Generate Python docs from pytorch/pytorch@ddf36c8

Author: pytorchbot

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@@ -2075,11 +2075,16 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <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>
+
 <span class="sd">        - Experimental or debug in-tree backends can be seen with `torch._dynamo.list_backends(None)`</span>
+
 <span class="sd">        - To register an out-of-tree custom backend: https://pytorch.org/docs/main/compile/custom-backends.html</span>
 <span class="sd">       mode (str): Can be either &quot;default&quot;, &quot;reduce-overhead&quot;, &quot;max-autotune&quot; or &quot;max-autotune-no-cudagraphs&quot;</span>
+
 <span class="sd">        - &quot;default&quot; is the default mode, which is a good balance between performance and overhead</span>
 
 <span class="sd">        - &quot;reduce-overhead&quot; is a mode that reduces the overhead of python with CUDA graphs,</span>
@@ -2098,13 +2103,21 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        - To see the exact configs that each mode sets you can call `torch._inductor.list_mode_options()`</span>
 
 <span class="sd">       options (dict): A dictionary of options to pass to the backend. Some notable ones to try out are</span>
+
 <span class="sd">        - `epilogue_fusion` which fuses pointwise ops into templates. Requires `max_autotune` to also be set</span>
+
 <span class="sd">        - `max_autotune` which will profile to pick the best matmul configuration</span>
+
 <span class="sd">        - `fallback_random` which is useful when debugging accuracy issues</span>
+
 <span class="sd">        - `shape_padding` which pads matrix shapes to better align loads on GPUs especially for tensor cores</span>
+
 <span class="sd">        - `triton.cudagraphs` which will reduce the overhead of python with CUDA graphs</span>
+
 <span class="sd">        - `trace.enabled` which is the most useful debugging flag to turn on</span>
+
 <span class="sd">        - `trace.graph_diagram` which will show you a picture of your graph after fusion</span>
+
 <span class="sd">        - For inductor you can see the full list of configs that it supports by calling `torch._inductor.list_options()`</span>
 <span class="sd">       disable (bool): Turn torch.compile() into a no-op for testing</span>
 

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