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<li class="navelem"><a class="el" href="dir_6b3ae6988449b0834e9596fad5d75199.html">gpu</a></li><li class="navelem"><a class="el" href="dir_49d1182a3b8dfb62757c53ae905481ad.html">impl</a></li> </ul>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment"></span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">/**</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * Copyright (c) 2015-present, Facebook, Inc.</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * All rights reserved.</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * This source code is licensed under the CC-by-NC license found in the</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> */</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">// Copyright 2004-present Facebook. All Rights Reserved.</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "L2Select.cuh"</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "../../FaissAssert.h"</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "../utils/DeviceUtils.h"</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "../utils/MathOperators.cuh"</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "../utils/Pair.cuh"</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "../utils/Reductions.cuh"</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "../utils/Select.cuh"</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include "../utils/Tensor.cuh"</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include "../utils/StaticUtils.h"</span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">// L2 + select kernel for k == 1, implements re-use of ||c||^2</span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> kRowsPerBlock, <span class="keywordtype">int</span> kBlockSize></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> __global__ <span class="keywordtype">void</span> l2SelectMin1(Tensor<T, 2, true> productDistances,</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  Tensor<T, 1, true> centroidDistances,</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  Tensor<T, 2, true> outDistances,</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  Tensor<int, 2, true> outIndices) {</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// Each block handles kRowsPerBlock rows of the distances (results)</span></div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  Pair<T, int> threadMin[kRowsPerBlock];</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  __shared__ Pair<T, int> blockMin[kRowsPerBlock * (kBlockSize / kWarpSize)];</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  T distance[kRowsPerBlock];</div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < kRowsPerBlock; ++i) {</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  threadMin[i].k = Limits<T>::getMax();</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  threadMin[i].v = -1;</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  }</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// blockIdx.x: which chunk of rows we are responsible for updating</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">int</span> rowStart = blockIdx.x * kRowsPerBlock;</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// FIXME: if we have exact multiples, don't need this</span></div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">bool</span> endRow = (blockIdx.x == gridDim.x - 1);</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">if</span> (endRow) {</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">if</span> (productDistances.getSize(0) % kRowsPerBlock == 0) {</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  endRow = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  }</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span> (endRow) {</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row < productDistances.getSize(0); ++row) {</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = threadIdx.x; col < productDistances.getSize(1);</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  col += blockDim.x) {</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  distance[0] = Math<T>::add(centroidDistances[col],</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  productDistances[row][col]);</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">if</span> (Math<T>::lt(distance[0], threadMin[0].k)) {</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  threadMin[0].k = distance[0];</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  threadMin[0].v = col;</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Reduce within the block</span></div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  threadMin[0] =</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  blockReduceAll<Pair<T, int>, Min<Pair<T, int> >, <span class="keyword">false</span>, <span class="keyword">false</span>>(</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  threadMin[0], Min<Pair<T, int> >(), blockMin);</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  outDistances[row][0] = threadMin[0].k;</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  outIndices[row][0] = threadMin[0].v;</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// so we can use the shared memory again</span></div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  __syncthreads();</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  threadMin[0].k = Limits<T>::getMax();</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  threadMin[0].v = -1;</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = threadIdx.x; col < productDistances.getSize(1);</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  col += blockDim.x) {</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  T centroidDistance = centroidDistances[col];</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  distance[row] = productDistances[rowStart + row][col];</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  }</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  distance[row] = Math<T>::add(distance[row], centroidDistance);</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">if</span> (Math<T>::lt(distance[row], threadMin[row].k)) {</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  threadMin[row].k = distance[row];</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  threadMin[row].v = col;</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Reduce within the block</span></div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  blockReduceAll<kRowsPerBlock, Pair<T, int>, Min<Pair<T, int> >, <span class="keyword">false</span>, <span class="keyword">false</span>>(</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  threadMin, Min<Pair<T, int> >(), blockMin);</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row < kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  outDistances[rowStart + row][0] = threadMin[row].k;</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  outIndices[rowStart + row][0] = threadMin[row].v;</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  }</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</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> <span class="comment">// L2 + select kernel for k > 1, no re-use of ||c||^2</span></div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> NumWarpQ, <span class="keywordtype">int</span> NumThreadQ, <span class="keywordtype">int</span> ThreadsPerBlock></div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span> __global__ <span class="keywordtype">void</span> l2SelectMinK(Tensor<T, 2, true> productDistances,</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  Tensor<T, 1, true> centroidDistances,</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  Tensor<T, 2, true> outDistances,</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  Tensor<int, 2, true> outIndices,</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">int</span> k, T initK) {</div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Each block handles a single row of the distances (results)</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  constexpr <span class="keywordtype">int</span> kNumWarps = ThreadsPerBlock / kWarpSize;</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  __shared__ T smemK[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  __shared__ <span class="keywordtype">int</span> smemV[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  BlockSelect<T, int, false, Comparator<T>,</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  NumWarpQ, NumThreadQ, ThreadsPerBlock></div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  heap(initK, -1, smemK, smemV, k);</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordtype">int</span> row = blockIdx.x;</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Whole warps must participate in the selection</span></div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">int</span> limit = utils::roundDown(productDistances.getSize(1), kWarpSize);</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordtype">int</span> i = threadIdx.x;</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">for</span> (; i < limit; i += blockDim.x) {</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  T v = Math<T>::add(centroidDistances[i],</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  productDistances[row][i]);</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  heap.add(v, i);</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span> (i < productDistances.getSize(1)) {</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  T v = Math<T>::add(centroidDistances[i],</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  productDistances[row][i]);</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  heap.addThreadQ(v, i);</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  heap.reduce();</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = threadIdx.x; i < k; i += blockDim.x) {</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  outDistances[row][i] = smemK[i];</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  outIndices[row][i] = smemV[i];</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  }</div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> }</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> <span class="comment">// FIXME: no TVec specialization</span></div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="keywordtype">void</span> runL2SelectMin(Tensor<T, 2, true>& productDistances,</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  Tensor<T, 1, true>& centroidDistances,</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  Tensor<T, 2, true>& outDistances,</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  Tensor<int, 2, true>& outIndices,</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  cudaStream_t stream) {</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  FAISS_ASSERT(productDistances.getSize(0) == outDistances.getSize(0));</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  FAISS_ASSERT(productDistances.getSize(0) == outIndices.getSize(0));</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  FAISS_ASSERT(centroidDistances.getSize(0) == productDistances.getSize(1));</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  FAISS_ASSERT(outDistances.getSize(1) == k);</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  FAISS_ASSERT(outIndices.getSize(1) == k);</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  FAISS_ASSERT(k <= 1024);</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">if</span> (k == 1) {</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  constexpr <span class="keywordtype">int</span> kThreadsPerBlock = 256;</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  constexpr <span class="keywordtype">int</span> kRowsPerBlock = 8;</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>  <span class="keyword">auto</span> block = dim3(kThreadsPerBlock);</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">auto</span> grid = dim3(utils::divUp(outDistances.getSize(0), kRowsPerBlock));</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  l2SelectMin1<T, kRowsPerBlock, kThreadsPerBlock></div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <<<grid, block, 0, stream>>>(productDistances, centroidDistances,</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  outDistances, outIndices);</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  constexpr <span class="keywordtype">int</span> kThreadsPerBlock = 128;</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>  <span class="keyword">auto</span> block = dim3(kThreadsPerBlock);</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">auto</span> grid = dim3(outDistances.getSize(0));</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> <span class="preprocessor">#define RUN_L2_SELECT(NUM_WARP_Q, NUM_THREAD_Q) \</span></div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="preprocessor"> do { \</span></div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="preprocessor"> l2SelectMinK<T, NUM_WARP_Q, NUM_THREAD_Q, kThreadsPerBlock> \</span></div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="preprocessor"> <<<grid, block, 0, stream>>>(productDistances, centroidDistances, \</span></div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="preprocessor"> outDistances, outIndices, \</span></div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="preprocessor"> k, Limits<T>::getMax()); \</span></div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="preprocessor"> } while (0)</span></div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">if</span> (k <= 32) {</div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  RUN_L2_SELECT(32, 2);</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k <= 64) {</div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  RUN_L2_SELECT(64, 3);</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k <= 128) {</div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  RUN_L2_SELECT(128, 3);</div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k <= 256) {</div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  RUN_L2_SELECT(256, 4);</div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k <= 512) {</div>
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  RUN_L2_SELECT(512, 8);</div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k <= 1024) {</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  RUN_L2_SELECT(1024, 8);</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  FAISS_ASSERT(<span class="keyword">false</span>);</div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  }</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> </div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  CUDA_VERIFY(cudaGetLastError());</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="keywordtype">void</span> runL2SelectMin(Tensor<float, 2, true>& productDistances,</div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  Tensor<float, 1, true>& centroidDistances,</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  Tensor<float, 2, true>& outDistances,</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  Tensor<int, 2, true>& outIndices,</div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  cudaStream_t stream) {</div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  runL2SelectMin<float>(productDistances,</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  centroidDistances,</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  outDistances,</div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  outIndices,</div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  k,</div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  stream);</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> </div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="preprocessor"></span><span class="keywordtype">void</span> runL2SelectMin(Tensor<half, 2, true>& productDistances,</div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  Tensor<half, 1, true>& centroidDistances,</div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  Tensor<half, 2, true>& outDistances,</div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  Tensor<int, 2, true>& outIndices,</div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  cudaStream_t stream) {</div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  runL2SelectMin<half>(productDistances,</div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  centroidDistances,</div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  outDistances,</div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  outIndices,</div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  k,</div>
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  stream);</div>
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span> }</div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> } } <span class="comment">// namespace</span></div>
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