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<div class="sphx-glr-download-link-note admonition note">
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<p class="admonition-title">Note</p>
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<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">here</span></a>
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to download the full example code</p>
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<div class="sphx-glr-example-title section" id="vector-addition">
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<span id="sphx-glr-getting-started-tutorials-01-vector-add-py"></span><h1>Vector Addition<a class="headerlink" href="#vector-addition" title="Permalink to this headline">¶</a></h1>
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<p>In this tutorial, you will write a simple, high-performance vector addition using Triton and learn about:</p>
|
||||
<ul class="simple">
|
||||
<li><p>The basic syntax of the Triton programming language</p></li>
|
||||
<li><p>The best practices for creating PyTorch custom operators using the <code class="code docutils literal notranslate"><span class="pre">triton.kernel</span></code> Python API</p></li>
|
||||
<li><p>The best practices for validating and benchmarking custom ops against native reference implementations</p></li>
|
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</ul>
|
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<div class="section" id="compute-kernel">
|
||||
<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Each compute kernel is declared using the <code class="code docutils literal notranslate"><span class="pre">__global__</span></code> attribute, and executed many times in parallel
|
||||
on different chunks of data (See the <a class="reference external" href="(https://en.wikipedia.org/wiki/SPMD">Single Program, Multiple Data</a>)
|
||||
programming model for more details).</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="n">__global__</span> <span class="kt">void</span> <span class="n">add</span><span class="p">(</span><span class="kt">float</span><span class="o">*</span> <span class="n">z</span><span class="p">,</span> <span class="kt">float</span><span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="kt">float</span><span class="o">*</span> <span class="n">y</span><span class="p">,</span> <span class="kt">int</span> <span class="n">N</span><span class="p">){</span>
|
||||
<span class="c1">// The `get_program_id(i)` returns the i-th coordinate</span>
|
||||
<span class="c1">// of the program in the overaching SPMD context</span>
|
||||
<span class="c1">// (a.k.a launch grid). This is what allows us to process</span>
|
||||
<span class="c1">// different chunks of data in parallel.</span>
|
||||
<span class="c1">// For those similar with CUDA, `get_program_id({0,1,2})`</span>
|
||||
<span class="c1">// is similar to blockIdx.{x,y,z}</span>
|
||||
<span class="kt">int</span> <span class="n">pid</span> <span class="o">=</span> <span class="n">get_program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span>
|
||||
<span class="c1">// In Triton, arrays are first-class citizen. In other words,</span>
|
||||
<span class="c1">// they are primitives data-types and are -- contrary to C and</span>
|
||||
<span class="c1">// CUDA -- not implemented as pointers to contiguous chunks of</span>
|
||||
<span class="c1">// memory.</span>
|
||||
<span class="c1">// In the few lines below, we create an array of `BLOCK` pointers</span>
|
||||
<span class="c1">// whose memory values are, e.g.:</span>
|
||||
<span class="c1">// [z + pid*BLOCK + 0, z + pid*BLOCK + 1, ..., z + pid*BLOCK + BLOCK - 1]</span>
|
||||
<span class="c1">// Note: here BLOCK is expected to be a pre-processor macro defined at compile-time</span>
|
||||
<span class="kt">int</span> <span class="n">offset</span><span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK</span> <span class="o">+</span> <span class="mi">0</span> <span class="p">...</span> <span class="n">BLOCK</span><span class="p">;</span>
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||||
<span class="kt">float</span><span class="o">*</span> <span class="n">pz</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">z</span> <span class="o">+</span> <span class="n">offset</span><span class="p">;</span>
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||||
<span class="kt">float</span><span class="o">*</span> <span class="n">px</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">offset</span><span class="p">;</span>
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||||
<span class="kt">float</span><span class="o">*</span> <span class="n">py</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">offset</span><span class="p">;</span>
|
||||
<span class="c1">// Simple element-wise control-flow for load/store operations can</span>
|
||||
<span class="c1">// be achieved using the the ternary operator `cond ? val_true : val_false`</span>
|
||||
<span class="c1">// or the conditional dereferencing operator `*?(cond)ptr</span>
|
||||
<span class="c1">// Here, we make sure that we do not access memory out-of-bounds when we</span>
|
||||
<span class="c1">// write-back `z`</span>
|
||||
<span class="kt">bool</span> <span class="n">check</span><span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">offset</span> <span class="o"><</span> <span class="n">N</span><span class="p">;</span>
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||||
<span class="o">*?</span><span class="p">(</span><span class="n">check</span><span class="p">)</span><span class="n">pz</span> <span class="o">=</span> <span class="o">*?</span><span class="p">(</span><span class="n">check</span><span class="p">)</span><span class="n">px</span> <span class="o">+</span> <span class="o">*?</span><span class="p">(</span><span class="n">check</span><span class="p">)</span><span class="n">py</span><span class="p">;</span>
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||||
<span class="p">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>The existence of arrays as a primitive data-type for Triton comes with a number of advantages that are highlighted in the <a class="reference external" href="http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf">MAPL’2019 Triton paper</a>.</p>
|
||||
</div>
|
||||
<div class="section" id="torch-bindings">
|
||||
<h2>Torch bindings<a class="headerlink" href="#torch-bindings" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The only thing that matters when it comes to Triton and Torch is the <code class="code docutils literal notranslate"><span class="pre">triton.kernel</span></code> class. This allows you to transform the above C-like function into a callable python object that can be used to modify <code class="code docutils literal notranslate"><span class="pre">torch.tensor</span></code> objects. To create a <code class="code docutils literal notranslate"><span class="pre">triton.kernel</span></code>, you only need three things:</p>
|
||||
<ul class="simple">
|
||||
<li><p><code class="code docutils literal notranslate"><span class="pre">source:</span> <span class="pre">string</span></code>: the source-code of the kernel you want to create</p></li>
|
||||
<li><p><code class="code docutils literal notranslate"><span class="pre">device:</span> <span class="pre">torch.device</span></code>: the device you want to compile this code for</p></li>
|
||||
<li><p><code class="code docutils literal notranslate"><span class="pre">defines:</span> <span class="pre">dict</span></code>: the set of macros that you want the pre-processor to <cite>#define</cite> for you</p></li>
|
||||
</ul>
|
||||
<div class="highlight-default 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">triton</span>
|
||||
|
||||
<span class="c1"># source-code for Triton compute kernel</span>
|
||||
<span class="c1"># here we just copy-paste the above code without the extensive comments.</span>
|
||||
<span class="c1"># you may prefer to store it in a .c file and load it from there instead.</span>
|
||||
<span class="n">_src</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2">__global__ void add(float* z, float* x, float* y, int N){</span>
|
||||
<span class="s2"> // program id</span>
|
||||
<span class="s2"> int pid = get_program_id(0);</span>
|
||||
<span class="s2"> // create arrays of pointers</span>
|
||||
<span class="s2"> int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;</span>
|
||||
<span class="s2"> float* pz[BLOCK] = z + offset;</span>
|
||||
<span class="s2"> float* px[BLOCK] = x + offset;</span>
|
||||
<span class="s2"> float* py[BLOCK] = y + offset;</span>
|
||||
<span class="s2"> // bounds checking</span>
|
||||
<span class="s2"> bool check[BLOCK] = offset < N;</span>
|
||||
<span class="s2"> // write-back</span>
|
||||
<span class="s2"> *?(check)pz = *?(check)px + *?(check)py;</span>
|
||||
<span class="s2">}</span>
|
||||
<span class="s2"> """</span>
|
||||
|
||||
|
||||
<span class="c1"># This function returns a callable `triton.kernel` object created from the above source code.</span>
|
||||
<span class="c1"># For portability, we maintain a cache of kernels for different `torch.device`</span>
|
||||
<span class="c1"># We compile the kernel with -DBLOCK=1024</span>
|
||||
<span class="k">def</span> <span class="nf">make_add_kernel</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
|
||||
<span class="n">cache</span> <span class="o">=</span> <span class="n">make_add_kernel</span><span class="o">.</span><span class="n">cache</span>
|
||||
<span class="k">if</span> <span class="n">device</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">cache</span><span class="p">:</span>
|
||||
<span class="n">defines</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'BLOCK'</span><span class="p">:</span> <span class="mi">1024</span><span class="p">}</span>
|
||||
<span class="n">cache</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">kernel</span><span class="p">(</span><span class="n">_src</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">defines</span><span class="o">=</span><span class="n">defines</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">cache</span><span class="p">[</span><span class="n">device</span><span class="p">]</span>
|
||||
|
||||
|
||||
<span class="n">make_add_kernel</span><span class="o">.</span><span class="n">cache</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
|
||||
|
||||
|
||||
<span class="c1"># This is a standard torch custom autograd Function;</span>
|
||||
<span class="c1"># The only difference is that we can now use the above kernel in the `forward` and `backward` functions.`</span>
|
||||
<span class="k">class</span> <span class="nc">_add</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
|
||||
<span class="nd">@staticmethod</span>
|
||||
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="c1"># constraints of the op</span>
|
||||
<span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
|
||||
<span class="c1"># *allocate output*</span>
|
||||
<span class="n">z</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="c1"># *create launch grid*:</span>
|
||||
<span class="c1"># this is a function which takes compilation parameters `opt`</span>
|
||||
<span class="c1"># as input and returns a tuple of int (i.e., launch grid) for the kernel.</span>
|
||||
<span class="c1"># triton.cdiv is a shortcut for ceil division:</span>
|
||||
<span class="c1"># triton.cdiv(a, b) = (a + b - 1) // b</span>
|
||||
<span class="n">N</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">opt</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</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">BLOCK</span><span class="p">),</span> <span class="p">)</span>
|
||||
<span class="c1"># *launch kernel*:</span>
|
||||
<span class="c1"># pointer to the data of torch tensors can be retrieved with</span>
|
||||
<span class="c1"># the `.data_ptr()` method</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">make_add_kernel</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">kernel</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">x</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">N</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="n">grid</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">z</span>
|
||||
|
||||
|
||||
<span class="c1"># Just like we standard PyTorch ops We use the :code:`.apply` method to create a callable object for our function</span>
|
||||
<span class="n">add</span> <span class="o">=</span> <span class="n">_add</span><span class="o">.</span><span class="n">apply</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="unit-test">
|
||||
<h2>Unit Test<a class="headerlink" href="#unit-test" title="Permalink to this headline">¶</a></h2>
|
||||
<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>
|
||||
<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="mi">98432</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">y</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="mi">98432</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">za</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
|
||||
<span class="n">zb</span> <span class="o">=</span> <span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">za</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">zb</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">za</span> <span class="o">-</span> <span class="n">zb</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([1.3713, 1.3076, 0.4940, ..., 0.6682, 1.1984, 1.2696], device='cuda:0')
|
||||
tensor([1.3713, 1.3076, 0.4940, ..., 0.6682, 1.1984, 1.2696], device='cuda:0')
|
||||
The maximum difference between torch and triton is 0.0
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="benchmarking">
|
||||
<h2>Benchmarking<a class="headerlink" href="#benchmarking" title="Permalink to this headline">¶</a></h2>
|
||||
<p>We can now benchmark our custom op for vectors of increasing sizes to get a sense of how it does</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">warmup</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
<span class="n">rep</span> <span class="o">=</span> <span class="mi">200</span>
|
||||
<span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="o">**</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]:</span>
|
||||
<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">N</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">y</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">N</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">triton_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">warmup</span><span class="o">=</span><span class="n">warmup</span><span class="p">,</span> <span class="n">rep</span><span class="o">=</span><span class="n">rep</span><span class="p">)</span>
|
||||
<span class="n">torch_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">,</span> <span class="n">warmup</span><span class="o">=</span><span class="n">warmup</span><span class="p">,</span> <span class="n">rep</span><span class="o">=</span><span class="n">rep</span><span class="p">)</span>
|
||||
<span class="c1"># print the performance of triton and torch as well as the achieved bandwidth</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="n">triton_ms</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="n">torch_ms</span><span class="si">:</span><span class="s1">.3f</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>131072 0.022 0.006
|
||||
262144 0.021 0.005
|
||||
524288 0.022 0.017
|
||||
1048576 0.037 0.037
|
||||
2097152 0.074 0.073
|
||||
4194304 0.144 0.143
|
||||
8388608 0.289 0.285
|
||||
16777216 0.566 0.562
|
||||
33554432 1.131 1.121
|
||||
</pre></div>
|
||||
</div>
|
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450
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Fused Softmax</a><ul>
|
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<li class="toctree-l3"><a class="reference internal" href="#motivations">Motivations</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="#compute-kernel">Compute Kernel</a></li>
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|
||||
to download the full example code</p>
|
||||
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|
||||
<div class="sphx-glr-example-title section" id="fused-softmax">
|
||||
<span id="sphx-glr-getting-started-tutorials-02-fused-softmax-py"></span><h1>Fused Softmax<a class="headerlink" href="#fused-softmax" title="Permalink to this headline">¶</a></h1>
|
||||
<p>In this tutorial, you will write a fused softmax layer that outperform’s PyTorch implementation and learn about:</p>
|
||||
<ul class="simple">
|
||||
<li><p>The benefits of kernel fusion for bandwidth-bound operations.</p></li>
|
||||
<li><p>The syntax and usage of reduction operators in Triton.</p></li>
|
||||
<li><p>The automatic vectorization capabilities of the Triton compiler.</p></li>
|
||||
</ul>
|
||||
<div class="section" id="motivations">
|
||||
<h2>Motivations<a class="headerlink" href="#motivations" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Custom GPU kernels for elementwise additions are educationally valuable but won’t get you very far in practice.
|
||||
Let us consider instead the case of a simple (numerically stabilized) softmax operation:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
|
||||
|
||||
|
||||
<span class="c1"># Compute the row-wise softmax of x</span>
|
||||
<span class="k">def</span> <span class="nf">naive_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
|
||||
<span class="c1"># read MN elements ; write M elements</span>
|
||||
<span class="n">x_max</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="c1"># read 2MN elements ; write MN elements</span>
|
||||
<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x_max</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
|
||||
<span class="c1"># read MN elements ; write MN elements</span>
|
||||
<span class="n">numerator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="c1"># read MN elements ; write M elements</span>
|
||||
<span class="n">denominator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="c1"># read 2MN elements ; write MN elements</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
|
||||
<span class="c1"># in total: read 7MN elements ; wrote 3MN + 2M elements</span>
|
||||
<span class="k">return</span> <span class="n">ret</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>When implemented naively in pytorch, computing <code class="code docutils literal notranslate"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">naive_softmax(x)</span></code> for <span class="math notranslate nohighlight">\(x \in R^{M \times N}\)</span> requires reading <span class="math notranslate nohighlight">\(7MN\)</span> elements from DRAM and writing back <span class="math notranslate nohighlight">\(3MN + 2M\)</span> elements.
|
||||
Instead, we want to write a custom “fused” pytorch operators that only reads X once and does all the necessary computations on-chip.
|
||||
This would require reading and writing back only <span class="math notranslate nohighlight">\(MN\)</span> bytes, so we could expect a theoretical speed-up of 5x.
|
||||
In practice, though, we expect less because our kernel will spend some time computing exponentials and moving data around in shared memory.</p>
|
||||
</div>
|
||||
<div class="section" id="compute-kernel">
|
||||
<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Our softmax kernel works as follows: each program loads a row of X and writes back a normalized row of Y. Note that one important limitation of Triton is that each block must have a power-of-two number of elements, which means that we need to guard the memory operations properly if we want to handle any possible input shapes:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="n">__global__</span> <span class="kt">void</span> <span class="n">softmax</span><span class="p">(</span><span class="kt">float</span><span class="o">*</span> <span class="n">Y</span><span class="p">,</span> <span class="kt">float</span><span class="o">*</span> <span class="n">X</span><span class="p">,</span> <span class="kt">int</span> <span class="n">stride_xm</span><span class="p">,</span> <span class="kt">int</span> <span class="n">stride_ym</span><span class="p">,</span> <span class="kt">int</span> <span class="n">M</span><span class="p">,</span> <span class="kt">int</span> <span class="n">N</span><span class="p">){</span>
|
||||
<span class="c1">// row index</span>
|
||||
<span class="kt">int</span> <span class="n">m</span> <span class="o">=</span> <span class="n">get_program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span>
|
||||
<span class="c1">// column indices</span>
|
||||
<span class="kt">int</span> <span class="n">n</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="p">...</span> <span class="n">BLOCK</span><span class="p">;</span>
|
||||
<span class="c1">// the memory address of all the elements</span>
|
||||
<span class="c1">// that we want to load can be computed as follows</span>
|
||||
<span class="kt">float</span><span class="o">*</span> <span class="n">px</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">m</span><span class="o">*</span><span class="n">stride_xm</span> <span class="o">+</span> <span class="n">n</span><span class="p">;</span>
|
||||
<span class="c1">// because BLOCK has to be a power of two</span>
|
||||
<span class="c1">// (per Triton-C specs), it is important</span>
|
||||
<span class="c1">// to guard each memory operation with predicates</span>
|
||||
<span class="c1">// or we will read out of bounds</span>
|
||||
<span class="kt">bool</span> <span class="n">check</span><span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span> <span class="o"><</span> <span class="n">N</span><span class="p">;</span>
|
||||
<span class="kt">float</span> <span class="n">x</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">check</span> <span class="o">?</span> <span class="o">*</span><span class="nl">px</span> <span class="p">:</span> <span class="o">-</span><span class="n">F32_INFINITY</span><span class="p">;</span>
|
||||
<span class="c1">// syntax for reduction in Triton is:</span>
|
||||
<span class="c1">// x[..., OPERATOR, ...]</span>
|
||||
<span class="c1">// ^</span>
|
||||
<span class="c1">// index</span>
|
||||
<span class="c1">// The operators currently supported are {min, max, +}</span>
|
||||
<span class="kt">float</span> <span class="n">z</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">max</span><span class="p">];</span>
|
||||
<span class="c1">// The exponential in Triton is fast but approximate</span>
|
||||
<span class="c1">// (i.e., like __expf in CUDA)</span>
|
||||
<span class="kt">float</span> <span class="n">num</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">);</span>
|
||||
<span class="kt">float</span> <span class="n">denom</span> <span class="o">=</span> <span class="n">num</span><span class="p">[</span><span class="o">+</span><span class="p">];</span>
|
||||
<span class="c1">// The result of the reduction is now stored in y</span>
|
||||
<span class="kt">float</span> <span class="n">y</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">num</span> <span class="o">/</span> <span class="n">denom</span><span class="p">;</span>
|
||||
<span class="c1">// We write it back</span>
|
||||
<span class="kt">float</span><span class="o">*</span> <span class="n">py</span> <span class="p">[</span><span class="n">BLOCK</span><span class="p">]</span> <span class="o">=</span> <span class="n">Y</span> <span class="o">+</span> <span class="n">m</span><span class="o">*</span><span class="n">stride_ym</span> <span class="o">+</span> <span class="n">n</span><span class="p">;</span>
|
||||
<span class="o">*?</span><span class="p">(</span><span class="n">check</span><span class="p">)</span><span class="n">py</span> <span class="o">=</span> <span class="n">y</span><span class="p">;</span>
|
||||
<span class="p">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
</div>
|
||||
<div class="section" id="torch-bindings">
|
||||
<h2>Torch Bindings<a class="headerlink" href="#torch-bindings" title="Permalink to this headline">¶</a></h2>
|
||||
<p>We need to make sure that BLOCK is the smallest power of two
|
||||
greater than the number of rows N of the input matrix.
|
||||
Different values of BLOCK will result in different kernels</p>
|
||||
<div class="highlight-default 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">triton</span>
|
||||
|
||||
<span class="c1"># Source code for the Triton kernel</span>
|
||||
<span class="n">_src</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2">__global__ void softmax(float* Y, float* X, int stride_ym, int stride_xm, int M, int N){</span>
|
||||
<span class="s2"> int m = get_program_id(0);</span>
|
||||
<span class="s2"> int n [BLOCK] = 0 ... BLOCK;</span>
|
||||
<span class="s2"> float* px [BLOCK] = X + m*stride_xm + n;</span>
|
||||
<span class="s2"> bool check[BLOCK] = n < N;</span>
|
||||
<span class="s2"> float x [BLOCK] = check ? *px : -F32_INFINITY;</span>
|
||||
<span class="s2"> float z [BLOCK] = x - x[max];</span>
|
||||
<span class="s2"> float num [BLOCK] = exp(z);</span>
|
||||
<span class="s2"> float denom = num[+];</span>
|
||||
<span class="s2"> float y [BLOCK] = num / denom;</span>
|
||||
<span class="s2"> float* py [BLOCK] = Y + m*stride_ym + n;</span>
|
||||
<span class="s2"> *?(check)py = y;</span>
|
||||
<span class="s2">}</span>
|
||||
<span class="s2">"""</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">next_power_of_2</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="n">n</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
<span class="n">n</span> <span class="o">|=</span> <span class="n">n</span> <span class="o">>></span> <span class="mi">1</span>
|
||||
<span class="n">n</span> <span class="o">|=</span> <span class="n">n</span> <span class="o">>></span> <span class="mi">2</span>
|
||||
<span class="n">n</span> <span class="o">|=</span> <span class="n">n</span> <span class="o">>></span> <span class="mi">4</span>
|
||||
<span class="n">n</span> <span class="o">|=</span> <span class="n">n</span> <span class="o">>></span> <span class="mi">8</span>
|
||||
<span class="n">n</span> <span class="o">|=</span> <span class="n">n</span> <span class="o">>></span> <span class="mi">16</span>
|
||||
<span class="n">n</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">n</span>
|
||||
|
||||
|
||||
<span class="n">_kernels</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">make_kernel</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
|
||||
<span class="n">BLOCK</span> <span class="o">=</span> <span class="n">next_power_of_2</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
|
||||
<span class="n">key</span> <span class="o">=</span> <span class="p">(</span><span class="n">BLOCK</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_kernels</span><span class="p">:</span>
|
||||
<span class="n">defines</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'BLOCK'</span><span class="p">:</span> <span class="n">BLOCK</span><span class="p">}</span>
|
||||
<span class="n">_kernels</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">kernel</span><span class="p">(</span><span class="n">_src</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">defines</span><span class="o">=</span><span class="n">defines</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_kernels</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">_softmax</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
|
||||
<span class="nd">@staticmethod</span>
|
||||
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="c1"># constraints of the op</span>
|
||||
<span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
|
||||
<span class="n">y</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="c1"># *create launch grid*:</span>
|
||||
<span class="c1"># here we just launch a grid of M programs</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">opt</span><span class="p">:</span> <span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="p">)</span>
|
||||
<span class="c1"># *launch kernel*:</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">make_kernel</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">kernel</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">x</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="n">grid</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">y</span>
|
||||
|
||||
|
||||
<span class="n">softmax</span> <span class="o">=</span> <span class="n">_softmax</span><span class="o">.</span><span class="n">apply</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="unit-test">
|
||||
<h2>Unit Test<a class="headerlink" href="#unit-test" title="Permalink to this headline">¶</a></h2>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1823</span><span class="p">,</span> <span class="mi">781</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">y_tri</span> <span class="o">=</span> <span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">y_ref</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">y_tri</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">y_ref</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_tri</span><span class="p">,</span> <span class="n">y_ref</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([[2.0935e-03, 6.4551e-04, 9.8605e-05, ..., 3.3981e-04, 2.7386e-03,
|
||||
9.1986e-05],
|
||||
[7.0923e-04, 6.7521e-04, 5.1366e-04, ..., 9.8392e-04, 2.6547e-04,
|
||||
6.9062e-04],
|
||||
[1.4032e-04, 5.8826e-04, 1.1694e-03, ..., 6.6423e-04, 1.8178e-04,
|
||||
6.7049e-04],
|
||||
...,
|
||||
[1.1767e-03, 4.2703e-03, 6.0596e-04, ..., 9.5274e-04, 1.1681e-03,
|
||||
6.4924e-04],
|
||||
[1.0772e-04, 7.4854e-04, 3.1912e-03, ..., 2.4980e-04, 1.9012e-03,
|
||||
5.2567e-04],
|
||||
[2.8518e-03, 8.1899e-04, 7.7046e-04, ..., 1.3403e-03, 5.3167e-04,
|
||||
4.3268e-04]], device='cuda:0')
|
||||
tensor([[2.0935e-03, 6.4551e-04, 9.8605e-05, ..., 3.3981e-04, 2.7386e-03,
|
||||
9.1986e-05],
|
||||
[7.0923e-04, 6.7521e-04, 5.1366e-04, ..., 9.8392e-04, 2.6547e-04,
|
||||
6.9062e-04],
|
||||
[1.4032e-04, 5.8826e-04, 1.1694e-03, ..., 6.6423e-04, 1.8178e-04,
|
||||
6.7049e-04],
|
||||
...,
|
||||
[1.1767e-03, 4.2703e-03, 6.0596e-04, ..., 9.5274e-04, 1.1681e-03,
|
||||
6.4924e-04],
|
||||
[1.0772e-04, 7.4854e-04, 3.1912e-03, ..., 2.4980e-04, 1.9012e-03,
|
||||
5.2567e-04],
|
||||
[2.8518e-03, 8.1899e-04, 7.7046e-04, ..., 1.3403e-03, 5.3167e-04,
|
||||
4.3268e-04]], device='cuda:0')
|
||||
True
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Seems to work!</p>
|
||||
</div>
|
||||
<div class="section" id="benchmarking">
|
||||
<h2>Benchmarking<a class="headerlink" href="#benchmarking" title="Permalink to this headline">¶</a></h2>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
|
||||
<span class="n">M</span> <span class="o">=</span> <span class="mi">4096</span>
|
||||
<span class="n">Ns</span> <span class="o">=</span> <span class="p">[</span><span class="mi">128</span> <span class="o">*</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</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">50</span><span class="p">)]</span>
|
||||
<span class="n">tri_ms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ref_ms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">def_ms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="n">Ns</span><span class="p">:</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">,</span> <span class="n">dtype</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">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">nelement</span><span class="p">()</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span> <span class="o">*</span> <span class="mf">1e-9</span> <span class="o">/</span> <span class="p">(</span><span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-3</span><span class="p">)</span>
|
||||
<span class="n">tri_ms</span> <span class="o">+=</span> <span class="p">[</span><span class="n">gbps</span><span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)))]</span>
|
||||
<span class="n">ref_ms</span> <span class="o">+=</span> <span class="p">[</span><span class="n">gbps</span><span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)))]</span>
|
||||
<span class="n">def_ms</span> <span class="o">+=</span> <span class="p">[</span><span class="n">gbps</span><span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">naive_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)))]</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Bandwidth (GB/s)'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Ns</span><span class="p">,</span> <span class="n">tri_ms</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Triton'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Ns</span><span class="p">,</span> <span class="n">ref_ms</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Torch'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Ns</span><span class="p">,</span> <span class="n">def_ms</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Naive'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="02 fused softmax" class="sphx-glr-single-img" src="../../_images/sphx_glr_02-fused-softmax_001.png" />
|
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|
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|
||||
<p><a class="reference download internal" download="" href="../../_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.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">02-fused-softmax.py</span></code></a></p>
|
||||
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|
||||
<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/034d953b6214fedce6ea03803c712b89/02-fused-softmax.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">02-fused-softmax.ipynb</span></code></a></p>
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|
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|
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|
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Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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|
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|
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241
getting-started/tutorials/index.html
Normal file
241
getting-started/tutorials/index.html
Normal file
@@ -0,0 +1,241 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
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|
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<div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
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<p class="caption"><span class="caption-text">Getting Started</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="01-vector-add.html">Vector Addition</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="02-fused-softmax.html">Fused Softmax</a></li>
|
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</ul>
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</li>
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</ul>
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<div class="section" id="tutorials">
|
||||
<span id="sphx-glr-getting-started-tutorials"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h1>
|
||||
<p>Below is a gallery of tutorials for writing various basic operations with Triton. It is recommended that you read through the tutorials in order, starting with the simplest one.</p>
|
||||
<div class="sphx-glr-thumbcontainer" tooltip="- The basic syntax of the Triton programming language - The best practices for creating PyTorch..."><div class="figure align-default" id="id1">
|
||||
<img alt="Vector Addition" src="../../_images/sphx_glr_01-vector-add_thumb.png" />
|
||||
<p class="caption"><span class="caption-text"><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
|
||||
</div>
|
||||
</div><div class="toctree-wrapper compound">
|
||||
</div>
|
||||
<div class="sphx-glr-thumbcontainer" tooltip="- The benefits of kernel fusion for bandwidth-bound operations. - The syntax and usage of reduc..."><div class="figure align-default" id="id2">
|
||||
<img alt="Fused Softmax" src="../../_images/sphx_glr_02-fused-softmax_thumb.png" />
|
||||
<p class="caption"><span class="caption-text"><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
|
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|
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227
getting-started/tutorials/sg_execution_times.html
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getting-started/tutorials/sg_execution_times.html
Normal file
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<span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
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<p><strong>00:08.983</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<table class="docutils align-default">
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<col style="width: 82%" />
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<col style="width: 7%" />
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<tr class="row-odd"><td><p><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
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<td><p>00:05.758</p></td>
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<td><p>0.0 MB</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td>
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<td><p>00:03.225</p></td>
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<td><p>0.0 MB</p></td>
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