[GH-PAGES] Updated website
This commit is contained in:
@@ -331,7 +331,7 @@ for different problem sizes.</p>
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@@ -340,7 +340,7 @@ 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 46.703 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 40.152 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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@@ -369,17 +369,17 @@ 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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1 384.0 585.142862 585.142862 151.703707
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0 256.0 512.000001 546.133347 186.181817
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1 384.0 585.142862 558.545450 151.703707
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4 768.0 722.823517 664.216187 163.839992
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.. ... ... ... ...
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93 12160.0 814.058574 405.755985 199.038365
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94 12288.0 814.111783 415.222812 199.298541
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95 12416.0 814.163950 412.149375 198.954424
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96 12544.0 814.214963 412.546756 199.111113
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97 12672.0 814.265046 411.679167 199.264875
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93 12160.0 814.058574 405.755985 198.631953
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95 12416.0 814.163950 412.149375 198.556711
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97 12672.0 814.265046 411.679167 198.873965
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[98 rows x 4 columns]
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</pre></div>
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@@ -392,7 +392,7 @@ 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 28.363 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 27.921 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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@@ -563,43 +563,43 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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<img alt="03 matrix multiplication" class="sphx-glr-single-img" src="../../_images/sphx_glr_03-matrix-multiplication_001.png" />
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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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1 384.0 7.372800 ... 7.899428 8.507077
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2 512.0 14.563555 ... 15.420235 15.420235
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 35.389441 34.028308
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5 896.0 37.971025 ... 40.140799 40.140799
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6 1024.0 49.932191 ... 53.773130 53.773130
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7 1152.0 45.242181 ... 48.161033 47.396572
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8 1280.0 51.200001 ... 57.690139 57.690139
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9 1408.0 64.138541 ... 69.009825 67.305878
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10 1536.0 79.526831 ... 80.430545 79.526831
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11 1664.0 63.372618 ... 63.372618 62.929456
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12 1792.0 72.983276 ... 63.142831 62.790080
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13 1920.0 68.776119 ... 71.257735 70.892307
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14 2048.0 73.908442 ... 78.398206 78.033565
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15 2176.0 83.155572 ... 87.115360 86.739860
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16 2304.0 68.251065 ... 78.064941 77.558029
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17 2432.0 71.305746 ... 75.726318 75.320281
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18 2560.0 77.833728 ... 82.539044 82.125311
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19 2688.0 83.552988 ... 91.625737 90.748936
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20 2816.0 79.587973 ... 82.290955 84.197315
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21 2944.0 81.232324 ... 83.477440 83.899046
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22 3072.0 81.121923 ... 89.451983 89.735509
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23 3200.0 84.656085 ... 96.240602 96.096095
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24 3328.0 83.905938 ... 83.323259 82.939284
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25 3456.0 82.519518 ... 86.876687 92.033756
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26 3584.0 87.808000 ... 100.017124 99.354022
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27 3712.0 84.874549 ... 88.561477 88.797643
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28 3840.0 84.292684 ... 92.159996 85.597527
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29 3968.0 91.816356 ... 87.976885 90.589410
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30 4096.0 87.552332 ... 94.519528 88.011627
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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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1 384.0 7.372800 ... 7.899428 8.507077
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2 512.0 14.563555 ... 15.420235 16.384000
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3 640.0 22.260869 ... 24.380953 24.380953
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4 768.0 32.768000 ... 35.389441 34.028308
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5 896.0 37.971025 ... 41.321411 39.025776
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6 1024.0 49.932191 ... 53.773130 53.773130
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7 1152.0 45.242181 ... 48.161033 47.396572
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8 1280.0 51.200001 ... 57.690139 57.690139
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9 1408.0 64.138541 ... 69.009825 68.147202
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10 1536.0 80.430545 ... 80.430545 80.430545
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11 1664.0 63.372618 ... 63.372618 62.929456
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12 1792.0 72.983276 ... 63.499573 63.142831
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13 1920.0 68.776119 ... 71.257735 70.892307
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14 2048.0 73.584279 ... 78.398206 78.033565
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15 2176.0 83.155572 ... 87.115360 86.739860
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16 2304.0 68.251065 ... 78.064941 77.558029
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17 2432.0 71.305746 ... 75.726318 75.522751
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18 2560.0 77.833728 ... 82.539044 82.331658
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19 2688.0 84.108772 ... 91.185232 90.966561
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20 2816.0 80.469019 ... 83.552120 84.523664
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21 2944.0 81.832567 ... 84.324925 83.758038
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22 3072.0 81.589488 ... 87.246694 89.451983
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23 3200.0 85.106381 ... 96.385543 95.952022
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24 3328.0 83.905938 ... 85.908470 86.113988
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25 3456.0 82.604067 ... 85.043848 89.480098
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26 3584.0 84.111686 ... 92.410473 94.747514
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27 3712.0 85.675250 ... 85.896254 88.404730
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28 3840.0 81.138664 ... 88.261772 91.701494
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29 3968.0 87.976885 ... 92.477403 85.811488
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30 4096.0 93.401342 ... 84.733417 86.872315
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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> ( 6 minutes 48.929 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 55.944 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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|
@@ -195,35 +195,35 @@ to download the full example code</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 1024.0 361.411758 97.912354 303.407414
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1 1536.0 409.599994 134.540150 341.333333
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2 2048.0 491.520012 161.154101 334.367350
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3 2560.0 461.954908 181.238943 330.322572
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4 3072.0 519.211251 192.501302 323.368415
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5 3584.0 554.941930 208.271186 311.652167
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1 1536.0 405.098894 134.540150 341.333333
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2 2048.0 496.484863 161.154101 323.368435
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3 2560.0 465.454542 181.238943 326.808501
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4 3072.0 519.211251 192.501302 321.956335
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5 3584.0 558.545477 208.271186 311.652167
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6 4096.0 561.737163 220.907859 297.890900
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7 4608.0 502.690905 232.825259 287.251954
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8 5120.0 525.128191 242.366855 284.444444
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9 5632.0 542.843364 243.107920 290.060087
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10 6144.0 544.118087 248.661056 286.879370
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11 6656.0 527.207907 256.000009 285.767438
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8 5120.0 525.128191 242.366855 287.102804
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9 5632.0 540.671974 243.107920 289.438969
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10 6144.0 546.133354 248.661056 286.879370
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11 6656.0 527.207907 256.000009 285.257135
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12 7168.0 503.017523 260.260201 284.821192
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13 7680.0 482.513091 262.938666 280.121579
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13 7680.0 483.779539 262.938666 280.121579
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14 8192.0 463.698115 266.406514 284.526763
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15 8704.0 414.476194 267.472468 284.987724
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16 9216.0 428.651187 271.724806 287.999990
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17 9728.0 437.213490 280.615388 290.027323
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17 9728.0 436.396262 280.278512 290.027323
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18 10240.0 446.025405 286.433562 290.153487
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19 10752.0 428.651173 246.935876 290.267711
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20 11264.0 426.397479 245.536784 286.980888
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21 11776.0 420.571432 249.888595 288.981596
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21 11776.0 421.198220 249.888595 288.981596
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22 12288.0 416.542386 254.673582 294.617366
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23 12800.0 410.695192 254.094291 289.811310
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24 13312.0 409.075539 253.160074 290.443638
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25 13824.0 404.604870 257.390218 292.056329
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23 12800.0 410.695192 254.094291 288.180121
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24 13312.0 409.075539 253.360814 290.443638
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25 13824.0 405.098897 257.390218 292.056329
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26 14336.0 394.116833 254.862216 286.959121
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27 14848.0 385.662341 257.852379 289.952797
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||||
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29 15872.0 370.192407 261.806182 290.341468
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28 15360.0 380.433442 257.970599 286.433562
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29 15872.0 370.552519 261.626369 290.341468
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||||
</pre></div>
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||||
</div>
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<div class="line-block">
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@@ -487,7 +487,7 @@ 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 14.282 seconds)</p>
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||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 14.484 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>14:18.601</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>14:18.825</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>06:48.929</p></td>
|
||||
<td><p>06:55.944</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>
|
||||
<td><p>03:28.363</p></td>
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||||
<td><p>03:27.921</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<td><p>02:14.282</p></td>
|
||||
<td><p>02:14.484</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<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>01:46.703</p></td>
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||||
<td><p>01:40.152</p></td>
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||||
<td><p>0.0 MB</p></td>
|
||||
</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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