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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="pipeline-parallelism">
<span id="id1"></span><h1>Pipeline Parallelism<a class="headerlink" href="#pipeline-parallelism" title="Permalink to this headline">¶</a></h1>
<p>Pipeline parallelism was original introduced in the
<a class="reference external" href="https://arxiv.org/abs/1811.06965">Gpipe</a> paper and is an efficient
technique to train large models on multiple GPUs.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Pipeline Parallelism is experimental and subject to change.</p>
</div>
<div class="section" id="model-parallelism-using-multiple-gpus">
<h2>Model Parallelism using multiple GPUs<a class="headerlink" href="#model-parallelism-using-multiple-gpus" title="Permalink to this headline">¶</a></h2>
<p>Typically for large models which don’t fit on a single GPU, model parallelism
is employed where certain parts of the model are placed on different GPUs.
Although, if this is done naively for sequential models, the training process
suffers from GPU under utilization since only one GPU is active at one time as
shown in the figure below:</p>
<div class="figure align-default" id="id2">
<img alt="_images/no_pipe.png" src="_images/no_pipe.png" />
<p class="caption"><span class="caption-text">The figure represents a model with 4 layers placed on 4 different GPUs
(vertical axis). The horizontal axis represents training this model through
time demonstrating that only 1 GPU is utilized at a time
(<a class="reference external" href="https://arxiv.org/abs/1811.06965">image source</a>).</span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div>
<div class="section" id="pipelined-execution">
<h2>Pipelined Execution<a class="headerlink" href="#pipelined-execution" title="Permalink to this headline">¶</a></h2>
<p>To alleviate this problem, pipeline parallelism splits the input minibatch into
multiple microbatches and pipelines the execution of these microbatches across
multiple GPUs. This is outlined in the figure below:</p>
<div class="figure align-default" id="id3">
<img alt="_images/pipe.png" src="_images/pipe.png" />
<p class="caption"><span class="caption-text">The figure represents a model with 4 layers placed on 4 different GPUs
(vertical axis). The horizontal axis represents training this model through
time demonstrating that the GPUs are utilized much more efficiently.
However, there still exists a bubble (as demonstrated in the figure) where
certain GPUs are not utilized.
(<a class="reference external" href="https://arxiv.org/abs/1811.06965">image source</a>).</span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div>
<div class="section" id="pipe-apis-in-pytorch">
<h2>Pipe APIs in PyTorch<a class="headerlink" href="#pipe-apis-in-pytorch" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="torch.distributed.pipeline.sync.Pipe">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.pipeline.sync.</span></code><code class="sig-name descname"><span class="pre">Pipe</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'except_last'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deferred_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/pipe.html#Pipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.Pipe" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps an arbitrary <a class="reference internal" href="generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Sequential</span></code></a> module
to train on using synchronous pipeline parallelism. If the module requires
lots of memory and doesn’t fit on a single GPU, pipeline parallelism is a
useful technique to employ for training.</p>
<p>The implementation is based on the <a class="reference external" href="https://arxiv.org/abs/2004.09910">torchgpipe</a> paper.</p>
<p>Pipe combines pipeline parallelism with checkpointing to reduce peak
memory required to train while minimizing device under-utilization.</p>
<p>You should place all the modules on the appropriate devices and wrap them
into an <a class="reference internal" href="generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Sequential</span></code></a> module defining the
desired order of execution. If a module does not contain any
parameters/buffers, it is assumed this module should be executed on CPU
and appropriate input tensors to the module are moved to CPU before
execution. This behavior can be overridden by the <code class="xref py py-class docutils literal notranslate"><span class="pre">WithDevice</span></code>
wrapper which can be used to explicitly specify which device a module
should run on.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<a class="reference internal" href="generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Sequential</span></code></a>) – sequential module to be parallelized using pipelining. Each module
in the sequence has to have all of its parameters on a single
device. Each module in the sequence has to either be an nn.Module
or <a class="reference internal" href="generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Sequential</span></code></a> (to combine multiple
sequential modules on a single device)</p></li>
<li><p><strong>chunks</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of micro-batches (default: <code class="docutils literal notranslate"><span class="pre">1</span></code>)</p></li>
<li><p><strong>checkpoint</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – when to enable checkpointing, one of <code class="docutils literal notranslate"><span class="pre">'always'</span></code>,
<code class="docutils literal notranslate"><span class="pre">'except_last'</span></code>, or <code class="docutils literal notranslate"><span class="pre">'never'</span></code> (default: <code class="docutils literal notranslate"><span class="pre">'except_last'</span></code>).
<code class="docutils literal notranslate"><span class="pre">'never'</span></code> disables checkpointing completely, <code class="docutils literal notranslate"><span class="pre">'except_last'</span></code>
enables checkpointing for all micro-batches except the last one
and <code class="docutils literal notranslate"><span class="pre">'always'</span></code> enables checkpointing for all micro-batches.</p></li>
<li><p><strong>deferred_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether to use deferred <code class="docutils literal notranslate"><span class="pre">BatchNorm</span></code> moving statistics (default:
<a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.10)"><code class="xref py py-data docutils literal notranslate"><span class="pre">False</span></code></a>). If set to <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.10)"><code class="xref py py-data docutils literal notranslate"><span class="pre">True</span></code></a>, we track statistics across
multiple micro-batches to update the running statistics per
mini-batch.</p></li>
</ul>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.10)"><strong>TypeError</strong></a> – the module is not a <a class="reference internal" href="generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Sequential</span></code></a>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – invalid arguments</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><p>Pipeline of two FC layers across GPUs 0 and 1.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Need to initialize RPC framework first.</span>
<span class="gp">>>> </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="gp">>>> </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="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s1">'worker'</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Build pipe.</span>
<span class="gp">>>> </span><span class="n">fc1</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">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fc2</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">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">model</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">fc1</span><span class="p">,</span> <span class="n">fc2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">Pipe</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output_rref</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You can wrap a <a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> model with
<a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a> only when the
checkpoint parameter of <a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> is <code class="docutils literal notranslate"><span class="pre">'never'</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> only supports intra-node pipelining currently, but
will be expanded to support inter-node pipelining in the future.
The forward function returns an <a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef" title="torch.distributed.rpc.RRef"><code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code></a>
to allow for inter-node pipelining in the future, where the output
might be on a remote host. For intra-node pipelinining you can use
<a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef.local_value" title="torch.distributed.rpc.RRef.local_value"><code class="xref py py-meth docutils literal notranslate"><span class="pre">local_value()</span></code></a> to retrieve the
output locally.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> is experimental and subject to change.</p>
</div>
<dl class="py method">
<dt id="torch.distributed.pipeline.sync.Pipe.forward">
<code class="sig-name descname"><span class="pre">forward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/pipe.html#Pipe.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.Pipe.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Processes a single input mini-batch through the pipe and returns an
<a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef" title="torch.distributed.rpc.RRef"><code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code></a> pointing to the output.
<a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> is a fairly transparent module wrapper. It doesn’t
modify the input and output signature of the underlying module. But
there’s type restriction. Input and output have to contain at least one
tensor. This restriction is applied at partition boundaries too.</p>
<p>The sequence of inputs are fed into the first stage of the pipeline as
<code class="docutils literal notranslate"><span class="pre">*inputs</span></code>. As a result the positional args for this function should
match the positional args for the first stage of the pipeline. The same
condition applies for output of one stage of the pipeline which is the
input for the next stage.</p>
<p>The input tensor is split into multiple micro-batches based on the
<code class="docutils literal notranslate"><span class="pre">chunks</span></code> parameter used to initialize <a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a>. The batch size
is assumed to be the first dimension of the tensor and if the batch
size is less than <code class="docutils literal notranslate"><span class="pre">chunks</span></code>, the number of micro-batches is equal to
the batch size.</p>
<p>Only tensors are split into multiple micro-batches, non-Tensor inputs
are just replicated as-is in each micro-batch. For non-Tensor outputs
in the last stage of the pipeline, they are aggregated as a <code class="docutils literal notranslate"><span class="pre">List</span></code>
and returned the user. For example, if you have 2 micro-batches
returning the integer 5, the user would receive the consolidated
output of <cite>[5, 5]</cite></p>
<p>All the input tensors need to be on the same device as the first
partition of the pipeline.</p>
<p>If a tensor is wrapped with the <code class="xref py py-class docutils literal notranslate"><span class="pre">NoChunk</span></code> wrapper, the tensor
is not split across micro-batches and is replicated as-is similar to
non-tensors.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>inputs</strong> – input mini-batch</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><a class="reference internal" href="rpc.html#torch.distributed.rpc.RRef" title="torch.distributed.rpc.RRef"><code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code></a> to the output of the mini-batch</p>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.10)"><strong>TypeError</strong></a> – input doesn’t contain at least one tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<div class="section" id="skip-connections">
<h3>Skip connections<a class="headerlink" href="#skip-connections" title="Permalink to this headline">¶</a></h3>
<p>Certain models like ResNeXt are not completely sequential and have skip
connections between layers. Naively implementing as part of pipeling
parallelism would imply that we need to copy outputs for certain layers through
multiple GPUs till we eventually reach the GPU where the layer for the skip
connection resides. To avoid this copy overhead, we provide APIs below to stash
and pop Tensors in different layers of the model.</p>
<dl class="py function">
<dt id="torch.distributed.pipeline.sync.skip.skippable.skippable">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.pipeline.sync.skip.skippable.</span></code><code class="sig-name descname"><span class="pre">skippable</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/skip/skippable.html#skippable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.skip.skippable.skippable" title="Permalink to this definition">¶</a></dt>
<dd><p>The decorator to define a <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code></a> with skip
connections. Decorated modules are called “skippable”. This functionality
works perfectly fine even when the module is not wrapped by
<a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a>.</p>
<p>Each skip tensor is managed by its name. Before manipulating skip tensors,
a skippable module must statically declare the names for skip tensors by
<cite>stash</cite> and/or <cite>pop</cite> parameters. Skip tensors with pre-declared name can be
stashed by <code class="docutils literal notranslate"><span class="pre">yield</span> <span class="pre">stash(name,</span> <span class="pre">tensor)</span></code> or popped by <code class="docutils literal notranslate"><span class="pre">tensor</span> <span class="pre">=</span> <span class="pre">yield</span>
<span class="pre">pop(name)</span></code>.</p>
<p>Here is an example with three layers. A skip tensor named “1to3” is stashed
and popped at the first and last layer, respectively:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@skippable</span><span class="p">(</span><span class="n">stash</span><span class="o">=</span><span class="p">[</span><span class="s1">'1to3'</span><span class="p">])</span>
<span class="k">class</span> <span class="nc">Layer1</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="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">yield</span> <span class="n">stash</span><span class="p">(</span><span class="s1">'1to3'</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f1</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Layer2</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="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="n">f2</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nd">@skippable</span><span class="p">(</span><span class="n">pop</span><span class="o">=</span><span class="p">[</span><span class="s1">'1to3'</span><span class="p">])</span>
<span class="k">class</span> <span class="nc">Layer3</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="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">skip_1to3</span> <span class="o">=</span> <span class="k">yield</span> <span class="n">pop</span><span class="p">(</span><span class="s1">'1to3'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f3</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="o">+</span> <span class="n">skip_1to3</span>
<span class="n">model</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">Layer1</span><span class="p">(),</span> <span class="n">Layer2</span><span class="p">(),</span> <span class="n">Layer3</span><span class="p">())</span>
</pre></div>
</div>
<p>One skippable module can stash or pop multiple skip tensors:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@skippable</span><span class="p">(</span><span class="n">stash</span><span class="o">=</span><span class="p">[</span><span class="s1">'alice'</span><span class="p">,</span> <span class="s1">'bob'</span><span class="p">],</span> <span class="n">pop</span><span class="o">=</span><span class="p">[</span><span class="s1">'carol'</span><span class="p">])</span>
<span class="k">class</span> <span class="nc">StashStashPop</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="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">yield</span> <span class="n">stash</span><span class="p">(</span><span class="s1">'alice'</span><span class="p">,</span> <span class="n">f_alice</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
<span class="k">yield</span> <span class="n">stash</span><span class="p">(</span><span class="s1">'bob'</span><span class="p">,</span> <span class="n">f_bob</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
<span class="n">carol</span> <span class="o">=</span> <span class="k">yield</span> <span class="n">pop</span><span class="p">(</span><span class="s1">'carol'</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">+</span> <span class="n">carol</span>
</pre></div>
</div>
<p>Every skip tensor must be associated with exactly one pair of <cite>stash</cite> and
<cite>pop</cite>. <a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> checks this
restriction automatically when wrapping a module. You can also check the
restriction by <a class="reference internal" href="#torch.distributed.pipeline.sync.skip.skippable.verify_skippables" title="torch.distributed.pipeline.sync.skip.skippable.verify_skippables"><code class="xref py py-func docutils literal notranslate"><span class="pre">verify_skippables()</span></code></a>
without <a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a>.</p>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.pipeline.sync.skip.skippable.stash">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.pipeline.sync.skip.skippable.</span></code><code class="sig-name descname"><span class="pre">stash</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/skip/skippable.html#stash"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.skip.skippable.stash" title="Permalink to this definition">¶</a></dt>
<dd><p>The command to stash a skip tensor.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">yield</span> <span class="n">stash</span><span class="p">(</span><span class="s1">'name'</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – name of skip tensor</p></li>
<li><p><strong>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><em>None</em></a>) – tensor to pass to the skip connection</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.pipeline.sync.skip.skippable.pop">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.pipeline.sync.skip.skippable.</span></code><code class="sig-name descname"><span class="pre">pop</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/skip/skippable.html#pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.skip.skippable.pop" title="Permalink to this definition">¶</a></dt>
<dd><p>The command to pop a skip tensor.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">skip</span> <span class="o">=</span> <span class="k">yield</span> <span class="n">pop</span><span class="p">(</span><span class="s1">'name'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="o">+</span> <span class="n">skip</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – name of skip tensor</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the skip tensor previously stashed by another layer under the same name</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.pipeline.sync.skip.skippable.verify_skippables">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.pipeline.sync.skip.skippable.</span></code><code class="sig-name descname"><span class="pre">verify_skippables</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipeline/sync/skip/skippable.html#verify_skippables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipeline.sync.skip.skippable.verify_skippables" title="Permalink to this definition">¶</a></dt>
<dd><p>Verifies if the underlying skippable modules satisfy integrity.</p>
<p>Every skip tensor must have only one pair of <cite>stash</cite> and <cite>pop</cite>. If there
are one or more unmatched pairs, it will raise <a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.10)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">TypeError</span></code></a> with the
detailed messages.</p>
<p>Here are a few failure cases. <a class="reference internal" href="#torch.distributed.pipeline.sync.skip.skippable.verify_skippables" title="torch.distributed.pipeline.sync.skip.skippable.verify_skippables"><code class="xref py py-func docutils literal notranslate"><span class="pre">verify_skippables()</span></code></a> will report failure
for these cases:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Layer1 stashes "1to3".</span>
<span class="c1"># Layer3 pops "1to3".</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">Layer1</span><span class="p">(),</span> <span class="n">Layer2</span><span class="p">())</span>
<span class="c1"># └──── ?</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">Layer2</span><span class="p">(),</span> <span class="n">Layer3</span><span class="p">())</span>
<span class="c1"># ? ────┘</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">Layer1</span><span class="p">(),</span> <span class="n">Layer2</span><span class="p">(),</span> <span class="n">Layer3</span><span class="p">(),</span> <span class="n">Layer3</span><span class="p">())</span>
<span class="c1"># └───────────────────┘ ^^^^^^</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">Layer1</span><span class="p">(),</span> <span class="n">Layer1</span><span class="p">(),</span> <span class="n">Layer2</span><span class="p">(),</span> <span class="n">Layer3</span><span class="p">())</span>
<span class="c1"># ^^^^^^ └───────────────────┘</span>
</pre></div>
</div>
<p>To use the same name for multiple skip tensors, they must be isolated by
different namespaces. See <code class="xref py py-meth docutils literal notranslate"><span class="pre">isolate()</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.10)"><strong>TypeError</strong></a> – one or more pairs of <cite>stash</cite> and <cite>pop</cite> are not matched.</p>
</dd>
</dl>
</dd></dl>
</div>
</div>
<div class="section" id="tutorials">
<h2>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h2>
<p>The following tutorials give a good overview of how to use the
<a class="reference internal" href="#torch.distributed.pipeline.sync.Pipe" title="torch.distributed.pipeline.sync.Pipe"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipe</span></code></a> API to train your models with the
rest of the components that PyTorch provides:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/pipeline_tutorial.html">Training Transformer models using Pipeline Parallelism</a></p></li>
<li><p><a class="reference external" href="https://pytorch.org/tutorials/advanced/ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></p></li>
</ul>
</div>
<div class="section" id="acknowledgements">
<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">¶</a></h2>
<p>The implementation for pipeline parallelism is based on <a class="reference external" href="https://github.com/facebookresearch/fairscale/tree/master/fairscale/nn/pipe">fairscale’s pipe implementation</a> and
<a class="reference external" href="https://github.com/kakaobrain/torchgpipe">torchgpipe</a>. We would like to
thank both teams for their contributions and guidance towards bringing pipeline
parallelism into PyTorch.</p>
</div>
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