[GH-PAGES] Updated website
This commit is contained in:
@@ -321,7 +321,7 @@ for different problem sizes.</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>vector-add-performance:
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size Triton Torch
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0 4096.0 8.000000 9.600000
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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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2 16384.0 38.400001 38.400001
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3 32768.0 76.800002 76.800002
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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 32.117 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.198 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">01-vector-add.py</span></code></a></p>
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@@ -374,17 +374,17 @@ We will then compare its performance against (1) <code class="code docutils lite
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
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N Triton Torch (native) Torch (jit)
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0 256.0 512.000001 546.133347 188.321838
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1 384.0 614.400016 585.142862 153.600004
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2 512.0 655.360017 585.142849 154.566038
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0 256.0 512.000001 546.133347 190.511628
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1 384.0 585.142862 585.142862 151.703707
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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 664.216187 162.754967
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4 768.0 722.823517 664.216187 163.839992
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.. ... ... ... ...
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93 12160.0 814.058574 406.179533 198.936606
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94 12288.0 814.111783 415.661740 199.197579
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95 12416.0 812.498981 412.149375 198.954424
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96 12544.0 812.566838 412.971190 199.209928
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97 12672.0 812.633240 412.097543 199.264875
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93 12160.0 812.359066 406.179533 199.038365
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94 12288.0 814.111783 415.222812 199.197579
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95 12416.0 814.163950 412.149375 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 20.245 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 20.954 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 ... 8.507077 8.507077
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2 512.0 14.563555 ... 16.384000 15.420235
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2 512.0 14.563555 ... 16.384000 16.384000
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 34.028308 34.028308
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5 896.0 39.025776 ... 40.140799 39.025776
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6 1024.0 51.150050 ... 52.428801 52.428801
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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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7 1152.0 45.242181 ... 46.656000 46.656000
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8 1280.0 51.200001 ... 56.888887 56.109587
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9 1408.0 64.138541 ... 67.305878 66.485074
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10 1536.0 80.430545 ... 79.526831 78.643199
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11 1664.0 62.929456 ... 62.061463 62.061463
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12 1792.0 72.512412 ... 71.588687 71.588687
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13 1920.0 69.120002 ... 70.530615 70.530615
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14 2048.0 73.908442 ... 76.959706 76.959706
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15 2176.0 83.155572 ... 86.367588 85.632545
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16 2304.0 68.251065 ... 76.809875 76.563695
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17 2432.0 71.305746 ... 85.393507 84.367759
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18 2560.0 78.019048 ... 80.908642 81.108913
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19 2688.0 83.737433 ... 89.044730 88.628636
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20 2816.0 79.587973 ... 82.916747 83.233226
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21 2944.0 81.564701 ... 82.373605 82.921853
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22 3072.0 82.181572 ... 89.451983 87.313963
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23 3200.0 83.116885 ... 92.753621 91.298148
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24 3328.0 80.617354 ... 84.895397 84.596116
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25 3456.0 82.099354 ... 91.097818 90.586029
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26 3584.0 82.189576 ... 91.563533 95.047985
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27 3712.0 85.309435 ... 90.981189 87.170458
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28 3840.0 81.079177 ... 85.070769 91.097196
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29 3968.0 85.752131 ... 91.266964 85.751184
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30 4096.0 93.336389 ... 92.245860 88.243079
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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 ... 72.512412 72.047592
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13 1920.0 69.467336 ... 70.172588 70.172588
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14 2048.0 73.908442 ... 76.959706 76.608294
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15 2176.0 83.500614 ... 85.998493 85.632545
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16 2304.0 68.348707 ... 76.809875 76.563695
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17 2432.0 71.305746 ... 75.118889 84.877538
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18 2560.0 77.833728 ... 81.310171 80.709358
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19 2688.0 83.369354 ... 90.316801 89.254248
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20 2816.0 83.074685 ... 83.552120 82.602666
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21 2944.0 81.832567 ... 80.251257 81.967162
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22 3072.0 82.062468 ... 88.473602 88.335577
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23 3200.0 82.262212 ... 91.822093 92.352095
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24 3328.0 83.130825 ... 84.596116 84.397770
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25 3456.0 80.300370 ... 88.595129 90.994998
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26 3584.0 87.594146 ... 95.756542 97.628001
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27 3712.0 85.163978 ... 86.304403 89.513749
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28 3840.0 80.139129 ... 91.701494 85.930069
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29 3968.0 90.859224 ... 84.040329 89.657558
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30 4096.0 86.339677 ... 93.206754 90.200084
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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 16.690 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 19.110 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-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">
|
||||
<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 1024.0 311.088617 99.096776 311.088617
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1 1536.0 351.085717 133.083026 338.201833
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2 2048.0 423.724127 161.684218 338.979315
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3 2560.0 461.954908 182.314537 323.368411
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0 1024.0 311.088617 99.497980 319.168844
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1 1536.0 354.461542 133.565214 341.333333
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2 2048.0 427.408686 159.067963 323.368435
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3 2560.0 461.954908 181.775141 328.556154
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4 3072.0 511.999982 191.005181 319.168834
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5 3584.0 554.941930 208.271186 306.106777
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6 4096.0 568.231237 219.919464 292.571431
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7 4608.0 498.162157 231.849059 289.507855
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8 5120.0 525.128191 242.845844 283.787523
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9 5632.0 538.517949 243.107920 290.683877
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10 6144.0 542.117638 248.661056 286.322318
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11 6656.0 527.207907 256.000009 286.279570
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12 7168.0 505.976473 262.243907 288.160801
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13 7680.0 481.253256 260.707203 277.172933
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14 8192.0 460.440290 269.326017 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 9728.0 438.857162 280.615388 288.950501
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18 10240.0 447.650282 286.433562 290.153487
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19 10752.0 430.079980 246.464170 290.267711
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20 11264.0 429.786952 245.091565 285.767446
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21 11776.0 421.198220 249.227509 288.686414
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22 12288.0 420.102570 254.453844 294.911986
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23 12800.0 415.135142 253.465340 289.811310
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24 13312.0 412.242569 252.559690 290.179836
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25 13824.0 405.098897 257.390218 292.571423
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26 14336.0 398.222222 254.862216 286.481278
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27 14848.0 384.414233 257.293872 289.246765
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28 15360.0 374.634130 257.610071 287.550706
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29 15872.0 366.805973 262.708969 291.229369
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5 3584.0 554.941930 207.267476 310.527060
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6 4096.0 568.231237 219.919464 302.473845
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7 4608.0 502.690905 233.316456 286.507772
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8 5120.0 531.948056 241.414550 284.444444
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9 5632.0 545.032265 244.426754 291.310338
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10 6144.0 548.163546 250.775512 286.879370
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11 6656.0 532.479975 256.000009 286.279570
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12 7168.0 513.528374 256.764187 281.098038
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13 7680.0 486.332448 264.827585 283.569230
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14 8192.0 464.794337 260.407952 278.876591
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15 8704.0 416.958106 267.472468 285.377055
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16 9216.0 431.157889 272.059034 287.999990
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17 9728.0 438.857162 280.278512 289.308559
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18 10240.0 446.836366 287.102804 291.530244
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19 10752.0 432.966444 246.699797 290.922209
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20 11264.0 429.104745 246.432094 288.512281
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21 11776.0 423.089806 249.007923 288.981596
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22 12288.0 421.905564 254.234486 294.617366
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23 12800.0 415.135142 253.884294 289.811310
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24 13312.0 411.711355 253.160074 289.916513
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25 13824.0 405.098897 256.991469 292.056329
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26 14336.0 397.761846 255.619613 288.886653
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27 14848.0 382.351933 257.293872 289.012175
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28 15360.0 377.318326 258.151252 287.438599
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29 15872.0 369.832994 261.986243 290.562936
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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 12.483 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 11.629 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>12:21.546</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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||||
<p><strong>12:30.902</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
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<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>05:16.690</p></td>
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<td><p>05:19.110</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>03:20.245</p></td>
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<td><p>03:20.954</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>02:12.483</p></td>
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<td><p>02:11.629</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>01:32.117</p></td>
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<td><p>01:39.198</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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|
Reference in New Issue
Block a user