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<section id="distributed-optimizers">
<h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" title="Permalink to this heading">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Distributed optimizer is not currently supported when using CUDA tensors</p>
</div>
<span class="target" id="module-torch.distributed.optim"></span><p><a class="reference internal" href="#module-torch.distributed.optim" title="torch.distributed.optim"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.distributed.optim</span></code></a> exposes DistributedOptimizer, which takes a list
of remote parameters (<a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef" title="torch.distributed.rpc.RRef"><code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code></a>) and runs the
optimizer locally on the workers where the parameters live. The distributed
optimizer can use any of the local optimizer <a class="reference internal" href="optim.html#optimizer-algorithms"><span class="std std-ref">Base class</span></a> to
apply the gradients on each worker.</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">DistributedOptimizer</span></span><span class="sig-paren">(</span><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">params_rref</span></span></em>, <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/optim/optimizer.html#DistributedOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>DistributedOptimizer takes remote references to parameters scattered
across workers and applies the given optimizer locally for each parameter.</p>
<p>This class uses <a class="reference internal" href="rpc.html#torch.distributed.autograd.get_gradients" title="torch.distributed.autograd.get_gradients"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_gradients()</span></code></a> in order
to retrieve the gradients for specific parameters.</p>
<p>Concurrent calls to
<a class="reference internal" href="#torch.distributed.optim.DistributedOptimizer.step" title="torch.distributed.optim.DistributedOptimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">step()</span></code></a>,
either from the same or different clients, will
be serialized on each worker – as each worker’s optimizer can only work
on one set of gradients at a time. However, there is no guarantee that
the full forward-backward-optimizer sequence will execute for one client
at a time. This means that the gradients being applied may not correspond
to the latest forward pass executed on a given worker. Also, there is no
guaranteed ordering across workers.</p>
<p><cite>DistributedOptimizer</cite> creates the local optimizer with TorchScript enabled
by default, so that optimizer updates are not blocked by the Python Global
Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
Model Parallel). This feature is currently enabled for most optimizers. You
can also follow <a class="reference external" href="https://github.com/pytorch/tutorials/pull/1465">the recipe</a> in PyTorch tutorials to enable TorchScript support
for your own custom optimizers.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>optimizer_class</strong> (<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><em>optim.Optimizer</em></a>) – the class of optimizer to
instantiate on each worker.</p></li>
<li><p><strong>params_rref</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a><em>[</em><a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef" title="torch.distributed.rpc.RRef"><em>RRef</em></a><em>]</em>) – list of RRefs to local or remote parameters
to optimize.</p></li>
<li><p><strong>args</strong> – arguments to pass to the optimizer constructor on each worker.</p></li>
<li><p><strong>kwargs</strong> – arguments to pass to the optimizer constructor on each worker.</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</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">DistributedOptimizer</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">dist_autograd</span><span class="o">.</span><span class="n">context</span><span class="p">()</span> <span class="k">as</span> <span class="n">context_id</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="c1"># Forward pass.</span>
<span class="gp">>>> </span> <span class="n">rref1</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">remote</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span> <span class="n">rref2</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">remote</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</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">rref1</span><span class="o">.</span><span class="n">to_here</span><span class="p">()</span> <span class="o">+</span> <span class="n">rref2</span><span class="o">.</span><span class="n">to_here</span><span class="p">()</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="c1"># Backward pass.</span>
<span class="gp">>>> </span> <span class="n">dist_autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">context_id</span><span class="p">,</span> <span class="p">[</span><span class="n">loss</span><span class="o">.</span><span class="n">sum</span><span class="p">()])</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="c1"># Optimizer.</span>
<span class="gp">>>> </span> <span class="n">dist_optim</span> <span class="o">=</span> <span class="n">DistributedOptimizer</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="p">[</span><span class="n">rref1</span><span class="p">,</span> <span class="n">rref2</span><span class="p">],</span>
<span class="gp">>>> </span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="p">)</span>
<span class="gp">>>> </span> <span class="n">dist_optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">context_id</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer.step">
<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step.</p>
<p>This will call <a class="reference internal" href="generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.step()</span></code></a> on each worker
containing parameters to be optimized, and will block until all workers
return. The provided <code class="docutils literal notranslate"><span class="pre">context_id</span></code> will be used to retrieve the
corresponding <a class="reference internal" href="rpc.html#torch.distributed.autograd.context" title="torch.distributed.autograd.context"><code class="xref py py-class docutils literal notranslate"><span class="pre">context</span></code></a> that
contains the gradients that should be applied to the parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>context_id</strong> – the autograd context id for which we should run the
optimizer step.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">PostLocalSGDOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averager</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>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">torch.optim.Optimizer</span></code></a> and runs <a class="reference external" href="https://arxiv.org/abs/1808.07217">post-local SGD</a>,
This optimizer runs local optimizer at every step.
After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>optim</strong> (<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.optimizer.Optimizer"><em>Optimizer</em></a>) – The local optimizer.</p></li>
<li><p><strong>averager</strong> (<em>ModelAverager</em>) – A model averager instance to run post-localSGD algorithm.</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</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.algorithms.model_averaging.averagers</span> <span class="k">as</span> <span class="nn">averagers</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">PostLocalSGDOptimizer</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook</span> <span class="kn">import</span> <span class="p">(</span>
<span class="gp">>>> </span> <span class="n">PostLocalSGDState</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">post_localSGD_hook</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="go">>>></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">parallel</span><span class="o">.</span><span class="n">DistributedDataParallel</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</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="n">output_device</span><span class="o">=</span><span class="n">rank</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Register a post-localSGD communication hook.</span>
<span class="gp">>>> </span><span class="n">state</span> <span class="o">=</span> <span class="n">PostLocalSGDState</span><span class="p">(</span><span class="n">process_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subgroup</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">start_localSGD_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">register_comm_hook</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">post_localSGD_hook</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Create a post-localSGD optimizer that wraps a local optimizer.</span>
<span class="gp">>>> </span><span class="c1"># Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as</span>
<span class="gp">>>> </span><span class="c1"># ``start_localSGD_iter`` used in ``PostLocalSGDState``.</span>
<span class="gp">>>> </span><span class="n">local_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">SGD</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">model</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">0.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">opt</span> <span class="o">=</span> <span class="n">PostLocalSGDOptimizer</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">optim</span><span class="o">=</span><span class="n">local_optim</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">averager</span><span class="o">=</span><span class="n">averagers</span><span class="o">.</span><span class="n">PeriodicModelAverager</span><span class="p">(</span><span class="n">period</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">warmup_steps</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># In the first 100 steps, DDP runs global gradient averaging at every step.</span>
<span class="gp">>>> </span><span class="c1"># After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),</span>
<span class="gp">>>> </span><span class="c1"># and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">200</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">opt</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">labels</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">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict">
<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This is the same as <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">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>,
but also restores model averager’s step value to the one
saved in the provided <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
<p>If there is no <code class="docutils literal notranslate"><span class="pre">"step"</span></code> entry in <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>,
it will raise a warning and initialize the model averager’s step to 0.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.state_dict">
<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This is the same as <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">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>,
but adds an extra entry to record model averager’s step to the checkpoint
to ensure reload does not cause unnecessary warm up again.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.step">
<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a single optimization step (parameter update).</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></span><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<span class="colon">:</span></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
or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> s giving all parameters, which will be sharded
across ranks.</p>
</dd>
<dt class="field-even">Keyword Arguments<span class="colon">:</span></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 v3.11)"><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 v3.11)"><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 class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group">
<span class="sig-name descname"><span class="pre">add_param_group</span></span><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<span class="colon">:</span></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 v3.11)"><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 class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict">
<span class="sig-name descname"><span class="pre">consolidate_state_dict</span></span><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<span class="colon">:</span></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 v3.11)"><em>int</em></a>) – the rank that receives the optimizer states (default: 0).</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.11)"><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 class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.join_hook">
<span class="sig-name descname"><span class="pre">join_hook</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">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<span class="colon">:</span></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 v3.11)"><em>dict</em></a>) – a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><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 class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict">
<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/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<span class="colon">:</span></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 v3.11)"><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<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.11)"><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 class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict">
<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/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<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#RuntimeError" title="(in Python v3.11)"><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>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.11)"><em>Dict</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><em>Any</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.step">
<span class="sig-name descname"><span class="pre">step</span></span><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<span class="colon">:</span></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<span class="colon">:</span></dt>
<dd class="field-even"><p>Optional loss depending on the underlying local optimizer.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.11)"><em>Optional</em></a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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