[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: 812898956dd0e9a6f8cc51a162e3dac8
+config: 6dd98656fe92bcf4cd765d9f8ef78d21
 tags: 645f666f9bcd5a90fca523b33c5a78b7

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

@@ -233,19 +233,19 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     vector-add-performance:
                size      Triton       Torch
     0        4096.0    9.600000    9.600000
-    1        8192.0   15.999999   15.999999
+    1        8192.0   19.200000   19.200000
     2       16384.0   38.400001   38.400001
-    3       32768.0   76.800002   76.800002
+    3       32768.0   63.999998   63.999998
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
-    6      262144.0  384.000001  384.000001
+    6      262144.0  384.000001  341.333321
     7      524288.0  472.615390  472.615390
     8     1048576.0  614.400016  614.400016
     9     2097152.0  722.823517  722.823517
     10    4194304.0  780.190482  780.190482
     11    8388608.0  812.429770  812.429770
     12   16777216.0  833.084721  833.084721
-    13   33554432.0  842.004273  843.811163
+    13   33554432.0  842.004273  842.004273
     14   67108864.0  847.448255  848.362445
     15  134217728.0  849.737435  850.656574
 
@@ -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  44.974 seconds)
+   **Total running time of the script:** ( 1 minutes  44.131 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

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

@@ -279,16 +279,16 @@ 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  546.133347      546.133347   188.321838
-    1     384.0  614.400016      585.142862   153.600004
+    1     384.0  614.400016      585.142862   151.703707
     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
+    4     768.0  722.823517      664.216187   163.839992
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      406.179533   198.733401
-    94  12288.0  812.429770      415.661740   198.995960
-    95  12416.0  812.498981      412.149375   198.655991
-    96  12544.0  810.925276      412.546756   198.864492
-    97  12672.0  811.007961      412.097543   198.971549
+    93  12160.0  812.359066      405.333344   198.936606
+    94  12288.0  812.429770      415.222812   199.096718
+    95  12416.0  812.498981      411.722274   198.904612
+    96  12544.0  810.925276      412.971190   199.012395
+    97  12672.0  811.007961      412.097543   199.167004
 
     [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  30.087 seconds)
+   **Total running time of the script:** ( 3 minutes  32.289 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

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

@@ -459,37 +459,37 @@ 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  ...   2.978909              2.978909
-    1    384.0   7.372800  ...   8.507077              7.899428
-    2    512.0  14.563555  ...  16.384000             15.420235
+    0    256.0   2.730667  ...   3.276800              2.978909
+    1    384.0   7.372800  ...   8.507077              8.507077
+    2    512.0  14.563555  ...  16.384000             16.384000
     3    640.0  22.260869  ...  24.380953             24.380953
     4    768.0  32.768000  ...  35.389441             34.028308
-    5    896.0  39.025776  ...  40.140799             39.025776
+    5    896.0  37.971025  ...  40.140799             39.025776
     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
     10  1536.0  80.430545  ...  81.355034             79.526831
-    11  1664.0  63.372618  ...  63.822072             62.492442
-    12  1792.0  72.983276  ...  73.943582             59.625589
-    13  1920.0  69.467336  ...  71.626943             71.257735
+    11  1664.0  62.929456  ...  63.372618             62.492442
+    12  1792.0  72.512412  ...  73.460287             59.467852
+    13  1920.0  69.120002  ...  71.257735             71.257735
     14  2048.0  73.908442  ...  78.398206             77.314362
-    15  2176.0  83.155572  ...  87.304326             85.998493
+    15  2176.0  83.500614  ...  87.494120             85.998493
     16  2304.0  68.446623  ...  78.064941             77.307030
-    17  2432.0  71.305746  ...  86.179335             85.653855
-    18  2560.0  77.833728  ...  82.956960             81.715711
-    19  2688.0  83.369354  ...  90.102270             89.464755
-    20  2816.0  80.099554  ...  84.687779             83.873477
-    21  2944.0  82.237674  ...  83.337844             82.102191
-    22  3072.0  81.589488  ...  89.877939             88.335577
-    23  3200.0  84.210524  ...  95.808380             93.841640
-    24  3328.0  84.003845  ...  85.398926             84.895397
-    25  3456.0  81.766291  ...  92.033756             91.200871
-    26  3584.0  86.125852  ...  92.220917             94.647779
-    27  3712.0  85.309435  ...  89.035062             82.287760
-    28  3840.0  84.485870  ...  92.817458             88.686451
-    29  3968.0  92.372393  ...  85.033178             90.724116
-    30  4096.0  86.202781  ...  92.820009             88.563330
+    17  2432.0  71.305746  ...  86.444504             83.614477
+    18  2560.0  77.833728  ...  82.747477             80.908642
+    19  2688.0  83.369354  ...  90.316801             89.359378
+    20  2816.0  80.026067  ...  83.712490             83.074685
+    21  2944.0  82.102191  ...  83.337844             82.784108
+    22  3072.0  81.707223  ...  89.735509             88.335577
+    23  3200.0  84.880639  ...  96.676741             95.665176
+    24  3328.0  83.130825  ...  84.596116             84.496824
+    25  3456.0  82.266905  ...  91.928814             91.200871
+    26  3584.0  83.101104  ...  93.224874             90.367227
+    27  3712.0  85.019017  ...  89.194055             82.287760
+    28  3840.0  84.550462  ...  91.587578             84.582788
+    29  3968.0  87.850207  ...  85.690965             90.054568
+    30  4096.0  86.370931  ...  85.981029             91.867031
 
     [31 rows x 5 columns]
 
@@ -499,7 +499,7 @@ 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  33.939 seconds)
+   **Total running time of the script:** ( 6 minutes  36.792 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

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

@@ -38,36 +38,36 @@ Layer Normalization
 
     layer-norm:
               N      Triton       Torch        Apex
-    0    1024.0  585.142849  277.694907  468.114273
+    0    1024.0  585.142849  277.694907  481.882344
     1    1536.0  630.153868  323.368435  511.999982
-    2    2048.0  682.666643  334.367358  520.126988
-    3    2560.0  694.237267  365.714281  512.000013
-    4    3072.0  712.347810  378.092307  496.484863
-    5    3584.0  725.873439  384.859062  448.000001
-    6    4096.0  728.177767  381.023256  455.111095
-    7    4608.0  670.254540  394.267384  426.173427
-    8    5120.0  688.403381  397.669909  422.268057
-    9    5632.0  704.000002  395.228063  415.262685
-    10   6144.0  697.191505  402.885254  409.600010
-    11   6656.0  705.271522  400.360920  400.360920
-    12   7168.0  690.891575  396.844306  387.459443
-    13   7680.0  678.895043  393.846167  386.415087
-    14   8192.0  636.271854  393.609605  371.308771
-    15   8704.0  627.315309  389.005597  380.502740
+    2    2048.0  668.734716  337.814445  520.126988
+    3    2560.0  694.237267  362.477870  518.481028
+    4    3072.0  712.347810  378.092307  506.721668
+    5    3584.0  725.873439  384.859062  455.111115
+    6    4096.0  736.359542  381.023256  455.111095
+    7    4608.0  670.254540  396.387087  426.173427
+    8    5120.0  688.403381  397.669909  426.666652
+    9    5632.0  704.000002  396.969169  411.470331
+    10   6144.0  702.171410  402.885254  411.313806
+    11   6656.0  700.631610  400.360920  400.360920
+    12   7168.0  690.891575  388.772874  384.859062
+    13   7680.0  678.895043  392.587863  386.415087
+    14   8192.0  633.198054  393.609605  377.729113
+    15   8704.0  624.502255  390.095225  380.502740
     16   9216.0  606.814809  407.337026  383.999986
-    17   9728.0  587.350922  409.599987  383.369452
-    18  10240.0  564.965524  408.578556  382.803739
-    19  10752.0  547.872604  411.559798  381.445676
-    20  11264.0  533.207081  406.826188  373.134567
-    21  11776.0  520.486200  409.599991  377.587162
-    22  12288.0  513.336807  413.911572  383.251457
-    23  12800.0  504.433489  410.420828  376.470582
-    24  13312.0  494.180982  405.699062  376.976995
-    25  13824.0  482.934503  411.888257  379.389355
-    26  14336.0  471.967074  406.695045  374.185964
-    27  14848.0  461.297068  408.192434  375.304904
-    28  15360.0  454.269882  406.214870  378.092307
-    29  15872.0  447.887117  406.974373  376.225175
+    17   9728.0  587.350922  409.599987  382.427505
+    18  10240.0  564.965524  409.600010  382.803739
+    19  10752.0  547.872604  411.559798  380.601764
+    20  11264.0  531.634232  403.185684  371.595879
+    21  11776.0  520.486200  410.492372  377.587162
+    22  12288.0  514.680630  413.911572  382.505826
+    23  12800.0  504.433489  410.420828  377.163903
+    24  13312.0  494.180982  407.250459  377.645399
+    25  13824.0  481.882350  411.888257  379.389355
+    26  14336.0  471.967074  403.830973  372.969090
+    27  14848.0  460.403127  406.794504  375.304904
+    28  15360.0  454.269882  406.214870  377.511515
+    29  15872.0  447.098578  408.282944  376.225175
 
 
 
@@ -393,7 +393,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  35.450 seconds)
+   **Total running time of the script:** ( 5 minutes  38.038 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:

master/_sources/getting-started/tutorials/06-fused-attention.rst.txt

@@ -390,7 +390,7 @@ This is a Triton implementation of the Flash Attention algorithm
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.075 seconds)
+   **Total running time of the script:** ( 0 minutes  0.073 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_06-fused-attention.py:

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

@@ -5,18 +5,18 @@
 
 Computation times
 =================
-**17:24.547** total execution time for **getting-started_tutorials** files:
+**17:31.345** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:33.939 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:36.792 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:35.450 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:38.038 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:30.087 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:32.289 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:44.974 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:44.131 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_06-fused-attention.py` (``06-fused-attention.py``)             | 00:00.075 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_06-fused-attention.py` (``06-fused-attention.py``)             | 00:00.073 | 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

@@ -325,24 +325,24 @@ for different problem sizes.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
            size      Triton       Torch
 0        4096.0    9.600000    9.600000
-1        8192.0   15.999999   15.999999
+1        8192.0   19.200000   19.200000
 2       16384.0   38.400001   38.400001
-3       32768.0   76.800002   76.800002
+3       32768.0   63.999998   63.999998
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
-6      262144.0  384.000001  384.000001
+6      262144.0  384.000001  341.333321
 7      524288.0  472.615390  472.615390
 8     1048576.0  614.400016  614.400016
 9     2097152.0  722.823517  722.823517
 10    4194304.0  780.190482  780.190482
 11    8388608.0  812.429770  812.429770
 12   16777216.0  833.084721  833.084721
-13   33554432.0  842.004273  843.811163
+13   33554432.0  842.004273  842.004273
 14   67108864.0  847.448255  848.362445
 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  44.974 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  44.131 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

@@ -372,16 +372,16 @@ We will then compare its performance against (1) <code class="code docutils lite
 <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  546.133347      546.133347   188.321838
-1     384.0  614.400016      585.142862   153.600004
+1     384.0  614.400016      585.142862   151.703707
 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
+4     768.0  722.823517      664.216187   163.839992
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      406.179533   198.733401
-94  12288.0  812.429770      415.661740   198.995960
-95  12416.0  812.498981      412.149375   198.655991
-96  12544.0  810.925276      412.546756   198.864492
-97  12672.0  811.007961      412.097543   198.971549
+93  12160.0  812.359066      405.333344   198.936606
+94  12288.0  812.429770      415.222812   199.096718
+95  12416.0  812.498981      411.722274   198.904612
+96  12544.0  810.925276      412.971190   199.012395
+97  12672.0  811.007961      412.097543   199.167004
 
 [98 rows x 4 columns]
 </pre></div>
@@ -394,7 +394,7 @@ 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  30.087 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  32.289 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

@@ -567,42 +567,42 @@ 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)
-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             15.420235
+0    256.0   2.730667  ...   3.276800              2.978909
+1    384.0   7.372800  ...   8.507077              8.507077
+2    512.0  14.563555  ...  16.384000             16.384000
 3    640.0  22.260869  ...  24.380953             24.380953
 4    768.0  32.768000  ...  35.389441             34.028308
-5    896.0  39.025776  ...  40.140799             39.025776
+5    896.0  37.971025  ...  40.140799             39.025776
 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
 10  1536.0  80.430545  ...  81.355034             79.526831
-11  1664.0  63.372618  ...  63.822072             62.492442
-12  1792.0  72.983276  ...  73.943582             59.625589
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master/getting-started/tutorials/05-layer-norm.html

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master/getting-started/tutorials/06-fused-attention.html

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