[GH-PAGES] Updated website

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

@@ -362,8 +362,8 @@ for different problem sizes.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
-<img alt="vector-add-performance" class="sphx-glr-single-img" src="../../_images/sphx_glr_01-vector-add_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.756 seconds)</p>
+<img alt="01 vector add" class="sphx-glr-single-img" src="../../_images/sphx_glr_01-vector-add_001.png" />
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  9.497 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

@@ -395,7 +395,7 @@ We will then compare its performance against (1) <code class="code docutils lite
 <span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
-<img alt="softmax-performance" class="sphx-glr-single-img" src="../../_images/sphx_glr_02-fused-softmax_001.png" />
+<img alt="02 fused softmax" class="sphx-glr-single-img" src="../../_images/sphx_glr_02-fused-softmax_001.png" />
 <p>In the above plot, we can see that:</p>
 <blockquote>
 <div><ul class="simple">
@@ -405,7 +405,7 @@ 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> ( 0 minutes  19.933 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  25.654 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

@@ -411,7 +411,13 @@ Here, we want to re-tune our kernel only when the shape of input matrices change
     <span class="n">cache</span> <span class="o">=</span> <span class="n">make_kernel</span><span class="o">.</span><span class="n">cache</span>
     <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">cache</span><span class="p">:</span>
         <span class="n">defines</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;TYPE&#39;</span><span class="p">:</span> <span class="n">dtype</span><span class="p">}</span>
-        <span class="n">cache</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">kernel</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">defines</span><span class="o">=</span><span class="n">defines</span><span class="p">,</span> <span class="n">autotune_vals</span><span class="o">=</span><span class="n">autotune_configs</span><span class="p">,</span> <span class="n">autotune_key</span><span class="o">=</span><span class="n">autotune_key</span><span class="p">)</span>
+        <span class="n">cache</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">kernel</span><span class="p">(</span>
+            <span class="n">src</span><span class="p">,</span>
+            <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
+            <span class="n">defines</span><span class="o">=</span><span class="n">defines</span><span class="p">,</span>
+            <span class="n">autotune_configs</span><span class="o">=</span><span class="n">autotune_configs</span><span class="p">,</span>
+            <span class="n">autotune_key</span><span class="o">=</span><span class="n">autotune_key</span><span class="p">,</span>
+        <span class="p">)</span>
     <span class="k">return</span> <span class="n">cache</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
 
 
@@ -515,7 +521,7 @@ make -j8 install
 Triton comes with some basic Python bindings for benchmarking CUTLASS. These will be compiled when the environment variables <code class="code docutils literal notranslate"><span class="pre">CUTLASS_INCLUDE_DIR</span></code> and <code class="code docutils literal notranslate"><span class="pre">CUTLASS_LIBRARY_DIR</span></code> are set during the installation process.
 To re-install Triton with the updated CUTLASS bindings, run the following command:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUTLASS_INCLUDE_DIR</span><span class="o">=</span>/tmp/cutlass/build/install/include/
-<span class="nb">export</span> <span class="nv">CUTLASS_LIBRARY_DIR</span><span class="o">=</span>/tmp/cutlass/build/install/lib/a
+<span class="nb">export</span> <span class="nv">CUTLASS_LIBRARY_DIR</span><span class="o">=</span>/tmp/cutlass/build/install/lib/
 pip uninstall -y triton
 pip install -e <span class="s2">&quot;git+https://github.com/ptillet/triton.git#egg=triton&amp;subdirectory=python&quot;</span>
 </pre></div>
@@ -549,8 +555,8 @@ True
         <span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">],</span>  <span class="c1"># argument names to use as an x-axis for the plot</span>
         <span class="n">x_vals</span><span class="o">=</span><span class="p">[</span><span class="mi">256</span> <span class="o">*</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">33</span><span class="p">)],</span>  <span class="c1"># different possible values for `x_name`</span>
         <span class="n">y_name</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>  <span class="c1"># argument name whose value corresponds to a different line in the plot</span>
-        <span class="n">y_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;torch&#39;</span><span class="p">,</span> <span class="s1">&#39;triton&#39;</span><span class="p">,</span> <span class="s1">&#39;cutlass&#39;</span><span class="p">],</span>  <span class="c1"># possible keys for `y_name`</span>
-        <span class="n">y_lines</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Torch&quot;</span><span class="p">,</span> <span class="s2">&quot;Triton&quot;</span><span class="p">,</span> <span class="s1">&#39;CUTLASS&#39;</span><span class="p">],</span>  <span class="c1"># label name for the lines</span>
+        <span class="n">y_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;cublas&#39;</span><span class="p">,</span> <span class="s1">&#39;triton&#39;</span><span class="p">,</span> <span class="s1">&#39;cutlass&#39;</span><span class="p">],</span>  <span class="c1"># possible keys for `y_name`</span>
+        <span class="n">y_lines</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;cuBLAS&quot;</span><span class="p">,</span> <span class="s2">&quot;Triton&quot;</span><span class="p">,</span> <span class="s1">&#39;CUTLASS&#39;</span><span class="p">],</span>  <span class="c1"># label name for the lines</span>
         <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;TFLOPS&quot;</span><span class="p">,</span>  <span class="c1"># label name for the y-axis</span>
         <span class="n">plot_name</span><span class="o">=</span><span class="s2">&quot;matmul-performance&quot;</span><span class="p">,</span>  <span class="c1"># name for the plot. Used also as a file name for saving the plot.</span>
         <span class="n">args</span><span class="o">=</span><span class="p">{}</span>
@@ -559,7 +565,7 @@ True
 <span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
     <span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="n">K</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
     <span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="n">K</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;torch&#39;</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;cublas&#39;</span><span class="p">:</span>
         <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton&#39;</span><span class="p">:</span>
         <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
@@ -572,9 +578,9 @@ True
 <span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
-<img alt="matmul-performance" class="sphx-glr-single-img" src="../../_images/sphx_glr_03-matrix-multiplication_001.png" />
+<img alt="03 matrix multiplication" class="sphx-glr-single-img" src="../../_images/sphx_glr_03-matrix-multiplication_001.png" />
 <p>As we can see, the performance of our kernel is pretty good. It is in fact faster than CUTLASS, and therefore probably comparable to the absolute best CUDA code an expert could write.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  6.502 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  5.861 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">
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getting-started/tutorials/sg_execution_times.html

@@ -167,7 +167,7 @@
             
   <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>01:34.190</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
+<p><strong>00:25.654</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 85%" />
@@ -175,16 +175,16 @@
 <col style="width: 6%" />
 </colgroup>
 <tbody>
+<tr class="row-odd"><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>00:25.654</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><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>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
 <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>
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-<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>00:19.933</p></td>
-<td><p>0.0 MB</p></td>
-</tr>
-<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>00:07.756</p></td>
+<td><p>00:00.000</p></td>
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