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authorDeterminant <[email protected]>2015-06-24 22:48:24 +0800
committerDeterminant <[email protected]>2015-06-24 22:48:24 +0800
commit5e407d74130accfbbf94d2cabcb03fc126a89410 (patch)
tree6d8998e904a31a95f85a6e64ac7f3940fb61af80
parent8f13607cba9d6cf4fc4a213ba5ae4bcd46f7e18d (diff)
separate non-Lua part of matrix code to a dedicated dir
-rw-r--r--nerv/Makefile43
-rw-r--r--nerv/common.c25
-rw-r--r--nerv/common.h45
-rw-r--r--nerv/lib/io/chunk_file.c18
-rw-r--r--nerv/lib/matrix/cuda_helper.h (renamed from nerv/matrix/cuda_helper.h)55
-rw-r--r--nerv/lib/matrix/cukernel.cu (renamed from nerv/matrix/cukernel.cu)0
-rw-r--r--nerv/lib/matrix/cukernel.h (renamed from nerv/matrix/cukernel.h)0
-rw-r--r--nerv/lib/matrix/cumatrix.c69
-rw-r--r--nerv/lib/matrix/cumatrix.h6
-rw-r--r--nerv/lib/matrix/generic/cukernel.cu571
-rw-r--r--nerv/lib/matrix/generic/cumatrix.c403
-rw-r--r--nerv/lib/matrix/generic/cumatrix.h50
-rw-r--r--nerv/lib/matrix/generic/elem_type.h22
-rw-r--r--nerv/lib/matrix/generic/matrix.c57
-rw-r--r--nerv/lib/matrix/generic/matrix.h4
-rw-r--r--nerv/lib/matrix/generic/mmatrix.c82
-rw-r--r--nerv/lib/matrix/generic/mmatrix.h7
-rw-r--r--nerv/lib/matrix/init.lua77
-rw-r--r--nerv/lib/matrix/matrix.h (renamed from nerv/matrix/generic/matrix.h)2
-rw-r--r--nerv/lib/matrix/mmatrix.c53
-rw-r--r--nerv/lib/matrix/mmatrix.h4
-rw-r--r--nerv/matrix/cumatrix.c44
-rw-r--r--nerv/matrix/generic/cumatrix.c479
-rw-r--r--nerv/matrix/generic/matrix.c102
-rw-r--r--nerv/matrix/generic/mmatrix.c80
-rw-r--r--nerv/matrix/init.c9
-rw-r--r--nerv/matrix/mmatrix.c34
-rw-r--r--nerv/nerv-scm-1.rockspec1
m---------speech0
29 files changed, 1805 insertions, 537 deletions
diff --git a/nerv/Makefile b/nerv/Makefile
index 4008453..06a439a 100644
--- a/nerv/Makefile
+++ b/nerv/Makefile
@@ -1,58 +1,65 @@
.PHONY: build install clean
SHELL := /bin/bash
BUILD_DIR := $(CURDIR)/build
-OBJS := nerv.o lib/luaT/luaT.o lib/io/chunk_file.o common.o \
- matrix/mmatrix.o matrix/cumatrix.o matrix/init.o matrix/cukernel.o \
+LIB_PATH := $(LUA_BINDIR)/../lib
+OBJ_DIR := $(BUILD_DIR)/objs
+CORE_OBJS := common.o lib/io/chunk_file.o lib/matrix/mmatrix.o lib/matrix/cumatrix.o lib/matrix/cukernel.o
+NERV_OBJS := nerv.o \
+ matrix/mmatrix.o matrix/cumatrix.o matrix/init.o \
io/init.o io/chunk_file.o \
examples/oop_example.o
-LIBS := libnerv.so
+LUAT_OBJS := lib/luaT/luaT.o
+CORE_OBJS := $(addprefix $(OBJ_DIR)/,$(CORE_OBJS))
+NERV_OBJS := $(addprefix $(OBJ_DIR)/,$(NERV_OBJS))
+LUAT_OBJS := $(addprefix $(OBJ_DIR)/,$(LUAT_OBJS))
+OBJS := $(CORE_OBJS) $(NERV_OBJS) $(LUAT_OBJS)
+LIBS := $(INST_LIBDIR)/libnerv.so $(LIB_PATH)/libnervcore.so $(LIB_PATH)/libluaT.so
LUA_LIBS := matrix/init.lua io/init.lua init.lua \
layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua \
layer/window.lua layer/bias.lua layer/combiner.lua layer/mse.lua \
nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua \
io/sgd_buffer.lua
INCLUDE := -I $(LUA_INCDIR) -DLUA_USE_APICHECK
-CUDA_BASE := /usr/local/cuda-6.5
-#CUDA_BASE := /usr/local/cuda-5.0
+#CUDA_BASE := /usr/local/cuda-6.5
+CUDA_BASE := /usr/local/cuda-5.0
CUDA_INCLUDE := -I $(CUDA_BASE)/include/
INCLUDE += $(CUDA_INCLUDE)
LDFLAGS := -L$(CUDA_BASE)/lib64/ -Wl,-rpath=$(CUDA_BASE)/lib64/ -lcudart -lcublas
CFLAGS := -Wall -Wextra -O2
-OBJ_DIR := $(BUILD_DIR)/objs
-SUBDIR := matrix io layer examples nn lib/io lib/luaT
+SUBDIR := matrix io layer examples nn lib/io lib/luaT lib/matrix
NVCC := $(CUDA_BASE)/bin/nvcc
NVCC_FLAGS := -Xcompiler -fPIC,-Wall,-Wextra
LUA_DIR = $(INST_LUADIR)/nerv
-OBJS := $(addprefix $(OBJ_DIR)/,$(OBJS))
OBJ_SUBDIR := $(addprefix $(OBJ_DIR)/,$(SUBDIR))
LUA_SUBDIR := $(addprefix $(LUA_DIR)/,$(SUBDIR))
-LIBS := $(addprefix $(INST_LIBDIR)/,$(LIBS))
LUA_LIBS := $(addprefix $(LUA_DIR)/,$(LUA_LIBS))
build: $(OBJ_DIR) $(OBJ_SUBDIR) $(OBJS)
+
$(OBJ_DIR) $(LUA_DIR) $(OBJ_SUBDIR) $(LUA_SUBDIR):
-mkdir -p $@
$(OBJ_DIR)/%.o: %.c $(patsubst /%.o,/%.c,$@)
gcc -c -o $@ $< $(INCLUDE) -fPIC $(CFLAGS)
-$(OBJ_DIR)/matrix/cukernel.o: matrix/cukernel.cu
+$(OBJ_DIR)/lib/matrix/cukernel.o: lib/matrix/cukernel.cu
$(NVCC) -c -o $@ $< $(INCLUDE) $(NVCC_FLAGS)
$(LUA_DIR)/%.lua: %.lua
cp $< $@
#$(OBJ_DIR)/luaT.o:
# gcc -c -o $@ luaT/luaT.c $(INCLUDE) -fPIC
-$(LIBS): $(OBJS)
- gcc -shared -o $@ $(OBJS) $(LDFLAGS)
+$(LIB_PATH)/libnervcore.so: $(CORE_OBJS)
+ gcc -shared -o $@ $^ $(LDFLAGS)
+$(LIB_PATH)/libluaT.so: $(LUAT_OBJS)
+ gcc -shared -o $@ $^ $(LDFLAGS)
+$(INST_LIBDIR)/libnerv.so: $(NERV_OBJS) $(LIB_PATH)/libnervcore.so $(LIB_PATH)/libluaT.so
+ gcc -shared -o $@ $(NERV_OBJS) $(LDFLAGS) -Wl,-rpath=$(LIB_PATH) -L$(LIB_PATH) -lnervcore -lluaT
$(OBJ_DIR)/matrix/cumatrix.o: matrix/generic/cumatrix.c matrix/generic/matrix.c matrix/generic/cukernel.cu
$(OBJ_DIR)/matrix/mmatrix.o: matrix/generic/mmatrix.c matrix/generic/matrix.c
-$(OBJ_DIR)/matrix/cukernel.o: matrix/generic/cukernel.cu
-
-.PHONY: speech
-speech:
- -mkdir -p build/objs/speech/tnet_io
- $(MAKE) -C speech/ BUILD_DIR=$(BUILD_DIR) LIB_DIR=$(LIB_DIR) OBJ_DIR=$(CURDIR)/build/objs/speech/ LUA_DIR=$(LUA_DIR)
+$(OBJ_DIR)/lib/matrix/cumatrix.o: lib/matrix/generic/cumatrix.c lib/matrix/generic/matrix.c lib/matrix/generic/cukernel.cu
+$(OBJ_DIR)/lib/matrix/mmatrix.o: lib/matrix/generic/mmatrix.c lib/matrix/generic/matrix.c
+$(OBJ_DIR)/lib/matrix/cukernel.o: lib/matrix/generic/cukernel.cu
clean:
-rm -rf $(OBJ_DIR)
diff --git a/nerv/common.c b/nerv/common.c
index b4e39e6..0584438 100644
--- a/nerv/common.c
+++ b/nerv/common.c
@@ -11,6 +11,31 @@ int nerv_error(lua_State *L, const char *err_mesg_fmt, ...) {
return 0;
}
+int nerv_error_status(lua_State *L, Status *status) {
+ const char *mmesg = NULL;
+ switch (status->err_code)
+ {
+ case MAT_GENERAL_ERR: mmesg = "general error"; break;
+ case MAT_INSUF_MEM: mmesg = "insufficient memory"; break;
+ case MAT_INVALID_FORMAT: mmesg = "invalid matrix format"; break;
+ case MAT_WRITE_ERROR: mmesg = "error while writing matrix"; break;
+ case MAT_INVALID_COPY_INTERVAL: mmesg = "invalid copy interval"; break;
+ case MAT_MISMATCH_DIM: mmesg = "mismatching matrix dimension"; break;
+ case MAT_WRONG_MULT_DIM: mmesg = "wrong multipier dimension"; break;
+ case MAT_ROW_VECTOR_EXP: mmesg = "row vector expected"; break;
+ case MAT_COL_VECTOR_EXP: mmesg = "column vector expected"; break;
+ case MAT_IDX_VECTOR_EXP: mmesg = "index vector expected"; break;
+ case MAT_INVALID_IDX: mmesg = "invalid index"; break;
+ case MAT_CUDA_ERR: mmesg = "cuda error"; break;
+ case MAT_CUBLAS_ERR: mmesg = "cublas error"; break;
+ }
+ if (status->msg)
+ nerv_error(L, "%s: %s @%s:%d", mmesg, status->msg,
+ status->file, status->lineno);
+ else
+ nerv_error(L, "%s @%s:%d", mmesg, status->file, status->lineno);
+}
+
int nerv_error_method_not_implemented(lua_State *L) {
return nerv_error(L, "method not implemented");
}
diff --git a/nerv/common.h b/nerv/common.h
index 6657dc4..413ca51 100644
--- a/nerv/common.h
+++ b/nerv/common.h
@@ -7,6 +7,50 @@
#include <stdio.h>
#include <stdlib.h>
+enum {
+ MAT_NORMAL,
+ MAT_GENERAL_ERR,
+ MAT_INSUF_MEM,
+ MAT_INVALID_FORMAT,
+ MAT_WRITE_ERROR,
+ MAT_INVALID_COPY_INTERVAL,
+ MAT_MISMATCH_DIM,
+ MAT_WRONG_MULT_DIM,
+ MAT_ROW_VECTOR_EXP,
+ MAT_COL_VECTOR_EXP,
+ MAT_IDX_VECTOR_EXP,
+ MAT_INVALID_IDX,
+ MAT_CUDA_ERR,
+ MAT_CUBLAS_ERR
+};
+
+typedef struct Status {
+ int err_code;
+ const char *file;
+ int lineno;
+ const char *msg;
+} Status;
+
+#define NERV_SET_STATUS(status, code, m) \
+ do { \
+ (status)->err_code = code; \
+ (status)->msg = m; \
+ (status)->file = __FILE__; \
+ (status)->lineno = __LINE__; \
+ } while (0)
+
+#define NERV_EXIT_STATUS(status, code, msg) \
+ do { \
+ NERV_SET_STATUS(status, code, msg); \
+ return; \
+ } while (0)
+
+#define NERV_LUA_CHECK_STATUS(L, status) \
+ do { \
+ if (status.err_code != MAT_NORMAL) \
+ nerv_error_status(L, &status); \
+ } while (0)
+
typedef struct HashNode {
const char *key;
void *val;
@@ -31,6 +75,7 @@ void hashmap_clear(HashMap *h);
size_t bkdr_hash(const char *key);
int nerv_error(lua_State *L, const char *err_mesg_fmt, ...);
+int nerv_error_status(lua_State *L, Status *status);
int nerv_error_method_not_implemented(lua_State *L);
void luaN_append_methods(lua_State *L, const luaL_Reg *mlist);
#endif
diff --git a/nerv/lib/io/chunk_file.c b/nerv/lib/io/chunk_file.c
index a305962..e70ffc9 100644
--- a/nerv/lib/io/chunk_file.c
+++ b/nerv/lib/io/chunk_file.c
@@ -91,6 +91,7 @@ static ChunkFile *open_write(const char *fn, int *status) {
}
cfp = (ChunkFile *)malloc(sizeof(ChunkFile));
cfp->fp = fp;
+ cfp->info = NULL;
cfp->status = CF_WRITE;
*status = CF_NORMAL;
return cfp;
@@ -111,8 +112,6 @@ static ChunkFile *open_read(const char *fn, int *status) {
return NULL;
}
cfp = (ChunkFile *)malloc(sizeof(ChunkFile));
- cfp->fp = fp;
- cfp->status = CF_READ;
offset = ftello(fp);
/* fprintf(stderr, "%d\n", (int)offset); */
for (i = 0;; offset += chunk_len, i++)
@@ -144,7 +143,9 @@ static ChunkFile *open_read(const char *fn, int *status) {
head = cip;
}
*status = CF_NORMAL;
+ cfp->fp = fp;
cfp->info = head;
+ cfp->status = CF_READ;
return cfp;
}
@@ -208,13 +209,16 @@ void nerv_chunk_file_close(ChunkFile *cfp) {
void nerv_chunk_file_destroy(ChunkFile *cfp) {
ChunkInfo *i, *ni;
- if (cfp->status != CF_CLOSED) fclose(cfp->fp);
- for (i = cfp->info; i; i = ni)
+ if (cfp->info)
{
- ni = i->next;
- free(i->metadata);
- free(i);
+ for (i = cfp->info; i; i = ni)
+ {
+ ni = i->next;
+ free(i->metadata);
+ free(i);
+ }
}
+ if (cfp->status != CF_CLOSED) fclose(cfp->fp);
free(cfp);
}
diff --git a/nerv/matrix/cuda_helper.h b/nerv/lib/matrix/cuda_helper.h
index fde6f18..8041efb 100644
--- a/nerv/matrix/cuda_helper.h
+++ b/nerv/lib/matrix/cuda_helper.h
@@ -4,33 +4,68 @@
#include "cuda_runtime.h"
#include "driver_types.h"
#include "cublas_v2.h"
-#define CUBLAS_SAFE_SYNC_CALL(call) \
+
+#define CUBLAS_SAFE_SYNC_CALL_RET(call, status) \
+ do { \
+ cublasStatus_t err = (call); \
+ if (err != CUBLAS_STATUS_SUCCESS) \
+ { \
+ NERV_SET_STATUS(status, MAT_CUBLAS_ERR, cublasGetErrorString(err)); \
+ return 0; \
+ } \
+ cudaDeviceSynchronize(); \
+ } while (0)
+
+#define CUBLAS_SAFE_SYNC_CALL(call, status) \
do { \
cublasStatus_t err = (call); \
if (err != CUBLAS_STATUS_SUCCESS) \
- nerv_error(L, "cumatrix cublas error: %s at %s:%d", \
- cublasGetErrorString(err), __FILE__, __LINE__); \
+ NERV_EXIT_STATUS(status, MAT_CUBLAS_ERR, cublasGetErrorString(err)); \
cudaDeviceSynchronize(); \
} while (0)
-#define CUDA_SAFE_CALL(call) \
+#define CUDA_SAFE_CALL_RET(call, status) \
+ do { \
+ cudaError_t err = (call); \
+ if (err != cudaSuccess) \
+ { \
+ NERV_SET_STATUS(status, MAT_CUDA_ERR, cudaGetErrorString(err)); \
+ return 0; \
+ } \
+ } while (0)
+
+#define CUDA_SAFE_CALL(call, status) \
do { \
cudaError_t err = (call); \
if (err != cudaSuccess) \
- nerv_error(L, "cumatrix CUDA error: %s at %s:%d", \
- cudaGetErrorString(err), __FILE__, __LINE__); \
+ NERV_EXIT_STATUS(status, MAT_CUDA_ERR, cudaGetErrorString(err)); \
+ } while (0)
+
+#define CUDA_SAFE_SYNC_CALL(call, status) \
+ do { \
+ CUDA_SAFE_CALL(call, status); \
+ cudaDeviceSynchronize(); \
} while (0)
-#define CUDA_SAFE_SYNC_CALL(call) \
+#define CUDA_SAFE_SYNC_CALL_RET(call, status) \
do { \
- CUDA_SAFE_CALL(call); \
+ CUDA_SAFE_CALL_RET(call, status); \
cudaDeviceSynchronize(); \
} while (0)
-#define CHECK_SAME_DIMENSION(a, b) \
+#define CHECK_SAME_DIMENSION(a, b, status) \
+ do { \
+ if (!(a->nrow == b->nrow && a->ncol == b->ncol)) \
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0); \
+ } while (0)
+
+#define CHECK_SAME_DIMENSION_RET(a, b, status) \
do { \
if (!(a->nrow == b->nrow && a->ncol == b->ncol)) \
- nerv_error(L, "matrices should be of the same dimension"); \
+ { \
+ NERV_SET_STATUS(status, MAT_MISMATCH_DIM, 0); \
+ return 0; \
+ } \
} while (0)
static const char *cublasGetErrorString(cublasStatus_t err) {
diff --git a/nerv/matrix/cukernel.cu b/nerv/lib/matrix/cukernel.cu
index a19030a..a19030a 100644
--- a/nerv/matrix/cukernel.cu
+++ b/nerv/lib/matrix/cukernel.cu
diff --git a/nerv/matrix/cukernel.h b/nerv/lib/matrix/cukernel.h
index 8a1494f..8a1494f 100644
--- a/nerv/matrix/cukernel.h
+++ b/nerv/lib/matrix/cukernel.h
diff --git a/nerv/lib/matrix/cumatrix.c b/nerv/lib/matrix/cumatrix.c
new file mode 100644
index 0000000..9641197
--- /dev/null
+++ b/nerv/lib/matrix/cumatrix.c
@@ -0,0 +1,69 @@
+#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;
+
+void nerv_cumatrix_print_profile() {
+ 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);
+ }
+ }
+}
+
+void nerv_cumatrix_clear_profile() {
+ hashmap_clear(profile);
+}
+
+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;
+}
+
+void nerv_cumatrix_init() {
+ cublasCreate(&cublas_handle);
+ cudaEventCreate(&profile_start);
+ cudaEventCreate(&profile_stop);
+ profile = hashmap_create(PROFILE_HASHMAP_SIZE, bkdr_hash, strcmp);
+}
+
+#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
+#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
+#include "generic/cumatrix.c"
diff --git a/nerv/lib/matrix/cumatrix.h b/nerv/lib/matrix/cumatrix.h
new file mode 100644
index 0000000..9f71507
--- /dev/null
+++ b/nerv/lib/matrix/cumatrix.h
@@ -0,0 +1,6 @@
+#ifndef NERV_CUMATRIX_H
+#define NERV_CUMATRIX_H
+void nerv_cumatrix_print_profile();
+void nerv_cumatrix_clear_profile();
+void nerv_cumatrix_init();
+#endif
diff --git a/nerv/lib/matrix/generic/cukernel.cu b/nerv/lib/matrix/generic/cukernel.cu
new file mode 100644
index 0000000..6111193
--- /dev/null
+++ b/nerv/lib/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/lib/matrix/generic/cumatrix.c b/nerv/lib/matrix/generic/cumatrix.c
new file mode 100644
index 0000000..11aacec
--- /dev/null
+++ b/nerv/lib/matrix/generic/cumatrix.c
@@ -0,0 +1,403 @@
+#ifdef NERV_GENERIC_CUMATRIX
+#include "matrix.h"
+#include "elem_type.h"
+#define MATRIX_DATA_FREE(ptr, status) cuda_matrix_(free)(ptr, status)
+#define MATRIX_DATA_ALLOC(dptr, stride, width, height, status) \
+ cuda_matrix_(alloc)(dptr, stride, width, height, status)
+
+#define NERV_GENERIC_MATRIX
+#define NERV_GENERIC_CUKERNEL
+#include "../../../common.h"
+#include "../cukernel.h"
+#include "../cuda_helper.h"
+
+void nerv_matrix_(add)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ CHECK_SAME_DIMENSION(a, c, status);
+ 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)),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(mul)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ int ta, int tb, Status *status) {
+#define SWAP(a, b) \
+ do { int t = (a); (a) = (b); (b) = t; } while (0)
+
+ 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_EXIT_STATUS(status, MAT_WRONG_MULT_DIM, 0);
+ /* 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)),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(sigmoid)(Matrix *a, const Matrix *b, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ PROFILE_START
+ cudak_(cuda_sigmoid)(b, a);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(sigmoid_grad)(Matrix *nerr, const Matrix *err,
+ const Matrix *output, Status *status) {
+ CHECK_SAME_DIMENSION(nerr, err, status);
+ CHECK_SAME_DIMENSION(nerr, output, status);
+ PROFILE_START
+ cudak_(cuda_sigmoid_grad)(output, err, nerr);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+Matrix *nerv_matrix_(softmax)(Matrix *b, const Matrix *a, Status *status) {
+ Matrix *max, *max_idx;
+ Matrix *dno;
+ CHECK_SAME_DIMENSION_RET(a, b, status);
+ max = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ max_idx = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ {
+ nerv_matrix_(destroy)(max, status);
+ return NULL;
+ }
+ dno = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ { /* FIXME: destroy may also fail? */
+ nerv_matrix_(destroy)(max, status);
+ nerv_matrix_(destroy)(max_idx, status);
+ return NULL;
+ }
+ 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_(destroy)(max, status);
+ nerv_matrix_(destroy)(dno, status);
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return max_idx;
+}
+
+Matrix *nerv_matrix_(rowsum)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ PROFILE_START
+ cudak_(cuda_rowsum)(a, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(colsum)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(1, a->ncol, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ PROFILE_START
+ cudak_(cuda_colsum)(a, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(colsame)(Matrix *a, const Matrix *ref,
+ Status *status) {
+ Matrix *b = nerv_matrix_(create)(1, a->ncol, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ CHECK_SAME_DIMENSION_RET(a, ref, status);
+ PROFILE_START
+ cudak_(cuda_colsame)(a, ref, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+Matrix *nerv_matrix_(rowmax)(Matrix *a, Status *status) {
+ Matrix *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ PROFILE_START
+ cudak_(cuda_rowmax)(a, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+void nerv_matrix_(rowmax_idx)(Matrix *a, Matrix **b, Matrix **idx,
+ Status *status) {
+ *b = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ return;
+ *idx = nerv_matrix_(create)(a->nrow, 1, status);
+ if (status->err_code != MAT_NORMAL)
+ {
+ /* FIXME: destroy may also fail? */
+ nerv_matrix_(destroy)(*b, status);
+ return;
+ }
+ PROFILE_START
+ cudak_(cuda_rowmax_idx)(a, *b, *idx);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(add_row)(Matrix *b, const Matrix *a, double beta,
+ Status *status) {
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (a->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_ROW_VECTOR_EXP, 0);
+ PROFILE_START
+ cudak_(cuda_add_row)(a, b, beta);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(fill)(Matrix *self, double val, Status *status) {
+ PROFILE_START
+ cudak_(cuda_fill)(self, val);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(copy_fromd)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status) {
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ 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),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(copy_fromh)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status) {
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ 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),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(copy_toh)(Matrix *a, const Matrix *b,
+ int a_begin, int a_end, int b_begin,
+ Status *status) {
+ if (!(0 <= a_begin && a_begin < a_end && a_end <= a->nrow &&
+ b_begin + a_end - a_begin <= b->nrow))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ if (b->ncol != a->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ 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),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+Matrix *nerv_matrix_(trans)(Matrix *a, Status *status) {
+ MATRIX_ELEM alpha = 1, beta = 0;
+ Matrix *b = nerv_matrix_(create)(a->ncol, a->nrow, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ /* FIXME: possible memory leak when lua error is raised */
+ PROFILE_START
+ CUBLAS_SAFE_SYNC_CALL_RET(
+ 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)),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+void nerv_matrix_(mul_elem)(Matrix *c, const Matrix *a, const Matrix *b,
+ Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ CHECK_SAME_DIMENSION(a, c, status);
+ PROFILE_START
+ cudak_(cuda_mul_elem)(a, b, c);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(log_elem)(Matrix *b, const Matrix *a, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ PROFILE_START
+ cudak_(cuda_log_elem)(a, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+Matrix *nerv_matrix_(decompress)(const Matrix *a, int orig_col, Status *status) {
+ Matrix *b;
+ if (a->ncol != 1)
+ {
+ NERV_SET_STATUS(status, MAT_COL_VECTOR_EXP, 0);
+ return NULL;
+ }
+ b = nerv_matrix_(create)(a->nrow, orig_col, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ PROFILE_START
+ cudak_(cuda_fill)(b, 0.0);
+ cudak_(cuda_decompress)(a, b);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return b;
+}
+
+void nerv_matrix_(copy_rows_fromh_by_idx)(Matrix *a, const Matrix *b,
+ const Matrix *idx, int b_begin, Status *status) {
+ long nrow = a->nrow;
+ if (!(0 <= b_begin && b_begin + nrow <= idx->ncol))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ long *idx_ptr = idx->data.i;
+ int i;
+ if (idx->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_IDX_VECTOR_EXP, 0);
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ 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_EXIT_STATUS(status, MAT_INVALID_IDX, 0);
+ CUDA_SAFE_CALL(cudaStreamCreate(streams + i), status);
+ CUDA_SAFE_CALL(cudaMemcpyAsync(MATRIX_ROW_PTR(a, i),
+ MATRIX_ROW_PTR(b, src_row),
+ b->stride,
+ cudaMemcpyHostToDevice, streams[i]), status);
+ }
+ for (i = 0; i < nrow; i++)
+ {
+ CUDA_SAFE_CALL(cudaStreamSynchronize(streams[i]), status);
+ CUDA_SAFE_CALL(cudaStreamDestroy(streams[i]), status);
+ }
+ free(streams);
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(expand_frm)(Matrix *a, const Matrix *b,
+ int context, Status *status) {
+ if (a->nrow != b->nrow)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (a->ncol != b->ncol * (context * 2 + 1))
+ NERV_EXIT_STATUS(status, MAT_GENERAL_ERR,
+ "the width should be 2 * context + 1");
+ PROFILE_START
+ cudak_(cuda_expand_frm)(b, a, context);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(rearrange_frm)(Matrix *a, const Matrix *b,
+ int step, Status *status) {
+ CHECK_SAME_DIMENSION(a, b, status);
+ if (b->ncol % step)
+ NERV_EXIT_STATUS(status, MAT_GENERAL_ERR,
+ "the dimension of columns is not divisible by step");
+ PROFILE_START
+ cudak_(cuda_rearrange_frm)(b, a, step);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(scale_rows_by_col)(Matrix *a, const Matrix *b,
+ Status *status) {
+ if (a->nrow != b->nrow)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (b->ncol != 1)
+ NERV_EXIT_STATUS(status, MAT_COL_VECTOR_EXP, 0);
+ PROFILE_START
+ cudak_(cuda_scale_rows_by_col)(b, a);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(scale_rows_by_row)(Matrix *a, const Matrix *b,
+ Status *status) {
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ if (b->nrow != 1)
+ NERV_EXIT_STATUS(status, MAT_ROW_VECTOR_EXP, 0);
+ PROFILE_START
+ cudak_(cuda_scale_rows_by_row)(b, a);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+static void cuda_matrix_(free)(MATRIX_ELEM *ptr, Status *status) {
+ CUDA_SAFE_SYNC_CALL(cudaFree(ptr), status);
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+static void cuda_matrix_(alloc)(MATRIX_ELEM **dptr,
+ size_t *stride, long width, long height,
+ Status *status) {
+ PROFILE_START
+ CUDA_SAFE_SYNC_CALL(cudaMallocPitch((void **)dptr, stride, width, height),
+ status);
+ PROFILE_STOP
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+#include "matrix.c"
+#endif
diff --git a/nerv/lib/matrix/generic/cumatrix.h b/nerv/lib/matrix/generic/cumatrix.h
new file mode 100644
index 0000000..9a4f87e
--- /dev/null
+++ b/nerv/lib/matrix/generic/cumatrix.h
@@ -0,0 +1,50 @@
+#include "../../../common.h"
+
+void nerv_matrix_(add)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ Status *status);
+void nerv_matrix_(mul)(Matrix *c, const Matrix *a, const Matrix *b,
+ MATRIX_ELEM alpha, MATRIX_ELEM beta,
+ int ta, int tb, Status *status);
+void nerv_matrix_(sigmoid)(Matrix *a, const Matrix *b, Status *status);
+void nerv_matrix_(sigmoid_grad)(Matrix *nerr, const Matrix *err,
+ const Matrix *output, Status *status);
+
+Matrix *nerv_matrix_(softmax)(Matrix *b, const Matrix *a, Status *status);
+Matrix *nerv_matrix_(rowsum)(Matrix *a, Status *status);
+Matrix *nerv_matrix_(colsum)(Matrix *a, Status *status);
+Matrix *nerv_matrix_(colsame)(Matrix *a, const Matrix *ref,
+ Status *status);
+Matrix *nerv_matrix_(rowmax)(Matrix *a, Status *status);
+void nerv_matrix_(rowmax_idx)(Matrix *a, Matrix **b, Matrix **idx,
+ Status *status);
+void nerv_matrix_(add_row)(Matrix *b, const Matrix *a, double beta,
+ Status *status);
+void nerv_matrix_(fill)(Matrix *self, double val, Status *status);
+void nerv_matrix_(copy_fromd)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status);
+void nerv_matrix_(copy_fromh)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status);
+void nerv_matrix_(copy_toh)(Matrix *a, const Matrix *b,
+ int a_begin, int a_end, int b_begin,
+ Status *status);
+Matrix *nerv_matrix_(trans)(Matrix *a, Status *status);
+void nerv_matrix_(mul_elem)(Matrix *c, const Matrix *a, const Matrix *b,
+ Status *status);
+
+void nerv_matrix_(log_elem)(Matrix *b, const Matrix *a, Status *status);
+
+Matrix *nerv_matrix_(decompress)(const Matrix *a, int orig_col, Status *status);
+void nerv_matrix_(copy_rows_fromh_by_idx)(Matrix *a, const Matrix *b,
+ const Matrix *idx, int b_begin, Status *status);
+
+void nerv_matrix_(expand_frm)(Matrix *a, const Matrix *b,
+ int context, Status *status);
+void nerv_matrix_(rearrange_frm)(Matrix *a, const Matrix *b,
+ int step, Status *status);
+void nerv_matrix_(scale_rows_by_col)(Matrix *a, const Matrix *b,
+ Status *status);
+void nerv_matrix_(scale_rows_by_row)(Matrix *a, const Matrix *b,
+ Status *status);
diff --git a/nerv/lib/matrix/generic/elem_type.h b/nerv/lib/matrix/generic/elem_type.h
new file mode 100644
index 0000000..bffe940
--- /dev/null
+++ b/nerv/lib/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/lib/matrix/generic/matrix.c b/nerv/lib/matrix/generic/matrix.c
new file mode 100644
index 0000000..91577e1
--- /dev/null
+++ b/nerv/lib/matrix/generic/matrix.c
@@ -0,0 +1,57 @@
+#ifdef NERV_GENERIC_MATRIX
+#include "../../../common.h"
+#include "matrix.h"
+/* FIXME: malloc failure detection */
+
+static void nerv_matrix_(data_free)(Matrix *self, Status *status) {
+ assert(*self->data_ref > 0);
+ if (--(*self->data_ref) == 0)
+ {
+ /* free matrix data */
+ MATRIX_DATA_FREE(MATRIX_ELEM_PTR(self), status);
+ free(self->data_ref);
+ free(self);
+ }
+ else NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+static void nerv_matrix_(data_retain)(Matrix *self) {
+ (*self->data_ref)++;
+}
+
+Matrix *nerv_matrix_(create)(long nrow, long ncol, Status *status) {
+ Matrix *self = (Matrix *)malloc(sizeof(Matrix));
+ self->nrow = nrow;
+ self->ncol = ncol;
+ self->nmax = self->nrow * self->ncol;
+ MATRIX_DATA_ALLOC(&MATRIX_ELEM_PTR(self), &self->stride,
+ sizeof(MATRIX_ELEM) * self->ncol, self->nrow,
+ status);
+ if (status->err_code != MAT_NORMAL)
+ {
+ free(self);
+ return NULL;
+ }
+ self->data_ref = (long *)malloc(sizeof(long));
+ *self->data_ref = 0;
+ nerv_matrix_(data_retain)(self);
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return self;
+}
+
+void nerv_matrix_(destroy)(Matrix *self, Status *status) {
+ nerv_matrix_(data_free)(self, status);
+}
+
+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;
+}
+#endif
diff --git a/nerv/lib/matrix/generic/matrix.h b/nerv/lib/matrix/generic/matrix.h
new file mode 100644
index 0000000..9d44e6d
--- /dev/null
+++ b/nerv/lib/matrix/generic/matrix.h
@@ -0,0 +1,4 @@
+#include "../matrix.h"
+Matrix *nerv_matrix_(create)(long nrow, long ncol, Status *status);
+void nerv_matrix_(destroy)(Matrix *self, Status *status);
+Matrix *nerv_matrix_(getrow)(Matrix *self, int row);
diff --git a/nerv/lib/matrix/generic/mmatrix.c b/nerv/lib/matrix/generic/mmatrix.c
new file mode 100644
index 0000000..e3d1f93
--- /dev/null
+++ b/nerv/lib/matrix/generic/mmatrix.c
@@ -0,0 +1,82 @@
+#ifdef NERV_GENERIC_MMATRIX
+#include "matrix.h"
+#include "elem_type.h"
+#define MATRIX_DATA_FREE(ptr, status) host_matrix_(free)(ptr, status)
+#define MATRIX_DATA_ALLOC(dptr, stride, width, height, status) \
+ host_matrix_(alloc)(dptr, stride, width, height, status)
+#define NERV_GENERIC_MATRIX
+#include "../../../common.h"
+#include "../../io/chunk_file.h"
+#include "string.h"
+
+static void host_matrix_(free)(MATRIX_ELEM *ptr, Status *status) {
+ free(ptr);
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+static void host_matrix_(alloc)(MATRIX_ELEM **dptr, size_t *stride,
+ long width, long height, Status *status) {
+ if ((*dptr = (MATRIX_ELEM *)malloc(width * height)) == NULL)
+ NERV_EXIT_STATUS(status, MAT_INSUF_MEM, 0);
+ *stride = width;
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+#include "matrix.c"
+Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status) {
+ int i, j;
+ long nrow, ncol;
+ FILE *fp = cdp->fp;
+ Matrix *self;
+ if (fscanf(fp, "%ld %ld", &nrow, &ncol) != 2)
+ NERV_EXIT_STATUS(status, MAT_INVALID_FORMAT, 0);
+ self = nerv_matrix_(create)(nrow, ncol, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ 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);
+ NERV_EXIT_STATUS(status, MAT_INVALID_FORMAT, 0);
+ }
+ }
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+ return self;
+}
+
+void nerv_matrix_(save)(Matrix *self, ChunkFile *cfp, Status *status) {
+ int i, j;
+ long nrow = self->nrow, ncol = self->ncol;
+ FILE *fp = cfp->fp;
+ if (fprintf(fp, "%ld %ld\n", nrow, ncol) < 0)
+ NERV_EXIT_STATUS(status, MAT_WRITE_ERROR, 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)
+ NERV_EXIT_STATUS(status, MAT_WRITE_ERROR, 0);
+ if (fprintf(fp, "\n") < 0)
+ NERV_EXIT_STATUS(status, MAT_WRITE_ERROR, 0);
+ }
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+void nerv_matrix_(copy_from)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status) {
+ if (!(0 <= b_begin && b_begin < b_end && b_end <= b->nrow &&
+ a_begin + b_end - b_begin <= a->nrow))
+ NERV_EXIT_STATUS(status, MAT_INVALID_COPY_INTERVAL, 0);
+ if (a->ncol != b->ncol)
+ NERV_EXIT_STATUS(status, MAT_MISMATCH_DIM, 0);
+ memmove(MATRIX_ROW_PTR(a, a_begin),
+ MATRIX_ROW_PTR(b, b_begin),
+ sizeof(MATRIX_ELEM) * b->ncol * (b_end - b_begin));
+ NERV_SET_STATUS(status, MAT_NORMAL, 0);
+}
+
+#endif
diff --git a/nerv/lib/matrix/generic/mmatrix.h b/nerv/lib/matrix/generic/mmatrix.h
new file mode 100644
index 0000000..5336e7a
--- /dev/null
+++ b/nerv/lib/matrix/generic/mmatrix.h
@@ -0,0 +1,7 @@
+#include "../../../common.h"
+
+Matrix *nerv_matrix_(load)(ChunkData *cdp, Status *status);
+void nerv_matrix_(save)(Matrix *self, ChunkFile *cfp, Status *status);
+void nerv_matrix_(copy_from)(Matrix *a, const Matrix *b,
+ int a_begin, int b_begin, int b_end,
+ Status *status);
diff --git a/nerv/lib/matrix/init.lua b/nerv/lib/matrix/init.lua
new file mode 100644
index 0000000..1a8925f
--- /dev/null
+++ b/nerv/lib/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/generic/matrix.h b/nerv/lib/matrix/matrix.h
index 833724b..cbf32c2 100644
--- a/nerv/matrix/generic/matrix.h
+++ b/nerv/lib/matrix/matrix.h
@@ -2,6 +2,7 @@
#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 */
@@ -15,5 +16,4 @@ typedef struct Matrix {
#define MATRIX_ROW_PTR(self, row) \
(MATRIX_ELEM *)((char *)MATRIX_ELEM_PTR(self) + (row) * (self)->stride)
-
#endif
diff --git a/nerv/lib/matrix/mmatrix.c b/nerv/lib/matrix/mmatrix.c
new file mode 100644
index 0000000..2f58e7f
--- /dev/null
+++ b/nerv/lib/matrix/mmatrix.c
@@ -0,0 +1,53 @@
+#define NERV_GENERIC_MMATRIX
+#include <stdlib.h>
+#include "../../common.h"
+
+#define MATRIX_USE_FLOAT
+#define host_matrix_(NAME) host_matrix_float_##NAME
+#define nerv_matrix_(NAME) nerv_matrix_host_float_##NAME
+#include "generic/matrix.h"
+#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
+#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
+#include "generic/mmatrix.c"
+
+Matrix *nerv_matrix_(perm_gen)(int ncol, Status *status) {
+ int i;
+ Matrix *self = nerv_matrix_(create)(1, ncol, status);
+ if (status->err_code != MAT_NORMAL)
+ return NULL;
+ 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;
+ }
+ return self;
+}
diff --git a/nerv/lib/matrix/mmatrix.h b/nerv/lib/matrix/mmatrix.h
new file mode 100644
index 0000000..df91e4c
--- /dev/null
+++ b/nerv/lib/matrix/mmatrix.h
@@ -0,0 +1,4 @@
+#ifndef NERV_MMATRIX_H
+#define NERV_MMATRIX_H
+Matrix *nerv_matrix_(perm_gen)(int ncol, Status *status);
+#endif
diff --git a/nerv/matrix/cumatrix.c b/nerv/matrix/cumatrix.c
index af34fb4..1bcb0f1 100644
--- a/nerv/matrix/cumatrix.c
+++ b/nerv/matrix/cumatrix.c
@@ -1,6 +1,7 @@
#define NERV_GENERIC_CUMATRIX
#include "../common.h"
-#include "cuda_helper.h"
+#include "../lib/matrix/cumatrix.h"
+#include "../lib/matrix/cuda_helper.h"
#include <string.h>
#define PROFILE_HASHMAP_SIZE 123457
static cublasHandle_t cublas_handle;
@@ -8,54 +9,29 @@ 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);
- }
- }
+ nerv_cumatrix_print_profile();
return 0;
}
static int clear_profile(lua_State *L) {
- (void)L;
- hashmap_clear(profile);
+ nerv_cumatrix_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);
+extern void nerv_matrix_cuda_float_lua_init(lua_State *L);
+extern void nerv_matrix_cuda_double_lua_init(lua_State *L);
-void nerv_cumatrix_init(lua_State *L) {
+void nerv_lua_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);
+ nerv_cumatrix_init();
+ nerv_matrix_cuda_float_lua_init(L);
+ nerv_matrix_cuda_double_lua_init(L);
}
#define MATRIX_USE_FLOAT
diff --git a/nerv/matrix/generic/cumatrix.c b/nerv/matrix/generic/cumatrix.c
index b5d1a35..859718e 100644
--- a/nerv/matrix/generic/cumatrix.c
+++ b/nerv/matrix/generic/cumatrix.c
@@ -1,10 +1,6 @@
#ifdef NERV_GENERIC_CUMATRIX
-#include "matrix.h"
+#include "../../lib/matrix/generic/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)
@@ -12,443 +8,303 @@
#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
-}
+#include "../../lib/matrix/generic/cumatrix.h"
-static int nerv_matrix_(add)(lua_State *L) {
+static int nerv_matrix_(lua_add)(lua_State *L) {
+ Status status;
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));
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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);
+ nerv_matrix_(add)(c, a, b, alpha, beta, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(get_cublas_op)(char ch) {
+static int nerv_matrix_(lua_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)
-
+static int nerv_matrix_(lua_mul)(lua_State *L) {
+ Status status;
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)) \
+ int ta = nargs > 5 ? nerv_matrix_(lua_get_cublas_op)(*luaL_checkstring(L, 6)) \
: CUBLAS_OP_N;
- int tb = nargs > 6 ? nerv_matrix_(get_cublas_op)(*luaL_checkstring(L, 7)) \
+ int tb = nargs > 6 ? nerv_matrix_(lua_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
+ nerv_matrix_(mul)(c, a, b, alpha, beta, ta, tb, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(create)(lua_State *L) {
+static int nerv_matrix_(lua_create)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = nerv_matrix_(new_)(L, a->nrow, a->ncol);
+ Matrix *b = nerv_matrix_(create)(a->nrow, a->ncol, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(sigmoid)(lua_State *L) {
+static int nerv_matrix_(lua_sigmoid)(lua_State *L) {
+ Status status;
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
+ nerv_matrix_(sigmoid)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(sigmoid_grad)(lua_State *L) {
+static int nerv_matrix_(lua_sigmoid_grad)(lua_State *L) {
+ Status status;
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
+ nerv_matrix_(sigmoid_grad)(nerr, err, output, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(softmax)(lua_State *L) {
+static int nerv_matrix_(lua_softmax)(lua_State *L) {
+ Status status;
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);
+ Matrix *max_idx = nerv_matrix_(softmax)(b, a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, max_idx, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(rowsum)(lua_State *L) {
+static int nerv_matrix_(lua_rowsum)(lua_State *L) {
+ Status status;
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
+ Matrix *b = nerv_matrix_(rowsum)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(colsum)(lua_State *L) {
+static int nerv_matrix_(lua_colsum)(lua_State *L) {
+ Status status;
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
+ Matrix *b = nerv_matrix_(colsum)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(colsame)(lua_State *L) {
+static int nerv_matrix_(lua_colsame)(lua_State *L) {
+ Status status;
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
+ const Matrix *ref = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ Matrix *b = nerv_matrix_(colsame)(a, ref, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(rowmax)(lua_State *L) {
+static int nerv_matrix_(lua_rowmax)(lua_State *L) {
+ Status status;
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
+ Matrix *b = nerv_matrix_(rowmax)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, b, nerv_matrix_(tname));
return 1;
}
-static int nerv_matrix_(rowmax_idx)(lua_State *L) {
+static int nerv_matrix_(lua_rowmax_idx)(lua_State *L) {
+ Status status;
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
+ Matrix *b;
+ Matrix *idx;
+ nerv_matrix_(rowmax_idx)(a, &b, &idx, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
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));
+static int nerv_matrix_(lua_add_row)(lua_State *L) {
+ Status status;
+ const 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
+ nerv_matrix_(add_row)(b, a, beta, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(fill)(lua_State *L) {
+static int nerv_matrix_(lua_fill)(lua_State *L) {
+ Status status;
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
double val = luaL_checknumber(L, 2);
- PROFILE_START
- cudak_(cuda_fill)(self, val);
- PROFILE_STOP
+ nerv_matrix_(fill)(self, val, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(copy_fromd)(lua_State *L) {
+static int nerv_matrix_(lua_copy_fromd)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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
+ nerv_matrix_(copy_fromd)(a, b, a_begin, b_begin, b_end, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
extern const char *MATRIX_CUMATRIX_HOST_TNAME;
-static int nerv_matrix_(copy_fromh)(lua_State *L) {
+static int nerv_matrix_(lua_copy_fromh)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ const 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
+ nerv_matrix_(copy_fromh)(a, b, a_begin, b_begin, b_end, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(copy_toh)(lua_State *L) {
+static int nerv_matrix_(lua_copy_toh)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ const 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
+ nerv_matrix_(copy_toh)(a, b, a_begin, a_end, b_begin, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(trans)(lua_State *L) {
+static int nerv_matrix_(lua_trans)(lua_State *L) {
+ Status status;
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
+ Matrix *b = nerv_matrix_(trans)(a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
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));
+static int nerv_matrix_(lua_mul_elem)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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
+ nerv_matrix_(mul_elem)(c, a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(log_elem)(lua_State *L) {
- Matrix *a = luaT_checkudata(L, 2, nerv_matrix_(tname));
+static int nerv_matrix_(lua_log_elem)(lua_State *L) {
+ Status status;
+ const 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
+ nerv_matrix_(log_elem)(b, a, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(decompress)(lua_State *L) {
- Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b;
+static int nerv_matrix_(lua_decompress)(lua_State *L) {
+ Status status;
+ const Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
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
+ Matrix *b = nerv_matrix_(decompress)(a, orig_col, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
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) {
+static int nerv_matrix_(lua_copy_rows_fromh_by_idx)(lua_State *L) {
+ Status status;
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);
+ const Matrix *b = luaT_checkudata(L, 2, MATRIX_CUMATRIX_HOST_TNAME);
+ const 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);
+ nerv_matrix_(copy_rows_fromh_by_idx)(a, b, idx, b_begin, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(expand_frm)(lua_State *L) {
+static int nerv_matrix_(lua_expand_frm)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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
+ nerv_matrix_(expand_frm)(a, b, context, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(rearrange_frm)(lua_State *L) {
+static int nerv_matrix_(lua_rearrange_frm)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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
+ nerv_matrix_(rearrange_frm)(a, b, step, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(scale_rows_by_col)(lua_State *L) {
+static int nerv_matrix_(lua_scale_rows_by_col)(lua_State *L) {
+ Status status;
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
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ nerv_matrix_(scale_rows_by_col)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(scale_rows_by_row)(lua_State *L) {
+static int nerv_matrix_(lua_scale_rows_by_row)(lua_State *L) {
+ Status status;
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
+ const Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ nerv_matrix_(scale_rows_by_row)(a, b, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
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)},
+ {"create", nerv_matrix_(lua_create)},
+ {"colsum", nerv_matrix_(lua_colsum)},
+ {"colsame", nerv_matrix_(lua_colsame)},
+ {"rowsum", nerv_matrix_(lua_rowsum)},
+ {"rowmax", nerv_matrix_(lua_rowmax)},
+ {"rowmax_idx", nerv_matrix_(lua_rowmax_idx)},
+ {"trans", nerv_matrix_(lua_trans)},
+ {"decompress", nerv_matrix_(lua_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)},
+ {"copy_fromh", nerv_matrix_(lua_copy_fromh)},
+ {"copy_fromd", nerv_matrix_(lua_copy_fromd)},
+ {"copy_toh", nerv_matrix_(lua_copy_toh)},
+ {"add", nerv_matrix_(lua_add)},
+ {"mul", nerv_matrix_(lua_mul)},
+ {"add_row", nerv_matrix_(lua_add_row)},
+ {"fill", nerv_matrix_(lua_fill)},
+ {"sigmoid", nerv_matrix_(lua_sigmoid)},
+ {"sigmoid_grad", nerv_matrix_(lua_sigmoid_grad)},
+ {"softmax", nerv_matrix_(lua_softmax)},
+ {"mul_elem", nerv_matrix_(lua_mul_elem)},
+ {"log_elem", nerv_matrix_(lua_log_elem)},
+ {"copy_rows_fromh_by_idx", nerv_matrix_(lua_copy_rows_fromh_by_idx)},
+ {"expand_frm", nerv_matrix_(lua_expand_frm)},
+ {"rearrange_frm", nerv_matrix_(lua_rearrange_frm)},
+ {"scale_rows_by_row", nerv_matrix_(lua_scale_rows_by_row)},
+ {"scale_rows_by_col", nerv_matrix_(lua_scale_rows_by_col)},
{NULL, NULL}
};
@@ -456,37 +312,34 @@ 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));
+int nerv_matrix_(lua_get_elem)(lua_State *L) {
+ return nerv_error_method_not_implemented(L);
}
-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
+int nerv_matrix_(lua_set_elem)(lua_State *L) {
+ return nerv_error_method_not_implemented(L);
}
static MATRIX_ELEM cuda_matrix_(read)(lua_State *L, MATRIX_ELEM *data,
- int idx) {
+ int idx) {
+ cudaError_t err;
MATRIX_ELEM res;
- CUDA_SAFE_SYNC_CALL(cudaMemcpy(&res, data + idx,
- sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost));
+ err = cudaMemcpy(&res, data + idx,
+ sizeof(MATRIX_ELEM), cudaMemcpyDeviceToHost);
+ if (err != cudaSuccess)
+ nerv_error(L, "cuda error: error while reading element");
+ cudaDeviceSynchronize();
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);
+ cudaError_t err;
+ err = cudaMemcpy(data + idx, &val,
+ sizeof(MATRIX_ELEM), cudaMemcpyHostToDevice);
+ if (err != cudaSuccess)
+ nerv_error(L, "cuda error: error while writing element");
+ cudaDeviceSynchronize();
}
#include "matrix.c"
diff --git a/nerv/matrix/generic/matrix.c b/nerv/matrix/generic/matrix.c
index e17fb42..9d2521b 100644
--- a/nerv/matrix/generic/matrix.c
+++ b/nerv/matrix/generic/matrix.c
@@ -1,68 +1,32 @@
#ifdef NERV_GENERIC_MATRIX
#include "../../common.h"
-#include "matrix.h"
+#include "../../lib/matrix/generic/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));
+int nerv_matrix_(lua_new)(lua_State *L) {
+ Status status;
+ Matrix *self = nerv_matrix_(create)(luaL_checkinteger(L, 1),
+ luaL_checkinteger(L, 2), &status);
+ NERV_LUA_CHECK_STATUS(L, status);
+ luaT_pushudata(L, self, nerv_matrix_(tname));
return 1;
}
-int nerv_matrix_(destroy)(lua_State *L) {
+int nerv_matrix_(lua_destroy)(lua_State *L) {
+ Status status;
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
- nerv_matrix_(data_free)(L, self);
+ nerv_matrix_(destroy)(self, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
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;
-}
+int nerv_matrix_(lua_get_elem)(lua_State *L);
+int nerv_matrix_(lua_set_elem)(lua_State *L);
-static int nerv_matrix_(newindex)(lua_State *L) {
+static int nerv_matrix_(lua_newindex)(lua_State *L) {
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
if (lua_isnumber(L, 2))
{
@@ -87,7 +51,8 @@ static int nerv_matrix_(newindex)(lua_State *L) {
}
-static int nerv_matrix_(index)(lua_State *L) {
+static int nerv_matrix_(lua_index)(lua_State *L) {
+ Status status;
Matrix *self = luaT_checkudata(L, 1, nerv_matrix_(tname));
if (lua_isnumber(L, 2))
{
@@ -102,7 +67,8 @@ static int nerv_matrix_(index)(lua_State *L) {
{
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));
+ luaT_pushudata(L, nerv_matrix_(getrow)(self, idx),
+ nerv_matrix_(tname));
}
lua_pushboolean(L, 1);
return 2;
@@ -114,38 +80,38 @@ static int nerv_matrix_(index)(lua_State *L) {
}
}
-static int nerv_matrix_(ncol)(lua_State *L) {
+static int nerv_matrix_(lua_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) {
+static int nerv_matrix_(lua_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 int nerv_matrix_(lua_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)},
+ {"get_elem", nerv_matrix_(lua_get_elem)},
+ {"set_elem", nerv_matrix_(lua_set_elem)},
+ {"ncol", nerv_matrix_(lua_ncol)},
+ {"nrow", nerv_matrix_(lua_nrow)},
+ {"get_dataref_value", nerv_matrix_(lua_get_dataref_value)},
+ {"__index__", nerv_matrix_(lua_index)},
+ {"__newindex__", nerv_matrix_(lua_newindex)},
{NULL, NULL}
};
-void nerv_matrix_(init)(lua_State *L) {
+void nerv_matrix_(lua_init)(lua_State *L) {
luaT_newmetatable(L, nerv_matrix_(tname), MATRIX_BASE_TNAME,
- nerv_matrix_(new), nerv_matrix_(destroy), NULL);
+ nerv_matrix_(lua_new), nerv_matrix_(lua_destroy), NULL);
luaL_register(L, NULL, nerv_matrix_(methods));
#ifdef MATRIX_INIT
MATRIX_INIT(L);
diff --git a/nerv/matrix/generic/mmatrix.c b/nerv/matrix/generic/mmatrix.c
index 697c9fc..233102a 100644
--- a/nerv/matrix/generic/mmatrix.c
+++ b/nerv/matrix/generic/mmatrix.c
@@ -1,9 +1,6 @@
#ifdef NERV_GENERIC_MMATRIX
-#include "matrix.h"
+#include "../../lib/matrix/generic/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)
@@ -11,17 +8,10 @@
#define NERV_GENERIC_MATRIX
#include "../../common.h"
#include "../../io/chunk_file.h"
+#include "../../lib/matrix/generic/mmatrix.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) {
+int nerv_matrix_(lua_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)
@@ -30,7 +20,7 @@ int nerv_matrix_(get_elem)(lua_State *L) {
return 1;
}
-int nerv_matrix_(set_elem)(lua_State *L) {
+int nerv_matrix_(lua_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);
@@ -50,72 +40,42 @@ static void host_matrix_(init)(lua_State *L) {
#include "matrix.c"
-int nerv_matrix_(load)(lua_State *L) {
+int nerv_matrix_(lua_load)(lua_State *L) {
+ Status status;
ChunkData *cdp = luaT_checkudata(L, 1, nerv_chunk_data_tname);
- Matrix *self;
- int i, j;
- long nrow, ncol;
- FILE *fp = cdp->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;
- }
- }
+ Matrix *self = nerv_matrix_(load)(cdp, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, self, nerv_matrix_(tname));
return 1;
}
-int nerv_matrix_(save)(lua_State *L) {
+int nerv_matrix_(lua_save)(lua_State *L) {
+ Status status;
ChunkFile *cfp = 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 = cfp->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;
- }
+ nerv_matrix_(save)(self, cfp, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
-static int nerv_matrix_(copy_from)(lua_State *L) {
+int nerv_matrix_(lua_copy_from)(lua_State *L) {
+ Status status;
Matrix *a = luaT_checkudata(L, 1, nerv_matrix_(tname));
- Matrix *b = luaT_checkudata(L, 2, nerv_matrix_(tname));
+ const 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));
+ nerv_matrix_(copy_from)(a, b, a_begin, b_begin, b_end, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
return 0;
}
static const luaL_Reg nerv_matrix_(extra_methods)[] = {
- {"load", nerv_matrix_(load)},
- {"save", nerv_matrix_(save)},
- {"copy_from", nerv_matrix_(copy_from)},
+ {"load", nerv_matrix_(lua_load)},
+ {"save", nerv_matrix_(lua_save)},
+ {"copy_from", nerv_matrix_(lua_copy_from)},
{NULL, NULL}
};
diff --git a/nerv/matrix/init.c b/nerv/matrix/init.c
index c29d7e9..27472b9 100644
--- a/nerv/matrix/init.c
+++ b/nerv/matrix/init.c
@@ -1,12 +1,11 @@
#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);
+void nerv_lua_cumatrix_init(lua_State *L);
+void nerv_lua_mmatrix_init(lua_State *L);
static const luaL_Reg matrix_methods[] = {
{"__tostring__", nerv_error_method_not_implemented },
@@ -25,11 +24,11 @@ void nerv_matrix_init(lua_State *L) {
/* CuMatrix inherits from Matrix */
luaT_newmetatable(L, nerv_matrix_cuda_tname, nerv_matrix_tname,
NULL, NULL, NULL);
- nerv_cumatrix_init(L);
+ nerv_lua_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);
+ nerv_lua_mmatrix_init(L);
lua_pop(L, 1);
}
diff --git a/nerv/matrix/mmatrix.c b/nerv/matrix/mmatrix.c
index d1d68b9..b25af03 100644
--- a/nerv/matrix/mmatrix.c
+++ b/nerv/matrix/mmatrix.c
@@ -1,15 +1,15 @@
#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_matrix_host_float_lua_init(lua_State *L);
+void nerv_matrix_host_double_lua_init(lua_State *L);
+void nerv_matrix_host_int_lua_init(lua_State *L);
-void nerv_mmatrix_init(lua_State *L) {
+void nerv_lua_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);
+ nerv_matrix_host_float_lua_init(L);
+ nerv_matrix_host_double_lua_init(L);
+ nerv_matrix_host_int_lua_init(L);
}
#define MATRIX_USE_FLOAT
@@ -52,26 +52,18 @@ static void host_matrix_(init_extra)(lua_State *L) {
}
#include "generic/mmatrix.c"
+#include "../lib/matrix/mmatrix.h"
-static int nerv_matrix_(perm_gen)(lua_State *L) {
+static int nerv_matrix_(lua_perm_gen)(lua_State *L) {
+ Status status;
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;
- }
+ Matrix *self = nerv_matrix_(perm_gen)(ncol, &status);
+ NERV_LUA_CHECK_STATUS(L, status);
luaT_pushudata(L, self, nerv_matrix_(tname));
return 1;
}
static const luaL_Reg nerv_matrix_(extra_methods_int)[] = {
- {"perm_gen", nerv_matrix_(perm_gen)},
+ {"perm_gen", nerv_matrix_(lua_perm_gen)},
{NULL, NULL}
};
-
diff --git a/nerv/nerv-scm-1.rockspec b/nerv/nerv-scm-1.rockspec
index 0b7e4cb..786b2df 100644
--- a/nerv/nerv-scm-1.rockspec
+++ b/nerv/nerv-scm-1.rockspec
@@ -24,6 +24,7 @@ build = {
LUA="$(LUA)",
},
install_variables = {
+ LUA_BINDIR="$(LUA_BINDIR)",
INST_PREFIX="$(PREFIX)",
INST_BINDIR="$(BINDIR)",
INST_LIBDIR="$(LIBDIR)",
diff --git a/speech b/speech
-Subproject 32eac093cc431849a92e5a2297c5fe646fd6055
+Subproject b6301089cde20f4c825c7f5deaf179082aad63d