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#ifdef NERV_GENERIC_CUMATRIX
#include "matrix.h"
#include "elem_type.h"
#define MATRIX_DATA_FREE(ptr) cuda_matrix_(free)(ptr)
#define MATRIX_DATA_ALLOC(dptr, stride, width, height) \
cuda_matrix_(alloc)(dptr, stride, width, height)
#define MATRIX_DATA_WRITE(data, idx, val) cuda_matrix_(write)(data, idx, val)
#define MATRIX_DATA_READ(data, idx) cuda_matrix_(read)(data, idx)
#define MATRIX_INIT(L) cuda_matrix_(init)(L)
#define MATRIX_BASE_TNAME nerv_matrix_cuda_tname
#define NERV_GENERIC_MATRIX
#define NERV_GENERIC_CUKERNEL
#include "../../common.h"
#include "../cukernel.h"
#include "cuda.h"
#include "cuda_runtime.h"
#include "driver_types.h"
#include "cublas_v2.h"
#define CHECK_SAME_DIMENSION(a, b) \
do { \
if (!(a->nrow == b->nrow && a->ncol == b->ncol)) \
nerv_error(L, "Matrices should be of the same dimension"); \
} while (0)
static cublasHandle_t cublas_handle;
Matrix *nerv_matrix_(new_)(long nrow, long ncol);
void nerv_matrix_(data_free)(Matrix *self);
static void nerv_matrix_(add_)(const Matrix *a, const Matrix *b,
const Matrix *c,
MATRIX_ELEM alpha, MATRIX_ELEM beta) {
NERV_CUBLAS_(geam)(cublas_handle, CUBLAS_OP_N, CUBLAS_OP_N,
a->ncol, a->nrow,
&alpha,
MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
&beta,
MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM),
MATRIX_ELEM_PTR(c), c->stride / sizeof(MATRIX_ELEM));
}
static int nerv_matrix_(add)(lua_State *L) {
Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
MATRIX_ELEM alpha = luaL_checknumber(L, 4); /* alpha */
MATRIX_ELEM beta = luaL_checknumber(L, 5); /* alpha */
CHECK_SAME_DIMENSION(a, b);
nerv_matrix_(add_)(a, b, c, alpha, beta);
return 0;
}
static int nerv_matrix_(get_cublas_op)(char ch) {
return (ch == 'T' || ch == 't') ? CUBLAS_OP_T : CUBLAS_OP_N;
}
static int nerv_matrix_(mul)(lua_State *L) {
#define SWAP(a, b) \
do { int t = (a); (a) = (b); (b) = t; } while (0)
Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
MATRIX_ELEM alpha = luaL_checknumber(L, 4);
MATRIX_ELEM beta = luaL_checknumber(L, 5);
int nargs = lua_gettop(L);
int ta = nargs > 5 ? nerv_matrix_(get_cublas_op)(*luaL_checkstring(L, 6)) \
: CUBLAS_OP_N;
int tb = nargs > 6 ? nerv_matrix_(get_cublas_op)(*luaL_checkstring(L, 7)) \
: CUBLAS_OP_N;
int am = a->nrow, an = a->ncol;
int bm = b->nrow, bn = b->ncol;
if (ta == CUBLAS_OP_T) SWAP(am, an);
if (tb == CUBLAS_OP_T) SWAP(bm, bn);
if (an != bm)
nerv_error(L, "Wrong dimension of multipliers");
/* MATRIX_ELEM alpha = 1.0f, beta = 0.0f; */
NERV_CUBLAS_(gemm)(cublas_handle, tb, ta,
bn, am, bm,
&alpha,
MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM),
MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
&beta,
MATRIX_ELEM_PTR(c), c->stride / sizeof(MATRIX_ELEM));
return 0;
}
static int nerv_matrix_(create)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
fprintf(stderr, "create\n");
Matrix *b = nerv_matrix_(new_)(a->nrow, a->ncol);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
static int nerv_matrix_(sigmoid)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
CHECK_SAME_DIMENSION(a, b);
cudak_(cuda_sigmoid)(b, a);
return 0;
}
static int nerv_matrix_(sigmoid_grad)(lua_State *L) {
Matrix *nerr = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *err = luaT_checkudata(L, 2, nerv_matrix_(tname));
Matrix *output = luaT_checkudata(L, 3, nerv_matrix_(tname));
CHECK_SAME_DIMENSION(nerr, err);
CHECK_SAME_DIMENSION(nerr, output);
cudak_(cuda_sigmoid_grad)(output, err, nerr);
return 0;
}
static int nerv_matrix_(softmax)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *max = nerv_matrix_(new_)(a->nrow, 1);
Matrix *dno = nerv_matrix_(new_)(a->nrow, 1);
cudak_(cuda_rowmax)(a, max);
cudak_(cuda_softmax_denominator)(a, max, dno);
cudak_(cuda_softmax_final)(a, max, dno, b);
nerv_matrix_(data_free)(max);
nerv_matrix_(data_free)(dno);
return 0;
}
static int nerv_matrix_(rowsum)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = nerv_matrix_(new_)(a->nrow, 1);
cudak_(cuda_rowsum)(a, b);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
static int nerv_matrix_(colsum)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = nerv_matrix_(new_)(1, a->ncol);
cudak_(cuda_colsum)(a, b);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
static int nerv_matrix_(rowmax)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = nerv_matrix_(new_)(a->nrow, 1);
cudak_(cuda_rowmax)(a, b);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
static int nerv_matrix_(add_row)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
double beta = luaL_checknumber(L, 3);
if (a->ncol != b->ncol)
nerv_error(L, "the number of columns is not the same");
if (a->nrow != 1)
nerv_error(L, "a row vector is expected");
cudak_(cuda_add_row)(a, b, beta);
return 0;
}
static int nerv_matrix_(fill)(lua_State *L) {
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
double val = luaL_checknumber(L, 2);
cudak_(cuda_fill)(self, val);
return 0;
}
extern const char *MATRIX_CUMATRIX_HOST_TNAME;
static int nerv_matrix_(copy_from)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
CHECK_SAME_DIMENSION(a, b);
cudaMemcpy2D(MATRIX_ELEM_PTR(a), a->stride,
MATRIX_ELEM_PTR(b), b->stride,
sizeof(MATRIX_ELEM) * b->ncol, b->nrow,
cudaMemcpyHostToDevice);
return 0;
}
static int nerv_matrix_(copy_to)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
CHECK_SAME_DIMENSION(a, b);
cudaMemcpy2D(MATRIX_ELEM_PTR(b), b->stride,
MATRIX_ELEM_PTR(a), a->stride,
sizeof(MATRIX_ELEM) * a->ncol, a->nrow,
cudaMemcpyDeviceToHost);
return 0;
}
static int nerv_matrix_(trans)(lua_State *L) {
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
Matrix *b = nerv_matrix_(new_)(a->ncol, a->nrow);
MATRIX_ELEM alpha = 1, beta = 0;
NERV_CUBLAS_(geam)(cublas_handle, CUBLAS_OP_T, CUBLAS_OP_T,
a->nrow, a->ncol,
&alpha,
MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
&beta,
MATRIX_ELEM_PTR(a), a->stride / sizeof(MATRIX_ELEM),
MATRIX_ELEM_PTR(b), b->stride / sizeof(MATRIX_ELEM));
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
static const luaL_Reg nerv_matrix_(extra_methods)[] = {
{"create", nerv_matrix_(create)},
{"softmax", nerv_matrix_(softmax)},
{"colsum", nerv_matrix_(colsum)},
{"rowsum", nerv_matrix_(rowsum)},
{"rowmax", nerv_matrix_(rowmax)},
{"copy_from", nerv_matrix_(copy_from)},
{"copy_to", nerv_matrix_(copy_to)},
{"trans", nerv_matrix_(trans)},
/* in-place calc */
{"add", nerv_matrix_(add)},
{"mul", nerv_matrix_(mul)},
{"add_row", nerv_matrix_(add_row)},
{"fill", nerv_matrix_(fill)},
{"sigmoid", nerv_matrix_(sigmoid)},
{"sigmoid_grad", nerv_matrix_(sigmoid_grad)},
{NULL, NULL}
};
static void cuda_matrix_(init)(lua_State *L) {
luaN_append_methods(L, nerv_matrix_(extra_methods));
cublasCreate(&cublas_handle);
}
static void cuda_matrix_(free)(MATRIX_ELEM *ptr) {
cudaFree(ptr);
}
static void cuda_matrix_(alloc)(MATRIX_ELEM **dptr, size_t *stride,
long width, long height) {
cudaMallocPitch((void **)dptr, stride, width, height);
}
static MATRIX_ELEM cuda_matrix_(read)(MATRIX_ELEM *data, int idx) {
MATRIX_ELEM res;
cudaMemcpy(&res, data + idx, sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost);
return res;
}
static void cuda_matrix_(write)(MATRIX_ELEM *data, int idx, MATRIX_ELEM val) {
cudaMemcpy(data + idx, &val, sizeof(MATRIX_ELEM), cudaMemcpyHostToDevice);
}
int nerv_matrix_(get_elem)(lua_State *L) {
return nerv_error_method_not_implemented(L);
}
int nerv_matrix_(set_elem)(lua_State *L) {
return nerv_error_method_not_implemented(L);
}
#include "matrix.c"
#endif
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