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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Optimize inference using torch.compile()","local":"optimize-inference-using-torchcompile","sections":[{"title":"Benefits of torch.compile","local":"benefits-of-torchcompile","sections":[],"depth":2},{"title":"Benchmarking code","local":"benchmarking-code","sections":[{"title":"Image Classification with ViT","local":"image-classification-with-vit","sections":[{"title":"Object Detection with DETR","local":"object-detection-with-detr","sections":[],"depth":4},{"title":"Image Segmentation with Segformer","local":"image-segmentation-with-segformer","sections":[],"depth":4}],"depth":3},{"title":"A100 (batch size: 1)","local":"a100-batch-size-1","sections":[],"depth":3},{"title":"A100 (batch size: 4)","local":"a100-batch-size-4","sections":[],"depth":3},{"title":"A100 (batch size: 16)","local":"a100-batch-size-16","sections":[],"depth":3},{"title":"V100 (batch size: 1)","local":"v100-batch-size-1","sections":[],"depth":3},{"title":"V100 (batch size: 4)","local":"v100-batch-size-4","sections":[],"depth":3},{"title":"V100 (batch size: 16)","local":"v100-batch-size-16","sections":[],"depth":3},{"title":"T4 (batch size: 1)","local":"t4-batch-size-1","sections":[],"depth":3},{"title":"T4 (batch size: 4)","local":"t4-batch-size-4","sections":[],"depth":3},{"title":"T4 (batch size: 16)","local":"t4-batch-size-16","sections":[],"depth":3}],"depth":2},{"title":"PyTorch Nightly","local":"pytorch-nightly","sections":[{"title":"A100","local":"a100","sections":[],"depth":3},{"title":"T4","local":"t4","sections":[],"depth":3},{"title":"V100","local":"v100","sections":[],"depth":3}],"depth":2},{"title":"Reduce Overhead","local":"reduce-overhead","sections":[{"title":"A100","local":"a100","sections":[],"depth":3},{"title":"T4","local":"t4","sections":[],"depth":3}],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/transformers/main/ja/_app/immutable/chunks/EditOnGithub.922df6ba.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Optimize inference using torch.compile()","local":"optimize-inference-using-torchcompile","sections":[{"title":"Benefits of torch.compile","local":"benefits-of-torchcompile","sections":[],"depth":2},{"title":"Benchmarking code","local":"benchmarking-code","sections":[{"title":"Image Classification with ViT","local":"image-classification-with-vit","sections":[{"title":"Object Detection with DETR","local":"object-detection-with-detr","sections":[],"depth":4},{"title":"Image Segmentation with Segformer","local":"image-segmentation-with-segformer","sections":[],"depth":4}],"depth":3},{"title":"A100 (batch size: 1)","local":"a100-batch-size-1","sections":[],"depth":3},{"title":"A100 (batch size: 4)","local":"a100-batch-size-4","sections":[],"depth":3},{"title":"A100 (batch size: 16)","local":"a100-batch-size-16","sections":[],"depth":3},{"title":"V100 (batch size: 1)","local":"v100-batch-size-1","sections":[],"depth":3},{"title":"V100 (batch size: 4)","local":"v100-batch-size-4","sections":[],"depth":3},{"title":"V100 (batch size: 16)","local":"v100-batch-size-16","sections":[],"depth":3},{"title":"T4 (batch size: 1)","local":"t4-batch-size-1","sections":[],"depth":3},{"title":"T4 (batch size: 4)","local":"t4-batch-size-4","sections":[],"depth":3},{"title":"T4 (batch size: 16)","local":"t4-batch-size-16","sections":[],"depth":3}],"depth":2},{"title":"PyTorch Nightly","local":"pytorch-nightly","sections":[{"title":"A100","local":"a100","sections":[],"depth":3},{"title":"T4","local":"t4","sections":[],"depth":3},{"title":"V100","local":"v100","sections":[],"depth":3}],"depth":2},{"title":"Reduce Overhead","local":"reduce-overhead","sections":[{"title":"A100","local":"a100","sections":[],"depth":3},{"title":"T4","local":"t4","sections":[],"depth":3}],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="optimize-inference-using-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="#optimize-inference-using-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>Optimize inference using torch.compile()</span></h1> <p data-svelte-h="svelte-irbci">このガイドは、<a href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html" rel="nofollow"><code>torch.compile()</code></a> を使用した推論速度の向上に関するベンチマークを提供することを目的としています。これは、<a href="https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending" rel="nofollow">🤗 Transformers のコンピュータビジョンモデル</a>向けのものです。</p> <h2 class="relative group"><a id="benefits-of-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="#benefits-of-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>Benefits of torch.compile</span></h2> <p data-svelte-h="svelte-1fbn4ig"><code>torch.compile()</code>の利点
	モデルとGPUによっては、torch.compile()は推論時に最大30%の高速化を実現します。 <code>torch.compile()</code>を使用するには、バージョン2.0以上のtorchをインストールするだけです。</p> <p data-svelte-h="svelte-1qahe0">モデルのコンパイルには時間がかかるため、毎回推論するのではなく、モデルを1度だけコンパイルする場合に役立ちます。
	任意のコンピュータビジョンモデルをコンパイルするには、以下のようにモデルに<code>torch.compile()</code>を呼び出します：</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 -->from transformers import AutoModelForImageClassification

	model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda")
	<span class="hljs-addition">+ model = torch.compile(model)</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1f6elgo"><code>compile()</code> は、コンパイルに関する異なるモードを備えており、基本的にはコンパイル時間と推論のオーバーヘッドが異なります。<code>max-autotune</code> は <code>reduce-overhead</code> よりも時間がかかりますが、推論速度が速くなります。デフォルトモードはコンパイルにおいては最速ですが、推論時間においては <code>reduce-overhead</code> に比べて効率が良くありません。このガイドでは、デフォルトモードを使用しました。詳細については、<a href="https://pytorch.org/get-started/pytorch-2.0/#user-experience" rel="nofollow">こちら</a> を参照してください。</p> <p data-svelte-h="svelte-zipvn5"><code>torch</code> バージョン 2.0.1 で異なるコンピュータビジョンモデル、タスク、ハードウェアの種類、およびバッチサイズを使用して <code>torch.compile</code> をベンチマークしました。</p> <h2 class="relative group"><a id="benchmarking-code" 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="#benchmarking-code"><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>Benchmarking code</span></h2> <p data-svelte-h="svelte-6affft">以下に、各タスクのベンチマークコードを示します。推論前にGPUをウォームアップし、毎回同じ画像を使用して300回の推論の平均時間を取得します。</p> <h3 class="relative group"><a id="image-classification-with-vit" 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="#image-classification-with-vit"><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>Image Classification with ViT</span></h3> <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">from</span> PIL <span class="hljs-keyword">import</span> Image
	<span class="hljs-keyword">import</span> requests
	<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
	<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, AutoModelForImageClassification

	url = <span class="hljs-string">'http://images.cocodataset.org/val2017/000000039769.jpg'</span>
	image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

	processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">"google/vit-base-patch16-224"</span>)
	model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">"google/vit-base-patch16-224"</span>).to(<span class="hljs-string">"cuda"</span>)
	model = torch.<span class="hljs-built_in">compile</span>(model)

	processed_input = processor(image, return_tensors=<span class="hljs-string">'pt'</span>).to(device=<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">with</span> torch.no_grad():
	_ = model(**processed_input)<!-- HTML_TAG_END --></pre></div> <h4 class="relative group"><a id="object-detection-with-detr" 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="#object-detection-with-detr"><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>Object Detection with DETR</span></h4> <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">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, AutoModelForObjectDetection

	processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">"facebook/detr-resnet-50"</span>)
	model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">"facebook/detr-resnet-50"</span>).to(<span class="hljs-string">"cuda"</span>)
	model = torch.<span class="hljs-built_in">compile</span>(model)

	texts = [<span class="hljs-string">"a photo of a cat"</span>, <span class="hljs-string">"a photo of a dog"</span>]
	inputs = processor(text=texts, images=image, return_tensors=<span class="hljs-string">"pt"</span>).to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">with</span> torch.no_grad():
	_ = model(**inputs)<!-- HTML_TAG_END --></pre></div> <h4 class="relative group"><a id="image-segmentation-with-segformer" 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="#image-segmentation-with-segformer"><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>Image Segmentation with Segformer</span></h4> <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">from</span> transformers <span class="hljs-keyword">import</span> SegformerImageProcessor, SegformerForSemanticSegmentation

	processor = SegformerImageProcessor.from_pretrained(<span class="hljs-string">"nvidia/segformer-b0-finetuned-ade-512-512"</span>)
	model = SegformerForSemanticSegmentation.from_pretrained(<span class="hljs-string">"nvidia/segformer-b0-finetuned-ade-512-512"</span>).to(<span class="hljs-string">"cuda"</span>)
	model = torch.<span class="hljs-built_in">compile</span>(model)
	seg_inputs = processor(images=image, return_tensors=<span class="hljs-string">"pt"</span>).to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">with</span> torch.no_grad():
	_ = model(**seg_inputs)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-15jicxa">以下は、私たちがベンチマークを行ったモデルのリストです。</p> <p data-svelte-h="svelte-1pzh5ag"><strong>Image Classification</strong></p> <ul data-svelte-h="svelte-i1xpay"><li><a href="https://huggingface.co/google/vit-base-patch16-224" rel="nofollow">google/vit-base-patch16-224</a></li> <li><a href="https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k" rel="nofollow">microsoft/beit-base-patch16-224-pt22k-ft22k</a></li> <li><a href="https://huggingface.co/facebook/convnext-large-224" rel="nofollow">facebook/convnext-large-224</a></li> <li><a href="https://huggingface.co/" rel="nofollow">microsoft/resnet-50</a></li></ul> <p data-svelte-h="svelte-vq25eq"><strong>Image Segmentation</strong></p> <ul data-svelte-h="svelte-1vcuz7e"><li><a href="https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512" rel="nofollow">nvidia/segformer-b0-finetuned-ade-512-512</a></li> <li><a href="https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic" rel="nofollow">facebook/mask2former-swin-tiny-coco-panoptic</a></li> <li><a href="https://huggingface.co/facebook/maskformer-swin-base-ade" rel="nofollow">facebook/maskformer-swin-base-ade</a></li> <li><a href="https://huggingface.co/google/deeplabv3_mobilenet_v2_1.0_513" rel="nofollow">google/deeplabv3_mobilenet_v2_1.0_513</a></li></ul> <p data-svelte-h="svelte-1e8jpwt"><strong>Object Detection</strong></p> <ul data-svelte-h="svelte-yhf9tm"><li><a href="https://huggingface.co/google/owlvit-base-patch32" rel="nofollow">google/owlvit-base-patch32</a></li> <li><a href="https://huggingface.co/facebook/detr-resnet-101" rel="nofollow">facebook/detr-resnet-101</a></li> <li><a href="https://huggingface.co/microsoft/conditional-detr-resnet-50" rel="nofollow">microsoft/conditional-detr-resnet-50</a></li></ul> <p data-svelte-h="svelte-1x7u157">以下は、<code>torch.compile()</code>を使用した場合と使用しない場合の推論時間の可視化と、異なるハードウェアとバッチサイズの各モデルに対するパフォーマンス向上の割合です。</p> <div class="flex" data-svelte-h="svelte-1jw9wmi"><div><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/a100_batch_comp.png"></div> <div><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/v100_batch_comp.png"></div> <div><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/t4_batch_comp.png"></div></div> <div class="flex" data-svelte-h="svelte-nlzsqo"><div><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/A100_1_duration.png"></div> <div><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/A100_1_percentage.png"></div></div> <p data-svelte-h="svelte-gdeipd"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/v100_1_duration.png" alt="Duration Comparison on V100 with Batch Size of 1"></p> <p data-svelte-h="svelte-1cusdpa"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/T4_4_percentage.png" alt="Percentage Improvement on T4 with Batch Size of 4"></p> <p data-svelte-h="svelte-4j9ezu">下記は、各モデルについて<code>compile()</code>を使用した場合と使用しなかった場合の推論時間（ミリ秒単位）です。なお、OwlViTは大きなバッチサイズでの使用時にメモリ不足（OOM）が発生することに注意してください。</p> <h3 class="relative group"><a id="a100-batch-size-1" 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="#a100-batch-size-1"><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>A100 (batch size: 1)</span></h3> <table data-svelte-h="svelte-6uvhqg"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">9.325</td> <td align="center">7.584</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">11.759</td> <td align="center">10.500</td></tr> <tr><td align="center">Object Detection/OwlViT</td> <td align="center">24.978</td> <td align="center">18.420</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">11.282</td> <td align="center">8.448</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">34.619</td> <td align="center">19.040</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">10.410</td> <td align="center">10.208</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">6.531</td> <td align="center">4.124</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">60.188</td> <td align="center">49.117</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">75.764</td> <td align="center">59.487</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">8.583</td> <td align="center">3.974</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">36.276</td> <td align="center">18.197</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">31.219</td> <td align="center">17.993</td></tr></tbody></table> <h3 class="relative group"><a id="a100-batch-size-4" 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="#a100-batch-size-4"><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>A100 (batch size: 4)</span></h3> <table data-svelte-h="svelte-f4zjoc"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">14.832</td> <td align="center">14.499</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">18.838</td> <td align="center">16.476</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">13.205</td> <td align="center">13.048</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">48.657</td> <td align="center">32.418</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">22.940</td> <td align="center">21.631</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">6.657</td> <td align="center">4.268</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">74.277</td> <td align="center">61.781</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">180.700</td> <td align="center">159.116</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">14.174</td> <td align="center">8.515</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">68.101</td> <td align="center">44.998</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">56.470</td> <td align="center">35.552</td></tr></tbody></table> <h3 class="relative group"><a id="a100-batch-size-16" 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="#a100-batch-size-16"><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>A100 (batch size: 16)</span></h3> <table data-svelte-h="svelte-9ju0ii"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">40.944</td> <td align="center">40.010</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">37.005</td> <td align="center">31.144</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">41.854</td> <td align="center">41.048</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">164.382</td> <td align="center">161.902</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">82.258</td> <td align="center">75.561</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">7.018</td> <td align="center">5.024</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">178.945</td> <td align="center">154.814</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">638.570</td> <td align="center">579.826</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">51.693</td> <td align="center">30.310</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">232.887</td> <td align="center">155.021</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">180.491</td> <td align="center">124.032</td></tr></tbody></table> <h3 class="relative group"><a id="v100-batch-size-1" 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="#v100-batch-size-1"><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>V100 (batch size: 1)</span></h3> <table data-svelte-h="svelte-18ncoxq"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">10.495</td> <td align="center">6.00</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">13.321</td> <td align="center">5.862</td></tr> <tr><td align="center">Object Detection/OwlViT</td> <td align="center">25.769</td> <td align="center">22.395</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">11.347</td> <td align="center">7.234</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">33.951</td> <td align="center">19.388</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">11.623</td> <td align="center">10.412</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">6.484</td> <td align="center">3.820</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">64.640</td> <td align="center">49.873</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">95.532</td> <td align="center">72.207</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">9.217</td> <td align="center">4.753</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">52.818</td> <td align="center">28.367</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">39.512</td> <td align="center">20.816</td></tr></tbody></table> <h3 class="relative group"><a id="v100-batch-size-4" 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="#v100-batch-size-4"><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>V100 (batch size: 4)</span></h3> <table data-svelte-h="svelte-15udyd3"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">15.181</td> <td align="center">14.501</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">16.787</td> <td align="center">16.188</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">15.171</td> <td align="center">14.753</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">88.529</td> <td align="center">64.195</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">29.574</td> <td align="center">27.085</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">6.109</td> <td align="center">4.731</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">90.402</td> <td align="center">76.926</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">234.261</td> <td align="center">205.456</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">24.623</td> <td align="center">14.816</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">134.672</td> <td align="center">101.304</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">97.464</td> <td align="center">69.739</td></tr></tbody></table> <h3 class="relative group"><a id="v100-batch-size-16" 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="#v100-batch-size-16"><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>V100 (batch size: 16)</span></h3> <table data-svelte-h="svelte-rw07j7"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">52.209</td> <td align="center">51.633</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">61.013</td> <td align="center">55.499</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">53.938</td> <td align="center">53.581</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">OOM</td> <td align="center">OOM</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">109.682</td> <td align="center">100.771</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">14.857</td> <td align="center">12.089</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">249.605</td> <td align="center">222.801</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">831.142</td> <td align="center">743.645</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">93.129</td> <td align="center">55.365</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">482.425</td> <td align="center">361.843</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">344.661</td> <td align="center">255.298</td></tr></tbody></table> <h3 class="relative group"><a id="t4-batch-size-1" 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="#t4-batch-size-1"><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>T4 (batch size: 1)</span></h3> <table data-svelte-h="svelte-37x5jw"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">16.520</td> <td align="center">15.786</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">16.116</td> <td align="center">14.205</td></tr> <tr><td align="center">Object Detection/OwlViT</td> <td align="center">53.634</td> <td align="center">51.105</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">16.464</td> <td align="center">15.710</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">73.100</td> <td align="center">53.99</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">32.932</td> <td align="center">30.845</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">6.031</td> <td align="center">4.321</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">79.192</td> <td align="center">66.815</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">200.026</td> <td align="center">188.268</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">18.908</td> <td align="center">11.997</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">106.622</td> <td align="center">82.566</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">77.594</td> <td align="center">56.984</td></tr></tbody></table> <h3 class="relative group"><a id="t4-batch-size-4" 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="#t4-batch-size-4"><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>T4 (batch size: 4)</span></h3> <table data-svelte-h="svelte-1mc5027"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">43.653</td> <td align="center">43.626</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">45.327</td> <td align="center">42.445</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">52.007</td> <td align="center">51.354</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">277.850</td> <td align="center">268.003</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">119.259</td> <td align="center">105.580</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">13.039</td> <td align="center">11.388</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">201.540</td> <td align="center">184.670</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">764.052</td> <td align="center">711.280</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">74.289</td> <td align="center">48.677</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">421.859</td> <td align="center">357.614</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">289.002</td> <td align="center">226.945</td></tr></tbody></table> <h3 class="relative group"><a id="t4-batch-size-16" 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="#t4-batch-size-16"><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>T4 (batch size: 16)</span></h3> <table data-svelte-h="svelte-10eiin7"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ViT</td> <td align="center">163.914</td> <td align="center">160.907</td></tr> <tr><td align="center">Image Segmentation/Segformer</td> <td align="center">192.412</td> <td align="center">163.620</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">188.978</td> <td align="center">187.976</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">OOM</td> <td align="center">OOM</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">422.886</td> <td align="center">388.078</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">44.114</td> <td align="center">37.604</td></tr> <tr><td align="center">Image Segmentation/Mask2former</td> <td align="center">756.337</td> <td align="center">695.291</td></tr> <tr><td align="center">Image Segmentation/Maskformer</td> <td align="center">2842.940</td> <td align="center">2656.88</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">299.003</td> <td align="center">201.942</td></tr> <tr><td align="center">Object Detection/Resnet-101</td> <td align="center">1619.505</td> <td align="center">1262.758</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">1137.513</td> <td align="center">897.390</td></tr></tbody></table> <h2 class="relative group"><a id="pytorch-nightly" 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="#pytorch-nightly"><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>PyTorch Nightly</span></h2> <p data-svelte-h="svelte-1wc2mx4">また、PyTorchのナイトリーバージョン（2.1.0dev）でのベンチマークを行い、コンパイルされていないモデルとコンパイル済みモデルの両方でレイテンシーの向上を観察しました。ホイールは<a href="https://download.pytorch.org/whl/nightly/cu118" rel="nofollow">こちら</a>から入手できます。</p> <h3 class="relative group"><a id="a100" 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="#a100"><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>A100</span></h3> <table data-svelte-h="svelte-1cg5nyy"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>Batch Size</strong></th> <th align="center"><strong>torch 2.0 - no compile</strong></th> <th align="center"><strong>torch 2.0 -<br> compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/BeiT</td> <td align="center">Unbatched</td> <td align="center">12.462</td> <td align="center">6.954</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">4</td> <td align="center">14.109</td> <td align="center">12.851</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">16</td> <td align="center">42.179</td> <td align="center">42.147</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">Unbatched</td> <td align="center">30.484</td> <td align="center">15.221</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">4</td> <td align="center">46.816</td> <td align="center">30.942</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">16</td> <td align="center">163.749</td> <td align="center">163.706</td></tr></tbody></table> <h3 class="relative group"><a id="t4" 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="#t4"><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>T4</span></h3> <table data-svelte-h="svelte-1nlzppe"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>Batch Size</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/BeiT</td> <td align="center">Unbatched</td> <td align="center">14.408</td> <td align="center">14.052</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">4</td> <td align="center">47.381</td> <td align="center">46.604</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">16</td> <td align="center">42.179</td> <td align="center">42.147</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">Unbatched</td> <td align="center">68.382</td> <td align="center">53.481</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">4</td> <td align="center">269.615</td> <td align="center">204.785</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">16</td> <td align="center">OOM</td> <td align="center">OOM</td></tr></tbody></table> <h3 class="relative group"><a id="v100" 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="#v100"><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>V100</span></h3> <table data-svelte-h="svelte-ok1p6e"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>Batch Size</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/BeiT</td> <td align="center">Unbatched</td> <td align="center">13.477</td> <td align="center">7.926</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">4</td> <td align="center">15.103</td> <td align="center">14.378</td></tr> <tr><td align="center">Image Classification/BeiT</td> <td align="center">16</td> <td align="center">52.517</td> <td align="center">51.691</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">Unbatched</td> <td align="center">28.706</td> <td align="center">19.077</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">4</td> <td align="center">88.402</td> <td align="center">62.949</td></tr> <tr><td align="center">Object Detection/DETR</td> <td align="center">16</td> <td align="center">OOM</td> <td align="center">OOM</td></tr></tbody></table> <h2 class="relative group"><a id="reduce-overhead" 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="#reduce-overhead"><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>Reduce Overhead</span></h2> <p data-svelte-h="svelte-1qqs4aq">NightlyビルドでA100およびT4向けの <code>reduce-overhead</code> コンパイルモードをベンチマークしました。</p> <h3 class="relative group"><a id="a100" 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="#a100"><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>A100</span></h3> <table data-svelte-h="svelte-13rnx0"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>Batch Size</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">Unbatched</td> <td align="center">11.758</td> <td align="center">7.335</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">4</td> <td align="center">23.171</td> <td align="center">21.490</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">Unbatched</td> <td align="center">7.435</td> <td align="center">3.801</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">4</td> <td align="center">7.261</td> <td align="center">2.187</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">Unbatched</td> <td align="center">32.823</td> <td align="center">11.627</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">4</td> <td align="center">50.622</td> <td align="center">33.831</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">Unbatched</td> <td align="center">9.869</td> <td align="center">4.244</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">4</td> <td align="center">14.385</td> <td align="center">7.946</td></tr></tbody></table> <h3 class="relative group"><a id="t4" 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="#t4"><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>T4</span></h3> <table data-svelte-h="svelte-oh2zql"><thead><tr><th align="center"><strong>Task/Model</strong></th> <th align="center"><strong>Batch Size</strong></th> <th align="center"><strong>torch 2.0 - <br>no compile</strong></th> <th align="center"><strong>torch 2.0 - <br>compile</strong></th></tr></thead> <tbody><tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">Unbatched</td> <td align="center">32.137</td> <td align="center">31.84</td></tr> <tr><td align="center">Image Classification/ConvNeXT</td> <td align="center">4</td> <td align="center">120.944</td> <td align="center">110.209</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">Unbatched</td> <td align="center">9.761</td> <td align="center">7.698</td></tr> <tr><td align="center">Image Classification/ResNet</td> <td align="center">4</td> <td align="center">15.215</td> <td align="center">13.871</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">Unbatched</td> <td align="center">72.150</td> <td align="center">57.660</td></tr> <tr><td align="center">Object Detection/Conditional-DETR</td> <td align="center">4</td> <td align="center">301.494</td> <td align="center">247.543</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">Unbatched</td> <td align="center">22.266</td> <td align="center">19.339</td></tr> <tr><td align="center">Image Segmentation/MobileNet</td> <td align="center">4</td> <td align="center">78.311</td> <td align="center">50.983</td></tr></tbody></table> <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_torch_compile.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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