[GH-PAGES] Updated website
This commit is contained in:
@@ -331,16 +331,16 @@ for different problem sizes.</p>
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</pre></div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 46.845 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 52.088 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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@@ -370,16 +370,16 @@ We will then compare its performance against (1) <code class="code docutils lite
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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 190.511628
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1 384.0 585.142862 558.545450 153.600004
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2 512.0 655.360017 585.142849 153.121496
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3 640.0 682.666684 620.606056 158.759699
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1 384.0 585.142862 558.545450 149.853661
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.. ... ... ... ...
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93 12160.0 814.058574 405.755985 198.936606
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[98 rows x 4 columns]
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</pre></div>
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@@ -392,7 +392,7 @@ 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 29.190 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 32.198 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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@@ -565,41 +565,41 @@ 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 ... 8.507077 8.507077
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2 512.0 14.563555 ... 15.420235 15.420235
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1 384.0 7.372800 ... 7.899428 8.507077
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2 512.0 14.563555 ... 15.420235 16.384000
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4 768.0 32.768000 ... 34.028308 34.028308
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5 896.0 37.971025 ... 40.140799 40.140799
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6 1024.0 49.932191 ... 53.773130 52.428801
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7 1152.0 45.242181 ... 48.161033 47.396572
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8 1280.0 51.200001 ... 57.690139 57.690139
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9 1408.0 64.138541 ... 69.009825 68.147202
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10 1536.0 80.430545 ... 80.430545 79.526831
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11 1664.0 62.929456 ... 62.929456 62.929456
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12 1792.0 72.983276 ... 63.142831 63.142831
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13 1920.0 68.776119 ... 71.257735 71.257735
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14 2048.0 73.584279 ... 78.398206 78.033565
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15 2176.0 82.813365 ... 86.739860 86.367588
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16 2304.0 68.251065 ... 77.558029 77.307030
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17 2432.0 71.125224 ... 75.726318 75.522751
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18 2560.0 77.283019 ... 82.331658 82.125311
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19 2688.0 83.552988 ... 91.185232 90.748936
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20 2816.0 79.443003 ... 83.792906 83.552120
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21 2944.0 82.237674 ... 84.040530 83.899046
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22 3072.0 81.589488 ... 85.598037 89.451983
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23 3200.0 84.432717 ... 88.520058 95.952022
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24 3328.0 81.854799 ... 85.703924 82.275764
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25 3456.0 80.220468 ... 92.244355 87.632137
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26 3584.0 83.101104 ... 91.284657 94.250936
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27 3712.0 85.675250 ... 82.902362 87.552452
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28 3840.0 81.079177 ... 87.493673 91.625518
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29 3968.0 87.035620 ... 90.388098 84.915752
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30 4096.0 91.366730 ... 83.208386 90.626421
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4 768.0 31.597714 ... 34.028308 34.028308
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5 896.0 36.971791 ... 40.140799 39.025776
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6 1024.0 48.770977 ... 52.428801 52.428801
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7 1152.0 43.911529 ... 46.656000 46.656000
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8 1280.0 49.951220 ... 56.888887 56.109587
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9 1408.0 62.664092 ... 67.305878 66.485074
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10 1536.0 78.643199 ... 78.643199 77.778988
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11 1664.0 61.636381 ... 62.061463 61.636381
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12 1792.0 71.588687 ... 67.707374 67.707374
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13 1920.0 67.764707 ... 70.172588 69.818184
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14 2048.0 72.005219 ... 76.608294 76.260072
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15 2176.0 81.803444 ... 85.632545 85.269692
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16 2304.0 67.100763 ... 76.563695 76.319081
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17 2432.0 69.886725 ... 74.323979 73.932798
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18 2560.0 76.382283 ... 80.908642 80.511054
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19 2688.0 82.642823 ... 89.676257 89.254248
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20 2816.0 82.602666 ... 82.916747 82.759409
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21 2944.0 81.166173 ... 82.509987 82.169877
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22 3072.0 81.005868 ... 88.197981 88.060814
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23 3200.0 83.660130 ... 95.522391 95.096582
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24 3328.0 82.369902 ... 84.795401 84.596116
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25 3456.0 81.026701 ... 91.304157 90.994998
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26 3584.0 86.374055 ... 98.699661 98.375705
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27 3712.0 84.874549 ... 88.876645 88.483034
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28 3840.0 84.036474 ... 92.545605 92.159996
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29 3968.0 92.232760 ... 91.062642 91.403695
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30 4096.0 93.206754 ... 93.012976 92.820009
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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> ( 6 minutes 21.623 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 41.514 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-03-matrix-multiplication-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/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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|
@@ -372,7 +372,7 @@ 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>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.011 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.411 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-04-low-memory-dropout-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/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>
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|
@@ -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 1024.0 356.173905 97.912354 303.407414
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1 1536.0 405.098894 135.032961 341.333333
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2 2048.0 486.653476 161.684218 336.657521
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3 2560.0 461.954908 182.857144 330.322572
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4 3072.0 519.211251 190.511624 319.168834
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5 3584.0 554.941930 207.267476 308.301075
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6 4096.0 564.965515 219.919464 297.890900
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7 4608.0 500.416301 232.825259 287.999990
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8 5120.0 527.381977 241.889751 285.104413
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9 5632.0 538.517949 242.236559 288.820505
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10 6144.0 546.133354 250.775512 288.000001
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11 6656.0 532.479975 254.775119 284.748652
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12 7168.0 505.976473 255.240352 279.272738
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13 7680.0 485.052616 264.447629 281.404588
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14 8192.0 464.794337 266.406514 282.077471
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15 8704.0 415.300208 262.762264 280.397305
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16 9216.0 428.651187 270.727053 287.065546
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17 9728.0 437.213490 279.942444 287.527089
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18 10240.0 442.810829 285.104413 289.469963
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19 10752.0 425.821771 248.123076 292.571421
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20 11264.0 427.071098 243.545956 282.778242
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21 11776.0 423.724129 248.569911 287.511689
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26 14336.0 393.215988 252.988236 284.585606
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27 14848.0 381.533186 256.552919 288.077610
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28 15360.0 377.318326 260.338991 290.267715
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29 15872.0 369.832994 262.708969 290.341468
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0 1024.0 356.173905 96.755900 299.707322
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1 1536.0 400.695643 133.083026 338.201833
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2 2048.0 486.653476 159.584422 321.254900
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3 2560.0 455.111129 179.649115 323.368411
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4 3072.0 508.468972 191.005181 320.556515
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5 3584.0 551.384634 206.769233 310.527060
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6 4096.0 558.545450 219.919464 297.890900
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7 4608.0 493.714279 231.364016 285.767436
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8 5120.0 518.481012 241.414550 283.133649
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9 5632.0 534.260858 241.803217 288.820505
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10 6144.0 540.131844 247.409397 285.214712
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11 6656.0 525.473708 255.182111 284.748652
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12 7168.0 503.017523 259.084340 283.881181
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13 7680.0 482.513091 262.190612 275.104486
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14 8192.0 463.698115 265.686491 283.296835
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15 8704.0 406.412440 266.789264 283.826081
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16 9216.0 419.703988 270.727053 287.251954
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17 9728.0 428.388977 279.942444 288.950501
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18 10240.0 437.295395 285.104413 286.767793
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19 10752.0 424.421071 245.292781 289.291486
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20 11264.0 423.724120 244.426754 286.069848
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21 11776.0 418.702211 247.915800 288.097854
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22 12288.0 414.784810 253.578674 293.737063
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23 12800.0 409.327110 252.631590 287.371378
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24 13312.0 408.030638 252.360194 289.653667
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25 13824.0 403.620451 256.197690 291.287079
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26 14336.0 394.116833 253.921779 286.242939
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27 14848.0 384.829370 257.108233 289.246765
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28 15360.0 380.041240 257.071134 284.884090
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29 15872.0 370.913333 260.731015 289.679087
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</pre></div>
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</div>
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<div class="line-block">
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@@ -487,7 +487,7 @@ 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>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 14.604 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 16.519 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">
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||||
<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>
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||||
|
@@ -174,7 +174,7 @@
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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>13:52.274</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>14:22.730</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>06:21.623</p></td>
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<td><p>06:41.514</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="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>03:29.190</p></td>
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<td><p>03:32.198</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-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>02:14.604</p></td>
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<td><p>02:16.519</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>01:46.845</p></td>
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<td><p>01:52.088</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-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>00:00.411</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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