[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: 77765ce6577c8ba49ed171ea824b3394
+config: 39a1592551a369f3686629da7defe5a5
 tags: 645f666f9bcd5a90fca523b33c5a78b7

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

@@ -234,11 +234,11 @@ 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   38.400001   38.400001
+    2       16384.0   31.999999   31.999999
-    3       32768.0   63.999998   63.999998
+    3       32768.0   63.999998   76.800002
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
-    6      262144.0  341.333321  384.000001
+    6      262144.0  341.333321  341.333321
     7      524288.0  472.615390  472.615390
     8     1048576.0  614.400016  614.400016
     9     2097152.0  722.823517  702.171410
@@ -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  49.775 seconds)
+   **Total running time of the script:** ( 1 minutes  43.751 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  512.000001      546.133347   190.511628
-    1     384.0  585.142862      585.142862   151.703707
+    1     384.0  614.400016      585.142862   153.600004
-    2     512.0  655.360017      585.142849   156.038096
+    2     512.0  655.360017      585.142849   154.566038
-    3     640.0  682.666684      640.000002   158.759699
+    3     640.0  706.206879      640.000002   160.000000
-    4     768.0  722.823517      646.736871   162.754967
+    4     768.0  722.823517      664.216187   162.754967
     ..      ...         ...             ...          ...
-    93  12160.0  814.058574      405.755985   198.834951
+    93  12160.0  814.058574      406.179533   199.038365
-    94  12288.0  815.800825      415.661740   198.995960
+    94  12288.0  815.800825      415.661740   199.197579
-    95  12416.0  814.163950      412.149375   198.755369
+    95  12416.0  814.163950      412.149375   198.954424
-    96  12544.0  814.214963      412.971190   198.864492
+    96  12544.0  814.214963      412.971190   199.061730
-    97  12672.0  814.265046      412.097543   199.069228
+    97  12672.0  814.265046      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  28.816 seconds)
+   **Total running time of the script:** ( 3 minutes  26.512 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

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

@@ -459,36 +459,36 @@ 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  ...   2.978909              2.978909
-    1    384.0   7.372800  ...   7.899428              7.899428
+    1    384.0   7.372800  ...   8.507077              7.899428
     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  ...  40.140799             39.025776
+    5    896.0  37.971025  ...  40.140799             40.140799
-    6   1024.0  49.932191  ...  53.773130             53.773130
+    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
+    8   1280.0  51.200001  ...  58.514284             57.690139
-    9   1408.0  64.138541  ...  69.009825             67.305878
+    9   1408.0  64.138541  ...  69.009825             69.009825
-    10  1536.0  80.430545  ...  80.430545             79.526831
+    10  1536.0  80.430545  ...  81.355034             80.430545
-    11  1664.0  62.929456  ...  63.372618             62.929456
+    11  1664.0  63.372618  ...  63.822072             63.372618
-    12  1792.0  72.983276  ...  63.142831             63.142831
+    12  1792.0  72.983276  ...  63.142831             62.790080
-    13  1920.0  68.776119  ...  71.257735             70.892307
+    13  1920.0  69.120002  ...  71.626943             70.892307
-    14  2048.0  73.584279  ...  78.398206             78.033565
+    14  2048.0  73.584279  ...  78.033565             78.033565
-    15  2176.0  83.155572  ...  87.115360             86.739860
+    15  2176.0  83.155572  ...  87.494120             86.739860
-    16  2304.0  68.446623  ...  77.558029             77.558029
+    16  2304.0  68.251065  ...  77.558029             77.558029
-    17  2432.0  71.305746  ...  75.930985             75.320281
+    17  2432.0  71.125224  ...  75.726318             75.320281
-    18  2560.0  77.833728  ...  82.125311             81.715711
+    18  2560.0  77.833728  ...  82.331658             82.125311
-    19  2688.0  83.552988  ...  90.966561             90.532356
+    19  2688.0  83.369354  ...  90.316801             90.748936
-    20  2816.0  81.369790  ...  83.873477             84.360174
+    20  2816.0  83.873477  ...  83.954203             84.035084
-    21  2944.0  81.832567  ...  83.617504             83.337844
+    21  2944.0  82.784108  ...  83.477440             84.182483
-    22  3072.0  79.750851  ...  89.451983             88.197981
+    22  3072.0  81.825298  ...  89.310890             88.335577
-    23  3200.0  79.503104  ...  96.822991             96.096095
+    23  3200.0  83.009080  ...  92.753621             95.952022
-    24  3328.0  83.034941  ...  82.558825             82.181847
+    24  3328.0  82.558825  ...  83.323259             84.003845
-    25  3456.0  81.849303  ...  91.407671             92.455926
+    25  3456.0  81.849303  ...  89.679166             90.484366
-    26  3584.0  87.211821  ...  91.750399             97.840469
+    26  3584.0  86.958797  ...  99.025764             99.795109
-    27  3712.0  85.896254  ...  86.341700             87.322855
+    27  3712.0  83.386762  ...  87.629253             88.326564
-    28  3840.0  80.313725  ...  85.597527             91.853823
+    28  3840.0  80.197243  ...  85.597527             91.701494
-    29  3968.0  86.174142  ...  84.154440             82.560175
+    29  3968.0  88.103928  ...  87.913500             85.992909
-    30  4096.0  89.240508  ...  84.201835             82.849652
+    30  4096.0  89.657802  ...  84.201835             90.748973
 
     [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  48.616 seconds)
+   **Total running time of the script:** ( 6 minutes  2.461 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  0.337 seconds)
+   **Total running time of the script:** ( 0 minutes  0.013 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  356.173905   99.497980  315.076934
+    0    1024.0  361.411758   99.497980  307.200008
-    1    1536.0  409.599994  134.050910  344.523365
+    1    1536.0  405.098894  134.540150  338.201833
-    2    2048.0  491.520012  159.067963  321.254900
+    2    2048.0  491.520012  158.554837  321.254900
-    3    2560.0  461.954908  182.314537  325.079368
+    3    2560.0  461.954908  182.314537  328.556154
-    4    3072.0  519.211251  191.501303  320.556515
+    4    3072.0  522.893629  191.999993  315.076914
-    5    3584.0  554.941930  207.768111  309.410081
+    5    3584.0  551.384634  208.271186  310.527060
-    6    4096.0  564.965515  220.907859  300.623865
+    6    4096.0  564.965515  219.919464  301.546004
-    7    4608.0  500.416301  232.336141  287.251954
+    7    4608.0  502.690905  233.316456  288.751954
-    8    5120.0  529.655159  243.809526  289.129408
+    8    5120.0  527.381977  240.469672  282.482762
-    9    5632.0  540.671974  244.426754  291.310338
+    9    5632.0  542.843364  241.371422  287.591490
-    10   6144.0  552.269672  251.202731  288.000001
+    10   6144.0  546.133354  250.349744  286.322318
-    11   6656.0  534.260858  255.590406  286.279570
+    11   6656.0  527.207907  255.182111  284.748652
-    12   7168.0  512.000004  253.734520  277.919225
+    12   7168.0  505.976473  256.000002  280.182402
-    13   7680.0  487.619051  266.743841  284.884090
+    13   7680.0  483.779539  264.447629  279.272719
-    14   8192.0  468.114289  258.354805  278.481578
+    14   8192.0  463.698115  265.327937  277.694924
-    15   8704.0  415.300208  267.472468  285.377055
+    15   8704.0  413.655443  263.757583  282.673891
-    16   9216.0  428.651187  272.394084  289.887291
+    16   9216.0  427.822068  270.065931  286.879380
-    17   9728.0  438.033784  279.942444  288.950501
+    17   9728.0  438.033784  279.942444  288.593329
-    18  10240.0  445.217381  287.102804  290.153487
+    18  10240.0  444.412281  285.435547  286.767793
-    19  10752.0  427.231788  246.935876  289.941565
+    19  10752.0  430.797982  246.699797  289.616170
-    20  11264.0  428.424741  245.536784  286.069848
+    20  11264.0  427.746848  244.205952  283.966395
-    21  11776.0  418.702211  249.667843  288.981596
+    21  11776.0  421.198220  249.227509  288.097854
-    22  12288.0  414.784810  254.453844  294.323369
+    22  12288.0  417.131525  253.578674  294.323369
-    23  12800.0  410.695192  254.094291  288.180121
+    23  12800.0  412.072450  253.465340  288.721817
-    24  13312.0  410.125805  252.559690  289.129403
+    24  13312.0  409.075539  253.763296  291.769875
-    25  13824.0  404.604870  256.991469  291.799461
+    25  13824.0  404.112047  257.190689  292.313649
-    26  14336.0  396.387109  255.809666  288.886653
+    26  14336.0  394.568805  252.246330  285.530296
-    27  14848.0  386.498925  257.665934  288.777966
+    27  14848.0  383.999990  257.479779  289.012175
-    28  15360.0  378.869469  258.513318  286.656296
+    28  15360.0  378.869469  260.338991  290.496454
-    29  15872.0  372.000001  261.626369  290.562936
+    29  15872.0  370.552519  263.800544  292.796308
 
 
 
@@ -339,7 +339,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  14.228 seconds)
+   **Total running time of the script:** ( 2 minutes  13.338 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
 =================
-**14:21.771** total execution time for **getting-started_tutorials** files:
+**13:26.076** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:48.616 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:02.461 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:28.816 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:26.512 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:14.228 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:13.338 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:49.775 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:43.751 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.337 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.013 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+

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

@@ -324,11 +324,11 @@ 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   38.400001   38.400001
+2       16384.0   31.999999   31.999999
-3       32768.0   63.999998   63.999998
+3       32768.0   63.999998   76.800002
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
-6      262144.0  341.333321  384.000001
+6      262144.0  341.333321  341.333321
 7      524288.0  472.615390  472.615390
 8     1048576.0  614.400016  614.400016
 9     2097152.0  722.823517  702.171410
@@ -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  49.775 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  43.751 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

@@ -370,16 +370,16 @@ 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  512.000001      546.133347   190.511628
-1     384.0  585.142862      585.142862   151.703707
+1     384.0  614.400016      585.142862   153.600004
-2     512.0  655.360017      585.142849   156.038096
+2     512.0  655.360017      585.142849   154.566038
-3     640.0  682.666684      640.000002   158.759699
+3     640.0  706.206879      640.000002   160.000000
-4     768.0  722.823517      646.736871   162.754967
+4     768.0  722.823517      664.216187   162.754967
 ..      ...         ...             ...          ...
-93  12160.0  814.058574      405.755985   198.834951
+93  12160.0  814.058574      406.179533   199.038365
-94  12288.0  815.800825      415.661740   198.995960
+94  12288.0  815.800825      415.661740   199.197579
-95  12416.0  814.163950      412.149375   198.755369
+95  12416.0  814.163950      412.149375   198.954424
-96  12544.0  814.214963      412.971190   198.864492
+96  12544.0  814.214963      412.971190   199.061730
-97  12672.0  814.265046      412.097543   199.069228
+97  12672.0  814.265046      412.097543   199.167004
 
 [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  28.816 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  26.512 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

@@ -565,41 +565,41 @@ torch_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3906,  24.4531, -3
 <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   2.730667  ...   2.978909              2.978909
-1    384.0   7.372800  ...   7.899428              7.899428
+1    384.0   7.372800  ...   8.507077              7.899428
 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  ...  40.140799             39.025776
+5    896.0  37.971025  ...  40.140799             40.140799
-6   1024.0  49.932191  ...  53.773130             53.773130
+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
+8   1280.0  51.200001  ...  58.514284             57.690139
-9   1408.0  64.138541  ...  69.009825             67.305878
+9   1408.0  64.138541  ...  69.009825             69.009825
-10  1536.0  80.430545  ...  80.430545             79.526831
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-11  1664.0  62.929456  ...  63.372618             62.929456
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-12  1792.0  72.983276  ...  63.142831             63.142831
+12  1792.0  72.983276  ...  63.142831             62.790080
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