[GH-PAGES] Updated website
This commit is contained in:
@@ -324,13 +324,13 @@ for different problem sizes.</p>
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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 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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8 1048576.0 614.400016 614.400016
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9 2097152.0 722.823517 702.171410
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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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@@ -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 45.148 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 37.395 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 606.814814 154.566038
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0 256.0 512.000001 512.000001 190.511628
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1 384.0 585.142862 585.142862 153.600004
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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 198.834951
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94 12288.0 814.111783 416.101597 199.197579
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95 12416.0 812.498981 412.149375 198.854847
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96 12544.0 812.566838 412.971190 199.111113
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97 12672.0 812.633240 412.097543 199.167004
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93 12160.0 812.359066 406.179533 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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97 12672.0 812.633240 412.097543 199.069228
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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 21.863 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 18.756 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 ... 2.978909 2.978909
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1 384.0 7.372800 ... 8.507077 7.899428
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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 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 ... 47.396572 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 ... 72.512412 72.047592
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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 83.500614 ... 86.367588 85.632545
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16 2304.0 68.251065 ... 77.057651 76.076024
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5 896.0 39.025776 ... 39.025776 37.971025
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6 1024.0 49.932191 ... 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 ... 72.047592 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 83.500614 ... 85.998493 85.632545
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16 2304.0 68.251065 ... 76.319081 76.563695
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17 2432.0 71.305746 ... 74.719317 84.877538
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18 2560.0 77.833728 ... 80.908642 80.511054
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19 2688.0 83.737433 ... 89.254248 89.888756
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20 2816.0 81.369790 ... 82.290955 83.233226
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21 2944.0 81.832567 ... 81.564701 82.509987
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22 3072.0 82.661468 ... 88.335577 88.060814
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23 3200.0 83.009080 ... 95.522391 95.238096
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24 3328.0 82.843841 ... 84.052885 84.200347
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25 3456.0 80.140726 ... 91.200871 90.892410
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26 3584.0 86.540320 ... 94.647779 94.997774
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27 3712.0 85.019017 ... 87.475786 87.706180
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28 3840.0 84.036474 ... 91.247522 89.295115
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29 3968.0 91.540836 ... 83.921126 91.198760
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30 4096.0 86.202781 ... 86.480498 91.118618
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18 2560.0 77.833728 ... 81.310171 80.908642
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19 2688.0 83.004501 ... 88.732296 89.044730
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20 2816.0 79.443003 ... 82.290955 83.074685
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21 2944.0 82.102191 ... 83.198715 82.373605
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22 3072.0 82.181572 ... 87.651868 82.661468
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23 3200.0 80.808083 ... 95.808380 94.955488
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24 3328.0 83.323259 ... 80.347427 84.596116
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25 3456.0 81.477080 ... 86.689860 90.281712
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26 3584.0 87.296493 ... 98.483450 93.564405
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27 3712.0 84.088676 ... 88.404730 86.791782
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28 3840.0 84.615146 ... 92.083268 84.102376
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29 3968.0 92.372393 ... 80.278903 84.299785
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30 4096.0 90.443212 ... 86.369197 89.538177
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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 25.100 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 18.032 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 311.088617
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1 1536.0 351.085717 133.083026 341.333333
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2 2048.0 423.724127 162.217818 338.979315
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3 2560.0 461.954908 182.857144 330.322572
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4 3072.0 511.999982 191.501303 315.076914
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5 3584.0 554.941930 208.271186 310.527060
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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 336.657521
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3 2560.0 461.954908 182.857144 328.556154
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4 3072.0 511.999982 191.501303 320.556515
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5 3584.0 554.941930 208.271186 308.301075
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6 4096.0 568.231237 220.412561 297.890900
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7 4608.0 498.162157 231.849059 290.267724
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8 5120.0 525.128191 242.845844 286.433562
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9 5632.0 538.517949 243.107920 290.683877
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10 6144.0 544.118087 248.661056 286.322318
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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 290.683877
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10 6144.0 542.117638 248.661056 285.767458
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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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12 7168.0 505.976473 261.844750 288.160801
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13 7680.0 482.513091 260.707203 277.590365
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14 8192.0 460.440290 268.957600 286.183409
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15 8704.0 416.958106 267.815384 285.377055
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16 9216.0 428.651187 272.729961 289.507855
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17 9728.0 439.683593 280.615388 288.950501
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18 10240.0 446.836366 286.767793 290.153487
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19 10752.0 430.079980 246.464170 289.941565
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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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17 9728.0 439.683593 280.278512 288.950501
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18 10240.0 447.650282 286.767793 290.496460
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19 10752.0 430.079980 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 254.453844 295.207195
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23 12800.0 415.696898 253.465340 288.180121
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23 12800.0 415.696898 253.465340 288.721817
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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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25 13824.0 405.098897 257.590056 292.571423
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||||
26 14336.0 397.761846 254.673567 286.481278
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27 14848.0 384.414233 257.108233 289.246765
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||||
28 15360.0 374.443863 257.790220 287.775181
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29 15872.0 366.982663 262.890274 291.006885
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28 15360.0 374.253788 257.790220 287.550706
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29 15872.0 366.982663 262.890274 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>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 10.934 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.525 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>12:43.056</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>12:26.718</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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@@ -183,19 +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>05:25.100</p></td>
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||||
<td><p>05:18.032</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>03:21.863</p></td>
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||||
<td><p>03:18.756</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>02:10.934</p></td>
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<td><p>02:12.525</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>01:45.148</p></td>
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<td><p>01:37.395</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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