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  • Lucas Hosseini's avatar
    Facebook sync (May 2019) + relicense (#838) · a8118acb
    Lucas Hosseini authored 
    Changelog:

- changed license: BSD+Patents -> MIT
- propagates exceptions raised in sub-indexes of IndexShards and IndexReplicas
- support for searching several inverted lists in parallel (parallel_mode != 0)
- better support for PQ codes where nbit != 8 or 16
- IVFSpectralHash implementation: spectral hash codes inside an IVF
- 6-bit per component scalar quantizer (4 and 8 bit were already supported)
- combinations of inverted lists: HStackInvertedLists and VStackInvertedLists
- configurable number of threads for OnDiskInvertedLists prefetching (including 0=no prefetch)
- more test and demo code compatible with Python 3 (print with parentheses)
- refactored benchmark code: data loading is now in a single file
    Unverified
    a8118acb
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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_5865795754b604b902e524ed1add5694.html">utils</a></li>  </ul>
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<div class="title">BlockSelectKernel.cuh</div>  </div>
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<div class="contents">
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/**</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) Facebook, Inc. and its affiliates.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<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>&#160;<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>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#pragma once</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;Float16.cuh&quot;</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;Select.cuh&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;</div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;</div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> K,</div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;          <span class="keyword">typename</span> IndexType,</div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;          <span class="keywordtype">bool</span> Dir,</div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;          <span class="keywordtype">int</span> NumWarpQ,</div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;          <span class="keywordtype">int</span> NumThreadQ,</div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;          <span class="keywordtype">int</span> ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;__global__ <span class="keywordtype">void</span> blockSelect(Tensor&lt;K, 2, true&gt; in,</div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;                            Tensor&lt;K, 2, true&gt; outK,</div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;                            Tensor&lt;IndexType, 2, true&gt; outV,</div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;                            K initK,</div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;                            IndexType initV,</div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;                            <span class="keywordtype">int</span> k) {</div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;  constexpr <span class="keywordtype">int</span> kNumWarps = ThreadsPerBlock / kWarpSize;</div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;</div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;  __shared__ K smemK[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;  __shared__ IndexType smemV[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;  BlockSelect&lt;K, IndexType, Dir, Comparator&lt;K&gt;,</div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;              NumWarpQ, NumThreadQ, ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    heap(initK, initV, smemK, smemV, k);</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;  <span class="comment">// Grid is exactly sized to rows available</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;  <span class="keywordtype">int</span> row = blockIdx.x;</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;  <span class="keywordtype">int</span> i = threadIdx.x;</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;  K* inStart = in[row][i].data();</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;  <span class="comment">// Whole warps must participate in the selection</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;  <span class="keywordtype">int</span> limit = utils::roundDown(in.getSize(1), kWarpSize);</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="keywordflow">for</span> (; i &lt; limit; i += ThreadsPerBlock) {</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    heap.add(*inStart, (IndexType) i);</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    inStart += ThreadsPerBlock;</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;  }</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  <span class="comment">// Handle last remainder fraction of a warp of elements</span></div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <span class="keywordflow">if</span> (i &lt; in.getSize(1)) {</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    heap.addThreadQ(*inStart, (IndexType) i);</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  }</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  heap.reduce();</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = threadIdx.x; i &lt; k; i += ThreadsPerBlock) {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    outK[row][i] = smemK[i];</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    outV[row][i] = smemV[i];</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  }</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;}</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> K,</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;          <span class="keyword">typename</span> IndexType,</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;          <span class="keywordtype">bool</span> Dir,</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;          <span class="keywordtype">int</span> NumWarpQ,</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;          <span class="keywordtype">int</span> NumThreadQ,</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;          <span class="keywordtype">int</span> ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;__global__ <span class="keywordtype">void</span> blockSelectPair(Tensor&lt;K, 2, true&gt; inK,</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;                                Tensor&lt;IndexType, 2, true&gt; inV,</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;                                Tensor&lt;K, 2, true&gt; outK,</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                                Tensor&lt;IndexType, 2, true&gt; outV,</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                                K initK,</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                                IndexType initV,</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;                                <span class="keywordtype">int</span> k) {</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  constexpr <span class="keywordtype">int</span> kNumWarps = ThreadsPerBlock / kWarpSize;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  __shared__ K smemK[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  __shared__ IndexType smemV[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;  BlockSelect&lt;K, IndexType, Dir, Comparator&lt;K&gt;,</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;              NumWarpQ, NumThreadQ, ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    heap(initK, initV, smemK, smemV, k);</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  <span class="comment">// Grid is exactly sized to rows available</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  <span class="keywordtype">int</span> row = blockIdx.x;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="keywordtype">int</span> i = threadIdx.x;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  K* inKStart = inK[row][i].data();</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  IndexType* inVStart = inV[row][i].data();</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  <span class="comment">// Whole warps must participate in the selection</span></div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  <span class="keywordtype">int</span> limit = utils::roundDown(inK.getSize(1), kWarpSize);</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <span class="keywordflow">for</span> (; i &lt; limit; i += ThreadsPerBlock) {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    heap.add(*inKStart, *inVStart);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    inKStart += ThreadsPerBlock;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    inVStart += ThreadsPerBlock;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  }</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="comment">// Handle last remainder fraction of a warp of elements</span></div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  <span class="keywordflow">if</span> (i &lt; inK.getSize(1)) {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    heap.addThreadQ(*inKStart, *inVStart);</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;  }</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  heap.reduce();</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = threadIdx.x; i &lt; k; i += ThreadsPerBlock) {</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    outK[row][i] = smemK[i];</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    outV[row][i] = smemV[i];</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  }</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;}</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;<span class="keywordtype">void</span> runBlockSelect(Tensor&lt;float, 2, true&gt;&amp; in,</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                    Tensor&lt;float, 2, true&gt;&amp; outKeys,</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                    Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                    <span class="keywordtype">bool</span> dir, <span class="keywordtype">int</span> k, cudaStream_t stream);</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;<span class="keywordtype">void</span> runBlockSelectPair(Tensor&lt;float, 2, true&gt;&amp; inKeys,</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;                        Tensor&lt;int, 2, true&gt;&amp; inIndices,</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;                        Tensor&lt;float, 2, true&gt;&amp; outKeys,</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;                        Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;                        <span class="keywordtype">bool</span> dir, <span class="keywordtype">int</span> k, cudaStream_t stream);</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;<span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;<span class="preprocessor"></span><span class="keywordtype">void</span> runBlockSelect(Tensor&lt;half, 2, true&gt;&amp; in,</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;                    Tensor&lt;half, 2, true&gt;&amp; outKeys,</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                    Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;                    <span class="keywordtype">bool</span> dir, <span class="keywordtype">int</span> k, cudaStream_t stream);</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;<span class="keywordtype">void</span> runBlockSelectPair(Tensor&lt;half, 2, true&gt;&amp; inKeys,</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;                        Tensor&lt;int, 2, true&gt;&amp; inIndices,</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;                        Tensor&lt;half, 2, true&gt;&amp; outKeys,</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;                        Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                        <span class="keywordtype">bool</span> dir, <span class="keywordtype">int</span> k, cudaStream_t stream);</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;} } <span class="comment">// namespace</span></div>
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