import{s as O,n as ee,o as te}from"../chunks/scheduler.defa9a21.js";import{S as ie,i as ae,g as o,s as n,r as v,A as ne,h as r,f as i,c as s,j as F,u as P,x as T,k as K,y as se,a,v as U,d as x,t as C,w as k}from"../chunks/index.fe795e71.js";import{C as le}from"../chunks/CodeBlock.204b6c34.js";import{H as X,E as oe}from"../chunks/getInferenceSnippets.2234a8dd.js";function re(Z){let l,M,_,L,p,H,m,Y='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.',A,c,E,h,j="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.",R,f,q="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:",S,u,Q="<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>",z,d,B,g,D='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/v1.10.1/en/package_reference/accelerator#accelerate.Accelerator.prepare">prepare()</a> method) breaks the weight tying and you’ll need to retie the weights.',G,$,I="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 <code>tie_weights</code> method to tie the weights.",J,y,N,w,V,b,W;return p=new X({props:{title:"TPU training",local:"tpu-training",headingTag:"h1"}}),c=new X({props:{title:"Compilation",local:"compilation",headingTag:"h2"}}),d=new X({props:{title:"Weight tying",local:"weight-tying",headingTag:"h2"}}),y=new le({props:{code:"aWYlMjBhY2NlbGVyYXRvci5kaXN0cmlidXRlZF90eXBlJTIwJTNEJTNEJTIwRGlzdHJpYnV0ZWRUeXBlLlRQVSUzQSUwQSUyMCUyMCUyMCUyMG1vZGVsLnRpZV93ZWlnaHRzKCk=",highlighted:`<span class="hljs-keyword">if</span> accelerator.distributed_type == DistributedType.TPU:
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