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Philippe Tillet
2021-07-31 05:27:59 +00:00
parent a86020efbc
commit 9e4ac7e794
21 changed files with 94 additions and 94 deletions
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4
_sources/getting-started/installation.rst.txt
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@@ -33,12 +33,12 @@ You can install the Python package from source by running the following commands
.. code-block:: bash
git clone https://github.com/ptillet/triton.git;
git clone https://github.com/openai/triton.git;
cd triton/python;
pip install cmake; # build time dependency
pip install -e .
Note that, if llvm-11 is not present on your system, the setup.py script will download LLVM static libraries on the web and link against that.
Note that, if llvm-11 is not present on your system, the setup.py script will download the official LLVM11 static libraries link against that.
You can then test your installation by running the unit tests:

10
_sources/getting-started/tutorials/01-vector-add.rst.txt
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@@ -216,13 +216,13 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
vector-add-performance:
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@@ -230,7 +230,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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**Total running time of the script:** ( 0 minutes 12.665 seconds)
.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

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_sources/getting-started/tutorials/02-fused-softmax.rst.txt
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@@ -261,17 +261,17 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
softmax-performance:
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4 768.0 702.171410 664.216187 273.066663
.. ... ... ... ...
93 12160.0 812.359066 405.755985 329.204728
93 12160.0 812.359066 406.179533 329.483481
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@@ -290,7 +290,7 @@ In the above plot, we can see that:
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 8.185 seconds)
**Total running time of the script:** ( 1 minutes 8.252 seconds)
.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

56
_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
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@@ -371,37 +371,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU)
0 128.0 0.455111 ... 0.512000 0.512000
1 256.0 2.730667 ... 3.276800 3.276800
2 384.0 7.372800 ... 8.507077 8.507077
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1 256.0 2.730667 ... 3.276800 2.978909
2 384.0 7.372800 ... 7.899428 7.899428
3 512.0 14.563555 ... 15.420235 15.420235
4 640.0 22.260869 ... 24.380953 24.380953
5 768.0 32.768000 ... 34.028308 34.028308
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31 4096.0 93.336389 ... 91.616198 91.118618
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@@ -411,7 +411,7 @@ 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 12.186 seconds)
**Total running time of the script:** ( 2 minutes 15.188 seconds)
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

8
_sources/getting-started/tutorials/sg_execution_times.rst.txt
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@@ -5,12 +5,12 @@
Computation times
=================
**03:31.335** total execution time for **getting-started_tutorials** files:
**03:36.105** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:12.186 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:15.188 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:08.185 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:08.252 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:10.964 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:12.665 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+

4
_sources/index.rst.txt
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@@ -1,7 +1,7 @@
Welcome to Triton's documentation!
==================================
Triton is an language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.
Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.
Getting Started
---------------
@@ -49,4 +49,4 @@ Check out the following documents to learn more about Triton and how it compares
:hidden:
programming-guide/chapter-1/introduction
programming-guide/chapter-2/related-work
programming-guide/chapter-2/related-work

4
getting-started/installation.html
View File

@@ -200,13 +200,13 @@
<div class="section" id="python-package">
<h3>Python Package<a class="headerlink" href="#python-package" title="Permalink to this headline"></a></h3>
<p>You can install the Python package from source by running the following commands:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/ptillet/triton.git<span class="p">;</span>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/openai/triton.git<span class="p">;</span>
<span class="nb">cd</span> triton/python<span class="p">;</span>
pip install cmake<span class="p">;</span> <span class="c1"># build time dependency</span>
pip install -e .
</pre></div>
</div>
<p>Note that, if llvm-11 is not present on your system, the setup.py script will download LLVM static libraries on the web and link against that.</p>
<p>Note that, if llvm-11 is not present on your system, the setup.py script will download the official LLVM11 static libraries link against that.</p>
<p>You can then test your installation by running the unit tests:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pytest -vs .
</pre></div>

10
getting-started/tutorials/01-vector-add.html
View File

@@ -305,13 +305,13 @@ for different problem sizes.</p>
<p class="sphx-glr-script-out">Out:</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 8.000000 9.600000
0 4096.0 9.600000 9.600000
1 8192.0 19.200000 19.200000
2 16384.0 38.400001 38.400001
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8 1048576.0 614.400016 614.400016
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@@ -319,11 +319,11 @@ for different problem sizes.</p>
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14 67108864.0 849.278610 848.362445
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</pre></div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.964 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 12.665 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>

12
getting-started/tutorials/02-fused-softmax.html
View File

@@ -346,17 +346,17 @@ 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 546.133347 273.066674
0 256.0 512.000001 512.000001 273.066674
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2 512.0 630.153853 585.142849 264.258068
3 640.0 682.666684 640.000002 265.974036
2 512.0 630.153853 606.814814 264.258068
3 640.0 682.666684 640.000002 269.473696
4 768.0 702.171410 664.216187 273.066663
.. ... ... ... ...
93 12160.0 812.359066 405.755985 329.204728
93 12160.0 812.359066 406.179533 329.483481
94 12288.0 812.429770 415.661740 329.602681
95 12416.0 810.840807 411.722274 329.173158
96 12544.0 810.925276 412.971190 329.292871
97 12672.0 811.007961 412.097543 329.142870
97 12672.0 811.007961 412.516771 329.142870
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</pre></div>
@@ -370,7 +370,7 @@ This means that – when temporary data is too large to fit entirely in the GPU
Note that our Triton kernel is not only faster than PyTorch’s CUDA kernel, it is also <strong>easier to read, understand and maintain</strong>.</p></li>
</ul>
</div></blockquote>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.185 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.252 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>

56
getting-started/tutorials/03-matrix-multiplication.html
View File

@@ -476,42 +476,42 @@ tensor(True, device=&#39;cuda:0&#39;)
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU)
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10 1408.0 64.138541 ... 65.684049 58.601554
11 1536.0 78.643199 ... 75.296679 75.296679
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11 1536.0 79.526831 ... 76.106321 75.296679
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19 2560.0 77.833728 ... 76.740048 74.812787
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.186 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 15.188 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.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">03-matrix-multiplication.py</span></code></a></p>

8
getting-started/tutorials/sg_execution_times.html
View File

@@ -174,7 +174,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline"></a></h1>
<p><strong>03:31.335</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
<p><strong>03:36.105</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
<table class="docutils align-default">
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@@ -183,15 +183,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
<td><p>02:12.186</p></td>
<td><p>02:15.188</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
<td><p>01:08.185</p></td>
<td><p>01:08.252</p></td>
<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td>
<td><p>00:10.964</p></td>
<td><p>00:12.665</p></td>
<td><p>0.0 MB</p></td>
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<div class="section" id="welcome-to-triton-s-documentation">
<h1>Welcome to Triton’s documentation!<a class="headerlink" href="#welcome-to-triton-s-documentation" title="Permalink to this headline"></a></h1>
<p>Triton is an language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.</p>
<p>Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.</p>
<div class="section" id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h2>
<ul class="simple">

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