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<section id="torch-compile">
<h1>torch.compile<a class="headerlink" href="#torch-compile" title="Permalink to this heading">¶</a></h1>
<p><a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> was introduced in <a class="reference external" href="https://pytorch.org/get-started/pytorch-2.0/">PyTorch 2.0</a></p>
<p>Our default and supported backend is <cite>inductor</cite> with benchmarks <a class="reference external" href="https://github.com/pytorch/pytorch/issues/93794">showing 30% to 2x speedups and 10% memory compression</a>
on real world models for both training and inference with a single line of code.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> API is experimental and subject to change.</p>
</div>
<p>The simplest possible interesting program is the below which we go over in a lot more detail in <a class="reference external" href="https://pytorch.org/docs/master/compile/get-started.html">getting started</a>
showing how to use <a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> to speed up inference on a variety of real world models from both TIMM and HuggingFace which we
co-announced <a class="reference external" href="https://pytorch.org/blog/Accelerating-Hugging-Face-and-TIMM-models/">here</a></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">compiled_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">fn</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()))</span>
</pre></div>
</div>
<p>If you happen to be running your model on an Ampere GPU, it’s crucial to enable tensor cores. We will actually warn you to set
<code class="docutils literal notranslate"><span class="pre">torch.set_float32_matmul_precision('high')</span></code></p>
<p><a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> works over <a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> as well as functions so you can pass in your entire training loop.</p>
<p>The above example was for inference but you can follow this tutorial for an <a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">example on training</a></p>
<section id="optimizations">
<h2>Optimizations<a class="headerlink" href="#optimizations" title="Permalink to this heading">¶</a></h2>
<p>Optimizations can be passed in <a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> with either a backend mode parameter or as passes. To understand what are the available options you can run
<code class="docutils literal notranslate"><span class="pre">torch._inductor.list_options</span></code> and <code class="docutils literal notranslate"><span class="pre">torch._inductor.list_mode_options()</span></code></p>
<p>The default backend is <cite>inductor</cite> which will likely be the most reliable and performant option for most users and library maintainers,
other backends are there for power users who don’t mind more experimental community support.</p>
<p>There is some nuance involved in benchmarking <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> so we’ve provided a utility to make this simpler with <code class="xref py py-func docutils literal notranslate"><span class="pre">bench_all()</span></code></p>
<p>You can get the full list of community backends by running <a class="reference internal" href="../_dynamo.html#torch._dynamo.list_backends" title="torch._dynamo.list_backends"><code class="xref py py-func docutils literal notranslate"><span class="pre">list_backends()</span></code></a></p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.compile"/><a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code></a></p></td>
<td><p>Optimizes given model/function using TorchDynamo and specified backend.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="troubleshooting-and-gotchas">
<h2>Troubleshooting and Gotchas<a class="headerlink" href="#troubleshooting-and-gotchas" title="Permalink to this heading">¶</a></h2>
<p>IF you experience issues with models failing to compile, running of out of memory, recompiling too often, not giving accurate results,
odds are you will find the right tool to solve your problem in our guides.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>A few features are still very much in development and not likely to work for most users. Please do not use these features
in production code and if you’re a library maintainer please do not expose these options to your users
Dynamic shapes <code class="docutils literal notranslate"><span class="pre">dynamic=true</span></code> and max autotune <code class="docutils literal notranslate"><span class="pre">mode="max-autotune"</span></code> which can be passed in to <a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>.
Distributed training has some quirks which you can follow in the troubleshooting guide below. Model export is not ready yet.</p>
</div>
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<li class="toctree-l1"><a class="reference internal" href="troubleshooting.html">PyTorch 2.0 Troubleshooting</a></li>
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<section id="learn-more">
<h2>Learn more<a class="headerlink" href="#learn-more" title="Permalink to this heading">¶</a></h2>
<p>If you can’t wait to get started and want to learn more about the internals of the PyTorch 2.0 stack then
please check out the references below.</p>
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