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WarpSelectKernel.cuh
1
2 /**
3 * Copyright (c) 2015-present, Facebook, Inc.
4 * All rights reserved.
5 *
6 * This source code is licensed under the CC-by-NC license found in the
7 * LICENSE file in the root directory of this source tree.
8 */
9
10 // Copyright 2004-present Facebook. All Rights Reserved.
11 #pragma once
12
13 #include "Float16.cuh"
14 #include "Select.cuh"
15
16 namespace faiss { namespace gpu {
17
18 template <typename K,
19 typename IndexType,
20 bool Dir,
21 int NumWarpQ,
22 int NumThreadQ,
23 int ThreadsPerBlock>
24 __global__ void warpSelect(Tensor<K, 2, true> in,
25 Tensor<K, 2, true> outK,
26 Tensor<IndexType, 2, true> outV,
27 K initK,
28 IndexType initV,
29 int k) {
30 constexpr int kNumWarps = ThreadsPerBlock / kWarpSize;
31
32 WarpSelect<K, IndexType, Dir, Comparator<K>,
33 NumWarpQ, NumThreadQ, ThreadsPerBlock>
34 heap(initK, initV, k);
35
36 int warpId = threadIdx.x / kWarpSize;
37 int row = blockIdx.x * kNumWarps + warpId;
38
39 if (row >= in.getSize(0)) {
40 return;
41 }
42
43 K* inStart = in[row].data();
44 int i = getLaneId();
45
46 // Whole warps must participate in the selection
47 int limit = utils::roundDown(in.getSize(1), kWarpSize);
48
49 for (; i < limit; i += kWarpSize) {
50 heap.add(inStart[i], (IndexType) i);
51 }
52
53 // Handle non-warp multiple remainder
54 if (i < in.getSize(1)) {
55 heap.addThreadQ(inStart[i], (IndexType) i);
56 }
57
58 heap.reduce();
59 heap.writeOut(outK[row].data(),
60 outV[row].data(), k);
61 }
62
63 void runWarpSelect(Tensor<float, 2, true>& in,
64 Tensor<float, 2, true>& outKeys,
65 Tensor<int, 2, true>& outIndices,
66 bool dir, int k, cudaStream_t stream);
67
68 #ifdef FAISS_USE_FLOAT16
69 void runWarpSelect(Tensor<half, 2, true>& in,
70 Tensor<half, 2, true>& outKeys,
71 Tensor<int, 2, true>& outIndices,
72 bool dir, int k, cudaStream_t stream);
73 #endif
74
75 } } // namespace