major change: use luarocks to manage project

author: Determinant <[email protected]> 2015-06-22 19:01:29 +0800
committer: Determinant <[email protected]> 2015-06-22 19:01:29 +0800
commit: 2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 (patch)
tree: 382f97575bd2df9ee6abb1662b11b279fc22d72b /nerv/matrix
parent: 196e9b48a3541caccdffc5743001cced70667091 (diff)
13 files changed, 1770 insertions, 0 deletions
diff --git a/nerv/matrix/cuda_helper.h b/nerv/matrix/cuda_helper.h
new file mode 100644
index 0000000..fde6f18
--- /dev/null
+++ b/nerv/matrix/cuda_helper.h
@@ -0,0 +1,75 @@
+#ifndef NERV_CUDA_HELPER_H
+#define NERV_CUDA_HELPER_H
+#include "cuda.h"
+#include "cuda_runtime.h"
+#include "driver_types.h"
+#include "cublas_v2.h"
+#define CUBLAS_SAFE_SYNC_CALL(call) \
+ do { \
+ cublasStatus_t err = (call); \
+ if (err != CUBLAS_STATUS_SUCCESS) \
+ nerv_error(L, "cumatrix cublas error: %s at %s:%d", \
+ cublasGetErrorString(err), __FILE__, __LINE__); \
+ cudaDeviceSynchronize(); \
+ } while (0)
+
+#define CUDA_SAFE_CALL(call) \
+ do { \
+ cudaError_t err = (call); \
+ if (err != cudaSuccess) \
+ nerv_error(L, "cumatrix CUDA error: %s at %s:%d", \
+ cudaGetErrorString(err), __FILE__, __LINE__); \
+ } while (0)
+
+#define CUDA_SAFE_SYNC_CALL(call) \
+ do { \
+ CUDA_SAFE_CALL(call); \
+ cudaDeviceSynchronize(); \
+ } while (0)
+
+#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 const char *cublasGetErrorString(cublasStatus_t err) {
+ switch (err)
+ {
+ case CUBLAS_STATUS_SUCCESS:
+ return "CUBLAS_STATUS_SUCCESS";
+ case CUBLAS_STATUS_NOT_INITIALIZED:
+ return "CUBLAS_STATUS_NOT_INITIALIZED";
+ case CUBLAS_STATUS_ALLOC_FAILED:
+ return "CUBLAS_STATUS_ALLOC_FAILED";
+ case CUBLAS_STATUS_INVALID_VALUE:
+ return "CUBLAS_STATUS_INVALID_VALUE";
+ case CUBLAS_STATUS_ARCH_MISMATCH:
+ return "CUBLAS_STATUS_ARCH_MISMATCH";
+ case CUBLAS_STATUS_MAPPING_ERROR:
+ return "CUBLAS_STATUS_MAPPING_ERROR";
+ case CUBLAS_STATUS_EXECUTION_FAILED:
+ return "CUBLAS_STATUS_EXECUTION_FAILED";
+ case CUBLAS_STATUS_INTERNAL_ERROR:
+ return "CUBLAS_STATUS_INTERNAL_ERROR";
+/* case CUBLAS_STATUS_NOT_SUPPORTED:
+ return "CUBLAS_STATUS_NOT_SUPPORTED";
+ case CUBLAS_STATUS_LICENSE_ERROR:
+ return "CUBLAS_STATUS_LICENSE_ERROR"; */
+ }
+ return "<unknown>";
+}
+
+#define PROFILE_START \
+ do { \
+ cudaEventRecord(profile_start, 0);
+#define PROFILE_STOP \
+ cudaEventRecord(profile_stop, 0); \
+ cudaEventSynchronize(profile_stop); \
+ float milliseconds = 0; \
+ cudaEventElapsedTime(&milliseconds, profile_start, profile_stop); \
+ accu_profile(__func__, milliseconds / 1000); \
+ } while (0);
+
+#define PROFILE_END
+#endif
diff --git a/nerv/matrix/cukernel.cu b/nerv/matrix/cukernel.cu
new file mode 100644
index 0000000..a19030a
--- /dev/null
+++ b/nerv/matrix/cukernel.cu
@@ -0,0 +1,17 @@
+#define NERV_GENERIC_CUKERNEL
+
+#define cudak_(NAME) cudak_float_ ## NAME
+#define MATRIX_USE_FLOAT
+#include "generic/elem_type.h"
+#include "generic/cukernel.cu"
+#undef cudak_
+#undef MATRIX_USE_FLOAT
+#undef MATRIX_ELEM
+#undef MATRIX_ELEM_PTR
+#undef MATRIX_ELEM_FMT
+#undef MATRIX_ELEM_WRITE_FMT
+
+#define cudak_(NAME) cudak_double_ ## NAME
+#define MATRIX_USE_DOUBLE
+#include "generic/elem_type.h"
+#include "generic/cukernel.cu"
diff --git a/nerv/matrix/cukernel.h b/nerv/matrix/cukernel.h
new file mode 100644
index 0000000..8a1494f
--- /dev/null
+++ b/nerv/matrix/cukernel.h
@@ -0,0 +1,20 @@
+#ifdef NERV_GENERIC_CUKERNEL
+void cudak_(cuda_mul_elem)(const Matrix *a, const Matrix *b, Matrix *c);
+void cudak_(cuda_log_elem)(const Matrix *a, Matrix *b);
+void cudak_(cuda_sigmoid)(const Matrix *a, Matrix *b);
+void cudak_(cuda_sigmoid_grad)(const Matrix *output, const Matrix *err, Matrix *nerr);
+void cudak_(cuda_rowsum)(const Matrix *a, Matrix *b);
+void cudak_(cuda_rowmax)(const Matrix *a, Matrix *b);
+void cudak_(cuda_rowmax_idx)(const Matrix *a, Matrix *b, Matrix *idx);
+void cudak_(cuda_colsum)(const Matrix *a, Matrix *b);
+void cudak_(cuda_colsame)(const Matrix *a, const Matrix *ref, Matrix *b);
+void cudak_(cuda_softmax_denominator)(const Matrix *a, const Matrix *max, Matrix *b);
+void cudak_(cuda_softmax_final)(const Matrix *a, const Matrix *max, const Matrix *deno, Matrix *b);
+void cudak_(cuda_add_row)(const Matrix *a, Matrix *b, double beta);
+void cudak_(cuda_fill)(Matrix *a, double val);
+void cudak_(cuda_expand_frm)(const Matrix *a, Matrix *b, int context);
+void cudak_(cuda_rearrange_frm)(const Matrix *a, Matrix *b, int step);
+void cudak_(cuda_scale_rows_by_row)(const Matrix *a, Matrix *b);
+void cudak_(cuda_scale_rows_by_col)(const Matrix *a, Matrix *b);
+void cudak_(cuda_decompress)(const Matrix *a, Matrix *b);
+#endif
diff --git a/nerv/matrix/cumatrix.c b/nerv/matrix/cumatrix.c
new file mode 100644
index 0000000..af34fb4
--- /dev/null
+++ b/nerv/matrix/cumatrix.c
@@ -0,0 +1,87 @@
+#define NERV_GENERIC_CUMATRIX
+#include "../common.h"
+#include "cuda_helper.h"
+#include <string.h>
+#define PROFILE_HASHMAP_SIZE 123457
+static cublasHandle_t cublas_handle;
+static cudaEvent_t profile_start, profile_stop;
+static HashMap *profile;
+
+static int print_profile(lua_State *L) {
+ (void)L;
+ size_t i;
+ fprintf(stderr, "*** [nerv cumatrix profile] **\n");
+ for (i = 0; i < profile->size; i++)
+ {
+ HashNode *ptr;
+ for (ptr = profile->bucket[i]; ptr; ptr = ptr->next)
+ {
+ fprintf(stderr, "%s:\t%.6f\n", ptr->key, *(float *)ptr->val);
+ }
+ }
+ return 0;
+}
+
+static int clear_profile(lua_State *L) {
+ (void)L;
+ hashmap_clear(profile);
+ return 0;
+}
+
+void accu_profile(const char *name, float delta) {
+ float *val = hashmap_getval(profile, name);
+ if (!val)
+ {
+ val = malloc(sizeof(float));
+ *val = 0;
+ hashmap_setval(profile, name, val);
+ }
+ *val += delta;
+}
+
+static const luaL_Reg cumatrix_methods[] = {
+ {"print_profile", print_profile},
+ {"clear_profile", clear_profile},
+ {NULL, NULL}
+};
+
+extern void nerv_matrix_cuda_float_init(lua_State *L);
+extern void nerv_matrix_cuda_double_init(lua_State *L);
+
+void nerv_cumatrix_init(lua_State *L) {
+ luaL_register(L, NULL, cumatrix_methods);
+ cublasCreate(&cublas_handle);
+ cudaEventCreate(&profile_start);
+ cudaEventCreate(&profile_stop);
+ profile = hashmap_create(PROFILE_HASHMAP_SIZE, bkdr_hash, strcmp);
+ nerv_matrix_cuda_float_init(L);
+ nerv_matrix_cuda_double_init(L);
+}
+
+#define MATRIX_USE_FLOAT
+#define cuda_matrix_(NAME) cuda_matrix_float_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_cuda_float_##NAME
+#define cudak_(NAME) cudak_float_ ## NAME
+#define NERV_CUBLAS_(NAME) cublasS##NAME
+#define MATRIX_CUMATRIX_HOST_TNAME nerv_matrix_host_float_tname
+const char *nerv_matrix_(tname) = "nerv.CuMatrixFloat";
+#include "generic/cumatrix.c"
+#undef NERV_CUBLAS_
+#undef cudak_
+#undef nerv_matrix_
+#undef cuda_matrix_
+#undef MATRIX_USE_FLOAT
+#undef MATRIX_ELEM
+#undef MATRIX_ELEM_PTR
+#undef MATRIX_ELEM_FMT
+#undef MATRIX_ELEM_WRITE_FMT
+#undef MATRIX_CUMATRIX_HOST_TNAME
+
+#define MATRIX_USE_DOUBLE
+#define cuda_matrix_(NAME) cuda_matrix_double_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_cuda_double_##NAME
+#define cudak_(NAME) cudak_double_ ## NAME
+#define NERV_CUBLAS_(NAME) cublasD##NAME
+#define MATRIX_CUMATRIX_HOST_TNAME nerv_matrix_host_double_tname
+const char *nerv_matrix_(tname) = "nerv.CuMatrixDouble";
+#include "generic/cumatrix.c"
diff --git a/nerv/matrix/generic/cukernel.cu b/nerv/matrix/generic/cukernel.cu
new file mode 100644
index 0000000..d6c8adc
--- /dev/null
+++ b/nerv/matrix/generic/cukernel.cu
@@ -0,0 +1,571 @@
+#ifdef NERV_GENERIC_CUKERNEL
+#include <assert.h>
+#include <stdio.h>
+#include "matrix.h"
+#include "cuda.h"
+#include "float.h"
+#define CUDA_THREADS_N 16
+#define CUDA_THREADS_NN ((CUDA_THREADS_N) * (CUDA_THREADS_N))
+#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
+__global__ void cudak_(log_elem)(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;
+ MATRIX_ELEM tmp;
+ if (i >= nrow || j >= ncol) return;
+ idx = j + i * stride;
+ tmp = a[idx];
+ if(tmp < FLT_MIN) tmp = FLT_MIN;
+ b[idx] = log(tmp);
+}
+
+__global__ void cudak_(mul_elem)(const MATRIX_ELEM *a, const MATRIX_ELEM *b,
+ MATRIX_ELEM *c, 
+ 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;
+ c[idx] = a[idx] * b[idx];
+}
+
+__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_(sigmoid_grad)(const MATRIX_ELEM *output,
+ const MATRIX_ELEM *err,
+ MATRIX_ELEM *nerr,
+ 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;
+ nerr[idx] = output[idx] * (1.0 - output[idx]) * err[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_colsame)(const MATRIX_ELEM *input,
+ const MATRIX_ELEM *ref_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] == \
+ ref_input[blockIdx.x + istride * i]) ? 1.0 : 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] : -FLT_MAX;
+ __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_(block_reduce_rowmax_idx)(const MATRIX_ELEM *input,
+ const MATRIX_ELEM *idx_input,
+ MATRIX_ELEM *output,
+ MATRIX_ELEM *idx_output,
+ const int istride, const int ostride,
+ const int n) {
+ extern __shared__ MATRIX_ELEM cudak_(arr)[];
+ MATRIX_ELEM *arr_val = cudak_(arr);
+ MATRIX_ELEM *arr_idx = arr_val + blockDim.x;
+ int j = blockIdx.x * blockDim.x + threadIdx.x;
+ arr_val[threadIdx.x] = j < n ? input[j + istride * blockIdx.y] : -FLT_MAX;
+ arr_idx[threadIdx.x] = j < n ? idx_input[j + istride * blockIdx.y] : 0;
+ __syncthreads();
+ for (int offset = blockDim.x >> 1; offset; offset >>= 1)
+ {
+ if (threadIdx.x < offset)
+ {
+ MATRIX_ELEM l = arr_val[threadIdx.x],
+ r = arr_val[threadIdx.x + offset];
+ if (r > l)
+ {
+ arr_val[threadIdx.x] = r;
+ arr_idx[threadIdx.x] = arr_idx[threadIdx.x + offset];
+ }
+ }
+ __syncthreads();
+ }
+ if (threadIdx.x == 0)
+ {
+ output[blockIdx.x + ostride * blockIdx.y] = arr_val[0];
+ idx_output[blockIdx.x + ostride * blockIdx.y] = arr_idx[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];
+}
+
+__global__ void cudak_(fill)(MATRIX_ELEM *a,
+ int nrow, int ncol, int stride, double val) {
+ int j = blockIdx.x * blockDim.x + threadIdx.x;
+ int i = blockIdx.y * blockDim.y + threadIdx.y;
+ if (i >= nrow || j >= ncol) return;
+ a[j + i * stride] = val;
+}
+
+__global__ void cudak_(expand_frm)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
+ int nrow, int ncol,
+ int enrow, int encol,
+ int stride, int estride,
+ int context) {
+ int j = blockIdx.x * blockDim.x + threadIdx.x;
+ int i = blockIdx.y * blockDim.y + threadIdx.y;
+ int ridx;
+ if (i >= enrow || j >= encol) return;
+ ridx = i + j / ncol - context;
+ if (ridx < 0) ridx = 0;
+ else if (ridx >= nrow) ridx = nrow - 1;
+ b[j + i * estride] = a[j % ncol + ridx * stride];
+}
+
+__global__ void cudak_(rearrange_frm)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
+ int nrow, int ncol,
+ int stride, int step, int orig_dim) {
+ 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] = a[j / step + (j % step) * orig_dim + i * stride];
+}
+
+__global__ void cudak_(scale_rows_by_col)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
+ int nrow, int ncol,
+ int astride, int bstride) {
+ 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 * bstride] *= a[i * astride];
+}
+
+__global__ void cudak_(scale_rows_by_row)(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;
+ if (i >= nrow || j >= ncol) return;
+ b[j + i * stride] *= a[j];
+}
+
+__global__ void cudak_(decompress)(const MATRIX_ELEM *a, MATRIX_ELEM *b,
+ int nrow, int ncol,
+ int stride_a, int stride_b) {
+ int j = blockIdx.x * blockDim.x + threadIdx.x;
+ int i = blockIdx.y * blockDim.y + threadIdx.y;
+ if (i >= nrow || j >= ncol) return;
+ b[lrintf(a[j + i * stride_a]) + i * stride_b] = 1.0;
+}
+
+__global__ void cudak_(gen_col_idx)(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;
+ if (i >= nrow || j >= ncol) return;
+ b[j + i * stride] = j;
+}
+
+extern "C" {
+#include "../cukernel.h"
+ void cudak_(cuda_log_elem)(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_(log_elem)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ b->nrow, b->ncol, b->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_mul_elem)(const Matrix *a, const Matrix *b,
+ Matrix *c) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(b->ncol, threadsPerBlock.x),
+ CEIL_DIV(b->nrow, threadsPerBlock.y));
+ cudak_(mul_elem)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ MATRIX_ELEM_PTR(c),
+ b->nrow, b->ncol, b->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ 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));
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_sigmoid_grad)(const Matrix *output,
+ const Matrix *err, Matrix *nerr) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(nerr->ncol, threadsPerBlock.x),
+ CEIL_DIV(nerr->nrow, threadsPerBlock.y));
+ cudak_(sigmoid_grad)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(output), MATRIX_ELEM_PTR(err),
+ MATRIX_ELEM_PTR(nerr),
+ nerr->nrow, nerr->ncol,
+ nerr->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ 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;
+ cudaStreamSynchronize(0);
+ 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);
+ cudaStreamSynchronize(0);
+ cudaFree(res);
+ }
+
+ void cudak_(cuda_colsame)(const Matrix *a, const Matrix *ref, Matrix *b) {
+ dim3 block(1, CUDA_THREADS_NN);
+ int nrow = a->nrow;
+ int blocks_per_col = CEIL_DIV(nrow, block.y);
+ dim3 grid(a->ncol, blocks_per_col);
+ MATRIX_ELEM *res;
+ size_t stride;
+ cudaMallocPitch(&res, &stride, a->ncol * sizeof(MATRIX_ELEM), blocks_per_col);
+ cudak_(block_reduce_colsame)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(ref), res,
+ a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+ nrow);
+ nrow = blocks_per_col;
+ assert((unsigned long)nrow <= block.y);
+ grid.y = 1;
+ cudaStreamSynchronize(0);
+ cudak_(block_reduce_colsum)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+ (res, MATRIX_ELEM_PTR(b),
+ stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+ nrow);
+ cudaStreamSynchronize(0);
+ 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.y);
+ dim3 grid(a->ncol, blocks_per_col);
+ MATRIX_ELEM *res;
+ size_t stride;
+ cudaMallocPitch(&res, &stride, a->ncol * sizeof(MATRIX_ELEM), blocks_per_col);
+ cudak_(block_reduce_colsum)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+ (MATRIX_ELEM_PTR(a), res,
+ a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+ nrow);
+ nrow = blocks_per_col;
+ assert((unsigned long)nrow <= block.y);
+ grid.y = 1;
+ cudaStreamSynchronize(0);
+ cudak_(block_reduce_colsum)<<<grid, block, block.y * sizeof(MATRIX_ELEM)>>> \
+ (res, MATRIX_ELEM_PTR(b),
+ stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+ nrow);
+ cudaStreamSynchronize(0);
+ cudaFree(res);
+ }
+
+ void cudak_(cuda_softmax_final)(const Matrix *a, const Matrix *max,
+ const Matrix *deno, 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_(softmax_final)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ MATRIX_ELEM_PTR(max), MATRIX_ELEM_PTR(deno),
+ b->nrow, b->ncol,
+ b->stride / sizeof(MATRIX_ELEM),
+ max->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_softmax_denominator)(const Matrix *a, const Matrix *max, 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;
+ assert(max->ncol == 1);
+ cudaMallocPitch(&res, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
+ cudak_(block_reduce_softmax_rowsum) \
+ <<<grid, block, block.x * sizeof(MATRIX_ELEM)>>> \
+ (MATRIX_ELEM_PTR(a), res, MATRIX_ELEM_PTR(max),
+ a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+ max->stride / sizeof(MATRIX_ELEM),
+ ncol);
+ ncol = blocks_per_row;
+ assert((unsigned long)ncol <= block.x);
+ grid.x = 1;
+ cudaStreamSynchronize(0);
+ 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);
+ cudaStreamSynchronize(0);
+ cudaFree(res);
+ }
+
+ void cudak_(cuda_rowmax)(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_rowmax)<<<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;
+ cudaStreamSynchronize(0);
+ cudak_(block_reduce_rowmax)<<<grid, block, block.x * sizeof(MATRIX_ELEM)>>> \
+ (res, MATRIX_ELEM_PTR(b),
+ stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+ ncol);
+ cudaStreamSynchronize(0);
+ cudaFree(res);
+ }
+
+ void cudak_(cuda_rowmax_idx)(const Matrix *a, Matrix *b, Matrix *b_idx) {
+ 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 *a_idx, *res, *res_idx;
+ size_t stride;
+ cudaMallocPitch(&a_idx, &stride, a->stride, a->nrow);
+ cudak_(gen_col_idx)<<<grid, block>>>(a_idx, a->nrow, ncol, stride / sizeof(MATRIX_ELEM));
+ cudaMallocPitch(&res, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
+ cudaMallocPitch(&res_idx, &stride, blocks_per_row * sizeof(MATRIX_ELEM), a->nrow);
+ cudaStreamSynchronize(0);
+ cudak_(block_reduce_rowmax_idx)<<<grid, block,
+ 2 * block.x * sizeof(MATRIX_ELEM)>>> \
+ (MATRIX_ELEM_PTR(a), a_idx, res, res_idx,
+ a->stride / sizeof(MATRIX_ELEM), stride / sizeof(MATRIX_ELEM),
+ ncol);
+ ncol = blocks_per_row;
+ assert((unsigned long)ncol <= block.x);
+ grid.x = 1;
+ cudaStreamSynchronize(0);
+ cudak_(block_reduce_rowmax_idx)<<<grid, block,
+ 2 * block.x * sizeof(MATRIX_ELEM)>>> \
+ (res, res_idx, MATRIX_ELEM_PTR(b), MATRIX_ELEM_PTR(b_idx),
+ stride / sizeof(MATRIX_ELEM), b->stride / sizeof(MATRIX_ELEM),
+ ncol);
+ cudaStreamSynchronize(0);
+ cudaFree(a_idx);
+ cudaFree(res);
+ cudaFree(res_idx);
+ }
+
+ /* in-place calc */
+ void cudak_(cuda_add_row)(const Matrix *a, Matrix *b, double beta) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(b->ncol, threadsPerBlock.x),
+ CEIL_DIV(b->nrow, threadsPerBlock.y));
+ cudak_(add_row)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b), b->nrow, b->ncol,
+ b->stride / sizeof(MATRIX_ELEM), beta);
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_fill)(Matrix *a, double val) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(a->ncol, threadsPerBlock.x),
+ CEIL_DIV(a->nrow, threadsPerBlock.y));
+ cudak_(fill)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), a->nrow, a->ncol,
+ a->stride / sizeof(MATRIX_ELEM), val);
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_expand_frm)(const Matrix *a, Matrix *b, int context) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(b->ncol, threadsPerBlock.x),
+ CEIL_DIV(b->nrow, threadsPerBlock.y));
+ cudak_(expand_frm)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ a->nrow, a->ncol,
+ b->nrow, b->ncol,
+ a->stride / sizeof(MATRIX_ELEM),
+ b->stride / sizeof(MATRIX_ELEM),
+ context);
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_rearrange_frm)(const Matrix *a, Matrix *b, int step) {
+ dim3 threadsPerBlock(CUDA_THREADS_N, CUDA_THREADS_N);
+ dim3 numBlocks(CEIL_DIV(b->ncol, threadsPerBlock.x),
+ CEIL_DIV(b->nrow, threadsPerBlock.y));
+ cudak_(rearrange_frm)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ b->nrow, b->ncol, b->stride / sizeof(MATRIX_ELEM),
+ step, b->ncol / step);
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_scale_rows_by_col)(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_(scale_rows_by_col)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ b->nrow, b->ncol,
+ a->stride / sizeof(MATRIX_ELEM),
+ b->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_scale_rows_by_row)(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_(scale_rows_by_row)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ b->nrow, b->ncol, b->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+
+ void cudak_(cuda_decompress)(const Matrix *a, Matrix *b) {
+ dim3 threadsPerBlock(1, CUDA_THREADS_NN);
+ dim3 numBlocks(1, CEIL_DIV(a->nrow, threadsPerBlock.y));
+ cudak_(decompress)<<<numBlocks, threadsPerBlock>>> \
+ (MATRIX_ELEM_PTR(a), MATRIX_ELEM_PTR(b),
+ a->nrow, a->ncol,
+ a->stride / sizeof(MATRIX_ELEM),
+ b->stride / sizeof(MATRIX_ELEM));
+ cudaStreamSynchronize(0);
+ }
+}
+#endif
diff --git a/nerv/matrix/generic/cumatrix.c b/nerv/matrix/generic/cumatrix.c
new file mode 100644
index 0000000..b5d1a35
--- /dev/null
+++ b/nerv/matrix/generic/cumatrix.c
@@ -0,0 +1,493 @@
+#ifdef NERV_GENERIC_CUMATRIX
+#include "matrix.h"
+#include "elem_type.h"
+
+#define MATRIX_DATA_FREE(L, ptr) cuda_matrix_(free)(L, ptr)
+#define MATRIX_DATA_ALLOC(L, dptr, stride, width, height) \
+ cuda_matrix_(alloc)(L, dptr, stride, width, height)
+#define MATRIX_DATA_WRITE(L, data, idx, val) cuda_matrix_(write)(L, data, idx, val)
+#define MATRIX_DATA_READ(L, data, idx) cuda_matrix_(read)(L, 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_helper.h"
+
+Matrix *nerv_matrix_(new_)(lua_State *L, long nrow, long ncol);
+void nerv_matrix_(data_free)(lua_State *L, Matrix *self);
+
+static void nerv_matrix_(add_)(lua_State *L, const Matrix *a, const Matrix *b,
+ const Matrix *c,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta) {
+ PROFILE_START
+ CUBLAS_SAFE_SYNC_CALL(
+ 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)));
+ PROFILE_STOP
+}
+
+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);
+ MATRIX_ELEM beta = luaL_checknumber(L, 5);
+ CHECK_SAME_DIMENSION(a, b);
+ CHECK_SAME_DIMENSION(a, c);
+ nerv_matrix_(add_)(L, 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; */
+ /* Because matrix in Nerv is row-major, here b comes first */
+ PROFILE_START
+ CUBLAS_SAFE_SYNC_CALL(
+ 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)));
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(create)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(new_)(L, 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);
+ PROFILE_START
+ cudak_(cuda_sigmoid)(b, a);
+ PROFILE_STOP
+ 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);
+ PROFILE_START
+ cudak_(cuda_sigmoid_grad)(output, err, nerr);
+ PROFILE_STOP
+ 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, *max_idx;
+ Matrix *dno;
+ CHECK_SAME_DIMENSION(a, b);
+ max = nerv_matrix_(new_)(L, a->nrow, 1);
+ max_idx = nerv_matrix_(new_)(L, a->nrow, 1);
+ dno = nerv_matrix_(new_)(L, a->nrow, 1);
+ PROFILE_START
+ cudak_(cuda_rowmax_idx)(a, max, max_idx);
+ cudak_(cuda_softmax_denominator)(a, max, dno);
+ cudak_(cuda_softmax_final)(a, max, dno, b);
+ PROFILE_STOP
+ nerv_matrix_(data_free)(L, max);
+ nerv_matrix_(data_free)(L, dno);
+ luaT_pushudata(L, max_idx, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(rowsum)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(new_)(L, a->nrow, 1);
+ PROFILE_START
+ cudak_(cuda_rowsum)(a, b);
+ PROFILE_STOP
+ 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_)(L, 1, a->ncol);
+ PROFILE_START
+ cudak_(cuda_colsum)(a, b);
+ PROFILE_STOP
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(colsame)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *ref = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(new_)(L, 1, a->ncol);
+ CHECK_SAME_DIMENSION(a, ref);
+ PROFILE_START
+ cudak_(cuda_colsame)(a, ref, b);
+ PROFILE_STOP
+ 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_)(L, a->nrow, 1);
+ PROFILE_START
+ cudak_(cuda_rowmax)(a, b);
+ PROFILE_STOP
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(rowmax_idx)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(new_)(L, a->nrow, 1);
+ Matrix *idx = nerv_matrix_(new_)(L, a->nrow, 1);
+ PROFILE_START
+ cudak_(cuda_rowmax_idx)(a, b, idx);
+ PROFILE_STOP
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ luaT_pushudata(L, idx, nerv_matrix_(tname));
+ return 2;
+}
+
+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");
+ PROFILE_START
+ cudak_(cuda_add_row)(a, b, beta);
+ PROFILE_STOP
+ 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);
+ PROFILE_START
+ cudak_(cuda_fill)(self, val);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(copy_fromd)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int nargs = lua_gettop(L);
+ int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+ int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+ int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ nerv_error(L, "invalid copy interval");
+ if (a->ncol != b->ncol)
+ nerv_error(L, "matrices should be of the same dimension");
+ PROFILE_START
+ CUDA_SAFE_SYNC_CALL(
+ cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
+ MATRIX_ROW_PTR(b, b_begin), b->stride,
+ sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
+ cudaMemcpyDeviceToDevice));
+ PROFILE_STOP
+ return 0;
+}
+
+extern const char *MATRIX_CUMATRIX_HOST_TNAME;
+static int nerv_matrix_(copy_fromh)(lua_State *L) { 
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ int nargs = lua_gettop(L);
+ int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+ int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+ int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ nerv_error(L, "invalid copy interval");
+ if (a->ncol != b->ncol)
+ nerv_error(L, "matrices should be of the same dimension");
+ PROFILE_START
+ CUDA_SAFE_SYNC_CALL(
+ cudaMemcpy2D(MATRIX_ROW_PTR(a, a_begin), a->stride,
+ MATRIX_ROW_PTR(b, b_begin), b->stride,
+ sizeof(MATRIX_ELEM) * b->ncol, b_end - b_begin,
+ cudaMemcpyHostToDevice));
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(copy_toh)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ int nargs = lua_gettop(L);
+ int a_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+ int a_end = nargs > 3 ? luaL_checkinteger(L, 4) : a->nrow;
+ int b_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+ if (!(0 <= a_begin && a_begin < a_end && a_end <= a->nrow &&
+ b_begin + a_end - a_begin <= b->nrow))
+ nerv_error(L, "invalid copy interval");
+ if (b->ncol != a->ncol)
+ nerv_error(L, "matrices should be of the same dimension");
+ PROFILE_START
+ CUDA_SAFE_SYNC_CALL(
+ cudaMemcpy2D(MATRIX_ROW_PTR(b, b_begin), b->stride,
+ MATRIX_ROW_PTR(a, a_begin), a->stride,
+ sizeof(MATRIX_ELEM) * a->ncol, a_end - a_begin,
+ cudaMemcpyDeviceToHost));
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(trans)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(new_)(L, a->ncol, a->nrow);
+ MATRIX_ELEM alpha = 1, beta = 0;
+ /* FIXME: possible memory leak when lua error is raised */
+ PROFILE_START
+ CUBLAS_SAFE_SYNC_CALL(
+ 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)));
+ PROFILE_STOP
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+static int nerv_matrix_(mul_elem)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 3, nerv_matrix_(tname));
+ Matrix *c = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ CHECK_SAME_DIMENSION(a, b);
+ CHECK_SAME_DIMENSION(a, c);
+ PROFILE_START
+ cudak_(cuda_mul_elem)(a, b, c);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(log_elem)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ CHECK_SAME_DIMENSION(a, b);
+ PROFILE_START
+ cudak_(cuda_log_elem)(a, b);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(decompress)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b;
+ int orig_col = luaL_checkinteger(L, 2);
+ if (a->ncol != 1)
+ nerv_error(L, "the compressed matrix must be a column vector");
+ b = nerv_matrix_(new_)(L, a->nrow, orig_col);
+ PROFILE_START
+ cudak_(cuda_fill)(b, 0.0);
+ cudak_(cuda_decompress)(a, b);
+ PROFILE_STOP
+ luaT_pushudata(L, b, nerv_matrix_(tname));
+ return 1;
+}
+
+extern const char *nerv_matrix_host_int_tname;
+static int nerv_matrix_(copy_rows_fromh_by_idx)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ Matrix *idx = luaT_checkudata(L, 3, nerv_matrix_host_int_tname);
+ long nrow = a->nrow;
+ int b_begin = lua_gettop(L) > 3 ? luaL_checkinteger(L, 4) : 0;
+ if (!(0 <= b_begin && b_begin + nrow <= idx->ncol))
+ nerv_error(L, "invalid copy interval");
+ long *idx_ptr = idx->data.i;
+ int i;
+ if (idx->nrow != 1)
+ nerv_error(L, "index should be a vector");
+ if (a->ncol != b->ncol)
+ nerv_error(L, "source/destination dimension mismatch");
+ cudaStream_t *streams = (cudaStream_t*)malloc(sizeof(cudaStream_t) * nrow);
+ for (i = 0; i < nrow; i++)
+ {
+ int src_row = idx_ptr[b_begin + i];
+ if (!(0 <= src_row && src_row < b->nrow))
+ nerv_error(L, "invalid index");
+ CUDA_SAFE_CALL(cudaStreamCreate(streams + i));
+ CUDA_SAFE_CALL(cudaMemcpyAsync(MATRIX_ROW_PTR(a, i),
+ MATRIX_ROW_PTR(b, src_row),
+ b->stride,
+ cudaMemcpyHostToDevice, streams[i]));
+ }
+ for (i = 0; i < nrow; i++)
+ {
+ CUDA_SAFE_CALL(cudaStreamSynchronize(streams[i]));
+ CUDA_SAFE_CALL(cudaStreamDestroy(streams[i]));
+ }
+ free(streams);
+ return 0;
+}
+
+static int nerv_matrix_(expand_frm)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int context = luaL_checkinteger(L, 3);
+ if (a->nrow != b->nrow)
+ nerv_error(L, "mismatching number of frames");
+ if (a->ncol != b->ncol * (context * 2 + 1))
+ nerv_error(L, "the width should be 2 * context + 1");
+ PROFILE_START
+ cudak_(cuda_expand_frm)(b, a, context);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(rearrange_frm)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int step = luaL_checkinteger(L, 3);
+ CHECK_SAME_DIMENSION(a, b);
+ if (b->ncol % step)
+ nerv_error(L, "the dimension of columns is not divisible by step");
+ PROFILE_START
+ cudak_(cuda_rearrange_frm)(b, a, step);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(scale_rows_by_col)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ if (a->nrow != b->nrow)
+ nerv_error(L, "the number of rows is not the same");
+ if (b->ncol != 1)
+ nerv_error(L, "a column vector is expected");
+ PROFILE_START
+ cudak_(cuda_scale_rows_by_col)(b, a);
+ PROFILE_STOP
+ return 0;
+}
+
+static int nerv_matrix_(scale_rows_by_row)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ if (a->ncol != b->ncol)
+ nerv_error(L, "the number of columns is not the same");
+ if (b->nrow != 1)
+ nerv_error(L, "a row vector is expected");
+ PROFILE_START
+ cudak_(cuda_scale_rows_by_row)(b, a);
+ PROFILE_STOP
+ return 0;
+}
+
+static const luaL_Reg nerv_matrix_(extra_methods)[] = {
+ {"create", nerv_matrix_(create)},
+ {"colsum", nerv_matrix_(colsum)},
+ {"colsame", nerv_matrix_(colsame)},
+ {"rowsum", nerv_matrix_(rowsum)},
+ {"rowmax", nerv_matrix_(rowmax)},
+ {"rowmax_idx", nerv_matrix_(rowmax_idx)},
+ {"trans", nerv_matrix_(trans)},
+ {"decompress", nerv_matrix_(decompress)},
+ /* in-place calc */
+ {"copy_fromh", nerv_matrix_(copy_fromh)},
+ {"copy_fromd", nerv_matrix_(copy_fromd)},
+ {"copy_toh", nerv_matrix_(copy_toh)},
+ {"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)},
+ {"softmax", nerv_matrix_(softmax)},
+ {"mul_elem", nerv_matrix_(mul_elem)},
+ {"log_elem", nerv_matrix_(log_elem)},
+ {"copy_rows_fromh_by_idx", nerv_matrix_(copy_rows_fromh_by_idx)},
+ {"expand_frm", nerv_matrix_(expand_frm)},
+ {"rearrange_frm", nerv_matrix_(rearrange_frm)},
+ {"scale_rows_by_row", nerv_matrix_(scale_rows_by_row)},
+ {"scale_rows_by_col", nerv_matrix_(scale_rows_by_col)},
+ {NULL, NULL}
+};
+
+static void cuda_matrix_(init)(lua_State *L) {
+ luaN_append_methods(L, nerv_matrix_(extra_methods));
+}
+
+static void cuda_matrix_(free)(lua_State *L, MATRIX_ELEM *ptr) {
+ CUDA_SAFE_SYNC_CALL(cudaFree(ptr));
+}
+
+static void cuda_matrix_(alloc)(lua_State *L, MATRIX_ELEM **dptr,
+ size_t *stride, long width, long height) {
+ PROFILE_START
+ CUDA_SAFE_SYNC_CALL(cudaMallocPitch((void **)dptr, stride, width, height));
+ PROFILE_STOP
+}
+
+static MATRIX_ELEM cuda_matrix_(read)(lua_State *L, MATRIX_ELEM *data,
+ int idx) {
+ MATRIX_ELEM res;
+ CUDA_SAFE_SYNC_CALL(cudaMemcpy(&res, data + idx,
+ sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost));
+ return res;
+}
+
+static void cuda_matrix_(write)(lua_State *L, MATRIX_ELEM *data,
+ int idx, MATRIX_ELEM val) {
+ CUDA_SAFE_SYNC_CALL(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
diff --git a/nerv/matrix/generic/elem_type.h b/nerv/matrix/generic/elem_type.h
new file mode 100644
index 0000000..bffe940
--- /dev/null
+++ b/nerv/matrix/generic/elem_type.h
@@ -0,0 +1,22 @@
+#ifdef MATRIX_USE_FLOAT
+
+#define MATRIX_ELEM float
+#define MATRIX_ELEM_FMT "%f"
+#define MATRIX_ELEM_WRITE_FMT "%.8f"
+#define MATRIX_ELEM_PTR(self) ((self)->data.f)
+
+#elif defined(MATRIX_USE_DOUBLE)
+
+#define MATRIX_ELEM double
+#define MATRIX_ELEM_FMT "%lf"
+#define MATRIX_ELEM_WRITE_FMT "%.8lf"
+#define MATRIX_ELEM_PTR(self) ((self)->data.d)
+
+#elif defined(MATRIX_USE_INT)
+
+#define MATRIX_ELEM long
+#define MATRIX_ELEM_FMT "%ld"
+#define MATRIX_ELEM_WRITE_FMT "%ld"
+#define MATRIX_ELEM_PTR(self) ((self)->data.i)
+
+#endif
diff --git a/nerv/matrix/generic/matrix.c b/nerv/matrix/generic/matrix.c
new file mode 100644
index 0000000..e17fb42
--- /dev/null
+++ b/nerv/matrix/generic/matrix.c
@@ -0,0 +1,155 @@
+#ifdef NERV_GENERIC_MATRIX
+#include "../../common.h"
+#include "matrix.h"
+
+extern const char *nerv_matrix_(tname);
+extern const char *MATRIX_BASE_TNAME;
+
+void nerv_matrix_(data_free)(lua_State *L, Matrix *self) {
+ (void)L;
+ assert(*self->data_ref > 0);
+ if (--(*self->data_ref) == 0)
+ {
+ /* free matrix data */
+ MATRIX_DATA_FREE(L, MATRIX_ELEM_PTR(self));
+ free(self->data_ref);
+ free(self);
+ }
+}
+
+void nerv_matrix_(data_retain)(Matrix *self) {
+ (*self->data_ref)++;
+}
+
+Matrix *nerv_matrix_(new_)(lua_State *L, long nrow, long ncol) {
+ Matrix *self = (Matrix *)malloc(sizeof(Matrix));
+ self->nrow = nrow;
+ self->ncol = ncol;
+ self->nmax = self->nrow * self->ncol;
+ MATRIX_DATA_ALLOC(L, &MATRIX_ELEM_PTR(self), &self->stride,
+ sizeof(MATRIX_ELEM) * self->ncol, self->nrow);
+ self->data_ref = (long *)malloc(sizeof(long));
+ *self->data_ref = 0;
+ nerv_matrix_(data_retain)(self);
+ return self;
+}
+
+int nerv_matrix_(new)(lua_State *L) {
+ luaT_pushudata(L, nerv_matrix_(new_)(L, luaL_checkinteger(L, 1),
+ luaL_checkinteger(L, 2)),
+ nerv_matrix_(tname));
+ return 1;
+}
+
+int nerv_matrix_(destroy)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ nerv_matrix_(data_free)(L, self);
+ return 1;
+}
+
+int nerv_matrix_(get_elem)(lua_State *L); 
+int nerv_matrix_(set_elem)(lua_State *L);
+
+static Matrix *nerv_matrix_(getrow)(Matrix *self, int row) {
+ Matrix *prow = (Matrix *)malloc(sizeof(Matrix));
+ prow->ncol = self->ncol;
+ prow->nrow = 1;
+ prow->stride = self->stride;
+ prow->nmax = prow->ncol;
+ MATRIX_ELEM_PTR(prow) = MATRIX_ROW_PTR(self, row);
+ prow->data_ref = self->data_ref;
+ nerv_matrix_(data_retain)(prow);
+ return prow;
+}
+
+static int nerv_matrix_(newindex)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ if (lua_isnumber(L, 2))
+ {
+ int idx = luaL_checkinteger(L, 2);
+ if (self->nrow == 1)
+ {
+ if (idx < 0 || idx >= self->ncol)
+ nerv_error(L, "index must be within range [0, %d)", self->ncol);
+ MATRIX_DATA_WRITE(L, MATRIX_ELEM_PTR(self), idx,
+ luaL_checknumber(L, 3));
+ }
+ else
+ nerv_error(L, "cannot assign to row vector");
+ lua_pushboolean(L, 1);
+ return 1;
+ }
+ else
+ {
+ lua_pushboolean(L, 0);
+ return 1;
+ }
+}
+
+
+static int nerv_matrix_(index)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ if (lua_isnumber(L, 2))
+ {
+ int idx = luaL_checkinteger(L, 2);
+ if (self->nrow == 1)
+ {
+ if (idx < 0 || idx >= self->ncol)
+ nerv_error(L, "index must be within range [0, %d)", self->ncol);
+ lua_pushnumber(L, MATRIX_DATA_READ(L, MATRIX_ELEM_PTR(self), idx));
+ }
+ else
+ {
+ if (idx < 0 || idx >= self->nrow)
+ nerv_error(L, "index must be within range [0, %d)", self->nrow);
+ luaT_pushudata(L, nerv_matrix_(getrow)(self, idx), nerv_matrix_(tname));
+ }
+ lua_pushboolean(L, 1);
+ return 2;
+ }
+ else
+ {
+ lua_pushboolean(L, 0);
+ return 1;
+ }
+}
+
+static int nerv_matrix_(ncol)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ lua_pushinteger(L, self->ncol);
+ return 1;
+}
+
+static int nerv_matrix_(nrow)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ lua_pushinteger(L, self->nrow);
+ return 1;
+}
+
+static int nerv_matrix_(get_dataref_value)(lua_State *L) { 
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname)); 
+ lua_pushinteger(L, *(self->data_ref)); 
+ return 1; 
+} 
+
+static const luaL_Reg nerv_matrix_(methods)[] = {
+ {"get_elem", nerv_matrix_(get_elem)},
+ {"set_elem", nerv_matrix_(set_elem)},
+ {"ncol", nerv_matrix_(ncol)},
+ {"nrow", nerv_matrix_(nrow)},
+ {"get_dataref_value", nerv_matrix_(get_dataref_value)},
+ {"__index__", nerv_matrix_(index)},
+ {"__newindex__", nerv_matrix_(newindex)},
+ {NULL, NULL}
+};
+
+void nerv_matrix_(init)(lua_State *L) {
+ luaT_newmetatable(L, nerv_matrix_(tname), MATRIX_BASE_TNAME,
+ nerv_matrix_(new), nerv_matrix_(destroy), NULL);
+ luaL_register(L, NULL, nerv_matrix_(methods));
+#ifdef MATRIX_INIT
+ MATRIX_INIT(L);
+#endif
+ lua_pop(L, 1);
+}
+#endif
diff --git a/nerv/matrix/generic/matrix.h b/nerv/matrix/generic/matrix.h
new file mode 100644
index 0000000..833724b
--- /dev/null
+++ b/nerv/matrix/generic/matrix.h
@@ -0,0 +1,19 @@
+#ifndef NERV_GENERIC_MATRIX_H
+#define NERV_GENERIC_MATRIX_H
+
+#include <stddef.h>
+typedef struct Matrix {
+ size_t stride; /* size of a row */
+ long ncol, nrow, nmax; /* dimension of the matrix */
+ union {
+ float *f;
+ double *d;
+ long *i;
+ } data; /* pointer to actual storage */
+ long *data_ref;
+} Matrix;
+
+#define MATRIX_ROW_PTR(self, row) \
+ (MATRIX_ELEM *)((char *)MATRIX_ELEM_PTR(self) + (row) * (self)->stride)
+
+#endif
diff --git a/nerv/matrix/generic/mmatrix.c b/nerv/matrix/generic/mmatrix.c
new file mode 100644
index 0000000..b0f0791
--- /dev/null
+++ b/nerv/matrix/generic/mmatrix.c
@@ -0,0 +1,122 @@
+#ifdef NERV_GENERIC_MMATRIX
+#include "matrix.h"
+#include "elem_type.h"
+#define MATRIX_DATA_FREE(L, ptr) free(ptr)
+#define MATRIX_DATA_ALLOC(L, dptr, stride, width, height) \
+ host_matrix_(alloc)(L, dptr, stride, width, height)
+#define MATRIX_DATA_WRITE(L, data, idx, val) (data[idx] = val)
+#define MATRIX_DATA_READ(L, data, idx) (data[idx])
+#define MATRIX_INIT(L) host_matrix_(init)(L)
+#define MATRIX_BASE_TNAME nerv_matrix_host_tname
+#define NERV_GENERIC_MATRIX
+#include "../../common.h"
+#include "../../io/chunk_file.h"
+#include "string.h"
+
+static void host_matrix_(alloc)(lua_State *L,
+ MATRIX_ELEM **dptr, size_t *stride,
+ long width, long height) {
+ if ((*dptr = (MATRIX_ELEM *)malloc(width * height)) == NULL)
+ nerv_error(L, "mmatrix insufficient memory");
+ *stride = width;
+}
+
+int nerv_matrix_(get_elem)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ int idx = luaL_checkinteger(L, 2);
+ if (idx < 0 || idx >= self->nmax)
+ nerv_error(L, "index must be within range [0, %d)", self->nmax);
+ lua_pushnumber(L, MATRIX_ELEM_PTR(self)[idx]);
+ return 1;
+}
+
+int nerv_matrix_(set_elem)(lua_State *L) {
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ int idx = luaL_checkinteger(L, 2);
+ MATRIX_ELEM v = luaL_checknumber(L, 3);
+ if (idx < 0 || idx >= self->nmax)
+ nerv_error(L, "index must be within range [0, %d)", self->nmax);
+ MATRIX_ELEM_PTR(self)[idx] = v;
+ return 0;
+}
+
+static const luaL_Reg nerv_matrix_(extra_methods)[];
+static void host_matrix_(init)(lua_State *L) {
+ luaN_append_methods(L, nerv_matrix_(extra_methods));
+#ifdef MMATRIX_INIT
+ MMATRIX_INIT(L);
+#endif
+}
+
+#include "matrix.c"
+
+int nerv_matrix_(load)(lua_State *L) {
+ ChunkData *chunk = luaT_checkudata(L, 1, nerv_chunk_data_tname);
+ Matrix *self;
+ int i, j;
+ long nrow, ncol;
+ FILE *fp = chunk->fp;
+ if (fscanf(fp, "%ld %ld", &nrow, &ncol) != 2)
+ return 0;
+ self = nerv_matrix_(new_)(L, nrow, ncol);
+ for (i = 0; i < nrow; i++)
+ {
+ MATRIX_ELEM *row = MATRIX_ROW_PTR(self, i);
+ for (j = 0; j < ncol; j++)
+ if (fscanf(fp, MATRIX_ELEM_FMT, row + j) != 1)
+ {
+ free(self);
+ return 0;
+ }
+ }
+ luaT_pushudata(L, self, nerv_matrix_(tname));
+ return 1;
+}
+
+int nerv_matrix_(save)(lua_State *L) {
+ ChunkFileHandle *chunk = luaT_checkudata(L, 2,
+ nerv_chunk_file_handle_tname);
+ Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ int i, j;
+ long nrow = self->nrow, ncol = self->ncol;
+ FILE *fp = chunk->fp;
+ if (fprintf(fp, "%ld %ld\n", nrow, ncol) < 0)
+ return 0;
+ for (i = 0; i < nrow; i++)
+ {
+ MATRIX_ELEM *row = MATRIX_ROW_PTR(self, i);
+ for (j = 0; j < ncol; j++)
+ if (fprintf(fp, MATRIX_ELEM_WRITE_FMT " ", row[j]) < 0)
+ return 0;
+ if (fprintf(fp, "\n") < 0)
+ return 0;
+ }
+ return 0;
+}
+
+static int nerv_matrix_(copy_from)(lua_State *L) {
+ Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
+ Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ int nargs = lua_gettop(L);
+ int b_begin = nargs > 2 ? luaL_checkinteger(L, 3) : 0;
+ int b_end = nargs > 3 ? luaL_checkinteger(L, 4) : b->nrow;
+ int a_begin = nargs > 4 ? luaL_checkinteger(L, 5) : 0;
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ nerv_error(L, "invalid copy interval");
+ if (a->ncol != b->ncol)
+ nerv_error(L, "matrices should be of the same dimension");
+ memmove(MATRIX_ROW_PTR(a, a_begin),
+ MATRIX_ROW_PTR(b, b_begin),
+ sizeof(MATRIX_ELEM) * b->ncol * (b_end - b_begin));
+ return 0;
+}
+
+static const luaL_Reg nerv_matrix_(extra_methods)[] = {
+ {"load", nerv_matrix_(load)},
+ {"save", nerv_matrix_(save)},
+ {"copy_from", nerv_matrix_(copy_from)},
+ {NULL, NULL}
+};
+
+#endif
diff --git a/nerv/matrix/init.c b/nerv/matrix/init.c
new file mode 100644
index 0000000..c29d7e9
--- /dev/null
+++ b/nerv/matrix/init.c
@@ -0,0 +1,35 @@
+#include "../common.h"
+#include "generic/matrix.h"
+
+const char *nerv_matrix_tname = "nerv.Matrix";
+const char *nerv_matrix_cuda_tname = "nerv.CuMatrix";
+const char *nerv_matrix_host_tname = "nerv.MMatrix";
+
+void nerv_cumatrix_init(lua_State *L);
+void nerv_mmatrix_init(lua_State *L);
+
+static const luaL_Reg matrix_methods[] = {
+ {"__tostring__", nerv_error_method_not_implemented },
+ {"__add__", nerv_error_method_not_implemented },
+ {"__sub__", nerv_error_method_not_implemented },
+ {"__mul__", nerv_error_method_not_implemented },
+ {NULL, NULL}
+};
+
+void nerv_matrix_init(lua_State *L) {
+ /* abstract base class: Matrix */
+ luaT_newmetatable(L, nerv_matrix_tname, NULL, NULL, NULL, NULL);
+ luaL_register(L, NULL, matrix_methods);
+ lua_pop(L, 1);
+
+ /* CuMatrix inherits from Matrix */
+ luaT_newmetatable(L, nerv_matrix_cuda_tname, nerv_matrix_tname,
+ NULL, NULL, NULL);
+ nerv_cumatrix_init(L);
+ lua_pop(L, 1);
+ /* MMatrix inherits from Matrix */
+ luaT_newmetatable(L, nerv_matrix_host_tname, nerv_matrix_tname,
+ NULL, NULL, NULL);
+ nerv_mmatrix_init(L);
+ lua_pop(L, 1);
+}
diff --git a/nerv/matrix/init.lua b/nerv/matrix/init.lua
new file mode 100644
index 0000000..1a8925f
--- /dev/null
+++ b/nerv/matrix/init.lua
@@ -0,0 +1,77 @@
+function nerv.Matrix:__tostring__()
+ local ncol = self:ncol()
+ local nrow = self:nrow()
+ local strt = {}
+ local fmt
+ if self.fmt then
+ fmt = self.fmt
+ else
+ fmt = "%.8f "
+ end
+ if nrow == 1 then
+ for col = 0, ncol - 1 do
+ table.insert(strt, string.format(fmt, self[col]))
+ end
+ table.insert(strt, "\n")
+ else
+ for row = 0, nrow - 1 do
+ local rp = self[row]
+ for col = 0, ncol - 1 do
+ table.insert(strt, string.format(fmt, rp[col]))
+ end
+ table.insert(strt, "\n")
+ end
+ end
+ table.insert(strt, string.format(
+ "[%s %d x %d]", self.__typename, nrow, ncol))
+ return table.concat(strt)
+end
+
+-- gen: a function takes take indices of the matrix and return the generated
+-- all entrys in the matrix will be assigned by calling gen(i, j)
+function nerv.Matrix:generate(gen)
+ if (self:nrow() == 1) then
+ for j = 0, self:ncol() - 1 do
+ self[j] = gen(j)
+ end
+ else
+ for i = 0, self:nrow() - 1 do
+ local row = self[i]
+ for j = 0, self:ncol() - 1 do
+ row[j] = gen(i, j)
+ end
+ end
+ end
+end
+
+nerv.MMatrixInt.fmt = "%d "
+
+function nerv.CuMatrix:__add__(b)
+ c = self:create()
+ c:add(self, b, 1.0, 1.0)
+ return c
+end
+
+function nerv.CuMatrix:__sub__(b)
+ c = self:create()
+ c:add(self, b, 1.0, -1.0)
+ return c
+end
+
+function nerv.CuMatrix:__mul__(b)
+ c = nerv.get_type(self.__typename)(self:nrow(), b:ncol())
+ c:mul(self, b, 1.0, 0.0, 'N', 'N')
+ return c
+end
+
+function nerv.CuMatrixFloat.new_from_host(mat)
+ local res = nerv.CuMatrixFloat(mat:nrow(), mat:ncol())
+ res:copy_fromh(mat)
+ return res
+end
+
+function nerv.CuMatrixFloat:new_to_host()
+ local res = nerv.MMatrixFloat(self:nrow(), self:ncol())
+ self:copy_toh(res)
+ return res
+end
diff --git a/nerv/matrix/mmatrix.c b/nerv/matrix/mmatrix.c
new file mode 100644
index 0000000..d1d68b9
--- /dev/null
+++ b/nerv/matrix/mmatrix.c
@@ -0,0 +1,77 @@
+#define NERV_GENERIC_MMATRIX
+#include <stdlib.h>
+#include "../common.h"
+void nerv_matrix_host_float_init(lua_State *L);
+void nerv_matrix_host_double_init(lua_State *L);
+void nerv_matrix_host_int_init(lua_State *L);
+
+void nerv_mmatrix_init(lua_State *L) {
+ srand(1);
+ nerv_matrix_host_float_init(L);
+ nerv_matrix_host_double_init(L);
+ nerv_matrix_host_int_init(L);
+}
+
+#define MATRIX_USE_FLOAT
+#define host_matrix_(NAME) host_matrix_float_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_host_float_##NAME
+const char *nerv_matrix_(tname) = "nerv.MMatrixFloat";
+#include "generic/mmatrix.c"
+#undef nerv_matrix_
+#undef host_matrix_
+#undef MATRIX_USE_FLOAT
+#undef MATRIX_ELEM
+#undef MATRIX_ELEM_PTR
+#undef MATRIX_ELEM_FMT
+#undef MATRIX_ELEM_WRITE_FMT
+
+#define NERV_GENERIC_MMATRIX
+#define MATRIX_USE_DOUBLE
+#define host_matrix_(NAME) host_matrix_double_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_host_double_##NAME
+const char *nerv_matrix_(tname) = "nerv.MMatrixDouble";
+#include "generic/mmatrix.c"
+#undef nerv_matrix_
+#undef host_matrix_
+#undef MATRIX_USE_DOUBLE
+#undef MATRIX_ELEM
+#undef MATRIX_ELEM_PTR
+#undef MATRIX_ELEM_FMT
+#undef MATRIX_ELEM_WRITE_FMT
+
+#define NERV_GENERIC_MMATRIX
+#define MATRIX_USE_INT
+#define host_matrix_(NAME) host_matrix_int_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_host_int_##NAME
+const char *nerv_matrix_(tname) = "nerv.MMatrixInt";
+#define MMATRIX_INIT(L) host_matrix_(init_extra)(L)
+
+static const luaL_Reg nerv_matrix_(extra_methods_int)[];
+static void host_matrix_(init_extra)(lua_State *L) {
+ luaN_append_methods(L, nerv_matrix_(extra_methods_int));
+}
+
+#include "generic/mmatrix.c"
+
+static int nerv_matrix_(perm_gen)(lua_State *L) {
+ int i, ncol = luaL_checkinteger(L, 1);
+ Matrix *self = nerv_matrix_(new_)(L, 1, ncol);
+ long *prow = self->data.i;
+ for (i = 0; i < ncol; i++)
+ prow[i] = i;
+ for (i = ncol - 1; i >= 0; i--)
+ {
+ size_t j = rand() % (i + 1);
+ long tmp = prow[i];
+ prow[i] = prow[j];
+ prow[j] = tmp;
+ }
+ luaT_pushudata(L, self, nerv_matrix_(tname));
+ return 1;
+}
+
+static const luaL_Reg nerv_matrix_(extra_methods_int)[] = {
+ {"perm_gen", nerv_matrix_(perm_gen)},
+ {NULL, NULL}
+};
+
author	Determinant <[email protected]>	2015-06-22 19:01:29 +0800
committer	Determinant <[email protected]>	2015-06-22 19:01:29 +0800
commit	2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 (patch)
tree	382f97575bd2df9ee6abb1662b11b279fc22d72b /nerv/matrix
parent	196e9b48a3541caccdffc5743001cced70667091 (diff)