258 lines
8.1 KiB
Plaintext
258 lines
8.1 KiB
Plaintext
|
|
/**
|
|
* 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 "L2Norm.cuh"
|
|
#include "../../FaissAssert.h"
|
|
#include "../utils/ConversionOperators.cuh"
|
|
#include "../utils/DeviceDefs.cuh"
|
|
#include "../utils/DeviceUtils.h"
|
|
#include "../utils/Float16.cuh"
|
|
#include "../utils/MathOperators.cuh"
|
|
#include "../utils/PtxUtils.cuh"
|
|
#include "../utils/StaticUtils.h"
|
|
#include "../utils/Reductions.cuh"
|
|
|
|
namespace faiss { namespace gpu {
|
|
|
|
// Input: (batch x dim), # repeats
|
|
// Output: (# repeats, norm of batch vector)
|
|
// Done under the presumption that the dimension size is not too large
|
|
// (<10k or so), since there wouldn't be enough parallelism applying a
|
|
// single block to the problem. Also that each vector is large enough
|
|
// (>64), since a single block works on multiple rows' norms at the
|
|
// same time.
|
|
// T: the type we are doing the math in (e.g., float, half)
|
|
// TVec: the potentially vectorized type we are loading in (e.g.,
|
|
// float4, half2)
|
|
template <typename T, typename TVec,
|
|
int RowTileSize, bool NormLoop, bool NormSquared>
|
|
__global__ void l2Norm(Tensor<TVec, 2, true> input,
|
|
Tensor<T, 1, true> output) {
|
|
extern __shared__ char smemByte[]; // #warps * RowTileSize elements
|
|
T* smem = (T*) smemByte;
|
|
|
|
int numWarps = utils::divUp(blockDim.x, kWarpSize);
|
|
int laneId = getLaneId();
|
|
int warpId = threadIdx.x / kWarpSize;
|
|
|
|
bool lastRowTile = (blockIdx.x == (gridDim.x - 1));
|
|
int rowStart = RowTileSize * blockIdx.x;
|
|
T rowNorm[RowTileSize];
|
|
|
|
if (lastRowTile) {
|
|
// We are handling the very end of the input matrix rows
|
|
for (int row = 0; row < input.getSize(0) - rowStart; ++row) {
|
|
if (NormLoop) {
|
|
rowNorm[0] = Math<T>::zero();
|
|
|
|
for (int col = threadIdx.x; col < input.getSize(1); col += blockDim.x) {
|
|
TVec val = input[rowStart + row][col];
|
|
val = Math<TVec>::mul(val, val);
|
|
rowNorm[0] = Math<T>::add(rowNorm[0], Math<TVec>::reduceAdd(val));
|
|
}
|
|
} else {
|
|
TVec val = input[rowStart + row][threadIdx.x];
|
|
val = Math<TVec>::mul(val, val);
|
|
rowNorm[0] = Math<TVec>::reduceAdd(val);
|
|
}
|
|
|
|
rowNorm[0] = warpReduceAllSum(rowNorm[0]);
|
|
if (laneId == 0) {
|
|
smem[row * numWarps + warpId] = rowNorm[0];
|
|
}
|
|
}
|
|
} else {
|
|
// We are guaranteed that all RowTileSize rows are available in
|
|
// [rowStart, rowStart + RowTileSize)
|
|
|
|
if (NormLoop) {
|
|
// A single block of threads is not big enough to span each
|
|
// vector
|
|
TVec tmp[RowTileSize];
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = Math<T>::zero();
|
|
}
|
|
|
|
for (int col = threadIdx.x; col < input.getSize(1); col += blockDim.x) {
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
tmp[row] = input[rowStart + row][col];
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
tmp[row] = Math<TVec>::mul(tmp[row], tmp[row]);
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = Math<T>::add(rowNorm[row],
|
|
Math<TVec>::reduceAdd(tmp[row]));
|
|
}
|
|
}
|
|
} else {
|
|
TVec tmp[RowTileSize];
|
|
|
|
// A block of threads is the exact size of the vector
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
tmp[row] = input[rowStart + row][threadIdx.x];
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
tmp[row] = Math<TVec>::mul(tmp[row], tmp[row]);
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = Math<TVec>::reduceAdd(tmp[row]);
|
|
}
|
|
}
|
|
|
|
// Sum up all parts in each warp
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = warpReduceAllSum(rowNorm[row]);
|
|
}
|
|
|
|
if (laneId == 0) {
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
smem[row * numWarps + warpId] = rowNorm[row];
|
|
}
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
// Sum across warps
|
|
if (warpId == 0) {
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = laneId < numWarps ?
|
|
smem[row * numWarps + laneId] : Math<T>::zero();
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
rowNorm[row] = warpReduceAllSum(rowNorm[row]);
|
|
}
|
|
|
|
// Write out answer
|
|
if (laneId == 0) {
|
|
#pragma unroll
|
|
for (int row = 0; row < RowTileSize; ++row) {
|
|
int outCol = rowStart + row;
|
|
|
|
if (lastRowTile) {
|
|
if (outCol < output.getSize(0)) {
|
|
output[outCol] =
|
|
NormSquared ? rowNorm[row] :
|
|
ConvertTo<T>::to(
|
|
sqrtf(ConvertTo<float>::to(rowNorm[row])));
|
|
}
|
|
} else {
|
|
output[outCol] =
|
|
NormSquared ? rowNorm[row] :
|
|
ConvertTo<T>::to(
|
|
sqrtf(ConvertTo<float>::to(rowNorm[row])));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename TVec>
|
|
void runL2Norm(Tensor<T, 2, true>& input,
|
|
Tensor<T, 1, true>& output,
|
|
bool normSquared,
|
|
cudaStream_t stream) {
|
|
FAISS_ASSERT(input.getSize(0) == output.getSize(0));
|
|
|
|
int maxThreads = getMaxThreadsCurrentDevice();
|
|
constexpr int rowTileSize = 8;
|
|
|
|
#define RUN_L2(TYPE_T, TYPE_TVEC, INPUT) \
|
|
do { \
|
|
if (normLoop) { \
|
|
if (normSquared) { \
|
|
l2Norm<TYPE_T, TYPE_TVEC, rowTileSize, true, true> \
|
|
<<<grid, block, smem, stream>>>(INPUT, output); \
|
|
} else { \
|
|
l2Norm<TYPE_T, TYPE_TVEC, rowTileSize, true, false> \
|
|
<<<grid, block, smem, stream>>>(INPUT, output); \
|
|
} \
|
|
} else { \
|
|
if (normSquared) { \
|
|
l2Norm<TYPE_T, TYPE_TVEC, rowTileSize, false, true> \
|
|
<<<grid, block, smem, stream>>>(INPUT, output); \
|
|
} else { \
|
|
l2Norm<TYPE_T, TYPE_TVEC, rowTileSize, false, false> \
|
|
<<<grid, block, smem, stream>>>(INPUT, output); \
|
|
} \
|
|
} \
|
|
} while (0)
|
|
|
|
if (input.template canCastResize<TVec>()) {
|
|
// Can load using the vectorized type
|
|
auto inputV = input.template castResize<TVec>();
|
|
|
|
int dim = inputV.getSize(1);
|
|
bool normLoop = dim > maxThreads;
|
|
int numThreads = min(dim, maxThreads);
|
|
|
|
auto grid = dim3(utils::divUp(inputV.getSize(0), rowTileSize));
|
|
auto block = dim3(numThreads);
|
|
|
|
auto smem = sizeof(T) * rowTileSize * utils::divUp(numThreads, kWarpSize);
|
|
|
|
RUN_L2(T, TVec, inputV);
|
|
} else {
|
|
// Can't load using the vectorized type
|
|
|
|
int dim = input.getSize(1);
|
|
bool normLoop = dim > maxThreads;
|
|
int numThreads = min(dim, maxThreads);
|
|
|
|
auto grid = dim3(utils::divUp(input.getSize(0), rowTileSize));
|
|
auto block = dim3(numThreads);
|
|
|
|
auto smem = sizeof(T) * rowTileSize * utils::divUp(numThreads, kWarpSize);
|
|
|
|
RUN_L2(T, T, input);
|
|
}
|
|
|
|
#undef RUN_L2
|
|
|
|
CUDA_VERIFY(cudaGetLastError());
|
|
}
|
|
|
|
void runL2Norm(Tensor<float, 2, true>& input,
|
|
Tensor<float, 1, true>& output,
|
|
bool normSquared,
|
|
cudaStream_t stream) {
|
|
runL2Norm<float, float4>(input, output, normSquared, stream);
|
|
}
|
|
|
|
#ifdef FAISS_USE_FLOAT16
|
|
void runL2Norm(Tensor<half, 2, true>& input,
|
|
Tensor<half, 1, true>& output,
|
|
bool normSquared,
|
|
cudaStream_t stream) {
|
|
runL2Norm<half, half2>(input, output, normSquared, stream);
|
|
}
|
|
#endif
|
|
|
|
} } // namespace
|