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<div class="section" id="torch-utils-checkpoint">
<h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" title="Permalink to this headline">¶</a></h1>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>Checkpointing is implemented by rerunning a forward-pass segment for
each checkpointed segment during backward. This can cause persistent
states like the RNG state to be advanced than they would without
checkpointing. By default, checkpointing includes logic to juggle
the RNG state such that checkpointed passes making use of RNG
(through dropout for example) have deterministic output as
compared to non-checkpointed passes. The logic to stash and restore
RNG states can incur a moderate performance hit depending on the runtime
of checkpointed operations. If deterministic output compared to
non-checkpointed passes is not required, supply <code class="docutils literal notranslate"><span class="pre">preserve_rng_state=False</span></code>
to <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> or <code class="docutils literal notranslate"><span class="pre">checkpoint_sequential</span></code> to omit stashing and
restoring the RNG state during each checkpoint.</p>
<p class="last">The stashing logic saves and restores the RNG state for the current device
and the device of all cuda Tensor arguments to the <code class="docutils literal notranslate"><span class="pre">run_fn</span></code>.
However, the logic has no way to anticipate if the user will move
Tensors to a new device within the <code class="docutils literal notranslate"><span class="pre">run_fn</span></code> itself. Therefore, if you move
Tensors to a new device (“new” meaning not belonging to the set of
[current device + devices of Tensor arguments]) within <code class="docutils literal notranslate"><span class="pre">run_fn</span></code>, deterministic
output compared to non-checkpointed passes is never guaranteed.</p>
</div>
<dl class="function">
<dt id="torch.utils.checkpoint.checkpoint">
<code class="descclassname">torch.utils.checkpoint.</code><code class="descname">checkpoint</code><span class="sig-paren">(</span><em>function</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">¶</a></dt>
<dd><p>Checkpoint a model or part of the model</p>
<p>Checkpointing works by trading compute for memory. Rather than storing all
intermediate activations of the entire computation graph for computing
backward, the checkpointed part does <strong>not</strong> save intermediate activations,
and instead recomputes them in backward pass. It can be applied on any part
of a model.</p>
<p>Specifically, in the forward pass, <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> will run in
<code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code> manner, i.e., not storing the intermediate
activations. Instead, the forward pass saves the inputs tuple and the
<code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> parameter. In the backwards pass, the saved inputs and
<code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> is retreived, and the forward pass is computed on
<code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> again, now tracking the intermediate activations, and then
the gradients are calculated using these activation values.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Checkpointing doesn’t work with <a class="reference internal" href="autograd.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.grad()</span></code></a>, but only
with <a class="reference internal" href="autograd.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.backward()</span></code></a>.</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> invocation during backward does anything different
than the one during forward, e.g., due to some global variable, the
checkpointed version won’t be equivalent, and unfortunately it can’t be
detected.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>function</strong> – describes what to run in the forward pass of the model or
part of the model. It should also know how to handle the inputs
passed as the tuple. For example, in LSTM, if user passes
<code class="docutils literal notranslate"><span class="pre">(activation,</span> <span class="pre">hidden)</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> should correctly use the
first input as <code class="docutils literal notranslate"><span class="pre">activation</span></code> and the second input as <code class="docutils literal notranslate"><span class="pre">hidden</span></code></li>
<li><strong>preserve_rng_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Omit stashing and restoring
the RNG state during each checkpoint.</li>
<li><strong>args</strong> – tuple containing inputs to the <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code></li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Output of running <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> on <code class="xref py py-attr docutils literal notranslate"><span class="pre">*args</span></code></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.utils.checkpoint.checkpoint_sequential">
<code class="descclassname">torch.utils.checkpoint.</code><code class="descname">checkpoint_sequential</code><span class="sig-paren">(</span><em>functions</em>, <em>segments</em>, <em>*inputs</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">¶</a></dt>
<dd><p>A helper function for checkpointing sequential models.</p>
<p>Sequential models execute a list of modules/functions in order
(sequentially). Therefore, we can divide such a model in various segments
and checkpoint each segment. All segments except the last will run in
<code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code> manner, i.e., not storing the intermediate
activations. The inputs of each checkpointed segment will be saved for
re-running the segment in the backward pass.</p>
<p>See <a class="reference internal" href="#torch.utils.checkpoint.checkpoint" title="torch.utils.checkpoint.checkpoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">checkpoint()</span></code></a> on how checkpointing works.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Checkpointing doesn’t work with <a class="reference internal" href="autograd.html#torch.autograd.grad" title="torch.autograd.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.grad()</span></code></a>, but only
with <a class="reference internal" href="autograd.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.backward()</span></code></a>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>functions</strong> – A <a class="reference internal" href="nn.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Sequential</span></code></a> or the list of modules or
functions (comprising the model) to run sequentially.</li>
<li><strong>segments</strong> – Number of chunks to create in the model</li>
<li><strong>inputs</strong> – tuple of Tensors that are inputs to <code class="xref py py-attr docutils literal notranslate"><span class="pre">functions</span></code></li>
<li><strong>preserve_rng_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Omit stashing and restoring
the RNG state during each checkpoint.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Output of running <code class="xref py py-attr docutils literal notranslate"><span class="pre">functions</span></code> sequentially on <code class="xref py py-attr docutils literal notranslate"><span class="pre">*inputs</span></code></p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</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="o">...</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">input_var</span> <span class="o">=</span> <span class="n">checkpoint_sequential</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">chunks</span><span class="p">,</span> <span class="n">input_var</span><span class="p">)</span>
</pre></div>
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