[GH-PAGES] Updated website
This commit is contained in:
@@ -322,7 +322,7 @@ for different problem sizes.</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
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size Triton Torch
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0 4096.0 9.600000 9.600000
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1 8192.0 19.200000 19.200000
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1 8192.0 15.999999 19.200000
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2 16384.0 38.400001 38.400001
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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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@@ -336,10 +336,10 @@ for different problem sizes.</p>
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12 16777216.0 833.084721 833.084721
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13 33554432.0 842.004273 842.004273
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14 67108864.0 847.448255 848.362445
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15 134217728.0 850.196756 850.656574
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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 44.439 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 44.691 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 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 151.703707
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2 512.0 655.360017 585.142849 154.566038
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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 156.038096
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3 640.0 682.666684 640.000002 160.000000
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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 406.179533 198.834951
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94 12288.0 814.111783 416.101597 199.096718
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95 12416.0 814.163950 412.577363 198.755369
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96 12544.0 812.566838 413.183734 198.913776
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93 12160.0 812.359066 405.755985 198.936606
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94 12288.0 814.111783 415.222812 199.096718
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95 12416.0 812.498981 412.149375 198.854847
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96 12544.0 812.566838 412.971190 199.012395
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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 25.535 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 24.321 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 ... 2.978909 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 15.420235
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3 640.0 22.260869 ... 24.380953 23.272727
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4 768.0 31.597714 ... 34.028308 34.028308
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1 384.0 7.372800 ... 8.507077 8.507077
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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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6 1024.0 49.932191 ... 52.428801 51.150050
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7 1152.0 43.911529 ... 45.938215 45.242181
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8 1280.0 49.951220 ... 55.351349 55.351349
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9 1408.0 62.664092 ... 65.684049 65.684049
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10 1536.0 78.643199 ... 77.778988 77.778988
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11 1664.0 62.061463 ... 61.217089 60.803457
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12 1792.0 71.588687 ... 71.135597 70.688200
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13 1920.0 68.098521 ... 69.120002 69.120002
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14 2048.0 72.628641 ... 75.573044 75.234154
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15 2176.0 81.803444 ... 84.199364 83.500614
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16 2304.0 67.100763 ... 75.119093 75.119093
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17 2432.0 69.886725 ... 83.614477 83.119713
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18 2560.0 76.204654 ... 79.727497 79.341404
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19 2688.0 82.284288 ... 88.216412 88.011732
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20 2816.0 82.759409 ... 82.135981 81.674548
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21 2944.0 81.166173 ... 81.431424 81.166173
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22 3072.0 81.121923 ... 87.381335 86.845249
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23 3200.0 83.660130 ... 94.395283 93.910490
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24 3328.0 82.181847 ... 83.808259 83.565058
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25 3456.0 80.864158 ... 90.484366 90.079964
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26 3584.0 86.291162 ... 97.628001 97.575029
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27 3712.0 84.730571 ... 87.860458 87.552452
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28 3840.0 84.228485 ... 91.322872 90.649182
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29 3968.0 92.442373 ... 90.522206 90.354633
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30 4096.0 93.142072 ... 91.929947 92.182504
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7 1152.0 44.566925 ... 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 79.526831 ... 79.526831 78.643199
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11 1664.0 63.372618 ... 62.492442 62.492442
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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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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 ... 76.441192 76.563695
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17 2432.0 71.305746 ... 79.587714 84.621881
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18 2560.0 77.833728 ... 80.908642 80.313727
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19 2688.0 83.369354 ... 89.676257 89.254248
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20 2816.0 84.035084 ... 82.759409 82.602666
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21 2944.0 82.373605 ... 83.198715 82.921853
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22 3072.0 81.943708 ... 87.855861 88.335577
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23 3200.0 84.321474 ... 89.761569 94.674553
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24 3328.0 82.939284 ... 79.548391 83.710812
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25 3456.0 81.683457 ... 89.480098 90.790053
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26 3584.0 87.211821 ... 98.591437 90.367227
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27 3712.0 85.019017 ... 88.718781 84.017953
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28 3840.0 84.679936 ... 92.390975 84.228485
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29 3968.0 92.723355 ... 85.271796 89.657558
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30 4096.0 92.787924 ... 92.372834 86.258181
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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 23.021 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 34.991 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">
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||||
<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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||||
|
@@ -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>
|
||||
</dl>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.011 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.110 seconds)</p>
|
||||
<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:
|
||||
N Triton Torch Apex
|
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0 1024.0 303.407414 98.698793 311.088617
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1 1536.0 347.773587 133.083026 341.333333
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2 2048.0 416.542360 157.538467 332.108094
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3 2560.0 451.764698 181.238943 328.556154
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4 3072.0 508.468972 190.511624 320.556515
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||||
5 3584.0 540.981122 206.769233 308.301075
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6 4096.0 558.545450 219.919464 298.796351
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7 4608.0 489.345125 231.364016 286.507772
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8 5120.0 520.677950 242.366855 285.767451
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9 5632.0 534.260858 243.545956 291.310338
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10 6144.0 544.118087 249.925419 286.879370
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11 6656.0 532.479975 254.775119 285.767438
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12 7168.0 515.065851 252.988236 277.024148
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13 7680.0 488.912481 265.590783 283.569230
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14 8192.0 464.794337 257.677592 277.303250
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15 8704.0 408.798442 266.448988 284.212242
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16 9216.0 422.106891 271.391419 289.129410
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17 9728.0 430.760152 279.272720 288.237038
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18 10240.0 438.074849 286.433562 289.129408
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19 10752.0 426.525614 245.760009 289.291486
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20 11264.0 427.071098 244.426754 285.465683
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21 11776.0 418.082825 248.569911 288.097854
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22 12288.0 416.542386 253.578674 293.737063
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23 12800.0 412.348979 253.047766 288.993430
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24 13312.0 410.125805 251.367424 288.607034
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25 13824.0 403.130022 256.197690 291.031592
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26 14336.0 395.021816 255.051144 288.402346
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27 14848.0 384.829370 256.737757 288.310684
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28 15360.0 376.547496 257.430175 287.550706
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29 15872.0 369.832994 260.731015 289.899545
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0 1024.0 307.200008 99.497980 315.076934
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1 1536.0 347.773587 134.050910 344.523365
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2 2048.0 423.724127 159.067963 323.368435
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3 2560.0 461.954908 182.314537 325.079368
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4 3072.0 515.580429 191.501303 319.168834
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5 3584.0 551.384634 207.768111 309.410081
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||||
6 4096.0 564.965515 220.907859 301.546004
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||||
7 4608.0 498.162157 232.825259 287.251954
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||||
8 5120.0 529.655159 243.809526 286.433562
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||||
9 5632.0 540.671974 244.426754 291.939522
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||||
10 6144.0 550.208948 251.202731 287.438593
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||||
11 6656.0 534.260858 255.590406 286.793541
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||||
12 7168.0 515.065851 253.734520 277.470965
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||||
13 7680.0 490.212752 266.743841 284.884090
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||||
14 8192.0 464.794337 258.354805 278.087683
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||||
15 8704.0 416.127506 267.815384 285.767450
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||||
16 9216.0 430.319054 272.059034 289.887291
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||||
17 9728.0 438.033784 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 246.699797 289.941565
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||||
20 11264.0 430.471331 245.313973 286.069848
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||||
21 11776.0 421.198220 249.447482 288.686414
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||||
22 12288.0 418.314886 254.673582 294.617366
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||||
23 12800.0 414.016170 254.094291 289.538159
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||||
24 13312.0 412.242569 252.559690 289.129403
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||||
25 13824.0 405.098897 256.991469 291.799461
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||||
26 14336.0 396.387109 256.000002 289.129416
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||||
27 14848.0 386.918555 257.665934 289.012175
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||||
28 15360.0 376.932517 258.332158 286.656296
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||||
29 15872.0 369.832994 261.986243 290.784741
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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>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 13.777 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.361 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>12:46.783</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:56.474</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 85%" />
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@@ -183,23 +183,23 @@
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</colgroup>
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<tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
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<td><p>05:23.021</p></td>
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<td><p>05:34.991</p></td>
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<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>03:25.535</p></td>
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||||
<td><p>03:24.321</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>02:13.777</p></td>
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<td><p>02:12.361</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>01:44.439</p></td>
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<td><p>01:44.691</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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<td><p>00:00.011</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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Block a user