import{s as te,n as ie,o as ae}from"../chunks/scheduler.b9285784.js";import{S as ne,i as se,e as o,s as n,c as _,h as le,a as r,d as i,b as s,f as O,g as b,j as v,k as ee,l as oe,m as a,n as P,t as x,o as C,p as U}from"../chunks/index.26bc89a1.js";import{C as re,H as q,E as pe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.7242ff19.js";import{C as me}from"../chunks/CodeBlock.c8ee1711.js";function fe(Y){let l,L,M,H,p,E,m,R,f,j='A <a href="https://cloud.google.com/tpu/docs/intro-to-tpu" rel="nofollow">TPU (Tensor Processing Unit)</a> is a type of hardware specifically designed for training models efficiently. Accelerate supports TPU training, but there are a few things you should be aware of, namely graph compilation. This tutorial briefly discusses compilation, and for more details, take a look at the <a href="../concept_guides/training_tpu">Training on TPUs with Accelerate</a> guide.',S,c,z,h,Q="A TPU creates a graph of all the operations in the training step such as the forward pass, backward pass and optimizer step. This is why the first training step always takes a while because building and compiling this graph takes time. But once compilation is complete, it is cached and all subsequent steps are much faster.",A,u,D="The key is to avoid compiling your code again or else training is super slow. This means all your operations must be exactly the same:",B,g,I="<li>all tensors in your batches must have the same length (for example, no dynamic padding for NLP tasks)</li> <li>your code must be static (for example, no layers with for loops that have different lengths depending on the input such as a LSTM)</li>",G,d,J,$,F='A common language model design is to tie the weights of the embedding and softmax layers. However, moving the model to a TPU (either yourself or passing it to the <a href="/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.prepare">prepare()</a> method) breaks the weight tying and you’ll need to retie the weights.',N,y,K='To add special behavior (like weight tying) in your script for TPUs, set <code>distributed_type</code> to <code>DistributedType.TPU</code> first. Then you can use the <a href="https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.tie_weights" rel="nofollow">tie_weights</a> method to tie the weights.',V,w,W,T,X,k,Z;return p=new re({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new q({props:{title:"TPU training",local:"tpu-training",headingTag:"h1"}}),c=new q({props:{title:"Compilation",local:"compilation",headingTag:"h2"}}),d=new q({props:{title:"Weight tying",local:"weight-tying",headingTag:"h2"}}),w=new me({props:{code:"aWYlMjBhY2NlbGVyYXRvci5kaXN0cmlidXRlZF90eXBlJTIwJTNEJTNEJTIwRGlzdHJpYnV0ZWRUeXBlLlRQVSUzQSUwQSUyMCUyMCUyMCUyMG1vZGVsLnRpZV93ZWlnaHRzKCk=",highlighted:`<span class="hljs-keyword">if</span> accelerator.distributed_type == DistributedType.TPU:
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