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<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<title>mlx.nn.QuantizedLinear &#8212; MLX 0.10.0 documentation</title>
<title>mlx.nn.QuantizedLinear &#8212; MLX 0.12.0 documentation</title>
@@ -36,7 +36,7 @@
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<link rel="index" title="Index" href="../../../genindex.html" />
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<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.new_stream.html">mlx.core.new_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.set_default_stream.html">mlx.core.set_default_stream</a></li>
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@@ -277,6 +279,7 @@
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.cummin.html">mlx.core.cummin</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.dequantize.html">mlx.core.dequantize</a></li>
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@@ -325,6 +328,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.outer.html">mlx.core.outer</a></li>
@@ -333,6 +337,7 @@
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.prod.html">mlx.core.prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.quantize.html">mlx.core.quantize</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.quantized_matmul.html">mlx.core.quantized_matmul</a></li>
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@@ -436,12 +441,14 @@
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<li class="toctree-l3 current active"><a class="current reference internal" href="#">mlx.nn.QuantizedLinear</a></li>
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<li class="toctree-l3"><a class="reference internal" href="mlx.nn.ReLU.html">mlx.nn.ReLU</a></li>
@@ -591,6 +599,7 @@
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.utils.tree_flatten.html">mlx.utils.tree_flatten</a></li>
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@@ -786,28 +795,33 @@ document.write(`
<p>It is the quantized equivalent of <a class="reference internal" href="mlx.nn.Linear.html#mlx.nn.Linear" title="mlx.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Linear</span></code></a>. For now its
parameters are frozen and will not be included in any gradient computation
but this will probably change in the future.</p>
<p>QuantizedLinear also provides two useful classmethods to convert linear
layers to QuantizedLinear layers.</p>
<ul class="simple">
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_linear()</span></code> returns a QuantizedLinear layer that applies the same
linear transformation up to the quantization error.</p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">quantize_module()</span></code> swaps all the linear layers of the passed module
with QuantizedLinear ones.</p></li>
</ul>
<p><a class="reference internal" href="#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a> also provides a classmethod <code class="xref py py-meth docutils literal notranslate"><span class="pre">from_linear()</span></code> to
convert linear layers to <a class="reference internal" href="#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a> layers.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input_dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the input features</p></li>
<li><p><strong>output_dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the output features</p></li>
<li><p><strong>input_dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the input features.</p></li>
<li><p><strong>output_dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the output features.</p></li>
<li><p><strong>bias</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If set to <code class="docutils literal notranslate"><span class="pre">False</span></code> then the layer will not use
a bias. (default: True).</p></li>
a bias. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
<li><p><strong>group_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The group size to use for the quantized
weight. See <a class="reference internal" href="../../_autosummary/mlx.core.quantize.html#mlx.core.quantize" title="mlx.core.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a>. (default: 64)</p></li>
weight. See <a class="reference internal" href="../../_autosummary/mlx.core.quantize.html#mlx.core.quantize" title="mlx.core.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a>. Default: <code class="docutils literal notranslate"><span class="pre">64</span></code>.</p></li>
<li><p><strong>bits</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The bit width to use for the quantized weight.
See <a class="reference internal" href="../../_autosummary/mlx.core.quantize.html#mlx.core.quantize" title="mlx.core.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a>. (default: 4)</p></li>
See <a class="reference internal" href="../../_autosummary/mlx.core.quantize.html#mlx.core.quantize" title="mlx.core.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a>. Default: <code class="docutils literal notranslate"><span class="pre">4</span></code>.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Methods</p>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_linear</span></code>(linear_layer[, group_size, bits])</p></td>
<td><p>Create a <a class="reference internal" href="#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a> layer from a <a class="reference internal" href="mlx.nn.Linear.html#mlx.nn.Linear" title="mlx.nn.Linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Linear</span></code></a> layer.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">unfreeze</span></code>(*args, **kwargs)</p></td>
<td><p>Wrap unfreeze so that we unfreeze any layers we might contain but our parameters will remain frozen.</p></td>
</tr>
</tbody>
</table>
</dd></dl>
</section>
@@ -824,12 +838,12 @@ See <a class="reference internal" href="../../_autosummary/mlx.core.quantize.htm
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