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  <div class="section" id="torch-ao-ns-numeric-suite">
<span id="id1"></span><h1>torch.ao.ns._numeric_suite<a class="headerlink" href="#torch-ao-ns-numeric-suite" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This module is an early prototype and is subject to change.</p>
</div>
<span class="target" id="module-torch.ao.ns._numeric_suite"></span><dl class="py function">
<dt id="torch.ao.ns._numeric_suite.compare_weights">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">compare_weights</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_weights" title="Permalink to this definition">¶</a></dt>
<dd><p>Compare the weights of the float module with its corresponding quantized
module. Return a dict with key corresponding to module names and each entry being
a dictionary with two keys ‘float’ and ‘quantized’, containing the float and
quantized weights. This dict can be used to compare and compute the quantization
error of the weights of float and quantized models.</p>
<p>Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wt_compare_dict</span> <span class="o">=</span> <span class="n">compare_weights</span><span class="p">(</span>
    <span class="n">float_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">qmodel</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">wt_compare_dict</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span>
        <span class="n">key</span><span class="p">,</span>
        <span class="n">compute_error</span><span class="p">(</span>
            <span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span>
            <span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()</span>
        <span class="p">)</span>
    <span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_dict</strong> – state dict of the float model</p></li>
<li><p><strong>quantized_dict</strong> – state dict of the quantized model</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>dict with key corresponding to module names and each entry being
a dictionary with two keys ‘float’ and ‘quantized’, containing the float and
quantized weights</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>weight_dict</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.get_logger_dict">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">get_logger_dict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_logger_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_logger_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Traverse the modules and save all logger stats into target dict.
This is mainly used for quantization accuracy debug.</p>
<dl class="simple">
<dt>Type of loggers supported:</dt><dd><p>ShadowLogger: used to log the outputs of the quantized module and its matching float shadow module,
OutputLogger: used to log the outputs of the modules</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mod</strong> – module we want to save all logger stats</p></li>
<li><p><strong>prefix</strong> – prefix for the current module</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the dictionary used to save all logger stats</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>target_dict</p>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt id="torch.ao.ns._numeric_suite.Logger">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">Logger</span></code><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for stats logging</p>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Logger.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger.forward" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="py class">
<dt id="torch.ao.ns._numeric_suite.ShadowLogger">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">ShadowLogger</span></code><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger" title="Permalink to this definition">¶</a></dt>
<dd><p>Class used in Shadow module to record the outputs of the original and
shadow modules.</p>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.ShadowLogger.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger.forward" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="py class">
<dt id="torch.ao.ns._numeric_suite.OutputLogger">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">OutputLogger</span></code><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger" title="Permalink to this definition">¶</a></dt>
<dd><p>Class used to log the outputs of the module</p>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.OutputLogger.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="py class">
<dt id="torch.ao.ns._numeric_suite.Shadow">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">Shadow</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow" title="Permalink to this definition">¶</a></dt>
<dd><p>Shadow module attaches the float module to its matching quantized module
as the shadow. Then it uses Logger module to process the outputs of both
modules.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>q_module</strong> – module quantized from float_module that we want to shadow</p></li>
<li><p><strong>float_module</strong> – float module used to shadow q_module</p></li>
<li><p><strong>logger_cls</strong> – type of logger used to process the outputs of q_module and
float_module. ShadowLogger or custom loggers can be used.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.forward" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.add">
<code class="sig-name descname"><span class="pre">add</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.add_scalar">
<code class="sig-name descname"><span class="pre">add_scalar</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_scalar" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.mul">
<code class="sig-name descname"><span class="pre">mul</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.mul_scalar">
<code class="sig-name descname"><span class="pre">mul_scalar</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul_scalar" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.cat">
<code class="sig-name descname"><span class="pre">cat</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.cat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.cat" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py method">
<dt id="torch.ao.ns._numeric_suite.Shadow.add_relu">
<code class="sig-name descname"><span class="pre">add_relu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_relu" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.prepare_model_with_stubs">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">prepare_model_with_stubs</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_with_stubs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_with_stubs" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepare the model by attaching the float module to its matching quantized
module as the shadow if the float module type is in module_swap_list.</p>
<p>Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">prepare_model_with_stubs</span><span class="p">(</span><span class="n">float_model</span><span class="p">,</span> <span class="n">q_model</span><span class="p">,</span> <span class="n">module_swap_list</span><span class="p">,</span> <span class="n">Logger</span><span class="p">)</span>
<span class="n">q_model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">ob_dict</span> <span class="o">=</span> <span class="n">get_logger_dict</span><span class="p">(</span><span class="n">q_model</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_module</strong> – float module used to generate the q_module</p></li>
<li><p><strong>q_module</strong> – module quantized from float_module</p></li>
<li><p><strong>module_swap_list</strong> – list of float module types to attach the shadow</p></li>
<li><p><strong>logger_cls</strong> – type of logger to be used in shadow module to process the outputs of
quantized module and its float shadow module</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.compare_model_stub">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">compare_model_stub</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">float_model</span></em>, <em class="sig-param"><span class="pre">q_model</span></em>, <em class="sig-param"><span class="pre">module_swap_list</span></em>, <em class="sig-param"><span class="pre">*data</span></em>, <em class="sig-param"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.ShadowLogger'&gt;</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_stub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_stub" title="Permalink to this definition">¶</a></dt>
<dd><p>Compare quantized module in a model with its floating point counterpart,
feeding both of them the same input. Return a dict with key corresponding to
module names and each entry being a dictionary with two keys ‘float’ and
‘quantized’, containing the output tensors of quantized and its matching
float shadow module. This dict can be used to compare and compute the module
level quantization error.</p>
<p>This function first call prepare_model_with_stubs() to swap the quantized
module that we want to compare with the Shadow module, which takes quantized
module, corresponding float module and logger as input, and creates a forward
path inside to make the float module to shadow quantized module sharing the
same input. The logger can be customizable, default logger is ShadowLogger
and it will save the outputs of the quantized module and float module that
can be used to compute the module level quantization error.</p>
<p>Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">module_swap_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">resnet</span><span class="o">.</span><span class="n">QuantizableBasicBlock</span><span class="p">]</span>
<span class="n">ob_dict</span> <span class="o">=</span> <span class="n">compare_model_stub</span><span class="p">(</span><span class="n">float_model</span><span class="p">,</span><span class="n">qmodel</span><span class="p">,</span><span class="n">module_swap_list</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">ob_dict</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()))</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_model</strong> – float model used to generate the q_model</p></li>
<li><p><strong>q_model</strong> – model quantized from float_model</p></li>
<li><p><strong>module_swap_list</strong> – list of float module types at which shadow modules will
be attached.</p></li>
<li><p><strong>data</strong> – input data used to run the prepared q_model</p></li>
<li><p><strong>logger_cls</strong> – type of logger to be used in shadow module to process the outputs of
quantized module and its float shadow module</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.get_matching_activations">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">get_matching_activations</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_matching_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_matching_activations" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the matching activation between float and quantized modules.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_module</strong> – float module used to generate the q_module</p></li>
<li><p><strong>q_module</strong> – module quantized from float_module</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>dict with key corresponding to quantized module names and each
entry being a dictionary with two keys ‘float’ and ‘quantized’, containing
the matching float and quantized activations</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>act_dict</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.prepare_model_outputs">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">prepare_model_outputs</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">float_module</span></em>, <em class="sig-param"><span class="pre">q_module</span></em>, <em class="sig-param"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></em>, <em class="sig-param"><span class="pre">allow_list=None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_outputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepare the model by attaching the logger to both float module
and quantized module if they are in the allow_list.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_module</strong> – float module used to generate the q_module</p></li>
<li><p><strong>q_module</strong> – module quantized from float_module</p></li>
<li><p><strong>logger_cls</strong> – type of logger to be attached to float_module and q_module</p></li>
<li><p><strong>allow_list</strong> – list of module types to attach logger</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="torch.ao.ns._numeric_suite.compare_model_outputs">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></code><code class="sig-name descname"><span class="pre">compare_model_outputs</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">float_model</span></em>, <em class="sig-param"><span class="pre">q_model</span></em>, <em class="sig-param"><span class="pre">*data</span></em>, <em class="sig-param"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></em>, <em class="sig-param"><span class="pre">allow_list=None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_outputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Compare output activations between float and quantized models at
corresponding locations for the same input. Return a dict with key corresponding
to quantized module names and each entry being a dictionary with two keys
‘float’ and ‘quantized’, containing the activations of quantized model and
float model at matching locations. This dict can be used to compare and
compute the propagation quantization error.</p>
<p>Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">act_compare_dict</span> <span class="o">=</span> <span class="n">compare_model_outputs</span><span class="p">(</span><span class="n">float_model</span><span class="p">,</span> <span class="n">qmodel</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">act_compare_dict</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span>
        <span class="n">key</span><span class="p">,</span>
        <span class="n">compute_error</span><span class="p">(</span>
            <span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span>
            <span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()</span>
        <span class="p">)</span>
    <span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_model</strong> – float model used to generate the q_model</p></li>
<li><p><strong>q_model</strong> – model quantized from float_model</p></li>
<li><p><strong>data</strong> – input data used to run the prepared float_model and q_model</p></li>
<li><p><strong>logger_cls</strong> – type of logger to be attached to float_module and q_module</p></li>
<li><p><strong>allow_list</strong> – list of module types to attach logger</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>dict with key corresponding to quantized module names
and each entry being a dictionary with two keys ‘float’ and ‘quantized’,
containing the matching float and quantized activations</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>act_compare_dict</p>
</dd>
</dl>
</dd></dl>

</div>


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