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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="distributed-optimizers">
<h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></code><code class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer_class</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">parameters_as_bucket_view</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">overlap_with_ddp</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>This class wraps an arbitrary <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">optim.Optimizer</span></code></a> and shards its states across ranks in the group as
described by <a class="reference external" href="https://arxiv.org/abs/1910.02054">ZeRO</a>. The local optimizer instance in each rank is only
responsible for updating approximately <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">world_size</span></code> parameters and
hence only needs to keep <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">world_size</span></code> optimizer states. After
parameters are updated locally, each rank will broadcast its parameters to
all other peers to keep all model replicas in the same state.
<code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> can be used in conjunction with
<a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a> to reduce per-rank peak
memory consumption.</p>
<p><code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> uses a sorted-greedy algorithm to pack a number
of parameters at each rank. Each parameter belongs to a single rank and is
not divided among ranks. The partition is arbitrary and might not match the
the parameter registration or usage order.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>params</strong> (<code class="docutils literal notranslate"><span class="pre">Iterable</span></code>) – an <code class="docutils literal notranslate"><span class="pre">Iterable</span></code> of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s
giving all parameters, which will be sharded across ranks.</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>optimizer_class</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Optimizer</span></code>) – the class of the local
optimizer.</p></li>
<li><p><strong>process_group</strong> (<code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code>, optional) – <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code>
<code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code> (default: <code class="docutils literal notranslate"><span class="pre">dist.group.WORLD</span></code> initialized by
<a class="reference internal" href="distributed.html#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.init_process_group()</span></code></a>).</p></li>
<li><p><strong>parameters_as_bucket_view</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – if <code class="docutils literal notranslate"><span class="pre">True</span></code>, parameters are
packed into buckets to speed up communication, and <code class="docutils literal notranslate"><span class="pre">param.data</span></code>
fields point to bucket views at different offsets; if <code class="docutils literal notranslate"><span class="pre">False</span></code>,
each individual parameter is communicated separately, and each
<code class="docutils literal notranslate"><span class="pre">params.data</span></code> stays intact (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
<li><p><strong>overlap_with_ddp</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – if <code class="docutils literal notranslate"><span class="pre">True</span></code>, <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="torch.distributed.optim.ZeroRedundancyOptimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">step()</span></code></a> is
overlapped with <code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> ‘s gradient
synchronization; this requires (1) either a functional optimizer
for the <code class="docutils literal notranslate"><span class="pre">optimizer_class</span></code> argument or one with a functional
equivalent and (2) registering a DDP communication hook
constructed from one of the functions in <code class="docutils literal notranslate"><span class="pre">ddp_zero_hook.py</span></code>;
parameters are packed into buckets matching those in
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>, meaning that the
<code class="docutils literal notranslate"><span class="pre">parameters_as_bucket_view</span></code> argument is ignored.
If <code class="docutils literal notranslate"><span class="pre">False</span></code>, <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="torch.distributed.optim.ZeroRedundancyOptimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">step()</span></code></a> runs disjointly after the backward pass
(per normal).
(default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>**defaults</strong> – any trailing arguments, which are forwarded to the local
optimizer.</p></li>
</ul>
</dd>
</dl>
<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.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.optim</span> <span class="kn">import</span> <span class="n">ZeroRedundancyOptimizer</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span> <span class="k">as</span> <span class="n">DDP</span>
<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">ddp</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="n">rank</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">opt</span> <span class="o">=</span> <span class="n">ZeroRedundancyOptimizer</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">ddp</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span>
<span class="gp">>>> </span> <span class="n">optimizer_class</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="gp">>>> </span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ddp</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Currently, <code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> requires that all of the
passed-in parameters are the same dense type.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If you pass <code class="docutils literal notranslate"><span class="pre">overlap_with_ddp=True</span></code>, be wary of the following: Given
the way that overlapping <code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> with
<a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="torch.distributed.optim.ZeroRedundancyOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code></a> is currently implemented, the first
two or three training iterations do not perform parameter updates in
the optimizer step, depending on if <code class="docutils literal notranslate"><span class="pre">static_graph=False</span></code> or
<code class="docutils literal notranslate"><span class="pre">static_graph=True</span></code>, respectively. This is because it needs
information about the gradient bucketing strategy used by
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>, which is not finalized until the
second forward pass if <code class="docutils literal notranslate"><span class="pre">static_graph=False</span></code> or until the third
forward pass if <code class="docutils literal notranslate"><span class="pre">static_graph=True</span></code>. To adjust for this, one option
is to prepend dummy inputs.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>ZeroRedundancyOptimizer is experimental and subject to change.</p>
</div>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group">
<code class="sig-name descname"><span class="pre">add_param_group</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Add a parameter group to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> ‘s <code class="docutils literal notranslate"><span class="pre">param_groups</span></code>.</p>
<p>This can be useful when fine tuning a pre-trained network, as frozen
layers can be made trainable and added to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> as
training progresses.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>param_group</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – specifies the parameters to be optimized and
group-specific optimization options.</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This method handles updating the shards on all partitions
but needs to be called on all ranks. Calling this on a subset of
the ranks will cause the training to hang because communication
primitives are called depending on the managed parameters and
expect all the ranks to participate on the same set of parameters.</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict">
<code class="sig-name descname"><span class="pre">consolidate_state_dict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.consolidate_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Consolidate a list of <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> s (one per rank) on the target
rank.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>to</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – the rank that receives the optimizer states (default: 0).</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.10)"><strong>RuntimeError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">overlap_with_ddp=True</span></code> and this method is
called before this <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="torch.distributed.optim.ZeroRedundancyOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code></a> instance
has been fully initialized, which happens once
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> gradient buckets have been
rebuilt.</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This needs to be called on all ranks.</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.join_hook">
<code class="sig-name descname"><span class="pre">join_hook</span></code><span class="sig-paren">(</span><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/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.join_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the ZeRO join hook, which enables training on uneven inputs by
shadowing the collective communications in the optimizer step.</p>
<p>Gradients must be properly set before this hook is called.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> containing any keyword arguments
to modify the behavior of the join hook at run time; all
<code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code> instances sharing the same join context
manager are forwarded the same value for <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>.</p>
</dd>
</dl>
<p>This hook does not support any keyword arguments; i.e. <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> is
unused.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict">
<code class="sig-name descname"><span class="pre">load_state_dict</span></code><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/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the state pertaining to the given rank from the input
<code class="docutils literal notranslate"><span class="pre">state_dict</span></code>, updating the local optimizer as needed.</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/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – optimizer state; should be an object returned
from a call to <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>.</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.10)"><strong>RuntimeError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">overlap_with_ddp=True</span></code> and this method is
called before this <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="torch.distributed.optim.ZeroRedundancyOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code></a> instance
has been fully initialized, which happens once
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> gradient buckets have been
rebuilt.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict">
<code class="sig-name descname"><span class="pre">state_dict</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the last global optimizer state known to this rank.</p>
<dl class="field-list simple">
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.10)"><strong>RuntimeError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">overlap_with_ddp=True</span></code> and this method is
called before this <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="torch.distributed.optim.ZeroRedundancyOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code></a> instance
has been fully initialized, which happens once
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> gradient buckets have been
rebuilt; or if this method is called without a preceding call
to <a class="reference internal" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">consolidate_state_dict()</span></code></a>.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.step">
<code class="sig-name descname"><span class="pre">step</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</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="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/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimizer step and syncs parameters across all ranks.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>closure</strong> (<em>callable</em>) – a closure that re-evaluates the model and
returns the loss; optional for most optimizers.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Optional loss depending on the underlying local optimizer.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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