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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.jit.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.jit.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>
<blockquote>
<div><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>
</div></blockquote>
<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.
At the same time OpenMP is not known for a good interoperability with other threading
libraries used by the application. In particular, 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 inter-op
threads will likely use different OpenMP thread pools for intra-op work.
This might result in a large number of threads used by the application.</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 align-default">
<colgroup>
<col style="width: 12%" />
<col style="width: 22%" />
<col style="width: 28%" />
<col style="width: 38%" />
</colgroup>
<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_THREADING</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 strongly 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 align-default">
<colgroup>
<col style="width: 17%" />
<col style="width: 42%" />
<col style="width: 41%" />
</colgroup>
<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, <a class="reference internal" href="../torch.html#module-torch" title="torch"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch</span></code></a> module)</p>
</td>
<td rowspan="2"><p><code class="docutils literal notranslate"><span class="pre">set*</span></code> functions can only be called once and only
during the startup, before the actual operators running;</p>
<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, <a class="reference internal" href="../torch.html#module-torch" title="torch"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch</span></code></a> 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 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>
</div>
</div>
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