[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: 3d670a0c1fdb74727f00ba92e45ed74d
+config: 62b3cc7c92dd3bc2cc535c8d5b7a6208
 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
@@ -245,7 +245,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     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  43.396 seconds)
+   **Total running time of the script:** ( 1 minutes  39.539 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  546.133347      546.133347   190.511628
-    1     384.0  614.400016      585.142862   153.600004
+    0     256.0  512.000001      546.133347   186.181817
+    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   198.936606
-    94  12288.0  812.429770      415.661740   199.197579
-    95  12416.0  812.498981      412.149375   198.854847
-    96  12544.0  810.925276      412.971190   199.111113
-    97  12672.0  811.007961      412.097543   199.167004
+    93  12160.0  812.359066      406.179533   198.530610
+    94  12288.0  812.429770      415.661740   198.794749
+    95  12416.0  812.498981      411.722274   198.457532
+    96  12544.0  812.566838      412.971190   198.667643
+    97  12672.0  811.007961      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  29.369 seconds)
+   **Total running time of the script:** ( 3 minutes  31.277 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   2.730667  ...   3.276800              2.978909
+    0    256.0   2.730667  ...   3.276800              3.276800
     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
-    6   1024.0  51.150050  ...  53.773130             52.428801
+    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             67.305878
+    9   1408.0  64.138541  ...  69.009825             68.147202
     10  1536.0  80.430545  ...  81.355034             79.526831
-    11  1664.0  62.929456  ...  63.372618             62.492442
-    12  1792.0  72.512412  ...  73.460287             59.467852
-    13  1920.0  68.776119  ...  71.626943             70.892307
+    11  1664.0  63.372618  ...  63.822072             62.492442
+    12  1792.0  72.983276  ...  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  ...  87.876193             86.367588
+    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             75.320281
-    18  2560.0  77.926278  ...  82.539044             81.310171
-    19  2688.0  83.369354  ...  90.532356             89.676257
-    20  2816.0  79.587973  ...  83.392363             83.153880
-    21  2944.0  81.967162  ...  83.337844             82.102191
-    22  3072.0  81.355034  ...  87.112467             88.750943
-    23  3200.0  84.880639  ...  96.676741             95.238096
-    24  3328.0  82.748617  ...  85.297742             84.101981
-    25  3456.0  82.688790  ...  91.615417             89.679166
-    26  3584.0  87.466332  ...  88.066008             95.960933
-    27  3712.0  81.548851  ...  87.629253             87.552452
-    28  3840.0  81.079177  ...  85.996889             90.058629
-    29  3968.0  86.849777  ...  91.609561             85.093402
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+    19  2688.0  83.186525  ...  90.102270             89.888756
+    20  2816.0  79.733474  ...  83.712490             82.446516
+    21  2944.0  81.564701  ...  83.477440             82.102191
+    22  3072.0  82.540970  ...  88.890270             88.060814
+    23  3200.0  83.879425  ...  96.240602             94.814812
+    24  3328.0  83.226931  ...  83.613586             84.795401
+    25  3456.0  82.604067  ...  90.180725             91.097818
+    26  3584.0  86.958797  ...  99.463928             93.661869
+    27  3712.0  83.247783  ...  89.433611             85.970176
+    28  3840.0  84.228485  ...  92.545605             84.423387
+    29  3968.0  91.266964  ...  86.327507             86.175099
+    30  4096.0  91.867031  ...  93.858555             86.285908
 
     [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:** ( 7 minutes  9.656 seconds)
+   **Total running time of the script:** ( 6 minutes  38.626 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  0.280 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

@@ -40,34 +40,34 @@ Layer Normalization
               N      Triton       Torch        Apex
     0    1024.0  585.142849  277.694907  481.882344
     1    1536.0  630.153868  323.368435  511.999982
-    2    2048.0  668.734716  334.367358  528.516136
-    3    2560.0  694.237267  365.714281  512.000013
-    4    3072.0  712.347810  378.092307  501.551037
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+    3    2560.0  694.237267  362.477870  518.481028
+    4    3072.0  712.347810  378.092307  496.484863
     5    3584.0  725.873439  384.859062  455.111115
     6    4096.0  728.177767  381.023256  455.111095
-    7    4608.0  670.254540  396.387087  428.651163
-    8    5120.0  688.403381  397.669909  422.268057
-    9    5632.0  704.000002  396.969169  417.185184
-    10   6144.0  697.191505  402.885254  411.313806
-    11   6656.0  705.271522  400.360920  400.360920
-    12   7168.0  690.891575  396.844306  387.459443
-    13   7680.0  682.666656  393.846167  387.634072
-    14   8192.0  639.375598  393.609605  371.308771
-    15   8704.0  627.315309  389.005597  380.502740
-    16   9216.0  606.814809  407.337026  383.999986
+    7    4608.0  670.254540  396.387087  423.724136
+    8    5120.0  688.403381  397.669909  420.102563
+    9    5632.0  704.000002  398.725657  411.470331
+    10   6144.0  697.191505  402.885254  409.600010
+    11   6656.0  700.631610  398.861429  400.360920
+    12   7168.0  690.891575  394.116833  387.459443
+    13   7680.0  678.895043  393.846167  385.203746
+    14   8192.0  636.271854  391.259714  372.363633
+    15   8704.0  627.315309  391.191007  380.502740
+    16   9216.0  606.814809  403.989025  381.023249
     17   9728.0  587.350922  409.599987  383.369452
-    18  10240.0  564.965524  408.578556  382.803739
+    18  10240.0  566.920437  408.578556  382.803739
     19  10752.0  547.872604  411.559798  381.445676
-    20  11264.0  533.207081  406.826188  373.134567
+    20  11264.0  533.207081  400.497785  370.069806
     21  11776.0  520.486200  409.599991  378.345375
-    22  12288.0  514.680630  414.784810  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
+    22  12288.0  514.680630  413.911572  383.251457
+    23  12800.0  504.433489  410.420828  377.163903
+    24  13312.0  494.180982  406.473303  378.316177
+    25  13824.0  481.882350  410.359948  378.739711
+    26  14336.0  471.967074  402.414053  375.410805
+    27  14848.0  461.297068  406.099164  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  32.086 seconds)
+   **Total running time of the script:** ( 5 minutes  36.228 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:

master/_sources/getting-started/tutorials/07-libdevice-function.rst.txt

@@ -152,7 +152,7 @@ We can also customize the libdevice library path by passing the path to the `lib
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.261 seconds)
+   **Total running time of the script:** ( 0 minutes  0.010 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_07-libdevice-function.py:

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

@@ -5,20 +5,20 @@
 
 Computation times
 =================
-**17:55.119** total execution time for **getting-started_tutorials** files:
+**17:25.764** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 07:09.656 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:38.626 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:32.086 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:36.228 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:29.369 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:31.277 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:43.396 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.280 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_07-libdevice-function.py` (``07-libdevice-function.py``)       | 00:00.261 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:39.539 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_getting-started_tutorials_06-fused-attention.py` (``06-fused-attention.py``)             | 00:00.072 | 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 |
++---------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_getting-started_tutorials_07-libdevice-function.py` (``07-libdevice-function.py``)       | 00:00.010 | 0.0 MB |
++---------------------------------------------------------------------------------------------------------+-----------+--------+

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

@@ -326,7 +326,7 @@ 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
@@ -337,12 +337,12 @@ for different problem sizes.</p>
 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  43.396 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  39.539 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

@@ -371,17 +371,17 @@ 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  546.133347      546.133347   190.511628
-1     384.0  614.400016      585.142862   153.600004
+0     256.0  512.000001      546.133347   186.181817
+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   198.936606
-94  12288.0  812.429770      415.661740   199.197579
-95  12416.0  812.498981      412.149375   198.854847
-96  12544.0  810.925276      412.971190   199.111113
-97  12672.0  811.007961      412.097543   199.167004
+93  12160.0  812.359066      406.179533   198.530610
+94  12288.0  812.429770      415.661740   198.794749
+95  12416.0  812.498981      411.722274   198.457532
+96  12544.0  812.566838      412.971190   198.667643
+97  12672.0  811.007961      412.097543   198.776477
 
 [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  29.369 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  31.277 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   2.730667  ...   3.276800              2.978909
+0    256.0   2.730667  ...   3.276800              3.276800
 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
-6   1024.0  51.150050  ...  53.773130             52.428801
+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             67.305878
+9   1408.0  64.138541  ...  69.009825             68.147202
 10  1536.0  80.430545  ...  81.355034             79.526831
-11  1664.0  62.929456  ...  63.372618             62.492442
-12  1792.0  72.512412  ...  73.460287             59.467852
-13  1920.0  68.776119  ...  71.626943             70.892307
+11  1664.0  63.372618  ...  63.822072             62.492442
+12  1792.0  72.983276  ...  73.460287             59.467852
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