[GH-PAGES] Updated website
This commit is contained in:
@@ -322,24 +322,24 @@ for different problem sizes.</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
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size Triton Torch
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0 4096.0 9.600000 9.600000
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1 8192.0 19.200000 19.200000
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1 8192.0 19.200000 15.999999
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2 16384.0 38.400001 38.400001
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||||
3 32768.0 76.800002 76.800002
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4 65536.0 127.999995 127.999995
|
||||
5 131072.0 219.428568 219.428568
|
||||
6 262144.0 341.333321 341.333321
|
||||
6 262144.0 341.333321 384.000001
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7 524288.0 472.615390 472.615390
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8 1048576.0 614.400016 614.400016
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9 2097152.0 722.823517 722.823517
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10 4194304.0 780.190482 780.190482
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11 8388608.0 812.429770 812.429770
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12 16777216.0 833.084721 833.084721
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13 33554432.0 842.004273 842.906750
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13 33554432.0 842.004273 843.811163
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14 67108864.0 847.448255 848.362445
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15 134217728.0 849.737435 850.656574
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 43.820 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 48.257 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-01-vector-add-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
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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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|
@@ -374,17 +374,17 @@ We will then compare its performance against (1) <code class="code docutils lite
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
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N Triton Torch (native) Torch (jit)
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0 256.0 512.000001 546.133347 188.321838
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1 384.0 585.142862 585.142862 151.703707
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0 256.0 512.000001 546.133347 186.181817
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1 384.0 614.400016 585.142862 153.600004
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2 512.0 655.360017 606.814814 154.566038
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3 640.0 682.666684 640.000002 160.000000
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4 768.0 722.823517 664.216187 163.839992
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4 768.0 722.823517 664.216187 162.754967
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.. ... ... ... ...
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93 12160.0 812.359066 406.179533 198.834951
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94 12288.0 814.111783 415.661740 198.995960
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95 12416.0 812.498981 411.296057 198.755369
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96 12544.0 812.566838 412.971190 199.012395
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97 12672.0 812.633240 412.097543 198.971549
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93 12160.0 814.058574 406.179533 198.530610
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94 12288.0 814.111783 416.101597 198.794749
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95 12416.0 812.498981 412.149375 198.457532
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96 12544.0 812.566838 412.971190 198.716830
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97 12672.0 812.633240 412.097543 198.873965
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[98 rows x 4 columns]
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</pre></div>
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@@ -397,7 +397,7 @@ We will then compare its performance against (1) <code class="code docutils lite
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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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</ul>
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</div></blockquote>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 24.302 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 24.240 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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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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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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|
@@ -569,7 +569,7 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
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M cuBLAS ... Triton Triton (+ LeakyReLU)
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0 256.0 2.730667 ... 2.978909 2.978909
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1 384.0 7.372800 ... 7.899428 7.899428
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1 384.0 7.372800 ... 8.507077 8.507077
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2 512.0 14.563555 ... 16.384000 16.384000
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 34.028308 34.028308
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@@ -579,31 +579,31 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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8 1280.0 51.200001 ... 56.888887 56.888887
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9 1408.0 64.138541 ... 67.305878 67.305878
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10 1536.0 80.430545 ... 80.430545 79.526831
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11 1664.0 62.929456 ... 62.492442 62.061463
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12 1792.0 72.983276 ... 72.512412 72.047592
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13 1920.0 69.120002 ... 70.530615 70.530615
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11 1664.0 63.372618 ... 62.492442 62.061463
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12 1792.0 72.983276 ... 72.047592 71.588687
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13 1920.0 69.120002 ... 70.530615 70.172588
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14 2048.0 73.908442 ... 77.314362 76.959706
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15 2176.0 83.500614 ... 85.998493 85.632545
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16 2304.0 68.251065 ... 76.809875 76.809875
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17 2432.0 71.305746 ... 74.719317 84.877538
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18 2560.0 77.833728 ... 81.108913 80.511054
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19 2688.0 83.369354 ... 89.676257 89.254248
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20 2816.0 79.154642 ... 83.552120 82.916747
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21 2944.0 81.967162 ... 82.646820 82.441740
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22 3072.0 82.540970 ... 89.593522 86.712254
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23 3200.0 80.503145 ... 91.756271 93.158662
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24 3328.0 82.275764 ... 84.795401 85.602017
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25 3456.0 82.688790 ... 91.046379 88.400840
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26 3584.0 87.381330 ... 97.522120 98.160909
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27 3712.0 84.159518 ... 88.326564 83.040189
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28 3840.0 84.164384 ... 92.159996 85.333335
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29 3968.0 92.723355 ... 84.038524 91.130650
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30 4096.0 87.495257 ... 86.536250 91.992956
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15 2176.0 83.500614 ... 86.367588 84.909907
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16 2304.0 68.251065 ... 76.809875 76.563695
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17 2432.0 71.305746 ... 74.918570 84.877538
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18 2560.0 78.019048 ... 81.310171 80.412266
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19 2688.0 83.552988 ... 89.464755 90.102270
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20 2816.0 79.587973 ... 80.617762 82.602666
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21 2944.0 82.921853 ... 78.979452 82.441740
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22 3072.0 81.472093 ... 85.792579 83.269271
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23 3200.0 84.656085 ... 92.888243 95.096582
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24 3328.0 82.041364 ... 83.130825 84.695641
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25 3456.0 81.518272 ... 91.511426 90.892410
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26 3584.0 86.457107 ... 91.563533 94.947616
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27 3712.0 83.806497 ... 86.192706 88.443856
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28 3840.0 80.255442 ... 90.574940 89.043476
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29 3968.0 92.512459 ... 83.807647 80.015697
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30 4096.0 93.271527 ... 84.360608 86.424811
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[31 rows x 5 columns]
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</pre></div>
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||||
</div>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 23.071 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 24.119 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>
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|
@@ -371,7 +371,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
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||||
<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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</dd>
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||||
</dl>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.011 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.010 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.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">04-low-memory-dropout.py</span></code></a></p>
|
||||
|
@@ -194,36 +194,36 @@ to download the full example code</p>
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||||
<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer-norm-backward:
|
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N Triton Torch Apex
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0 1024.0 311.088617 93.801531 292.571431
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1 1536.0 351.085717 135.529409 344.523365
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2 2048.0 423.724127 161.684218 325.509933
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3 2560.0 461.954908 183.402991 330.322572
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4 3072.0 519.211251 191.999993 317.793096
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5 3584.0 551.384634 208.271186 309.410081
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6 4096.0 564.965515 220.907859 301.546004
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7 4608.0 500.416301 233.316456 292.571431
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8 5120.0 529.655159 242.845844 287.775181
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9 5632.0 536.380957 243.545956 290.683877
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10 6144.0 544.118087 250.349744 286.322318
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11 6656.0 534.260858 255.182111 284.748652
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12 7168.0 510.480705 255.619613 278.820105
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13 7680.0 486.332448 265.208635 280.975614
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14 8192.0 460.440290 266.046015 282.482757
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15 8704.0 417.791980 264.425310 283.826081
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16 9216.0 428.651187 270.065931 286.879380
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17 9728.0 438.857162 281.971008 288.593329
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18 10240.0 446.025405 286.100109 289.129408
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0 1024.0 307.200008 99.096776 307.200008
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1 1536.0 351.085717 133.083026 338.201833
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2 2048.0 423.724127 162.217818 327.679984
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3 2560.0 461.954908 182.857144 325.079368
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4 3072.0 515.580429 191.501303 319.168834
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5 3584.0 554.941930 208.271186 308.301075
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6 4096.0 568.231237 220.412561 298.796351
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7 4608.0 498.162157 231.849059 287.251954
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8 5120.0 525.128191 242.845844 286.433562
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9 5632.0 538.517949 243.545956 290.683877
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10 6144.0 542.117638 248.661056 286.322318
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11 6656.0 527.207907 256.000009 286.279570
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12 7168.0 507.469040 262.243907 288.644296
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13 7680.0 481.253256 260.707203 277.172933
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||||
14 8192.0 460.440290 268.957600 286.600589
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||||
15 8704.0 416.958106 267.472468 284.987724
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||||
16 9216.0 428.651187 273.066667 289.507855
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||||
17 9728.0 439.683593 280.278512 288.950501
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||||
18 10240.0 446.836366 286.433562 290.153487
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||||
19 10752.0 429.364408 246.699797 290.267711
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||||
20 11264.0 429.104745 245.091565 286.372873
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||||
21 11776.0 423.724129 248.569911 288.097854
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||||
22 12288.0 420.701865 252.493141 294.323369
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||||
23 12800.0 415.135142 253.465340 287.640454
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||||
24 13312.0 411.711355 253.160074 290.707920
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||||
25 13824.0 406.588243 257.790206 292.571423
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||||
26 14336.0 396.844280 254.109315 287.438588
|
||||
27 14848.0 386.080180 256.737757 290.662311
|
||||
28 15360.0 373.874218 259.422943 289.811315
|
||||
29 15872.0 370.913333 263.071829 291.452168
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||||
20 11264.0 429.104745 245.091565 285.767446
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||||
21 11776.0 421.198220 249.227509 288.981596
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||||
22 12288.0 420.102570 254.453844 294.911986
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||||
23 12800.0 415.696898 253.465340 288.180121
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||||
24 13312.0 412.242569 252.559690 290.179836
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||||
25 13824.0 405.098897 257.390218 292.571423
|
||||
26 14336.0 397.761846 254.673567 286.242939
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||||
27 14848.0 384.414233 257.108233 289.246765
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||||
28 15360.0 374.253788 257.790220 286.656296
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||||
29 15872.0 366.982663 262.708969 291.229369
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||||
</pre></div>
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||||
</div>
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||||
<div class="line-block">
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||||
@@ -477,7 +477,7 @@ to download the full example code</p>
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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">'.'</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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||||
</pre></div>
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||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.781 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.043 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>
|
||||
|
@@ -174,7 +174,7 @@
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||||
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||||
<div class="section" id="computation-times">
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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>
|
||||
<p><strong>12:42.984</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:48.670</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 85%" />
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@@ -183,23 +183,23 @@
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||||
</colgroup>
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<tbody>
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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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<td><p>05:23.071</p></td>
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||||
<td><p>05:24.119</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
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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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<td><p>03:24.302</p></td>
|
||||
<td><p>03:24.240</p></td>
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||||
<td><p>0.0 MB</p></td>
|
||||
</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>02:11.781</p></td>
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<td><p>02:12.043</p></td>
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<td><p>0.0 MB</p></td>
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</tr>
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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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<td><p>01:43.820</p></td>
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<td><p>01:48.257</p></td>
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<td><p>0.0 MB</p></td>
|
||||
</tr>
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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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<td><p>00:00.011</p></td>
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<td><p>00:00.010</p></td>
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<td><p>0.0 MB</p></td>
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||||
</tr>
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||||
</tbody>
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||||
|
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Block a user