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Generate Python docs from pytorch/pytorch@8072ebc
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Expand Down Expand Up @@ -1170,6 +1170,26 @@ <h1>Source code for torch._functorch.partitioners</h1><div class="highlight"><pr
<span class="ow">and</span> <span class="n">user</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;ac_graph_id&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;ac_graph_id&quot;</span><span class="p">]</span>
<span class="p">):</span>
<span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;recompute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">CheckpointPolicy</span><span class="o">.</span><span class="n">MUST_SAVE</span>
<span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;has_backward_hook&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
<span class="n">must_recompute</span><span class="p">(</span><span class="n">user</span><span class="p">)</span> <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">users</span>
<span class="p">):</span>
<span class="c1"># If node is AC region output and has a backward hook on it, we intentionally choose to save it.</span>
<span class="c1"># This is to work around circular dependencies in Traceable FSDP2+AC.</span>
<span class="c1"># Example:</span>
<span class="c1"># ```</span>
<span class="c1"># out = fully_shard(utils.checkpoint(module))(x)</span>
<span class="c1"># norm_out = layer_norm(out)</span>
<span class="c1"># ```</span>
<span class="c1"># Here there is a circular dependency:</span>
<span class="c1"># 1. In backward, grad_input of layer_norm aka. `out_grad` is actually dependent on `out`.</span>
<span class="c1"># 2. `out` depends on `out`&#39;s backward hook created by FSDP2 (which does all-gather for `module` weights)</span>
<span class="c1"># in order to be recomputed.</span>
<span class="c1"># 3. `out`&#39;s backward hook, as is the case for all eager backward hooks, depends on `out_grad`</span>
<span class="c1"># -&gt; circular dependency with (1)!</span>
<span class="c1">#</span>
<span class="c1"># Solution: check whether `out` has a backward hook, and if so, intentionally save `out`</span>
<span class="c1"># in forward graph outputs. With this, we can break the above circular dependency.</span>
<span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;recompute&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">CheckpointPolicy</span><span class="o">.</span><span class="n">MUST_SAVE</span>
<span class="k">return</span> <span class="n">joint_module</span>


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