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Philippe Tillet
2021-09-03 00:13:53 +00:00
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_sources/getting-started/tutorials/01-vector-add.rst.txt
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_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
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Computation times
=================
**03:51.756** total execution time for **getting-started_tutorials** files:
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| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:14.737 | 0.0 MB |
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| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:13.131 | 0.0 MB |
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| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:11.053 | 0.0 MB |
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getting-started/tutorials/01-vector-add.html
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getting-started/tutorials/02-fused-softmax.html
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getting-started/tutorials/03-matrix-multiplication.html
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8
getting-started/tutorials/sg_execution_times.html
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<span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">heuristics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.heuristics" title="Permalink to this definition"></a></dt>
<dd><p>Decorator for specifying how the values of certain meta-parameters may be computed.
This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@heuristics</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="mi">2</span> <span class="o">**</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">])))})</span>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">heuristics</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="mi">2</span> <span class="o">**</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">])))})</span>
<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">x_ptr</span><span class="p">,</span> <span class="n">x_size</span><span class="p">,</span> <span class="o">**</span><span class="n">META</span><span class="p">):</span>
<span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">META</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]</span> <span class="c1"># smallest power-of-two &gt;= x_size</span>

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