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fused_scale_mask_softmax.cu
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#include <assert.h>
#include <cuda.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <math_constants.h>
#include <time.h>
#include <algorithm>
#include <cub/cub.cuh>
#include <iostream>
using namespace std;
#define CUDA_CHECK() \
if ((cudaPeekAtLastError()) != cudaSuccess) { \
printf("Error %s at %s:%d\n", cudaGetErrorString(cudaGetLastError()), __FILE__, __LINE__ - 1); \
exit(-1); \
}
constexpr int kWarpSize = 32;
template <typename T>
struct SumOp {
__device__ __forceinline__ T operator()(const T& a, const T& b) const { return a + b; }
};
template <typename T>
struct MaxOp {
__device__ __forceinline__ T operator()(const T& a, const T& b) const { return max(a, b); }
};
template <template <typename> class ReductionOp, typename T, int thread_group_width = kWarpSize>
__inline__ __device__ T WarpAllReduce(T val) {
for (int mask = thread_group_width / 2; mask > 0; mask /= 2) {
val = ReductionOp<T>()(val, __shfl_xor_sync(0xffffffff, val, mask));
}
return val;
}
template <template <typename> class ReductionOp, typename T, int block_size>
__inline__ __device__ T BlockAllReduce(T val) {
typedef cub::BlockReduce<T, block_size> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
__shared__ T result_broadcast;
T result = BlockReduce(temp_storage).Reduce(val, ReductionOp<T>());
if (threadIdx.x == 0) {
result_broadcast = result;
}
__syncthreads();
return result_broadcast;
}
template <typename T>
__inline__ __device__ T Inf();
template <>
__inline__ __device__ float Inf<float>() {
return CUDART_INF_F;
}
template <>
__inline__ __device__ double Inf<double>() {
return CUDART_INF;
}
template <typename T>
__inline__ __device__ T Exp(T x);
template <>
__inline__ __device__ float Exp<float>(float x) {
#ifdef OF_SOFTMAX_USE_FAST_MATH
return __expf(x);
#else
return exp(x);
#endif
}
template <>
__inline__ __device__ double Exp<double>(double x) {
return exp(x);
}
template <typename T>
__inline__ __device__ T Div(T a, T b);
template <>
__inline__ __device__ float Div<float>(float a, float b) {
#ifdef OF_SOFTMAX_USE_FAST_MATH
return __fdividef(a, b);
#else
return a / b;
#endif
}
template <>
__inline__ __device__ double Div<double>(double a, double b) {
return a / b;
}
template <typename T>
__inline__ __device__ T Log(T x);
template <>
__inline__ __device__ float Log<float>(float x) {
#ifdef OF_SOFTMAX_USE_FAST_MATH
return __logf(x);
#else
return log(x);
#endif
}
template <>
__inline__ __device__ double Log<double>(double x) {
return log(x);
}
inline cudaError_t GetNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, int* num_blocks) {
int dev;
{
cudaError_t err = cudaGetDevice(&dev);
if (err != cudaSuccess) {
return err;
}
}
int sm_count;
{
cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev);
if (err != cudaSuccess) {
return err;
}
}
int tpm;
{
cudaError_t err = cudaDeviceGetAttribute(&tpm, cudaDevAttrMaxThreadsPerMultiProcessor, dev);
if (err != cudaSuccess) {
return err;
}
}
*num_blocks = std::max<int>(1, std::min<int64_t>(max_blocks, sm_count * tpm / block_size * waves));
return cudaSuccess;
}
template <typename T>
struct DefaultComputeType {
using type = T;
};
template <>
struct DefaultComputeType<half> {
using type = float;
};
template <typename T, int N>
struct GetPackType {
using type = typename std::aligned_storage<N * sizeof(T), N * sizeof(T)>::type;
};
template <typename T, int N>
using PackType = typename GetPackType<T, N>::type;
template <typename T, int N>
union Pack {
static_assert(sizeof(PackType<T, N>) == sizeof(T) * N, "");
__device__ Pack() {}
PackType<T, N> storage;
T elem[N];
};
template <typename SRC, typename DST>
struct DirectLoad {
DirectLoad(const SRC* src, int64_t row_size) : src(src), row_size(row_size) {}
template <int N>
__device__ void load(DST* dst, int64_t row, int64_t col) const {
Pack<SRC, N> pack;
const int64_t offset = (row * row_size + col) / N;
pack.storage = *(reinterpret_cast<const PackType<SRC, N>*>(src) + offset);
#pragma unroll
for (int i = 0; i < N; ++i) {
dst[i] = static_cast<DST>(pack.elem[i]);
}
}
const SRC* src;
int64_t row_size;
};
template <typename SRC, typename DST>
struct DirectStore {
DirectStore(DST* dst, int64_t row_size) : dst(dst), row_size(row_size) {}
template <int N>
__device__ void store(const SRC* src, int64_t row, int64_t col) {
Pack<DST, N> pack;
const int64_t offset = (row * row_size + col) / N;
#pragma unroll
for (int i = 0; i < N; ++i) {
pack.elem[i] = static_cast<DST>(src[i]);
}
*(reinterpret_cast<PackType<DST, N>*>(dst) + offset) = pack.storage;
}
DST* dst;
int64_t row_size;
};
enum class Algorithm {
kSoftmax = 0,
kLogSoftmax = 1,
};
template <typename LOAD,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
bool padding,
Algorithm algorithm>
__global__ void SoftmaxWarpImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols) {
static_assert(cols_per_thread % pack_size == 0, "");
static_assert(thread_group_width <= kWarpSize, "");
static_assert(kWarpSize % thread_group_width == 0, "");
constexpr int num_packs = cols_per_thread / pack_size;
assert(cols <= cols_per_thread * thread_group_width);
ComputeType buf[rows_per_access][cols_per_thread];
const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y;
const int num_global_thread_group = gridDim.x * blockDim.y;
const int lane_id = threadIdx.x;
const int64_t step = num_global_thread_group * rows_per_access;
for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) {
ComputeType thread_max[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
thread_max[row_id] = -Inf<ComputeType>();
ComputeType* row_buf = buf[row_id];
#pragma unroll
for (int pack_id = 0; pack_id < num_packs; ++pack_id) {
const int pack_offset = pack_id * pack_size;
const int col = (pack_id * thread_group_width + lane_id) * pack_size;
if (!padding || col < cols) {
load.template load<pack_size>(row_buf + pack_offset, row + row_id, col);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
thread_max[row_id] = max(thread_max[row_id], row_buf[pack_offset + i]);
}
} else {
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
row_buf[pack_offset + i] = -Inf<ComputeType>();
}
}
}
}
ComputeType warp_max[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
warp_max[row_id] = WarpAllReduce<MaxOp, ComputeType, thread_group_width>(thread_max[row_id]);
}
ComputeType thread_sum[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
thread_sum[row_id] = 0;
ComputeType* row_buf = buf[row_id];
#pragma unroll
for (int i = 0; i < cols_per_thread; ++i) {
if (algorithm == Algorithm::kSoftmax) {
row_buf[i] = Exp(row_buf[i] - warp_max[row_id]);
thread_sum[row_id] += row_buf[i];
} else if (algorithm == Algorithm::kLogSoftmax) {
row_buf[i] -= warp_max[row_id];
thread_sum[row_id] += Exp(row_buf[i]);
} else {
__trap();
}
}
}
ComputeType warp_sum[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
warp_sum[row_id] = WarpAllReduce<SumOp, ComputeType, thread_group_width>(thread_sum[row_id]);
}
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
ComputeType* row_buf = buf[row_id];
#pragma unroll
for (int i = 0; i < cols_per_thread; ++i) {
if (algorithm == Algorithm::kSoftmax) {
row_buf[i] = Div(row_buf[i], warp_sum[row_id]);
} else if (algorithm == Algorithm::kLogSoftmax) {
row_buf[i] -= Log(warp_sum[row_id]);
} else {
__trap();
}
}
#pragma unroll
for (int i = 0; i < num_packs; ++i) {
const int col = (i * thread_group_width + lane_id) * pack_size;
if (!padding || col < cols) {
store.template store<pack_size>(row_buf + i * pack_size, row + row_id, col);
}
}
}
}
}
template <typename LOAD,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
bool padding,
Algorithm algorithm>
inline cudaError_t LaunchSoftmaxWarpImpl(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
constexpr int block_size = 128;
constexpr int waves = 32;
static_assert(block_size % thread_group_width == 0, "");
constexpr int thread_groups_per_block = block_size / thread_group_width;
dim3 block_dim(thread_group_width, thread_groups_per_block);
const int64_t num_blocks = (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block;
int grid_dim_x;
{
cudaError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x);
if (err != cudaSuccess) {
return err;
}
}
SoftmaxWarpImpl<LOAD, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, padding, algorithm>
<<<grid_dim_x, block_dim, 0, stream>>>(load, store, rows, cols);
return cudaPeekAtLastError();
}
template <typename LOAD,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
Algorithm algorithm>
inline cudaError_t DispatchSoftmaxWarpImplPadding(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
if (cols == cols_per_thread * thread_group_width) {
return LaunchSoftmaxWarpImpl<LOAD, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, false, algorithm>(
stream, load, store, rows, cols);
} else {
return LaunchSoftmaxWarpImpl<LOAD, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, true, algorithm>(
stream, load, store, rows, cols);
}
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
typename std::enable_if<pack_size == 1, cudaError_t>::type DispatchSoftmaxWarpImplCols(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols <= 0) {
return cudaErrorInvalidValue;
}
#define DEFINE_ONE_ELIF(thread_group_width) \
else if (cols <= (thread_group_width) * pack_size) { \
if (rows % 2 == 0) { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, pack_size, thread_group_width, 2, algorithm>( \
stream, load, store, rows, cols); \
} else { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, pack_size, thread_group_width, 1, algorithm>( \
stream, load, store, rows, cols); \
} \
}
DEFINE_ONE_ELIF(1)
DEFINE_ONE_ELIF(2)
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
#define DEFINE_ONE_ELIF(col) \
else if (cols <= (col) * kWarpSize) { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, col, kWarpSize, 1, algorithm>(stream, load, store, rows, cols); \
}
DEFINE_ONE_ELIF(2)
DEFINE_ONE_ELIF(3)
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(5)
DEFINE_ONE_ELIF(6)
DEFINE_ONE_ELIF(7)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(9)
DEFINE_ONE_ELIF(10)
DEFINE_ONE_ELIF(11)
DEFINE_ONE_ELIF(12)
DEFINE_ONE_ELIF(13)
DEFINE_ONE_ELIF(14)
DEFINE_ONE_ELIF(15)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(17)
DEFINE_ONE_ELIF(18)
DEFINE_ONE_ELIF(19)
DEFINE_ONE_ELIF(20)
DEFINE_ONE_ELIF(21)
DEFINE_ONE_ELIF(22)
DEFINE_ONE_ELIF(23)
DEFINE_ONE_ELIF(24)
DEFINE_ONE_ELIF(25)
DEFINE_ONE_ELIF(26)
DEFINE_ONE_ELIF(27)
DEFINE_ONE_ELIF(28)
DEFINE_ONE_ELIF(29)
DEFINE_ONE_ELIF(30)
DEFINE_ONE_ELIF(31)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
else {
return cudaErrorInvalidValue;
}
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
typename std::enable_if<pack_size == 2, cudaError_t>::type DispatchSoftmaxWarpImplCols(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols <= 0) {
return cudaErrorInvalidValue;
}
#define DEFINE_ONE_ELIF(thread_group_width) \
else if (cols <= (thread_group_width) * pack_size) { \
if (rows % 2 == 0) { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, pack_size, thread_group_width, 2, algorithm>( \
stream, load, store, rows, cols); \
} else { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, pack_size, thread_group_width, 1, algorithm>( \
stream, load, store, rows, cols); \
} \
}
DEFINE_ONE_ELIF(1)
DEFINE_ONE_ELIF(2)
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
#define DEFINE_ONE_ELIF(col) \
else if (cols <= (col) * kWarpSize) { \
return DispatchSoftmaxWarpImplPadding<LOAD, STORE, ComputeType, pack_size, col, kWarpSize, 1, algorithm>(stream, load, store, rows, cols); \
}
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(6)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(10)
DEFINE_ONE_ELIF(12)
DEFINE_ONE_ELIF(14)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(18)
DEFINE_ONE_ELIF(20)
DEFINE_ONE_ELIF(22)
DEFINE_ONE_ELIF(24)
DEFINE_ONE_ELIF(26)
DEFINE_ONE_ELIF(28)
DEFINE_ONE_ELIF(30)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
else {
return cudaErrorInvalidValue;
}
}
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
struct DispatchSoftmaxWarpImplPackSize {
cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
if (cols % 2 == 0) {
return DispatchSoftmaxWarpImplCols<LOAD, STORE, ComputeType, 2, algorithm>(stream, load, store, rows, cols);
} else {
return DispatchSoftmaxWarpImplCols<LOAD, STORE, ComputeType, 1, algorithm>(stream, load, store, rows, cols);
}
}
};
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
inline cudaError_t DispatchSoftmaxWarpImpl(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
return DispatchSoftmaxWarpImplPackSize<LOAD, STORE, ComputeType, algorithm>()(stream, load, store, rows, cols);
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, int block_size, Algorithm algorithm>
__global__ void SoftmaxBlockSMemImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols) {
extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[];
auto* buf = reinterpret_cast<ComputeType*>(shared_buf);
const int tid = threadIdx.x;
assert(cols % pack_size == 0);
const int num_packs = cols / pack_size;
for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) {
ComputeType thread_max = -Inf<ComputeType>();
for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) {
ComputeType pack[pack_size];
load.template load<pack_size>(pack, row, pack_id * pack_size);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
buf[i * num_packs + pack_id] = pack[i];
thread_max = max(thread_max, pack[i]);
}
}
const ComputeType row_max = BlockAllReduce<MaxOp, ComputeType, block_size>(thread_max);
ComputeType thread_sum = 0;
for (int col = tid; col < cols; col += block_size) {
if (algorithm == Algorithm::kSoftmax) {
const ComputeType exp_x = Exp(buf[col] - row_max);
buf[col] = exp_x;
thread_sum += exp_x;
} else {
const ComputeType x = buf[col] - row_max;
buf[col] = x;
thread_sum += Exp(x);
}
}
const ComputeType row_sum = BlockAllReduce<SumOp, ComputeType, block_size>(thread_sum);
for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) {
ComputeType pack[pack_size];
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
if (algorithm == Algorithm::kSoftmax) {
pack[i] = Div(buf[i * num_packs + pack_id], row_sum);
} else if (algorithm == Algorithm::kLogSoftmax) {
pack[i] = buf[i * num_packs + pack_id] - Log(row_sum);
} else {
__trap();
}
}
store.template store<pack_size>(pack, row, pack_id * pack_size);
}
}
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, int block_size, Algorithm algorithm>
inline cudaError_t LaunchSoftmaxBlockSMemImpl(cudaStream_t stream, LOAD load, STORE store, int smem, const int64_t rows, const int64_t cols) {
constexpr int waves = 32;
int grid_dim_x;
{
cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x);
if (err != cudaSuccess) {
return err;
}
}
SoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size, algorithm>
<<<grid_dim_x, block_size, smem, stream>>>(load, store, rows, cols);
return cudaPeekAtLastError();
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols,
bool* success) {
constexpr int block_size_conf_1 = 128;
constexpr int block_size_conf_2 = 256;
constexpr int block_size_conf_3 = 512;
constexpr int block_size_conf_4 = 1024;
const size_t smem = cols * sizeof(ComputeType);
int max_active_blocks_conf_1;
{
cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&max_active_blocks_conf_1, SoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_1, algorithm>, block_size_conf_1,
smem);
if (err != cudaSuccess) {
return err;
}
}
if (max_active_blocks_conf_1 <= 0) {
*success = false;
return cudaSuccess;
}
int max_active_blocks_conf_4;
{
cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&max_active_blocks_conf_4, SoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_4, algorithm>, block_size_conf_4,
smem);
if (err != cudaSuccess) {
return err;
}
}
if (max_active_blocks_conf_4 == max_active_blocks_conf_1) {
*success = true;
return LaunchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_4, algorithm>(stream, load, store, smem, rows, cols);
}
int max_active_blocks_conf_3;
{
cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&max_active_blocks_conf_3, SoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_3, algorithm>, block_size_conf_3,
smem);
if (err != cudaSuccess) {
return err;
}
}
if (max_active_blocks_conf_3 == max_active_blocks_conf_1) {
*success = true;
return LaunchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_3, algorithm>(stream, load, store, smem, rows, cols);
}
int max_active_blocks_conf_2;
{
cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&max_active_blocks_conf_2, SoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_2, algorithm>, block_size_conf_2,
smem);
if (err != cudaSuccess) {
return err;
}
}
if (max_active_blocks_conf_2 == max_active_blocks_conf_1) {
*success = true;
return LaunchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_2, algorithm>(stream, load, store, smem, rows, cols);
}
*success = true;
return LaunchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, pack_size, block_size_conf_1, algorithm>(stream, load, store, smem, rows, cols);
}
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
struct TryDispatchSoftmaxBlockSMemImplPackSize {
cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, bool* success) {
if (cols % 2 == 0) {
return TryDispatchSoftmaxBlockSMemImplBlockSize<LOAD, STORE, ComputeType, 2, algorithm>(stream, load, store, rows, cols, success);
} else {
return TryDispatchSoftmaxBlockSMemImplBlockSize<LOAD, STORE, ComputeType, 1, algorithm>(stream, load, store, rows, cols, success);
}
}
};
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
inline cudaError_t TryDispatchSoftmaxBlockSMemImpl(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols,
bool* success) {
return TryDispatchSoftmaxBlockSMemImplPackSize<LOAD, STORE, ComputeType, algorithm>()(stream, load, store, rows, cols, success);
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, int block_size, Algorithm algorithm>
__global__ void SoftmaxBlockUncachedImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols) {
const int tid = threadIdx.x;
assert(cols % pack_size == 0);
const int num_packs = cols / pack_size;
for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) {
ComputeType thread_max = -Inf<ComputeType>();
for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) {
ComputeType pack[pack_size];
load.template load<pack_size>(pack, row, pack_id * pack_size);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
thread_max = max(thread_max, pack[i]);
}
}
const ComputeType row_max = BlockAllReduce<MaxOp, ComputeType, block_size>(thread_max);
ComputeType thread_sum = 0;
for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) {
ComputeType pack[pack_size];
load.template load<pack_size>(pack, row, pack_id * pack_size);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
thread_sum += Exp(pack[i] - row_max);
}
}
const ComputeType row_sum = BlockAllReduce<SumOp, ComputeType, block_size>(thread_sum);
for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) {
ComputeType pack[pack_size];
load.template load<pack_size>(pack, row, pack_id * pack_size);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
if (algorithm == Algorithm::kSoftmax) {
pack[i] = Div(Exp(pack[i] - row_max), row_sum);
} else if (algorithm == Algorithm::kLogSoftmax) {
pack[i] = (pack[i] - row_max) - Log(row_sum);
} else {
__trap();
}
}
store.template store<pack_size>(pack, row, pack_id * pack_size);
}
}
}
template <typename LOAD, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
inline cudaError_t LaunchSoftmaxBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
constexpr int block_size = 1024;
constexpr int waves = 32;
int grid_dim_x;
{
cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x);
if (err != cudaSuccess) {
return err;
}
}
SoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, pack_size, block_size, algorithm>
<<<grid_dim_x, block_size, 0, stream>>>(load, store, rows, cols);
return cudaPeekAtLastError();
}
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
struct DispatchSoftmaxBlockUncachedImplPackSize {
cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
if (cols % 2 == 0) {
return LaunchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, 2, algorithm>(stream, load, store, rows, cols);
} else {
return LaunchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, 1, algorithm>(stream, load, store, rows, cols);
}
}
};
template <typename LOAD, typename STORE, typename ComputeType, Algorithm algorithm>
inline cudaError_t DispatchSoftmaxBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) {
return DispatchSoftmaxBlockUncachedImplPackSize<LOAD, STORE, ComputeType, algorithm>()(stream, load, store, rows, cols);
}
template <typename LOAD, typename STORE, typename ComputeType>
inline typename std::enable_if<!std::is_same<ComputeType, double>::value, cudaError_t>::type DispatchSoftmax(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols < 1024) {
return DispatchSoftmaxWarpImpl<LOAD, STORE, ComputeType, Algorithm::kSoftmax>(stream, load, store, rows, cols);
} else {
bool dispatch_smem_impl_success;
{
cudaError_t err = TryDispatchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, Algorithm::kSoftmax>(stream, load, store, rows, cols,
&dispatch_smem_impl_success);
if (err != cudaSuccess) {
return err;
}
}
if (!dispatch_smem_impl_success) {
return DispatchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, Algorithm::kSoftmax>(stream, load, store, rows, cols);
}
return cudaSuccess;
}
}
template <typename LOAD, typename STORE, typename ComputeType>
inline typename std::enable_if<std::is_same<ComputeType, double>::value, cudaError_t>::type DispatchSoftmax(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
return DispatchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, Algorithm::kSoftmax>(stream, load, store, rows, cols);
}
template <typename LOAD, typename STORE, typename ComputeType>
inline typename std::enable_if<!std::is_same<ComputeType, double>::value, cudaError_t>::type DispatchLogSoftmax(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols <= 1024) {
return DispatchSoftmaxWarpImpl<LOAD, STORE, ComputeType, Algorithm::kLogSoftmax>(stream, load, store, rows, cols);
} else {
bool dispatch_smem_impl_success;
{
cudaError_t err = TryDispatchSoftmaxBlockSMemImpl<LOAD, STORE, ComputeType, Algorithm::kLogSoftmax>(stream, load, store, rows, cols,
&dispatch_smem_impl_success);
if (err != cudaSuccess) {
return err;
}
}
if (!dispatch_smem_impl_success) {
return DispatchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, Algorithm::kLogSoftmax>(stream, load, store, rows, cols);
}
return cudaSuccess;
}
}
template <typename LOAD, typename STORE, typename ComputeType>
inline typename std::enable_if<std::is_same<ComputeType, double>::value, cudaError_t>::type DispatchLogSoftmax(cudaStream_t stream,
LOAD load,
STORE store,
const int64_t rows,
const int64_t cols) {
return DispatchSoftmaxBlockUncachedImpl<LOAD, STORE, ComputeType, Algorithm::kLogSoftmax>(stream, load, store, rows, cols);
}
template <typename LOAD_Y,
typename LOAD_DY,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
bool padding,
Algorithm algorithm>
__global__ void SoftmaxGradWarpImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) {
static_assert(cols_per_thread % pack_size == 0, "");
constexpr int pack_per_thread = cols_per_thread / pack_size;
assert(cols <= cols_per_thread * thread_group_width);
static_assert(thread_group_width <= kWarpSize, "");
static_assert(kWarpSize % thread_group_width == 0, "");
ComputeType y_buf[rows_per_access][cols_per_thread];
ComputeType dy_buf[rows_per_access][cols_per_thread];
const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y;
const int num_global_thread_group = gridDim.x * blockDim.y;
const int lane_id = threadIdx.x;
const int64_t step = num_global_thread_group * rows_per_access;
for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) {
ComputeType thread_sum[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
thread_sum[row_id] = 0;
ComputeType* row_y_buf = y_buf[row_id];
ComputeType* row_dy_buf = dy_buf[row_id];
#pragma unroll
for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) {
const int pack_offset = pack_id * pack_size;
const int col = (pack_id * thread_group_width + lane_id) * pack_size;
if (!padding || col < cols) {
load_y.template load<pack_size>(row_y_buf + pack_offset, row + row_id, col);
load_dy.template load<pack_size>(row_dy_buf + pack_offset, row + row_id, col);
#pragma unroll
for (int i = 0; i < pack_size; ++i) {
if (algorithm == Algorithm::kSoftmax) {
thread_sum[row_id] += row_y_buf[pack_offset + i] * row_dy_buf[pack_offset + i];
} else if (algorithm == Algorithm::kLogSoftmax) {
thread_sum[row_id] += row_dy_buf[pack_offset + i];
} else {
__trap();
}
}
}
}
}
ComputeType warp_sum[rows_per_access];
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
warp_sum[row_id] = WarpAllReduce<SumOp, ComputeType, thread_group_width>(thread_sum[row_id]);
}
#pragma unroll
for (int row_id = 0; row_id < rows_per_access; ++row_id) {
ComputeType* row_y_buf = y_buf[row_id];
ComputeType* row_dy_buf = dy_buf[row_id];
#pragma unroll
for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) {
const int pack_offset = pack_id * pack_size;
const int col = (pack_id * thread_group_width + lane_id) * pack_size;
if (!padding || col < cols) {
for (int i = 0; i < pack_size; ++i) {
if (algorithm == Algorithm::kSoftmax) {
row_dy_buf[pack_offset + i] = (row_dy_buf[pack_offset + i] - warp_sum[row_id]) * row_y_buf[pack_offset + i];
} else if (algorithm == Algorithm::kLogSoftmax) {
row_dy_buf[pack_offset + i] -= Exp(row_y_buf[pack_offset + i]) * warp_sum[row_id];
} else {
__trap();
}
}
store.template store<pack_size>(row_dy_buf + pack_offset, row + row_id, col);
}
}
}
}
}
template <typename LOAD_Y,
typename LOAD_DY,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
bool padding,
Algorithm algorithm>
inline cudaError_t LaunchSoftmaxGradWarpImpl(cudaStream_t stream,
LOAD_Y load_y,
LOAD_DY load_dy,
STORE store,
const int64_t rows,
const int64_t cols) {
constexpr int block_size = 128;
constexpr int waves = 32;
static_assert(block_size % thread_group_width == 0, "");
constexpr int thread_groups_per_block = block_size / thread_group_width;
dim3 block_dim(thread_group_width, thread_groups_per_block);
const int64_t num_blocks = (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block;
int grid_dim_x;
{
cudaError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x);
if (err != cudaSuccess) {
return err;
}
}
SoftmaxGradWarpImpl<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, padding, algorithm>
<<<grid_dim_x, block_dim, 0, stream>>>(load_y, load_dy, store, rows, cols);
return cudaPeekAtLastError();
}
template <typename LOAD_Y,
typename LOAD_DY,
typename STORE,
typename ComputeType,
int pack_size,
int cols_per_thread,
int thread_group_width,
int rows_per_access,
Algorithm algorithm>
inline cudaError_t DispatchSoftmaxGradWarpImplPadding(cudaStream_t stream,
LOAD_Y load_y,
LOAD_DY load_dy,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols == cols_per_thread * thread_group_width) {
return LaunchSoftmaxGradWarpImpl<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, false,
algorithm>(stream, load_y, load_dy, store, rows, cols);
} else {
return LaunchSoftmaxGradWarpImpl<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, cols_per_thread, thread_group_width, rows_per_access, true,
algorithm>(stream, load_y, load_dy, store, rows, cols);
}
}
template <typename LOAD_Y, typename LOAD_DY, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
typename std::enable_if<pack_size == 1, cudaError_t>::type DispatchSoftmaxGradWarpImplCols(cudaStream_t stream,
LOAD_Y load_y,
LOAD_DY load_dy,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols <= 0) {
return cudaErrorInvalidValue;
}
#define DEFINE_ONE_ELIF(thread_group_width) \
else if (cols <= (thread_group_width) * pack_size) { \
if (rows % 2 == 0) { \
return DispatchSoftmaxGradWarpImplPadding<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, pack_size, thread_group_width, 2, algorithm>( \
stream, load_y, load_dy, store, rows, cols); \
} else { \
return DispatchSoftmaxGradWarpImplPadding<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, pack_size, thread_group_width, 1, algorithm>( \
stream, load_y, load_dy, store, rows, cols); \
} \
}
DEFINE_ONE_ELIF(1)
DEFINE_ONE_ELIF(2)
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
#define DEFINE_ONE_ELIF(col) \
else if (cols <= (col) * kWarpSize) { \
return DispatchSoftmaxGradWarpImplPadding<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, col, kWarpSize, 1, algorithm>( \
stream, load_y, load_dy, store, rows, cols); \
}
DEFINE_ONE_ELIF(2)
DEFINE_ONE_ELIF(3)
DEFINE_ONE_ELIF(4)
DEFINE_ONE_ELIF(5)
DEFINE_ONE_ELIF(6)
DEFINE_ONE_ELIF(7)
DEFINE_ONE_ELIF(8)
DEFINE_ONE_ELIF(9)
DEFINE_ONE_ELIF(10)
DEFINE_ONE_ELIF(11)
DEFINE_ONE_ELIF(12)
DEFINE_ONE_ELIF(13)
DEFINE_ONE_ELIF(14)
DEFINE_ONE_ELIF(15)
DEFINE_ONE_ELIF(16)
DEFINE_ONE_ELIF(17)
DEFINE_ONE_ELIF(18)
DEFINE_ONE_ELIF(19)
DEFINE_ONE_ELIF(20)
DEFINE_ONE_ELIF(21)
DEFINE_ONE_ELIF(22)
DEFINE_ONE_ELIF(23)
DEFINE_ONE_ELIF(24)
DEFINE_ONE_ELIF(25)
DEFINE_ONE_ELIF(26)
DEFINE_ONE_ELIF(27)
DEFINE_ONE_ELIF(28)
DEFINE_ONE_ELIF(29)
DEFINE_ONE_ELIF(30)
DEFINE_ONE_ELIF(31)
DEFINE_ONE_ELIF(32)
#undef DEFINE_ONE_ELIF
else {
return cudaErrorInvalidValue;
}
}
template <typename LOAD_Y, typename LOAD_DY, typename STORE, typename ComputeType, int pack_size, Algorithm algorithm>
typename std::enable_if<pack_size == 2, cudaError_t>::type DispatchSoftmaxGradWarpImplCols(cudaStream_t stream,
LOAD_Y load_y,
LOAD_DY load_dy,
STORE store,
const int64_t rows,
const int64_t cols) {
if (cols <= 0) {
return cudaErrorInvalidValue;
}
#define DEFINE_ONE_ELIF(thread_group_width) \
else if (cols <= (thread_group_width) * pack_size) { \
if (rows % 2 == 0) { \
return DispatchSoftmaxGradWarpImplPadding<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, pack_size, thread_group_width, 2, algorithm>( \
stream, load_y, load_dy, store, rows, cols); \
} else { \
return DispatchSoftmaxGradWarpImplPadding<LOAD_Y, LOAD_DY, STORE, ComputeType, pack_size, pack_size, thread_group_width, 1, algorithm>( \
stream, load_y, load_dy, store, rows, cols); \