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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<section id="quantization-api-reference">
<h1>Quantization API Reference<a class="headerlink" href="#quantization-api-reference" title="Permalink to this heading">¶</a></h1>
<section id="torch-quantization">
<h2>torch.quantization<a class="headerlink" href="#torch-quantization" title="Permalink to this heading">¶</a></h2>
<p>This module contains Eager mode quantization APIs.</p>
<section id="top-level-apis">
<h3>Top level APIs<a class="headerlink" href="#top-level-apis" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.quantize"/><a class="reference internal" href="generated/torch.quantization.quantize.html#torch.quantization.quantize" title="torch.quantization.quantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize</span></code></a></p></td>
<td><p>Quantize the input float model with post training static quantization.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.quantize_dynamic"/><a class="reference internal" href="generated/torch.quantization.quantize_dynamic.html#torch.quantization.quantize_dynamic" title="torch.quantization.quantize_dynamic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_dynamic</span></code></a></p></td>
<td><p>Converts a float model to dynamic (i.e.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.quantize_qat"/><a class="reference internal" href="generated/torch.quantization.quantize_qat.html#torch.quantization.quantize_qat" title="torch.quantization.quantize_qat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_qat</span></code></a></p></td>
<td><p>Do quantization aware training and output a quantized model</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.prepare"/><a class="reference internal" href="generated/torch.quantization.prepare.html#torch.quantization.prepare" title="torch.quantization.prepare"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare</span></code></a></p></td>
<td><p>Prepares a copy of the model for quantization calibration or quantization-aware training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.prepare_qat"/><a class="reference internal" href="generated/torch.quantization.prepare_qat.html#torch.quantization.prepare_qat" title="torch.quantization.prepare_qat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_qat</span></code></a></p></td>
<td><p>Prepares a copy of the model for quantization calibration or quantization-aware training and converts it to quantized version.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.convert"/><a class="reference internal" href="generated/torch.quantization.convert.html#torch.quantization.convert" title="torch.quantization.convert"><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert</span></code></a></p></td>
<td><p>Converts submodules in input module to a different module according to <cite>mapping</cite> by calling <cite>from_float</cite> method on the target module class.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="preparing-model-for-quantization">
<h3>Preparing model for quantization<a class="headerlink" href="#preparing-model-for-quantization" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.fuse_modules"/><a class="reference internal" href="generated/torch.quantization.fuse_modules.html#torch.quantization.fuse_modules" title="torch.quantization.fuse_modules"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fuse_modules</span></code></a></p></td>
<td><p>Fuses a list of modules into a single module</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.QuantStub"/><a class="reference internal" href="generated/torch.quantization.QuantStub.html#torch.quantization.QuantStub" title="torch.quantization.QuantStub"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantStub</span></code></a></p></td>
<td><p>Quantize stub module, before calibration, this is same as an observer, it will be swapped as <cite>nnq.Quantize</cite> in <cite>convert</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.DeQuantStub"/><a class="reference internal" href="generated/torch.quantization.DeQuantStub.html#torch.quantization.DeQuantStub" title="torch.quantization.DeQuantStub"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeQuantStub</span></code></a></p></td>
<td><p>Dequantize stub module, before calibration, this is same as identity, this will be swapped as <cite>nnq.DeQuantize</cite> in <cite>convert</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.QuantWrapper"/><a class="reference internal" href="generated/torch.quantization.QuantWrapper.html#torch.quantization.QuantWrapper" title="torch.quantization.QuantWrapper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantWrapper</span></code></a></p></td>
<td><p>A wrapper class that wraps the input module, adds QuantStub and DeQuantStub and surround the call to module with call to quant and dequant modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.add_quant_dequant"/><a class="reference internal" href="generated/torch.quantization.add_quant_dequant.html#torch.quantization.add_quant_dequant" title="torch.quantization.add_quant_dequant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_quant_dequant</span></code></a></p></td>
<td><p>Wrap the leaf child module in QuantWrapper if it has a valid qconfig Note that this function will modify the children of module inplace and it can return a new module which wraps the input module as well.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="utility-functions">
<h3>Utility functions<a class="headerlink" href="#utility-functions" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.add_observer_"/><a class="reference internal" href="generated/torch.quantization.add_observer_.html#torch.quantization.add_observer_" title="torch.quantization.add_observer_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_observer_</span></code></a></p></td>
<td><p>Add observer for the leaf child of the module.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.swap_module"/><a class="reference internal" href="generated/torch.quantization.swap_module.html#torch.quantization.swap_module" title="torch.quantization.swap_module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">swap_module</span></code></a></p></td>
<td><p>Swaps the module if it has a quantized counterpart and it has an <cite>observer</cite> attached.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.propagate_qconfig_"/><a class="reference internal" href="generated/torch.quantization.propagate_qconfig_.html#torch.quantization.propagate_qconfig_" title="torch.quantization.propagate_qconfig_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">propagate_qconfig_</span></code></a></p></td>
<td><p>Propagate qconfig through the module hierarchy and assign <cite>qconfig</cite> attribute on each leaf module</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.default_eval_fn"/><a class="reference internal" href="generated/torch.quantization.default_eval_fn.html#torch.quantization.default_eval_fn" title="torch.quantization.default_eval_fn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_eval_fn</span></code></a></p></td>
<td><p>Default evaluation function takes a torch.utils.data.Dataset or a list of input Tensors and run the model on the dataset</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.get_observer_dict"/><a class="reference internal" href="generated/torch.quantization.get_observer_dict.html#torch.quantization.get_observer_dict" title="torch.quantization.get_observer_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_observer_dict</span></code></a></p></td>
<td><p>Traverse the modules and save all observers into dict.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="torch-quantization-quantize-fx">
<h2>torch.quantization.quantize_fx<a class="headerlink" href="#torch-quantization-quantize-fx" title="Permalink to this heading">¶</a></h2>
<p>This module contains FX graph mode quantization APIs (prototype).</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.quantize_fx.prepare_fx"/><a class="reference internal" href="generated/torch.quantization.quantize_fx.prepare_fx.html#torch.quantization.quantize_fx.prepare_fx" title="torch.quantization.quantize_fx.prepare_fx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_fx</span></code></a></p></td>
<td><p>Prepare a model for post training static quantization</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.quantize_fx.prepare_qat_fx"/><a class="reference internal" href="generated/torch.quantization.quantize_fx.prepare_qat_fx.html#torch.quantization.quantize_fx.prepare_qat_fx" title="torch.quantization.quantize_fx.prepare_qat_fx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_qat_fx</span></code></a></p></td>
<td><p>Prepare a model for quantization aware training</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.quantize_fx.convert_fx"/><a class="reference internal" href="generated/torch.quantization.quantize_fx.convert_fx.html#torch.quantization.quantize_fx.convert_fx" title="torch.quantization.quantize_fx.convert_fx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert_fx</span></code></a></p></td>
<td><p>Convert a calibrated or trained model to a quantized model</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.quantize_fx.fuse_fx"/><a class="reference internal" href="generated/torch.quantization.quantize_fx.fuse_fx.html#torch.quantization.quantize_fx.fuse_fx" title="torch.quantization.quantize_fx.fuse_fx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fuse_fx</span></code></a></p></td>
<td><p>Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-ao-quantization-qconfig-mapping">
<h2>torch.ao.quantization.qconfig_mapping<a class="headerlink" href="#torch-ao-quantization-qconfig-mapping" title="Permalink to this heading">¶</a></h2>
<p>This module contains QConfigMapping for configuring FX graph mode quantization.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.qconfig_mapping.QConfigMapping"/><a class="reference internal" href="generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html#torch.ao.quantization.qconfig_mapping.QConfigMapping" title="torch.ao.quantization.qconfig_mapping.QConfigMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QConfigMapping</span></code></a></p></td>
<td><p>Mapping from model ops to <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.quantization.QConfig</span></code> s.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping"/><a class="reference internal" href="generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html#torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping" title="torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_default_qconfig_mapping</span></code></a></p></td>
<td><p>Return the default QConfigMapping for post training quantization.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping"/><a class="reference internal" href="generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html#torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping" title="torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_default_qat_qconfig_mapping</span></code></a></p></td>
<td><p>Return the default QConfigMapping for quantization aware training.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-ao-quantization-backend-config">
<h2>torch.ao.quantization.backend_config<a class="headerlink" href="#torch-ao-quantization-backend-config" title="Permalink to this heading">¶</a></h2>
<p>This module contains BackendConfig, a config object that defines how quantization is supported
in a backend. Currently only used by FX Graph Mode Quantization, but we may extend Eager Mode
Quantization to work with this as well.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.backend_config.BackendConfig"/><a class="reference internal" href="generated/torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig" title="torch.ao.quantization.backend_config.BackendConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackendConfig</span></code></a></p></td>
<td><p>Config that defines the set of patterns that can be quantized on a given backend, and how reference quantized models can be produced from these patterns.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.quantization.backend_config.BackendPatternConfig"/><a class="reference internal" href="generated/torch.ao.quantization.backend_config.BackendPatternConfig.html#torch.ao.quantization.backend_config.BackendPatternConfig" title="torch.ao.quantization.backend_config.BackendPatternConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackendPatternConfig</span></code></a></p></td>
<td><p>Config for ops defined in <a class="reference internal" href="generated/torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig" title="torch.ao.quantization.backend_config.BackendConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackendConfig</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.backend_config.DTypeConfig"/><a class="reference internal" href="generated/torch.ao.quantization.backend_config.DTypeConfig.html#torch.ao.quantization.backend_config.DTypeConfig" title="torch.ao.quantization.backend_config.DTypeConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DTypeConfig</span></code></a></p></td>
<td><p>Config for the set of supported input/output activation, weight, and bias data types for the patterns defined in <a class="reference internal" href="generated/torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig" title="torch.ao.quantization.backend_config.BackendConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackendConfig</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.quantization.backend_config.ObservationType"/><a class="reference internal" href="generated/torch.ao.quantization.backend_config.ObservationType.html#torch.ao.quantization.backend_config.ObservationType" title="torch.ao.quantization.backend_config.ObservationType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ObservationType</span></code></a></p></td>
<td><p>An enum that represents different ways of how an operator/operator pattern should be observed</p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-ao-quantization-fx-custom-config">
<h2>torch.ao.quantization.fx.custom_config<a class="headerlink" href="#torch-ao-quantization-fx-custom-config" title="Permalink to this heading">¶</a></h2>
<p>This module contains a few CustomConfig classes that’s used in both eager mode and FX graph mode quantization</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.fx.custom_config.FuseCustomConfig"/><a class="reference internal" href="generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html#torch.ao.quantization.fx.custom_config.FuseCustomConfig" title="torch.ao.quantization.fx.custom_config.FuseCustomConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FuseCustomConfig</span></code></a></p></td>
<td><p>Custom configuration for <a class="reference internal" href="generated/torch.quantization.quantize_fx.fuse_fx.html#torch.quantization.quantize_fx.fuse_fx" title="torch.ao.quantization.quantize_fx.fuse_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuse_fx()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig"/><a class="reference internal" href="generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html#torch.ao.quantization.fx.custom_config.PrepareCustomConfig" title="torch.ao.quantization.fx.custom_config.PrepareCustomConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code></a></p></td>
<td><p>Custom configuration for <a class="reference internal" href="generated/torch.quantization.quantize_fx.prepare_fx.html#torch.quantization.quantize_fx.prepare_fx" title="torch.ao.quantization.quantize_fx.prepare_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_fx()</span></code></a> and <a class="reference internal" href="generated/torch.quantization.quantize_fx.prepare_qat_fx.html#torch.quantization.quantize_fx.prepare_qat_fx" title="torch.ao.quantization.quantize_fx.prepare_qat_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_qat_fx()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig"/><a class="reference internal" href="generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html#torch.ao.quantization.fx.custom_config.ConvertCustomConfig" title="torch.ao.quantization.fx.custom_config.ConvertCustomConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code></a></p></td>
<td><p>Custom configuration for <a class="reference internal" href="generated/torch.quantization.quantize_fx.convert_fx.html#torch.quantization.quantize_fx.convert_fx" title="torch.ao.quantization.quantize_fx.convert_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert_fx()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry"/><a class="reference internal" href="generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html#torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry" title="torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StandaloneModuleConfigEntry</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-quantization-related-functions">
<h2>torch (quantization related functions)<a class="headerlink" href="#torch-quantization-related-functions" title="Permalink to this heading">¶</a></h2>
<p>This describes the quantization related functions of the <cite>torch</cite> namespace.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantize_per_tensor"/><a class="reference internal" href="generated/torch.quantize_per_tensor.html#torch.quantize_per_tensor" title="torch.quantize_per_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_per_tensor</span></code></a></p></td>
<td><p>Converts a float tensor to a quantized tensor with given scale and zero point.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantize_per_channel"/><a class="reference internal" href="generated/torch.quantize_per_channel.html#torch.quantize_per_channel" title="torch.quantize_per_channel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_per_channel</span></code></a></p></td>
<td><p>Converts a float tensor to a per-channel quantized tensor with given scales and zero points.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.dequantize"/><a class="reference internal" href="generated/torch.dequantize.html#torch.dequantize" title="torch.dequantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code></a></p></td>
<td><p>Returns an fp32 Tensor by dequantizing a quantized Tensor</p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-tensor-quantization-related-methods">
<h2>torch.Tensor (quantization related methods)<a class="headerlink" href="#torch-tensor-quantization-related-methods" title="Permalink to this heading">¶</a></h2>
<p>Quantized Tensors support a limited subset of data manipulation methods of the
regular full-precision tensor.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.Tensor.view"/><a class="reference internal" href="generated/torch.Tensor.view.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view</span></code></a></p></td>
<td><p>Returns a new tensor with the same data as the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor but of a different <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.as_strided"/><a class="reference internal" href="generated/torch.Tensor.as_strided.html#torch.Tensor.as_strided" title="torch.Tensor.as_strided"><code class="xref py py-obj docutils literal notranslate"><span class="pre">as_strided</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.as_strided.html#torch.as_strided" title="torch.as_strided"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_strided()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.expand"/><a class="reference internal" href="generated/torch.Tensor.expand.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expand</span></code></a></p></td>
<td><p>Returns a new view of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor with singleton dimensions expanded to a larger size.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.flatten"/><a class="reference internal" href="generated/torch.Tensor.flatten.html#torch.Tensor.flatten" title="torch.Tensor.flatten"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flatten</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.flatten.html#torch.flatten" title="torch.flatten"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.flatten()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.select"/><a class="reference internal" href="generated/torch.Tensor.select.html#torch.Tensor.select" title="torch.Tensor.select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.select.html#torch.select" title="torch.select"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.select()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.ne"/><a class="reference internal" href="generated/torch.Tensor.ne.html#torch.Tensor.ne" title="torch.Tensor.ne"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ne</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.ne.html#torch.ne" title="torch.ne"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ne()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.eq"/><a class="reference internal" href="generated/torch.Tensor.eq.html#torch.Tensor.eq" title="torch.Tensor.eq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eq</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.eq.html#torch.eq" title="torch.eq"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.eq()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.ge"/><a class="reference internal" href="generated/torch.Tensor.ge.html#torch.Tensor.ge" title="torch.Tensor.ge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ge</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.ge.html#torch.ge" title="torch.ge"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ge()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.le"/><a class="reference internal" href="generated/torch.Tensor.le.html#torch.Tensor.le" title="torch.Tensor.le"><code class="xref py py-obj docutils literal notranslate"><span class="pre">le</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.le.html#torch.le" title="torch.le"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.le()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.gt"/><a class="reference internal" href="generated/torch.Tensor.gt.html#torch.Tensor.gt" title="torch.Tensor.gt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gt</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.gt.html#torch.gt" title="torch.gt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.gt()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.lt"/><a class="reference internal" href="generated/torch.Tensor.lt.html#torch.Tensor.lt" title="torch.Tensor.lt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lt</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.lt.html#torch.lt" title="torch.lt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.lt()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.copy_"/><a class="reference internal" href="generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_</span></code></a></p></td>
<td><p>Copies the elements from <code class="xref py py-attr docutils literal notranslate"><span class="pre">src</span></code> into <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor and returns <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.clone"/><a class="reference internal" href="generated/torch.Tensor.clone.html#torch.Tensor.clone" title="torch.Tensor.clone"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clone</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.clone.html#torch.clone" title="torch.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.clone()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.dequantize"/><a class="reference internal" href="generated/torch.Tensor.dequantize.html#torch.Tensor.dequantize" title="torch.Tensor.dequantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code></a></p></td>
<td><p>Given a quantized Tensor, dequantize it and return the dequantized float Tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.equal"/><a class="reference internal" href="generated/torch.Tensor.equal.html#torch.Tensor.equal" title="torch.Tensor.equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">equal</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.equal.html#torch.equal" title="torch.equal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.equal()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.int_repr"/><a class="reference internal" href="generated/torch.Tensor.int_repr.html#torch.Tensor.int_repr" title="torch.Tensor.int_repr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">int_repr</span></code></a></p></td>
<td><p>Given a quantized Tensor, <code class="docutils literal notranslate"><span class="pre">self.int_repr()</span></code> returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.max"/><a class="reference internal" href="generated/torch.Tensor.max.html#torch.Tensor.max" title="torch.Tensor.max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.max.html#torch.max" title="torch.max"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.max()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.mean"/><a class="reference internal" href="generated/torch.Tensor.mean.html#torch.Tensor.mean" title="torch.Tensor.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mean()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.min"/><a class="reference internal" href="generated/torch.Tensor.min.html#torch.Tensor.min" title="torch.Tensor.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.min.html#torch.min" title="torch.min"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.min()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.q_scale"/><a class="reference internal" href="generated/torch.Tensor.q_scale.html#torch.Tensor.q_scale" title="torch.Tensor.q_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">q_scale</span></code></a></p></td>
<td><p>Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.q_zero_point"/><a class="reference internal" href="generated/torch.Tensor.q_zero_point.html#torch.Tensor.q_zero_point" title="torch.Tensor.q_zero_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">q_zero_point</span></code></a></p></td>
<td><p>Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.q_per_channel_scales"/><a class="reference internal" href="generated/torch.Tensor.q_per_channel_scales.html#torch.Tensor.q_per_channel_scales" title="torch.Tensor.q_per_channel_scales"><code class="xref py py-obj docutils literal notranslate"><span class="pre">q_per_channel_scales</span></code></a></p></td>
<td><p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.q_per_channel_zero_points"/><a class="reference internal" href="generated/torch.Tensor.q_per_channel_zero_points.html#torch.Tensor.q_per_channel_zero_points" title="torch.Tensor.q_per_channel_zero_points"><code class="xref py py-obj docutils literal notranslate"><span class="pre">q_per_channel_zero_points</span></code></a></p></td>
<td><p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.q_per_channel_axis"/><a class="reference internal" href="generated/torch.Tensor.q_per_channel_axis.html#torch.Tensor.q_per_channel_axis" title="torch.Tensor.q_per_channel_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">q_per_channel_axis</span></code></a></p></td>
<td><p>Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.resize_"/><a class="reference internal" href="generated/torch.Tensor.resize_.html#torch.Tensor.resize_" title="torch.Tensor.resize_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_</span></code></a></p></td>
<td><p>Resizes <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to the specified size.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.Tensor.sort"/><a class="reference internal" href="generated/torch.Tensor.sort.html#torch.Tensor.sort" title="torch.Tensor.sort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sort</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.sort.html#torch.sort" title="torch.sort"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sort()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.Tensor.topk"/><a class="reference internal" href="generated/torch.Tensor.topk.html#torch.Tensor.topk" title="torch.Tensor.topk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">topk</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.topk.html#torch.topk" title="torch.topk"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.topk()</span></code></a></p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-quantization-observer">
<h2>torch.quantization.observer<a class="headerlink" href="#torch-quantization-observer" title="Permalink to this heading">¶</a></h2>
<p>This module contains observers which are used to collect statistics about
the values observed during calibration (PTQ) or training (QAT).</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.ObserverBase"/><a class="reference internal" href="generated/torch.quantization.observer.ObserverBase.html#torch.quantization.observer.ObserverBase" title="torch.quantization.observer.ObserverBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ObserverBase</span></code></a></p></td>
<td><p>Base observer Module.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.MinMaxObserver"/><a class="reference internal" href="generated/torch.quantization.observer.MinMaxObserver.html#torch.quantization.observer.MinMaxObserver" title="torch.quantization.observer.MinMaxObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinMaxObserver</span></code></a></p></td>
<td><p>Observer module for computing the quantization parameters based on the running min and max values.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.MovingAverageMinMaxObserver"/><a class="reference internal" href="generated/torch.quantization.observer.MovingAverageMinMaxObserver.html#torch.quantization.observer.MovingAverageMinMaxObserver" title="torch.quantization.observer.MovingAverageMinMaxObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MovingAverageMinMaxObserver</span></code></a></p></td>
<td><p>Observer module for computing the quantization parameters based on the moving average of the min and max values.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.PerChannelMinMaxObserver"/><a class="reference internal" href="generated/torch.quantization.observer.PerChannelMinMaxObserver.html#torch.quantization.observer.PerChannelMinMaxObserver" title="torch.quantization.observer.PerChannelMinMaxObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PerChannelMinMaxObserver</span></code></a></p></td>
<td><p>Observer module for computing the quantization parameters based on the running per channel min and max values.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.MovingAveragePerChannelMinMaxObserver"/><a class="reference internal" href="generated/torch.quantization.observer.MovingAveragePerChannelMinMaxObserver.html#torch.quantization.observer.MovingAveragePerChannelMinMaxObserver" title="torch.quantization.observer.MovingAveragePerChannelMinMaxObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></code></a></p></td>
<td><p>Observer module for computing the quantization parameters based on the running per channel min and max values.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.HistogramObserver"/><a class="reference internal" href="generated/torch.quantization.observer.HistogramObserver.html#torch.quantization.observer.HistogramObserver" title="torch.quantization.observer.HistogramObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HistogramObserver</span></code></a></p></td>
<td><p>The module records the running histogram of tensor values along with min/max values.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.PlaceholderObserver"/><a class="reference internal" href="generated/torch.quantization.observer.PlaceholderObserver.html#torch.quantization.observer.PlaceholderObserver" title="torch.quantization.observer.PlaceholderObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PlaceholderObserver</span></code></a></p></td>
<td><p>Observer that doesn't do anything and just passes its configuration to the quantized module's <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.RecordingObserver"/><a class="reference internal" href="generated/torch.quantization.observer.RecordingObserver.html#torch.quantization.observer.RecordingObserver" title="torch.quantization.observer.RecordingObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordingObserver</span></code></a></p></td>
<td><p>The module is mainly for debug and records the tensor values during runtime.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.NoopObserver"/><a class="reference internal" href="generated/torch.quantization.observer.NoopObserver.html#torch.quantization.observer.NoopObserver" title="torch.quantization.observer.NoopObserver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NoopObserver</span></code></a></p></td>
<td><p>Observer that doesn't do anything and just passes its configuration to the quantized module's <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.get_observer_state_dict"/><a class="reference internal" href="generated/torch.quantization.observer.get_observer_state_dict.html#torch.quantization.observer.get_observer_state_dict" title="torch.quantization.observer.get_observer_state_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_observer_state_dict</span></code></a></p></td>
<td><p>Returns the state dict corresponding to the observer stats.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.load_observer_state_dict"/><a class="reference internal" href="generated/torch.quantization.observer.load_observer_state_dict.html#torch.quantization.observer.load_observer_state_dict" title="torch.quantization.observer.load_observer_state_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_observer_state_dict</span></code></a></p></td>
<td><p>Given input model and a state_dict containing model observer stats, load the stats back into the model.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.default_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_observer.html#torch.quantization.observer.default_observer" title="torch.quantization.observer.default_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.default_placeholder_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_placeholder_observer.html#torch.quantization.observer.default_placeholder_observer" title="torch.quantization.observer.default_placeholder_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_placeholder_observer</span></code></a></p></td>
<td><p>alias of <a class="reference internal" href="generated/torch.quantization.observer.PlaceholderObserver.html#torch.quantization.observer.PlaceholderObserver" title="torch.ao.quantization.observer.PlaceholderObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">PlaceholderObserver</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.default_debug_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_debug_observer.html#torch.quantization.observer.default_debug_observer" title="torch.quantization.observer.default_debug_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_debug_observer</span></code></a></p></td>
<td><p>alias of <a class="reference internal" href="generated/torch.quantization.observer.RecordingObserver.html#torch.quantization.observer.RecordingObserver" title="torch.ao.quantization.observer.RecordingObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingObserver</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.default_weight_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_weight_observer.html#torch.quantization.observer.default_weight_observer" title="torch.quantization.observer.default_weight_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_weight_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.default_histogram_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_histogram_observer.html#torch.quantization.observer.default_histogram_observer" title="torch.quantization.observer.default_histogram_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_histogram_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.default_per_channel_weight_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_per_channel_weight_observer.html#torch.quantization.observer.default_per_channel_weight_observer" title="torch.quantization.observer.default_per_channel_weight_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_per_channel_weight_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.observer.default_dynamic_quant_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_dynamic_quant_observer.html#torch.quantization.observer.default_dynamic_quant_observer" title="torch.quantization.observer.default_dynamic_quant_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_dynamic_quant_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.observer.default_float_qparams_observer"/><a class="reference internal" href="generated/torch.quantization.observer.default_float_qparams_observer.html#torch.quantization.observer.default_float_qparams_observer" title="torch.quantization.observer.default_float_qparams_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_float_qparams_observer</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-quantization-fake-quantize">
<h2>torch.quantization.fake_quantize<a class="headerlink" href="#torch-quantization-fake-quantize" title="Permalink to this heading">¶</a></h2>
<p>This module implements modules which are used to perform fake quantization
during QAT.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.FakeQuantizeBase"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.FakeQuantizeBase.html#torch.quantization.fake_quantize.FakeQuantizeBase" title="torch.quantization.fake_quantize.FakeQuantizeBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FakeQuantizeBase</span></code></a></p></td>
<td><p>Base fake quantize module Any fake quantize implementation should derive from this class.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.FakeQuantize"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.FakeQuantize.html#torch.quantization.fake_quantize.FakeQuantize" title="torch.quantization.fake_quantize.FakeQuantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FakeQuantize</span></code></a></p></td>
<td><p>Simulate the quantize and dequantize operations in training time. The output of this module is given by::.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.FixedQParamsFakeQuantize"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.FixedQParamsFakeQuantize.html#torch.quantization.fake_quantize.FixedQParamsFakeQuantize" title="torch.quantization.fake_quantize.FixedQParamsFakeQuantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FixedQParamsFakeQuantize</span></code></a></p></td>
<td><p>Simulate quantize and dequantize with fixed quantization parameters in training time.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html#torch.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize" title="torch.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FusedMovingAvgObsFakeQuantize</span></code></a></p></td>
<td><p>Fused module that is used to observe the input tensor (compute min/max), compute scale/zero_point and fake_quantize the tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.default_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_fake_quant.html#torch.quantization.fake_quantize.default_fake_quant" title="torch.quantization.fake_quantize.default_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.default_weight_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_weight_fake_quant.html#torch.quantization.fake_quantize.default_weight_fake_quant" title="torch.quantization.fake_quantize.default_weight_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_weight_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.default_per_channel_weight_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_per_channel_weight_fake_quant.html#torch.quantization.fake_quantize.default_per_channel_weight_fake_quant" title="torch.quantization.fake_quantize.default_per_channel_weight_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_per_channel_weight_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.default_histogram_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_histogram_fake_quant.html#torch.quantization.fake_quantize.default_histogram_fake_quant" title="torch.quantization.fake_quantize.default_histogram_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_histogram_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.default_fused_act_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_fused_act_fake_quant.html#torch.quantization.fake_quantize.default_fused_act_fake_quant" title="torch.quantization.fake_quantize.default_fused_act_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_fused_act_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.default_fused_wt_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_fused_wt_fake_quant.html#torch.quantization.fake_quantize.default_fused_wt_fake_quant" title="torch.quantization.fake_quantize.default_fused_wt_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_fused_wt_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html#torch.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant" title="torch.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_fused_per_channel_wt_fake_quant</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.disable_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.disable_fake_quant.html#torch.quantization.fake_quantize.disable_fake_quant" title="torch.quantization.fake_quantize.disable_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_fake_quant</span></code></a></p></td>
<td><p>Disable fake quantization for this module, if applicable. Example usage::.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.enable_fake_quant"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.enable_fake_quant.html#torch.quantization.fake_quantize.enable_fake_quant" title="torch.quantization.fake_quantize.enable_fake_quant"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_fake_quant</span></code></a></p></td>
<td><p>Enable fake quantization for this module, if applicable. Example usage::.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.fake_quantize.disable_observer"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.disable_observer.html#torch.quantization.fake_quantize.disable_observer" title="torch.quantization.fake_quantize.disable_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_observer</span></code></a></p></td>
<td><p>Disable observation for this module, if applicable. Example usage::.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.fake_quantize.enable_observer"/><a class="reference internal" href="generated/torch.quantization.fake_quantize.enable_observer.html#torch.quantization.fake_quantize.enable_observer" title="torch.quantization.fake_quantize.enable_observer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_observer</span></code></a></p></td>
<td><p>Enable observation for this module, if applicable. Example usage::.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="torch-quantization-qconfig">
<h2>torch.quantization.qconfig<a class="headerlink" href="#torch-quantization-qconfig" title="Permalink to this heading">¶</a></h2>
<p>This module defines <cite>QConfig</cite> objects which are used
to configure quantization settings for individual ops.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.QConfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.QConfig.html#torch.quantization.qconfig.QConfig" title="torch.quantization.qconfig.QConfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QConfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.default_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_qconfig.html#torch.quantization.qconfig.default_qconfig" title="torch.quantization.qconfig.default_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.default_debug_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_debug_qconfig.html#torch.quantization.qconfig.default_debug_qconfig" title="torch.quantization.qconfig.default_debug_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_debug_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.default_per_channel_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_per_channel_qconfig.html#torch.quantization.qconfig.default_per_channel_qconfig" title="torch.quantization.qconfig.default_per_channel_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_per_channel_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.default_dynamic_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_dynamic_qconfig.html#torch.quantization.qconfig.default_dynamic_qconfig" title="torch.quantization.qconfig.default_dynamic_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_dynamic_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.float16_dynamic_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.float16_dynamic_qconfig.html#torch.quantization.qconfig.float16_dynamic_qconfig" title="torch.quantization.qconfig.float16_dynamic_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float16_dynamic_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.float16_static_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.float16_static_qconfig.html#torch.quantization.qconfig.float16_static_qconfig" title="torch.quantization.qconfig.float16_static_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float16_static_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.per_channel_dynamic_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.per_channel_dynamic_qconfig.html#torch.quantization.qconfig.per_channel_dynamic_qconfig" title="torch.quantization.qconfig.per_channel_dynamic_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">per_channel_dynamic_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.float_qparams_weight_only_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.float_qparams_weight_only_qconfig.html#torch.quantization.qconfig.float_qparams_weight_only_qconfig" title="torch.quantization.qconfig.float_qparams_weight_only_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float_qparams_weight_only_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.default_qat_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_qat_qconfig.html#torch.quantization.qconfig.default_qat_qconfig" title="torch.quantization.qconfig.default_qat_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_qat_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.default_weight_only_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_weight_only_qconfig.html#torch.quantization.qconfig.default_weight_only_qconfig" title="torch.quantization.qconfig.default_weight_only_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_weight_only_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.quantization.qconfig.default_activation_only_qconfig"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_activation_only_qconfig.html#torch.quantization.qconfig.default_activation_only_qconfig" title="torch.quantization.qconfig.default_activation_only_qconfig"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_activation_only_qconfig</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantization.qconfig.default_qat_qconfig_v2"/><a class="reference internal" href="generated/torch.quantization.qconfig.default_qat_qconfig_v2.html#torch.quantization.qconfig.default_qat_qconfig_v2" title="torch.quantization.qconfig.default_qat_qconfig_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_qat_qconfig_v2</span></code></a></p></td>
<td><p>Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-torch.ao.nn.intrinsic">
<span id="torch-ao-nn-intrinsic"></span><h2>torch.ao.nn.intrinsic<a class="headerlink" href="#module-torch.ao.nn.intrinsic" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.ao.nn.intrinsic.modules"></span><p>This module implements the combined (fused) modules conv + relu which can
then be quantized.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.ConvReLU1d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU1d.html#torch.ao.nn.intrinsic.ConvReLU1d" title="torch.ao.nn.intrinsic.ConvReLU1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU1d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv1d and ReLU modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.ConvReLU2d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU2d.html#torch.ao.nn.intrinsic.ConvReLU2d" title="torch.ao.nn.intrinsic.ConvReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv2d and ReLU modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.ConvReLU3d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU3d.html#torch.ao.nn.intrinsic.ConvReLU3d" title="torch.ao.nn.intrinsic.ConvReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv3d and ReLU modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.LinearReLU"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.LinearReLU.html#torch.ao.nn.intrinsic.LinearReLU" title="torch.ao.nn.intrinsic.LinearReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearReLU</span></code></a></p></td>
<td><p>This is a sequential container which calls the Linear and ReLU modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.ConvBn1d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn1d.html#torch.ao.nn.intrinsic.ConvBn1d" title="torch.ao.nn.intrinsic.ConvBn1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn1d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.ConvBn2d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn2d.html#torch.ao.nn.intrinsic.ConvBn2d" title="torch.ao.nn.intrinsic.ConvBn2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn2d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.ConvBn3d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn3d.html#torch.ao.nn.intrinsic.ConvBn3d" title="torch.ao.nn.intrinsic.ConvBn3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn3d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.ConvBnReLU1d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html#torch.ao.nn.intrinsic.ConvBnReLU1d" title="torch.ao.nn.intrinsic.ConvBnReLU1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU1d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.ConvBnReLU2d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html#torch.ao.nn.intrinsic.ConvBnReLU2d" title="torch.ao.nn.intrinsic.ConvBnReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU2d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.ConvBnReLU3d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html#torch.ao.nn.intrinsic.ConvBnReLU3d" title="torch.ao.nn.intrinsic.ConvBnReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU3d</span></code></a></p></td>
<td><p>This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.ao.nn.intrinsic.BNReLU2d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU2d.html#torch.ao.nn.intrinsic.BNReLU2d" title="torch.ao.nn.intrinsic.BNReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNReLU2d</span></code></a></p></td>
<td><p>This is a sequential container which calls the BatchNorm 2d and ReLU modules.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ao.nn.intrinsic.BNReLU3d"/><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU3d.html#torch.ao.nn.intrinsic.BNReLU3d" title="torch.ao.nn.intrinsic.BNReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNReLU3d</span></code></a></p></td>
<td><p>This is a sequential container which calls the BatchNorm 3d and ReLU modules.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-torch.nn.intrinsic.qat">
<span id="torch-nn-intrinsic-qat"></span><h2>torch.nn.intrinsic.qat<a class="headerlink" href="#module-torch.nn.intrinsic.qat" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.nn.intrinsic.qat.modules"></span><p>This module implements the versions of those fused operations needed for
quantization aware training.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.LinearReLU"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.LinearReLU.html#torch.nn.intrinsic.qat.LinearReLU" title="torch.nn.intrinsic.qat.LinearReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearReLU</span></code></a></p></td>
<td><p>A LinearReLU module fused from Linear and ReLU modules, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.qat.ConvBn1d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBn1d.html#torch.nn.intrinsic.qat.ConvBn1d" title="torch.nn.intrinsic.qat.ConvBn1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn1d</span></code></a></p></td>
<td><p>A ConvBn1d module is a module fused from Conv1d and BatchNorm1d, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.ConvBnReLU1d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBnReLU1d.html#torch.nn.intrinsic.qat.ConvBnReLU1d" title="torch.nn.intrinsic.qat.ConvBnReLU1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU1d</span></code></a></p></td>
<td><p>A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.qat.ConvBn2d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBn2d.html#torch.nn.intrinsic.qat.ConvBn2d" title="torch.nn.intrinsic.qat.ConvBn2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn2d</span></code></a></p></td>
<td><p>A ConvBn2d module is a module fused from Conv2d and BatchNorm2d, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.ConvBnReLU2d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBnReLU2d.html#torch.nn.intrinsic.qat.ConvBnReLU2d" title="torch.nn.intrinsic.qat.ConvBnReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU2d</span></code></a></p></td>
<td><p>A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.qat.ConvReLU2d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvReLU2d.html#torch.nn.intrinsic.qat.ConvReLU2d" title="torch.nn.intrinsic.qat.ConvReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a></p></td>
<td><p>A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.ConvBn3d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBn3d.html#torch.nn.intrinsic.qat.ConvBn3d" title="torch.nn.intrinsic.qat.ConvBn3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBn3d</span></code></a></p></td>
<td><p>A ConvBn3d module is a module fused from Conv3d and BatchNorm3d, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.qat.ConvBnReLU3d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvBnReLU3d.html#torch.nn.intrinsic.qat.ConvBnReLU3d" title="torch.nn.intrinsic.qat.ConvBnReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvBnReLU3d</span></code></a></p></td>
<td><p>A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.ConvReLU3d"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.ConvReLU3d.html#torch.nn.intrinsic.qat.ConvReLU3d" title="torch.nn.intrinsic.qat.ConvReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a></p></td>
<td><p>A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.qat.update_bn_stats"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.update_bn_stats.html#torch.nn.intrinsic.qat.update_bn_stats" title="torch.nn.intrinsic.qat.update_bn_stats"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_bn_stats</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.qat.freeze_bn_stats"/><a class="reference internal" href="generated/torch.nn.intrinsic.qat.freeze_bn_stats.html#torch.nn.intrinsic.qat.freeze_bn_stats" title="torch.nn.intrinsic.qat.freeze_bn_stats"><code class="xref py py-obj docutils literal notranslate"><span class="pre">freeze_bn_stats</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-torch.nn.intrinsic.quantized">
<span id="torch-nn-intrinsic-quantized"></span><h2>torch.nn.intrinsic.quantized<a class="headerlink" href="#module-torch.nn.intrinsic.quantized" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.nn.intrinsic.quantized.modules"></span><p>This module implements the quantized implementations of fused operations
like conv + relu. No BatchNorm variants as it’s usually folded into convolution
for inference.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.quantized.BNReLU2d"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.BNReLU2d.html#torch.nn.intrinsic.quantized.BNReLU2d" title="torch.nn.intrinsic.quantized.BNReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNReLU2d</span></code></a></p></td>
<td><p>A BNReLU2d module is a fused module of BatchNorm2d and ReLU</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.quantized.BNReLU3d"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.BNReLU3d.html#torch.nn.intrinsic.quantized.BNReLU3d" title="torch.nn.intrinsic.quantized.BNReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNReLU3d</span></code></a></p></td>
<td><p>A BNReLU3d module is a fused module of BatchNorm3d and ReLU</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.quantized.ConvReLU1d"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.ConvReLU1d.html#torch.nn.intrinsic.quantized.ConvReLU1d" title="torch.nn.intrinsic.quantized.ConvReLU1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU1d</span></code></a></p></td>
<td><p>A ConvReLU1d module is a fused module of Conv1d and ReLU</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.quantized.ConvReLU2d"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.ConvReLU2d.html#torch.nn.intrinsic.quantized.ConvReLU2d" title="torch.nn.intrinsic.quantized.ConvReLU2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a></p></td>
<td><p>A ConvReLU2d module is a fused module of Conv2d and ReLU</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.quantized.ConvReLU3d"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.ConvReLU3d.html#torch.nn.intrinsic.quantized.ConvReLU3d" title="torch.nn.intrinsic.quantized.ConvReLU3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a></p></td>
<td><p>A ConvReLU3d module is a fused module of Conv3d and ReLU</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.intrinsic.quantized.LinearReLU"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.LinearReLU.html#torch.nn.intrinsic.quantized.LinearReLU" title="torch.nn.intrinsic.quantized.LinearReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearReLU</span></code></a></p></td>
<td><p>A LinearReLU module fused from Linear and ReLU modules</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-torch.nn.intrinsic.quantized.dynamic">
<span id="torch-nn-intrinsic-quantized-dynamic"></span><h2>torch.nn.intrinsic.quantized.dynamic<a class="headerlink" href="#module-torch.nn.intrinsic.quantized.dynamic" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.nn.intrinsic.quantized.dynamic.modules"></span><p>This module implements the quantized dynamic implementations of fused operations
like linear + relu.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.intrinsic.quantized.dynamic.LinearReLU"/><a class="reference internal" href="generated/torch.nn.intrinsic.quantized.dynamic.LinearReLU.html#torch.nn.intrinsic.quantized.dynamic.LinearReLU" title="torch.nn.intrinsic.quantized.dynamic.LinearReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearReLU</span></code></a></p></td>
<td><p>A LinearReLU module fused from Linear and ReLU modules that can be used for dynamic quantization.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-torch.ao.nn.qat">
<span id="torch-ao-nn-qat"></span><h2>torch.ao.nn.qat<a class="headerlink" href="#module-torch.ao.nn.qat" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.ao.nn.qat.modules"></span><p>This module implements versions of the key nn modules <strong>Conv2d()</strong> and
<strong>Linear()</strong> which run in FP32 but with rounding applied to simulate the
effect of INT8 quantization.</p>
<table class="autosummary longtable docutils align-default">
<tbody>