[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 35.577 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.304 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 186.181817
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1 384.0 614.400016 585.142862 153.600004
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2 512.0 655.360017 606.814814 154.566038
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3 640.0 682.666684 640.000002 160.000000
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4 768.0 722.823517 664.216187 162.754967
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.. ... ... ... ...
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93 12160.0 814.058574 406.179533 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 412.546756 199.012395
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93 12160.0 813.207932 406.179533 198.834951
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94 12288.0 814.111783 415.222812 199.096718
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95 12416.0 812.498981 412.149375 198.655991
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96 12544.0 812.566838 412.546756 198.913776
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97 12672.0 812.633240 412.097543 199.069228
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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 22.257 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 22.855 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 ... 8.507077 7.899428
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0 256.0 2.978909 ... 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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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 ... 39.025776 39.025776
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6 1024.0 51.150050 ... 53.773130 52.428801
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5 896.0 39.025776 ... 40.140799 39.025776
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6 1024.0 49.932191 ... 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 56.888887
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9 1408.0 64.138541 ... 67.305878 65.684049
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10 1536.0 79.526831 ... 79.526831 78.643199
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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 63.372618 ... 62.492442 62.061463
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12 1792.0 72.983276 ... 71.135597 71.135597
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12 1792.0 72.983276 ... 72.047592 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 ... 76.809875 76.563695
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17 2432.0 71.125224 ... 74.918570 84.749516
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18 2560.0 77.833728 ... 81.310171 80.908642
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19 2688.0 83.922689 ... 89.464755 89.888756
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20 2816.0 83.552120 ... 82.290955 83.392363
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21 2944.0 82.784108 ... 79.865439 82.509987
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22 3072.0 82.181572 ... 88.335577 88.473602
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23 3200.0 84.768213 ... 95.380032 94.814812
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24 3328.0 83.905938 ... 85.806075 84.695641
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25 3456.0 82.604067 ... 91.407671 91.097818
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26 3584.0 84.745889 ... 93.661869 87.042978
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27 3712.0 85.896254 ... 90.981189 87.783251
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28 3840.0 82.654712 ... 89.839159 88.332269
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29 3968.0 89.855624 ... 84.385406 89.591729
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30 4096.0 91.739479 ... 93.336389 90.260743
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17 2432.0 71.125224 ... 75.118889 84.367759
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18 2560.0 77.833728 ... 81.108913 80.908642
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19 2688.0 83.737433 ... 89.676257 89.888756
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20 2816.0 80.617762 ... 83.552120 82.759409
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21 2944.0 82.784108 ... 83.060049 82.921853
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22 3072.0 82.540970 ... 85.662786 88.612060
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23 3200.0 84.544253 ... 95.952022 95.665176
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24 3328.0 83.419811 ... 84.895397 84.298943
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25 3456.0 82.266905 ... 91.407671 86.596744
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26 3584.0 86.457107 ... 94.747514 96.475743
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27 3712.0 85.748791 ... 88.483034 87.706180
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28 3840.0 82.654712 ... 88.121115 91.247522
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29 3968.0 87.913500 ... 91.472214 87.409694
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30 4096.0 93.858555 ... 93.401342 90.260743
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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 52.235 seconds)</p>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 40.148 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 0.111 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.110 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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|
@@ -194,35 +194,35 @@ 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 99.096776 311.088617
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0 1024.0 307.200008 99.497980 311.088617
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1 1536.0 351.085717 133.083026 341.333333
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2 2048.0 423.724127 162.217818 336.657521
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3 2560.0 461.954908 182.857144 330.322572
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4 3072.0 511.999982 191.501303 320.556515
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2 2048.0 423.724127 162.217818 327.679984
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3 2560.0 461.954908 182.857144 326.808501
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4 3072.0 511.999982 191.501303 317.793096
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5 3584.0 554.941930 208.271186 308.301075
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6 4096.0 568.231237 220.412561 297.890900
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7 4608.0 498.162157 231.849059 287.251954
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8 5120.0 525.128191 242.845844 283.787523
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9 5632.0 536.380957 243.545956 291.310338
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10 6144.0 542.117638 248.661056 286.322318
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11 6656.0 527.207907 256.000009 286.793541
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12 7168.0 507.469040 262.243907 288.160801
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13 7680.0 482.513091 260.707203 277.172933
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14 8192.0 460.440290 268.957600 286.600589
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15 8704.0 416.958106 267.815384 285.377055
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6 4096.0 568.231237 220.412561 294.323343
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7 4608.0 495.928261 231.849059 291.031570
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8 5120.0 525.128191 242.845844 287.102804
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9 5632.0 536.380957 243.545956 290.683877
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10 6144.0 542.117638 248.661056 285.767458
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11 6656.0 527.207907 256.410903 286.793541
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12 7168.0 507.469040 261.844750 288.644296
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13 7680.0 482.513091 260.707203 277.590365
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14 8192.0 460.440290 269.141693 286.600589
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15 8704.0 416.958106 267.815384 284.987724
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16 9216.0 428.651187 273.066667 289.507855
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17 9728.0 439.683593 280.278512 288.950501
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18 10240.0 447.650282 286.433562 290.153487
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19 10752.0 429.364408 246.464170 290.267711
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20 11264.0 429.104745 245.091565 285.767446
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18 10240.0 447.650282 286.767793 289.811322
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19 10752.0 428.651173 246.464170 290.267711
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20 11264.0 429.104745 245.313973 285.767446
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21 11776.0 421.198220 249.447482 288.686414
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22 12288.0 420.102570 254.453844 295.207195
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22 12288.0 420.102570 254.673582 294.911986
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23 12800.0 415.135142 253.465340 288.180121
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24 13312.0 412.242569 252.759501 290.179836
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25 13824.0 404.604870 257.390218 292.571423
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26 14336.0 397.761846 254.862216 286.481278
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||||
27 14848.0 383.999990 257.108233 289.246765
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28 15360.0 374.634130 257.610071 288.000007
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26 14336.0 397.761846 254.673567 286.481278
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27 14848.0 384.207000 257.293872 289.246765
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28 15360.0 374.253788 257.610071 286.433562
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29 15872.0 366.982663 262.890274 291.229369
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</pre></div>
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</div>
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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 12.038 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.292 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>13:02.218</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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||||
<p><strong>12:55.709</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>05:52.235</p></td>
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<td><p>05:40.148</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>03:22.258</p></td>
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<td><p>03:22.855</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>02:12.038</p></td>
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<td><p>02:12.292</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>01:35.577</p></td>
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<td><p>01:40.304</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>00:00.111</p></td>
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<td><p>00:00.110</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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