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Philippe Tillet
2022-08-05 00:53:59 +00:00
parent b254e2b165
commit 844e79e14c
161 changed files with 272 additions and 272 deletions
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6
v1.1.2/getting-started/tutorials/01-vector-add.html
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@@ -322,8 +322,8 @@ for different problem sizes.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
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16
v1.1.2/getting-started/tutorials/02-fused-softmax.html
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@@ -374,17 +374,17 @@ We will then compare its performance against (1) <code class="code docutils lite
<p class="sphx-glr-script-out">Out:</p>
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N Triton Torch (native) Torch (jit)
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.. ... ... ... ...
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93 12160.0 814.058574 406.179533 198.834951
94 12288.0 814.111783 415.661740 199.096718
95 12416.0 812.498981 412.149375 198.755369
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@@ -397,7 +397,7 @@ We will then compare its performance against (1) <code class="code docutils lite
Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
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48
v1.1.2/getting-started/tutorials/03-matrix-multiplication.html
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@@ -568,42 +568,42 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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0 256.0 2.730667 ... 2.978909 2.978909
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9 1408.0 64.138541 ... 67.305878 66.485074
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22 3072.0 81.121923 ... 88.335577 88.473602
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17 2432.0 71.305746 ... 74.918570 84.877538
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19 2688.0 83.737433 ... 89.888756 89.044730
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22 3072.0 81.707223 ... 87.516392 88.060814
23 3200.0 80.402009 ... 93.841640 92.219022
24 3328.0 81.530349 ... 84.895397 84.397770
25 3456.0 81.932484 ... 85.043848 88.497878
26 3584.0 86.457107 ... 98.808123 93.176571
27 3712.0 81.615477 ... 86.716441 87.399253
28 3840.0 83.027026 ... 90.909991 87.355452
29 3968.0 88.873953 ... 84.097346 90.054568
30 4096.0 92.820009 ... 88.534120 90.871857
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2
v1.1.2/getting-started/tutorials/04-low-memory-dropout.html
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@@ -371,7 +371,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
<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>
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52
v1.1.2/getting-started/tutorials/05-layer-norm.html
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@@ -194,36 +194,36 @@ to download the full example code</p>
<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
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11 6656.0 527.207907 256.000009 286.279570
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17 9728.0 438.857162 280.615388 288.950501
18 10240.0 447.650282 286.767793 290.496460
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17 9728.0 438.857162 280.278512 288.950501
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26 14336.0 398.222222 254.673567 286.481278
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<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">&#39;.&#39;</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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12
v1.1.2/getting-started/tutorials/sg_execution_times.html
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@@ -174,7 +174,7 @@
<div class="section" id="computation-times">
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