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<li class="toctree-l2 current"><a class="current reference internal" href="#">Vector Addition</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#compute-kernel">Compute Kernel</a></li>
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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 programming model of Triton</p></li>
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<li><p>The <cite>triton.jit</cite> decorator, which is used to define Triton kernels.</p></li>
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<li><p>The best practices for validating and benchmarking your 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="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
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<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
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<span class="k">def</span> <span class="nf">add_kernel</span><span class="p">(</span>
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<span class="n">x_ptr</span><span class="p">,</span> <span class="c1"># *Pointer* to first input vector</span>
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<span class="n">y_ptr</span><span class="p">,</span> <span class="c1"># *Pointer* to second input vector</span>
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<span class="n">output_ptr</span><span class="p">,</span> <span class="c1"># *Pointer* to output vector</span>
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<span class="n">n_elements</span><span class="p">,</span> <span class="c1"># Size of the vector</span>
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<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="c1"># Number of elements each program should process</span>
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<span class="c1"># NOTE: `constexpr` so it can be used as a shape value</span>
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<span class="p">):</span>
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<span class="c1"># There are multiple 'program's processing different data. We identify which program</span>
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<span class="c1"># we are here</span>
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<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># We use a 1D launch grid so axis is 0</span>
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<span class="c1"># This program will process inputs that are offset from the initial data.</span>
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<span class="c1"># for instance, if you had a vector of length 256 and block_size of 64, the programs</span>
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<span class="c1"># would each access the elements [0:64, 64:128, 128:192, 192:256].</span>
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<span class="c1"># Note that offsets is a list of pointers</span>
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<span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
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<span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
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<span class="c1"># Create a mask to guard memory operations against 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_elements</span>
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<span class="c1"># Load x and y from DRAM, masking out any extra elements in case the input is not a</span>
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<span class="c1"># multiple of the block size</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
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<span class="n">y</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">y_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
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<span class="n">output</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="c1"># Write x + y back to DRAM</span>
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<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>Let’s also declare a helper function to (1) allocate the <cite>z</cite> tensor
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and (2) enqueue the above kernel with appropriate grid/block sizes.</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">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
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<span class="c1"># We need to preallocate the output</span>
|
||||
<span class="n">output</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="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">is_cuda</span>
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<span class="n">n_elements</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
|
||||
<span class="c1"># The SPMD launch grid denotes the number of kernel instances that run 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="c1"># In this case, we use a 1D grid where the size is the number of blocks</span>
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||||
<span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK_SIZE'</span><span class="p">]),)</span>
|
||||
<span class="c1"># NOTE:</span>
|
||||
<span class="c1"># - each torch.tensor object is implicitly converted into a pointer to its first element.</span>
|
||||
<span class="c1"># - `triton.jit`'ed functions can be index 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>
|
||||
<span class="n">add_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
|
||||
<span class="c1"># We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still</span>
|
||||
<span class="c1"># running asynchronously at this point.</span>
|
||||
<span class="k">return</span> <span class="n">output</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>We can now use the above function to compute the element-wise sum of two <cite>torch.tensor</cite> objects and test its correctness:</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">size</span> <span class="o">=</span> <span class="mi">98432</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">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">output_torch</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
|
||||
<span class="n">output_triton</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">output_torch</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">output_triton</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span>
|
||||
<span class="sa">f</span><span class="s1">'The maximum difference between torch and triton is '</span>
|
||||
<span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output_torch</span> <span class="o">-</span> <span class="n">output_triton</span><span class="p">))</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
|
||||
tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], 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="benchmark">
|
||||
<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
|
||||
<p>We can now benchmark our custom op on 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 your custom ops
|
||||
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="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">line_arg</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">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">'triton'</span><span class="p">,</span> <span class="s1">'torch'</span><span class="p">],</span> <span class="c1"># possible values for `line_arg`</span>
|
||||
<span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Triton'</span><span class="p">,</span> <span class="s1">'Torch'</span><span class="p">],</span> <span class="c1"># label name for the lines</span>
|
||||
<span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">)],</span> <span class="c1"># line styles</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="s1">'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="s1">'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>print_data=True</cite> to see the performance number, <cite>show_plots=True</cite> to plot them, 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">print_data</span><span class="o">=</span><span class="kc">True</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>
|
||||
</div>
|
||||
<img alt="01 vector add" class="sphx-glr-single-img" src="../../_images/sphx_glr_01-vector-add_001.png" />
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
|
||||
size Triton Torch
|
||||
0 4096.0 9.600000 9.600000
|
||||
1 8192.0 19.200000 19.200000
|
||||
2 16384.0 38.400001 38.400001
|
||||
3 32768.0 76.800002 76.800002
|
||||
4 65536.0 127.999995 127.999995
|
||||
5 131072.0 219.428568 219.428568
|
||||
6 262144.0 341.333321 384.000001
|
||||
7 524288.0 472.615390 472.615390
|
||||
8 1048576.0 614.400016 614.400016
|
||||
9 2097152.0 722.823517 722.823517
|
||||
10 4194304.0 780.190482 780.190482
|
||||
11 8388608.0 812.429770 812.429770
|
||||
12 16777216.0 833.084721 833.084721
|
||||
13 33554432.0 842.004273 842.004273
|
||||
14 67108864.0 847.448255 848.362445
|
||||
15 134217728.0 849.737435 850.656574
|
||||
</pre></div>
|
||||
</div>
|
||||
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|
||||
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|
||||
<p><a class="reference download internal" download="" href="../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.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">01-vector-add.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/f191ee1e78dc52eb5f7cba88f71cef2f/01-vector-add.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">01-vector-add.ipynb</span></code></a></p>
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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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|
||||
<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 operation that is significantly faster
|
||||
than PyTorch’s native op for a particular class of matrices: those whose rows can fit in
|
||||
the GPU’s SRAM.
|
||||
You will learn about:</p>
|
||||
<ul class="simple">
|
||||
<li><p>The benefits of kernel fusion for bandwidth-bound operations.</p></li>
|
||||
<li><p>Reduction operators in Triton.</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="kn">import</span> <span class="nn">triton</span>
|
||||
<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
|
||||
|
||||
|
||||
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</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="sd">"""Compute row-wise softmax of X using native pytorch</span>
|
||||
|
||||
<span class="sd"> We subtract the maximum element in order to avoid overflows. Softmax is invariant to</span>
|
||||
<span class="sd"> this shift.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># read MN elements ; write M elements</span>
|
||||
<span class="n">x_max</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">dim</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 MN + M 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">z</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">numerator</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="c1"># read MN + M 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 5MN + 2M 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">\(5MN + 2M\)</span> elements from DRAM and writing back <span class="math notranslate nohighlight">\(3MN + 2M\)</span> elements.
|
||||
This is obviously wasteful; we’d prefer to have a custom “fused” kernel that only reads
|
||||
X once and does all the necessary computations on-chip.
|
||||
Doing so would require reading and writing back only <span class="math notranslate nohighlight">\(MN\)</span> bytes, so we could
|
||||
expect a theoretical speed-up of ~4x (i.e., <span class="math notranslate nohighlight">\((8MN + 4M) / 2MN\)</span>).
|
||||
The <cite>torch.jit.script</cite> flags aims to perform this kind of “kernel fusion” automatically
|
||||
but, as we will see later, it is still far from ideal.</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 the input matrix X,
|
||||
normalizes it and writes back the result to the output Y.
|
||||
Note that one important limitation of Triton is that each block must have a
|
||||
power-of-two number of elements, so we need to internally “pad” each row and guard the
|
||||
memory operations properly if we want to handle any possible input shapes:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">softmax_kernel</span><span class="p">(</span>
|
||||
<span class="n">output_ptr</span><span class="p">,</span> <span class="n">input_ptr</span><span class="p">,</span> <span class="n">input_row_stride</span><span class="p">,</span> <span class="n">output_row_stride</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span>
|
||||
<span class="p">):</span>
|
||||
<span class="c1"># The rows of the softmax are independent, so we parallelize across those</span>
|
||||
<span class="n">row_idx</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="c1"># The stride represents how much we need to increase the pointer to advance 1 row</span>
|
||||
<span class="n">row_start_ptr</span> <span class="o">=</span> <span class="n">input_ptr</span> <span class="o">+</span> <span class="n">row_idx</span> <span class="o">*</span> <span class="n">input_row_stride</span>
|
||||
<span class="c1"># The block size is the next power of two greater than n_cols, so we can fit each</span>
|
||||
<span class="c1"># row in a single block</span>
|
||||
<span class="n">col_offsets</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
|
||||
<span class="n">input_ptrs</span> <span class="o">=</span> <span class="n">row_start_ptr</span> <span class="o">+</span> <span class="n">col_offsets</span>
|
||||
<span class="c1"># Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">input_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">col_offsets</span> <span class="o"><</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">other</span><span class="o">=-</span><span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">))</span>
|
||||
<span class="c1"># Substract maximum for numerical stability</span>
|
||||
<span class="n">row_minus_max</span> <span class="o">=</span> <span class="n">row</span> <span class="o">-</span> <span class="n">tl</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="c1"># Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)</span>
|
||||
<span class="n">numerator</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">row_minus_max</span><span class="p">)</span>
|
||||
<span class="n">denominator</span> <span class="o">=</span> <span class="n">tl</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">0</span><span class="p">)</span>
|
||||
<span class="n">softmax_output</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span>
|
||||
<span class="c1"># Write back output to DRAM</span>
|
||||
<span class="n">output_row_start_ptr</span> <span class="o">=</span> <span class="n">output_ptr</span> <span class="o">+</span> <span class="n">row_idx</span> <span class="o">*</span> <span class="n">output_row_stride</span>
|
||||
<span class="n">output_ptrs</span> <span class="o">=</span> <span class="n">output_row_start_ptr</span> <span class="o">+</span> <span class="n">col_offsets</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptrs</span><span class="p">,</span> <span class="n">softmax_output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">col_offsets</span> <span class="o"><</span> <span class="n">n_cols</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
|
||||
<span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="c1"># The block size is the smallest power of two greater than the number of columns in `x`</span>
|
||||
<span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">next_power_of_2</span><span class="p">(</span><span class="n">n_cols</span><span class="p">)</span>
|
||||
<span class="c1"># Another trick we can use is to ask the compiler to use more threads per row by</span>
|
||||
<span class="c1"># increasing the number of warps (`num_warps`) over which each row is distributed.</span>
|
||||
<span class="c1"># You will see in the next tutorial how to auto-tune this value in a more natural</span>
|
||||
<span class="c1"># way so you don't have to come up with manual heuristics yourself.</span>
|
||||
<span class="n">num_warps</span> <span class="o">=</span> <span class="mi">4</span>
|
||||
<span class="k">if</span> <span class="n">BLOCK_SIZE</span> <span class="o">>=</span> <span class="mi">2048</span><span class="p">:</span>
|
||||
<span class="n">num_warps</span> <span class="o">=</span> <span class="mi">8</span>
|
||||
<span class="k">if</span> <span class="n">BLOCK_SIZE</span> <span class="o">>=</span> <span class="mi">4096</span><span class="p">:</span>
|
||||
<span class="n">num_warps</span> <span class="o">=</span> <span class="mi">16</span>
|
||||
<span class="c1"># Allocate output</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"># Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o</span>
|
||||
<span class="c1"># f the input matrix</span>
|
||||
<span class="n">softmax_kernel</span><span class="p">[(</span><span class="n">n_rows</span><span class="p">,)](</span>
|
||||
<span class="n">y</span><span class="p">,</span>
|
||||
<span class="n">x</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">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">n_cols</span><span class="p">,</span>
|
||||
<span class="n">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">y</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>
|
||||
<p>We make sure that we test our kernel on a matrix with an irregular number of rows and columns.
|
||||
This will allow us to verify that our padding mechanism works.</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">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_triton</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_torch</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="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_triton</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">),</span> <span class="p">(</span><span class="n">y_triton</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>As expected, the results are identical.</p>
|
||||
</div>
|
||||
<div class="section" id="benchmark">
|
||||
<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Here we will benchmark our operation as a function of the number of columns in the input matrix – assuming 4096 rows.
|
||||
We will then compare its performance against (1) <code class="code docutils literal notranslate"><span class="pre">torch.softmax</span></code> and (2) the <code class="code docutils literal notranslate"><span class="pre">naive_softmax</span></code> defined above.</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">'N'</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">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">100</span><span class="p">)</span>
|
||||
<span class="p">],</span> <span class="c1"># different possible values for `x_name`</span>
|
||||
<span class="n">line_arg</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">line_vals</span><span class="o">=</span><span class="p">[</span>
|
||||
<span class="s1">'triton'</span><span class="p">,</span>
|
||||
<span class="s1">'torch-native'</span><span class="p">,</span>
|
||||
<span class="s1">'torch-jit'</span><span class="p">,</span>
|
||||
<span class="p">],</span> <span class="c1"># possible values for `line_arg``</span>
|
||||
<span class="n">line_names</span><span class="o">=</span><span class="p">[</span>
|
||||
<span class="s2">"Triton"</span><span class="p">,</span>
|
||||
<span class="s2">"Torch (native)"</span><span class="p">,</span>
|
||||
<span class="s2">"Torch (jit)"</span><span class="p">,</span>
|
||||
<span class="p">],</span> <span class="c1"># label name for the lines</span>
|
||||
<span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'--'</span><span class="p">)],</span> <span class="c1"># line styles</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">"softmax-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="s1">'M'</span><span class="p">:</span> <span class="mi">4096</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">M</span><span class="p">,</span> <span class="n">N</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">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="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'torch-native'</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">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="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">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'torch-jit'</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">naive_softmax</span><span class="p">(</span><span class="n">x</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">2</span> <span class="o">*</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="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>
|
||||
|
||||
|
||||
<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> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</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" />
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
|
||||
N Triton Torch (native) Torch (jit)
|
||||
0 256.0 546.133347 546.133347 188.321838
|
||||
1 384.0 585.142862 585.142862 151.703707
|
||||
2 512.0 655.360017 606.814814 154.566038
|
||||
3 640.0 682.666684 640.000002 158.759699
|
||||
4 768.0 722.823517 664.216187 163.839992
|
||||
.. ... ... ... ...
|
||||
93 12160.0 814.058574 405.755985 198.631953
|
||||
94 12288.0 814.111783 415.661740 198.794749
|
||||
95 12416.0 814.163950 412.149375 198.457532
|
||||
96 12544.0 814.214963 412.971190 198.716830
|
||||
97 12672.0 814.265046 411.679167 198.776477
|
||||
|
||||
[98 rows x 4 columns]
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In the above plot, we can see that:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p>Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.</p></li>
|
||||
<li><p>Triton is noticeably faster than <code class="code docutils literal notranslate"><span class="pre">torch.softmax</span></code> – in addition to being <strong>easier to read, understand and maintain</strong>.
|
||||
Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 22.664 seconds)</p>
|
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
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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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<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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<li class="toctree-l2"><a class="reference internal" href="02-fused-softmax.html">Fused Softmax</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Matrix Multiplication</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><ul>
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<li class="toctree-l4"><a class="reference internal" href="#pointer-arithmetics">Pointer Arithmetics</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#l2-cache-optimizations">L2 Cache Optimizations</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#benchmark">Benchmark</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="#square-matrix-performance">Square Matrix Performance</a></li>
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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-03-matrix-multiplication-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="matrix-multiplication">
|
||||
<span id="sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"></span><h1>Matrix Multiplication<a class="headerlink" href="#matrix-multiplication" title="Permalink to this headline">¶</a></h1>
|
||||
<p>In this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication
|
||||
kernel that achieves performance on par with cuBLAS.
|
||||
You will specifically learn about:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Block-level matrix multiplications</p></li>
|
||||
<li><p>Multi-dimensional pointer arithmetic</p></li>
|
||||
<li><p>Program re-ordering for improved L2 cache hit rate</p></li>
|
||||
<li><p>Automatic performance tuning</p></li>
|
||||
</ul>
|
||||
<div class="section" id="motivations">
|
||||
<h2>Motivations<a class="headerlink" href="#motivations" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Matrix multiplications are a key building block of most modern high-performance computing systems.
|
||||
They are notoriously hard to optimize, hence their implementation is generally done by
|
||||
hardware vendors themselves as part of so-called “kernel libraries” (e.g., cuBLAS).
|
||||
Unfortunately, these libraries are often proprietary and cannot be easily customized
|
||||
to accomodate the needs of modern deep learning workloads (e.g., fused activation functions).
|
||||
In this tutorial, you will learn how to implement efficient matrix multiplications by
|
||||
yourself with Triton, in a way that is easy to customize and extend.</p>
|
||||
<p>Roughly speaking, the kernel that we will write will implement the following blocked
|
||||
algorithm to multiply a (M, K) by a (K, N) matrix:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># do in parallel</span>
|
||||
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</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">BLOCK_SIZE_M</span><span class="p">):</span>
|
||||
<span class="c1"># do in parallel</span>
|
||||
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">):</span>
|
||||
<span class="n">acc</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">):</span>
|
||||
<span class="n">a</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">]</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">B</span><span class="p">[</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</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="n">BLOCK_SIZE_N</span><span class="p">]</span>
|
||||
<span class="n">acc</span> <span class="o">+=</span> <span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
|
||||
<span class="n">C</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</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="n">BLOCK_SIZE_N</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span><span class="p">;</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>where each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.</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>The above algorithm is, actually, fairly straightforward to implement in Triton.
|
||||
The main difficulty comes from the computation of the memory locations at which blocks
|
||||
of <code class="code docutils literal notranslate"><span class="pre">A</span></code> and <code class="code docutils literal notranslate"><span class="pre">B</span></code> must be read in the inner loop. For that, we need
|
||||
multi-dimensional pointer arithmetics.</p>
|
||||
<div class="section" id="pointer-arithmetics">
|
||||
<h3>Pointer Arithmetics<a class="headerlink" href="#pointer-arithmetics" title="Permalink to this headline">¶</a></h3>
|
||||
<p>For a row-major 2D tensor <code class="code docutils literal notranslate"><span class="pre">X</span></code>, the memory location of <code class="code docutils literal notranslate"><span class="pre">X[i,</span> <span class="pre">j]</span></code> is given b
|
||||
y <code class="code docutils literal notranslate"><span class="pre">&X[i,</span> <span class="pre">j]</span> <span class="pre">=</span> <span class="pre">X</span> <span class="pre">+</span> <span class="pre">i*stride_xi</span> <span class="pre">+</span> <span class="pre">j*stride_xj</span></code>.
|
||||
Therefore, blocks of pointers for <code class="code docutils literal notranslate"><span class="pre">A[m</span> <span class="pre">:</span> <span class="pre">m+BLOCK_SIZE_M,</span> <span class="pre">k:k+BLOCK_SIZE_K]</span></code> and
|
||||
<code class="code docutils literal notranslate"><span class="pre">B[k</span> <span class="pre">:</span> <span class="pre">k+BLOCK_SIZE_K,</span> <span class="pre">n</span> <span class="pre">:</span> <span class="pre">n+BLOCK_SIZE_N]</span></code> can be defined in pseudo-code as:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">&</span><span class="n">A</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">k</span><span class="p">:</span><span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">]</span> <span class="o">=</span> <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">A</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="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">A</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
|
||||
<span class="o">&</span><span class="n">B</span><span class="p">[</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</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="n">BLOCK_SIZE_N</span><span class="p">]</span> <span class="o">=</span> <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">B</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="o">+</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="n">BLOCK_SIZE_N</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">B</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>Which means that pointers for blocks of A and B can be initialized (i.e., <code class="code docutils literal notranslate"><span class="pre">k=0</span></code>) in Triton as:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">offs_am</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">offs_bn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">offs_k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">)</span>
|
||||
<span class="n">a_ptrs</span> <span class="o">=</span> <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_am</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">stride_am</span> <span class="o">+</span> <span class="n">offs_k</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">stride_ak</span><span class="p">)</span>
|
||||
<span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_k</span> <span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">stride_bk</span> <span class="o">+</span> <span class="n">offs_bn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">stride_bn</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>And then updated in the inner loop as follows:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">pa</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_ak</span><span class="p">;</span>
|
||||
<span class="n">pb</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_bk</span><span class="p">;</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
</div>
|
||||
<div class="section" id="l2-cache-optimizations">
|
||||
<h3>L2 Cache Optimizations<a class="headerlink" href="#l2-cache-optimizations" title="Permalink to this headline">¶</a></h3>
|
||||
<p>As mentioned above, each program instance computes a <code class="code docutils literal notranslate"><span class="pre">[BLOCK_SIZE_M,</span> <span class="pre">BLOCK_SIZE_N]</span></code>
|
||||
block of <code class="code docutils literal notranslate"><span class="pre">C</span></code>.
|
||||
It is important to remember that the order in which these blocks are computed does
|
||||
matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
|
||||
a simple row-major ordering</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><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>
|
||||
<span class="n">grid_m</span> <span class="o">=</span> <span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">BLOCK_SIZE_M</span><span class="p">;</span>
|
||||
<span class="n">grid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">BLOCK_SIZE_N</span><span class="p">;</span>
|
||||
<span class="n">pid_m</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">/</span> <span class="n">grid_n</span><span class="p">;</span>
|
||||
<span class="n">pid_n</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">%</span> <span class="n">grid_n</span><span class="p">;</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>is just not going to cut it.</p>
|
||||
<p>One possible solution is to launch blocks in an order that promotes data reuse.
|
||||
This can be done by ‘super-grouping’ blocks in groups of <code class="code docutils literal notranslate"><span class="pre">GROUP_M</span></code> rows before
|
||||
switching to the next column:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># program ID</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="c1"># number of program ids along the M axis</span>
|
||||
<span class="n">num_pid_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="c1"># number of programs ids along the N axis</span>
|
||||
<span class="n">num_pid_n</span> <span class="o">=</span> <span class="n">tl</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">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="c1"># number of programs in group</span>
|
||||
<span class="n">num_pid_in_group</span> <span class="o">=</span> <span class="n">GROUP_SIZE_M</span> <span class="o">*</span> <span class="n">num_pid_n</span>
|
||||
<span class="c1"># id of the group this program is in</span>
|
||||
<span class="n">group_id</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">//</span> <span class="n">num_pid_in_group</span>
|
||||
<span class="c1"># row-id of the first program in the group</span>
|
||||
<span class="n">first_pid_m</span> <span class="o">=</span> <span class="n">group_id</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</span>
|
||||
<span class="c1"># if `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller</span>
|
||||
<span class="n">group_size_m</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_pid_m</span> <span class="o">-</span> <span class="n">first_pid_m</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">)</span>
|
||||
<span class="c1"># *within groups*, programs are ordered in a column-major order</span>
|
||||
<span class="c1"># row-id of the program in the *launch grid*</span>
|
||||
<span class="n">pid_m</span> <span class="o">=</span> <span class="n">first_pid_m</span> <span class="o">+</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">group_size_m</span><span class="p">)</span>
|
||||
<span class="c1"># col-id of the program in the *launch grid*</span>
|
||||
<span class="n">pid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">num_pid_in_group</span><span class="p">)</span> <span class="o">//</span> <span class="n">group_size_m</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
|
||||
we can see that if we compute the output in row-major ordering, we need to load 90
|
||||
blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
|
||||
ordering, we only need to load 54 blocks.</p>
|
||||
<blockquote>
|
||||
<div><img alt="../../_images/grouped_vs_row_major_ordering.png" src="../../_images/grouped_vs_row_major_ordering.png" />
|
||||
</div></blockquote>
|
||||
<p>In practice, this can improve the performance of our matrix multiplication kernel by
|
||||
more than 10% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="final-result">
|
||||
<h2>Final Result<a class="headerlink" href="#final-result" 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">torch</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">triton</span>
|
||||
<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
|
||||
|
||||
<span class="c1"># %</span>
|
||||
<span class="c1"># :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`</span>
|
||||
<span class="c1"># decorator, which consumes:</span>
|
||||
<span class="c1"># - A list of :code:`triton.Config` objects that define different configurations of</span>
|
||||
<span class="c1"># meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try</span>
|
||||
<span class="c1"># - An autotuning *key* whose change in values will trigger evaluation of all the</span>
|
||||
<span class="c1"># provided configs</span>
|
||||
|
||||
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">autotune</span><span class="p">(</span>
|
||||
<span class="n">configs</span><span class="o">=</span><span class="p">[</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_N'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'BLOCK_SIZE_K'</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">'GROUP_SIZE_M'</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
||||
<span class="p">],</span>
|
||||
<span class="n">key</span><span class="o">=</span><span class="p">[</span><span class="s1">'M'</span><span class="p">,</span> <span class="s1">'N'</span><span class="p">,</span> <span class="s1">'K'</span><span class="p">],</span>
|
||||
<span class="p">)</span>
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">matmul_kernel</span><span class="p">(</span>
|
||||
<span class="c1"># Pointers to matrices</span>
|
||||
<span class="n">a_ptr</span><span class="p">,</span> <span class="n">b_ptr</span><span class="p">,</span> <span class="n">c_ptr</span><span class="p">,</span>
|
||||
<span class="c1"># Matrix dimensions</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span>
|
||||
<span class="c1"># The stride variables represent how much to increase the ptr by when moving by 1</span>
|
||||
<span class="c1"># element in a particular dimension. E.g. stride_am is how much to increase a_ptr</span>
|
||||
<span class="c1"># by to get the element one row down (A has M rows)</span>
|
||||
<span class="n">stride_am</span><span class="p">,</span> <span class="n">stride_ak</span><span class="p">,</span>
|
||||
<span class="n">stride_bk</span><span class="p">,</span> <span class="n">stride_bn</span><span class="p">,</span>
|
||||
<span class="n">stride_cm</span><span class="p">,</span> <span class="n">stride_cn</span><span class="p">,</span>
|
||||
<span class="c1"># Meta-parameters</span>
|
||||
<span class="n">BLOCK_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
|
||||
<span class="n">GROUP_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
|
||||
<span class="n">ACTIVATION</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
|
||||
<span class="p">):</span>
|
||||
<span class="sd">"""Kernel for computing the matmul C = A x B.</span>
|
||||
<span class="sd"> A has shape (M, K), B has shape (K, N) and C has shape (M, N)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># -----------------------------------------------------------</span>
|
||||
<span class="c1"># Map program ids `pid` to the block of C it should compute.</span>
|
||||
<span class="c1"># This is done in a grouped ordering to promote L2 data reuse</span>
|
||||
<span class="c1"># See above `L2 Cache Optimizations` section for details</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">num_pid_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">num_pid_n</span> <span class="o">=</span> <span class="n">tl</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">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">num_pid_in_group</span> <span class="o">=</span> <span class="n">GROUP_SIZE_M</span> <span class="o">*</span> <span class="n">num_pid_n</span>
|
||||
<span class="n">group_id</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">//</span> <span class="n">num_pid_in_group</span>
|
||||
<span class="n">first_pid_m</span> <span class="o">=</span> <span class="n">group_id</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</span>
|
||||
<span class="n">group_size_m</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_pid_m</span> <span class="o">-</span> <span class="n">first_pid_m</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">pid_m</span> <span class="o">=</span> <span class="n">first_pid_m</span> <span class="o">+</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">group_size_m</span><span class="p">)</span>
|
||||
<span class="n">pid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">num_pid_in_group</span><span class="p">)</span> <span class="o">//</span> <span class="n">group_size_m</span>
|
||||
|
||||
<span class="c1"># ----------------------------------------------------------</span>
|
||||
<span class="c1"># Create pointers for the first blocks of A and B.</span>
|
||||
<span class="c1"># We will advance this pointer as we move in the K direction</span>
|
||||
<span class="c1"># and accumulate</span>
|
||||
<span class="c1"># a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers</span>
|
||||
<span class="c1"># b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers</span>
|
||||
<span class="c1"># see above `Pointer Arithmetics` section for details</span>
|
||||
<span class="n">offs_am</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">offs_bn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">offs_k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">)</span>
|
||||
<span class="n">a_ptrs</span> <span class="o">=</span> <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_am</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_am</span> <span class="o">+</span> <span class="n">offs_k</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">stride_ak</span><span class="p">)</span>
|
||||
<span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_k</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_bk</span> <span class="o">+</span> <span class="n">offs_bn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">stride_bn</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># -----------------------------------------------------------</span>
|
||||
<span class="c1"># Iterate to compute a block of the C matrix</span>
|
||||
<span class="c1"># We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block</span>
|
||||
<span class="c1"># of fp32 values for higher accuracy.</span>
|
||||
<span class="c1"># `accumulator` will be converted back to fp16 after the loop</span>
|
||||
<span class="n">accumulator</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">):</span>
|
||||
<span class="c1"># Note that for simplicity, we don't apply a mask here.</span>
|
||||
<span class="c1"># This means that if K is not a multiple of BLOCK_SIZE_K,</span>
|
||||
<span class="c1"># this will access out-of-bounds memory and produce an</span>
|
||||
<span class="c1"># error or (worse!) incorrect results.</span>
|
||||
<span class="n">a</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">a_ptrs</span><span class="p">)</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">)</span>
|
||||
<span class="c1"># We accumulate along the K dimension</span>
|
||||
<span class="n">accumulator</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
|
||||
<span class="c1"># Advance the ptrs to the next K block</span>
|
||||
<span class="n">a_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_ak</span>
|
||||
<span class="n">b_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_bk</span>
|
||||
<span class="c1"># you can fuse arbitrary activation functions here</span>
|
||||
<span class="c1"># while the accumulator is still in FP32!</span>
|
||||
<span class="k">if</span> <span class="n">ACTIVATION</span><span class="p">:</span>
|
||||
<span class="n">accumulator</span> <span class="o">=</span> <span class="n">ACTIVATION</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># -----------------------------------------------------------</span>
|
||||
<span class="c1"># Write back the block of the output matrix C</span>
|
||||
<span class="n">offs_cm</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">offs_cn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">c_ptrs</span> <span class="o">=</span> <span class="n">c_ptr</span> <span class="o">+</span> <span class="n">stride_cm</span> <span class="o">*</span> <span class="n">offs_cm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">stride_cn</span> <span class="o">*</span> <span class="n">offs_cn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
|
||||
<span class="n">c_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">offs_cm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o"><</span> <span class="n">M</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">offs_cn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o"><</span> <span class="n">N</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">c_ptrs</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">c_mask</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="c1"># we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`</span>
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">leaky_relu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="mf">0.01</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>We can now create a convenience wrapper function that only takes two input tensors
|
||||
and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="c1"># checks constraints</span>
|
||||
<span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</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="s2">"incompatible dimensions"</span>
|
||||
<span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(),</span> <span class="s2">"matrix A must be contiguous"</span>
|
||||
<span class="k">assert</span> <span class="n">b</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(),</span> <span class="s2">"matrix B must be contiguous"</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">K</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="n">K</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="k">assert</span> <span class="p">(</span>
|
||||
<span class="n">K</span> <span class="o">%</span> <span class="mi">32</span> <span class="o">==</span> <span class="mi">0</span>
|
||||
<span class="p">),</span> <span class="s2">"We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"</span>
|
||||
<span class="c1"># allocates output</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</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="n">a</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="c1"># 1D launch kernel where each block gets its own program.</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">M</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s1">'BLOCK_SIZE_M'</span><span class="p">])</span> <span class="o">*</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_SIZE_N'</span><span class="p">]),</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">matmul_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span>
|
||||
<span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span>
|
||||
<span class="n">a</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">a</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
|
||||
<span class="n">b</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">b</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
|
||||
<span class="n">c</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">c</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
|
||||
<span class="n">ACTIVATION</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">c</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>
|
||||
<p>We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)</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">a</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">512</span><span class="p">,</span> <span class="mi">512</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">float16</span><span class="p">)</span>
|
||||
<span class="n">b</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">512</span><span class="p">,</span> <span class="mi">512</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">float16</span><span class="p">)</span>
|
||||
<span class="n">triton_output</span> <span class="o">=</span> <span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">torch_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"triton_output=</span><span class="si">{</span><span class="n">triton_output</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"torch_output=</span><span class="si">{</span><span class="n">torch_output</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">triton_output</span><span class="p">,</span> <span class="n">torch_output</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"✅ Triton and Torch match"</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"❌ Triton and Torch differ"</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>triton_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3984, 24.4531, -32.3438],
|
||||
[ 6.3555, -19.6094, 34.0938, ..., -5.8945, 5.2891, 6.8867],
|
||||
[-32.0625, 5.9492, 15.3984, ..., -21.3906, -23.9844, -10.1328],
|
||||
...,
|
||||
[ -5.7031, 7.4492, 8.2656, ..., -10.6953, -40.0000, 17.7500],
|
||||
[ 25.5000, 24.3281, -8.4688, ..., -18.9375, 32.5312, -29.9219],
|
||||
[ -5.3477, 4.9844, 11.8906, ..., 5.5898, 6.4023, -17.3125]],
|
||||
device='cuda:0', dtype=torch.float16)
|
||||
torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -32.3438],
|
||||
[ 6.3516, -19.6094, 34.0938, ..., -5.8906, 5.2812, 6.8828],
|
||||
[-32.0625, 5.9531, 15.3984, ..., -21.4062, -23.9844, -10.1328],
|
||||
...,
|
||||
[ -5.7070, 7.4492, 8.2656, ..., -10.6953, -40.0000, 17.7500],
|
||||
[ 25.5000, 24.3438, -8.4609, ..., -18.9375, 32.5312, -29.9219],
|
||||
[ -5.3477, 4.9805, 11.8828, ..., 5.5859, 6.4023, -17.3125]],
|
||||
device='cuda:0', dtype=torch.float16)
|
||||
✅ Triton and Torch match
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="benchmark">
|
||||
<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
|
||||
<div class="section" id="square-matrix-performance">
|
||||
<h3>Square Matrix Performance<a class="headerlink" href="#square-matrix-performance" title="Permalink to this headline">¶</a></h3>
|
||||
<p>We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.</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">'M'</span><span class="p">,</span> <span class="s1">'N'</span><span class="p">,</span> <span class="s1">'K'</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">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">33</span><span class="p">)</span>
|
||||
<span class="p">],</span> <span class="c1"># different possible values for `x_name`</span>
|
||||
<span class="n">line_arg</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="c1"># possible values for `line_arg``</span>
|
||||
<span class="n">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">'cublas'</span><span class="p">,</span> <span class="s1">'cublas + relu'</span><span class="p">,</span> <span class="s1">'triton'</span><span class="p">,</span> <span class="s1">'triton + relu'</span><span class="p">],</span>
|
||||
<span class="c1"># label name for the lines</span>
|
||||
<span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"cuBLAS"</span><span class="p">,</span> <span class="s2">"cuBLAS (+ torch.nn.LeakyReLU)"</span><span class="p">,</span> <span class="s2">"Triton"</span><span class="p">,</span> <span class="s2">"Triton (+ LeakyReLU)"</span><span class="p">],</span>
|
||||
<span class="c1"># line styles</span>
|
||||
<span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'--'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'--'</span><span class="p">)],</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="s2">"TFLOPS"</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">"matmul-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="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">benchmark</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">K</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
|
||||
<span class="n">a</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">K</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">float16</span><span class="p">)</span>
|
||||
<span class="n">b</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">K</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">float16</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'cublas'</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">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
|
||||
<span class="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">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'cublas + relu'</span><span class="p">:</span>
|
||||
<span class="n">torch_relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</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">torch_relu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'triton + relu'</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">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">leaky_relu</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">perf</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">M</span> <span class="o">*</span> <span class="n">N</span> <span class="o">*</span> <span class="n">K</span> <span class="o">*</span> <span class="mf">1e-12</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="k">return</span> <span class="n">perf</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</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> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="03 matrix multiplication" class="sphx-glr-single-img" src="../../_images/sphx_glr_03-matrix-multiplication_001.png" />
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
|
||||
M cuBLAS ... Triton Triton (+ LeakyReLU)
|
||||
0 256.0 2.978909 ... 3.276800 2.978909
|
||||
1 384.0 7.372800 ... 7.899428 8.507077
|
||||
2 512.0 14.563555 ... 16.384000 16.384000
|
||||
3 640.0 22.260869 ... 24.380953 24.380953
|
||||
4 768.0 32.768000 ... 35.389441 34.028308
|
||||
5 896.0 37.971025 ... 40.140799 39.025776
|
||||
6 1024.0 49.932191 ... 53.773130 52.428801
|
||||
7 1152.0 45.242181 ... 48.161033 47.396572
|
||||
8 1280.0 51.200001 ... 57.690139 57.690139
|
||||
9 1408.0 64.138541 ... 68.147202 67.305878
|
||||
10 1536.0 79.526831 ... 79.526831 79.526831
|
||||
11 1664.0 62.929456 ... 63.372618 62.929456
|
||||
12 1792.0 72.512412 ... 63.142831 62.790080
|
||||
13 1920.0 69.120002 ... 71.257735 70.892307
|
||||
14 2048.0 73.262953 ... 78.033565 77.672296
|
||||
15 2176.0 83.155572 ... 87.115360 86.739860
|
||||
16 2304.0 68.251065 ... 77.810656 77.810656
|
||||
17 2432.0 71.305746 ... 75.522751 75.320281
|
||||
18 2560.0 77.833728 ... 82.539044 82.125311
|
||||
19 2688.0 83.737433 ... 91.185232 90.748936
|
||||
20 2816.0 84.523664 ... 81.445766 84.523664
|
||||
21 2944.0 81.298583 ... 83.899046 84.040530
|
||||
22 3072.0 82.540970 ... 90.164177 89.735509
|
||||
23 3200.0 84.656085 ... 96.385543 96.240602
|
||||
24 3328.0 83.226931 ... 85.602017 84.249616
|
||||
25 3456.0 81.849303 ... 85.133652 84.864807
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Low-Memory Dropout</a><ul>
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<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-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="low-memory-dropout">
|
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<span id="sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"></span><h1>Low-Memory Dropout<a class="headerlink" href="#low-memory-dropout" title="Permalink to this headline">¶</a></h1>
|
||||
<p>In this tutorial, you will write a memory-efficient implementation of dropout whose state
|
||||
will be composed of a single int32 seed. This differs from more traditional implementations of dropout,
|
||||
whose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:</p>
|
||||
<ul class="simple">
|
||||
<li><p>The limitations of naive implementations of Dropout with PyTorch</p></li>
|
||||
<li><p>Parallel pseudo-random number generation in Triton</p></li>
|
||||
</ul>
|
||||
<div class="section" id="baseline">
|
||||
<h2>Baseline<a class="headerlink" href="#baseline" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The <em>dropout</em> operator was first introduced in <a class="reference internal" href="#srivastava2014" id="id1"><span>[SRIVASTAVA2014]</span></a> as a way to improve the performance
|
||||
of deep neural networks in low-data regime (i.e. regularization).</p>
|
||||
<p>It takes a vector as input and produces a vector of the same shape as output. Each scalar in the
|
||||
output has a probability <span class="math notranslate nohighlight">\(p\)</span> of being changed to zero and otherwise it is copied from the input.
|
||||
This forces the network to perform well even when only <span class="math notranslate nohighlight">\(1 - p\)</span> scalars from the input are available.</p>
|
||||
<p>At evaluation time we want to use the full power of the network so we set <span class="math notranslate nohighlight">\(p=0\)</span>. Naively this would
|
||||
increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease
|
||||
in the output softmax temperature). To prevent this we multiply the output by <span class="math notranslate nohighlight">\(\frac{1}{1 - p}\)</span>, which
|
||||
keeps the norm consistent regardless of the dropout probability.</p>
|
||||
<p>Let’s first take a look at the baseline implementation.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tabulate</span>
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">triton</span>
|
||||
<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
|
||||
|
||||
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">_dropout</span><span class="p">(</span>
|
||||
<span class="n">x_ptr</span><span class="p">,</span> <span class="c1"># pointer to the input</span>
|
||||
<span class="n">x_keep_ptr</span><span class="p">,</span> <span class="c1"># pointer to a mask of 0s and 1s</span>
|
||||
<span class="n">output_ptr</span><span class="p">,</span> <span class="c1"># pointer to the output</span>
|
||||
<span class="n">n_elements</span><span class="p">,</span> <span class="c1"># number of elements in the `x` tensor</span>
|
||||
<span class="n">p</span><span class="p">,</span> <span class="c1"># probability that an element of `x` is changed to zero</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
|
||||
<span class="p">):</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
|
||||
<span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o"><</span> <span class="n">n_elements</span>
|
||||
<span class="c1"># Load data</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">x_keep</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_keep_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="c1"># The line below is the crucial part, described in the paragraph above!</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="c1"># Write-back output</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_keep</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
|
||||
<span class="n">output</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="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
|
||||
<span class="n">n_elements</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</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_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK_SIZE'</span><span class="p">]),)</span>
|
||||
<span class="n">_dropout</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">x_keep</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">output</span>
|
||||
|
||||
|
||||
<span class="c1"># Input tensor</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">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="c1"># Dropout mask</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="mf">0.5</span>
|
||||
<span class="n">x_keep</span> <span class="o">=</span> <span class="p">(</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="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span> <span class="o">></span> <span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_keep</span><span class="o">=</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">tabulate</span><span class="o">.</span><span class="n">tabulate</span><span class="p">([</span>
|
||||
<span class="p">[</span><span class="s2">"input"</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
|
||||
<span class="p">[</span><span class="s2">"keep mask"</span><span class="p">]</span> <span class="o">+</span> <span class="n">x_keep</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
|
||||
<span class="p">[</span><span class="s2">"output"</span><span class="p">]</span> <span class="o">+</span> <span class="n">output</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</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>--------- ------- --------- -------- -------- -------- -------- -------- -------- --------- ---------
|
||||
input 1.541 -0.293429 -2.17879 0.568431 -1.08452 -1.3986 0.403347 0.838026 -0.719258 -0.403344
|
||||
keep mask 1 1 0 1 0 1 1 0 0 0
|
||||
output 3.08199 -0.586858 0 1.13686 0 -2.79719 0.806694 0 0 0
|
||||
--------- ------- --------- -------- -------- -------- -------- -------- -------- --------- ---------
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="seeded-dropout">
|
||||
<h2>Seeded dropout<a class="headerlink" href="#seeded-dropout" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly
|
||||
we need to store the dropout mask for backpropagation. Secondly, dropout state management can get
|
||||
very tricky when using recompute/checkpointing (e.g. see all the notes about <cite>preserve_rng_state</cite> in
|
||||
<a class="reference external" href="https://pytorch.org/docs/1.9.0/checkpoint.html">https://pytorch.org/docs/1.9.0/checkpoint.html</a>). In this tutorial we’ll describe an alternative implementation
|
||||
that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management
|
||||
of persisting randomness across multiple invocations of the kernel.</p>
|
||||
<p>Pseudorandom number generation in Triton is simple! In this tutorial we will use the
|
||||
<code class="code docutils literal notranslate"><span class="pre">triton.language.rand</span></code> function which generates a block of uniformly distributed <code class="code docutils literal notranslate"><span class="pre">float32</span></code>
|
||||
values in [0, 1), given a seed and a block of <code class="code docutils literal notranslate"><span class="pre">int32</span></code> offsets. But if you need it, Triton also provides
|
||||
other <a class="reference internal" href="../../python-api/triton.language.html#random-number-generation"><span class="std std-ref">random number generation strategies</span></a>.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Triton’s implementation of PRNG is based on the Philox algorithm (described on <a class="reference internal" href="#salmon2011" id="id2"><span>[SALMON2011]</span></a>).</p>
|
||||
</div>
|
||||
<p>Let’s put it all together.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">_seeded_dropout</span><span class="p">(</span>
|
||||
<span class="n">x_ptr</span><span class="p">,</span>
|
||||
<span class="n">output_ptr</span><span class="p">,</span>
|
||||
<span class="n">n_elements</span><span class="p">,</span>
|
||||
<span class="n">p</span><span class="p">,</span>
|
||||
<span class="n">seed</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
|
||||
<span class="p">):</span>
|
||||
<span class="c1"># compute memory offsets of elements handled by this instance</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
|
||||
<span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
|
||||
<span class="c1"># load data from x</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o"><</span> <span class="n">n_elements</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="c1"># randomly prune it</span>
|
||||
<span class="n">random</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span>
|
||||
<span class="n">x_keep</span> <span class="o">=</span> <span class="n">random</span> <span class="o">></span> <span class="n">p</span>
|
||||
<span class="c1"># write-back</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span>
|
||||
<span class="n">output</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="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
|
||||
<span class="n">n_elements</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</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_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">'BLOCK_SIZE'</span><span class="p">]),)</span>
|
||||
<span class="n">_seeded_dropout</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">output</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">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="c1"># Compare this to the baseline - dropout mask is never instantiated!</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
|
||||
<span class="n">output2</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
|
||||
<span class="n">output3</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">512</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">tabulate</span><span class="o">.</span><span class="n">tabulate</span><span class="p">([</span>
|
||||
<span class="p">[</span><span class="s2">"input"</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
|
||||
<span class="p">[</span><span class="s2">"output (seed = 123)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">output</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
|
||||
<span class="p">[</span><span class="s2">"output (seed = 123)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">output2</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
|
||||
<span class="p">[</span><span class="s2">"output (seed = 512)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">output3</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</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>------------------- --------- -------- -------- ------- -------- -------- --------- --------- --------- ---------
|
||||
input -0.952835 0.371721 0.408716 1.42142 0.149397 -0.67086 -0.214186 -0.431969 -0.707878 -0.106434
|
||||
output (seed = 123) 0 0.743443 0 0 0 -1.34172 0 0 -1.41576 -0.212868
|
||||
output (seed = 123) 0 0.743443 0 0 0 -1.34172 0 0 -1.41576 -0.212868
|
||||
output (seed = 512) 0 0 0.817432 2.84284 0 -1.34172 -0.428372 0 0 0
|
||||
------------------- --------- -------- -------- ------- -------- -------- --------- --------- --------- ---------
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same!
|
||||
If you’d like explore further applications of pseudorandomness in GPU programming, we encourage you
|
||||
to explore the <cite>triton/language/random</cite> folder!</p>
|
||||
</div>
|
||||
<div class="section" id="exercises">
|
||||
<h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this headline">¶</a></h2>
|
||||
<ol class="arabic simple">
|
||||
<li><p>Extend the kernel to operate over a matrix and use a vector of seeds - one per row.</p></li>
|
||||
<li><p>Add support for striding.</p></li>
|
||||
<li><p>(challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.</p></li>
|
||||
</ol>
|
||||
</div>
|
||||
<div class="section" id="references">
|
||||
<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="citation">
|
||||
<dt class="label" id="salmon2011"><span class="brackets"><a class="fn-backref" href="#id2">SALMON2011</a></span></dt>
|
||||
<dd><p>John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, “Parallel Random Numbers: As Easy as 1, 2, 3”, 2011</p>
|
||||
</dd>
|
||||
<dt class="label" id="srivastava2014"><span class="brackets"><a class="fn-backref" href="#id1">SRIVASTAVA2014</a></span></dt>
|
||||
<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
|
||||
</dd>
|
||||
</dl>
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<li class="toctree-l2"><a class="reference internal" href="04-low-memory-dropout.html">Low-Memory Dropout</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Layer Normalization</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.html">triton</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-1/introduction.html">Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-2/related-work.html">Related Work</a></li>
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<a href="../../_sources/getting-started/tutorials/05-layer-norm.rst.txt" rel="nofollow"> View page source</a>
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<div itemprop="articleBody">
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<div class="sphx-glr-download-link-note admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">here</span></a>
|
||||
to download the full example code</p>
|
||||
</div>
|
||||
<div class="sphx-glr-example-title section" id="layer-normalization">
|
||||
<span id="sphx-glr-getting-started-tutorials-05-layer-norm-py"></span><h1>Layer Normalization<a class="headerlink" href="#layer-normalization" title="Permalink to this headline">¶</a></h1>
|
||||
<img alt="05 layer norm" class="sphx-glr-single-img" src="../../_images/sphx_glr_05-layer-norm_001.png" />
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer-norm-backward:
|
||||
N Triton Torch Apex
|
||||
0 1024.0 361.411758 97.912354 303.407414
|
||||
1 1536.0 409.599994 134.540150 341.333333
|
||||
2 2048.0 491.520012 161.154101 323.368435
|
||||
3 2560.0 461.954908 181.238943 326.808501
|
||||
4 3072.0 515.580429 192.501302 320.556515
|
||||
5 3584.0 554.941930 208.271186 311.652167
|
||||
6 4096.0 561.737163 220.907859 294.323343
|
||||
7 4608.0 502.690905 232.825259 291.799469
|
||||
8 5120.0 525.128191 242.366855 289.129408
|
||||
9 5632.0 540.671974 243.107920 288.820505
|
||||
10 6144.0 546.133354 248.661056 286.322318
|
||||
11 6656.0 527.207907 256.000009 285.257135
|
||||
12 7168.0 503.017523 260.063480 285.293536
|
||||
13 7680.0 483.779539 262.938666 275.928134
|
||||
14 8192.0 463.698115 266.767970 284.526763
|
||||
15 8704.0 413.655443 267.472468 284.987724
|
||||
16 9216.0 427.822068 271.724806 288.375482
|
||||
17 9728.0 436.396262 280.615388 289.667485
|
||||
18 10240.0 446.025405 286.433562 290.153487
|
||||
19 10752.0 428.651173 246.935876 290.267711
|
||||
20 11264.0 426.397479 245.536784 286.980888
|
||||
21 11776.0 420.571432 249.667843 288.981596
|
||||
22 12288.0 416.542386 254.673582 294.617366
|
||||
23 12800.0 410.695192 253.884294 288.180121
|
||||
24 13312.0 409.075539 252.959629 290.443638
|
||||
25 13824.0 404.604870 257.390218 292.056329
|
||||
26 14336.0 394.116833 254.862216 286.959121
|
||||
27 14848.0 385.662341 257.852379 289.952797
|
||||
28 15360.0 380.433442 257.970599 286.433562
|
||||
29 15872.0 370.192407 261.626369 290.562936
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="line-block">
|
||||
<div class="line"><br /></div>
|
||||
</div>
|
||||
<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="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="c1"># This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it</span>
|
||||
<span class="c1"># should not be added to extras_require in setup.py.</span>
|
||||
<span class="kn">import</span> <span class="nn">apex</span>
|
||||
<span class="n">HAS_APEX</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
|
||||
<span class="n">HAS_APEX</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
|
||||
<span class="c1"># Forward Pass</span>
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">_layer_norm_fwd_fused</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">W</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
|
||||
<span class="c1"># position of elements processed by this program</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">cols</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">cols</span> <span class="o"><</span> <span class="n">N</span>
|
||||
<span class="c1"># offset data pointers to start at the row of interest</span>
|
||||
<span class="n">X</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
|
||||
<span class="n">Y</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
|
||||
<span class="c1"># load data and cast to float32</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="c1"># compute mean</span>
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</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">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
|
||||
<span class="c1"># compute std</span>
|
||||
<span class="n">xmean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">x</span> <span class="o">-</span> <span class="n">mean</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xmean</span> <span class="o">*</span> <span class="n">xmean</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
|
||||
<span class="n">rstd</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">tl</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">var</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">xhat</span> <span class="o">=</span> <span class="n">xmean</span> <span class="o">*</span> <span class="n">rstd</span>
|
||||
<span class="c1"># write-back mean/rstd</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">row</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">V</span> <span class="o">+</span> <span class="n">row</span><span class="p">,</span> <span class="n">rstd</span><span class="p">)</span>
|
||||
<span class="c1"># multiply by weight and add bias</span>
|
||||
<span class="n">w</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">W</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">B</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">xhat</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">b</span>
|
||||
<span class="c1"># write-back</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">Y</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="c1"># Backward pass (DX + partial DW + partial DB)</span>
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">_layer_norm_bwd_dx_fused</span><span class="p">(</span><span class="n">DX</span><span class="p">,</span> <span class="n">DY</span><span class="p">,</span> <span class="n">DW</span><span class="p">,</span> <span class="n">DB</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Lock</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
|
||||
<span class="n">GROUP_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
|
||||
<span class="c1"># position of elements processed by this program</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">cols</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">cols</span> <span class="o"><</span> <span class="n">N</span>
|
||||
<span class="c1"># offset data pointers to start at the row of interest</span>
|
||||
<span class="n">X</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
|
||||
<span class="n">DY</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
|
||||
<span class="n">DX</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
|
||||
<span class="c1"># offset locks and weight/bias gradient pointer</span>
|
||||
<span class="c1"># each kernel instance accumulates partial sums for</span>
|
||||
<span class="c1"># DW and DB into one of GROUP_SIZE_M independent buffers</span>
|
||||
<span class="c1"># these buffers stay in the L2, which allow this kernel</span>
|
||||
<span class="c1"># to be fast</span>
|
||||
<span class="n">lock_id</span> <span class="o">=</span> <span class="n">row</span> <span class="o">%</span> <span class="n">GROUP_SIZE_M</span>
|
||||
<span class="n">Lock</span> <span class="o">+=</span> <span class="n">lock_id</span>
|
||||
<span class="n">Count</span> <span class="o">=</span> <span class="n">Lock</span> <span class="o">+</span> <span class="n">GROUP_SIZE_M</span>
|
||||
<span class="n">DW</span> <span class="o">=</span> <span class="n">DW</span> <span class="o">+</span> <span class="n">lock_id</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span>
|
||||
<span class="n">DB</span> <span class="o">=</span> <span class="n">DB</span> <span class="o">+</span> <span class="n">lock_id</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span>
|
||||
<span class="c1"># load data to SRAM</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="n">dy</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DY</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="n">w</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">W</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">row</span><span class="p">)</span>
|
||||
<span class="n">rstd</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">V</span> <span class="o">+</span> <span class="n">row</span><span class="p">)</span>
|
||||
<span class="c1"># compute dx</span>
|
||||
<span class="n">xhat</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">*</span> <span class="n">rstd</span>
|
||||
<span class="n">wdy</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">dy</span>
|
||||
<span class="n">xhat</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">xhat</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">wdy</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">wdy</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">mean1</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xhat</span> <span class="o">*</span> <span class="n">wdy</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
|
||||
<span class="n">mean2</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wdy</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
|
||||
<span class="n">dx</span> <span class="o">=</span> <span class="p">(</span><span class="n">wdy</span> <span class="o">-</span> <span class="p">(</span><span class="n">xhat</span> <span class="o">*</span> <span class="n">mean1</span> <span class="o">+</span> <span class="n">mean2</span><span class="p">))</span> <span class="o">*</span> <span class="n">rstd</span>
|
||||
<span class="c1"># write-back dx</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DX</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="c1"># accumulate partial sums for dw/db</span>
|
||||
<span class="n">partial_dw</span> <span class="o">=</span> <span class="p">(</span><span class="n">dy</span> <span class="o">*</span> <span class="n">xhat</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="n">partial_db</span> <span class="o">=</span> <span class="p">(</span><span class="n">dy</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="k">while</span> <span class="n">tl</span><span class="o">.</span><span class="n">atomic_cas</span><span class="p">(</span><span class="n">Lock</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">pass</span>
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">Count</span><span class="p">)</span>
|
||||
<span class="c1"># first store doesn't accumulate</span>
|
||||
<span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">atomic_xchg</span><span class="p">(</span><span class="n">Count</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">partial_dw</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">partial_db</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DB</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">partial_dw</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DB</span><span class="p">,</span> <span class="n">partial_db</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="c1"># release lock</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">atomic_xchg</span><span class="p">(</span><span class="n">Lock</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Backward pass (total DW + total DB)</span>
|
||||
|
||||
|
||||
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
|
||||
<span class="k">def</span> <span class="nf">_layer_norm_bwd_dwdb</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">DB</span><span class="p">,</span> <span class="n">FINAL_DW</span><span class="p">,</span> <span class="n">FINAL_DB</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">BLOCK_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">cols</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
|
||||
<span class="n">dw</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
<span class="n">db</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</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">0</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">):</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">rows</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o"><</span> <span class="n">M</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o"><</span> <span class="n">N</span><span class="p">)</span>
|
||||
<span class="n">offs</span> <span class="o">=</span> <span class="n">rows</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
|
||||
<span class="n">dw</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DW</span> <span class="o">+</span> <span class="n">offs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">db</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DB</span> <span class="o">+</span> <span class="n">offs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">sum_dw</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dw</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">sum_db</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">db</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">FINAL_DW</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">sum_dw</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">cols</span> <span class="o"><</span> <span class="n">N</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">FINAL_DB</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">sum_db</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">cols</span> <span class="o"><</span> <span class="n">N</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">LayerNorm</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">normalized_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
|
||||
<span class="c1"># allocate output</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"># reshape input data into 2D tensor</span>
|
||||
<span class="n">x_arg</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">x_arg</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">M</span><span class="p">,</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">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">rstd</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">M</span><span class="p">,</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">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="c1"># Less than 64KB per feature: enqueue fused kernel</span>
|
||||
<span class="n">MAX_FUSED_SIZE</span> <span class="o">=</span> <span class="mi">65536</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="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">MAX_FUSED_SIZE</span><span class="p">,</span> <span class="n">triton</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="k">if</span> <span class="n">N</span> <span class="o">></span> <span class="n">BLOCK_SIZE</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"This layer norm doesn't support feature dim >= 64KB."</span><span class="p">)</span>
|
||||
<span class="c1"># heuristics for number of warps</span>
|
||||
<span class="n">num_warps</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">BLOCK_SIZE</span> <span class="o">//</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="mi">8</span><span class="p">)</span>
|
||||
<span class="c1"># enqueue kernel</span>
|
||||
<span class="n">_layer_norm_fwd_fused</span><span class="p">[(</span><span class="n">M</span><span class="p">,)](</span><span class="n">x_arg</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">rstd</span><span class="p">,</span>
|
||||
<span class="n">x_arg</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">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">)</span>
|
||||
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">rstd</span><span class="p">)</span>
|
||||
<span class="n">ctx</span><span class="o">.</span><span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">BLOCK_SIZE</span>
|
||||
<span class="n">ctx</span><span class="o">.</span><span class="n">num_warps</span> <span class="o">=</span> <span class="n">num_warps</span>
|
||||
<span class="n">ctx</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
|
||||
<span class="k">return</span> <span class="n">y</span>
|
||||
|
||||
<span class="nd">@staticmethod</span>
|
||||
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dy</span><span class="p">):</span>
|
||||
<span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
|
||||
<span class="c1"># heuristics for amount of parallel reduction stream for DG/DB</span>
|
||||
<span class="n">N</span> <span class="o">=</span> <span class="n">w</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">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">64</span>
|
||||
<span class="k">if</span> <span class="n">N</span> <span class="o"><=</span> <span class="mi">8192</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">96</span>
|
||||
<span class="k">if</span> <span class="n">N</span> <span class="o"><=</span> <span class="mi">4096</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">128</span>
|
||||
<span class="k">if</span> <span class="n">N</span> <span class="o"><=</span> <span class="mi">1024</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">256</span>
|
||||
<span class="c1"># allocate output</span>
|
||||
<span class="n">locks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</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">int32</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">_dw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">w</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">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">_db</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">w</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">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">dw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">w</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">dtype</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">db</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">w</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">dtype</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
|
||||
<span class="n">dx</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">dy</span><span class="p">)</span>
|
||||
<span class="c1"># enqueue kernel using forward pass heuristics</span>
|
||||
<span class="c1"># also compute partial sums for DW and DB</span>
|
||||
<span class="n">x_arg</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">x_arg</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="n">_layer_norm_bwd_dx_fused</span><span class="p">[(</span><span class="n">M</span><span class="p">,)](</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">_dw</span><span class="p">,</span> <span class="n">_db</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">locks</span><span class="p">,</span>
|
||||
<span class="n">x_arg</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">N</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE_N</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">BLOCK_SIZE</span><span class="p">,</span>
|
||||
<span class="n">GROUP_SIZE_M</span><span class="o">=</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span>
|
||||
<span class="n">num_warps</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">num_warps</span><span class="p">)</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_SIZE_N'</span><span class="p">])]</span>
|
||||
<span class="c1"># accumulate partial sums in separate kernel</span>
|
||||
<span class="n">_layer_norm_bwd_dwdb</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">_dw</span><span class="p">,</span> <span class="n">_db</span><span class="p">,</span> <span class="n">dw</span><span class="p">,</span> <span class="n">db</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE_M</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE_N</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">dx</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dw</span><span class="p">,</span> <span class="n">db</span><span class="p">,</span> <span class="kc">None</span>
|
||||
|
||||
|
||||
<span class="n">layer_norm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="o">.</span><span class="n">apply</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">test_layer_norm</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">dtype</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</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="c1"># create data</span>
|
||||
<span class="n">x_shape</span> <span class="o">=</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">w_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">)</span>
|
||||
<span class="n">weight</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">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">bias</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">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.3</span> <span class="o">+</span> <span class="mf">0.5</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">x_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">dy</span> <span class="o">=</span> <span class="mf">.1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">x</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="c1"># forward pass</span>
|
||||
<span class="n">y_tri</span> <span class="o">=</span> <span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</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">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="c1"># backward pass (triton)</span>
|
||||
<span class="n">y_tri</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">dx_tri</span><span class="p">,</span> <span class="n">dw_tri</span><span class="p">,</span> <span class="n">db_tri</span> <span class="o">=</span> <span class="p">[</span><span class="n">_</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]]</span>
|
||||
<span class="n">x</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="c1"># backward pass (torch)</span>
|
||||
<span class="n">y_ref</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">dx_ref</span><span class="p">,</span> <span class="n">dw_ref</span><span class="p">,</span> <span class="n">db_ref</span> <span class="o">=</span> <span class="p">[</span><span class="n">_</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]]</span>
|
||||
<span class="c1"># compare</span>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</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>
|
||||
<span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">dx_tri</span><span class="p">,</span> <span class="n">dx_ref</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">assert_almost_equal</span><span class="p">(</span><span class="n">db_tri</span><span class="p">,</span> <span class="n">db_ref</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="mi">1</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">assert_almost_equal</span><span class="p">(</span><span class="n">dw_tri</span><span class="p">,</span> <span class="n">dw_ref</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="mi">1</span><span class="p">)</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">'N'</span><span class="p">],</span>
|
||||
<span class="n">x_vals</span><span class="o">=</span><span class="p">[</span><span class="mi">512</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">32</span><span class="p">)],</span>
|
||||
<span class="n">line_arg</span><span class="o">=</span><span class="s1">'provider'</span><span class="p">,</span>
|
||||
<span class="n">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">'triton'</span><span class="p">,</span> <span class="s1">'torch'</span><span class="p">]</span> <span class="o">+</span> <span class="p">([</span><span class="s1">'apex'</span><span class="p">]</span> <span class="k">if</span> <span class="n">HAS_APEX</span> <span class="k">else</span> <span class="p">[]),</span>
|
||||
<span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Triton'</span><span class="p">,</span> <span class="s1">'Torch'</span><span class="p">]</span> <span class="o">+</span> <span class="p">([</span><span class="s1">'Apex'</span><span class="p">]</span> <span class="k">if</span> <span class="n">HAS_APEX</span> <span class="k">else</span> <span class="p">[]),</span>
|
||||
<span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'green'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'orange'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">)],</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="s1">'GB/s'</span><span class="p">,</span>
|
||||
<span class="n">plot_name</span><span class="o">=</span><span class="s1">'layer-norm-backward'</span><span class="p">,</span>
|
||||
<span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">'M'</span><span class="p">:</span> <span class="mi">4096</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="s1">'mode'</span><span class="p">:</span> <span class="s1">'backward'</span><span class="p">}</span>
|
||||
<span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">def</span> <span class="nf">bench_layer_norm</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">dtype</span><span class="p">,</span> <span class="n">provider</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'backward'</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</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="c1"># create data</span>
|
||||
<span class="n">x_shape</span> <span class="o">=</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">w_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">)</span>
|
||||
<span class="n">weight</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">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">bias</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">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.3</span> <span class="o">+</span> <span class="mf">0.5</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">x_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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">dy</span> <span class="o">=</span> <span class="mf">.1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">x</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="c1"># utility functions</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">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</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">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'apex'</span><span class="p">:</span>
|
||||
<span class="n">apex_layer_norm</span> <span class="o">=</span> <span class="n">apex</span><span class="o">.</span><span class="n">normalization</span><span class="o">.</span><span class="n">FusedLayerNorm</span><span class="p">(</span><span class="n">w_shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="n">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">apex_layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="c1"># forward pass</span>
|
||||
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">'forward'</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">2</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</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="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</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="n">y_fwd</span><span class="p">,</span> <span class="n">rep</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
|
||||
<span class="c1"># backward pass</span>
|
||||
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">'backward'</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">3</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</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="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">y_fwd</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">y</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
|
||||
<span class="n">grad_to_none</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">rep</span><span class="o">=</span><span class="mi">500</span><span class="p">)</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>
|
||||
|
||||
|
||||
<span class="n">bench_layer_norm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">save_path</span><span class="o">=</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
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|
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|
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<p><a class="reference download internal" download="" href="../../_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.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">05-layer-norm.py</span></code></a></p>
|
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|
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<p><a class="reference download internal" download="" href="../../_downloads/ae7fff29e1b574187bc930ed94bcc353/05-layer-norm.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">05-layer-norm.ipynb</span></code></a></p>
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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<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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<li class="toctree-l2"><a class="reference internal" href="03-matrix-multiplication.html">Matrix Multiplication</a></li>
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<li class="toctree-l2"><a class="reference internal" href="04-low-memory-dropout.html">Low-Memory Dropout</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="05-layer-norm.html">Layer Normalization</a></li>
|
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</ul>
|
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</li>
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</ul>
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<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.html">triton</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.language.html">triton.language</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.testing.html">triton.testing</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-1/introduction.html">Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-2/related-work.html">Related Work</a></li>
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<div class="section" id="tutorials">
|
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<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>
|
||||
<p>To install the dependencies for the tutorials:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> triton
|
||||
pip install -e <span class="s1">'./python[tutorials]'</span>
|
||||
</pre></div>
|
||||
</div>
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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>13:45.813</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<tr class="row-odd"><td><p><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
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<tr class="row-even"><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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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="05-layer-norm.html#sphx-glr-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">Layer Normalization</span></a> (<code class="docutils literal notranslate"><span class="pre">05-layer-norm.py</span></code>)</p></td>
|
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<td><p>0.0 MB</p></td>
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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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<tr class="row-odd"><td><p><a class="reference internal" href="04-low-memory-dropout.html#sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">Low-Memory Dropout</span></a> (<code class="docutils literal notranslate"><span class="pre">04-low-memory-dropout.py</span></code>)</p></td>
|
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