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<div class="title">BroadcastSum.cu</div> </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/**</span></div>
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<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) Facebook, Inc. and its affiliates.</span></div>
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<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * This source code is licensed under the MIT license found in the</span></div>
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<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
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<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> */</span></div>
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<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div>
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<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div>
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<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <algorithm></span></div>
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<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "../../FaissAssert.h"</span></div>
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<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div>
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<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "../utils/DeviceUtils.h"</span></div>
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<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "../utils/MathOperators.cuh"</span></div>
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<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include "../utils/Tensor.cuh"</span></div>
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<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "../utils/StaticUtils.h"</span></div>
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<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div>
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<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
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<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div>
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<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> kRowsPerBlock, <span class="keywordtype">int</span> kRowUnroll, <span class="keywordtype">int</span> kColLoad></div>
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<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> __global__ <span class="keywordtype">void</span> sumAlongColumns(Tensor<T, 1, true> input,</div>
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<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  Tensor<T, 2, true> output) {</div>
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<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  static_assert(kRowsPerBlock % kRowUnroll == 0, <span class="stringliteral">"must fit rows"</span>);</div>
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<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div>
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<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="comment">// blockIdx.x: which chunk of rows we are responsible for updating</span></div>
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<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="comment">// blockIdx.y: which chunk of columns we are responsible for</span></div>
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<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="comment">// updating</span></div>
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<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordtype">int</span> rowStart = blockIdx.x * kRowsPerBlock;</div>
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<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">int</span> rowEnd = rowStart + kRowsPerBlock;</div>
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<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordtype">int</span> colStart = blockIdx.y * blockDim.x * kColLoad;</div>
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<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div>
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<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// FIXME: if we have exact multiples, don't need this</span></div>
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<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordtype">bool</span> endRow = (blockIdx.x == gridDim.x - 1);</div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">bool</span> endCol = (blockIdx.y == gridDim.y - 1);</div>
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<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div>
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<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">if</span> (endRow) {</div>
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<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">if</span> (output.getSize(0) % kRowsPerBlock == 0) {</div>
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<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  endRow = <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  }</div>
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<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  }</div>
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<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
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<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">if</span> (endCol) {</div>
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<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = colStart + threadIdx.x;</div>
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<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  col < input.getSize(0); col += blockDim.x) {</div>
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<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  T val = input[col];</div>
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<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div>
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<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">if</span> (endRow) {</div>
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<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < output.getSize(0); ++row) {</div>
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<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  T out = output[row][col];</div>
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<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  out = Math<T>::add(out, val);</div>
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<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  output[row][col] = out;</div>
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<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div>
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<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  T rows[kRowUnroll];</div>
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<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div>
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<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < rowEnd; row += kRowUnroll) {</div>
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<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  rows[i] = output[row + i][col];</div>
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<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div>
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<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div>
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<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  rows[i] = Math<T>::add(rows[i], val);</div>
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<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div>
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<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div>
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<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  output[row + i][col] = rows[i];</div>
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<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div>
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<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</div>
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<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</div>
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<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div>
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<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">int</span> col = colStart + threadIdx.x;</div>
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<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div>
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<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  T val[kColLoad];</div>
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<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div>
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<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kColLoad; ++i) {</div>
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<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  val[i] = input[col + i * blockDim.x];</div>
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<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div>
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<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div>
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<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">if</span> (endRow) {</div>
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<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < output.getSize(0); ++row) {</div>
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<div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kColLoad; ++i) {</div>
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<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  T out = output[row][col + i * blockDim.x];</div>
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<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  out = Math<T>::add(out, val[i]);</div>
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<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  output[row][col + i * blockDim.x] = out;</div>
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<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div>
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<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  }</div>
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<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  T rows[kRowUnroll * kColLoad];</div>
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<div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div>
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<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < rowEnd; row += kRowUnroll) {</div>
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<div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kColLoad; ++j) {</div>
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<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  rows[i * kColLoad + j] =</div>
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<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  output[row + i][col + j * blockDim.x];</div>
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<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  }</div>
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<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div>
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<div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div>
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<div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kColLoad; ++j) {</div>
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<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  rows[i * kColLoad + j] =</div>
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<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  Math<T>::add(rows[i * kColLoad + j], val[j]);</div>
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<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  }</div>
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<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div>
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<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div>
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<div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="preprocessor">#pragma unroll</span></div>
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<div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
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<div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kColLoad; ++j) {</div>
|
|
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  output[row + i][col + j * blockDim.x] =</div>
|
|
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  rows[i * kColLoad + j];</div>
|
|
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div>
|
|
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div>
|
|
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div>
|
|
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div>
|
|
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div>
|
|
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span> }</div>
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<div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div>
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<div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> kRowsPerBlock, <span class="keywordtype">int</span> kRowUnroll, <span class="keywordtype">int</span> kColLoad></div>
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|
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> __global__ <span class="keywordtype">void</span> assignAlongColumns(Tensor<T, 1, true> input,</div>
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|
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  Tensor<T, 2, true> output) {</div>
|
|
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  static_assert(kRowsPerBlock % kRowUnroll == 0, <span class="stringliteral">"must fit rows"</span>);</div>
|
|
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div>
|
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<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="comment">// blockIdx.x: which chunk of rows we are responsible for updating</span></div>
|
|
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// blockIdx.y: which chunk of columns we are responsible for</span></div>
|
|
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// updating</span></div>
|
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<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordtype">int</span> rowStart = blockIdx.x * kRowsPerBlock;</div>
|
|
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordtype">int</span> rowEnd = rowStart + kRowsPerBlock;</div>
|
|
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordtype">int</span> colStart = blockIdx.y * blockDim.x * kColLoad;</div>
|
|
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div>
|
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<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// FIXME: if we have exact multiples, don't need this</span></div>
|
|
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">bool</span> endRow = (blockIdx.x == gridDim.x - 1);</div>
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<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordtype">bool</span> endCol = (blockIdx.y == gridDim.y - 1);</div>
|
|
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div>
|
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<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">if</span> (endRow) {</div>
|
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<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">if</span> (output.getSize(0) % kRowsPerBlock == 0) {</div>
|
|
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  endRow = <span class="keyword">false</span>;</div>
|
|
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div>
|
|
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  }</div>
|
|
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div>
|
|
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">if</span> (endCol) {</div>
|
|
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = colStart + threadIdx.x;</div>
|
|
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  col < input.getSize(0); col += blockDim.x) {</div>
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|
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  T val = input[col];</div>
|
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<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div>
|
|
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">if</span> (endRow) {</div>
|
|
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < output.getSize(0); ++row) {</div>
|
|
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  output[row][col] = val;</div>
|
|
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  }</div>
|
|
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < rowEnd; row += kRowUnroll) {</div>
|
|
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
|
|
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  output[row + i][col] = val;</div>
|
|
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div>
|
|
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div>
|
|
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div>
|
|
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  }</div>
|
|
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordtype">int</span> col = colStart + threadIdx.x;</div>
|
|
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div>
|
|
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  T val[kColLoad];</div>
|
|
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div>
|
|
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kColLoad; ++i) {</div>
|
|
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  val[i] = input[col + i * blockDim.x];</div>
|
|
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  }</div>
|
|
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div>
|
|
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">if</span> (endRow) {</div>
|
|
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < output.getSize(0); ++row) {</div>
|
|
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kColLoad; ++i) {</div>
|
|
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  output[row][col + i * blockDim.x] = val[i];</div>
|
|
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div>
|
|
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div>
|
|
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < rowEnd; row += kRowUnroll) {</div>
|
|
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowUnroll; ++i) {</div>
|
|
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="preprocessor">#pragma unroll</span></div>
|
|
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kColLoad; ++j) {</div>
|
|
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  output[row + i][col + j * blockDim.x] = val[j];</div>
|
|
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div>
|
|
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  }</div>
|
|
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  }</div>
|
|
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div>
|
|
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div>
|
|
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> }</div>
|
|
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div>
|
|
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">bool</span> ZeroClamp></div>
|
|
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> __global__ <span class="keywordtype">void</span> sumAlongRows(Tensor<T, 1, true> input,</div>
|
|
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  Tensor<T, 2, true> output) {</div>
|
|
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  __shared__ T sval;</div>
|
|
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div>
|
|
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">int</span> row = blockIdx.x;</div>
|
|
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div>
|
|
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
|
|
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  sval = input[row];</div>
|
|
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div>
|
|
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div>
|
|
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  __syncthreads();</div>
|
|
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div>
|
|
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  T val = sval;</div>
|
|
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div>
|
|
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// FIXME: speed up</span></div>
|
|
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = threadIdx.x; i < output.getSize(1); i += blockDim.x) {</div>
|
|
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  T out = output[row][i];</div>
|
|
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  out = Math<T>::add(out, val);</div>
|
|
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  out = Math<T>::lt(out, Math<T>::zero()) ? Math<T>::zero() : out;</div>
|
|
<div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div>
|
|
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  output[row][i] = out;</div>
|
|
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div>
|
|
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span> }</div>
|
|
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div>
|
|
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> TVec></div>
|
|
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keywordtype">void</span> runSumAlongColumns(Tensor<T, 1, true>& input,</div>
|
|
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  Tensor<T, 2, true>& output,</div>
|
|
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  FAISS_ASSERT(input.getSize(0) == output.getSize(1));</div>
|
|
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div>
|
|
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordtype">int</span> threadsPerBlock = 256;</div>
|
|
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  constexpr <span class="keywordtype">int</span> kRowUnroll = 4;</div>
|
|
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  constexpr <span class="keywordtype">int</span> kRowsPerBlock = kRowUnroll * 4;</div>
|
|
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  constexpr <span class="keywordtype">int</span> kColLoad = 4;</div>
|
|
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div>
|
|
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">auto</span> block = dim3(threadsPerBlock);</div>
|
|
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div>
|
|
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">if</span> (input.template canCastResize<TVec>() &&</div>
|
|
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  output.template canCastResize<TVec>()) {</div>
|
|
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">auto</span> inputV = input.template castResize<TVec>();</div>
|
|
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">auto</span> outputV = output.template castResize<TVec>();</div>
|
|
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div>
|
|
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keyword">auto</span> grid =</div>
|
|
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  dim3(utils::divUp(outputV.getSize(0), kRowsPerBlock),</div>
|
|
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  utils::divUp(outputV.getSize(1), threadsPerBlock * kColLoad));</div>
|
|
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div>
|
|
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  sumAlongColumns<TVec, kRowsPerBlock, kRowUnroll, kColLoad></div>
|
|
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <<<grid, block, 0, stream>>>(inputV, outputV);</div>
|
|
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">auto</span> grid =</div>
|
|
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  dim3(utils::divUp(output.getSize(0), kRowsPerBlock),</div>
|
|
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  utils::divUp(output.getSize(1), threadsPerBlock * kColLoad));</div>
|
|
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div>
|
|
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  sumAlongColumns<T, kRowsPerBlock, kRowUnroll, kColLoad></div>
|
|
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <<<grid, block, 0, stream>>>(input, output);</div>
|
|
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  }</div>
|
|
<div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div>
|
|
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  CUDA_TEST_ERROR();</div>
|
|
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> }</div>
|
|
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div>
|
|
<div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="keywordtype">void</span> runSumAlongColumns(Tensor<float, 1, true>& input,</div>
|
|
<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  Tensor<float, 2, true>& output,</div>
|
|
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  runSumAlongColumns<float, float4>(input, output, stream);</div>
|
|
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> }</div>
|
|
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div>
|
|
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
|
|
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="preprocessor"></span><span class="keywordtype">void</span> runSumAlongColumns(Tensor<half, 1, true>& input,</div>
|
|
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  Tensor<half, 2, true>& output,</div>
|
|
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  runSumAlongColumns<half, half2>(input, output, stream);</div>
|
|
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> }</div>
|
|
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="preprocessor">#endif</span></div>
|
|
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="preprocessor"></span></div>
|
|
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> TVec></div>
|
|
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="keywordtype">void</span> runAssignAlongColumns(Tensor<T, 1, true>& input,</div>
|
|
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  Tensor<T, 2, true>& output,</div>
|
|
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  FAISS_ASSERT(input.getSize(0) == output.getSize(1));</div>
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<div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div>
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<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordtype">int</span> threadsPerBlock = 256;</div>
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<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  constexpr <span class="keywordtype">int</span> kRowUnroll = 4;</div>
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<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  constexpr <span class="keywordtype">int</span> kRowsPerBlock = kRowUnroll * 4;</div>
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<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  constexpr <span class="keywordtype">int</span> kColLoad = 4;</div>
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<div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div>
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<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">auto</span> block = dim3(threadsPerBlock);</div>
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<div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div>
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<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordflow">if</span> (input.template canCastResize<TVec>() &&</div>
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<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  output.template canCastResize<TVec>()) {</div>
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<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">auto</span> inputV = input.template castResize<TVec>();</div>
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<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">auto</span> outputV = output.template castResize<TVec>();</div>
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<div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div>
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<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keyword">auto</span> grid =</div>
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<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  dim3(utils::divUp(outputV.getSize(0), kRowsPerBlock),</div>
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<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  utils::divUp(outputV.getSize(1), threadsPerBlock * kColLoad));</div>
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<div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div>
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<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  assignAlongColumns<TVec, kRowsPerBlock, kRowUnroll, kColLoad></div>
|
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<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <<<grid, block, 0, stream>>>(inputV, outputV);</div>
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<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  } <span class="keywordflow">else</span> {</div>
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<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">auto</span> grid =</div>
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|
<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  dim3(utils::divUp(output.getSize(0), kRowsPerBlock),</div>
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<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  utils::divUp(output.getSize(1), threadsPerBlock * kColLoad));</div>
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<div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div>
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<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  assignAlongColumns<T, kRowsPerBlock, kRowUnroll, kColLoad></div>
|
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<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <<<grid, block, 0, stream>>>(input, output);</div>
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<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  }</div>
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<div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div>
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<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  CUDA_TEST_ERROR();</div>
|
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<div class="line"><a name="l00307"></a><span class="lineno"> 307</span> }</div>
|
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<div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div>
|
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<div class="line"><a name="l00309"></a><span class="lineno"> 309</span> <span class="keywordtype">void</span> runAssignAlongColumns(Tensor<float, 1, true>& input,</div>
|
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<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  Tensor<float, 2, true>& output,</div>
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<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  cudaStream_t stream) {</div>
|
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<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  runAssignAlongColumns<float, float4>(input, output, stream);</div>
|
|
<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> }</div>
|
|
<div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div>
|
|
<div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
|
|
<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="preprocessor"></span><span class="keywordtype">void</span> runAssignAlongColumns(Tensor<half, 1, true>& input,</div>
|
|
<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  Tensor<half, 2, true>& output,</div>
|
|
<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  runAssignAlongColumns<half, half2>(input, output, stream);</div>
|
|
<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> }</div>
|
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<div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="preprocessor">#endif</span></div>
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<div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="preprocessor"></span></div>
|
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<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
|
|
<div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="keywordtype">void</span> runSumAlongRows(Tensor<T, 1, true>& input,</div>
|
|
<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  Tensor<T, 2, true>& output,</div>
|
|
<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordtype">bool</span> zeroClamp,</div>
|
|
<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  FAISS_ASSERT(input.getSize(0) == output.getSize(0));</div>
|
|
<div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div>
|
|
<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordtype">int</span> threadsPerBlock =</div>
|
|
<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  std::min(output.getSize(1), getMaxThreadsCurrentDevice());</div>
|
|
<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">auto</span> grid = dim3(output.getSize(0));</div>
|
|
<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">auto</span> block = dim3(threadsPerBlock);</div>
|
|
<div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div>
|
|
<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">if</span> (zeroClamp) {</div>
|
|
<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  sumAlongRows<T, true><<<grid, block, 0, stream>>>(input, output);</div>
|
|
<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  } <span class="keywordflow">else</span> {</div>
|
|
<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  sumAlongRows<T, false><<<grid, block, 0, stream>>>(input, output);</div>
|
|
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  }</div>
|
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<div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div>
|
|
<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  CUDA_TEST_ERROR();</div>
|
|
<div class="line"><a name="l00342"></a><span class="lineno"> 342</span> }</div>
|
|
<div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div>
|
|
<div class="line"><a name="l00344"></a><span class="lineno"> 344</span> <span class="keywordtype">void</span> runSumAlongRows(Tensor<float, 1, true>& input,</div>
|
|
<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  Tensor<float, 2, true>& output,</div>
|
|
<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordtype">bool</span> zeroClamp,</div>
|
|
<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  runSumAlongRows<float>(input, output, zeroClamp, stream);</div>
|
|
<div class="line"><a name="l00349"></a><span class="lineno"> 349</span> }</div>
|
|
<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div>
|
|
<div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
|
|
<div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="preprocessor"></span><span class="keywordtype">void</span> runSumAlongRows(Tensor<half, 1, true>& input,</div>
|
|
<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  Tensor<half, 2, true>& output,</div>
|
|
<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordtype">bool</span> zeroClamp,</div>
|
|
<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  cudaStream_t stream) {</div>
|
|
<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  runSumAlongRows<half>(input, output, zeroClamp, stream);</div>
|
|
<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> }</div>
|
|
<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="preprocessor">#endif</span></div>
|
|
<div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="preprocessor"></span></div>
|
|
<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> } } <span class="comment">// namespace</span></div>
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