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<section id="mps-backend">
<span id="id1"></span><h1>MPS backend<a class="headerlink" href="#mps-backend" title="Permalink to this heading">¶</a></h1>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">mps</span></code> device enables high-performance
training on GPU for MacOS devices with Metal programming framework. It
introduces a new device to map Machine Learning computational graphs and
primitives on highly efficient Metal Performance Shaders Graph framework and
tuned kernels provided by Metal Performance Shaders framework respectively.</p>
<p>The new MPS backend extends the PyTorch ecosystem and provides existing scripts
capabilities to setup and run operations on GPU.</p>
<p>To get started, simply move your Tensor and Module to the <code class="docutils literal notranslate"><span class="pre">mps</span></code> device:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Check that MPS is available</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">is_built</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"MPS not available because the current PyTorch install was not "</span>
<span class="s2">"built with MPS enabled."</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"MPS not available because the current MacOS version is not 12.3+ "</span>
<span class="s2">"and/or you do not have an MPS-enabled device on this machine."</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">mps_device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"mps"</span><span class="p">)</span>
<span class="c1"># Create a Tensor directly on the mps device</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">mps_device</span><span class="p">)</span>
<span class="c1"># Or</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"mps"</span><span class="p">)</span>
<span class="c1"># Any operation happens on the GPU</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="c1"># Move your model to mps just like any other device</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">YourFavoriteNet</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">mps_device</span><span class="p">)</span>
<span class="c1"># Now every call runs on the GPU</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
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