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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<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>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
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<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</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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<div class="section" id="distributed-pipeline-parallelism-using-rpc">
<h1>Distributed Pipeline Parallelism Using RPC<a class="headerlink" href="#distributed-pipeline-parallelism-using-rpc" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://mrshenli.github.io/">Shen Li</a></p>
<p>Prerequisites:</p>
<ul class="simple">
<li><a class="reference external" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
<li><a class="reference external" href="https://tutorials.pytorch.kr/intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a></li>
<li><a class="reference external" href="https://tutorials.pytorch.kr/intermediate/rpc_tutorial.html">Getting started with Distributed RPC Framework</a></li>
<li>RRef helper functions:
<a class="reference external" href="https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.RRef.rpc_sync">RRef.rpc_sync()</a>,
<a class="reference external" href="https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.RRef.rpc_async">RRef.rpc_async()</a>, and
<a class="reference external" href="https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.RRef.remote">RRef.remote()</a></li>
</ul>
<p>This tutorial uses a Resnet50 model to demonstrate implementing distributed
pipeline parallelism with <a class="reference external" href="https://pytorch.org/docs/master/rpc.html">torch.distributed.rpc</a>
APIs. This can be viewed as the distributed counterpart of the multi-GPU
pipeline parallelism discussed in
<a class="reference external" href="model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This tutorial requires PyTorch v1.6.0 or above.</p>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Full source code of this tutorial can be found at
<a class="reference external" href="https://github.com/pytorch/examples/tree/master/distributed/rpc/pipeline">pytorch/examples</a>.</p>
</div>
<div class="section" id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">ยถ</a></h2>
<p>The previous tutorial, <a class="reference external" href="rpc_tutorial.html">Getting Started with Distributed RPC Framework</a>
shows how to use <a class="reference external" href="https://pytorch.org/docs/master/rpc.html">torch.distributed.rpc</a>
to implement distributed model parallelism for an RNN model. That tutorial uses
one GPU to host the <code class="docutils literal notranslate"><span class="pre">EmbeddingTable</span></code>, and the provided code works fine.
However, if a model lives on multiple GPUs, it would require some extra steps to
increase the amortized utilization of all GPUs. Pipeline parallelism is one type
of paradigm that can help in this case.</p>
<p>In this tutorial, we use <code class="docutils literal notranslate"><span class="pre">ResNet50</span></code> as an example model which is also used by
the <a class="reference external" href="model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a>
tutorial. Similarly, the <code class="docutils literal notranslate"><span class="pre">ResNet50</span></code> model is divided into two shards and
the input batch is partitioned into multiple splits and fed into the two model
shards in a pipelined fashion. The difference is that, instead of parallelizing
the execution using CUDA streams, this tutorial invokes asynchronous RPCs. So,
the solution presented in this tutorial also works across machine boundaries.
The remainder of this tutorial presents the implementation in four steps.</p>
</div>
<div class="section" id="step-1-partition-resnet50-model">
<h2>Step 1: Partition ResNet50 Model<a class="headerlink" href="#step-1-partition-resnet50-model" title="Permalink to this headline">ยถ</a></h2>
<p>This is the preparation step which implements <code class="docutils literal notranslate"><span class="pre">ResNet50</span></code> in two model shards.
The code below is borrowed from the
<a class="reference external" href="https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L124">ResNet implementation in torchvision</a>.
The <code class="docutils literal notranslate"><span class="pre">ResNetBase</span></code> module contains the common building blocks and attributes for
the two ResNet shards.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">threading</span>
<span class="kn">import</span> <span class="nn">torch</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">from</span> <span class="nn">torchvision.models.resnet</span> <span class="kn">import</span> <span class="n">Bottleneck</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="k">def</span> <span class="nf">conv1x1</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">ResNetBase</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">block</span><span class="p">,</span> <span class="n">inplanes</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">width_per_group</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ResNetBase</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">_lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_block</span> <span class="o">=</span> <span class="n">block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="n">inplanes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dilation</span> <span class="o">=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">groups</span> <span class="o">=</span> <span class="n">groups</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_width</span> <span class="o">=</span> <span class="n">width_per_group</span>
<span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">blocks</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="n">norm_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span>
<span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">previous_dilation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span>
<span class="k">if</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">!=</span> <span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</span><span class="p">:</span>
<span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">conv1x1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</span><span class="p">,</span> <span class="n">stride</span><span class="p">),</span>
<span class="n">norm_layer</span><span class="p">(</span><span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_width</span><span class="p">,</span> <span class="n">previous_dilation</span><span class="p">,</span> <span class="n">norm_layer</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</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">1</span><span class="p">,</span> <span class="n">blocks</span><span class="p">):</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span>
<span class="n">base_width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">base_width</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span>
<span class="n">norm_layer</span><span class="o">=</span><span class="n">norm_layer</span><span class="p">))</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</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="k">return</span> <span class="p">[</span><span class="n">RRef</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">()]</span>
</pre></div>
</div>
<p>Now, we are ready to define the two model shards. For the constructor, we
simply split all ResNet50 layers into two parts and move each part into the
provided device. The <code class="docutils literal notranslate"><span class="pre">forward</span></code> functions of both shards take an <code class="docutils literal notranslate"><span class="pre">RRef</span></code> of
the input data, fetch the data locally, and then move it to the expected device.
After applying all layers to the input, it moves the output to CPU and returns.
It is because the RPC API requires tensors to reside on CPU to avoid invalid
device errors when the numbers of devices in the caller and the callee do not
match.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">ResNetShard1</span><span class="p">(</span><span class="n">ResNetBase</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">device</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ResNetShard1</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="n">Bottleneck</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
<span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'fan_out'</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</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_rref</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x_rref</span><span class="o">.</span><span class="n">to_here</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">ResNetShard2</span><span class="p">(</span><span class="n">ResNetBase</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">device</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ResNetShard2</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="n">Bottleneck</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
<span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</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">512</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</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_rref</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x_rref</span><span class="o">.</span><span class="n">to_here</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="step-2-stitch-resnet50-model-shards-into-one-module">
<h2>Step 2: Stitch ResNet50 Model Shards Into One Module<a class="headerlink" href="#step-2-stitch-resnet50-model-shards-into-one-module" title="Permalink to this headline">ยถ</a></h2>
<p>Then, we create a <code class="docutils literal notranslate"><span class="pre">DistResNet50</span></code> module to assemble the two shards and
implement the pipeline parallel logic. In the constructor, we use two
<code class="docutils literal notranslate"><span class="pre">rpc.remote</span></code> calls to put the two shards on two different RPC workers
respectively and hold on to the <code class="docutils literal notranslate"><span class="pre">RRef</span></code> to the two model parts so that they
can be referenced in the forward pass. The <code class="docutils literal notranslate"><span class="pre">forward</span></code> function
splits the input batch into multiple micro-batches, and feeds these
micro-batches to the two model parts in a pipelined fashion. It first uses an
<code class="docutils literal notranslate"><span class="pre">rpc.remote</span></code> call to apply the first shard to a micro-batch and then forwards
the returned intermediate output <code class="docutils literal notranslate"><span class="pre">RRef</span></code> to the second model shard. After that,
it collects the <code class="docutils literal notranslate"><span class="pre">Future</span></code> of all micro-outputs, and waits for all of them after
the loop. Note that both <code class="docutils literal notranslate"><span class="pre">remote()</span></code> and <code class="docutils literal notranslate"><span class="pre">rpc_async()</span></code> return immediately and
run asynchronously. Therefore, the entire loop is non-blocking, and will launch
multiple RPCs concurrently. The execution order of one micro-batch on two model
parts are preserved by intermediate output <code class="docutils literal notranslate"><span class="pre">y_rref</span></code>. The execution order
across micro-batches does not matter. In the end, the forward function
concatenates outputs of all micro-batches into one single output tensor and
returns. The <code class="docutils literal notranslate"><span class="pre">parameter_rrefs</span></code> function is a helper to
simplify distributed optimizer construction, which will be used later.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DistResNet50</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">num_split</span><span class="p">,</span> <span class="n">workers</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">DistResNet50</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">num_split</span> <span class="o">=</span> <span class="n">num_split</span>
<span class="c1"># Put the first part of the ResNet50 on workers[0]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">p1_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">workers</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">ResNetShard1</span><span class="p">,</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"cuda:0"</span><span class="p">,)</span> <span class="o">+</span> <span class="n">args</span><span class="p">,</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
<span class="p">)</span>
<span class="c1"># Put the second part of the ResNet50 on workers[1]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">p2_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">workers</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">ResNetShard2</span><span class="p">,</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"cuda:1"</span><span class="p">,)</span> <span class="o">+</span> <span class="n">args</span><span class="p">,</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</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">xs</span><span class="p">):</span>
<span class="n">out_futures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">iter</span><span class="p">(</span><span class="n">xs</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_split</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)):</span>
<span class="n">x_rref</span> <span class="o">=</span> <span class="n">RRef</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y_rref</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1_rref</span><span class="o">.</span><span class="n">remote</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x_rref</span><span class="p">)</span>
<span class="n">z_fut</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2_rref</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">y_rref</span><span class="p">)</span>
<span class="n">out_futures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z_fut</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">wait_all</span><span class="p">(</span><span class="n">out_futures</span><span class="p">))</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="n">remote_params</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">p1_rref</span><span class="o">.</span><span class="n">remote</span><span class="p">()</span><span class="o">.</span><span class="n">parameter_rrefs</span><span class="p">()</span><span class="o">.</span><span class="n">to_here</span><span class="p">())</span>
<span class="n">remote_params</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">p2_rref</span><span class="o">.</span><span class="n">remote</span><span class="p">()</span><span class="o">.</span><span class="n">parameter_rrefs</span><span class="p">()</span><span class="o">.</span><span class="n">to_here</span><span class="p">())</span>
<span class="k">return</span> <span class="n">remote_params</span>
</pre></div>
</div>
</div>
<div class="section" id="step-3-define-the-training-loop">
<h2>Step 3: Define The Training Loop<a class="headerlink" href="#step-3-define-the-training-loop" title="Permalink to this headline">ยถ</a></h2>
<p>After defining the model, let us implement the training loop. We use a
dedicated โmasterโ worker to prepare random inputs and labels, and control the
distributed backward pass and distributed optimizer step. It first creates an
instance of the <code class="docutils literal notranslate"><span class="pre">DistResNet50</span></code> module. It specifies the number of
micro-batches for each batch, and also provides the name of the two RPC workers
(i.e., โworker1โ, and โworker2โ). Then it defines the loss function and creates
a <code class="docutils literal notranslate"><span class="pre">DistributedOptimizer</span></code> using the <code class="docutils literal notranslate"><span class="pre">parameter_rrefs()</span></code> helper to acquire a
list of parameter <code class="docutils literal notranslate"><span class="pre">RRefs</span></code>. Then, the main training loop is very similar to
regular local training, except that it uses <code class="docutils literal notranslate"><span class="pre">dist_autograd</span></code> to launch
backward and provides the <code class="docutils literal notranslate"><span class="pre">context_id</span></code> for both backward and optimizer
<code class="docutils literal notranslate"><span class="pre">step()</span></code>.</p>
<div class="code python 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="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.optim</span> <span class="kn">import</span> <span class="n">DistributedOptimizer</span>
<span class="n">num_batches</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">120</span>
<span class="n">image_w</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">image_h</span> <span class="o">=</span> <span class="mi">128</span>
<span class="k">def</span> <span class="nf">run_master</span><span class="p">(</span><span class="n">num_split</span><span class="p">):</span>
<span class="c1"># put the two model parts on worker1 and worker2 respectively</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">DistResNet50</span><span class="p">(</span><span class="n">num_split</span><span class="p">,</span> <span class="p">[</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="s2">"worker2"</span><span class="p">])</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">()</span>
<span class="n">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">one_hot_indices</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_size</span><span class="p">)</span> \
<span class="o">.</span><span class="n">random_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> \
<span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_batches</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Processing batch </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># generate random inputs and labels</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">image_w</span><span class="p">,</span> <span class="n">image_h</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> \
<span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">one_hot_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</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">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</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="p">[</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)])</span>
<span class="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>
</pre></div>
</div>
</div>
<div class="section" id="step-4-launch-rpc-processes">
<h2>Step 4: Launch RPC Processes<a class="headerlink" href="#step-4-launch-rpc-processes" title="Permalink to this headline">ยถ</a></h2>
<p>Finally, the code below shows the target function for all processes. The main
logic is defined in <code class="docutils literal notranslate"><span class="pre">run_master</span></code>. The workers passively waiting for
commands from the master, and hence simply runs <code class="docutils literal notranslate"><span class="pre">init_rpc</span></code> and <code class="docutils literal notranslate"><span class="pre">shutdown</span></code>,
where the <code class="docutils literal notranslate"><span class="pre">shutdown</span></code> by default will block until all RPC participants finish.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</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">num_split</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="n">options</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">TensorPipeRpcBackendOptions</span><span class="p">(</span><span class="n">num_worker_threads</span><span class="o">=</span><span class="mi">128</span><span class="p">)</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="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
<span class="s2">"master"</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">rpc_backend_options</span><span class="o">=</span><span class="n">options</span>
<span class="p">)</span>
<span class="n">run_master</span><span class="p">(</span><span class="n">num_split</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="sa">f</span><span class="s2">"worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">"</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">rpc_backend_options</span><span class="o">=</span><span class="n">options</span>
<span class="p">)</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">3</span>
<span class="k">for</span> <span class="n">num_split</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">]:</span>
<span class="n">tik</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</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">world_size</span><span class="p">,</span> <span class="n">num_split</span><span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">tok</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"number of splits = </span><span class="si">{</span><span class="n">num_split</span><span class="si">}</span><span class="s2">, execution time = </span><span class="si">{</span><span class="n">tok</span> <span class="o">-</span> <span class="n">tik</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
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<ul>
<li><a class="reference internal" href="#">Distributed Pipeline Parallelism Using RPC</a><ul>
<li><a class="reference internal" href="#basics">Basics</a></li>
<li><a class="reference internal" href="#step-1-partition-resnet50-model">Step 1: Partition ResNet50 Model</a></li>
<li><a class="reference internal" href="#step-2-stitch-resnet50-model-shards-into-one-module">Step 2: Stitch ResNet50 Model Shards Into One Module</a></li>
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