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<div class="section" id="module-torch.distributed.fsdp">
<span id="fullyshardeddataparallel"></span><h1>FullyShardedDataParallel<a class="headerlink" href="#module-torch.distributed.fsdp" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullyShardedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</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">sharding_strategy</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">cpu_offload</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">auto_wrap_policy</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">backward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BackwardPrefetch.BACKWARD_PRE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</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">ignored_modules</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">param_init_fn</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">device_id</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">sync_module_states</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">forward_prefetch</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">limit_all_gathers</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="n"><span class="pre">use_orig_params</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">ignored_states</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">device_mesh</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/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>A wrapper for sharding module parameters across data parallel workers.</p>
<p>This is inspired by <a class="reference external" href="https://arxiv.org/abs/2004.13336">Xu et al.</a> as well as the ZeRO Stage 3 from <a class="reference external" href="https://www.deepspeed.ai/">DeepSpeed</a>.
FullyShardedDataParallel is commonly shortened to FSDP.</p>
<p>To understand FSDP internals, refer to the
<a class="reference internal" href="notes/fsdp.html#fsdp-notes"><span class="std std-ref">FSDP Notes</span></a>.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.fsdp</span> <span class="kn">import</span> <span class="n">FullyShardedDataParallel</span> <span class="k">as</span> <span class="n">FSDP</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device_id</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">sharded_module</span> <span class="o">=</span> <span class="n">FSDP</span><span class="p">(</span><span class="n">my_module</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">sharded_module</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">sharded_module</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">]))</span>
<span class="gp">>>> </span><span class="n">loss</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>Using FSDP involves wrapping your module and then initializing your
optimizer after. This is required since FSDP changes the parameter
variables.</p>
<p>When setting up FSDP, you need to consider the destination CUDA
device. If the device has an ID (<code class="docutils literal notranslate"><span class="pre">dev_id</span></code>), you have three options:</p>
<ul class="simple">
<li><p>Place the module on that device</p></li>
<li><p>Set the device using <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device(dev_id)</span></code></p></li>
<li><p>Pass <code class="docutils literal notranslate"><span class="pre">dev_id</span></code> into the <code class="docutils literal notranslate"><span class="pre">device_id</span></code> constructor argument.</p></li>
</ul>
<p>This ensures that the FSDP instance’s compute device is the
destination device. For option 1 and 3, the FSDP initialization
always occurs on GPU. For option 2, the FSDP initialization
happens on module’s current device, which may be a CPU.</p>
<p>If you’re using the <code class="docutils literal notranslate"><span class="pre">sync_module_states=True</span></code> flag, you need to
ensure that the module is on a GPU or use the <code class="docutils literal notranslate"><span class="pre">device_id</span></code>
argument to specify a CUDA device that FSDP will move the module
to in the FSDP constructor. This is necessary because
<code class="docutils literal notranslate"><span class="pre">sync_module_states=True</span></code> requires GPU communication.</p>
<p>FSDP also takes care of moving input tensors to the forward method
to the GPU compute device, so you don’t need to manually move them
from CPU.</p>
<p>For <code class="docutils literal notranslate"><span class="pre">use_orig_params=True</span></code>,
<code class="docutils literal notranslate"><span class="pre">ShardingStrategy.SHARD_GRAD_OP</span></code> exposes the unsharded
parameters, not the sharded parameters after forward, unlike
<code class="docutils literal notranslate"><span class="pre">ShardingStrategy.FULL_SHARD</span></code>. If you want
to inspect the gradients, you can use the <code class="docutils literal notranslate"><span class="pre">summon_full_params</span></code>
method with <code class="docutils literal notranslate"><span class="pre">with_grads=True</span></code>.</p>
<p>With <code class="docutils literal notranslate"><span class="pre">limit_all_gathers=True</span></code>, you may see a gap in the FSDP
pre-forward where the CPU thread is not issuing any kernels. This is
intentional and shows the rate limiter in effect. Synchronizing the CPU
thread in that way prevents over-allocating memory for subsequent
all-gathers, and it should not actually delay GPU kernel execution.</p>
<p>FSDP replaces managed modules’ parameters with <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>
views during forward and backward computation for autograd-related
reasons. If your module’s forward relies on saved references to
the parameters instead of reacquiring the references each
iteration, then it will not see FSDP’s newly created views,
and autograd will not work correctly.</p>
<p>Finally, when using <code class="docutils literal notranslate"><span class="pre">sharding_strategy=ShardingStrategy.HYBRID_SHARD</span></code>
with the sharding process group being intra-node and the
replication process group being inter-node, setting
<code class="docutils literal notranslate"><span class="pre">NCCL_CROSS_NIC=1</span></code> can help improve the all-reduce times over
the replication process group for some cluster setups.</p>
<p><strong>Limitations</strong></p>
<p>There are several limitations to be aware of when using FSDP:</p>
<ul class="simple">
<li><p>FSDP currently does not support gradient accumulation outside
<code class="docutils literal notranslate"><span class="pre">no_sync()</span></code> when using CPU offloading. This is because FSDP
uses the newly-reduced gradient instead of accumulating with any
existing gradient, which can lead to incorrect results.</p></li>
<li><p>FSDP does not support running the forward pass of a submodule
that is contained in an FSDP instance. This is because the
submodule’s parameters will be sharded, but the submodule itself
is not an FSDP instance, so its forward pass will not all-gather
the full parameters appropriately.</p></li>
<li><p>FSDP does not work with double backwards due to the way it
registers backward hooks.</p></li>
<li><p>FSDP has some constraints when freezing parameters.
For <code class="docutils literal notranslate"><span class="pre">use_orig_params=False</span></code>, each FSDP instance must manage
parameters that are all frozen or all non-frozen. For
<code class="docutils literal notranslate"><span class="pre">use_orig_params=True</span></code>, FSDP supports mixing frozen and
non-frozen parameters, but it’s recommended to avoid doing so to
prevent higher than expected gradient memory usage.</p></li>
<li><p>As of PyTorch 1.12, FSDP offers limited support for shared
parameters. If enhanced shared parameter support is needed for
your use case, please post in
<a class="reference external" href="https://github.com/pytorch/pytorch/issues/77724">this issue</a>.</p></li>
<li><p>You should avoid modifying the parameters between forward and
backward without using the <code class="docutils literal notranslate"><span class="pre">summon_full_params</span></code> context, as
the modifications may not persist.</p></li>
</ul>
<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="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a>) – This is the module to be wrapped with FSDP.</p></li>
<li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><em>ProcessGroup</em><em>, </em><em>Tuple</em><em>[</em><em>ProcessGroup</em><em>, </em><em>ProcessGroup</em><em>]</em><em>]</em><em>]</em>) – This is the process group over which the model is sharded and thus
the one used for FSDP’s all-gather and reduce-scatter collective
communications. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then FSDP uses the default process
group. For hybrid sharding strategies such as
<code class="docutils literal notranslate"><span class="pre">ShardingStrategy.HYBRID_SHARD</span></code>, users can pass in a tuple of
process groups, representing the groups over which to shard and
replicate, respectively. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then FSDP constructs process
groups for the user to shard intra-node and replicate inter-node.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>sharding_strategy</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.fsdp.ShardingStrategy" title="torch.distributed.fsdp.ShardingStrategy"><em>ShardingStrategy</em></a><em>]</em>) – This configures the sharding strategy, which may trade off memory
saving and communication overhead. See <a class="reference internal" href="#torch.distributed.fsdp.ShardingStrategy" title="torch.distributed.fsdp.ShardingStrategy"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShardingStrategy</span></code></a>
for details. (Default: <code class="docutils literal notranslate"><span class="pre">FULL_SHARD</span></code>)</p></li>
<li><p><strong>cpu_offload</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.fsdp.CPUOffload" title="torch.distributed.fsdp.CPUOffload"><em>CPUOffload</em></a><em>]</em>) – This configures CPU offloading. If this is set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, then
no CPU offloading happens. See <a class="reference internal" href="#torch.distributed.fsdp.CPUOffload" title="torch.distributed.fsdp.CPUOffload"><code class="xref py py-class docutils literal notranslate"><span class="pre">CPUOffload</span></code></a> for details.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>auto_wrap_policy</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><em>Callable</em><em>[</em><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>]</em><em>, </em><em>ModuleWrapPolicy</em><em>, </em><em>CustomPolicy</em><em>]</em><em>]</em>) – <p>This specifies a policy to apply FSDP to submodules of <code class="docutils literal notranslate"><span class="pre">module</span></code>,
which is needed for communication and computation overlap and thus
affects performance. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then FSDP only applies to
<code class="docutils literal notranslate"><span class="pre">module</span></code>, and users should manually apply FSDP to parent modules
themselves (proceeding bottom-up). For convenience, this accepts
<code class="docutils literal notranslate"><span class="pre">ModuleWrapPolicy</span></code> directly, which allows users to specify the
module classes to wrap (e.g. the transformer block). Otherwise,
this should be a callable that takes in three arguments
<code class="docutils literal notranslate"><span class="pre">module:</span> <span class="pre">nn.Module</span></code>, <code class="docutils literal notranslate"><span class="pre">recurse:</span> <span class="pre">bool</span></code>, and
<code class="docutils literal notranslate"><span class="pre">nonwrapped_numel:</span> <span class="pre">int</span></code> and should return a <code class="docutils literal notranslate"><span class="pre">bool</span></code> specifying
whether the passed-in <code class="docutils literal notranslate"><span class="pre">module</span></code> should have FSDP applied if
<code class="docutils literal notranslate"><span class="pre">recurse=False</span></code> or if the traversal should continue into the
module’s subtree if <code class="docutils literal notranslate"><span class="pre">recurse=True</span></code>. Users may add additional
arguments to the callable. The <code class="docutils literal notranslate"><span class="pre">size_based_auto_wrap_policy</span></code> in
<code class="docutils literal notranslate"><span class="pre">torch.distributed.fsdp.wrap.py</span></code> gives an example callable that
applies FSDP to a module if the parameters in its subtree exceed
100M numel. We recommend printing the model after applying FSDP
and adjusting as needed.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_auto_wrap_policy</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">recurse</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">nonwrapped_numel</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="c1"># Additional custom arguments</span>
<span class="gp">>>> </span> <span class="n">min_num_params</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e8</span><span class="p">),</span>
<span class="gp">>>> </span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">nonwrapped_numel</span> <span class="o">>=</span> <span class="n">min_num_params</span>
<span class="gp">>>> </span><span class="c1"># Configure a custom `min_num_params`</span>
<span class="gp">>>> </span><span class="n">my_auto_wrap_policy</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">custom_auto_wrap_policy</span><span class="p">,</span> <span class="n">min_num_params</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mf">1e5</span><span class="p">))</span>
</pre></div>
</div>
</p></li>
<li><p><strong>backward_prefetch</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.fsdp.BackwardPrefetch" title="torch.distributed.fsdp.BackwardPrefetch"><em>BackwardPrefetch</em></a><em>]</em>) – This configures explicit backward prefetching of all-gathers. If
<code class="docutils literal notranslate"><span class="pre">None</span></code>, then FSDP does not backward prefetch, and there is no
communication and computation overlap in the backward pass. See
<a class="reference internal" href="#torch.distributed.fsdp.BackwardPrefetch" title="torch.distributed.fsdp.BackwardPrefetch"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackwardPrefetch</span></code></a> for details. (Default: <code class="docutils literal notranslate"><span class="pre">BACKWARD_PRE</span></code>)</p></li>
<li><p><strong>mixed_precision</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.fsdp.MixedPrecision" title="torch.distributed.fsdp.MixedPrecision"><em>MixedPrecision</em></a><em>]</em>) – This configures native mixed precision for FSDP. If this is set to
<code class="docutils literal notranslate"><span class="pre">None</span></code>, then no mixed precision is used. Otherwise, parameter,
buffer, and gradient reduction dtypes can be set. See
<a class="reference internal" href="#torch.distributed.fsdp.MixedPrecision" title="torch.distributed.fsdp.MixedPrecision"><code class="xref py py-class docutils literal notranslate"><span class="pre">MixedPrecision</span></code></a> for details. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>ignored_modules</strong> (<em>Optional</em><em>[</em><em>Iterable</em><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em>]</em><em>]</em>) – Modules whose
own parameters and child modules’ parameters and buffers are
ignored by this instance. None of the modules directly in
<code class="docutils literal notranslate"><span class="pre">ignored_modules</span></code> should be <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code></a>
instances, and any child modules that are already-constructed
<a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code></a> instances will not be ignored if
they are nested under this instance. This argument may be used to
avoid sharding specific parameters at module granularity when using an
<code class="docutils literal notranslate"><span class="pre">auto_wrap_policy</span></code> or if parameters’ sharding is not managed by
FSDP. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>param_init_fn</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>[</em><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>nn.Module</em></a><em>]</em><em>, </em><em>None</em><em>]</em><em>]</em>) – <p>A <code class="docutils literal notranslate"><span class="pre">Callable[torch.nn.Module]</span> <span class="pre">-></span> <span class="pre">None</span></code> that
specifies how modules that are currently on the meta device should
be initialized onto an actual device. As of v1.12, FSDP detects
modules with parameters or buffers on meta device via <code class="docutils literal notranslate"><span class="pre">is_meta</span></code>
and either applies <code class="docutils literal notranslate"><span class="pre">param_init_fn</span></code> if specified or calls
<code class="docutils literal notranslate"><span class="pre">nn.Module.reset_parameters()</span></code> otherwise. For both cases, the
implementation should <em>only</em> initialize the parameters/buffers of
the module, not those of its submodules. This is to avoid
re-initialization. In addition, FSDP also supports deferred
initialization via torchdistX’s (<a class="reference external" href="https://github.com/pytorch/torchdistX">https://github.com/pytorch/torchdistX</a>)
<code class="docutils literal notranslate"><span class="pre">deferred_init()</span></code> API, where the deferred modules are initialized
by calling <code class="docutils literal notranslate"><span class="pre">param_init_fn</span></code> if specified or torchdistX’s default
<code class="docutils literal notranslate"><span class="pre">materialize_module()</span></code> otherwise. If <code class="docutils literal notranslate"><span class="pre">param_init_fn</span></code> is
specified, then it is applied to all meta-device modules, meaning
that it should probably case on the module type. FSDP calls the
initialization function before parameter flattening and sharding.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">module</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"meta"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">my_init_fn</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="c1"># E.g. initialize depending on the module type</span>
<span class="gp">>>> </span> <span class="o">...</span>
<span class="gp">>>> </span><span class="n">fsdp_model</span> <span class="o">=</span> <span class="n">FSDP</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">param_init_fn</span><span class="o">=</span><span class="n">my_init_fn</span><span class="p">,</span> <span class="n">auto_wrap_policy</span><span class="o">=</span><span class="n">size_based_auto_wrap_policy</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">fsdp_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="c1"># current CUDA device</span>
<span class="gp">>>> </span><span class="c1"># With torchdistX</span>
<span class="gp">>>> </span><span class="n">module</span> <span class="o">=</span> <span class="n">deferred_init</span><span class="o">.</span><span class="n">deferred_init</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># Will initialize via deferred_init.materialize_module().</span>
<span class="gp">>>> </span><span class="n">fsdp_model</span> <span class="o">=</span> <span class="n">FSDP</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">auto_wrap_policy</span><span class="o">=</span><span class="n">size_based_auto_wrap_policy</span><span class="p">)</span>
</pre></div>
</div>
</p></li>
<li><p><strong>device_id</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><em>torch.device</em></a><em>]</em><em>]</em>) – An <code class="docutils literal notranslate"><span class="pre">int</span></code> or
<code class="docutils literal notranslate"><span class="pre">torch.device</span></code> giving the CUDA device on which FSDP
initialization takes place, including the module initialization
if needed and the parameter sharding. This should be specified to
improve initialization speed if <code class="docutils literal notranslate"><span class="pre">module</span></code> is on CPU. If the
default CUDA device was set (e.g. via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device</span></code>),
then the user may pass <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_device</span></code> to this.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>sync_module_states</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then each FSDP module will
broadcast module parameters and buffers from rank 0 to ensure that
they are replicated across ranks (adding communication overhead to
this constructor). This can help load <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> checkpoints
via <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> in a memory efficient way. See
<a class="reference internal" href="#torch.distributed.fsdp.FullStateDictConfig" title="torch.distributed.fsdp.FullStateDictConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullStateDictConfig</span></code></a> for an example of this. (Default:
<code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>forward_prefetch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then FSDP <em>explicitly</em> prefetches
the next forward-pass all-gather before the current forward
computation. This is only useful for CPU-bound workloads, in which
case issuing the next all-gather earlier may improve overlap. This
should only be used for static-graph models since the prefetching
follows the first iteration’s execution order. (Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>limit_all_gathers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then FSDP explicitly
synchronizes the CPU thread to ensure GPU memory usage from only
<em>two</em> consecutive FSDP instances (the current instance running
computation and the next instance whose all-gather is prefetched).
If <code class="docutils literal notranslate"><span class="pre">False</span></code>, then FSDP allows the CPU thread to issue all-gathers
without any extra synchronization. (Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) We often
refer to this feature as the “rate limiter”. This flag should only
be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> for specific CPU-bound workloads with low
memory pressure in which case the CPU thread can aggressively issue
all kernels without concern for the GPU memory usage.</p></li>
<li><p><strong>use_orig_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Setting this to <code class="docutils literal notranslate"><span class="pre">True</span></code> has FSDP use
<code class="docutils literal notranslate"><span class="pre">module</span></code> ‘s original parameters. FSDP exposes those original
parameters to the user via <code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.named_parameters()</span></code>
instead of FSDP’s internal <code class="xref py py-class docutils literal notranslate"><span class="pre">FlatParameter</span></code> s. This means
that the optimizer step runs on the original parameters, enabling
per-original-parameter hyperparameters. FSDP preserves the original
parameter variables and manipulates their data between unsharded
and sharded forms, where they are always views into the underlying
unsharded or sharded <code class="xref py py-class docutils literal notranslate"><span class="pre">FlatParameter</span></code>, respectively. With the
current algorithm, the sharded form is always 1D, losing the
original tensor structure. An original parameter may have all,
some, or none of its data present for a given rank. In the none
case, its data will be like a size-0 empty tensor. Users should not
author programs relying on what data is present for a given
original parameter in its sharded form. <code class="docutils literal notranslate"><span class="pre">True</span></code> is required to
use <code class="docutils literal notranslate"><span class="pre">torch.compile()</span></code>. Setting this to <code class="docutils literal notranslate"><span class="pre">False</span></code> exposes FSDP’s
internal <code class="xref py py-class docutils literal notranslate"><span class="pre">FlatParameter</span></code> s to the user via
<code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.named_parameters()</span></code>. (Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>ignored_states</strong> (<em>Optional</em><em>[</em><em>Iterable</em><em>[</em><em>torch.nn.Parameter</em><em>]</em><em>]</em><em>, </em><em>Optional</em><em>[</em><em>Iterable</em><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em>]</em><em>]</em>) – Ignored parameters or modules that will not be managed by this FSDP
instance, meaning that the parameters are not sharded and their
gradients are not reduced across ranks. This argument unifies with
the existing <code class="docutils literal notranslate"><span class="pre">ignored_modules</span></code> argument, and we may deprecate
<code class="docutils literal notranslate"><span class="pre">ignored_modules</span></code> soon. For backward compatibility, we keep both
<code class="docutils literal notranslate"><span class="pre">ignored_states</span></code> and <cite>ignored_modules`</cite>, but FSDP only allows one
of them to be specified as not <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
<li><p><strong>device_mesh</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="distributed.html#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><em>DeviceMesh</em></a><em>]</em>) – DeviceMesh can be used as an altenative to
process_group. When device_mesh is passed, FSDP will use the underlying process
groups for all-gather and reduce-scatter collective communications. Therefore,
these two args need to be mutually exclusive. For hybrid sharding strategies such as
<code class="docutils literal notranslate"><span class="pre">ShardingStrategy.HYBRID_SHARD</span></code>, users can pass in a 2D DeviceMesh instead
of a tuple of process groups. For 2D FSDP + TP, users are required to pass in
device_mesh instead of process_group. For more DeviceMesh info, please visit:
<a class="reference external" href="https://pytorch.org/tutorials/recipes/distributed_device_mesh.html">https://pytorch.org/tutorials/recipes/distributed_device_mesh.html</a></p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.apply">
<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.apply" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
<p>Typical use includes initializing the parameters of a model (see also <a class="reference internal" href="nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
<p>Compared to <code class="docutils literal notranslate"><span class="pre">torch.nn.Module.apply</span></code>, this version additionally gathers
the full parameters before applying <code class="docutils literal notranslate"><span class="pre">fn</span></code>. It should not be called from
within another <code class="docutils literal notranslate"><span class="pre">summon_full_params</span></code> context.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>fn</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> -> None) – function to be applied to each submodule</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><p id="torch.nn.Module"/><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module">Module</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.check_is_root">
<span class="sig-name descname"><span class="pre">check_is_root</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.check_is_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.check_is_root" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if this instance is a root FSDP module.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_">
<span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
<dd><p>Clip the gradient norm of all parameters.</p>
<p>The norm is computed over all parameters’ gradients as viewed as a single vector, and the
gradients are modified in-place.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>max_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – max norm of the gradients</p></li>
<li><p><strong>norm_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – type of the used p-norm. Can be <code class="docutils literal notranslate"><span class="pre">'inf'</span></code>
for infinity norm.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Total norm of the parameters (viewed as a single vector).</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p>If every FSDP instance uses <code class="docutils literal notranslate"><span class="pre">NO_SHARD</span></code>, meaning that no
gradients are sharded across ranks, then you may directly use
<a class="reference internal" href="generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_" title="torch.nn.utils.clip_grad_norm_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.utils.clip_grad_norm_()</span></code></a>.</p>
<p>If at least some FSDP instance uses a sharded strategy (i.e.
one other than <code class="docutils literal notranslate"><span class="pre">NO_SHARD</span></code>), then you should use this method
instead of <a class="reference internal" href="generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_" title="torch.nn.utils.clip_grad_norm_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.utils.clip_grad_norm_()</span></code></a> since this method
handles the fact that gradients are sharded across ranks.</p>
<p>The total norm returned will have the “largest” dtype across
all parameters/gradients as defined by PyTorch’s type promotion
semantics. For example, if <em>all</em> parameters/gradients use a low
precision dtype, then the returned norm’s dtype will be that low
precision dtype, but if there exists at least one parameter/
gradient using FP32, then the returned norm’s dtype will be FP32.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This needs to be called on all ranks since it uses
collective communications.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">flatten_sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sharded_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.flatten_sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Flatten a sharded optimizer state-dict.</p>
<p>The API is similar to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>. The only
difference is that the input <code class="docutils literal notranslate"><span class="pre">sharded_optim_state_dict</span></code> should be
returned from <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sharded_optim_state_dict()</span></code></a>. Therefore, there will
be all-gather calls on each rank to gather <code class="docutils literal notranslate"><span class="pre">ShardedTensor</span></code> s.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sharded_optim_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 v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>]</em>) – Optimizer state dict
corresponding to the unflattened parameters and holding the
sharded optimizer state.</p></li>
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – Refer to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>.</p></li>
<li><p><strong>optim</strong> (<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><em>torch.optim.Optimizer</em></a>) – Optimizer for <code class="docutils literal notranslate"><span class="pre">model</span></code> ‘s
parameters.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Refer to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>.</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.Dict" title="(in Python v3.13)"><em>Dict</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.forward">
<span class="sig-name descname"><span class="pre">forward</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/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Run the forward pass for the wrapped module, inserting FSDP-specific pre- and post-forward sharding logic.</p>
<dl class="field-list simple">
<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.Any" title="(in Python v3.13)"><em>Any</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fsdp_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.fsdp_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules" title="Permalink to this definition">¶</a></dt>
<dd><p>Return all nested FSDP instances.</p>
<p>This possibly includes <code class="docutils literal notranslate"><span class="pre">module</span></code> itself and only includes FSDP root modules if <code class="docutils literal notranslate"><span class="pre">root_only=True</span></code>.</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="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – Root module, which may or may not be an
<code class="docutils literal notranslate"><span class="pre">FSDP</span></code> module.</p></li>
<li><p><strong>root_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to return only FSDP root modules.
(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>FSDP modules that are nested in
the input <code class="docutils literal notranslate"><span class="pre">module</span></code>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List[<a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel">FullyShardedDataParallel</a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</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">rank0_only</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="n"><span class="pre">group</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/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the full optimizer state-dict.</p>
<p>Consolidates the full optimizer state on rank 0 and returns it
as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> following the convention of
<a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.state_dict()</span></code></a>, i.e. with keys <code class="docutils literal notranslate"><span class="pre">"state"</span></code>
and <code class="docutils literal notranslate"><span class="pre">"param_groups"</span></code>. The flattened parameters in <code class="docutils literal notranslate"><span class="pre">FSDP</span></code> modules
contained in <code class="docutils literal notranslate"><span class="pre">model</span></code> are mapped back to their unflattened parameters.</p>
<p>This needs to be called on all ranks since it uses
collective communications. However, if <code class="docutils literal notranslate"><span class="pre">rank0_only=True</span></code>, then
the state dict is only populated on rank 0, and all other ranks
return an empty <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
<p>Unlike <code class="docutils literal notranslate"><span class="pre">torch.optim.Optimizer.state_dict()</span></code>, this method
uses full parameter names as keys instead of parameter IDs.</p>
<p>Like in <a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.state_dict()</span></code></a>, the tensors
contained in the optimizer state dict are not cloned, so there may
be aliasing surprises. For best practices, consider saving the
returned optimizer state dict immediately, e.g. using
<code class="docutils literal notranslate"><span class="pre">torch.save()</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – Root module (which may or may not be a
<a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code></a> instance) whose parameters
were passed into the optimizer <code class="docutils literal notranslate"><span class="pre">optim</span></code>.</p></li>
<li><p><strong>optim</strong> (<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><em>torch.optim.Optimizer</em></a>) – Optimizer for <code class="docutils literal notranslate"><span class="pre">model</span></code> ‘s
parameters.</p></li>
<li><p><strong>optim_input</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><em>List</em><em>[</em><em>Dict</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>]</em><em>]</em><em>, </em><em>Iterable</em><em>[</em><em>torch.nn.Parameter</em><em>]</em><em>]</em><em>]</em>) – Input passed into the optimizer <code class="docutils literal notranslate"><span class="pre">optim</span></code> representing either a
<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> of parameter groups or an iterable of parameters;
if <code class="docutils literal notranslate"><span class="pre">None</span></code>, then this method assumes the input was
<code class="docutils literal notranslate"><span class="pre">model.parameters()</span></code>. This argument is deprecated, and there
is no need to pass it in anymore. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>rank0_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, saves the populated <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>
only on rank 0; if <code class="docutils literal notranslate"><span class="pre">False</span></code>, saves it on all ranks. (Default:
<code class="docutils literal notranslate"><span class="pre">True</span></code>)</p></li>
<li><p><strong>group</strong> (<em>dist.ProcessGroup</em>) – Model’s process group or <code class="docutils literal notranslate"><span class="pre">None</span></code> if using
the default process group. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> containing the optimizer state for
<code class="docutils literal notranslate"><span class="pre">model</span></code> ‘s original unflattened parameters and including keys
“state” and “param_groups” following the convention of
<a class="reference internal" href="generated/torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.state_dict()</span></code></a>. If <code class="docutils literal notranslate"><span class="pre">rank0_only=True</span></code>,
then nonzero ranks return an empty <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Dict[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, Any]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.get_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the state_dict_type and the corresponding configurations for the FSDP modules rooted at <code class="docutils literal notranslate"><span class="pre">module</span></code>.</p>
<p>The target module does not have to be an FSDP module.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <code class="docutils literal notranslate"><span class="pre">StateDictSettings</span></code> containing the state_dict_type and
state_dict / optim_state_dict configs that are currently set.</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>AssertionError` if the StateDictSettings for differen</strong> – </p></li>
<li><p><strong>FSDP submodules differ.</strong> – </p></li>
</ul>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.fsdp.StateDictSettings" title="torch.distributed.fsdp.api.StateDictSettings"><em>StateDictSettings</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.module">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">module</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a></em><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.module" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the wrapped module.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.named_buffers">
<span class="sig-name descname"><span class="pre">named_buffers</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/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_buffers" title="Permalink to this definition">¶</a></dt>
<dd><p>Return an iterator over module buffers, yielding both the name of the buffer and the buffer itself.</p>
<p>Intercepts buffer names and removes all occurrences of the FSDP-specific flattened buffer prefix
when inside the <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summon_full_params()</span></code></a> context manager.</p>
<dl class="field-list simple">
<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.Iterator" title="(in Python v3.13)"><em>Iterator</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.13)"><em>Tuple</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>]]</p>