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<div class="section" id="torch-ao-ns-numeric-suite-fx">
<span id="id1"></span><h1>torch.ao.ns._numeric_suite_fx<a class="headerlink" href="#torch-ao-ns-numeric-suite-fx" 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_fx"></span><p>This module contains tooling to compare weights and activations
across models. Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.quantization.quantize_fx</span> <span class="k">as</span> <span class="nn">quantize_fx</span>
<span class="kn">import</span> <span class="nn">torch.ao.ns._numeric_suite_fx</span> <span class="k">as</span> <span class="nn">ns</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">mp</span> <span class="o">=</span> <span class="n">quantize_fx</span><span class="o">.</span><span class="n">prepare_fx</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="p">{</span><span class="s1">''</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">default_qconfig</span><span class="p">})</span>
<span class="c1"># We convert a copy because we need the original prepared model</span>
<span class="c1"># to be available for comparisons, and `quantize_fx.convert_fx` is inplace.</span>
<span class="n">mq</span> <span class="o">=</span> <span class="n">quantize_fx</span><span class="o">.</span><span class="n">convert_fx</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
<span class="c1">#</span>
<span class="c1"># Comparing weights</span>
<span class="c1">#</span>
<span class="c1"># extract weight pairs</span>
<span class="n">weight_comparison</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">extract_weights</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="n">mp</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="n">mq</span><span class="p">)</span>
<span class="c1"># add SQNR for each comparison, inplace</span>
<span class="n">ns</span><span class="o">.</span><span class="n">extend_logger_results_with_comparison</span><span class="p">(</span>
<span class="n">weight_comparison</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">ns</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">compute_sqnr</span><span class="p">,</span>
<span class="s1">'sqnr'</span><span class="p">)</span>
<span class="c1"># weight_comparison contains the weights from `mp` and `mq` stored</span>
<span class="c1"># in pairs, and can be used for further analysis.</span>
<span class="c1">#</span>
<span class="c1"># Comparing activations, with error propagation</span>
<span class="c1">#</span>
<span class="c1"># add loggers</span>
<span class="n">mp_ns</span><span class="p">,</span> <span class="n">mq_ns</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">add_loggers</span><span class="p">(</span>
<span class="s1">'a'</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">mp</span><span class="p">),</span>
<span class="s1">'b'</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">mq</span><span class="p">),</span>
<span class="n">ns</span><span class="o">.</span><span class="n">OutputLogger</span><span class="p">)</span>
<span class="c1"># send an example datum to capture intermediate activations</span>
<span class="n">datum</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">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">mp_ns</span><span class="p">(</span><span class="n">datum</span><span class="p">)</span>
<span class="n">mq_ns</span><span class="p">(</span><span class="n">datum</span><span class="p">)</span>
<span class="c1"># extract intermediate activations</span>
<span class="n">act_comparison</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">extract_logger_info</span><span class="p">(</span>
<span class="n">mp_ns</span><span class="p">,</span> <span class="n">mq_ns</span><span class="p">,</span> <span class="n">ns</span><span class="o">.</span><span class="n">OutputLogger</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">)</span>
<span class="c1"># add SQNR for each comparison, inplace</span>
<span class="n">ns</span><span class="o">.</span><span class="n">extend_logger_results_with_comparison</span><span class="p">(</span>
<span class="n">act_comparison</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">ns</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">compute_sqnr</span><span class="p">,</span>
<span class="s1">'sqnr'</span><span class="p">)</span>
<span class="c1"># act_comparison contains the activations from `mp_ns` and `mq_ns` stored</span>
<span class="c1"># in pairs, and can be used for further analysis.</span>
<span class="c1">#</span>
<span class="c1"># Comparing activations, without error propagation</span>
<span class="c1">#</span>
<span class="c1"># create shadow model</span>
<span class="n">mp_shadows_mq</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">add_shadow_loggers</span><span class="p">(</span>
<span class="s1">'a'</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">mp</span><span class="p">),</span>
<span class="s1">'b'</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">mq</span><span class="p">),</span>
<span class="n">ns</span><span class="o">.</span><span class="n">OutputLogger</span><span class="p">)</span>
<span class="c1"># send an example datum to capture intermediate activations</span>
<span class="n">datum</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">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">mp_shadows_mq</span><span class="p">(</span><span class="n">datum</span><span class="p">)</span>
<span class="c1"># extract intermediate activations</span>
<span class="n">shadow_act_comparison</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">extract_shadow_logger_info</span><span class="p">(</span>
<span class="n">mp_shadows_mq</span><span class="p">,</span> <span class="n">ns</span><span class="o">.</span><span class="n">OutputLogger</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">)</span>
<span class="c1"># add SQNR for each comparison, inplace</span>
<span class="n">ns</span><span class="o">.</span><span class="n">extend_logger_results_with_comparison</span><span class="p">(</span>
<span class="n">shadow_act_comparison</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">ns</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">compute_sqnr</span><span class="p">,</span>
<span class="s1">'sqnr'</span><span class="p">)</span>
<span class="c1"># shadow_act_comparison contains the activations from `mp_ns` and `mq_ns` stored</span>
<span class="c1"># in pairs, and can be used for further analysis.</span>
</pre></div>
</div>
<dl class="py class">
<dt id="torch.ao.ns._numeric_suite_fx.OutputLogger">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">OutputLogger</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_within_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_of_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fqn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for capturing intermediate values.</p>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite_fx.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_fx.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.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_fx.NSTracer">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">NSTracer</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skipped_module_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skipped_module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer" title="Permalink to this definition">¶</a></dt>
<dd><p>Just like a regular FX quantization tracer, but treats observers and fake_quantize
modules as leaf modules.</p>
<dl class="py method">
<dt id="torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module">
<code class="sig-name descname"><span class="pre">is_leaf_module</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.extract_weights">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">extract_weights</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</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">unmatchable_types_map</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">op_to_type_to_weight_extraction_fn</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/ao/ns/_numeric_suite_fx.html#extract_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_weights" title="Permalink to this definition">¶</a></dt>
<dd><p>Extract weights from model A and model B, and return a comparison.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_name_a</strong> – string name of model A to use in results</p></li>
<li><p><strong>model_a</strong> – model A</p></li>
<li><p><strong>model_name_b</strong> – string name of model B to use in results</p></li>
<li><p><strong>model_b</strong> – model B</p></li>
<li><p><strong>base_name_to_sets_of_related_ops</strong> – optional override of subgraph base nodes, subject to change</p></li>
<li><p><strong>unmatchable_types_map</strong> – optional override of unmatchable types, subject to change</p></li>
<li><p><strong>op_to_type_to_weight_extraction_fn</strong> – optional override of function which extracts weight
from a type, subject to change</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>NSResultsType, containing the weight comparisons</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.add_loggers">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">add_loggers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</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">unmatchable_types_map</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/ao/ns/_numeric_suite_fx.html#add_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_loggers" title="Permalink to this definition">¶</a></dt>
<dd><p>Instrument model A and model B with loggers.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_name_a</strong> – string name of model A to use in results</p></li>
<li><p><strong>model_a</strong> – model A</p></li>
<li><p><strong>model_name_b</strong> – string name of model B to use in results</p></li>
<li><p><strong>model_b</strong> – model B</p></li>
<li><p><strong>logger_cls</strong> – class of Logger to use</p></li>
<li><p><strong>base_name_to_sets_of_related_ops</strong> – optional override of subgraph base nodes, subject to change</p></li>
<li><p><strong>unmatchable_types_map</strong> – optional override of unmatchable types, subject to change</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Returns a tuple of (model_a_with_loggers, model_b_with_loggers). Modifies both models inplace.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.extract_logger_info">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">extract_logger_info</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_logger_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Traverse all loggers in <cite>model_a</cite> and <cite>model_b</cite>, and extract the logged
information.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_a</strong> – model A</p></li>
<li><p><strong>model_b</strong> – model B</p></li>
<li><p><strong>logger_cls</strong> – class of Logger to use</p></li>
<li><p><strong>model_name_to_use_for_layer_names</strong> – string name of model to use for
layer names in the output</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>NSResultsType, containing the logged comparisons</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.add_shadow_loggers">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">add_shadow_loggers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</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">node_type_to_io_type_map</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">unmatchable_types_map</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/ao/ns/_numeric_suite_fx.html#add_shadow_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_shadow_loggers" title="Permalink to this definition">¶</a></dt>
<dd><p>Instrument model A and model B with shadow loggers.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_name_a</strong> – string name of model A to use in results</p></li>
<li><p><strong>model_a</strong> – model A</p></li>
<li><p><strong>model_name_b</strong> – string name of model B to use in results</p></li>
<li><p><strong>model_b</strong> – model B</p></li>
<li><p><strong>logger_cls</strong> – class of Logger to use</p></li>
<li><p><strong>should_log_inputs</strong> – whether to log inputs</p></li>
<li><p><strong>base_name_to_sets_of_related_ops</strong> – optional override of subgraph base nodes, subject to change</p></li>
<li><p><strong>unmatchable_types_map</strong> – optional override of unmatchable types, subject to change</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">extract_shadow_logger_info</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a_shadows_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_shadow_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Traverse all loggers in a shadow model, and extract the logged
information.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_a_shadows_b</strong> – shadow model</p></li>
<li><p><strong>logger_cls</strong> – class of Logger to use</p></li>
<li><p><strong>model_name_to_use_for_layer_names</strong> – string name of model to use for
layer names in the output</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>NSResultsType, containing the logged comparisons</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></code><code class="sig-name descname"><span class="pre">extend_logger_results_with_comparison</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extend_logger_results_with_comparison"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison" title="Permalink to this definition">¶</a></dt>
<dd><p>Compares the logged values from <cite>model_name_2</cite> against the corresponding
values in <cite>model_name_1</cite>, using <cite>comparison_fn</cite>. Records the result
in <cite>model_name_2</cite>’s results under <cite>comparison_name</cite>. Modifies <cite>results</cite> inplace.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>results</strong> – the result data structure from <cite>extract_logger_info</cite> or
<cite>extract_shadow_logger_info</cite>.</p></li>
<li><p><strong>model_name_1</strong> – string name of model 1</p></li>
<li><p><strong>model_name_2</strong> – string name of model 2</p></li>
<li><p><strong>comparison_fn</strong> – function to compare two Tensors</p></li>
<li><p><strong>model_name_to_use_for_layer_names</strong> – string name of model to use for
layer names in the output</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="torch-ao-ns-fx-utils">
<h1>torch.ao.ns.fx.utils<a class="headerlink" href="#torch-ao-ns-fx-utils" 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>
<dl class="py function">
<dt id="torch.ao.ns.fx.utils.compute_sqnr">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></code><code class="sig-name descname"><span class="pre">compute_sqnr</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/fx/utils.html#compute_sqnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_sqnr" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py function">
<dt id="torch.ao.ns.fx.utils.compute_normalized_l2_error">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></code><code class="sig-name descname"><span class="pre">compute_normalized_l2_error</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/fx/utils.html#compute_normalized_l2_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_normalized_l2_error" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py function">
<dt id="torch.ao.ns.fx.utils.compute_cosine_similarity">
<code class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></code><code class="sig-name descname"><span class="pre">compute_cosine_similarity</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/fx/utils.html#compute_cosine_similarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_cosine_similarity" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
</article>
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// Add class to links that have code blocks, since we cannot create links in code blocks
$("article.pytorch-article a span.pre").each(function(e) {
$(this).closest("a").addClass("has-code");
});
})
</script>
</body>
</html>