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#ifdef NERV_GENERIC_CUKERNEL
#include <assert.h>
#include <stdio.h>
#include "matrix.h"
#include "cuda.h"
#define CUDA_THREADS_N 16
#define CUDA_THREADS_NN (16 * 16)
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
__global__ void cudak_(sigmoid)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
int nrow, int ncol, int stride) {
int j = blockIdx.x * blockDim.x + threadIdx.x;
int i = blockIdx.y * blockDim.y + threadIdx.y;
long idx;
if (i >= nrow || j >= ncol) return;
idx = j + i * stride;
b[idx] = 1.0 / (1.0 + exp(-a[idx]));
}
__global__ void cudak_(softmax_final)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
const MATRIX_ELEM *max, const MATRIX_ELEM *deno,
int nrow, int ncol, int stride, int mstride) {
int j = blockIdx.x * blockDim.x + threadIdx.x;
int i = blockIdx.y * blockDim.y + threadIdx.y;
long idx;
if (i >= nrow || j >= ncol) return;
idx = j + i * stride;
b[idx] = exp(a[idx] - max[0 + i * mstride]) / deno[0 + i * mstride];
}
__global__ void cudak_(block_reduce_rowsum)(const MATRIX_ELEM *input,
MATRIX_ELEM *output,
const int istride, const int ostride,
const int n) {
extern __shared__ MATRIX_ELEM cudak_(arr)[];
int j = blockIdx.x * blockDim.x + threadIdx.x;
cudak_(arr)[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : 0;
__syncthreads();
for (int offset = blockDim.x >> 1; offset; offset >>= 1)
{
if (threadIdx.x < offset)
cudak_(arr)[threadIdx.x] += cudak_(arr)[threadIdx.x + offset];
__syncthreads();
}
if (threadIdx.x == 0)
output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
}
__global__ void cudak_(block_reduce_colsum)(const MATRIX_ELEM *input,
MATRIX_ELEM *output,
const int istride, const int ostride,
const int n) {
extern __shared__ MATRIX_ELEM cudak_(arr)[];
int i = blockIdx.y * blockDim.y + threadIdx.y;
cudak_(arr)[threadIdx.y] = i < n ? input[blockIdx.x + istride * i] : 0;
__syncthreads();
for (int offset = blockDim.y >> 1; offset; offset >>= 1)
{
if (threadIdx.y < offset)
cudak_(arr)[threadIdx.y] += cudak_(arr)[threadIdx.y + offset];
__syncthreads();
}
if (threadIdx.y == 0)
output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
}
__global__ void cudak_(block_reduce_softmax_rowsum)(const MATRIX_ELEM *input,
MATRIX_ELEM *output,
const MATRIX_ELEM *max,
const int istride, const int ostride,
const int mstride, const int n) {
extern __shared__ MATRIX_ELEM cudak_(arr)[];
int j = blockIdx.x * blockDim.x + threadIdx.x;
cudak_(arr)[threadIdx.x] = j < n ? exp(input[j + istride * blockIdx.y] - \
max[0 + mstride * blockIdx.y]) : 0;
__syncthreads();
for (int offset = blockDim.x >> 1; offset; offset >>= 1)
{
if (threadIdx.x < offset)
cudak_(arr)[threadIdx.x] += cudak_(arr)[threadIdx.x + offset];
__syncthreads();
}
if (threadIdx.x == 0)
output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
}
__global__ void cudak_(block_reduce_rowmax)(const MATRIX_ELEM *input,
MATRIX_ELEM *output,
const int istride, const int ostride,
const int n) {
extern __shared__ MATRIX_ELEM cudak_(arr)[];
int j = blockIdx.x * blockDim.x + threadIdx.x;
cudak_(arr)[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : 0;
__syncthreads();
for (int offset = blockDim.x >> 1; offset; offset >>= 1)
{
if (threadIdx.x < offset)
{
MATRIX_ELEM l = cudak_(arr)[threadIdx.x],
r = cudak_(arr)[threadIdx.x + offset];
if (r > l) cudak_(arr)[threadIdx.x] = r;
}
__syncthreads();
}
if (threadIdx.x == 0)
output[blockIdx.x + ostride * blockIdx.y] = cudak_(arr)[0];
}
__global__ void cudak_(add_row)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
int nrow, int ncol, int stride, double beta) {
int j = blockIdx.x * blockDim.x + threadIdx.x;
int i = blockIdx.y * blockDim.y + threadIdx.y;
if (i >= nrow || j >= ncol) return;
b[j + i * stride] += beta * a[j];
}
extern "C" {
#include "../cukernel.h"
void cudak_(cuda_sigmoid)(const Matrix *a, Matrix *b) {
dim3 threadsPerBlock(CUDA_THREADS_N,
CUDA_THREADS_N);
dim3 numBlocks(CEIL_DIV(b->ncol, threadsPerBlock.x),
CEIL_DIV(b->nrow, threadsPerBlock.y));
cudak_(sigmoid)<<<numBlocks, threadsPerBlock>>> \
(MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b), b->nrow, b->ncol,
b->stride / sizeof(MATRIX_ELEM));
}
void cudak_(cuda_rowsum)(const Matrix *a, Matrix *b) {
dim3 block(CUDA_THREADS_NN, 1);
int ncol = a->ncol;
int blocks_per_row = CEIL_DIV(ncol, block.x);
dim3 grid(blocks_per_row, a->nrow);
MATRIX_ELEM *res;
size_t stride;
cudaMallocPitch(&res, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
cudak_(block_reduce_rowsum)<<<grid, block, block.x * sizeof(MATRIX_ELEM)>>> \
(MATRIX_ELEM_PTR(a), res,
a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
ncol);
ncol = blocks_per_row;
assert((unsigned long)ncol <= block.x);
grid.x = 1;
cudak_(block_reduce_rowsum)<<<grid, block, block.x * sizeof(MATRIX_ELEM)>>> \
(res, MATRIX_ELEM_PTR(b),
stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
ncol);
cudaFree(res);
}
void cudak_(cuda_colsum)(const Matrix *a, Matrix *b) {
dim3 block(1, CUDA_THREADS_NN);
int nrow = a->nrow;
int blocks_per_col = CEIL_DIV(nrow, block.x);
dim3 grid(a->ncol, blocks_per_col);
MATRIX_ELEM *res;
size_t stride;
cudaMallocPitch(&res, &stride, a->ncol * sizeof(MATRIX_ELEM), blocks_per_col
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