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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
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_sources/getting-started/tutorials/02-fused-softmax.rst.txt
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.. rst-class:: sphx-glr-timing
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.. _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:
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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 |
+---------------------------------------------------------------------------------------------------------+-----------+--------+

2
_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
---------------

4
getting-started/installation.html
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@@ -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
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<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:
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.. ... ... ... ...
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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">
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<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:
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14 1920.0 69.467336 ... 68.776119 69.120002
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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">
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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 @@
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<td><p>02:12.186</p></td>
<td><p>02:15.188</p></td>
<td><p>0.0 MB</p></td>
</tr>
<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>
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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>
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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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