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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 programming model used by Triton</p></li>
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<li><p>The <cite>triton.jit</cite> decorator, which constitutes the main entry point for writing Triton kernels.</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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<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="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
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<span class="k">def</span> <span class="nf">_add</span><span class="p">(</span>
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<span class="n">X</span><span class="p">,</span> <span class="c1"># *Pointer* to first input vector</span>
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<span class="n">Y</span><span class="p">,</span> <span class="c1"># *Pointer* to second input vector</span>
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<span class="n">Z</span><span class="p">,</span> <span class="c1"># *Pointer* to output vector</span>
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<span class="n">N</span><span class="p">,</span> <span class="c1"># Size of the vector</span>
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<span class="o">**</span><span class="n">meta</span> <span class="c1"># Optional meta-parameters for the kernel</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">triton</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<span class="c1"># Create an offset for the blocks of pointers to be</span>
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<span class="c1"># processed by this program instance</span>
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<span class="n">offsets</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK'</span><span class="p">]</span> <span class="o">+</span> <span class="n">triton</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">meta</span><span class="p">[</span><span class="s1">'BLOCK'</span><span class="p">])</span>
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<span class="c1"># Create a mask to guard memory operations against</span>
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<span class="c1"># out-of-bounds accesses</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</span>
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<span class="c1"># Load x</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</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">y</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">Y</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="c1"># Write back x + y</span>
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<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
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<span class="n">triton</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">Z</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>We can also declara a helper function that handles allocating the output vector
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and enqueueing the kernel.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
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<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>
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||
<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>
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||
<span class="c1"># The SPMD launch grid denotes the number of kernel instances that should execute in parallel.</span>
|
||
<span class="c1"># It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]</span>
|
||
<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</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK'</span><span class="p">]),</span> <span class="p">)</span>
|
||
<span class="c1"># NOTE:</span>
|
||
<span class="c1"># - torch.tensor objects are implicitly converted to pointers to their first element.</span>
|
||
<span class="c1"># - `triton.jit`'ed functions can be subscripted with a launch grid to obtain a callable GPU kernel</span>
|
||
<span class="c1"># - don't forget to pass meta-parameters as keywords arguments</span>
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<span class="n">_add</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">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">BLOCK</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
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<span class="c1"># We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still</span>
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<span class="c1"># running asynchronously.</span>
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<span class="k">return</span> <span class="n">z</span>
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||
</pre></div>
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</div>
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<p>We can now use the above function to compute the sum of two <cite>torch.tensor</cite> objects and test our results:</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">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">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">za</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="n">za</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">zb</span><span class="p">)</span>
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<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, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
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||
tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
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||
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="benchmark">
|
||
<h2>Benchmark<a class="headerlink" href="#benchmark" 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.
|
||
To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of our custom op.
|
||
for different problem sizes.</p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">perf_report</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">Benchmark</span><span class="p">(</span>
|
||
<span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">'size'</span><span class="p">],</span> <span class="c1"># argument names to use as an x-axis for the plot</span>
|
||
<span class="n">x_vals</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">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">)],</span> <span class="c1"># different possible values for `x_name`</span>
|
||
<span class="n">x_log</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># x axis is logarithmic</span>
|
||
<span class="n">y_name</span><span class="o">=</span><span class="s1">'provider'</span><span class="p">,</span> <span class="c1"># argument name whose value corresponds to a different line in the plot</span>
|
||
<span class="n">y_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">'torch'</span><span class="p">,</span> <span class="s1">'triton'</span><span class="p">],</span> <span class="c1"># possible keys for `y_name`</span>
|
||
<span class="n">y_lines</span><span class="o">=</span><span class="p">[</span><span class="s2">"Torch"</span><span class="p">,</span> <span class="s2">"Triton"</span><span class="p">],</span> <span class="c1"># label name for the lines</span>
|
||
<span class="n">ylabel</span><span class="o">=</span><span class="s2">"GB/s"</span><span class="p">,</span> <span class="c1"># label name for the y-axis</span>
|
||
<span class="n">plot_name</span><span class="o">=</span><span class="s2">"vector-add-performance"</span><span class="p">,</span> <span class="c1"># name for the plot. Used also as a file name for saving the plot.</span>
|
||
<span class="n">args</span><span class="o">=</span><span class="p">{}</span> <span class="c1"># values for function arguments not in `x_names` and `y_name`</span>
|
||
<span class="p">)</span>
|
||
<span class="p">)</span>
|
||
<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">provider</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">size</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">size</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="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'torch'</span><span class="p">:</span>
|
||
<span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_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="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'triton'</span><span class="p">:</span>
|
||
<span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_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">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">12</span> <span class="o">*</span> <span class="n">size</span> <span class="o">/</span> <span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
|
||
<span class="k">return</span> <span class="n">gbps</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>We can now run the decorated function above. Pass <cite>show_plots=True</cite> to see the plots and/or
|
||
<a href="#id1"><span class="problematic" id="id2">`</span></a>save_path=’/path/to/results/’ to save them to disk along with raw CSV data</p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
</pre></div>
|
||
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|
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