[GH-PAGES] Updated website

master/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 2733d834a878ced23a6c2f6f4c64be8c
+config: 29a6392189b919d314768a8cb78a5eab
 tags: 645f666f9bcd5a90fca523b33c5a78b7

master/_sources/getting-started/tutorials/01-vector-add.rst.txt

@@ -234,7 +234,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
                size      Triton       Torch
     0        4096.0    9.600000    9.600000
     1        8192.0   19.200000   19.200000
-    2       16384.0   31.999999   31.999999
+    2       16384.0   38.400001   38.400001
     3       32768.0   76.800002   76.800002
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
@@ -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  40.152 seconds)
+   **Total running time of the script:** ( 1 minutes  40.796 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

master/_sources/getting-started/tutorials/02-fused-softmax.rst.txt

@@ -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   186.181817
-    1     384.0  585.142862      558.545450   151.703707
+    0     256.0  512.000001      512.000001   186.181817
+    1     384.0  585.142862      585.142862   151.703707
     2     512.0  655.360017      606.814814   154.566038
-    3     640.0  682.666684      640.000002   158.759699
-    4     768.0  722.823517      664.216187   163.839992
+    3     640.0  682.666684      640.000002   160.000000
+    4     768.0  722.823517      664.216187   162.754967
     ..      ...         ...             ...          ...
-    93  12160.0  814.058574      405.755985   198.631953
-    94  12288.0  814.111783      415.661740   198.895304
-    95  12416.0  814.163950      412.149375   198.556711
-    96  12544.0  814.214963      412.971190   198.716830
-    97  12672.0  814.265046      411.679167   198.873965
+    93  12160.0  814.058574      405.755985   198.834951
+    94  12288.0  814.111783      415.222812   199.096718
+    95  12416.0  814.163950      411.722274   198.854847
+    96  12544.0  814.214963      412.546756   198.913776
+    97  12672.0  814.265046      411.679167   199.069228
 
     [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  27.921 seconds)
+   **Total running time of the script:** ( 3 minutes  19.085 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

master/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt

@@ -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   2.978909  ...   3.276800              2.978909
-    1    384.0   7.372800  ...   7.899428              8.507077
-    2    512.0  14.563555  ...  15.420235             16.384000
+    0    256.0   2.730667  ...    3.276800              2.978909
+    1    384.0   7.372800  ...    8.507077              8.507077
+    2    512.0  14.563555  ...   15.420235             15.420235
     3    640.0  22.260869  ...   24.380953             24.380953
-    4    768.0  32.768000  ...  35.389441             34.028308
-    5    896.0  37.971025  ...  41.321411             39.025776
-    6   1024.0  49.932191  ...  53.773130             53.773130
+    4    768.0  32.768000  ...   34.028308             34.028308
+    5    896.0  37.971025  ...   41.321411             40.140799
+    6   1024.0  49.932191  ...   53.773130             52.428801
     7   1152.0  45.242181  ...   48.161033             47.396572
     8   1280.0  51.200001  ...   57.690139             57.690139
-    9   1408.0  64.138541  ...  69.009825             68.147202
+    9   1408.0  64.138541  ...   68.147202             68.147202
     10  1536.0  80.430545  ...   80.430545             80.430545
-    11  1664.0  63.372618  ...  63.372618             62.929456
+    11  1664.0  62.929456  ...   63.372618             62.929456
     12  1792.0  72.983276  ...   63.499573             63.142831
-    13  1920.0  68.776119  ...  71.257735             70.892307
+    13  1920.0  68.776119  ...   70.892307             70.892307
     14  2048.0  73.584279  ...   78.398206             78.033565
     15  2176.0  83.155572  ...   87.115360             86.739860
-    16  2304.0  68.251065  ...  78.064941             77.558029
+    16  2304.0  68.251065  ...   77.810656             77.558029
     17  2432.0  71.305746  ...   75.726318             75.522751
-    18  2560.0  77.833728  ...  82.539044             82.331658
-    19  2688.0  84.108772  ...  91.185232             90.966561
-    20  2816.0  80.469019  ...  83.552120             84.523664
-    21  2944.0  81.832567  ...  84.324925             83.758038
-    22  3072.0  81.589488  ...  87.246694             89.451983
-    23  3200.0  85.106381  ...  96.385543             95.952022
-    24  3328.0  83.905938  ...  85.908470             86.113988
-    25  3456.0  82.604067  ...  85.043848             89.480098
-    26  3584.0  84.111686  ...  92.410473             94.747514
-    27  3712.0  85.675250  ...  85.896254             88.404730
-    28  3840.0  81.138664  ...  88.261772             91.701494
-    29  3968.0  87.976885  ...  92.477403             85.811488
-    30  4096.0  93.401342  ...  84.733417             86.872315
+    18  2560.0  77.833728  ...   81.715711             81.920002
+    19  2688.0  83.737433  ...   90.966561             90.316801
+    20  2816.0  84.035084  ...   83.552120             83.873477
+    21  2944.0  82.509987  ...   83.899046             84.040530
+    22  3072.0  81.589488  ...   86.184329             89.735509
+    23  3200.0  84.993363  ...   96.240602             96.240602
+    24  3328.0  83.226931  ...   85.806075             84.895397
+    25  3456.0  82.604067  ...   84.068369             88.692595
+    26  3584.0  87.211821  ...  100.017124            100.351999
+    27  3712.0  85.091436  ...   89.513749             87.094458
+    28  3840.0  84.228485  ...   92.390975             85.531326
+    29  3968.0  91.954739  ...   85.034103             91.540836
+    30  4096.0  87.495257  ...   89.597949             93.336389
 
     [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:** ( 6 minutes  55.944 seconds)
+   **Total running time of the script:** ( 6 minutes  7.559 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

master/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt

@@ -240,7 +240,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.323 seconds)
+   **Total running time of the script:** ( 0 minutes  0.012 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py:

master/_sources/getting-started/tutorials/05-layer-norm.rst.txt

@@ -38,36 +38,36 @@ Layer Normalization
 
     layer-norm-backward:
               N      Triton       Torch        Apex
-    0    1024.0  361.411758   97.912354  303.407414
-    1    1536.0  405.098894  134.540150  341.333333
-    2    2048.0  496.484863  161.154101  323.368435
-    3    2560.0  465.454542  181.238943  326.808501
-    4    3072.0  519.211251  192.501302  321.956335
-    5    3584.0  558.545477  208.271186  311.652167
-    6    4096.0  561.737163  220.907859  297.890900
-    7    4608.0  502.690905  232.825259  287.251954
-    8    5120.0  525.128191  242.366855  287.102804
-    9    5632.0  540.671974  243.107920  289.438969
-    10   6144.0  546.133354  248.661056  286.879370
-    11   6656.0  527.207907  256.000009  285.257135
-    12   7168.0  503.017523  260.260201  284.821192
-    13   7680.0  483.779539  262.938666  280.121579
-    14   8192.0  463.698115  266.406514  284.526763
-    15   8704.0  414.476194  267.472468  284.987724
-    16   9216.0  428.651187  271.724806  287.999990
-    17   9728.0  436.396262  280.278512  290.027323
-    18  10240.0  446.025405  286.433562  290.153487
-    19  10752.0  428.651173  246.935876  290.267711
-    20  11264.0  426.397479  245.536784  286.980888
-    21  11776.0  421.198220  249.888595  288.981596
-    22  12288.0  416.542386  254.673582  294.617366
-    23  12800.0  410.695192  254.094291  288.180121
-    24  13312.0  409.075539  253.360814  290.443638
-    25  13824.0  405.098897  257.390218  292.056329
-    26  14336.0  394.116833  254.862216  286.959121
-    27  14848.0  385.662341  257.852379  289.952797
-    28  15360.0  380.433442  257.970599  286.433562
-    29  15872.0  370.552519  261.626369  290.341468
+    0    1024.0  361.411758   99.902435  315.076934
+    1    1536.0  405.098894  133.083026  344.523365
+    2    2048.0  496.484863  158.554837  334.367350
+    3    2560.0  461.954908  182.857144  330.322572
+    4    3072.0  519.211251  191.501303  320.556515
+    5    3584.0  554.941930  208.271186  309.410081
+    6    4096.0  564.965515  220.412561  298.796351
+    7    4608.0  502.690905  231.849059  286.507772
+    8    5120.0  527.381977  244.294240  286.433562
+    9    5632.0  542.843364  244.426754  292.571431
+    10   6144.0  550.208948  251.202731  288.000001
+    11   6656.0  534.260858  256.000009  286.793541
+    12   7168.0  513.528374  253.734520  277.470965
+    13   7680.0  488.912481  266.743841  284.884090
+    14   8192.0  468.114289  258.694729  278.087683
+    15   8704.0  415.300208  267.472468  286.158893
+    16   9216.0  429.483477  273.066667  289.129410
+    17   9728.0  438.857162  279.942444  289.308559
+    18  10240.0  444.412281  287.102804  290.153487
+    19  10752.0  427.940303  246.935876  290.104546
+    20  11264.0  429.104745  246.432094  287.897767
+    21  11776.0  421.198220  249.888595  289.573776
+    22  12288.0  417.131525  254.673582  294.323369
+    23  12800.0  411.244989  253.884294  290.084977
+    24  13312.0  409.599999  252.959629  289.391298
+    25  13824.0  405.594132  256.991469  292.056329
+    26  14336.0  395.475867  255.619613  288.402346
+    27  14848.0  385.245405  256.922861  288.544136
+    28  15360.0  380.041240  258.332158  288.450715
+    29  15872.0  372.000001  263.071829  290.341468
 
 
 
@@ -339,7 +339,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  14.484 seconds)
+   **Total running time of the script:** ( 2 minutes  13.781 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:

master/_sources/getting-started/tutorials/sg_execution_times.rst.txt

@@ -5,16 +5,16 @@
 
 Computation times
 =================
-**14:18.825** total execution time for **getting-started_tutorials** files:
+**13:21.234** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:55.944 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:07.559 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:27.921 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:19.085 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:14.484 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:13.781 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:40.152 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:40.796 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.323 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.012 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+

master/getting-started/tutorials/01-vector-add.html

@@ -324,7 +324,7 @@ for different problem sizes.</p>
            size      Triton       Torch
 0        4096.0    9.600000    9.600000
 1        8192.0   19.200000   19.200000
-2       16384.0   31.999999   31.999999
+2       16384.0   38.400001   38.400001
 3       32768.0   76.800002   76.800002
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
@@ -340,7 +340,7 @@ for different problem sizes.</p>
 15  134217728.0  849.737435  850.656574
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  40.152 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  40.796 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <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>

master/getting-started/tutorials/02-fused-softmax.html

@@ -369,17 +369,17 @@ We will then compare its performance against (1) <code class="code docutils lite
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
           N      Triton  Torch (native)  Torch (jit)
-0     256.0  512.000001      546.133347   186.181817
-1     384.0  585.142862      558.545450   151.703707
+0     256.0  512.000001      512.000001   186.181817
+1     384.0  585.142862      585.142862   151.703707
 2     512.0  655.360017      606.814814   154.566038
-3     640.0  682.666684      640.000002   158.759699
-4     768.0  722.823517      664.216187   163.839992
+3     640.0  682.666684      640.000002   160.000000
+4     768.0  722.823517      664.216187   162.754967
 ..      ...         ...             ...          ...
-93  12160.0  814.058574      405.755985   198.631953
-94  12288.0  814.111783      415.661740   198.895304
-95  12416.0  814.163950      412.149375   198.556711
-96  12544.0  814.214963      412.971190   198.716830
-97  12672.0  814.265046      411.679167   198.873965
+93  12160.0  814.058574      405.755985   198.834951
+94  12288.0  814.111783      415.222812   199.096718
+95  12416.0  814.163950      411.722274   198.854847
+96  12544.0  814.214963      412.546756   198.913776
+97  12672.0  814.265046      411.679167   199.069228
 
 [98 rows x 4 columns]
 </pre></div>
@@ -392,7 +392,7 @@ We will then compare its performance against (1) <code class="code docutils lite
 Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
 </ul>
 </div></blockquote>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  27.921 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  19.085 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <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>

master/getting-started/tutorials/03-matrix-multiplication.html

@@ -564,42 +564,42 @@ torch_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3906,  24.4531, -3
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
          M     cuBLAS  ...      Triton  Triton (+ LeakyReLU)
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-1    384.0   7.372800  ...   7.899428              8.507077
-2    512.0  14.563555  ...  15.420235             16.384000
+0    256.0   2.730667  ...    3.276800              2.978909
+1    384.0   7.372800  ...    8.507077              8.507077
+2    512.0  14.563555  ...   15.420235             15.420235
 3    640.0  22.260869  ...   24.380953             24.380953
-4    768.0  32.768000  ...  35.389441             34.028308
-5    896.0  37.971025  ...  41.321411             39.025776
-6   1024.0  49.932191  ...  53.773130             53.773130
+4    768.0  32.768000  ...   34.028308             34.028308
+5    896.0  37.971025  ...   41.321411             40.140799
+6   1024.0  49.932191  ...   53.773130             52.428801
 7   1152.0  45.242181  ...   48.161033             47.396572
 8   1280.0  51.200001  ...   57.690139             57.690139
-9   1408.0  64.138541  ...  69.009825             68.147202
+9   1408.0  64.138541  ...   68.147202             68.147202
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