[GH-PAGES] Updated website
This commit is contained in:
@@ -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 43.477 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.524 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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@@ -374,16 +374,16 @@ 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 190.511628
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0 256.0 512.000001 512.000001 188.321838
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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 158.759699
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4 768.0 722.823517 646.736871 162.754967
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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 12160.0 812.359066 405.755985 198.834951
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94 12288.0 814.111783 415.661740 199.096718
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95 12416.0 812.498981 412.149375 198.755369
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96 12544.0 812.566838 412.971190 199.012395
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93 12160.0 812.359066 406.179533 199.038365
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94 12288.0 814.111783 415.661740 199.197579
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95 12416.0 812.498981 411.722274 198.904612
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96 12544.0 812.566838 412.546756 199.012395
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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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@@ -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 24.818 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 22.390 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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1 384.0 7.372800 ... 7.899428 7.899428
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0 256.0 2.730667 ... 3.276800 2.978909
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1 384.0 7.372800 ... 8.507077 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 ... 39.025776 37.971025
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4 768.0 31.597714 ... 34.028308 34.028308
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5 896.0 37.971025 ... 39.025776 39.025776
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6 1024.0 49.932191 ... 52.428801 52.428801
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7 1152.0 44.566925 ... 46.656000 46.656000
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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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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 72.983276 ... 72.047592 71.588687
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13 1920.0 68.776119 ... 70.172588 70.172588
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12 1792.0 72.983276 ... 71.588687 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 ... 85.134737 83.366361
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18 2560.0 77.649287 ... 80.709358 80.313727
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19 2688.0 83.186525 ... 89.676257 89.044730
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20 2816.0 84.035084 ... 83.233226 83.233226
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21 2944.0 80.380696 ... 83.060049 82.921853
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22 3072.0 82.420822 ... 87.516392 89.100084
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23 3200.0 79.601989 ... 93.841640 91.168092
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24 3328.0 82.275764 ... 84.795401 84.496824
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25 3456.0 81.683457 ... 91.200871 90.994998
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26 3584.0 84.905939 ... 91.470385 94.448944
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27 3712.0 85.675250 ... 88.404730 87.706180
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28 3840.0 81.019778 ... 87.217666 91.549669
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29 3968.0 86.053553 ... 91.816356 83.807647
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30 4096.0 94.386588 ... 86.424811 85.217605
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16 2304.0 68.251065 ... 76.319081 76.563695
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17 2432.0 71.305746 ... 74.918570 83.864074
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18 2560.0 77.833728 ... 81.310171 80.908642
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19 2688.0 83.186525 ... 90.102270 89.254248
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20 2816.0 82.135981 ... 83.074685 83.392363
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21 2944.0 80.902653 ... 82.646820 82.509987
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22 3072.0 81.589488 ... 87.787755 86.447489
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23 3200.0 80.604535 ... 95.952022 94.814812
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24 3328.0 82.939284 ... 82.181847 81.901361
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25 3456.0 81.518272 ... 86.596744 90.180725
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26 3584.0 87.211821 ... 98.375705 96.579370
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27 3712.0 82.491612 ... 88.679403 81.153437
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28 3840.0 84.744825 ... 92.313853 84.355978
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29 3968.0 92.723355 ... 85.271796 88.744681
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30 4096.0 89.090569 ... 92.884244 86.202781
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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 35.509 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 17.895 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 0.011 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.010 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 1024.0 307.200008 99.497980 311.088617
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1 1536.0 347.773587 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 3072.0 519.211251 191.501303 321.956335
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5 3584.0 551.384634 207.768111 308.301075
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6 4096.0 564.965515 220.907859 299.707322
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7 4608.0 498.162157 232.336141 287.251954
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8 5120.0 527.381977 243.326731 286.433562
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9 5632.0 540.671974 244.426754 291.310338
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10 6144.0 550.208948 251.202731 286.879370
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11 6656.0 534.260858 255.590406 286.793541
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12 7168.0 516.612607 253.734520 277.919225
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13 7680.0 488.912481 266.743841 284.884090
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14 8192.0 463.698115 258.354805 278.087683
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15 8704.0 416.127506 267.815384 285.377055
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16 9216.0 429.483477 272.394084 290.267724
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17 9728.0 437.213490 279.942444 288.950501
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18 10240.0 446.836366 287.438599 290.496460
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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 11776.0 421.198220 249.447482 288.686414
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22 12288.0 418.314886 254.453844 294.617366
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23 12800.0 414.016170 254.094291 288.450715
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24 13312.0 412.242569 252.360194 289.129403
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25 13824.0 404.604870 256.991469 291.799461
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26 14336.0 395.930964 255.904799 289.129416
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27 14848.0 386.498925 257.479779 289.012175
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28 15360.0 376.547496 258.332158 287.775181
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29 15872.0 369.116300 261.806182 290.784741
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0 1024.0 307.200008 99.497980 315.076934
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1 1536.0 347.773587 133.083026 338.201833
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2 2048.0 423.724127 161.684218 325.509933
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3 2560.0 458.507457 182.314537 325.079368
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4 3072.0 515.580429 190.511624 316.429186
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5 3584.0 551.384634 208.271186 310.527060
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6 4096.0 568.231237 220.907859 299.707322
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7 4608.0 498.162157 231.364016 286.507772
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8 5120.0 527.381977 242.366855 284.444444
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9 5632.0 540.671974 242.671458 288.820505
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10 6144.0 540.131844 249.925419 285.767458
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11 6656.0 528.953642 256.000009 285.257135
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12 7168.0 508.970395 255.240352 277.470965
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13 7680.0 485.052616 266.358392 283.569230
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14 8192.0 461.521112 260.753323 276.134828
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15 8704.0 416.958106 265.433292 282.291896
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||||
16 9216.0 427.822068 269.736580 286.693456
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17 9728.0 438.857162 282.996365 291.112221
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18 10240.0 445.217381 286.433562 290.496460
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19 10752.0 423.724151 245.760009 290.267711
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||||
20 11264.0 428.424741 243.765566 284.864065
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21 11776.0 424.360356 249.667843 288.981596
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22 12288.0 420.701865 253.578674 294.617366
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23 12800.0 416.260178 254.304635 290.909089
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||||
24 13312.0 412.775186 253.360814 291.237929
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||||
25 13824.0 405.098897 257.990666 292.571423
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||||
26 14336.0 400.540153 252.802351 287.438588
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27 14848.0 383.586664 260.110958 292.811844
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||||
28 15360.0 378.869469 261.076480 290.267715
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29 15872.0 369.116300 261.267482 289.239176
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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 12.572 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.224 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">
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||||
<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>12:56.386</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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||||
<p><strong>12:33.042</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
||||
<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>05:35.509</p></td>
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<td><p>05:17.895</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>03:24.818</p></td>
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<td><p>03:22.390</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>02:12.572</p></td>
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<td><p>02:12.224</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>01:43.477</p></td>
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<td><p>01:40.524</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>00:00.010</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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