[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: c805eb194ad6515efc001bb542757901
+config: 85ad6ae80796007cd5ed69917efce6b6
 tags: 645f666f9bcd5a90fca523b33c5a78b7

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

@@ -238,14 +238,14 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     3       32768.0   63.999998   63.999998
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
-    6      262144.0  341.333321  384.000001
+    6      262144.0  384.000001  384.000001
     7      524288.0  472.615390  472.615390
     8     1048576.0  614.400016  614.400016
-    9     2097152.0  722.823517  702.171410
+    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
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+    13   33554432.0  842.004273  843.811163
     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  45.313 seconds)
+   **Total running time of the script:** ( 2 minutes  39.483 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   188.321838
-    1     384.0  614.400016      585.142862   151.703707
-    2     512.0  655.360017      585.142849   154.566038
+    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
-    4     768.0  722.823517      646.736871   163.839992
+    4     768.0  722.823517      664.216187   162.754967
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      405.333344   199.038365
-    94  12288.0  812.429770      415.222812   199.298541
-    95  12416.0  812.498981      411.296057   198.954424
-    96  12544.0  810.925276      412.971190   199.111113
-    97  12672.0  811.007961      412.097543   199.264875
+    93  12160.0  812.359066      406.179533   198.631953
+    94  12288.0  812.429770      415.222812   198.895304
+    95  12416.0  812.498981      412.149375   198.556711
+    96  12544.0  810.925276      412.546756   198.815254
+    97  12672.0  811.007961      412.097543   198.873965
 
     [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  30.839 seconds)
+   **Total running time of the script:** ( 3 minutes  31.979 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  ...   8.507077              7.899428
+    0    256.0   2.978909  ...   2.978909              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  49.932191  ...  53.773130             52.428801
+    6   1024.0  51.150050  ...  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  ...  68.147202             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  72.983276  ...  73.460287             59.467852
-    13  1920.0  69.120002  ...  71.257735             70.892307
-    14  2048.0  73.584279  ...  78.033565             76.959706
+    11  1664.0  63.372618  ...  63.372618             62.492442
+    12  1792.0  72.512412  ...  73.460287             59.467852
+    13  1920.0  69.120002  ...  71.626943             71.257735
+    14  2048.0  73.908442  ...  78.398206             77.314362
     15  2176.0  83.155572  ...  87.494120             85.998493
-    16  2304.0  68.446623  ...  78.064941             77.057651
-    17  2432.0  71.125224  ...  85.915795             75.320281
-    18  2560.0  77.833728  ...  82.125311             81.512437
-    19  2688.0  83.737433  ...  90.532356             89.676257
-    20  2816.0  83.074685  ...  84.687779             84.035084
-    21  2944.0  81.698415  ...  84.040530             83.060049
-    22  3072.0  81.707223  ...  90.164177             89.310890
-    23  3200.0  84.544253  ...  96.969694             95.665176
-    24  3328.0  83.905938  ...  86.320498             85.602017
-    25  3456.0  79.508447  ...  91.824110             85.585527
-    26  3584.0  85.633710  ...  92.600816             95.756542
-    27  3712.0  84.159518  ...  93.100971             87.783251
-    28  3840.0  80.901241  ...  87.980905             92.236860
-    29  3968.0  87.913500  ...  91.747320             86.114283
-    30  4096.0  93.336389  ...  86.928580             85.271746
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+    17  2432.0  71.305746  ...  86.711310             75.320281
+    18  2560.0  77.833728  ...  82.331658             81.108913
+    19  2688.0  83.922689  ...  90.748936             89.676257
+    20  2816.0  79.879498  ...  82.446516             83.873477
+    21  2944.0  81.967162  ...  83.617504             83.337844
+    22  3072.0  81.825298  ...  89.593522             88.750943
+    23  3200.0  84.993363  ...  96.822991             95.808380
+    24  3328.0  83.130825  ...  84.596116             84.200347
+    25  3456.0  81.518272  ...  91.407671             90.943675
+    26  3584.0  85.797134  ...  91.470385             91.470385
+    27  3712.0  84.088676  ...  89.273764             83.247783
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+    29  3968.0  85.809605  ...  92.232760             86.175099
+    30  4096.0  93.433852  ...  85.871008             92.245860
 
     [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  36.741 seconds)
+   **Total running time of the script:** ( 6 minutes  36.303 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  0.164 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  468.114273
     1    1536.0  630.153868  323.368435  511.999982
-    2    2048.0  668.734716  334.367358  520.126988
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-    4    3072.0  702.171410  378.092307  501.551037
-    5    3584.0  725.873439  384.859062  451.527536
-    6    4096.0  728.177767  383.251446  451.972420
-    7    4608.0  670.254540  396.387087  428.651163
-    8    5120.0  688.403381  395.748783  420.102563
-    9    5632.0  704.000002  396.969169  415.262685
-    10   6144.0  702.171410  402.885254  411.313806
-    11   6656.0  700.631610  398.861429  398.861429
-    12   7168.0  686.754468  388.772874  384.859062
+    2    2048.0  682.666643  334.367358  520.126988
+    3    2560.0  694.237267  365.714281  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  394.267384  421.302872
+    8    5120.0  688.403381  397.669909  424.455959
+    9    5632.0  704.000002  395.228063  411.470331
+    10   6144.0  697.191505  402.885254  411.313806
+    11   6656.0  700.631610  400.360920  398.861429
+    12   7168.0  690.891575  396.844306  387.459443
     13   7680.0  678.895043  393.846167  386.415087
-    14   8192.0  636.271854  390.095241  374.491442
-    15   8704.0  627.315309  396.763538  380.502740
-    16   9216.0  604.327881  405.098894  383.002605
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+    16   9216.0  604.327881  407.337026  383.002605
     17   9728.0  585.142883  409.599987  383.369452
-    18  10240.0  564.965524  409.600010  381.911416
+    18  10240.0  564.965524  408.578556  382.803739
     19  10752.0  546.133312  411.559798  381.445676
-    20  11264.0  531.634232  403.185684  373.908711
-    21  11776.0  519.052343  409.599991  377.587162
-    22  12288.0  513.336807  413.911572  382.505826
-    23  12800.0  504.433489  410.420828  377.859783
-    24  13312.0  494.180982  404.159395  377.645399
-    25  13824.0  481.882350  411.122660  378.739711
-    26  14336.0  471.967074  400.307157  372.969090
-    27  14848.0  461.297068  404.027214  374.712936
+    20  11264.0  533.207081  406.826188  373.134567
+    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
+    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  447.098578  405.027112  375.668625
+    29  15872.0  447.098578  406.974373  376.225175
 
 
 
@@ -393,7 +393,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  38.066 seconds)
+   **Total running time of the script:** ( 5 minutes  37.477 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  0.075 seconds)
+   **Total running time of the script:** ( 0 minutes  0.367 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  0.128 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
 =================
-**17:31.058** total execution time for **getting-started_tutorials** files:
+**18:25.900** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:36.741 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:36.303 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:38.066 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 05:37.477 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:30.839 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:31.979 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:45.313 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 02:39.483 | 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``)             | 00:00.367 | 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``)       | 00:00.164 | 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``)       | 00:00.128 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+

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

@@ -330,19 +330,19 @@ for different problem sizes.</p>
 3       32768.0   63.999998   63.999998
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
-6      262144.0  341.333321  384.000001
+6      262144.0  384.000001  384.000001
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-9     2097152.0  722.823517  702.171410
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 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  45.313 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  39.483 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   188.321838
-1     384.0  614.400016      585.142862   151.703707
-2     512.0  655.360017      585.142849   154.566038
+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
-4     768.0  722.823517      646.736871   163.839992
+4     768.0  722.823517      664.216187   162.754967
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      405.333344   199.038365
-94  12288.0  812.429770      415.222812   199.298541
-95  12416.0  812.498981      411.296057   198.954424
-96  12544.0  810.925276      412.971190   199.111113
-97  12672.0  811.007961      412.097543   199.264875
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+96  12544.0  810.925276      412.546756   198.815254
+97  12672.0  811.007961      412.097543   198.873965
 
 [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  30.839 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  31.979 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  ...   8.507077              7.899428
+0    256.0   2.978909  ...   2.978909              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
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