[GH-PAGES] Updated website
This commit is contained in:
@@ -321,10 +321,10 @@ 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 9.600000 9.600000
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0 4096.0 8.000000 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 63.999998 63.999998
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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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5 131072.0 219.428568 219.428568
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6 262144.0 341.333321 384.000001
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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 42.557 seconds)</p>
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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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<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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@@ -376,15 +376,15 @@ We will then compare its performance against (1) <code class="code docutils lite
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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 606.814814 154.566038
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3 640.0 682.666684 640.000002 158.759699
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2 512.0 655.360017 585.142849 154.566038
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3 640.0 682.666684 640.000002 160.000000
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4 768.0 722.823517 664.216187 162.754967
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.. ... ... ... ...
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93 12160.0 814.058574 406.179533 198.733401
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94 12288.0 814.111783 416.101597 199.096718
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95 12416.0 812.498981 412.149375 198.655991
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96 12544.0 812.566838 412.546756 198.913776
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97 12672.0 812.633240 412.097543 199.069228
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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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[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 23.007 seconds)</p>
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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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<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 2.978909 ... 3.276800 2.978909
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0 256.0 2.730667 ... 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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2 512.0 14.563555 ... 16.384000 15.420235
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 34.028308 34.028308
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5 896.0 39.025776 ... 40.140799 39.025776
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6 1024.0 51.150050 ... 52.428801 52.428801
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7 1152.0 45.242181 ... 46.656000 46.656000
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8 1280.0 51.200001 ... 56.888887 56.888887
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9 1408.0 64.138541 ... 67.305878 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 ... 71.588687 71.588687
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13 1920.0 68.776119 ... 70.530615 70.530615
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14 2048.0 73.908442 ... 77.314362 76.959706
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15 2176.0 83.155572 ... 85.998493 85.632545
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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 85.134737
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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 83.369354 ... 89.888756 89.044730
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20 2816.0 83.552120 ... 83.712490 82.759409
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21 2944.0 81.564701 ... 81.698415 81.832567
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22 3072.0 82.062468 ... 88.473602 88.335577
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23 3200.0 84.544253 ... 95.238096 95.096582
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24 3328.0 83.130825 ... 84.101981 84.003845
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25 3456.0 79.430113 ... 88.595129 88.400840
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26 3584.0 87.296493 ... 98.160909 98.160909
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27 3712.0 82.423549 ... 88.561477 85.822459
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28 3840.0 84.809814 ... 92.006659 87.286505
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29 3968.0 93.148045 ... 82.953048 80.917732
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30 4096.0 93.045216 ... 92.884244 88.243079
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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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[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 25.861 seconds)</p>
|
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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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<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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|
@@ -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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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 162.217818 325.509933
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3 2560.0 461.954908 182.857144 326.808501
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4 3072.0 511.999982 191.501303 317.793096
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5 3584.0 554.941930 208.271186 308.301075
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6 4096.0 568.231237 220.412561 294.323343
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7 4608.0 498.162157 231.849059 291.031570
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8 5120.0 525.128191 242.845844 287.102804
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9 5632.0 538.517949 243.107920 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 286.793541
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12 7168.0 505.976473 262.243907 288.644296
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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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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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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 9728.0 439.683593 280.278512 288.950501
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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 10752.0 429.364408 246.464170 290.267711
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20 11264.0 429.445578 245.091565 285.767446
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21 11776.0 421.826879 249.227509 288.686414
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22 12288.0 420.102570 254.453844 295.207195
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23 12800.0 415.696898 253.465340 288.180121
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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 14336.0 397.761846 254.673567 286.481278
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27 14848.0 383.586664 257.293872 289.246765
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28 15360.0 374.253788 257.610071 286.433562
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29 15872.0 366.982663 262.708969 291.229369
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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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||||
</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 11.602 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.483 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">
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||||
<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>12:43.037</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
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<p><strong>12:21.546</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
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||||
<colgroup>
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<col style="width: 85%" />
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@@ -183,19 +183,19 @@
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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>05:25.861</p></td>
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<td><p>05:16.690</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>03:23.007</p></td>
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||||
<td><p>03:20.245</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>02:11.602</p></td>
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<td><p>02:12.483</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>01:42.557</p></td>
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||||
<td><p>01:32.117</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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|
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