[GH-PAGES] Updated website
This commit is contained in:
@@ -327,7 +327,7 @@ for different problem sizes.</p>
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3 32768.0 76.800002 76.800002
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4 65536.0 127.999995 127.999995
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6 262144.0 341.333321 384.000001
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@@ -339,7 +339,7 @@ for different problem sizes.</p>
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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 45.701 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.333 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 192.752942
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0 256.0 512.000001 546.133347 186.181817
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1 384.0 585.142862 585.142862 151.703707
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2 512.0 655.360017 585.142849 156.038096
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3 640.0 682.666684 640.000002 160.000000
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2 512.0 655.360017 606.814814 156.038096
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3 640.0 682.666684 640.000002 158.759699
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4 768.0 722.823517 646.736871 163.839992
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.. ... ... ... ...
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93 12160.0 812.359066 405.755985 198.834951
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94 12288.0 814.111783 415.661740 198.995960
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95 12416.0 812.498981 412.149375 198.755369
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96 12544.0 812.566838 412.971190 198.913776
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97 12672.0 812.633240 412.097543 199.069228
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93 12160.0 812.359066 406.179533 199.038365
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94 12288.0 814.111783 415.661740 199.298541
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95 12416.0 812.498981 411.722274 198.954424
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96 12544.0 812.566838 412.971190 199.111113
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97 12672.0 812.633240 412.097543 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 22.745 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 21.436 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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|
@@ -568,42 +568,42 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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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>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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0 256.0 2.730667 ... 3.276800 2.978909
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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 37.971025 ... 40.140799 39.025776
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6 1024.0 51.150050 ... 53.773130 52.428801
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6 1024.0 49.932191 ... 52.428801 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 56.888887
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9 1408.0 64.138541 ... 67.305878 67.305878
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9 1408.0 64.138541 ... 67.305878 66.485074
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10 1536.0 80.430545 ... 79.526831 79.526831
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11 1664.0 63.372618 ... 62.492442 62.061463
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12 1792.0 72.512412 ... 72.047592 71.588687
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12 1792.0 72.983276 ... 59.154861 71.588687
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13 1920.0 69.120002 ... 70.172588 70.172588
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14 2048.0 73.908442 ... 76.959706 76.608294
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15 2176.0 83.155572 ... 85.998493 85.269692
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16 2304.0 68.251065 ... 76.563695 76.563695
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17 2432.0 71.125224 ... 82.874527 84.115159
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18 2560.0 77.833728 ... 81.310171 80.511054
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19 2688.0 83.737433 ... 89.676257 89.044730
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20 2816.0 83.552120 ... 83.074685 82.602666
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21 2944.0 82.237674 ... 82.784108 82.784108
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22 3072.0 81.825298 ... 87.651868 86.845249
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23 3200.0 82.156612 ... 89.761569 88.275863
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24 3328.0 79.548391 ... 80.527177 83.710812
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25 3456.0 81.683457 ... 91.511426 91.097818
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26 3584.0 85.552231 ... 90.549237 96.683219
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27 3712.0 85.455380 ... 88.248537 88.640059
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28 3840.0 82.778440 ... 87.252072 88.971840
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29 3968.0 93.076994 ... 84.094627 91.335278
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30 4096.0 86.590337 ... 86.480498 92.182504
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16 2304.0 68.251065 ... 76.563695 76.319081
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17 2432.0 71.305746 ... 84.877538 84.621881
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18 2560.0 77.833728 ... 81.310171 80.709358
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19 2688.0 83.737433 ... 89.464755 89.888756
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20 2816.0 83.552120 ... 83.712490 82.916747
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21 2944.0 81.967162 ... 80.902653 82.102191
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22 3072.0 82.062468 ... 88.197981 87.924073
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23 3200.0 84.544253 ... 95.380032 94.955488
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24 3328.0 82.939284 ... 84.695641 81.162679
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25 3456.0 80.061141 ... 82.183044 87.632137
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26 3584.0 87.254137 ... 90.460980 94.349836
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27 3712.0 85.601834 ... 89.194055 91.313847
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28 3840.0 84.100380 ... 84.972723 88.686451
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29 3968.0 93.148045 ... 79.236324 80.971427
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30 4096.0 91.647477 ... 83.390136 89.181212
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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 23.977 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 18.978 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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|
@@ -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 1024.0 307.200008 99.902435 315.076934
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1 1536.0 351.085717 134.050910 344.523365
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2 2048.0 423.724127 159.067963 334.367350
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3 2560.0 458.507457 182.314537 330.322572
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4 3072.0 515.580429 191.501303 321.956335
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5 3584.0 547.872604 208.271186 309.410081
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6 4096.0 568.231237 220.907859 300.623865
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7 4608.0 500.416301 232.336141 287.251954
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8 5120.0 527.381977 243.809526 286.433562
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9 5632.0 542.843364 244.869560 291.939522
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10 6144.0 548.163546 251.202731 288.000001
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11 6656.0 534.260858 256.000009 286.793541
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12 7168.0 516.612607 253.734520 277.919225
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13 7680.0 490.212752 266.743841 284.444450
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14 8192.0 464.794337 258.694729 278.481578
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15 8704.0 416.958106 267.472468 284.987724
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16 9216.0 431.157889 272.394084 289.887291
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17 9728.0 439.683593 280.278512 288.950501
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18 10240.0 446.836366 287.438599 289.811322
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19 10752.0 430.079980 246.935876 289.941565
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20 11264.0 430.471331 245.536784 286.069848
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21 11776.0 420.571432 249.447482 288.981596
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22 12288.0 418.909088 254.673582 294.323369
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23 12800.0 414.016170 254.094291 289.811310
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24 13312.0 411.181478 252.360194 289.129403
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25 13824.0 404.604870 257.190689 291.799461
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26 14336.0 395.475867 256.190622 289.129416
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27 14848.0 384.829370 257.479779 289.012175
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28 15360.0 377.318326 258.332158 288.225185
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29 15872.0 369.832994 261.626369 290.562936
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0 1024.0 307.200008 98.303995 303.407414
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1 1536.0 351.085717 133.565214 338.201833
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2 2048.0 423.724127 158.554837 321.254900
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3 2560.0 458.507457 180.705883 328.556154
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4 3072.0 515.580429 190.020625 321.956335
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5 3584.0 551.384634 206.769233 310.527060
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6 4096.0 568.231237 221.905193 301.546004
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7 4608.0 498.162157 232.825259 287.251954
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8 5120.0 527.381977 241.414550 283.787523
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9 5632.0 542.843364 242.671458 288.820505
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10 6144.0 548.163546 250.775512 285.767458
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11 6656.0 532.479975 255.182111 285.257135
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12 7168.0 508.970395 253.360829 275.692317
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13 7680.0 481.253256 264.447629 279.272719
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14 8192.0 459.364487 258.354805 273.827300
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15 8704.0 415.300208 266.789264 284.599455
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16 9216.0 428.651187 270.396088 286.879380
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17 9728.0 438.033784 282.311967 290.388056
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||||
18 10240.0 447.650282 284.774046 287.775181
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||||
19 10752.0 431.518385 247.409390 291.250566
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||||
20 11264.0 427.746848 242.671458 283.966395
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21 11776.0 423.089806 251.221344 291.064881
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22 12288.0 418.314886 254.015505 294.029924
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||||
23 12800.0 416.824953 254.304635 288.450715
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||||
24 13312.0 411.711355 252.360194 289.653667
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||||
25 13824.0 405.594132 257.790206 292.056329
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||||
26 14336.0 396.844280 254.297107 286.481278
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||||
27 14848.0 387.760604 258.600868 290.188916
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||||
28 15360.0 375.397138 260.155264 289.583654
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||||
29 15872.0 368.758973 263.253636 292.796308
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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>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.766 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.437 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>12:44.201</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:31.196</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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@@ -183,19 +183,19 @@
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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>05:23.977</p></td>
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||||
<td><p>05:18.978</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>03:22.745</p></td>
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||||
<td><p>03:21.436</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>02:11.766</p></td>
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<td><p>02:11.437</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>01:45.701</p></td>
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||||
<td><p>01:39.333</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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