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kkawa14 merged 3 commits into from
Mar 9, 2026
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[RedSuns Review]Add documentation for the progress visualization feature #1670

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add progress visualization document
kkawa14 Mar 6, 2026
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resolve conflict
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update variable names in docs
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2 changes: 1 addition & 1 deletion docs/api/api_docs/classes/BitWidthConfig.html
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />

<title>BitWidthConfig &#8212; MCT Documentation: ver 2.6.0</title>
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<title>Data Generation Configuration &#8212; MCT Documentation: ver 2.6.0</title>
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16 changes: 8 additions & 8 deletions docs/api/api_docs/classes/DefaultDict.html
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<title>DefaultDict Class &#8212; MCT Documentation: ver 2.6.0</title>
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<dd><p>Get the value of the inner dictionary by the given key, If key is not in dictionary,
it uses the default_factory to return a default value.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></span></p>
</dd>
<dt class="field-even">Parameters<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>key</strong> – Key to use in inner dictionary.</p>
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>key</strong> – Key to use in inner dictionary.</p>
</dd>
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>Value of the inner dictionary by the given key, or a default value if not exist.
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Value of the inner dictionary by the given key, or a default value if not exist.
If default_factory was not passed at initialization, it returns None.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></p>
</dd>
</dl>
</dd></dl>

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<title>FrameworkInfo Class &#8212; MCT Documentation: ver 2.6.0</title>
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<p class="rubric">Examples</p>
<p>When quantizing a Keras model, if we want to quantize the kernels of Conv2D layers only, we can
set, and we know it’s kernel out/in channel indices are (3, 2) respectivly:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_ops</span> <span class="o">=</span> <span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_channels_mapping</span> <span class="o">=</span> <span class="n">DefaultDict</span><span class="p">({</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)})</span>
</pre></div>
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<title>GradientPTQConfig Class &#8212; MCT Documentation: ver 2.6.0</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />

<title>MixedPrecisionQuantizationConfig &#8212; MCT Documentation: ver 2.6.0</title>
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<title>Pruning Configuration &#8212; MCT Documentation: ver 2.6.0</title>
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<title>Pruning Information &#8212; MCT Documentation: ver 2.6.0</title>
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<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>Dict[BaseNode, np.ndarray]</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">BaseNode</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>]</p>
</dd>
</dl>
</dd></dl>

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<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>Dict[BaseNode, np.ndarray]</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">BaseNode</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>]</p>
</dd>
</dl>
</dd></dl>

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4 changes: 2 additions & 2 deletions docs/api/api_docs/classes/QuantizationConfig.html
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<title>QuantizationConfig &#8212; MCT Documentation: ver 2.6.0</title>
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activations using thresholds, with weight threshold selection based on MSE and activation threshold selection
using NOCLIPPING (min/max), while enabling relu_bound_to_power_of_2 and weights_bias_correction,
you can instantiate a quantization configuration like this:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">model_compression_toolkit</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mct</span>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">model_compression_toolkit</span> <span class="k">as</span> <span class="nn">mct</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">qc</span> <span class="o">=</span> <span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationConfig</span><span class="p">(</span><span class="n">activation_error_method</span><span class="o">=</span><span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationErrorMethod</span><span class="o">.</span><span class="n">NOCLIPPING</span><span class="p">,</span> <span class="n">weights_error_method</span><span class="o">=</span><span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationErrorMethod</span><span class="o">.</span><span class="n">MSE</span><span class="p">,</span> <span class="n">relu_bound_to_power_of_2</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights_bias_correction</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
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<title>QuantizationErrorMethod &#8212; MCT Documentation: ver 2.6.0</title>
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<title>wrapper &#8212; MCT Documentation: ver 2.6.0</title>
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<span class="sig-name descname"><span class="pre">quantize_and_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">representative_dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">framework</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pytorch'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'PTQ'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_mixed_precision</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">param_items</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="headerlink" href="#model_compression_toolkit.wrapper.mct_wrapper.MCTWrapper.quantize_and_export" title="Link to this definition">¶</a></dt>
<dd><p>Main function to perform model quantization and export.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-data docutils literal notranslate"><span class="pre">Tuple</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>, <code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code>]</span></p>
</dd>
<dt class="field-even">Parameters<span class="colon">:</span></dt>
<dd class="field-even"><ul class="simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>float_model</strong> – The float model to be quantized.</p></li>
<li><p><strong>representative_dataset</strong> (<em>Callable</em><em>, </em><em>np.array</em><em>, </em><em>tf.Tensor</em>) – Representative dataset for calibration.</p></li>
<li><p><strong>framework</strong> (<em>str</em>) – ‘tensorflow’ or ‘pytorch’.
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[[key,value],…]. Default: None</p></li>
</ul>
</dd>
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>tuple (quantization success flag, quantized model)</p>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>tuple (quantization success flag, quantized model)</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>Import MCT</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">model_compression_toolkit</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mct</span>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">model_compression_toolkit</span> <span class="k">as</span> <span class="nn">mct</span>
</pre></div>
</div>
<p>Prepare the float model and dataset</p>
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</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="xref py py-data docutils literal notranslate"><span class="pre">Tuple</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>, <code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code>]</p>
</dd>
</dl>
</dd></dl>

</dd></dl>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />

<title>XQuant Configuration &#8212; MCT Documentation: ver 2.6.0</title>
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<title>API Docs &#8212; MCT Documentation: ver 2.6.0</title>
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<section id="api-docs">
<span id="ug-api-docs"></span><h1>API Docs<a class="headerlink" href="#api-docs" title="Link to this heading">¶</a></h1>
<p><strong>Init module for MCT API.</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">model_compression_toolkit</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mct</span>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">model_compression_toolkit</span> <span class="k">as</span> <span class="nn">mct</span>
</pre></div>
</div>
<div class="line-block">
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