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<li><a class="reference internal" href="#module-brevitas.export.onnx.finn.function.acc">brevitas.export.onnx.finn.function.acc module</a></li>
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<div class="section" id="brevitas-export-onnx-finn-function-package">
<h1>brevitas.export.onnx.finn.function package<a class="headerlink" href="#brevitas-export-onnx-finn-function-package" title="Permalink to this headline">¶</a></h1>
<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.finn.function.acc">
<span id="brevitas-export-onnx-finn-function-acc-module"></span><h2>brevitas.export.onnx.finn.function.acc module<a class="headerlink" href="#module-brevitas.export.onnx.finn.function.acc" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="brevitas.export.onnx.finn.function.acc.QuantAvgPool2dPlaceholderFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.finn.function.acc.</code><code class="sig-name descname">QuantAvgPool2dPlaceholderFunction</code><a class="headerlink" href="#brevitas.export.onnx.finn.function.acc.QuantAvgPool2dPlaceholderFunction" 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.finn.function.acc.QuantAvgPool2dPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">kernel</span></em>, <em class="sig-param"><span class="n">stride</span></em>, <em class="sig-param"><span class="n">signed</span></em>, <em class="sig-param"><span class="n">ibits</span></em>, <em class="sig-param"><span class="n">obits</span></em>, <em class="sig-param"><span class="n">scale</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.acc.QuantAvgPool2dPlaceholderFunction.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.finn.function.acc.QuantAvgPool2dPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">kernel</span></em>, <em class="sig-param"><span class="n">stride</span></em>, <em class="sig-param"><span class="n">signed</span></em>, <em class="sig-param"><span class="n">ibits</span></em>, <em class="sig-param"><span class="n">obits</span></em>, <em class="sig-param"><span class="n">scale</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.acc.QuantAvgPool2dPlaceholderFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-brevitas.export.onnx.finn.function.act">
<span id="brevitas-export-onnx-finn-function-act-module"></span><h2>brevitas.export.onnx.finn.function.act module<a class="headerlink" href="#module-brevitas.export.onnx.finn.function.act" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="brevitas.export.onnx.finn.function.act.QuantHardTanhPlaceholderFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.finn.function.act.</code><code class="sig-name descname">QuantHardTanhPlaceholderFunction</code><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantHardTanhPlaceholderFunction" 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.finn.function.act.QuantHardTanhPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em>, <em class="sig-param"><span class="n">thres</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">scale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantHardTanhPlaceholderFunction.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.finn.function.act.QuantHardTanhPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em>, <em class="sig-param"><span class="n">thres</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">scale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantHardTanhPlaceholderFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="brevitas.export.onnx.finn.function.act.QuantReLUPlaceholderFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.finn.function.act.</code><code class="sig-name descname">QuantReLUPlaceholderFunction</code><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantReLUPlaceholderFunction" 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.finn.function.act.QuantReLUPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em>, <em class="sig-param"><span class="n">thres</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">scale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantReLUPlaceholderFunction.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.finn.function.act.QuantReLUPlaceholderFunction.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">input</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em>, <em class="sig-param"><span class="n">thres</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">scale</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.act.QuantReLUPlaceholderFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-brevitas.export.onnx.finn.function.parameter">
<span id="brevitas-export-onnx-finn-function-parameter-module"></span><h2>brevitas.export.onnx.finn.function.parameter module<a class="headerlink" href="#module-brevitas.export.onnx.finn.function.parameter" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="brevitas.export.onnx.finn.function.parameter.QuantizedConvNdPlaceholderFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.finn.function.parameter.</code><code class="sig-name descname">QuantizedConvNdPlaceholderFunction</code><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedConvNdPlaceholderFunction" 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.finn.function.parameter.QuantizedConvNdPlaceholderFunction.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">W</span></em>, <em class="sig-param"><span class="n">scale_factor</span></em>, <em class="sig-param"><span class="n">qnt_type</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">pads</span></em>, <em class="sig-param"><span class="n">strides</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">in_scale</span></em>, <em class="sig-param"><span class="n">in_qnt_type</span></em>, <em class="sig-param"><span class="n">kernel_shape</span></em>, <em class="sig-param"><span class="n">groups</span></em>, <em class="sig-param"><span class="n">dilations</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedConvNdPlaceholderFunction.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.finn.function.parameter.QuantizedConvNdPlaceholderFunction.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">W</span></em>, <em class="sig-param"><span class="n">scale_factor</span></em>, <em class="sig-param"><span class="n">w_qnt_type</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">pads</span></em>, <em class="sig-param"><span class="n">strides</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">in_scale</span></em>, <em class="sig-param"><span class="n">in_qnt_type</span></em>, <em class="sig-param"><span class="n">kernel_shape</span></em>, <em class="sig-param"><span class="n">groups</span></em>, <em class="sig-param"><span class="n">dilations</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedConvNdPlaceholderFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="brevitas.export.onnx.finn.function.parameter.QuantizedLinearPlaceholderFunction">
<em class="property">class </em><code class="sig-prename descclassname">brevitas.export.onnx.finn.function.parameter.</code><code class="sig-name descname">QuantizedLinearPlaceholderFunction</code><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedLinearPlaceholderFunction" 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.finn.function.parameter.QuantizedLinearPlaceholderFunction.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">Wt</span></em>, <em class="sig-param"><span class="n">scale_factor</span></em>, <em class="sig-param"><span class="n">w_qnt_type</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">in_scale</span></em>, <em class="sig-param"><span class="n">in_qnt_type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedLinearPlaceholderFunction.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.finn.function.parameter.QuantizedLinearPlaceholderFunction.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">Wt</span></em>, <em class="sig-param"><span class="n">scale_factor</span></em>, <em class="sig-param"><span class="n">w_qnt_type</span></em>, <em class="sig-param"><span class="n">out_shape</span></em>, <em class="sig-param"><span class="n">bias</span></em>, <em class="sig-param"><span class="n">in_scale</span></em>, <em class="sig-param"><span class="n">in_qnt_type</span></em><span class="sig-paren">)</span><a class="headerlink" href="#brevitas.export.onnx.finn.function.parameter.QuantizedLinearPlaceholderFunction.symbolic" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-brevitas.export.onnx.finn.function">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-brevitas.export.onnx.finn.function" title="Permalink to this headline">¶</a></h2>
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