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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>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</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="class">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer">
<em class="property">class </em><code class="sig-prename descclassname">torch.distributed.optim.</code><code class="sig-name descname">ZeroRedundancyOptimizer</code><span class="sig-paren">(</span><em class="sig-param">params</em>, <em class="sig-param">optimizer_class</em>, <em class="sig-param">group=None</em>, <em class="sig-param">parameters_as_bucket_view=False</em>, <em class="sig-param">**default</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link">[source]</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 optimizer instance in each rank is only responsible for
updating <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 <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataparallel</span></code> to
reduce per-rank peak memory consumption.</p>
<p><code class="docutils literal notranslate"><span class="pre">ZeroRedundancyOptimizer</span></code> use a 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</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>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">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_views</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – when enabled, parameters will
be packed into larger buckets to speed up communication and
<code class="docutils literal notranslate"><span class="pre">param.data</span></code> fields will point to bucket views at different
offsets. When disabled, each individual parameter will be
communicated separately, but <code class="docutils literal notranslate"><span class="pre">params.data</span></code> will stay intact.</p></li>
<li><p><strong>**default</strong> – all trailing arguments will be forwarded to the given 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>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group">
<code class="sig-name descname">add_param_group</code><span class="sig-paren">(</span><em class="sig-param">param_group</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">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Add a param 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 v3.9)"><em>dict</em></a>) – Specifies what Tensors should be optimized
along with group specific optimization options.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict">
<code class="sig-name descname">consolidate_state_dict</code><span class="sig-paren">(</span><em class="sig-param">to=0</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">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Update the consolidated state_dict list, one per 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 v3.9)"><em>int</em></a>) – the rank that receives the global states. (default: 0)</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict">
<code class="sig-name descname">load_state_dict</code><span class="sig-paren">(</span><em class="sig-param">state_dict</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">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Restore the global parameter groups as well as the shard.</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 v3.9)"><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>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.local_state_dict">
<code class="sig-name descname">local_state_dict</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.local_state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.local_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets this rank’s <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p><p>The state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.
It contains two entries:</p>
<ul class="simple">
<li><dl class="simple">
<dt>state - a dict holding current optimization state. Its content</dt><dd><p>differs between optimizer classes.</p>
</dd>
</dl>
</li>
<li><p>param_groups - a dict containing all parameter groups</p></li>
</ul>
</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.partition_parameters">
<code class="sig-name descname">partition_parameters</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.partition_parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.partition_parameters" title="Permalink to this definition">¶</a></dt>
<dd><p>Partitions parameters across distributed data parallel ranks.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>a list of <code class="docutils literal notranslate"><span class="pre">param_groups</span></code> (which is a list of dict) where each
element of the list contains the param_groups for a rank. Element 0
corresponds to rank 0, etc. We need all the ranks for the broadcast
inside <code class="docutils literal notranslate"><span class="pre">step()</span></code>.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.rank_local_state_dict">
<em class="property">static </em><code class="sig-name descname">rank_local_state_dict</code><span class="sig-paren">(</span><em class="sig-param">rank</em>, <em class="sig-param">state_dict</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.rank_local_state_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.rank_local_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the local_state_dict for a given rank.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – rank to get <code class="docutils literal notranslate"><span class="pre">local_state_dict</span></code> for</p></li>
<li><p><strong>state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)"><em>dict</em></a>) – global <code class="docutils literal notranslate"><span class="pre">state_dict</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict">
<code class="sig-name descname">state_dict</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">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the last known global optimizer state, which consist of a list of
the shards.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.distributed.optim.ZeroRedundancyOptimizer.step">
<code class="sig-name descname">step</code><span class="sig-paren">(</span><em class="sig-param">closure=None</em>, <em class="sig-param">**kwargs</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">[source]</span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step (parameter update).</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 reevaluates 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, depends on the underlying optimizer</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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