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	<link href="/docs/transformers/main/zh/_app/immutable/assets/0.tn0RQdqM.css" rel="modulepreload"> <!--[--><!--[0--><!--[--><!--[0--><!--[--><p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" 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><!----></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg><!----></button></div> <!--[-1--><!--]--></div><!----> <!--[0--><h1 class="relative group"><a id="使用-torchcompile-优化推理" 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="#使用-torchcompile-优化推理"><span><svg 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>使用 torch.compile() 优化推理</span></h1><!--]--><!----> <p>本指南旨在为使用<a href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html" rel="nofollow"><code>torch.compile()</code></a>在<a href="https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending" rel="nofollow">🤗 Transformers中的计算机视觉模型</a>中引入的推理速度提升提供一个基准。</p> <!--[1--><h2 class="relative group"><a id="torchcompile-的优势" 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="#torchcompile-的优势"><span><svg 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>torch.compile 的优势</span></h2><!--]--><!----> <p>根据模型和GPU的不同，<code>torch.compile()</code>在推理过程中可以提高多达30%的速度。要使用<code>torch.compile()</code>，只需安装2.0及以上版本的<code>torch</code>即可。</p> <p>编译模型需要时间，因此如果您只需要编译一次模型而不是每次推理都编译，那么它非常有用。
	要编译您选择的任何计算机视觉模型，请按照以下方式调用<code>torch.compile()</code>：</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 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="language-diff "><!---->from transformers import AutoModelForImageClassification

	model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda")
	<span class="hljs-addition">+ model = torch.compile(model)</span><!----></pre></div><!----> <p><code>compile()</code> 提供了多种编译模式，它们在编译时间和推理开销上有所不同。<code>max-autotune</code> 比 <code>reduce-overhead</code> 需要更长的时间，但会得到更快的推理速度。默认模式在编译时最快，但在推理时间上与 <code>reduce-overhead</code> 相比效率较低。在本指南中，我们使用了默认模式。您可以在<a href="https://pytorch.org/get-started/pytorch-2.0/#user-experience" rel="nofollow">这里</a>了解更多信息。</p> <p>我们在 PyTorch 2.0.1 版本上使用不同的计算机视觉模型、任务、硬件类型和数据批量大小对 <code>torch.compile</code> 进行了基准测试。</p> <!--[1--><h2 class="relative group"><a id="基准测试代码" 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="#基准测试代码"><span><svg 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>基准测试代码</span></h2><!--]--><!----> <p>以下是每个任务的基准测试代码。我们在推理之前”预热“GPU，并取300次推理的平均值，每次使用相同的图像。</p> <!--[2--><h3 class="relative group"><a id="使用-vit-进行图像分类" 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="#使用-vit-进行图像分类"><span><svg 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>使用 ViT 进行图像分类</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 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="language-python "><!----><span class="hljs-keyword">import</span> torch
	<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)
	<!----></pre></div><!----> <!--[3--><h4 class="relative group"><a id="使用-detr-进行目标检测" 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="#使用-detr-进行目标检测"><span><svg 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>使用 DETR 进行目标检测</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 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="language-python "><!----><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)<!----></pre></div><!----> <!--[3--><h4 class="relative group"><a id="使用-segformer-进行图像分割" 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="#使用-segformer-进行图像分割"><span><svg 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>使用 Segformer 进行图像分割</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 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="language-python "><!----><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)<!----></pre></div><!----> <p>以下是我们进行基准测试的模型列表。</p> <p><strong>图像分类</strong></p> <ul><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><strong>图像分割</strong></p> <ul><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><strong>目标检测</strong></p> <ul><li><p><a href="https://huggingface.co/google/owlvit-base-patch32" rel="nofollow">google/owlvit-base-patch32</a></p></li> <li><p><a href="https://huggingface.co/facebook/detr-resnet-101" rel="nofollow">facebook/detr-resnet-101</a></p></li> <li><p><a href="https://huggingface.co/microsoft/conditional-detr-resnet-50" rel="nofollow">microsoft/conditional-detr-resnet-50</a></p> <p>下面是使用和不使用<code>torch.compile()</code>的推理持续时间可视化，以及每个模型在不同硬件和数据批量大小下的改进百分比。</p></li></ul> <div class="flex"><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"><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><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><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>下面可以找到每个模型使用和不使用<code>compile()</code>的推理时间（毫秒）。请注意，OwlViT在大批量大小下会导致内存溢出。</p> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[1--><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 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>我们还在 PyTorch Nightly 版本（2.1.0dev）上进行了基准测试，可以在<a href="https://download.pytorch.org/whl/nightly/cu118" rel="nofollow">这里</a>找到 Nightly 版本的安装包，并观察到了未编译和编译模型的延迟性能改善。</p> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[2--><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 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><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> <!--[1--><h2 class="relative group"><a id="降低开销" 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="#降低开销"><span><svg 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>降低开销</span></h2><!--]--><!----> <p>我们在 PyTorch Nightly 版本中为 A100 和 T4 进行了 <code>reduce-overhead</code> 编译模式的性能基准测试。</p> <!--[2--><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 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><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> <!--[2--><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 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><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/zh/perf_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><span class="underline">Update</span> on GitHub</span></a><!----> <p></p><!--]--><!----><!--]--><!--]--><!--]--> <!--[-1--><!--]--><!--]-->

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