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<div class="section" id="features-for-large-scale-deployments">
<h1>Features for large-scale deployments<a class="headerlink" href="#features-for-large-scale-deployments" title="Permalink to this headline">¶</a></h1>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#fleet-wide-operator-profiling" id="id1">Fleet-wide operator profiling</a></p></li>
<li><p><a class="reference internal" href="#api-usage-logging" id="id2">API usage logging</a></p></li>
<li><p><a class="reference internal" href="#attaching-metadata-to-saved-torchscript-models" id="id3">Attaching metadata to saved TorchScript models</a></p></li>
<li><p><a class="reference internal" href="#build-environment-considerations" id="id4">Build environment considerations</a></p></li>
<li><p><a class="reference internal" href="#common-extension-points" id="id5">Common extension points</a></p></li>
</ul>
</div>
<p>This note talks about several extension points and tricks that might be useful
when running PyTorch within a larger system or operating multiple systems using
PyTorch in a larger organization.</p>
<p>It doesn’t cover topics of deploying models to production. Check
<a class="reference internal" href="../jit.html#module-torch.jit" title="torch.jit"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.jit</span></code></a> or one of the corresponding tutorials.</p>
<p>The note assumes that you either build PyTorch from source in your
organization or have an ability to statically link additional code to be loaded
when PyTorch is used. Therefore, many of the hooks are exposed as C++ APIs that
can be triggered once in a centralized place, e.g. in static initialization
code.</p>
<div class="section" id="fleet-wide-operator-profiling">
<h2><a class="toc-backref" href="#id1">Fleet-wide operator profiling</a><a class="headerlink" href="#fleet-wide-operator-profiling" title="Permalink to this headline">¶</a></h2>
<p>PyTorch comes with <code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.autograd.profiler</span></code> capable of measuring time
taken by individual operators on demand. One can use the same mechanism to do
“always ON” measurements for any process running PyTorch. It might be useful for
gathering information about PyTorch workloads running in a given process or
across the entire set of machines.</p>
<p>New callbacks for any operator invocation can be added with
<code class="docutils literal notranslate"><span class="pre">torch::autograd::profiler::pushCallback</span></code>. Hooks will be called with
<code class="docutils literal notranslate"><span class="pre">torch::autograd::profiler::RecordFunction</span></code> struct that describes invocation
context (e.g. <cite>name</cite>). If enabled, <code class="docutils literal notranslate"><span class="pre">RecordFunction::inputs()</span></code> contains arguments
of the function represented as <code class="docutils literal notranslate"><span class="pre">torch::IValue</span></code> variant type. Note, that inputs
logging is relatively expensive and thus has to be enabled explicitly.</p>
<p>The operator callbacks also have access to <code class="docutils literal notranslate"><span class="pre">at::getThreadLocalDebugInfo()</span></code>
interface that returns a pointer to the struct holding the debug information. This
debug information is supposed to be set earlier with the corresponding
<code class="docutils literal notranslate"><span class="pre">at::setThreadLocalDebugInfo(debug_info)</span></code> call. Debug information is propagated
through the forward (including async <code class="docutils literal notranslate"><span class="pre">fork</span></code> tasks) and backward passes and can
be useful for passing some extra information about execution environment
(e.g. model id) from the higher layers of the application down to the operator
callbacks.</p>
<p>Invoking callbacks adds some overhead, so usually it’s useful to just randomly
sample operator invocations. This can be enabled on per-callback basis with a
global sampling rate specified by
<cite>torch::autograd::profiler::setSamplingProbability</cite>.</p>
<p>Note, that <code class="docutils literal notranslate"><span class="pre">pushCallback</span></code> and <code class="docutils literal notranslate"><span class="pre">setSamplingProbability</span></code> are not thread-safe
and can be called only when no PyTorch operator is running. Usually, it’s a good
idea to call them once during initialization.</p>
<p>Here’s an example:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Called somewhere in the program beginning</span>
<span class="kt">void</span> <span class="nf">init</span><span class="p">()</span> <span class="p">{</span>
<span class="c1">// Sample one in a hundred operator runs randomly</span>
<span class="n">torch</span><span class="o">::</span><span class="n">autograd</span><span class="o">::</span><span class="n">setSamplingProbability</span><span class="p">(</span><span class="mf">0.01</span><span class="p">);</span>
<span class="n">pushCallback</span><span class="p">(</span>
<span class="o">&</span><span class="n">onFunctionEnter</span><span class="p">,</span>
<span class="o">&</span><span class="n">onFunctionExit</span><span class="p">,</span>
<span class="cm">/* needs_inputs */</span> <span class="nb">true</span><span class="p">,</span>
<span class="cm">/* sampled */</span> <span class="nb">true</span>
<span class="p">);</span>
<span class="p">}</span>
<span class="kt">void</span> <span class="nf">onFunctionEnter</span><span class="p">(</span><span class="k">const</span> <span class="n">RecordFunction</span><span class="o">&</span> <span class="n">fn</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"Before function "</span> <span class="o"><<</span> <span class="n">fn</span><span class="p">.</span><span class="n">name</span><span class="p">()</span>
<span class="o"><<</span> <span class="s">" with "</span> <span class="o"><<</span> <span class="n">fn</span><span class="p">.</span><span class="n">inputs</span><span class="p">().</span><span class="n">size</span><span class="p">()</span> <span class="o"><<</span> <span class="s">" inputs"</span> <span class="o"><<</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="p">}</span>
<span class="kt">void</span> <span class="nf">onFunctionExit</span><span class="p">(</span><span class="k">const</span> <span class="n">RecordFunction</span><span class="o">&</span> <span class="n">fn</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"After function "</span> <span class="o"><<</span> <span class="n">fn</span><span class="p">.</span><span class="n">name</span><span class="p">();</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="api-usage-logging">
<h2><a class="toc-backref" href="#id2">API usage logging</a><a class="headerlink" href="#api-usage-logging" title="Permalink to this headline">¶</a></h2>
<p>When running in a broader ecosystem, for example in managed job scheduler, it’s
often useful to track which binaries invoke particular PyTorch APIs. There
exists simple instrumentation injected at several important API points that
triggers a given callback. Because usually PyTorch is invoked in one-off python
scripts, the callback fires only once for a given process for each of the APIs.</p>
<p><code class="docutils literal notranslate"><span class="pre">c10::SetAPIUsageHandler</span></code> can be used to register API usage instrumentation
handler. Passed argument is going to be an “api key” identifying used point, for
example <code class="docutils literal notranslate"><span class="pre">python.import</span></code> for PyTorch extension import or
<code class="docutils literal notranslate"><span class="pre">torch.script.compile</span></code> if TorchScript compilation was triggered.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">SetAPIUsageLogger</span><span class="p">([](</span><span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&</span> <span class="n">event_name</span><span class="p">)</span> <span class="p">{</span>
<span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o"><<</span> <span class="s">"API was used: "</span> <span class="o"><<</span> <span class="n">event_name</span> <span class="o"><<</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="p">});</span>
</pre></div>
</div>
<p>Note for developers: new API trigger points can be added in code with
<code class="docutils literal notranslate"><span class="pre">C10_LOG_API_USAGE_ONCE("my_api")</span></code> in C++ or
<code class="docutils literal notranslate"><span class="pre">torch._C._log_api_usage_once("my.api")</span></code> in Python.</p>
</div>
<div class="section" id="attaching-metadata-to-saved-torchscript-models">
<h2><a class="toc-backref" href="#id3">Attaching metadata to saved TorchScript models</a><a class="headerlink" href="#attaching-metadata-to-saved-torchscript-models" title="Permalink to this headline">¶</a></h2>
<p>TorchScript modules can be saved as an archive file that bundles serialized
parameters and module code as TorchScript (see <a class="reference internal" href="../jit.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.jit.save()</span></code></a>). It’s
often convenient to bundle additional information together with the model, for
example, description of model producer or auxiliary artifacts.</p>
<p>It can be achieved by passing the <code class="docutils literal notranslate"><span class="pre">_extra_files</span></code> argument to
<a class="reference internal" href="../jit.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.jit.save()</span></code></a> and <code class="docutils literal notranslate"><span class="pre">torch::jit::load</span></code> to store and retrieve
arbitrary binary blobs during saving process. Since TorchScript files are
regular ZIP archives, extra information gets stored as regular files inside
archive’s <code class="docutils literal notranslate"><span class="pre">extra/</span></code> directory.</p>
<p>There’s also a global hook allowing to attach extra files to any TorchScript
archive produced in the current process. It might be useful to tag models with
producer metadata, akin to JPEG metadata produced by digital cameras. Example
usage might look like:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">SetExportModuleExtraFilesHook</span><span class="p">([](</span><span class="k">const</span> <span class="n">Module</span><span class="o">&</span><span class="p">)</span> <span class="p">{</span>
<span class="n">ExtraFilesMap</span> <span class="n">files</span><span class="p">;</span>
<span class="n">files</span><span class="p">[</span><span class="s">"producer_info.json"</span><span class="p">]</span> <span class="o">=</span> <span class="s">"{</span><span class="se">\"</span><span class="s">user</span><span class="se">\"</span><span class="s">: </span><span class="se">\"</span><span class="s">"</span> <span class="o">+</span> <span class="n">getenv</span><span class="p">(</span><span class="s">"USER"</span><span class="p">)</span> <span class="o">+</span> <span class="s">"</span><span class="se">\"</span><span class="s">}"</span><span class="p">;</span>
<span class="k">return</span> <span class="n">files</span><span class="p">;</span>
<span class="p">});</span>
</pre></div>
</div>
</div>
<div class="section" id="build-environment-considerations">
<h2><a class="toc-backref" href="#id4">Build environment considerations</a><a class="headerlink" href="#build-environment-considerations" title="Permalink to this headline">¶</a></h2>
<p>TorchScript’s compilation needs to have access to the original python files as
it uses python’s <code class="docutils literal notranslate"><span class="pre">inspect.getsource</span></code> call. In certain production environments
it might require explicitly deploying <code class="docutils literal notranslate"><span class="pre">.py</span></code> files along with precompiled
<code class="docutils literal notranslate"><span class="pre">.pyc</span></code>.</p>
</div>
<div class="section" id="common-extension-points">
<h2><a class="toc-backref" href="#id5">Common extension points</a><a class="headerlink" href="#common-extension-points" title="Permalink to this headline">¶</a></h2>
<p>PyTorch APIs are generally loosely coupled and it’s easy to replace a component
with specialized version. Common extension points include:</p>
<ul class="simple">
<li><p>Custom operators implemented in C++ - see <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html">tutorial for more details</a>.</p></li>
<li><p>Custom data reading can be often integrated directly by invoking corresponding python library. Existing functionality of <a class="reference internal" href="../data.html#module-torch.utils.data" title="torch.utils.data"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.utils.data</span></code></a> can be utilized by extending <a class="reference internal" href="../data.html#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> or <a class="reference internal" href="../data.html#torch.utils.data.IterableDataset" title="torch.utils.data.IterableDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">IterableDataset</span></code></a>.</p></li>
</ul>
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<li><a class="reference internal" href="#fleet-wide-operator-profiling">Fleet-wide operator profiling</a></li>
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<li><a class="reference internal" href="#attaching-metadata-to-saved-torchscript-models">Attaching metadata to saved TorchScript models</a></li>
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