[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: e4eafa0628e28e2bfe39e33d79ac7192
+config: 9db37abf114d2bf6098b799c8c2e818a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

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

@@ -235,17 +235,17 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     0        4096.0    9.600000    9.600000
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     2       16384.0   38.400001   38.400001
-    3       32768.0   76.800002   76.800002
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     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  722.823517
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     11    8388608.0  812.429770  812.429770
     12   16777216.0  833.084721  833.084721
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     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  43.980 seconds)
+   **Total running time of the script:** ( 1 minutes  38.773 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   190.511628
-    1     384.0  614.400016      585.142862   153.600004
-    2     512.0  655.360017      606.814814   154.566038
-    3     640.0  706.206879      640.000002   160.000000
+    1     384.0  585.142862      585.142862   151.703707
+    2     512.0  655.360017      606.814814   156.038096
+    3     640.0  682.666684      640.000002   160.000000
     4     768.0  722.823517      664.216187   162.754967
     ..      ...         ...             ...          ...
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-    95  12416.0  812.498981      412.149375   198.606350
-    96  12544.0  810.925276      412.971190   198.864492
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+    97  12672.0  811.007961      412.097543   198.679085
 
     [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  30.496 seconds)
+   **Total running time of the script:** ( 3 minutes  26.216 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              8.507077
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     3    640.0  22.260869  ...  24.380953             24.380953
     4    768.0  32.768000  ...  35.389441             34.028308
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     6   1024.0  51.150050  ...  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
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     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  69.120002  ...  71.626943             70.892307
+    13  1920.0  69.120002  ...  71.257735             70.892307
     14  2048.0  73.908442  ...  78.398206             77.314362
-    15  2176.0  83.500614  ...  87.876193             86.367588
-    16  2304.0  68.446623  ...  78.064941             77.307030
-    17  2432.0  71.305746  ...  85.915795             75.320281
-    18  2560.0  78.019048  ...  82.747477             81.512437
-    19  2688.0  83.186525  ...  90.640519             89.254248
-    20  2816.0  82.135981  ...  84.035084             83.873477
-    21  2944.0  81.298583  ...  81.431424             83.337844
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-    24  3328.0  84.101981  ...  85.398926             84.695641
-    25  3456.0  81.766291  ...  91.824110             90.790053
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-    28  3840.0  80.990111  ...  93.563449             84.777307
-    29  3968.0  93.112506  ...  82.840416             84.915752
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+    30  4096.0  93.466385  ...  83.156833             85.652663
 
     [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  34.019 seconds)
+   **Total running time of the script:** ( 6 minutes  17.908 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  0.013 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
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-    7    4608.0  670.254540  394.267384  421.302872
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-    13   7680.0  678.895043  393.846167  386.415087
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-    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
-    19  10752.0  547.872604  411.559798  381.445676
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+    11   6656.0  700.631610  400.360920  398.861429
+    12   7168.0  686.754468  387.459443  387.459443
+    13   7680.0  678.895043  392.587863  385.203746
+    14   8192.0  636.271854  391.259714  371.308771
+    15   8704.0  621.714277  390.095225  379.465939
+    16   9216.0  604.327881  405.098894  383.002605
+    17   9728.0  585.142883  409.599987  382.427505
+    18  10240.0  564.965524  409.600010  382.803739
+    19  10752.0  546.133312  410.577576  380.601764
+    20  11264.0  530.070590  401.389743  368.556239
+    21  11776.0  520.486200  409.599991  376.831982
+    22  12288.0  516.031509  413.911572  383.251457
+    23  12800.0  504.433489  409.599981  377.163903
+    24  13312.0  494.180982  408.030638  376.976995
+    25  13824.0  482.934503  412.656711  379.389355
+    26  14336.0  471.967074  402.414053  370.558967
+    27  14848.0  461.297068  406.099164  374.712936
+    28  15360.0  454.269882  406.887417  378.092307
+    29  15872.0  447.098578  407.627589  376.225175
 
 
 
@@ -393,7 +393,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  34.799 seconds)
+   **Total running time of the script:** ( 5 minutes  34.534 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  0.080 seconds)
+   **Total running time of the script:** ( 0 minutes  0.076 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_06-fused-attention.py:

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

@@ -5,20 +5,20 @@
 
 Computation times
 =================
-**17:23.396** total execution time for **getting-started_tutorials** files:
+**16:57.529** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:34.019 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:17.908 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:34.799 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:34.534 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:30.496 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:26.216 | 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_01-vector-add.py` (``01-vector-add.py``)                       | 01:38.773 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_06-fused-attention.py` (``06-fused-attention.py``)             | 00:00.080 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_06-fused-attention.py` (``06-fused-attention.py``)             | 00:00.076 | 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_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

@@ -327,22 +327,22 @@ for different problem sizes.</p>
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-3       32768.0   76.800002   76.800002
+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
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 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  43.980 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  38.773 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   190.511628
-1     384.0  614.400016      585.142862   153.600004
-2     512.0  655.360017      606.814814   154.566038
-3     640.0  706.206879      640.000002   160.000000
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+3     640.0  682.666684      640.000002   160.000000
 4     768.0  722.823517      664.216187   162.754967
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      406.179533   198.631953
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 [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>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  26.216 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              8.507077
-2    512.0  14.563555  ...  16.384000             16.384000
+0    256.0   2.730667  ...   2.978909              2.978909
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+2    512.0  14.563555  ...  15.420235             15.420235
 3    640.0  22.260869  ...  24.380953             24.380953
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