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<div class="section" id="module-torch.distributed.elastic.multiprocessing.errors">
<span id="error-propagation"></span><span id="elastic-errors-api"></span><h1>Error Propagation<a class="headerlink" href="#module-torch.distributed.elastic.multiprocessing.errors" title="Permalink to this heading">¶</a></h1>
<p>Each host in a distributed PyTorch job runs with a single TorchElastic agent,
and multiple workers (as children processes of the TorchElastic agent).
Since the workers are user-provided (your PyTorch script/job), TorchElastic
has a way to propagate errors on the trainers through the agent and up to the
scheduler, which ultimately informs the end-user about the state of the job
and applies any retry policies.</p>
<p>TorchElastic categorizes errors into 3 categories:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Category</p></th>
<th class="head"><p>Sub-Category</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td rowspan="2"><p>User Error</p></td>
<td><p>Input Error</p></td>
<td><p>invalid inputs to TorchElastic APIs (e.g. min > max nodes)</p></td>
</tr>
<tr class="row-odd"><td><p>Worker Failure</p></td>
<td><p>any failures on the worker child process</p></td>
</tr>
<tr class="row-even"><td><p>Platform Error</p></td>
<td><p>n/a</p></td>
<td><p>failures caused by the agent</p></td>
</tr>
<tr class="row-odd"><td><p>Infra Error</p></td>
<td><p>n/a</p></td>
<td><p>failures outside the domain of the agent and workers
(e.g. host failures)</p></td>
</tr>
</tbody>
</table>
<p>All errors other than “Worker Failure” are either raised canonically from the
agent process or implicitly or explicitly crash the agent process. So the
standard language (python) provided exception handling strategies apply.</p>
<p>Worker Failures are special because the exception/failure originates on a different
process from the agent so the error needs to be propagated inter-process
(e.g. the agent cannot simply <code class="docutils literal notranslate"><span class="pre">try-catch</span></code> an exception raised on the worker process).</p>
<p>TorchElastic agents use <a class="reference internal" href="multiprocessing.html#torch.distributed.elastic.multiprocessing.start_processes" title="torch.distributed.elastic.multiprocessing.start_processes"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.elastic.multiprocessing.start_processes()</span></code></a>
to launch the workers which has a simple file based inter-process error propagation
built-in.</p>
<p>Any function or binary entrypoint decorated with <a class="reference internal" href="#torch.distributed.elastic.multiprocessing.errors.record" title="torch.distributed.elastic.multiprocessing.errors.record"><code class="xref py py-func docutils literal notranslate"><span class="pre">record()</span></code></a>
will write uncaught exceptions (with the trace information) to a file specified by the
environment variable <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_ERROR_FILE</span></code>. The parent process (e.g. agent)
sets this env var on each child it launches, then aggregates the error files for all
children, and propagates the one with the <strong>smallest</strong> timestamp (e.g. the <strong>first</strong> error).</p>
<div class="section" id="methods-and-classes">
<h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.record">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_handler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.7.0/torch/distributed/elastic/multiprocessing/errors/__init__.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.record" title="Permalink to this definition">¶</a></dt>
<dd><p>Syntactic sugar to record errors/exceptions that happened in the decorated
function using the provided <code class="docutils literal notranslate"><span class="pre">error_handler</span></code>.</p>
<p>Using this decorator is equivalent to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">error_handler</span> <span class="o">=</span> <span class="n">get_error_handler</span><span class="p">()</span>
<span class="n">error_handler</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">foobar</span><span class="p">()</span>
<span class="k">except</span> <span class="n">ChildFailedError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">failure</span> <span class="o">=</span> <span class="n">e</span><span class="o">.</span><span class="n">get_first_failure</span><span class="p">()</span>
<span class="n">error_handler</span><span class="o">.</span><span class="n">dump_error_file</span><span class="p">(</span><span class="n">failure</span><span class="o">.</span><span class="n">error_file</span><span class="p">,</span> <span class="n">failure</span><span class="o">.</span><span class="n">exitcode</span><span class="p">)</span>
<span class="k">raise</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">error_handler</span><span class="o">.</span><span class="n">record_exception</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="k">raise</span>
</pre></div>
</div>
<div class="admonition important">
<p class="admonition-title">Important</p>
<p>use this decorator once per process at the top level method,
typically this is the main method.</p>
</div>
<p>Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@record</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="k">pass</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">main</span><span class="p">()</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a>[[…], <em>T</em>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ChildFailedError">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ChildFailedError</span></span><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">failures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ChildFailedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.7.0/torch/distributed/elastic/multiprocessing/errors/__init__.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ChildFailedError" title="Permalink to this definition">¶</a></dt>
<dd><p>Special exception type that can be raised from a function annotated with the
<code class="docutils literal notranslate"><span class="pre">@record</span></code> decorator to have the child process’ (root exception) propagate
up the stack as-is (e.g. without being wrapped in the parent’s traceback).</p>
<p>Useful in cases where the parent is a simple nanny process
and the child (worker) processes are actually doing meaningful compute.
In this case, errors typically occur on the child process as the parent
is not doing anything non-trivial, and child errors should be propagated
to the scheduler for accurate root cause diagnostics.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The propagation relies on error files rather than exception handling to
support both function and binary launches.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># process tree on a host (container)</span>
<span class="mi">0</span><span class="p">:</span> <span class="n">scheduler</span><span class="o">-</span><span class="n">init</span><span class="o">-</span><span class="n">process</span><span class="p">:</span>
<span class="o">|-</span> <span class="mi">1</span><span class="p">:</span> <span class="n">torchelastic_agent</span><span class="p">:</span>
<span class="o">|-</span> <span class="mi">2</span><span class="p">:</span> <span class="n">trainer_0</span> <span class="p">(</span><span class="n">ok</span><span class="p">)</span>
<span class="o">|-</span> <span class="mi">3</span><span class="p">:</span> <span class="n">trainer_1</span> <span class="p">(</span><span class="n">fail</span><span class="p">)</span> <span class="o">-></span> <span class="n">error</span><span class="o">.</span><span class="n">json</span>
<span class="o">|-</span> <span class="o">...</span>
<span class="o">|-</span> <span class="n">n</span><span class="o">+</span><span class="mi">2</span><span class="p">:</span> <span class="n">trainer_n</span> <span class="p">(</span><span class="n">ok</span><span class="p">)</span>
<span class="o">|-</span> <span class="n">n</span><span class="o">+</span><span class="mi">3</span><span class="p">:</span> <span class="n">other</span> <span class="n">processes</span>
<span class="o">|-</span> <span class="o">...</span>
</pre></div>
</div>
<p>In the example above, trainer 1’s failure (written into error.json) is
the root cause and should be reported to the scheduler’s init process.
The torchelastic agent raises a <code class="docutils literal notranslate"><span class="pre">ChildFailedError("trainer",</span> <span class="pre">{1:</span> <span class="pre">"trainer_1/error.json"})</span></code>
upon detecting trainer 1’s failure which would propagate the contents
of trainer 1’s error file to the scheduler’s init process.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ErrorHandler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ErrorHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors/error_handler.html#ErrorHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.7.0/torch/distributed/elastic/multiprocessing/errors/error_handler.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ErrorHandler" title="Permalink to this definition">¶</a></dt>
<dd><p>Write the provided exception object along with some other metadata about
the error in a structured way in JSON format to an error file specified by the
environment variable: <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_ERROR_FILE</span></code>. If this environment
variable is not set, then simply logs the contents of what would have been
written to the error file.</p>
<p>This handler may be subclassed to customize the handling of the error.
Subclasses should override <code class="docutils literal notranslate"><span class="pre">initialize()</span></code> and <code class="docutils literal notranslate"><span class="pre">record_exception()</span></code>.</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ProcessFailure">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ProcessFailure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exitcode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ProcessFailure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.7.0/torch/distributed/elastic/multiprocessing/errors/__init__.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ProcessFailure" title="Permalink to this definition">¶</a></dt>
<dd><p>Represent the failed process result. When the worker process fails, it may record failure root cause into the file.</p>
<p>Tries to read the failure timestamp from the provided <code class="docutils literal notranslate"><span class="pre">error_file</span></code>,
if the <code class="docutils literal notranslate"><span class="pre">error_file</span></code> does not exist, the timestamp is the current
timestamp (seconds since epoch).</p>
<p>The <code class="docutils literal notranslate"><span class="pre">message</span></code> field is a concise explanation of the failure. If
the error file exists then the message is obtained from the error file.
Otherwise one is generated based on the failure signature.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is assumed that the <code class="docutils literal notranslate"><span class="pre">error_file</span></code> is written by
<code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic.multiprocessing.errors.error_handler.ErrorHandler</span></code>.
Otherwise the behavior is undefined.</p>
</div>
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