[GH-PAGES] Updated website
This commit is contained in:
@@ -334,12 +334,12 @@ for different problem sizes.</p>
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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 46.080 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 43.227 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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0 256.0 512.000001 546.133347 186.181817
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1 384.0 585.142862 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 162.754967
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.. ... ... ... ...
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93 12160.0 814.058574 406.179533 198.530610
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94 12288.0 814.111783 415.222812 198.794749
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95 12416.0 812.498981 412.149375 198.358474
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96 12544.0 812.566838 413.396498 198.716830
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97 12672.0 812.633240 412.097543 198.776477
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93 12160.0 814.058574 406.179533 199.038365
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94 12288.0 814.111783 415.222812 199.197579
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95 12416.0 812.498981 412.149375 198.755369
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96 12544.0 812.566838 412.971190 199.111113
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97 12672.0 812.633240 411.679167 199.167004
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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 25.043 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 21.808 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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@@ -569,41 +569,41 @@ 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 ... 3.276800 2.978909
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1 384.0 7.372800 ... 8.507077 8.507077
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1 384.0 7.372800 ... 7.899428 7.899428
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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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5 896.0 39.025776 ... 40.140799 39.025776
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6 1024.0 51.150050 ... 53.773130 52.428801
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7 1152.0 45.242181 ... 46.656000 46.656000
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8 1280.0 51.200001 ... 56.888887 56.109587
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9 1408.0 64.138541 ... 67.305878 66.485074
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10 1536.0 80.430545 ... 79.526831 78.643199
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11 1664.0 62.929456 ... 62.492442 62.061463
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12 1792.0 72.512412 ... 72.512412 71.588687
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13 1920.0 69.120002 ... 70.172588 70.172588
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14 2048.0 73.584279 ... 76.959706 76.608294
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15 2176.0 83.155572 ... 85.998493 85.269692
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16 2304.0 68.446623 ... 77.057651 76.319081
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17 2432.0 71.305746 ... 85.134737 84.877538
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18 2560.0 78.019048 ... 80.908642 81.108913
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19 2688.0 83.004501 ... 89.888756 89.464755
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20 2816.0 83.233226 ... 83.552120 81.904619
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21 2944.0 82.784108 ... 81.034195 81.431424
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22 3072.0 82.062468 ... 88.612060 88.473602
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23 3200.0 83.009080 ... 95.238096 94.955488
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24 3328.0 83.808259 ... 84.695641 84.496824
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25 3456.0 81.108217 ... 84.820164 89.480098
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26 3584.0 87.381330 ... 94.997774 90.188780
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27 3712.0 85.019017 ... 86.192706 86.491211
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28 3840.0 80.255442 ... 85.399230 91.701494
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29 3968.0 87.913500 ... 90.994735 83.751926
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30 4096.0 92.627833 ... 85.434583 82.441739
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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 ... 79.526831 79.526831
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11 1664.0 62.929456 ... 62.929456 62.061463
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12 1792.0 72.983276 ... 72.512412 72.047592
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13 1920.0 68.776119 ... 70.172588 70.172588
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14 2048.0 73.584279 ... 76.608294 76.608294
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15 2176.0 83.155572 ... 85.632545 84.909907
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16 2304.0 68.643310 ... 76.809875 76.563695
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17 2432.0 71.305746 ... 85.393507 84.877538
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18 2560.0 78.019048 ... 80.908642 80.709358
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19 2688.0 83.186525 ... 89.676257 89.464755
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20 2816.0 83.074685 ... 83.074685 82.759409
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21 2944.0 81.832567 ... 80.640830 81.298583
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22 3072.0 82.181572 ... 88.750943 86.712254
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23 3200.0 83.989503 ... 93.704243 94.256261
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24 3328.0 82.369902 ... 83.808259 83.613586
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25 3456.0 77.820048 ... 86.318594 89.183149
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26 3584.0 87.042978 ... 92.696281 94.448944
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27 3712.0 85.675250 ... 85.091436 88.170647
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28 3840.0 83.591840 ... 88.332269 91.625518
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29 3968.0 86.114283 ... 91.403695 84.915752
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30 4096.0 93.271527 ... 83.468735 84.413665
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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 51.018 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 24.995 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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|
@@ -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>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.108 seconds)</p>
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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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<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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|
@@ -194,33 +194,33 @@ 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 98.303995 303.407414
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0 1024.0 307.200008 98.303995 303.407414
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1 1536.0 351.085717 134.050910 341.333333
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2 2048.0 420.102553 161.684218 325.509933
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3 2560.0 461.954908 181.238943 325.079368
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4 3072.0 511.999982 192.501302 319.168834
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5 3584.0 551.384634 208.271186 311.652167
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2 2048.0 420.102553 161.154101 334.367350
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3 2560.0 465.454542 181.238943 330.322572
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4 3072.0 511.999982 191.999993 320.556515
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5 3584.0 551.384634 207.768111 310.527060
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6 4096.0 568.231237 219.919464 299.707322
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7 4608.0 500.416301 232.825259 286.507772
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8 5120.0 525.128191 242.366855 285.104413
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9 5632.0 540.671974 243.107920 289.438969
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10 6144.0 544.118087 248.242431 285.767458
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8 5120.0 525.128191 242.366855 284.444444
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9 5632.0 540.671974 243.545956 288.820505
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10 6144.0 542.117638 248.242431 285.767458
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11 6656.0 530.710976 256.000009 285.767438
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12 7168.0 505.976473 260.654538 286.242939
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13 7680.0 481.253256 262.564106 279.272719
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14 8192.0 462.607053 267.130429 284.526763
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15 8704.0 417.791980 267.815384 284.987724
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16 9216.0 430.319054 272.059034 288.751954
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17 9728.0 438.033784 280.278512 289.667485
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16 9216.0 430.319054 272.394084 288.751954
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17 9728.0 438.857162 280.278512 289.667485
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18 10240.0 447.650282 286.433562 290.496460
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19 10752.0 428.651173 247.172406 290.922209
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20 11264.0 429.104745 245.536784 286.676558
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21 11776.0 422.457417 249.667843 288.686414
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22 12288.0 420.102570 254.453844 294.323369
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21 11776.0 423.089806 249.667843 288.686414
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22 12288.0 420.102570 254.673582 294.323369
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23 12800.0 414.574901 253.465340 289.811310
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24 13312.0 412.242569 252.759501 289.916513
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25 13824.0 406.090579 257.190689 291.799461
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26 14336.0 395.930964 254.297107 286.959121
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24 13312.0 412.775186 252.559690 289.916513
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25 13824.0 406.090579 257.190689 292.056329
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26 14336.0 395.930964 254.297107 286.719986
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27 14848.0 386.498925 257.665934 289.246765
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28 15360.0 373.495460 257.790220 287.102804
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29 15872.0 370.192407 261.626369 289.899545
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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>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.343 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.626 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>
|
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<p><strong>13:14.591</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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||||
<p><strong>12:42.667</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
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<table class="docutils align-default">
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<colgroup>
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<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>05:51.018</p></td>
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<td><p>05:24.995</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>03:25.043</p></td>
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<td><p>03:21.808</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>02:12.343</p></td>
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<td><p>02:12.626</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>01:46.080</p></td>
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<td><p>01:43.227</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>00:00.108</p></td>
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<td><p>00:00.011</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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