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<div class="section" id="distributed-data-parallel">
<span id="ddp"></span><h1>Distributed Data Parallel<a class="headerlink" href="#distributed-data-parallel" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The implementation of <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>
evolves over time. This design note is written based on the state as of v1.4.</p>
</div>
<p><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> (DDP) transparently performs
distributed data parallel training. This page describes how it works and reveals
implementation details.</p>
<div class="section" id="example">
<h2>Example<a class="headerlink" href="#example" title="Permalink to this headline">¶</a></h2>
<p>Let us start with a simple <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>
example. This example uses a <a class="reference internal" href="../generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Linear</span></code></a> as the local model, wraps
it with DDP, and then runs one forward pass, one backward pass, and an optimizer
step on the DDP model. After that, parameters on the local model will be
updated, and all models on different processes should be exactly the same.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</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="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
<span class="c1"># create default process group</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="s2">"gloo"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">)</span>
<span class="c1"># create local model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</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="c1"># construct DDP model</span>
<span class="n">ddp_model</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="c1"># define loss function and optimizer</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">()</span>
<span class="n">optimizer</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">ddp_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">0.001</span><span class="p">)</span>
<span class="c1"># forward pass</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">ddp_model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</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="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</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="c1"># backward pass</span>
<span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="c1"># update parameters</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">world_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">example</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,),</span>
<span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
<span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="internal-design">
<h2>Internal Design<a class="headerlink" href="#internal-design" title="Permalink to this headline">¶</a></h2>
<p>This section reveals how it works under the hood of
<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> by diving into details of
every step in one iteration.</p>
<ul class="simple">
<li><p><strong>Prerequisite</strong>: DDP relies on c10d <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code> for communications.
Hence, applications must create <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code> instances before constructing
DDP.</p></li>
<li><p><strong>Construction</strong>: The DDP constructor takes a reference to the local module,
and broadcasts <code class="docutils literal notranslate"><span class="pre">state_dict()</span></code> from the process with rank 0 to all other
processes in the group to make sure that all model replicas start from the
exact same state. Then, each DDP process creates a local <code class="docutils literal notranslate"><span class="pre">Reducer</span></code>, which
later will take care of the gradients synchronization during the backward
pass. To improve communication efficiency, the <code class="docutils literal notranslate"><span class="pre">Reducer</span></code> organizes parameter
gradients into buckets, and reduces one bucket at a time. Bucket size can be
configured by setting the <cite>bucket_cap_mb</cite> argument in DDP constructor. The
mapping from parameter gradients to buckets is determined at the construction
time, based on the bucket size limit and parameter sizes. Model parameters are
allocated into buckets in (roughly) the reverse order of
<code class="docutils literal notranslate"><span class="pre">Model.parameters()</span></code> from the given model. The reason for using the reverse
order is because DDP expects gradients to become ready during the backward
pass in approximately that order. The figure below shows an example. Note
that, the <code class="docutils literal notranslate"><span class="pre">grad0</span></code> and <code class="docutils literal notranslate"><span class="pre">grad1</span></code> are in <code class="docutils literal notranslate"><span class="pre">bucket1</span></code>, and the other two
gradients are in <code class="docutils literal notranslate"><span class="pre">bucket0</span></code>. Of course, this assumption might not always
be true, and when that happens it could hurt DDP backward speed as the
<code class="docutils literal notranslate"><span class="pre">Reducer</span></code> cannot kick off the communication at the earliest possible time.
Besides bucketing, the <code class="docutils literal notranslate"><span class="pre">Reducer</span></code> also registers autograd hooks during
construction, one hook per parameter. These hooks will be triggered during
the backward pass when the gradient becomes ready.</p></li>
<li><p><strong>Forward Pass</strong>: The DDP takes the input and passes it to the local model,
and then analyzes the output from the local model if
<code class="docutils literal notranslate"><span class="pre">find_unused_parameters</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This mode allows running
backward on a subgraph of the model, and DDP finds out which parameters are
involved in the backward pass by traversing the autograd graph from the model
output and marking all unused parameters as ready for reduction. During the
backward pass, the <code class="docutils literal notranslate"><span class="pre">Reducer</span></code> would only wait for unready parameters, but it
would still reduce all buckets. Marking a parameter gradient as ready does not
help DDP skip buckets as for now, but it will prevent DDP from waiting for
absent gradients forever during the backward pass. Note that traversing the
autograd graph introduces extra overheads, so applications should only set
<code class="docutils literal notranslate"><span class="pre">find_unused_parameters</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> when necessary.</p></li>
<li><p><strong>Backward Pass</strong>: The <code class="docutils literal notranslate"><span class="pre">backward()</span></code> function is directly invoked on the loss
<code class="docutils literal notranslate"><span class="pre">Tensor</span></code>, which is out of DDP’s control, and DDP uses autograd hooks
registered at construction time to trigger gradients synchronizations. When
one gradient becomes ready, its corresponding DDP hook on that grad
accumulator will fire, and DDP will then mark that parameter gradient as
ready for reduction. When gradients in one bucket are all ready, the
<code class="docutils literal notranslate"><span class="pre">Reducer</span></code> kicks off an asynchronous <code class="docutils literal notranslate"><span class="pre">allreduce</span></code> on that bucket to
calculate mean of gradients across all processes. When all buckets are ready,
the <code class="docutils literal notranslate"><span class="pre">Reducer</span></code> will block waiting for all <code class="docutils literal notranslate"><span class="pre">allreduce</span></code> operations to finish.
When this is done, averaged gradients are written to the <code class="docutils literal notranslate"><span class="pre">param.grad</span></code> field
of all parameters. So after the backward pass, the <cite>grad</cite> field on the same
corresponding parameter across different DDP processes should be the same.</p></li>
<li><p><strong>Optimizer Step</strong>: From the optimizer’s perspective, it is optimizing a local
model. Model replicas on all DDP processes can keep in sync because they all
start from the same state and they have the same averaged gradients in
every iteration.</p></li>
</ul>
<a class="reference internal image-reference" href="https://user-images.githubusercontent.com/16999635/72401724-d296d880-371a-11ea-90ab-737f86543df9.png"><img alt="ddp_grad_sync.png" src="https://user-images.githubusercontent.com/16999635/72401724-d296d880-371a-11ea-90ab-737f86543df9.png" style="width: 700px;" /></a>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>DDP requires <code class="docutils literal notranslate"><span class="pre">Reducer</span></code> instances on all processes to invoke <code class="docutils literal notranslate"><span class="pre">allreduce</span></code>
in exactly the same order, which is done by always running <code class="docutils literal notranslate"><span class="pre">allreduce</span></code>
in the bucket index order instead of actual bucket ready order. Mismatched
<code class="docutils literal notranslate"><span class="pre">allreduce</span></code> order across processes can lead to wrong results or DDP backward
hang.</p>
</div>
</div>
<div class="section" id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline">¶</a></h2>
<p>Below are pointers to the DDP implementation components. The stacked graph shows
the structure of the code.</p>
<div class="section" id="processgroup">
<h3>ProcessGroup<a class="headerlink" href="#processgroup" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v1.7.0/torch/lib/c10d/ProcessGroup.hpp">ProcessGroup.hpp</a>:
contains the abstract API of all process group implementations. The <code class="docutils literal notranslate"><span class="pre">c10d</span></code>
library provides 3 implementations out of the box, namely,
<cite>ProcessGroupGloo</cite>, <cite>ProcessGroupNCCL</cite>, and <cite>ProcessGroupMPI</cite>.
<code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code> uses <code class="docutils literal notranslate"><span class="pre">ProcessGroup::broadcast()</span></code> to send
model states from the process with rank 0 to others during initialization
and <code class="docutils literal notranslate"><span class="pre">ProcessGroup::allreduce()</span></code> to sum gradients.</p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v1.7.0/torch/lib/c10d/Store.hpp">Store.hpp</a>:
assists the rendezvous service for process group instances to find each other.</p></li>
</ul>
</div>
<div class="section" id="distributeddataparallel">
<h3>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v1.7.0/torch/nn/parallel/distributed.py">distributed.py</a>:
is the Python entry point for DDP. It implements the initialization steps and
the <code class="docutils literal notranslate"><span class="pre">forward</span></code> function for the <code class="docutils literal notranslate"><span class="pre">nn.parallel.DistributedDataParallel</span></code>
module which call into C++ libraries. Its <code class="docutils literal notranslate"><span class="pre">_sync_param</span></code> function performs
intra-process parameter synchronization when one DDP process works on multiple
devices, and it also broadcasts model buffers from the process with rank 0 to
all other processes. The inter-process parameter synchronization happens in
<code class="docutils literal notranslate"><span class="pre">Reducer.cpp</span></code>.</p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v1.7.0/torch/csrc/distributed/c10d/comm.h">comm.h</a>:
implements the coalesced broadcast helper function which is invoked to
broadcast model states during initialization and synchronize model buffers
before the forward pass.</p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v1.7.0/torch/csrc/distributed/c10d/reducer.h">reducer.h</a>:
provides the core implementation for gradient synchronization in the backward
pass. It has three entry point functions:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">Reducer</span></code>: The constructor is called in <code class="docutils literal notranslate"><span class="pre">distributed.py</span></code> which registers
<code class="docutils literal notranslate"><span class="pre">Reducer::autograd_hook()</span></code> to gradient accumulators.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">autograd_hook()</span></code> function will be invoked by the autograd engine when
a gradient becomes ready.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">prepare_for_backward()</span></code> is called at the end of DDP forward pass in
<code class="docutils literal notranslate"><span class="pre">distributed.py</span></code>. It traverses the autograd graph to find unused
parameters when <code class="docutils literal notranslate"><span class="pre">find_unused_parameters</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code> in DDP
constructor.</p></li>
</ul>
</li>
</ul>
<a class="reference internal image-reference" href="https://user-images.githubusercontent.com/16999635/72313120-4e7c1c80-3658-11ea-9c6d-44336b2daeac.png"><img alt="ddp_code.png" src="https://user-images.githubusercontent.com/16999635/72313120-4e7c1c80-3658-11ea-9c6d-44336b2daeac.png" style="width: 400px;" /></a>
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<ul>
<li><a class="reference internal" href="#">Distributed Data Parallel</a><ul>
<li><a class="reference internal" href="#example">Example</a></li>
<li><a class="reference internal" href="#internal-design">Internal Design</a></li>
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