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<meta property="og:description" content="Author: Shen Li Prerequisites: PyTorch Distributed Overview, RPC API documents. This tutorial uses two simple examples to demonstrate how to build distributed training with the torch.distributed.rpc package which was first introduced as an experimental feature in PyTorch v1.4. Source code of the ..." />
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">모든 레시피 보기</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">파이토치(PyTorch) 기본 익히기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">빠른 시작(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">텐서(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset과 DataLoader</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>를 사용한 자동 미분</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">예제로 배우는 파이토치(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> 이 <em>실제로</em> 무엇인가요?</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">적대적 예제 생성(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN 튜토리얼</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 과 함께 SDPA 사용하기</a></li>
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<li class="toctree-l1"><a class="reference internal" href="ddp_tutorial.html">분산 데이터 병렬 처리 시작하기</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Getting Started with Distributed RPC Framework</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
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<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li>
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<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li>
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<div class="section" id="getting-started-with-distributed-rpc-framework">
<h1>Getting Started with Distributed RPC Framework<a class="headerlink" href="#getting-started-with-distributed-rpc-framework" title="이 제목에 대한 퍼머링크">¶</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://mrshenli.github.io/">Shen Li</a></p>
<div class="admonition note">
<p class="admonition-title">참고</p>
<p><a class="reference internal" href="../_images/pencil-16.png"><img alt="edit" src="../_images/pencil-16.png" style="width: 16px; height: 16px;" /></a> View and edit this tutorial in <a class="reference external" href="https://github.com/pytorch/tutorials/blob/main/intermediate_source/rpc_tutorial.rst">github</a>.</p>
</div>
<p>Prerequisites:</p>
<ul class="simple">
<li><p><a class="reference external" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></p></li>
<li><p><a class="reference external" href="https://pytorch.org/docs/master/rpc.html">RPC API documents</a></p></li>
</ul>
<p>This tutorial uses two simple examples to demonstrate how to build distributed
training with the <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a>
package which was first introduced as an experimental feature in PyTorch v1.4.
Source code of the two examples can be found in
<a class="reference external" href="https://github.com/pytorch/examples">PyTorch examples</a>.</p>
<p>Previous tutorials,
<a class="reference external" href="ddp_tutorial.html">Getting Started With Distributed Data Parallel</a>
and <a class="reference external" href="dist_tuto.html">Writing Distributed Applications With PyTorch</a>,
described <a class="reference external" href="https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html">DistributedDataParallel</a>
which supports a specific training paradigm where the model is replicated across
multiple processes and each process handles a split of the input data.
Sometimes, you might run into scenarios that require different training
paradigms. For example:</p>
<ol class="arabic simple">
<li><p>In reinforcement learning, it might be relatively expensive to acquire
training data from environments while the model itself can be quite small. In
this case, it might be useful to spawn multiple observers running in parallel
and share a single agent. In this case, the agent takes care of the training
locally, but the application would still need libraries to send and receive
data between observers and the trainer.</p></li>
<li><p>Your model might be too large to fit in GPUs on a single machine, and hence
would need a library to help split the model onto multiple machines. Or you
might be implementing a <a class="reference external" href="https://www.cs.cmu.edu/~muli/file/parameter_server_osdi14.pdf">parameter server</a>
training framework, where model parameters and trainers live on different
machines.</p></li>
</ol>
<p>The <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a> package
can help with the above scenarios. In case 1, <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#rpc">RPC</a>
and <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#rref">RRef</a> allow sending data
from one worker to another while easily referencing remote data objects. In
case 2, <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#distributed-autograd-framework">distributed autograd</a>
and <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#module-torch.distributed.optim">distributed optimizer</a>
make executing backward pass and optimizer step as if it is local training. In
the next two sections, we will demonstrate APIs of
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a> using a
reinforcement learning example and a language model example. Please note, this
tutorial does not aim at building the most accurate or efficient models to
solve given problems, instead, the main goal here is to show how to use the
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a> package to
build distributed training applications.</p>
<div class="section" id="distributed-reinforcement-learning-using-rpc-and-rref">
<h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" href="#distributed-reinforcement-learning-using-rpc-and-rref" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>This section describes steps to build a toy distributed reinforcement learning
model using RPC to solve CartPole-v1 from <a class="reference external" href="https://gym.openai.com">OpenAI Gym</a>.
The policy code is mostly borrowed from the existing single-thread
<a class="reference external" href="https://github.com/pytorch/examples/blob/master/reinforcement_learning">example</a>
as shown below. We will skip details of the <code class="docutils literal notranslate"><span class="pre">Policy</span></code> design, and focus on RPC
usages.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="k">class</span> <span class="nc">Policy</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Policy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">affine1</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">4</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">affine2</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">128</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">affine1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">action_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">affine2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">action_scores</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>We are ready to present the observer. In this example, each observer creates its
own environment, and waits for the agent’s command to run an episode. In each
episode, one observer loops at most <code class="docutils literal notranslate"><span class="pre">n_steps</span></code> iterations, and in each
iteration, it uses RPC to pass its environment state to the agent and gets an
action back. Then it applies that action to its environment, and gets the reward
and the next state from the environment. After that, the observer uses another
RPC to report the reward to the agent. Again, please note that, this is
obviously not the most efficient observer implementation. For example, one
simple optimization could be packing current state and last reward in one RPC to
reduce the communication overhead. However, the goal is to demonstrate RPC API
instead of building the best solver for CartPole. So, let’s keep the logic
simple and the two steps explicit in this example.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">gym</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">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span>
<span class="n">description</span><span class="o">=</span><span class="s2">"RPC Reinforcement Learning Example"</span><span class="p">,</span>
<span class="n">formatter_class</span><span class="o">=</span><span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentDefaultsHelpFormatter</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--world_size'</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">'W'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'number of workers'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--log_interval'</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">'N'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'interval between training status logs'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--gamma'</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">'G'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'how much to value future rewards'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--seed'</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">'S'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'random seed for reproducibility'</span><span class="p">)</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">Observer</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">get_worker_info</span><span class="p">()</span><span class="o">.</span><span class="n">id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">env</span> <span class="o">=</span> <span class="n">gym</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">'CartPole-v1'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">agent_rref</span><span class="p">):</span>
<span class="n">state</span><span class="p">,</span> <span class="n">ep_reward</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">(),</span> <span class="mi">0</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">10000</span><span class="p">):</span>
<span class="c1"># send the state to the agent to get an action</span>
<span class="n">action</span> <span class="o">=</span> <span class="n">agent_rref</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">()</span><span class="o">.</span><span class="n">select_action</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span>
<span class="c1"># apply the action to the environment, and get the reward</span>
<span class="n">state</span><span class="p">,</span> <span class="n">reward</span><span class="p">,</span> <span class="n">done</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">action</span><span class="p">)</span>
<span class="c1"># report the reward to the agent for training purpose</span>
<span class="n">agent_rref</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">()</span><span class="o">.</span><span class="n">report_reward</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="n">reward</span><span class="p">)</span>
<span class="c1"># finishes after the number of self.env._max_episode_steps</span>
<span class="k">if</span> <span class="n">done</span><span class="p">:</span>
<span class="k">break</span>
</pre></div>
</div>
<p>The code for agent is a little more complex, and we will break it into multiple
pieces. In this example, the agent serves as both the trainer and the master,
such that it sends command to multiple distributed observers to run episodes,
and it also records all actions and rewards locally which will be used during
the training phase after each episode. The code below shows <code class="docutils literal notranslate"><span class="pre">Agent</span></code>
constructor where most lines are initializing various components. The loop at
the end initializes observers remotely on other workers, and holds <code class="docutils literal notranslate"><span class="pre">RRefs</span></code> to
those observers locally. The agent will use those observer <code class="docutils literal notranslate"><span class="pre">RRefs</span></code> later to
send commands. Applications don’t need to worry about the lifetime of <code class="docutils literal notranslate"><span class="pre">RRefs</span></code>.
The owner of each <code class="docutils literal notranslate"><span class="pre">RRef</span></code> maintains a reference counting map to track its
lifetime, and guarantees the remote data object will not be deleted as long as
there is any live user of that <code class="docutils literal notranslate"><span class="pre">RRef</span></code>. Please refer to the <code class="docutils literal notranslate"><span class="pre">RRef</span></code>
<a class="reference external" href="https://pytorch.org/docs/master/notes/rref.html">design doc</a> for details.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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="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.distributed.rpc</span> <span class="kn">import</span> <span class="n">RRef</span><span class="p">,</span> <span class="n">rpc_async</span><span class="p">,</span> <span class="n">remote</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">Categorical</span>
<span class="k">class</span> <span class="nc">Agent</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ob_rrefs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">agent_rref</span> <span class="o">=</span> <span class="n">RRef</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rewards</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_log_probs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">=</span> <span class="n">Policy</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</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="bp">self</span><span class="o">.</span><span class="n">policy</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">1e-2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">running_reward</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reward_threshold</span> <span class="o">=</span> <span class="n">gym</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">'CartPole-v1'</span><span class="p">)</span><span class="o">.</span><span class="n">spec</span><span class="o">.</span><span class="n">reward_threshold</span>
<span class="k">for</span> <span class="n">ob_rank</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
<span class="n">ob_info</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">get_worker_info</span><span class="p">(</span><span class="n">OBSERVER_NAME</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ob_rank</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ob_rrefs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">remote</span><span class="p">(</span><span class="n">ob_info</span><span class="p">,</span> <span class="n">Observer</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">[</span><span class="n">ob_info</span><span class="o">.</span><span class="n">id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_log_probs</span><span class="p">[</span><span class="n">ob_info</span><span class="o">.</span><span class="n">id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
</pre></div>
</div>
<p>Next, the agent exposes two APIs to observers for selecting actions and
reporting rewards. Those functions only run locally on the agent, but will
be triggered by observers through RPC.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Agent</span><span class="p">:</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">select_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ob_id</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">state</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">Categorical</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="n">action</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_log_probs</span><span class="p">[</span><span class="n">ob_id</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">action</span><span class="p">))</span>
<span class="k">return</span> <span class="n">action</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">report_reward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ob_id</span><span class="p">,</span> <span class="n">reward</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">[</span><span class="n">ob_id</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">reward</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s add a <code class="docutils literal notranslate"><span class="pre">run_episode</span></code> function on agent which tells all observers
to execute an episode. In this function, it first creates a list to collect
futures from asynchronous RPCs, and then loop over all observer <code class="docutils literal notranslate"><span class="pre">RRefs</span></code> to
make asynchronous RPCs. In these RPCs, the agent also passes an <code class="docutils literal notranslate"><span class="pre">RRef</span></code> of
itself to the observer, so that the observer can call functions on the agent as
well. As shown above, each observer will make RPCs back to the agent, which are
nested RPCs. After each episode, the <code class="docutils literal notranslate"><span class="pre">saved_log_probs</span></code> and <code class="docutils literal notranslate"><span class="pre">rewards</span></code> will
contain the recorded action probs and rewards.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Agent</span><span class="p">:</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">run_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">futs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ob_rref</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ob_rrefs</span><span class="p">:</span>
<span class="c1"># make async RPC to kick off an episode on all observers</span>
<span class="n">futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="n">rpc_async</span><span class="p">(</span>
<span class="n">ob_rref</span><span class="o">.</span><span class="n">owner</span><span class="p">(),</span>
<span class="n">ob_rref</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">()</span><span class="o">.</span><span class="n">run_episode</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">agent_rref</span><span class="p">,)</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="c1"># wait until all obervers have finished this episode</span>
<span class="k">for</span> <span class="n">fut</span> <span class="ow">in</span> <span class="n">futs</span><span class="p">:</span>
<span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
</pre></div>
</div>
<p>Finally, after one episode, the agent needs to train the model, which
is implemented in the <code class="docutils literal notranslate"><span class="pre">finish_episode</span></code> function below. There is no RPCs in
this function and it is mostly borrowed from the single-thread
<a class="reference external" href="https://github.com/pytorch/examples/blob/master/reinforcement_learning">example</a>.
Hence, we skip describing its contents.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Agent</span><span class="p">:</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">finish_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># joins probs and rewards from different observers into lists</span>
<span class="n">R</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">rewards</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ob_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">:</span>
<span class="n">probs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">saved_log_probs</span><span class="p">[</span><span class="n">ob_id</span><span class="p">])</span>
<span class="n">rewards</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">[</span><span class="n">ob_id</span><span class="p">])</span>
<span class="c1"># use the minimum observer reward to calculate the running reward</span>
<span class="n">min_reward</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">[</span><span class="n">ob_id</span><span class="p">])</span> <span class="k">for</span> <span class="n">ob_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">running_reward</span> <span class="o">=</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="n">min_reward</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="mf">0.05</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_reward</span>
<span class="c1"># clear saved probs and rewards</span>
<span class="k">for</span> <span class="n">ob_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">[</span><span class="n">ob_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_log_probs</span><span class="p">[</span><span class="n">ob_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">policy_loss</span><span class="p">,</span> <span class="n">returns</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">rewards</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
<span class="n">R</span> <span class="o">=</span> <span class="n">r</span> <span class="o">+</span> <span class="n">args</span><span class="o">.</span><span class="n">gamma</span> <span class="o">*</span> <span class="n">R</span>
<span class="n">returns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">R</span><span class="p">)</span>
<span class="n">returns</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">returns</span><span class="p">)</span>
<span class="n">returns</span> <span class="o">=</span> <span class="p">(</span><span class="n">returns</span> <span class="o">-</span> <span class="n">returns</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">/</span> <span class="p">(</span><span class="n">returns</span><span class="o">.</span><span class="n">std</span><span class="p">()</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
<span class="k">for</span> <span class="n">log_prob</span><span class="p">,</span> <span class="n">R</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">returns</span><span class="p">):</span>
<span class="n">policy_loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">log_prob</span> <span class="o">*</span> <span class="n">R</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">policy_loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">policy_loss</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">policy_loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="k">return</span> <span class="n">min_reward</span>
</pre></div>
</div>
<p>With <code class="docutils literal notranslate"><span class="pre">Policy</span></code>, <code class="docutils literal notranslate"><span class="pre">Observer</span></code>, and <code class="docutils literal notranslate"><span class="pre">Agent</span></code> classes, we are ready to launch
multiple processes to perform the distributed training. In this example, all
processes run the same <code class="docutils literal notranslate"><span class="pre">run_worker</span></code> function, and they use the rank to
distinguish their role. Rank 0 is always the agent, and all other ranks are
observers. The agent serves as master by repeatedly calling <code class="docutils literal notranslate"><span class="pre">run_episode</span></code> and
<code class="docutils literal notranslate"><span class="pre">finish_episode</span></code> until the running reward surpasses the reward threshold
specified by the environment. All observers passively waiting for commands
from the agent. The code is wrapped by
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.init_rpc">rpc.init_rpc</a> and
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.shutdown">rpc.shutdown</a>,
which initializes and terminates RPC instances respectively. More details are
available in the <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">API page</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">count</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="n">AGENT_NAME</span> <span class="o">=</span> <span class="s2">"agent"</span>
<span class="n">OBSERVER_NAME</span><span class="o">=</span><span class="s2">"obs</span><span class="si">{}</span><span class="s2">"</span>
<span class="k">def</span> <span class="nf">run_worker</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">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># rank0 is the agent</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="n">AGENT_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="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span><span class="n">world_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"This will run until reward threshold of </span><span class="si">{</span><span class="n">agent</span><span class="o">.</span><span class="n">reward_threshold</span><span class="si">}</span><span class="s2">"</span>
<span class="s2">" is reached. Ctrl+C to exit."</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i_episode</span> <span class="ow">in</span> <span class="n">count</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="n">agent</span><span class="o">.</span><span class="n">run_episode</span><span class="p">()</span>
<span class="n">last_reward</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">finish_episode</span><span class="p">()</span>
<span class="k">if</span> <span class="n">i_episode</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Episode </span><span class="si">{</span><span class="n">i_episode</span><span class="si">}</span><span class="se">\t</span><span class="s2">Last reward: </span><span class="si">{</span><span class="n">last_reward</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\t</span><span class="s2">Average reward: "</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span> <span class="o">></span> <span class="n">agent</span><span class="o">.</span><span class="n">reward_threshold</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Solved! Running reward is now </span><span class="si">{</span><span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span><span class="si">}</span><span class="s2">!"</span><span class="p">)</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># other ranks are the observer</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="n">OBSERVER_NAME</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">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"># observers passively waiting for instructions from the agent</span>
<span class="c1"># block until all rpcs finish, and shutdown the RPC instance</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span>
<span class="n">run_worker</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span>
<span class="n">nprocs</span><span class="o">=</span><span class="n">args</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>
</pre></div>
</div>
<p>Below are some sample outputs when training with <cite>world_size=2</cite>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>This will run until reward threshold of 475.0 is reached. Ctrl+C to exit.
Episode 10 Last reward: 26.00 Average reward: 10.01
Episode 20 Last reward: 16.00 Average reward: 11.27
Episode 30 Last reward: 49.00 Average reward: 18.62
Episode 40 Last reward: 45.00 Average reward: 26.09
Episode 50 Last reward: 44.00 Average reward: 30.03
Episode 60 Last reward: 111.00 Average reward: 42.23
Episode 70 Last reward: 131.00 Average reward: 70.11
Episode 80 Last reward: 87.00 Average reward: 76.51
Episode 90 Last reward: 86.00 Average reward: 95.93
Episode 100 Last reward: 13.00 Average reward: 123.93
Episode 110 Last reward: 33.00 Average reward: 91.39
Episode 120 Last reward: 73.00 Average reward: 76.38
Episode 130 Last reward: 137.00 Average reward: 88.08
Episode 140 Last reward: 89.00 Average reward: 104.96
Episode 150 Last reward: 97.00 Average reward: 98.74
Episode 160 Last reward: 150.00 Average reward: 100.87
Episode 170 Last reward: 126.00 Average reward: 104.38
Episode 180 Last reward: 500.00 Average reward: 213.74
Episode 190 Last reward: 322.00 Average reward: 300.22
Episode 200 Last reward: 165.00 Average reward: 272.71
Episode 210 Last reward: 168.00 Average reward: 233.11
Episode 220 Last reward: 184.00 Average reward: 195.02
Episode 230 Last reward: 284.00 Average reward: 208.32
Episode 240 Last reward: 395.00 Average reward: 247.37
Episode 250 Last reward: 500.00 Average reward: 335.42
Episode 260 Last reward: 500.00 Average reward: 386.30
Episode 270 Last reward: 500.00 Average reward: 405.29
Episode 280 Last reward: 500.00 Average reward: 443.29
Episode 290 Last reward: 500.00 Average reward: 464.65
Solved! Running reward is now 475.3163778435275!
</pre></div>
</div>
<p>In this example, we show how to use RPC as the communication vehicle to pass
data across workers, and how to use RRef to reference remote objects. It is true
that you could build the entire structure directly on top of <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code>
<code class="docutils literal notranslate"><span class="pre">send</span></code> and <code class="docutils literal notranslate"><span class="pre">recv</span></code> APIs or use other communication/RPC libraries. However,
by using <cite>torch.distributed.rpc</cite>, you can get the native support and
continuously optimized performance under the hood.</p>
<p>Next, we will show how to combine RPC and RRef with distributed autograd and
distributed optimizer to perform distributed model parallel training.</p>
</div>
<div class="section" id="distributed-rnn-using-distributed-autograd-and-distributed-optimizer">
<h2>Distributed RNN using Distributed Autograd and Distributed Optimizer<a class="headerlink" href="#distributed-rnn-using-distributed-autograd-and-distributed-optimizer" title="이 제목에 대한 퍼머링크">¶</a></h2>
<p>In this section, we use an RNN model to show how to build distributed model
parallel training with the RPC API. The example RNN model is very small and
can easily fit into a single GPU, but we still divide its layers onto two
different workers to demonstrate the idea. Developer can apply the similar
techniques to distribute much larger models across multiple devices and
machines.</p>
<p>The RNN model design is borrowed from the word language model in PyTorch
<a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">example</a>
repository, which contains three main components, an embedding table, an
<code class="docutils literal notranslate"><span class="pre">LSTM</span></code> layer, and a decoder. The code below wraps the embedding table and the
decoder into sub-modules, so that their constructors can be passed to the RPC
API. In the <code class="docutils literal notranslate"><span class="pre">EmbeddingTable</span></code> sub-module, we intentionally put the
<code class="docutils literal notranslate"><span class="pre">Embedding</span></code> layer on GPU to cover the use case. In v1.4, RPC always creates
CPU tensor arguments or return values on the destination worker. If the function
takes a GPU tensor, you need to move it to the proper device explicitly.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EmbeddingTable</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Encoding layers of the RNNModel</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">dropout</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">EmbeddingTable</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">Decoder</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">dropout</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Decoder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</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="n">nhid</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">output</span><span class="p">))</span>
</pre></div>
</div>
<p>With the above sub-modules, we can now piece them together using RPC to
create an RNN model. In the code below <code class="docutils literal notranslate"><span class="pre">ps</span></code> represents a parameter server,
which hosts parameters of the embedding table and the decoder. The constructor
uses the <a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.remote">remote</a>
API to create an <code class="docutils literal notranslate"><span class="pre">EmbeddingTable</span></code> object and a <code class="docutils literal notranslate"><span class="pre">Decoder</span></code> object on the
parameter server, and locally creates the <code class="docutils literal notranslate"><span class="pre">LSTM</span></code> sub-module. During the
forward pass, the trainer uses the <code class="docutils literal notranslate"><span class="pre">EmbeddingTable</span></code> <code class="docutils literal notranslate"><span class="pre">RRef</span></code> to find the
remote sub-module and passes the input data to the <code class="docutils literal notranslate"><span class="pre">EmbeddingTable</span></code> using RPC
and fetches the lookup results. Then, it runs the embedding through the local
<code class="docutils literal notranslate"><span class="pre">LSTM</span></code> layer, and finally uses another RPC to send the output to the
<code class="docutils literal notranslate"><span class="pre">Decoder</span></code> sub-module. In general, to implement distributed model parallel
training, developers can divide the model into sub-modules, invoke RPC to create
sub-module instances remotely, and use on <code class="docutils literal notranslate"><span class="pre">RRef</span></code> to find them when necessary.
As you can see in the code below, it looks very similar to single-machine model
parallel training. The main difference is replacing <code class="docutils literal notranslate"><span class="pre">Tensor.to(device)</span></code> with
RPC functions.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RNNModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ps</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">RNNModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># setup embedding table remotely</span>
<span class="bp">self</span><span class="o">.</span><span class="n">emb_table_rref</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">ps</span><span class="p">,</span> <span class="n">EmbeddingTable</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">dropout</span><span class="p">))</span>
<span class="c1"># setup LSTM locally</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
<span class="c1"># setup decoder remotely</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder_rref</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">ps</span><span class="p">,</span> <span class="n">Decoder</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">dropout</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">):</span>
<span class="c1"># pass input to the remote embedding table and fetch emb tensor back</span>
<span class="n">emb</span> <span class="o">=</span> <span class="n">_remote_method</span><span class="p">(</span><span class="n">EmbeddingTable</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emb_table_rref</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">emb</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
<span class="c1"># pass output to the rremote decoder and get the decoded output back</span>
<span class="n">decoded</span> <span class="o">=</span> <span class="n">_remote_method</span><span class="p">(</span><span class="n">Decoder</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder_rref</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">decoded</span><span class="p">,</span> <span class="n">hidden</span>
</pre></div>
</div>
<p>Before introducing the distributed optimizer, let’s add a helper function to
generate a list of RRefs of model parameters, which will be consumed by the
distributed optimizer. In local training, applications could call
<code class="docutils literal notranslate"><span class="pre">Module.parameters()</span></code> to grab references to all parameter tensors, and pass it
to the local optimizer for subsequent updates. However, the same API does not
work in distributed training scenarios as some parameters live on remote
machines. Therefore, instead of taking a list of parameter <code class="docutils literal notranslate"><span class="pre">Tensors</span></code>, the
distributed optimizer takes a list of <code class="docutils literal notranslate"><span class="pre">RRefs</span></code>, one <code class="docutils literal notranslate"><span class="pre">RRef</span></code> per model
parameter for both local and remote model parameters. The helper function is
pretty simple, just call <code class="docutils literal notranslate"><span class="pre">Module.parameters()</span></code> and creates a local <code class="docutils literal notranslate"><span class="pre">RRef</span></code> on
each of the parameters.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_parameter_rrefs</span><span class="p">(</span><span class="n">module</span><span class="p">):</span>
<span class="n">param_rrefs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">param_rrefs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">RRef</span><span class="p">(</span><span class="n">param</span><span class="p">))</span>
<span class="k">return</span> <span class="n">param_rrefs</span>
</pre></div>
</div>
<p>Then, as the <code class="docutils literal notranslate"><span class="pre">RNNModel</span></code> contains three sub-modules, we need to call
<code class="docutils literal notranslate"><span class="pre">_parameter_rrefs</span></code> three times, and wrap that into another helper function.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RNNModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">parameter_rrefs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">remote_params</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># get RRefs of embedding table</span>
<span class="n">remote_params</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_remote_method</span><span class="p">(</span><span class="n">_parameter_rrefs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emb_table_rref</span><span class="p">))</span>
<span class="c1"># create RRefs for local parameters</span>
<span class="n">remote_params</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_parameter_rrefs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">))</span>
<span class="c1"># get RRefs of decoder</span>
<span class="n">remote_params</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_remote_method</span><span class="p">(</span><span class="n">_parameter_rrefs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder_rref</span><span class="p">))</span>
<span class="k">return</span> <span class="n">remote_params</span>
</pre></div>
</div>
<p>Now, we are ready to implement the training loop. After initializing model
arguments, we create the <code class="docutils literal notranslate"><span class="pre">RNNModel</span></code> and the <code class="docutils literal notranslate"><span class="pre">DistributedOptimizer</span></code>. The
distributed optimizer will take a list of parameter <code class="docutils literal notranslate"><span class="pre">RRefs</span></code>, find all distinct
owner workers, and create the given local optimizer (i.e., <code class="docutils literal notranslate"><span class="pre">SGD</span></code> in this case,
you can use other local optimizers as well) on each of the owner worker using
the given arguments (i.e., <code class="docutils literal notranslate"><span class="pre">lr=0.05</span></code>).</p>
<p>In the training loop, it first creates a distributed autograd context, which
will help the distributed autograd engine to find gradients and involved RPC
send/recv functions. The design details of the distributed autograd engine can
be found in its <a class="reference external" href="https://pytorch.org/docs/master/notes/distributed_autograd.html">design note</a>.
Then, it kicks off the forward pass as if it is a local
model, and run the distributed backward pass. For the distributed backward, you
only need to specify a list of roots, in this case, it is the loss <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>.
The distributed autograd engine will traverse the distributed graph
automatically and write gradients properly. Next, it runs the <code class="docutils literal notranslate"><span class="pre">step</span></code>
function on the distributed optimizer, which will reach out to all involved
local optimizers to update model parameters. Compared to local training, one
minor difference is that you don’t need to run <code class="docutils literal notranslate"><span class="pre">zero_grad()</span></code> because each
autograd context has dedicated space to store gradients, and as we create a
context per iteration, those gradients from different iterations will not
accumulate to the same set of <code class="docutils literal notranslate"><span class="pre">Tensors</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_trainer</span><span class="p">():</span>
<span class="n">batch</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">ntoken</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">ninp</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">nhid</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">nindices</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">nlayers</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">hidden</span> <span class="o">=</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="n">nlayers</span><span class="p">,</span> <span class="n">nindices</span><span class="p">,</span> <span class="n">nhid</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="n">nlayers</span><span class="p">,</span> <span class="n">nindices</span><span class="p">,</span> <span class="n">nhid</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">RNNModel</span><span class="p">(</span><span class="s1">'ps'</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">)</span>
<span class="c1"># setup distributed optimizer</span>
<span class="n">opt</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="n">model</span><span class="o">.</span><span class="n">parameter_rrefs</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="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">get_next_batch</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">5</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">nindices</span><span class="p">)</span> <span class="o">%</span> <span class="n">ntoken</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">)</span> <span class="o">%</span> <span class="n">nindices</span>
<span class="k">yield</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span>
<span class="c1"># train for 10 iterations</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">get_next_batch</span><span class="p">():</span>
<span class="c1"># create 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="n">hidden</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">detach_</span><span class="p">()</span>
<span class="n">hidden</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">detach_</span><span class="p">()</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="c1"># run distributed 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"># run distributed optimizer</span>
<span class="n">opt</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="c1"># not necessary to zero grads since they are</span>
<span class="c1"># accumulated into the distributed autograd context</span>
<span class="c1"># which is reset every iteration.</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Training epoch </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">epoch</span><span class="p">))</span>
</pre></div>
</div>
<p>Finally, let’s add some glue code to launch the parameter server and the trainer
processes.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_worker</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">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"trainer"</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="n">_run_trainer</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"ps"</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"># parameter server do nothing</span>
<span class="k">pass</span>
<span class="c1"># block until all rpcs finish</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="s2">"__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">run_worker</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="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>
</pre></div>
</div>
</div>
</div>