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<div class="section" id="torch-nn-qat">
<h1>torch.nn.qat<a class="headerlink" href="#torch-nn-qat" title="Permalink to this headline">¶</a></h1>
<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>
<span class="target" id="module-torch.nn.qat"></span><div class="section" id="conv2d">
<h2>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="torch.nn.qat.Conv2d">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.nn.qat.</span></code><code class="sig-name descname"><span class="pre">Conv2d</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/qat/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.qat.Conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>A Conv2d module attached with FakeQuantize modules for weight,
used for quantization aware training.</p>
<p>We adopt the same interface as <cite>torch.nn.Conv2d</cite>, please see
<a class="reference external" href="https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d">https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d</a>
for documentation.</p>
<p>Similar to <cite>torch.nn.Conv2d</cite>, with FakeQuantize modules initialized to
default.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>~Conv2d.weight_fake_quant</strong> – fake quant module for weight</p>
</dd>
</dl>
<dl class="py method">
<dt id="torch.nn.qat.Conv2d.from_float">
<em class="property"><span class="pre">classmethod</span> </em><code class="sig-name descname"><span class="pre">from_float</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/qat/modules/conv.html#Conv2d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.qat.Conv2d.from_float" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a qat module from a float module or qparams_dict</p>
<p>Args: <cite>mod</cite> a float module, either produced by torch.quantization utilities
or directly from user</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="linear">
<h2>Linear<a class="headerlink" href="#linear" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="torch.nn.qat.Linear">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.nn.qat.</span></code><code class="sig-name descname"><span class="pre">Linear</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/qat/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.qat.Linear" title="Permalink to this definition">¶</a></dt>
<dd><p>A linear module attached with FakeQuantize modules for weight,
used for quantization aware training.</p>
<p>We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a>
for documentation.</p>
<p>Similar to <cite>torch.nn.Linear</cite>, with FakeQuantize modules initialized to
default.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>~Linear.weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – fake quant module for weight</p>
</dd>
</dl>
<dl class="py method">
<dt id="torch.nn.qat.Linear.from_float">
<em class="property"><span class="pre">classmethod</span> </em><code class="sig-name descname"><span class="pre">from_float</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/qat/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.qat.Linear.from_float" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a qat module from a float module or qparams_dict</p>
<p>Args: <cite>mod</cite> a float module, either produced by torch.quantization utilities
or directly from user</p>
</dd></dl>
</dd></dl>
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