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<div class="section" id="lnstructured">
<h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.nn.utils.prune.LnStructured">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.utils.prune.</code><code class="sig-name descname">LnStructured</code><span class="sig-paren">(</span><em class="sig-param">amount</em>, <em class="sig-param">n</em>, <em class="sig-param">dim=-1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured" title="Permalink to this definition">¶</a></dt>
<dd><p>Prune entire (currently unpruned) channels in a tensor based on their
Ln-norm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>amount</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – quantity of channels to prune.
If <code class="docutils literal notranslate"><span class="pre">float</span></code>, should be between 0.0 and 1.0 and represent the
fraction of parameters to prune. If <code class="docutils literal notranslate"><span class="pre">int</span></code>, it represents the
absolute number of parameters to prune.</p></li>
<li><p><strong>n</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>inf</em><em>, </em><em>-inf</em><em>, </em><em>'fro'</em><em>, </em><em>'nuc'</em>) – See documentation of valid
entries for argument <code class="docutils literal notranslate"><span class="pre">p</span></code> in <a class="reference internal" href="torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.norm()</span></code></a>.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – index of the dim along which we define
channels to prune. Default: -1.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torch.nn.utils.prune.LnStructured.apply">
<em class="property">classmethod </em><code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">module</em>, <em class="sig-param">name</em>, <em class="sig-param">amount</em>, <em class="sig-param">n</em>, <em class="sig-param">dim</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.apply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds the forward pre-hook that enables pruning on the fly and
the reparametrization of a tensor in terms of the original tensor
and the pruning mask.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a>) – module containing the tensor to prune</p></li>
<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – parameter name within <code class="docutils literal notranslate"><span class="pre">module</span></code> on which pruning
will act.</p></li>
<li><p><strong>amount</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – quantity of parameters to prune.
If <code class="docutils literal notranslate"><span class="pre">float</span></code>, should be between 0.0 and 1.0 and represent the
fraction of parameters to prune. If <code class="docutils literal notranslate"><span class="pre">int</span></code>, it represents the
absolute number of parameters to prune.</p></li>
<li><p><strong>n</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>inf</em><em>, </em><em>-inf</em><em>, </em><em>'fro'</em><em>, </em><em>'nuc'</em>) – See documentation of valid
entries for argument <code class="docutils literal notranslate"><span class="pre">p</span></code> in <a class="reference internal" href="torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.norm()</span></code></a>.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – index of the dim along which we define channels to
prune.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.nn.utils.prune.LnStructured.apply_mask">
<code class="sig-name descname">apply_mask</code><span class="sig-paren">(</span><em class="sig-param">module</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
<dd><p>Simply handles the multiplication between the parameter being
pruned and the generated mask.
Fetches the mask and the original tensor from the module
and returns the pruned version of the tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>module</strong> (<a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a>) – module containing the tensor to prune</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>pruned version of the input tensor</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pruned_tensor (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">torch.Tensor</a>)</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.nn.utils.prune.LnStructured.compute_mask">
<code class="sig-name descname">compute_mask</code><span class="sig-paren">(</span><em class="sig-param">t</em>, <em class="sig-param">default_mask</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.compute_mask"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.
Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
if the tensor has not been pruned yet), generate a mask to apply on
top of the <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> by zeroing out the channels along the
specified dim with the lowest Ln-norm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>t</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – tensor representing the parameter to prune</p></li>
<li><p><strong>default_mask</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Base mask from previous pruning
iterations, that need to be respected after the new mask is
applied. Same dims as <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>mask to apply to <code class="docutils literal notranslate"><span class="pre">t</span></code>, of same dims as <code class="docutils literal notranslate"><span class="pre">t</span></code></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>mask (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">torch.Tensor</a>)</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#IndexError" title="(in Python v3.9)"><strong>IndexError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">self.dim</span> <span class="pre">>=</span> <span class="pre">len(t.shape)</span></code></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.nn.utils.prune.LnStructured.prune">
<code class="sig-name descname">prune</code><span class="sig-paren">(</span><em class="sig-param">t</em>, <em class="sig-param">default_mask=None</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.prune" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>
according to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="torch.nn.utils.prune.LnStructured.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>t</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – tensor to prune (of same dimensions as
<code class="docutils literal notranslate"><span class="pre">default_mask</span></code>).</p></li>
<li><p><strong>default_mask</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – mask from previous pruning
iteration, if any. To be considered when determining what
portion of the tensor that pruning should act on. If None,
default to a mask of ones.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>pruned version of tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.nn.utils.prune.LnStructured.remove">
<code class="sig-name descname">remove</code><span class="sig-paren">(</span><em class="sig-param">module</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.remove" title="Permalink to this definition">¶</a></dt>
<dd><p>Removes the pruning reparameterization from a module. The pruned
parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned, and the parameter
named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list. Similarly,
the buffer named <code class="docutils literal notranslate"><span class="pre">name+'_mask'</span></code> is removed from the buffers.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Pruning itself is NOT undone or reversed!</p>
</div>
</dd></dl>
</dd></dl>
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