[GH-PAGES] Updated website
This commit is contained in:
Philippe Tillet
@@ -326,7 +326,7 @@ for different problem sizes.</p>
|
||||
size Triton Torch
|
||||
0 4096.0 9.600000 9.600000
|
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1 8192.0 19.200000 19.200000
|
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2 16384.0 38.400001 38.400001
|
||||
2 16384.0 31.999999 31.999999
|
||||
3 32768.0 76.800002 76.800002
|
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4 65536.0 127.999995 127.999995
|
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5 131072.0 219.428568 219.428568
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@@ -342,7 +342,7 @@ for different problem sizes.</p>
|
||||
15 134217728.0 849.737435 850.656574
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</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.248 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.087 seconds)</p>
|
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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">
|
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<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>
|
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|
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@@ -371,17 +371,17 @@ We will then compare its performance against (1) <code class="code docutils lite
|
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<p class="sphx-glr-script-out">Out:</p>
|
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<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 512.000001 186.181817
|
||||
1 384.0 614.400016 585.142862 153.600004
|
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0 256.0 512.000001 512.000001 190.511628
|
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1 384.0 614.400016 558.545450 153.600004
|
||||
2 512.0 655.360017 585.142849 154.566038
|
||||
3 640.0 682.666684 640.000002 158.759699
|
||||
3 640.0 706.206879 640.000002 160.000000
|
||||
4 768.0 722.823517 664.216187 162.754967
|
||||
.. ... ... ... ...
|
||||
93 12160.0 812.359066 406.179533 198.834951
|
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94 12288.0 812.429770 415.222812 199.197579
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95 12416.0 810.840807 412.149375 198.854847
|
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96 12544.0 810.925276 412.546756 199.111113
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97 12672.0 811.007961 412.097543 199.167004
|
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93 12160.0 812.359066 406.179533 199.038365
|
||||
94 12288.0 812.429770 415.222812 199.298541
|
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95 12416.0 812.498981 412.149375 198.954424
|
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96 12544.0 810.925276 412.546756 199.209928
|
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97 12672.0 811.007961 412.097543 199.264875
|
||||
|
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[98 rows x 4 columns]
|
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</pre></div>
|
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@@ -394,7 +394,7 @@ We will then compare its performance against (1) <code class="code docutils lite
|
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Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
|
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</ul>
|
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</div></blockquote>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 25.192 seconds)</p>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 26.158 seconds)</p>
|
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<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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<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>
|
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|
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@@ -430,8 +430,8 @@ more than 10% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).</
|
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<span class="n">b_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_bk</span>
|
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<span class="c1"># you can fuse arbitrary activation functions here</span>
|
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<span class="c1"># while the accumulator is still in FP32!</span>
|
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<span class="k">if</span> <span class="n">ACTIVATION</span><span class="p">:</span>
|
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<span class="n">accumulator</span> <span class="o">=</span> <span class="n">ACTIVATION</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">ACTIVATION</span> <span class="o">==</span> <span class="s2">"leaky_relu"</span><span class="p">:</span>
|
||||
<span class="n">accumulator</span> <span class="o">=</span> <span class="n">leaky_relu</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
|
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|
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<span class="c1"># -----------------------------------------------------------</span>
|
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@@ -452,7 +452,7 @@ more than 10% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).</
|
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</div>
|
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<p>We can now create a convenience wrapper function that only takes two input tensors
|
||||
and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">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="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">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="n">activation</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span>
|
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<span class="c1"># checks constraints</span>
|
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<span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</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="s2">"incompatible dimensions"</span>
|
||||
<span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(),</span> <span class="s2">"matrix A must be contiguous"</span>
|
||||
@@ -554,7 +554,7 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
|
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<span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">'triton + relu'</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">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="n">activation</span><span class="o">=</span><span class="n">leaky_relu</span><span class="p">)</span>
|
||||
<span class="k">lambda</span><span class="p">:</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="n">activation</span><span class="o">=</span><span class="s2">"leaky_relu"</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">perf</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">M</span> <span class="o">*</span> <span class="n">N</span> <span class="o">*</span> <span class="n">K</span> <span class="o">*</span> <span class="mf">1e-12</span> <span class="o">/</span> <span class="p">(</span><span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-3</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">perf</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
|
||||
@@ -567,42 +567,42 @@ 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 ... 2.978909 2.978909
|
||||
1 384.0 7.372800 ... 7.899428 7.899428
|
||||
0 256.0 2.978909 ... 2.978909 2.978909
|
||||
1 384.0 7.372800 ... 8.507077 8.507077
|
||||
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|
||||
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 45.242181 ... 47.396572 47.396572
|
||||
7 1152.0 45.242181 ... 48.161033 47.396572
|
||||
8 1280.0 51.200001 ... 57.690139 57.690139
|
||||
9 1408.0 64.138541 ... 68.147202 67.305878
|
||||
10 1536.0 80.430545 ... 80.430545 79.526831
|
||||
10 1536.0 80.430545 ... 81.355034 79.526831
|
||||
11 1664.0 63.372618 ... 63.372618 62.492442
|
||||
12 1792.0 72.983276 ... 73.460287 59.467852
|
||||
13 1920.0 68.776119 ... 71.626943 71.257735
|
||||
14 2048.0 73.908442 ... 78.398206 77.314362
|
||||
15 2176.0 83.500614 ... 87.494120 85.998493
|
||||
16 2304.0 68.446623 ... 78.064941 77.307030
|
||||
17 2432.0 71.125224 ... 86.179335 85.653855
|
||||
18 2560.0 77.833728 ... 82.331658 81.108913
|
||||
19 2688.0 83.737433 ... 91.185232 89.888756
|
||||
20 2816.0 83.233216 ... 84.441840 84.197315
|
||||
21 2944.0 81.564701 ... 83.758038 82.373605
|
||||
22 3072.0 82.540970 ... 89.593522 88.335577
|
||||
23 3200.0 83.989503 ... 95.096582 89.012517
|
||||
24 3328.0 82.464255 ... 82.939284 84.596116
|
||||
25 3456.0 81.932484 ... 90.994998 91.200871
|
||||
26 3584.0 87.127323 ... 99.354022 92.600816
|
||||
27 3712.0 84.159518 ... 89.353616 83.247783
|
||||
28 3840.0 85.136259 ... 93.484358 86.738820
|
||||
29 3968.0 92.302520 ... 87.976885 90.926929
|
||||
30 4096.0 91.741443 ... 90.933416 91.304576
|
||||
13 1920.0 68.776119 ... 71.257735 70.892307
|
||||
14 2048.0 73.584279 ... 78.033565 76.959706
|
||||
15 2176.0 83.155572 ... 87.494120 85.998493
|
||||
16 2304.0 68.446623 ... 78.320893 77.558029
|
||||
17 2432.0 71.305746 ... 86.711310 75.320281
|
||||
18 2560.0 77.833728 ... 82.747477 81.715711
|
||||
19 2688.0 83.552988 ... 90.532356 89.676257
|
||||
20 2816.0 83.552120 ... 84.035084 83.392363
|
||||
21 2944.0 81.832567 ... 83.758038 81.967162
|
||||
22 3072.0 82.540970 ... 89.877939 89.170242
|
||||
23 3200.0 84.321474 ... 96.822991 95.380032
|
||||
24 3328.0 83.034941 ... 85.806075 84.596116
|
||||
25 3456.0 82.183044 ... 91.928814 87.632137
|
||||
26 3584.0 87.381330 ... 92.696281 96.891584
|
||||
27 3712.0 84.694652 ... 87.244203 88.092894
|
||||
28 3840.0 85.136259 ... 88.900318 90.279183
|
||||
29 3968.0 88.008611 ... 92.547541 84.268854
|
||||
30 4096.0 93.368854 ... 87.781379 86.592080
|
||||
|
||||
[31 rows x 5 columns]
|
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</pre></div>
|
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</div>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 44.471 seconds)</p>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 21.318 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>
|
||||
|
||||
@@ -374,7 +374,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
|
||||
<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
|
||||
</dd>
|
||||
</dl>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.275 seconds)</p>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.279 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py">
|
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.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">04-low-memory-dropout.py</span></code></a></p>
|
||||
|
||||
@@ -200,31 +200,31 @@ to download the full example code</p>
|
||||
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||||
4 3072.0 712.347810 378.092307 501.551037
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||||
5 3584.0 725.873439 384.859062 458.751978
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6 4096.0 728.177767 381.023256 458.293714
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||||
7 4608.0 670.254540 396.387087 426.173427
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||||
8 5120.0 688.403381 397.669909 426.666652
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9 5632.0 698.542675 395.228063 413.357796
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4 3072.0 712.347810 375.206126 496.484863
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5 3584.0 725.873439 384.859062 451.527536
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||||
6 4096.0 728.177767 381.023256 455.111095
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||||
7 4608.0 670.254540 394.267384 421.302872
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||||
8 5120.0 688.403381 397.669909 424.455959
|
||||
9 5632.0 698.542675 395.228063 411.470331
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||||
10 6144.0 697.191505 402.885254 409.600010
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||||
11 6656.0 700.631610 398.861429 398.861429
|
||||
12 7168.0 690.891575 396.844306 387.459443
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||||
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21 11776.0 520.486200 409.599991 377.587162
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25 13824.0 481.882350 411.122660 379.389355
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||||
27 14848.0 461.297068 408.192434 375.304904
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||||
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||||
28 15360.0 454.269882 406.214870 377.511515
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||||
29 15872.0 447.887117 406.974373 376.225175
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -543,7 +543,7 @@ to download the full example code</p>
|
||||
<span class="n">bench_layer_norm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">save_path</span><span class="o">=</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
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</pre></div>
|
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</div>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 28.983 seconds)</p>
|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 33.112 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-05-layer-norm-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.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">05-layer-norm.py</span></code></a></p>
|
||||
|
||||
@@ -276,7 +276,7 @@ tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:
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The maximum difference between torch and triton is 2.384185791015625e-07
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</pre></div>
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</div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.249 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.257 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-07-libdevice-function-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/3ff29f967ace7985da24aab10352fc76/07-libdevice-function.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">07-libdevice-function.py</span></code></a></p>
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@@ -174,7 +174,7 @@
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<div class="section" id="computation-times">
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<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>
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<p><strong>17:21.490</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<p><strong>16:59.282</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
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<table class="docutils align-default">
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<td><p>06:44.471</p></td>
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<td><p>06:21.318</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="05-layer-norm.html#sphx-glr-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">Layer Normalization</span></a> (<code class="docutils literal notranslate"><span class="pre">05-layer-norm.py</span></code>)</p></td>
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<td><p>05:28.983</p></td>
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<td><p>05:33.112</p></td>
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<td><p>0.0 MB</p></td>
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<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>
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<td><p>03:25.192</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="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>
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<td><p>01:42.248</p></td>
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<td><p>01:38.087</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="04-low-memory-dropout.html#sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">Low-Memory Dropout</span></a> (<code class="docutils literal notranslate"><span class="pre">04-low-memory-dropout.py</span></code>)</p></td>
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<td><p>00:00.275</p></td>
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<td><p>00:00.279</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="07-libdevice-function.html#sphx-glr-getting-started-tutorials-07-libdevice-function-py"><span class="std std-ref">Libdevice function</span></a> (<code class="docutils literal notranslate"><span class="pre">07-libdevice-function.py</span></code>)</p></td>
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||||
<td><p>00:00.249</p></td>
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<td><p>00:00.257</p></td>
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<td><p>0.0 MB</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="06-fused-attention.html#sphx-glr-getting-started-tutorials-06-fused-attention-py"><span class="std std-ref">Fused Attention</span></a> (<code class="docutils literal notranslate"><span class="pre">06-fused-attention.py</span></code>)</p></td>
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||||
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||||
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