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Expand Down Expand Up @@ -2187,8 +2187,8 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
<span class="n">device_type</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device_type</span><span class="p">)</span><span class="o">.</span><span class="n">type</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="vm">__name__</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">device_type</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;The runtime module of &#39;</span><span class="si">{}</span><span class="s2">&#39; has already &quot;</span>
<span class="s2">&quot;been registered with &#39;</span><span class="si">{}</span><span class="s2">&#39;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device_type</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">device_type</span><span class="p">)))</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The runtime module of &#39;</span><span class="si">{</span><span class="n">device_type</span><span class="si">}</span><span class="s2">&#39; has already &quot;</span>
<span class="sa">f</span><span class="s2">&quot;been registered with &#39;</span><span class="si">{</span><span class="nb">getattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="w"> </span><span class="n">device_type</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">device_type</span><span class="p">,</span> <span class="n">module</span><span class="p">)</span>
<span class="n">torch_module_name</span> <span class="o">=</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">device_type</span><span class="p">])</span>
<span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="n">torch_module_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">module</span>
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Expand Down Expand Up @@ -1073,8 +1073,8 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>

<span class="k">if</span> <span class="n">m</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">n</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;LPBPCG algorithm is not applicable when the number of A rows (=</span><span class="si">{}</span><span class="s2">)&quot;</span>
<span class="s2">&quot; is smaller than 3 x the number of requested eigenpairs (=</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="sa">f</span><span class="s2">&quot;LPBPCG algorithm is not applicable when the number of A rows (=</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">)&quot;</span>
<span class="sa">f</span><span class="s2">&quot; is smaller than 3 x the number of requested eigenpairs (=</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">)&quot;</span>
<span class="p">)</span>

<span class="n">method</span> <span class="o">=</span> <span class="s2">&quot;ortho&quot;</span> <span class="k">if</span> <span class="n">method</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">method</span>
Expand Down Expand Up @@ -1610,9 +1610,7 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>
<span class="k">assert</span> <span class="n">B</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;Overdetermined shape of U:&quot;</span>
<span class="s2">&quot; #B-cols(=</span><span class="si">{}</span><span class="s2">) &gt;= #U-cols(=</span><span class="si">{}</span><span class="s2">) + #V-cols(=</span><span class="si">{}</span><span class="s2">) must hold&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">B</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">U</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">V</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="sa">f</span><span class="s2">&quot; #B-cols(=</span><span class="si">{</span><span class="n">B</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">) &gt;= #U-cols(=</span><span class="si">{</span><span class="n">U</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">) + #V-cols(=</span><span class="si">{</span><span class="n">V</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">) must hold&quot;</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ivars</span><span class="p">[</span><span class="s2">&quot;ortho_i&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ivars</span><span class="p">[</span><span class="s2">&quot;ortho_j&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">j</span>
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Expand Down Expand Up @@ -777,8 +777,8 @@ <h1>Source code for torch.ao.nn.quantizable.modules.rnn</h1><div class="highligh
<span class="k">if</span> <span class="n">num_layers</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;dropout option adds dropout after all but last &quot;</span>
<span class="s2">&quot;recurrent layer, so non-zero dropout expects &quot;</span>
<span class="s2">&quot;num_layers greater than 1, but got dropout=</span><span class="si">{}</span><span class="s2"> &quot;</span>
<span class="s2">&quot;and num_layers=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dropout</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">))</span>
<span class="sa">f</span><span class="s2">&quot;num_layers greater than 1, but got dropout=</span><span class="si">{</span><span class="n">dropout</span><span class="si">}</span><span class="s2"> &quot;</span>
<span class="sa">f</span><span class="s2">&quot;and num_layers=</span><span class="si">{</span><span class="n">num_layers</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

<span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">_LSTMLayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
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Expand Down Expand Up @@ -564,7 +564,7 @@ <h1>Source code for torch.ao.nn.quantized.dynamic.modules.linear</h1><div class=
<span class="n">weight_observer</span> <span class="o">=</span> <span class="n">default_dynamic_qconfig</span><span class="o">.</span><span class="n">weight</span><span class="p">()</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">weight_observer</span><span class="o">.</span><span class="n">dtype</span>
<span class="k">assert</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">],</span> <span class="s2">&quot;The only supported dtypes for &quot;</span> \
<span class="s2">&quot;dynamic quantized linear are qint8 and float16 got: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="sa">f</span><span class="s2">&quot;dynamic quantized linear are qint8 and float16 got: </span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="n">weight_observer</span><span class="p">(</span><span class="n">mod</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">:</span>
<span class="n">qweight</span> <span class="o">=</span> <span class="n">_quantize_weight</span><span class="p">(</span><span class="n">mod</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="n">weight_observer</span><span class="p">)</span>
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