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L2Select.cu
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the CC-by-NC license found in the
* LICENSE file in the root directory of this source tree.
*/
// Copyright 2004-present Facebook. All Rights Reserved.
#include "L2Select.cuh"
#include "../../FaissAssert.h"
#include "../utils/DeviceUtils.h"
#include "../utils/MathOperators.cuh"
#include "../utils/Pair.cuh"
#include "../utils/Reductions.cuh"
#include "../utils/Select.cuh"
#include "../utils/Tensor.cuh"
#include "../utils/StaticUtils.h"
namespace faiss { namespace gpu {
// L2 + select kernel for k == 1, implements re-use of ||c||^2
template <typename T, int kRowsPerBlock, int kBlockSize>
__global__ void l2SelectMin1(Tensor<T, 2, true> productDistances,
Tensor<T, 1, true> centroidDistances,
Tensor<T, 2, true> outDistances,
Tensor<int, 2, true> outIndices) {
// Each block handles kRowsPerBlock rows of the distances (results)
Pair<T, int> threadMin[kRowsPerBlock];
__shared__ Pair<T, int> blockMin[kRowsPerBlock * (kBlockSize / kWarpSize)];
T distance[kRowsPerBlock];
#pragma unroll
for (int i = 0; i < kRowsPerBlock; ++i) {
threadMin[i].k = Limits<T>::getMax();
threadMin[i].v = -1;
}
// blockIdx.x: which chunk of rows we are responsible for updating
int rowStart = blockIdx.x * kRowsPerBlock;
// FIXME: if we have exact multiples, don't need this
bool endRow = (blockIdx.x == gridDim.x - 1);
if (endRow) {
if (productDistances.getSize(0) % kRowsPerBlock == 0) {
endRow = false;
}
}
if (endRow) {
for (int row = rowStart; row < productDistances.getSize(0); ++row) {
for (int col = threadIdx.x; col < productDistances.getSize(1);
col += blockDim.x) {
distance[0] = Math<T>::add(centroidDistances[col],
productDistances[row][col]);
if (Math<T>::lt(distance[0], threadMin[0].k)) {
threadMin[0].k = distance[0];
threadMin[0].v = col;
}
}
// Reduce within the block
threadMin[0] =
blockReduceAll<Pair<T, int>, Min<Pair<T, int> >, false, false>(
threadMin[0], Min<Pair<T, int> >(), blockMin);
if (threadIdx.x == 0) {
outDistances[row][0] = threadMin[0].k;
outIndices[row][0] = threadMin[0].v;
}
// so we can use the shared memory again
__syncthreads();
threadMin[0].k = Limits<T>::getMax();
threadMin[0].v = -1;
}
} else {
for (int col = threadIdx.x; col < productDistances.getSize(1);
col += blockDim.x) {
T centroidDistance = centroidDistances[col];
#pragma unroll
for (int row = 0; row < kRowsPerBlock; ++row) {
distance[row] = productDistances[rowStart + row][col];
}
#pragma unroll
for (int row = 0; row < kRowsPerBlock; ++row) {
distance[row] = Math<T>::add(distance[row], centroidDistance);
}
#pragma unroll
for (int row = 0; row < kRowsPerBlock; ++row) {
if (Math<T>::lt(distance[row], threadMin[row].k)) {
threadMin[row].k = distance[row];
threadMin[row].v = col;
}
}
}
// Reduce within the block
blockReduceAll<kRowsPerBlock, Pair<T, int>, Min<Pair<T, int> >, false, false>(
threadMin, Min<Pair<T, int> >(), blockMin);
if (threadIdx.x == 0) {
#pragma unroll
for (int row = 0; row < kRowsPerBlock; ++row) {
outDistances[rowStart + row][0] = threadMin[row].k;
outIndices[rowStart + row][0] = threadMin[row].v;
}
}
}
}
// L2 + select kernel for k > 1, no re-use of ||c||^2
template <typename T, int NumWarpQ, int NumThreadQ, int ThreadsPerBlock>
__global__ void l2SelectMinK(Tensor<T, 2, true> productDistances,
Tensor<T, 1, true> centroidDistances,
Tensor<T, 2, true> outDistances,
Tensor<int, 2, true> outIndices,
int k, T initK) {
// Each block handles a single row of the distances (results)
constexpr int kNumWarps = ThreadsPerBlock / kWarpSize;
__shared__ T smemK[kNumWarps * NumWarpQ];
__shared__ int smemV[kNumWarps * NumWarpQ];
BlockSelect<T, int, false, Comparator<T>,
NumWarpQ, NumThreadQ, ThreadsPerBlock>
heap(initK, -1, smemK, smemV, k);
int row = blockIdx.x;
// Whole warps must participate in the selection
int limit = utils::roundDown(productDistances.getSize(1), kWarpSize);
int i = threadIdx.x;
for (; i < limit; i += blockDim.x) {
T v = Math<T>::add(centroidDistances[i],
productDistances[row][i]);
heap.add(v, i);
}
if (i < productDistances.getSize(1)) {
T v = Math<T>::add(centroidDistances[i],
productDistances[row][i]);
heap.addThreadQ(v, i);
}
heap.reduce();
for (int i = threadIdx.x; i < k; i += blockDim.x) {
outDistances[row][i] = smemK[i];
outIndices[row][i] = smemV[i];
}
}
// FIXME: no TVec specialization
template <typename T>
void runL2SelectMin(Tensor<T, 2, true>& productDistances,
Tensor<T, 1, true>& centroidDistances,
Tensor<T, 2, true>& outDistances,
Tensor<int, 2, true>& outIndices,
int k,
cudaStream_t stream) {
FAISS_ASSERT(productDistances.getSize(0) == outDistances.getSize(0));
FAISS_ASSERT(productDistances.getSize(0) == outIndices.getSize(0));
FAISS_ASSERT(centroidDistances.getSize(0) == productDistances.getSize(1));
FAISS_ASSERT(outDistances.getSize(1) == k);
FAISS_ASSERT(outIndices.getSize(1) == k);
FAISS_ASSERT(k <= 1024);
if (k == 1) {
constexpr int kThreadsPerBlock = 256;
constexpr int kRowsPerBlock = 8;
auto block = dim3(kThreadsPerBlock);
auto grid = dim3(utils::divUp(outDistances.getSize(0), kRowsPerBlock));
l2SelectMin1<T, kRowsPerBlock, kThreadsPerBlock>
<<<grid, block, 0, stream>>>(productDistances, centroidDistances,
outDistances, outIndices);
} else {
constexpr int kThreadsPerBlock = 128;
auto block = dim3(kThreadsPerBlock);
auto grid = dim3(outDistances.getSize(0));
#define RUN_L2_SELECT(NUM_WARP_Q, NUM_THREAD_Q) \
do { \
l2SelectMinK<T, NUM_WARP_Q, NUM_THREAD_Q, kThreadsPerBlock> \
<<<grid, block, 0, stream>>>(productDistances, centroidDistances, \
outDistances, outIndices, \
k, Limits<T>::getMax()); \
} while (0)
if (k <= 32) {
RUN_L2_SELECT(32, 2);
} else if (k <= 64) {
RUN_L2_SELECT(64, 3);
} else if (k <= 128) {
RUN_L2_SELECT(128, 3);
} else if (k <= 256) {
RUN_L2_SELECT(256, 4);
} else if (k <= 512) {
RUN_L2_SELECT(512, 8);
} else if (k <= 1024) {
RUN_L2_SELECT(1024, 8);
} else {
FAISS_ASSERT(false);
}
}
CUDA_VERIFY(cudaGetLastError());
}
void runL2SelectMin(Tensor<float, 2, true>& productDistances,
Tensor<float, 1, true>& centroidDistances,
Tensor<float, 2, true>& outDistances,
Tensor<int, 2, true>& outIndices,
int k,
cudaStream_t stream) {
runL2SelectMin<float>(productDistances,
centroidDistances,
outDistances,
outIndices,
k,
stream);
}
#ifdef FAISS_USE_FLOAT16
void runL2SelectMin(Tensor<half, 2, true>& productDistances,
Tensor<half, 1, true>& centroidDistances,
Tensor<half, 2, true>& outDistances,
Tensor<int, 2, true>& outIndices,
int k,
cudaStream_t stream) {
runL2SelectMin<half>(productDistances,
centroidDistances,
outDistances,
outIndices,
k,
stream);
}
#endif
} } // namespace