[GH-PAGES] Updated website

This commit is contained in:
Philippe Tillet
2022-02-16 00:38:53 +00:00
parent 729d03602e
commit c1c43fcf06
158 changed files with 280 additions and 280 deletions

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@@ -235,10 +235,10 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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1 8192.0 19.200000 19.200000
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3 32768.0 76.800002 76.800002
3 32768.0 63.999998 63.999998
4 65536.0 127.999995 127.999995
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6 262144.0 341.333321 384.000001
7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016
9 2097152.0 722.823517 722.823517
@@ -255,7 +255,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 41.422 seconds)
**Total running time of the script:** ( 1 minutes 43.089 seconds)
.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

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@@ -278,17 +278,17 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
softmax-performance:
N Triton Torch (native) Torch (jit)
0 256.0 512.000001 546.133347 188.321838
0 256.0 512.000001 546.133347 190.511628
1 384.0 614.400016 585.142862 153.600004
2 512.0 655.360017 606.814814 154.566038
3 640.0 706.206879 640.000002 160.000000
4 768.0 722.823517 664.216187 162.754967
.. ... ... ... ...
93 12160.0 814.058574 406.179533 198.834951
94 12288.0 815.800825 415.222812 199.197579
95 12416.0 814.163950 412.149375 198.755369
96 12544.0 814.214963 412.971190 199.012395
97 12672.0 814.265046 412.097543 198.971549
93 12160.0 814.058574 406.179533 198.631953
94 12288.0 814.111783 415.661740 198.895304
95 12416.0 814.163950 412.577363 198.457532
96 12544.0 814.214963 412.971190 198.716830
97 12672.0 814.265046 412.097543 198.776477
[98 rows x 4 columns]
@@ -306,7 +306,7 @@ In the above plot, we can see that:
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 3 minutes 18.383 seconds)
**Total running time of the script:** ( 3 minutes 21.816 seconds)
.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

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@@ -458,37 +458,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU)
0 256.0 2.730667 ... 3.276800 2.978909
0 256.0 2.978909 ... 2.978909 2.978909
1 384.0 7.372800 ... 8.507077 7.899428
2 512.0 14.563555 ... 16.384000 16.384000
3 640.0 23.272727 ... 24.380953 24.380953
3 640.0 22.260869 ... 24.380953 24.380953
4 768.0 32.768000 ... 34.028308 34.028308
5 896.0 39.025776 ... 40.140799 39.025776
5 896.0 39.025776 ... 39.025776 39.025776
6 1024.0 51.150050 ... 52.428801 52.428801
7 1152.0 45.242181 ... 46.656000 46.656000
8 1280.0 51.200001 ... 56.888887 56.888887
9 1408.0 64.138541 ... 67.305878 66.485074
10 1536.0 80.430545 ... 79.526831 78.643199
11 1664.0 62.929456 ... 62.492442 62.061463
12 1792.0 72.512412 ... 72.047592 71.588687
12 1792.0 72.983276 ... 71.588687 72.047592
13 1920.0 69.120002 ... 70.172588 70.172588
14 2048.0 73.262953 ... 76.608294 76.608294
15 2176.0 83.155572 ... 85.998493 85.269692
16 2304.0 68.643310 ... 76.319081 76.319081
17 2432.0 71.305746 ... 74.719317 85.134737
18 2560.0 78.019048 ... 80.709358 80.511054
19 2688.0 83.552988 ... 89.888756 89.676257
20 2816.0 82.916747 ... 83.392363 82.135981
21 2944.0 82.237674 ... 82.646820 82.921853
22 3072.0 81.707223 ... 86.978653 84.010539
23 3200.0 84.993363 ... 88.033012 94.048494
24 3328.0 79.990330 ... 84.003845 84.496824
25 3456.0 81.600781 ... 85.585527 90.281712
26 3584.0 87.381330 ... 98.591437 95.654673
27 3712.0 84.159518 ... 87.475786 85.418853
28 3840.0 83.781816 ... 91.473945 84.582788
29 3968.0 90.724116 ... 85.093402 90.455103
30 4096.0 86.313653 ... 87.438257 91.678778
15 2176.0 83.155572 ... 85.998493 85.632545
16 2304.0 68.446623 ... 76.319081 76.319081
17 2432.0 71.305746 ... 84.621881 84.115159
18 2560.0 78.019048 ... 81.108913 80.709358
19 2688.0 83.004501 ... 89.464755 89.464755
20 2816.0 82.916747 ... 82.759409 82.290955
21 2944.0 81.298583 ... 82.509987 82.646820
22 3072.0 81.825298 ... 84.135370 89.030036
23 3200.0 84.488448 ... 94.955488 94.604578
24 3328.0 82.939284 ... 81.346098 83.905938
25 3456.0 81.766291 ... 87.775250 90.790053
26 3584.0 87.296493 ... 97.947050 90.276496
27 3712.0 84.159518 ... 87.018592 85.748791
28 3840.0 84.874902 ... 92.313853 84.552479
29 3968.0 92.302520 ... 85.152783 91.028675
30 4096.0 86.703957 ... 85.434583 91.211502
[31 rows x 5 columns]
@@ -498,7 +498,7 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 5 minutes 59.778 seconds)
**Total running time of the script:** ( 6 minutes 1.969 seconds)
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

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@@ -240,7 +240,7 @@ References
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.481 seconds)
**Total running time of the script:** ( 0 minutes 0.479 seconds)
.. _sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py:

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@@ -39,35 +39,35 @@ Layer Normalization
layer-norm-backward:
N Triton Torch Apex
0 1024.0 311.088617 98.303995 307.200008
1 1536.0 347.773587 134.540150 341.333333
2 2048.0 423.724127 161.684218 334.367350
3 2560.0 458.507457 181.775141 326.808501
4 3072.0 511.999982 192.501302 319.168834
5 3584.0 551.384634 208.271186 310.527060
6 4096.0 568.231237 220.412561 293.444785
7 4608.0 507.302750 232.825259 291.031570
8 5120.0 531.948056 242.845844 287.102804
9 5632.0 545.032265 243.545956 289.438969
10 6144.0 548.163546 248.661056 286.879370
11 6656.0 534.260858 256.000009 285.767438
1 1536.0 347.773587 134.050910 341.333333
2 2048.0 420.102553 161.154101 334.367350
3 2560.0 458.507457 181.238943 330.322572
4 3072.0 511.999982 191.999993 320.556515
5 3584.0 551.384634 207.768111 310.527060
6 4096.0 564.965515 220.412561 297.890900
7 4608.0 504.986315 232.825259 286.507772
8 5120.0 529.655159 242.845844 285.104413
9 5632.0 542.843364 243.545956 289.438969
10 6144.0 546.133354 248.661056 286.879370
11 6656.0 532.479975 256.000009 285.767438
12 7168.0 507.469040 260.654538 286.242939
13 7680.0 479.999983 262.564106 279.696505
14 8192.0 462.607053 267.493874 284.526763
15 8704.0 418.629245 267.815384 285.377055
16 9216.0 432.000001 272.729961 289.129410
17 9728.0 439.683593 280.615388 290.027323
18 10240.0 450.109870 286.767793 290.496460
19 10752.0 427.940303 247.172406 290.922209
20 11264.0 427.071098 245.760001 286.676558
21 11776.0 423.089806 249.667843 288.981596
22 12288.0 419.504980 254.673582 294.323369
23 12800.0 414.016170 253.674644 289.811310
24 13312.0 411.181478 252.759501 290.179836
25 13824.0 404.604870 257.190689 292.313649
26 14336.0 393.440813 254.485198 286.959121
27 14848.0 385.245405 257.665934 289.246765
28 15360.0 373.874218 257.970599 287.326580
29 15872.0 371.274849 261.806182 290.120338
13 7680.0 479.999983 262.190612 278.850215
14 8192.0 463.698115 267.130429 284.939124
15 8704.0 417.791980 267.815384 284.987724
16 9216.0 431.157889 272.394084 288.751954
17 9728.0 438.857162 280.278512 290.027323
18 10240.0 449.287041 286.767793 290.153487
19 10752.0 426.525614 247.172406 290.594591
20 11264.0 427.071098 245.536784 286.676558
21 11776.0 423.089806 249.778170 288.981596
22 12288.0 419.504980 254.673582 294.176573
23 12800.0 414.016170 253.465340 289.538159
24 13312.0 411.181478 252.759501 289.916513
25 13824.0 404.112047 257.190689 292.056329
26 14336.0 393.215988 254.485198 286.719986
27 14848.0 384.829370 257.665934 289.012175
28 15360.0 373.495460 257.970599 287.326580
29 15872.0 371.274849 261.806182 289.679087
@@ -339,7 +339,7 @@ Layer Normalization
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 2 minutes 10.738 seconds)
**Total running time of the script:** ( 2 minutes 12.779 seconds)
.. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:

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@@ -5,16 +5,16 @@
Computation times
=================
**13:10.803** total execution time for **getting-started_tutorials** files:
**13:20.131** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 05:59.778 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:01.969 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 03:18.383 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 03:21.816 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``) | 02:10.738 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``) | 02:12.779 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 01:41.422 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 01:43.089 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``) | 00:00.481 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``) | 00:00.479 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+