),\n- Button(id=\"w11\"),\n- Input(id=\"w12\"),\n- Input(id=\"w13\"),\n- id=\"w8\",\n- ),\n- id=\"w1\",\n+ with app._context():\n+ app.push_screen(Screen())\n+\n+ screen = app.screen\n+\n+ from textual.containers import Horizontal, VerticalScroll\n+ from textual.widgets import Button, Input, Switch\n+\n+ screen._add_children(\n+ VerticalScroll(\n+ Horizontal(\n+ Input(id=\"w3\"),\n+ Switch(id=\"w4\"),\n+ Input(id=\"w5\"),\n+ Button(id=\"w6\"),\n+ Switch(id=\"w7\"),\n+ id=\"w2\",\n+ ),\n+ Horizontal(\n+ Button(id=\"w9\"),\n+ Switch(id=\"w10\"),\n+ Button(id=\"w11\"),\n+ Input(id=\"w12\"),\n+ Input(id=\"w13\"),\n+ id=\"w8\",\n+ ),\n+ id=\"w1\",\n+ )\n )\n- )\n \n- screen.set_focus(screen.query_one(\"#w3\"))\n- assert screen.focused.id == \"w3\"\n+ screen.set_focus(screen.query_one(\"#w3\"))\n+ assert screen.focused.id == \"w3\"\n \n- assert screen.focus_next(Button).id == \"w6\"\n- assert screen.focus_next(Switch).id == \"w7\"\n- assert screen.focus_next(Input).id == \"w12\"\n+ assert screen.focus_next(Button).id == \"w6\"\n+ assert screen.focus_next(Switch).id == \"w7\"\n+ assert screen.focus_next(Input).id == \"w12\"\n \n- assert screen.focus_previous(Button).id == \"w11\"\n- assert screen.focus_previous(Switch).id == \"w10\"\n- assert screen.focus_previous(Button).id == \"w9\"\n- assert screen.focus_previous(Input).id == \"w5\"\n+ assert screen.focus_previous(Button).id == \"w11\"\n+ assert screen.focus_previous(Switch).id == \"w10\"\n+ assert screen.focus_previous(Button).id == \"w9\"\n+ assert screen.focus_previous(Input).id == \"w5\"\n \n \n def test_focus_next_and_previous_with_str_selector_without_self(screen: Screen):\ndiff --git a/tests/test_gc.py b/tests/test_gc.py\nnew file mode 100644\nindex 0000000000..b21a7d9b72\n--- /dev/null\n+++ b/tests/test_gc.py\n@@ -0,0 +1,87 @@\n+import asyncio\n+import gc\n+\n+import pytest\n+\n+from textual.app import App, ComposeResult\n+from textual.containers import Vertical\n+from textual.widgets import Footer, Header, Label\n+\n+\n+def count_nodes() -> int:\n+ \"\"\"Count number of references to DOMNodes.\"\"\"\n+ dom_nodes = [\n+ obj\n+ for obj in gc.get_objects()\n+ if any(cls.__name__ == \"DOMNode\" for cls in obj.__class__.__mro__)\n+ ]\n+ print(dom_nodes)\n+ return len(dom_nodes)\n+\n+\n+async def run_app() -> None:\n+ \"\"\"Run a dummy app.\"\"\"\n+\n+ class DummyApp(App):\n+ \"\"\"Dummy app with a few widgets.\"\"\"\n+\n+ def compose(self) -> ComposeResult:\n+ yield Header()\n+ with Vertical():\n+ yield Label(\"foo\")\n+ yield Label(\"bar\")\n+ yield Footer()\n+\n+ app = DummyApp()\n+\n+ async with app.run_test() as pilot:\n+ # We should have a bunch of DOMNodes while the test is running\n+ assert count_nodes() > 0\n+ await pilot.press(\"ctrl+c\")\n+\n+ assert not app._running\n+\n+ # Force a GC collection\n+ gc.collect()\n+\n+ # After the test, all DOMNodes will have been torn down\n+ assert count_nodes() == 1\n+\n+\n+async def _count_app_nodes() -> None:\n+ \"\"\"Regression test for https://github.com/Textualize/textual/issues/4959\"\"\"\n+\n+ # Should be no DOMNodes yet\n+ assert count_nodes() == 0\n+\n+ await run_app()\n+ await asyncio.sleep(0)\n+\n+ gc.collect()\n+\n+ nodes_remaining = count_nodes()\n+\n+ if nodes_remaining:\n+ print(\"NODES REMAINING\")\n+\n+ import objgraph\n+\n+ objgraph.show_backrefs(\n+ [\n+ obj\n+ for obj in gc.get_objects()\n+ if any(cls.__name__ == \"App\" for cls in obj.__class__.__mro__)\n+ ],\n+ filename=\"graph.png\",\n+ max_depth=15,\n+ )\n+\n+ assert nodes_remaining == 0\n+\n+\n+# It looks like PyTest holds on to references to DOMNodes\n+# So this will only pass if ran in isolation\[email protected]\n+async def test_gc():\n+ \"\"\"Regression test for https://github.com/Textualize/textual/issues/4959\"\"\"\n+ await _count_app_nodes()\ndiff --git a/tests/test_path.py b/tests/test_path.py\nindex d7088f8be7..3d5203a6e8 100644\n--- a/tests/test_path.py\n+++ b/tests/test_path.py\n@@ -30,14 +30,15 @@ class ListPathApp(App[None]):\n \n \n @pytest.mark.parametrize(\n- \"app,expected_css_path_attribute\",\n+ \"app_class,expected_css_path_attribute\",\n [\n- (RelativePathObjectApp(), [APP_DIR / \"test.tcss\"]),\n- (RelativePathStrApp(), [APP_DIR / \"test.tcss\"]),\n- (AbsolutePathObjectApp(), [Path(\"/tmp/test.tcss\")]),\n- (AbsolutePathStrApp(), [Path(\"/tmp/test.tcss\")]),\n- (ListPathApp(), [APP_DIR / \"test.tcss\", Path(\"/another/path.tcss\")]),\n+ (RelativePathObjectApp, [APP_DIR / \"test.tcss\"]),\n+ (RelativePathStrApp, [APP_DIR / \"test.tcss\"]),\n+ (AbsolutePathObjectApp, [Path(\"/tmp/test.tcss\")]),\n+ (AbsolutePathStrApp, [Path(\"/tmp/test.tcss\")]),\n+ (ListPathApp, [APP_DIR / \"test.tcss\", Path(\"/another/path.tcss\")]),\n ],\n )\n-def test_css_paths_of_various_types(app, expected_css_path_attribute):\n+def test_css_paths_of_various_types(app_class, expected_css_path_attribute):\n+ app = app_class()\n assert app.css_path == [path.absolute() for path in expected_css_path_attribute]\ndiff --git a/tests/test_screens.py b/tests/test_screens.py\nindex 00f3504361..a53abb7049 100644\n--- a/tests/test_screens.py\n+++ b/tests/test_screens.py\n@@ -74,89 +74,88 @@ async def test_screens():\n # There should be nothing in the children since the app hasn't run yet\n assert not app._nodes\n assert not app.children\n- app._set_active()\n-\n- with pytest.raises(ScreenStackError):\n- app.screen\n-\n- assert not app._installed_screens\n-\n- screen1 = Screen(name=\"screen1\")\n- screen2 = Screen(name=\"screen2\")\n- screen3 = Screen(name=\"screen3\")\n-\n- # installs screens\n- app.install_screen(screen1, \"screen1\")\n- app.install_screen(screen2, \"screen2\")\n-\n- # Installing a screen does not add it to the DOM\n- assert not app._nodes\n- assert not app.children\n+ with app._context():\n+ with pytest.raises(ScreenStackError):\n+ app.screen\n+\n+ assert not app._installed_screens\n+\n+ screen1 = Screen(name=\"screen1\")\n+ screen2 = Screen(name=\"screen2\")\n+ screen3 = Screen(name=\"screen3\")\n+\n+ # installs screens\n+ app.install_screen(screen1, \"screen1\")\n+ app.install_screen(screen2, \"screen2\")\n+\n+ # Installing a screen does not add it to the DOM\n+ assert not app._nodes\n+ assert not app.children\n+\n+ # Check they are installed\n+ assert app.is_screen_installed(\"screen1\")\n+ assert app.is_screen_installed(\"screen2\")\n+\n+ assert app.get_screen(\"screen1\") is screen1\n+ with pytest.raises(KeyError):\n+ app.get_screen(\"foo\")\n+\n+ # Check screen3 is not installed\n+ assert not app.is_screen_installed(\"screen3\")\n+\n+ # Installs screen3\n+ app.install_screen(screen3, \"screen3\")\n+ # Confirm installed\n+ assert app.is_screen_installed(\"screen3\")\n+\n+ # Check screen stack is empty\n+ assert app.screen_stack == []\n+ # Push a screen\n+ await app.push_screen(\"screen1\")\n+ # Check it is on the stack\n+ assert app.screen_stack == [screen1]\n+ # Check it is current\n+ assert app.screen is screen1\n+ # There should be one item in the children view\n+ assert app.children == (screen1,)\n+\n+ # Switch to another screen\n+ await app.switch_screen(\"screen2\")\n+ # Check it has changed the stack and that it is current\n+ assert app.screen_stack == [screen2]\n+ assert app.screen is screen2\n+ assert app.children == (screen2,)\n+\n+ # Push another screen\n+ await app.push_screen(\"screen3\")\n+ assert app.screen_stack == [screen2, screen3]\n+ assert app.screen is screen3\n+ # Only the current screen is in children\n+ assert app.children == (screen3,)\n+\n+ # Pop a screen\n+ await app.pop_screen()\n+ assert app.screen is screen2\n+ assert app.screen_stack == [screen2]\n+\n+ # Uninstall screens\n+ app.uninstall_screen(screen1)\n+ assert not app.is_screen_installed(screen1)\n+ app.uninstall_screen(\"screen3\")\n+ assert not app.is_screen_installed(screen1)\n+\n+ # Check we can't uninstall a screen on the stack\n+ with pytest.raises(ScreenStackError):\n+ app.uninstall_screen(screen2)\n \n- # Check they are installed\n- assert app.is_screen_installed(\"screen1\")\n- assert app.is_screen_installed(\"screen2\")\n-\n- assert app.get_screen(\"screen1\") is screen1\n- with pytest.raises(KeyError):\n- app.get_screen(\"foo\")\n-\n- # Check screen3 is not installed\n- assert not app.is_screen_installed(\"screen3\")\n-\n- # Installs screen3\n- app.install_screen(screen3, \"screen3\")\n- # Confirm installed\n- assert app.is_screen_installed(\"screen3\")\n-\n- # Check screen stack is empty\n- assert app.screen_stack == []\n- # Push a screen\n- await app.push_screen(\"screen1\")\n- # Check it is on the stack\n- assert app.screen_stack == [screen1]\n- # Check it is current\n- assert app.screen is screen1\n- # There should be one item in the children view\n- assert app.children == (screen1,)\n-\n- # Switch to another screen\n- await app.switch_screen(\"screen2\")\n- # Check it has changed the stack and that it is current\n- assert app.screen_stack == [screen2]\n- assert app.screen is screen2\n- assert app.children == (screen2,)\n-\n- # Push another screen\n- await app.push_screen(\"screen3\")\n- assert app.screen_stack == [screen2, screen3]\n- assert app.screen is screen3\n- # Only the current screen is in children\n- assert app.children == (screen3,)\n-\n- # Pop a screen\n- await app.pop_screen()\n- assert app.screen is screen2\n- assert app.screen_stack == [screen2]\n-\n- # Uninstall screens\n- app.uninstall_screen(screen1)\n- assert not app.is_screen_installed(screen1)\n- app.uninstall_screen(\"screen3\")\n- assert not app.is_screen_installed(screen1)\n-\n- # Check we can't uninstall a screen on the stack\n- with pytest.raises(ScreenStackError):\n- app.uninstall_screen(screen2)\n-\n- # Check we can't pop last screen\n- with pytest.raises(ScreenStackError):\n- app.pop_screen()\n+ # Check we can't pop last screen\n+ with pytest.raises(ScreenStackError):\n+ app.pop_screen()\n \n- screen1.remove()\n- screen2.remove()\n- screen3.remove()\n- await app._shutdown()\n+ screen1.remove()\n+ screen2.remove()\n+ screen3.remove()\n+ await app._shutdown()\n \n \n async def test_auto_focus_on_screen_if_app_auto_focus_is_none():\ndiff --git a/tests/test_unmount.py b/tests/test_unmount.py\nindex 324a3812a3..36c0acbeb1 100644\n--- a/tests/test_unmount.py\n+++ b/tests/test_unmount.py\n@@ -50,4 +50,7 @@ async def on_mount(self) -> None:\n \"MyScreen#main\",\n ]\n \n+ print(unmount_ids)\n+ print(expected)\n+\n assert unmount_ids == expected\ndiff --git a/tests/test_widget.py b/tests/test_widget.py\nindex 2652aec54d..6643916bbb 100644\n--- a/tests/test_widget.py\n+++ b/tests/test_widget.py\n@@ -57,22 +57,21 @@ def render(self) -> str:\n widget3 = TextWidget(\"foo\\nbar\\nbaz\", id=\"widget3\")\n \n app = App()\n- app._set_active()\n+ with app._context():\n+ width = widget1.get_content_width(Size(20, 20), Size(80, 24))\n+ height = widget1.get_content_height(Size(20, 20), Size(80, 24), width)\n+ assert width == 3\n+ assert height == 1\n \n- width = widget1.get_content_width(Size(20, 20), Size(80, 24))\n- height = widget1.get_content_height(Size(20, 20), Size(80, 24), width)\n- assert width == 3\n- assert height == 1\n+ width = widget2.get_content_width(Size(20, 20), Size(80, 24))\n+ height = widget2.get_content_height(Size(20, 20), Size(80, 24), width)\n+ assert width == 3\n+ assert height == 2\n \n- width = widget2.get_content_width(Size(20, 20), Size(80, 24))\n- height = widget2.get_content_height(Size(20, 20), Size(80, 24), width)\n- assert width == 3\n- assert height == 2\n-\n- width = widget3.get_content_width(Size(20, 20), Size(80, 24))\n- height = widget3.get_content_height(Size(20, 20), Size(80, 24), width)\n- assert width == 3\n- assert height == 3\n+ width = widget3.get_content_width(Size(20, 20), Size(80, 24))\n+ height = widget3.get_content_height(Size(20, 20), Size(80, 24), width)\n+ assert width == 3\n+ assert height == 3\n \n \n class GetByIdApp(App):\n@@ -87,34 +86,38 @@ def compose(self) -> ComposeResult:\n id=\"parent\",\n )\n \n+ @property\n+ def parent(self) -> Widget:\n+ return self.query_one(\"#parent\")\n+\n \n @pytest.fixture\n async def hierarchy_app():\n app = GetByIdApp()\n- async with app.run_test():\n- yield app\n-\n-\[email protected]\n-async def parent(hierarchy_app):\n- yield hierarchy_app.get_widget_by_id(\"parent\")\n+ yield app\n \n \n-def test_get_child_by_id_gets_first_child(parent):\n- child = parent.get_child_by_id(id=\"child1\")\n- assert child.id == \"child1\"\n- assert child.get_child_by_id(id=\"grandchild1\").id == \"grandchild1\"\n- assert parent.get_child_by_id(id=\"child2\").id == \"child2\"\n+async def test_get_child_by_id_gets_first_child(hierarchy_app):\n+ async with hierarchy_app.run_test():\n+ parent = hierarchy_app.parent\n+ child = parent.get_child_by_id(id=\"child1\")\n+ assert child.id == \"child1\"\n+ assert child.get_child_by_id(id=\"grandchild1\").id == \"grandchild1\"\n+ assert parent.get_child_by_id(id=\"child2\").id == \"child2\"\n \n \n-def test_get_child_by_id_no_matching_child(parent):\n- with pytest.raises(NoMatches):\n- parent.get_child_by_id(id=\"doesnt-exist\")\n+async def test_get_child_by_id_no_matching_child(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ with pytest.raises(NoMatches):\n+ parent.get_child_by_id(id=\"doesnt-exist\")\n \n \n-def test_get_child_by_id_only_immediate_descendents(parent):\n- with pytest.raises(NoMatches):\n- parent.get_child_by_id(id=\"grandchild1\")\n+async def test_get_child_by_id_only_immediate_descendents(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ with pytest.raises(NoMatches):\n+ parent.get_child_by_id(id=\"grandchild1\")\n \n \n async def test_get_child_by_type():\n@@ -135,51 +138,65 @@ def compose(self) -> ComposeResult:\n app.get_child_by_type(Label)\n \n \n-def test_get_widget_by_id_no_matching_child(parent):\n- with pytest.raises(NoMatches):\n- parent.get_widget_by_id(id=\"i-dont-exist\")\n+async def test_get_widget_by_id_no_matching_child(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ with pytest.raises(NoMatches):\n+ parent.get_widget_by_id(id=\"i-dont-exist\")\n \n \n-def test_get_widget_by_id_non_immediate_descendants(parent):\n- result = parent.get_widget_by_id(\"grandchild1\")\n- assert result.id == \"grandchild1\"\n+async def test_get_widget_by_id_non_immediate_descendants(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ result = parent.get_widget_by_id(\"grandchild1\")\n+ assert result.id == \"grandchild1\"\n \n \n-def test_get_widget_by_id_immediate_descendants(parent):\n- result = parent.get_widget_by_id(\"child1\")\n- assert result.id == \"child1\"\n+async def test_get_widget_by_id_immediate_descendants(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ result = parent.get_widget_by_id(\"child1\")\n+ assert result.id == \"child1\"\n \n \n-def test_get_widget_by_id_doesnt_return_self(parent):\n- with pytest.raises(NoMatches):\n- parent.get_widget_by_id(\"parent\")\n+async def test_get_widget_by_id_doesnt_return_self(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ with pytest.raises(NoMatches):\n+ parent.get_widget_by_id(\"parent\")\n \n \n-def test_get_widgets_app_delegated(hierarchy_app, parent):\n+async def test_get_widgets_app_delegated(hierarchy_app):\n # Check that get_child_by_id finds the parent, which is a child of the default Screen\n- queried_parent = hierarchy_app.get_child_by_id(\"parent\")\n- assert queried_parent is parent\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ queried_parent = hierarchy_app.get_child_by_id(\"parent\")\n+ assert queried_parent is parent\n \n- # Check that the grandchild (descendant of the default screen) is found\n- grandchild = hierarchy_app.get_widget_by_id(\"grandchild1\")\n- assert grandchild.id == \"grandchild1\"\n+ # Check that the grandchild (descendant of the default screen) is found\n+ grandchild = hierarchy_app.get_widget_by_id(\"grandchild1\")\n+ assert grandchild.id == \"grandchild1\"\n \n \n-def test_widget_mount_ids_must_be_unique_mounting_all_in_one_go(parent):\n- widget1 = Widget(id=\"hello\")\n- widget2 = Widget(id=\"hello\")\n+async def test_widget_mount_ids_must_be_unique_mounting_all_in_one_go(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ widget1 = Widget(id=\"hello\")\n+ widget2 = Widget(id=\"hello\")\n \n- with pytest.raises(MountError):\n- parent.mount(widget1, widget2)\n+ with pytest.raises(MountError):\n+ parent.mount(widget1, widget2)\n \n \n-def test_widget_mount_ids_must_be_unique_mounting_multiple_calls(parent):\n- widget1 = Widget(id=\"hello\")\n- widget2 = Widget(id=\"hello\")\n+async def test_widget_mount_ids_must_be_unique_mounting_multiple_calls(hierarchy_app):\n+ async with hierarchy_app.run_test() as pilot:\n+ parent = pilot.app.parent\n+ widget1 = Widget(id=\"hello\")\n+ widget2 = Widget(id=\"hello\")\n \n- parent.mount(widget1)\n- with pytest.raises(DuplicateIds):\n- parent.mount(widget2)\n+ parent.mount(widget1)\n+ with pytest.raises(DuplicateIds):\n+ parent.mount(widget2)\n \n \n def test_get_pseudo_class_state():\ndiff --git a/tests/test_widget_mounting.py b/tests/test_widget_mounting.py\nindex 7babed4781..79f7364ad6 100644\n--- a/tests/test_widget_mounting.py\n+++ b/tests/test_widget_mounting.py\n@@ -116,8 +116,10 @@ async def test_mount_via_app() -> None:\n await pilot.app.mount(Static(), before=\"Static\")\n \n \n-def test_mount_error() -> None:\n+async def test_mount_error() -> None:\n \"\"\"Mounting a widget on an un-mounted widget should raise an error.\"\"\"\n- with pytest.raises(MountError):\n- widget = Widget()\n- widget.mount(Static())\n+ app = App()\n+ async with app.run_test():\n+ with pytest.raises(MountError):\n+ widget = Widget()\n+ widget.mount(Static())\ndiff --git a/tests/text_area/test_history.py b/tests/text_area/test_history.py\nindex 8d50a63f83..1d0c7a0b0b 100644\n--- a/tests/text_area/test_history.py\n+++ b/tests/text_area/test_history.py\n@@ -6,7 +6,6 @@\n \n from textual.app import App, ComposeResult\n from textual.events import Paste\n-from textual.pilot import Pilot\n from textual.widgets import TextArea\n from textual.widgets.text_area import EditHistory, Selection\n \n@@ -57,300 +56,346 @@ async def text_area(pilot):\n return pilot.app.text_area\n \n \n-async def test_simple_undo_redo(pilot, text_area: TextArea):\n- text_area.insert(\"123\", (0, 0))\n+async def test_simple_undo_redo():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.insert(\"123\", (0, 0))\n \n- assert text_area.text == \"123\"\n- text_area.undo()\n- assert text_area.text == \"\"\n- text_area.redo()\n- assert text_area.text == \"123\"\n+ assert text_area.text == \"123\"\n+ text_area.undo()\n+ assert text_area.text == \"\"\n+ text_area.redo()\n+ assert text_area.text == \"123\"\n \n \n-async def test_undo_selection_retained(pilot: Pilot, text_area: TextArea):\n+async def test_undo_selection_retained():\n # Select a range of text and press backspace.\n- text_area.text = SIMPLE_TEXT\n- text_area.selection = Selection((0, 0), (2, 3))\n- await pilot.press(\"backspace\")\n- assert text_area.text == \"NO\\nPQRST\\nUVWXY\\nZ\\n\"\n- assert text_area.selection == Selection.cursor((0, 0))\n-\n- # Undo the deletion - the text comes back, and the selection is restored.\n- text_area.undo()\n- assert text_area.selection == Selection((0, 0), (2, 3))\n- assert text_area.text == SIMPLE_TEXT\n-\n- # Redo the deletion - the text is gone again. The selection goes to the post-delete location.\n- text_area.redo()\n- assert text_area.text == \"NO\\nPQRST\\nUVWXY\\nZ\\n\"\n- assert text_area.selection == Selection.cursor((0, 0))\n-\n-\n-async def test_undo_checkpoint_created_on_cursor_move(\n- pilot: Pilot, text_area: TextArea\n-):\n- text_area.text = SIMPLE_TEXT\n- # Characters are inserted on line 0 and 1.\n- checkpoint_one = text_area.text\n- checkpoint_one_selection = text_area.selection\n- await pilot.press(\"1\") # Added to initial batch.\n-\n- # This cursor movement ensures a new checkpoint is created.\n- post_insert_one_location = text_area.selection\n- await pilot.press(\"down\")\n-\n- checkpoint_two = text_area.text\n- checkpoint_two_selection = text_area.selection\n- await pilot.press(\"2\") # Added to new batch.\n-\n- checkpoint_three = text_area.text\n- checkpoint_three_selection = text_area.selection\n-\n- # Going back to checkpoint two\n- text_area.undo()\n- assert text_area.text == checkpoint_two\n- assert text_area.selection == checkpoint_two_selection\n-\n- # Back again to checkpoint one (initial state)\n- text_area.undo()\n- assert text_area.text == checkpoint_one\n- assert text_area.selection == checkpoint_one_selection\n-\n- # Redo to move forward to checkpoint two.\n- text_area.redo()\n- assert text_area.text == checkpoint_two\n- assert text_area.selection == post_insert_one_location\n-\n- # Redo to move forward to checkpoint three.\n- text_area.redo()\n- assert text_area.text == checkpoint_three\n- assert text_area.selection == checkpoint_three_selection\n-\n-\n-async def test_setting_text_property_resets_history(pilot: Pilot, text_area: TextArea):\n- await pilot.press(\"1\")\n-\n- # Programmatically setting text, which should invalidate the history\n- text = \"Hello, world!\"\n- text_area.text = text\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = SIMPLE_TEXT\n+ text_area.selection = Selection((0, 0), (2, 3))\n+ await pilot.press(\"backspace\")\n+ assert text_area.text == \"NO\\nPQRST\\nUVWXY\\nZ\\n\"\n+ assert text_area.selection == Selection.cursor((0, 0))\n \n- # The undo doesn't do anything, since we set the `text` property.\n- text_area.undo()\n- assert text_area.text == text\n+ # Undo the deletion - the text comes back, and the selection is restored.\n+ text_area.undo()\n+ assert text_area.selection == Selection((0, 0), (2, 3))\n+ assert text_area.text == SIMPLE_TEXT\n \n+ # Redo the deletion - the text is gone again. The selection goes to the post-delete location.\n+ text_area.redo()\n+ assert text_area.text == \"NO\\nPQRST\\nUVWXY\\nZ\\n\"\n+ assert text_area.selection == Selection.cursor((0, 0))\n \n-async def test_edits_batched_by_time(pilot: Pilot, text_area: TextArea):\n- # The first \"12\" is batched since they happen within 2 seconds.\n- text_area.history.mock_time = 0\n- await pilot.press(\"1\")\n \n- text_area.history.mock_time = 1.0\n- await pilot.press(\"2\")\n+async def test_undo_checkpoint_created_on_cursor_move():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = SIMPLE_TEXT\n+ # Characters are inserted on line 0 and 1.\n+ checkpoint_one = text_area.text\n+ checkpoint_one_selection = text_area.selection\n+ await pilot.press(\"1\") # Added to initial batch.\n+\n+ # This cursor movement ensures a new checkpoint is created.\n+ post_insert_one_location = text_area.selection\n+ await pilot.press(\"down\")\n+\n+ checkpoint_two = text_area.text\n+ checkpoint_two_selection = text_area.selection\n+ await pilot.press(\"2\") # Added to new batch.\n+\n+ checkpoint_three = text_area.text\n+ checkpoint_three_selection = text_area.selection\n+\n+ # Going back to checkpoint two\n+ text_area.undo()\n+ assert text_area.text == checkpoint_two\n+ assert text_area.selection == checkpoint_two_selection\n+\n+ # Back again to checkpoint one (initial state)\n+ text_area.undo()\n+ assert text_area.text == checkpoint_one\n+ assert text_area.selection == checkpoint_one_selection\n+\n+ # Redo to move forward to checkpoint two.\n+ text_area.redo()\n+ assert text_area.text == checkpoint_two\n+ assert text_area.selection == post_insert_one_location\n+\n+ # Redo to move forward to checkpoint three.\n+ text_area.redo()\n+ assert text_area.text == checkpoint_three\n+ assert text_area.selection == checkpoint_three_selection\n+\n+\n+async def test_setting_text_property_resets_history():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ await pilot.press(\"1\")\n \n- # Since \"3\" appears 10 seconds later, it's in a separate batch.\n- text_area.history.mock_time += 10.0\n- await pilot.press(\"3\")\n+ # Programmatically setting text, which should invalidate the history\n+ text = \"Hello, world!\"\n+ text_area.text = text\n \n- assert text_area.text == \"123\"\n+ # The undo doesn't do anything, since we set the `text` property.\n+ text_area.undo()\n+ assert text_area.text == text\n \n- text_area.undo()\n- assert text_area.text == \"12\"\n \n- text_area.undo()\n- assert text_area.text == \"\"\n+async def test_edits_batched_by_time():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ # The first \"12\" is batched since they happen within 2 seconds.\n+ text_area.history.mock_time = 0\n+ await pilot.press(\"1\")\n \n+ text_area.history.mock_time = 1.0\n+ await pilot.press(\"2\")\n \n-async def test_undo_checkpoint_character_limit_reached(\n- pilot: Pilot, text_area: TextArea\n-):\n- await pilot.press(\"1\")\n- # Since the insertion below is > 100 characters it goes to a new batch.\n- text_area.insert(\"2\" * 120)\n+ # Since \"3\" appears 10 seconds later, it's in a separate batch.\n+ text_area.history.mock_time += 10.0\n+ await pilot.press(\"3\")\n \n- text_area.undo()\n- assert text_area.text == \"1\"\n- text_area.undo()\n- assert text_area.text == \"\"\n+ assert text_area.text == \"123\"\n \n+ text_area.undo()\n+ assert text_area.text == \"12\"\n \n-async def test_redo_with_no_undo_is_noop(text_area: TextArea):\n- text_area.text = SIMPLE_TEXT\n- text_area.redo()\n- assert text_area.text == SIMPLE_TEXT\n+ text_area.undo()\n+ assert text_area.text == \"\"\n \n \n-async def test_undo_with_empty_undo_stack_is_noop(text_area: TextArea):\n- text_area.text = SIMPLE_TEXT\n- text_area.undo()\n- assert text_area.text == SIMPLE_TEXT\n+async def test_undo_checkpoint_character_limit_reached():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ await pilot.press(\"1\")\n+ # Since the insertion below is > 100 characters it goes to a new batch.\n+ text_area.insert(\"2\" * 120)\n \n+ text_area.undo()\n+ assert text_area.text == \"1\"\n+ text_area.undo()\n+ assert text_area.text == \"\"\n \n-async def test_redo_stack_cleared_on_edit(pilot: Pilot, text_area: TextArea):\n- text_area.text = \"\"\n- await pilot.press(\"1\")\n- text_area.history.checkpoint()\n- await pilot.press(\"2\")\n- text_area.history.checkpoint()\n- await pilot.press(\"3\")\n \n- text_area.undo()\n- text_area.undo()\n- text_area.undo()\n- assert text_area.text == \"\"\n- assert text_area.selection == Selection.cursor((0, 0))\n+async def test_redo_with_no_undo_is_noop():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = SIMPLE_TEXT\n+ text_area.redo()\n+ assert text_area.text == SIMPLE_TEXT\n \n- # Redo stack has 3 edits in it now.\n- await pilot.press(\"f\")\n- assert text_area.text == \"f\"\n- assert text_area.selection == Selection.cursor((0, 1))\n \n- # Redo stack is cleared because of the edit, so redo has no effect.\n- text_area.redo()\n- assert text_area.text == \"f\"\n- assert text_area.selection == Selection.cursor((0, 1))\n- text_area.redo()\n- assert text_area.text == \"f\"\n- assert text_area.selection == Selection.cursor((0, 1))\n+async def test_undo_with_empty_undo_stack_is_noop():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = SIMPLE_TEXT\n+ text_area.undo()\n+ assert text_area.text == SIMPLE_TEXT\n \n \n-async def test_inserts_not_batched_with_deletes(pilot: Pilot, text_area: TextArea):\n+async def test_redo_stack_cleared_on_edit():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = \"\"\n+ await pilot.press(\"1\")\n+ text_area.history.checkpoint()\n+ await pilot.press(\"2\")\n+ text_area.history.checkpoint()\n+ await pilot.press(\"3\")\n+\n+ text_area.undo()\n+ text_area.undo()\n+ text_area.undo()\n+ assert text_area.text == \"\"\n+ assert text_area.selection == Selection.cursor((0, 0))\n+\n+ # Redo stack has 3 edits in it now.\n+ await pilot.press(\"f\")\n+ assert text_area.text == \"f\"\n+ assert text_area.selection == Selection.cursor((0, 1))\n+\n+ # Redo stack is cleared because of the edit, so redo has no effect.\n+ text_area.redo()\n+ assert text_area.text == \"f\"\n+ assert text_area.selection == Selection.cursor((0, 1))\n+ text_area.redo()\n+ assert text_area.text == \"f\"\n+ assert text_area.selection == Selection.cursor((0, 1))\n+\n+\n+async def test_inserts_not_batched_with_deletes():\n # 3 batches here: __1___ ___________2____________ __3__\n- await pilot.press(*\"123\", \"backspace\", \"backspace\", *\"23\")\n-\n- assert text_area.text == \"123\"\n-\n- # Undo batch 1: the \"23\" insertion.\n- text_area.undo()\n- assert text_area.text == \"1\"\n \n- # Undo batch 2: the double backspace.\n- text_area.undo()\n- assert text_area.text == \"123\"\n-\n- # Undo batch 3: the \"123\" insertion.\n- text_area.undo()\n- assert text_area.text == \"\"\n+ app = TextAreaApp()\n \n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ await pilot.press(*\"123\", \"backspace\", \"backspace\", *\"23\")\n \n-async def test_paste_is_an_isolated_batch(pilot: Pilot, text_area: TextArea):\n- pilot.app.post_message(Paste(\"hello \"))\n- pilot.app.post_message(Paste(\"world\"))\n- await pilot.pause()\n+ assert text_area.text == \"123\"\n \n- assert text_area.text == \"hello world\"\n+ # Undo batch 1: the \"23\" insertion.\n+ text_area.undo()\n+ assert text_area.text == \"1\"\n \n- await pilot.press(\"!\")\n+ # Undo batch 2: the double backspace.\n+ text_area.undo()\n+ assert text_area.text == \"123\"\n \n- # The insertion of \"!\" does not get batched with the paste of \"world\".\n- text_area.undo()\n- assert text_area.text == \"hello world\"\n+ # Undo batch 3: the \"123\" insertion.\n+ text_area.undo()\n+ assert text_area.text == \"\"\n \n- text_area.undo()\n- assert text_area.text == \"hello \"\n \n- text_area.undo()\n- assert text_area.text == \"\"\n+async def test_paste_is_an_isolated_batch():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ pilot.app.post_message(Paste(\"hello \"))\n+ pilot.app.post_message(Paste(\"world\"))\n+ await pilot.pause()\n \n+ assert text_area.text == \"hello world\"\n \n-async def test_focus_creates_checkpoint(pilot: Pilot, text_area: TextArea):\n- await pilot.press(*\"123\")\n- text_area.has_focus = False\n- text_area.has_focus = True\n- await pilot.press(*\"456\")\n- assert text_area.text == \"123456\"\n+ await pilot.press(\"!\")\n \n- # Since we re-focused, a checkpoint exists between 123 and 456,\n- # so when we use undo, only the 456 is removed.\n- text_area.undo()\n- assert text_area.text == \"123\"\n+ # The insertion of \"!\" does not get batched with the paste of \"world\".\n+ text_area.undo()\n+ assert text_area.text == \"hello world\"\n \n+ text_area.undo()\n+ assert text_area.text == \"hello \"\n \n-async def test_undo_redo_deletions_batched(pilot: Pilot, text_area: TextArea):\n- text_area.text = SIMPLE_TEXT\n- text_area.selection = Selection((0, 2), (1, 2))\n+ text_area.undo()\n+ assert text_area.text == \"\"\n \n- # Perform a single delete of some selected text. It'll live in it's own\n- # batch since it's a multi-line operation.\n- await pilot.press(\"backspace\")\n- checkpoint_one = \"ABHIJ\\nKLMNO\\nPQRST\\nUVWXY\\nZ\\n\"\n- assert text_area.text == checkpoint_one\n- assert text_area.selection == Selection.cursor((0, 2))\n \n- # Pressing backspace a few times to delete more characters.\n- await pilot.press(\"backspace\", \"backspace\", \"backspace\")\n- checkpoint_two = \"HIJ\\nKLMNO\\nPQRST\\nUVWXY\\nZ\\n\"\n- assert text_area.text == checkpoint_two\n- assert text_area.selection == Selection.cursor((0, 0))\n+async def test_focus_creates_checkpoint():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ await pilot.press(*\"123\")\n+ text_area.has_focus = False\n+ text_area.has_focus = True\n+ await pilot.press(*\"456\")\n+ assert text_area.text == \"123456\"\n \n- # When we undo, the 3 deletions above should be batched, but not\n- # the original deletion since it contains a newline character.\n- text_area.undo()\n- assert text_area.text == checkpoint_one\n- assert text_area.selection == Selection.cursor((0, 2))\n+ # Since we re-focused, a checkpoint exists between 123 and 456,\n+ # so when we use undo, only the 456 is removed.\n+ text_area.undo()\n+ assert text_area.text == \"123\"\n \n- # Undoing again restores us back to our initial text and selection.\n- text_area.undo()\n- assert text_area.text == SIMPLE_TEXT\n- assert text_area.selection == Selection((0, 2), (1, 2))\n \n- # At this point, the undo stack contains two items, so we can redo twice.\n+async def test_undo_redo_deletions_batched():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ text_area.text = SIMPLE_TEXT\n+ text_area.selection = Selection((0, 2), (1, 2))\n+\n+ # Perform a single delete of some selected text. It'll live in it's own\n+ # batch since it's a multi-line operation.\n+ await pilot.press(\"backspace\")\n+ checkpoint_one = \"ABHIJ\\nKLMNO\\nPQRST\\nUVWXY\\nZ\\n\"\n+ assert text_area.text == checkpoint_one\n+ assert text_area.selection == Selection.cursor((0, 2))\n+\n+ # Pressing backspace a few times to delete more characters.\n+ await pilot.press(\"backspace\", \"backspace\", \"backspace\")\n+ checkpoint_two = \"HIJ\\nKLMNO\\nPQRST\\nUVWXY\\nZ\\n\"\n+ assert text_area.text == checkpoint_two\n+ assert text_area.selection == Selection.cursor((0, 0))\n+\n+ # When we undo, the 3 deletions above should be batched, but not\n+ # the original deletion since it contains a newline character.\n+ text_area.undo()\n+ assert text_area.text == checkpoint_one\n+ assert text_area.selection == Selection.cursor((0, 2))\n+\n+ # Undoing again restores us back to our initial text and selection.\n+ text_area.undo()\n+ assert text_area.text == SIMPLE_TEXT\n+ assert text_area.selection == Selection((0, 2), (1, 2))\n+\n+ # At this point, the undo stack contains two items, so we can redo twice.\n+\n+ # Redo to go back to checkpoint one.\n+ text_area.redo()\n+ assert text_area.text == checkpoint_one\n+ assert text_area.selection == Selection.cursor((0, 2))\n+\n+ # Redo again to go back to checkpoint two\n+ text_area.redo()\n+ assert text_area.text == checkpoint_two\n+ assert text_area.selection == Selection.cursor((0, 0))\n+\n+ # Redo again does nothing.\n+ text_area.redo()\n+ assert text_area.text == checkpoint_two\n+ assert text_area.selection == Selection.cursor((0, 0))\n+\n+\n+async def test_max_checkpoints():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ assert len(text_area.history.undo_stack) == 0\n+ for index in range(MAX_CHECKPOINTS):\n+ # Press enter since that will ensure a checkpoint is created.\n+ await pilot.press(\"enter\")\n \n- # Redo to go back to checkpoint one.\n- text_area.redo()\n- assert text_area.text == checkpoint_one\n- assert text_area.selection == Selection.cursor((0, 2))\n+ assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS\n+ await pilot.press(\"enter\")\n+ # Ensure we don't go over the limit.\n+ assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS\n \n- # Redo again to go back to checkpoint two\n- text_area.redo()\n- assert text_area.text == checkpoint_two\n- assert text_area.selection == Selection.cursor((0, 0))\n \n- # Redo again does nothing.\n- text_area.redo()\n- assert text_area.text == checkpoint_two\n- assert text_area.selection == Selection.cursor((0, 0))\n+async def test_redo_stack():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ assert len(text_area.history.redo_stack) == 0\n+ await pilot.press(\"enter\")\n+ await pilot.press(*\"123\")\n+ assert len(text_area.history.undo_stack) == 2\n+ assert len(text_area.history.redo_stack) == 0\n+ text_area.undo()\n+ assert len(text_area.history.undo_stack) == 1\n+ assert len(text_area.history.redo_stack) == 1\n+ text_area.undo()\n+ assert len(text_area.history.undo_stack) == 0\n+ assert len(text_area.history.redo_stack) == 2\n+ text_area.redo()\n+ assert len(text_area.history.undo_stack) == 1\n+ assert len(text_area.history.redo_stack) == 1\n+ text_area.redo()\n+ assert len(text_area.history.undo_stack) == 2\n+ assert len(text_area.history.redo_stack) == 0\n+\n+\n+async def test_backward_selection_undo_redo():\n+ app = TextAreaApp()\n+ async with app.run_test() as pilot:\n+ text_area = app.text_area\n+ # Failed prior to https://github.com/Textualize/textual/pull/4352\n+ text_area.text = SIMPLE_TEXT\n+ text_area.selection = Selection((3, 2), (0, 0))\n \n+ await pilot.press(\"a\")\n \n-async def test_max_checkpoints(pilot: Pilot, text_area: TextArea):\n- assert len(text_area.history.undo_stack) == 0\n- for index in range(MAX_CHECKPOINTS):\n- # Press enter since that will ensure a checkpoint is created.\n- await pilot.press(\"enter\")\n+ text_area.undo()\n+ await pilot.press(\"down\", \"down\", \"down\", \"down\")\n \n- assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS\n- await pilot.press(\"enter\")\n- # Ensure we don't go over the limit.\n- assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS\n-\n-\n-async def test_redo_stack(pilot: Pilot, text_area: TextArea):\n- assert len(text_area.history.redo_stack) == 0\n- await pilot.press(\"enter\")\n- await pilot.press(*\"123\")\n- assert len(text_area.history.undo_stack) == 2\n- assert len(text_area.history.redo_stack) == 0\n- text_area.undo()\n- assert len(text_area.history.undo_stack) == 1\n- assert len(text_area.history.redo_stack) == 1\n- text_area.undo()\n- assert len(text_area.history.undo_stack) == 0\n- assert len(text_area.history.redo_stack) == 2\n- text_area.redo()\n- assert len(text_area.history.undo_stack) == 1\n- assert len(text_area.history.redo_stack) == 1\n- text_area.redo()\n- assert len(text_area.history.undo_stack) == 2\n- assert len(text_area.history.redo_stack) == 0\n-\n-\n-async def test_backward_selection_undo_redo(pilot: Pilot, text_area: TextArea):\n- # Failed prior to https://github.com/Textualize/textual/pull/4352\n- text_area.text = SIMPLE_TEXT\n- text_area.selection = Selection((3, 2), (0, 0))\n-\n- await pilot.press(\"a\")\n-\n- text_area.undo()\n- await pilot.press(\"down\", \"down\", \"down\", \"down\")\n-\n- assert text_area.text == SIMPLE_TEXT\n+ assert text_area.text == SIMPLE_TEXT\n" }
[ { "diff_hunk": "@@ -50,4 +50,7 @@ async def on_mount(self) -> None:\n \"MyScreen#main\",\n ]\n \n+ print(unmount_ids)\n+ print(expected)", "line": null, "original_line": 54, "original_start_line": 53, "path": "tests/test_unmount.py", "start_line": null, "text": "@user1:\n```suggestion\r\n```" } ]
8ad78c74dde897f2f5e51e542910243d606ed9dd
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-from functools import lru_cache, partial +from functools import partial from inspect import isawaitable, signature from typing import TYPE_CHECKING, Any, Callable @@ -16,16 +16,24 @@ def count_parameters(func: Callable) -> int: """Count the number of parameters in a callable""" + try: + return func._param_count + except AttributeError: + pass if isinstance(func, partial): - return _count_parameters(func.func) + len(func.args) - if hasattr(func, "__self__"): + param_count = _count_parameters(func.func) - ( + len(func.args) + len(func.keywords) + ) + elif hasattr(func, "__self__"): # Bound method func = func.__func__ # type: ignore - return _count_parameters(func) - 1 - return _count_parameters(func) + param_count = _count_parameters(func) - 1 + else: + param_count = _count_parameters(func) + func._param_count = param_count + return param_count -@lru_cache(maxsize=2048) def _count_parameters(func: Callable) -> int: """Count the number of parameters in a callable""" return len(signature(func).parameters) diff --git a/src/textual/_compositor.py b/src/textual/_compositor.py index f18417d001..0639945b5a 100644 --- a/src/textual/_compositor.py +++ b/src/textual/_compositor.py @@ -309,6 +309,15 @@ def __init__(self) -> None: # Mapping of line numbers on to lists of widget and regions self._layers_visible: list[list[tuple[Widget, Region, Region]]] | None = None + def clear(self) -> None: + """Remove all references to widgets (used when the screen closes).""" + self._full_map.clear() + self._visible_map = None + self._layers = None + self.widgets.clear() + self._visible_widgets = None + self._layers_visible = None + @classmethod def _regions_to_spans( cls, regions: Iterable[Region] diff --git a/src/textual/_context.py b/src/textual/_context.py index b1b20b4d29..039bb7b4ef 100644 --- a/src/textual/_context.py +++ b/src/textual/_context.py @@ -1,7 +1,7 @@ from __future__ import annotations from contextvars import ContextVar -from typing import TYPE_CHECKING, Callable +from typing import TYPE_CHECKING, Any, Callable if TYPE_CHECKING: from .app import App @@ -14,8 +14,9 @@ class NoActiveAppError(RuntimeError): """Runtime error raised if we try to retrieve the active app when there is none.""" -active_app: ContextVar["App[object]"] = ContextVar("active_app") +active_app: ContextVar["App[Any]"] = ContextVar("active_app") active_message_pump: ContextVar["MessagePump"] = ContextVar("active_message_pump") + prevent_message_types_stack: ContextVar[list[set[type[Message]]]] = ContextVar( "prevent_message_types_stack" ) diff --git a/src/textual/_node_list.py b/src/textual/_node_list.py index 52555f9d61..abae4c298f 100644 --- a/src/textual/_node_list.py +++ b/src/textual/_node_list.py @@ -1,6 +1,7 @@ from __future__ import annotations import sys +import weakref from operator import attrgetter from typing import TYPE_CHECKING, Any, Callable, Iterator, Sequence, overload @@ -26,7 +27,13 @@ class NodeList(Sequence["Widget"]): """ def __init__(self, parent: DOMNode | None = None) -> None: - self._parent = parent + """Initialize a node list. + + Args: + parent: The parent node which holds a reference to this object, or `None` if + there is no parent. + """ + self._parent = None if parent is None else weakref.ref(parent) # The nodes in the list self._nodes: list[Widget] = [] self._nodes_set: set[Widget] = set() @@ -57,7 +64,9 @@ def __contains__(self, widget: object) -> bool: def updated(self) -> None: """Mark the nodes as having been updated.""" self._updates += 1 - node = self._parent + node = None if self._parent is None else self._parent() + if node is None: + return while node is not None and (node := node._parent) is not None: node._nodes._updates += 1 diff --git a/src/textual/app.py b/src/textual/app.py index b721507249..8f3fe1089c 100644 --- a/src/textual/app.py +++ b/src/textual/app.py @@ -809,6 +809,17 @@ def _end_batch(self) -> None: if not self._batch_count: self.check_idle() + @contextmanager + def _context(self) -> Generator[None, None, None]: + """Context manager to set ContextVars.""" + app_reset_token = active_app.set(self) + message_pump_reset_token = active_message_pump.set(self) + try: + yield + finally: + active_message_pump.reset(message_pump_reset_token) + active_app.reset(app_reset_token) + def animate( self, attribute: str, @@ -1046,10 +1057,6 @@ def get_default_screen(self) -> Screen: """ return Screen(id="_default") - def _set_active(self) -> None: - """Set this app to be the currently active app.""" - active_app.set(self) - def compose(self) -> ComposeResult: """Yield child widgets for a container. @@ -1355,8 +1362,8 @@ def call_from_thread( async def run_callback() -> CallThreadReturnType: """Run the callback, set the result or error on the future.""" - self._set_active() - return await invoke(callback_with_args) + with self._context(): + return await invoke(callback_with_args) # Post the message to the main loop future: Future[CallThreadReturnType] = asyncio.run_coroutine_threadsafe( @@ -1667,41 +1674,39 @@ async def run_app(app: App) -> None: app: App to run. """ - try: - if message_hook is not None: - message_hook_context_var.set(message_hook) - app._loop = asyncio.get_running_loop() - app._thread_id = threading.get_ident() - await app._process_messages( - ready_callback=on_app_ready, - headless=headless, - terminal_size=size, - ) - finally: - app_ready_event.set() + with app._context(): + try: + if message_hook is not None: + message_hook_context_var.set(message_hook) + app._loop = asyncio.get_running_loop() + app._thread_id = threading.get_ident() + await app._process_messages( + ready_callback=on_app_ready, + headless=headless, + terminal_size=size, + ) + finally: + app_ready_event.set() # Launch the app in the "background" - active_message_pump.set(app) + app_task = create_task(run_app(app), name=f"run_test {app}") # Wait until the app has performed all startup routines. await app_ready_event.wait() - - # Get the app in an active state. - app._set_active() - - # Context manager returns pilot object to manipulate the app - try: - pilot = Pilot(app) - await pilot._wait_for_screen() - yield pilot - finally: - # Shutdown the app cleanly - await app._shutdown() - await app_task - # Re-raise the exception which caused panic so test frameworks are aware - if self._exception: - raise self._exception + with app._context(): + # Context manager returns pilot object to manipulate the app + try: + pilot = Pilot(app) + await pilot._wait_for_screen() + yield pilot + finally: + # Shutdown the app cleanly + await app._shutdown() + await app_task + # Re-raise the exception which caused panic so test frameworks are aware + if self._exception: + raise self._exception async def run_async( self, @@ -1751,14 +1756,14 @@ async def app_ready() -> None: async def run_auto_pilot( auto_pilot: AutopilotCallbackType, pilot: Pilot ) -> None: - try: - await auto_pilot(pilot) - except Exception: - app.exit() - raise + with self._context(): + try: + await auto_pilot(pilot) + except Exception: + app.exit() + raise pilot = Pilot(app) - active_message_pump.set(self) auto_pilot_task = create_task( run_auto_pilot(auto_pilot, pilot), name=repr(pilot) ) @@ -1816,18 +1821,19 @@ async def run_app() -> None: """Run the app.""" self._loop = asyncio.get_running_loop() self._thread_id = threading.get_ident() - try: - await self.run_async( - headless=headless, - inline=inline, - inline_no_clear=inline_no_clear, - mouse=mouse, - size=size, - auto_pilot=auto_pilot, - ) - finally: - self._loop = None - self._thread_id = 0 + with self._context(): + try: + await self.run_async( + headless=headless, + inline=inline, + inline_no_clear=inline_no_clear, + mouse=mouse, + size=size, + auto_pilot=auto_pilot, + ) + finally: + self._loop = None + self._thread_id = 0 if _ASYNCIO_GET_EVENT_LOOP_IS_DEPRECATED: # N.B. This doesn't work with Python<3.10, as we end up with 2 event loops: @@ -2680,6 +2686,17 @@ def _build_driver( ) return driver + async def _init_devtools(self): + """Initialize developer tools.""" + if self.devtools is not None: + from textual_dev.client import DevtoolsConnectionError + + try: + await self.devtools.connect() + self.log.system(f"Connected to devtools ( {self.devtools.url} )") + except DevtoolsConnectionError: + self.log.system(f"Couldn't connect to devtools ( {self.devtools.url} )") + async def _process_messages( self, ready_callback: CallbackType | None = None, @@ -2690,54 +2707,49 @@ async def _process_messages( terminal_size: tuple[int, int] | None = None, message_hook: Callable[[Message], None] | None = None, ) -> None: - self._set_active() - active_message_pump.set(self) + async def app_prelude() -> bool: + """Work required before running the app. - if self.devtools is not None: - from textual_dev.client import DevtoolsConnectionError + Returns: + `True` if the app should continue, or `False` if there was a problem starting. + """ + await self._init_devtools() + self.log.system("---") + self.log.system(loop=asyncio.get_running_loop()) + self.log.system(features=self.features) + if constants.LOG_FILE is not None: + _log_path = os.path.abspath(constants.LOG_FILE) + self.log.system(f"Writing logs to {_log_path!r}") try: - await self.devtools.connect() - self.log.system(f"Connected to devtools ( {self.devtools.url} )") - except DevtoolsConnectionError: - self.log.system(f"Couldn't connect to devtools ( {self.devtools.url} )") - - self.log.system("---") - - self.log.system(loop=asyncio.get_running_loop()) - self.log.system(features=self.features) - if constants.LOG_FILE is not None: - _log_path = os.path.abspath(constants.LOG_FILE) - self.log.system(f"Writing logs to {_log_path!r}") - - try: - if self.css_path: - self.stylesheet.read_all(self.css_path) - for read_from, css, tie_breaker, scope in self._get_default_css(): - self.stylesheet.add_source( - css, - read_from=read_from, - is_default_css=True, - tie_breaker=tie_breaker, - scope=scope, - ) - if self.CSS: - try: - app_path = inspect.getfile(self.__class__) - except (TypeError, OSError): - app_path = "" - read_from = (app_path, f"{self.__class__.__name__}.CSS") - self.stylesheet.add_source( - self.CSS, read_from=read_from, is_default_css=False - ) - except Exception as error: - self._handle_exception(error) - self._print_error_renderables() - return + if self.css_path: + self.stylesheet.read_all(self.css_path) + for read_from, css, tie_breaker, scope in self._get_default_css(): + self.stylesheet.add_source( + css, + read_from=read_from, + is_default_css=True, + tie_breaker=tie_breaker, + scope=scope, + ) + if self.CSS: + try: + app_path = inspect.getfile(self.__class__) + except (TypeError, OSError): + app_path = "" + read_from = (app_path, f"{self.__class__.__name__}.CSS") + self.stylesheet.add_source( + self.CSS, read_from=read_from, is_default_css=False + ) + except Exception as error: + self._handle_exception(error) + self._print_error_renderables() + return False - if self.css_monitor: - self.set_interval(0.25, self.css_monitor, name="css monitor") - self.log.system("STARTED", self.css_monitor) + if self.css_monitor: + self.set_interval(0.25, self.css_monitor, name="css monitor") + self.log.system("STARTED", self.css_monitor) + return True async def run_process_messages(): """The main message loop, invoke below.""" @@ -2788,40 +2800,44 @@ async def invoke_ready_callback() -> None: finally: await Timer._stop_all(self._timers) - self._running = True - try: - load_event = events.Load() - await self._dispatch_message(load_event) + with self._context(): + if not await app_prelude(): + return + self._running = True + try: + load_event = events.Load() + await self._dispatch_message(load_event) - driver = self._driver = self._build_driver( - headless=headless, - inline=inline, - mouse=mouse, - size=terminal_size, - ) - self.log(driver=driver) + driver = self._driver = self._build_driver( + headless=headless, + inline=inline, + mouse=mouse, + size=terminal_size, + ) + self.log(driver=driver) - if not self._exit: - driver.start_application_mode() - try: - with redirect_stdout(self._capture_stdout): - with redirect_stderr(self._capture_stderr): - await run_process_messages() + if not self._exit: + driver.start_application_mode() + try: + with redirect_stdout(self._capture_stdout): + with redirect_stderr(self._capture_stderr): + await run_process_messages() - finally: - if self._driver.is_inline: - cursor_x, cursor_y = self._previous_cursor_position - self._driver.write( - Control.move(-cursor_x, -cursor_y + 1).segment.text - ) - if inline_no_clear and not not self.app._exit_renderables: - console = Console() - console.print(self.screen._compositor) - console.print() + finally: + Reactive._clear_watchers(self) + if self._driver.is_inline: + cursor_x, cursor_y = self._previous_cursor_position + self._driver.write( + Control.move(-cursor_x, -cursor_y + 1).segment.text + ) + if inline_no_clear and not not self.app._exit_renderables: + console = Console() + console.print(self.screen._compositor) + console.print() - driver.stop_application_mode() - except Exception as error: - self._handle_exception(error) + driver.stop_application_mode() + except Exception as error: + self._handle_exception(error) async def _pre_process(self) -> bool: """Special case for the app, which doesn't need the functionality in MessagePump.""" @@ -3029,6 +3045,8 @@ async def _close_all(self) -> None: if stack_screen._running: await self._prune(stack_screen) stack.clear() + self._installed_screens.clear() + self._modes.clear() # Close any remaining nodes # Should be empty by now @@ -3045,12 +3063,13 @@ async def _shutdown(self) -> None: await self._close_all() await self._close_messages() - await self._dispatch_message(events.Unmount()) if self._driver is not None: self._driver.close() + self._nodes._clear() + if self.devtools is not None and self.devtools.is_connected: await self._disconnect_devtools() diff --git a/src/textual/command.py b/src/textual/command.py index 54bc5a4c73..55789e0bb5 100644 --- a/src/textual/command.py +++ b/src/textual/command.py @@ -241,6 +241,7 @@ async def _wait_init(self) -> None: """Wait for initialization.""" if self._init_task is not None: await self._init_task + self._init_task = None async def startup(self) -> None: """Called after the Provider is initialized, but before any calls to `search`.""" diff --git a/src/textual/css/_styles_builder.py b/src/textual/css/_styles_builder.py index 3107375a83..96cc8e2b9a 100644 --- a/src/textual/css/_styles_builder.py +++ b/src/textual/css/_styles_builder.py @@ -1,7 +1,6 @@ from __future__ import annotations -from functools import lru_cache -from typing import Iterable, NoReturn, Sequence, cast +from typing import Iterable, NoReturn, cast import rich.repr @@ -137,19 +136,6 @@ def add_declaration(self, declaration: Declaration) -> None: except Exception as error: self.error(declaration.name, declaration.token, str(error)) - @lru_cache(maxsize=None) - def _get_processable_rule_names(self) -> Sequence[str]: - """ - Returns the list of CSS properties we can manage - - i.e. the ones for which we have a `process_[property name]` method - - Returns: - All the "Python-ised" CSS property names this class can handle. - - Example: ("width", "background", "offset_x", ...) - """ - return [attr[8:] for attr in dir(self) if attr.startswith("process_")] - def _process_enum_multiple( self, name: str, tokens: list[Token], valid_values: set[str], count: int ) -> tuple[str, ...]: @@ -1154,4 +1140,7 @@ def _get_suggested_property_name_for_rule(self, rule_name: str) -> str | None: Example: returns "background" for rule_name "bkgrund", "offset_x" for "ofset_x" """ - return get_suggestion(rule_name, self._get_processable_rule_names()) + processable_rules_name = [ + attr[8:] for attr in dir(self) if attr.startswith("process_") + ] + return get_suggestion(rule_name, processable_rules_name) diff --git a/src/textual/css/styles.py b/src/textual/css/styles.py index c2d14ea02b..8fe230bc75 100644 --- a/src/textual/css/styles.py +++ b/src/textual/css/styles.py @@ -1,7 +1,7 @@ from __future__ import annotations from dataclasses import dataclass, field -from functools import lru_cache, partial +from functools import partial from operator import attrgetter from typing import TYPE_CHECKING, Any, Callable, Iterable, cast @@ -554,7 +554,6 @@ def is_animatable(cls, rule: str) -> bool: return rule in cls.ANIMATABLE @classmethod - @lru_cache(maxsize=1024) def parse( cls, css: str, read_from: CSSLocation, *, node: DOMNode | None = None ) -> Styles: diff --git a/src/textual/message_pump.py b/src/textual/message_pump.py index 317fd0fcd3..5231bfaaac 100644 --- a/src/textual/message_pump.py +++ b/src/textual/message_pump.py @@ -229,7 +229,7 @@ def app(self) -> "App[object]": if node is None: raise NoActiveAppError() node = node._parent - active_app.set(node) + return node @property @@ -501,24 +501,26 @@ def _start_messages(self) -> None: async def _process_messages(self) -> None: self._running = True - active_message_pump.set(self) - if not await self._pre_process(): - self._running = False - return + with self._context(): + if not await self._pre_process(): + self._running = False + return - try: - await self._process_messages_loop() - except CancelledError: - pass - finally: - self._running = False try: - if self._timers: - await Timer._stop_all(self._timers) - self._timers.clear() + await self._process_messages_loop() + except CancelledError: + pass finally: - await self._message_loop_exit() + self._running = False + try: + if self._timers: + await Timer._stop_all(self._timers) + self._timers.clear() + Reactive._clear_watchers(self) + finally: + await self._message_loop_exit() + self._task = None async def _message_loop_exit(self) -> None: """Called when the message loop has completed.""" @@ -558,6 +560,15 @@ def _close_messages_no_wait(self) -> None: """Request the message queue to immediately exit.""" self._message_queue.put_nowait(messages.CloseMessages()) + @contextmanager + def _context(self) -> Generator[None, None, None]: + """Context manager to set ContextVars.""" + reset_token = active_message_pump.set(self) + try: + yield + finally: + active_message_pump.reset(reset_token) + async def _on_close_messages(self, message: messages.CloseMessages) -> None: await self._close_messages() diff --git a/src/textual/reactive.py b/src/textual/reactive.py index 64b6e007ce..aa38893ee8 100644 --- a/src/textual/reactive.py +++ b/src/textual/reactive.py @@ -24,7 +24,6 @@ from . import events from ._callback import count_parameters -from ._context import active_message_pump from ._types import ( MessageTarget, WatchCallbackBothValuesType, @@ -82,8 +81,8 @@ def invoke_watcher( _rich_traceback_omit = True param_count = count_parameters(watch_function) - reset_token = active_message_pump.set(watcher_object) - try: + + with watcher_object._context(): if param_count == 2: watch_result = cast(WatchCallbackBothValuesType, watch_function)( old_value, value @@ -97,8 +96,6 @@ def invoke_watcher( watcher_object.call_next( partial(await_watcher, watcher_object, watch_result) ) - finally: - active_message_pump.reset(reset_token) @rich.repr.auto @@ -152,6 +149,18 @@ def __rich_repr__(self) -> rich.repr.Result: yield "bindings", self._bindings, False yield "name", getattr(self, "name", None), None + @classmethod + def _clear_watchers(cls, obj: Reactable) -> None: + """Clear any watchers on a given object. + + Args: + obj: A reactive object. + """ + try: + getattr(obj, "__watchers").clear() + except AttributeError: + pass + @property def owner(self) -> Type[MessageTarget]: """The owner (class) where the reactive was declared.""" @@ -468,9 +477,8 @@ def _watch( """ if not hasattr(obj, "__watchers"): setattr(obj, "__watchers", {}) - watchers: dict[str, list[tuple[Reactable, WatchCallbackType]]] = getattr( - obj, "__watchers" - ) + watchers: dict[str, list[tuple[Reactable, WatchCallbackType]]] + watchers = getattr(obj, "__watchers") watcher_list = watchers.setdefault(attribute_name, []) if any(callback == callback_from_list for _, callback_from_list in watcher_list): return diff --git a/src/textual/screen.py b/src/textual/screen.py index 7697e38f20..4ea8eaba26 100644 --- a/src/textual/screen.py +++ b/src/textual/screen.py @@ -892,7 +892,8 @@ async def _on_idle(self, event: events.Idle) -> None: finally: if self._bindings_updated: self._bindings_updated = False - self.app.call_later(self.bindings_updated_signal.publish, self) + if self.is_attached and not self.app._exit: + self.app.call_later(self.bindings_updated_signal.publish, self) def _compositor_refresh(self) -> None: """Perform a compositor refresh.""" @@ -978,11 +979,8 @@ async def _invoke_and_clear_callbacks(self) -> None: callbacks = self._callbacks[:] self._callbacks.clear() for callback, message_pump in callbacks: - reset_token = active_message_pump.set(message_pump) - try: + with message_pump._context(): await invoke(callback) - finally: - active_message_pump.reset(reset_token) def _invoke_later(self, callback: CallbackType, sender: MessagePump) -> None: """Enqueue a callback to be invoked after the screen is repainted. @@ -1012,6 +1010,16 @@ def _push_result_callback( ResultCallback[Optional[ScreenResultType]](requester, callback, future) ) + async def _message_loop_exit(self) -> None: + await super()._message_loop_exit() + self._compositor.clear() + self._dirty_widgets.clear() + self._dirty_regions.clear() + self._arrangement_cache.clear() + self.screen_layout_refresh_signal.unsubscribe(self) + self._nodes._clear() + self._task = None + def _pop_result_callback(self) -> None: """Remove the latest result callback from the stack.""" self._result_callbacks.pop() diff --git a/src/textual/signal.py b/src/textual/signal.py index 5960ea687d..2e9a946e90 100644 --- a/src/textual/signal.py +++ b/src/textual/signal.py @@ -10,7 +10,7 @@ from __future__ import annotations from typing import TYPE_CHECKING, Any, Awaitable, Callable, Generic, TypeVar, Union -from weakref import WeakKeyDictionary +from weakref import WeakKeyDictionary, ref import rich.repr @@ -41,17 +41,22 @@ def __init__(self, owner: DOMNode, name: str) -> None: owner: The owner of this signal. name: An identifier for debugging purposes. """ - self._owner = owner + self._owner = ref(owner) self._name = name self._subscriptions: WeakKeyDictionary[ MessagePump, list[SignalCallbackType] ] = WeakKeyDictionary() def __rich_repr__(self) -> rich.repr.Result: - yield "owner", self._owner + yield "owner", self.owner yield "name", self._name yield "subscriptions", list(self._subscriptions.keys()) + @property + def owner(self) -> DOMNode | None: + """The owner of this Signal, or `None` if there is no owner.""" + return self._owner() + def subscribe( self, node: MessagePump, @@ -108,9 +113,12 @@ def publish(self, data: SignalT) -> None: """ # Don't publish if the DOM is not ready or shutting down - if not self._owner.is_attached or self._owner._pruning: + owner = self.owner + if owner is None: + return + if not owner.is_attached or owner._pruning: return - for ancestor_node in self._owner.ancestors_with_self: + for ancestor_node in owner.ancestors_with_self: if not ancestor_node.is_running: return diff --git a/src/textual/timer.py b/src/textual/timer.py index d158962fd5..e593203ee3 100644 --- a/src/textual/timer.py +++ b/src/textual/timer.py @@ -84,7 +84,7 @@ def _start(self) -> None: """Start the timer.""" self._task = create_task(self._run_timer(), name=self.name) - def stop(self) -> Task: + def stop(self) -> None: """Stop the timer. Returns: @@ -92,15 +92,11 @@ def stop(self) -> Task: """ if self._task is None: - - async def noop() -> None: - """A dummy task.""" - - return create_task(noop()) + return self._active.set() self._task.cancel() - return self._task + self._task = None @classmethod async def _stop_all(cls, timers: Iterable[Timer]) -> None: @@ -123,6 +119,7 @@ async def stop_timer(timer: Timer) -> None: await timer._task except CancelledError: pass + timer._task = None await gather(*[stop_timer(timer) for timer in list(timers)]) diff --git a/src/textual/widget.py b/src/textual/widget.py index b71cc971ae..58305d0497 100644 --- a/src/textual/widget.py +++ b/src/textual/widget.py @@ -49,7 +49,7 @@ from ._animator import DEFAULT_EASING, Animatable, BoundAnimator, EasingFunction from ._arrange import DockArrangeResult, arrange from ._compose import compose -from ._context import NoActiveAppError, active_app +from ._context import NoActiveAppError from ._debug import get_caller_file_and_line from ._dispatch_key import dispatch_key from ._easing import DEFAULT_SCROLL_EASING @@ -1194,9 +1194,10 @@ async def recompose(self) -> None: return async with self.batch(): - await self.query("*").exclude(".-textual-system").remove() + await self.query_children("*").exclude(".-textual-system").remove() if self.is_attached: - await self.mount_all(compose(self)) + compose_nodes = compose(self) + await self.mount_all(compose_nodes) def _post_register(self, app: App) -> None: """Called when the instance is registered. @@ -1883,7 +1884,7 @@ def _console(self) -> Console: Returns: A Rich console object. """ - return active_app.get().console + return self.app.console @property def _has_relative_children_width(self) -> bool: @@ -3676,6 +3677,11 @@ async def _message_loop_exit(self) -> None: parent._nodes._remove(self) self.app._registry.discard(self) self._detach() + self._arrangement_cache.clear() + self._nodes._clear() + self._render_cache = _RenderCache(NULL_SIZE, []) + self._component_styles.clear() + self._query_one_cache.clear() async def _on_idle(self, event: events.Idle) -> None: """Called when there are no more events on the queue. diff --git a/src/textual/widgets/_footer.py b/src/textual/widgets/_footer.py index 0d51f9f76e..98214a56f7 100644 --- a/src/textual/widgets/_footer.py +++ b/src/textual/widgets/_footer.py @@ -227,15 +227,15 @@ def compose(self) -> ComposeResult: ) break + async def bindings_changed(self, screen: Screen) -> None: + self._bindings_ready = True + if not screen.app.app_focus: + return + if self.is_attached and screen is self.screen: + await self.recompose() + def on_mount(self) -> None: - async def bindings_changed(screen: Screen) -> None: - self._bindings_ready = True - if not screen.app.app_focus: - return - if self.is_attached and screen is self.screen: - await self.recompose() - - self.screen.bindings_updated_signal.subscribe(self, bindings_changed) + self.screen.bindings_updated_signal.subscribe(self, self.bindings_changed) def on_unmount(self) -> None: self.screen.bindings_updated_signal.unsubscribe(self) diff --git a/src/textual/worker.py b/src/textual/worker.py index 9ad60a64ac..c0b1cbbffd 100644 --- a/src/textual/worker.py +++ b/src/textual/worker.py @@ -359,30 +359,30 @@ async def _run(self, app: App) -> None: Args: app: App instance. """ - app._set_active() - active_worker.set(self) + with app._context(): + active_worker.set(self) - self.state = WorkerState.RUNNING - app.log.worker(self) - try: - self._result = await self.run() - except asyncio.CancelledError as error: - self.state = WorkerState.CANCELLED - self._error = error - app.log.worker(self) - except Exception as error: - self.state = WorkerState.ERROR - self._error = error - app.log.worker(self, "failed", repr(error)) - from rich.traceback import Traceback - - app.log.worker(Traceback()) - if self.exit_on_error: - worker_failed = WorkerFailed(self._error) - app._handle_exception(worker_failed) - else: - self.state = WorkerState.SUCCESS + self.state = WorkerState.RUNNING app.log.worker(self) + try: + self._result = await self.run() + except asyncio.CancelledError as error: + self.state = WorkerState.CANCELLED + self._error = error + app.log.worker(self) + except Exception as error: + self.state = WorkerState.ERROR + self._error = error + app.log.worker(self, "failed", repr(error)) + from rich.traceback import Traceback + + app.log.worker(Traceback()) + if self.exit_on_error: + worker_failed = WorkerFailed(self._error) + app._handle_exception(worker_failed) + else: + self.state = WorkerState.SUCCESS + app.log.worker(self) def _start( self, app: App, done_callback: Callable[[Worker], None] | None = None diff --git a/tests/layouts/test_horizontal.py b/tests/layouts/test_horizontal.py index 05ce4c8905..62d766aecb 100644 --- a/tests/layouts/test_horizontal.py +++ b/tests/layouts/test_horizontal.py @@ -19,11 +19,11 @@ def compose(self) -> ComposeResult: yield self.horizontal app = HorizontalAutoWidth() - async with app.run_test(): - yield app + yield app async def test_horizontal_get_content_width(app): - size = app.screen.size - width = app.horizontal.get_content_width(size, size) - assert width == 15 + async with app.run_test(): + size = app.screen.size + width = app.horizontal.get_content_width(size, size) + assert width == 15 diff --git a/tests/test_count_parameters.py b/tests/test_count_parameters.py new file mode 100644 index 0000000000..be3adda3b8 --- /dev/null +++ b/tests/test_count_parameters.py @@ -0,0 +1,57 @@ +from functools import partial + +from textual._callback import count_parameters + + +def test_functions() -> None: + """Test count parameters of functions.""" + + def foo(): ... + def bar(a): ... + def baz(a, b): ... + + # repeat to allow for caching + for _ in range(3): + assert count_parameters(foo) == 0 + assert count_parameters(bar) == 1 + assert count_parameters(baz) == 2 + + +def test_methods() -> None: + """Test count parameters of methods.""" + + class Foo: + def foo(self): ... + def bar(self, a): ... + def baz(self, a, b): ... + + foo = Foo() + + # repeat to allow for caching + for _ in range(3): + assert count_parameters(foo.foo) == 0 + assert count_parameters(foo.bar) == 1 + assert count_parameters(foo.baz) == 2 + + +def test_partials() -> None: + """Test count parameters of partials.""" + + class Foo: + def method(self, a, b, c, d): ... + + foo = Foo() + + partial0 = partial(foo.method) + partial1 = partial(foo.method, 10) + partial2 = partial(foo.method, b=10, c=20) + + for _ in range(3): + assert count_parameters(partial0) == 4 + assert count_parameters(partial0) == 4 + + assert count_parameters(partial1) == 3 + assert count_parameters(partial1) == 3 + + assert count_parameters(partial2) == 2 + assert count_parameters(partial2) == 2 diff --git a/tests/test_focus.py b/tests/test_focus.py index ee955e23e5..a23046bc94 100644 --- a/tests/test_focus.py +++ b/tests/test_focus.py @@ -22,46 +22,47 @@ class ChildrenFocusableOnly(Widget, can_focus=False, can_focus_children=True): @pytest.fixture def screen() -> Screen: app = App() - app._set_active() - app.push_screen(Screen()) - screen = app.screen + with app._context(): + app.push_screen(Screen()) - # The classes even/odd alternate along the focus chain. - # The classes in/out identify nested widgets. - screen._add_children( - Focusable(id="foo", classes="a"), - NonFocusable(id="bar"), - Focusable(Focusable(id="Paul", classes="c"), id="container1", classes="b"), - NonFocusable(Focusable(id="Jessica", classes="a"), id="container2"), - Focusable(id="baz", classes="b"), - ChildrenFocusableOnly(Focusable(id="child", classes="c")), - ) + screen = app.screen - return screen + # The classes even/odd alternate along the focus chain. + # The classes in/out identify nested widgets. + screen._add_children( + Focusable(id="foo", classes="a"), + NonFocusable(id="bar"), + Focusable(Focusable(id="Paul", classes="c"), id="container1", classes="b"), + NonFocusable(Focusable(id="Jessica", classes="a"), id="container2"), + Focusable(id="baz", classes="b"), + ChildrenFocusableOnly(Focusable(id="child", classes="c")), + ) + + return screen def test_focus_chain(): app = App() - app._set_active() - app.push_screen(Screen()) + with app._context(): + app.push_screen(Screen()) - screen = app.screen + screen = app.screen - # Check empty focus chain - assert not screen.focus_chain + # Check empty focus chain + assert not screen.focus_chain - app.screen._add_children( - Focusable(id="foo"), - NonFocusable(id="bar"), - Focusable(Focusable(id="Paul"), id="container1"), - NonFocusable(Focusable(id="Jessica"), id="container2"), - Focusable(id="baz"), - ChildrenFocusableOnly(Focusable(id="child")), - ) + app.screen._add_children( + Focusable(id="foo"), + NonFocusable(id="bar"), + Focusable(Focusable(id="Paul"), id="container1"), + NonFocusable(Focusable(id="Jessica"), id="container2"), + Focusable(id="baz"), + ChildrenFocusableOnly(Focusable(id="child")), + ) - focus_chain = [widget.id for widget in screen.focus_chain] - assert focus_chain == ["foo", "container1", "Paul", "baz", "child"] + focus_chain = [widget.id for widget in screen.focus_chain] + assert focus_chain == ["foo", "container1", "Paul", "baz", "child"] def test_allow_focus(): @@ -90,18 +91,19 @@ def allow_focus_children(self) -> bool: return False app = App() - app._set_active() - app.push_screen(Screen()) - app.screen._add_children( - Focusable(id="foo"), - NonFocusable(id="bar"), - FocusableContainer(Button("egg", id="egg")), - NonFocusableContainer(Button("EGG", id="qux")), - ) - assert [widget.id for widget in app.screen.focus_chain] == ["foo", "egg"] - assert focusable_allow_focus_called - assert non_focusable_allow_focus_called + with app._context(): + app.push_screen(Screen()) + + app.screen._add_children( + Focusable(id="foo"), + NonFocusable(id="bar"), + FocusableContainer(Button("egg", id="egg")), + NonFocusableContainer(Button("EGG", id="qux")), + ) + assert [widget.id for widget in app.screen.focus_chain] == ["foo", "egg"] + assert focusable_allow_focus_called + assert non_focusable_allow_focus_called def test_focus_next_and_previous(screen: Screen): @@ -188,47 +190,47 @@ def test_focus_next_and_previous_with_str_selector(screen: Screen): def test_focus_next_and_previous_with_type_selector_without_self(): """Test moving the focus with a selector that does not match the currently focused node.""" app = App() - app._set_active() - app.push_screen(Screen()) - - screen = app.screen - - from textual.containers import Horizontal, VerticalScroll - from textual.widgets import Button, Input, Switch - - screen._add_children( - VerticalScroll( - Horizontal( - Input(id="w3"), - Switch(id="w4"), - Input(id="w5"), - Button(id="w6"), - Switch(id="w7"), - id="w2", - ), - Horizontal( - Button(id="w9"), - Switch(id="w10"), - Button(id="w11"), - Input(id="w12"), - Input(id="w13"), - id="w8", - ), - id="w1", + with app._context(): + app.push_screen(Screen()) + + screen = app.screen + + from textual.containers import Horizontal, VerticalScroll + from textual.widgets import Button, Input, Switch + + screen._add_children( + VerticalScroll( + Horizontal( + Input(id="w3"), + Switch(id="w4"), + Input(id="w5"), + Button(id="w6"), + Switch(id="w7"), + id="w2", + ), + Horizontal( + Button(id="w9"), + Switch(id="w10"), + Button(id="w11"), + Input(id="w12"), + Input(id="w13"), + id="w8", + ), + id="w1", + ) ) - ) - screen.set_focus(screen.query_one("#w3")) - assert screen.focused.id == "w3" + screen.set_focus(screen.query_one("#w3")) + assert screen.focused.id == "w3" - assert screen.focus_next(Button).id == "w6" - assert screen.focus_next(Switch).id == "w7" - assert screen.focus_next(Input).id == "w12" + assert screen.focus_next(Button).id == "w6" + assert screen.focus_next(Switch).id == "w7" + assert screen.focus_next(Input).id == "w12" - assert screen.focus_previous(Button).id == "w11" - assert screen.focus_previous(Switch).id == "w10" - assert screen.focus_previous(Button).id == "w9" - assert screen.focus_previous(Input).id == "w5" + assert screen.focus_previous(Button).id == "w11" + assert screen.focus_previous(Switch).id == "w10" + assert screen.focus_previous(Button).id == "w9" + assert screen.focus_previous(Input).id == "w5" def test_focus_next_and_previous_with_str_selector_without_self(screen: Screen): diff --git a/tests/test_gc.py b/tests/test_gc.py new file mode 100644 index 0000000000..b21a7d9b72 --- /dev/null +++ b/tests/test_gc.py @@ -0,0 +1,87 @@ +import asyncio +import gc + +import pytest + +from textual.app import App, ComposeResult +from textual.containers import Vertical +from textual.widgets import Footer, Header, Label + + +def count_nodes() -> int: + """Count number of references to DOMNodes.""" + dom_nodes = [ + obj + for obj in gc.get_objects() + if any(cls.__name__ == "DOMNode" for cls in obj.__class__.__mro__) + ] + print(dom_nodes) + return len(dom_nodes) + + +async def run_app() -> None: + """Run a dummy app.""" + + class DummyApp(App): + """Dummy app with a few widgets.""" + + def compose(self) -> ComposeResult: + yield Header() + with Vertical(): + yield Label("foo") + yield Label("bar") + yield Footer() + + app = DummyApp() + + async with app.run_test() as pilot: + # We should have a bunch of DOMNodes while the test is running + assert count_nodes() > 0 + await pilot.press("ctrl+c") + + assert not app._running + + # Force a GC collection + gc.collect() + + # After the test, all DOMNodes will have been torn down + assert count_nodes() == 1 + + +async def _count_app_nodes() -> None: + """Regression test for https://github.com/Textualize/textual/issues/4959""" + + # Should be no DOMNodes yet + assert count_nodes() == 0 + + await run_app() + await asyncio.sleep(0) + + gc.collect() + + nodes_remaining = count_nodes() + + if nodes_remaining: + print("NODES REMAINING") + + import objgraph + + objgraph.show_backrefs( + [ + obj + for obj in gc.get_objects() + if any(cls.__name__ == "App" for cls in obj.__class__.__mro__) + ], + filename="graph.png", + max_depth=15, + ) + + assert nodes_remaining == 0 + + +# It looks like PyTest holds on to references to DOMNodes +# So this will only pass if ran in isolation [email protected] +async def test_gc(): + """Regression test for https://github.com/Textualize/textual/issues/4959""" + await _count_app_nodes() diff --git a/tests/test_path.py b/tests/test_path.py index d7088f8be7..3d5203a6e8 100644 --- a/tests/test_path.py +++ b/tests/test_path.py @@ -30,14 +30,15 @@ class ListPathApp(App[None]): @pytest.mark.parametrize( - "app,expected_css_path_attribute", + "app_class,expected_css_path_attribute", [ - (RelativePathObjectApp(), [APP_DIR / "test.tcss"]), - (RelativePathStrApp(), [APP_DIR / "test.tcss"]), - (AbsolutePathObjectApp(), [Path("/tmp/test.tcss")]), - (AbsolutePathStrApp(), [Path("/tmp/test.tcss")]), - (ListPathApp(), [APP_DIR / "test.tcss", Path("/another/path.tcss")]), + (RelativePathObjectApp, [APP_DIR / "test.tcss"]), + (RelativePathStrApp, [APP_DIR / "test.tcss"]), + (AbsolutePathObjectApp, [Path("/tmp/test.tcss")]), + (AbsolutePathStrApp, [Path("/tmp/test.tcss")]), + (ListPathApp, [APP_DIR / "test.tcss", Path("/another/path.tcss")]), ], ) -def test_css_paths_of_various_types(app, expected_css_path_attribute): +def test_css_paths_of_various_types(app_class, expected_css_path_attribute): + app = app_class() assert app.css_path == [path.absolute() for path in expected_css_path_attribute] diff --git a/tests/test_screens.py b/tests/test_screens.py index 00f3504361..a53abb7049 100644 --- a/tests/test_screens.py +++ b/tests/test_screens.py @@ -74,89 +74,88 @@ async def test_screens(): # There should be nothing in the children since the app hasn't run yet assert not app._nodes assert not app.children - app._set_active() - - with pytest.raises(ScreenStackError): - app.screen - - assert not app._installed_screens - - screen1 = Screen(name="screen1") - screen2 = Screen(name="screen2") - screen3 = Screen(name="screen3") - - # installs screens - app.install_screen(screen1, "screen1") - app.install_screen(screen2, "screen2") - - # Installing a screen does not add it to the DOM - assert not app._nodes - assert not app.children + with app._context(): + with pytest.raises(ScreenStackError): + app.screen + + assert not app._installed_screens + + screen1 = Screen(name="screen1") + screen2 = Screen(name="screen2") + screen3 = Screen(name="screen3") + + # installs screens + app.install_screen(screen1, "screen1") + app.install_screen(screen2, "screen2") + + # Installing a screen does not add it to the DOM + assert not app._nodes + assert not app.children + + # Check they are installed + assert app.is_screen_installed("screen1") + assert app.is_screen_installed("screen2") + + assert app.get_screen("screen1") is screen1 + with pytest.raises(KeyError): + app.get_screen("foo") + + # Check screen3 is not installed + assert not app.is_screen_installed("screen3") + + # Installs screen3 + app.install_screen(screen3, "screen3") + # Confirm installed + assert app.is_screen_installed("screen3") + + # Check screen stack is empty + assert app.screen_stack == [] + # Push a screen + await app.push_screen("screen1") + # Check it is on the stack + assert app.screen_stack == [screen1] + # Check it is current + assert app.screen is screen1 + # There should be one item in the children view + assert app.children == (screen1,) + + # Switch to another screen + await app.switch_screen("screen2") + # Check it has changed the stack and that it is current + assert app.screen_stack == [screen2] + assert app.screen is screen2 + assert app.children == (screen2,) + + # Push another screen + await app.push_screen("screen3") + assert app.screen_stack == [screen2, screen3] + assert app.screen is screen3 + # Only the current screen is in children + assert app.children == (screen3,) + + # Pop a screen + await app.pop_screen() + assert app.screen is screen2 + assert app.screen_stack == [screen2] + + # Uninstall screens + app.uninstall_screen(screen1) + assert not app.is_screen_installed(screen1) + app.uninstall_screen("screen3") + assert not app.is_screen_installed(screen1) + + # Check we can't uninstall a screen on the stack + with pytest.raises(ScreenStackError): + app.uninstall_screen(screen2) - # Check they are installed - assert app.is_screen_installed("screen1") - assert app.is_screen_installed("screen2") - - assert app.get_screen("screen1") is screen1 - with pytest.raises(KeyError): - app.get_screen("foo") - - # Check screen3 is not installed - assert not app.is_screen_installed("screen3") - - # Installs screen3 - app.install_screen(screen3, "screen3") - # Confirm installed - assert app.is_screen_installed("screen3") - - # Check screen stack is empty - assert app.screen_stack == [] - # Push a screen - await app.push_screen("screen1") - # Check it is on the stack - assert app.screen_stack == [screen1] - # Check it is current - assert app.screen is screen1 - # There should be one item in the children view - assert app.children == (screen1,) - - # Switch to another screen - await app.switch_screen("screen2") - # Check it has changed the stack and that it is current - assert app.screen_stack == [screen2] - assert app.screen is screen2 - assert app.children == (screen2,) - - # Push another screen - await app.push_screen("screen3") - assert app.screen_stack == [screen2, screen3] - assert app.screen is screen3 - # Only the current screen is in children - assert app.children == (screen3,) - - # Pop a screen - await app.pop_screen() - assert app.screen is screen2 - assert app.screen_stack == [screen2] - - # Uninstall screens - app.uninstall_screen(screen1) - assert not app.is_screen_installed(screen1) - app.uninstall_screen("screen3") - assert not app.is_screen_installed(screen1) - - # Check we can't uninstall a screen on the stack - with pytest.raises(ScreenStackError): - app.uninstall_screen(screen2) - - # Check we can't pop last screen - with pytest.raises(ScreenStackError): - app.pop_screen() + # Check we can't pop last screen + with pytest.raises(ScreenStackError): + app.pop_screen() - screen1.remove() - screen2.remove() - screen3.remove() - await app._shutdown() + screen1.remove() + screen2.remove() + screen3.remove() + await app._shutdown() async def test_auto_focus_on_screen_if_app_auto_focus_is_none(): diff --git a/tests/test_unmount.py b/tests/test_unmount.py index 324a3812a3..3da75d1247 100644 --- a/tests/test_unmount.py +++ b/tests/test_unmount.py @@ -50,4 +50,5 @@ async def on_mount(self) -> None: "MyScreen#main", ] + assert unmount_ids == expected diff --git a/tests/test_widget.py b/tests/test_widget.py index 2652aec54d..6643916bbb 100644 --- a/tests/test_widget.py +++ b/tests/test_widget.py @@ -57,22 +57,21 @@ def render(self) -> str: widget3 = TextWidget("foo\nbar\nbaz", id="widget3") app = App() - app._set_active() + with app._context(): + width = widget1.get_content_width(Size(20, 20), Size(80, 24)) + height = widget1.get_content_height(Size(20, 20), Size(80, 24), width) + assert width == 3 + assert height == 1 - width = widget1.get_content_width(Size(20, 20), Size(80, 24)) - height = widget1.get_content_height(Size(20, 20), Size(80, 24), width) - assert width == 3 - assert height == 1 + width = widget2.get_content_width(Size(20, 20), Size(80, 24)) + height = widget2.get_content_height(Size(20, 20), Size(80, 24), width) + assert width == 3 + assert height == 2 - width = widget2.get_content_width(Size(20, 20), Size(80, 24)) - height = widget2.get_content_height(Size(20, 20), Size(80, 24), width) - assert width == 3 - assert height == 2 - - width = widget3.get_content_width(Size(20, 20), Size(80, 24)) - height = widget3.get_content_height(Size(20, 20), Size(80, 24), width) - assert width == 3 - assert height == 3 + width = widget3.get_content_width(Size(20, 20), Size(80, 24)) + height = widget3.get_content_height(Size(20, 20), Size(80, 24), width) + assert width == 3 + assert height == 3 class GetByIdApp(App): @@ -87,34 +86,38 @@ def compose(self) -> ComposeResult: id="parent", ) + @property + def parent(self) -> Widget: + return self.query_one("#parent") + @pytest.fixture async def hierarchy_app(): app = GetByIdApp() - async with app.run_test(): - yield app - - [email protected] -async def parent(hierarchy_app): - yield hierarchy_app.get_widget_by_id("parent") + yield app -def test_get_child_by_id_gets_first_child(parent): - child = parent.get_child_by_id(id="child1") - assert child.id == "child1" - assert child.get_child_by_id(id="grandchild1").id == "grandchild1" - assert parent.get_child_by_id(id="child2").id == "child2" +async def test_get_child_by_id_gets_first_child(hierarchy_app): + async with hierarchy_app.run_test(): + parent = hierarchy_app.parent + child = parent.get_child_by_id(id="child1") + assert child.id == "child1" + assert child.get_child_by_id(id="grandchild1").id == "grandchild1" + assert parent.get_child_by_id(id="child2").id == "child2" -def test_get_child_by_id_no_matching_child(parent): - with pytest.raises(NoMatches): - parent.get_child_by_id(id="doesnt-exist") +async def test_get_child_by_id_no_matching_child(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + with pytest.raises(NoMatches): + parent.get_child_by_id(id="doesnt-exist") -def test_get_child_by_id_only_immediate_descendents(parent): - with pytest.raises(NoMatches): - parent.get_child_by_id(id="grandchild1") +async def test_get_child_by_id_only_immediate_descendents(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + with pytest.raises(NoMatches): + parent.get_child_by_id(id="grandchild1") async def test_get_child_by_type(): @@ -135,51 +138,65 @@ def compose(self) -> ComposeResult: app.get_child_by_type(Label) -def test_get_widget_by_id_no_matching_child(parent): - with pytest.raises(NoMatches): - parent.get_widget_by_id(id="i-dont-exist") +async def test_get_widget_by_id_no_matching_child(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + with pytest.raises(NoMatches): + parent.get_widget_by_id(id="i-dont-exist") -def test_get_widget_by_id_non_immediate_descendants(parent): - result = parent.get_widget_by_id("grandchild1") - assert result.id == "grandchild1" +async def test_get_widget_by_id_non_immediate_descendants(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + result = parent.get_widget_by_id("grandchild1") + assert result.id == "grandchild1" -def test_get_widget_by_id_immediate_descendants(parent): - result = parent.get_widget_by_id("child1") - assert result.id == "child1" +async def test_get_widget_by_id_immediate_descendants(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + result = parent.get_widget_by_id("child1") + assert result.id == "child1" -def test_get_widget_by_id_doesnt_return_self(parent): - with pytest.raises(NoMatches): - parent.get_widget_by_id("parent") +async def test_get_widget_by_id_doesnt_return_self(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + with pytest.raises(NoMatches): + parent.get_widget_by_id("parent") -def test_get_widgets_app_delegated(hierarchy_app, parent): +async def test_get_widgets_app_delegated(hierarchy_app): # Check that get_child_by_id finds the parent, which is a child of the default Screen - queried_parent = hierarchy_app.get_child_by_id("parent") - assert queried_parent is parent + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + queried_parent = hierarchy_app.get_child_by_id("parent") + assert queried_parent is parent - # Check that the grandchild (descendant of the default screen) is found - grandchild = hierarchy_app.get_widget_by_id("grandchild1") - assert grandchild.id == "grandchild1" + # Check that the grandchild (descendant of the default screen) is found + grandchild = hierarchy_app.get_widget_by_id("grandchild1") + assert grandchild.id == "grandchild1" -def test_widget_mount_ids_must_be_unique_mounting_all_in_one_go(parent): - widget1 = Widget(id="hello") - widget2 = Widget(id="hello") +async def test_widget_mount_ids_must_be_unique_mounting_all_in_one_go(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + widget1 = Widget(id="hello") + widget2 = Widget(id="hello") - with pytest.raises(MountError): - parent.mount(widget1, widget2) + with pytest.raises(MountError): + parent.mount(widget1, widget2) -def test_widget_mount_ids_must_be_unique_mounting_multiple_calls(parent): - widget1 = Widget(id="hello") - widget2 = Widget(id="hello") +async def test_widget_mount_ids_must_be_unique_mounting_multiple_calls(hierarchy_app): + async with hierarchy_app.run_test() as pilot: + parent = pilot.app.parent + widget1 = Widget(id="hello") + widget2 = Widget(id="hello") - parent.mount(widget1) - with pytest.raises(DuplicateIds): - parent.mount(widget2) + parent.mount(widget1) + with pytest.raises(DuplicateIds): + parent.mount(widget2) def test_get_pseudo_class_state(): diff --git a/tests/test_widget_mounting.py b/tests/test_widget_mounting.py index 7babed4781..79f7364ad6 100644 --- a/tests/test_widget_mounting.py +++ b/tests/test_widget_mounting.py @@ -116,8 +116,10 @@ async def test_mount_via_app() -> None: await pilot.app.mount(Static(), before="Static") -def test_mount_error() -> None: +async def test_mount_error() -> None: """Mounting a widget on an un-mounted widget should raise an error.""" - with pytest.raises(MountError): - widget = Widget() - widget.mount(Static()) + app = App() + async with app.run_test(): + with pytest.raises(MountError): + widget = Widget() + widget.mount(Static()) diff --git a/tests/text_area/test_history.py b/tests/text_area/test_history.py index 8d50a63f83..1d0c7a0b0b 100644 --- a/tests/text_area/test_history.py +++ b/tests/text_area/test_history.py @@ -6,7 +6,6 @@ from textual.app import App, ComposeResult from textual.events import Paste -from textual.pilot import Pilot from textual.widgets import TextArea from textual.widgets.text_area import EditHistory, Selection @@ -57,300 +56,346 @@ async def text_area(pilot): return pilot.app.text_area -async def test_simple_undo_redo(pilot, text_area: TextArea): - text_area.insert("123", (0, 0)) +async def test_simple_undo_redo(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.insert("123", (0, 0)) - assert text_area.text == "123" - text_area.undo() - assert text_area.text == "" - text_area.redo() - assert text_area.text == "123" + assert text_area.text == "123" + text_area.undo() + assert text_area.text == "" + text_area.redo() + assert text_area.text == "123" -async def test_undo_selection_retained(pilot: Pilot, text_area: TextArea): +async def test_undo_selection_retained(): # Select a range of text and press backspace. - text_area.text = SIMPLE_TEXT - text_area.selection = Selection((0, 0), (2, 3)) - await pilot.press("backspace") - assert text_area.text == "NO\nPQRST\nUVWXY\nZ\n" - assert text_area.selection == Selection.cursor((0, 0)) - - # Undo the deletion - the text comes back, and the selection is restored. - text_area.undo() - assert text_area.selection == Selection((0, 0), (2, 3)) - assert text_area.text == SIMPLE_TEXT - - # Redo the deletion - the text is gone again. The selection goes to the post-delete location. - text_area.redo() - assert text_area.text == "NO\nPQRST\nUVWXY\nZ\n" - assert text_area.selection == Selection.cursor((0, 0)) - - -async def test_undo_checkpoint_created_on_cursor_move( - pilot: Pilot, text_area: TextArea -): - text_area.text = SIMPLE_TEXT - # Characters are inserted on line 0 and 1. - checkpoint_one = text_area.text - checkpoint_one_selection = text_area.selection - await pilot.press("1") # Added to initial batch. - - # This cursor movement ensures a new checkpoint is created. - post_insert_one_location = text_area.selection - await pilot.press("down") - - checkpoint_two = text_area.text - checkpoint_two_selection = text_area.selection - await pilot.press("2") # Added to new batch. - - checkpoint_three = text_area.text - checkpoint_three_selection = text_area.selection - - # Going back to checkpoint two - text_area.undo() - assert text_area.text == checkpoint_two - assert text_area.selection == checkpoint_two_selection - - # Back again to checkpoint one (initial state) - text_area.undo() - assert text_area.text == checkpoint_one - assert text_area.selection == checkpoint_one_selection - - # Redo to move forward to checkpoint two. - text_area.redo() - assert text_area.text == checkpoint_two - assert text_area.selection == post_insert_one_location - - # Redo to move forward to checkpoint three. - text_area.redo() - assert text_area.text == checkpoint_three - assert text_area.selection == checkpoint_three_selection - - -async def test_setting_text_property_resets_history(pilot: Pilot, text_area: TextArea): - await pilot.press("1") - - # Programmatically setting text, which should invalidate the history - text = "Hello, world!" - text_area.text = text + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = SIMPLE_TEXT + text_area.selection = Selection((0, 0), (2, 3)) + await pilot.press("backspace") + assert text_area.text == "NO\nPQRST\nUVWXY\nZ\n" + assert text_area.selection == Selection.cursor((0, 0)) - # The undo doesn't do anything, since we set the `text` property. - text_area.undo() - assert text_area.text == text + # Undo the deletion - the text comes back, and the selection is restored. + text_area.undo() + assert text_area.selection == Selection((0, 0), (2, 3)) + assert text_area.text == SIMPLE_TEXT + # Redo the deletion - the text is gone again. The selection goes to the post-delete location. + text_area.redo() + assert text_area.text == "NO\nPQRST\nUVWXY\nZ\n" + assert text_area.selection == Selection.cursor((0, 0)) -async def test_edits_batched_by_time(pilot: Pilot, text_area: TextArea): - # The first "12" is batched since they happen within 2 seconds. - text_area.history.mock_time = 0 - await pilot.press("1") - text_area.history.mock_time = 1.0 - await pilot.press("2") +async def test_undo_checkpoint_created_on_cursor_move(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = SIMPLE_TEXT + # Characters are inserted on line 0 and 1. + checkpoint_one = text_area.text + checkpoint_one_selection = text_area.selection + await pilot.press("1") # Added to initial batch. + + # This cursor movement ensures a new checkpoint is created. + post_insert_one_location = text_area.selection + await pilot.press("down") + + checkpoint_two = text_area.text + checkpoint_two_selection = text_area.selection + await pilot.press("2") # Added to new batch. + + checkpoint_three = text_area.text + checkpoint_three_selection = text_area.selection + + # Going back to checkpoint two + text_area.undo() + assert text_area.text == checkpoint_two + assert text_area.selection == checkpoint_two_selection + + # Back again to checkpoint one (initial state) + text_area.undo() + assert text_area.text == checkpoint_one + assert text_area.selection == checkpoint_one_selection + + # Redo to move forward to checkpoint two. + text_area.redo() + assert text_area.text == checkpoint_two + assert text_area.selection == post_insert_one_location + + # Redo to move forward to checkpoint three. + text_area.redo() + assert text_area.text == checkpoint_three + assert text_area.selection == checkpoint_three_selection + + +async def test_setting_text_property_resets_history(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + await pilot.press("1") - # Since "3" appears 10 seconds later, it's in a separate batch. - text_area.history.mock_time += 10.0 - await pilot.press("3") + # Programmatically setting text, which should invalidate the history + text = "Hello, world!" + text_area.text = text - assert text_area.text == "123" + # The undo doesn't do anything, since we set the `text` property. + text_area.undo() + assert text_area.text == text - text_area.undo() - assert text_area.text == "12" - text_area.undo() - assert text_area.text == "" +async def test_edits_batched_by_time(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + # The first "12" is batched since they happen within 2 seconds. + text_area.history.mock_time = 0 + await pilot.press("1") + text_area.history.mock_time = 1.0 + await pilot.press("2") -async def test_undo_checkpoint_character_limit_reached( - pilot: Pilot, text_area: TextArea -): - await pilot.press("1") - # Since the insertion below is > 100 characters it goes to a new batch. - text_area.insert("2" * 120) + # Since "3" appears 10 seconds later, it's in a separate batch. + text_area.history.mock_time += 10.0 + await pilot.press("3") - text_area.undo() - assert text_area.text == "1" - text_area.undo() - assert text_area.text == "" + assert text_area.text == "123" + text_area.undo() + assert text_area.text == "12" -async def test_redo_with_no_undo_is_noop(text_area: TextArea): - text_area.text = SIMPLE_TEXT - text_area.redo() - assert text_area.text == SIMPLE_TEXT + text_area.undo() + assert text_area.text == "" -async def test_undo_with_empty_undo_stack_is_noop(text_area: TextArea): - text_area.text = SIMPLE_TEXT - text_area.undo() - assert text_area.text == SIMPLE_TEXT +async def test_undo_checkpoint_character_limit_reached(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + await pilot.press("1") + # Since the insertion below is > 100 characters it goes to a new batch. + text_area.insert("2" * 120) + text_area.undo() + assert text_area.text == "1" + text_area.undo() + assert text_area.text == "" -async def test_redo_stack_cleared_on_edit(pilot: Pilot, text_area: TextArea): - text_area.text = "" - await pilot.press("1") - text_area.history.checkpoint() - await pilot.press("2") - text_area.history.checkpoint() - await pilot.press("3") - text_area.undo() - text_area.undo() - text_area.undo() - assert text_area.text == "" - assert text_area.selection == Selection.cursor((0, 0)) +async def test_redo_with_no_undo_is_noop(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = SIMPLE_TEXT + text_area.redo() + assert text_area.text == SIMPLE_TEXT - # Redo stack has 3 edits in it now. - await pilot.press("f") - assert text_area.text == "f" - assert text_area.selection == Selection.cursor((0, 1)) - # Redo stack is cleared because of the edit, so redo has no effect. - text_area.redo() - assert text_area.text == "f" - assert text_area.selection == Selection.cursor((0, 1)) - text_area.redo() - assert text_area.text == "f" - assert text_area.selection == Selection.cursor((0, 1)) +async def test_undo_with_empty_undo_stack_is_noop(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = SIMPLE_TEXT + text_area.undo() + assert text_area.text == SIMPLE_TEXT -async def test_inserts_not_batched_with_deletes(pilot: Pilot, text_area: TextArea): +async def test_redo_stack_cleared_on_edit(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = "" + await pilot.press("1") + text_area.history.checkpoint() + await pilot.press("2") + text_area.history.checkpoint() + await pilot.press("3") + + text_area.undo() + text_area.undo() + text_area.undo() + assert text_area.text == "" + assert text_area.selection == Selection.cursor((0, 0)) + + # Redo stack has 3 edits in it now. + await pilot.press("f") + assert text_area.text == "f" + assert text_area.selection == Selection.cursor((0, 1)) + + # Redo stack is cleared because of the edit, so redo has no effect. + text_area.redo() + assert text_area.text == "f" + assert text_area.selection == Selection.cursor((0, 1)) + text_area.redo() + assert text_area.text == "f" + assert text_area.selection == Selection.cursor((0, 1)) + + +async def test_inserts_not_batched_with_deletes(): # 3 batches here: __1___ ___________2____________ __3__ - await pilot.press(*"123", "backspace", "backspace", *"23") - - assert text_area.text == "123" - - # Undo batch 1: the "23" insertion. - text_area.undo() - assert text_area.text == "1" - # Undo batch 2: the double backspace. - text_area.undo() - assert text_area.text == "123" - - # Undo batch 3: the "123" insertion. - text_area.undo() - assert text_area.text == "" + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + await pilot.press(*"123", "backspace", "backspace", *"23") -async def test_paste_is_an_isolated_batch(pilot: Pilot, text_area: TextArea): - pilot.app.post_message(Paste("hello ")) - pilot.app.post_message(Paste("world")) - await pilot.pause() + assert text_area.text == "123" - assert text_area.text == "hello world" + # Undo batch 1: the "23" insertion. + text_area.undo() + assert text_area.text == "1" - await pilot.press("!") + # Undo batch 2: the double backspace. + text_area.undo() + assert text_area.text == "123" - # The insertion of "!" does not get batched with the paste of "world". - text_area.undo() - assert text_area.text == "hello world" + # Undo batch 3: the "123" insertion. + text_area.undo() + assert text_area.text == "" - text_area.undo() - assert text_area.text == "hello " - text_area.undo() - assert text_area.text == "" +async def test_paste_is_an_isolated_batch(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + pilot.app.post_message(Paste("hello ")) + pilot.app.post_message(Paste("world")) + await pilot.pause() + assert text_area.text == "hello world" -async def test_focus_creates_checkpoint(pilot: Pilot, text_area: TextArea): - await pilot.press(*"123") - text_area.has_focus = False - text_area.has_focus = True - await pilot.press(*"456") - assert text_area.text == "123456" + await pilot.press("!") - # Since we re-focused, a checkpoint exists between 123 and 456, - # so when we use undo, only the 456 is removed. - text_area.undo() - assert text_area.text == "123" + # The insertion of "!" does not get batched with the paste of "world". + text_area.undo() + assert text_area.text == "hello world" + text_area.undo() + assert text_area.text == "hello " -async def test_undo_redo_deletions_batched(pilot: Pilot, text_area: TextArea): - text_area.text = SIMPLE_TEXT - text_area.selection = Selection((0, 2), (1, 2)) + text_area.undo() + assert text_area.text == "" - # Perform a single delete of some selected text. It'll live in it's own - # batch since it's a multi-line operation. - await pilot.press("backspace") - checkpoint_one = "ABHIJ\nKLMNO\nPQRST\nUVWXY\nZ\n" - assert text_area.text == checkpoint_one - assert text_area.selection == Selection.cursor((0, 2)) - # Pressing backspace a few times to delete more characters. - await pilot.press("backspace", "backspace", "backspace") - checkpoint_two = "HIJ\nKLMNO\nPQRST\nUVWXY\nZ\n" - assert text_area.text == checkpoint_two - assert text_area.selection == Selection.cursor((0, 0)) +async def test_focus_creates_checkpoint(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + await pilot.press(*"123") + text_area.has_focus = False + text_area.has_focus = True + await pilot.press(*"456") + assert text_area.text == "123456" - # When we undo, the 3 deletions above should be batched, but not - # the original deletion since it contains a newline character. - text_area.undo() - assert text_area.text == checkpoint_one - assert text_area.selection == Selection.cursor((0, 2)) + # Since we re-focused, a checkpoint exists between 123 and 456, + # so when we use undo, only the 456 is removed. + text_area.undo() + assert text_area.text == "123" - # Undoing again restores us back to our initial text and selection. - text_area.undo() - assert text_area.text == SIMPLE_TEXT - assert text_area.selection == Selection((0, 2), (1, 2)) - # At this point, the undo stack contains two items, so we can redo twice. +async def test_undo_redo_deletions_batched(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + text_area.text = SIMPLE_TEXT + text_area.selection = Selection((0, 2), (1, 2)) + + # Perform a single delete of some selected text. It'll live in it's own + # batch since it's a multi-line operation. + await pilot.press("backspace") + checkpoint_one = "ABHIJ\nKLMNO\nPQRST\nUVWXY\nZ\n" + assert text_area.text == checkpoint_one + assert text_area.selection == Selection.cursor((0, 2)) + + # Pressing backspace a few times to delete more characters. + await pilot.press("backspace", "backspace", "backspace") + checkpoint_two = "HIJ\nKLMNO\nPQRST\nUVWXY\nZ\n" + assert text_area.text == checkpoint_two + assert text_area.selection == Selection.cursor((0, 0)) + + # When we undo, the 3 deletions above should be batched, but not + # the original deletion since it contains a newline character. + text_area.undo() + assert text_area.text == checkpoint_one + assert text_area.selection == Selection.cursor((0, 2)) + + # Undoing again restores us back to our initial text and selection. + text_area.undo() + assert text_area.text == SIMPLE_TEXT + assert text_area.selection == Selection((0, 2), (1, 2)) + + # At this point, the undo stack contains two items, so we can redo twice. + + # Redo to go back to checkpoint one. + text_area.redo() + assert text_area.text == checkpoint_one + assert text_area.selection == Selection.cursor((0, 2)) + + # Redo again to go back to checkpoint two + text_area.redo() + assert text_area.text == checkpoint_two + assert text_area.selection == Selection.cursor((0, 0)) + + # Redo again does nothing. + text_area.redo() + assert text_area.text == checkpoint_two + assert text_area.selection == Selection.cursor((0, 0)) + + +async def test_max_checkpoints(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + assert len(text_area.history.undo_stack) == 0 + for index in range(MAX_CHECKPOINTS): + # Press enter since that will ensure a checkpoint is created. + await pilot.press("enter") - # Redo to go back to checkpoint one. - text_area.redo() - assert text_area.text == checkpoint_one - assert text_area.selection == Selection.cursor((0, 2)) + assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS + await pilot.press("enter") + # Ensure we don't go over the limit. + assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS - # Redo again to go back to checkpoint two - text_area.redo() - assert text_area.text == checkpoint_two - assert text_area.selection == Selection.cursor((0, 0)) - # Redo again does nothing. - text_area.redo() - assert text_area.text == checkpoint_two - assert text_area.selection == Selection.cursor((0, 0)) +async def test_redo_stack(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + assert len(text_area.history.redo_stack) == 0 + await pilot.press("enter") + await pilot.press(*"123") + assert len(text_area.history.undo_stack) == 2 + assert len(text_area.history.redo_stack) == 0 + text_area.undo() + assert len(text_area.history.undo_stack) == 1 + assert len(text_area.history.redo_stack) == 1 + text_area.undo() + assert len(text_area.history.undo_stack) == 0 + assert len(text_area.history.redo_stack) == 2 + text_area.redo() + assert len(text_area.history.undo_stack) == 1 + assert len(text_area.history.redo_stack) == 1 + text_area.redo() + assert len(text_area.history.undo_stack) == 2 + assert len(text_area.history.redo_stack) == 0 + + +async def test_backward_selection_undo_redo(): + app = TextAreaApp() + async with app.run_test() as pilot: + text_area = app.text_area + # Failed prior to https://github.com/Textualize/textual/pull/4352 + text_area.text = SIMPLE_TEXT + text_area.selection = Selection((3, 2), (0, 0)) + await pilot.press("a") -async def test_max_checkpoints(pilot: Pilot, text_area: TextArea): - assert len(text_area.history.undo_stack) == 0 - for index in range(MAX_CHECKPOINTS): - # Press enter since that will ensure a checkpoint is created. - await pilot.press("enter") + text_area.undo() + await pilot.press("down", "down", "down", "down") - assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS - await pilot.press("enter") - # Ensure we don't go over the limit. - assert len(text_area.history.undo_stack) == MAX_CHECKPOINTS - - -async def test_redo_stack(pilot: Pilot, text_area: TextArea): - assert len(text_area.history.redo_stack) == 0 - await pilot.press("enter") - await pilot.press(*"123") - assert len(text_area.history.undo_stack) == 2 - assert len(text_area.history.redo_stack) == 0 - text_area.undo() - assert len(text_area.history.undo_stack) == 1 - assert len(text_area.history.redo_stack) == 1 - text_area.undo() - assert len(text_area.history.undo_stack) == 0 - assert len(text_area.history.redo_stack) == 2 - text_area.redo() - assert len(text_area.history.undo_stack) == 1 - assert len(text_area.history.redo_stack) == 1 - text_area.redo() - assert len(text_area.history.undo_stack) == 2 - assert len(text_area.history.redo_stack) == 0 - - -async def test_backward_selection_undo_redo(pilot: Pilot, text_area: TextArea): - # Failed prior to https://github.com/Textualize/textual/pull/4352 - text_area.text = SIMPLE_TEXT - text_area.selection = Selection((3, 2), (0, 0)) - - await pilot.press("a") - - text_area.undo() - await pilot.press("down", "down", "down", "down") - - assert text_area.text == SIMPLE_TEXT + assert text_area.text == SIMPLE_TEXT
{ "difficulty": "high", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
Textualize__textual-5022@2e612e8
Textualize/textual
Python
5,022
fix(app): fix `inline_no_clear`
Fixes #5019. I'm afraid I don't fully understand commit 8c01ef7, but this change seems to fix the broken `inline_no_clear` without any regression related to issue #4882 which I believe this commit intended to fix. - [ ] Docstrings on all new or modified functions / classes - [ ] Updated documentation - [x] Updated CHANGELOG.md (where appropriate)
2024-09-18T22:03:56Z
`inline_no_clear` broken in v0.77.0 It looks like the `inline_no_clear` option was broken in v0.77.0, from a quick git bisect the culprit seems to be commit 8c01ef7. If you run the app below where `inline_no_clear=True`, the app output is cleared on exit: ```python from textual.app import App, ComposeResult from textual.widgets import Static class InlineNoClearApp(App): def compose(self) -> ComposeResult: yield Static("Quitting should not clear the app output") if __name__ == "__main__": app = InlineNoClearApp() app.run(inline=True, inline_no_clear=True) ```
Thank you for your issue. Give us a little time to review it. PS. You might want to check the [FAQ](https://textual.textualize.io/FAQ/) if you haven't done so already. This is an automated reply, generated by [FAQtory](https://github.com/willmcgugan/faqtory)
[ { "body": "It looks like the `inline_no_clear` option was broken in v0.77.0, from a quick git bisect the culprit seems to be commit 8c01ef7.\r\n\r\nIf you run the app below where `inline_no_clear=True`, the app output is cleared on exit:\r\n\r\n```python\r\nfrom textual.app import App, ComposeResult\r\nfrom textual.widgets import Static\r\n\r\n\r\nclass InlineNoClearApp(App):\r\n def compose(self) -> ComposeResult:\r\n yield Static(\"Quitting should not clear the app output\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app = InlineNoClearApp()\r\n app.run(inline=True, inline_no_clear=True)\r\n\r\n```", "number": 5019, "title": "`inline_no_clear` broken in v0.77.0" } ]
8b8c10d0f1bd32a60ee74cbe00dc32a18bdb7a7f
{ "head_commit": "2e612e8c24de395a6152bfbec46b96e328701859", "head_commit_message": "update changelog", "patch_to_review": "diff --git a/CHANGELOG.md b/CHANGELOG.md\nindex ef0b9537c5..882d5c1672 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -38,6 +38,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).\n - Fixed flicker when setting `dark` reactive on startup https://github.com/Textualize/textual/pull/4989\n - Fixed command palette not sorting search results by their match score https://github.com/Textualize/textual/pull/4994\n - Fixed `DataTable` cached height issue on re-populating the table when using auto-height rows https://github.com/Textualize/textual/pull/4992\n+- Fixed inline app output being cleared when `inline_no_clear=True` https://github.com/Textualize/textual/issues/5019\n \n ## [0.79.1] - 2024-08-31\n \ndiff --git a/src/textual/app.py b/src/textual/app.py\nindex 9bd851c927..9b4ce38b12 100644\n--- a/src/textual/app.py\n+++ b/src/textual/app.py\n@@ -2909,10 +2909,14 @@ async def invoke_ready_callback() -> None:\n self._driver.write(\n Control.move(-cursor_x, -cursor_y + 1).segment.text\n )\n- if inline_no_clear and not not self.app._exit_renderables:\n+ if inline_no_clear:\n console = Console()\n- console.print(self.screen._compositor)\n- console.print()\n+ try:\n+ console.print(self.screen._compositor)\n+ except ScreenStackError:\n+ pass\n+ else:\n+ console.print()\n \n driver.stop_application_mode()\n except Exception as error:\n" }
[ { "diff_hunk": "@@ -2909,10 +2909,14 @@ async def invoke_ready_callback() -> None:\n self._driver.write(\n Control.move(-cursor_x, -cursor_y + 1).segment.text\n )\n- if inline_no_clear and not not self.app._exit_renderables:", "line": 2962, "original_line": 2962, "original_start_line": null, "path": "src/textual/app.py", "start_line": null, "text": "@user1:\nThe \"not not\" is a bug. I think the reasoning was that if there are errors (in the exit renderables) they should be printed rather than the last frame." } ]
23844b1221715bafb1681a5ed298636f4449fa86
diff --git a/CHANGELOG.md b/CHANGELOG.md index cb52627175..8a25970f27 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -46,6 +46,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/). - Fixed flicker when setting `dark` reactive on startup https://github.com/Textualize/textual/pull/4989 - Fixed command palette not sorting search results by their match score https://github.com/Textualize/textual/pull/4994 - Fixed `DataTable` cached height issue on re-populating the table when using auto-height rows https://github.com/Textualize/textual/pull/4992 +- Fixed inline app output being cleared when `inline_no_clear=True` https://github.com/Textualize/textual/issues/5019 ## [0.79.1] - 2024-08-31 diff --git a/src/textual/app.py b/src/textual/app.py index e612e25675..9a6267bc3f 100644 --- a/src/textual/app.py +++ b/src/textual/app.py @@ -2959,10 +2959,18 @@ async def invoke_ready_callback() -> None: self._driver.write( Control.move(-cursor_x, -cursor_y + 1).segment.text ) - if inline_no_clear and not not self.app._exit_renderables: + if inline_no_clear and not self.app._exit_renderables: console = Console() - console.print(self.screen._compositor) - console.print() + try: + console.print(self.screen._compositor) + except ScreenStackError: + console.print() + else: + self._driver.write( + Control.move( + -cursor_x, -self.INLINE_PADDING - 1 + ).segment.text + ) driver.stop_application_mode() except Exception as error: diff --git a/src/textual/drivers/linux_inline_driver.py b/src/textual/drivers/linux_inline_driver.py index 734bbb3445..c12eb3219f 100644 --- a/src/textual/drivers/linux_inline_driver.py +++ b/src/textual/drivers/linux_inline_driver.py @@ -310,8 +310,8 @@ def stop_application_mode(self) -> None: """Stop application mode, restore state.""" self._disable_bracketed_paste() self.disable_input() - - self.write("\x1b[2A\x1b[J") + self.write("\x1b[<u") # Disable kitty protocol + self.write("\x1b[J") if self.attrs_before is not None: try: diff --git 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class="terminal-3682463994-r7" x="12.2" y="410.4" textLength="951.6" clip-path="url(#terminal-3682463994-line-16)">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class="terminal-3682463994-r7" x="963.8" y="410.4" textLength="12.2" clip-path="url(#terminal-3682463994-line-16)">โ–Ž</text><text class="terminal-3682463994-r2" x="976" y="410.4" textLength="12.2" clip-path="url(#terminal-3682463994-line-16)"> +</text><text class="terminal-3682463994-r7" x="0" y="434.8" textLength="12.2" clip-path="url(#terminal-3682463994-line-17)">โ–Š</text><text class="terminal-3682463994-r7" x="12.2" y="434.8" textLength="951.6" clip-path="url(#terminal-3682463994-line-17)">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class="terminal-3682463994-r7" x="963.8" y="434.8" textLength="12.2" clip-path="url(#terminal-3682463994-line-17)">โ–Ž</text><text class="terminal-3682463994-r2" x="976" y="434.8" textLength="12.2" clip-path="url(#terminal-3682463994-line-17)"> +</text><text class="terminal-3682463994-r7" x="0" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)">โ–Š</text><text class="terminal-3682463994-r12" x="24.4" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)">โ–</text><text class="terminal-3682463994-r13" x="36.6" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)">X</text><text class="terminal-3682463994-r12" x="48.8" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)">โ–Œ</text><text class="terminal-3682463994-r8" x="61" y="459.2" textLength="890.6" clip-path="url(#terminal-3682463994-line-18)">&#160;Simple&#160;SelectionList&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-3682463994-r7" x="963.8" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)">โ–Ž</text><text class="terminal-3682463994-r2" x="976" y="459.2" textLength="12.2" clip-path="url(#terminal-3682463994-line-18)"> +</text><text class="terminal-3682463994-r7" x="0" y="483.6" textLength="12.2" clip-path="url(#terminal-3682463994-line-19)">โ–Š</text><text class="terminal-3682463994-r7" x="963.8" y="483.6" 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textLength="12.2" clip-path="url(#terminal-3682463994-line-21)"> +</text><text class="terminal-3682463994-r2" x="976" y="556.8" textLength="12.2" clip-path="url(#terminal-3682463994-line-22)"> </text> </g> </g>
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-6613@b06cd17
vllm-project/vllm
Python
6,613
[Core][VLM] Support image embeddings as input
This PR adds the support for passing image embeddings as input so that they can be directly consumed by the language model. Example usage ``` python # Refer to the HuggingFace repo for the correct format to use prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:" # Image embedding generated from a separate vision tower component, directly # used to be merged with text embedding. image_embeds: torch.Tensor = ... # shape of (1, image_feature_size, hidden_size of LM) # Single prompt inference outputs = llm.generate({ "prompt": prompt, "multi_modal_data": {"image": image_embeds}, }) ``` FIXES #6604 Follow-up TODO: Support initializing VLM with only language model backbone. --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-07-21T04:42:04Z
[Feature]: MultiModal LLM with vector API ### ๐Ÿš€ The feature, motivation and pitch Consider a scenario where a large model is deployed in the cloud, and the application is deployed on a computationally limited embedded device. If we want to support multimodal dialogue interaction with vision and language, each request would send an image (considering the dialogue history, there would be many images). Given network bandwidth and other factors, this would cause a lot of latency. Therefore, if the VLM's image encoder and projector are deployed on the embedded device, and if we could send the encoded vector instead during requests, the data transmission volume would be much smaller. This would reduce latency and improve the user experience. ### Alternatives The suggestted usage method is as follow ``` python # Refer to the HuggingFace repo for the correct format to use prompt = "USER: <vector>\nWhat is the content of this image?\nASSISTANT:" # Image encoded vector vector = np.array([x, x,x, x]) # Single prompt inference outputs = llm.generate({ "prompt": prompt, "multi_modal_data": {"vector": vector}, }) ``` For this usage, deploying only a single-model LLM model could support multi-modal model usage, and the modality is not limited. ### Additional context _No response_
Hey @qZhang88 thanks for the issue! Supporting image embeddings as inputs is indeed in our Q3 roadmap that you can check in #4194
[ { "body": "### ๐Ÿš€ The feature, motivation and pitch\n\nConsider a scenario where a large model is deployed in the cloud, and the application is deployed on a computationally limited embedded device.\r\n\r\nIf we want to support multimodal dialogue interaction with vision and language, each request would send an image (considering the dialogue history, there would be many images). Given network bandwidth and other factors, this would cause a lot of latency.\r\n\r\nTherefore, if the VLM's image encoder and projector are deployed on the embedded device, and if we could send the encoded vector instead during requests, the data transmission volume would be much smaller. This would reduce latency and improve the user experience.\r\n\n\n### Alternatives\n\nThe suggestted usage method is as follow\r\n\r\n``` python\r\n# Refer to the HuggingFace repo for the correct format to use\r\nprompt = \"USER: <vector>\\nWhat is the content of this image?\\nASSISTANT:\"\r\n\r\n# Image encoded vector\r\nvector = np.array([x, x,x, x])\r\n\r\n# Single prompt inference\r\noutputs = llm.generate({\r\n \"prompt\": prompt,\r\n \"multi_modal_data\": {\"vector\": vector},\r\n})\r\n```\r\n\r\nFor this usage, deploying only a single-model LLM model could support multi-modal model usage, and the modality is not limited.\n\n### Additional context\n\n_No response_", "number": 6604, "title": "[Feature]: MultiModal LLM with vector API" } ]
86ab567bae0698425095e28cce67e9a31a261b72
{ "head_commit": "b06cd17cc79bf12e7a81713c24648957200f2a0d", "head_commit_message": "revert", "patch_to_review": "diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py\nindex fdea8ee30ce6..66e4b6acb566 100644\n--- a/vllm/model_executor/models/fuyu.py\n+++ b/vllm/model_executor/models/fuyu.py\n@@ -16,7 +16,7 @@\n # limitations under the License.\n \"\"\" PyTorch Fuyu model.\"\"\"\n import math\n-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict\n+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union\n \n import torch\n import torch.nn as nn\n@@ -62,6 +62,14 @@ class FuyuImagePixelInputs(TypedDict):\n \"\"\"\n \n \n+class FuyuImageEmbeddingInputs(TypedDict):\n+ type: Literal[\"image_embeds\"]\n+ data: torch.Tensor\n+\n+\n+FuyuImageInputs = Union[FuyuImagePixelInputs, FuyuImageEmbeddingInputs]\n+\n+\n def _calculate_num_image_tokens(\n height: int,\n width: int,\n@@ -249,6 +257,16 @@ def _parse_and_validate_image_input(self, **kwargs: object):\n data=image_patches)\n return None\n \n+ def _process_image_input(self,\n+ image_input: FuyuImageInputs) -> torch.Tensor:\n+\n+ if image_input[\"type\"] == \"image_embeds\":\n+ return image_input[\"data\"]\n+\n+ assert self.vision_embed_tokens is not None\n+ vision_embeddings, _ = self.vision_embed_tokens(image_input[\"data\"])\n+ return vision_embeddings\n+\n def forward(\n self,\n input_ids: torch.Tensor,\n@@ -261,8 +279,7 @@ def forward(\n image_input = self._parse_and_validate_image_input(**kwargs)\n \n if image_input is not None:\n- vision_embeddings, _ = self.vision_embed_tokens(\n- image_input[\"data\"])\n+ vision_embeddings = self._process_image_input(image_input)\n inputs_embeds = self.language_model.model.embed_tokens(input_ids)\n inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,\n vision_embeddings,\ndiff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py\nindex b5dddd519219..d7dcd558a8f7 100644\n--- a/vllm/model_executor/models/llava.py\n+++ b/vllm/model_executor/models/llava.py\n@@ -1,4 +1,4 @@\n-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict\n+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union\n \n import torch\n import torch.nn as nn\n@@ -59,7 +59,12 @@ class LlavaImagePixelInputs(TypedDict):\n \"\"\"Shape: `(batch_size, num_channels, height, width)`\"\"\"\n \n \n-LlavaImageInputs = LlavaImagePixelInputs\n+class LlavaImageEmbeddingInputs(TypedDict):\n+ type: Literal[\"image_embeds\"]\n+ data: torch.Tensor\n+\n+\n+LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]\n \n \n def get_max_llava_image_tokens(ctx: InputContext):\n@@ -174,18 +179,28 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:\n def _parse_and_validate_image_input(\n self, **kwargs: object) -> Optional[LlavaImageInputs]:\n pixel_values = kwargs.pop(\"pixel_values\", None)\n+ image_embeds = kwargs.pop(\"image_embeds\", None)\n \n- if pixel_values is None:\n+ if pixel_values is None and image_embeds is None:\n return None\n \n- if not isinstance(pixel_values, torch.Tensor):\n- raise ValueError(\"Incorrect type of pixel values. \"\n- f\"Got type: {type(pixel_values)}\")\n-\n- return LlavaImagePixelInputs(\n- type=\"pixel_values\",\n- data=self._validate_pixel_values(pixel_values),\n- )\n+ if pixel_values is not None:\n+ if not isinstance(pixel_values, torch.Tensor):\n+ raise ValueError(\"Incorrect type of pixel values. \"\n+ f\"Got type: {type(pixel_values)}\")\n+ return LlavaImagePixelInputs(\n+ type=\"pixel_values\",\n+ data=self._validate_pixel_values(pixel_values),\n+ )\n+\n+ if image_embeds is not None:\n+ if not isinstance(image_embeds, torch.Tensor):\n+ raise ValueError(\"Incorrect type of image embeddings. \"\n+ f\"Got type: {type(image_embeds)}\")\n+ return LlavaImageEmbeddingInputs(\n+ type=\"image_embeds\",\n+ data=image_embeds,\n+ )\n \n def _select_image_features(self, image_features: torch.Tensor, *,\n strategy: str) -> torch.Tensor:\n@@ -219,6 +234,10 @@ def _process_image_pixels(self,\n \n def _process_image_input(self,\n image_input: LlavaImageInputs) -> torch.Tensor:\n+\n+ if image_input[\"type\"] == \"image_embeds\":\n+ return image_input[\"data\"]\n+\n assert self.vision_tower is not None\n image_features = self._process_image_pixels(image_input)\n return self.multi_modal_projector(image_features)\ndiff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py\nindex 0c89eed88f21..e4583e361ffa 100644\n--- a/vllm/model_executor/models/llava_next.py\n+++ b/vllm/model_executor/models/llava_next.py\n@@ -59,7 +59,13 @@ class LlavaNextImagePixelInputs(TypedDict):\n \"\"\"\n \n \n-LlavaNextImageInputs = LlavaNextImagePixelInputs\n+class LlavaNextImageEmbeddingInputs(TypedDict):\n+ type: Literal[\"image_embeds\"]\n+ data: torch.Tensor\n+\n+\n+LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,\n+ LlavaNextImageEmbeddingInputs]\n \n \n # Taken from: https://github.com/huggingface/text-generation-inference/blob/v2.0.4/server/text_generation_server/models/vlm_causal_lm.py#L91\n@@ -187,7 +193,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):\n input_width=width,\n )\n elif isinstance(image_data, torch.Tensor):\n- raise NotImplementedError(\"Embeddings input is not supported yet\")\n+ return\n else:\n raise TypeError(f\"Invalid image type: {type(image_data)}\")\n \n@@ -285,26 +291,38 @@ def _validate_shape(d: torch.Tensor):\n return data\n \n def _parse_and_validate_image_input(\n- self, **kwargs: object) -> Optional[LlavaNextImagePixelInputs]:\n+ self, **kwargs: object) -> Optional[LlavaNextImageInputs]:\n pixel_values = kwargs.pop(\"pixel_values\", None)\n image_sizes = kwargs.pop(\"image_sizes\", None)\n+ image_embeds = kwargs.pop(\"image_embeds\", None)\n \n- if pixel_values is None:\n+ if pixel_values is None and image_embeds is None:\n return None\n \n- if not isinstance(pixel_values, (torch.Tensor, list)):\n- raise ValueError(\"Incorrect type of pixel values. \"\n- f\"Got type: {type(pixel_values)}\")\n+ if pixel_values is not None:\n+ if not isinstance(pixel_values, (torch.Tensor, list)):\n+ raise ValueError(\"Incorrect type of pixel values. \"\n+ f\"Got type: {type(pixel_values)}\")\n \n- if not isinstance(image_sizes, torch.Tensor):\n- raise ValueError(\"Incorrect type of image sizes. \"\n- f\"Got type: {type(image_sizes)}\")\n+ if not isinstance(image_sizes, torch.Tensor):\n+ raise ValueError(\"Incorrect type of image sizes. \"\n+ f\"Got type: {type(image_sizes)}\")\n \n- return LlavaNextImagePixelInputs(\n- type=\"pixel_values\",\n- data=self._validate_pixel_values(pixel_values),\n- image_sizes=self._validate_image_sizes(image_sizes),\n- )\n+ return LlavaNextImagePixelInputs(\n+ type=\"pixel_values\",\n+ data=self._validate_pixel_values(pixel_values),\n+ image_sizes=self._validate_image_sizes(image_sizes),\n+ )\n+\n+ if image_embeds is not None:\n+ if not isinstance(image_embeds, torch.Tensor):\n+ raise ValueError(\"Incorrect type of image embeds. \"\n+ f\"Got type: {type(image_embeds)}\")\n+\n+ return LlavaNextImageEmbeddingInputs(\n+ type=\"image_embeds\",\n+ data=image_embeds,\n+ )\n \n def _select_image_features(self, image_features: torch.Tensor, *,\n strategy: str) -> torch.Tensor:\n@@ -425,6 +443,10 @@ def _process_image_pixels(\n \n def _process_image_input(\n self, image_input: LlavaNextImageInputs) -> BatchedTensors:\n+\n+ if image_input[\"type\"] == \"image_embeds\":\n+ return [image_input[\"data\"]]\n+\n patch_embeddings = self._process_image_pixels(image_input)\n \n image_sizes = image_input.get(\"image_sizes\")\ndiff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py\nindex 8a2bacbd96b6..985cf07159b0 100644\n--- a/vllm/model_executor/models/paligemma.py\n+++ b/vllm/model_executor/models/paligemma.py\n@@ -1,4 +1,4 @@\n-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict\n+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union\n \n import torch\n from PIL import Image\n@@ -148,7 +148,13 @@ class PaliGemmaImagePixelInputs(TypedDict):\n \"\"\"Shape: (batch_size, num_channels, height, width)\"\"\"\n \n \n-PaliGemmaImageInputs = PaliGemmaImagePixelInputs\n+class PaliGemmaImageEmbeddingInputs(TypedDict):\n+ type: Literal[\"image_embeds\"]\n+ data: torch.Tensor\n+\n+\n+PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs,\n+ PaliGemmaImageEmbeddingInputs]\n \n \n @MULTIMODAL_REGISTRY.register_image_input_mapper()\n@@ -198,18 +204,28 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:\n def _parse_and_validate_image_input(\n self, **kwargs: object) -> Optional[PaliGemmaImageInputs]:\n pixel_values = kwargs.pop(\"pixel_values\", None)\n+ image_embeds = kwargs.pop(\"image_embeds\", None)\n \n- if pixel_values is None:\n+ if pixel_values is None and image_embeds is None:\n return None\n \n- if not isinstance(pixel_values, torch.Tensor):\n- raise ValueError(\"Incorrect type of pixel values. \"\n- f\"Got type: {type(pixel_values)}\")\n-\n- return PaliGemmaImagePixelInputs(\n- type=\"pixel_values\",\n- data=self._validate_pixel_values(pixel_values),\n- )\n+ if pixel_values is not None:\n+ if not isinstance(pixel_values, torch.Tensor):\n+ raise ValueError(\"Incorrect type of pixel values. \"\n+ f\"Got type: {type(pixel_values)}\")\n+ return PaliGemmaImagePixelInputs(\n+ type=\"pixel_values\",\n+ data=self._validate_pixel_values(pixel_values),\n+ )\n+\n+ if image_embeds is not None:\n+ if not isinstance(image_embeds, torch.Tensor):\n+ raise ValueError(\"Incorrect type of image embeddings. \"\n+ f\"Got type: {type(image_embeds)}\")\n+ return PaliGemmaImageEmbeddingInputs(\n+ type=\"image_embeds\",\n+ data=image_embeds,\n+ )\n \n def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,\n pixel_values: torch.Tensor) -> torch.Tensor:\n@@ -233,6 +249,9 @@ def _process_image_pixels(\n def _process_image_input(\n self, image_input: PaliGemmaImageInputs) -> torch.Tensor:\n \n+ if image_input[\"type\"] == \"pixel_values\":\n+ return image_input[\"data\"]\n+\n assert self.vision_tower is not None\n image_features = self._process_image_pixels(image_input)\n \ndiff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py\nindex 8b2c425289f0..2bbaa19df796 100644\n--- a/vllm/model_executor/models/phi3v.py\n+++ b/vllm/model_executor/models/phi3v.py\n@@ -503,22 +503,31 @@ def _parse_and_validate_image_input(\n self, **kwargs: object) -> Optional[Phi3VImagePixelInputs]:\n pixel_values = kwargs.pop(\"pixel_values\", None)\n image_sizes = kwargs.pop(\"image_sizes\", None)\n+ image_embeds = kwargs.pop(\"image_embeds\", None)\n \n if pixel_values is None:\n return None\n \n- if not isinstance(pixel_values, (torch.Tensor, list)):\n- raise ValueError(\"Incorrect type of pixel values. \"\n- f\"Got type: {type(pixel_values)}\")\n+ if pixel_values is None and image_embeds is None:\n+ return None\n+\n+ if pixel_values is not None:\n+ if not isinstance(pixel_values, (torch.Tensor, list)):\n+ raise ValueError(\"Incorrect type of pixel values. \"\n+ f\"Got type: {type(pixel_values)}\")\n+\n+ if not isinstance(image_sizes, torch.Tensor):\n+ raise ValueError(\"Incorrect type of image sizes. \"\n+ f\"Got type: {type(image_sizes)}\")\n \n- if not isinstance(image_sizes, torch.Tensor):\n- raise ValueError(\"Incorrect type of image sizes. \"\n- f\"Got type: {type(image_sizes)}\")\n+ return Phi3VImagePixelInputs(\n+ type=\"pixel_values\",\n+ data=self._validate_pixel_values(pixel_values),\n+ image_sizes=self._validate_image_sizes(image_sizes))\n \n- return Phi3VImagePixelInputs(\n- type=\"pixel_values\",\n- data=self._validate_pixel_values(pixel_values),\n- image_sizes=self._validate_image_sizes(image_sizes))\n+ # TODO: Enable image embeddings for Phi3-Vision\n+ if image_embeds is not None:\n+ raise NotImplementedError(\"Embeddings input is not supported yet\")\n \n def forward(self,\n input_ids: torch.Tensor,\ndiff --git a/vllm/multimodal/image.py b/vllm/multimodal/image.py\nindex 3b37ce9149fb..61d18a604818 100644\n--- a/vllm/multimodal/image.py\n+++ b/vllm/multimodal/image.py\n@@ -113,6 +113,8 @@ def _get_hf_image_processor(self, model_config: ModelConfig):\n def _default_input_mapper(self, ctx: InputContext,\n data: object) -> MultiModalInputs:\n model_config = ctx.model_config\n+\n+ # Raw image\n if isinstance(data, Image.Image):\n image_processor = self._get_hf_image_processor(model_config)\n if image_processor is None:\n@@ -127,8 +129,10 @@ def _default_input_mapper(self, ctx: InputContext,\n raise\n \n return MultiModalInputs(batch_data)\n+\n+ # Image embedding\n elif isinstance(data, torch.Tensor):\n- raise NotImplementedError(\"Embeddings input is not supported yet\")\n+ return MultiModalInputs({\"image_embeds\": data})\n \n raise TypeError(f\"Invalid image type: {type(data)}\")\n \n" }
[ { "diff_hunk": "@@ -187,7 +193,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):\n input_width=width,\n )\n elif isinstance(image_data, torch.Tensor):\n- raise NotImplementedError(\"Embeddings input is not supported yet\")\n+ return", "line": null, "original_line": 196, "original_start_line": null, "path": "vllm/model_executor/models/llava_next.py", "start_line": null, "text": "@user1:\n```suggestion\r\n image_feature_size = image_data.shape[1]\r\n```\n\n@author:\nGood catch!" } ]
307d705d7ba6289d904195acf2fc38470f2f0f8b
diff --git a/docs/source/models/vlm.rst b/docs/source/models/vlm.rst index a385605c9f8f..236e37b51d47 100644 --- a/docs/source/models/vlm.rst +++ b/docs/source/models/vlm.rst @@ -49,6 +49,17 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptI "multi_modal_data": {"image": image}, }) + for o in outputs: + generated_text = o.outputs[0].text + print(generated_text) + + # Inference with image embeddings as input + image_embeds = torch.load(...) # torch.Tensor of shape (1, image_feature_size, hidden_size of LM) + outputs = llm.generate({ + "prompt": prompt, + "multi_modal_data": {"image": image_embeds}, + }) + for o in outputs: generated_text = o.outputs[0].text print(generated_text) diff --git a/tests/models/test_llava_image_embeds.py b/tests/models/test_llava_image_embeds.py new file mode 100644 index 000000000000..63ccd1f6625c --- /dev/null +++ b/tests/models/test_llava_image_embeds.py @@ -0,0 +1,159 @@ +from typing import List, Optional, Tuple, Type + +import pytest +from transformers import AutoConfig, AutoTokenizer + +from vllm.sequence import SampleLogprobs + +from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets +from .utils import check_logprobs_close + +pytestmark = pytest.mark.vlm + +HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ + "stop_sign": + "USER: <image>\nWhat's the content of the image?\nASSISTANT:", + "cherry_blossom": + "USER: <image>\nWhat is the season?\nASSISTANT:", +}) + +models = [ + "llava-hf/llava-1.5-7b-hf", +] + + +def vllm_to_hf_output(vllm_output: Tuple[List[int], str, + Optional[SampleLogprobs]], + model: str): + """Sanitize vllm output to be comparable with hf output.""" + output_ids, output_str, out_logprobs = vllm_output + + config = AutoConfig.from_pretrained(model) + image_token_id = config.image_token_index + + tokenizer = AutoTokenizer.from_pretrained(model) + eos_token_id = tokenizer.eos_token_id + + hf_output_ids = [ + token_id for idx, token_id in enumerate(output_ids) + if token_id != image_token_id or output_ids[idx - 1] != image_token_id + ] + + assert output_str[0] == " " + hf_output_str = output_str[1:] + if hf_output_ids[-1] == eos_token_id: + hf_output_str = hf_output_str + tokenizer.decode(eos_token_id) + + return hf_output_ids, hf_output_str, out_logprobs + + +def run_test( + hf_runner: Type[HfRunner], + vllm_runner: Type[VllmRunner], + image_assets: _ImageAssets, + model: str, + *, + size_factors: List[float], + dtype: str, + max_tokens: int, + num_logprobs: int, + tensor_parallel_size: int, + distributed_executor_backend: Optional[str] = None, +): + """Inference result should be the same between hf and vllm. + + All the image fixtures for the test is under tests/images. + For huggingface runner, we provide the PIL images as input. + For vllm runner, we provide MultiModalDataDict objects + and corresponding vision language config as input. + Note, the text input is also adjusted to abide by vllm contract. + The text output is sanitized to be able to compare with hf. + """ + + # vLLM to load from image embeddings + vllm_images = [asset.image_embeds for asset in image_assets] + + # transformers to load from PIL images + hf_images = [asset.pil_image for asset in image_assets] + + vllm_inputs_per_image = [( + [prompt for _ in size_factors], + [image for _ in size_factors], + ) for image, prompt in zip(vllm_images, HF_IMAGE_PROMPTS)] + + hf_inputs_per_image = [( + [prompt for _ in size_factors], + [image for _ in size_factors], + ) for image, prompt in zip(hf_images, HF_IMAGE_PROMPTS)] + + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). + + # max_model_len should be greater than image_feature_size + with vllm_runner(model, + dtype=dtype, + tensor_parallel_size=tensor_parallel_size, + distributed_executor_backend=distributed_executor_backend, + enforce_eager=True) as vllm_model: + vllm_outputs_per_image = [ + vllm_model.generate_greedy_logprobs(prompts, + max_tokens, + num_logprobs=num_logprobs, + images=images) + for prompts, images in vllm_inputs_per_image + ] + + with hf_runner(model, dtype=dtype, is_vision_model=True) as hf_model: + hf_outputs_per_image = [ + hf_model.generate_greedy_logprobs_limit(prompts, + max_tokens, + num_logprobs=num_logprobs, + images=images) + for prompts, images in hf_inputs_per_image + ] + + for hf_outputs, vllm_outputs in zip(hf_outputs_per_image, + vllm_outputs_per_image): + # TODO: Check whether using original CLIPVisionModel can improve + # consistency against HF + check_logprobs_close( + outputs_0_lst=hf_outputs, + outputs_1_lst=[ + vllm_to_hf_output(vllm_output, model) + for vllm_output in vllm_outputs + ], + name_0="hf", + name_1="vllm", + ) + + [email protected]("model", models) [email protected]( + "size_factors", + [ + # No image + [], + # Single-scale + [1.0], + # Single-scale, batched + [1.0, 1.0, 1.0], + ], +) [email protected]("dtype", ["half"]) [email protected]("max_tokens", [128]) [email protected]("num_logprobs", [5]) +def test_models(hf_runner, vllm_runner, image_assets, model, size_factors, + dtype: str, max_tokens: int, num_logprobs: int) -> None: + run_test( + hf_runner, + vllm_runner, + image_assets, + model, + size_factors=size_factors, + dtype=dtype, + max_tokens=max_tokens, + num_logprobs=num_logprobs, + tensor_parallel_size=1, + ) diff --git a/vllm/assets/image.py b/vllm/assets/image.py index 376150574e3b..5eec78c32890 100644 --- a/vllm/assets/image.py +++ b/vllm/assets/image.py @@ -1,6 +1,7 @@ from dataclasses import dataclass from typing import Literal +import torch from PIL import Image from vllm.assets.base import get_vllm_public_assets @@ -18,3 +19,12 @@ def pil_image(self) -> Image.Image: image_path = get_vllm_public_assets(filename=f"{self.name}.jpg", s3_prefix=VLM_IMAGES_DIR) return Image.open(image_path) + + @property + def image_embeds(self) -> torch.Tensor: + """ + Image embeddings, only used for testing purposes with llava 1.5. + """ + image_path = get_vllm_public_assets(filename=f"{self.name}.pt", + s3_prefix=VLM_IMAGES_DIR) + return torch.load(image_path) diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index e00e6c080695..084cbf35533b 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -1,4 +1,4 @@ -from typing import Iterable, List, Literal, Optional, Tuple, TypedDict +from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union import torch import torch.nn as nn @@ -28,6 +28,29 @@ "language_model.model": "language_model", } +# We use this internally as placeholders since there is no image token +# defined on the HuggingFace repo +BLIP2_IMAGE_TOKEN = "<image>" +BLIP2_IMAGE_TOKEN_ID = 50265 + + +class Blip2ImagePixelInputs(TypedDict): + type: Literal["pixel_values"] + data: torch.Tensor + """Shape: (batch_size, num_channels, height, width)""" + + +class Blip2ImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: torch.Tensor + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +Blip2ImageInputs = Union[Blip2ImagePixelInputs, Blip2ImageEmbeddingInputs] + class Blip2QFormerMultiHeadAttention(nn.Module): @@ -375,20 +398,6 @@ def forward( return sequence_output -class Blip2ImagePixelInputs(TypedDict): - type: Literal["pixel_values"] - data: torch.Tensor - """Shape: (batch_size, num_channels, height, width)""" - - -Blip2ImageInputs = Blip2ImagePixelInputs - -# We use this internally as placeholders since there is no image token -# defined on the HuggingFace repo -BLIP2_IMAGE_TOKEN = "<image>" -BLIP2_IMAGE_TOKEN_ID = 50265 - - def get_blip2_image_feature_size(hf_config: Blip2Config) -> int: return hf_config.num_query_tokens @@ -506,18 +515,31 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[Blip2ImageInputs]: pixel_values = kwargs.pop("pixel_values", None) + image_embeds = kwargs.pop("image_embeds", None) - if pixel_values is None: + if pixel_values is None and image_embeds is None: return None - if not isinstance(pixel_values, torch.Tensor): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") + if pixel_values is not None: + if not isinstance(pixel_values, torch.Tensor): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") - return Blip2ImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - ) + return Blip2ImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + ) + + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeddings. " + f"Got type: {type(image_embeds)}") + return Blip2ImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + + raise AssertionError("This line should be unreachable.") def _image_pixels_to_features(self, vision_model: BlipVisionModel, pixel_values: torch.Tensor) -> torch.Tensor: @@ -538,6 +560,10 @@ def _process_image_pixels(self, def _process_image_input(self, image_input: Blip2ImageInputs) -> torch.Tensor: + + if image_input["type"] == "image_embeds": + return image_input["data"] + assert self.vision_model is not None image_features = self._process_image_pixels(image_input) diff --git a/vllm/model_executor/models/clip.py b/vllm/model_executor/models/clip.py index 805ade39389d..8ec72eeb14e5 100644 --- a/vllm/model_executor/models/clip.py +++ b/vllm/model_executor/models/clip.py @@ -88,7 +88,13 @@ def input_processor_for_clip( tokenizer = cached_get_tokenizer(model_config.tokenizer) if image_feature_size_override is None: - image_feature_size = get_clip_image_feature_size(hf_config) + image_data = multi_modal_data["image"] + if isinstance(image_data, Image.Image): + image_feature_size = get_clip_image_feature_size(hf_config) + elif isinstance(image_data, torch.Tensor): + image_feature_size = image_data.shape[0] + else: + raise TypeError(f"Invalid image type: {type(image_data)}") else: image_feature_size = image_feature_size_override diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py index c4738263c305..bb49349e7954 100644 --- a/vllm/model_executor/models/fuyu.py +++ b/vllm/model_executor/models/fuyu.py @@ -234,7 +234,8 @@ def __init__(self, cache_config=cache_config, quant_config=quant_config) - def _parse_and_validate_image_input(self, **kwargs: object): + def _parse_and_validate_image_input( + self, **kwargs: object) -> Optional[FuyuImagePixelInputs]: image_patches = kwargs.pop("image_patches", None) if isinstance(image_patches, torch.Tensor): @@ -249,6 +250,13 @@ def _parse_and_validate_image_input(self, **kwargs: object): data=image_patches) return None + def _process_image_input( + self, image_input: FuyuImagePixelInputs) -> torch.Tensor: + + assert self.vision_embed_tokens is not None + vision_embeddings, _ = self.vision_embed_tokens(image_input["data"]) + return vision_embeddings + def forward( self, input_ids: torch.Tensor, @@ -261,8 +269,7 @@ def forward( image_input = self._parse_and_validate_image_input(**kwargs) if image_input is not None: - vision_embeddings, _ = self.vision_embed_tokens( - image_input["data"]) + vision_embeddings = self._process_image_input(image_input) inputs_embeds = self.language_model.model.embed_tokens(input_ids) inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, vision_embeddings, diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index 49f9a4c85f2d..26c02d46a18e 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -50,6 +50,19 @@ class InternVLImagePixelInputs(TypedDict): """ +class InternVLImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: Union[torch.Tensor, List[torch.Tensor]] + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +InternVLImageInputs = Union[InternVLImagePixelInputs, + InternVLImageEmbeddingInputs] + + # copied from https://huggingface.co/OpenGVLab/InternVL2-1B def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD @@ -193,8 +206,10 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs): # add thumbnail image if num_blocks > 1 if hf_config.use_thumbnail and num_blocks > 1: num_blocks += 1 + image_feature_size = num_blocks * num_patches + elif isinstance(image_data, torch.Tensor): - raise NotImplementedError("Embeddings input is not supported yet") + image_feature_size = image_data.shape[0] else: raise TypeError(f"Invalid image type: {type(image_data)}") @@ -205,7 +220,7 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs): prompt_token_ids = llm_inputs["prompt_token_ids"] if prompt is None: prompt = tokenizer.decode(prompt_token_ids) - image_prompt = IMG_START + IMG_CONTEXT * num_blocks * num_patches + IMG_END + image_prompt = IMG_START + IMG_CONTEXT * image_feature_size + IMG_END new_prompt = prompt.replace('<image>', image_prompt, 1) new_prompt_token_ids = tokenizer.encode(new_prompt) @@ -378,23 +393,49 @@ def _validate_shape(d: torch.Tensor): return data def _parse_and_validate_image_input( - self, **kwargs: object) -> Optional[InternVLImagePixelInputs]: + self, **kwargs: object) -> Optional[InternVLImageInputs]: pixel_values = kwargs.pop("pixel_values", None) image_token_id = kwargs.pop("image_token_id", None) + image_embeds = kwargs.pop("image_embeds", None) - if pixel_values is None: + if pixel_values is None and image_embeds is None: return None + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeddings. " + f"Got type: {type(image_embeds)}") + return InternVLImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + self.img_context_token_id = image_token_id[0] - if not isinstance(pixel_values, (torch.Tensor, list)): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") + if pixel_values is not None: + if not isinstance(pixel_values, (torch.Tensor, list)): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") + + return InternVLImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + ) + + raise AssertionError("This line should be unreachable.") + + def _process_image_input( + self, + image_input: InternVLImageInputs, + ) -> torch.Tensor: + + if image_input["type"] == "image_embeds": + return image_input["data"] + + assert self.vision_model is not None + image_embeds = self.extract_feature(image_input["data"]) - return InternVLImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - ) + return image_embeds def forward( self, @@ -409,9 +450,9 @@ def forward( if image_input is not None: inputs_embeds = self.language_model.model.get_input_embeddings( input_ids) - vit_embeds = self.extract_feature(image_input["data"]) + vision_embeddings = self._process_image_input(image_input) inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, - vit_embeds, + vision_embeddings, self.img_context_token_id) input_ids = None else: diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 9a11bcc4c54c..0ff68943b510 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -27,6 +27,24 @@ merge_vision_embeddings) +class LlavaImagePixelInputs(TypedDict): + type: Literal["pixel_values"] + data: torch.Tensor + """Shape: `(batch_size, num_channels, height, width)`""" + + +class LlavaImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: torch.Tensor + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs] + + # TODO(xwjiang): Run benchmark and decide if TP. class LlavaMultiModalProjector(nn.Module): @@ -49,15 +67,6 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor: return hidden_states -class LlavaImagePixelInputs(TypedDict): - type: Literal["pixel_values"] - data: torch.Tensor - """Shape: `(batch_size, num_channels, height, width)`""" - - -LlavaImageInputs = LlavaImagePixelInputs - - def get_max_llava_image_tokens(ctx: InputContext): hf_config = ctx.get_hf_config(LlavaConfig) vision_config = hf_config.vision_config @@ -210,18 +219,30 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[LlavaImageInputs]: pixel_values = kwargs.pop("pixel_values", None) + image_embeds = kwargs.pop("image_embeds", None) - if pixel_values is None: + if pixel_values is None and image_embeds is None: return None - if not isinstance(pixel_values, torch.Tensor): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") - - return LlavaImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - ) + if pixel_values is not None: + if not isinstance(pixel_values, torch.Tensor): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") + return LlavaImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + ) + + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeddings. " + f"Got type: {type(image_embeds)}") + return LlavaImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + + raise AssertionError("This line should be unreachable.") def _select_image_features(self, image_features: torch.Tensor, *, strategy: str) -> torch.Tensor: @@ -258,6 +279,10 @@ def _process_image_pixels(self, def _process_image_input(self, image_input: LlavaImageInputs) -> torch.Tensor: + + if image_input["type"] == "image_embeds": + return image_input["data"] + assert self.vision_tower is not None image_features = self._process_image_pixels(image_input) return self.multi_modal_projector(image_features) diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 9abc480f60de..d94af966162f 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -60,7 +60,17 @@ class LlavaNextImagePixelInputs(TypedDict): """ -LlavaNextImageInputs = LlavaNextImagePixelInputs +class LlavaNextImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: torch.Tensor + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +LlavaNextImageInputs = Union[LlavaNextImagePixelInputs, + LlavaNextImageEmbeddingInputs] # Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79 @@ -208,7 +218,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs): input_width=width, ) elif isinstance(image_data, torch.Tensor): - raise NotImplementedError("Embeddings input is not supported yet") + image_feature_size = image_data.shape[0] else: raise TypeError(f"Invalid image type: {type(image_data)}") @@ -320,26 +330,40 @@ def _validate_shape(d: torch.Tensor): return data def _parse_and_validate_image_input( - self, **kwargs: object) -> Optional[LlavaNextImagePixelInputs]: + self, **kwargs: object) -> Optional[LlavaNextImageInputs]: pixel_values = kwargs.pop("pixel_values", None) image_sizes = kwargs.pop("image_sizes", None) + image_embeds = kwargs.pop("image_embeds", None) - if pixel_values is None: + if pixel_values is None and image_embeds is None: return None - if not isinstance(pixel_values, (torch.Tensor, list)): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") + if pixel_values is not None: + if not isinstance(pixel_values, (torch.Tensor, list)): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") - if not isinstance(image_sizes, torch.Tensor): - raise ValueError("Incorrect type of image sizes. " - f"Got type: {type(image_sizes)}") + if not isinstance(image_sizes, torch.Tensor): + raise ValueError("Incorrect type of image sizes. " + f"Got type: {type(image_sizes)}") - return LlavaNextImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - image_sizes=self._validate_image_sizes(image_sizes), - ) + return LlavaNextImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + image_sizes=self._validate_image_sizes(image_sizes), + ) + + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeds. " + f"Got type: {type(image_embeds)}") + + return LlavaNextImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + + raise AssertionError("This line should be unreachable.") def _select_image_features(self, image_features: torch.Tensor, *, strategy: str) -> torch.Tensor: @@ -466,6 +490,10 @@ def _process_image_input( self, image_input: LlavaNextImageInputs, ) -> Union[torch.Tensor, List[torch.Tensor]]: + + if image_input["type"] == "image_embeds": + return [image_input["data"]] + patch_embeddings = self._process_image_pixels(image_input) image_sizes = image_input.get("image_sizes") diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index 9ba53b8b59a2..c6d59db643bb 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -1,4 +1,4 @@ -from typing import Iterable, List, Literal, Optional, Tuple, TypedDict +from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union import torch from torch import nn @@ -31,6 +31,25 @@ } +class PaliGemmaImagePixelInputs(TypedDict): + type: Literal["pixel_values"] + data: torch.Tensor + """Shape: (batch_size, num_channels, height, width)""" + + +class PaliGemmaImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: torch.Tensor + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs, + PaliGemmaImageEmbeddingInputs] + + def get_max_paligemma_image_tokens(ctx: InputContext): hf_config = ctx.get_hf_config(PaliGemmaConfig) vision_config = hf_config.vision_config @@ -107,15 +126,6 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor: return hidden_states -class PaliGemmaImagePixelInputs(TypedDict): - type: Literal["pixel_values"] - data: torch.Tensor - """Shape: (batch_size, num_channels, height, width)""" - - -PaliGemmaImageInputs = PaliGemmaImagePixelInputs - - @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_paligemma_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma) @@ -163,18 +173,30 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[PaliGemmaImageInputs]: pixel_values = kwargs.pop("pixel_values", None) + image_embeds = kwargs.pop("image_embeds", None) - if pixel_values is None: + if pixel_values is None and image_embeds is None: return None - if not isinstance(pixel_values, torch.Tensor): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") - - return PaliGemmaImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - ) + if pixel_values is not None: + if not isinstance(pixel_values, torch.Tensor): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") + return PaliGemmaImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + ) + + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeddings. " + f"Got type: {type(image_embeds)}") + return PaliGemmaImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + + raise AssertionError("This line should be unreachable.") def _image_pixels_to_features( self, @@ -187,27 +209,21 @@ def _image_pixels_to_features( return image_features - def _process_image_pixels( + def _process_image_input( self, - inputs: PaliGemmaImagePixelInputs, + image_input: PaliGemmaImageInputs, ) -> torch.Tensor: - assert self.vision_tower is not None - pixel_values = inputs["data"] + if image_input["type"] == "image_embeds": + return image_input["data"] - return self._image_pixels_to_features( + assert self.vision_tower is not None + pixel_values = image_input["data"] + image_features = self._image_pixels_to_features( self.vision_tower, pixel_values, ) - def _process_image_input( - self, - image_input: PaliGemmaImageInputs, - ) -> torch.Tensor: - - assert self.vision_tower is not None - image_features = self._process_image_pixels(image_input, ) - return self.multi_modal_projector(image_features) def forward(self, diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index d39cf15a1bb9..51d3a75ea6ff 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -70,6 +70,36 @@ projection_dim=768) +class Phi3VImagePixelInputs(TypedDict): + type: Literal["pixel_values"] + data: Union[torch.Tensor, List[torch.Tensor]] + """ + Shape: `(batch_size, 1 + num_patches, num_channels, height, width)` + + Note that `num_patches` may be different for each batch, in which case + the data is passed as a list instead of a batched tensor. + """ + + image_sizes: torch.Tensor + """ + Shape: `(batch_size, 2)` + + This should be in `(height, width)` format. + """ + + +class Phi3VImageEmbeddingInputs(TypedDict): + type: Literal["image_embeds"] + data: Union[torch.Tensor, List[torch.Tensor]] + """Shape: `(batch_size, image_feature_size, hidden_size)` + + `hidden_size` must match the hidden size of language model backbone. + """ + + +Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs] + + class Phi3ImageEmbeddingBase(nn.Module): def __init__(self) -> None: @@ -257,24 +287,6 @@ def add_image_newline(self, image_features_hd): return image_features_hd_newline -class Phi3VImagePixelInputs(TypedDict): - type: Literal["pixel_values"] - data: Union[torch.Tensor, List[torch.Tensor]] - """ - Shape: `(batch_size, 1 + num_patches, num_channels, height, width)` - - Note that `num_patches` may be different for each batch, in which case - the data is passed as a list instead of a batched tensor. - """ - - image_sizes: torch.Tensor - """ - Shape: `(batch_size, 2)` - - This should be in `(height, width)` format. - """ - - # Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L57 def _calc_padded_size(*, width: int, height: int, padding_unit: int = 336): target_height = int(np.ceil(height / padding_unit) * padding_unit) @@ -390,7 +402,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs): input_width=w, input_height=h) elif isinstance(image_data, torch.Tensor): - raise NotImplementedError("Embeddings input is not supported yet") + image_feature_size = image_data.shape[0] else: raise TypeError(f"Invalid image type: {type(image_data)}") @@ -494,25 +506,55 @@ def _validate_shape(d: torch.Tensor): return data def _parse_and_validate_image_input( - self, **kwargs: object) -> Optional[Phi3VImagePixelInputs]: + self, **kwargs: object) -> Optional[Phi3VImageInputs]: pixel_values = kwargs.pop("pixel_values", None) image_sizes = kwargs.pop("image_sizes", None) + image_embeds = kwargs.pop("image_embeds", None) if pixel_values is None: return None - if not isinstance(pixel_values, (torch.Tensor, list)): - raise ValueError("Incorrect type of pixel values. " - f"Got type: {type(pixel_values)}") + if pixel_values is None and image_embeds is None: + return None + + if pixel_values is not None: + if not isinstance(pixel_values, (torch.Tensor, list)): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") + + if not isinstance(image_sizes, torch.Tensor): + raise ValueError("Incorrect type of image sizes. " + f"Got type: {type(image_sizes)}") + + return Phi3VImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + image_sizes=self._validate_image_sizes(image_sizes)) + + if image_embeds is not None: + if not isinstance(image_embeds, torch.Tensor): + raise ValueError("Incorrect type of image embeddings. " + f"Got type: {type(image_embeds)}") + return Phi3VImageEmbeddingInputs( + type="image_embeds", + data=image_embeds, + ) + + raise AssertionError("This line should be unreachable.") + + def _process_image_input( + self, + image_input: Phi3VImageInputs, + ) -> torch.Tensor: + + if image_input["type"] == "image_embeds": + return image_input["data"] - if not isinstance(image_sizes, torch.Tensor): - raise ValueError("Incorrect type of image sizes. " - f"Got type: {type(image_sizes)}") + assert self.vision_embed_tokens is not None + image_embeds = self.vision_embed_tokens(image_input["data"], + image_input["image_sizes"]) - return Phi3VImagePixelInputs( - type="pixel_values", - data=self._validate_pixel_values(pixel_values), - image_sizes=self._validate_image_sizes(image_sizes)) + return image_embeds def forward(self, input_ids: torch.Tensor, @@ -524,8 +566,7 @@ def forward(self, image_input = self._parse_and_validate_image_input(**kwargs) if image_input is not None: - vision_embeddings = self.vision_embed_tokens( - image_input["data"], image_input["image_sizes"]) + vision_embeddings = self._process_image_input(image_input) inputs_embeds = self.model.get_input_embeddings(input_ids) inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds, vision_embeddings, diff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py index 5ba14f73394f..afe57bf573ad 100644 --- a/vllm/model_executor/models/siglip.py +++ b/vllm/model_executor/models/siglip.py @@ -97,7 +97,13 @@ def input_processor_for_siglip( tokenizer = cached_get_tokenizer(model_config.tokenizer) if image_feature_size_override is None: - image_feature_size = get_siglip_image_feature_size(hf_config) + image_data = multi_modal_data["image"] + if isinstance(image_data, Image.Image): + image_feature_size = get_siglip_image_feature_size(hf_config) + elif isinstance(image_data, torch.Tensor): + image_feature_size = image_data.shape[0] + else: + raise TypeError(f"Invalid image type: {type(image_data)}") else: image_feature_size = image_feature_size_override diff --git a/vllm/multimodal/image.py b/vllm/multimodal/image.py index db50229bda31..a1c16d6957c2 100644 --- a/vllm/multimodal/image.py +++ b/vllm/multimodal/image.py @@ -115,6 +115,7 @@ def _default_input_mapper(self, ctx: InputContext, data: object) -> MultiModalInputs: model_config = ctx.model_config + # PIL image if isinstance(data, Image.Image) or is_list_of(data, Image.Image): image_processor = self._get_hf_image_processor(model_config) if image_processor is None: @@ -129,8 +130,10 @@ def _default_input_mapper(self, ctx: InputContext, raise return MultiModalInputs(batch_data) + + # Image embedding elif isinstance(data, torch.Tensor) or is_list_of(data, torch.Tensor): - raise NotImplementedError("Embeddings input is not supported yet") + return MultiModalInputs({"image_embeds": data}) raise TypeError(f"Invalid image type: {type(data)}")
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
vllm-project__vllm-5654@46ef3ac
vllm-project/vllm
Python
5,654
[Bugfix] AsyncLLMEngine hangs with asyncio.run
This root cause is that https://github.com/vllm-project/vllm/blob/97b030005c7f5cde7c1b97c718a8841db7d6220b/vllm/engine/async_llm_engine.py#L509 triggered https://github.com/python/cpython/issues/86296 The Python bug is fixed in Python 3.12 but will not be backported. We fix it by switching to `asyncio.timeout` for Python 3.11+, and another `async_timeout` implementation for Python 3.8+. This PR is largely based on https://github.com/python-websockets/websockets/pull/1307. FIX #4789 FIX #3996 **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-06-18T19:22:28Z
[Usage]: Using AsyncLLMEngine with asyncio.run ### Your current environment ```text Collecting environment information... PyTorch version: 2.1.2+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 GPU 1: NVIDIA RTX A6000 Nvidia driver version: 550.54.15 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: GenuineIntel Model name: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 1 Stepping: 7 CPU max MHz: 4800.0000 CPU min MHz: 1200.0000 BogoMIPS: 6999.82 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vnni md_clear flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 384 KiB (12 instances) L1i cache: 384 KiB (12 instances) L2 cache: 12 MiB (12 instances) L3 cache: 19.3 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-23 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Versions of relevant libraries: [pip3] flake8==7.0.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.1 [pip3] torch==2.1.2 [pip3] triton==2.1.0 [conda] torch 2.1.2 pypi_0 pypi [conda] triton 2.1.0 pypi_0 pypiROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.0.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS 0-23 0 N/A GPU1 SYS X 0-23 0 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ``` ### How would you like to use vllm I want to run batch offline inference in a command line tool using `AsyncLLMEngine` in a set of coroutines being managed by a top level call to `asyncio.run`. I have a demo script that ultimately does the right thing but doesn't tear down or complete correctly. It will either: 1. Hang until asyncio times out 2. Throw an error from `_raise_exception_on_finish` being cancelled The minimal working example of what I want to do is: ```python import asyncio from uuid import uuid4 from vllm import AsyncEngineArgs, AsyncLLMEngine, SamplingParams def main(): engine = AsyncLLMEngine.from_engine_args( AsyncEngineArgs( model="mistralai/Mistral-7B-Instruct-v0.2", ) ) params = SamplingParams( top_k=1, temperature=0.8, max_tokens=64, stop=[], ) async def run_query(query: str): request_id = uuid4() outputs = engine.generate(query, params, request_id) async for output in outputs: final_output = output responses = [] for output in final_output.outputs: responses.append(output.text) return responses async def process(): queries = [ "I have cats, do you?", "If you were a muppet, what type of muppet would you be?", ] tasks = [asyncio.create_task(run_query(q)) for q in queries] results = [] for task in asyncio.as_completed(tasks): result = await task results.append(result) return results results = asyncio.run(process()) print(results) if __name__ == "__main__": main() ``` My guess is that this is due to `AsyncLLMEngine` calling `asyncio.get_event_loop` and that somehow conflicting with my top level runner calling `asyncio.run`. [Bug]: Async engine hangs with 0.4.* releases ### Your current environment ```text Collecting environment information... PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.29.3 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.5.0-1020-oem-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.1.105 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX 6000 Ada Generation Nvidia driver version: 545.29.06 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Vendor ID: GenuineIntel Model name: 13th Gen Intel(R) Core(TM) i9-13900K CPU family: 6 Model: 183 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 1 Stepping: 1 CPU max MHz: 5800.0000 CPU min MHz: 800.0000 BogoMIPS: 5990.40 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 896 KiB (24 instances) L1i cache: 1.3 MiB (24 instances) L2 cache: 32 MiB (12 instances) L3 cache: 36 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-31 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] torch==2.3.0 [pip3] triton==2.3.0 [pip3] vllm-nccl-cu12==2.18.1.0.4.0 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.2 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-31 0 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ``` ### ๐Ÿ› Describe the bug The code below worked fine with vllm==0.3.3 (I could see the generated output printed to the console). However, when I try to use it with 0.4.0, 0.4.1 or 0.4.2 it hangs. ```python import argparse import asyncio from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine, AsyncStream from vllm.sampling_params import SamplingParams def parse_args(): parser = argparse.ArgumentParser() parser = AsyncEngineArgs.add_cli_args(parser) return parser.parse_args() async def iterate_over_output_for_one_prompt(output_iterator: AsyncStream) -> str: last_text = "" prompt = "???" async for output in output_iterator: prompt = output.prompt last_text = output.outputs[0].text return prompt + last_text async def generate(engine: AsyncLLMEngine, prompts: list[str]) -> list[str]: sampling_params = SamplingParams(n=1, max_tokens=100, ignore_eos=True) output_iterators = [await engine.add_request(f"req_{i}", prompt, sampling_params) for i, prompt in enumerate(prompts)] outputs = await asyncio.gather(*[iterate_over_output_for_one_prompt(output_iterator) for output_iterator in output_iterators]) return list(outputs) def main(): args = parse_args() engine = AsyncLLMEngine.from_engine_args(AsyncEngineArgs.from_cli_args(args)) prompts = ["I've never been to", "I would like to see"] outputs = asyncio.run(generate(engine, prompts)) for output in outputs: print(output) print("FINISHED") if __name__ == '__main__': main() ``` The last logs before iterrupting the execution are ``` INFO 05-13 14:28:39 async_llm_engine.py:120] Finished request req_0. INFO 05-13 14:28:39 async_llm_engine.py:120] Finished request req_1. ``` And after finally pressing Ctrl+C I get ```Traceback (most recent call last): File "/workdir/repo/llmvpr/vllm/minimal.py", line 46, in <module> main() File "/workdir/repo/llmvpr/vllm/minimal.py", line 39, in main outputs = asyncio.run(generate(engine, prompts)) File "/usr/lib/python3.10/asyncio/runners.py", line 47, in run _cancel_all_tasks(loop) File "/usr/lib/python3.10/asyncio/runners.py", line 63, in _cancel_all_tasks loop.run_until_complete(tasks.gather(*to_cancel, return_exceptions=True)) File "/usr/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete self.run_forever() File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever self._run_once() File "/usr/lib/python3.10/asyncio/base_events.py", line 1871, in _run_once event_list = self._selector.select(timeout) File "/usr/lib/python3.10/selectors.py", line 469, in select fd_event_list = self._selector.poll(timeout, max_ev) KeyboardInterrupt Task was destroyed but it is pending! task: <Task pending name='Task-2' coro=<AsyncLLMEngine.run_engine_loop() running at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:490> wait_for=<Future pending cb=[Task.task_wakeup()]> cb=[_raise_exception_on_finish(error_callback=<bound method...7851eba052d0>>)() at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:30, <1 more>, gather.<locals>._done_callback() at /usr/lib/python3.10/asyncio/tasks.py:720]> ``` Is there something wrong with my code or is it a bug in `vllm`?
Is there a reason why you cannot use the following? ```python from vllm import LLM model = LLM("mistralai/Mistral-7B-Instruct-v0.2") model.generate([prompts]) ``` The `LLM` class is intended for offline usage The class you are using is not really intended to be used externally That's my current backup solution which works just fine. But if I can get a scenario like the above working that'd be ideal. Okay, is there a reason that doesn't work? Just curious so we can improve the interface. I would say that looking at `vllm/entrypoints/api_server.py` is the best place for an example using the `AsyncLLMEngine`. I think it will be tricky to have multiple asyncio loops running. The `AsyncLLMEngine` class is supposed to handle the concurrency Otherwise I think your best bet is to reimplement `AsyncLLMEngine` for your needs As far as I can tell, when using the `LLM` engine my co-routines all run in sequence rather than doing anything parallel. When I activate the `AsyncLLMEngine` manually, everything works pretty smoothly but it never exits the top level co-routine and instead times out. I'll have to look deep into the engine to see what its doing that's interacting badly with `asyncio.run`. Alternative, I might just stand up vLLM servers as sub-processes and call them. What is AsyncLLMEngine? What is the difference between AsyncLLMEngine vs LLM class? @fozziethebeat I got the same issue, did you figure an workaround? thanks. Our solution was to fork off a sub-process that ran the server and waited until it was ready and then just call it via REST requests. Works well enough @robertgshaw2-neuralmagic Hi, Robert, how can we set `distrubted_execution_backend="mp"` while using the `LLM`? This is the same as https://github.com/vllm-project/vllm/issues/4789. I'll try to send a PR to fix it. > If you experienced crashes or hangs, it would be helpful to run vllm with export VLLM_TRACE_FUNCTION=1 . All the function calls in vllm will be recorded. Inspect these log files, and tell which function crashes or hangs. Quote from issue templates. [vllm_trace_function.log](https://github.com/vllm-project/vllm/files/15305960/vllm_trace_function.log) The requested log file is too large to send in its entirety, but I am attaching the last 10MB. When you initialize the async engine I think it expects to be in an running event loop, not sure why though. If you change the code to ```diff diff --git a/original.py b/repro.py index 37ede83..8881b6f 100644 --- a/original.py +++ b/repro.py @@ -32,15 +32,15 @@ async def generate(engine: AsyncLLMEngine, prompts: list[str]) -> list[str]: return list(outputs) -def main(): +async def main(): args = parse_args() engine = AsyncLLMEngine.from_engine_args(AsyncEngineArgs.from_cli_args(args)) prompts = ["I've never been to", "I would like to see"] - outputs = asyncio.run(generate(engine, prompts)) + outputs = await generate(engine, prompts) for output in outputs: print(output) print("FINISHED") if __name__ == '__main__': - main() + asyncio.run(main()) ``` The prints should succeed. However, the script still will not exit. So there's still a bug. Saw the same issue. I tested and the culprit commit should be #3015. It's not clear to me what's the root cause though. So the problem is that https://github.com/vllm-project/vllm/blob/97b030005c7f5cde7c1b97c718a8841db7d6220b/vllm/engine/async_llm_engine.py#L509 triggered https://github.com/python/cpython/issues/86296 The bug is fixed in Python 3.12 but will not be backported. It's very easy to workaround it in Python 3.11 with `asyncio.timeout`. However, the workaround for Python 3.8 - 3.10 is not straightforward. @zifeitong I am facing exactly the same issue. Setting engine_use_ray and worker_use_ray to True can help me walk around this issue.
[ { "body": "### Your current environment\n\n```text\r\nCollecting environment information...\r\nPyTorch version: 2.1.2+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.4 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: Could not collect\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration:\r\nGPU 0: NVIDIA GeForce RTX 3060\r\nGPU 1: NVIDIA RTX A6000\r\n\r\nNvidia driver version: 550.54.15\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 46 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 24\r\nOn-line CPU(s) list: 0-23\r\nVendor ID: GenuineIntel\r\nModel name: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz\r\nCPU family: 6\r\nModel: 85\r\nThread(s) per core: 2\r\nCore(s) per socket: 12\r\nSocket(s): 1\r\nStepping: 7\r\nCPU max MHz: 4800.0000\r\nCPU min MHz: 1200.0000\r\nBogoMIPS: 6999.82\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vnni md_clear flush_l1d arch_capabilities\r\nVirtualization: VT-x\r\nL1d cache: 384 KiB (12 instances)\r\nL1i cache: 384 KiB (12 instances)\r\nL2 cache: 12 MiB (12 instances)\r\nL3 cache: 19.3 MiB (1 instance)\r\nNUMA node(s): 1\r\nNUMA node0 CPU(s): 0-23\r\nVulnerability Gather data sampling: Mitigation; Microcode\r\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Retbleed: Mitigation; Enhanced IBRS\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Mitigation; TSX disabled\r\n\r\nVersions of relevant libraries:\r\n[pip3] flake8==7.0.0\r\n[pip3] mypy-extensions==1.0.0\r\n[pip3] numpy==1.26.1\r\n[pip3] torch==2.1.2\r\n[pip3] triton==2.1.0\r\n[conda] torch 2.1.2 pypi_0 pypi\r\n[conda] triton 2.1.0 pypi_0 pypiROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.4.0.post1\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0\tGPU1\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\r\nGPU0\t X \tSYS\t0-23\t0\t\tN/A\r\nGPU1\tSYS\t X \t0-23\t0\t\tN/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n```\r\n\n\n### How would you like to use vllm\n\nI want to run batch offline inference in a command line tool using `AsyncLLMEngine` in a set of coroutines being managed by a top level call to `asyncio.run`.\r\n\r\nI have a demo script that ultimately does the right thing but doesn't tear down or complete correctly. It will either:\r\n1. Hang until asyncio times out\r\n2. Throw an error from `_raise_exception_on_finish` being cancelled\r\n\r\nThe minimal working example of what I want to do is:\r\n\r\n```python\r\nimport asyncio\r\nfrom uuid import uuid4\r\nfrom vllm import AsyncEngineArgs, AsyncLLMEngine, SamplingParams\r\n\r\n\r\ndef main():\r\n engine = AsyncLLMEngine.from_engine_args(\r\n AsyncEngineArgs(\r\n model=\"mistralai/Mistral-7B-Instruct-v0.2\",\r\n )\r\n )\r\n params = SamplingParams(\r\n top_k=1,\r\n temperature=0.8,\r\n max_tokens=64,\r\n stop=[],\r\n )\r\n\r\n async def run_query(query: str):\r\n request_id = uuid4()\r\n outputs = engine.generate(query, params, request_id)\r\n async for output in outputs:\r\n final_output = output\r\n responses = []\r\n for output in final_output.outputs:\r\n responses.append(output.text)\r\n return responses\r\n\r\n async def process():\r\n queries = [\r\n \"I have cats, do you?\",\r\n \"If you were a muppet, what type of muppet would you be?\",\r\n ]\r\n tasks = [asyncio.create_task(run_query(q)) for q in queries]\r\n results = []\r\n for task in asyncio.as_completed(tasks):\r\n result = await task\r\n results.append(result)\r\n return results\r\n\r\n results = asyncio.run(process())\r\n print(results)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nMy guess is that this is due to `AsyncLLMEngine` calling `asyncio.get_event_loop` and that somehow conflicting with my top level runner calling `asyncio.run`.", "number": 3996, "title": "[Usage]: Using AsyncLLMEngine with asyncio.run" }, { "body": "### Your current environment\n\n```text\r\nCollecting environment information...\r\nPyTorch version: 2.3.0+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.29.3\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-6.5.0-1020-oem-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 12.1.105\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: GPU 0: NVIDIA RTX 6000 Ada Generation\r\nNvidia driver version: 545.29.06\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 46 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 32\r\nOn-line CPU(s) list: 0-31\r\nVendor ID: GenuineIntel\r\nModel name: 13th Gen Intel(R) Core(TM) i9-13900K\r\nCPU family: 6\r\nModel: 183\r\nThread(s) per core: 2\r\nCore(s) per socket: 24\r\nSocket(s): 1\r\nStepping: 1\r\nCPU max MHz: 5800.0000\r\nCPU min MHz: 800.0000\r\nBogoMIPS: 5990.40\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities\r\nVirtualization: VT-x\r\nL1d cache: 896 KiB (24 instances)\r\nL1i cache: 1.3 MiB (24 instances)\r\nL2 cache: 32 MiB (12 instances)\r\nL3 cache: 36 MiB (1 instance)\r\nNUMA node(s): 1\r\nNUMA node0 CPU(s): 0-31\r\nVulnerability Gather data sampling: Not affected\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Not affected\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] mypy-extensions==1.0.0\r\n[pip3] numpy==1.26.4\r\n[pip3] nvidia-nccl-cu12==2.20.5\r\n[pip3] torch==2.3.0\r\n[pip3] triton==2.3.0\r\n[pip3] vllm-nccl-cu12==2.18.1.0.4.0\r\n[conda] Could not collectROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.4.2\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\n\u001b[4mGPU0\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\u001b[0m\r\nGPU0\t X \t0-31\t0\t\tN/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n\r\n```\r\n\n\n### ๐Ÿ› Describe the bug\n\nThe code below worked fine with vllm==0.3.3 (I could see the generated output printed to the console).\r\nHowever, when I try to use it with 0.4.0, 0.4.1 or 0.4.2 it hangs.\r\n```python\r\nimport argparse\r\nimport asyncio\r\n\r\nfrom vllm.engine.arg_utils import AsyncEngineArgs\r\nfrom vllm.engine.async_llm_engine import AsyncLLMEngine, AsyncStream\r\nfrom vllm.sampling_params import SamplingParams\r\n\r\n\r\ndef parse_args():\r\n parser = argparse.ArgumentParser()\r\n parser = AsyncEngineArgs.add_cli_args(parser)\r\n return parser.parse_args()\r\n\r\n\r\nasync def iterate_over_output_for_one_prompt(output_iterator: AsyncStream) -> str:\r\n last_text = \"\"\r\n prompt = \"???\"\r\n\r\n async for output in output_iterator:\r\n prompt = output.prompt\r\n last_text = output.outputs[0].text\r\n\r\n return prompt + last_text\r\n\r\n\r\nasync def generate(engine: AsyncLLMEngine, prompts: list[str]) -> list[str]:\r\n sampling_params = SamplingParams(n=1, max_tokens=100, ignore_eos=True)\r\n output_iterators = [await engine.add_request(f\"req_{i}\", prompt, sampling_params)\r\n for i, prompt in enumerate(prompts)]\r\n outputs = await asyncio.gather(*[iterate_over_output_for_one_prompt(output_iterator)\r\n for output_iterator in output_iterators])\r\n return list(outputs)\r\n\r\n\r\ndef main():\r\n args = parse_args()\r\n engine = AsyncLLMEngine.from_engine_args(AsyncEngineArgs.from_cli_args(args))\r\n prompts = [\"I've never been to\", \"I would like to see\"]\r\n outputs = asyncio.run(generate(engine, prompts))\r\n for output in outputs:\r\n print(output)\r\n print(\"FINISHED\")\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\nThe last logs before iterrupting the execution are\r\n```\r\nINFO 05-13 14:28:39 async_llm_engine.py:120] Finished request req_0.\r\nINFO 05-13 14:28:39 async_llm_engine.py:120] Finished request req_1.\r\n```\r\nAnd after finally pressing Ctrl+C I get\r\n```Traceback (most recent call last):\r\n File \"/workdir/repo/llmvpr/vllm/minimal.py\", line 46, in <module>\r\n main()\r\n File \"/workdir/repo/llmvpr/vllm/minimal.py\", line 39, in main\r\n outputs = asyncio.run(generate(engine, prompts))\r\n File \"/usr/lib/python3.10/asyncio/runners.py\", line 47, in run\r\n _cancel_all_tasks(loop)\r\n File \"/usr/lib/python3.10/asyncio/runners.py\", line 63, in _cancel_all_tasks\r\n loop.run_until_complete(tasks.gather(*to_cancel, return_exceptions=True))\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 636, in run_until_complete\r\n self.run_forever()\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\r\n self._run_once()\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1871, in _run_once\r\n event_list = self._selector.select(timeout)\r\n File \"/usr/lib/python3.10/selectors.py\", line 469, in select\r\n fd_event_list = self._selector.poll(timeout, max_ev)\r\nKeyboardInterrupt\r\nTask was destroyed but it is pending!\r\ntask: <Task pending name='Task-2' coro=<AsyncLLMEngine.run_engine_loop() running at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:490> wait_for=<Future pending cb=[Task.task_wakeup()]> cb=[_raise_exception_on_finish(error_callback=<bound method...7851eba052d0>>)() at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:30, <1 more>, gather.<locals>._done_callback() at /usr/lib/python3.10/asyncio/tasks.py:720]>\r\n```\r\nIs there something wrong with my code or is it a bug in `vllm`?", "number": 4789, "title": "[Bug]: Async engine hangs with 0.4.* releases" } ]
8a173382c80d6730e1bbc81f932ac3721ab2cd9d
{ "head_commit": "46ef3ac227f44526db4ecfd0825a62b78ab5fd9e", "head_commit_message": "Fix format", "patch_to_review": "diff --git a/tests/async_engine/test_async_llm_engine.py b/tests/async_engine/test_async_llm_engine.py\nindex 10a46422887e..127dba2c48f0 100644\n--- a/tests/async_engine/test_async_llm_engine.py\n+++ b/tests/async_engine/test_async_llm_engine.py\n@@ -3,7 +3,8 @@\n \n import pytest\n \n-from vllm.engine.async_llm_engine import AsyncLLMEngine\n+from vllm import SamplingParams\n+from vllm.engine.async_llm_engine import AsyncEngineArgs, AsyncLLMEngine\n \n \n @dataclass\n@@ -94,3 +95,29 @@ async def test_new_requests_event():\n assert engine.get_model_config() is not None\n assert engine.get_tokenizer() is not None\n assert engine.get_decoding_config() is not None\n+\n+\n+def test_asyncio_run():\n+ engine = AsyncLLMEngine.from_engine_args(\n+ AsyncEngineArgs(model=\"facebook/opt-125m\"))\n+\n+ async def run(prompt: str):\n+ sampling_params = SamplingParams(\n+ temperature=0,\n+ max_tokens=32,\n+ )\n+\n+ async for output in engine.generate(prompt,\n+ sampling_params,\n+ request_id=prompt):\n+ final_output = output\n+ return final_output\n+\n+ async def generate():\n+ return await asyncio.gather(\n+ run(\"test0\"),\n+ run(\"test1\"),\n+ )\n+\n+ results = asyncio.run(generate())\n+ assert len(results) == 2\ndiff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py\nindex ab312850b9ec..8863d3807255 100644\n--- a/vllm/engine/async_llm_engine.py\n+++ b/vllm/engine/async_llm_engine.py\n@@ -1,8 +1,12 @@\n import asyncio\n+import enum\n+import sys\n import time\n+import warnings\n from functools import partial\n-from typing import (AsyncIterator, Callable, Dict, Iterable, List, Optional,\n- Set, Tuple, Type, Union)\n+from types import TracebackType\n+from typing import (Any, AsyncIterator, Callable, Dict, Iterable, List,\n+ Optional, Set, Tuple, Type, Union)\n \n from transformers import PreTrainedTokenizer\n \n@@ -29,6 +33,199 @@ class AsyncEngineDeadError(RuntimeError):\n pass\n \n \n+# From https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py\n+# Licensed under the Apache License (Apache-2.0)\n+\n+if sys.version_info[:2] >= (3, 11):\n+ from asyncio import timeout as asyncio_timeout # noqa: F401\n+else:\n+\n+ def asyncio_timeout(delay: Optional[float]) -> \"Timeout\":\n+ \"\"\"timeout context manager.\n+\n+ Useful in cases when you want to apply timeout logic around block\n+ of code or in cases when asyncio.wait_for is not suitable. For example:\n+\n+ >>> async with timeout(0.001):\n+ ... async with aiohttp.get('https://github.com') as r:\n+ ... await r.text()\n+\n+\n+ delay - value in seconds or None to disable timeout logic\n+ \"\"\"\n+ loop = asyncio.get_running_loop()\n+ deadline = loop.time() + delay if delay is not None else None\n+ return Timeout(deadline, loop)\n+\n+ class _State(enum.Enum):\n+ INIT = \"INIT\"\n+ ENTER = \"ENTER\"\n+ TIMEOUT = \"TIMEOUT\"\n+ EXIT = \"EXIT\"\n+\n+ class Timeout:\n+ # Internal class, please don't instantiate it directly\n+ # Use timeout() and timeout_at() public factories instead.\n+ #\n+ # Implementation note: `async with timeout()` is preferred\n+ # over `with timeout()`.\n+ # While technically the Timeout class implementation\n+ # doesn't need to be async at all,\n+ # the `async with` statement explicitly points that\n+ # the context manager should be used from async function context.\n+ #\n+ # This design allows to avoid many silly misusages.\n+ #\n+ # TimeoutError is raised immediately when scheduled\n+ # if the deadline is passed.\n+ # The purpose is to time out as soon as possible\n+ # without waiting for the next await expression.\n+\n+ __slots__ = (\"_deadline\", \"_loop\", \"_state\", \"_timeout_handler\")\n+\n+ def __init__(self, deadline: Optional[float],\n+ loop: asyncio.AbstractEventLoop) -> None:\n+ self._loop = loop\n+ self._state = _State.INIT\n+\n+ self._timeout_handler = None # type: Optional[asyncio.Handle]\n+ if deadline is None:\n+ self._deadline = None # type: Optional[float]\n+ else:\n+ self.update(deadline)\n+\n+ def __enter__(self) -> \"Timeout\":\n+ warnings.warn(\n+ \"with timeout() is deprecated, use async with timeout()\",\n+ DeprecationWarning,\n+ stacklevel=2,\n+ )\n+ self._do_enter()\n+ return self\n+\n+ def __exit__(\n+ self,\n+ exc_type: Optional[Type[BaseException]],\n+ exc_val: Optional[BaseException],\n+ exc_tb: Optional[TracebackType],\n+ ) -> Optional[bool]:\n+ self._do_exit(exc_type)\n+ return None\n+\n+ async def __aenter__(self) -> \"Timeout\":\n+ self._do_enter()\n+ return self\n+\n+ async def __aexit__(\n+ self,\n+ exc_type: Optional[Type[BaseException]],\n+ exc_val: Optional[BaseException],\n+ exc_tb: Optional[TracebackType],\n+ ) -> Optional[bool]:\n+ self._do_exit(exc_type)\n+ return None\n+\n+ @property\n+ def expired(self) -> bool:\n+ \"\"\"Is timeout expired during execution?\"\"\"\n+ return self._state == _State.TIMEOUT\n+\n+ @property\n+ def deadline(self) -> Optional[float]:\n+ return self._deadline\n+\n+ def reject(self) -> None:\n+ \"\"\"Reject scheduled timeout if any.\"\"\"\n+ # cancel is maybe better name but\n+ # task.cancel() raises CancelledError in asyncio world.\n+ if self._state not in (_State.INIT, _State.ENTER):\n+ raise RuntimeError(f\"invalid state {self._state.value}\")\n+ self._reject()\n+\n+ def _reject(self) -> None:\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+ self._timeout_handler = None\n+\n+ def shift(self, delay: float) -> None:\n+ \"\"\"Advance timeout on delay seconds.\n+\n+ The delay can be negative.\n+\n+ Raise RuntimeError if shift is called when deadline is not scheduled\n+ \"\"\"\n+ deadline = self._deadline\n+ if deadline is None:\n+ raise RuntimeError(\n+ \"cannot shift timeout if deadline is not scheduled\")\n+ self.update(deadline + delay)\n+\n+ def update(self, deadline: float) -> None:\n+ \"\"\"Set deadline to absolute value.\n+\n+ deadline argument points on the time in the same clock system\n+ as loop.time().\n+\n+ If new deadline is in the past the timeout is raised immediately.\n+\n+ Please note: it is not POSIX time but a time with\n+ undefined starting base, e.g. the time of the system power on.\n+ \"\"\"\n+ if self._state == _State.EXIT:\n+ raise RuntimeError(\n+ \"cannot reschedule after exit from context manager\")\n+ if self._state == _State.TIMEOUT:\n+ raise RuntimeError(\"cannot reschedule expired timeout\")\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+ self._deadline = deadline\n+ if self._state != _State.INIT:\n+ self._reschedule()\n+\n+ def _reschedule(self) -> None:\n+ assert self._state == _State.ENTER\n+ deadline = self._deadline\n+ if deadline is None:\n+ return\n+\n+ now = self._loop.time()\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+\n+ task = asyncio.current_task()\n+ if deadline <= now:\n+ self._timeout_handler = self._loop.call_soon(\n+ self._on_timeout, task)\n+ else:\n+ self._timeout_handler = self._loop.call_at(\n+ deadline, self._on_timeout, task)\n+\n+ def _do_enter(self) -> None:\n+ if self._state != _State.INIT:\n+ raise RuntimeError(f\"invalid state {self._state.value}\")\n+ self._state = _State.ENTER\n+ self._reschedule()\n+\n+ def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:\n+ if exc_type is asyncio.CancelledError and \\\n+ self._state == _State.TIMEOUT:\n+ self._timeout_handler = None\n+ raise asyncio.TimeoutError\n+ # timeout has not expired\n+ self._state = _State.EXIT\n+ self._reject()\n+ return None\n+\n+ def _on_timeout(self, task: \"Optional[asyncio.Task[Any]]\") -> None:\n+ if task:\n+ task.cancel()\n+ self._state = _State.TIMEOUT\n+ # drop the reference early\n+ self._timeout_handler = None\n+\n+ # End https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py\n+\n+\n def _log_task_completion(task: asyncio.Task,\n error_callback: Callable[[Exception], None]) -> None:\n \"\"\"This function is only intended for the `engine.run_engine_loop()` task.\n@@ -540,8 +737,8 @@ async def run_engine_loop(self):\n # Abort if iteration takes too long due to unrecoverable errors\n # (eg. NCCL timeouts).\n try:\n- has_requests_in_progress = await asyncio.wait_for(\n- self.engine_step(), ENGINE_ITERATION_TIMEOUT_S)\n+ async with asyncio_timeout(ENGINE_ITERATION_TIMEOUT_S):\n+ has_requests_in_progress = await self.engine_step()\n except asyncio.TimeoutError as exc:\n logger.error(\n \"Engine iteration timed out. This should never happen!\")\n" }
[ { "diff_hunk": "@@ -29,6 +33,199 @@ class AsyncEngineDeadError(RuntimeError):\n pass\n \n \n+# From https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py\n+# Licensed under the Apache License (Apache-2.0)\n+\n+if sys.version_info[:2] >= (3, 11):\n+ from asyncio import timeout as asyncio_timeout # noqa: F401\n+else:\n+\n+ def asyncio_timeout(delay: Optional[float]) -> \"Timeout\":\n+ \"\"\"timeout context manager.\n+\n+ Useful in cases when you want to apply timeout logic around block\n+ of code or in cases when asyncio.wait_for is not suitable. For example:\n+\n+ >>> async with timeout(0.001):\n+ ... async with aiohttp.get('https://github.com') as r:\n+ ... await r.text()\n+\n+\n+ delay - value in seconds or None to disable timeout logic\n+ \"\"\"\n+ loop = asyncio.get_running_loop()\n+ deadline = loop.time() + delay if delay is not None else None\n+ return Timeout(deadline, loop)\n+\n+ class _State(enum.Enum):\n+ INIT = \"INIT\"\n+ ENTER = \"ENTER\"\n+ TIMEOUT = \"TIMEOUT\"\n+ EXIT = \"EXIT\"\n+\n+ class Timeout:\n+ # Internal class, please don't instantiate it directly\n+ # Use timeout() and timeout_at() public factories instead.\n+ #\n+ # Implementation note: `async with timeout()` is preferred\n+ # over `with timeout()`.\n+ # While technically the Timeout class implementation\n+ # doesn't need to be async at all,\n+ # the `async with` statement explicitly points that\n+ # the context manager should be used from async function context.\n+ #\n+ # This design allows to avoid many silly misusages.\n+ #\n+ # TimeoutError is raised immediately when scheduled\n+ # if the deadline is passed.\n+ # The purpose is to time out as soon as possible\n+ # without waiting for the next await expression.\n+\n+ __slots__ = (\"_deadline\", \"_loop\", \"_state\", \"_timeout_handler\")\n+\n+ def __init__(self, deadline: Optional[float],\n+ loop: asyncio.AbstractEventLoop) -> None:\n+ self._loop = loop\n+ self._state = _State.INIT\n+\n+ self._timeout_handler = None # type: Optional[asyncio.Handle]\n+ if deadline is None:\n+ self._deadline = None # type: Optional[float]\n+ else:\n+ self.update(deadline)\n+\n+ def __enter__(self) -> \"Timeout\":\n+ warnings.warn(\n+ \"with timeout() is deprecated, use async with timeout()\",\n+ DeprecationWarning,\n+ stacklevel=2,\n+ )\n+ self._do_enter()\n+ return self\n+\n+ def __exit__(\n+ self,\n+ exc_type: Optional[Type[BaseException]],\n+ exc_val: Optional[BaseException],\n+ exc_tb: Optional[TracebackType],\n+ ) -> Optional[bool]:\n+ self._do_exit(exc_type)\n+ return None\n+\n+ async def __aenter__(self) -> \"Timeout\":\n+ self._do_enter()\n+ return self\n+\n+ async def __aexit__(\n+ self,\n+ exc_type: Optional[Type[BaseException]],\n+ exc_val: Optional[BaseException],\n+ exc_tb: Optional[TracebackType],\n+ ) -> Optional[bool]:\n+ self._do_exit(exc_type)\n+ return None\n+\n+ @property\n+ def expired(self) -> bool:\n+ \"\"\"Is timeout expired during execution?\"\"\"\n+ return self._state == _State.TIMEOUT\n+\n+ @property\n+ def deadline(self) -> Optional[float]:\n+ return self._deadline\n+\n+ def reject(self) -> None:\n+ \"\"\"Reject scheduled timeout if any.\"\"\"\n+ # cancel is maybe better name but\n+ # task.cancel() raises CancelledError in asyncio world.\n+ if self._state not in (_State.INIT, _State.ENTER):\n+ raise RuntimeError(f\"invalid state {self._state.value}\")\n+ self._reject()\n+\n+ def _reject(self) -> None:\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+ self._timeout_handler = None\n+\n+ def shift(self, delay: float) -> None:\n+ \"\"\"Advance timeout on delay seconds.\n+\n+ The delay can be negative.\n+\n+ Raise RuntimeError if shift is called when deadline is not scheduled\n+ \"\"\"\n+ deadline = self._deadline\n+ if deadline is None:\n+ raise RuntimeError(\n+ \"cannot shift timeout if deadline is not scheduled\")\n+ self.update(deadline + delay)\n+\n+ def update(self, deadline: float) -> None:\n+ \"\"\"Set deadline to absolute value.\n+\n+ deadline argument points on the time in the same clock system\n+ as loop.time().\n+\n+ If new deadline is in the past the timeout is raised immediately.\n+\n+ Please note: it is not POSIX time but a time with\n+ undefined starting base, e.g. the time of the system power on.\n+ \"\"\"\n+ if self._state == _State.EXIT:\n+ raise RuntimeError(\n+ \"cannot reschedule after exit from context manager\")\n+ if self._state == _State.TIMEOUT:\n+ raise RuntimeError(\"cannot reschedule expired timeout\")\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+ self._deadline = deadline\n+ if self._state != _State.INIT:\n+ self._reschedule()\n+\n+ def _reschedule(self) -> None:\n+ assert self._state == _State.ENTER\n+ deadline = self._deadline\n+ if deadline is None:\n+ return\n+\n+ now = self._loop.time()\n+ if self._timeout_handler is not None:\n+ self._timeout_handler.cancel()\n+\n+ task = asyncio.current_task()\n+ if deadline <= now:\n+ self._timeout_handler = self._loop.call_soon(\n+ self._on_timeout, task)\n+ else:\n+ self._timeout_handler = self._loop.call_at(\n+ deadline, self._on_timeout, task)\n+\n+ def _do_enter(self) -> None:\n+ if self._state != _State.INIT:\n+ raise RuntimeError(f\"invalid state {self._state.value}\")\n+ self._state = _State.ENTER\n+ self._reschedule()\n+\n+ def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:\n+ if exc_type is asyncio.CancelledError and \\\n+ self._state == _State.TIMEOUT:\n+ self._timeout_handler = None\n+ raise asyncio.TimeoutError\n+ # timeout has not expired\n+ self._state = _State.EXIT\n+ self._reject()\n+ return None\n+\n+ def _on_timeout(self, task: \"Optional[asyncio.Task[Any]]\") -> None:\n+ if task:\n+ task.cancel()\n+ self._state = _State.TIMEOUT\n+ # drop the reference early\n+ self._timeout_handler = None\n+\n+ # End https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py", "line": null, "original_line": 226, "original_start_line": 36, "path": "vllm/engine/async_llm_engine.py", "start_line": null, "text": "@user1:\nlet's put this in its own file" } ]
cd44c86afdbe4b70139c5654c941c8f9e75b3fbb
diff --git a/tests/async_engine/test_async_llm_engine.py b/tests/async_engine/test_async_llm_engine.py index 10a46422887e..52d3394a96a1 100644 --- a/tests/async_engine/test_async_llm_engine.py +++ b/tests/async_engine/test_async_llm_engine.py @@ -2,8 +2,12 @@ from dataclasses import dataclass import pytest +import torch -from vllm.engine.async_llm_engine import AsyncLLMEngine +from vllm import SamplingParams +from vllm.engine.async_llm_engine import AsyncEngineArgs, AsyncLLMEngine + +from ..utils import wait_for_gpu_memory_to_clear @dataclass @@ -94,3 +98,35 @@ async def test_new_requests_event(): assert engine.get_model_config() is not None assert engine.get_tokenizer() is not None assert engine.get_decoding_config() is not None + + +def test_asyncio_run(): + wait_for_gpu_memory_to_clear( + devices=list(range(torch.cuda.device_count())), + threshold_bytes=2 * 2**30, + timeout_s=60, + ) + + engine = AsyncLLMEngine.from_engine_args( + AsyncEngineArgs(model="facebook/opt-125m")) + + async def run(prompt: str): + sampling_params = SamplingParams( + temperature=0, + max_tokens=32, + ) + + async for output in engine.generate(prompt, + sampling_params, + request_id=prompt): + final_output = output + return final_output + + async def generate(): + return await asyncio.gather( + run("test0"), + run("test1"), + ) + + results = asyncio.run(generate()) + assert len(results) == 2 diff --git a/tests/spec_decode/e2e/conftest.py b/tests/spec_decode/e2e/conftest.py index 86103cf85484..60dfe33f2918 100644 --- a/tests/spec_decode/e2e/conftest.py +++ b/tests/spec_decode/e2e/conftest.py @@ -1,5 +1,4 @@ import asyncio -import time from itertools import cycle from typing import Dict, List, Optional, Tuple, Union @@ -7,12 +6,6 @@ import ray import torch -from vllm.utils import is_hip - -if (not is_hip()): - from pynvml import (nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, - nvmlInit) - from vllm import LLM from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine @@ -26,6 +19,7 @@ from vllm.utils import Counter, random_uuid from ...conftest import cleanup +from ...utils import wait_for_gpu_memory_to_clear class AsyncLLM: @@ -291,38 +285,3 @@ def run_greedy_equality_correctness_test(baseline_llm_generator, print(f'{i=} {baseline_token_ids=}') print(f'{i=} {spec_token_ids=}') assert baseline_token_ids == spec_token_ids - - -def wait_for_gpu_memory_to_clear(devices: List[int], - threshold_bytes: int, - timeout_s: float = 120) -> None: - # Use nvml instead of pytorch to reduce measurement error from torch cuda - # context. - nvmlInit() - start_time = time.time() - while True: - output: Dict[int, str] = {} - output_raw: Dict[int, float] = {} - for device in devices: - dev_handle = nvmlDeviceGetHandleByIndex(device) - mem_info = nvmlDeviceGetMemoryInfo(dev_handle) - gb_used = mem_info.used / 2**30 - output_raw[device] = gb_used - output[device] = f'{gb_used:.02f}' - - print('gpu memory used (GB): ', end='') - for k, v in output.items(): - print(f'{k}={v}; ', end='') - print('') - - dur_s = time.time() - start_time - if all(v <= (threshold_bytes / 2**30) for v in output_raw.values()): - print(f'Done waiting for free GPU memory on devices {devices=} ' - f'({threshold_bytes/2**30=}) {dur_s=:.02f}') - break - - if dur_s >= timeout_s: - raise ValueError(f'Memory of devices {devices=} not free after ' - f'{dur_s=:.02f} ({threshold_bytes/2**30=})') - - time.sleep(5) diff --git a/tests/utils.py b/tests/utils.py index f2b2d22b1ebc..bc30515c8310 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -4,7 +4,7 @@ import time import warnings from contextlib import contextmanager -from typing import List +from typing import Dict, List import openai import ray @@ -13,7 +13,11 @@ from vllm.distributed import (ensure_model_parallel_initialized, init_distributed_environment) from vllm.entrypoints.openai.cli_args import make_arg_parser -from vllm.utils import get_open_port +from vllm.utils import get_open_port, is_hip + +if (not is_hip()): + from pynvml import (nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, + nvmlInit) # Path to root of repository so that utilities can be imported by ray workers VLLM_PATH = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir)) @@ -154,3 +158,38 @@ def error_on_warning(): warnings.simplefilter("error") yield + + +def wait_for_gpu_memory_to_clear(devices: List[int], + threshold_bytes: int, + timeout_s: float = 120) -> None: + # Use nvml instead of pytorch to reduce measurement error from torch cuda + # context. + nvmlInit() + start_time = time.time() + while True: + output: Dict[int, str] = {} + output_raw: Dict[int, float] = {} + for device in devices: + dev_handle = nvmlDeviceGetHandleByIndex(device) + mem_info = nvmlDeviceGetMemoryInfo(dev_handle) + gb_used = mem_info.used / 2**30 + output_raw[device] = gb_used + output[device] = f'{gb_used:.02f}' + + print('gpu memory used (GB): ', end='') + for k, v in output.items(): + print(f'{k}={v}; ', end='') + print('') + + dur_s = time.time() - start_time + if all(v <= (threshold_bytes / 2**30) for v in output_raw.values()): + print(f'Done waiting for free GPU memory on devices {devices=} ' + f'({threshold_bytes/2**30=}) {dur_s=:.02f}') + break + + if dur_s >= timeout_s: + raise ValueError(f'Memory of devices {devices=} not free after ' + f'{dur_s=:.02f} ({threshold_bytes/2**30=})') + + time.sleep(5) diff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py index ab312850b9ec..fb4457c78be2 100644 --- a/vllm/engine/async_llm_engine.py +++ b/vllm/engine/async_llm_engine.py @@ -10,6 +10,7 @@ from vllm.config import DecodingConfig, ModelConfig from vllm.core.scheduler import SchedulerOutputs from vllm.engine.arg_utils import AsyncEngineArgs +from vllm.engine.async_timeout import asyncio_timeout from vllm.engine.llm_engine import LLMEngine from vllm.executor.ray_utils import initialize_ray_cluster, ray from vllm.inputs import LLMInputs, PromptInputs @@ -540,8 +541,8 @@ async def run_engine_loop(self): # Abort if iteration takes too long due to unrecoverable errors # (eg. NCCL timeouts). try: - has_requests_in_progress = await asyncio.wait_for( - self.engine_step(), ENGINE_ITERATION_TIMEOUT_S) + async with asyncio_timeout(ENGINE_ITERATION_TIMEOUT_S): + has_requests_in_progress = await self.engine_step() except asyncio.TimeoutError as exc: logger.error( "Engine iteration timed out. This should never happen!") diff --git a/vllm/engine/async_timeout.py b/vllm/engine/async_timeout.py new file mode 100644 index 000000000000..4b1842625212 --- /dev/null +++ b/vllm/engine/async_timeout.py @@ -0,0 +1,189 @@ +# Workaround for https://github.com/python/cpython/issues/86296 +# +# From https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py +# Licensed under the Apache License (Apache-2.0) + +import asyncio +import enum +import sys +import warnings +from types import TracebackType +from typing import Any, Optional, Type + +if sys.version_info[:2] >= (3, 11): + from asyncio import timeout as asyncio_timeout +else: + + def asyncio_timeout(delay: Optional[float]) -> "Timeout": + """timeout context manager. + Useful in cases when you want to apply timeout logic around block + of code or in cases when asyncio.wait_for is not suitable. For example: + >>> async with timeout(0.001): + ... async with aiohttp.get('https://github.com') as r: + ... await r.text() + delay - value in seconds or None to disable timeout logic + """ + loop = asyncio.get_running_loop() + deadline = loop.time() + delay if delay is not None else None + return Timeout(deadline, loop) + + class _State(enum.Enum): + INIT = "INIT" + ENTER = "ENTER" + TIMEOUT = "TIMEOUT" + EXIT = "EXIT" + + class Timeout: + # Internal class, please don't instantiate it directly + # Use timeout() and timeout_at() public factories instead. + # + # Implementation note: `async with timeout()` is preferred + # over `with timeout()`. + # While technically the Timeout class implementation + # doesn't need to be async at all, + # the `async with` statement explicitly points that + # the context manager should be used from async function context. + # + # This design allows to avoid many silly misusages. + # + # TimeoutError is raised immediately when scheduled + # if the deadline is passed. + # The purpose is to time out as soon as possible + # without waiting for the next await expression. + + __slots__ = ("_deadline", "_loop", "_state", "_timeout_handler") + + def __init__(self, deadline: Optional[float], + loop: asyncio.AbstractEventLoop) -> None: + self._loop = loop + self._state = _State.INIT + + self._timeout_handler = None # type: Optional[asyncio.Handle] + if deadline is None: + self._deadline = None # type: Optional[float] + else: + self.update(deadline) + + def __enter__(self) -> "Timeout": + warnings.warn( + "with timeout() is deprecated, use async with timeout()", + DeprecationWarning, + stacklevel=2, + ) + self._do_enter() + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> Optional[bool]: + self._do_exit(exc_type) + return None + + async def __aenter__(self) -> "Timeout": + self._do_enter() + return self + + async def __aexit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> Optional[bool]: + self._do_exit(exc_type) + return None + + @property + def expired(self) -> bool: + """Is timeout expired during execution?""" + return self._state == _State.TIMEOUT + + @property + def deadline(self) -> Optional[float]: + return self._deadline + + def reject(self) -> None: + """Reject scheduled timeout if any.""" + # cancel is maybe better name but + # task.cancel() raises CancelledError in asyncio world. + if self._state not in (_State.INIT, _State.ENTER): + raise RuntimeError(f"invalid state {self._state.value}") + self._reject() + + def _reject(self) -> None: + if self._timeout_handler is not None: + self._timeout_handler.cancel() + self._timeout_handler = None + + def shift(self, delay: float) -> None: + """Advance timeout on delay seconds. + The delay can be negative. + Raise RuntimeError if shift is called when deadline is not scheduled + """ + deadline = self._deadline + if deadline is None: + raise RuntimeError( + "cannot shift timeout if deadline is not scheduled") + self.update(deadline + delay) + + def update(self, deadline: float) -> None: + """Set deadline to absolute value. + deadline argument points on the time in the same clock system + as loop.time(). + If new deadline is in the past the timeout is raised immediately. + Please note: it is not POSIX time but a time with + undefined starting base, e.g. the time of the system power on. + """ + if self._state == _State.EXIT: + raise RuntimeError( + "cannot reschedule after exit from context manager") + if self._state == _State.TIMEOUT: + raise RuntimeError("cannot reschedule expired timeout") + if self._timeout_handler is not None: + self._timeout_handler.cancel() + self._deadline = deadline + if self._state != _State.INIT: + self._reschedule() + + def _reschedule(self) -> None: + assert self._state == _State.ENTER + deadline = self._deadline + if deadline is None: + return + + now = self._loop.time() + if self._timeout_handler is not None: + self._timeout_handler.cancel() + + task = asyncio.current_task() + if deadline <= now: + self._timeout_handler = self._loop.call_soon( + self._on_timeout, task) + else: + self._timeout_handler = self._loop.call_at( + deadline, self._on_timeout, task) + + def _do_enter(self) -> None: + if self._state != _State.INIT: + raise RuntimeError(f"invalid state {self._state.value}") + self._state = _State.ENTER + self._reschedule() + + def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None: + if exc_type is asyncio.CancelledError and \ + self._state == _State.TIMEOUT: + self._timeout_handler = None + raise asyncio.TimeoutError + # timeout has not expired + self._state = _State.EXIT + self._reject() + return None + + def _on_timeout(self, task: "Optional[asyncio.Task[Any]]") -> None: + if task: + task.cancel() + self._state = _State.TIMEOUT + # drop the reference early + self._timeout_handler = None
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-5669@2e27316
vllm-project/vllm
Python
5,669
[distributed][misc] use fork by default for mp
fixes https://github.com/vllm-project/vllm/issues/5637 `fork` is not safe after we create cuda context. we should already avoid initializing cuda context before we create workers, so it should be fine to use `fork`, which can remove the necessity of `if __name__ = "__main__"` in user's code.
2024-06-19T05:24:41Z
[Bug]: RuntimeError with tensor_parallel_size > 1 in Process Bootstrapping Phase ### Your current environment ```text The output of `python collect_env.py` Collecting environment information... PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Rocky Linux release 8.9 (Green Obsidian) (x86_64) GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-20) Clang version: 16.0.6 (Red Hat 16.0.6-2.module+el8.9.0+1651+e10a8f6d) CMake version: version 3.29.5 Libc version: glibc-2.28 Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-PCIE-40GB GPU 1: NVIDIA A100-PCIE-40GB GPU 2: NVIDIA A100-PCIE-40GB GPU 3: NVIDIA A100-PCIE-40GB GPU 4: NVIDIA A100-PCIE-40GB GPU 5: NVIDIA A100-PCIE-40GB GPU 6: NVIDIA A100-PCIE-40GB GPU 7: NVIDIA A100-PCIE-40GB Nvidia driver version: 550.54.15 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Thread(s) per core: 1 Core(s) per socket: 24 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 106 Model name: Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz Stepping: 6 CPU MHz: 3500.000 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 5600.00 Virtualization: VT-x L1d cache: 48K L1i cache: 32K L2 cache: 1280K L3 cache: 36864K NUMA node0 CPU(s): 0-23 NUMA node1 CPU(s): 24-47 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] pytorch-lightning==2.3.0 [pip3] torch==2.3.0 [pip3] torchaudio==2.3.1 [pip3] torchmetrics==1.4.0.post0 [pip3] torchvision==0.18.1 [pip3] transformers==4.42.0.dev0 [pip3] triton==2.3.0 [conda] blas 2.116 mkl conda-forge [conda] blas-devel 3.9.0 16_linux64_mkl conda-forge [conda] libblas 3.9.0 16_linux64_mkl conda-forge [conda] libcblas 3.9.0 16_linux64_mkl conda-forge [conda] liblapack 3.9.0 16_linux64_mkl conda-forge [conda] liblapacke 3.9.0 16_linux64_mkl conda-forge [conda] libopenvino-pytorch-frontend 2024.1.0 he02047a_7 conda-forge [conda] mkl 2022.1.0 h84fe81f_915 conda-forge [conda] mkl-devel 2022.1.0 ha770c72_916 conda-forge [conda] mkl-include 2022.1.0 h84fe81f_915 conda-forge [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch [conda] pytorch-lightning 2.3.0 pyhd8ed1ab_0 conda-forge [conda] pytorch-mutex 1.0 cuda pytorch [conda] torch 2.3.0 pypi_0 pypi [conda] torchaudio 2.3.1 py310_cu121 pytorch [conda] torchmetrics 1.4.0.post0 pyhd8ed1ab_0 conda-forge [conda] torchvision 0.18.1 py310_cu121 pytorch [conda] transformers 4.42.0.dev0 pypi_0 pypi [conda] triton 2.3.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.0 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 SYS SYS SYS SYS SYS SYS 0-23 0 N/A GPU1 NV12 X SYS SYS SYS SYS SYS SYS 0-23 0 N/A GPU2 SYS SYS X NV12 SYS SYS SYS SYS 0-23 0 N/A GPU3 SYS SYS NV12 X SYS SYS SYS SYS 0-23 0 N/A GPU4 SYS SYS SYS SYS X NV12 SYS SYS 24-47 1 N/A GPU5 SYS SYS SYS SYS NV12 X SYS SYS 24-47 1 N/A GPU6 SYS SYS SYS SYS SYS SYS X NV12 24-47 1 N/A GPU7 SYS SYS SYS SYS SYS SYS NV12 X 24-47 1 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ``` ### ๐Ÿ› Describe the bug ### Description When setting `tensor_parallel_size` to a value greater than 1, the program gets stuck and raises a `RuntimeError` related to the bootstrapping phase of new processes. This issue does not occur when using version v0.4.3, but persists in versions v0.5.0.post1 and v0.5.0. ### Steps to Reproduce 1. Install version v0.5.0.post1 or v0.5.0 of the library. 2. Run the following Python script with `tensor_parallel_size` set to 2: ```python import os import argparse import ray from vllm import SamplingParams, LLM from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) ray.init(address="auto") def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--model_path", type=str, default=os.path.join(os.environ.get("LLAMA_MODEL_FOLDER"))) return parser.parse_args() args = parse_args() MODEL_PATH = args.model_path llm = LLM(model=MODEL_PATH, tensor_parallel_size=2) ``` ```bash python load.py --model_path $JOBFS/fine_tuned_models/checkpoint-1857 ``` ### Expected Behavior The program should run without any issues regarding process bootstrapping, similar to how it behaves with version v0.4.3. ### Observed Behavior The program raises the following `RuntimeError` when `tensor_parallel_size` is set to 2: ``` python load.py --model_path $JOBFS/fine_tuned_models/checkpoint-1857 INFO 06-18 21:35:42 config.py:623] Defaulting to use mp for distributed inference INFO 06-18 21:35:42 llm_engine.py:161] Initializing an LLM engine (v0.5.0) with config: model='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', speculative_config=None, tokenizer='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857) INFO 06-18 21:35:44 config.py:623] Defaulting to use mp for distributed inference INFO 06-18 21:35:44 llm_engine.py:161] Initializing an LLM engine (v0.5.0) with config: model='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', speculative_config=None, tokenizer='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857) Traceback (most recent call last): File "<string>", line 1, in <module> File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 125, in _main prepare(preparation_data) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/codes/kgqa/load.py", line 20, in <module> llm = LLM(model = MODEL_PATH, tensor_parallel_size=2) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 144, in __init__ self.llm_engine = LLMEngine.from_engine_args( File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 360, in from_engine_args engine = cls( File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 223, in __init__ self.model_executor = executor_class( File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__ super().__init__(*args, **kwargs) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 41, in __init__ self._init_executor() File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 48, in _init_executor self.workers = [ File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 49, in <listcomp> ProcessWorkerWrapper( File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_worker_utils.py", line 162, in __init__ self.process.start() File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/process.py", line 121, in start self._popen = self._Popen(self) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/context.py", line 288, in _Popen return Popen(process_obj) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ERROR 06-18 21:35:45 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 3449547 died, exit code: 1 INFO 06-18 21:35:45 multiproc_worker_utils.py:123] Killing local vLLM worker processes ``` ### Environment - OS: GNU/Linux - Python Version: Python 3.10.14 - Library Version: v0.5.0.post1 and v0.5.0 ### Additional Context Reverting back to version v0.4.3 resolves the issue. It appears there might be a change in how processes are handled in newer versions that could be causing this error.
We are seeing the same error for LLM, LLMEngine, and AsyncLLMEngine. Interestingly, we find wrapping everything in the python script within `if __name__ == '__main__':` can temporarily bypass the issue. For `test.py` being the following ``` import os from vllm import LLM, SamplingParams prompts = ['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful chatbot who always responds to requests.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is ramen?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'] sampling_params = SamplingParams(temperature=0.0, max_tokens=512) llm = LLM( model="meta-llama/Meta-Llama-3-8B-Instruct", tensor_parallel_size=2, disable_log_stats=False, ) outputs = llm.generate(prompts, sampling_params) ``` Running `python test.py` gives ``` (venv-vllm) ben@sakura-h100-3:/nvme0n1/ben/test$ python test.py /nvme0n1/ben/venvs/venv-vllm/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( INFO 06-19 01:40:01 config.py:632] Defaulting to use mp for distributed inference INFO 06-19 01:40:01 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='meta-llama/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=meta-llama/Meta-Llama-3-8B-Instruct) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. /nvme0n1/ben/venvs/venv-vllm/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( INFO 06-19 01:40:04 config.py:632] Defaulting to use mp for distributed inference INFO 06-19 01:40:04 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='meta-llama/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=meta-llama/Meta-Llama-3-8B-Instruct) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. Traceback (most recent call last): File "<string>", line 1, in <module> File "/usr/lib/python3.10/multiprocessing/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/usr/lib/python3.10/multiprocessing/spawn.py", line 125, in _main prepare(preparation_data) File "/usr/lib/python3.10/multiprocessing/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/usr/lib/python3.10/multiprocessing/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/usr/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/usr/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/usr/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/nvme0n1/ben/test/test.py", line 8, in <module> llm = LLM( File "/nvme0n1/ben/vllm/vllm/entrypoints/llm.py", line 144, in __init__ self.llm_engine = LLMEngine.from_engine_args( File "/nvme0n1/ben/vllm/vllm/engine/llm_engine.py", line 371, in from_engine_args engine = cls( File "/nvme0n1/ben/vllm/vllm/engine/llm_engine.py", line 223, in __init__ self.model_executor = executor_class( File "/nvme0n1/ben/vllm/vllm/executor/distributed_gpu_executor.py", line 25, in __init__ super().__init__(*args, **kwargs) File "/nvme0n1/ben/vllm/vllm/executor/executor_base.py", line 41, in __init__ self._init_executor() File "/nvme0n1/ben/vllm/vllm/executor/multiproc_gpu_executor.py", line 48, in _init_executor self.workers = [ File "/nvme0n1/ben/vllm/vllm/executor/multiproc_gpu_executor.py", line 49, in <listcomp> ProcessWorkerWrapper( File "/nvme0n1/ben/vllm/vllm/executor/multiproc_worker_utils.py", line 162, in __init__ self.process.start() File "/usr/lib/python3.10/multiprocessing/process.py", line 121, in start self._popen = self._Popen(self) File "/usr/lib/python3.10/multiprocessing/context.py", line 288, in _Popen return Popen(process_obj) File "/usr/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/usr/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/usr/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/usr/lib/python3.10/multiprocessing/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/usr/lib/python3.10/multiprocessing/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ERROR 06-19 01:40:05 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 2334411 died, exit code: 1 INFO 06-19 01:40:05 multiproc_worker_utils.py:123] Killing local vLLM worker processes ``` For `test-wrap.py` being the following ``` import os from vllm import LLM, SamplingParams if __name__ == '__main__': prompts = ['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful chatbot who always responds to requests.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is ramen?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'] sampling_params = SamplingParams(temperature=0.0, max_tokens=512) llm = LLM( model="meta-llama/Meta-Llama-3-8B-Instruct", tensor_parallel_size=2, disable_log_stats=False, ) outputs = llm.generate(prompts, sampling_params) ``` Running `python test-wrap.py` gives ``` (venv-vllm) ben@sakura-h100-3:/nvme0n1/ben/test$ python test-wrapped.py /nvme0n1/ben/venvs/venv-vllm/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( INFO 06-19 01:43:57 config.py:632] Defaulting to use mp for distributed inference INFO 06-19 01:43:57 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='meta-llama/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=meta-llama/Meta-Llama-3-8B-Instruct) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:00 multiproc_worker_utils.py:215] Worker ready; awaiting tasks (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:00 utils.py:672] Found nccl from library libnccl.so.2 INFO 06-19 01:44:00 utils.py:672] Found nccl from library libnccl.so.2 (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:00 pynccl.py:63] vLLM is using nccl==2.20.5 INFO 06-19 01:44:00 pynccl.py:63] vLLM is using nccl==2.20.5 INFO 06-19 01:44:01 custom_all_reduce_utils.py:196] generating GPU P2P access cache in /home/ben/.config/vllm/gpu_p2p_access_cache_for_0,1.json INFO 06-19 01:44:06 custom_all_reduce_utils.py:208] reading GPU P2P access cache from /home/ben/.config/vllm/gpu_p2p_access_cache_for_0,1.json (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:06 custom_all_reduce_utils.py:208] reading GPU P2P access cache from /home/ben/.config/vllm/gpu_p2p_access_cache_for_0,1.json INFO 06-19 01:44:07 weight_utils.py:218] Using model weights format ['*.safetensors'] (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:07 weight_utils.py:218] Using model weights format ['*.safetensors'] INFO 06-19 01:44:09 model_runner.py:160] Loading model weights took 7.4829 GB (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:10 model_runner.py:160] Loading model weights took 7.4829 GB INFO 06-19 01:44:11 distributed_gpu_executor.py:56] # GPU blocks: 61915, # CPU blocks: 4096 (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:12 model_runner.py:889] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:12 model_runner.py:893] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage. INFO 06-19 01:44:12 model_runner.py:889] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. INFO 06-19 01:44:12 model_runner.py:893] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage. (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:16 custom_all_reduce.py:267] Registering 2275 cuda graph addresses INFO 06-19 01:44:16 custom_all_reduce.py:267] Registering 2275 cuda graph addresses INFO 06-19 01:44:16 model_runner.py:965] Graph capturing finished in 4 secs. (VllmWorkerProcess pid=2335588) INFO 06-19 01:44:16 model_runner.py:965] Graph capturing finished in 4 secs. Processed prompts: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1/1 [00:01<00:00, 1.98s/it, est. speed input: 16.18 toks/s, output: 145.13 toks/s] [rank0]:[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors] /usr/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' ``` Note that there is a CUDA IPC Tensor Error and a Leaked Semaphore Objects error at the end, but at least the text generation task can be successfully completed. This might be related to [unprotected usage of python's concurrent.futures library](https://stackoverflow.com/a/74709889), although the only place I could find vllm using this library is [here](https://github.com/vllm-project/vllm/blob/7879f24dcce75665d83865ee8281f2ef1bbb7e74/vllm/model_executor/guided_decoding/outlines_decoding.py#L71). Just checked. For v0.4.3, default backend (ray) works, albeit with a small error `[rank0]:[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]` at the end. If we set `distributed_executor_backend='mp'`, it is broken with the same error described in the threads above. cc @njhill hi, folks, can you try to set an environment variable `export VLLM_WORKER_MULTIPROC_METHOD=fork` , and then run vllm again? > hi, folks, can you try to set an environment variable `export VLLM_WORKER_MULTIPROC_METHOD=fork` , and then run vllm again? I also encountered a similar error above, adding this environment variable resolved it. I attempted to set the environment variable export VLLM_WORKER_MULTIPROC_METHOD=fork as suggested and reran the VLLM application. Unfortunately, I'm still encountering errors. This time, a KeyError occurred in the multiprocessing.resource_tracker module, indicating a potential issue with process management under the fork start method. The traceback highlights a removal operation on a missing key in a resource cache. Hereโ€™s the relevant part of the error message: ```bash (llm) z5327441@k091:/scratch/pbs.5466392.kman.restech.unsw.edu.au $ export VLLM_WORKER_MULTIPROC_METHOD=fork (llm) z5327441@k091:/scratch/pbs.5466392.kman.restech.unsw.edu.au $ python test.py 2024-06-19 20:27:18,261 INFO worker.py:1568 -- Connecting to existing Ray cluster at address: 10.197.40.91:6379... 2024-06-19 20:27:18,269 INFO worker.py:1744 -- Connected to Ray cluster. View the dashboard at 10.197.40.91:8265 INFO 06-19 20:27:18 config.py:623] Defaulting to use mp for distributed inference INFO 06-19 20:27:18 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. (VllmWorkerProcess pid=3599698) INFO 06-19 20:27:20 multiproc_worker_utils.py:215] Worker ready; awaiting tasks (VllmWorkerProcess pid=3599698) INFO 06-19 20:27:20 utils.py:637] Found nccl from library libnccl.so.2 INFO 06-19 20:27:20 utils.py:637] Found nccl from library libnccl.so.2 INFO 06-19 20:27:20 pynccl.py:63] vLLM is using nccl==2.20.5 (VllmWorkerProcess pid=3599698) INFO 06-19 20:27:20 pynccl.py:63] vLLM is using nccl==2.20.5 INFO 06-19 20:27:20 custom_all_reduce_utils.py:170] generating GPU P2P access cache in /home/z5327441/.config/vllm/gpu_p2p_access_cache_for_7,6,5,4,3,2,1,0.json 2024-06-19 20:27:22,430 INFO worker.py:1568 -- Connecting to existing Ray cluster at address: 10.197.40.91:6379... 2024-06-19 20:27:22,438 INFO worker.py:1744 -- Connected to Ray cluster. View the dashboard at 10.197.40.91:8265 2024-06-19 20:27:22,475 INFO worker.py:1568 -- Connecting to existing Ray cluster at address: 10.197.40.91:6379... 2024-06-19 20:27:22,483 INFO worker.py:1744 -- Connected to Ray cluster. View the dashboard at 10.197.40.91:8265 INFO 06-19 20:27:22 config.py:623] Defaulting to use mp for distributed inference INFO 06-19 20:27:22 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct) INFO 06-19 20:27:22 config.py:623] Defaulting to use mp for distributed inference INFO 06-19 20:27:22 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', speculative_config=None, tokenizer='/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5466392.kman.restech.unsw.edu.au/models/Meta-Llama-3-8B-Instruct) Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. (VllmWorkerProcess pid=3600016) INFO 06-19 20:27:23 multiproc_worker_utils.py:215] Worker ready; awaiting tasks (VllmWorkerProcess pid=3600019) INFO 06-19 20:27:23 multiproc_worker_utils.py:215] Worker ready; awaiting tasks INFO 06-19 20:27:23 utils.py:637] Found nccl from library libnccl.so.2 (VllmWorkerProcess pid=3600016) INFO 06-19 20:27:23 utils.py:637] Found nccl from library libnccl.so.2 (VllmWorkerProcess pid=3600016) INFO 06-19 20:27:23 pynccl.py:63] vLLM is using nccl==2.20.5 INFO 06-19 20:27:23 pynccl.py:63] vLLM is using nccl==2.20.5 INFO 06-19 20:27:23 utils.py:637] Found nccl from library libnccl.so.2 (VllmWorkerProcess pid=3600019) INFO 06-19 20:27:23 utils.py:637] Found nccl from library libnccl.so.2 INFO 06-19 20:27:23 pynccl.py:63] vLLM is using nccl==2.20.5 (VllmWorkerProcess pid=3600019) INFO 06-19 20:27:23 pynccl.py:63] vLLM is using nccl==2.20.5 Traceback (most recent call last): File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/resource_tracker.py", line 209, in main cache[rtype].remove(name) KeyError: '/psm_deecb814' INFO 06-19 20:27:24 custom_all_reduce_utils.py:170] generating GPU P2P access cache in /home/z5327441/.config/vllm/gpu_p2p_access_cache_for_7,6,5,4,3,2,1,0.json [rank0]: Traceback (most recent call last): [rank0]: File "<string>", line 1, in <module> [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 116, in spawn_main [rank0]: exitcode = _main(fd, parent_sentinel) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 125, in _main [rank0]: prepare(preparation_data) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 236, in prepare [rank0]: _fixup_main_from_path(data['init_main_from_path']) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 287, in _fixup_main_from_path [rank0]: main_content = runpy.run_path(main_path, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 289, in run_path [rank0]: return _run_module_code(code, init_globals, run_name, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 96, in _run_module_code [rank0]: _run_code(code, mod_globals, init_globals, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 86, in _run_code [rank0]: exec(code, run_globals) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/test.py", line 13, in <module> [rank0]: llm = LLM( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 144, in __init__ [rank0]: self.llm_engine = LLMEngine.from_engine_args( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 363, in from_engine_args [rank0]: engine = cls( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 223, in __init__ [rank0]: self.model_executor = executor_class( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__ [rank0]: super().__init__(*args, **kwargs) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 41, in __init__ [rank0]: self._init_executor() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 65, in _init_executor [rank0]: self._run_workers("init_device") [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 119, in _run_workers [rank0]: driver_worker_output = driver_worker_method(*args, **kwargs) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/worker/worker.py", line 115, in init_device [rank0]: init_worker_distributed_environment(self.parallel_config, self.rank, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/worker/worker.py", line 357, in init_worker_distributed_environment [rank0]: ensure_model_parallel_initialized(parallel_config.tensor_parallel_size, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 655, in ensure_model_parallel_initialized [rank0]: initialize_model_parallel(tensor_model_parallel_size, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 616, in initialize_model_parallel [rank0]: _TP = GroupCoordinator( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 157, in __init__ [rank0]: self.ca_comm = CustomAllreduce( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 174, in __init__ [rank0]: if not _can_p2p(rank, world_size): [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 78, in _can_p2p [rank0]: if not gpu_p2p_access_check(rank, i): [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 174, in gpu_p2p_access_check [rank0]: cache[f"{_i}->{_j}"] = can_actually_p2p(_i, _j) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 123, in can_actually_p2p [rank0]: pi.start() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/process.py", line 121, in start [rank0]: self._popen = self._Popen(self) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/context.py", line 288, in _Popen [rank0]: return Popen(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 32, in __init__ [rank0]: super().__init__(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ [rank0]: self._launch(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 42, in _launch [rank0]: prep_data = spawn.get_preparation_data(process_obj._name) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 154, in get_preparation_data [rank0]: _check_not_importing_main() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 134, in _check_not_importing_main [rank0]: raise RuntimeError(''' [rank0]: RuntimeError: [rank0]: An attempt has been made to start a new process before the [rank0]: current process has finished its bootstrapping phase. [rank0]: This probably means that you are not using fork to start your [rank0]: child processes and you have forgotten to use the proper idiom [rank0]: in the main module: [rank0]: if __name__ == '__main__': [rank0]: freeze_support() [rank0]: ... [rank0]: The "freeze_support()" line can be omitted if the program [rank0]: is not going to be frozen to produce an executable. Traceback (most recent call last): File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/resource_tracker.py", line 209, in main cache[rtype].remove(name) KeyError: '/psm_2038dbcf' INFO 06-19 20:27:24 custom_all_reduce_utils.py:170] generating GPU P2P access cache in /home/z5327441/.config/vllm/gpu_p2p_access_cache_for_7,6,5,4,3,2,1,0.json [rank0]: Traceback (most recent call last): [rank0]: File "<string>", line 1, in <module> [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 116, in spawn_main [rank0]: exitcode = _main(fd, parent_sentinel) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 125, in _main [rank0]: prepare(preparation_data) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 236, in prepare [rank0]: _fixup_main_from_path(data['init_main_from_path']) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 287, in _fixup_main_from_path [rank0]: main_content = runpy.run_path(main_path, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 289, in run_path [rank0]: return _run_module_code(code, init_globals, run_name, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 96, in _run_module_code [rank0]: _run_code(code, mod_globals, init_globals, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py", line 86, in _run_code [rank0]: exec(code, run_globals) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/test.py", line 13, in <module> [rank0]: llm = LLM( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 144, in __init__ [rank0]: self.llm_engine = LLMEngine.from_engine_args( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 363, in from_engine_args [rank0]: engine = cls( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 223, in __init__ [rank0]: self.model_executor = executor_class( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__ [rank0]: super().__init__(*args, **kwargs) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 41, in __init__ [rank0]: self._init_executor() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 65, in _init_executor [rank0]: self._run_workers("init_device") [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py", line 119, in _run_workers [rank0]: driver_worker_output = driver_worker_method(*args, **kwargs) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/worker/worker.py", line 115, in init_device [rank0]: init_worker_distributed_environment(self.parallel_config, self.rank, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/worker/worker.py", line 357, in init_worker_distributed_environment [rank0]: ensure_model_parallel_initialized(parallel_config.tensor_parallel_size, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 655, in ensure_model_parallel_initialized [rank0]: initialize_model_parallel(tensor_model_parallel_size, [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 616, in initialize_model_parallel [rank0]: _TP = GroupCoordinator( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 157, in __init__ [rank0]: self.ca_comm = CustomAllreduce( [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 174, in __init__ [rank0]: if not _can_p2p(rank, world_size): [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 78, in _can_p2p [rank0]: if not gpu_p2p_access_check(rank, i): [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 174, in gpu_p2p_access_check [rank0]: cache[f"{_i}->{_j}"] = can_actually_p2p(_i, _j) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 123, in can_actually_p2p [rank0]: pi.start() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/process.py", line 121, in start [rank0]: self._popen = self._Popen(self) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/context.py", line 288, in _Popen [rank0]: return Popen(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 32, in __init__ [rank0]: super().__init__(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ [rank0]: self._launch(process_obj) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py", line 42, in _launch [rank0]: prep_data = spawn.get_preparation_data(process_obj._name) [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 154, in get_preparation_data [rank0]: _check_not_importing_main() [rank0]: File "/scratch/pbs.5466392.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py", line 134, in _check_not_importing_main [rank0]: raise RuntimeError(''' [rank0]: RuntimeError: [rank0]: An attempt has been made to start a new process before the [rank0]: current process has finished its bootstrapping phase. [rank0]: This probably means that you are not using fork to start your [rank0]: child processes and you have forgotten to use the proper idiom [rank0]: in the main module: [rank0]: if __name__ == '__main__': [rank0]: freeze_support() [rank0]: ... [rank0]: The "freeze_support()" line can be omitted if the program [rank0]: is not going to be frozen to produce an executable. *** SIGTERM received at time=1718792844 on cpu 39 *** PC: @ 0x14ee9615645c (unknown) pthread_cond_wait@@GLIBC_2.3.2 @ 0x14ee9615acf0 (unknown) (unknown) [2024-06-19 20:27:24,847 E 3600016 3599783] logging.cc:343: *** SIGTERM received at time=1718792844 on cpu 39 *** [2024-06-19 20:27:24,847 E 3600016 3599783] logging.cc:343: PC: @ 0x14ee9615645c (unknown) pthread_cond_wait@@GLIBC_2.3.2 [2024-06-19 20:27:24,847 E 3600016 3599783] logging.cc:343: @ 0x14ee9615acf0 (unknown) (unknown) *** SIGTERM received at time=1718792844 on cpu 37 *** PC: @ 0x14bb61b8c45c (unknown) pthread_cond_wait@@GLIBC_2.3.2 @ 0x14bb61b90cf0 (unknown) (unknown) [2024-06-19 20:27:24,892 E 3600019 3599784] logging.cc:343: *** SIGTERM received at time=1718792844 on cpu 37 *** [2024-06-19 20:27:24,892 E 3600019 3599784] logging.cc:343: PC: @ 0x14bb61b8c45c (unknown) pthread_cond_wait@@GLIBC_2.3.2 [2024-06-19 20:27:24,892 E 3600019 3599784] logging.cc:343: @ 0x14bb61b90cf0 (unknown) (unknown) ```
[ { "body": "### Your current environment\r\n\r\n```text\r\nThe output of `python collect_env.py`\r\nCollecting environment information...\r\nPyTorch version: 2.3.0+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Rocky Linux release 8.9 (Green Obsidian) (x86_64)\r\nGCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-20)\r\nClang version: 16.0.6 (Red Hat 16.0.6-2.module+el8.9.0+1651+e10a8f6d)\r\nCMake version: version 3.29.5\r\nLibc version: glibc-2.28\r\n\r\nPython version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime)\r\nPython platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: \r\nGPU 0: NVIDIA A100-PCIE-40GB\r\nGPU 1: NVIDIA A100-PCIE-40GB\r\nGPU 2: NVIDIA A100-PCIE-40GB\r\nGPU 3: NVIDIA A100-PCIE-40GB\r\nGPU 4: NVIDIA A100-PCIE-40GB\r\nGPU 5: NVIDIA A100-PCIE-40GB\r\nGPU 6: NVIDIA A100-PCIE-40GB\r\nGPU 7: NVIDIA A100-PCIE-40GB\r\n\r\nNvidia driver version: 550.54.15\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nByte Order: Little Endian\r\nCPU(s): 48\r\nOn-line CPU(s) list: 0-47\r\nThread(s) per core: 1\r\nCore(s) per socket: 24\r\nSocket(s): 2\r\nNUMA node(s): 2\r\nVendor ID: GenuineIntel\r\nCPU family: 6\r\nModel: 106\r\nModel name: Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz\r\nStepping: 6\r\nCPU MHz: 3500.000\r\nCPU max MHz: 3500.0000\r\nCPU min MHz: 800.0000\r\nBogoMIPS: 5600.00\r\nVirtualization: VT-x\r\nL1d cache: 48K\r\nL1i cache: 32K\r\nL2 cache: 1280K\r\nL3 cache: 36864K\r\nNUMA node0 CPU(s): 0-23\r\nNUMA node1 CPU(s): 24-47\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.26.4\r\n[pip3] nvidia-nccl-cu12==2.20.5\r\n[pip3] pytorch-lightning==2.3.0\r\n[pip3] torch==2.3.0\r\n[pip3] torchaudio==2.3.1\r\n[pip3] torchmetrics==1.4.0.post0\r\n[pip3] torchvision==0.18.1\r\n[pip3] transformers==4.42.0.dev0\r\n[pip3] triton==2.3.0\r\n[conda] blas 2.116 mkl conda-forge\r\n[conda] blas-devel 3.9.0 16_linux64_mkl conda-forge\r\n[conda] libblas 3.9.0 16_linux64_mkl conda-forge\r\n[conda] libcblas 3.9.0 16_linux64_mkl conda-forge\r\n[conda] liblapack 3.9.0 16_linux64_mkl conda-forge\r\n[conda] liblapacke 3.9.0 16_linux64_mkl conda-forge\r\n[conda] libopenvino-pytorch-frontend 2024.1.0 he02047a_7 conda-forge\r\n[conda] mkl 2022.1.0 h84fe81f_915 conda-forge\r\n[conda] mkl-devel 2022.1.0 ha770c72_916 conda-forge\r\n[conda] mkl-include 2022.1.0 h84fe81f_915 conda-forge\r\n[conda] numpy 1.26.4 pypi_0 pypi\r\n[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi\r\n[conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch\r\n[conda] pytorch-lightning 2.3.0 pyhd8ed1ab_0 conda-forge\r\n[conda] pytorch-mutex 1.0 cuda pytorch\r\n[conda] torch 2.3.0 pypi_0 pypi\r\n[conda] torchaudio 2.3.1 py310_cu121 pytorch\r\n[conda] torchmetrics 1.4.0.post0 pyhd8ed1ab_0 conda-forge\r\n[conda] torchvision 0.18.1 py310_cu121 pytorch\r\n[conda] transformers 4.42.0.dev0 pypi_0 pypi\r\n[conda] triton 2.3.0 pypi_0 pypi\r\nROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.5.0\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID\r\nGPU0 X NV12 SYS SYS SYS SYS SYS SYS 0-23 0 N/A\r\nGPU1 NV12 X SYS SYS SYS SYS SYS SYS 0-23 0 N/A\r\nGPU2 SYS SYS X NV12 SYS SYS SYS SYS 0-23 0 N/A\r\nGPU3 SYS SYS NV12 X SYS SYS SYS SYS 0-23 0 N/A\r\nGPU4 SYS SYS SYS SYS X NV12 SYS SYS 24-47 1 N/A\r\nGPU5 SYS SYS SYS SYS NV12 X SYS SYS 24-47 1 N/A\r\nGPU6 SYS SYS SYS SYS SYS SYS X NV12 24-47 1 N/A\r\nGPU7 SYS SYS SYS SYS SYS SYS NV12 X 24-47 1 N/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n```\r\n\r\n\r\n### ๐Ÿ› Describe the bug\r\n\r\n### Description\r\n\r\nWhen setting `tensor_parallel_size` to a value greater than 1, the program gets stuck and raises a `RuntimeError` related to the bootstrapping phase of new processes. This issue does not occur when using version v0.4.3, but persists in versions v0.5.0.post1 and v0.5.0.\r\n\r\n### Steps to Reproduce\r\n\r\n1. Install version v0.5.0.post1 or v0.5.0 of the library.\r\n\r\n2. Run the following Python script with `tensor_parallel_size` set to 2:\r\n\r\n ```python\r\n import os\r\n import argparse\r\n import ray\r\n from vllm import SamplingParams, LLM\r\n from dotenv import load_dotenv, find_dotenv\r\n _ = load_dotenv(find_dotenv())\r\n \r\n ray.init(address=\"auto\")\r\n \r\n def parse_args():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"--model_path\", type=str, default=os.path.join(os.environ.get(\"LLAMA_MODEL_FOLDER\")))\r\n return parser.parse_args() \r\n \r\n args = parse_args()\r\n MODEL_PATH = args.model_path\r\n \r\n llm = LLM(model=MODEL_PATH, tensor_parallel_size=2)\r\n ```\r\n```bash\r\npython load.py --model_path $JOBFS/fine_tuned_models/checkpoint-1857\r\n```\r\n### Expected Behavior\r\n\r\nThe program should run without any issues regarding process bootstrapping, similar to how it behaves with version v0.4.3.\r\n\r\n### Observed Behavior\r\n\r\nThe program raises the following `RuntimeError` when `tensor_parallel_size` is set to 2:\r\n\r\n```\r\npython load.py --model_path $JOBFS/fine_tuned_models/checkpoint-1857\r\nINFO 06-18 21:35:42 config.py:623] Defaulting to use mp for distributed inference\r\nINFO 06-18 21:35:42 llm_engine.py:161] Initializing an LLM engine (v0.5.0) with config: model='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', speculative_config=None, tokenizer='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857)\r\nINFO 06-18 21:35:44 config.py:623] Defaulting to use mp for distributed inference\r\nINFO 06-18 21:35:44 llm_engine.py:161] Initializing an LLM engine (v0.5.0) with config: model='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', speculative_config=None, tokenizer='/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5401450.kman.restech.unsw.edu.au/fine_tuned_models/checkpoint-1857)\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 116, in spawn_main\r\n exitcode = _main(fd, parent_sentinel)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 125, in _main\r\n prepare(preparation_data)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 236, in prepare\r\n _fixup_main_from_path(data['init_main_from_path'])\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 287, in _fixup_main_from_path\r\n main_content = runpy.run_path(main_path,\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py\", line 289, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py\", line 96, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/codes/kgqa/load.py\", line 20, in <module>\r\n llm = LLM(model = MODEL_PATH, tensor_parallel_size=2)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/entrypoints/llm.py\", line 144, in __init__\r\n self.llm_engine = LLMEngine.from_engine_args(\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py\", line 360, in from_engine_args\r\n engine = cls(\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/engine/llm_engine.py\", line 223, in __init__\r\n self.model_executor = executor_class(\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/distributed_gpu_executor.py\", line 25, in __init__\r\n super().__init__(*args, **kwargs)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/executor_base.py\", line 41, in __init__\r\n self._init_executor()\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py\", line 48, in _init_executor\r\n self.workers = [\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_gpu_executor.py\", line 49, in <listcomp>\r\n ProcessWorkerWrapper(\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/site-packages/vllm/executor/multiproc_worker_utils.py\", line 162, in __init__\r\n self.process.start()\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/process.py\", line 121, in start\r\n self._popen = self._Popen(self)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/context.py\", line 288, in _Popen\r\n return Popen(process_obj)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py\", line 32, in __init__\r\n super().__init__(process_obj)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_fork.py\", line 19, in __init__\r\n self._launch(process_obj)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/popen_spawn_posix.py\", line 42, in _launch\r\n prep_data = spawn.get_preparation_data(process_obj._name)\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 154, in get_preparation_data\r\n _check_not_importing_main()\r\n File \"/scratch/pbs.5401450.kman.restech.unsw.edu.au/miniforge3/envs/llm/lib/python3.10/multiprocessing/spawn.py\", line 134, in _check_not_importing_main\r\n raise RuntimeError('''\r\nRuntimeError: \r\n An attempt has been made to start a new process before the\r\n current process has finished its bootstrapping phase.\r\n\r\n This probably means that you are not using fork to start your\r\n child processes and you have forgotten to use the proper idiom\r\n in the main module:\r\n\r\n if __name__ == '__main__':\r\n freeze_support()\r\n ...\r\n\r\n The \"freeze_support()\" line can be omitted if the program\r\n is not going to be frozen to produce an executable.\r\nERROR 06-18 21:35:45 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 3449547 died, exit code: 1\r\nINFO 06-18 21:35:45 multiproc_worker_utils.py:123] Killing local vLLM worker processes\r\n```\r\n\r\n### Environment\r\n\r\n- OS: GNU/Linux\r\n- Python Version: Python 3.10.14\r\n- Library Version: v0.5.0.post1 and v0.5.0\r\n\r\n### Additional Context\r\n\r\nReverting back to version v0.4.3 resolves the issue. It appears there might be a change in how processes are handled in newer versions that could be causing this error.\r\n\r\n", "number": 5637, "title": "[Bug]: RuntimeError with tensor_parallel_size > 1 in Process Bootstrapping Phase" } ]
3eea74889fe29534808bae41fca251e0e74c0962
{ "head_commit": "2e2731637f1b25d7ca3086b7951a7e671e023f71", "head_commit_message": "add comments", "patch_to_review": "diff --git a/vllm/distributed/device_communicators/custom_all_reduce_utils.py b/vllm/distributed/device_communicators/custom_all_reduce_utils.py\nindex e0641a54c419..1bb8b8011def 100644\n--- a/vllm/distributed/device_communicators/custom_all_reduce_utils.py\n+++ b/vllm/distributed/device_communicators/custom_all_reduce_utils.py\n@@ -1,6 +1,9 @@\n import ctypes\n import json\n import os\n+import pickle\n+import subprocess\n+import sys\n from itertools import product\n from typing import Dict, List, Optional, Sequence\n \n@@ -198,7 +201,19 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool:\n ids = list(range(num_dev))\n # batch of all pairs of GPUs\n batch_src, batch_tgt = zip(*list(product(ids, ids)))\n- result = can_actually_p2p(batch_src, batch_tgt)\n+ # NOTE: we use `subprocess` rather than `multiprocessing` here\n+ # because the caller might not have `if __name__ == \"__main__\":`,\n+ # in that case we cannot use spawn method in multiprocessing.\n+ # However, `can_actually_p2p` requires spawn method.\n+ # The fix is, we use `subprocess` to call the function,\n+ # where we have `if __name__ == \"__main__\":` in this file.\n+ input_bytes = pickle.dumps((batch_src, batch_tgt))\n+ returned = subprocess.run([sys.executable, __file__],\n+ input=input_bytes,\n+ capture_output=True)\n+ # check if the subprocess is successful\n+ returned.check_returncode()\n+ result = pickle.loads(returned.stdout)\n for _i, _j, r in zip(batch_src, batch_tgt, result):\n cache[f\"{_i}->{_j}\"] = r\n with open(path, \"w\") as f:\n@@ -213,3 +228,8 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool:\n \n \n __all__ = [\"gpu_p2p_access_check\"]\n+\n+if __name__ == \"__main__\":\n+ batch_src, batch_tgt = pickle.loads(sys.stdin.buffer.read())\n+ result = can_actually_p2p(batch_src, batch_tgt)\n+ sys.stdout.buffer.write(pickle.dumps(result))\ndiff --git a/vllm/envs.py b/vllm/envs.py\nindex f03b69f4b886..ae2fcd0826fb 100644\n--- a/vllm/envs.py\n+++ b/vllm/envs.py\n@@ -29,7 +29,7 @@\n VLLM_CPU_KVCACHE_SPACE: int = 0\n VLLM_XLA_CACHE_PATH: str = \"~/.vllm/xla_cache/\"\n VLLM_USE_RAY_COMPILED_DAG: bool = False\n- VLLM_WORKER_MULTIPROC_METHOD: str = \"spawn\"\n+ VLLM_WORKER_MULTIPROC_METHOD: str = \"fork\"\n VLLM_IMAGE_FETCH_TIMEOUT: int = 5\n VLLM_TARGET_DEVICE: str = \"cuda\"\n MAX_JOBS: Optional[str] = None\n@@ -212,7 +212,7 @@\n # Use dedicated multiprocess context for workers.\n # Both spawn and fork work\n \"VLLM_WORKER_MULTIPROC_METHOD\":\n- lambda: os.getenv(\"VLLM_WORKER_MULTIPROC_METHOD\", \"spawn\"),\n+ lambda: os.getenv(\"VLLM_WORKER_MULTIPROC_METHOD\", \"fork\"),\n \n # Timeout for fetching images when serving multimodal models\n # Default is 5 seconds\n" }
[ { "diff_hunk": "@@ -198,7 +201,19 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool:\n ids = list(range(num_dev))\n # batch of all pairs of GPUs\n batch_src, batch_tgt = zip(*list(product(ids, ids)))\n- result = can_actually_p2p(batch_src, batch_tgt)\n+ # NOTE: we use `subprocess` rather than `multiprocessing` here\n+ # because the caller might not have `if __name__ == \"__main__\":`,\n+ # in that case we cannot use spawn method in multiprocessing.\n+ # However, `can_actually_p2p` requires spawn method.\n+ # The fix is, we use `subprocess` to call the function,\n+ # where we have `if __name__ == \"__main__\":` in this file.\n+ input_bytes = pickle.dumps((batch_src, batch_tgt))\n+ returned = subprocess.run([sys.executable, __file__],\n+ input=input_bytes,\n+ capture_output=True)\n+ # check if the subprocess is successful\n+ returned.check_returncode()", "line": null, "original_line": 215, "original_start_line": null, "path": "vllm/distributed/device_communicators/custom_all_reduce_utils.py", "start_line": null, "text": "@user1:\nWrap raised exception with one with clearer \"p2p unsupported\" message?\n\n@author:\nfixed in [288c99a](https://github.com/vllm-project/vllm/pull/5669/commits/288c99a304730e701ac715b1452031d539d000c8)" } ]
8348ef58bb55c57dafcc1e27c53bf98c44254ddd
diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 5afe3730210e..67437ef7ea4b 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -37,6 +37,9 @@ steps: working_dir: "/vllm-workspace/tests" num_gpus: 2 commands: + # FIXIT: find out which code initialize cuda before running the test + # before the fix, we need to use spawn to test it + - export VLLM_WORKER_MULTIPROC_METHOD=spawn - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py @@ -55,6 +58,9 @@ steps: working_dir: "/vllm-workspace/tests" num_gpus: 4 commands: + # FIXIT: find out which code initialize cuda before running the test + # before the fix, we need to use spawn to test it + - export VLLM_WORKER_MULTIPROC_METHOD=spawn - pytest -v -s distributed/test_pynccl.py # We want to test that models which use 2 GPUs work with 4 GPUs, which is why we duplicate them here. # See https://github.com/vllm-project/vllm/pull/5473#issuecomment-2166601837 for context. @@ -145,6 +151,9 @@ steps: num_gpus: 4 # This test runs llama 13B, so it is required to run on 4 GPUs. commands: + # FIXIT: find out which code initialize cuda before running the test + # before the fix, we need to use spawn to test it + - export VLLM_WORKER_MULTIPROC_METHOD=spawn - pytest -v -s -x lora/test_long_context.py - label: Tensorizer Test diff --git a/vllm/distributed/device_communicators/custom_all_reduce_utils.py b/vllm/distributed/device_communicators/custom_all_reduce_utils.py index e0641a54c419..d3e41fa71067 100644 --- a/vllm/distributed/device_communicators/custom_all_reduce_utils.py +++ b/vllm/distributed/device_communicators/custom_all_reduce_utils.py @@ -1,6 +1,9 @@ import ctypes import json import os +import pickle +import subprocess +import sys from itertools import product from typing import Dict, List, Optional, Sequence @@ -198,7 +201,25 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool: ids = list(range(num_dev)) # batch of all pairs of GPUs batch_src, batch_tgt = zip(*list(product(ids, ids))) - result = can_actually_p2p(batch_src, batch_tgt) + # NOTE: we use `subprocess` rather than `multiprocessing` here + # because the caller might not have `if __name__ == "__main__":`, + # in that case we cannot use spawn method in multiprocessing. + # However, `can_actually_p2p` requires spawn method. + # The fix is, we use `subprocess` to call the function, + # where we have `if __name__ == "__main__":` in this file. + input_bytes = pickle.dumps((batch_src, batch_tgt)) + returned = subprocess.run([sys.executable, __file__], + input=input_bytes, + capture_output=True) + # check if the subprocess is successful + try: + returned.check_returncode() + except Exception as e: + # wrap raised exception to provide more information + raise RuntimeError( + f"Error happened when batch testing " + f"peer-to-peer access from {batch_src} to {batch_tgt}") from e + result = pickle.loads(returned.stdout) for _i, _j, r in zip(batch_src, batch_tgt, result): cache[f"{_i}->{_j}"] = r with open(path, "w") as f: @@ -213,3 +234,8 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool: __all__ = ["gpu_p2p_access_check"] + +if __name__ == "__main__": + batch_src, batch_tgt = pickle.loads(sys.stdin.buffer.read()) + result = can_actually_p2p(batch_src, batch_tgt) + sys.stdout.buffer.write(pickle.dumps(result)) diff --git a/vllm/envs.py b/vllm/envs.py index f03b69f4b886..ae2fcd0826fb 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -29,7 +29,7 @@ VLLM_CPU_KVCACHE_SPACE: int = 0 VLLM_XLA_CACHE_PATH: str = "~/.vllm/xla_cache/" VLLM_USE_RAY_COMPILED_DAG: bool = False - VLLM_WORKER_MULTIPROC_METHOD: str = "spawn" + VLLM_WORKER_MULTIPROC_METHOD: str = "fork" VLLM_IMAGE_FETCH_TIMEOUT: int = 5 VLLM_TARGET_DEVICE: str = "cuda" MAX_JOBS: Optional[str] = None @@ -212,7 +212,7 @@ # Use dedicated multiprocess context for workers. # Both spawn and fork work "VLLM_WORKER_MULTIPROC_METHOD": - lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn"), + lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "fork"), # Timeout for fetching images when serving multimodal models # Default is 5 seconds
{ "difficulty": "medium", "estimated_review_effort": 4, "problem_domain": "Bug Fixes" }
vllm-project__vllm-5473@b89b8e3
vllm-project/vllm
Python
5,473
Add `cuda_device_count_stateless`
Calling `torch.cuda.device_count()` before `CUDA_VISIBLE_DEVICES` is set to the desired value (during worker init) will lead to issues, as the env var will be read and persisted in memory, meaning later modifications will not affect it. This PR adds a way to obtain the device count without that issue. FIX #4969 FIX #4981
2024-06-12T23:09:31Z
[Usage]: Is it possible to start 8 tp=1 LLMEngine on a 8-GPU machine? ### Your current environment ```text Collecting environment information... /home/corvo/.local/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead. warnings.warn( PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.28.3 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.10.213-201.855.amzn2.x86_64-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.4.99 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB GPU 1: NVIDIA A100-SXM4-40GB GPU 2: NVIDIA A100-SXM4-40GB GPU 3: NVIDIA A100-SXM4-40GB GPU 4: NVIDIA A100-SXM4-40GB GPU 5: NVIDIA A100-SXM4-40GB GPU 6: NVIDIA A100-SXM4-40GB GPU 7: NVIDIA A100-SXM4-40GB Nvidia driver version: 535.161.08 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 96 On-line CPU(s) list: 0-95 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 Stepping: 7 BogoMIPS: 5999.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke Hypervisor vendor: KVM Virtualization type: full L1d cache: 1.5 MiB (48 instances) L1i cache: 1.5 MiB (48 instances) L2 cache: 48 MiB (48 instances) L3 cache: 71.5 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Vulnerable Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.24.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] onnx==1.15.0rc2 [pip3] optree==0.10.0 [pip3] pytorch-quantization==2.1.2 [pip3] pytorch-triton==2.2.0+e28a256d7 [pip3] torch==2.3.0 [pip3] torch-tensorrt==2.3.0a0 [pip3] torchdata==0.7.1a0 [pip3] torchtext==0.17.0a0 [pip3] torchvision==0.18.0a0 [pip3] triton==2.3.0 [pip3] vllm-nccl-cu12==2.18.1.0.4.0 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.2 vLLM Build Flags: CUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 0-23,48-71 0 N/A GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 0-23,48-71 0 N/A GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 0-23,48-71 0 N/A GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 0-23,48-71 0 N/A GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 24-47,72-95 1 N/A GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 24-47,72-95 1 N/A GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 24-47,72-95 1 N/A GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X 24-47,72-95 1 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ``` ### How would you like to use vllm Below code will try to init LLM on the 1st GPU, causing GPU OOM. ``` from vllm import LLM for i in range(8): llm = LLM( model="/models/mistral-7b", tensor_parallel_size=1, ) ``` [Usage]: How to start vLLM on a particular GPU? ### Your current environment ``` Collecting environment information... PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.29.3 Libc version: glibc-2.31 Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-1056-azure-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe GPU 1: NVIDIA A100 80GB PCIe Nvidia driver version: 545.23.08 cuDNN version: Probably one of the following: /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 48 bits physical, 48 bits virtual CPU(s): 48 On-line CPU(s) list: 0-47 Thread(s) per core: 1 Core(s) per socket: 48 Socket(s): 1 NUMA node(s): 2 Vendor ID: AuthenticAMD CPU family: 25 Model: 1 Model name: AMD EPYC 7V13 64-Core Processor Stepping: 1 CPU MHz: 2445.437 BogoMIPS: 4890.87 Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 1.5 MiB L1i cache: 1.5 MiB L2 cache: 24 MiB L3 cache: 192 MiB NUMA node0 CPU(s): 0-23 NUMA node1 CPU(s): 24-47 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] torch==2.3.0 [pip3] triton==2.3.0 [pip3] vllm_nccl_cu12==2.18.1.0.4.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] torch 2.3.0 pypi_0 pypi [conda] triton 2.3.0 pypi_0 pypi [conda] vllm-nccl-cu12 2.18.1.0.4.0 pypi_0 pypiROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.2 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 SYS 0-23 0 N/A GPU1 NV12 X SYS 24-47 1 N/A NIC0 SYS SYS X Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks NIC Legend: NIC0: mlx5_0 ``` ### How would you like to use vllm I have two GPUs in my VM... I am already using vLLM on one of the GPUs and the other one is vacant. How can I start a second vLLM instance on the second GPU of mine? I tried: ```bash --device cuda | --device auto | --device cuda:1 ``` but they don't seem to work as I was expecting... Could you please tell me what am I missing here? Regards!
Hi @sfc-gh-zhwang, I don't think this is easily doable in vLLM at the moment within a single python process. Possibly you could construct each model on GPU 0 and move each to GPU X before moving on. I would recommend starting a separate process for each LLM and specifying `CUDA_VISIBLE_DEVICES` for each i.e. `CUDA_VISIBLE_DEVICES=0 python script.py`, `CUDA_VISIBLE_DEVICES=1 python script.py`, etc Vllm always use cuda:0. That's bad. Same with #4981 You can use `CUDA_VISIBLE_DEVICES` environment variable when running the command. I changed CUDA_VISIBLE_DEVICES, and when I delete CUDA_VISIBLE_DEVICES to load another model. I got an error: CUDA error: invalid device ordinal. > I changed CUDA_VISIBLE_DEVICES, and when I delete CUDA_VISIBLE_DEVICES to load another model. I got an error: CUDA error: invalid device ordinal. Can you show the commands (including env variables) which you used to run vLLM? > > ๆˆ‘ๆ›ดๆ”นไบ†CUDA_VISIBLE_DEVICES๏ผŒๅฝ“ๆˆ‘ๅˆ ้™คCUDA_VISIBLE_DEVICESไปฅๅŠ ่ฝฝๅฆไธ€ไธชๆจกๅž‹ๆ—ถใ€‚ๆˆ‘ๆ”ถๅˆฐ้”™่ฏฏ๏ผšCUDA ้”™่ฏฏ๏ผš่ฎพๅค‡ๅบๅทๆ— ๆ•ˆใ€‚ > > ๆ‚จ่ƒฝๅฑ•็คบ็”จไบŽ่ฟ่กŒ vLLM ็š„ๅ‘ฝไปค๏ผˆๅŒ…ๆ‹ฌ env ๅ˜้‡๏ผ‰ๅ—๏ผŸ I use an script to select GPU of most memory. So I have to del CUDA_VISIBLE_DEVICES env variable after I load a model, and then to load another model. However, When I move new model to the device I select. I got the error. Actually, I think this bug is not caused by vllm. Even I don't use vllm, when I set CUDA_VISIBLE_DEVICES and then unset CUDA_VISIBLE_DEVICES to load another model, I will got an error. I don't think set CUDA_VISIBLE_DEVICES is a good way to set GPU. > > ๆˆ‘ๆ›ดๆ”นไบ†CUDA_VISIBLE_DEVICES๏ผŒๅฝ“ๆˆ‘ๅˆ ้™คCUDA_VISIBLE_DEVICESไปฅๅŠ ่ฝฝๅฆไธ€ไธชๆจกๅž‹ๆ—ถใ€‚ๆˆ‘ๆ”ถๅˆฐ้”™่ฏฏ๏ผšCUDA ้”™่ฏฏ๏ผš่ฎพๅค‡ๅบๅทๆ— ๆ•ˆใ€‚ > > ๆ‚จ่ƒฝๅฑ•็คบ็”จไบŽ่ฟ่กŒ vLLM ็š„ๅ‘ฝไปค๏ผˆๅŒ…ๆ‹ฌ env ๅ˜้‡๏ผ‰ๅ—๏ผŸ It appears that if you set the CUDA_VISIBLE_DEVICES environment variable, for example, os.environ["CUDA_VISIBLE_DEVICES"] = "2,3", then in your code, the device indices will start from 0. That is, cuda:0 corresponds to the actual cuda:2, and cuda:1 corresponds to the actual cuda:3 > > > ๆˆ‘ๆ›ดๆ”นไบ†CUDA_VISIBLE_DEVICES๏ผŒๅฝ“ๆˆ‘ๅˆ ้™คCUDA_VISIBLE_DEVICESไปฅๅŠ ่ฝฝๅฆไธ€ไธชๆจกๅž‹ๆ—ถใ€‚ๆˆ‘ๆ”ถๅˆฐ้”™่ฏฏ๏ผšCUDA ้”™่ฏฏ๏ผš่ฎพๅค‡ๅบๅทๆ— ๆ•ˆใ€‚ > > > > > > ๆ‚จ่ƒฝๅฑ•็คบ็”จไบŽ่ฟ่กŒ vLLM ็š„ๅ‘ฝไปค๏ผˆๅŒ…ๆ‹ฌ env ๅ˜้‡๏ผ‰ๅ—๏ผŸ > > It appears that if you set the CUDA_VISIBLE_DEVICES environment variable, for example, os.environ["CUDA_VISIBLE_DEVICES"] = "2,3", then in your code, the device indices will start from 0. That is, cuda:0 corresponds to the actual cuda:2, and cuda:1 corresponds to the actual cuda:3 Usually, I set the environment variable in the command line instead of inside Python, e.g.: ``` CUDA_VISIBLE_DEVICES=0,1 python -m <command> ``` This is because the environment variable needs to be updated before importing PyTorch in order for it to properly take effect, which is difficult to rely on. > > > > ๆˆ‘ๆ›ดๆ”นไบ†CUDA_VISIBLE_DEVICES๏ผŒๅฝ“ๆˆ‘ๅˆ ้™คCUDA_VISIBLE_DEVICESไปฅๅŠ ่ฝฝๅฆไธ€ไธชๆจกๅž‹ๆ—ถใ€‚ๆˆ‘ๆ”ถๅˆฐ้”™่ฏฏ๏ผšCUDA ้”™่ฏฏ๏ผš่ฎพๅค‡ๅบๅทๆ— ๆ•ˆใ€‚ > > > > > > > > > ๆ‚จ่ƒฝๅฑ•็คบ็”จไบŽ่ฟ่กŒ vLLM ็š„ๅ‘ฝไปค๏ผˆๅŒ…ๆ‹ฌ env ๅ˜้‡๏ผ‰ๅ—๏ผŸ > > > > > > ๅฆ‚ๆžœๆ‚จ่ฎพ็ฝฎไบ†CUDA_VISIBLE_DEVICES็Žฏๅขƒๅ˜้‡๏ผŒไพ‹ๅฆ‚ os.environ[โ€œCUDA_VISIBLE_DEVICESโ€] = โ€œ2,3โ€๏ผŒ้‚ฃไนˆๅœจๆ‚จ็š„ไปฃ็ ไธญ๏ผŒ่ฎพๅค‡็ดขๅผ•ๅฐ†ไปŽ 0 ๅผ€ๅง‹ใ€‚ไนŸๅฐฑๆ˜ฏ่ฏด๏ผŒcuda๏ผš0 ๅฏนๅบ”ไบŽๅฎž้™…็š„ cuda๏ผš2๏ผŒ่€Œ cuda๏ผš1 ๅฏนๅบ”ไบŽๅฎž้™…็š„ cuda๏ผš3 > > ้€šๅธธ๏ผŒๆˆ‘ๅœจๅ‘ฝไปค่กŒไธญ่€Œไธๆ˜ฏๅœจ Python ไธญ่ฎพ็ฝฎ็Žฏๅขƒๅ˜้‡๏ผŒไพ‹ๅฆ‚๏ผš > > ``` > CUDA_VISIBLE_DEVICES=0,1 python -m <command> > ``` > > ่ฟ™ๆ˜ฏๅ› ไธบๅœจๅฏผๅ…ฅ PyTorch ไน‹ๅ‰้œ€่ฆๆ›ดๆ–ฐ็Žฏๅขƒๅ˜้‡ๆ‰่ƒฝไฝฟๅ…ถๆญฃ็กฎ็”Ÿๆ•ˆ๏ผŒ่ฟ™ๅพˆ้šพไพ่ต–ใ€‚ I have several model and gpu. So I have to set CUDA_VISIBLE_DEVICES several times, and get error. Set CUDA_VISIBLE_DEVICES is not a good way. I think when people have several model and gpu, they need a device paramter. You can run multiple vLLM commands simultaneously, each with a different GPU. I have decided not to use vllm. Vllm has a DeviceConfig configuration, and you can pass a device paramter to vllm.LLM. but the kv-cache does not use it and always uses cuda:0. This is too messy.
[ { "body": "### Your current environment\n\n```text\r\nCollecting environment information...\r\n/home/corvo/.local/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\r\n warnings.warn(\r\nPyTorch version: 2.3.0+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.4 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.28.3\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-5.10.213-201.855.amzn2.x86_64-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 12.4.99\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration:\r\nGPU 0: NVIDIA A100-SXM4-40GB\r\nGPU 1: NVIDIA A100-SXM4-40GB\r\nGPU 2: NVIDIA A100-SXM4-40GB\r\nGPU 3: NVIDIA A100-SXM4-40GB\r\nGPU 4: NVIDIA A100-SXM4-40GB\r\nGPU 5: NVIDIA A100-SXM4-40GB\r\nGPU 6: NVIDIA A100-SXM4-40GB\r\nGPU 7: NVIDIA A100-SXM4-40GB\r\n\r\nNvidia driver version: 535.161.08\r\ncuDNN version: Probably one of the following:\r\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0\r\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 46 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 96\r\nOn-line CPU(s) list: 0-95\r\nVendor ID: GenuineIntel\r\nModel name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz\r\nCPU family: 6\r\nModel: 85\r\nThread(s) per core: 2\r\nCore(s) per socket: 24\r\nSocket(s): 2\r\nStepping: 7\r\nBogoMIPS: 5999.99\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke\r\nHypervisor vendor: KVM\r\nVirtualization type: full\r\nL1d cache: 1.5 MiB (48 instances)\r\nL1i cache: 1.5 MiB (48 instances)\r\nL2 cache: 48 MiB (48 instances)\r\nL3 cache: 71.5 MiB (2 instances)\r\nNUMA node(s): 2\r\nNUMA node0 CPU(s): 0-23,48-71\r\nNUMA node1 CPU(s): 24-47,72-95\r\nVulnerability Gather data sampling: Unknown: Dependent on hypervisor status\r\nVulnerability Itlb multihit: KVM: Mitigation: VMX unsupported\r\nVulnerability L1tf: Mitigation; PTE Inversion\r\nVulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\r\nVulnerability Meltdown: Mitigation; PTI\r\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\r\nVulnerability Retbleed: Vulnerable\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Vulnerable\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.24.4\r\n[pip3] nvidia-nccl-cu12==2.20.5\r\n[pip3] onnx==1.15.0rc2\r\n[pip3] optree==0.10.0\r\n[pip3] pytorch-quantization==2.1.2\r\n[pip3] pytorch-triton==2.2.0+e28a256d7\r\n[pip3] torch==2.3.0\r\n[pip3] torch-tensorrt==2.3.0a0\r\n[pip3] torchdata==0.7.1a0\r\n[pip3] torchtext==0.17.0a0\r\n[pip3] torchvision==0.18.0a0\r\n[pip3] triton==2.3.0\r\n[pip3] vllm-nccl-cu12==2.18.1.0.4.0\r\n[conda] Could not collectROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.4.2\r\nvLLM Build Flags:\r\nCUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0\tGPU1\tGPU2\tGPU3\tGPU4\tGPU5\tGPU6\tGPU7\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\r\nGPU0\t X \tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\t0-23,48-71\t0\t\tN/A\r\nGPU1\tNV12\t X \tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\t0-23,48-71\t0\t\tN/A\r\nGPU2\tNV12\tNV12\t X \tNV12\tNV12\tNV12\tNV12\tNV12\t0-23,48-71\t0\t\tN/A\r\nGPU3\tNV12\tNV12\tNV12\t X \tNV12\tNV12\tNV12\tNV12\t0-23,48-71\t0\t\tN/A\r\nGPU4\tNV12\tNV12\tNV12\tNV12\t X \tNV12\tNV12\tNV12\t24-47,72-95\t1\t\tN/A\r\nGPU5\tNV12\tNV12\tNV12\tNV12\tNV12\t X \tNV12\tNV12\t24-47,72-95\t1\t\tN/A\r\nGPU6\tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\t X \tNV12\t24-47,72-95\t1\t\tN/A\r\nGPU7\tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\tNV12\t X \t24-47,72-95\t1\t\tN/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n```\r\n\n\n### How would you like to use vllm\n\nBelow code will try to init LLM on the 1st GPU, causing GPU OOM.\r\n```\r\nfrom vllm import LLM\r\n\r\nfor i in range(8):\r\n llm = LLM(\r\n model=\"/models/mistral-7b\",\r\n tensor_parallel_size=1,\r\n )\r\n```", "number": 4969, "title": "[Usage]: Is it possible to start 8 tp=1 LLMEngine on a 8-GPU machine?" }, { "body": "### Your current environment\r\n\r\n```\r\nCollecting environment information...\r\nPyTorch version: 2.3.0+cu121\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 20.04.6 LTS (x86_64)\r\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 \r\nClang version: Could not collect\r\nCMake version: version 3.29.3\r\nLibc version: glibc-2.31\r\n\r\nPython version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-1056-azure-x86_64-with-glibc2.31\r\nIs CUDA available: True\r\nCUDA runtime version: 11.8.89\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration:\r\nGPU 0: NVIDIA A100 80GB PCIe\r\nGPU 1: NVIDIA A100 80GB PCIe\r\n\r\nNvidia driver version: 545.23.08\r\ncuDNN version: Probably one of the following:\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\r\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit \r\nByte Order: Little Endian \r\nAddress sizes: 48 bits physical, 48 bits virtual\r\nCPU(s): 48\r\nOn-line CPU(s) list: 0-47\r\nThread(s) per core: 1\r\nCore(s) per socket: 48\r\nSocket(s): 1\r\nNUMA node(s): 2\r\nVendor ID: AuthenticAMD \r\nCPU family: 25\r\nModel: 1\r\nModel name: AMD EPYC 7V13 64-Core Processor\r\nStepping: 1\r\nCPU MHz: 2445.437\r\nBogoMIPS: 4890.87\r\nHypervisor vendor: Microsoft\r\nVirtualization type: full\r\nL1d cache: 1.5 MiB\r\nL1i cache: 1.5 MiB\r\nL2 cache: 24 MiB\r\nL3 cache: 192 MiB\r\nNUMA node0 CPU(s): 0-23\r\nNUMA node1 CPU(s): 24-47\r\nVulnerability Gather data sampling: Not affected \r\nVulnerability Itlb multihit: Not affected \r\nVulnerability L1tf: Not affected \r\nVulnerability Mds: Not affected \r\nVulnerability Meltdown: Not affected \r\nVulnerability Mmio stale data: Not affected \r\nVulnerability Retbleed: Not affected \r\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\r\nVulnerability Spec store bypass: Vulnerable\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected \r\nVulnerability Tsx async abort: Not affected \r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.26.4\r\n[pip3] nvidia-nccl-cu12==2.20.5\r\n[pip3] torch==2.3.0\r\n[pip3] triton==2.3.0\r\n[pip3] vllm_nccl_cu12==2.18.1.0.4.0\r\n[conda] numpy 1.26.4 pypi_0 pypi\r\n[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi\r\n[conda] torch 2.3.0 pypi_0 pypi\r\n[conda] triton 2.3.0 pypi_0 pypi\r\n[conda] vllm-nccl-cu12 2.18.1.0.4.0 pypi_0 pypiROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: 0.4.2\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0 GPU1 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID\r\nGPU0 X NV12 SYS 0-23 0 N/A\r\nGPU1 NV12 X SYS 24-47 1 N/A\r\nNIC0 SYS SYS X\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n\r\nNIC Legend:\r\n\r\n NIC0: mlx5_0\r\n```\r\n\r\n\r\n### How would you like to use vllm\r\n\r\nI have two GPUs in my VM... I am already using vLLM on one of the GPUs and the other one is vacant.\r\nHow can I start a second vLLM instance on the second GPU of mine?\r\n\r\nI tried:\r\n```bash\r\n--device cuda | --device auto | --device cuda:1\r\n```\r\nbut they don't seem to work as I was expecting...\r\n\r\n\r\nCould you please tell me what am I missing here?\r\n\r\n\r\nRegards!", "number": 4981, "title": "[Usage]: How to start vLLM on a particular GPU?" } ]
7d19de2e9c9a94658c36b55011b803a7991d0335
{ "head_commit": "b89b8e366ea1b323e47311598d611c90d9a1f3af", "head_commit_message": "Add `get_num_gpus_available_isolated`", "patch_to_review": "diff --git a/tests/conftest.py b/tests/conftest.py\nindex e0680467d78b..554a11a74521 100644\n--- a/tests/conftest.py\n+++ b/tests/conftest.py\n@@ -21,7 +21,7 @@\n from vllm.multimodal import MultiModalData\n from vllm.multimodal.image import ImageFeatureData, ImagePixelData\n from vllm.sequence import SampleLogprobs\n-from vllm.utils import is_cpu\n+from vllm.utils import is_cpu, lazy_num_gpus_available\n \n logger = init_logger(__name__)\n \n@@ -537,15 +537,4 @@ def num_gpus_available():\n \"\"\"Get number of GPUs without initializing the CUDA context\n in current process.\"\"\"\n \n- try:\n- out = subprocess.run([\n- sys.executable, \"-c\",\n- \"import torch; print(torch.cuda.device_count())\"\n- ],\n- capture_output=True,\n- check=True,\n- text=True)\n- except subprocess.CalledProcessError as e:\n- logger.warning(\"Failed to get number of GPUs.\", exc_info=e)\n- return 0\n- return int(out.stdout.strip())\n+ return lazy_num_gpus_available()\ndiff --git a/vllm/config.py b/vllm/config.py\nindex 2513d43ce8e6..89d256ed9826 100644\n--- a/vllm/config.py\n+++ b/vllm/config.py\n@@ -11,7 +11,7 @@\n from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS\n from vllm.model_executor.models import ModelRegistry\n from vllm.transformers_utils.config import get_config, get_hf_text_config\n-from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron, is_tpu\n+from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron, is_tpu, get_num_gpus_available_isolated\n \n if TYPE_CHECKING:\n from ray.util.placement_group import PlacementGroup\n@@ -605,12 +605,11 @@ def __init__(\n if self.distributed_executor_backend is None and self.world_size > 1:\n # We use multiprocessing by default if world_size fits on the\n # current node and we aren't in a ray placement group.\n- from torch.cuda import device_count\n \n from vllm.executor import ray_utils\n backend = \"mp\"\n ray_found = ray_utils.ray is not None\n- if device_count() < self.world_size:\n+ if get_num_gpus_available_isolated() < self.world_size:\n if not ray_found:\n raise ValueError(\"Unable to load Ray which is \"\n \"required for multi-node inference\")\ndiff --git a/vllm/distributed/device_communicators/custom_all_reduce.py b/vllm/distributed/device_communicators/custom_all_reduce.py\nindex bbc2284f8a36..2f3f77787d61 100644\n--- a/vllm/distributed/device_communicators/custom_all_reduce.py\n+++ b/vllm/distributed/device_communicators/custom_all_reduce.py\n@@ -12,6 +12,7 @@\n from vllm.distributed.parallel_state import (\n get_local_rank, get_tensor_model_parallel_cpu_group, is_in_the_same_node)\n from vllm.logger import init_logger\n+from vllm.utils import get_num_gpus_available_isolated\n \n try:\n import pynvml\n@@ -149,7 +150,7 @@ def __init__(self,\n if cuda_visible_devices:\n device_ids = list(map(int, cuda_visible_devices.split(\",\")))\n else:\n- device_ids = list(range(torch.cuda.device_count()))\n+ device_ids = list(range(get_num_gpus_available_isolated()))\n \n physical_device_id = device_ids[device.index]\n tensor = torch.tensor([physical_device_id],\ndiff --git a/vllm/distributed/device_communicators/custom_all_reduce_utils.py b/vllm/distributed/device_communicators/custom_all_reduce_utils.py\nindex 4b89a23dfc46..3f6816445eb8 100644\n--- a/vllm/distributed/device_communicators/custom_all_reduce_utils.py\n+++ b/vllm/distributed/device_communicators/custom_all_reduce_utils.py\n@@ -13,6 +13,7 @@\n import vllm.envs as envs\n from vllm.distributed.parallel_state import get_cpu_world_group, get_local_rank\n from vllm.logger import init_logger\n+from vllm.utils import get_num_gpus_available_isolated\n \n logger = init_logger(__name__)\n \n@@ -153,7 +154,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:\n \n is_distributed = dist.is_initialized()\n \n- num_dev = torch.cuda.device_count()\n+ num_dev = get_num_gpus_available_isolated()\n cuda_visible_devices = envs.CUDA_VISIBLE_DEVICES\n if cuda_visible_devices is None:\n cuda_visible_devices = \",\".join(str(i) for i in range(num_dev))\ndiff --git a/vllm/executor/multiproc_gpu_executor.py b/vllm/executor/multiproc_gpu_executor.py\nindex 99c9e52034cc..86869b7e621c 100644\n--- a/vllm/executor/multiproc_gpu_executor.py\n+++ b/vllm/executor/multiproc_gpu_executor.py\n@@ -10,7 +10,7 @@\n from vllm.logger import init_logger\n from vllm.sequence import ExecuteModelRequest, SamplerOutput\n from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,\n- get_vllm_instance_id, make_async)\n+ get_vllm_instance_id, make_async, get_num_gpus_available_isolated)\n \n logger = init_logger(__name__)\n \n@@ -33,8 +33,7 @@ def _init_executor(self) -> None:\n # Disable torch async compiling which won't work with daemonic processes\n os.environ[\"TORCHINDUCTOR_COMPILE_THREADS\"] = \"1\"\n \n- from torch.cuda import device_count\n- assert world_size <= device_count(), (\n+ assert world_size <= get_num_gpus_available_isolated(), (\n \"please set tensor_parallel_size to less than max local gpu count\")\n \n distributed_init_method = get_distributed_init_method(\ndiff --git a/vllm/utils.py b/vllm/utils.py\nindex af585929d1a0..4b64465ef9c9 100644\n--- a/vllm/utils.py\n+++ b/vllm/utils.py\n@@ -693,3 +693,26 @@ def inner(*args, **kwargs):\n return inner # type: ignore\n \n return wrapper\n+\n+\n+@lru_cache(maxsize=None)\n+def get_num_gpus_available_isolated() -> int:\n+ \"\"\"Get number of GPUs without initializing the CUDA context\n+ in current process.\n+ \n+ This should be used instead of torch.cuda.device_count()\n+ unless CUDA_VISIBLE_DEVICES has already been set to the desired\n+ value.\"\"\"\n+\n+ try:\n+ out = subprocess.run([\n+ sys.executable, \"-c\",\n+ \"import torch; print(torch.cuda.device_count())\"\n+ ],\n+ capture_output=True,\n+ check=True,\n+ text=True)\n+ except subprocess.CalledProcessError as e:\n+ logger.warning(\"Failed to get number of GPUs.\", exc_info=e)\n+ return 0\n+ return int(out.stdout.strip())\n" }
[ { "diff_hunk": "@@ -537,15 +537,4 @@ def num_gpus_available():\n \"\"\"Get number of GPUs without initializing the CUDA context\n in current process.\"\"\"\n \n- try:\n- out = subprocess.run([\n- sys.executable, \"-c\",\n- \"import torch; print(torch.cuda.device_count())\"\n- ],\n- capture_output=True,\n- check=True,\n- text=True)\n- except subprocess.CalledProcessError as e:\n- logger.warning(\"Failed to get number of GPUs.\", exc_info=e)\n- return 0\n- return int(out.stdout.strip())\n+ return lazy_num_gpus_available()", "line": null, "original_line": 540, "original_start_line": null, "path": "tests/conftest.py", "start_line": null, "text": "@author:\n```suggestion\r\n return get_num_gpus_available_isolated()\r\n```" }, { "diff_hunk": "@@ -21,7 +21,7 @@\n from vllm.multimodal import MultiModalData\n from vllm.multimodal.image import ImageFeatureData, ImagePixelData\n from vllm.sequence import SampleLogprobs\n-from vllm.utils import is_cpu\n+from vllm.utils import is_cpu, lazy_num_gpus_available", "line": null, "original_line": 24, "original_start_line": null, "path": "tests/conftest.py", "start_line": null, "text": "@author:\n```suggestion\r\nfrom vllm.utils import is_cpu, get_num_gpus_available_isolated\r\n```" } ]
bf43ffbe1ab6579302774ef81d74867b45597c48
diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 6b12d19ba611..6a2932db9f2d 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -48,6 +48,7 @@ steps: - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py - pytest -v -s spec_decode/e2e/test_integration_dist.py - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py + - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s distributed/test_utils.py - label: Distributed Tests (Multiple Groups) #mirror_hardwares: [amd] diff --git a/tests/conftest.py b/tests/conftest.py index e0680467d78b..5e482466e1c6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,8 +1,6 @@ import contextlib import gc import os -import subprocess -import sys from typing import Any, Dict, List, Optional, Tuple, TypeVar import pytest @@ -21,7 +19,7 @@ from vllm.multimodal import MultiModalData from vllm.multimodal.image import ImageFeatureData, ImagePixelData from vllm.sequence import SampleLogprobs -from vllm.utils import is_cpu +from vllm.utils import cuda_device_count_stateless, is_cpu logger = init_logger(__name__) @@ -537,15 +535,4 @@ def num_gpus_available(): """Get number of GPUs without initializing the CUDA context in current process.""" - try: - out = subprocess.run([ - sys.executable, "-c", - "import torch; print(torch.cuda.device_count())" - ], - capture_output=True, - check=True, - text=True) - except subprocess.CalledProcessError as e: - logger.warning("Failed to get number of GPUs.", exc_info=e) - return 0 - return int(out.stdout.strip()) + return cuda_device_count_stateless() diff --git a/tests/distributed/test_utils.py b/tests/distributed/test_utils.py new file mode 100644 index 000000000000..b7ec59c7a2cc --- /dev/null +++ b/tests/distributed/test_utils.py @@ -0,0 +1,31 @@ +import os + +import ray + +from vllm.utils import cuda_device_count_stateless + + [email protected] +class _CUDADeviceCountStatelessTestActor(): + + def get_count(self): + return cuda_device_count_stateless() + + def set_cuda_visible_devices(self, cuda_visible_devices: str): + os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices + + def get_cuda_visible_devices(self): + return os.environ["CUDA_VISIBLE_DEVICES"] + + +def test_cuda_device_count_stateless(): + """Test that cuda_device_count_stateless changes return value if + CUDA_VISIBLE_DEVICES is changed.""" + + actor = _CUDADeviceCountStatelessTestActor.options(num_gpus=2).remote() + assert ray.get(actor.get_cuda_visible_devices.remote()) == "0,1" + assert ray.get(actor.get_count.remote()) == 2 + ray.get(actor.set_cuda_visible_devices.remote("0")) + assert ray.get(actor.get_count.remote()) == 1 + ray.get(actor.set_cuda_visible_devices.remote("")) + assert ray.get(actor.get_count.remote()) == 0 diff --git a/vllm/config.py b/vllm/config.py index 2513d43ce8e6..a0bd6b0975a1 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -11,7 +11,8 @@ from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.model_executor.models import ModelRegistry from vllm.transformers_utils.config import get_config, get_hf_text_config -from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron, is_tpu +from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu, + is_hip, is_neuron, is_tpu) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup @@ -605,12 +606,11 @@ def __init__( if self.distributed_executor_backend is None and self.world_size > 1: # We use multiprocessing by default if world_size fits on the # current node and we aren't in a ray placement group. - from torch.cuda import device_count from vllm.executor import ray_utils backend = "mp" ray_found = ray_utils.ray is not None - if device_count() < self.world_size: + if cuda_device_count_stateless() < self.world_size: if not ray_found: raise ValueError("Unable to load Ray which is " "required for multi-node inference") diff --git a/vllm/distributed/device_communicators/custom_all_reduce.py b/vllm/distributed/device_communicators/custom_all_reduce.py index bbc2284f8a36..2f8ffe87d480 100644 --- a/vllm/distributed/device_communicators/custom_all_reduce.py +++ b/vllm/distributed/device_communicators/custom_all_reduce.py @@ -12,6 +12,7 @@ from vllm.distributed.parallel_state import ( get_local_rank, get_tensor_model_parallel_cpu_group, is_in_the_same_node) from vllm.logger import init_logger +from vllm.utils import cuda_device_count_stateless try: import pynvml @@ -149,7 +150,7 @@ def __init__(self, if cuda_visible_devices: device_ids = list(map(int, cuda_visible_devices.split(","))) else: - device_ids = list(range(torch.cuda.device_count())) + device_ids = list(range(cuda_device_count_stateless())) physical_device_id = device_ids[device.index] tensor = torch.tensor([physical_device_id], diff --git a/vllm/distributed/device_communicators/custom_all_reduce_utils.py b/vllm/distributed/device_communicators/custom_all_reduce_utils.py index 4b89a23dfc46..b3d397de72cc 100644 --- a/vllm/distributed/device_communicators/custom_all_reduce_utils.py +++ b/vllm/distributed/device_communicators/custom_all_reduce_utils.py @@ -13,6 +13,7 @@ import vllm.envs as envs from vllm.distributed.parallel_state import get_cpu_world_group, get_local_rank from vllm.logger import init_logger +from vllm.utils import cuda_device_count_stateless logger = init_logger(__name__) @@ -153,7 +154,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool: is_distributed = dist.is_initialized() - num_dev = torch.cuda.device_count() + num_dev = cuda_device_count_stateless() cuda_visible_devices = envs.CUDA_VISIBLE_DEVICES if cuda_visible_devices is None: cuda_visible_devices = ",".join(str(i) for i in range(num_dev)) diff --git a/vllm/executor/multiproc_gpu_executor.py b/vllm/executor/multiproc_gpu_executor.py index 99c9e52034cc..8385e56f88b3 100644 --- a/vllm/executor/multiproc_gpu_executor.py +++ b/vllm/executor/multiproc_gpu_executor.py @@ -9,7 +9,8 @@ ResultHandler, WorkerMonitor) from vllm.logger import init_logger from vllm.sequence import ExecuteModelRequest, SamplerOutput -from vllm.utils import (get_distributed_init_method, get_ip, get_open_port, +from vllm.utils import (cuda_device_count_stateless, + get_distributed_init_method, get_ip, get_open_port, get_vllm_instance_id, make_async) logger = init_logger(__name__) @@ -33,8 +34,7 @@ def _init_executor(self) -> None: # Disable torch async compiling which won't work with daemonic processes os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1" - from torch.cuda import device_count - assert world_size <= device_count(), ( + assert world_size <= cuda_device_count_stateless(), ( "please set tensor_parallel_size to less than max local gpu count") distributed_init_method = get_distributed_init_method( diff --git a/vllm/utils.py b/vllm/utils.py index af585929d1a0..b5c42605ba35 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -693,3 +693,38 @@ def inner(*args, **kwargs): return inner # type: ignore return wrapper + + +@lru_cache(maxsize=8) +def _cuda_device_count_stateless( + cuda_visible_devices: Optional[str] = None) -> int: + # Note: cuda_visible_devices is not used, but we keep it as an argument for + # LRU Cache purposes. + + # Code below is based on + # https://github.com/pytorch/pytorch/blob/ + # c1cd946818442aca8c7f812b16d187ce1586c3bc/ + # torch/cuda/__init__.py#L831C1-L831C17 + import torch.cuda + import torch.version + + if not torch.cuda._is_compiled(): + return 0 + # bypass _device_count_nvml() if rocm (not supported) + nvml_count = -1 if torch.version.hip else torch.cuda._device_count_nvml() + r = torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count + return r + + +def cuda_device_count_stateless() -> int: + """Get number of CUDA devices, caching based on the value of + CUDA_VISIBLE_DEVICES at the time of call. + + This should be used instead of torch.cuda.device_count() + unless CUDA_VISIBLE_DEVICES has already been set to the desired + value.""" + + # This can be removed and simply replaced with torch.cuda.get_device_count + # after https://github.com/pytorch/pytorch/pull/122815 is released. + + return _cuda_device_count_stateless(envs.CUDA_VISIBLE_DEVICES)
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "New Feature Additions" }
Textualize__textual-4874@67c5ad8
Textualize/textual
Python
4,874
command palette dismiss logic
Fixes https://github.com/Textualize/textual/issues/4856 Screwed up the diff a bit here. Most of the updated snapshots are from a previous PR.
2024-08-12T11:34:21Z
Command palette cancel If you bring up the command palette (with `ctrl+\`) you have to press ESC twice to cancel it. This makes sense when there is an auto-complete menu showing. But I think that a single ESC should dismiss the command palette in the following circumstances: 1. There is nothing in the input 2. There are no items in the auto-complete
Could you please assign this issue to me? I want to work on it. Sorry, we have a PR for this one. Will add more GFIs soon.
[ { "body": "If you bring up the command palette (with `ctrl+\\`) you have to press ESC twice to cancel it.\r\n\r\nThis makes sense when there is an auto-complete menu showing. But I think that a single ESC should dismiss the command palette in the following circumstances:\r\n\r\n1. There is nothing in the input\r\n2. There are no items in the auto-complete", "number": 4856, "title": "Command palette cancel" } ]
ee5ff3ad166419599df5aff6748921e51bcfa96e
{ "head_commit": "67c5ad8cf0c531db78727a1b9eab6a638f40b703", "head_commit_message": "snapshots", "patch_to_review": "diff --git a/src/textual/command.py b/src/textual/command.py\nindex ad519deeb8..698cf22105 100644\n--- a/src/textual/command.py\n+++ b/src/textual/command.py\n@@ -575,6 +575,8 @@ def __init__(self) -> None:\n \"\"\"Keeps track of if there are 'No matches found' message waiting to be displayed.\"\"\"\n self._providers: list[Provider] = []\n \"\"\"List of Provider instances involved in searches.\"\"\"\n+ self._hit_count: int = 0\n+ \"\"\"Number of hits displayed.\"\"\"\n \n @staticmethod\n def is_open(app: App) -> bool:\n@@ -735,6 +737,8 @@ def _show_no_matches() -> None:\n # discovered is a situation we don't need to highlight.\n self._list_visible = False\n \n+ self._hit_count = 0\n+\n self._no_matches_timer = self.set_timer(\n self._NO_MATCHES_COUNTDOWN,\n _show_no_matches,\n@@ -883,6 +887,7 @@ def _refresh_command_list(\n if highlighted is not None and highlighted.id:\n command_list.highlighted = command_list.get_option_index(highlighted.id)\n self._list_visible = bool(command_list.option_count)\n+ self._hit_count = command_list.option_count\n \n _RESULT_BATCH_TIME: Final[float] = 0.25\n \"\"\"How long to wait before adding commands to the command list.\"\"\"\n@@ -1049,6 +1054,7 @@ def _select_command(self, event: OptionList.OptionSelected) -> None:\n input.action_end()\n self._list_visible = False\n self.query_one(CommandList).clear_options()\n+ self._hit_count = 0\n if self.run_on_select:\n self._select_or_command()\n \n@@ -1090,8 +1096,11 @@ def _stop_event_leak(self, event: OptionList.OptionHighlighted) -> None:\n \n def _action_escape(self) -> None:\n \"\"\"Handle a request to escape out of the command palette.\"\"\"\n- if self._list_visible:\n+ input = self.query_one(CommandInput)\n+ # Hide the options if there are result and there is input\n+ if self._list_visible and (self._hit_count and input.value):\n self._list_visible = False\n+ # Otherwise dismiss modal\n else:\n self._cancel_gather_commands()\n self.app.post_message(CommandPalette.Closed(option_selected=False))\ndiff --git a/tests/snapshot_tests/__snapshots__/test_snapshots/test_auto_grid_default_height.svg b/tests/snapshot_tests/__snapshots__/test_snapshots/test_auto_grid_default_height.svg\nindex 0569ad0263..d7fcfbdf18 100644\n--- a/tests/snapshot_tests/__snapshots__/test_snapshots/test_auto_grid_default_height.svg\n+++ b/tests/snapshot_tests/__snapshots__/test_snapshots/test_auto_grid_default_height.svg\n@@ -19,137 +19,138 @@\n font-weight: 700;\n }\n \n- .terminal-2854661859-matrix {\n+ .terminal-3709062841-matrix {\n font-family: Fira Code, monospace;\n font-size: 20px;\n line-height: 24.4px;\n font-variant-east-asian: full-width;\n }\n \n- .terminal-2854661859-title {\n+ .terminal-3709062841-title {\n font-size: 18px;\n font-weight: bold;\n font-family: arial;\n }\n \n- .terminal-2854661859-r1 { fill: #c5c8c6 }\n-.terminal-2854661859-r2 { 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clip-path=\"url(#terminal-2999677337-line-5)\">sample</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-5)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-5)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-6)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"166.4\" textLength=\"97.6\" clip-path=\"url(#terminal-2999677337-line-6)\">sentence</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-6)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-6)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-7)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"190.8\" textLength=\"36.6\" clip-path=\"url(#terminal-2999677337-line-7)\">and</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-7)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-7)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-8)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"215.2\" textLength=\"48.8\" clip-path=\"url(#terminal-2999677337-line-8)\">here</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-8)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-8)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-9)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"239.6\" textLength=\"36.6\" clip-path=\"url(#terminal-2999677337-line-9)\">are</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-9)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-9)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-10)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"264\" textLength=\"48.8\" clip-path=\"url(#terminal-2999677337-line-10)\">some</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-10)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-10)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-11)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"288.4\" textLength=\"109.8\" clip-path=\"url(#terminal-2999677337-line-11)\">wordsthis</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-11)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-11)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-12)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"312.8\" textLength=\"24.4\" clip-path=\"url(#terminal-2999677337-line-12)\">is</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-12)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-12)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-13)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-13)\">a</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-13)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-13)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-14)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"361.6\" textLength=\"73.2\" clip-path=\"url(#terminal-2999677337-line-14)\">sample</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-14)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-14)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-15)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"386\" textLength=\"97.6\" clip-path=\"url(#terminal-2999677337-line-15)\">sentence</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-15)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-15)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-16)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"410.4\" textLength=\"36.6\" clip-path=\"url(#terminal-2999677337-line-16)\">and</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-16)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-16)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-17)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"434.8\" textLength=\"48.8\" clip-path=\"url(#terminal-2999677337-line-17)\">here</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-17)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-17)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-18)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"459.2\" textLength=\"36.6\" clip-path=\"url(#terminal-2999677337-line-18)\">are</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-18)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-18)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-19)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"483.6\" textLength=\"48.8\" clip-path=\"url(#terminal-2999677337-line-19)\">some</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-19)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-19)\">\n-</text><text class=\"terminal-2999677337-r4\" x=\"0\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-20)\">โ”‚</text><text class=\"terminal-2999677337-r3\" x=\"12.2\" y=\"508\" textLength=\"61\" clip-path=\"url(#terminal-2999677337-line-20)\">words</text><text class=\"terminal-2999677337-r4\" x=\"122\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-20)\">โ”‚</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-20)\">\n-</text><text class=\"terminal-2999677337-r5\" x=\"0\" y=\"532.4\" textLength=\"48.8\" clip-path=\"url(#terminal-2999677337-line-21)\">&#160;^q&#160;</text><text class=\"terminal-2999677337-r6\" x=\"48.8\" y=\"532.4\" textLength=\"61\" clip-path=\"url(#terminal-2999677337-line-21)\">Quit&#160;</text><text class=\"terminal-2999677337-r5\" x=\"817.4\" y=\"532.4\" textLength=\"24.4\" clip-path=\"url(#terminal-2999677337-line-21)\">^p</text><text class=\"terminal-2999677337-r6\" x=\"841.8\" y=\"532.4\" textLength=\"97.6\" clip-path=\"url(#terminal-2999677337-line-21)\">&#160;palette</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-21)\">\n-</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-22)\">\n-</text><text class=\"terminal-2999677337-r1\" x=\"976\" y=\"581.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2999677337-line-23)\">\n-</text><text class=\"terminal-2999677337-r8\" x=\"951.6\" y=\"605.6\" textLength=\"24.4\" clip-path=\"url(#terminal-2999677337-line-24)\">โ–‡โ–‡</text>\n+ <g class=\"terminal-645436058-matrix\">\n+ <text class=\"terminal-645436058-r2\" x=\"12.2\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-0)\">โญ˜</text><text class=\"terminal-645436058-r2\" x=\"414.8\" y=\"20\" textLength=\"85.4\" clip-path=\"url(#terminal-645436058-line-0)\">TestApp</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-0)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"44.4\" textLength=\"134.2\" clip-path=\"url(#terminal-645436058-line-1)\">โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-1)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-2)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"68.8\" textLength=\"48.8\" clip-path=\"url(#terminal-645436058-line-2)\">this</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-2)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-2)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-3)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"93.2\" textLength=\"24.4\" clip-path=\"url(#terminal-645436058-line-3)\">is</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-3)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-3)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-4)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-4)\">a</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-4)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-4)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-5)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"142\" textLength=\"73.2\" clip-path=\"url(#terminal-645436058-line-5)\">sample</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-5)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-5)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-6)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"166.4\" textLength=\"97.6\" clip-path=\"url(#terminal-645436058-line-6)\">sentence</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-6)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-6)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-7)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"190.8\" textLength=\"36.6\" clip-path=\"url(#terminal-645436058-line-7)\">and</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-7)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-7)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-8)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"215.2\" textLength=\"48.8\" clip-path=\"url(#terminal-645436058-line-8)\">here</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-8)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-8)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-9)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"239.6\" textLength=\"36.6\" clip-path=\"url(#terminal-645436058-line-9)\">are</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-9)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-9)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-10)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"264\" textLength=\"48.8\" clip-path=\"url(#terminal-645436058-line-10)\">some</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-10)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-10)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-11)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"288.4\" textLength=\"109.8\" clip-path=\"url(#terminal-645436058-line-11)\">wordsthis</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-11)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-11)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-12)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"312.8\" textLength=\"24.4\" clip-path=\"url(#terminal-645436058-line-12)\">is</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-12)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-12)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-13)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-13)\">a</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-13)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-13)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-14)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"361.6\" textLength=\"73.2\" clip-path=\"url(#terminal-645436058-line-14)\">sample</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-14)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-14)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-15)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"386\" textLength=\"97.6\" clip-path=\"url(#terminal-645436058-line-15)\">sentence</text><text class=\"terminal-645436058-r4\" x=\"122\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-15)\">โ”‚</text><text class=\"terminal-645436058-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-15)\">\n+</text><text class=\"terminal-645436058-r4\" x=\"0\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-645436058-line-16)\">โ”‚</text><text class=\"terminal-645436058-r3\" x=\"12.2\" y=\"410.4\" textLength=\"36.6\" 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clip-path=\"url(#terminal-693292760-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"142\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"142\" textLength=\"390.4\" clip-path=\"url(#terminal-693292760-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-5)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"166.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"166.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-6)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"190.8\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"190.8\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-7)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"215.2\" textLength=\"390.4\" clip-path=\"url(#terminal-693292760-line-8)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"215.2\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-8)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"215.2\" textLength=\"390.4\" clip-path=\"url(#terminal-693292760-line-8)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-8)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"239.6\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-9)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r5\" x=\"195.2\" y=\"239.6\" textLength=\"585.6\" clip-path=\"url(#terminal-693292760-line-9)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r3\" x=\"780.8\" y=\"239.6\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-9)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-9)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"195.2\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"378.2\" y=\"264\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"264\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"768.6\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"780.8\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-10)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"195.2\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"378.2\" y=\"288.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"288.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"768.6\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"780.8\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-11)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"195.2\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"378.2\" y=\"312.8\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"312.8\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r5\" x=\"768.6\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"780.8\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-12)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"337.2\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-13)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r5\" x=\"195.2\" y=\"337.2\" textLength=\"585.6\" 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class=\"terminal-693292760-r3\" x=\"585.6\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-15)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"386\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-15)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-15)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-15)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"410.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r5\" x=\"573.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"410.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-16)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"434.8\" textLength=\"390.4\" clip-path=\"url(#terminal-693292760-line-17)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r5\" x=\"390.4\" y=\"434.8\" textLength=\"195.2\" clip-path=\"url(#terminal-693292760-line-17)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r3\" x=\"585.6\" y=\"434.8\" textLength=\"390.4\" clip-path=\"url(#terminal-693292760-line-17)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-17)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"459.2\" textLength=\"976\" clip-path=\"url(#terminal-693292760-line-18)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-18)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"483.6\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"483.6\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"573.4\" y=\"483.6\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"483.6\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-19)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-19)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"508\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"508\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"573.4\" y=\"508\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"508\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-20)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-20)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚</text><text class=\"terminal-693292760-r3\" x=\"183\" y=\"532.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"378.2\" y=\"532.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"573.4\" y=\"532.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"768.6\" y=\"532.4\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚โ”‚</text><text class=\"terminal-693292760-r3\" x=\"963.8\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-21)\">โ”‚</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-21)\">\n-</text><text class=\"terminal-693292760-r3\" x=\"0\" y=\"556.8\" textLength=\"976\" clip-path=\"url(#terminal-693292760-line-22)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-693292760-r2\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-693292760-line-22)\">\n-</text><text class=\"terminal-693292760-r7\" x=\"0\" y=\"581.2\" textLength=\"36.6\" clip-path=\"url(#terminal-693292760-line-23)\">&#160;n&#160;</text><text class=\"terminal-693292760-r8\" x=\"36.6\" y=\"581.2\" textLength=\"109.8\" clip-path=\"url(#terminal-693292760-line-23)\">New&#160;Game&#160;</text><text class=\"terminal-693292760-r7\" x=\"146.4\" y=\"581.2\" textLength=\"36.6\" clip-path=\"url(#terminal-693292760-line-23)\">&#160;?&#160;</text><text class=\"terminal-693292760-r8\" x=\"183\" y=\"581.2\" textLength=\"61\" clip-path=\"url(#terminal-693292760-line-23)\">Help&#160;</text><text class=\"terminal-693292760-r7\" x=\"244\" y=\"581.2\" textLength=\"36.6\" clip-path=\"url(#terminal-693292760-line-23)\">&#160;q&#160;</text><text class=\"terminal-693292760-r8\" x=\"280.6\" y=\"581.2\" textLength=\"61\" clip-path=\"url(#terminal-693292760-line-23)\">Quit&#160;</text><text class=\"terminal-693292760-r7\" x=\"341.6\" y=\"581.2\" textLength=\"48.8\" clip-path=\"url(#terminal-693292760-line-23)\">&#160;^d&#160;</text><text class=\"terminal-693292760-r8\" x=\"390.4\" y=\"581.2\" textLength=\"207.4\" clip-path=\"url(#terminal-693292760-line-23)\">Toggle&#160;Dark&#160;Mode&#160;</text><text class=\"terminal-693292760-r7\" x=\"841.8\" y=\"581.2\" textLength=\"24.4\" clip-path=\"url(#terminal-693292760-line-23)\">^p</text><text class=\"terminal-693292760-r8\" x=\"866.2\" y=\"581.2\" textLength=\"97.6\" clip-path=\"url(#terminal-693292760-line-23)\">&#160;palette</text>\n+ <g class=\"terminal-2114580142-matrix\">\n+ <text class=\"terminal-2114580142-r1\" x=\"0\" y=\"20\" textLength=\"378.2\" clip-path=\"url(#terminal-2114580142-line-0)\">5x5&#160;--&#160;A&#160;little&#160;annoying&#160;puzzle</text><text class=\"terminal-2114580142-r1\" x=\"585.6\" y=\"20\" textLength=\"97.6\" clip-path=\"url(#terminal-2114580142-line-0)\">Moves:&#160;0</text><text class=\"terminal-2114580142-r1\" x=\"780.8\" y=\"20\" textLength=\"109.8\" clip-path=\"url(#terminal-2114580142-line-0)\">Filled:&#160;5</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-0)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"44.4\" textLength=\"976\" clip-path=\"url(#terminal-2114580142-line-1)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-1)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-2)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"183\" y=\"68.8\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-2)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"378.2\" y=\"68.8\" textLength=\"24.4\" 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clip-path=\"url(#terminal-2114580142-line-3)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"573.4\" y=\"93.2\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-3)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"768.6\" y=\"93.2\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-3)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"963.8\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-3)\">โ”‚</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-3)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"117.6\" textLength=\"976\" clip-path=\"url(#terminal-2114580142-line-4)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-4)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"142\" textLength=\"390.4\" clip-path=\"url(#terminal-2114580142-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-2114580142-r5\" x=\"390.4\" y=\"142\" textLength=\"195.2\" clip-path=\"url(#terminal-2114580142-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-2114580142-r3\" x=\"585.6\" y=\"142\" textLength=\"390.4\" clip-path=\"url(#terminal-2114580142-line-5)\">โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-5)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"183\" y=\"166.4\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"378.2\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"390.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"573.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"585.6\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"768.6\" y=\"166.4\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"963.8\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">โ”‚</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-6)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"183\" y=\"190.8\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"378.2\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"390.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"573.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"585.6\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"768.6\" y=\"190.8\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"963.8\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">โ”‚</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-7)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"215.2\" textLength=\"390.4\" clip-path=\"url(#terminal-2114580142-line-8)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-2114580142-r5\" x=\"390.4\" y=\"215.2\" textLength=\"195.2\" clip-path=\"url(#terminal-2114580142-line-8)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-2114580142-r3\" x=\"585.6\" y=\"215.2\" textLength=\"390.4\" 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clip-path=\"url(#terminal-2114580142-line-10)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"963.8\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-10)\">โ”‚</text><text class=\"terminal-2114580142-r2\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-10)\">\n+</text><text class=\"terminal-2114580142-r3\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-11)\">โ”‚</text><text class=\"terminal-2114580142-r3\" x=\"183\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-11)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"195.2\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2114580142-line-11)\">โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"378.2\" y=\"288.4\" textLength=\"24.4\" clip-path=\"url(#terminal-2114580142-line-11)\">โ”‚โ”‚</text><text class=\"terminal-2114580142-r5\" x=\"573.4\" y=\"288.4\" textLength=\"24.4\" 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clip-path=\"url(#terminal-4103379692-line-6)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-6)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-6)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-7)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"190.8\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-7)\">Canceron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-7)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-7)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-8)\">โ–Š</text><text class=\"terminal-4103379692-r9\" x=\"170.8\" y=\"215.2\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-8)\">Caprica&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-8)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-8)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-9)\">โ–Š</text><text class=\"terminal-4103379692-r8\" x=\"170.8\" y=\"239.6\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-9)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-9)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-9)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-10)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"264\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-10)\">Gemenon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-10)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-10)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-11)\">โ–Š</text><text class=\"terminal-4103379692-r8\" x=\"170.8\" y=\"288.4\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-11)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-11)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-11)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-12)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"312.8\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-12)\">Leonis&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-12)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-12)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-13)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"337.2\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-13)\">Libran&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-13)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-13)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-14)\">โ–Š</text><text class=\"terminal-4103379692-r8\" x=\"170.8\" y=\"361.6\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-14)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-14)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-14)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-15)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"386\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-15)\">Picon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r10\" x=\"780.8\" y=\"386\" textLength=\"24.4\" clip-path=\"url(#terminal-4103379692-line-15)\">โ–โ–</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-15)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-15)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-16)\">โ–Š</text><text class=\"terminal-4103379692-r8\" x=\"170.8\" y=\"410.4\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-16)\">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-16)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-16)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-17)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"434.8\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-17)\">Sagittaron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-17)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-17)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-18)\">โ–Š</text><text class=\"terminal-4103379692-r7\" x=\"170.8\" y=\"459.2\" textLength=\"610\" clip-path=\"url(#terminal-4103379692-line-18)\">Scorpia&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-18)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-18)\">\n-</text><text class=\"terminal-4103379692-r4\" x=\"146.4\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-19)\">โ–Š</text><text class=\"terminal-4103379692-r5\" x=\"158.6\" y=\"483.6\" textLength=\"658.8\" clip-path=\"url(#terminal-4103379692-line-19)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4103379692-r5\" x=\"817.4\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-19)\">โ–Ž</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-19)\">\n-</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-20)\">\n-</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-21)\">\n-</text><text class=\"terminal-4103379692-r1\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4103379692-line-22)\">\n-</text><text class=\"terminal-4103379692-r12\" x=\"841.8\" y=\"581.2\" textLength=\"24.4\" clip-path=\"url(#terminal-4103379692-line-23)\">^p</text><text class=\"terminal-4103379692-r13\" x=\"866.2\" y=\"581.2\" textLength=\"97.6\" clip-path=\"url(#terminal-4103379692-line-23)\">&#160;palette</text>\n+ <g class=\"terminal-2741156546-matrix\">\n+ <text class=\"terminal-2741156546-r2\" x=\"12.2\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-0)\">โญ˜</text><text class=\"terminal-2741156546-r2\" x=\"390.4\" y=\"20\" textLength=\"158.6\" clip-path=\"url(#terminal-2741156546-line-0)\">OptionListApp</text><text class=\"terminal-2741156546-r1\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-0)\">\n+</text><text class=\"terminal-2741156546-r1\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-1)\">\n+</text><text class=\"terminal-2741156546-r1\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-2)\">\n+</text><text class=\"terminal-2741156546-r4\" x=\"146.4\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-3)\">โ–Š</text><text class=\"terminal-2741156546-r5\" x=\"158.6\" y=\"93.2\" textLength=\"658.8\" clip-path=\"url(#terminal-2741156546-line-3)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-2741156546-r5\" x=\"817.4\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-3)\">โ–Ž</text><text class=\"terminal-2741156546-r1\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-3)\">\n+</text><text class=\"terminal-2741156546-r4\" x=\"146.4\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-4)\">โ–Š</text><text class=\"terminal-2741156546-r6\" x=\"170.8\" y=\"117.6\" textLength=\"610\" clip-path=\"url(#terminal-2741156546-line-4)\">Aerilon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-2741156546-r5\" x=\"817.4\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-4)\">โ–Ž</text><text class=\"terminal-2741156546-r1\" x=\"976\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-4)\">\n+</text><text class=\"terminal-2741156546-r4\" x=\"146.4\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-2741156546-line-5)\">โ–Š</text><text class=\"terminal-2741156546-r7\" x=\"170.8\" y=\"142\" textLength=\"610\" 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clip-path=\"url(#terminal-225900743-line-7)\">Caprica&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-7)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-7)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-8)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"215.2\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-8)\">Gemenon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-8)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-8)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-9)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"239.6\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-9)\">Leonis&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-9)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-9)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-10)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"264\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-10)\">Libran&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-10)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-10)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-11)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"288.4\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-11)\">Picon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-11)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-11)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-12)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"312.8\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-12)\">Sagittaron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-12)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-12)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-13)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"337.2\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-13)\">Scorpia&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-13)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-13)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-14)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"361.6\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-14)\">Tauron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-14)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-14)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-15)\">โ–Š</text><text class=\"terminal-225900743-r7\" x=\"170.8\" y=\"386\" textLength=\"634.4\" clip-path=\"url(#terminal-225900743-line-15)\">Virgon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-15)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-15)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-16)\">โ–Š</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-16)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-16)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-17)\">โ–Š</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-17)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-17)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-18)\">โ–Š</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-18)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-18)\">\n-</text><text class=\"terminal-225900743-r4\" x=\"146.4\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-19)\">โ–Š</text><text class=\"terminal-225900743-r5\" x=\"158.6\" y=\"483.6\" textLength=\"658.8\" clip-path=\"url(#terminal-225900743-line-19)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-225900743-r5\" x=\"817.4\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-19)\">โ–Ž</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-19)\">\n-</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-20)\">\n-</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-21)\">\n-</text><text class=\"terminal-225900743-r1\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-225900743-line-22)\">\n-</text><text class=\"terminal-225900743-r9\" x=\"841.8\" y=\"581.2\" textLength=\"24.4\" clip-path=\"url(#terminal-225900743-line-23)\">^p</text><text class=\"terminal-225900743-r10\" x=\"866.2\" y=\"581.2\" textLength=\"97.6\" clip-path=\"url(#terminal-225900743-line-23)\">&#160;palette</text>\n+ <g class=\"terminal-4032960684-matrix\">\n+ <text class=\"terminal-4032960684-r2\" x=\"12.2\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-0)\">โญ˜</text><text class=\"terminal-4032960684-r2\" x=\"390.4\" y=\"20\" textLength=\"158.6\" clip-path=\"url(#terminal-4032960684-line-0)\">OptionListApp</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-0)\">\n+</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-1)\">\n+</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-2)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-3)\">โ–Š</text><text class=\"terminal-4032960684-r5\" x=\"158.6\" y=\"93.2\" textLength=\"658.8\" clip-path=\"url(#terminal-4032960684-line-3)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-3)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-3)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-4)\">โ–Š</text><text class=\"terminal-4032960684-r6\" x=\"170.8\" y=\"117.6\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-4)\">Aerilon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-4)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-4)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-5)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"142\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-5)\">Aquaria&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-5)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-5)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-6)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"166.4\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-6)\">Canceron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-6)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-6)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-7)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"190.8\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-7)\">Caprica&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-7)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-7)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-8)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"215.2\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-8)\">Gemenon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-8)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-8)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-9)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"239.6\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-9)\">Leonis&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-9)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-9)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-10)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"264\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-10)\">Libran&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-10)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-10)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-11)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"288.4\" textLength=\"634.4\" clip-path=\"url(#terminal-4032960684-line-11)\">Picon&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-4032960684-r5\" x=\"817.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-11)\">โ–Ž</text><text class=\"terminal-4032960684-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-11)\">\n+</text><text class=\"terminal-4032960684-r4\" x=\"146.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4032960684-line-12)\">โ–Š</text><text class=\"terminal-4032960684-r7\" x=\"170.8\" y=\"312.8\" textLength=\"634.4\" 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clip-path=\"url(#terminal-496130370-line-8)\">Welcome&#160;to&#160;line&#160;986&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-8)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-8)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-9)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"239.6\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-9)\">Welcome&#160;to&#160;line&#160;987&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-9)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-9)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-10)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"264\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-10)\">Welcome&#160;to&#160;line&#160;988&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-10)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-10)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-11)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"288.4\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-11)\">Welcome&#160;to&#160;line&#160;989&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-11)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-11)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-12)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"312.8\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-12)\">Welcome&#160;to&#160;line&#160;990&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-12)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-12)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-13)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"337.2\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-13)\">Welcome&#160;to&#160;line&#160;991&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-13)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-13)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-14)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"361.6\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-14)\">Welcome&#160;to&#160;line&#160;992&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-14)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-14)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-15)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"386\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-15)\">Welcome&#160;to&#160;line&#160;993&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-15)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-15)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-16)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"410.4\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-16)\">Welcome&#160;to&#160;line&#160;994&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-16)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-16)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-17)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"434.8\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-17)\">Welcome&#160;to&#160;line&#160;995&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-17)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-17)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-18)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"459.2\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-18)\">Welcome&#160;to&#160;line&#160;996&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-18)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-18)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-19)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"483.6\" textLength=\"927.2\" clip-path=\"url(#terminal-496130370-line-19)\">Welcome&#160;to&#160;line&#160;997&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-496130370-r3\" x=\"963.8\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-19)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-19)\">\n-</text><text class=\"terminal-496130370-r3\" x=\"0\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-496130370-line-20)\">โ”‚</text><text class=\"terminal-496130370-r1\" x=\"12.2\" y=\"508\" textLength=\"927.2\" 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clip-path=\"url(#terminal-244021528-line-0)\">ScrollViewTester</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-0)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"44.4\" textLength=\"976\" clip-path=\"url(#terminal-244021528-line-1)\">โ•ญโ”€&#160;1&#160;โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-1)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-2)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"68.8\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-2)\">Welcome&#160;to&#160;line&#160;980&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-2)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-2)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-3)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"93.2\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-3)\">Welcome&#160;to&#160;line&#160;981&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-3)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-3)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-4)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"117.6\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-4)\">Welcome&#160;to&#160;line&#160;982&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-4)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"117.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-4)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-5)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"142\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-5)\">Welcome&#160;to&#160;line&#160;983&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-5)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-5)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-6)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"166.4\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-6)\">Welcome&#160;to&#160;line&#160;984&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-6)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-6)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-7)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"190.8\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-7)\">Welcome&#160;to&#160;line&#160;985&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-7)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-7)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-8)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"215.2\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-8)\">Welcome&#160;to&#160;line&#160;986&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-8)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-8)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-9)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"239.6\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-9)\">Welcome&#160;to&#160;line&#160;987&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-9)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-9)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-10)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"264\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-10)\">Welcome&#160;to&#160;line&#160;988&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-10)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-10)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-11)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"288.4\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-11)\">Welcome&#160;to&#160;line&#160;989&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-11)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-11)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-12)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"312.8\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-12)\">Welcome&#160;to&#160;line&#160;990&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-12)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-12)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-13)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"337.2\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-13)\">Welcome&#160;to&#160;line&#160;991&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-13)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-13)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-14)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"361.6\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-14)\">Welcome&#160;to&#160;line&#160;992&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-14)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-14)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-15)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"386\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-15)\">Welcome&#160;to&#160;line&#160;993&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-15)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-15)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-16)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"410.4\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-16)\">Welcome&#160;to&#160;line&#160;994&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-16)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-16)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-17)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"434.8\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-17)\">Welcome&#160;to&#160;line&#160;995&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-17)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-17)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-18)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"459.2\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-18)\">Welcome&#160;to&#160;line&#160;996&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-18)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-18)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-19)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"483.6\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-19)\">Welcome&#160;to&#160;line&#160;997&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-19)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-19)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-20)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"508\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-20)\">Welcome&#160;to&#160;line&#160;998&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-20)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-20)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-21)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"12.2\" y=\"532.4\" textLength=\"927.2\" clip-path=\"url(#terminal-244021528-line-21)\">Welcome&#160;to&#160;line&#160;999&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-244021528-r3\" x=\"963.8\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-21)\">โ”‚</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-21)\">\n+</text><text class=\"terminal-244021528-r3\" x=\"0\" y=\"556.8\" textLength=\"976\" clip-path=\"url(#terminal-244021528-line-22)\">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class=\"terminal-244021528-r1\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-22)\">\n+</text><text class=\"terminal-244021528-r6\" x=\"829.6\" y=\"581.2\" textLength=\"12.2\" clip-path=\"url(#terminal-244021528-line-23)\">โ–</text><text class=\"terminal-244021528-r7\" x=\"841.8\" y=\"581.2\" textLength=\"24.4\" clip-path=\"url(#terminal-244021528-line-23)\">^p</text><text class=\"terminal-244021528-r8\" x=\"866.2\" y=\"581.2\" textLength=\"97.6\" clip-path=\"url(#terminal-244021528-line-23)\">&#160;palette</text>\n </g>\n </g>\n </svg>\ndiff --git a/tests/snapshot_tests/__snapshots__/test_snapshots/test_selection_list_selected.svg 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clip-path=\"url(#terminal-4058341162-line-6)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-6)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"36.6\" y=\"190.8\" textLength=\"134.2\" clip-path=\"url(#terminal-4058341162-line-7)\">&#160;out_quint&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-7)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-7)\">โ–Ž</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-7)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"190.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-7)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"215.2\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-8)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-8)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-8)\">โ–Ž</text><text class=\"terminal-4058341162-r15\" x=\"658.8\" y=\"215.2\" textLength=\"231.8\" clip-path=\"url(#terminal-4058341162-line-8)\">Welcome&#160;to&#160;Textual!</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-8)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"215.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-8)\">\n-</text><text class=\"terminal-4058341162-r1\" x=\"0\" y=\"239.6\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-9)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-9)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-9)\">โ–Ž</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-9)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"239.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-9)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"36.6\" y=\"264\" textLength=\"134.2\" clip-path=\"url(#terminal-4058341162-line-10)\">&#160;out_quart&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-10)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-10)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"264\" textLength=\"195.2\" clip-path=\"url(#terminal-4058341162-line-10)\">I&#160;must&#160;not&#160;fear.</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-10)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-10)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"288.4\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-11)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-11)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-11)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"288.4\" textLength=\"146.4\" clip-path=\"url(#terminal-4058341162-line-11)\">Fear&#160;is&#160;the&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-11)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-11)\">\n-</text><text class=\"terminal-4058341162-r1\" x=\"0\" y=\"312.8\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-12)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-12)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-12)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"312.8\" textLength=\"146.4\" clip-path=\"url(#terminal-4058341162-line-12)\">mind-killer.</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-12)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"312.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-12)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"48.8\" y=\"337.2\" textLength=\"122\" clip-path=\"url(#terminal-4058341162-line-13)\">&#160;out_quad&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-13)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-13)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"337.2\" textLength=\"146.4\" clip-path=\"url(#terminal-4058341162-line-13)\">Fear&#160;is&#160;the&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-13)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"337.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-13)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"361.6\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-14)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-14)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-14)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"361.6\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-14)\">little-death&#160;that&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-14)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"361.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-14)\">\n-</text><text class=\"terminal-4058341162-r1\" x=\"0\" y=\"386\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-15)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-15)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-15)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"386\" textLength=\"158.6\" clip-path=\"url(#terminal-4058341162-line-15)\">brings&#160;total&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-15)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"386\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-15)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"48.8\" y=\"410.4\" textLength=\"122\" clip-path=\"url(#terminal-4058341162-line-16)\">&#160;out_expo&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-16)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-16)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"410.4\" textLength=\"158.6\" clip-path=\"url(#terminal-4058341162-line-16)\">obliteration.</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-16)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"410.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-16)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"434.8\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-17)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-17)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-17)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"434.8\" textLength=\"244\" clip-path=\"url(#terminal-4058341162-line-17)\">I&#160;will&#160;face&#160;my&#160;fear.</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-17)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"434.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-17)\">\n-</text><text class=\"terminal-4058341162-r1\" x=\"0\" y=\"459.2\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-18)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-18)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-18)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"459.2\" textLength=\"244\" clip-path=\"url(#terminal-4058341162-line-18)\">I&#160;will&#160;permit&#160;it&#160;to&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-18)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"459.2\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-18)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"24.4\" y=\"483.6\" textLength=\"158.6\" clip-path=\"url(#terminal-4058341162-line-19)\">&#160;out_elastic&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-19)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-19)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"483.6\" textLength=\"207.4\" clip-path=\"url(#terminal-4058341162-line-19)\">pass&#160;over&#160;me&#160;and&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-19)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"483.6\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-19)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"508\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-20)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-20)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-20)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"508\" textLength=\"134.2\" clip-path=\"url(#terminal-4058341162-line-20)\">through&#160;me.</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-20)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"508\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-20)\">\n-</text><text class=\"terminal-4058341162-r1\" x=\"0\" y=\"532.4\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-21)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-21)\">โ–</text><text class=\"terminal-4058341162-r12\" x=\"634.4\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-21)\">โ–Ž</text><text class=\"terminal-4058341162-r16\" x=\"658.8\" y=\"532.4\" textLength=\"256.2\" clip-path=\"url(#terminal-4058341162-line-21)\">And&#160;when&#160;it&#160;has&#160;gone&#160;</text><text class=\"terminal-4058341162-r14\" x=\"951.6\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-21)\">โ–Š</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"532.4\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-21)\">\n-</text><text class=\"terminal-4058341162-r9\" x=\"36.6\" y=\"556.8\" textLength=\"134.2\" clip-path=\"url(#terminal-4058341162-line-22)\">&#160;out_cubic&#160;</text><text class=\"terminal-4058341162-r12\" x=\"610\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-22)\">โ–</text><text class=\"terminal-4058341162-r3\" x=\"976\" y=\"556.8\" textLength=\"12.2\" clip-path=\"url(#terminal-4058341162-line-22)\">\n-</text><text class=\"terminal-4058341162-r7\" x=\"0\" y=\"581.2\" textLength=\"219.6\" clip-path=\"url(#terminal-4058341162-line-23)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-4058341162-r17\" x=\"244\" y=\"581.2\" textLength=\"48.8\" clip-path=\"url(#terminal-4058341162-line-23)\">&#160;^b&#160;</text><text class=\"terminal-4058341162-r18\" x=\"292.8\" y=\"581.2\" textLength=\"146.4\" clip-path=\"url(#terminal-4058341162-line-23)\">Toggle&#160;Dark&#160;</text><text class=\"terminal-4058341162-r17\" x=\"841.8\" y=\"581.2\" textLength=\"24.4\" clip-path=\"url(#terminal-4058341162-line-23)\">^p</text><text class=\"terminal-4058341162-r18\" x=\"866.2\" y=\"581.2\" textLength=\"97.6\" clip-path=\"url(#terminal-4058341162-line-23)\">&#160;palette</text>\n+ <g class=\"terminal-1586593536-matrix\">\n+ <text class=\"terminal-1586593536-r1\" x=\"0\" y=\"20\" textLength=\"219.6\" clip-path=\"url(#terminal-1586593536-line-0)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-1586593536-r3\" x=\"976\" y=\"20\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-0)\">\n+</text><text class=\"terminal-1586593536-r4\" x=\"61\" y=\"44.4\" textLength=\"85.4\" clip-path=\"url(#terminal-1586593536-line-1)\">&#160;round&#160;</text><text class=\"terminal-1586593536-r5\" x=\"512.4\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-1)\">โ–Š</text><text class=\"terminal-1586593536-r5\" x=\"524.6\" y=\"44.4\" textLength=\"341.6\" clip-path=\"url(#terminal-1586593536-line-1)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-1586593536-r5\" x=\"866.2\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-1)\">โ–Ž</text><text class=\"terminal-1586593536-r3\" x=\"976\" y=\"44.4\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-1)\">\n+</text><text class=\"terminal-1586593536-r7\" x=\"0\" y=\"68.8\" textLength=\"219.6\" clip-path=\"url(#terminal-1586593536-line-2)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-1586593536-r6\" x=\"268.4\" y=\"68.8\" textLength=\"231.8\" clip-path=\"url(#terminal-1586593536-line-2)\">Animation&#160;Duration:</text><text class=\"terminal-1586593536-r5\" x=\"512.4\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-2)\">โ–Š</text><text class=\"terminal-1586593536-r8\" x=\"536.8\" y=\"68.8\" textLength=\"317.2\" clip-path=\"url(#terminal-1586593536-line-2)\">1.0&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class=\"terminal-1586593536-r5\" x=\"866.2\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-2)\">โ–Ž</text><text class=\"terminal-1586593536-r3\" x=\"976\" y=\"68.8\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-2)\">\n+</text><text class=\"terminal-1586593536-r1\" x=\"0\" y=\"93.2\" textLength=\"219.6\" clip-path=\"url(#terminal-1586593536-line-3)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-1586593536-r5\" x=\"512.4\" y=\"93.2\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-3)\">โ–Š</text><text class=\"terminal-1586593536-r5\" x=\"524.6\" y=\"93.2\" textLength=\"341.6\" 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clip-path=\"url(#terminal-1586593536-line-5)\">โ–โ–</text><text class=\"terminal-1586593536-r12\" x=\"610\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-5)\">โ–</text><text class=\"terminal-1586593536-r3\" x=\"976\" y=\"142\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-5)\">\n+</text><text class=\"terminal-1586593536-r1\" x=\"0\" y=\"166.4\" textLength=\"219.6\" clip-path=\"url(#terminal-1586593536-line-6)\">โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”โ–”</text><text class=\"terminal-1586593536-r13\" x=\"244\" y=\"166.4\" textLength=\"366\" clip-path=\"url(#terminal-1586593536-line-6)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-1586593536-r12\" x=\"610\" y=\"166.4\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-6)\">โ–</text><text class=\"terminal-1586593536-r12\" x=\"634.4\" y=\"166.4\" textLength=\"329.4\" 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class=\"terminal-1586593536-r12\" x=\"634.4\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-10)\">โ–Ž</text><text class=\"terminal-1586593536-r16\" x=\"658.8\" y=\"264\" textLength=\"195.2\" clip-path=\"url(#terminal-1586593536-line-10)\">I&#160;must&#160;not&#160;fear.</text><text class=\"terminal-1586593536-r14\" x=\"951.6\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-10)\">โ–Š</text><text class=\"terminal-1586593536-r3\" x=\"976\" y=\"264\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-10)\">\n+</text><text class=\"terminal-1586593536-r7\" x=\"0\" y=\"288.4\" textLength=\"219.6\" clip-path=\"url(#terminal-1586593536-line-11)\">โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–โ–</text><text class=\"terminal-1586593536-r12\" x=\"610\" y=\"288.4\" textLength=\"12.2\" clip-path=\"url(#terminal-1586593536-line-11)\">โ–</text><text class=\"terminal-1586593536-r12\" x=\"634.4\" y=\"288.4\" textLength=\"12.2\" 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b/tests/snapshot_tests/test_snapshots.py\nindex 472475b796..22940f7644 100644\n--- a/tests/snapshot_tests/test_snapshots.py\n+++ b/tests/snapshot_tests/test_snapshots.py\n@@ -4,6 +4,7 @@\n \n from tests.snapshot_tests.language_snippets import SNIPPETS\n from textual.pilot import Pilot\n+from textual.app import App\n from textual.widgets.text_area import Selection, BUILTIN_LANGUAGES\n from textual.widgets import RichLog, TextArea, Input, Button\n from textual.widgets.text_area import TextAreaTheme\n@@ -1195,7 +1196,7 @@ def test_example_color_command(snap_compare):\n \"\"\"Test the color_command example.\"\"\"\n assert snap_compare(\n EXAMPLES_DIR / \"color_command.py\",\n- press=[\"ctrl+p\", \"r\", \"e\", \"d\", \"down\", \"enter\"],\n+ press=[App.COMMAND_PALETTE_BINDING, \"r\", \"e\", \"d\", \"down\", \"enter\"],\n )\n \n \n@@ -1410,3 +1411,32 @@ def test_auto_height_scrollbar(snap_compare):\n def test_bind_override(snap_compare):\n \"\"\"Regression test for https://github.com/Textualize/textual/issues/4755\"\"\"\n assert snap_compare(SNAPSHOT_APPS_DIR / \"bind_override.py\")\n+\n+\n+def test_command_palette_dismiss_escape(snap_compare):\n+ \"\"\"Regression test for https://github.com/Textualize/textual/issues/4856\"\"\"\n+\n+ async def run_before(pilot: Pilot):\n+ await pilot.press(App.COMMAND_PALETTE_BINDING)\n+ await pilot.pause()\n+ await pilot.press(\"escape\")\n+\n+ assert snap_compare(\n+ SNAPSHOT_APPS_DIR / \"command_palette_dismiss.py\", run_before=run_before\n+ )\n+\n+\n+def test_command_palette_dismiss_escape_no_results(snap_compare):\n+ \"\"\"Regression test for https://github.com/Textualize/textual/issues/4856\"\"\"\n+\n+ async def run_before(pilot: Pilot):\n+ await pilot.press(App.COMMAND_PALETTE_BINDING)\n+ await pilot.pause()\n+ await pilot.press(*\"foo\")\n+ await pilot.pause()\n+ await pilot.pause()\n+ await pilot.press(\"escape\")\n+\n+ assert snap_compare(\n+ SNAPSHOT_APPS_DIR / \"command_palette_dismiss.py\", run_before=run_before\n+ )\n" }
[ { "diff_hunk": "@@ -735,6 +737,8 @@ def _show_no_matches() -> None:\n # discovered is a situation we don't need to highlight.\n self._list_visible = False\n \n+ self._hit_count = 0", "line": null, "original_line": 740, "original_start_line": null, "path": "src/textual/command.py", "start_line": null, "text": "@user1:\nMinor but `_start_no_matches_countdown` seems like a strange place for this (seems out of scope). I'd put it inside `gather_commands` where we check `if option_list.options_count == 0`. Take it or leave it ๐Ÿ˜„" } ]
46bd07d6ea43c6151ee864e45fb1a738ce501a78
diff --git a/src/textual/command.py b/src/textual/command.py index ad519deeb8..44a03ca72c 100644 --- a/src/textual/command.py +++ b/src/textual/command.py @@ -575,6 +575,8 @@ def __init__(self) -> None: """Keeps track of if there are 'No matches found' message waiting to be displayed.""" self._providers: list[Provider] = [] """List of Provider instances involved in searches.""" + self._hit_count: int = 0 + """Number of hits displayed.""" @staticmethod def is_open(app: App) -> bool: @@ -883,6 +885,7 @@ def _refresh_command_list( if highlighted is not None and highlighted.id: command_list.highlighted = command_list.get_option_index(highlighted.id) self._list_visible = bool(command_list.option_count) + self._hit_count = command_list.option_count _RESULT_BATCH_TIME: Final[float] = 0.25 """How long to wait before adding commands to the command list.""" @@ -1014,6 +1017,7 @@ async def _gather_commands(self, search_value: str) -> None: # mean nothing was found. Give the user positive feedback to that # effect. if command_list.option_count == 0 and not worker.is_cancelled: + self._hit_count = 0 self._start_no_matches_countdown(search_value) def _cancel_gather_commands(self) -> None: @@ -1049,6 +1053,7 @@ def _select_command(self, event: OptionList.OptionSelected) -> None: input.action_end() self._list_visible = False self.query_one(CommandList).clear_options() + self._hit_count = 0 if self.run_on_select: self._select_or_command() @@ -1090,8 +1095,11 @@ def _stop_event_leak(self, event: OptionList.OptionHighlighted) -> None: def _action_escape(self) -> None: """Handle a request to escape out of the command palette.""" - if self._list_visible: + input = self.query_one(CommandInput) + # Hide the options if there are result and there is input + if self._list_visible and (self._hit_count and input.value): self._list_visible = False + # Otherwise dismiss modal else: self._cancel_gather_commands() 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x="170.8" y="239.6" textLength="610" clip-path="url(#terminal-4103379692-line-9)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-4103379692-r5" x="817.4" y="239.6" textLength="12.2" clip-path="url(#terminal-4103379692-line-9)">โ–Ž</text><text class="terminal-4103379692-r1" x="976" y="239.6" textLength="12.2" clip-path="url(#terminal-4103379692-line-9)"> -</text><text class="terminal-4103379692-r4" x="146.4" y="264" textLength="12.2" clip-path="url(#terminal-4103379692-line-10)">โ–Š</text><text class="terminal-4103379692-r7" x="170.8" y="264" textLength="610" 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clip-path="url(#terminal-4103379692-line-16)">โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€</text><text class="terminal-4103379692-r5" x="817.4" y="410.4" textLength="12.2" clip-path="url(#terminal-4103379692-line-16)">โ–Ž</text><text class="terminal-4103379692-r1" x="976" y="410.4" textLength="12.2" clip-path="url(#terminal-4103379692-line-16)"> -</text><text class="terminal-4103379692-r4" x="146.4" y="434.8" textLength="12.2" clip-path="url(#terminal-4103379692-line-17)">โ–Š</text><text class="terminal-4103379692-r7" x="170.8" y="434.8" textLength="610" clip-path="url(#terminal-4103379692-line-17)">Sagittaron&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text 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clip-path="url(#terminal-496130370-line-22)">โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ</text><text class="terminal-496130370-r1" x="976" y="556.8" textLength="12.2" clip-path="url(#terminal-496130370-line-22)"> -</text><text class="terminal-496130370-r6" x="841.8" y="581.2" textLength="24.4" clip-path="url(#terminal-496130370-line-23)">^p</text><text class="terminal-496130370-r7" x="866.2" y="581.2" textLength="97.6" clip-path="url(#terminal-496130370-line-23)">&#160;palette</text> + <g class="terminal-244021528-matrix"> + <text class="terminal-244021528-r2" x="12.2" y="20" textLength="12.2" clip-path="url(#terminal-244021528-line-0)">โญ˜</text><text class="terminal-244021528-r2" x="378.2" y="20" textLength="195.2" clip-path="url(#terminal-244021528-line-0)">ScrollViewTester</text><text class="terminal-244021528-r1" x="976" y="20" textLength="12.2" clip-path="url(#terminal-244021528-line-0)"> +</text><text class="terminal-244021528-r3" x="0" y="44.4" textLength="976" clip-path="url(#terminal-244021528-line-1)">โ•ญโ”€&#160;1&#160;โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ</text><text class="terminal-244021528-r1" x="976" y="44.4" textLength="12.2" clip-path="url(#terminal-244021528-line-1)"> +</text><text class="terminal-244021528-r3" x="0" y="68.8" textLength="12.2" clip-path="url(#terminal-244021528-line-2)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="68.8" textLength="927.2" clip-path="url(#terminal-244021528-line-2)">Welcome&#160;to&#160;line&#160;980&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="68.8" textLength="12.2" clip-path="url(#terminal-244021528-line-2)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="68.8" textLength="12.2" clip-path="url(#terminal-244021528-line-2)"> +</text><text class="terminal-244021528-r3" x="0" y="93.2" textLength="12.2" clip-path="url(#terminal-244021528-line-3)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="93.2" textLength="927.2" clip-path="url(#terminal-244021528-line-3)">Welcome&#160;to&#160;line&#160;981&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="93.2" textLength="12.2" clip-path="url(#terminal-244021528-line-3)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="93.2" textLength="12.2" clip-path="url(#terminal-244021528-line-3)"> +</text><text class="terminal-244021528-r3" x="0" y="117.6" textLength="12.2" clip-path="url(#terminal-244021528-line-4)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="117.6" textLength="927.2" clip-path="url(#terminal-244021528-line-4)">Welcome&#160;to&#160;line&#160;982&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="117.6" textLength="12.2" clip-path="url(#terminal-244021528-line-4)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="117.6" textLength="12.2" clip-path="url(#terminal-244021528-line-4)"> +</text><text class="terminal-244021528-r3" x="0" y="142" textLength="12.2" clip-path="url(#terminal-244021528-line-5)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="142" textLength="927.2" clip-path="url(#terminal-244021528-line-5)">Welcome&#160;to&#160;line&#160;983&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="142" textLength="12.2" clip-path="url(#terminal-244021528-line-5)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="142" textLength="12.2" clip-path="url(#terminal-244021528-line-5)"> +</text><text class="terminal-244021528-r3" x="0" y="166.4" textLength="12.2" clip-path="url(#terminal-244021528-line-6)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="166.4" textLength="927.2" clip-path="url(#terminal-244021528-line-6)">Welcome&#160;to&#160;line&#160;984&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="166.4" textLength="12.2" clip-path="url(#terminal-244021528-line-6)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="166.4" textLength="12.2" clip-path="url(#terminal-244021528-line-6)"> +</text><text class="terminal-244021528-r3" x="0" y="190.8" textLength="12.2" clip-path="url(#terminal-244021528-line-7)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="190.8" textLength="927.2" clip-path="url(#terminal-244021528-line-7)">Welcome&#160;to&#160;line&#160;985&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="190.8" textLength="12.2" clip-path="url(#terminal-244021528-line-7)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="190.8" textLength="12.2" clip-path="url(#terminal-244021528-line-7)"> +</text><text class="terminal-244021528-r3" x="0" y="215.2" textLength="12.2" clip-path="url(#terminal-244021528-line-8)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="215.2" textLength="927.2" clip-path="url(#terminal-244021528-line-8)">Welcome&#160;to&#160;line&#160;986&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="215.2" textLength="12.2" clip-path="url(#terminal-244021528-line-8)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="215.2" textLength="12.2" clip-path="url(#terminal-244021528-line-8)"> +</text><text class="terminal-244021528-r3" x="0" y="239.6" textLength="12.2" clip-path="url(#terminal-244021528-line-9)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="239.6" textLength="927.2" 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clip-path="url(#terminal-244021528-line-10)">Welcome&#160;to&#160;line&#160;988&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="264" textLength="12.2" clip-path="url(#terminal-244021528-line-10)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="264" textLength="12.2" clip-path="url(#terminal-244021528-line-10)"> +</text><text class="terminal-244021528-r3" x="0" y="288.4" textLength="12.2" clip-path="url(#terminal-244021528-line-11)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="288.4" textLength="927.2" clip-path="url(#terminal-244021528-line-11)">Welcome&#160;to&#160;line&#160;989&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="288.4" textLength="12.2" clip-path="url(#terminal-244021528-line-11)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="288.4" textLength="12.2" clip-path="url(#terminal-244021528-line-11)"> +</text><text class="terminal-244021528-r3" x="0" y="312.8" textLength="12.2" clip-path="url(#terminal-244021528-line-12)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="312.8" textLength="927.2" 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clip-path="url(#terminal-244021528-line-15)">Welcome&#160;to&#160;line&#160;993&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;</text><text class="terminal-244021528-r3" x="963.8" y="386" textLength="12.2" clip-path="url(#terminal-244021528-line-15)">โ”‚</text><text class="terminal-244021528-r1" x="976" y="386" textLength="12.2" clip-path="url(#terminal-244021528-line-15)"> +</text><text class="terminal-244021528-r3" x="0" y="410.4" textLength="12.2" clip-path="url(#terminal-244021528-line-16)">โ”‚</text><text class="terminal-244021528-r1" x="12.2" y="410.4" textLength="927.2" 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a/tests/snapshot_tests/snapshot_apps/command_palette_dismiss.py b/tests/snapshot_tests/snapshot_apps/command_palette_dismiss.py new file mode 100644 index 0000000000..5325ea5e0a --- /dev/null +++ b/tests/snapshot_tests/snapshot_apps/command_palette_dismiss.py @@ -0,0 +1,12 @@ +from textual.app import App, ComposeResult +from textual.widgets import Label + + +class CPApp(App): + def compose(self) -> ComposeResult: + yield Label("Command palette test app") + + +if __name__ == "__main__": + app = CPApp() + app.run() diff --git a/tests/snapshot_tests/test_snapshots.py b/tests/snapshot_tests/test_snapshots.py index 472475b796..22940f7644 100644 --- a/tests/snapshot_tests/test_snapshots.py +++ b/tests/snapshot_tests/test_snapshots.py @@ -4,6 +4,7 @@ from tests.snapshot_tests.language_snippets import SNIPPETS from textual.pilot import Pilot +from textual.app import App from textual.widgets.text_area import Selection, BUILTIN_LANGUAGES from textual.widgets import RichLog, TextArea, Input, Button from textual.widgets.text_area import TextAreaTheme @@ -1195,7 +1196,7 @@ def test_example_color_command(snap_compare): """Test the color_command example.""" assert snap_compare( EXAMPLES_DIR / "color_command.py", - press=["ctrl+p", "r", "e", "d", "down", "enter"], + press=[App.COMMAND_PALETTE_BINDING, "r", "e", "d", "down", "enter"], ) @@ -1410,3 +1411,32 @@ def test_auto_height_scrollbar(snap_compare): def test_bind_override(snap_compare): """Regression test for https://github.com/Textualize/textual/issues/4755""" assert snap_compare(SNAPSHOT_APPS_DIR / "bind_override.py") + + +def test_command_palette_dismiss_escape(snap_compare): + """Regression test for https://github.com/Textualize/textual/issues/4856""" + + async def run_before(pilot: Pilot): + await pilot.press(App.COMMAND_PALETTE_BINDING) + await pilot.pause() + await pilot.press("escape") + + assert snap_compare( + SNAPSHOT_APPS_DIR / "command_palette_dismiss.py", run_before=run_before + ) + + +def test_command_palette_dismiss_escape_no_results(snap_compare): + """Regression test for https://github.com/Textualize/textual/issues/4856""" + + async def run_before(pilot: Pilot): + await pilot.press(App.COMMAND_PALETTE_BINDING) + await pilot.pause() + await pilot.press(*"foo") + await pilot.pause() + await pilot.pause() + await pilot.press("escape") + + assert snap_compare( + SNAPSHOT_APPS_DIR / "command_palette_dismiss.py", run_before=run_before + )
{ "difficulty": "low", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
vllm-project__vllm-5402@6c3e723
vllm-project/vllm
Python
5,402
[Bugfix] Add device assertion to TorchSDPA
Fix #5351 Added a device assertion to avoid using TorchSDPA with NVIDIA GPUs. --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
2024-06-11T01:49:53Z
[Bug]: TorchSDPAMetadata is out of date ### Your current environment alexr@roxy:~$ python collect_env.py Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.26.3 Libc version: glibc-2.35 Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-105-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.5.119 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070 Nvidia driver version: 535.161.08 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: GenuineIntel Model name: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz CPU family: 6 Model: 165 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 5 CPU max MHz: 5100.0000 CPU min MHz: 800.0000 BogoMIPS: 7599.80 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities L1d cache: 256 KiB (8 instances) L1i cache: 256 KiB (8 instances) L2 cache: 2 MiB (8 instances) L3 cache: 16 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Mitigation; Microcode Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flake8==6.0.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] nvidia-nccl-cu11==2.14.3 [pip3] sentence-transformers==2.2.2 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [pip3] transformers==4.26.0 [pip3] triton==2.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-15 0 N/A Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks ### ๐Ÿ› Describe the bug I believe the TorchSDPA backend for attention is no longer compatible. I received a crash message that said as much and did some debugging. If you set `export VLLM_ATTENTION_BACKEND=TORCH_SDPA`, then the metadata is set with ``` attn_metadata = self.attn_backend.make_metadata( num_prefills=0, num_prefill_tokens=0, num_decode_tokens=batch_size, slot_mapping=slot_mapping[:batch_size], seq_lens=None, seq_lens_tensor=seq_lens[:batch_size], max_query_len=None, max_prefill_seq_len=0, max_decode_seq_len=self.max_seq_len_to_capture, query_start_loc=None, seq_start_loc=None, context_lens_tensor=None, block_tables=block_tables[:batch_size], use_cuda_graph=True, ) ``` which is sent to ``` @staticmethod def make_metadata(*args, **kwargs) -> "TorchSDPAMetadata": return TorchSDPAMetadata(*args, **kwargs) ``` and neither TorchSDPAMetadata (nor its superclasses) have most of those values ``` @dataclass class TorchSDPAMetadata(AttentionMetadata, PagedAttentionMetadata): """Metadata for TorchSDPABackend. """ # Currently, input sequences can only contain all prompts # or all decoding. True if all sequences are prompts. is_prompt: bool slot_mapping: torch.Tensor seq_lens: Optional[List[int]] # Maximum query length in the batch. None for decoding. max_query_len: Optional[int] ``` Am I correct in my diagnosis? I can send a crash log if necessary, but I believe this is pretty self evidently an error (and a CI test should be added or the backend should be removed probably.)
cc @bigPYJ1151 @jikunshang @zhouyuan I'm surprised this is the case since this backend should be already be tested on CPU machine @Reichenbachian If I understand correctly, you are using the TORCH_SDPA backend with NVIDIA GPUs. The backend is for Intel (x86) CPUs and should not be used with NVIDIA GPUs. (cc @simon-mo)
[ { "body": "### Your current environment\n\nalexr@roxy:~$ python collect_env.py\r\nCollecting environment information...\r\nPyTorch version: 2.0.0+cu117\r\nIs debug build: False\r\nCUDA used to build PyTorch: 11.7\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 22.04.1 LTS (x86_64)\r\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.26.3\r\nLibc version: glibc-2.35\r\n\r\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-105-generic-x86_64-with-glibc2.35\r\nIs CUDA available: True\r\nCUDA runtime version: 11.5.119\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070\r\nNvidia driver version: 535.161.08\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture: x86_64\r\nCPU op-mode(s): 32-bit, 64-bit\r\nAddress sizes: 39 bits physical, 48 bits virtual\r\nByte Order: Little Endian\r\nCPU(s): 16\r\nOn-line CPU(s) list: 0-15\r\nVendor ID: GenuineIntel\r\nModel name: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz\r\nCPU family: 6\r\nModel: 165\r\nThread(s) per core: 2\r\nCore(s) per socket: 8\r\nSocket(s): 1\r\nStepping: 5\r\nCPU max MHz: 5100.0000\r\nCPU min MHz: 800.0000\r\nBogoMIPS: 7599.80\r\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\r\nL1d cache: 256 KiB (8 instances)\r\nL1i cache: 256 KiB (8 instances)\r\nL2 cache: 2 MiB (8 instances)\r\nL3 cache: 16 MiB (1 instance)\r\nNUMA node(s): 1\r\nNUMA node0 CPU(s): 0-15\r\nVulnerability Gather data sampling: Mitigation; Microcode\r\nVulnerability Itlb multihit: KVM: Mitigation: VMX unsupported\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\r\nVulnerability Retbleed: Mitigation; Enhanced IBRS\r\nVulnerability Spec rstack overflow: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\r\nVulnerability Srbds: Mitigation; Microcode\r\nVulnerability Tsx async abort: Not affected\r\n\r\nVersions of relevant libraries:\r\n[pip3] flake8==6.0.0\r\n[pip3] mypy-extensions==1.0.0\r\n[pip3] numpy==1.24.2\r\n[pip3] nvidia-nccl-cu11==2.14.3\r\n[pip3] sentence-transformers==2.2.2\r\n[pip3] torch==2.0.0\r\n[pip3] torchvision==0.15.1\r\n[pip3] transformers==4.26.0\r\n[pip3] triton==2.0.0\r\n[conda] Could not collect\r\nROCM Version: Could not collect\r\nNeuron SDK Version: N/A\r\nvLLM Version: N/A\r\nvLLM Build Flags:\r\nCUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled\r\nGPU Topology:\r\nGPU0\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\r\nGPU0\t X \t0-15\t0\t\tN/A\r\n\r\nLegend:\r\n\r\n X = Self\r\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\r\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\r\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\r\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\r\n PIX = Connection traversing at most a single PCIe bridge\r\n NV# = Connection traversing a bonded set of # NVLinks\r\n\n\n### ๐Ÿ› Describe the bug\n\nI believe the TorchSDPA backend for attention is no longer compatible. I received a crash message that said as much and did some debugging. If you set `export VLLM_ATTENTION_BACKEND=TORCH_SDPA`, then the metadata is set with\r\n```\r\n attn_metadata = self.attn_backend.make_metadata(\r\n num_prefills=0,\r\n num_prefill_tokens=0,\r\n num_decode_tokens=batch_size,\r\n slot_mapping=slot_mapping[:batch_size],\r\n seq_lens=None,\r\n seq_lens_tensor=seq_lens[:batch_size],\r\n max_query_len=None,\r\n max_prefill_seq_len=0,\r\n max_decode_seq_len=self.max_seq_len_to_capture,\r\n query_start_loc=None,\r\n seq_start_loc=None,\r\n context_lens_tensor=None,\r\n block_tables=block_tables[:batch_size],\r\n use_cuda_graph=True,\r\n )\r\n```\r\n\r\nwhich is sent to \r\n\r\n```\r\n @staticmethod\r\n def make_metadata(*args, **kwargs) -> \"TorchSDPAMetadata\":\r\n return TorchSDPAMetadata(*args, **kwargs)\r\n```\r\n\r\nand neither TorchSDPAMetadata (nor its superclasses) have most of those values\r\n```\r\n@dataclass\r\nclass TorchSDPAMetadata(AttentionMetadata, PagedAttentionMetadata):\r\n \"\"\"Metadata for TorchSDPABackend.\r\n \"\"\"\r\n # Currently, input sequences can only contain all prompts\r\n # or all decoding. True if all sequences are prompts.\r\n is_prompt: bool\r\n slot_mapping: torch.Tensor\r\n seq_lens: Optional[List[int]]\r\n # Maximum query length in the batch. None for decoding.\r\n max_query_len: Optional[int]\r\n```\r\n\r\nAm I correct in my diagnosis? I can send a crash log if necessary, but I believe this is pretty self evidently an error (and a CI test should be added or the backend should be removed probably.)", "number": 5351, "title": "[Bug]: TorchSDPAMetadata is out of date" } ]
246598a6b1e22616630b7f1bf11bd9bcb31dc860
{ "head_commit": "6c3e7239bfca6363fc812a7a8049028f06a1bf71", "head_commit_message": "Add is_cpu assertion to TorchSDPA", "patch_to_review": "diff --git a/vllm/attention/backends/torch_sdpa.py b/vllm/attention/backends/torch_sdpa.py\nindex 9b50adec5244..b3cd383abe7c 100644\n--- a/vllm/attention/backends/torch_sdpa.py\n+++ b/vllm/attention/backends/torch_sdpa.py\n@@ -10,6 +10,7 @@\n AttentionMetadata)\n from vllm.attention.ops.paged_attn import (PagedAttention,\n PagedAttentionMetadata)\n+from vllm.utils import is_cpu\n \n \n class TorchSDPABackend(AttentionBackend):\n@@ -104,6 +105,9 @@ def __init__(\n ) -> None:\n assert blocksparse_params is None, ValueError(\n \"Torch SPDA does not support block-sparse attention.\")\n+ assert is_cpu(), RuntimeError(\n+ \"Torch SDPA is only used for the CPU backend.\")\n+\n self.num_heads = num_heads\n self.head_size = head_size\n self.scale = float(scale)\n" }
[ { "diff_hunk": "@@ -104,6 +105,9 @@ def __init__(\n ) -> None:\n assert blocksparse_params is None, ValueError(\n \"Torch SPDA does not support block-sparse attention.\")\n+ assert is_cpu(), RuntimeError(", "line": null, "original_line": 108, "original_start_line": null, "path": "vllm/attention/backends/torch_sdpa.py", "start_line": null, "text": "@user1:\nCould this instead be moved to `selector.py`?" } ]
9e58c42a02e4e2ce933a7da623fb342634c8620c
diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index 7253483f9a0b..7ffaf030ae99 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -57,6 +57,9 @@ def get_attn_backend( ROCmFlashAttentionBackend) return ROCmFlashAttentionBackend elif backend == _Backend.TORCH_SDPA: + # TODO: make XPU backend available here. + assert is_cpu(), RuntimeError( + "Torch SDPA backend is only used for the CPU device.") logger.info("Using Torch SDPA backend.") from vllm.attention.backends.torch_sdpa import TorchSDPABackend return TorchSDPABackend
{ "difficulty": "low", "estimated_review_effort": 2, "problem_domain": "Bug Fixes" }
Textualize__textual-4828@05dad38
Textualize/textual
Python
4,828
mutate via data bind
Fixes https://github.com/Textualize/textual/issues/4825 - Ensures that watchers are called on data bound children, when calling `mutate_reactive`. - Also enforces a new instance when data binding. Previously parent and child would end up sharing an instance, which would sure be a common source of errors.
2024-08-01T09:32:18Z
mutate_reactive doesn't work with data binding and recompose=False Note that in the following example recompose is set to False on both reactive members. Adding an item to the list in on_mount is working as expected, and `watch_msg` is called. But changing an element in `on_button_pressed`, doesn't call `watch_msg`. Apparently only changing the size of the list, is considered a change. ```python from textual.app import App, ComposeResult from textual.widgets import Static, Button from textual.reactive import reactive from textual.events import Mount class TestWidget(Static): msg: reactive[list[str]] = reactive(list, recompose=False) count = reactive(0) def compute_count(self): return len(self.msg) def watch_msg(self, old, new): if new and len(new) > 0: self.mount(Static(self.msg[0]["text"])) class TestApp(App): msg: reactive[list[str]] = reactive(list, recompose=False) def on_mount(self, event: Mount) -> None: self.msg.append( { "text": "Hello" } ) self.mutate_reactive(TestApp.msg) def compose(self) -> ComposeResult: yield TestWidget().data_bind(TestApp.msg) yield Button("Click me", id="button") def on_button_pressed(self, event: Button.Pressed) -> None: self.msg[0] = {"text": "World!"} self.mutate_reactive(TestApp.msg) if __name__ == "__main__": app = TestApp() app.run()```
[ { "body": "Note that in the following example recompose is set to False on both reactive members.\r\nAdding an item to the list in on_mount is working as expected, and `watch_msg` is called. But changing an element in `on_button_pressed`, doesn't call `watch_msg`. Apparently only changing the size of the list, is considered a change.\r\n\r\n```python\r\nfrom textual.app import App, ComposeResult\r\nfrom textual.widgets import Static, Button\r\nfrom textual.reactive import reactive\r\nfrom textual.events import Mount\r\n\r\nclass TestWidget(Static):\r\n msg: reactive[list[str]] = reactive(list, recompose=False)\r\n count = reactive(0)\r\n\r\n def compute_count(self):\r\n return len(self.msg)\r\n\r\n def watch_msg(self, old, new):\r\n if new and len(new) > 0:\r\n self.mount(Static(self.msg[0][\"text\"]))\r\n\r\nclass TestApp(App):\r\n msg: reactive[list[str]] = reactive(list, recompose=False)\r\n\r\n def on_mount(self, event: Mount) -> None:\r\n self.msg.append( { \"text\": \"Hello\" } )\r\n self.mutate_reactive(TestApp.msg)\r\n\r\n def compose(self) -> ComposeResult:\r\n yield TestWidget().data_bind(TestApp.msg)\r\n yield Button(\"Click me\", id=\"button\")\r\n\r\n def on_button_pressed(self, event: Button.Pressed) -> None:\r\n self.msg[0] = {\"text\": \"World!\"}\r\n self.mutate_reactive(TestApp.msg)\r\n\r\nif __name__ == \"__main__\":\r\n app = TestApp()\r\n app.run()```", "number": 4825, "title": "mutate_reactive doesn't work with data binding and recompose=False" } ]
13ec4c3298e3993b4a87159adece0116dfb603cb
{ "head_commit": "05dad38fcd8941fb8e7006e3a802394763a47c3f", "head_commit_message": "better test", "patch_to_review": "diff --git a/src/textual/dom.py b/src/textual/dom.py\nindex 55ce52afea..d3b4b83e9e 100644\n--- a/src/textual/dom.py\n+++ b/src/textual/dom.py\n@@ -8,6 +8,7 @@\n \n import re\n import threading\n+from copy import copy\n from functools import lru_cache, partial\n from inspect import getfile\n from typing import (\n@@ -41,7 +42,7 @@\n from .css.styles import RenderStyles, Styles\n from .css.tokenize import IDENTIFIER\n from .message_pump import MessagePump\n-from .reactive import Reactive, ReactiveError, _watch\n+from .reactive import Reactive, ReactiveError, _Mutated, _watch\n from .timer import Timer\n from .walk import walk_breadth_first, walk_depth_first\n from .worker_manager import WorkerManager\n@@ -334,7 +335,10 @@ def setter(value: object) -> None:\n \"\"\"Set bound data.\"\"\"\n _rich_traceback_omit = True\n Reactive._initialize_object(self)\n- setattr(self, variable_name, value)\n+ # Copy the bound value\n+ # Sharing the same instance with mutable objects can lead to confusing behavior\n+ # Wrap the value in `_Mutated` so the setter knows to invoke watchers etc\n+ setattr(self, variable_name, _Mutated(copy(value)))\n \n return setter\n \ndiff --git a/src/textual/reactive.py b/src/textual/reactive.py\nindex 17b109d77e..64b6e007ce 100644\n--- a/src/textual/reactive.py\n+++ b/src/textual/reactive.py\n@@ -42,6 +42,13 @@\n ReactableType = TypeVar(\"ReactableType\", bound=\"DOMNode\")\n \n \n+class _Mutated:\n+ \"\"\"A wrapper to indicate a value was mutated.\"\"\"\n+\n+ def __init__(self, value: Any) -> None:\n+ self.value = value\n+\n+\n class ReactiveError(Exception):\n \"\"\"Base class for reactive errors.\"\"\"\n \n@@ -273,6 +280,10 @@ def _set(self, obj: Reactable, value: ReactiveType, always: bool = False) -> Non\n f\"Node is missing data; Check you are calling super().__init__(...) in the {obj.__class__.__name__}() constructor, before setting reactives.\"\n )\n \n+ if isinstance(value, _Mutated):\n+ value = value.value\n+ always = True\n+\n self._initialize_reactive(obj, self.name)\n \n if hasattr(obj, self.compute_name):\ndiff --git a/tests/test_reactive.py b/tests/test_reactive.py\nindex ad2da5724d..49ab1fd0f6 100644\n--- a/tests/test_reactive.py\n+++ b/tests/test_reactive.py\n@@ -783,3 +783,46 @@ def compose(self) -> ComposeResult:\n widget.mutate_reactive(TestWidget.names)\n # Watcher should be invoked\n assert watched_names == [[], [\"Paul\"], [\"Jessica\"]]\n+\n+\n+async def test_mutate_reactive_data_bind() -> None:\n+ \"\"\"https://github.com/Textualize/textual/issues/4825\"\"\"\n+\n+ # Record mutations to TestWidget.messages\n+ widget_messages: list[list[str]] = []\n+\n+ class TestWidget(Widget):\n+ messages: reactive[list[str]] = reactive(list, init=False)\n+\n+ def watch_messages(self, names: list[str]) -> None:\n+ widget_messages.append(names.copy())\n+\n+ class TestApp(App):\n+ messages: reactive[list[str]] = reactive(list, init=False)\n+\n+ def compose(self) -> ComposeResult:\n+ yield TestWidget().data_bind(TestApp.messages)\n+\n+ app = TestApp()\n+ async with app.run_test():\n+ test_widget = app.query_one(TestWidget)\n+ assert widget_messages == [[]]\n+ assert test_widget.messages == []\n+\n+ # Previously setting a mutable object would lead to shared references\n+ assert app.messages is not test_widget.messages\n+\n+ # Mutate app\n+ app.messages.append(\"foo\")\n+ # Mutations aren't detected\n+ assert widget_messages == [[]]\n+ assert app.messages == [\"foo\"]\n+ assert test_widget.messages == []\n+ # Explicitly mutate app reactive\n+ app.mutate_reactive(TestApp.messages)\n+ # Mutating app, will also invoke watchers on any data binds\n+ assert widget_messages == [[], [\"foo\"]]\n+ assert app.messages == [\"foo\"]\n+ assert test_widget.messages == [\"foo\"]\n+\n+ assert app.messages is not test_widget.messages\n" }
[ { "diff_hunk": "@@ -334,7 +335,10 @@ def setter(value: object) -> None:\n \"\"\"Set bound data.\"\"\"\n _rich_traceback_omit = True\n Reactive._initialize_object(self)\n- setattr(self, variable_name, value)\n+ # Copy the bound value", "line": null, "original_line": 338, "original_start_line": null, "path": "src/textual/dom.py", "start_line": null, "text": "@user1:\nI feel like this should be documented since copying can be expensive or potentially crash the program.\r\n\r\nI'm guessing shallow copying means it also wouldn't fix the confusion for e.g. a list of mutable objects. For example, if one of the objects in the list is mutated and I call `mutate_reactive`, the prior confusing scenario still arises.\n\n@author:\nI'm reconsidering the copy.\r\n\r\nIt's not clear what the dev would expect when modifying a child attribute that has been bound. I'm not sure if they would expect the changes to be reflected on the parent. But I don't know if they would expect the inverse either.\r\n\r\nGoing to remove the copy, until I have a better idea what it would fix (if anything)\n\n@user1:\nI agree. I actually think dealing with the mutation via offering APIs like this is likely to be filled with more footguns, \"things we need to document\", and generally worse than just saying \"only use immutable values for reactives, and watchers only fire when you assign\"." } ]
3cdc6537a410ca200ab89ae457f7f5273ce6e8a9
diff --git a/docs/guide/reactivity.md b/docs/guide/reactivity.md index ff7a1cebdf..eb058a130b 100644 --- a/docs/guide/reactivity.md +++ b/docs/guide/reactivity.md @@ -410,7 +410,8 @@ Note the call to `mutate_reactive`. Without it, the display would not update whe ## Data binding -Reactive attributes from one widget may be *bound* (connected) to another widget, so that changes to a single reactive will automatically update another widget (potentially more than one). +Reactive attributes may be *bound* (connected) to attributes on child widgets, so that changes to the parent are automatically reflected in the children. +This can simplify working with compound widgets where the value of an attribute might be used in multiple places. To bind reactive attributes, call [data_bind][textual.dom.DOMNode.data_bind] on a widget. This method accepts reactives (as class attributes) in positional arguments or keyword arguments. diff --git a/src/textual/dom.py b/src/textual/dom.py index 55ce52afea..6e84f53ee3 100644 --- a/src/textual/dom.py +++ b/src/textual/dom.py @@ -41,7 +41,7 @@ from .css.styles import RenderStyles, Styles from .css.tokenize import IDENTIFIER from .message_pump import MessagePump -from .reactive import Reactive, ReactiveError, _watch +from .reactive import Reactive, ReactiveError, _Mutated, _watch from .timer import Timer from .walk import walk_breadth_first, walk_depth_first from .worker_manager import WorkerManager @@ -253,6 +253,10 @@ def mutate_reactive(self, reactive: Reactive[ReactiveType]) -> None: this method after your reactive is updated. This will ensure that all the reactive _superpowers_ work. + !!! note + + This method will cause watchers to be called, even if the value hasn't changed. + Args: reactive: A reactive property (use the class scope syntax, i.e. `MyClass.my_reactive`). """ @@ -279,6 +283,7 @@ def compose(self) -> ComposeResult: yield WorldClock("Asia/Tokyo").data_bind(WorldClockApp.time) ``` + Raises: ReactiveError: If the data wasn't bound. @@ -334,7 +339,8 @@ def setter(value: object) -> None: """Set bound data.""" _rich_traceback_omit = True Reactive._initialize_object(self) - setattr(self, variable_name, value) + # Wrap the value in `_Mutated` so the setter knows to invoke watchers etc. + setattr(self, variable_name, _Mutated(value)) return setter diff --git a/src/textual/reactive.py b/src/textual/reactive.py index 17b109d77e..64b6e007ce 100644 --- a/src/textual/reactive.py +++ b/src/textual/reactive.py @@ -42,6 +42,13 @@ ReactableType = TypeVar("ReactableType", bound="DOMNode") +class _Mutated: + """A wrapper to indicate a value was mutated.""" + + def __init__(self, value: Any) -> None: + self.value = value + + class ReactiveError(Exception): """Base class for reactive errors.""" @@ -273,6 +280,10 @@ def _set(self, obj: Reactable, value: ReactiveType, always: bool = False) -> Non f"Node is missing data; Check you are calling super().__init__(...) in the {obj.__class__.__name__}() constructor, before setting reactives." ) + if isinstance(value, _Mutated): + value = value.value + always = True + self._initialize_reactive(obj, self.name) if hasattr(obj, self.compute_name): diff --git a/tests/test_reactive.py b/tests/test_reactive.py index ad2da5724d..54b57e337e 100644 --- a/tests/test_reactive.py +++ b/tests/test_reactive.py @@ -783,3 +783,44 @@ def compose(self) -> ComposeResult: widget.mutate_reactive(TestWidget.names) # Watcher should be invoked assert watched_names == [[], ["Paul"], ["Jessica"]] + + +async def test_mutate_reactive_data_bind() -> None: + """https://github.com/Textualize/textual/issues/4825""" + + # Record mutations to TestWidget.messages + widget_messages: list[list[str]] = [] + + class TestWidget(Widget): + messages: reactive[list[str]] = reactive(list, init=False) + + def watch_messages(self, names: list[str]) -> None: + widget_messages.append(names.copy()) + + class TestApp(App): + messages: reactive[list[str]] = reactive(list, init=False) + + def compose(self) -> ComposeResult: + yield TestWidget().data_bind(TestApp.messages) + + app = TestApp() + async with app.run_test(): + test_widget = app.query_one(TestWidget) + assert widget_messages == [[]] + assert test_widget.messages == [] + + # Should be the same instance + assert app.messages is test_widget.messages + + # Mutate app + app.messages.append("foo") + # Mutations aren't detected + assert widget_messages == [[]] + assert app.messages == ["foo"] + assert test_widget.messages == ["foo"] + # Explicitly mutate app reactive + app.mutate_reactive(TestApp.messages) + # Mutating app, will also invoke watchers on any data binds + assert widget_messages == [[], ["foo"]] + assert app.messages == ["foo"] + assert test_widget.messages == ["foo"]
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }
Textualize__textual-4333@74ab967
Textualize/textual
Python
4,333
Tests re-raise exceptions that happen inside `Widget.compose`
This will fix #4282. The fix was to simply leverage the machinery introduced in #2754; for some reason, exceptions caught inside `Widget.compose` or workers were directly being sent to `App.panic` instead of using `App._handle_exception`.
2024-03-25T14:46:38Z
Exception not being raised in tests Consider such an example: ```python from __future__ import annotations from textual import work from textual.app import App class SimpleAppThatCrashes(App[None]): def on_mount(self) -> None: self.crash() @work(name="crash") async def crash(self) -> None: raise ValueError("I'm crashing!") async def test_exception_not_being_raised(): async with SimpleAppThatCrashes().run_test() as pilot: await pilot.pause(1) # SimpleAppThatCrashes().run() # Uncomment and run as regular app ``` when running via pilot in pytest - everything is green. But when running this app as regular : ```python โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Traceback (most recent call last) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:365 in _run โ”‚ โ”‚ โ”‚ โ”‚ 362 โ”‚ โ”‚ self.state = WorkerState.RUNNING โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โ”‚ 363 โ”‚ โ”‚ app.log.worker(self) โ”‚ app = SimpleAppThatCrashes(title='SimpleAppThatCrashes', classes={'-dark-mode'}) โ”‚ โ”‚ โ”‚ 364 โ”‚ โ”‚ try: โ”‚ error = ValueError("I'm crashing!") โ”‚ โ”‚ โ”‚ โฑ 365 โ”‚ โ”‚ โ”‚ self._result = await self.run() โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚ โ”‚ 366 โ”‚ โ”‚ except asyncio.CancelledError as error: โ”‚ Traceback = <class 'rich.traceback.Traceback'> โ”‚ โ”‚ โ”‚ 367 โ”‚ โ”‚ โ”‚ self.state = WorkerState.CANCELLED โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚ โ”‚ 368 โ”‚ โ”‚ โ”‚ self._error = error โ”‚ โ”‚ โ”‚ โ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:349 in run โ”‚ โ”‚ โ”‚ โ”‚ 346 โ”‚ โ”‚ Returns: โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โ”‚ 347 โ”‚ โ”‚ โ”‚ Return value of the work. โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚ โ”‚ 348 โ”‚ โ”‚ """ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚ โ”‚ โฑ 349 โ”‚ โ”‚ return await ( โ”‚ โ”‚ 350 โ”‚ โ”‚ โ”‚ self._run_threaded() if self._thread_worker else self._run_async() โ”‚ โ”‚ 351 โ”‚ โ”‚ ) โ”‚ โ”‚ 352 โ”‚ โ”‚ โ”‚ โ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:334 in _run_async โ”‚ โ”‚ โ”‚ โ”‚ 331 โ”‚ โ”‚ โ”‚ or hasattr(self._work, "func") โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โ”‚ 332 โ”‚ โ”‚ โ”‚ and inspect.iscoroutinefunction(self._work.func) โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚ โ”‚ 333 โ”‚ โ”‚ ): โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚ โ”‚ โฑ 334 โ”‚ โ”‚ โ”‚ return await self._work() โ”‚ โ”‚ 335 โ”‚ โ”‚ elif inspect.isawaitable(self._work): โ”‚ โ”‚ 336 โ”‚ โ”‚ โ”‚ return await self._work โ”‚ โ”‚ 337 โ”‚ โ”‚ elif callable(self._work): โ”‚ โ”‚ โ”‚ โ”‚ /home/mzebrak/1workspace/textual/textual_pytest_exceptions.py:13 in crash โ”‚ โ”‚ โ”‚ โ”‚ 10 โ”‚ โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โ”‚ 11 โ”‚ @work(name="crash") โ”‚ self = SimpleAppThatCrashes(title='SimpleAppThatCrashes', classes={'-dark-mode'}) โ”‚ โ”‚ โ”‚ 12 โ”‚ async def crash(self) -> None: โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚ โ”‚ โฑ 13 โ”‚ โ”‚ raise ValueError("I'm crashing!") โ”‚ โ”‚ 14 โ”‚ โ”‚ 15 โ”‚ โ”‚ 16 async def test_exception_not_being_raised(): โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ ValueError: I'm crashing! ```
Thank you for your issue. Give us a little time to review it. PS. You might want to check the [FAQ](https://textual.textualize.io/FAQ/) if you haven't done so already. This is an automated reply, generated by [FAQtory](https://github.com/willmcgugan/faqtory) Another intresting (even simplier) case: ```python from __future__ import annotations from textual.app import App, ComposeResult from textual.screen import Screen class SomeScreen(Screen): def compose(self) -> ComposeResult: self.query_one("#not-existing") return [] class SimpleAppThatCrashes(App[None]): def on_mount(self) -> None: self.push_screen(SomeScreen()) async def test_exception_not_being_raised(): async with SimpleAppThatCrashes().run_test() as pilot: await pilot.pause(1) assert isinstance(pilot.app.screen, SomeScreen) # SimpleAppThatCrashes().run() # Uncomment and run as regular app ``` I think we can make `run_test()` re-raise fatal errors, such as those raised from workers.
[ { "body": "Consider such an example:\r\n\r\n```python\r\nfrom __future__ import annotations\r\n\r\nfrom textual import work\r\nfrom textual.app import App\r\n\r\n\r\nclass SimpleAppThatCrashes(App[None]):\r\n def on_mount(self) -> None:\r\n self.crash()\r\n\r\n @work(name=\"crash\")\r\n async def crash(self) -> None:\r\n raise ValueError(\"I'm crashing!\")\r\n\r\n\r\nasync def test_exception_not_being_raised():\r\n async with SimpleAppThatCrashes().run_test() as pilot:\r\n await pilot.pause(1)\r\n\r\n# SimpleAppThatCrashes().run() # Uncomment and run as regular app\r\n```\r\n\r\nwhen running via pilot in pytest - everything is green.\r\n\r\nBut when running this app as regular :\r\n\r\n```python\r\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Traceback (most recent call last) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\r\nโ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:365 in _run โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ 362 โ”‚ โ”‚ self.state = WorkerState.RUNNING โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚\r\nโ”‚ 363 โ”‚ โ”‚ app.log.worker(self) โ”‚ app = SimpleAppThatCrashes(title='SimpleAppThatCrashes', classes={'-dark-mode'}) โ”‚ โ”‚\r\nโ”‚ 364 โ”‚ โ”‚ try: โ”‚ error = ValueError(\"I'm crashing!\") โ”‚ โ”‚\r\nโ”‚ โฑ 365 โ”‚ โ”‚ โ”‚ self._result = await self.run() โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚\r\nโ”‚ 366 โ”‚ โ”‚ except asyncio.CancelledError as error: โ”‚ Traceback = <class 'rich.traceback.Traceback'> โ”‚ โ”‚\r\nโ”‚ 367 โ”‚ โ”‚ โ”‚ self.state = WorkerState.CANCELLED โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚\r\nโ”‚ 368 โ”‚ โ”‚ โ”‚ self._error = error โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:349 in run โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ 346 โ”‚ โ”‚ Returns: โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚\r\nโ”‚ 347 โ”‚ โ”‚ โ”‚ Return value of the work. โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚\r\nโ”‚ 348 โ”‚ โ”‚ \"\"\" โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚\r\nโ”‚ โฑ 349 โ”‚ โ”‚ return await ( โ”‚\r\nโ”‚ 350 โ”‚ โ”‚ โ”‚ self._run_threaded() if self._thread_worker else self._run_async() โ”‚\r\nโ”‚ 351 โ”‚ โ”‚ ) โ”‚\r\nโ”‚ 352 โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ /home/mzebrak/.pyenv/versions/textual/lib/python3.10/site-packages/textual/worker.py:334 in _run_async โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ 331 โ”‚ โ”‚ โ”‚ or hasattr(self._work, \"func\") โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚\r\nโ”‚ 332 โ”‚ โ”‚ โ”‚ and inspect.iscoroutinefunction(self._work.func) โ”‚ self = <Worker ERROR name='crash' description='crash()'> โ”‚ โ”‚\r\nโ”‚ 333 โ”‚ โ”‚ ): โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚\r\nโ”‚ โฑ 334 โ”‚ โ”‚ โ”‚ return await self._work() โ”‚\r\nโ”‚ 335 โ”‚ โ”‚ elif inspect.isawaitable(self._work): โ”‚\r\nโ”‚ 336 โ”‚ โ”‚ โ”‚ return await self._work โ”‚\r\nโ”‚ 337 โ”‚ โ”‚ elif callable(self._work): โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ /home/mzebrak/1workspace/textual/textual_pytest_exceptions.py:13 in crash โ”‚\r\nโ”‚ โ”‚\r\nโ”‚ 10 โ”‚ โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ locals โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚\r\nโ”‚ 11 โ”‚ @work(name=\"crash\") โ”‚ self = SimpleAppThatCrashes(title='SimpleAppThatCrashes', classes={'-dark-mode'}) โ”‚ โ”‚\r\nโ”‚ 12 โ”‚ async def crash(self) -> None: โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โ”‚\r\nโ”‚ โฑ 13 โ”‚ โ”‚ raise ValueError(\"I'm crashing!\") โ”‚\r\nโ”‚ 14 โ”‚\r\nโ”‚ 15 โ”‚\r\nโ”‚ 16 async def test_exception_not_being_raised(): โ”‚\r\nโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\r\nValueError: I'm crashing!\r\n```\r\n", "number": 4282, "title": "Exception not being raised in tests" } ]
ea8138913eec098ca2036294dcb9911c197e0f2b
{ "head_commit": "74ab96763fdb02583b92050ea58d897ed3d848d1", "head_commit_message": "Add test to ensure exception reraised in tests.", "patch_to_review": "diff --git a/CHANGELOG.md b/CHANGELOG.md\nindex 3a0cb30a51..3239310763 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).\n - Fixed a crash in `TextArea` when undoing an edit to a selection the selection was made backwards https://github.com/Textualize/textual/issues/4301\n - Fixed issue with flickering scrollbars https://github.com/Textualize/textual/pull/4315\n - Fix progress bar ETA not updating when setting `total` reactive https://github.com/Textualize/textual/pull/4316\n+- Exceptions inside `Widget.compose` weren't bubbling up in tests https://github.com/Textualize/textual/issues/4282\n \n ### Changed \n \ndiff --git a/src/textual/widget.py b/src/textual/widget.py\nindex aac76e8778..4bfaabdee0 100644\n--- a/src/textual/widget.py\n+++ b/src/textual/widget.py\n@@ -3618,10 +3618,8 @@ async def _compose(self) -> None:\n raise TypeError(\n f\"{self!r} compose() method returned an invalid result; {error}\"\n ) from error\n- except Exception:\n- from rich.traceback import Traceback\n-\n- self.app.panic(Traceback())\n+ except Exception as error:\n+ self.app._handle_exception(error)\n else:\n self._extend_compose(widgets)\n await self.mount_composed_widgets(widgets)\ndiff --git a/tests/test_pilot.py b/tests/test_pilot.py\nindex d436f8b5fc..10ea623453 100644\n--- a/tests/test_pilot.py\n+++ b/tests/test_pilot.py\n@@ -7,6 +7,7 @@\n from textual.binding import Binding\n from textual.containers import Center, Middle\n from textual.pilot import OutOfBounds\n+from textual.screen import Screen\n from textual.widgets import Button, Label\n \n KEY_CHARACTERS_TO_TEST = \"akTW03\" + punctuation\n@@ -56,7 +57,7 @@ def on_key(self, event: events.Key) -> None:\n assert keys_pressed[-1] == char\n \n \n-async def test_pilot_exception_catching_compose():\n+async def test_pilot_exception_catching_app_compose():\n \"\"\"Ensuring that test frameworks are aware of exceptions\n inside compose methods when running via Pilot run_test().\"\"\"\n \n@@ -70,6 +71,21 @@ def compose(self) -> ComposeResult:\n pass\n \n \n+async def test_pilot_exception_cathing_widget_compose():\n+ class SomeScreen(Screen[None]):\n+ def compose(self) -> ComposeResult:\n+ 1 / 0\n+ yield Label(\"Beep\")\n+\n+ class FailingApp(App[None]):\n+ def on_mount(self) -> None:\n+ self.push_screen(SomeScreen())\n+\n+ with pytest.raises(ZeroDivisionError):\n+ async with FailingApp().run_test():\n+ pass\n+\n+\n async def test_pilot_exception_catching_action():\n \"\"\"Ensure that exceptions inside action handlers are presented\n to the test framework when running via Pilot run_test().\"\"\"\n" }
[ { "diff_hunk": "@@ -70,6 +71,21 @@ def compose(self) -> ComposeResult:\n pass\n \n \n+async def test_pilot_exception_cathing_widget_compose():", "line": null, "original_line": 74, "original_start_line": null, "path": "tests/test_pilot.py", "start_line": null, "text": "@user1:\n```suggestion\r\nasync def test_pilot_exception_catching_widget_compose():\r\n```" } ]
809f38341f35effd073d43c0625fe984f4d9e311
diff --git a/CHANGELOG.md b/CHANGELOG.md index 3a0cb30a51..ecbc5dd4b5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/). - Fixed a crash in `TextArea` when undoing an edit to a selection the selection was made backwards https://github.com/Textualize/textual/issues/4301 - Fixed issue with flickering scrollbars https://github.com/Textualize/textual/pull/4315 - Fix progress bar ETA not updating when setting `total` reactive https://github.com/Textualize/textual/pull/4316 +- Exceptions inside `Widget.compose` or workers weren't bubbling up in tests https://github.com/Textualize/textual/issues/4282 ### Changed diff --git a/src/textual/widget.py b/src/textual/widget.py index aac76e8778..4bfaabdee0 100644 --- a/src/textual/widget.py +++ b/src/textual/widget.py @@ -3618,10 +3618,8 @@ async def _compose(self) -> None: raise TypeError( f"{self!r} compose() method returned an invalid result; {error}" ) from error - except Exception: - from rich.traceback import Traceback - - self.app.panic(Traceback()) + except Exception as error: + self.app._handle_exception(error) else: self._extend_compose(widgets) await self.mount_composed_widgets(widgets) diff --git a/src/textual/worker.py b/src/textual/worker.py index 7719cd4dca..f7f10b60ae 100644 --- a/src/textual/worker.py +++ b/src/textual/worker.py @@ -375,7 +375,8 @@ async def _run(self, app: App) -> None: app.log.worker(Traceback()) if self.exit_on_error: - app._fatal_error() + worker_failed = WorkerFailed(self._error) + app._handle_exception(worker_failed) else: self.state = WorkerState.SUCCESS app.log.worker(self) diff --git a/tests/test_pilot.py b/tests/test_pilot.py index d436f8b5fc..9451237fab 100644 --- a/tests/test_pilot.py +++ b/tests/test_pilot.py @@ -2,12 +2,14 @@ import pytest -from textual import events +from textual import events, work from textual.app import App, ComposeResult from textual.binding import Binding from textual.containers import Center, Middle from textual.pilot import OutOfBounds +from textual.screen import Screen from textual.widgets import Button, Label +from textual.worker import WorkerFailed KEY_CHARACTERS_TO_TEST = "akTW03" + punctuation """Test some "simple" characters (letters + digits) and all punctuation.""" @@ -56,7 +58,7 @@ def on_key(self, event: events.Key) -> None: assert keys_pressed[-1] == char -async def test_pilot_exception_catching_compose(): +async def test_pilot_exception_catching_app_compose(): """Ensuring that test frameworks are aware of exceptions inside compose methods when running via Pilot run_test().""" @@ -70,6 +72,21 @@ def compose(self) -> ComposeResult: pass +async def test_pilot_exception_catching_widget_compose(): + class SomeScreen(Screen[None]): + def compose(self) -> ComposeResult: + 1 / 0 + yield Label("Beep") + + class FailingApp(App[None]): + def on_mount(self) -> None: + self.push_screen(SomeScreen()) + + with pytest.raises(ZeroDivisionError): + async with FailingApp().run_test(): + pass + + async def test_pilot_exception_catching_action(): """Ensure that exceptions inside action handlers are presented to the test framework when running via Pilot run_test().""" @@ -85,6 +102,21 @@ def action_beep(self) -> None: await pilot.press("b") +async def test_pilot_exception_catching_worker(): + class SimpleAppThatCrashes(App[None]): + def on_mount(self) -> None: + self.crash() + + @work(name="crash") + async def crash(self) -> None: + 1 / 0 + + with pytest.raises(WorkerFailed) as exc: + async with SimpleAppThatCrashes().run_test(): + pass + assert exc.type is ZeroDivisionError + + async def test_pilot_click_screen(): """Regression test for https://github.com/Textualize/textual/issues/3395. diff --git a/tests/workers/test_worker.py b/tests/workers/test_worker.py index 7b553d122f..61af91f638 100644 --- a/tests/workers/test_worker.py +++ b/tests/workers/test_worker.py @@ -113,10 +113,10 @@ class ErrorApp(App): pass app = ErrorApp() - async with app.run_test(): - worker: Worker[str] = Worker(app, run_error) - worker._start(app) - with pytest.raises(WorkerFailed): + with pytest.raises(WorkerFailed): + async with app.run_test(): + worker: Worker[str] = Worker(app, run_error) + worker._start(app) await worker.wait() @@ -218,12 +218,12 @@ async def self_referential_work(): await get_current_worker().wait() app = App() - async with app.run_test(): - worker = Worker(app, self_referential_work) - worker._start(app) - with pytest.raises(WorkerFailed) as exc: + with pytest.raises(WorkerFailed) as exc: + async with app.run_test(): + worker = Worker(app, self_referential_work) + worker._start(app) await worker.wait() - assert exc.type is DeadlockError + assert exc.type is DeadlockError async def test_wait_without_start():
{ "difficulty": "medium", "estimated_review_effort": 3, "problem_domain": "Bug Fixes" }