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<div class="section" id="distributed-checkpoint-torch-distributed-checkpoint">
<h1>Distributed Checkpoint - torch.distributed.checkpoint<a class="headerlink" href="#distributed-checkpoint-torch-distributed-checkpoint" title="Permalink to this heading">¶</a></h1>
<p>Distributed Checkpoint (DCP) support loading and saving models from multiple ranks in parallel.
It handles load-time resharding which enables saving in one cluster topology and loading into another.</p>
<p>DCP is different than <cite>torch.save</cite> and <cite>torch.load</cite> in a few significant ways:</p>
<ul class="simple">
<li><p>It produces multiple files per checkpoint, with at least one per rank.</p></li>
<li><p>It operates in place, meaning that the model should allocate its data first and DCP uses that storage instead.</p></li>
</ul>
<p>The entrypoints to load and save a checkpoint are the following:</p>
<span class="target" id="module-torch.distributed.checkpoint"></span><dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.load">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</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">planner</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="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a distributed <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> in SPMD style.</p>
<p>Each rank will try to read the least amount of data necessary
to fullfill the requested <cite>state_dict</cite>. When loading <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code>
or <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> instances, each rank only reads data for their local shards.</p>
<p>For each <code class="docutils literal notranslate"><span class="pre">Stateful</span></code> object (having both a <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and a <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>),
load will first call <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> before attempting deserialization, followed by
<code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> once the deserialization is complete.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>All tensors in <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> must be allocated on their
destination device <em>prior to</em> calling this function.</p>
<p>All non-tensor data is loaded using <cite>torch.load()</cite> and modified in place
on state_dict.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Users must call <cite>load_state_dict</cite> on the root module to ensure load
pos-processing and non-tensor data properly propagates.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_dict</strong> (<em>Dict</em><em>[</em><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>Any</em><em>]</em>) – The state_dict to load. Note that this
state dict will updated in place.</p></li>
<li><p><strong>storage_reader</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.StorageReader" title="torch.distributed.checkpoint.StorageReader"><em>StorageReader</em></a>) – StorageReader used to load data from.</p></li>
<li><p><strong>process_group</strong> (<em>ProcessGroup</em>) – ProcessGroup to be used for cross-rank synchronization.</p></li>
<li><p><strong>coordinator_rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Rank to use to coordinate the checkpoint.
rank0 is used by default.</p></li>
<li><p><strong>no_dist</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, distributed checkpoint will not load
in SPMD style. (Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>None.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>None</p>
</dd>
</dl>
<dl>
<dt>Examples</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adagrad</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">model_state_dict</span> <span class="o">=</span> <span class="n">my_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">fs_storage_reader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">FileSystemReader</span><span class="p">(</span><span class="s2">"/checkpoint/1"</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="o">=</span><span class="n">model_state_dict</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">storage_reader</span><span class="o">=</span><span class="n">fs_storage_reader</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># module.load_state_dict() function might have customized steps</span>
<span class="gp">>>> </span><span class="c1"># to flush the state_dict, must call it to</span>
<span class="gp">>>> </span><span class="c1"># ensure correct behavior.</span>
<span class="gp">>>> </span><span class="n">my_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>load_state_dict uses collectives to coordinate reads across ranks.
For NCCL-based process groups, internal tensor representations of
objects must be moved to the GPU device before communication takes place.
In this case, the device used is given by <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_device()</span></code>
and it is the user’s responsibility to ensure that this is set so that each
rank has an individual GPU, via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device()</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.save">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</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">planner</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="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Save a distributed model in SPMD style.</p>
<p>This function is different from <code class="docutils literal notranslate"><span class="pre">torch.save()</span></code> as it handles
<code class="docutils literal notranslate"><span class="pre">ShardedTensor</span></code> , and <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> by having each rank only save their local shards.</p>
<p>For each <code class="docutils literal notranslate"><span class="pre">Stateful</span></code> object (having both a <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and a <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>),
save will call <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> before serialization.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>There is no guarantees of Backwards Compatibility across PyTorch versions
for saved state_dicts.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If using the <cite>process_group</cite> argument, make sure that only its ranks
call <cite>save_state_dict</cite> and that all data in state_dict belong to it.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When saving checkpoint for FSDP’s <cite>ShardingStrategy.HYBRID_SHARD</cite>, only one of
the shard_group should be calling <cite>save_state_dict</cite> and the corresponding process
group needs to be passed in.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function can be used to save a state_dict without having a process group
initialized by passing <code class="docutils literal notranslate"><span class="pre">no_dist=True</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>state_dict</strong> (<em>Dict</em><em>[</em><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>Any</em><em>]</em>) – The state_dict to save.</p></li>
<li><p><strong>storage_writer</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.StorageWriter" title="torch.distributed.checkpoint.StorageWriter"><em>StorageWriter</em></a>) – Instance of StorageWrite use to perform writes.</p></li>
<li><p><strong>process_group</strong> (<em>ProcessGroup</em>) – ProcessGroup to be used for cross-rank synchronization.</p></li>
<li><p><strong>coordinator_rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Rank to use to coordinate the checkpoint.
rank0 is used by default.</p></li>
<li><p><strong>no_dist</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, distributed checkpoint will not save
in SPMD style. (Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Metadata object for the saved checkpoint.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Metadata</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">my_model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model_state_dict</span> <span class="o">=</span> <span class="n">my_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">fs_storage_writer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">FileSystemWriter</span><span class="p">(</span><span class="s2">"/checkpoint/1"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">save_state_dict</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="o">=</span><span class="n">model_state_dict</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">storage_writer</span><span class="o">=</span><span class="n">fs_storage_writer</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>save_state_dict uses collectives to coordinate writes across ranks.
For NCCL-based process groups, internal tensor representations of
objects must be moved to the GPU device before communication takes place.
In this case, the device used is given by <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_device()</span></code>
and it is the user’s responsibility to ensure that this is set so that
each rank has an individual GPU, via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device()</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.load_state_dict">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</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">planner</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="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This method is deprecated. Please switch to ‘load’.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.save_state_dict">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</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">planner</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="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.save_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This method is deprecated. Please switch to ‘save’.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>Metadata</em></p>
</dd>
</dl>
</dd></dl>
<p>In addition to the above entrypoints, <cite>Stateful</cite> objects, as described below, provide additional customization during saving/loading
.. automodule:: torch.distributed.checkpoint.stateful</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</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/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
<dd><p>Stateful protocol for objects that can be checkpointed and restored.</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.load_state_dict">
<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Restore the object’s state from the provided state_dict.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><em>Dict</em></a><em>[</em><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><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><em>Any</em></a><em>]</em>) – The state dict to restore from</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.state_dict">
<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Objects should return their state_dict representation as a dictionary.
The output of this function will be checkpointed, and later restored in
<cite>load_state_dict()</cite>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Because of the inplace nature of restoring a checkpoint, this function
is also called during <cite>torch.distributed.checkpoint.load</cite>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The objects state dict</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Dict</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<p>This <a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/torch/distributed/checkpoint/examples/fsdp_checkpoint_example.py">example</a> shows how to use Pytorch Distributed Checkpoint to save a FSDP model.</p>
<p>The following types define the IO interface used during checkpoint:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
<dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> to read from storage.</p>
<p>One StorageReader instance acts as both the coordinator and the follower
in a distributed checkpoint. As part of initialization, each instance
is told its role.</p>
<p>A subclass should expected the following sequence of calls by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>:</p>
<ol class="arabic simple">
<li><p>(all ranks) read_metadata()</p></li>
<li><p>(all ranks) set_up_storage_reader()</p></li>
<li><p>(all ranks) prepare_local_plan()</p></li>
<li><p>(coordinator) prepare_global_plan()</p></li>
<li><p>(all ranks) read_data()</p></li>
</ol>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_global_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform centralized planning of storage loading.</p>
<p>This method is only called on the coordinator instance.</p>
<p>While this method can produce a completely different plan, the preferred
way is to store storage specific data in LoadPlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plans</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a><em>]</em>) – A list of <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> instances, one for each rank.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of transformed <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> after storage global planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a>[<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_local_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform storage-specific local planning.</p>
<p>While this method can produce a completely different plan, the recommended
way is to store storage specific data in LoadPlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.LoadPlan"><em>LoadPlan</em></a>) – The local plan from the <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> in use.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A transformed <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> after storage local planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_data">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Read all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
<p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::load_bytes</span></code> to deserialize a BytesIO
object into the right place.</p>
<p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::resolve_tensor</span></code> to get access to the
tensors that in should load data into.</p>
<p>It’s the StorageLayer responsibility to properly schedule any cross device copies
required.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.LoadPlan"><em>LoadPlan</em></a>) – The local plan to execute on</p></li>
<li><p><strong>planner</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlanner" title="torch.distributed.checkpoint.LoadPlanner"><em>LoadPlanner</em></a>) – The planner object to use to resolve items.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A future that completes once all reads are finished.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="futures.html#torch.futures.Future" title="torch.jit.Future"><em>Future</em></a>[None]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_metadata">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
<dd><p>Read the checkpoint metadata.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The metadata object associated with the checkpoint being loaded.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><em>Metadata</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.set_up_storage_reader">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize this instance.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>metadata</strong> (<em>Metadata</em>) – The metadata schema to use.</p></li>
<li><p><strong>is_coordinator</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether this instance is responsible for coordinating
the checkpoint.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageWriter</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter" title="Permalink to this definition">¶</a></dt>
<dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">save_state_dict</span></code> to write to storage.</p>
<p>One StorageWriter instance acts as both the coordinator and the follower
in a distributed checkpoint. As part of initialization, each instance
is told its role.</p>
<p>A subclass should expect the following sequence of calls.</p>
<ol class="arabic simple">
<li><p>(all ranks) set_up_storage_writer()</p></li>
<li><p>(all ranks) prepare_local_plan()</p></li>
<li><p>(coordinator) prepare_global_plan()</p></li>
<li><p>(all ranks) write_data()</p></li>
<li><p>(coordinator) finish()</p></li>
</ol>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.finish">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.finish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.finish" title="Permalink to this definition">¶</a></dt>
<dd><p>Write the metadata and marks the current checkpoint as successful.</p>
<p>The actual format/schema used for serializing <cite>metadata</cite> is an
implementation detail. The only requirement is that it’s recoverable
in to the same object graph.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>metadata</strong> (<em>Metadata</em>) – metadata for the new checkpoint</p></li>
<li><p><strong>results</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><em>WriteResult</em><em>]</em><em>]</em>) – A list of WriteResults from all ranks.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>None</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>None</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_global_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform centralized planning of storage.</p>
<p>This method is only called on the coordinator instance.</p>
<p>While this method can produce a completely different plan, the preferred
way is to store storage specific data in SavePlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plans</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.SavePlan" title="torch.distributed.checkpoint.planner.SavePlan"><em>SavePlan</em></a><em>]</em>) – A list of <code class="docutils literal notranslate"><span class="pre">SavePlan</span></code> instances, one for each rank.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of transformed <code class="docutils literal notranslate"><span class="pre">SavePlan</span></code> after storage global planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a>[<a class="reference internal" href="#torch.distributed.checkpoint.SavePlan" title="torch.distributed.checkpoint.planner.SavePlan"><em>SavePlan</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_local_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform storage-specific local planning.</p>
<p>While this method can produce a completely different plan, the recommended
way is to store storage specific data in SavePlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.SavePlan" title="torch.distributed.checkpoint.SavePlan"><em>SavePlan</em></a>) – The local plan from the <code class="docutils literal notranslate"><span class="pre">SavePlanner</span></code> in use.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A transformed <code class="docutils literal notranslate"><span class="pre">SavePlan</span></code> after storage local planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.checkpoint.SavePlan" title="torch.distributed.checkpoint.planner.SavePlan"><em>SavePlan</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.set_up_storage_writer">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_writer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.set_up_storage_writer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.set_up_storage_writer" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize this instance.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>is_coordinator</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether this instance is responsible for coordinating
the checkpoint.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.write_data">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.write_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.write_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Write all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
<p>A subclass should call <code class="docutils literal notranslate"><span class="pre">SavePlanner::resolve_data</span></code> on each item
from the plan to get access to the underlying object to write.</p>
<p>Subclasses should lazily call <cite>resolve_data</cite> as it can allocate memory.
In case of tensors, make following assumptions:</p>
<ul class="simple">
<li><p>They might be on any device, including not matching the one on <code class="docutils literal notranslate"><span class="pre">WriteItem::tensor_data</span></code></p></li>
<li><p>They might be views or not contiguous. Only the projection needs to be saved.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.SavePlan" title="torch.distributed.checkpoint.SavePlan"><em>SavePlan</em></a>) – The save plan to execute.</p></li>
<li><p><strong>planner</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.SavePlanner" title="torch.distributed.checkpoint.SavePlanner"><em>SavePlanner</em></a>) – Planner object to be used to resolve items to data.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A future that completes to a list of WriteResult</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="futures.html#torch.futures.Future" title="torch.jit.Future"><em>Future</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a>[<em>WriteResult</em>]]</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<p>The following types define the planner interface used during checkpoint:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner" title="Permalink to this definition">¶</a></dt>
<dd><p>Abstract class defining the protocol used by load_state_dict to plan the load process.</p>
<p>LoadPlanner are stateful objects that can be used to customize the whole load process.</p>
<p>LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
will be visible to the whole process.</p>
<p>A planner subclass can expect the following sequence of calls during load_state_dict:</p>
<ol class="arabic simple">
<li><dl class="simple">
<dt>set_up_planner - called on all ranks.</dt><dd><p>Signals the start of loading a checkpoint.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>create_local_plan - called on all ranks.</dt><dd><p>Process the state_dict and produces a <cite>LoadPlan</cite> that will be sent for global planning.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>create_global_plan - called on the coordinator rank only.</dt><dd><p>Takes the LoadPlan from all ranks and make any global decision.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>load_bytes - called multiple times on each rank</dt><dd><p>This is called once per non-tensor value in state_dict.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>resolve_tensor and commit_tensor - called multiple times on each rank</dt><dd><p>They are called in pair for each Tensor value in state_dict.</p>
</dd>
</dl>
</li>
</ol>
<p>Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
most changes can be expressed by changes in a single method.</p>
<p>There are two usual patterns of extension:</p>
<p>Rewriting state_dict. This is the simplest way to extend the load process as it
doesn’t requite understanding the intrincacies of how LoadPlan works. We need
to keep a reference to the original state_dict as load happens in place so
we need to be able to perform it in place</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">RenamePlanner</span><span class="p">(</span><span class="n">DefaultLoadPlanner</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">set_up_planner</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">is_coordinator</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">original_state_dict</span> <span class="o">=</span> <span class="n">state_dict</span>
<span class="gp">>>> </span> <span class="n">state_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"foo_"</span> <span class="o">+</span> <span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten_sharded_tensors</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">state_dict</span> <span class="o">=</span> <span class="n">_flatten_sharded_tensors</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten_state_dict</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mappings</span> <span class="o">=</span> <span class="n">flatten_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span> <span class="o">=</span> <span class="n">state_dict</span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">is_coordinator</span> <span class="o">=</span> <span class="n">is_coordinator</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">load_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">read_item</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="c1"># Remove the "foo_" prefix</span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">original_state_dict</span><span class="p">[</span><span class="n">read_item</span><span class="o">.</span><span class="n">dest_index</span><span class="o">.</span><span class="n">fqn</span><span class="p">[</span><span class="mi">4</span><span class="p">:]]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
</pre></div>
</div>
<p>Modifying resolve_tensor and commit_tensor to handle load time transformation.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">MetaModelMaterialize</span><span class="p">(</span><span class="n">DefaultSavePlanner</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">resolve_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">read_item</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">tensor</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">resolve_tensor</span><span class="p">(</span><span class="n">read_item</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="k">def</span> <span class="nf">commit_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">read_item</span><span class="p">,</span> <span class="n">tensor</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">[</span><span class="n">read_item</span><span class="o">.</span><span class="n">dest_index</span><span class="o">.</span><span class="n">fqn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tensor</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.commit_tensor">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">commit_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.commit_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.commit_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Call once the StorageReader finished loading data into <code class="docutils literal notranslate"><span class="pre">tensor</span></code>.</p>
<p>The provided tensor is the same one returned by the call to <code class="docutils literal notranslate"><span class="pre">resolve_tensor</span></code>.
This method is only needed if this LoadPlanner needs to post process <code class="docutils literal notranslate"><span class="pre">tensor</span></code> prior to
copying it back to the one in the state_dict.</p>
<p>The contents of tensor will follow its device synchronization model.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.create_global_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the global load plan and return plans for each rank.</p>