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<div class="section" id="module-torch.nn.quantized.dynamic">
<span id="torch-nn-quantized-dynamic"></span><h1>torch.nn.quantized.dynamic<a class="headerlink" href="#module-torch.nn.quantized.dynamic" title="Permalink to this headline">¶</a></h1>
<div class="section" id="linear">
<h2>Linear<a class="headerlink" href="#linear" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.dynamic.Linear">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.dynamic.</code><code class="sig-name descname">Linear</code><span class="sig-paren">(</span><em class="sig-param">in_features</em>, <em class="sig-param">out_features</em>, <em class="sig-param">bias_=True</em>, <em class="sig-param">dtype=torch.qint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/linear.html#Linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
<dd><p>A dynamic quantized linear module with floating point tensor as inputs and outputs.
We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
<a class="reference external" href="https://pytorch.org/docs/1.7.1/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a> for documentation.</p>
<p>Similar to <a class="reference internal" href="generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Linear</span></code></a>, attributes will be randomly
initialized at module creation time and will be overwritten later</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>~Linear.weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the non-learnable quantized weights of the module which are of
shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_features</mtext><mo separator="true">,</mo><mtext>in_features</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_features}, \text{in\_features})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_features</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_features</span></span><span class="mclose">)</span></span></span></span>
</span>.</p></li>
<li><p><strong>~Linear.bias</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the non-learnable floating point bias of the module of shape
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_features</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_features})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_features</span></span><span class="mclose">)</span></span></span></span>
</span>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">bias</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>,
the values are initialized to zero.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">dynamic</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="go">torch.Size([128, 30])</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.nn.quantized.dynamic.Linear.from_float">
<em class="property">classmethod </em><code class="sig-name descname">from_float</code><span class="sig-paren">(</span><em class="sig-param">mod</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/linear.html#Linear.from_float"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.Linear.from_float" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a dynamic quantized module from a float module or qparams_dict</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mod</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>Module</em></a>) – a float module, either produced by torch.quantization
utilities or provided by the user</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="lstm">
<h2>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.dynamic.LSTM">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.dynamic.</code><code class="sig-name descname">LSTM</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/rnn.html#LSTM"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.LSTM" title="Permalink to this definition">¶</a></dt>
<dd><p>A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
We adopt the same interface as <cite>torch.nn.LSTM</cite>, please see
<a class="reference external" href="https://pytorch.org/docs/1.7.1/nn.html#torch.nn.LSTM">https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM</a> for documentation.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span><span class="p">,</span> <span class="p">(</span><span class="n">hn</span><span class="p">,</span> <span class="n">cn</span><span class="p">)</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="p">(</span><span class="n">h0</span><span class="p">,</span> <span class="n">c0</span><span class="p">))</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="lstmcell">
<h2>LSTMCell<a class="headerlink" href="#lstmcell" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.dynamic.LSTMCell">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.dynamic.</code><code class="sig-name descname">LSTMCell</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/rnn.html#LSTMCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.LSTMCell" title="Permalink to this definition">¶</a></dt>
<dd><p>A long short-term memory (LSTM) cell.</p>
<p>A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.LSTMCell</cite>,
please see <a class="reference external" href="https://pytorch.org/docs/1.7.1/nn.html#torch.nn.LSTMCell">https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell</a> for documentation.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTMCell</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">hx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="p">[]</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">):</span>
<span class="go"> hx, cx = rnn(input[i], (hx, cx))</span>
<span class="go"> output.append(hx)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="grucell">
<h2>GRUCell<a class="headerlink" href="#grucell" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.dynamic.GRUCell">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.dynamic.</code><code class="sig-name descname">GRUCell</code><span class="sig-paren">(</span><em class="sig-param">input_size</em>, <em class="sig-param">hidden_size</em>, <em class="sig-param">bias=True</em>, <em class="sig-param">dtype=torch.qint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/rnn.html#GRUCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.GRUCell" title="Permalink to this definition">¶</a></dt>
<dd><p>A gated recurrent unit (GRU) cell</p>
<p>A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.GRUCell</cite>,
please see <a class="reference external" href="https://pytorch.org/docs/1.7.1/nn.html#torch.nn.GRUCell">https://pytorch.org/docs/stable/nn.html#torch.nn.GRUCell</a> for documentation.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GRUCell</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">hx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="p">[]</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">):</span>
<span class="go"> hx = rnn(input[i], hx)</span>
<span class="go"> output.append(hx)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="rnncell">
<h2>RNNCell<a class="headerlink" href="#rnncell" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.dynamic.RNNCell">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.dynamic.</code><code class="sig-name descname">RNNCell</code><span class="sig-paren">(</span><em class="sig-param">input_size</em>, <em class="sig-param">hidden_size</em>, <em class="sig-param">bias=True</em>, <em class="sig-param">nonlinearity='tanh'</em>, <em class="sig-param">dtype=torch.qint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/dynamic/modules/rnn.html#RNNCell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.dynamic.RNNCell" title="Permalink to this definition">¶</a></dt>
<dd><p>An Elman RNN cell with tanh or ReLU non-linearity.
A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.RNNCell</cite>,
please see <a class="reference external" href="https://pytorch.org/docs/1.7.1/nn.html#torch.nn.RNNCell">https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell</a> for documentation.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">RNNCell</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">hx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="p">[]</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">):</span>
<span class="go"> hx = rnn(input[i], hx)</span>
<span class="go"> output.append(hx)</span>
</pre></div>
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