[GH-PAGES] Updated website
This commit is contained in:
@@ -324,7 +324,7 @@ for different problem sizes.</p>
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@@ -334,12 +334,12 @@ for different problem sizes.</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 47.967 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 47.807 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 192.752942
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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 186.181817
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.. ... ... ... ...
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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 23.856 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 23.484 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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@@ -568,7 +568,7 @@ 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 ... 3.276800 2.978909
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0 256.0 2.730667 ... 3.276800 3.276800
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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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@@ -579,31 +579,31 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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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 78.643199
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11 1664.0 62.929456 ... 62.061463 61.636381
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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 ... 76.959706 76.959706
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15 2176.0 83.500614 ... 86.367588 85.269692
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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 80.908642
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19 2688.0 83.277839 ... 89.254248 88.422041
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20 2816.0 79.298560 ... 82.759409 83.233226
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21 2944.0 82.373605 ... 82.373605 80.510553
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22 3072.0 82.003045 ... 88.473602 87.516392
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23 3200.0 84.656085 ... 95.736729 94.674553
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24 3328.0 82.891535 ... 83.130825 81.162679
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25 3456.0 81.683457 ... 91.407671 90.994998
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26 3584.0 84.905948 ... 90.364394 95.553020
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27 3712.0 85.528545 ... 82.220033 88.640059
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28 3840.0 81.859361 ... 86.130841 90.872641
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29 3968.0 85.812429 ... 90.589410 85.751184
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30 4096.0 93.142072 ... 91.304576 88.243079
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13 1920.0 69.120002 ... 70.530615 70.530615
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14 2048.0 73.908442 ... 77.314362 76.959706
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15 2176.0 83.500614 ... 85.998493 85.632545
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16 2304.0 68.446623 ... 76.809875 76.563695
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17 2432.0 71.305746 ... 83.366361 84.877538
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18 2560.0 77.833728 ... 81.310171 81.108913
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19 2688.0 82.823267 ... 89.888756 89.254248
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20 2816.0 79.154642 ... 83.552120 82.602666
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21 2944.0 82.237674 ... 82.373605 83.060049
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22 3072.0 78.697851 ... 88.473602 89.170242
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23 3200.0 83.879425 ... 94.604578 94.256261
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24 3328.0 82.369902 ... 84.348328 84.298943
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25 3456.0 82.773682 ... 84.156124 87.870919
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26 3584.0 87.296493 ... 97.100854 88.849200
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27 3712.0 84.088676 ... 83.247783 86.641231
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28 3840.0 84.615146 ... 85.863352 91.097196
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29 3968.0 85.812429 ... 84.156250 90.254387
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30 4096.0 89.210850 ... 93.271527 86.313653
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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 25.284 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 26.564 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 0.010 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>
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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>
|
||||
|
@@ -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 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 325.509933
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3 2560.0 461.954908 182.857144 325.079368
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4 3072.0 511.999982 191.501303 319.168834
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5 3584.0 554.941930 208.271186 309.410081
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6 4096.0 568.231237 220.412561 300.623865
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7 4608.0 498.162157 231.849059 287.999990
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8 5120.0 525.128191 242.845844 287.102804
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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 288.644296
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13 7680.0 481.253256 260.707203 277.172933
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14 8192.0 460.440290 268.957600 287.018988
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15 8704.0 416.958106 267.472468 284.987724
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16 9216.0 428.651187 273.066667 289.507855
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17 9728.0 438.857162 280.278512 288.950501
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18 10240.0 447.650282 286.767793 289.811322
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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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21 11776.0 421.198220 249.447482 288.686414
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22 12288.0 420.102570 254.673582 295.207195
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23 12800.0 415.696898 253.465340 288.180121
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24 13312.0 412.242569 252.559690 290.179836
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25 13824.0 405.098897 257.190689 292.571423
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26 14336.0 397.761846 254.673567 286.481278
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27 14848.0 383.586664 257.108233 289.246765
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28 15360.0 374.634130 257.790220 286.656296
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29 15872.0 366.982663 262.890274 291.229369
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0 1024.0 311.088617 98.698793 303.407414
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||||
1 1536.0 351.085717 135.529409 344.523365
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2 2048.0 427.408686 160.104230 323.368435
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3 2560.0 465.454542 182.314537 326.808501
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4 3072.0 515.580429 192.501302 316.429186
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5 3584.0 554.941930 208.271186 308.301075
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6 4096.0 571.534884 220.907859 297.890900
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7 4608.0 500.416301 232.825259 291.799469
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8 5120.0 527.381977 241.414550 285.104413
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9 5632.0 538.517949 243.545956 290.060087
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||||
10 6144.0 546.133354 249.081070 286.322318
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||||
11 6656.0 528.953642 256.000009 287.309361
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||||
12 7168.0 512.000004 258.694737 283.881181
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||||
13 7680.0 482.513091 264.447629 280.975614
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||||
14 8192.0 460.440290 267.493874 285.767442
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||||
15 8704.0 416.958106 268.159180 285.377055
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||||
16 9216.0 429.483477 272.394084 288.751954
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||||
17 9728.0 438.033784 280.278512 289.308559
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||||
18 10240.0 446.025405 287.102804 290.840246
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||||
19 10752.0 430.797982 246.699797 290.594591
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||||
20 11264.0 427.746848 246.432094 288.512281
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||||
21 11776.0 421.826879 249.667843 288.686414
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||||
22 12288.0 419.504980 254.453844 294.617366
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||||
23 12800.0 415.696898 253.884294 289.811310
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||||
24 13312.0 411.711355 252.559690 290.443638
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||||
25 13824.0 407.087128 256.991469 292.056329
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||||
26 14336.0 396.844280 253.921779 286.719986
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||||
27 14848.0 384.414233 257.293872 289.012175
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||||
28 15360.0 376.932517 257.970599 288.676598
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||||
29 15872.0 369.116300 263.253636 291.452168
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||||
</pre></div>
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||||
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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>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.368 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.911 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>
|
||||
|
@@ -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>12:49.485</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:50.776</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
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<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>05:25.284</p></td>
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<td><p>05:26.564</p></td>
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<td><p>0.0 MB</p></td>
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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>03:23.856</p></td>
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||||
<td><p>03:23.484</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>02:12.368</p></td>
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<td><p>02:12.911</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>01:47.967</p></td>
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<td><p>01:47.807</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>00:00.010</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