PyTorch-China · zeshengzong · Dec 17, 2024 · Dec 17, 2024
diff --git a/docs/.buildinfo b/docs/.buildinfo
@@ -1,4 +1,4 @@
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 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 1c6c44e0140fc763c96b46c891ab7a11
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diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
diff --git a/docs/.doctrees/index.doctree b/docs/.doctrees/index.doctree
diff --git a/docs/.doctrees/intermediate/compiled_autograd_tutorial.doctree b/docs/.doctrees/intermediate/compiled_autograd_tutorial.doctree
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
@@ -3,7 +3,7 @@
 
 **PyTorch 新增教程**
 
-* `使用自定义的 Triton 内核与 torch.compile <https://pytorch-cn.com/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html>`__
+* `Compiled Autograd: 为 torch.compile 捕获更大的后向图 <https://pytorch-cn.com/tutorials/intermediate/compiled_autograd_tutorial.html>`__
 * `通过区域编译减少 torch.compile 冷启动编译时间 <https://pytorch-cn.com/tutorials/recipes/regional_compilation.html>`__
 * `使用 Tensor Parallel (TP) 进行大规模 Transformer 模型训练 <https://pytorch-cn.com/tutorials/intermediate/TP_tutorial.html>`__
 * `利用半结构化(2:4)稀疏性加速 BERT <https://pytorch-cn.com/tutorials/advanced/semi_structured_sparse.html>`__

diff --git a/docs/_sources/intermediate/compiled_autograd_tutorial.rst.txt b/docs/_sources/intermediate/compiled_autograd_tutorial.rst.txt
@@ -4,21 +4,18 @@ Compiled Autograd: 为 ``torch.compile`` 捕获更大的后向图
 
 .. grid:: 2
 
-    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+    .. grid-item-card:: :octicon:`mortar-board;1em;` 你将学到什么
        :class-card: card-prerequisites
 
-       * How compiled autograd interacts with ``torch.compile``
-       * How to use the compiled autograd API
-       * How to inspect logs using ``TORCH_LOGS``
        * 编译自动微分如何与 ``torch.compile`` 交互
        * 如何使用编译自动微分API
        * 如何使用 ``TORCH_LOGS`` 检查日志
 
-    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+    .. grid-item-card:: :octicon:`list-unordered;1em;` 预备知识
        :class-card: card-prerequisites
 
        * PyTorch 2.4
-       * 完成 `torch.compile介绍` <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
+       * 完成 `torch.compile介绍 <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
        * 阅读 `开始使用PyTorch 2.x <https://pytorch.org/get-started/pytorch-2.0/>`_ 中的TorchDynamo和AOTAutograd部分
 
 概览

diff --git a/docs/index.html b/docs/index.html
@@ -595,7 +595,7 @@
 <h1>欢迎来到 PyTorch 教程<a class="headerlink" href="#pytorch" title="Permalink to this heading">¶</a></h1>
 <p><strong>PyTorch 新增教程</strong></p>
 <ul class="simple">
-<li><p><a class="reference external" href="https://pytorch-cn.com/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html">使用自定义的 Triton 内核与 torch.compile</a></p></li>
+<li><p><a class="reference external" href="https://pytorch-cn.com/tutorials/intermediate/compiled_autograd_tutorial.html">Compiled Autograd: 为 torch.compile 捕获更大的后向图</a></p></li>
 <li><p><a class="reference external" href="https://pytorch-cn.com/tutorials/recipes/regional_compilation.html">通过区域编译减少 torch.compile 冷启动编译时间</a></p></li>
 <li><p><a class="reference external" href="https://pytorch-cn.com/tutorials/intermediate/TP_tutorial.html">使用 Tensor Parallel (TP) 进行大规模 Transformer 模型训练</a></p></li>
 <li><p><a class="reference external" href="https://pytorch-cn.com/tutorials/advanced/semi_structured_sparse.html">利用半结构化(2:4)稀疏性加速 BERT</a></p></li>

diff --git a/docs/intermediate/compiled_autograd_tutorial.html b/docs/intermediate/compiled_autograd_tutorial.html
@@ -599,11 +599,8 @@ <h1>Compiled Autograd: 为 <code class="docutils literal notranslate"><span clas
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm card-prerequisites docutils">
 <div class="sd-card-body docutils">
 <div class="sd-card-title sd-font-weight-bold docutils">
-<svg version="1.1" width="1.0em" height="1.0em" class="sd-octicon sd-octicon-mortar-board" viewBox="0 0 16 16" aria-hidden="true"><path d="M7.693 1.066a.747.747 0 0 1 .614 0l7.25 3.25a.75.75 0 0 1 0 1.368L13 6.831v2.794c0 1.024-.81 1.749-1.66 2.173-.893.447-2.075.702-3.34.702-.278 0-.55-.012-.816-.036a.75.75 0 0 1 .133-1.494c.22.02.45.03.683.03 1.082 0 2.025-.221 2.67-.543.69-.345.83-.682.83-.832V7.503L8.307 8.934a.747.747 0 0 1-.614 0L4 7.28v1.663c.296.105.575.275.812.512.438.438.688 1.059.688 1.796v3a.75.75 0 0 1-.75.75h-3a.75.75 0 0 1-.75-.75v-3c0-.737.25-1.358.688-1.796.237-.237.516-.407.812-.512V6.606L.443 5.684a.75.75 0 0 1 0-1.368ZM2.583 5 8 7.428 13.416 5 8 2.572ZM2.5 11.25v2.25H4v-2.25c0-.388-.125-.611-.25-.735a.697.697 0 0 0-.5-.203.707.707 0 0 0-.5.203c-.125.124-.25.347-.25.735Z"></path></svg> What you will learn</div>
+<svg version="1.1" width="1.0em" height="1.0em" class="sd-octicon sd-octicon-mortar-board" viewBox="0 0 16 16" aria-hidden="true"><path d="M7.693 1.066a.747.747 0 0 1 .614 0l7.25 3.25a.75.75 0 0 1 0 1.368L13 6.831v2.794c0 1.024-.81 1.749-1.66 2.173-.893.447-2.075.702-3.34.702-.278 0-.55-.012-.816-.036a.75.75 0 0 1 .133-1.494c.22.02.45.03.683.03 1.082 0 2.025-.221 2.67-.543.69-.345.83-.682.83-.832V7.503L8.307 8.934a.747.747 0 0 1-.614 0L4 7.28v1.663c.296.105.575.275.812.512.438.438.688 1.059.688 1.796v3a.75.75 0 0 1-.75.75h-3a.75.75 0 0 1-.75-.75v-3c0-.737.25-1.358.688-1.796.237-.237.516-.407.812-.512V6.606L.443 5.684a.75.75 0 0 1 0-1.368ZM2.583 5 8 7.428 13.416 5 8 2.572ZM2.5 11.25v2.25H4v-2.25c0-.388-.125-.611-.25-.735a.697.697 0 0 0-.5-.203.707.707 0 0 0-.5.203c-.125.124-.25.347-.25.735Z"></path></svg> 你将学到什么</div>
 <ul class="simple">
-<li><p class="sd-card-text">How compiled autograd interacts with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></p></li>
-<li><p class="sd-card-text">How to use the compiled autograd API</p></li>
-<li><p class="sd-card-text">How to inspect logs using <code class="docutils literal notranslate"><span class="pre">TORCH_LOGS</span></code></p></li>
 <li><p class="sd-card-text">编译自动微分如何与 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 交互</p></li>
 <li><p class="sd-card-text">如何使用编译自动微分API</p></li>
 <li><p class="sd-card-text">如何使用 <code class="docutils literal notranslate"><span class="pre">TORCH_LOGS</span></code> 检查日志</p></li>
@@ -615,10 +612,10 @@ <h1>Compiled Autograd: 为 <code class="docutils literal notranslate"><span clas
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm card-prerequisites docutils">
 <div class="sd-card-body docutils">
 <div class="sd-card-title sd-font-weight-bold docutils">
-<svg version="1.1" width="1.0em" height="1.0em" class="sd-octicon sd-octicon-list-unordered" viewBox="0 0 16 16" aria-hidden="true"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg> Prerequisites</div>
+<svg version="1.1" width="1.0em" height="1.0em" class="sd-octicon sd-octicon-list-unordered" viewBox="0 0 16 16" aria-hidden="true"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg> 预备知识</div>
 <ul class="simple">
 <li><p class="sd-card-text">PyTorch 2.4</p></li>
-<li><p class="sd-card-text">完成 <cite>torch.compile介绍</cite> &lt;<a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html</a>&gt;`_</p></li>
+<li><p class="sd-card-text">完成 <a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">torch.compile介绍</a></p></li>
 <li><p class="sd-card-text">阅读 <a class="reference external" href="https://pytorch.org/get-started/pytorch-2.0/">开始使用PyTorch 2.x</a> 中的TorchDynamo和AOTAutograd部分</p></li>
 </ul>
 </div>

diff --git a/docs/searchindex.js b/docs/searchindex.js
diff --git a/intermediate_source/compiled_autograd_tutorial.rst b/intermediate_source/compiled_autograd_tutorial.rst
@@ -4,21 +4,18 @@ Compiled Autograd: 为 ``torch.compile`` 捕获更大的后向图
 
 .. grid:: 2
 
-    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+    .. grid-item-card:: :octicon:`mortar-board;1em;` 你将学到什么
        :class-card: card-prerequisites
 
-       * How compiled autograd interacts with ``torch.compile``
-       * How to use the compiled autograd API
-       * How to inspect logs using ``TORCH_LOGS``
        * 编译自动微分如何与 ``torch.compile`` 交互
        * 如何使用编译自动微分API
        * 如何使用 ``TORCH_LOGS`` 检查日志
 
-    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+    .. grid-item-card:: :octicon:`list-unordered;1em;` 预备知识
        :class-card: card-prerequisites
 
        * PyTorch 2.4
-       * 完成 `torch.compile介绍` <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
+       * 完成 `torch.compile介绍 <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
        * 阅读 `开始使用PyTorch 2.x <https://pytorch.org/get-started/pytorch-2.0/>`_ 中的TorchDynamo和AOTAutograd部分
 
 概览