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| <link rel="modulepreload" href="/docs/transformers/pr_33913/ja/_app/immutable/chunks/EditOnGithub.922df6ba.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Efficient Training on CPU","local":"efficient-training-on-cpu","sections":[{"title":"Mixed precision with IPEX","local":"mixed-precision-with-ipex","sections":[{"title":"IPEX installation:","local":"ipex-installation","sections":[],"depth":3},{"title":"Trainerでの使用方法","local":"trainerでの使用方法","sections":[],"depth":3},{"title":"Practice example","local":"practice-example","sections":[],"depth":3}],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="efficient-training-on-cpu" 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="#efficient-training-on-cpu"><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>Efficient Training on CPU</span></h1> <p data-svelte-h="svelte-11xh6oz">このガイドは、CPU上で大規模なモデルを効率的にトレーニングする方法に焦点を当てています。</p> <h2 class="relative group"><a id="mixed-precision-with-ipex" 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="#mixed-precision-with-ipex"><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>Mixed precision with IPEX</span></h2> <p data-svelte-h="svelte-hle050">IPEXはAVX-512以上のCPUに最適化されており、AVX2のみのCPUでも機能的に動作します。そのため、AVX-512以上のIntel CPU世代ではパフォーマンスの向上が期待されますが、AVX2のみのCPU（例：AMD CPUまたは古いIntel CPU）ではIPEXの下でより良いパフォーマンスが得られるかもしれませんが、保証されません。IPEXは、Float32とBFloat16の両方でCPUトレーニングのパフォーマンスを最適化します。以下のセクションでは、BFloat16の使用に重点を置いて説明します。</p> <p data-svelte-h="svelte-uvmlso">低精度データ型であるBFloat16は、AVX512命令セットを備えた第3世代Xeon® Scalable Processors（別名Cooper Lake）でネイティブサポートされており、さらに高性能なIntel® Advanced Matrix Extensions（Intel® AMX）命令セットを備えた次世代のIntel® Xeon® Scalable Processorsでもサポートされます。CPUバックエンド用の自動混合精度がPyTorch-1.10以降で有効になっています。同時に、Intel® Extension for PyTorchでのCPU用BFloat16の自動混合精度サポートと、オペレーターのBFloat16最適化のサポートが大幅に向上し、一部がPyTorchのメインブランチにアップストリームされています。ユーザーはIPEX Auto Mixed Precisionを使用することで、より優れたパフォーマンスとユーザーエクスペリエンスを得ることができます。</p> <p data-svelte-h="svelte-1xnbmj5">詳細な情報については、<a href="https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/amp.html" rel="nofollow">Auto Mixed Precision</a>を確認してください。</p> <h3 class="relative group"><a id="ipex-installation" 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="#ipex-installation"><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>IPEX installation:</span></h3> <p data-svelte-h="svelte-1qi2p9c">IPEXのリリースはPyTorchに従っており、pipを使用してインストールできます：</p> <table data-svelte-h="svelte-okkx16"><thead><tr><th align="center">PyTorch Version</th> <th align="center">IPEX version</th></tr></thead> <tbody><tr><td align="center">1.13</td> <td align="center">1.13.0+cpu</td></tr> <tr><td align="center">1.12</td> <td align="center">1.12.300+cpu</td></tr> <tr><td align="center">1.11</td> <td align="center">1.11.200+cpu</td></tr> <tr><td align="center">1.10</td> <td align="center">1.10.100+cpu</td></tr></tbody></table> <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 -->pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1xti9kg"><a href="https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html" rel="nofollow">IPEXのインストール方法</a>について、さらなるアプローチを確認してください。</p> <h3 class="relative group"><a id="trainerでの使用方法" 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="#trainerでの使用方法"><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>Trainerでの使用方法</span></h3> <p data-svelte-h="svelte-1szp3tw">TrainerでIPEXの自動混合精度を有効にするには、ユーザーはトレーニングコマンド引数に <code>use_ipex</code>、<code>bf16</code>、および <code>no_cuda</code> を追加する必要があります。</p> <p data-svelte-h="svelte-yud8x"><a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering" rel="nofollow">Transformersの質問応答</a>のユースケースを例に説明します。</p> <ul data-svelte-h="svelte-jovvsy"><li>CPU上でBF16自動混合精度を使用してIPEXでトレーニングを行う場合：<pre> python run_qa.py \ | |
| --model_name_or_path google-bert/bert-base-uncased \ | |
| --dataset_name squad \ | |
| --do_train \ | |
| --do_eval \ | |
| --per_device_train_batch_size 12 \ | |
| --learning_rate 3e-5 \ | |
| --num_train_epochs 2 \ | |
| --max_seq_length 384 \ | |
| --doc_stride 128 \ | |
| --output_dir /tmp/debug_squad/ \ | |
| <b>--use_ipex \</b> | |
| <b>--bf16 --no_cuda</b></pre></li></ul> <h3 class="relative group"><a id="practice-example" 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="#practice-example"><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>Practice example</span></h3> <p data-svelte-h="svelte-w7zs5j">Blog: <a href="https://huggingface.co/blog/intel-sapphire-rapids" rel="nofollow">Accelerating PyTorch Transformers with Intel Sapphire Rapids</a></p> <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/ja/perf_train_cpu.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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