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Philippe Tillet
2021-04-21 01:58:48 -04:00
parent 92126eb098
commit 4d0ba90bc7
44 changed files with 204 additions and 887 deletions
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2
_downloads/034d953b6214fedce6ea03803c712b89/02-fused-softmax.ipynb
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@@ -76,7 +76,7 @@
},
"outputs": [],
"source": [
"def next_power_of_2(n):\n n -= 1\n n |= n >> 1\n n |= n >> 2\n n |= n >> 4\n n |= n >> 8\n n |= n >> 16\n n += 1\n return n\n\n\ndef softmax(x):\n M, N = x.shape\n # The block size is the smallest power of two greater than the number of columns in `x`\n BLOCK = next_power_of_2(N)\n # Another trick we can use is to ask the compiler to parallelize each\n # row-normalization more aggressively -- i.e., with more warps -- vectors\n # that are longer\n # You will see in the next tutorial how to auto-tune this value in a more natural\n # way so you don't have to come up with manual heuristics yourself\n num_warps = 4\n if BLOCK >= 2048: num_warps = 8\n if BLOCK >= 4096: num_warps = 16\n # Allocate output\n y = torch.empty_like(x)\n # Enqueue kernel. The launch grid is simple: we have one kernel instance per row of the input matrix\n _softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, BLOCK=BLOCK)\n return y"
"def next_power_of_2(n):\n n -= 1\n n |= n >> 1\n n |= n >> 2\n n |= n >> 4\n n |= n >> 8\n n |= n >> 16\n n += 1\n return n\n\n\ndef softmax(x):\n M, N = x.shape\n # The block size is the smallest power of two greater than the number of columns in `x`\n BLOCK = next_power_of_2(N)\n # Another trick we can use is to ask the compiler to parallelize each\n # row-normalization more aggressively -- i.e., with more warps -- vectors\n # that are longer\n # You will see in the next tutorial how to auto-tune this value in a more natural\n # way so you don't have to come up with manual heuristics yourself\n num_warps = 4\n if BLOCK >= 2048: num_warps = 8\n if BLOCK >= 4096: num_warps = 16\n # Allocate output\n y = torch.empty_like(x)\n # Enqueue kernel. The launch grid is simple: we have one kernel instance per row of the input matrix\n _softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, num_warps=num_warps, BLOCK=BLOCK)\n return y"
]
},
{

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_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py
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@@ -100,7 +100,7 @@ def softmax(x):
# Allocate output
y = torch.empty_like(x)
# Enqueue kernel. The launch grid is simple: we have one kernel instance per row of the input matrix
_softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, BLOCK=BLOCK)
_softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, num_warps=num_warps, BLOCK=BLOCK)
return y

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_sources/getting-started/tutorials/01-vector-add.rst.txt
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@@ -212,7 +212,7 @@ We can now run the decorated function above. Pass `show_plots=True` to see the p
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 5.812 seconds)
**Total running time of the script:** ( 0 minutes 7.044 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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@@ -151,7 +151,7 @@ We can create a helper function that enqueues the kernel and its (meta-)argument
# Allocate output
y = torch.empty_like(x)
# Enqueue kernel. The launch grid is simple: we have one kernel instance per row of the input matrix
_softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, BLOCK=BLOCK)
_softmax[(M, )](y, x, x.stride(0), y.stride(0), M, N, num_warps=num_warps, BLOCK=BLOCK)
return y
@@ -264,7 +264,7 @@ In the above plot, we can see that:
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 20.767 seconds)
**Total running time of the script:** ( 0 minutes 20.176 seconds)
.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

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_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
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@@ -287,32 +287,32 @@ We can test our custom matrix multiplication operation against a native torch im
.. code-block:: none
tensor([[-0.0000e+00, 2.9438e+01, -1.3113e-06, ..., 9.7266e+00,
-3.4237e-04, -0.0000e+00],
[-1.7615e-01, -0.0000e+00, 6.1914e+00, ..., 3.7562e+01,
-0.0000e+00, -0.0000e+00],
[ 9.9531e+00, 1.9078e+01, -0.0000e+00, ..., 3.6934e+00,
1.6578e+01, 2.1031e+01],
tensor([[-5.9605e-08, 5.1094e+01, -1.8477e-05, ..., 2.6547e+01,
-7.2598e-05, -4.2510e-04],
[-2.7100e-01, -3.0220e-05, 5.9414e+00, ..., 2.8340e+00,
-1.8644e-04, 1.3094e+01],
[-1.5332e-01, 4.8125e+00, 8.4277e-01, ..., 3.6387e+00,
4.3375e+01, 1.6865e+00],
...,
[ 2.6547e+01, -1.1802e-05, 7.7852e+00, ..., 5.2156e+01,
3.5469e+01, 1.5602e+01],
[-0.0000e+00, -0.0000e+00, 1.6531e+01, ..., 2.1211e+00,
1.7412e+00, 1.1422e+01],
[-2.6550e-02, -1.1325e-05, 3.0344e+01, ..., -9.1248e-03,
-1.5199e-05, 3.8164e+00]], device='cuda:0', dtype=torch.float16)
tensor([[-0.0000e+00, 2.9438e+01, -1.3113e-06, ..., 9.7266e+00,
-3.4261e-04, -0.0000e+00],
[-1.7615e-01, -0.0000e+00, 6.1914e+00, ..., 3.7562e+01,
-0.0000e+00, -0.0000e+00],
[ 9.9531e+00, 1.9078e+01, -0.0000e+00, ..., 3.6934e+00,
1.6578e+01, 2.1031e+01],
[-0.0000e+00, 2.9453e+01, -4.7684e-07, ..., 6.2617e+00,
4.1133e+00, -0.0000e+00],
[ 1.6562e+01, -8.1539e-04, 1.3836e+01, ..., 1.9844e+00,
-1.1238e-02, 8.4375e+00],
[-1.0876e-01, -2.7295e-01, 3.2156e+01, ..., -1.6907e-02,
-0.0000e+00, -0.0000e+00]], device='cuda:0', dtype=torch.float16)
tensor([[-5.9605e-08, 5.1094e+01, -1.8537e-05, ..., 2.6547e+01,
-7.2658e-05, -4.2605e-04],
[-2.7100e-01, -3.0220e-05, 5.9414e+00, ..., 2.8340e+00,
-1.8632e-04, 1.3094e+01],
[-1.5332e-01, 4.8125e+00, 8.4277e-01, ..., 3.6387e+00,
4.3375e+01, 1.6875e+00],
...,
[ 2.6547e+01, -1.1802e-05, 7.7852e+00, ..., 5.2156e+01,
3.5469e+01, 1.5602e+01],
[-0.0000e+00, -0.0000e+00, 1.6531e+01, ..., 2.1211e+00,
1.7412e+00, 1.1422e+01],
[-2.6550e-02, -1.1325e-05, 3.0344e+01, ..., -9.1324e-03,
-1.5199e-05, 3.8164e+00]], device='cuda:0', dtype=torch.float16)
[-0.0000e+00, 2.9453e+01, -4.7684e-07, ..., 6.2617e+00,
4.1133e+00, -0.0000e+00],
[ 1.6562e+01, -8.1778e-04, 1.3836e+01, ..., 1.9844e+00,
-1.1238e-02, 8.4375e+00],
[-1.0876e-01, -2.7295e-01, 3.2156e+01, ..., -1.6891e-02,
-0.0000e+00, -0.0000e+00]], device='cuda:0', dtype=torch.float16)
tensor(True, device='cuda:0')
@@ -373,36 +373,36 @@ We can now compare the performance of our kernel against CUTLASS. Here we focus
M cuBLAS Triton
0 512.0 20.164923 15.420235
1 768.0 58.982401 42.130286
1 768.0 58.982401 40.215272
2 1024.0 91.180520 72.315584
3 1280.0 157.538463 117.028568
4 1536.0 150.593357 147.455995
5 1792.0 212.064605 193.783168
6 2048.0 197.379013 151.146088
7 2304.0 243.753804 179.608068
8 2560.0 237.449270 217.006622
9 2816.0 233.231062 200.987140
4 1536.0 153.867127 144.446699
5 1792.0 208.137481 190.498706
6 2048.0 199.728763 152.520144
7 2304.0 246.266731 178.267699
8 2560.0 235.741014 215.578957
9 2816.0 231.990461 198.246398
10 3072.0 236.916752 221.184001
11 3328.0 234.499328 210.500857
11 3328.0 239.173747 210.500857
12 3584.0 248.385067 230.552287
13 3840.0 252.493157 223.418188
14 4096.0 263.689066 244.922869
15 4352.0 247.295210 231.639115
16 4608.0 274.573240 254.803966
17 4864.0 266.298229 245.366501
18 5120.0 259.548513 238.312729
19 5376.0 252.676487 237.081606
20 5632.0 270.685535 249.046163
21 5888.0 264.382140 242.069377
22 6144.0 262.447761 240.565495
23 6400.0 257.028108 235.078047
24 6656.0 254.386204 232.699140
25 6912.0 252.040861 232.926171
26 7168.0 253.193644 231.815375
27 7424.0 251.789150 232.860938
28 7680.0 250.988932 231.727608
29 7936.0 253.622108 232.094986
30 8192.0 253.121589 231.859598
13 3840.0 251.917998 222.519114
14 4096.0 263.172024 244.032234
15 4352.0 249.595626 232.307632
16 4608.0 276.560014 254.803966
17 4864.0 266.614125 245.366501
18 5120.0 257.003930 238.096276
19 5376.0 252.676487 236.527241
20 5632.0 270.057027 248.514009
21 5888.0 264.206935 242.511113
22 6144.0 259.441481 241.205983
23 6400.0 257.157204 235.078047
24 6656.0 254.161678 232.699140
25 6912.0 251.844029 233.178785
26 7168.0 253.282797 231.740709
27 7424.0 251.868505 230.377264
28 7680.0 250.988932 231.606284
29 7936.0 253.293068 229.692102
30 8192.0 253.002304 231.360005
@@ -410,7 +410,7 @@ We can now compare the performance of our kernel against CUTLASS. Here we focus
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 36.230 seconds)
**Total running time of the script:** ( 0 minutes 32.933 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
=================
**00:36.230** total execution time for **getting-started_tutorials** files:
**01:00.154** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 00:36.230 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 00:32.933 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:00.000 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 00:20.176 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 00:00.000 | 0.0 MB |
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:07.044 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+

2
getting-started/tutorials/01-vector-add.html
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@@ -295,7 +295,7 @@ for different problem sizes.</p>
</pre></div>
</div>
<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 5.812 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 7.044 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>

4
getting-started/tutorials/02-fused-softmax.html
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@@ -280,7 +280,7 @@ so we need to internally “pad” tiles and guard the memory operations properl
<span class="c1"># Allocate output</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># Enqueue kernel. The launch grid is simple: we have one kernel instance per row of the input matrix</span>
<span class="n">_softmax</span><span class="p">[(</span><span class="n">M</span><span class="p">,</span> <span class="p">)](</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</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">BLOCK</span><span class="o">=</span><span class="n">BLOCK</span><span class="p">)</span>
<span class="n">_softmax</span><span class="p">[(</span><span class="n">M</span><span class="p">,</span> <span class="p">)](</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</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">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">,</span> <span class="n">BLOCK</span><span class="o">=</span><span class="n">BLOCK</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
</pre></div>
</div>
@@ -343,7 +343,7 @@ 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 20.767 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 20.176 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>

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getting-started/tutorials/03-matrix-multiplication.html
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@@ -406,32 +406,32 @@ and (1) checks any shape constraint; (2) allocates the output; (3) launches the
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.0000e+00, 2.9438e+01, -1.3113e-06, ..., 9.7266e+00,
-3.4237e-04, -0.0000e+00],
[-1.7615e-01, -0.0000e+00, 6.1914e+00, ..., 3.7562e+01,
-0.0000e+00, -0.0000e+00],
[ 9.9531e+00, 1.9078e+01, -0.0000e+00, ..., 3.6934e+00,
1.6578e+01, 2.1031e+01],
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-5.9605e-08, 5.1094e+01, -1.8477e-05, ..., 2.6547e+01,
-7.2598e-05, -4.2510e-04],
[-2.7100e-01, -3.0220e-05, 5.9414e+00, ..., 2.8340e+00,
-1.8644e-04, 1.3094e+01],
[-1.5332e-01, 4.8125e+00, 8.4277e-01, ..., 3.6387e+00,
4.3375e+01, 1.6865e+00],
...,
[ 2.6547e+01, -1.1802e-05, 7.7852e+00, ..., 5.2156e+01,
3.5469e+01, 1.5602e+01],
[-0.0000e+00, -0.0000e+00, 1.6531e+01, ..., 2.1211e+00,
1.7412e+00, 1.1422e+01],
[-2.6550e-02, -1.1325e-05, 3.0344e+01, ..., -9.1248e-03,
-1.5199e-05, 3.8164e+00]], device=&#39;cuda:0&#39;, dtype=torch.float16)
tensor([[-0.0000e+00, 2.9438e+01, -1.3113e-06, ..., 9.7266e+00,
-3.4261e-04, -0.0000e+00],
[-1.7615e-01, -0.0000e+00, 6.1914e+00, ..., 3.7562e+01,
-0.0000e+00, -0.0000e+00],
[ 9.9531e+00, 1.9078e+01, -0.0000e+00, ..., 3.6934e+00,
1.6578e+01, 2.1031e+01],
[-0.0000e+00, 2.9453e+01, -4.7684e-07, ..., 6.2617e+00,
4.1133e+00, -0.0000e+00],
[ 1.6562e+01, -8.1539e-04, 1.3836e+01, ..., 1.9844e+00,
-1.1238e-02, 8.4375e+00],
[-1.0876e-01, -2.7295e-01, 3.2156e+01, ..., -1.6907e-02,
-0.0000e+00, -0.0000e+00]], device=&#39;cuda:0&#39;, dtype=torch.float16)
tensor([[-5.9605e-08, 5.1094e+01, -1.8537e-05, ..., 2.6547e+01,
-7.2658e-05, -4.2605e-04],
[-2.7100e-01, -3.0220e-05, 5.9414e+00, ..., 2.8340e+00,
-1.8632e-04, 1.3094e+01],
[-1.5332e-01, 4.8125e+00, 8.4277e-01, ..., 3.6387e+00,
4.3375e+01, 1.6875e+00],
...,
[ 2.6547e+01, -1.1802e-05, 7.7852e+00, ..., 5.2156e+01,
3.5469e+01, 1.5602e+01],
[-0.0000e+00, -0.0000e+00, 1.6531e+01, ..., 2.1211e+00,
1.7412e+00, 1.1422e+01],
[-2.6550e-02, -1.1325e-05, 3.0344e+01, ..., -9.1324e-03,
-1.5199e-05, 3.8164e+00]], device=&#39;cuda:0&#39;, dtype=torch.float16)
[-0.0000e+00, 2.9453e+01, -4.7684e-07, ..., 6.2617e+00,
4.1133e+00, -0.0000e+00],
[ 1.6562e+01, -8.1778e-04, 1.3836e+01, ..., 1.9844e+00,
-1.1238e-02, 8.4375e+00],
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28 7680.0 250.988932 231.606284
29 7936.0 253.293068 229.692102
30 8192.0 253.002304 231.360005
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12
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30
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@@ -42,9 +42,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a></li>
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@@ -42,9 +42,7 @@
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@@ -203,10 +181,6 @@
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@@ -95,26 +93,8 @@
<li class="toctree-l1"><a class="reference internal" href="../../../getting-started/tutorials/index.html">Tutorials</a></li>
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<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Python API</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#creation-ops">Creation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#shape-manipulation-ops">Shape Manipulation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#linear-algebra-ops">Linear Algebra Ops</a></li>
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<li class="toctree-l2 current"><a class="reference internal" href="../index.html#math-ops">Math Ops</a><ul class="current">
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.exp</a></li>
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<li class="toctree-l3"><a class="reference internal" href="triton.softmax.html">triton.softmax</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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<li><a href="../index.html">Python API</a> &raquo;</li>
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<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Python API</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#creation-ops">Creation Ops</a></li>
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<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.load</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.store.html">triton.store</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.atomic_cas.html">triton.atomic_cas</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.atomic_xchg.html">triton.atomic_xchg</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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@@ -42,9 +42,7 @@
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@@ -95,26 +93,8 @@
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<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Python API</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#memory-ops">Memory Ops</a></li>
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<li class="toctree-l3"><a class="reference internal" href="triton.exp.html">triton.exp</a></li>
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.log</a></li>
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<li class="toctree-l3"><a class="reference internal" href="triton.softmax.html">triton.softmax</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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@@ -42,9 +42,7 @@
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<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Python API</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
<li class="toctree-l2 current"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a><ul class="current">
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<li class="toctree-l3"><a class="reference internal" href="triton.min.html">triton.min</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.sum.html">triton.sum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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@@ -42,9 +42,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../getting-started/tutorials/index.html">Tutorials</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="triton.sum.html">triton.sum</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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<li><a href="../index.html">Python API</a> &raquo;</li>
<li>triton.min</li>
@@ -202,10 +181,6 @@
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@@ -42,9 +42,7 @@
<link rel="index" title="Index" href="../../../genindex.html" />
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@@ -95,24 +93,8 @@
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<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#creation-ops">Creation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#shape-manipulation-ops">Shape Manipulation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#linear-algebra-ops">Linear Algebra Ops</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
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<li class="toctree-l2 current"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a><ul class="current">
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.minimum</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.maximum.html">triton.maximum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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<li><a href="../../../index.html" class="icon icon-home"></a> &raquo;</li>
<li><a href="../index.html">Python API</a> &raquo;</li>
<li>triton.minimum</li>
@@ -201,10 +181,6 @@
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@@ -42,9 +42,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../index.html#linear-algebra-ops">Linear Algebra Ops</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
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@@ -42,9 +42,7 @@
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<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.sigmoid</a></li>
<li class="toctree-l3"><a class="reference internal" href="triton.softmax.html">triton.softmax</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
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<li><a href="../../../index.html" class="icon icon-home"></a> &raquo;</li>
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@@ -95,25 +93,8 @@
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<li><a href="../../../index.html" class="icon icon-home"></a> &raquo;</li>
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@@ -202,10 +181,6 @@
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@@ -42,9 +42,7 @@
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@@ -95,23 +93,8 @@
<li class="toctree-l1"><a class="reference internal" href="../../../getting-started/tutorials/index.html">Tutorials</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Reference</span></p>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#shape-manipulation-ops">Shape Manipulation Ops</a></li>
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<li class="toctree-l2 current"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a><ul class="current">
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.where</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#compiler-hint-ops">Compiler Hint Ops</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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<li><a href="../../../index.html" class="icon icon-home"></a> &raquo;</li>
<li><a href="../index.html">Python API</a> &raquo;</li>
<li>triton.where</li>
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30
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@@ -42,9 +42,7 @@
<link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" />
<link rel="next" title="triton.broadcast_to" href="triton.broadcast_to.html" />
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<link rel="search" title="Search" href="../../../search.html" />
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@@ -95,24 +93,8 @@
<li class="toctree-l1"><a class="reference internal" href="../../../getting-started/tutorials/index.html">Tutorials</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Reference</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Python API</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../index.html#programming-model">Programming Model</a></li>
<li class="toctree-l2 current"><a class="reference internal" href="../index.html#creation-ops">Creation Ops</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="triton.arange.html">triton.arange</a></li>
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.zeros</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#shape-manipulation-ops">Shape Manipulation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#linear-algebra-ops">Linear Algebra Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#memory-ops">Memory Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#indexing-ops">Indexing Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../index.html#math-ops">Math Ops</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../index.html#comparison-ops">Comparison ops</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../index.html">Python API</a></li>
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<p class="caption"><span class="caption-text">Programming Guide</span></p>
<ul>
@@ -166,8 +148,6 @@
<li><a href="../../../index.html" class="icon icon-home"></a> &raquo;</li>
<li><a href="../index.html">Python API</a> &raquo;</li>
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@@ -211,10 +191,6 @@
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@@ -43,7 +43,7 @@
<link rel="index" title="Index" href="../../genindex.html" />
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@@ -97,59 +97,16 @@
<p class="caption"><span class="caption-text">Language Reference</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">Python API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#programming-model">Programming Model</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="generated/triton.num_programs.html">triton.num_programs</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#creation-ops">Creation Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.arange.html">triton.arange</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.zeros.html">triton.zeros</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#shape-manipulation-ops">Shape Manipulation Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.broadcast_to.html">triton.broadcast_to</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.reshape.html">triton.reshape</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.ravel.html">triton.ravel</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#linear-algebra-ops">Linear Algebra Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.dot.html">triton.dot</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#memory-ops">Memory Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.load.html">triton.load</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.store.html">triton.store</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#indexing-ops">Indexing Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.where.html">triton.where</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="generated/triton.exp.html">triton.exp</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.log.html">triton.log</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#reduction-ops">Reduction Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.max.html">triton.max</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.min.html">triton.min</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.sum.html">triton.sum</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#comparison-ops">Comparison ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.minimum.html">triton.minimum</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.maximum.html">triton.maximum</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#compiler-hint-ops">Compiler Hint Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.multiple_of.html">triton.multiple_of</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#programming-model">Programming Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="#creation-ops">Creation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#shape-manipulation-ops">Shape Manipulation Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#linear-algebra-ops">Linear Algebra Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#memory-ops">Memory Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#indexing-ops">Indexing Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#math-ops">Math Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="#compiler-hint-ops">Compiler Hint Ops</a></li>
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.program_id.html#triton.program_id" title="triton.program_id"><code class="xref py py-obj docutils literal notranslate"><span class="pre">program_id</span></code></a></p></td>
<td><p>Returns the id of the current program instance along the given <cite>axis</cite>. Triton uses an SPMD model in which different &#64;triton.jit functions run in parallel with different <a href="#id1"><span class="problematic" id="id2">`</span></a>program_id`s.</p></td>
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<td><p>Returns the number of program instances launched along the given <cite>axis</cite>.</p></td>
</tr>
</tbody>
</table>
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@@ -251,12 +202,6 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.arange.html#triton.arange" title="triton.arange"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arange</span></code></a></p></td>
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</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.zeros.html#triton.zeros" title="triton.zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zeros</span></code></a></p></td>
<td><p>Returns a block filled with the scalar value 0 and the given shape.</p></td>
</tr>
</tbody>
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.broadcast_to.html#triton.broadcast_to" title="triton.broadcast_to"><code class="xref py py-obj docutils literal notranslate"><span class="pre">broadcast_to</span></code></a></p></td>
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</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.reshape.html#triton.reshape" title="triton.reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape</span></code></a></p></td>
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</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.ravel.html#triton.ravel" title="triton.ravel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ravel</span></code></a></p></td>
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.dot.html#triton.dot" title="triton.dot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dot</span></code></a></p></td>
<td><p>Returns the matrix product of two blocks.</p></td>
</tr>
</tbody>
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@@ -302,18 +235,6 @@
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.load.html#triton.load" title="triton.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a></p></td>
<td><p>Return a block of data whose values are, elementwise, loaded from memory at location defined by <cite>pointer</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.store.html#triton.store" title="triton.store"><code class="xref py py-obj docutils literal notranslate"><span class="pre">store</span></code></a></p></td>
<td><p>Stores <cite>value</cite> block of elements in memory, element-wise, at the memory locations specified by <cite>pointer</cite>.</p></td>
</tr>
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<td><p></p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.atomic_xchg.html#triton.atomic_xchg" title="triton.atomic_xchg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_xchg</span></code></a></p></td>
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</tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.where.html#triton.where" title="triton.where"><code class="xref py py-obj docutils literal notranslate"><span class="pre">where</span></code></a></p></td>
<td><p>Returns a block of elements from either <cite>x</cite> or <cite>y</cite>, depending on <cite>condition</cite>.</p></td>
</tr>
</tbody>
</table>
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.exp.html#triton.exp" title="triton.exp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exp</span></code></a></p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.log.html#triton.log" title="triton.log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code></a></p></td>
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