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ColossalAI/colossalai/kernel/cuda_native/csrc/kernels/include/block_reduce.h

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/* Copyright 2021 The LightSeq Team
Copyright Tencent/TurboTransformers
This block_reduce_n is adapted from Tencent/TurboTransformers
*/
#pragma once
#include <cuda.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
enum class ReduceType { kMax = 0, kSum };
const unsigned int WARP_REDUCE_MASK = 0xffffffff;
const float REDUCE_FLOAT_INF_NEG = -100000000.f;
const float REDUCE_FLOAT_INF_POS = 100000000.f;
const unsigned int WARP_REDUCE_SIZE = 32;
template <typename T>
__forceinline__ __device__ T warpReduceSum(T val) {
for (int mask = (WARP_REDUCE_SIZE >> 1); mask > 0; mask >>= 1)
val += __shfl_xor_sync(WARP_REDUCE_MASK, val, mask, WARP_REDUCE_SIZE);
return val;
}
/* Calculate the sum of all elements in a block */
template <typename T>
__forceinline__ __device__ T blockReduceSum(T val) {
static __shared__ T shared[32];
int lane = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
val = warpReduceSum<T>(val);
if (lane == 0) shared[wid] = val;
__syncthreads();
val = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : (T)0.0f;
val = warpReduceSum<T>(val);
return val;
}
template <ReduceType Rtype, int Num>
__inline__ __device__ void blockReduce(float *pval);
// use template to make code more concise
template <ReduceType Rtype, int Num>
__inline__ __device__ void warpReduce(float *pval);
// static
template <>
__inline__ __device__ void warpReduce<ReduceType::kMax, 1>(float *pval) {
*pval = max(*pval, __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 16, 32));
*pval = max(*pval, __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 8, 32));
*pval = max(*pval, __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 4, 32));
*pval = max(*pval, __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 2, 32));
*pval = max(*pval, __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 1, 32));
}
template <>
__inline__ __device__ void warpReduce<ReduceType::kMax, 2>(float *pval) {
float val0_tmp, val1_tmp;
#define WarpReduceMaxOneStep(a, b) \
val0_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval), a, b); \
val1_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 1), a, b); \
*(pval) = max(val0_tmp, *(pval)); \
*(pval + 1) = max(val1_tmp, *(pval + 1));
WarpReduceMaxOneStep(16, 32);
WarpReduceMaxOneStep(8, 32);
WarpReduceMaxOneStep(4, 32);
WarpReduceMaxOneStep(2, 32);
WarpReduceMaxOneStep(1, 32);
#undef WarpReduceMaxOneStep
}
template <>
__inline__ __device__ void warpReduce<ReduceType::kSum, 1>(float *pval) {
*pval += __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 16, 32);
*pval += __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 8, 32);
*pval += __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 4, 32);
*pval += __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 2, 32);
*pval += __shfl_xor_sync(WARP_REDUCE_MASK, *pval, 1, 32);
}
/*
* Unorll for loop for warpreduce to
* imporve instruction issue efficiency
* ElemX means there are X numbers to be summed
*/
template <>
__inline__ __device__ void warpReduce<ReduceType::kSum, 2>(float *pval) {
float val0_tmp, val1_tmp;
#define WarpReduceSumOneStep(a, b) \
val0_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 0), a, b); \
val1_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 1), a, b); \
*(pval + 0) += val0_tmp; \
*(pval + 1) += val1_tmp
WarpReduceSumOneStep(16, 32);
WarpReduceSumOneStep(8, 32);
WarpReduceSumOneStep(4, 32);
WarpReduceSumOneStep(2, 32);
WarpReduceSumOneStep(1, 32);
#undef WarpReduceSumOneStep
}
template <>
__inline__ __device__ void warpReduce<ReduceType::kSum, 4>(float *pval) {
float val0_tmp, val1_tmp, val2_tmp, val3_tmp;
#define WarpReduceSumOneStep(a, b) \
val0_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 0), a, b); \
val1_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 1), a, b); \
val2_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 2), a, b); \
val3_tmp = __shfl_xor_sync(WARP_REDUCE_MASK, *(pval + 3), a, b); \
*(pval + 0) += val0_tmp; \
*(pval + 1) += val1_tmp; \
*(pval + 2) += val2_tmp; \
*(pval + 3) += val3_tmp
WarpReduceSumOneStep(16, 32);
WarpReduceSumOneStep(8, 32);
WarpReduceSumOneStep(4, 32);
WarpReduceSumOneStep(2, 32);
WarpReduceSumOneStep(1, 32);
#undef WarpReduceSumOneStep
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kSum, 1>(float *pval) {
const int num = 1;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kSum, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = 0.f;
}
}
warpReduce<ReduceType::kSum, num>(pval);
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kSum, 2>(float *pval) {
const int num = 2;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kSum, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = 0.f;
}
}
warpReduce<ReduceType::kSum, num>(pval);
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kSum, 4>(float *pval) {
const int num = 4;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kSum, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = 0.f;
}
}
warpReduce<ReduceType::kSum, num>(pval);
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kMax, 1>(float *pval) {
const int num = 1;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kMax, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = REDUCE_FLOAT_INF_NEG;
}
}
warpReduce<ReduceType::kMax, num>(pval);
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kMax, 2>(float *pval) {
const int num = 1;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kMax, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = REDUCE_FLOAT_INF_NEG;
}
}
warpReduce<ReduceType::kMax, num>(pval);
}
template <>
__inline__ __device__ void blockReduce<ReduceType::kMax, 4>(float *pval) {
const int num = 1;
static __shared__ float shared[num][32];
int lane_id = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
warpReduce<ReduceType::kMax, num>(pval);
if (lane_id == 0) {
#pragma unroll
for (int i = 0; i < num; ++i) {
shared[i][wid] = *(pval + i);
}
}
__syncthreads();
if (threadIdx.x < (blockDim.x >> 5)) {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = shared[i][lane_id];
}
} else {
#pragma unroll
for (int i = 0; i < num; ++i) {
*(pval + i) = REDUCE_FLOAT_INF_NEG;
}
}
warpReduce<ReduceType::kMax, num>(pval);
}
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