Generate Python docs from pytorch/pytorch@85a7535

Author: pytorchbot

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

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@@ -400,7 +400,6 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
 <span class="kn">from</span> <span class="nn">torch.nn.intrinsic</span> <span class="kn">import</span> <span class="n">_FusedModule</span>
 <span class="kn">from</span> <span class="nn">.fx</span> <span class="kn">import</span> <span class="n">Fuser</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span> <span class="nn">.fx</span> <span class="kn">import</span> <span class="n">prepare</span><span class="p">,</span> <span class="n">convert</span>  <span class="c1"># noqa: F401</span>
-<span class="kn">from</span> <span class="nn">.fx</span> <span class="kn">import</span> <span class="n">get_fbgemm_backend_config_dict</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span> <span class="nn">.fx</span> <span class="kn">import</span> <span class="n">get_tensorrt_backend_config_dict</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span> <span class="nn">.fx.graph_module</span> <span class="kn">import</span> <span class="n">ObservedGraphModule</span><span class="p">,</span> <span class="n">QuantizedGraphModule</span>
 <span class="kn">from</span> <span class="nn">.fx.qconfig_utils</span> <span class="kn">import</span> <span class="p">(</span>
@@ -953,6 +952,7 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
     <span class="n">convert_custom_config_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">is_standalone_module</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
     <span class="n">_remove_qconfig</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">qconfig_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantizedGraphModule</span><span class="p">:</span>
     <span class="sd">&quot;&quot;&quot; `is_standalone_module`: see docs in :func:`~torch.ao.quantization.prepare_standalone_module_fx`</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -968,6 +968,7 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
         <span class="n">convert_custom_config_dict</span><span class="p">,</span>
         <span class="n">is_standalone_module</span><span class="p">,</span>
         <span class="n">_remove_qconfig_flag</span><span class="o">=</span><span class="n">_remove_qconfig</span><span class="p">,</span>
+        <span class="n">convert_qconfig_dict</span><span class="o">=</span><span class="n">qconfig_dict</span><span class="p">,</span>
     <span class="p">)</span>
 
     <span class="n">preserved_attributes</span> <span class="o">=</span> <span class="n">convert_custom_config_dict</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;preserved_attributes&quot;</span><span class="p">,</span> <span class="p">[])</span>
@@ -981,6 +982,7 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
     <span class="n">is_reference</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
     <span class="n">convert_custom_config_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">_remove_qconfig</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">qconfig_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantizedGraphModule</span><span class="p">:</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot; Convert a calibrated or trained model to a quantized model</span>
 
@@ -1030,6 +1032,28 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
 
 <span class="sd">        * `_remove_qconfig`: Option to remove the qconfig attributes in the model after convert.</span>
 
+<span class="sd">        * `qconfig_dict`: qconfig_dict with either same keys as what is passed to</span>
+<span class="sd">          the qconfig_dict in `prepare_fx` API, with same values or `None`, or</span>
+<span class="sd">          additional keys with values set to `None`</span>
+
+<span class="sd">          For each entry whose value is set to None, we skip quantizing that entry in the model::</span>
+
+<span class="sd">            qconfig_dict = {</span>
+<span class="sd">              # used for object_type, skip quantizing torch.nn.functional.add</span>
+<span class="sd">              &quot;object_type&quot;: [</span>
+<span class="sd">                (torch.nn.functional.add, None),</span>
+<span class="sd">                (torch.nn.functional.linear, qconfig_from_prepare)</span>
+<span class="sd">                ...,</span>
+<span class="sd">              ],</span>
+
+<span class="sd">              # sed for module names, skip quantizing &quot;foo.bar&quot;</span>
+<span class="sd">              &quot;module_name&quot;: [</span>
+<span class="sd">                (&quot;foo.bar&quot;, None)</span>
+<span class="sd">                ...,</span>
+<span class="sd">              ],</span>
+<span class="sd">            }</span>
+
+
 <span class="sd">    Return:</span>
 <span class="sd">        A quantized model (GraphModule)</span>
 
@@ -1045,6 +1069,7 @@ <h1>Source code for torch.ao.quantization.quantize_fx</h1><div class="highlight"
         <span class="n">is_reference</span><span class="p">,</span>
         <span class="n">convert_custom_config_dict</span><span class="p">,</span>
         <span class="n">_remove_qconfig</span><span class="o">=</span><span class="n">_remove_qconfig</span><span class="p">,</span>
+        <span class="n">qconfig_dict</span><span class="o">=</span><span class="n">qconfig_dict</span><span class="p">,</span>
     <span class="p">)</span></div>
 
 

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

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@@ -706,6 +706,11 @@ <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
                                 <span class="n">_prepare_profiler</span><span class="p">,</span> <span class="n">_enable_profiler</span><span class="p">,</span> <span class="n">_disable_profiler</span><span class="p">)</span>
 
 <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">profiler</span>
+
+<span class="k">def</span> <span class="nf">register_py_tensor_class_for_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="bp">cls</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="nb">type</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;cls isn&#39;t a typeinfo object&quot;</span><span class="p">)</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_autograd</span><span class="o">.</span><span class="n">_register_py_class_for_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span>
 </pre></div>
 
              </article>

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