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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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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 heading">¶</a></h1>
<div class="admonition note">
<p class="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>The stashing logic saves and restores the RNG state for CPU and another
device type (infer the device type from Tensor arguments excluding CPU
tensors by <code class="docutils literal notranslate"><span class="pre">_infer_device_type</span></code>) to the <code class="docutils literal notranslate"><span class="pre">run_fn</span></code>. If there are multiple
device, device state will only be saved for devices of a single device type,
and the remaining devices will be ignored. Consequently, if any checkpointed
functions involve randomness, this may result in incorrect gradients. (Note
that if CUDA devices are among the devices detected, it will be prioritized;
otherwise, the first device encountered will be selected.) If there are no
CPU-tensors, the default device type state (default value is <cite>cuda</cite>, and it
could be set to other device by <code class="docutils literal notranslate"><span class="pre">DefaultDeviceType</span></code>) will be saved and restored.
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="py function">
<dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint">
<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=<function</span> <span class="pre">noop_context_fn></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></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>Activation checkpointing is a technique that trades compute for memory.
Instead of keeping tensors needed for backward alive until they are used in
gradient computation during backward, forward computation in checkpointed
regions omits saving tensors for backward and recomputes them during the
backward pass. Activation checkpointing can be applied to any part of a
model.</p>
<p>There are currently two checkpointing implementations available, determined
by the <code class="xref py py-attr docutils literal notranslate"><span class="pre">use_reentrant</span></code> parameter. It is recommended that you use
<code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>. Please refer the note below for a discussion of
their differences.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If the <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> invocation during the backward pass differs
from the forward pass, e.g., due to a global variable, the checkpointed
checkpointed version may not be equivalent, potentially causing an
error being raised or leading to silently incorrect gradients.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If you are using the <code class="docutils literal notranslate"><span class="pre">use_reentrant=True</span></code> variant (this is currently
the default), please refer to the note below for important
considerations and potential limitations.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The reentrant variant of checkpoint (<code class="docutils literal notranslate"><span class="pre">use_reentrant=True</span></code>) and
the non-reentrant variant of checkpoint (<code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>)
differ in the following ways:</p>
<ul class="simple">
<li><p>Non-reentrant checkpoint stops recomputation as soon as all needed
intermediate activations have been recomputed. This feature is enabled
by default, but can be disabled with <code class="xref py py-func docutils literal notranslate"><span class="pre">set_checkpoint_early_stop()</span></code>.
Reentrant checkpoint always recomputes <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code> in its
entirety during the backward pass.</p></li>
<li><p>The reentrant variant does not record the autograd graph during the
forward pass, as it runs with the forward pass under
<a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code></a>. The non-reentrant version does record the
autograd graph, allowing one to perform backward on the graph within
checkpointed regions.</p></li>
<li><p>The reentrant checkpoint only supports the
<a class="reference internal" href="generated/torch.autograd.backward.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> API for the backward pass without its
<cite>inputs</cite> argument, while the non-reentrant version supports all ways
of performing the backward pass.</p></li>
<li><p>At least one input and output must have <code class="docutils literal notranslate"><span class="pre">requires_grad=True</span></code> for the
reentrant variant. If this condition is unmet, the checkpointed part
of the model will not have gradients. The non-reentrant version does
not have this requirement.</p></li>
<li><p>The reentrant version does not consider tensors in nested structures
(e.g., custom objects, lists, dicts, etc) as participating in
autograd, while the non-reentrant version does.</p></li>
<li><p>The reentrant checkpoint does not support checkpointed regions with
detached tensors from the computational graph, whereas the
non-reentrant version does. For the reentrant variant, if the
checkpointed segment contains tensors detached using <code class="docutils literal notranslate"><span class="pre">detach()</span></code> or
with <a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code></a>, the backward pass will raise an error.
This is because <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> makes all the outputs require gradients
and this causes issues when a tensor is defined to have no gradient in
the model. To avoid this, detach the tensors outside of the
<code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> function.</p></li>
</ul>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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></p></li>
<li><p><strong>preserve_rng_state</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>) – Omit stashing and restoring
the RNG state during each checkpoint.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>use_reentrant</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>) – Use checkpointing
implementation that requires re-entrant autograd.
If <code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code> is specified, <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> will use an
implementation that does not require re-entrant autograd. This
allows <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> to support additional functionality, such as
working as expected with <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code> and support for
keyword arguments input into the checkpointed function. Note that future
versions of PyTorch will default to <code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>context_fn</strong> (<em>Callable</em><em>, </em><em>optional</em>) – A callable returning a tuple of two
context managers. The function and its recomputation will be run
under the first and second context managers respectively.
This argument is only supported if <code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>.</p></li>
<li><p><strong>determinism_check</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – A string specifying the determinism
check to perform. By default it is set to <code class="docutils literal notranslate"><span class="pre">"default"</span></code> which
compares the shapes, dtypes, and devices of the recomputed tensors
against those the saved tensors. To turn off this check, specify
<code class="docutils literal notranslate"><span class="pre">"none"</span></code>. Currently these are the only two supported values.
Please open an issue if you would like to see more determinism
checks. This argument is only supported if <code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>,
if <code class="docutils literal notranslate"><span class="pre">use_reentrant=True</span></code>, the determinism check is always disabled.</p></li>
<li><p><strong>debug</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 <code class="docutils literal notranslate"><span class="pre">True</span></code>, error messages will also include
a trace of the operators ran during the original forward computation
as well as the recomputation. This argument is only supported if
<code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code>.</p></li>
<li><p><strong>args</strong> – tuple containing inputs to the <code class="xref py py-attr docutils literal notranslate"><span class="pre">function</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>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>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint_sequential">
<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link"><span class="pre">[source]</span></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 not store
the intermediate activations. The inputs of each checkpointed segment will
be saved for re-running the segment in the backward pass.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If you are using the <code class="docutils literal notranslate"><span class="pre">use_reentrant=True`</span> <span class="pre">variant</span> <span class="pre">(this</span> <span class="pre">is</span> <span class="pre">the</span>
<span class="pre">default),</span> <span class="pre">please</span> <span class="pre">see</span> <span class="pre">:func:`~torch.utils.checkpoint.checkpoint`</span> <span class="pre">for</span>
<span class="pre">the</span> <span class="pre">important</span> <span class="pre">considerations</span> <span class="pre">and</span> <span class="pre">limitations</span> <span class="pre">of</span> <span class="pre">this</span> <span class="pre">variant.</span> <span class="pre">It</span> <span class="pre">is</span>
<span class="pre">recommended</span> <span class="pre">that</span> <span class="pre">you</span> <span class="pre">use</span> <span class="pre">``use_reentrant=False</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>functions</strong> – A <a class="reference internal" href="generated/torch.nn.Sequential.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.</p></li>
<li><p><strong>segments</strong> – Number of chunks to create in the model</p></li>
<li><p><strong>input</strong> – A Tensor that is input to <code class="xref py py-attr docutils literal notranslate"><span class="pre">functions</span></code></p></li>
<li><p><strong>preserve_rng_state</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>) – Omit stashing and restoring
the RNG state during each checkpoint.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>use_reentrant</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>) – Use checkpointing
implementation that requires re-entrant autograd.
If <code class="docutils literal notranslate"><span class="pre">use_reentrant=False</span></code> is specified, <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> will use an
implementation that does not require re-entrant autograd. This
allows <code class="docutils literal notranslate"><span class="pre">checkpoint</span></code> to support additional functionality, such as
working as expected with <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code> and support for
keyword arguments input into the checkpointed function.
Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>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>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest 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>
</div>
</dd></dl>
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