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          <section id="emitters">
<h1>Emitters<a class="headerlink" href="#emitters" title="Permalink to this heading">#</a></h1>
<section id="module-cutlass.emit.common">
<span id="common"></span><h2>Common<a class="headerlink" href="#module-cutlass.emit.common" title="Permalink to this heading">#</a></h2>
<p>Common utilities for emitting CUTLASS kernels</p>
</section>
<section id="module-cutlass.emit.pytorch">
<span id="pytorch"></span><h2>PyTorch<a class="headerlink" href="#module-cutlass.emit.pytorch" title="Permalink to this heading">#</a></h2>
<p>Utilities for generating source for building a PyTorch CUDA extension that using a CUTLASS kernel.
If specified, the extension can be JIT compiled via PyTorch’s <code class="docutils literal notranslate"><span class="pre">cpp_extension.load</span></code> method.</p>
<p>Example usage with JIT compilation:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plan</span> <span class="o">=</span> <span class="n">cutlass</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">Gemm</span><span class="p">(</span><span class="n">element</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">cutlass</span><span class="o">.</span><span class="n">LayoutType</span><span class="o">.</span><span class="n">RowMajor</span><span class="p">)</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">plan</span><span class="o">.</span><span class="n">construct</span><span class="p">()</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">cutlass</span><span class="o">.</span><span class="n">emit</span><span class="o">.</span><span class="n">pytorch</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="s1">&#39;cutlass_gemm&#39;</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="n">jit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># Generate inputs for the GEMM</span>
<span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>

<span class="c1"># Run the module</span>
<span class="n">D</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
</pre></div>
</div>
<p>Example usage without JIT compilation:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plan</span> <span class="o">=</span> <span class="n">cutlass</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">Gemm</span><span class="p">(</span><span class="n">element</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">cutlass</span><span class="o">.</span><span class="n">LayoutType</span><span class="o">.</span><span class="n">RowMajor</span><span class="p">)</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">plan</span><span class="o">.</span><span class="n">construct</span><span class="p">()</span>
<span class="n">cutlass</span><span class="o">.</span><span class="n">emit</span><span class="o">.</span><span class="n">pytorch</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="s1">&#39;cutlass_gemm&#39;</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="n">jit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sourcedir</span><span class="o">=</span><span class="s1">&#39;output&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>After this call, the directory <code class="docutils literal notranslate"><span class="pre">output</span></code> contains <code class="docutils literal notranslate"><span class="pre">setup.py</span></code>,
<code class="docutils literal notranslate"><span class="pre">cutlass_gemm.cpp</span></code>, and <code class="docutils literal notranslate"><span class="pre">cutlass_gemm_kernel.cu</span></code>. The module can be built from
within <code class="docutils literal notranslate"><span class="pre">output</span></code> by running: <code class="docutils literal notranslate"><span class="pre">TORCH_CUDA_ARCH_LIST=&quot;8.0&quot;</span> <span class="pre">python</span> <span class="pre">setup.py</span> <span class="pre">develop</span> <span class="pre">--user</span></code>.</p>
<p>The module can later be used in Python via:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">cutlass_gemm</span>

<span class="c1"># Generate inputs for the GEMM</span>
<span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>

<span class="c1"># Run the module</span>
<span class="n">D</span> <span class="o">=</span> <span class="n">cutlass_gemm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>
</pre></div>
</div>
<dl class="py function">
<dt class="sig sig-object py" id="cutlass.emit.pytorch.pytorch">
<span class="sig-prename descclassname"><span class="pre">cutlass.emit.pytorch.</span></span><span class="sig-name descname"><span class="pre">pytorch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sourcedir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cutlass/emit/pytorch.html#pytorch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cutlass.emit.pytorch.pytorch" title="Permalink to this definition">#</a></dt>
<dd><p>Generates source for building a PyTorch CUDA module that leverages the CUTLASS kernel
specified by <code class="docutils literal notranslate"><span class="pre">op</span></code>. If the <code class="docutils literal notranslate"><span class="pre">jit</span></code> parameter is set to true, the module is just-in-time
compiled, loaded, and returned.</p>
<p>The result of this method is files within <code class="docutils literal notranslate"><span class="pre">sourcedir</span></code> that can be used for building
a PyTorch module.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>op</strong> – operation to emit in the module</p></li>
<li><p><strong>name</strong> (<em>str</em>) – name of the module to generate</p></li>
<li><p><strong>cc</strong> (<em>int</em>) – compute capability of the device the module should target</p></li>
<li><p><strong>jit</strong> (<em>bool</em>) – whether the module should be just-in-time compiled</p></li>
<li><p><strong>sourcedir</strong> (<em>str</em>) – directory to which generated source files should be written</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>loaded PyTorch module (if <code class="docutils literal notranslate"><span class="pre">jit=True</span></code>) or None</p>
</dd>
</dl>
</dd></dl>

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<li><a class="reference internal" href="#cutlass.emit.pytorch.pytorch"><code class="docutils literal notranslate"><span class="pre">pytorch()</span></code></a></li>
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