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<div class="section" id="cpu-threading-and-torchscript-inference">
<span id="cpu-threading-torchscript-inference"></span><h1>CPU threading and TorchScript inference<a class="headerlink" href="#cpu-threading-and-torchscript-inference" title="Permalink to this headline">¶</a></h1>
<p>PyTorch allows using multiple CPU threads during TorchScript model inference.
The following figure shows different levels of parallelism one would find in a
typical application:</p>
<a class="reference internal image-reference" href="../_images/cpu_threading_torchscript_inference.svg"><img alt="../_images/cpu_threading_torchscript_inference.svg" src="../_images/cpu_threading_torchscript_inference.svg" width="75%" /></a>
<p>One or more inference threads execute a model’s forward pass on the given inputs.
Each inference thread invokes a JIT interpreter that executes the ops
of a model inline, one by one. A model can utilize a <code class="docutils literal notranslate"><span class="pre">fork</span></code> TorchScript
primitive to launch an asynchronous task. Forking several operations at once
results in a task that is executed in parallel. The <code class="docutils literal notranslate"><span class="pre">fork</span></code> operator returns a
<code class="docutils literal notranslate"><span class="pre">Future</span></code> object which can be used to synchronize on later, for example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">compute_z</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w_z</span><span class="p">)</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="c1"># launch compute_z asynchronously:</span>
<span class="n">fut</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_fork</span><span class="p">(</span><span class="n">compute_z</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="c1"># execute the next operation in parallel to compute_z:</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w_y</span><span class="p">)</span>
<span class="c1"># wait for the result of compute_z:</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_wait</span><span class="p">(</span><span class="n">fut</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
</pre></div>
</div>
<p>PyTorch uses a single thread pool for the inter-op parallelism, this thread pool
is shared by all inference tasks that are forked within the application process.</p>
<p>In addition to the inter-op parallelism, PyTorch can also utilize multiple threads
within the ops (<cite>intra-op parallelism</cite>). This can be useful in many cases,
including element-wise ops on large tensors, convolutions, GEMMs, embedding
lookups and others.</p>
<div class="section" id="build-options">
<h2>Build options<a class="headerlink" href="#build-options" title="Permalink to this headline">¶</a></h2>
<p>PyTorch uses an internal ATen library to implement ops. In addition to that,
PyTorch can also be built with support of external libraries, such as <a class="reference external" href="https://software.intel.com/en-us/mkl">MKL</a> and <a class="reference external" href="https://github.com/intel/mkl-dnn">MKL-DNN</a>,
to speed up computations on CPU.</p>
<p>ATen, MKL and MKL-DNN support intra-op parallelism and depend on the
following parallelization libraries to implement it:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://www.openmp.org/">OpenMP</a> - a standard (and a library, usually shipped with a compiler), widely used in external libraries;</p></li>
<li><p><a class="reference external" href="https://github.com/intel/tbb">TBB</a> - a newer parallelization library optimized for task-based parallelism and concurrent environments.</p></li>
</ul>
<p>OpenMP historically has been used by a large number of libraries. It is known
for a relative ease of use and support for loop-based parallelism and other primitives.</p>
<p>TBB is used to a lesser extent in external libraries, but, at the same time,
is optimized for the concurrent environments. PyTorch’s TBB backend guarantees that
there’s a separate, single, per-process intra-op thread pool used by all of the
ops running in the application.</p>
<p>Depending of the use case, one might find one or another parallelization
library a better choice in their application.</p>
<p>PyTorch allows selecting of the parallelization backend used by ATen and other
libraries at the build time with the following build options:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Library</p></th>
<th class="head"><p>Build Option</p></th>
<th class="head"><p>Values</p></th>
<th class="head"><p>Notes</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>ATen</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">ATEN_THREADING</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">OMP</span></code> (default), <code class="docutils literal notranslate"><span class="pre">TBB</span></code></p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>MKL</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">MKL_THREADING</span></code></p></td>
<td><p>(same)</p></td>
<td><p>To enable MKL use <code class="docutils literal notranslate"><span class="pre">BLAS=MKL</span></code></p></td>
</tr>
<tr class="row-even"><td><p>MKL-DNN</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">MKLDNN_CPU_RUNTIME</span></code></p></td>
<td><p>(same)</p></td>
<td><p>To enable MKL-DNN use <code class="docutils literal notranslate"><span class="pre">USE_MKLDNN=1</span></code></p></td>
</tr>
</tbody>
</table>
<p>It is recommended not to mix OpenMP and TBB within one build.</p>
<p>Any of the <code class="docutils literal notranslate"><span class="pre">TBB</span></code> values above require <code class="docutils literal notranslate"><span class="pre">USE_TBB=1</span></code> build setting (default: OFF).
A separate setting <code class="docutils literal notranslate"><span class="pre">USE_OPENMP=1</span></code> (default: ON) is required for OpenMP parallelism.</p>
</div>
<div class="section" id="runtime-api">
<h2>Runtime API<a class="headerlink" href="#runtime-api" title="Permalink to this headline">¶</a></h2>
<p>The following API is used to control thread settings:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Type of parallelism</p></th>
<th class="head"><p>Settings</p></th>
<th class="head"><p>Notes</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Inter-op parallelism</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">at::set_num_interop_threads</span></code>,
<code class="docutils literal notranslate"><span class="pre">at::get_num_interop_threads</span></code> (C++)</p>
<p><code class="docutils literal notranslate"><span class="pre">set_num_interop_threads</span></code>,
<code class="docutils literal notranslate"><span class="pre">get_num_interop_threads</span></code> (Python, <code class="xref py py-mod docutils literal notranslate"><span class="pre">torch</span></code> module)</p>
</td>
<td rowspan="2"><p>Default number of threads: number of CPU cores.</p></td>
</tr>
<tr class="row-odd"><td><p>Intra-op parallelism</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">at::set_num_threads</span></code>,
<code class="docutils literal notranslate"><span class="pre">at::get_num_threads</span></code> (C++)
<code class="docutils literal notranslate"><span class="pre">set_num_threads</span></code>,
<code class="docutils literal notranslate"><span class="pre">get_num_threads</span></code> (Python, <code class="xref py py-mod docutils literal notranslate"><span class="pre">torch</span></code> module)</p>
<p>Environment variables:
<code class="docutils literal notranslate"><span class="pre">OMP_NUM_THREADS</span></code> and <code class="docutils literal notranslate"><span class="pre">MKL_NUM_THREADS</span></code></p>
</td>
</tr>
</tbody>
</table>
<p>For the intra-op parallelism settings, <code class="docutils literal notranslate"><span class="pre">at::set_num_threads</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.set_num_threads</span></code> always take precedence
over environment variables, <code class="docutils literal notranslate"><span class="pre">MKL_NUM_THREADS</span></code> variable takes precedence over <code class="docutils literal notranslate"><span class="pre">OMP_NUM_THREADS</span></code>.</p>
</div>
<div class="section" id="tuning-the-number-of-threads">
<h2>Tuning the number of threads<a class="headerlink" href="#tuning-the-number-of-threads" title="Permalink to this headline">¶</a></h2>
<p>The following simple script shows how a runtime of matrix multiplication changes with the number of threads:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timeit</span>
<span class="n">runtimes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">threads</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">49</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">threads</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="n">setup</span> <span class="o">=</span> <span class="s2">"import torch; x = torch.randn(1024, 1024); y = torch.randn(1024, 1024)"</span><span class="p">,</span> <span class="n">stmt</span><span class="o">=</span><span class="s2">"torch.mm(x, y)"</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">runtimes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
<span class="c1"># ... plotting (threads, runtimes) ...</span>
</pre></div>
</div>
<p>Running the script on a system with 24 physical CPU cores (Xeon E5-2680, MKL and OpenMP based build) results in the following runtimes:</p>
<a class="reference internal image-reference" href="../_images/cpu_threading_runtimes.svg"><img alt="../_images/cpu_threading_runtimes.svg" src="../_images/cpu_threading_runtimes.svg" width="75%" /></a>
<p>The following considerations should be taken into account when tuning the number of intra- and inter-op threads:</p>
<ul class="simple">
<li><p>When choosing the number of threads one needs to avoid <cite>oversubscription</cite> (using too many threads, leads to performance degradation). For example, in an application that uses a large application thread pool or heavily relies on
inter-op parallelism, one might find disabling intra-op parallelism as a possible option (i.e. by calling <code class="docutils literal notranslate"><span class="pre">set_num_threads(1)</span></code>);</p></li>
<li><p>In a typical application one might encounter a trade off between <cite>latency</cite> (time spent on processing an inference request) and <cite>throughput</cite> (amount of work done per unit of time). Tuning the number of threads can be a useful
tool to adjust this trade off in one way or another. For example, in latency critical applications one might want to increase the number of intra-op threads to process each request as fast as possible. At the same time, parallel implementations
of ops may add an extra overhead that increases amount work done per single request and thus reduces the overall throughput.</p></li>
</ul>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>OpenMP does not guarantee that a single per-process intra-op thread
pool is going to be used in the application. On the contrary, two different application or inter-op
threads may use different OpenMP thread pools for intra-op work.
This might result in a large number of threads used by the application.
Extra care in tuning the number of threads is needed to avoid
oversubscription in multi-threaded applications in OpenMP case.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Pre-built PyTorch releases are compiled with OpenMP support.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">parallel_info</span></code> utility prints information about thread settings and can be used for debugging.
Similar output can be also obtained in Python with <code class="docutils literal notranslate"><span class="pre">torch.__config__.parallel_info()</span></code> call.</p>
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