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<li class="navelem"><a class="el" href="dir_5956a3e80a20e8e03eb577bedb92689f.html">gpu</a></li><li class="navelem"><a class="el" href="dir_2be73404b46ec2282840cd36fdb9a907.html">impl</a></li> </ul>
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<div class="title">IVFFlatScan.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>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) Facebook, Inc. and its affiliates.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div>
<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>
<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>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> */</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "IVFFlatScan.cuh"</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "../GpuResources.h"</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "IVFUtils.cuh"</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "../utils/ConversionOperators.cuh"</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "../utils/DeviceDefs.cuh"</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include "../utils/DeviceUtils.h"</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "../utils/DeviceTensor.cuh"</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "../utils/Float16.cuh"</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "../utils/MathOperators.cuh"</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "../utils/LoadStoreOperators.cuh"</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "../utils/PtxUtils.cuh"</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include "../utils/Reductions.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> <span class="preprocessor">#include <thrust/host_vector.h></span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</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></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">inline</span> __device__ <span class="keyword">typename</span> Math<T>::ScalarType l2Distance(T a, T b) {</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  a = Math<T>::sub(a, b);</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  a = Math<T>::mul(a, a);</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> <a class="code" href="structfaiss_1_1gpu_1_1Math.html#a4b17f0b5d014f300e76dde5b24af8014">Math<T>::reduceAdd</a>(a);</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> }</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">inline</span> __device__ <span class="keyword">typename</span> Math<T>::ScalarType ipDistance(T a, T b) {</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">return</span> <a class="code" href="structfaiss_1_1gpu_1_1Math.html#a4b17f0b5d014f300e76dde5b24af8014">Math<T>::reduceAdd</a>(Math<T>::mul(a, b));</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> </div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="comment">// For list scanning, even if the input data is `half`, we perform all</span></div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment">// math in float32, because the code is memory b/w bound, and the</span></div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment">// added precision for accumulation is useful</span></div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"></span></div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment">/// The class that we use to provide scan specializations</span></div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keywordtype">int</span> Dims, <span class="keywordtype">bool</span> L2, <span class="keyword">typename</span> T></div>
<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="structfaiss_1_1gpu_1_1IVFFlatScan.html"> 44</a></span> <span class="keyword">struct </span><a class="code" href="structfaiss_1_1gpu_1_1IVFFlatScan.html">IVFFlatScan</a> {</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> </div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment">// Fallback implementation: works for any dimension size</span></div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> L2, <span class="keyword">typename</span> T></div>
<div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="structfaiss_1_1gpu_1_1IVFFlatScan_3-1_00_01L2_00_01T_01_4.html"> 49</a></span> <span class="keyword">struct </span><a class="code" href="structfaiss_1_1gpu_1_1IVFFlatScan.html">IVFFlatScan</a><-1, L2, T> {</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">static</span> __device__ <span class="keywordtype">void</span> scan(<span class="keywordtype">float</span>* query,</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordtype">void</span>* vecData,</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">int</span> numVecs,</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">int</span> dim,</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">float</span>* distanceOut) {</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">extern</span> __shared__ <span class="keywordtype">float</span> smem[];</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  T* vecs = (T*) vecData;</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> vec = 0; vec < numVecs; ++vec) {</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Reduce in dist</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">float</span> dist = 0.0f;</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">for</span> (<span class="keywordtype">int</span> d = threadIdx.x; d < dim; d += blockDim.x) {</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">float</span> vecVal = <a class="code" href="structfaiss_1_1gpu_1_1ConvertTo.html">ConvertTo<float>::to</a>(vecs[vec * dim + d]);</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">float</span> queryVal = query[d];</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">float</span> curDist;</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>  <span class="keywordflow">if</span> (L2) {</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  curDist = l2Distance(queryVal, vecVal);</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  curDist = ipDistance(queryVal, vecVal);</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</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>  dist += curDist;</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// Reduce distance within block</span></div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  dist = blockReduceAllSum<float, false, true>(dist, smem);</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  distanceOut[vec] = dist;</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  }</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> };</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment">// implementation: works for # dims == blockDim.x</span></div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> L2, <span class="keyword">typename</span> T></div>
<div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="structfaiss_1_1gpu_1_1IVFFlatScan_3_010_00_01L2_00_01T_01_4.html"> 88</a></span> <span class="keyword">struct </span><a class="code" href="structfaiss_1_1gpu_1_1IVFFlatScan.html">IVFFlatScan</a><0, L2, T> {</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">static</span> __device__ <span class="keywordtype">void</span> scan(<span class="keywordtype">float</span>* query,</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordtype">void</span>* vecData,</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">int</span> numVecs,</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordtype">int</span> dim,</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">float</span>* distanceOut) {</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">extern</span> __shared__ <span class="keywordtype">float</span> smem[];</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  T* vecs = (T*) vecData;</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">float</span> queryVal = query[threadIdx.x];</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>  constexpr <span class="keywordtype">int</span> kUnroll = 4;</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">int</span> limit = utils::roundDown(numVecs, kUnroll);</div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < limit; i += kUnroll) {</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">float</span> vecVal[kUnroll];</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> <span class="preprocessor">#pragma unroll</span></div>
<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> j = 0; j < kUnroll; ++j) {</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  vecVal[j] = <a class="code" href="structfaiss_1_1gpu_1_1ConvertTo.html">ConvertTo<float>::to</a>(vecs[(i + j) * dim + threadIdx.x]);</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kUnroll; ++j) {</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">if</span> (L2) {</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  vecVal[j] = l2Distance(queryVal, vecVal[j]);</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  vecVal[j] = ipDistance(queryVal, vecVal[j]);</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</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>  blockReduceAllSum<kUnroll, float, false, true>(vecVal, smem);</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>  <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="preprocessor"></span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < kUnroll; ++j) {</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  distanceOut[i + j] = vecVal[j];</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  }</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Handle remainder</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = limit; i < numVecs; ++i) {</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">float</span> vecVal = <a class="code" href="structfaiss_1_1gpu_1_1ConvertTo.html">ConvertTo<float>::to</a>(vecs[i * dim + threadIdx.x]);</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">if</span> (L2) {</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  vecVal = l2Distance(queryVal, vecVal);</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  vecVal = ipDistance(queryVal, vecVal);</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  }</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>  vecVal = blockReduceAllSum<float, false, true>(vecVal, smem);</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="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  distanceOut[i] = vecVal;</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</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>  }</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> <span class="keyword">template</span> <<span class="keywordtype">int</span> Dims, <span class="keywordtype">bool</span> L2, <span class="keyword">typename</span> T></div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> __global__ <span class="keywordtype">void</span></div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> ivfFlatScan(<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<float, 2, true></a> queries,</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<int, 2, true></a> listIds,</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">void</span>** allListData,</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordtype">int</span>* listLengths,</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<int, 2, true></a> prefixSumOffsets,</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<float, 1, true></a> distance) {</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">auto</span> queryId = blockIdx.y;</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">auto</span> probeId = blockIdx.x;</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="comment">// This is where we start writing out data</span></div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="comment">// We ensure that before the array (at offset -1), there is a 0 value</span></div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordtype">int</span> outBase = *(prefixSumOffsets[queryId][probeId].<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>() - 1);</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>  <span class="keyword">auto</span> listId = listIds[queryId][probeId];</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// Safety guard in case NaNs in input cause no list ID to be generated</span></div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span> (listId == -1) {</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">auto</span> query = queries[queryId].<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>();</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">auto</span> vecs = allListData[listId];</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">auto</span> numVecs = listLengths[listId];</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">auto</span> dim = queries.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1);</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">auto</span> distanceOut = distance[outBase].<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">data</a>();</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  IVFFlatScan<Dims, L2, T>::scan(query, vecs, numVecs, dim, distanceOut);</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> </div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="keywordtype">void</span></div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> runIVFFlatScanTile(Tensor<float, 2, true>& queries,</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  Tensor<int, 2, true>& listIds,</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  thrust::device_vector<void*>& listData,</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  thrust::device_vector<void*>& listIndices,</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  IndicesOptions indicesOptions,</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  thrust::device_vector<int>& listLengths,</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  Tensor<char, 1, true>& thrustMem,</div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  Tensor<int, 2, true>& prefixSumOffsets,</div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  Tensor<float, 1, true>& allDistances,</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  Tensor<float, 3, true>& heapDistances,</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  Tensor<int, 3, true>& heapIndices,</div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordtype">bool</span> l2Distance,</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordtype">bool</span> useFloat16,</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  Tensor<float, 2, true>& outDistances,</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  Tensor<long, 2, true>& outIndices,</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  cudaStream_t stream) {</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">// Calculate offset lengths, so we know where to write out</span></div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="comment">// intermediate results</span></div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  runCalcListOffsets(listIds, listLengths, prefixSumOffsets, thrustMem, stream);</div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="comment">// Calculate distances for vectors within our chunk of lists</span></div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  constexpr <span class="keywordtype">int</span> kMaxThreadsIVF = 512;</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="comment">// FIXME: if `half` and # dims is multiple of 2, halve the</span></div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// threadblock size</span></div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordtype">int</span> dim = queries.getSize(1);</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordtype">int</span> numThreads = std::min(dim, kMaxThreadsIVF);</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>  <span class="keyword">auto</span> grid = dim3(listIds.getSize(1),</div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  listIds.getSize(0));</div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">auto</span> block = dim3(numThreads);</div>
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// All exact dim kernels are unrolled by 4, hence the `4`</span></div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keyword">auto</span> smem = <span class="keyword">sizeof</span>(float) * utils::divUp(numThreads, kWarpSize) * 4;</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> <span class="preprocessor">#define RUN_IVF_FLAT(DIMS, L2, T) \</span></div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="preprocessor"> do { \</span></div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="preprocessor"> ivfFlatScan<DIMS, L2, T> \</span></div>
<div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="preprocessor"> <<<grid, block, smem, stream>>>( \</span></div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="preprocessor"> queries, \</span></div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="preprocessor"> listIds, \</span></div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span> <span class="preprocessor"> listData.data().get(), \</span></div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span> <span class="preprocessor"> listLengths.data().get(), \</span></div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="preprocessor"> prefixSumOffsets, \</span></div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="preprocessor"> allDistances); \</span></div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="preprocessor"> } while (0)</span></div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="preprocessor">#define HANDLE_DIM_CASE(DIMS) \</span></div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="preprocessor"> do { \</span></div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="preprocessor"> if (l2Distance) { \</span></div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="preprocessor"> if (useFloat16) { \</span></div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, true, half); \</span></div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, true, float); \</span></div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="preprocessor"> if (useFloat16) { \</span></div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, false, half); \</span></div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, false, float); \</span></div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="preprocessor"> } while (0)</span></div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="preprocessor">#define HANDLE_DIM_CASE(DIMS) \</span></div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="preprocessor"> do { \</span></div>
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="preprocessor"> if (l2Distance) { \</span></div>
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="preprocessor"> if (useFloat16) { \</span></div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor"> FAISS_ASSERT(false); \</span></div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, true, float); \</span></div>
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="preprocessor"> if (useFloat16) { \</span></div>
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="preprocessor"> FAISS_ASSERT(false); \</span></div>
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="preprocessor"> } else { \</span></div>
<div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="preprocessor"> RUN_IVF_FLAT(DIMS, false, float); \</span></div>
<div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="preprocessor"> } \</span></div>
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="preprocessor"> } while (0)</span></div>
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="preprocessor">#endif // FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">if</span> (dim <= kMaxThreadsIVF) {</div>
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  HANDLE_DIM_CASE(0);</div>
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  HANDLE_DIM_CASE(-1);</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> </div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  CUDA_TEST_ERROR();</div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="preprocessor">#undef HANDLE_DIM_CASE</span></div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="preprocessor"></span><span class="preprocessor">#undef RUN_IVF_FLAT</span></div>
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="comment">// k-select the output in chunks, to increase parallelism</span></div>
<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  runPass1SelectLists(prefixSumOffsets,</div>
<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  allDistances,</div>
<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  listIds.getSize(1),</div>
<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  k,</div>
<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  !l2Distance, <span class="comment">// L2 distance chooses smallest</span></div>
<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  heapDistances,</div>
<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  heapIndices,</div>
<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  stream);</div>
<div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div>
<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// k-select final output</span></div>
<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">auto</span> flatHeapDistances = heapDistances.downcastInner<2>();</div>
<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">auto</span> flatHeapIndices = heapIndices.downcastInner<2>();</div>
<div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div>
<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  runPass2SelectLists(flatHeapDistances,</div>
<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  flatHeapIndices,</div>
<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  listIndices,</div>
<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  indicesOptions,</div>
<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  prefixSumOffsets,</div>
<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  listIds,</div>
<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  k,</div>
<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  !l2Distance, <span class="comment">// L2 distance chooses smallest</span></div>
<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  outDistances,</div>
<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  outIndices,</div>
<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  stream);</div>
<div class="line"><a name="l00302"></a><span class="lineno"> 302</span> }</div>
<div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div>
<div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="keywordtype">void</span></div>
<div class="line"><a name="l00305"></a><span class="lineno"> 305</span> runIVFFlatScan(Tensor<float, 2, true>& queries,</div>
<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  Tensor<int, 2, true>& listIds,</div>
<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  thrust::device_vector<void*>& listData,</div>
<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  thrust::device_vector<void*>& listIndices,</div>
<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  IndicesOptions indicesOptions,</div>
<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  thrust::device_vector<int>& listLengths,</div>
<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordtype">int</span> maxListLength,</div>
<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordtype">bool</span> l2Distance,</div>
<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordtype">bool</span> useFloat16,</div>
<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">// output</span></div>
<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  Tensor<float, 2, true>& outDistances,</div>
<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="comment">// output</span></div>
<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  Tensor<long, 2, true>& outIndices,</div>
<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  GpuResources* res) {</div>
<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  constexpr <span class="keywordtype">int</span> kMinQueryTileSize = 8;</div>
<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  constexpr <span class="keywordtype">int</span> kMaxQueryTileSize = 128;</div>
<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  constexpr <span class="keywordtype">int</span> kThrustMemSize = 16384;</div>
<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div>
<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordtype">int</span> nprobe = listIds.getSize(1);</div>
<div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div>
<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keyword">auto</span>& mem = res->getMemoryManagerCurrentDevice();</div>
<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keyword">auto</span> stream = res->getDefaultStreamCurrentDevice();</div>
<div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div>
<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="comment">// Make a reservation for Thrust to do its dirty work (global memory</span></div>
<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">// cross-block reduction space); hopefully this is large enough.</span></div>
<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  DeviceTensor<char, 1, true> thrustMem1(</div>
<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  mem, {kThrustMemSize}, stream);</div>
<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  DeviceTensor<char, 1, true> thrustMem2(</div>
<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  mem, {kThrustMemSize}, stream);</div>
<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  DeviceTensor<char, 1, true>* thrustMem[2] =</div>
<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  {&thrustMem1, &thrustMem2};</div>
<div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div>
<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// How much temporary storage is available?</span></div>
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="comment">// If possible, we'd like to fit within the space available.</span></div>
<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordtype">size_t</span> sizeAvailable = mem.getSizeAvailable();</div>
<div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div>
<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="comment">// We run two passes of heap selection</span></div>
<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="comment">// This is the size of the first-level heap passes</span></div>
<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  constexpr <span class="keywordtype">int</span> kNProbeSplit = 8;</div>
<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordtype">int</span> pass2Chunks = std::min(nprobe, kNProbeSplit);</div>
<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div>
<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordtype">size_t</span> sizeForFirstSelectPass =</div>
<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  pass2Chunks * k * (<span class="keyword">sizeof</span>(float) + <span class="keyword">sizeof</span>(<span class="keywordtype">int</span>));</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>  <span class="comment">// How much temporary storage we need per each query</span></div>
<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordtype">size_t</span> sizePerQuery =</div>
<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  2 * <span class="comment">// # streams</span></div>
<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  ((nprobe * <span class="keyword">sizeof</span>(int) + <span class="keyword">sizeof</span>(<span class="keywordtype">int</span>)) + <span class="comment">// prefixSumOffsets</span></div>
<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  nprobe * maxListLength * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>) + <span class="comment">// allDistances</span></div>
<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  sizeForFirstSelectPass);</div>
<div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div>
<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordtype">int</span> queryTileSize = (int) (sizeAvailable / sizePerQuery);</div>
<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div>
<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span> (queryTileSize < kMinQueryTileSize) {</div>
<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  queryTileSize = kMinQueryTileSize;</div>
<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (queryTileSize > kMaxQueryTileSize) {</div>
<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  queryTileSize = kMaxQueryTileSize;</div>
<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  }</div>
<div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div>
<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="comment">// FIXME: we should adjust queryTileSize to deal with this, since</span></div>
<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// indexing is in int32</span></div>
<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  FAISS_ASSERT(queryTileSize * nprobe * maxListLength <</div>
<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  std::numeric_limits<int>::max());</div>
<div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div>
<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">// Temporary memory buffers</span></div>
<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// Make sure there is space prior to the start which will be 0, and</span></div>
<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// will handle the boundary condition without branches</span></div>
<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  DeviceTensor<int, 1, true> prefixSumOffsetSpace1(</div>
<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  mem, {queryTileSize * nprobe + 1}, stream);</div>
<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  DeviceTensor<int, 1, true> prefixSumOffsetSpace2(</div>
<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  mem, {queryTileSize * nprobe + 1}, stream);</div>
<div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div>
<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  DeviceTensor<int, 2, true> prefixSumOffsets1(</div>
<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  prefixSumOffsetSpace1[1].data(),</div>
<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  {queryTileSize, nprobe});</div>
<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  DeviceTensor<int, 2, true> prefixSumOffsets2(</div>
<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  prefixSumOffsetSpace2[1].data(),</div>
<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  {queryTileSize, nprobe});</div>
<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  DeviceTensor<int, 2, true>* prefixSumOffsets[2] =</div>
<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  {&prefixSumOffsets1, &prefixSumOffsets2};</div>
<div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div>
<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="comment">// Make sure the element before prefixSumOffsets is 0, since we</span></div>
<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="comment">// depend upon simple, boundary-less indexing to get proper results</span></div>
<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  CUDA_VERIFY(cudaMemsetAsync(prefixSumOffsetSpace1.data(),</div>
<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  0,</div>
<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keyword">sizeof</span>(int),</div>
<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  stream));</div>
<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  CUDA_VERIFY(cudaMemsetAsync(prefixSumOffsetSpace2.data(),</div>
<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  0,</div>
<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">sizeof</span>(int),</div>
<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  stream));</div>
<div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div>
<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  DeviceTensor<float, 1, true> allDistances1(</div>
<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  mem, {queryTileSize * nprobe * maxListLength}, stream);</div>
<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  DeviceTensor<float, 1, true> allDistances2(</div>
<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  mem, {queryTileSize * nprobe * maxListLength}, stream);</div>
<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  DeviceTensor<float, 1, true>* allDistances[2] =</div>
<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  {&allDistances1, &allDistances2};</div>
<div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div>
<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  DeviceTensor<float, 3, true> heapDistances1(</div>
<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  mem, {queryTileSize, pass2Chunks, k}, stream);</div>
<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  DeviceTensor<float, 3, true> heapDistances2(</div>
<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  mem, {queryTileSize, pass2Chunks, k}, stream);</div>
<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  DeviceTensor<float, 3, true>* heapDistances[2] =</div>
<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  {&heapDistances1, &heapDistances2};</div>
<div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div>
<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  DeviceTensor<int, 3, true> heapIndices1(</div>
<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  mem, {queryTileSize, pass2Chunks, k}, stream);</div>
<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  DeviceTensor<int, 3, true> heapIndices2(</div>
<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  mem, {queryTileSize, pass2Chunks, k}, stream);</div>
<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  DeviceTensor<int, 3, true>* heapIndices[2] =</div>
<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  {&heapIndices1, &heapIndices2};</div>
<div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div>
<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">auto</span> streams = res->getAlternateStreamsCurrentDevice();</div>
<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  streamWait(streams, {stream});</div>
<div class="line"><a name="l00421"></a><span class="lineno"> 421</span> </div>
<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keywordtype">int</span> curStream = 0;</div>
<div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div>
<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> query = 0; query < queries.getSize(0); query += queryTileSize) {</div>
<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordtype">int</span> numQueriesInTile =</div>
<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  std::min(queryTileSize, queries.getSize(0) - query);</div>
<div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div>
<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keyword">auto</span> prefixSumOffsetsView =</div>
<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  prefixSumOffsets[curStream]->narrowOutermost(0, numQueriesInTile);</div>
<div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div>
<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">auto</span> listIdsView =</div>
<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  listIds.narrowOutermost(query, numQueriesInTile);</div>
<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">auto</span> queryView =</div>
<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  queries.narrowOutermost(query, numQueriesInTile);</div>
<div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div>
<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">auto</span> heapDistancesView =</div>
<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  heapDistances[curStream]->narrowOutermost(0, numQueriesInTile);</div>
<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">auto</span> heapIndicesView =</div>
<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  heapIndices[curStream]->narrowOutermost(0, numQueriesInTile);</div>
<div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div>
<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">auto</span> outDistanceView =</div>
<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  outDistances.narrowOutermost(query, numQueriesInTile);</div>
<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">auto</span> outIndicesView =</div>
<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  outIndices.narrowOutermost(query, numQueriesInTile);</div>
<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div>
<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  runIVFFlatScanTile(queryView,</div>
<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  listIdsView,</div>
<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  listData,</div>
<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  listIndices,</div>
<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  indicesOptions,</div>
<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  listLengths,</div>
<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  *thrustMem[curStream],</div>
<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  prefixSumOffsetsView,</div>
<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  *allDistances[curStream],</div>
<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  heapDistancesView,</div>
<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  heapIndicesView,</div>
<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  k,</div>
<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  l2Distance,</div>
<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  useFloat16,</div>
<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  outDistanceView,</div>
<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  outIndicesView,</div>
<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  streams[curStream]);</div>
<div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div>
<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  curStream = (curStream + 1) % 2;</div>
<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  }</div>
<div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div>
<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  streamWait({stream}, streams);</div>
<div class="line"><a name="l00468"></a><span class="lineno"> 468</span> }</div>
<div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div>
<div class="line"><a name="l00470"></a><span class="lineno"> 470</span> } } <span class="comment">// namespace</span></div>
<div class="ttc" id="structfaiss_1_1gpu_1_1Math_html_a4b17f0b5d014f300e76dde5b24af8014"><div class="ttname"><a href="structfaiss_1_1gpu_1_1Math.html#a4b17f0b5d014f300e76dde5b24af8014">faiss::gpu::Math::reduceAdd</a></div><div class="ttdeci">static __device__ T reduceAdd(T v)</div><div class="ttdoc">For a vector type, this is a horizontal add, returning sum(v_i) </div><div class="ttdef"><b>Definition:</b> <a href="MathOperators_8cuh_source.html#l00042">MathOperators.cuh:42</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6699c311648457f257afa340c61f417c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">faiss::gpu::Tensor::getSize</a></div><div class="ttdeci">__host__ __device__ IndexT getSize(int i) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00222">Tensor.cuh:222</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a50411ce4d0fa32ef715e3321b6e33212"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212">faiss::gpu::Tensor::data</a></div><div class="ttdeci">__host__ __device__ DataPtrType data()</div><div class="ttdoc">Returns a raw pointer to the start of our data. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00174">Tensor.cuh:174</a></div></div>
<div class="ttc" id="structfaiss_1_1gpu_1_1ConvertTo_html"><div class="ttname"><a href="structfaiss_1_1gpu_1_1ConvertTo.html">faiss::gpu::ConvertTo</a></div><div class="ttdef"><b>Definition:</b> <a href="ConversionOperators_8cuh_source.html#l00028">ConversionOperators.cuh:28</a></div></div>
<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html">faiss::gpu::Tensor</a></div><div class="ttdoc">Our tensor type. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00028">Tensor.cuh:28</a></div></div>
<div class="ttc" id="structfaiss_1_1gpu_1_1IVFFlatScan_html"><div class="ttname"><a href="structfaiss_1_1gpu_1_1IVFFlatScan.html">faiss::gpu::IVFFlatScan</a></div><div class="ttdoc">The class that we use to provide scan specializations. </div><div class="ttdef"><b>Definition:</b> <a href="IVFFlatScan_8cu_source.html#l00044">IVFFlatScan.cu:44</a></div></div>
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