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@@ -701,7 +701,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
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<span class="c1"># All strings are unicode in Python 3.</span>
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<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_tensor_str</span><span class="o">.</span><span class="n">_str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
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<div class="viewcode-block" id="Tensor.backward"><a class="viewcode-back" href="../../generated/torch.Tensor.backward.html#torch.Tensor.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">register_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span>
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<span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">,</span>
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<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
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<span class="c1"># bins: The number of bins used for histogram calculation.</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">HistogramObserver</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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<span class="n">factory_kwargs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">factory_kwargs</span><span class="p">(</span><span class="n">factory_kwargs</span><span class="p">)</span>
@@ -1816,7 +1820,7 @@ <h1>Source code for torch.ao.quantization.observer</h1><div class="highlight"><p
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<span class="c1"># Restrict activations to be in the range (0,127)</span>
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<span class="n">default_observer</span> <span class="o">=</span> <span class="n">MinMaxObserver</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">reduce_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">default_observer</span> <span class="o">=</span> <span class="n">MinMaxObserver</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">quant_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">quant_max</span><span class="o">=</span><span class="mi">127</span><span class="p">)</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">Default observer for static quantization, usually used for debugging.</span>
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<span class="sd">Default weight observer.</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="n">default_histogram_observer</span> <span class="o">=</span> <span class="n">HistogramObserver</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">reduce_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">default_histogram_observer</span> <span class="o">=</span> <span class="n">HistogramObserver</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">quant_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">quant_max</span><span class="o">=</span><span class="mi">127</span><span class="p">)</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">Default histogram observer, usually used for PTQ.</span>
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<span class="sd">&quot;&quot;&quot;</span>

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<span class="kn">import</span> <span class="nn">warnings</span>
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<span class="k">class</span> <span class="nc">QConfig</span><span class="p">(</span><span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;QConfig&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">,</span> <span class="s1">&#39;weight&#39;</span><span class="p">])):</span>
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<div class="viewcode-block" id="QConfig"><a class="viewcode-back" href="../../../../generated/torch.quantization.qconfig.QConfig.html#torch.quantization.qconfig.QConfig">[docs]</a><span class="k">class</span> <span class="nc">QConfig</span><span class="p">(</span><span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;QConfig&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">,</span> <span class="s1">&#39;weight&#39;</span><span class="p">])):</span>
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<span class="sd"> Describes how to quantize a layer or a part of the network by providing</span>
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<span class="sd"> settings (observer classes) for activations and weights respectively.</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
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<span class="s2">&quot;Use MyObserver.with_args(x=1) to override arguments to constructor if needed&quot;</span><span class="p">)</span>
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<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">QConfig</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span>
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<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">QConfig</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
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<span class="k">class</span> <span class="nc">QConfigDynamic</span><span class="p">(</span><span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;QConfigDynamic&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">,</span> <span class="s1">&#39;weight&#39;</span><span class="p">])):</span>

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