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<!DOCTYPE html>
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<li><a class="reference internal" href="#">brevitas.export.onnx.standard package</a><ul>
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<li><a class="reference internal" href="#module-brevitas.export.onnx.standard.function">brevitas.export.onnx.standard.function module</a></li>
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<div class="section" id="brevitas-export-onnx-standard-package">
<h1>brevitas.export.onnx.standard package<a class="headerlink" href="#brevitas-export-onnx-standard-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="subpackages">
<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html">brevitas.export.onnx.standard.handler package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#module-brevitas.export.onnx.standard.handler.act">brevitas.export.onnx.standard.handler.act module</a></li>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#module-brevitas.export.onnx.standard.handler.base">brevitas.export.onnx.standard.handler.base module</a></li>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#module-brevitas.export.onnx.standard.handler.parameter">brevitas.export.onnx.standard.handler.parameter module</a></li>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#module-brevitas.export.onnx.standard.handler.pool">brevitas.export.onnx.standard.handler.pool module</a></li>
<li class="toctree-l2"><a class="reference internal" href="brevitas.export.onnx.standard.handler.html#module-brevitas.export.onnx.standard.handler">Module contents</a></li>
</ul>
</li>
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<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-brevitas.export.onnx.standard.function">
<span id="brevitas-export-onnx-standard-function-module"></span><h2>brevitas.export.onnx.standard.function module<a class="headerlink" href="#module-brevitas.export.onnx.standard.function" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="brevitas.export.onnx.standard.function.DequantizeLinearFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.standard.function.</code><code class="sig-name descname">DequantizeLinearFunction</code><a class="headerlink" href="#brevitas.export.onnx.standard.function.DequantizeLinearFunction" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.function.Function</span></code></p>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.DequantizeLinearFunction.forward">
<em class="property">static </em><code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ctx</span></em>, <em class="sig-param"><span class="n">int_x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">axis</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.DequantizeLinearFunction.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs the operation.</p>
<p>This function is to be overridden by all subclasses.</p>
<p>It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).</p>
<p>The context can be used to store tensors that can be then retrieved
during the backward pass.</p>
</dd></dl>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.DequantizeLinearFunction.symbolic">
<em class="property">static </em><code class="sig-name descname">symbolic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">g</span></em>, <em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">axis</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.DequantizeLinearFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="brevitas.export.onnx.standard.function.QLinearConvFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.standard.function.</code><code class="sig-name descname">QLinearConvFunction</code><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearConvFunction" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.function.Function</span></code></p>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QLinearConvFunction.forward">
<em class="property">static </em><code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ctx</span></em>, <em class="sig-param"><span class="n">int_x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">int_weight</span></em>, <em class="sig-param"><span class="n">weight_scale</span></em>, <em class="sig-param"><span class="n">weight_zero_point</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">output_zero_point</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">kernel_size</span></em>, <em class="sig-param"><span class="n">padding</span></em>, <em class="sig-param"><span class="n">stride</span></em>, <em class="sig-param"><span class="n">groups</span></em>, <em class="sig-param"><span class="n">dilation</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearConvFunction.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs the operation.</p>
<p>This function is to be overridden by all subclasses.</p>
<p>It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).</p>
<p>The context can be used to store tensors that can be then retrieved
during the backward pass.</p>
</dd></dl>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QLinearConvFunction.symbolic">
<em class="property">static </em><code class="sig-name descname">symbolic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">g</span></em>, <em class="sig-param"><span class="n">int_x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">int_weight</span></em>, <em class="sig-param"><span class="n">weight_scale</span></em>, <em class="sig-param"><span class="n">weight_zero_point</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">ouput_zero_point</span></em>, <em class="sig-param"><span class="n">int_bias</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">kernel_size</span></em>, <em class="sig-param"><span class="n">padding</span></em>, <em class="sig-param"><span class="n">stride</span></em>, <em class="sig-param"><span class="n">groups</span></em>, <em class="sig-param"><span class="n">dilation</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearConvFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="brevitas.export.onnx.standard.function.QLinearMatMulFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.standard.function.</code><code class="sig-name descname">QLinearMatMulFunction</code><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearMatMulFunction" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.function.Function</span></code></p>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QLinearMatMulFunction.forward">
<em class="property">static </em><code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ctx</span></em>, <em class="sig-param"><span class="n">int_x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">int_weight</span></em>, <em class="sig-param"><span class="n">weight_scale</span></em>, <em class="sig-param"><span class="n">weight_zero_point</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">output_zero_point</span></em>, <em class="sig-param"><span class="n">out_shape</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearMatMulFunction.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs the operation.</p>
<p>This function is to be overridden by all subclasses.</p>
<p>It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).</p>
<p>The context can be used to store tensors that can be then retrieved
during the backward pass.</p>
</dd></dl>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QLinearMatMulFunction.symbolic">
<em class="property">static </em><code class="sig-name descname">symbolic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">g</span></em>, <em class="sig-param"><span class="n">int_x</span></em>, <em class="sig-param"><span class="n">input_scale</span></em>, <em class="sig-param"><span class="n">input_zero_point</span></em>, <em class="sig-param"><span class="n">int_weight</span></em>, <em class="sig-param"><span class="n">weight_scale</span></em>, <em class="sig-param"><span class="n">weight_zero_point</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">ouput_zero_point</span></em>, <em class="sig-param"><span class="n">out_shape</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QLinearMatMulFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="brevitas.export.onnx.standard.function.QuantizeLinearFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.standard.function.</code><code class="sig-name descname">QuantizeLinearFunction</code><a class="headerlink" href="#brevitas.export.onnx.standard.function.QuantizeLinearFunction" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.function.Function</span></code></p>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QuantizeLinearFunction.forward">
<em class="property">static </em><code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ctx</span></em>, <em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">ouput_zero_point</span></em>, <em class="sig-param"><span class="n">axis</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QuantizeLinearFunction.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs the operation.</p>
<p>This function is to be overridden by all subclasses.</p>
<p>It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).</p>
<p>The context can be used to store tensors that can be then retrieved
during the backward pass.</p>
</dd></dl>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.function.QuantizeLinearFunction.symbolic">
<em class="property">static </em><code class="sig-name descname">symbolic</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">g</span></em>, <em class="sig-param"><span class="n">x</span></em>, <em class="sig-param"><span class="n">output_scale</span></em>, <em class="sig-param"><span class="n">ouput_zero_point</span></em>, <em class="sig-param"><span class="n">axis</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.function.QuantizeLinearFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-brevitas.export.onnx.standard.manager">
<span id="brevitas-export-onnx-standard-manager-module"></span><h2>brevitas.export.onnx.standard.manager module<a class="headerlink" href="#module-brevitas.export.onnx.standard.manager" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="brevitas.export.onnx.standard.manager.StdONNXManager">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.standard.manager.</code><code class="sig-name descname">StdONNXManager</code><a class="headerlink" href="#brevitas.export.onnx.standard.manager.StdONNXManager" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="brevitas.export.onnx.html#brevitas.export.onnx.base.ONNXBaseManager" title="brevitas.export.onnx.base.ONNXBaseManager"><code class="xref py py-class docutils literal notranslate"><span class="pre">brevitas.export.onnx.base.ONNXBaseManager</span></code></a></p>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.manager.StdONNXManager.export_onnx">
<em class="property">classmethod </em><code class="sig-name descname">export_onnx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">module</span></em>, <em class="sig-param"><span class="n">input_shape</span></em>, <em class="sig-param"><span class="n">export_path</span></em>, <em class="sig-param"><span class="n">input_t</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.manager.StdONNXManager.export_onnx" title="Permalink to this definition">¶</a></dt>
<dd><ul class="simple">
<li><p>input_shape : tuple describing the shape of network input e.g. (1, 1, 28, 28)</p></li>
<li><p>export_path : ONNX filename to export to</p></li>
<li><dl class="simple">
<dt>input_t<span class="classifier">if specified, do an initial forward pass with this value. this</span></dt><dd><p>may be necessary for QuantTensor caching.</p>
</dd>
</dl>
</li>
<li><p>torch_onnx_kwargs : will be passed as kwargs to torch.onnx.export</p></li>
</ul>
</dd></dl>
<dl class="py attribute">
<dt id="brevitas.export.onnx.standard.manager.StdONNXManager.handlers">
<code class="sig-name descname">handlers</code><em class="property"> = [<class 'brevitas.export.onnx.standard.handler.parameter.StdONNXQuantConv1dHandler'>, <class 'brevitas.export.onnx.standard.handler.parameter.StdONNXQuantConv2dHandler'>, <class 'brevitas.export.onnx.standard.handler.parameter.StdONNXQuantLinearHandler'>, <class 'brevitas.export.onnx.standard.handler.act.StdONNXQuantReLUHandler'>, <class 'brevitas.export.onnx.standard.handler.act.StdONNXQuantHardTanhHandler'>, <class 'brevitas.export.onnx.standard.handler.act.StdONNXQuantIdentityHandler'>, <class 'brevitas.export.onnx.standard.handler.act.StdONNXQuantTanhHandler'>, <class 'brevitas.export.onnx.standard.handler.act.StdONNXQuantSigmoidHandler'>, <class 'brevitas.export.onnx.standard.handler.pool.StdONNXQuantMaxPool1d'>, <class 'brevitas.export.onnx.standard.handler.pool.StdONNXQuantMaxPool2d'>]</em><a class="headerlink" href="#brevitas.export.onnx.standard.manager.StdONNXManager.handlers" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt id="brevitas.export.onnx.standard.manager.StdONNXManager.onnx_passes">
<code class="sig-name descname">onnx_passes</code><em class="property"> = ['eliminate_unused_initializer']</em><a class="headerlink" href="#brevitas.export.onnx.standard.manager.StdONNXManager.onnx_passes" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="brevitas.export.onnx.standard.manager.StdONNXManager.solve_enable_onnx_checker">
<em class="property">classmethod </em><code class="sig-name descname">solve_enable_onnx_checker</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">export_kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.standard.manager.StdONNXManager.solve_enable_onnx_checker" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-brevitas.export.onnx.standard">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-brevitas.export.onnx.standard" title="Permalink to this headline">¶</a></h2>
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