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Generate Python docs from pytorch/pytorch@45ed7c1
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2 changes: 1 addition & 1 deletion nightly/_modules/index.html
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<div class="pytorch-left-menu-search">

<div class="version">
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27 changes: 13 additions & 14 deletions nightly/_modules/torch/_functorch/aot_autograd.html
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Expand Down Expand Up @@ -969,14 +969,7 @@ <h1>Source code for torch._functorch.aot_autograd</h1><div class="highlight"><pr
<span class="n">python_dispatcher_mode</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">enable_python_dispatcher</span><span class="p">()</span> <span class="k">if</span> <span class="n">shape_env</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
<span class="p">)</span>

<span class="k">def</span> <span class="nf">try_record_chromium_data</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="c1"># `backend_compile` only exists as an event if we are compiling with dynamo</span>
<span class="c1"># In some unit tests we don&#39;t use dynamo, so we ignore those cases</span>
<span class="n">chromium_log</span> <span class="o">=</span> <span class="n">get_chromium_event_logger</span><span class="p">()</span>
<span class="k">if</span> <span class="s2">&quot;backend_compile&quot;</span> <span class="ow">in</span> <span class="n">chromium_log</span><span class="o">.</span><span class="n">get_stack</span><span class="p">():</span>
<span class="n">chromium_log</span><span class="o">.</span><span class="n">add_event_data</span><span class="p">(</span><span class="s2">&quot;backend_compile&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<span class="n">chromium_log</span> <span class="o">=</span> <span class="n">get_chromium_event_logger</span><span class="p">()</span>
<span class="c1"># See NOTE: [Deferring tensor pack/unpack hooks until runtime]</span>
<span class="c1"># If any saved tensor hooks are active, we **don&#39;t** want to trace them.</span>
<span class="c1"># Instead, we&#39;ll let them run at runtime, around the custom autograd.Function</span>
Expand Down Expand Up @@ -1030,8 +1023,8 @@ <h1>Source code for torch._functorch.aot_autograd</h1><div class="highlight"><pr
<span class="n">req_subclass_dispatch</span> <span class="o">=</span> <span class="n">requires_subclass_dispatch</span><span class="p">(</span>
<span class="n">fake_flat_args</span><span class="p">,</span> <span class="n">fw_metadata</span>
<span class="p">)</span>
<span class="n">try_record_chromium_data</span><span class="p">(</span>
<span class="n">requires_subclass_dispatch</span><span class="o">=</span><span class="n">req_subclass_dispatch</span>
<span class="n">chromium_log</span><span class="o">.</span><span class="n">try_add_event_data</span><span class="p">(</span>
<span class="s2">&quot;backend_compile&quot;</span><span class="p">,</span> <span class="n">requires_subclass_dispatch</span><span class="o">=</span><span class="n">req_subclass_dispatch</span>
<span class="p">)</span>

<span class="n">output_and_mutation_safe</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
Expand Down Expand Up @@ -1150,13 +1143,19 @@ <h1>Source code for torch._functorch.aot_autograd</h1><div class="highlight"><pr
<span class="k">if</span> <span class="n">aot_config</span><span class="o">.</span><span class="n">is_export</span><span class="p">:</span>
<span class="c1"># export uses just the &quot;graph bits&quot;, whereas the other</span>
<span class="c1"># two dispatchers include some extra work around handling a runtime epilogue</span>
<span class="n">try_record_chromium_data</span><span class="p">(</span><span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;export&quot;</span><span class="p">)</span>
<span class="n">chromium_log</span><span class="o">.</span><span class="n">try_add_event_data</span><span class="p">(</span>
<span class="s2">&quot;backend_compile&quot;</span><span class="p">,</span> <span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;export&quot;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">partial</span><span class="p">(</span><span class="n">aot_dispatch_export</span><span class="p">,</span> <span class="n">needs_autograd</span><span class="o">=</span><span class="n">needs_autograd</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">needs_autograd</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">aot_config</span><span class="o">.</span><span class="n">pre_dispatch</span><span class="p">:</span>
<span class="n">try_record_chromium_data</span><span class="p">(</span><span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;autograd&quot;</span><span class="p">)</span>
<span class="n">chromium_log</span><span class="o">.</span><span class="n">try_add_event_data</span><span class="p">(</span>
<span class="s2">&quot;backend_compile&quot;</span><span class="p">,</span> <span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;autograd&quot;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">aot_dispatch_autograd</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">try_record_chromium_data</span><span class="p">(</span><span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;inference&quot;</span><span class="p">)</span>
<span class="n">chromium_log</span><span class="o">.</span><span class="n">try_add_event_data</span><span class="p">(</span>
<span class="s2">&quot;backend_compile&quot;</span><span class="p">,</span> <span class="n">dispatch_mode</span><span class="o">=</span><span class="s2">&quot;inference&quot;</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">aot_dispatch_base</span>

<span class="n">compiler_fn</span> <span class="o">=</span> <span class="n">choose_dispatcher</span><span class="p">(</span><span class="n">needs_autograd</span><span class="p">,</span> <span class="n">aot_config</span><span class="p">)</span>
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6 changes: 4 additions & 2 deletions nightly/_modules/torch/_functorch/partitioners.html
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Expand Down Expand Up @@ -2045,7 +2045,7 @@ <h1>Source code for torch._functorch.partitioners</h1><div class="highlight"><pr

<span class="n">input_storages</span> <span class="o">=</span> <span class="p">{</span><span class="n">get_node_storage</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">node_info</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>

<span class="k">def</span> <span class="nf">get_recomputable_banned_nodes</span><span class="p">(</span><span class="n">banned_nodes</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">]:</span>
<span class="k">def</span> <span class="nf">get_recomputable_banned_nodes</span><span class="p">(</span><span class="n">banned_nodes</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">[</span>
<span class="n">i</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">banned_nodes</span>
Expand All @@ -2057,6 +2057,8 @@ <h1>Source code for torch._functorch.partitioners</h1><div class="highlight"><pr
<span class="p">]</span>

<span class="n">recomputable_banned_nodes</span> <span class="o">=</span> <span class="n">get_recomputable_banned_nodes</span><span class="p">(</span><span class="n">banned_nodes</span><span class="p">)</span>
<span class="c1"># sort first by name, to ensure determinism when multiple nodes have same size</span>
<span class="n">recomputable_banned_nodes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">recomputable_banned_nodes</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>

<span class="c1"># default: runtime_optimized_saved_values</span>
<span class="c1"># more aggressive: more_aggressive_saved_values</span>
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<a href='https://pytorch.org/functorch/versions.html'>nightly (2.6.0a0+git45ed7c1) &#x25BC</a>
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