/
cum_kernel.cu
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/
cum_kernel.cu
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/reverse.h>
#include <thrust/scan.h>
#include "paddle/phi/kernels/cumsum_kernel.h"
#ifdef __NVCC__
#include <cub/cub.cuh>
#endif
#ifdef __HIPCC__
#include <hipcub/hipcub.hpp>
namespace cub = hipcub;
#endif
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/hostdevice.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, int BLOCK_SIZE>
__device__ void BlockReverse(
const T* idata, T* odata, int src_base, int dst_base, int valid_item) {
__shared__ T sh_mem[BLOCK_SIZE];
int tx = threadIdx.x;
int offset = tx;
int in_index = src_base + offset;
if (offset >= valid_item) {
sh_mem[offset] = 0;
} else {
int sh_mem_index = BLOCK_SIZE - offset - 1;
T data = idata[in_index];
sh_mem[sh_mem_index] = data;
}
__syncthreads();
int out_index = dst_base - offset;
if (offset < valid_item) {
int sh_mem_index = BLOCK_SIZE - offset - 1;
odata[out_index] = sh_mem[sh_mem_index];
}
}
template <typename T>
__global__ void MatrixRowReverse(const T* matrix_data,
T* reverse_data,
int reverse_size,
int outer_size,
int inner_size) {
int bx = blockIdx.x;
int by = blockIdx.y;
int item_per_block = 1024;
for (int block_offset = 0; block_offset < reverse_size;
block_offset += item_per_block) {
int valid_item = (reverse_size - block_offset > item_per_block)
? item_per_block
: reverse_size - block_offset;
int src_offset =
bx * reverse_size + block_offset + by * (inner_size * reverse_size);
int dst_offset = bx * reverse_size + by * (inner_size * reverse_size) +
reverse_size - 1 - block_offset;
if (reverse_size < item_per_block) {
valid_item = reverse_size;
}
BlockReverse<T, 1024>(
matrix_data, reverse_data, src_offset, dst_offset, valid_item);
}
}
template <typename T, typename Op>
struct BlockPrefixCallbackOp {
// Running prefix
T running_total_;
Op op_;
__device__ BlockPrefixCallbackOp(T running_total, Op op)
: running_total_(running_total), op_(op) {}
// Callback operator to be entered by the first warp of threads in the block.
// tid 0 is responsible for returning a value for seeding the block-wide scan.
__device__ T operator()(T block_aggregate) {
T old_prefix = running_total_;
running_total_ = op_(old_prefix, block_aggregate);
return old_prefix;
}
};
// No bank-conflict transpose
template <typename T, int TILE_DIM, int BLOCK_ROWS>
__global__ void MatrixTranspose(T* odata,
const T* idata,
size_t height,
size_t width) {
__shared__ T tile[TILE_DIM][TILE_DIM + 1];
int x = blockIdx.x * TILE_DIM + threadIdx.x;
int y = blockIdx.y * TILE_DIM + threadIdx.y;
for (int j = 0; j < TILE_DIM; j += BLOCK_ROWS) {
if (x < width && (y + j) < height) {
tile[threadIdx.y + j][threadIdx.x] = idata[(y + j) * width + x];
} else {
tile[threadIdx.y + j][threadIdx.x] = 0;
}
}
__syncthreads();
x = blockIdx.y * TILE_DIM + threadIdx.x; // transpose block offset
y = blockIdx.x * TILE_DIM + threadIdx.y;
for (int j = 0; j < TILE_DIM; j += BLOCK_ROWS) {
if (x < height && (y + j) < width) {
odata[(y + j) * height + x] = tile[threadIdx.x][threadIdx.y + j];
}
}
}
struct LogAddExp {
template <typename T>
__host__ __device__ __forceinline__ T operator()(const T& a,
const T& b) const {
return std::log(1 + std::exp(std::min(a, b) - std::max(a, b))) +
std::max(a, b);
}
};
template <typename T, typename op>
struct Identity;
template <typename T>
struct Identity<T, cub::Sum> {
static constexpr T value = 0;
};
template <typename T>
struct Identity<T, LogAddExp> {
static constexpr T value = std::numeric_limits<T>::lowest();
};
template <typename T, int BLOCK_THREADS, int ITEMS_PER_THREAD, typename Op>
__global__ void BlockScanKernel(T* d_out,
const T* d_in,
int inner_size,
int outer_size,
int scan_size,
bool exclusive,
Op op) {
// Specialize BlockLoad, BlockStore, and BlockRadixSort collective types
typedef cub::
BlockLoad<T, BLOCK_THREADS, ITEMS_PER_THREAD, cub::BLOCK_LOAD_TRANSPOSE>
BlockLoadT;
typedef cub::
BlockStore<T, BLOCK_THREADS, ITEMS_PER_THREAD, cub::BLOCK_STORE_TRANSPOSE>
BlockStoreT;
typedef cub::BlockScan<T, BLOCK_THREADS> BlockScanT;
// Allocate type-safe, repurposable shared memory for collectives
__shared__ union {
typename BlockLoadT::TempStorage load;
typename BlockStoreT::TempStorage store;
typename BlockScanT::TempStorage scan;
} temp_storage;
int bx = blockIdx.x;
int by = blockIdx.y;
BlockPrefixCallbackOp<T, Op> prefix_op(Identity<T, Op>::value, op);
T block_aggregate = static_cast<T>(0);
// Obtain this block's segment of consecutive keys (blocked across threads)
int item_per_block = BLOCK_THREADS * ITEMS_PER_THREAD;
for (int block_offset = 0; block_offset < scan_size;
block_offset += BLOCK_THREADS * ITEMS_PER_THREAD) {
int valid_item = (scan_size - block_offset > item_per_block)
? item_per_block
: (scan_size - block_offset);
if (scan_size < item_per_block) {
valid_item = scan_size;
}
int offset = bx * scan_size + block_offset + by * (inner_size * scan_size);
T thread_keys[ITEMS_PER_THREAD];
BlockLoadT(temp_storage.load)
.Load(d_in + offset, thread_keys, valid_item, 0);
__syncthreads();
if (exclusive) {
BlockScanT(temp_storage.scan)
.ExclusiveScan(thread_keys, thread_keys, op, prefix_op);
} else {
BlockScanT(temp_storage.scan)
.InclusiveScan(thread_keys, thread_keys, op, prefix_op);
}
__syncthreads();
BlockStoreT(temp_storage.store)
.Store(d_out + offset, thread_keys, valid_item);
}
}
template <typename T, typename Context, typename Op>
void ScanKernel(const Context& dev_ctx,
const DenseTensor& x,
int axis,
bool flatten,
bool exclusive,
bool reverse,
Op op,
DenseTensor* out) {
auto out_dims = out->dims();
auto size = x.numel();
PADDLE_ENFORCE_EQ(
axis < out_dims.size() && axis >= (0 - out_dims.size()),
true,
phi::errors::OutOfRange(
"Attr(axis) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(axis) = %d.",
out_dims.size(),
out_dims.size() - 1,
axis));
if (axis < 0) {
axis += out_dims.size();
}
T* out_data = dev_ctx.template Alloc<T>(out);
const T* in_data = x.data<T>();
size_t height = 1;
size_t width = 1;
for (size_t i = 0; i <= axis; i++) {
height *= out_dims[i];
}
for (size_t i = axis + 1; i < out_dims.size(); i++) {
width *= out_dims[i];
}
int scan_size = out_dims[axis];
bool transpose = (axis != out_dims.size() - 1);
int tile_size = 32;
dim3 blocks(32, 8);
dim3 transpose_grids((width + tile_size - 1) / tile_size,
(height + tile_size - 1) / tile_size);
out->Resize(out_dims);
auto* tmp_data = out->data<T>();
T* next_in_data = out_data;
T* next_out_data = tmp_data;
if (transpose) {
MatrixTranspose<T, 32, 8><<<transpose_grids, blocks, 0, dev_ctx.stream()>>>(
out_data, in_data, height, width);
next_in_data = out_data;
next_out_data = tmp_data;
}
auto swap_ptr = [](T*& ptr1, T*& ptr2) {
T* tmp = ptr2;
ptr2 = ptr1;
ptr1 = tmp;
};
int outer_size = height / scan_size;
int inner_size = width;
// Consider the size of shared memory, here block size is 128
dim3 scan_grid(outer_size, inner_size);
dim3 reverse_grid = scan_grid;
if (reverse) {
if (transpose) {
reverse_grid.x = scan_grid.y;
reverse_grid.y = scan_grid.x;
MatrixRowReverse<T><<<reverse_grid, 1024, 0, dev_ctx.stream()>>>(
next_in_data, next_out_data, scan_size, outer_size, inner_size);
if (!transpose) next_in_data = tmp_data;
swap_ptr(next_in_data, next_out_data);
} else {
MatrixRowReverse<T><<<reverse_grid, 1024, 0, dev_ctx.stream()>>>(
in_data, out_data, scan_size, outer_size, inner_size);
}
}
if (!transpose && !reverse) {
BlockScanKernel<T, 128, 4, Op><<<scan_grid, 128, 0, dev_ctx.stream()>>>(
out_data, in_data, outer_size, inner_size, scan_size, exclusive, op);
} else {
BlockScanKernel<T, 128, 4, Op>
<<<scan_grid, 128, 0, dev_ctx.stream()>>>(next_out_data,
next_in_data,
outer_size,
inner_size,
scan_size,
exclusive,
op);
}
swap_ptr(next_in_data, next_out_data);
if (reverse) {
MatrixRowReverse<T><<<reverse_grid, 1024, 0, dev_ctx.stream()>>>(
next_in_data, next_out_data, scan_size, outer_size, inner_size);
swap_ptr(next_in_data, next_out_data);
}
if (transpose) {
transpose_grids.x = (height + tile_size - 1) / tile_size;
transpose_grids.y = (width + tile_size - 1) / tile_size;
MatrixTranspose<T, 32, 8><<<transpose_grids, blocks, 0, dev_ctx.stream()>>>(
next_out_data, next_in_data, width, height);
}
}
template <typename T, typename Context>
void CumsumKernel(const Context& dev_ctx,
const DenseTensor& x,
int axis,
bool flatten,
bool exclusive,
bool reverse,
DenseTensor* out) {
using Op = cub::Sum;
auto op = Op();
ScanKernel<T, Context, Op>(
dev_ctx, x, axis, flatten, exclusive, reverse, op, out);
}
template <typename T, typename Context>
void LogcumsumexpKernel(const Context& dev_ctx,
const DenseTensor& x,
int axis,
bool flatten,
bool exclusive,
bool reverse,
DenseTensor* out) {
using Op = LogAddExp;
auto op = Op();
ScanKernel<T, Context, Op>(
dev_ctx, x, axis, flatten, exclusive, reverse, op, out);
}
} // namespace phi
PD_REGISTER_KERNEL(cumsum,
GPU,
ALL_LAYOUT,
phi::CumsumKernel,
float,
double,
int16_t,
int,
int64_t) {}
PD_REGISTER_KERNEL(logcumsumexp,
GPU,
ALL_LAYOUT,
phi::LogcumsumexpKernel,
float,
double) {}