[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 15.999999 19.200000
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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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4 65536.0 127.999995 127.999995
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@@ -334,12 +334,12 @@ for different problem sizes.</p>
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10 4194304.0 780.190482 780.190482
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11 8388608.0 812.429770 812.429770
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12 16777216.0 833.084721 833.084721
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13 33554432.0 842.004273 843.811163
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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 134217728.0 849.737435 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 46.040 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 43.896 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 186.181817
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1 384.0 614.400016 585.142862 153.600004
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0 256.0 512.000001 546.133347 188.321838
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1 384.0 585.142862 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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3 640.0 682.666684 640.000002 158.759699
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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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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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95 12416.0 814.163950 411.296057 198.755369
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96 12544.0 812.566838 412.546756 199.012395
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97 12672.0 812.633240 412.097543 199.069228
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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 22.897 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 21.154 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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|
@@ -568,12 +568,12 @@ 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 2.978909 ... 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 ... 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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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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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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@@ -581,29 +581,29 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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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 ... 72.047592 71.588687
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13 1920.0 69.120002 ... 70.530615 70.172588
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14 2048.0 73.908442 ... 77.314362 76.959706
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15 2176.0 83.155572 ... 86.367588 85.269692
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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 83.500614 ... 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 ... 74.918570 84.877538
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18 2560.0 77.833728 ... 81.310171 81.108913
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19 2688.0 83.737433 ... 89.888756 89.044730
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20 2816.0 79.879498 ... 83.074685 82.446516
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21 2944.0 82.921853 ... 82.646820 83.060049
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22 3072.0 81.707223 ... 87.516392 88.060814
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23 3200.0 80.402009 ... 93.841640 92.219022
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24 3328.0 81.530349 ... 84.895397 84.397770
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25 3456.0 81.932484 ... 85.043848 88.497878
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26 3584.0 86.457107 ... 98.808123 93.176571
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27 3712.0 81.615477 ... 86.716441 87.399253
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28 3840.0 83.027026 ... 90.909991 87.355452
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29 3968.0 88.873953 ... 84.097346 90.054568
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30 4096.0 92.820009 ... 88.534120 90.871857
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17 2432.0 71.305746 ... 85.393507 85.134737
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18 2560.0 77.926278 ... 80.709358 81.108913
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19 2688.0 83.186525 ... 89.044730 89.254248
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20 2816.0 79.879498 ... 79.733474 83.233226
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21 2944.0 82.102191 ... 83.198715 83.060049
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22 3072.0 80.202695 ... 88.060814 89.310890
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23 3200.0 84.432717 ... 95.451158 94.534716
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24 3328.0 82.843841 ... 83.130825 84.695641
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25 3456.0 82.688790 ... 90.994998 86.596744
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26 3584.0 84.745889 ... 93.176571 93.467144
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27 3712.0 85.309435 ... 87.783251 88.092894
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28 3840.0 84.228485 ... 92.545605 86.265212
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29 3968.0 93.504929 ... 86.419216 82.616073
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30 4096.0 93.271527 ... 83.365047 83.938538
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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 56.082 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 23.618 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>
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</dl>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.130 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.011 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">
|
||||
<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>
|
||||
|
@@ -194,36 +194,36 @@ to download the full example code</p>
|
||||
<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 307.200008
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1 1536.0 351.085717 133.083026 338.201833
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2 2048.0 423.724127 162.217818 334.367350
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3 2560.0 461.954908 182.857144 330.322572
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4 3072.0 515.580429 191.501303 320.556515
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5 3584.0 551.384634 208.271186 308.301075
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0 1024.0 307.200008 99.497980 311.088617
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1 1536.0 351.085717 133.565214 344.523365
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2 2048.0 423.724127 158.554837 332.108094
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3 2560.0 461.954908 183.402991 332.108113
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4 3072.0 511.999982 193.005236 317.793096
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5 3584.0 551.384634 208.271186 311.652167
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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 5632.0 538.517949 243.545956 291.310338
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10 6144.0 544.118087 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 7680.0 482.513091 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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16 9216.0 428.651187 273.066667 289.507855
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17 9728.0 438.857162 280.278512 288.950501
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18 10240.0 447.650282 286.767793 290.153487
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19 10752.0 430.079980 246.464170 290.267711
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20 11264.0 429.104745 245.202718 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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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 13824.0 405.098897 257.190689 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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||||
29 15872.0 366.982663 262.890274 291.229369
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7 4608.0 500.416301 232.825259 291.031570
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||||
8 5120.0 527.381977 240.941184 285.767451
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||||
9 5632.0 540.671974 242.671458 288.820505
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||||
10 6144.0 550.208948 250.775512 287.438593
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||||
11 6656.0 537.858601 255.590406 286.793541
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||||
12 7168.0 512.000004 256.381525 280.639473
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||||
13 7680.0 485.052616 262.938666 280.975614
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||||
14 8192.0 462.607053 265.327937 278.087683
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||||
15 8704.0 417.791980 268.159180 286.945050
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||||
16 9216.0 429.483477 271.724806 289.129410
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||||
17 9728.0 438.857162 282.653752 289.667485
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||||
18 10240.0 446.025405 285.435547 288.112552
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||||
19 10752.0 432.241202 246.229020 288.967529
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||||
20 11264.0 427.746848 244.426754 284.564206
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||||
21 11776.0 421.826879 250.553197 289.277383
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||||
22 12288.0 422.510018 254.015505 294.911986
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||||
23 12800.0 415.696898 254.304635 288.450715
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||||
24 13312.0 412.242569 252.559690 291.503659
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||||
25 13824.0 405.842204 257.790206 292.313649
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||||
26 14336.0 396.844280 253.547537 286.242939
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||||
27 14848.0 382.762626 259.165092 291.375307
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||||
28 15360.0 373.117425 260.155264 289.811315
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||||
29 15872.0 365.749395 261.267482 289.019722
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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 12.430 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 10.846 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>13:17.580</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:39.525</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>05:56.082</p></td>
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||||
<td><p>05:23.618</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
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||||
<tr class="row-even"><td><p><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
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<td><p>03:22.897</p></td>
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<td><p>03:21.154</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>02:12.430</p></td>
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<td><p>02:10.846</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>01:46.040</p></td>
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<td><p>01:43.896</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>00:00.130</p></td>
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<td><p>00:00.011</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