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| <link rel="modulepreload" href="/docs/transformers/pr_36839/en/_app/immutable/chunks/EditOnGithub.7faefd25.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"ExecuTorch","local":"executorch","sections":[],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="executorch" 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="#executorch"><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>ExecuTorch</span></h1> <p data-svelte-h="svelte-uwjuic"><a href="https://pytorch.org/executorch/stable/index.html" rel="nofollow">ExecuTorch</a> is a platform that enables PyTorch training and inference programs to be run on mobile and edge devices. It is powered by <a href="https://pytorch.org/docs/stable/torch.compiler.html" rel="nofollow">torch.compile</a> and <a href="https://pytorch.org/docs/main/export.html" rel="nofollow">torch.export</a> for performance and deployment.</p> <p data-svelte-h="svelte-mn86fo">You can use ExecuTorch with Transformers with <a href="https://pytorch.org/docs/main/export.html" rel="nofollow">torch.export</a>. The <a href="/docs/transformers/pr_36839/en/main_classes/executorch#transformers.convert_and_export_with_cache">convert_and_export_with_cache()</a> method converts a <a href="/docs/transformers/pr_36839/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> into an exportable module. Under the hood, it uses <a href="https://pytorch.org/docs/main/export.html" rel="nofollow">torch.export</a> to export the model, ensuring compatibility with ExecuTorch.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" 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="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> torch | |
| <span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> LlamaForCausalLM, AutoTokenizer, GenerationConfig | |
| <span class="hljs-keyword">from</span> transformers.integrations.executorch <span class="hljs-keyword">import</span>( | |
| TorchExportableModuleWithStaticCache, | |
| convert_and_export_with_cache | |
| ) | |
| generation_config = GenerationConfig( | |
| use_cache=<span class="hljs-literal">True</span>, | |
| cache_implementation=<span class="hljs-string">"static"</span>, | |
| cache_config={ | |
| <span class="hljs-string">"batch_size"</span>: <span class="hljs-number">1</span>, | |
| <span class="hljs-string">"max_cache_len"</span>: <span class="hljs-number">20</span>, | |
| } | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"meta-llama/Llama-3.2-1B"</span>, pad_token=<span class="hljs-string">"</s>"</span>, padding_side=<span class="hljs-string">"right"</span>) | |
| model = LlamaForCausalLM.from_pretrained(<span class="hljs-string">"meta-llama/Llama-3.2-1B"</span>, device_map=<span class="hljs-string">"auto"</span>, torch_dtype=torch.bfloat16, attn_implementation=<span class="hljs-string">"sdpa"</span>, generation_config=generation_config) | |
| exported_program = convert_and_export_with_cache(model)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-4zcxej">The exported PyTorch model is now ready to be used with ExecuTorch. Wrap the model with <a href="/docs/transformers/pr_36839/en/main_classes/executorch#transformers.TorchExportableModuleWithStaticCache">TorchExportableModuleWithStaticCache</a> to generate text.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" 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="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->prompts = [<span class="hljs-string">"Simply put, the theory of relativity states that "</span>] | |
| prompt_tokens = tokenizer(prompts, return_tensors=<span class="hljs-string">"pt"</span>, padding=<span class="hljs-literal">True</span>).to(model.device) | |
| prompt_token_ids = prompt_tokens[<span class="hljs-string">"input_ids"</span>] | |
| generated_ids = TorchExportableModuleWithStaticCache.generate( | |
| exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=<span class="hljs-number">20</span>, | |
| ) | |
| generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>) | |
| <span class="hljs-built_in">print</span>(generated_text) | |
| [<span class="hljs-string">'Simply put, the theory of relativity states that 1) the speed of light is the'</span>]<!-- HTML_TAG_END --></pre></div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/en/executorch.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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