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<div class="section" id="distributed-rpc-framework">
<span id="id1"></span><h1>Distributed RPC Framework<a class="headerlink" href="#distributed-rpc-framework" title="Permalink to this headline">¶</a></h1>
<p>The distributed RPC framework provides mechanisms for multi-machine model
training through a set of primitives to allow for remote communication, and a
higher-level API to automatically differentiate models split across several
machines.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The RPC API is experimental and subject to change.</p>
</div>
<div class="section" id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">¶</a></h2>
<p>The distributed RPC framework makes it easy to run functions remotely, supports
referencing remote objects without copying the real data around, and provides
autograd and optimizer APIs to transparently run backward and update parameters
across RPC boundaries. These features can be categorized into four sets of APIs.</p>
<ol class="arabic simple">
<li><p><strong>Remote Procedure Call (RPC)</strong> supports running a function on the specified
destination worker with the given arguments and getting the return value back
or creating a reference to the return value. There are three main RPC APIs:
<code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_sync()</span></code> (synchronous),
<code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code> (asynchronous), and
<code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code> (asynchronous and returns a reference
to the remote return value). Use the synchronous API if the user code cannot
proceed without the return value. Otherwise, use the asynchronous API to get
a future, and wait on the future when the return value is needed on the
caller. The <code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code> API is useful when the
requirement is to create something remotely but never need to fetch it to
the caller. Imagine the case that a driver process is setting up a parameter
server and a trainer. The driver can create an embedding table on the
parameter server and then share the reference to the embedding table with the
trainer, but itself will never use the embedding table locally. In this case,
<code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_sync()</span></code> and
<code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code> are no longer appropriate, as they
always imply that the return value will be returned to the caller
immediately or in the future.</p></li>
<li><p><strong>Remote Reference (RRef)</strong> serves as a distributed shared pointer to a local
or remote object. It can be shared with other workers and reference counting
will be handled transparently. Each RRef only has one owner and the object
only lives on that owner. Non-owner workers holding RRefs can get copies of
the object from the owner by explicitly requesting it. This is useful when
a worker needs to access some data object, but itself is neither the creator
(the caller of <code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code>) or the owner of the
object. The distributed optimizer, as we will discuss below, is one example
of such use cases.</p></li>
<li><p><strong>Distributed Autograd</strong> stitches together local autograd engines on all the
workers involved in the forward pass, and automatically reach out to them
during the backward pass to compute gradients. This is especially helpful if
the forward pass needs to span multiple machines when conducting, e.g.,
distributed model parallel training, parameter-server training, etc. With
this feature, user code no longer needs to worry about how to send gradients
across RPC boundaries and in which order should the local autograd engines
be launched, which can become quite complicated where there are nested and
inter-dependent RPC calls in the forward pass.</p></li>
<li><p><strong>Distributed Optimizer</strong>’s constructor takes a
<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Optimizer()</span></code></a> (e.g., <a class="reference internal" href="optim.html#torch.optim.SGD" title="torch.optim.SGD"><code class="xref py py-meth docutils literal notranslate"><span class="pre">SGD()</span></code></a>,
<a class="reference internal" href="optim.html#torch.optim.Adagrad" title="torch.optim.Adagrad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Adagrad()</span></code></a>, etc.) and a list of parameter RRefs, creates an
<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Optimizer()</span></code></a> instance on each distinct RRef owner, and
updates parameters accordingly when running <cite>step()</cite>. When you have
distributed forward and backward passes, parameters and gradients will be
scattered across multiple workers, and hence it requires an optimizer on each
of the involved workers. Distributed Optimizer wraps all those local
optimizers into one, and provides a concise constructor and <cite>step()</cite> API.</p></li>
</ol>
</div>
<div class="section" id="rpc">
<span id="id2"></span><h2>RPC<a class="headerlink" href="#rpc" title="Permalink to this headline">¶</a></h2>
<p>Before using RPC and distributed autograd primitives, initialization must take
place. To initialize the RPC framework we need to use
<code class="xref py py-meth docutils literal notranslate"><span class="pre">init_rpc()</span></code> which would initialize the RPC
framework, RRef framework and distributed autograd. By default, this will also
initialize the <cite>ProcessGroup</cite> (<code class="xref py py-meth docutils literal notranslate"><span class="pre">init_process_group()</span></code>)
backend for RPC communication. The <cite>ProcessGroup</cite> backend internally uses gloo
for communication.</p>
<p>The following APIs provide primitives allowing users to remotely execute
functions as well as create (RRefs) to remote data objects.</p>
</div>
<div class="section" id="rref">
<span id="id3"></span><h2>RRef<a class="headerlink" href="#rref" title="Permalink to this headline">¶</a></h2>
<p>An <cite>RRef</cite> (Remote REFerence) is a reference to a value of some type <cite>T</cite>
(e.g. <cite>Tensor</cite>) on a remote worker. This handle keeps the referenced remote
value alive on the owner, but there is no implication that the value will be
transferred to the local worker in the future. RRefs can be used in
multi-machine training by holding references to <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Modules</a> that exist on
other workers, and calling the appropriate functions to retrieve or modify their
parameters during training. See <a class="reference internal" href="notes/rref.html#remote-reference-protocol"><span class="std std-ref">Remote Reference Protocol</span></a> for more
details.</p>
</div>
<div class="section" id="distributed-autograd-framework">
<h2>Distributed Autograd Framework<a class="headerlink" href="#distributed-autograd-framework" title="Permalink to this headline">¶</a></h2>
<p>This module provides an RPC-based distributed autograd framework that can be
used for applications such as model parallel training. In short, applications
may send and receive gradient recording tensors over RPC. In the forward pass,
we record when gradient recording tensors are sent over RPC and during the
backward pass we use this information to perform a distributed backward pass
using RPC. For more details see <a class="reference internal" href="notes/distributed_autograd.html#distributed-autograd-design"><span class="std std-ref">Distributed Autograd Design</span></a>.</p>
<span class="target" id="module-torch.distributed.autograd"></span><dl class="class">
<dt id="torch.distributed.autograd.context">
<em class="property">class </em><code class="sig-prename descclassname">torch.distributed.autograd.</code><code class="sig-name descname">context</code><a class="reference internal" href="_modules/torch/distributed/autograd.html#context"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.autograd.context" title="Permalink to this definition">¶</a></dt>
<dd><p>Context object to wrap forward and backward passes when using
distributed autograd. The <code class="docutils literal notranslate"><span class="pre">context_id</span></code> generated in the <code class="docutils literal notranslate"><span class="pre">with</span></code>
statement is required to uniquely identify a distributed backward pass
on all workers. Each worker stores metadata associated with this
<code class="docutils literal notranslate"><span class="pre">context_id</span></code>, which is required to correctly execute a distributed
autograd pass.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">>></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="o">>></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="o">>></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="o">>></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="o">>></span> <span class="n">loss</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">t1</span><span class="p">,</span> <span class="n">t2</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">dist_autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">([</span><span class="n">loss</span><span class="p">])</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="distributed-optimizer">
<h2>Distributed Optimizer<a class="headerlink" href="#distributed-optimizer" title="Permalink to this headline">¶</a></h2>
</div>
</div>
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