peft/pr_3206/en/developer_guides/torch_compile.html

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The reason why it won’t always work is because PEFT is highly dynamic in certain places (loading and switching between multiple adapters, for instance), which can cause trouble for <code>torch.compile</code>. In other places, <code>torch.compile</code> may work, but won’t be as fast as expected because of graph breaks.</p> <p data-svelte-h="svelte-ekv2oq">If you don’t see an error, it doesn’t necessarily mean that <code>torch.compile</code> worked correctly. It might give you an output, but the output is incorrect. This guide describes what works with <code>torch.compile</code> and what doesn’t. For your own testing, we recommend using the latest PyTorch version, as <code>torch.compile</code> is constantly being improved.</p> <blockquote class="tip" data-svelte-h="svelte-kdzi31"><p>Unless indicated otherwise, the default <code>torch.compile</code> settings were used.</p></blockquote>  <h2 class="relative group"><a id="training-and-inference-with-torchcompile" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-and-inference-with-torchcompile"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Training and inference with torch.compile</span></h2> <p data-svelte-h="svelte-10kwh8m">These features <strong>work</strong> with <code>torch.compile</code>. Everything listed below was tested with a causal LM:</p> <ul data-svelte-h="svelte-1bf32c1"><li>Training with <code>Trainer</code> from 🤗 transformers</li> <li>Training with a custom PyTorch loop</li> <li>Inference</li> <li>Generation</li></ul> <p data-svelte-h="svelte-1u6npfd">The following adapters were tested successfully:</p> <ul data-svelte-h="svelte-1fkyxy4"><li>AdaLoRA</li> <li>BOFT</li> <li>IA³</li> <li>Layer Norm Tuning</li> <li>LoHa</li> <li>LoKr</li> <li>LoRA</li> <li>LoRA + DoRA</li> <li>LoRA applied to embedding layers</li> <li>OFT</li> <li>VeRA</li> <li>HRA</li></ul>  <h2 class="relative group"><a id="advanced-peft-features-with-torchcompile" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#advanced-peft-features-with-torchcompile"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Advanced PEFT features with torch.compile</span></h2> <p data-svelte-h="svelte-gy3aka">Below are some of the more advanced PEFT features that <strong>work</strong>. They were all tested with LoRA.</p> <ul data-svelte-h="svelte-142zgr"><li><code>modules_to_save</code> (i.e. <code>config = LoraConfig(..., modules_to_save=...)</code>)</li> <li>Merging adapters (one or multiple)</li> <li>Merging multiple adapters into one adapter (i.e. calling <code>model.add_weighted_adapter(...)</code>)</li> <li>Using PEFT adapters with quantization (bitsandbytes)</li> <li>Disabling adapters (i.e. using <code>with model.disable_adapter()</code>)</li> <li>Unloading (i.e. calling <code>model.merge_and_unload()</code>)</li> <li>Mixed adapter batches (i.e. calling <code>model(batch, adapter_names=[&quot;__base__&quot;, &quot;default&quot;, &quot;other&quot;, ...])</code>)</li> <li>Inference with multiple adapters (i.e. using <code>model.add_adapter</code> or <code>model.load_adapter</code> to load more than 1 adapter); for this, only call <code>torch.compile</code> <em>after</em> loading all adapters</li></ul> <p data-svelte-h="svelte-x3yaa4">Generally, we can expect that if a feature works correctly with LoRA and is also supported by other adapter types, it should also work for that adapter type.</p>  <h2 class="relative group"><a id="test-cases" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#test-cases"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Test cases</span></h2> <p data-svelte-h="svelte-1ks3zgv">All the use cases listed above are tested inside of <a href="https://github.com/huggingface/peft/blob/main/tests/test_torch_compile.py" rel="nofollow"><code>peft/tests/test_torch_compile.py</code></a>. If you want to check in more detail how we tested a certain feature, please go to that file and check the test that corresponds to your use case.</p> <blockquote class="tip" data-svelte-h="svelte-1degoe8"><p>If you have another use case where you know that <code>torch.compile</code> does or does not work with PEFT, please contribute by letting us know or by opening a PR to add this use case to the covered test cases.</p></blockquote> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/peft/blob/main/docs/source/developer_guides/torch_compile.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a>  <p></p> 
			
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