Generate Python docs from pytorch/pytorch@65bce81

Author: pytorchbot

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@@ -2066,7 +2066,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="compile"><a class="viewcode-back" href="../generated/torch.compile.html#torch.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span>
             <span class="n">fullgraph</span><span class="p">:</span> <span class="n">builtins</span><span class="o">.</span><span class="n">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-            <span class="n">dynamic</span><span class="p">:</span> <span class="n">builtins</span><span class="o">.</span><span class="n">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="n">dynamic</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">builtins</span><span class="o">.</span><span class="n">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
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@@ -2088,12 +2088,13 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">    Args:</span>
 <span class="sd">       model (Callable): Module/function to optimize</span>
 <span class="sd">       fullgraph (bool): Whether it is ok to break model into several subgraphs</span>
-<span class="sd">       dynamic (bool): Use dynamic shape tracing.  When this is True, we will up-front attempt</span>
+<span class="sd">       dynamic (bool or None): Use dynamic shape tracing.  When this is True, we will up-front attempt</span>
 <span class="sd">        to generate a kernel that is as dynamic as possible to avoid recompilations when</span>
 <span class="sd">        sizes change.  This may not always work as some operations/optimizations will</span>
 <span class="sd">        force specialization; use TORCH_LOGS=dynamic to debug overspecialization.</span>
-<span class="sd">        In particular, if you use &quot;reduce-overhead&quot;, this will force sizes to be static</span>
-<span class="sd">        even with dynamic=True.</span>
+<span class="sd">        When this is False, we will NEVER generate dynamic kernels, we will always specialize.</span>
+<span class="sd">        By default (None), we automatically detect if dynamism has occurred and compile a more</span>
+<span class="sd">        dynamic kernel upon recompile.</span>
 <span class="sd">       backend (str or Callable): backend to be used</span>
 <span class="sd">        - &quot;inductor&quot; is the default backend, which is a good balance between performance and overhead</span>
 <span class="sd">        - Non experimental in-tree backends can be seen with `torch._dynamo.list_backends()`</span>

docs/main/_modules/torch.html

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