[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: f96c64ba45a28721a5c3fed48369efcf
+config: 0181fbac89417cfa54f7b17040b579bd
 tags: 645f666f9bcd5a90fca523b33c5a78b7

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

@@ -233,9 +233,9 @@ 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   19.200000   19.200000
+    1        8192.0   15.999999   19.200000
     2       16384.0   38.400001   38.400001
-    3       32768.0   63.999998   63.999998
+    3       32768.0   76.800002   76.800002
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
     6      262144.0  341.333321  341.333321
@@ -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  47.206 seconds)
+   **Total running time of the script:** ( 1 minutes  43.980 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      512.000001   188.321838
-    1     384.0  614.400016      585.142862   153.600004
-    2     512.0  655.360017      585.142849   154.566038
-    3     640.0  706.206879      640.000002   158.759699
-    4     768.0  722.823517      664.216187   162.754967
+    0     256.0  546.133347      546.133347   188.321838
+    1     384.0  585.142862      558.545450   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      646.736871   163.839992
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      406.179533   198.733401
-    94  12288.0  812.429770      415.661740   198.995960
-    95  12416.0  810.840807      412.149375   198.655991
-    96  12544.0  810.925276      412.971190   198.913776
-    97  12672.0  811.007961      412.097543   198.971549
+    93  12160.0  812.359066      405.755985   198.936606
+    94  12288.0  812.429770      415.661740   199.197579
+    95  12416.0  812.498981      412.149375   198.904612
+    96  12544.0  810.925276      412.546756   199.012395
+    97  12672.0  811.007961      411.679167   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  31.827 seconds)
+   **Total running time of the script:** ( 3 minutes  29.352 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  ...   2.978909              2.978909
-    1    384.0   7.372800  ...   7.899428              7.899428
-    2    512.0  14.563555  ...  15.420235             15.420235
+    0    256.0   2.978909  ...   2.978909              2.978909
+    1    384.0   7.372800  ...   8.507077              8.507077
+    2    512.0  14.563555  ...  15.420235             16.384000
     3    640.0  22.260869  ...  24.380953             24.380953
-    4    768.0  31.597714  ...  34.028308             34.028308
+    4    768.0  32.768000  ...  35.389441             34.028308
     5    896.0  37.971025  ...  40.140799             39.025776
     6   1024.0  49.932191  ...  53.773130             52.428801
-    7   1152.0  45.242181  ...  47.396572             47.396572
+    7   1152.0  45.242181  ...  48.161033             47.396572
     8   1280.0  51.200001  ...  57.690139             57.690139
     9   1408.0  64.138541  ...  68.147202             67.305878
-    10  1536.0  80.430545  ...  81.355034             79.526831
-    11  1664.0  62.929456  ...  63.372618             62.492442
-    12  1792.0  72.983276  ...  73.460287             59.467852
-    13  1920.0  69.467336  ...  71.257735             70.892307
-    14  2048.0  73.584279  ...  78.398206             77.314362
-    15  2176.0  83.155572  ...  87.494120             85.998493
-    16  2304.0  68.056616  ...  78.064941             77.307030
-    17  2432.0  71.125224  ...  86.179335             85.653855
-    18  2560.0  77.833728  ...  82.747477             81.512437
-    19  2688.0  83.737433  ...  90.966561             89.888756
-    20  2816.0  84.523664  ...  85.017948             84.197315
-    21  2944.0  82.034625  ...  83.899046             82.373605
-    22  3072.0  81.943708  ...  89.735509             88.890270
-    23  3200.0  84.656085  ...  96.969694             94.674553
-    24  3328.0  83.226931  ...  85.703924             84.995628
-    25  3456.0  82.519518  ...  91.928814             88.595129
-    26  3584.0  83.177979  ...  88.412386             94.947616
-    27  3712.0  85.822459  ...  84.766519             86.867254
-    28  3840.0  84.744825  ...  87.286505             86.738820
-    29  3968.0  93.219206  ...  85.992909             91.403695
-    30  4096.0  86.369197  ...  87.324485             91.804194
+    10  1536.0  80.430545  ...  80.430545             78.643199
+    11  1664.0  63.372618  ...  63.372618             62.492442
+    12  1792.0  72.983276  ...  72.983276             59.467852
+    13  1920.0  69.120002  ...  71.257735             70.892307
+    14  2048.0  73.908442  ...  78.033565             76.959706
+    15  2176.0  83.155572  ...  87.494120             86.367588
+    16  2304.0  68.446623  ...  78.064941             77.558029
+    17  2432.0  71.305746  ...  86.711310             85.134737
+    18  2560.0  77.833728  ...  82.956960             81.512437
+    19  2688.0  83.737433  ...  90.966561             89.254248
+    20  2816.0  80.173175  ...  83.392363             84.035084
+    21  2944.0  82.102191  ...  83.060049             83.337844
+    22  3072.0  80.430545  ...  89.735509             87.787755
+    23  3200.0  83.116885  ...  95.096582             96.096095
+    24  3328.0  83.419811  ...  84.447271             83.130825
+    25  3456.0  81.932484  ...  87.252780             91.407671
+    26  3584.0  87.296493  ...  99.574077             98.699661
+    27  3712.0  83.247783  ...  89.553872             81.415926
+    28  3840.0  84.679936  ...  93.405401             84.679936
+    29  3968.0  92.652949  ...  88.744681             85.361267
+    30  4096.0  91.553703  ...  85.001726             84.626564
 
     [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  35.166 seconds)
+   **Total running time of the script:** ( 6 minutes  32.019 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  0.012 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

@@ -39,35 +39,35 @@ Layer Normalization
     layer-norm:
               N      Triton       Torch        Apex
     0    1024.0  585.142849  277.694907  468.114273
-    1    1536.0  630.153868  323.368435  511.999982
+    1    1536.0  630.153868  327.679984  511.999982
     2    2048.0  668.734716  334.367358  520.126988
-    3    2560.0  694.237267  362.477870  512.000013
-    4    3072.0  712.347810  375.206126  496.484863
-    5    3584.0  725.873439  384.859062  451.527536
-    6    4096.0  728.177767  381.023256  455.111095
-    7    4608.0  670.254540  394.267384  421.302872
+    3    2560.0  694.237267  362.477870  518.481028
+    4    3072.0  702.171410  375.206126  501.551037
+    5    3584.0  725.873439  384.859062  455.111115
+    6    4096.0  728.177767  381.023256  451.972420
+    7    4608.0  670.254540  394.267384  418.909088
     8    5120.0  688.403381  397.669909  424.455959
-    9    5632.0  698.542675  395.228063  411.470331
-    10   6144.0  697.191505  402.885254  411.313806
-    11   6656.0  700.631610  398.861429  398.861429
-    12   7168.0  690.891575  396.844306  387.459443
-    13   7680.0  678.895043  392.587863  386.415087
-    14   8192.0  633.198054  393.609605  372.363633
-    15   8704.0  627.315309  389.005597  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
+    9    5632.0  704.000002  395.228063  411.470331
+    10   6144.0  697.191505  402.885254  409.600010
+    11   6656.0  700.631610  398.861429  400.360920
+    12   7168.0  686.754468  394.116833  384.859062
+    13   7680.0  678.895043  393.846167  385.203746
+    14   8192.0  633.198054  388.937680  369.216905
+    15   8704.0  624.502255  390.095225  379.465939
+    16   9216.0  604.327881  407.337026  381.023249
+    17   9728.0  587.350922  408.524944  382.427505
+    18  10240.0  564.965524  409.600010  382.803739
     19  10752.0  546.133312  411.559798  381.445676
-    20  11264.0  533.207081  406.826188  373.134567
+    20  11264.0  533.207081  405.909906  375.466676
     21  11776.0  520.486200  409.599991  377.587162
-    22  12288.0  516.031509  413.911572  383.251457
-    23  12800.0  504.433489  410.420828  376.470582
+    22  12288.0  513.336807  413.911572  383.251457
+    23  12800.0  504.433489  409.599981  375.779805
     24  13312.0  494.180982  405.699062  376.310952
-    25  13824.0  481.882350  411.122660  379.389355
-    26  14336.0  470.997935  406.695045  374.185964
-    27  14848.0  460.403127  408.192434  375.304904
-    28  15360.0  454.269882  406.214870  378.092307
-    29  15872.0  447.887117  406.974373  376.225175
+    25  13824.0  481.882350  411.888257  378.739711
+    26  14336.0  470.997935  405.257949  371.158581
+    27  14848.0  460.403127  407.492270  374.712936
+    28  15360.0  454.269882  406.887417  377.511515
+    29  15872.0  446.312810  406.974373  375.668625
 
 
 
@@ -393,7 +393,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  41.246 seconds)
+   **Total running time of the script:** ( 5 minutes  37.208 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:

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

@@ -385,7 +385,7 @@ This is a Triton implementation of the Flash Attention algorithm
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.072 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/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  0.010 seconds)
+   **Total running time of the script:** ( 0 minutes  0.011 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
 =================
-**17:35.538** total execution time for **getting-started_tutorials** files:
+**17:22.656** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:35.166 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:32.019 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:41.246 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:37.208 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:31.827 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:29.352 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:47.206 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:43.980 | 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_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 |
+| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.013 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_07-libdevice-function.py` (``07-libdevice-function.py``)       | 00:00.010 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_07-libdevice-function.py` (``07-libdevice-function.py``)       | 00:00.011 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+

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

@@ -325,9 +325,9 @@ 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   19.200000   19.200000
+1        8192.0   15.999999   19.200000
 2       16384.0   38.400001   38.400001
-3       32768.0   63.999998   63.999998
+3       32768.0   76.800002   76.800002
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
 6      262144.0  341.333321  341.333321
@@ -342,7 +342,7 @@ 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  47.206 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  43.980 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  512.000001      512.000001   188.321838
-1     384.0  614.400016      585.142862   153.600004
-2     512.0  655.360017      585.142849   154.566038
-3     640.0  706.206879      640.000002   158.759699
-4     768.0  722.823517      664.216187   162.754967
+0     256.0  546.133347      546.133347   188.321838
+1     384.0  585.142862      558.545450   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      646.736871   163.839992
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      406.179533   198.733401
-94  12288.0  812.429770      415.661740   198.995960
-95  12416.0  810.840807      412.149375   198.655991
-96  12544.0  810.925276      412.971190   198.913776
-97  12672.0  811.007961      412.097543   198.971549
+93  12160.0  812.359066      405.755985   198.936606
+94  12288.0  812.429770      415.661740   199.197579
+95  12416.0  812.498981      412.149375   198.904612
+96  12544.0  810.925276      412.546756   199.012395
+97  12672.0  811.007961      411.679167   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  31.827 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  29.352 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  ...   2.978909              2.978909
-1    384.0   7.372800  ...   7.899428              7.899428
-2    512.0  14.563555  ...  15.420235             15.420235
+0    256.0   2.978909  ...   2.978909              2.978909
+1    384.0   7.372800  ...   8.507077              8.507077
+2    512.0  14.563555  ...  15.420235             16.384000
 3    640.0  22.260869  ...  24.380953             24.380953
-4    768.0  31.597714  ...  34.028308             34.028308
+4    768.0  32.768000  ...  35.389441             34.028308
 5    896.0  37.971025  ...  40.140799             39.025776
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