Generate Python docs from pytorch/pytorch@ca96784

Author: pytorchbot

docs/main/_images/RReLU.png

@@ -242,7 +242,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.3.0a0+gitca96784 ) &#x25BC</a>
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@@ -596,7 +596,9 @@ <h1>All modules for which code is available</h1>
 <ul><li><a href="torch/distributed/elastic/multiprocessing/api.html">torch.distributed.elastic.multiprocessing.api</a></li>
 <li><a href="torch/distributed/elastic/multiprocessing/errors.html">torch.distributed.elastic.multiprocessing.errors</a></li>
 <ul><li><a href="torch/distributed/elastic/multiprocessing/errors/error_handler.html">torch.distributed.elastic.multiprocessing.errors.error_handler</a></li>
-</ul></ul><li><a href="torch/distributed/elastic/rendezvous/api.html">torch.distributed.elastic.rendezvous.api</a></li>
+</ul><li><a href="torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.html">torch.distributed.elastic.multiprocessing.subprocess_handler.handlers</a></li>
+<li><a href="torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.html">torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler</a></li>
+</ul><li><a href="torch/distributed/elastic/rendezvous/api.html">torch.distributed.elastic.rendezvous.api</a></li>
 <li><a href="torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend</a></li>
 <li><a href="torch/distributed/elastic/rendezvous/dynamic_rendezvous.html">torch.distributed.elastic.rendezvous.dynamic_rendezvous</a></li>
 <li><a href="torch/distributed/elastic/rendezvous/etcd_rendezvous.html">torch.distributed.elastic.rendezvous.etcd_rendezvous</a></li>

docs/main/_modules/index.html

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     <div class="version">
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@@ -1364,7 +1364,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">    A handful of CUDA operations are nondeterministic if the CUDA version is</span>
 <span class="sd">    10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8``</span>
 <span class="sd">    or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more</span>
-<span class="sd">    details: `&lt;https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility&gt;`_</span>
+<span class="sd">    details: `&lt;https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility&gt;`_</span>
 <span class="sd">    If one of these environment variable configurations is not set, a :class:`RuntimeError`</span>
 <span class="sd">    will be raised from these operations when called with CUDA tensors:</span>
 
@@ -2247,6 +2247,10 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_</span><span class="p">,</span> <span class="n">inputs_</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">compiler_fn</span><span class="p">(</span><span class="n">model_</span><span class="p">,</span> <span class="n">inputs_</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
 
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">compiler_fn</span><span class="p">,</span> <span class="s2">&quot;reset&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">compiler_fn</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+
 
 <div class="viewcode-block" id="compile"><a class="viewcode-back" href="../generated/torch.compile.html#torch.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span>
             <span class="n">fullgraph</span><span class="p">:</span> <span class="n">builtins</span><span class="o">.</span><span class="n">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>

docs/main/_modules/torch.html

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docs/main/_modules/torch/__config__.html

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docs/main/_modules/torch/__future__.html

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docs/main/_modules/torch/_tensor.html

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docs/main/_modules/torch/_tensor_str.html

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docs/main/_modules/torch/_utils.html

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docs/main/_modules/torch/ao/nn/intrinsic/qat/modules/linear_relu.html

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docs/main/_modules/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.html

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docs/main/_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html

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docs/main/_modules/torch/ao/nn/qat/modules/linear.html

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docs/main/_modules/torch/ao/nn/quantizable/modules/activation.html

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@@ -785,7 +785,7 @@ <h1>Source code for torch.ao.nn.quantizable.modules.rnn</h1><div class="highligh
         <span class="bp">self</span><span class="o">.</span><span class="n">batch_first</span> <span class="o">=</span> <span class="n">batch_first</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="o">=</span> <span class="n">bidirectional</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># We don&#39;t want to train using this module</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># Default to eval mode. If we want to train, we will explicitly set to training.</span>
         <span class="n">num_directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dropout</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">dropout</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> \
@@ -874,9 +874,14 @@ <h1>Source code for torch.ao.nn.quantizable.modules.rnn</h1><div class="highligh
         <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">num_layers</span><span class="p">):</span>
             <span class="n">observed</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">_LSTMLayer</span><span class="o">.</span><span class="n">from_float</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">qconfig</span><span class="p">,</span>
                                                          <span class="n">batch_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="c1"># TODO: Remove setting observed to eval to enable QAT.</span>
-        <span class="n">observed</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="n">observed</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">observed</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="c1"># Prepare the model</span>
+        <span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">training</span><span class="p">:</span>
+            <span class="n">observed</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+            <span class="n">observed</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">observed</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">observed</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+            <span class="n">observed</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">observed</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">observed</span>
 
     <span class="nd">@classmethod</span>

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