import{s as vt,n as wt,o as Pt}from"../chunks/scheduler.b9285784.js";import{S as xt,i as St,e as l,s as r,c as s,h as Dt,a as d,d as t,b as o,f as g,g as c,j as y,k as h,l as n,m as i,n as p,t as f,o as m,p as u}from"../chunks/index.26bc89a1.js";import{C as Ft,H as T,E as Tt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.7242ff19.js";import{D as v}from"../chunks/Docstring.9ffdb58c.js";function Ct(dt){let w,ge,ue,he,O,be,U,ye,L,$e,P,M,je,te,st="Enables RAM efficient loading of Hugging Face models for FSDP in the environment.",ve,E,we,x,I,Ge,ae,ct="Disables RAM efficient loading of Hugging Face models for FSDP in the environment.",Pe,R,xe,$,A,Ke,re,pt=`Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
<code>SHARDED_STATE_DICT</code> was used for the model. Weights will be saved to <code>{output_path}/model.safetensors</code> if
<code>safe_serialization</code> else <code>pytorch_model.bin</code>.`,Je,oe,ft="Note: this is a CPU-bound process.",Se,H,De,_,V,Qe,ne,mt="This plugin is used to enable fully sharded data parallelism.",Xe,C,W,Ye,ie,ut=`Given <code>model</code>, creates an <code>auto_wrap_policy</code> based on the passed in policy and if we can use the
<code>transformer_cls_to_wrap</code>`,Ze,k,q,et,le,_t="Sets the mixed precision policy for FSDP",tt,N,B,at,de,gt="Set the state dict config based on the <code>StateDictType</code>.",rt,z,j,ot,se,ht="Validates the mixed precision policy, abstracted away to not bring in the imports if not needed.",Fe,G,Te,S,K,nt,ce,bt=`Loads the full state dict (could be only on rank 0) into the sharded model. This is done by broadcasting the
parameters from rank 0 to all other ranks. This function modifies the model in-place.`,Ce,J,ke,D,Q,it,pe,yt=`Switches the parameters of the optimizer to new ones (sharded parameters in usual case). This function modifies the
optimizer in-place.`,Ne,X,ze,F,Y,lt,fe,$t="Prepares the model for FSDP2 in-place. Also returns the model to avoid misuse of the original model.",Oe,Z,Ue,ee,Le,_e,Me;return O=new Ft({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),U=new T({props:{title:"Fully Sharded Data Parallel utilities",local:"fully-sharded-data-parallel-utilities",headingTag:"h1"}}),L=new T({props:{title:"enable_fsdp_ram_efficient_loading",local:"accelerate.utils.enable_fsdp_ram_efficient_loading",headingTag:"h2"}}),M=new v({props:{name:"accelerate.utils.enable_fsdp_ram_efficient_loading",anchor:"accelerate.utils.enable_fsdp_ram_efficient_loading",parameters:[],source:"https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/fsdp_utils.py#L39"}}),E=new T({props:{title:"disable_fsdp_ram_efficient_loading",local:"accelerate.utils.disable_fsdp_ram_efficient_loading",headingTag:"h2"}}),I=new v({props:{name:"accelerate.utils.disable_fsdp_ram_efficient_loading",anchor:"accelerate.utils.disable_fsdp_ram_efficient_loading",parameters:[],source:"https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/fsdp_utils.py#L49"}}),R=new T({props:{title:"merge_fsdp_weights",local:"accelerate.utils.merge_fsdp_weights",headingTag:"h2"}}),A=new v({props:{name:"accelerate.utils.merge_fsdp_weights",anchor:"accelerate.utils.merge_fsdp_weights",parameters:[{name:"checkpoint_dir",val:": str"},{name:"output_path",val:": str"},{name:"safe_serialization",val:": bool = True"},{name:"remove_checkpoint_dir",val:": bool = False"}],parametersDescription:[{anchor:"accelerate.utils.merge_fsdp_weights.checkpoint_dir",description:`<strong>checkpoint_dir</strong> (<code>str</code>) &#x2014;
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Sharding strategy to use. Should be either a <code>str</code> or an instance of
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<code>reshard_after_forward</code>.`,name:"sharding_strategy"},{anchor:"accelerate.FullyShardedDataParallelPlugin.reshard_after_forward",description:`<strong>reshard_after_forward</strong> (<code>Union[str, torch.distributed.fsdp.ShardingStrategy, bool]</code>, defaults to <code>&apos;FULL_SHARD&apos;</code> for <code>fsdp_version=1</code> and <code>True</code> for <code>fsdp_version=2</code>) &#x2014;
Sharding strategy to use. Should be a bool if <code>fsdp_version</code> is set to 2 else a <code>str</code> or an instance of
<code>torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy</code>.`,name:"reshard_after_forward"},{anchor:"accelerate.FullyShardedDataParallelPlugin.backward_prefetch",description:`<strong>backward_prefetch</strong> (<code>Union[str, torch.distributed.fsdp.BackwardPrefetch]</code>, defaults to <code>&apos;NO_PREFETCH&apos;</code>) &#x2014;
Backward prefetch strategy to use. Should be either a <code>str</code> or an instance of
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A config to enable mixed precision training with FullyShardedDataParallel. If passing in a <code>dict</code>, it
should have the following keys: <code>param_dtype</code>, <code>reduce_dtype</code>, and <code>buffer_dtype</code>, can be an instance of
<code>torch.distributed.fsdp.MixedPrecisionPolicy</code> if <code>fsdp_version</code> is set to 2. If passing in a <code>str</code>, it
should be one of the following values: fp8, fp16, bf16, fp32, and used to set <code>param_dtype</code>,
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Whether to offload parameters to CPU. Should be either a <code>bool</code> or an instance of
<code>torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffload</code> or
<code>torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffloadPolicy</code> if <code>fsdp_version</code> is set to 2.`,name:"cpu_offload"},{anchor:"accelerate.FullyShardedDataParallelPlugin.ignored_modules",description:`<strong>ignored_modules</strong> (<code>Optional[Union[Iterable[torch.nn.Module], str]]</code>, defaults to <code>None</code>) &#x2014;
A list of modules to ignore when wrapping with FSDP. When passing a string, will match the modules by name
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State dict type to use. If a string, it must be one of <code>full_state_dict</code>, <code>local_state_dict</code>, or
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State dict config to use. Is determined based on the <code>state_dict_type</code> if not passed in.`,name:"state_dict_config"},{anchor:"accelerate.FullyShardedDataParallelPlugin.optim_state_dict_config",description:`<strong>optim_state_dict_config</strong> (<code>Optional[Union[torch.distributed.fsdp.FullOptimStateDictConfig, torch.distributed.fsdp.ShardedOptimStateDictConfig]</code>, defaults to <code>None</code>) &#x2014;
Optim state dict config to use. Is determined based on the <code>state_dict_type</code> if not passed in.`,name:"optim_state_dict_config"},{anchor:"accelerate.FullyShardedDataParallelPlugin.limit_all_gathers",description:`<strong>limit_all_gathers</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to have FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers. This
bool only affects the sharded strategies that schedule all-gathers. Enabling this can help lower the number
of CUDA malloc retries.`,name:"limit_all_gathers"},{anchor:"accelerate.FullyShardedDataParallelPlugin.use_orig_params",description:`<strong>use_orig_params</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
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A <code>Callable[torch.nn.Module] -&gt; None</code> that specifies how modules that are currently on the meta device
should be initialized onto an actual device. Only applicable when <code>sync_module_states</code> is <code>True</code>. By
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Whether each individually wrapped FSDP unit should broadcast module parameters from rank 0 to ensure they
are the same across all ranks after initialization. Defaults to <code>False</code> unless <code>cpu_ram_efficient_loading</code>
is <code>True</code>, then will be forcibly enabled.`,name:"sync_module_states"},{anchor:"accelerate.FullyShardedDataParallelPlugin.forward_prefetch",description:`<strong>forward_prefetch</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to have FSDP explicitly prefetches the next upcoming all-gather while executing in the forward
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A technique to reduce memory usage by clearing activations of certain layers and recomputing them during a
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If True, only the first process loads the pretrained model checkoint while all other processes have empty
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A list of transformer layer class names to wrap. Only applicable when <code>auto_wrap_policy</code> is
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The minimum number of parameters a module must have to be wrapped. Only applicable when <code>auto_wrap_policy</code>
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<ul>
<li><code>KeyError</code> —
If a parameter in the optimizer couldn’t be switched to its sharded version. This should never happen and
indicates a bug. If we kept the original params instead of raising, the training wouldn’t be numerically
correct and weights wouldn’t get updated.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>KeyError</code></p>
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<p>Prepared model</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.nn.Module</code></p>
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