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WarpSelectKernel.cuh
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 #pragma once
10
11 #include "Float16.cuh"
12 #include "Select.cuh"
13
14 namespace faiss { namespace gpu {
15
16 template <typename K,
17 typename IndexType,
18 bool Dir,
19 int NumWarpQ,
20 int NumThreadQ,
21 int ThreadsPerBlock>
22 __global__ void warpSelect(Tensor<K, 2, true> in,
23 Tensor<K, 2, true> outK,
24 Tensor<IndexType, 2, true> outV,
25 K initK,
26 IndexType initV,
27 int k) {
28 constexpr int kNumWarps = ThreadsPerBlock / kWarpSize;
29
30 WarpSelect<K, IndexType, Dir, Comparator<K>,
31 NumWarpQ, NumThreadQ, ThreadsPerBlock>
32 heap(initK, initV, k);
33
34 int warpId = threadIdx.x / kWarpSize;
35 int row = blockIdx.x * kNumWarps + warpId;
36
37 if (row >= in.getSize(0)) {
38 return;
39 }
40
41 int i = getLaneId();
42 K* inStart = in[row][i].data();
43
44 // Whole warps must participate in the selection
45 int limit = utils::roundDown(in.getSize(1), kWarpSize);
46
47 for (; i < limit; i += kWarpSize) {
48 heap.add(*inStart, (IndexType) i);
49 inStart += kWarpSize;
50 }
51
52 // Handle non-warp multiple remainder
53 if (i < in.getSize(1)) {
54 heap.addThreadQ(*inStart, (IndexType) i);
55 }
56
57 heap.reduce();
58 heap.writeOut(outK[row].data(),
59 outV[row].data(), k);
60 }
61
62 void runWarpSelect(Tensor<float, 2, true>& in,
63 Tensor<float, 2, true>& outKeys,
64 Tensor<int, 2, true>& outIndices,
65 bool dir, int k, cudaStream_t stream);
66
67 #ifdef FAISS_USE_FLOAT16
68 void runWarpSelect(Tensor<half, 2, true>& in,
69 Tensor<half, 2, true>& outKeys,
70 Tensor<int, 2, true>& outIndices,
71 bool dir, int k, cudaStream_t stream);
72 #endif
73
74 } } // namespace