Generate Python docs from pytorch/pytorch@5ccf28d

Author: pytorchbot

docs/master/__config__.html

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docs/master/_modules/torch/ao/quantization/qconfig.html

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docs/master/_modules/torch/ao/quantization/quantize.html

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@@ -792,15 +792,17 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
 <span class="sd">               (&quot;submodule.standalone&quot;,</span>
 <span class="sd">                None,  # qconfig_dict for the prepare function called in the submodule,</span>
 <span class="sd">                       # None means use qconfig from parent qconfig_dict</span>
-<span class="sd">                {&quot;input_quantized_idxs&quot;: [], &quot;output_quantized_idxs&quot;: []})  # prepare_custom_config_dict</span>
+<span class="sd">                {&quot;input_quantized_idxs&quot;: [], &quot;output_quantized_idxs&quot;: []}),  # prepare_custom_config_dict</span>
+<span class="sd">                {}  # backend_config_dict, TODO: point to README doc when it&#39;s ready</span>
 <span class="sd">            ],</span>
 
 <span class="sd">            &quot;standalone_module_class&quot;: [</span>
 <span class="sd">                # module_class, qconfig_dict, prepare_custom_config_dict</span>
 <span class="sd">                (StandaloneModule,</span>
 <span class="sd">                 None,  # qconfig_dict for the prepare function called in the submodule,</span>
 <span class="sd">                        # None means use qconfig from parent qconfig_dict</span>
-<span class="sd">                {&quot;input_quantized_idxs&quot;: [0], &quot;output_quantized_idxs&quot;: [0]})  # prepare_custom_config_dict</span>
+<span class="sd">                {&quot;input_quantized_idxs&quot;: [0], &quot;output_quantized_idxs&quot;: [0]},  # prepare_custom_config_dict</span>
+<span class="sd">                {})  # backend_config_dict, TODO: point to README doc when it&#39;s ready</span>
 <span class="sd">            ],</span>
 
 <span class="sd">            # user will manually define the corresponding observed</span>

docs/master/_modules/torch/ao/quantization/quantize_fx.html

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@@ -1278,6 +1278,7 @@ <h1>Source code for torch.autograd.gradcheck</h1><div class="highlight"><pre>
     <span class="k">with</span> <span class="n">fwAD</span><span class="o">.</span><span class="n">dual_level</span><span class="p">():</span>
         <span class="n">fw_grads</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">dual_inputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">tensor_indices</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">inp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
             <span class="k">if</span> <span class="n">is_tensor_like</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span> <span class="ow">and</span> <span class="n">inp</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">inp</span><span class="o">.</span><span class="n">layout</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">_mkldnn</span><span class="p">:</span>  <span class="c1"># type: ignore[attr-defined]</span>
@@ -1287,12 +1288,15 @@ <h1>Source code for torch.autograd.gradcheck</h1><div class="highlight"><pre>
                 <span class="c1"># If inp is a differentiable view, the dual might not be the tangent given to</span>
                 <span class="c1"># make_dual, so read it explicitly from the dual tensor</span>
                 <span class="n">fw_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fwAD</span><span class="o">.</span><span class="n">unpack_dual</span><span class="p">(</span><span class="n">inp</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">tensor_indices</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
             <span class="n">dual_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span>
 
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">fw_grad</span><span class="p">,</span> <span class="n">u</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">fw_grads</span><span class="p">,</span> <span class="n">all_u</span><span class="p">)):</span>
             <span class="n">fw_grad</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">fw_grad</span><span class="p">))</span>
 
-        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">inp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tensor_inputs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">inp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">idx</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">tensor_indices</span><span class="p">:</span>
+                <span class="k">continue</span>
             <span class="n">dual_inp_obj</span> <span class="o">=</span> <span class="n">dual_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
 
             <span class="c1"># case 1 (Materialized Zero Tensor Tangent)</span>
@@ -1777,8 +1781,6 @@ <h1>Source code for torch.autograd.gradcheck</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">assert</span> <span class="n">check_forward_ad</span> <span class="ow">or</span> <span class="n">check_backward_ad</span><span class="p">,</span> \
         <span class="s2">&quot;Expected at least one of check_forward_ad or check_backward_ad to be True&quot;</span>
-    <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="n">check_undefined_grad</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">check_backward_ad</span><span class="p">),</span> \
-        <span class="s2">&quot;Setting check_undefined_grad=True requires check_backward_ad to be True&quot;</span>
     <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="n">check_batched_grad</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">check_backward_ad</span><span class="p">),</span> <span class="p">(</span>
         <span class="s2">&quot;Setting check_batched_grad=True requires check_backward_ad to be True&quot;</span><span class="p">)</span>
     <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="n">check_batched_forward_grad</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">check_forward_ad</span><span class="p">),</span> <span class="p">(</span>
@@ -1823,7 +1825,7 @@ <h1>Source code for torch.autograd.gradcheck</h1><div class="highlight"><pre>
 
     <span class="n">_test_backward_mul_by_grad_output</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">tupled_inputs</span><span class="p">,</span> <span class="n">check_sparse_nnz</span><span class="p">)</span>
 
-    <span class="k">if</span> <span class="n">check_undefined_grad</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">check_undefined_grad</span> <span class="ow">and</span> <span class="n">check_backward_ad</span><span class="p">:</span>
         <span class="n">_test_undefined_backward_mode</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">tupled_inputs</span><span class="p">)</span>
     <span class="k">return</span> <span class="kc">True</span>
 

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