[GH-PAGES] Updated website

_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py

@@ -55,7 +55,7 @@ def add(x: torch.Tensor, y: torch.Tensor):
     # We need to preallocate the output
     output = torch.empty_like(x)
     assert x.is_cuda and y.is_cuda and output.is_cuda
-    n_elements = output.shape[0]
+    n_elements = output.numel()
     # The SPMD launch grid denotes the number of kernel instances that run in parallel.
     # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
     # In this case, we use a 1D grid where the size is the number of blocks

_downloads/f191ee1e78dc52eb5f7cba88f71cef2f/01-vector-add.ipynb

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def add(x: torch.Tensor, y: torch.Tensor):\n    # We need to preallocate the output\n    output = torch.empty_like(x)\n    assert x.is_cuda and y.is_cuda and output.is_cuda\n    n_elements = output.shape[0]\n    # The SPMD launch grid denotes the number of kernel instances that run in parallel.\n    # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]\n    # In this case, we use a 1D grid where the size is the number of blocks\n    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n    # NOTE:\n    #  - each torch.tensor object is implicitly converted into a pointer to its first element.\n    #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel\n    #  - don't forget to pass meta-parameters as keywords arguments\n    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)\n    # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still\n    # running asynchronously at this point.\n    return output"
+        "def add(x: torch.Tensor, y: torch.Tensor):\n    # We need to preallocate the output\n    output = torch.empty_like(x)\n    assert x.is_cuda and y.is_cuda and output.is_cuda\n    n_elements = output.numel()\n    # The SPMD launch grid denotes the number of kernel instances that run in parallel.\n    # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]\n    # In this case, we use a 1D grid where the size is the number of blocks\n    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n    # NOTE:\n    #  - each torch.tensor object is implicitly converted into a pointer to its first element.\n    #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel\n    #  - don't forget to pass meta-parameters as keywords arguments\n    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)\n    # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still\n    # running asynchronously at this point.\n    return output"
       ]
     },
     {

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

@@ -92,7 +92,7 @@ and (2) enqueue the above kernel with appropriate grid/block sizes.
         # We need to preallocate the output
         output = torch.empty_like(x)
         assert x.is_cuda and y.is_cuda and output.is_cuda
-        n_elements = output.shape[0]
+        n_elements = output.numel()
         # The SPMD launch grid denotes the number of kernel instances that run in parallel.
         # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
         # In this case, we use a 1D grid where the size is the number of blocks
@@ -234,17 +234,17 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     0        4096.0    9.600000    9.600000
     1        8192.0   19.200000   19.200000
     2       16384.0   38.400001   38.400001
-    3       32768.0   63.999998   76.800002
+    3       32768.0   63.999998   63.999998
     4       65536.0  127.999995  127.999995
     5      131072.0  219.428568  219.428568
-    6      262144.0  384.000001  384.000001
+    6      262144.0  341.333321  384.000001
     7      524288.0  472.615390  472.615390
     8     1048576.0  614.400016  614.400016
     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
-    13   33554432.0  843.811163  842.004273
+    13   33554432.0  843.811163  843.811163
     14   67108864.0  849.278610  848.362445
     15  134217728.0  851.577704  850.656574
 
@@ -254,7 +254,7 @@ 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:** ( 0 minutes  12.966 seconds)
+   **Total running time of the script:** ( 0 minutes  11.041 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

_sources/getting-started/tutorials/02-fused-softmax.rst.txt

@@ -302,15 +302,15 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
               N      Triton  Torch (native)  Torch (jit)
     0     256.0  512.000001      546.133347   186.181817
     1     384.0  585.142862      585.142862   153.600004
-    2     512.0  630.153853      585.142849   154.566038
+    2     512.0  630.153853      606.814814   154.566038
-    3     640.0  660.645170      640.000002   158.759699
+    3     640.0  682.666684      640.000002   160.000000
-    4     768.0  702.171410      664.216187   162.754967
+    4     768.0  702.171410      664.216187   163.839992
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      406.179533   198.733401
+    93  12160.0  812.359066      406.179533   199.038365
-    94  12288.0  812.429770      415.661740   198.995960
+    94  12288.0  812.429770      416.101597   199.298541
-    95  12416.0  810.840807      411.296057   198.655991
+    95  12416.0  810.840807      412.149375   198.954424
-    96  12544.0  810.925276      412.971190   198.815254
+    96  12544.0  810.925276      412.971190   199.111113
-    97  12672.0  809.389265      412.097543   198.873965
+    97  12672.0  809.389265      412.097543   199.264875
 
     [98 rows x 4 columns]
 
@@ -328,7 +328,7 @@ In the above plot, we can see that:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  12.677 seconds)
+   **Total running time of the script:** ( 1 minutes  13.141 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

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

@@ -462,37 +462,37 @@ 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              3.276800
+    0    256.0   2.978909  ...   2.978909              2.978909
-    1    384.0   7.372800  ...   8.507077              8.507077
+    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  ...  34.028308             34.028308
     5    896.0  39.025776  ...  40.140799             39.025776
     6   1024.0  49.932191  ...  53.773130             52.428801
-    7   1152.0  44.566925  ...  46.656000             45.938215
+    7   1152.0  44.566925  ...  46.656000             46.656000
-    8   1280.0  51.200001  ...  56.109587             56.109587
+    8   1280.0  51.200001  ...  56.888887             56.109587
-    9   1408.0  64.138541  ...  64.902096             64.138541
+    9   1408.0  64.138541  ...  64.902096             64.902096
-    10  1536.0  78.643199  ...  76.106321             76.106321
+    10  1536.0  80.430545  ...  76.933564             76.106321
-    11  1664.0  62.929456  ...  62.061463             62.061463
+    11  1664.0  63.372618  ...  62.492442             62.492442
-    12  1792.0  72.983276  ...  69.810085             69.379162
+    12  1792.0  72.983276  ...  70.246402             69.810085
-    13  1920.0  69.120002  ...  70.530615             70.530615
+    13  1920.0  65.207547  ...  69.467336             67.764707
-    14  2048.0  73.908442  ...  75.234154             68.487170
+    14  2048.0  73.908442  ...  74.898285             73.584279
-    15  2176.0  82.813365  ...  77.998640             79.855747
+    15  2176.0  82.813365  ...  81.143743             81.143743
     16  2304.0  68.446623  ...  73.728002             73.501144
     17  2432.0  71.305746  ...  82.147552             81.908060
-    18  2560.0  77.649287  ...  76.027843             77.465723
+    18  2560.0  77.649287  ...  77.465723             77.465723
-    19  2688.0  78.536346  ...  82.284288             80.880718
+    19  2688.0  80.027544  ...  81.401408             81.576466
-    20  2816.0  82.602666  ...  77.605356             79.443003
+    20  2816.0  81.369790  ...  79.879498             79.443003
-    21  2944.0  81.431424  ...  80.122235             76.670818
+    21  2944.0  83.060049  ...  78.979452             77.265163
-    22  3072.0  81.472093  ...  82.903517             82.181572
+    22  3072.0  82.540970  ...  82.782312             82.181572
-    23  3200.0  83.116885  ...  85.106381             88.642656
+    23  3200.0  82.368085  ...  86.253369             90.523334
-    24  3328.0  84.101981  ...  86.424125             84.895397
+    24  3328.0  83.613586  ...  82.653612             82.088138
-    25  3456.0  81.849303  ...  81.766291             85.950501
+    25  3456.0  81.849303  ...  85.133652             84.775569
-    26  3584.0  87.127323  ...  88.761490             90.549237
+    26  3584.0  87.551500  ...  94.747514             92.220917
-    27  3712.0  85.822459  ...  81.883070             84.088676
+    27  3712.0  81.482335  ...  84.230479             82.971218
-    28  3840.0  84.679936  ...  88.615388             88.402879
+    28  3840.0  81.138664  ...  84.292684             83.027026
-    29  3968.0  92.512459  ...  87.850207             87.787005
+    29  3968.0  89.068569  ...  84.562670             80.864108
-    30  4096.0  93.271527  ...  83.313299             90.626421
+    30  4096.0  92.948562  ...  89.717734             90.018600
 
     [31 rows x 5 columns]
 
@@ -502,7 +502,7 @@ 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:** ( 2 minutes  5.134 seconds)
+   **Total running time of the script:** ( 2 minutes  12.082 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

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

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**03:30.777** total execution time for **getting-started_tutorials** files:
+**03:36.265** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:05.134 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:12.082 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 01:12.677 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 01:13.141 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 00:12.966 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 00:11.041 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+

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

@@ -240,7 +240,7 @@ and (2) enqueue the above kernel with appropriate grid/block sizes.</p>
     <span class="c1"># We need to preallocate the output</span>
     <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
     <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">is_cuda</span>
-    <span class="n">n_elements</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">n_elements</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
     <span class="c1"># The SPMD launch grid denotes the number of kernel instances that run in parallel.</span>
     <span class="c1"># It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -&gt; Tuple[int]</span>
     <span class="c1"># In this case, we use a 1D grid where the size is the number of blocks</span>
@@ -322,22 +322,22 @@ for different problem sizes.</p>
 0        4096.0    9.600000    9.600000
 1        8192.0   19.200000   19.200000
 2       16384.0   38.400001   38.400001
-3       32768.0   63.999998   76.800002
+3       32768.0   63.999998   63.999998
 4       65536.0  127.999995  127.999995
 5      131072.0  219.428568  219.428568
-6      262144.0  384.000001  384.000001
+6      262144.0  341.333321  384.000001
 7      524288.0  472.615390  472.615390
 8     1048576.0  614.400016  614.400016
 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
-13   33554432.0  843.811163  842.004273
+13   33554432.0  843.811163  843.811163
 14   67108864.0  849.278610  848.362445
 15  134217728.0  851.577704  850.656574
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  12.966 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  11.041 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>

getting-started/tutorials/02-fused-softmax.html

@@ -387,15 +387,15 @@ We will then compare its performance against (1) <code class="code docutils lite
           N      Triton  Torch (native)  Torch (jit)
 0     256.0  512.000001      546.133347   186.181817
 1     384.0  585.142862      585.142862   153.600004
-2     512.0  630.153853      585.142849   154.566038
+2     512.0  630.153853      606.814814   154.566038
-3     640.0  660.645170      640.000002   158.759699
+3     640.0  682.666684      640.000002   160.000000
-4     768.0  702.171410      664.216187   162.754967
+4     768.0  702.171410      664.216187   163.839992
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      406.179533   198.733401
+93  12160.0  812.359066      406.179533   199.038365
-94  12288.0  812.429770      415.661740   198.995960
+94  12288.0  812.429770      416.101597   199.298541
-95  12416.0  810.840807      411.296057   198.655991
+95  12416.0  810.840807      412.149375   198.954424
-96  12544.0  810.925276      412.971190   198.815254
+96  12544.0  810.925276      412.971190   199.111113
-97  12672.0  809.389265      412.097543   198.873965
+97  12672.0  809.389265      412.097543   199.264875
 
 [98 rows x 4 columns]
 </pre></div>
@@ -408,7 +408,7 @@ 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> ( 1 minutes  12.677 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  13.141 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>

getting-started/tutorials/03-matrix-multiplication.html

@@ -566,42 +566,42 @@ 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              3.276800
+0    256.0   2.978909  ...   2.978909              2.978909
-1    384.0   7.372800  ...   8.507077              8.507077
+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  ...  34.028308             34.028308
 5    896.0  39.025776  ...  40.140799             39.025776
 6   1024.0  49.932191  ...  53.773130             52.428801
-7   1152.0  44.566925  ...  46.656000             45.938215
+7   1152.0  44.566925  ...  46.656000             46.656000
-8   1280.0  51.200001  ...  56.109587             56.109587
+8   1280.0  51.200001  ...  56.888887             56.109587
-9   1408.0  64.138541  ...  64.902096             64.138541
+9   1408.0  64.138541  ...  64.902096             64.902096
-10  1536.0  78.643199  ...  76.106321             76.106321
+10  1536.0  80.430545  ...  76.933564             76.106321
-11  1664.0  62.929456  ...  62.061463             62.061463
+11  1664.0  63.372618  ...  62.492442             62.492442
-12  1792.0  72.983276  ...  69.810085             69.379162
+12  1792.0  72.983276  ...  70.246402             69.810085
-13  1920.0  69.120002  ...  70.530615             70.530615
+13  1920.0  65.207547  ...  69.467336             67.764707
-14  2048.0  73.908442  ...  75.234154             68.487170
+14  2048.0  73.908442  ...  74.898285             73.584279
-15  2176.0  82.813365  ...  77.998640             79.855747
+15  2176.0  82.813365  ...  81.143743             81.143743
 16  2304.0  68.446623  ...  73.728002             73.501144
 17  2432.0  71.305746  ...  82.147552             81.908060
-18  2560.0  77.649287  ...  76.027843             77.465723
+18  2560.0  77.649287  ...  77.465723             77.465723
-19  2688.0  78.536346  ...  82.284288             80.880718
+19  2688.0  80.027544  ...  81.401408             81.576466
-20  2816.0  82.602666  ...  77.605356             79.443003
+20  2816.0  81.369790  ...  79.879498             79.443003
-21  2944.0  81.431424  ...  80.122235             76.670818
+21  2944.0  83.060049  ...  78.979452             77.265163
-22  3072.0  81.472093  ...  82.903517             82.181572
+22  3072.0  82.540970  ...  82.782312             82.181572
-23  3200.0  83.116885  ...  85.106381             88.642656
+23  3200.0  82.368085  ...  86.253369             90.523334
-24  3328.0  84.101981  ...  86.424125             84.895397
+24  3328.0  83.613586  ...  82.653612             82.088138
-25  3456.0  81.849303  ...  81.766291             85.950501
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getting-started/tutorials/sg_execution_times.html

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