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<li class="toctree-l2 current"><a class="current reference internal" href="#">Libdevice function</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#asin-kernel">asin Kernel</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#using-the-default-libdevice-library-path">Using the default libdevice library path</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#customize-the-libdevice-library-path">Customize the libdevice library path</a></li>
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<div class="sphx-glr-example-title section" id="libdevice-function">
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<span id="sphx-glr-getting-started-tutorials-07-libdevice-function-py"></span><h1>Libdevice function<a class="headerlink" href="#libdevice-function" title="Permalink to this headline">¶</a></h1>
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<p>Triton can invoke a custom function from an external library.
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In this example, we will use the <cite>libdevice</cite> library to apply <cite>asin</cite> on a tensor.
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Please refer to <a class="reference external" href="https://docs.nvidia.com/cuda/libdevice-users-guide/index.html">https://docs.nvidia.com/cuda/libdevice-users-guide/index.html</a> regarding the semantics of all available libdevice functions.</p>
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<p>In <cite>trition/language/libdevice.py</cite>, we try to aggregate functions with the same computation but different data types together.
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For example, both <cite>__nv_asin</cite> and <cite>__nvasinf</cite> calculate the principal value of the arc sine of the input, but <cite>__nv_asin</cite> operates on <cite>double</cite> and <cite>__nv_asinf</cite> operates on <cite>float</cite>.
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Using triton, you can simply call <cite>tl.libdevice.asinf</cite>.
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triton automatically selects the correct underlying device function to invoke based on input and output types.</p>
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<div class="section" id="asin-kernel">
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<h2>asin Kernel<a class="headerlink" href="#asin-kernel" title="Permalink to this headline">¶</a></h2>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="kn">import</span> <span class="nn">triton</span>
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<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
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<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
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<span class="k">def</span> <span class="nf">asin_kernel</span><span class="p">(</span>
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<span class="n">x_ptr</span><span class="p">,</span>
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<span class="n">y_ptr</span><span class="p">,</span>
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<span class="n">n_elements</span><span class="p">,</span>
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<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
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<span class="p">):</span>
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<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
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<span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
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<span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o"><</span> <span class="n">n_elements</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">libdevice</span><span class="o">.</span><span class="n">asin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">y_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="using-the-default-libdevice-library-path">
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<h2>Using the default libdevice library path<a class="headerlink" href="#using-the-default-libdevice-library-path" title="Permalink to this headline">¶</a></h2>
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<p>We can use the default libdevice library path encoded in <cite>triton/language/libdevice.py</cite></p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">size</span> <span class="o">=</span> <span class="mi">98432</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
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<span class="n">output_triton</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
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<span class="n">output_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">asin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">output_triton</span><span class="o">.</span><span class="n">is_cuda</span>
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<span class="n">n_elements</span> <span class="o">=</span> <span class="n">output_torch</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
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<span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK_SIZE'</span><span class="p">]),)</span>
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<span class="n">asin_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">output_triton</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">output_torch</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">output_triton</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span>
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<span class="sa">f</span><span class="s1">'The maximum difference between torch and triton is '</span>
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<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output_torch</span> <span class="o">-</span> <span class="n">output_triton</span><span class="p">))</span><span class="si">}</span><span class="s1">'</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
|
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tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
|
|
The maximum difference between torch and triton is 2.384185791015625e-07
|
|
</pre></div>
|
|
</div>
|
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</div>
|
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<div class="section" id="customize-the-libdevice-library-path">
|
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<h2>Customize the libdevice library path<a class="headerlink" href="#customize-the-libdevice-library-path" title="Permalink to this headline">¶</a></h2>
|
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<p>We can also customize the libdevice library path by passing the path to the <cite>libdevice</cite> library to the <cite>asin</cite> kernel.</p>
|
|
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">output_triton</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
|
<span class="n">asin_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">output_triton</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
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|
<span class="n">extern_libs</span><span class="o">=</span><span class="p">{</span><span class="s1">'libdevice'</span><span class="p">:</span> <span class="s1">'/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'</span><span class="p">})</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">output_torch</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">output_triton</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span>
|
|
<span class="sa">f</span><span class="s1">'The maximum difference between torch and triton is '</span>
|
|
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output_torch</span> <span class="o">-</span> <span class="n">output_triton</span><span class="p">))</span><span class="si">}</span><span class="s1">'</span>
|
|
<span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
|
<p class="sphx-glr-script-out">Out:</p>
|
|
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
|
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tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
|
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The maximum difference between torch and triton is 2.384185791015625e-07
|
|
</pre></div>
|
|
</div>
|
|
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<p><a class="reference download internal" download="" href="../../_downloads/3ff29f967ace7985da24aab10352fc76/07-libdevice-function.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">07-libdevice-function.py</span></code></a></p>
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