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Slowly refactoring and pushing to GPU

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This commit is contained in:
Joseph Redmon 2014-05-02 15:20:34 -07:00
parent 00d483697a
commit 5ef74c2031
8 changed files with 31 additions and 333 deletions
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16
Makefile
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@ -1,29 +1,29 @@
CC=gcc
GPU=1
COMMON=-Wall `pkg-config --cflags opencv` -I/usr/local/cuda/include/
COMMON=-Wall -Werror -Wfatal-errors `pkg-config --cflags opencv` -I/usr/local/cuda/include/
ifeq ($(GPU), 1)
COMMON+=-DGPU
else
endif
UNAME = $(shell uname)
OPTS=-O3
OPTS=-O3 -flto
ifeq ($(UNAME), Darwin)
COMMON+= -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
ifeq ($(GPU), 1)
LDFLAGS= -framework OpenCL
endif
else
OPTS+= -march=native
ifeq ($(GPU), 1)
LDFLAGS= -lOpenCL
endif
endif
CFLAGS= $(COMMON) $(OPTS)
CFLAGS= $(COMMON) -O0 -g
#CFLAGS= $(COMMON) -O0 -g
LDFLAGS+=`pkg-config --libs opencv` -lm
VPATH=./src/
EXEC=cnn
OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o cpu_gemm.o normalization_layer.o
ifeq ($(GPU), 1)
OBJ+=gpu_gemm.o opencl.o
endif
OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o gemm.o normalization_layer.o opencl.o im2col.o col2im.o axpy.o
all: $(EXEC)

17
src/convolutional_layer.c
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@ -100,7 +100,7 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
float *b = layer.col_image;
float *c = layer.output;
for(i = 0; i < layer.batch; ++i){
im2col_cpu(in+i*(n/layer.batch), layer.c, layer.h, layer.w, layer.size, layer.stride, b+i*(n/layer.batch));
im2col_gpu(in+i*(n/layer.batch), layer.c, layer.h, layer.w, layer.size, layer.stride, b+i*(n/layer.batch));
}
gemm(0,0,m,n,k,1,a,k,b,n,0,c,n);
activate_array(layer.output, m*n, layer.activation);
@ -162,16 +162,13 @@ void backward_convolutional_layer(convolutional_layer layer, float *delta)
void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
{
int i;
int size = layer.size*layer.size*layer.c*layer.n;
for(i = 0; i < layer.n; ++i){
layer.biases[i] += step*layer.bias_updates[i];
layer.bias_updates[i] *= momentum;
}
for(i = 0; i < size; ++i){
layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
layer.filter_updates[i] *= momentum;
}
axpy_cpu(layer.n, step, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.n, momentum, layer.bias_updates, 1);
scal_cpu(size, 1.-step*decay, layer.filters, 1);
axpy_cpu(size, step, layer.filter_updates, 1, layer.filters, 1);
scal_cpu(size, momentum, layer.filter_updates, 1);
}
void test_convolutional_layer()

2
src/data.c
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@ -123,7 +123,7 @@ data load_cifar10_data(char *filename)
{
data d;
d.shallow = 0;
unsigned long i,j;
long i,j;
matrix X = make_matrix(10000, 3072);
matrix y = make_matrix(10000, 10);
d.X = X;

236
src/gpu_gemm.c
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@ -1,236 +0,0 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <math.h>
#include "opencl.h"
#include "mini_blas.h"
#define STR_HELPER(x) #x
#define STR(x) STR_HELPER(x)
#define BLOCK 8
cl_kernel get_gemm_kernel()
{
static int init = 0;
static cl_kernel gemm_kernel;
if(!init){
gemm_kernel = get_kernel("src/gemm.cl", "gemm", "-D BLOCK=" STR(BLOCK) );
init = 1;
}
return gemm_kernel;
}
void gpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float BETA,
float *C, int ldc)
{
cl_setup();
cl_kernel gemm_kernel = get_gemm_kernel();
cl_context context = cl.context;
cl_command_queue queue = cl.queue;
size_t size = sizeof(float)*(TA ? lda*K:lda*M);
cl_mem A_gpu = clCreateBuffer(context,
CL_MEM_READ_ONLY|CL_MEM_COPY_HOST_PTR,
size, A, &cl.error);
check_error(cl);
size = sizeof(float)*(TB ? ldb*N:ldb*K);
cl_mem B_gpu = clCreateBuffer(context,
CL_MEM_READ_ONLY|CL_MEM_COPY_HOST_PTR,
size, B, &cl.error);
check_error(cl);
size = sizeof(float)*(ldc*M);
cl_mem C_gpu = clCreateBuffer(context,
CL_MEM_WRITE_ONLY|CL_MEM_COPY_HOST_PTR,
size, C, &cl.error);
check_error(cl);
cl_uint i = 0;
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TA), (void*) &TA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TB), (void*) &TB);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(M), (void*) &M);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(N), (void*) &N);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(K), (void*) &K);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ALPHA), (void*) &ALPHA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(A_gpu), (void*) &A_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(lda), (void*) &lda);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(B_gpu), (void*) &B_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldb), (void*) &ldb);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(BETA), (void*) &BETA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(C_gpu), (void*) &C_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldc), (void*) &ldc);
check_error(cl);
const size_t global_size[] = {ceil((float)M/BLOCK)*BLOCK, ceil((float)N/BLOCK)*BLOCK};
const size_t local_size[] = {BLOCK, BLOCK};
//printf("%zd %zd %zd %zd\n", global_size[0], global_size[1], local_size[0], local_size[1]);
clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, local_size, 0, 0, 0);
check_error(cl);
clEnqueueReadBuffer(queue, C_gpu, CL_TRUE, 0, size, C, 0, 0, 0);
check_error(cl);
clReleaseMemObject(A_gpu);
clReleaseMemObject(B_gpu);
clReleaseMemObject(C_gpu);
}
void time_gpu_random_matrix(int TA, int TB, int m, int k, int n)
{
float *a;
if(!TA) a = random_matrix(m,k);
else a = random_matrix(k,m);
int lda = (!TA)?k:m;
float *b;
if(!TB) b = random_matrix(k,n);
else b = random_matrix(n,k);
int ldb = (!TB)?n:k;
float *c = random_matrix(m,n);
int i;
clock_t start = clock(), end;
for(i = 0; i<1000; ++i){
gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
}
end = clock();
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC);
free(a);
free(b);
free(c);
}
void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
{
srand(0);
float *a;
if(!TA) a = random_matrix(m,k);
else a = random_matrix(k,m);
int lda = (!TA)?k:m;
float *b;
if(!TB) b = random_matrix(k,n);
else b = random_matrix(n,k);
int ldb = (!TB)?n:k;
float *c = random_matrix(m,n);
float *c_gpu = random_matrix(m,n);
memset(c, 0, m*n*sizeof(float));
memset(c_gpu, 0, m*n*sizeof(float));
int i;
//pm(m,k,b);
gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c_gpu,n);
//pm(m, n, c_gpu);
cpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
//pm(m, n, c);
double sse = 0;
for(i = 0; i < m*n; ++i) {
//printf("%f %f\n", c[i], c_gpu[i]);
sse += pow(c[i]-c_gpu[i], 2);
}
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %g MSE\n",m,k,k,n, TA, TB, sse/(m*n));
free(a);
free(b);
free(c);
}
void test_gpu_blas()
{
test_gpu_accuracy(0,0,17,10,10);
test_gpu_accuracy(1,0,17,10,10);
test_gpu_accuracy(0,1,17,10,10);
test_gpu_accuracy(1,1,17,10,10);
test_gpu_accuracy(0,0,1000,10,100);
test_gpu_accuracy(1,0,1000,10,100);
test_gpu_accuracy(0,1,1000,10,100);
test_gpu_accuracy(1,1,1000,10,100);
time_gpu_random_matrix(0,0,1000,1000,100);
time_random_matrix(0,0,1000,1000,100);
time_gpu_random_matrix(0,1,1000,1000,100);
time_random_matrix(0,1,1000,1000,100);
time_gpu_random_matrix(1,0,1000,1000,100);
time_random_matrix(1,0,1000,1000,100);
time_gpu_random_matrix(1,1,1000,1000,100);
time_random_matrix(1,1,1000,1000,100);
}
/*
cl_kernel get_gemm_kernel_slow()
{
static int init = 0;
static cl_kernel gemm_kernel;
if(!init){
gemm_kernel = get_kernel("src/gemm.cl", "gemm_slow");
init = 1;
}
return gemm_kernel;
}
void gpu_gemm_slow(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float BETA,
float *C, int ldc)
{
cl_setup();
cl_kernel gemm_kernel = get_gemm_kernel_slow();
cl_context context = cl.context;
cl_command_queue queue = cl.queue;
size_t size = sizeof(float)*(TA ? lda*K:lda*M);
cl_mem A_gpu = clCreateBuffer(context,
CL_MEM_READ_ONLY|CL_MEM_COPY_HOST_PTR,
size, A, &cl.error);
check_error(cl);
size = sizeof(float)*(TB ? ldb*N:ldb*K);
cl_mem B_gpu = clCreateBuffer(context,
CL_MEM_READ_ONLY|CL_MEM_COPY_HOST_PTR,
size, B, &cl.error);
check_error(cl);
size = sizeof(float)*(ldc*M);
cl_mem C_gpu = clCreateBuffer(context,
CL_MEM_READ_ONLY|CL_MEM_COPY_HOST_PTR,
size, C, &cl.error);
check_error(cl);
cl_uint i = 0;
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TA), (void*) &TA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TB), (void*) &TB);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(M), (void*) &M);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(N), (void*) &N);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(K), (void*) &K);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ALPHA), (void*) &ALPHA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(A_gpu), (void*) &A_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(lda), (void*) &lda);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(B_gpu), (void*) &B_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldb), (void*) &ldb);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(BETA), (void*) &BETA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(C_gpu), (void*) &C_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldc), (void*) &ldc);
check_error(cl);
const size_t global_size[] = {M, N};
clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, 0, 0, 0, 0);
clEnqueueReadBuffer(queue, C_gpu, CL_TRUE, 0, size, C, 0, 0, 0);
clReleaseMemObject(A_gpu);
clReleaseMemObject(B_gpu);
clReleaseMemObject(C_gpu);
}
*/

2
src/list.c
View File

@ -11,6 +11,7 @@ list *make_list()
return l;
}
/*
void transfer_node(list *s, list *d, node *n)
{
node *prev, *next;
@ -22,6 +23,7 @@ void transfer_node(list *s, list *d, node *n)
if(s->front == n) s->front = next;
if(s->back == n) s->back = prev;
}
*/
void *list_pop(list *l){
if(!l->back) return 0;

75
src/mini_blas.c
View File

@ -1,4 +1,3 @@
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
@ -18,77 +17,7 @@ void pm(int M, int N, float *A)
printf("\n");
}
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float BETA,
float *C, int ldc)
{
gpu_gemm( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
}
void im2row(float *image, int h, int w, int c, int size, int stride, float *matrix)
{
int i;
int mc = c;
int mw = (size*size);
int mh = ((h-size)/stride+1)*((w-size)/stride+1);
int msize = mc*mw*mh;
for(i = 0; i < msize; ++i){
int channel = i/(mh*mw);
int block = (i%(mh*mw))/mw;
int position = i%mw;
int block_h = block/((w-size)/stride+1);
int block_w = block%((w-size)/stride+1);
int ph, pw, pc;
ph = position/size+block_h;
pw = position%size+block_w;
pc = channel;
matrix[i] = image[pc*h*w+ph*w+pw];
}
}
void im2col(float *image, int h, int w, int c, int size, int stride, float *matrix)
{
int b,p;
int blocks = ((h-size)/stride+1)*((w-size)/stride+1);
int pixels = (size*size*c);
for(b = 0; b < blocks; ++b){
int block_h = b/((w-size)/stride+1);
int block_w = b%((w-size)/stride+1);
for(p = 0; p < pixels; ++p){
int ph, pw, pc;
int position = p%(size*size);
pc = p/(size*size);
ph = position/size+block_h;
pw = position%size+block_w;
matrix[b+p*blocks] = image[pc*h*w+ph*w+pw];
}
}
}
//From Berkeley Vision's Caffe!
void im2col_cpu(float* data_im, const int channels,
const int height, const int width, const int ksize, const int stride,
float* data_col)
{
int c,h,w;
int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1;
int channels_col = channels * ksize * ksize;
for ( c = 0; c < channels_col; ++c) {
int w_offset = c % ksize;
int h_offset = (c / ksize) % ksize;
int c_im = c / ksize / ksize;
for ( h = 0; h < height_col; ++h) {
for ( w = 0; w < width_col; ++w) {
data_col[(c * height_col + h) * width_col + w] =
data_im[(c_im * height + h * stride + h_offset) * width
+ w * stride + w_offset];
}
}
}
}
//This one might be too, can't remember.
void col2im_cpu(float* data_col, const int channels,
const int height, const int width, const int ksize, const int stride,
float* data_im)
@ -135,7 +64,7 @@ void time_random_matrix(int TA, int TB, int m, int k, int n)
int i;
clock_t start = clock(), end;
for(i = 0; i<1000; ++i){
cpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
}
end = clock();
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC);

11
src/mini_blas.h
View File

@ -6,8 +6,9 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
float *C, int ldc);
float *random_matrix(int rows, int cols);
void time_random_matrix(int TA, int TB, int m, int k, int n);
void im2row(float *image, int h, int w, int c, int size, int stride, float *matrix);
void im2col(float *image, int h, int w, int c, int size, int stride, float *matrix);
void im2col_gpu(float* data_im, const int channels,
const int height, const int width, const int ksize, const int stride,
float* data_col);
void im2col_cpu(float* data_im, const int channels,
const int height, const int width, const int ksize, const int stride,
float* data_col);
@ -16,14 +17,16 @@ void col2im_cpu(float* data_col, const int channels,
float* data_im);
void test_blas();
void gpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA,
void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float BETA,
float *C, int ldc);
void cpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA,
void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
float *B, int ldb,
float BETA,
float *C, int ldc);
void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
void scal_cpu(int N, float ALPHA, float *X, int INCX);
void test_gpu_blas();

5
src/opencl.c
View File

@ -1,3 +1,4 @@
#ifdef GPU
#include "opencl.h"
#include <stdio.h>
#include <stdlib.h>
@ -12,6 +13,7 @@ void check_error(cl_info info)
{
if (info.error != CL_SUCCESS) {
printf("\n Error number %d", info.error);
exit(1);
}
}
@ -66,6 +68,7 @@ cl_program cl_fprog(char *filename, char *options, cl_info info)
clGetProgramBuildInfo( prog, info.device, CL_PROGRAM_BUILD_LOG, 4096, build_c, 0);
fprintf(stderr, "Build Log for %s program:\n%s\n", filename, build_c);
}
check_error(info);
return prog;
}
@ -85,4 +88,4 @@ cl_kernel get_kernel(char *filename, char *kernelname, char *options)
return kernel;
}
#endif