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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 vector addition using Triton and learn about:</p>
|
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<ul class="simple">
|
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<li><p>The basic syntax of the Triton programming language</p></li>
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<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>
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<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">
|
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<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
|
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<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
|
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on different chunks of data (See the <a class="reference external" href="(https://en.wikipedia.org/wiki/SPMD">Single Program, Multiple Data</a>)
|
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programming model for more details).</p>
|
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<blockquote>
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<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<p>We can now use the above function to compute the sum of two <cite>torch.tensor</cite> objects:</p>
|
||
</div>
|
||
<div class="section" id="unit-test">
|
||
<h2>Unit Test<a class="headerlink" href="#unit-test" title="Permalink to this headline">¶</a></h2>
|
||
<p>Of course, the first thing that we should check is that whether kernel is correct. This is pretty easy to test, as shown below:</p>
|
||
<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>
|
||
<p>Seems like we’re good to go!</p>
|
||
</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 relative to PyTorch.</p>
|
||
<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="c1"># There are three tensors of 4N bytes each. So the bandwidth of a given kernel</span>
|
||
<span class="c1"># is 12N / time_ms * 1e-6 GB/s</span>
|
||
<span class="n">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">N</span><span class="p">,</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">12</span> <span class="o">*</span> <span class="n">N</span> <span class="o">/</span> <span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
|
||
<span class="c1"># We want to benchmark small and large vector alike</span>
|
||
<span class="n">sizes</span> <span class="o">=</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">12</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span>
|
||
<span class="n">triton_bw</span> <span class="o">=</span> <span class="p">[]</span>
|
||
<span class="n">torch_bw</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">sizes</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">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">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">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="c1"># Triton provide a do_bench utility function that can be used to benchmark</span>
|
||
<span class="c1"># arbitrary workloads. It supports a `warmup` parameter that is used to stabilize</span>
|
||
<span class="c1"># GPU clock speeds as well as a `rep` parameter that controls the number of times</span>
|
||
<span class="c1"># the benchmark is repeated. Importantly, we set `clear_l2 = True` to make sure</span>
|
||
<span class="c1"># that the L2 cache does not contain any element of x before each kernel call when</span>
|
||
<span class="c1"># N is small.</span>
|
||
<span class="n">do_bench</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">fn</span><span class="p">:</span> <span class="n">gbps</span><span class="p">(</span><span class="n">N</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="n">fn</span><span class="p">,</span> <span class="n">warmup</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">rep</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">clear_l2</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||
<span class="n">triton_bw</span> <span class="o">+=</span> <span class="p">[</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">torch_bw</span> <span class="o">+=</span> <span class="p">[</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="c1"># We plot the results as a semi-log</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">triton_bw</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">semilogx</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">torch_bw</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">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="01 vector add" class="sphx-glr-single-img" src="../../_images/sphx_glr_01-vector-add_001.png" />
|
||
<p>Seems like our simple element-wise operation operates at peak bandwidth. While this is a fairly low bar for a custom GPU programming language, this is a good start before we move to more advanced operations.</p>
|
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