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[GH-PAGES] Updated website

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Philippe Tillet
2021-08-03 00:13:32 +00:00
parent b01d514815
commit e7c395b279
19 changed files with 89 additions and 89 deletions
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24
getting-started/tutorials/02-fused-softmax.html
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@@ -216,7 +216,7 @@ Let us consider instead the case of a simple (numerically stabilized) softmax op
<span class="c1"># read 2MN elements ; write MN elements</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x_max</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
<span class="c1"># read MN elements ; write MN elements</span>
<span class="n">numerator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="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 2MN elements ; write MN elements</span>
@@ -346,17 +346,17 @@ We will then compare its performance against (1) <code class="code docutils lite
<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)
0 256.0 512.000001 546.133347 273.066674
1 384.0 585.142862 585.142862 267.130429
2 512.0 630.153853 606.814814 264.258068
3 640.0 682.666684 640.000002 269.473696
4 768.0 702.171410 664.216187 273.066663
0 256.0 512.000001 546.133347 186.181817
1 384.0 585.142862 585.142862 153.600004
2 512.0 630.153853 606.814814 154.566038
3 640.0 682.666684 640.000002 160.000000
4 768.0 702.171410 664.216187 162.754967
.. ... ... ... ...
93 12160.0 812.359066 406.179533 329.483481
94 12288.0 812.429770 415.661740 329.602681
95 12416.0 810.840807 412.149375 329.173158
96 12544.0 810.925276 412.546756 329.292871
97 12672.0 811.007961 412.097543 329.410251
93 12160.0 812.359066 406.179533 198.936606
94 12288.0 812.429770 416.101597 199.298541
95 12416.0 810.840807 412.149375 198.854847
96 12544.0 810.925276 412.971190 199.111113
97 12672.0 811.007961 412.097543 199.167004
[98 rows x 4 columns]
</pre></div>
@@ -370,7 +370,7 @@ This means that – when temporary data is too large to fit entirely in the GPU
Note that our Triton kernel is not only faster than PyTorch’s CUDA kernel, it is also <strong>easier to read, understand and maintain</strong>.</p></li>
</ul>
</div></blockquote>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.174 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 13.269 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<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>