[GH-PAGES] Updated website

This commit is contained in:
Philippe Tillet
2022-09-08 00:52:31 +00:00
parent 8e1a3b0434
commit 762f8c9f51
163 changed files with 264 additions and 264 deletions

View File

@@ -324,22 +324,22 @@ for different problem sizes.</p>
0 4096.0 9.600000 9.600000
1 8192.0 19.200000 19.200000
2 16384.0 38.400001 38.400001
3 32768.0 63.999998 63.999998
3 32768.0 76.800002 76.800002
4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568
6 262144.0 384.000001 384.000001
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
13 33554432.0 842.004273 843.811163
14 67108864.0 847.448255 848.362445
15 134217728.0 849.737435 850.656574
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.326 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 44.518 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<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>

View File

@@ -375,16 +375,16 @@ We will then compare its performance against (1) <code class="code docutils lite
<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 190.511628
1 384.0 614.400016 585.142862 153.600004
2 512.0 655.360017 585.142849 156.038096
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 160.000000
4 768.0 722.823517 664.216187 163.839992
.. ... ... ... ...
93 12160.0 814.058574 405.333344 199.038365
94 12288.0 814.111783 415.222812 199.298541
95 12416.0 812.498981 411.722274 198.954424
96 12544.0 812.566838 412.971190 199.111113
97 12672.0 812.633240 411.679167 199.264875
93 12160.0 814.058574 405.755985 198.530610
94 12288.0 814.111783 415.661740 198.895304
95 12416.0 812.498981 411.296057 198.556711
96 12544.0 812.566838 412.971190 198.815254
97 12672.0 812.633240 412.097543 198.873965
[98 rows x 4 columns]
</pre></div>
@@ -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>
</ul>
</div></blockquote>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 22.377 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 22.299 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>

View File

@@ -568,8 +568,8 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
<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 ... 8.507077 8.507077
0 256.0 2.978909 ... 3.276800 3.276800
1 384.0 7.372800 ... 8.507077 7.899428
2 512.0 14.563555 ... 16.384000 16.384000
3 640.0 22.260869 ... 24.380953 24.380953
4 768.0 32.768000 ... 34.028308 34.028308
@@ -580,30 +580,30 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
9 1408.0 64.138541 ... 67.305878 66.485074
10 1536.0 80.430545 ... 79.526831 78.643199
11 1664.0 62.929456 ... 62.061463 62.061463
12 1792.0 72.512412 ... 72.047592 71.588687
13 1920.0 69.120002 ... 70.172588 70.530615
12 1792.0 72.512412 ... 71.588687 71.588687
13 1920.0 69.120002 ... 70.530615 70.530615
14 2048.0 73.908442 ... 77.314362 76.959706
15 2176.0 83.500614 ... 86.367588 85.269692
16 2304.0 68.251065 ... 76.809875 76.563695
15 2176.0 83.500614 ... 86.367588 85.632545
16 2304.0 68.446623 ... 76.809875 76.076024
17 2432.0 71.305746 ... 74.918570 84.367759
18 2560.0 77.833728 ... 80.709358 80.908642
19 2688.0 83.186525 ... 88.836198 89.149366
20 2816.0 79.733474 ... 82.916747 82.446516
21 2944.0 82.237674 ... 82.102191 81.832567
22 3072.0 81.589488 ... 88.473602 88.473602
23 3200.0 84.656085 ... 92.485553 95.238096
24 3328.0 83.516586 ... 84.101981 84.695641
25 3456.0 81.518272 ... 89.480098 90.892410
26 3584.0 87.636833 ... 93.661869 96.424013
27 3712.0 85.528545 ... 84.766519 87.361037
28 3840.0 82.654712 ... 87.080314 91.097196
29 3968.0 86.973584 ... 91.266964 84.856701
30 4096.0 93.466385 ... 89.359338 87.267706
18 2560.0 77.833728 ... 81.512437 80.511054
19 2688.0 83.922689 ... 88.836198 89.254248
20 2816.0 79.587973 ... 82.916747 83.233226
21 2944.0 81.967162 ... 79.737653 82.646820
22 3072.0 82.540970 ... 87.381335 87.246694
23 3200.0 82.368085 ... 91.822093 93.430660
24 3328.0 79.901550 ... 84.895397 85.096096
25 3456.0 82.099354 ... 90.994998 88.595129
26 3584.0 86.540320 ... 93.661869 95.047985
27 3712.0 81.682211 ... 92.326568 87.514102
28 3840.0 80.139129 ... 84.550462 91.625518
29 3968.0 85.811488 ... 91.266964 83.606668
30 4096.0 93.792965 ... 91.304576 87.267706
[31 rows x 5 columns]
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 19.704 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 22.782 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.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">03-matrix-multiplication.py</span></code></a></p>

View File

@@ -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
0 1024.0 311.088617 94.160917 292.571431
1 1536.0 351.085717 135.032961 341.333333
2 2048.0 427.408686 161.684218 323.368435
3 2560.0 465.454542 183.677138 326.808501
4 3072.0 519.211251 191.999993 317.793096
5 3584.0 554.941930 207.768111 310.527060
6 4096.0 568.231237 220.412561 299.707322
7 4608.0 500.416301 233.316456 287.999990
8 5120.0 531.948056 242.845844 288.450695
9 5632.0 542.843364 241.371422 287.591490
10 6144.0 540.131844 250.775512 288.000001
11 6656.0 532.479975 256.000009 286.279570
12 7168.0 510.480705 256.764187 281.558103
13 7680.0 483.779539 262.938666 280.547947
14 8192.0 461.521112 262.493992 276.134828
15 8704.0 416.127506 264.760452 282.673891
16 9216.0 429.483477 270.065931 286.507772
17 9728.0 438.033784 280.278512 289.308559
18 10240.0 445.217381 285.435547 289.811322
19 10752.0 430.797982 246.229020 290.594591
20 11264.0 428.424741 245.983625 286.980888
21 11776.0 423.724129 249.447482 288.981596
22 12288.0 421.302872 253.360821 294.323369
23 12800.0 414.574901 253.047766 288.993430
24 13312.0 410.652963 251.763593 290.048115
25 13824.0 403.620451 256.792581 291.799461
26 14336.0 396.844280 252.988236 285.767449
27 14848.0 380.311643 257.108233 289.246765
28 15360.0 376.932517 259.605636 288.225185
29 15872.0 368.758973 264.349752 292.122692
0 1024.0 311.088617 99.096776 303.407414
1 1536.0 351.085717 135.529409 341.333333
2 2048.0 427.408686 160.104230 323.368435
3 2560.0 465.454542 182.314537 330.322572
4 3072.0 519.211251 192.501302 319.168834
5 3584.0 554.941930 208.776702 310.527060
6 4096.0 571.534884 220.412561 300.623865
7 4608.0 502.690905 233.316456 290.267724
8 5120.0 527.381977 241.414550 286.433562
9 5632.0 540.671974 241.803217 288.820505
10 6144.0 538.160602 250.775512 288.000001
11 6656.0 528.953642 256.000009 285.257135
12 7168.0 510.480705 250.775516 274.373205
13 7680.0 481.253256 264.068761 280.121579
14 8192.0 461.521112 265.686491 281.673345
15 8704.0 416.958106 264.425310 282.673891
16 9216.0 431.157889 271.391419 287.625496
17 9728.0 438.857162 280.615388 288.593329
18 10240.0 445.217381 286.433562 288.450695
19 10752.0 429.364408 246.935876 290.267711
20 11264.0 429.104745 243.765566 284.264977
21 11776.0 421.826879 249.227509 288.686414
22 12288.0 421.905564 253.578674 294.617366
23 12800.0 416.260178 253.465340 288.180121
24 13312.0 410.125805 252.360194 289.653667
25 13824.0 407.087128 257.190689 291.799461
26 14336.0 398.222222 251.692749 284.115601
27 14848.0 385.245405 255.266469 286.687039
28 15360.0 376.163261 260.522978 289.583654
29 15872.0 370.552519 263.618003 292.122692
</pre></div>
</div>
<div class="line-block">
@@ -477,7 +477,7 @@ to download the full example code</p>
<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>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.098 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.871 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-05-layer-norm-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<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>

View File

@@ -174,7 +174,7 @@
<div class="section" id="computation-times">
<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>
<p><strong>12:33.515</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
<p><strong>12:41.480</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -183,19 +183,19 @@
</colgroup>
<tbody>
<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>
<td><p>05:19.704</p></td>
<td><p>05:22.782</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
<td><p>03:22.377</p></td>
<td><p>03:22.299</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
<td><p>02:11.098</p></td>
<td><p>02:11.871</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
<td><p>01:40.326</p></td>
<td><p>01:44.518</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>