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<div class="section" id="generic-join-context-manager">
<h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-context-manager" title="Permalink to this headline">¶</a></h1>
<p>The generic join context manager facilitates distributed training on uneven
inputs. This page outlines the API of the relevant classes: <code class="xref py py-class docutils literal notranslate"><span class="pre">Join</span></code>,
<code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code>, and <code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code>. For a tutorial, see
<a class="reference external" href="https://pytorch.org/tutorials/advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a>.</p>
<dl class="py class">
<dt id="torch.distributed.algorithms.Join">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></code><code class="sig-name descname"><span class="pre">Join</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join" title="Permalink to this definition">¶</a></dt>
<dd><p>This class defines the generic join context manager, which allows custom
hooks to be called after a process joins. These hooks should shadow the
collective communications of non-joined processes to prevent hanging and
erroring and to ensure algorithmic correctness. Refer to <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a>
for details about the hook definition.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The context manager requires each participating <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> to
call the method <a class="reference internal" href="#torch.distributed.algorithms.Join.notify_join_context" title="torch.distributed.algorithms.Join.notify_join_context"><code class="xref py py-meth docutils literal notranslate"><span class="pre">notify_join_context()</span></code></a> before its own per-
iteration collective communications to ensure correctness.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The context manager requires that all <code class="docutils literal notranslate"><span class="pre">process_group</span></code> attributes in
the <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> objects are the same. If there are multiple
<a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> objects, then the <code class="docutils literal notranslate"><span class="pre">device</span></code> of the first is used.
The process group and device information is used for checking for non-
joined processes and for notifying processes to throw an exception if
<code class="docutils literal notranslate"><span class="pre">throw_on_early_termination</span></code> is enabled, both of which using an all-
reduce.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>joinables</strong> (<em>List</em><em>[</em><a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><em>Joinable</em></a><em>]</em>) – a list of the participating
<a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> s; their hooks are iterated over in the given
order.</p></li>
<li><p><strong>enable</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – a flag enabling uneven input detection; setting to
<code class="docutils literal notranslate"><span class="pre">False</span></code> disables the context manager’s functionality and should
only be set when the user knows the inputs will not be uneven
(default: <code class="docutils literal notranslate"><span class="pre">True</span></code>).</p></li>
<li><p><strong>throw_on_early_termination</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – a flag controlling whether to throw an
exception upon detecting uneven inputs (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">os</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.nn.parallel.DistributedDataParallel</span> <span class="k">as</span> <span class="nn">DDP</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.optim.ZeroRedundancyOptimizer</span> <span class="k">as</span> <span class="nn">ZeRO</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.algorithms.join</span> <span class="kn">import</span> <span class="n">Join</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># On each spawned worker</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="s2">"nccl"</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="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">model</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">torch</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">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">),</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="n">rank</span><span class="p">])</span>
<span class="gp">>>> </span> <span class="n">optim</span> <span class="o">=</span> <span class="n">ZeRO</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">torch</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="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="c1"># Rank 1 gets one more input than rank 0</span>
<span class="gp">>>> </span> <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</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">10</span> <span class="o">+</span> <span class="n">rank</span><span class="p">)]</span>
<span class="gp">>>> </span> <span class="k">with</span> <span class="n">Join</span><span class="p">([</span><span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="p">]):</span>
<span class="gp">>>> </span> <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="c1"># All ranks reach here without hanging/erroring</span>
</pre></div>
</div>
<dl class="py method">
<dt id="torch.distributed.algorithms.Join.notify_join_context">
<em class="property"><span class="pre">static</span> </em><code class="sig-name descname"><span class="pre">notify_join_context</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join.notify_join_context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join.notify_join_context" title="Permalink to this definition">¶</a></dt>
<dd><p>Notifies the join context manager that the calling process has not yet
joined; then, if <code class="docutils literal notranslate"><span class="pre">throw_on_early_termination=True</span></code>, checks if uneven
inputs have been detected (i.e. if one process has already joined) and
throws an exception if so.</p>
<p>This method should be called from a <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> object before
its per-iteration collective communications. For example, this should
be called at the beginning of the forward pass in
<code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>.</p>
<p>Only the first <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> object passed into the context
manager performs the collective communications in this method, and
for the others, this method is vacuous.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>joinable</strong> (<a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><em>Joinable</em></a>) – the <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> object calling this
method.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An async work handle for the all-reduce meant to notify the context
manager that the process has not yet joined if <code class="docutils literal notranslate"><span class="pre">joinable</span></code> is the
first one passed into the context manager; <code class="docutils literal notranslate"><span class="pre">None</span></code> otherwise.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.algorithms.Joinable">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></code><code class="sig-name descname"><span class="pre">Joinable</span></code><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable" title="Permalink to this definition">¶</a></dt>
<dd><p>This defines an abstract base class for joinable classes. A joinable class
(inheriting from <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>) should implement <a class="reference internal" href="#torch.distributed.algorithms.Joinable.join_hook" title="torch.distributed.algorithms.Joinable.join_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_hook()</span></code></a>,
which returns a <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> instance, in addition to
<a class="reference internal" href="#torch.distributed.algorithms.Joinable.join_device" title="torch.distributed.algorithms.Joinable.join_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_device()</span></code></a> and <a class="reference internal" href="#torch.distributed.algorithms.Joinable.join_process_group" title="torch.distributed.algorithms.Joinable.join_process_group"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_process_group()</span></code></a> that return device and
process group information, respectively.</p>
<dl class="py method">
<dt id="torch.distributed.algorithms.Joinable.join_device">
<em class="property"><span class="pre">abstract</span> <span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">join_device</span></code><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_device" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the device from which to perform collective communications
needed by the join context manager implementation itself.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.algorithms.Joinable.join_hook">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">join_hook</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">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> instance for the given <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> containing any keyword arguments
to modify the behavior of the join hook at run time; all
<a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a> instances sharing the same join context
manager are forwarded the same value for <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.algorithms.Joinable.join_process_group">
<em class="property"><span class="pre">abstract</span> <span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">join_process_group</span></code><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_process_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the process group for the collective communications needed by
the join context manager itself.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.algorithms.JoinHook">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></code><code class="sig-name descname"><span class="pre">JoinHook</span></code><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook" title="Permalink to this definition">¶</a></dt>
<dd><p>This defines a join hook, which provides two entry points in the join
context manager: a main hook, which is called repeatedly while there exists
a non-joined process, and a post-hook, which is called once all processes
have joined.</p>
<p>To implement a join hook for the generic join context manager, define a
class that inherits from <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> and override <code class="docutils literal notranslate"><span class="pre">main_hook()</span></code> and
<code class="docutils literal notranslate"><span class="pre">post_hook()</span></code> as appropriate.</p>
<dl class="py method">
<dt id="torch.distributed.algorithms.JoinHook.main_hook">
<code class="sig-name descname"><span class="pre">main_hook</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.main_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.main_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>This hook is called repeatedly while there exists a non-joined process
to shadow collective communications in one training iteration (i.e. in
one forward pass, backward pass, and optimizer step).</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.algorithms.JoinHook.post_hook">
<code class="sig-name descname"><span class="pre">post_hook</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_joiner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.post_hook" title="Permalink to this definition">¶</a></dt>
<dd><p>This hook is called after all processes have joined. It is passed an
additional <code class="docutils literal notranslate"><span class="pre">bool</span></code> argument <code class="docutils literal notranslate"><span class="pre">is_last_joiner</span></code>, which indicates if the
rank is one of the last to join.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>is_last_joiner</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – <code class="docutils literal notranslate"><span class="pre">True</span></code> if the rank is one of the last to
join; <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
</dd>
</dl>
</dd></dl>
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