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<section id="distributed-autograd-design">
<span id="id1"></span><h1>Distributed Autograd Design<a class="headerlink" href="#distributed-autograd-design" title="Permalink to this heading">¶</a></h1>
<p>This note will present the detailed design for distributed autograd and walk
through the internals of the same. Make sure you’re familiar with
<a class="reference internal" href="../notes/autograd.html#autograd-mechanics"><span class="std std-ref">Autograd mechanics</span></a> and the <a class="reference internal" href="../rpc.html#distributed-rpc-framework"><span class="std std-ref">Distributed RPC Framework</span></a> before
proceeding.</p>
<section id="background">
<h2>Background<a class="headerlink" href="#background" title="Permalink to this heading">¶</a></h2>
<p>Let’s say you have two nodes and a very simple model partitioned across two
nodes. This can be implemented using <a class="reference internal" href="../rpc.html#module-torch.distributed.rpc" title="torch.distributed.rpc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.distributed.rpc</span></code></a> as follows:</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.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="k">def</span> <span class="nf">my_add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span>
<span class="c1"># On worker 0:</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Perform some computation remotely.</span>
<span class="n">t3</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">my_add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">))</span>
<span class="c1"># Perform some computation locally based on remote result.</span>
<span class="n">t4</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">t5</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">t3</span><span class="p">,</span> <span class="n">t4</span><span class="p">)</span>
<span class="c1"># Compute some loss.</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">t5</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>
</div>
<p>The main motivation behind distributed autograd is to enable running a backward
pass on such distributed models with the <code class="docutils literal notranslate"><span class="pre">loss</span></code> that we’ve computed and
record appropriate gradients for all tensors that require gradients.</p>
</section>
<section id="autograd-recording-during-the-forward-pass">
<h2>Autograd recording during the forward pass<a class="headerlink" href="#autograd-recording-during-the-forward-pass" title="Permalink to this heading">¶</a></h2>
<p>PyTorch builds the autograd graph during the forward pass and this graph is
used to execute the backward pass. For more details see
<a class="reference internal" href="../notes/autograd.html#how-autograd-encodes-history"><span class="std std-ref">How autograd encodes the history</span></a>.</p>
<p>For distributed autograd, we need to keep track of all RPCs during the forward
pass to ensure the backward pass is executed appropriately. For this purpose,
we attach <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> functions to the autograd graph when we perform
an RPC.</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">send</span></code> function is attached to the source of the RPC and its output
edges point to the autograd function for the input tensors of the RPC.
The input for this function during the backward pass is received from the
destination as the output of the appropriate <code class="docutils literal notranslate"><span class="pre">recv</span></code> function.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">recv</span></code> function is attached to the destination of the RPC and its
inputs are retrieved from operators executed on the destination using the
input tensors. The output gradients of this function are sent to the source
node to the appropriate <code class="docutils literal notranslate"><span class="pre">send</span></code> function during the backward pass.</p></li>
<li><p>Each <code class="docutils literal notranslate"><span class="pre">send-recv</span></code> pair is assigned a globally unique <code class="docutils literal notranslate"><span class="pre">autograd_message_id</span></code>
to uniquely identify the pair. This is useful to look up the corresponding
function on a remote node during the backward pass.</p></li>
<li><p>For <a class="reference internal" href="../rpc.html#rref"><span class="std std-ref">RRef</span></a>, whenever we call <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.rpc.RRef.to_here()</span></code>
we attach an appropriate <code class="docutils literal notranslate"><span class="pre">send-recv</span></code> pair for the tensors involved.</p></li>
</ul>
<p>As an example, this is what the autograd graph for our example above would look
like (t5.sum() excluded for simplicity):</p>
<img alt="../_images/send_recv_functions.png" src="../_images/send_recv_functions.png" />
</section>
<section id="distributed-autograd-context">
<h2>Distributed Autograd Context<a class="headerlink" href="#distributed-autograd-context" title="Permalink to this heading">¶</a></h2>
<p>Each forward and backward pass that uses distributed autograd is assigned a
unique <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">torch.distributed.autograd.context</span></code></a> and this context has a
globally unique <code class="docutils literal notranslate"><span class="pre">autograd_context_id</span></code>. This context is created on each node
as needed.</p>
<p>This context serves the following purpose:</p>
<ol class="arabic simple">
<li><p>Multiple nodes running distributed backward passes might accumulate
gradients on the same tensor and as a result the <code class="docutils literal notranslate"><span class="pre">.grad</span></code> field of the
tensor would have gradients from a variety of distributed backward passes
before we have the opportunity to run the optimizer. This is similar to
calling <a class="reference internal" href="../generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.autograd.backward()</span></code></a> multiple times locally. In order to
provide a way of separating out the gradients for each backward pass, the
gradients are accumulated in the <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">torch.distributed.autograd.context</span></code></a>
for each backward pass.</p></li>
<li><p>During the forward pass we store the <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> functions for
each autograd pass in this context. This ensures we hold references to the
appropriate nodes in the autograd graph to keep it alive. In addition to
this, it is easy to look up the appropriate <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> functions
during the backward pass.</p></li>
<li><p>In general we also use this context to store some metadata for each
distributed autograd pass.</p></li>
</ol>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>From the user’s perspective the autograd context is setup as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="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="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">()</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="n">loss</span><span class="p">)</span>
</pre></div>
</div>
<p>It is important to note that your model’s forward pass must be invoked within
the distributed autograd context manager, as a valid context is needed in
order to ensure that all <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> functions are stored properly
to run the backward pass across all participating nodes.</p>
</section>
<section id="distributed-backward-pass">
<h2>Distributed Backward Pass<a class="headerlink" href="#distributed-backward-pass" title="Permalink to this heading">¶</a></h2>
<p>In this section we outline the challenge of computing dependencies accurately
during a distributed backward pass and describe a couple of algorithms (with
tradeoffs) on how we can execute a distributed backward pass.</p>
<section id="computing-dependencies">
<h3>Computing dependencies<a class="headerlink" href="#computing-dependencies" title="Permalink to this heading">¶</a></h3>
<p>Consider the following piece of code being run on a single machine</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="n">c</span>
<span class="n">d</span><span class="o">.</span><span class="n">sum</span><span class="o">.</span><span class="p">()</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
</pre></div>
</div>
<p>This is what the autograd graph for the code above would look like:</p>
<a class="reference internal image-reference" href="../_images/local_dependencies.png"><img alt="../_images/local_dependencies.png" src="../_images/local_dependencies.png" style="width: 372.0px; height: 264.8px;" /></a>
<p>The first step the autograd engine performs as part of the backward pass is
computing the number of dependencies for each node in the autograd graph. This
helps the autograd engine know when a node in the graph is ready for execution.
The numbers in brackets for <code class="docutils literal notranslate"><span class="pre">add(1)</span></code> and <code class="docutils literal notranslate"><span class="pre">mul(0)</span></code> denote the number of
dependencies. As you can see, this means during the backward pass the <code class="docutils literal notranslate"><span class="pre">add</span></code>
node needs 1 input and the <code class="docutils literal notranslate"><span class="pre">mul</span></code> node doesn’t need any inputs (in other
words doesn’t need to be executed). The local autograd engine computes these
dependencies by traversing the graph from the root nodes (<code class="docutils literal notranslate"><span class="pre">d</span></code> in this case).</p>
<p>The fact that certain nodes in the autograd graph might not be executed in the
backward pass poses a challenge for distributed autograd. Consider this piece
of code which uses RPC.</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.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</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">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</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">mul</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">))</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>
</div>
<p>The associated autograd graph for the code above would be:</p>
<img alt="../_images/distributed_dependencies.png" src="../_images/distributed_dependencies.png" />
<p>Computing dependencies of this distributed autograd graph is much more
challenging and requires some overhead (either in terms of computation or
network communication).</p>
<p>For performance sensitive applications we can avoid a
lot of overhead by assuming every <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> function are valid as
part of the backward pass (most applications don’t perform RPCs that aren’t
used). This simplifies the distributed autograd algorithm and is much more
efficient, but at the cost that the application needs to be aware of the
limitations. This algorithm is called the <a class="reference internal" href="#id2">FAST mode algorithm</a> and is
described in detail below.</p>
<p>In the general case it might not be necessary that every <code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code>
function is valid as part of the backward pass. To address this, we have
proposed a <a class="reference internal" href="#smart-mode-algorithm">SMART mode algorithm</a> which is described in a later section.
Please note that currently, only the <cite>FAST</cite> mode algorithm is implemented.</p>
</section>
<section id="fast-mode-algorithm">
<span id="id2"></span><h3>FAST mode algorithm<a class="headerlink" href="#fast-mode-algorithm" title="Permalink to this heading">¶</a></h3>
<p>The key assumption of this algorithm is that each <code class="docutils literal notranslate"><span class="pre">send</span></code> function has a
dependency of 1 when we run a backward pass. In other words, we assume we’ll
receive a gradient over RPC from another node.</p>
<p>The algorithm is as follows:</p>
<ol class="arabic simple">
<li><p>We start from the worker which has the roots for the backward pass
(all roots must be local).</p></li>
<li><p>Lookup all the <code class="docutils literal notranslate"><span class="pre">send</span></code> functions for the current
<a class="reference internal" href="#distributed-autograd-context">Distributed Autograd Context</a>.</p></li>
<li><p>Compute dependencies locally starting from the provided roots and all the
<code class="docutils literal notranslate"><span class="pre">send</span></code> functions we retrieved.</p></li>
<li><p>After computing dependencies, kick off the local autograd engine with the
provided roots.</p></li>
<li><p>When the autograd engine executes the <code class="docutils literal notranslate"><span class="pre">recv</span></code> function, the <code class="docutils literal notranslate"><span class="pre">recv</span></code>
function sends the input gradients via RPC to the appropriate worker.
Each <code class="docutils literal notranslate"><span class="pre">recv</span></code> function knows the destination worker id since it is recorded
as part of the forward pass. The <code class="docutils literal notranslate"><span class="pre">recv</span></code> function also sends over the
<code class="docutils literal notranslate"><span class="pre">autograd_context_id</span></code> and <code class="docutils literal notranslate"><span class="pre">autograd_message_id</span></code> to the remote host.</p></li>
<li><p>When this request is received on the remote host, we use the
<code class="docutils literal notranslate"><span class="pre">autograd_context_id</span></code> and <code class="docutils literal notranslate"><span class="pre">autograd_message_id</span></code> to look up the
appropriate <code class="docutils literal notranslate"><span class="pre">send</span></code> function.</p></li>
<li><p>If this is the first time a worker has received a request for the given
<code class="docutils literal notranslate"><span class="pre">autograd_context_id</span></code>, it will compute dependencies locally as described
in points 1-3 above.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">send</span></code> function retrieved in 6. is then enqueued for execution on the
local autograd engine for that worker.</p></li>
<li><p>Finally, instead of accumulating the gradients on the <code class="docutils literal notranslate"><span class="pre">.grad</span></code> field of the
Tensor, we accumulate the gradients separately per
<a class="reference internal" href="#distributed-autograd-context">Distributed Autograd Context</a>. The gradients are stored in a
<code class="docutils literal notranslate"><span class="pre">Dict[Tensor,</span> <span class="pre">Tensor]</span></code>, which is basically a map from Tensor to its
associated gradient and this map can be retrieved using the
<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> API.</p></li>
</ol>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>As an example the complete code with distributed autograd would be as follows:</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.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</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="k">def</span> <span class="nf">my_add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span>
<span class="c1"># On worker 0:</span>
<span class="c1"># Setup the autograd context. Computations that take</span>
<span class="c1"># part in the distributed backward pass must be within</span>
<span class="c1"># the distributed autograd context manager.</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="n">t1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Perform some computation remotely.</span>
<span class="n">t3</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">my_add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">))</span>
<span class="c1"># Perform some computation locally based on remote result.</span>
<span class="n">t4</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">t5</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">t3</span><span class="p">,</span> <span class="n">t4</span><span class="p">)</span>
<span class="c1"># Compute some loss.</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">t5</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c1"># Run the backward pass.</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="p">])</span>
<span class="c1"># Retrieve the gradients from the context.</span>
<span class="n">dist_autograd</span><span class="o">.</span><span class="n">get_gradients</span><span class="p">(</span><span class="n">context_id</span><span class="p">)</span>
</pre></div>
</div>
<p>The distributed autograd graph with dependencies would be as follows (t5.sum() excluded for simplicity):</p>
<img alt="../_images/distributed_dependencies_computed.png" src="../_images/distributed_dependencies_computed.png" />
<p>The <a class="reference internal" href="#id2">FAST mode algorithm</a> applied to the above example would be as follows:</p>
<ol class="arabic simple">
<li><p>On <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">0</span></code> we start from the roots <code class="docutils literal notranslate"><span class="pre">loss</span></code> and <code class="docutils literal notranslate"><span class="pre">send1</span></code> to compute
dependencies. As a result <code class="docutils literal notranslate"><span class="pre">send1</span></code> is marked with a dependency of 1 and <code class="docutils literal notranslate"><span class="pre">mul</span></code>
on <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">0</span></code> is marked with a dependency of 1.</p></li>
<li><p>Now, we kickoff the local autograd engine on <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">0</span></code>. We first execute
the <code class="docutils literal notranslate"><span class="pre">mul</span></code> function, accumulate its output in the autograd context as the
gradient for <code class="docutils literal notranslate"><span class="pre">t4</span></code>. Then, we execute <code class="docutils literal notranslate"><span class="pre">recv2</span></code> which sends the gradients to
<code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">1</span></code>.</p></li>
<li><p>Since this is the first time <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">1</span></code> has heard about this backward pass,
it starts dependency computation and marks the dependencies for <code class="docutils literal notranslate"><span class="pre">send2</span></code>,
<code class="docutils literal notranslate"><span class="pre">add</span></code> and <code class="docutils literal notranslate"><span class="pre">recv1</span></code> appropriately.</p></li>
<li><p>Next, we enqueue <code class="docutils literal notranslate"><span class="pre">send2</span></code> on the local autograd engine of <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">1</span></code>, which
in turn executes <code class="docutils literal notranslate"><span class="pre">add</span></code> and <code class="docutils literal notranslate"><span class="pre">recv1</span></code>.</p></li>
<li><p>When <code class="docutils literal notranslate"><span class="pre">recv1</span></code> is executed it sends the gradients over to <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">0</span></code>.</p></li>
<li><p>Since <code class="docutils literal notranslate"><span class="pre">Worker</span> <span class="pre">0</span></code> has already computed dependencies for this backward pass,
it just enqueues and executes <code class="docutils literal notranslate"><span class="pre">send1</span></code> locally.</p></li>
<li><p>Finally, gradients for <code class="docutils literal notranslate"><span class="pre">t1</span></code>, <code class="docutils literal notranslate"><span class="pre">t2</span></code> and <code class="docutils literal notranslate"><span class="pre">t4</span></code> are accumulated in the
<a class="reference internal" href="#distributed-autograd-context">Distributed Autograd Context</a>.</p></li>
</ol>
</section>
<section id="smart-mode-algorithm">
<h3>SMART mode algorithm<a class="headerlink" href="#smart-mode-algorithm" title="Permalink to this heading">¶</a></h3>
<p>Full details of this algorithm are still in the works, but for the general idea
you can refer to <strong>Distributed Autograd Algorithm Smart mode</strong> section in the
<a class="reference external" href="https://github.com/pytorch/pytorch/issues/23110">RFC</a>.</p>
</section>
</section>
<section id="distributed-optimizer">
<h2>Distributed Optimizer<a class="headerlink" href="#distributed-optimizer" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="../distributed.optim.html#torch.distributed.optim.DistributedOptimizer" title="torch.distributed.optim.DistributedOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedOptimizer</span></code></a> operates as follows:</p>
<ol class="arabic simple">
<li><p>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>) to
optimize. These could also be local parameters wrapped within a local
<code class="docutils literal notranslate"><span class="pre">RRef</span></code>.</p></li>
<li><p>Takes a <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">Optimizer</span></code></a> class as the local
optimizer to run on all distinct <code class="docutils literal notranslate"><span class="pre">RRef</span></code> owners.</p></li>
<li><p>The distributed optimizer creates an instance of the local <code class="docutils literal notranslate"><span class="pre">Optimizer</span></code> on
each of the worker nodes and holds an <code class="docutils literal notranslate"><span class="pre">RRef</span></code> to them.</p></li>
<li><p>When <a class="reference internal" href="../distributed.optim.html#torch.distributed.optim.DistributedOptimizer.step" title="torch.distributed.optim.DistributedOptimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.optim.DistributedOptimizer.step()</span></code></a> is invoked,
the distributed optimizer uses RPC to remotely execute all the local
optimizers on the appropriate remote workers. A distributed autograd
<code class="docutils literal notranslate"><span class="pre">context_id</span></code> must be provided as input to
<a class="reference internal" href="../distributed.optim.html#torch.distributed.optim.DistributedOptimizer.step" title="torch.distributed.optim.DistributedOptimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.optim.DistributedOptimizer.step()</span></code></a>. This is used
by local optimizers to apply gradients stored in the corresponding
context.</p></li>
<li><p>If multiple concurrent distributed optimizers are updating the same
parameters on a worker, these updates are serialized via a lock.</p></li>
</ol>
</section>
<section id="simple-end-to-end-example">
<h2>Simple end to end example<a class="headerlink" href="#simple-end-to-end-example" title="Permalink to this heading">¶</a></h2>
<p>Putting it all together, the following is a simple end to end example using
distributed autograd and the distributed optimizer. If the code is placed into a
file called “dist_autograd_simple.py”, it can be run with the command
<code class="code docutils literal notranslate"><span class="pre">MASTER_ADDR="localhost"</span> <span class="pre">MASTER_PORT=29500</span> <span class="pre">python</span> <span class="pre">dist_autograd_simple.py</span></code>:</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.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</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="kn">from</span> <span class="nn">torch.distributed</span> <span class="kn">import</span> <span class="n">rpc</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</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="k">def</span> <span class="nf">random_tensor</span><span class="p">():</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_run_process</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">dst_rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="s2">"worker</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
<span class="n">dst_name</span> <span class="o">=</span> <span class="s2">"worker</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dst_rank</span><span class="p">)</span>
<span class="c1"># Initialize RPC.</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="n">name</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"># Use a distributed autograd context.</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="c1"># Forward pass (create references on remote nodes).</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="n">dst_name</span><span class="p">,</span> <span class="n">random_tensor</span><span class="p">)</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="n">dst_name</span><span class="p">,</span> <span class="n">random_tensor</span><span class="p">)</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="c1"># Backward pass (run distributed autograd).</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="c1"># Build DistributedOptimizer.</span>
<span class="n">dist_optim</span> <span class="o">=</span> <span class="n">DistributedOptimizer</span><span class="p">(</span>
<span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">,</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="n">lr</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Run the distributed optimizer step.</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>
<span class="k">def</span> <span class="nf">run_process</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="n">dst_rank</span> <span class="o">=</span> <span class="p">(</span><span class="n">rank</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">world_size</span>
<span class="n">_run_process</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">dst_rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">)</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="c1"># Run world_size workers</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">run_process</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>
</pre></div>
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