#include "gemm.h"
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#include "utils.h"
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#include "im2col.h"
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#include "cuda.h"
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#if defined(_OPENMP)
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#include <omp.h>
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#endif
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void gemm_bin(int M, int N, int K, float ALPHA,
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char *A, int lda,
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float *B, int ldb,
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float *C, int ldc)
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{
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int i,j,k;
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for(i = 0; i < M; ++i){
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for(k = 0; k < K; ++k){
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char A_PART = A[i*lda+k];
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if(A_PART){
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for(j = 0; j < N; ++j){
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C[i*ldc+j] += B[k*ldb+j];
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}
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} else {
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for(j = 0; j < N; ++j){
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C[i*ldc+j] -= B[k*ldb+j];
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}
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}
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}
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}
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}
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float *random_matrix(int rows, int cols)
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{
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int i;
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float *m = calloc(rows*cols, sizeof(float));
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for(i = 0; i < rows*cols; ++i){
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m[i] = (float)rand()/RAND_MAX;
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}
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return m;
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}
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void time_random_matrix(int TA, int TB, int m, int k, int n)
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{
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float *a;
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if(!TA) a = random_matrix(m,k);
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else a = random_matrix(k,m);
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int lda = (!TA)?k:m;
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float *b;
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if(!TB) b = random_matrix(k,n);
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else b = random_matrix(n,k);
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int ldb = (!TB)?n:k;
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float *c = random_matrix(m,n);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i<10; ++i){
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gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
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}
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end = clock();
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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);
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free(a);
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free(b);
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free(c);
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}
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|
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void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
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float *A, int lda,
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float *B, int ldb,
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float BETA,
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float *C, int ldc)
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{
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gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
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}
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//--------------------------------------------
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// XNOR bitwise GEMM for binary neural network
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//--------------------------------------------
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#include <stdint.h>
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static inline unsigned char xnor(unsigned char a, unsigned char b) {
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//return a == b;
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return !(a^b);
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}
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// INT-32
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static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) {
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size_t src_i = index / 32;
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int src_shift = index % 32;
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unsigned char val = (src[src_i] & (1 << src_shift)) > 0;
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return val;
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}
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static inline uint32_t xnor_int32(uint32_t a, uint32_t b) {
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return ~(a^b);
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}
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static inline uint64_t xnor_int64(uint64_t a, uint64_t b) {
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return ~(a^b);
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}
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|
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static inline uint32_t fill_bit_int32(char src) {
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if (src == 0) return 0x00000000;
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else return 0xFFFFFFFF;
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}
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static inline uint64_t fill_bit_int64(char src) {
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if (src == 0) return 0x0000000000000000;
|
else return 0xFFFFFFFFFFFFFFFF;
|
}
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void binary_int32_printf(uint32_t src) {
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int i;
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for (i = 0; i < 32; ++i) {
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if (src & 1) printf("1");
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else printf("0");
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src = src >> 1;
|
}
|
printf("\n");
|
}
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|
void binary_int64_printf(uint64_t src) {
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int i;
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for (i = 0; i < 64; ++i) {
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if (src & 1) printf("1");
|
else printf("0");
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src = src >> 1;
|
}
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printf("\n");
|
}
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/*
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void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
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unsigned char *B, int ldb,
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float *C, int ldc, float *mean_arr)
|
{
|
int *count_arr = calloc(M*N, sizeof(int));
|
|
int i, j, k;
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for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
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for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
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char a_bit = get_bit(A, i*lda + k);
|
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
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char b_bit = get_bit(B, k*ldb + j);
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count_arr[i*ldc + j] += xnor(a_bit, b_bit);
|
}
|
}
|
}
|
|
for (i = 0; i < M; ++i) {
|
float mean_val = mean_arr[i];
|
for (j = 0; j < N; ++j) {
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
}
|
}
|
free(count_arr);
|
}
|
*/
|
|
/*
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
|
unsigned char *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int *count_arr = calloc(M*N, sizeof(int));
|
|
int i, j, k;
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
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char a_bit = get_bit(A, i*lda + k);
|
char b_bit = get_bit(B, j*ldb + k);
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count_arr[i*ldc + j] += xnor(a_bit, b_bit);
|
}
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}
|
}
|
|
for (i = 0; i < M; ++i) {
|
float mean_val = mean_arr[i];
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for (j = 0; j < N; ++j) {
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
}
|
}
|
free(count_arr);
|
}
|
*/
|
|
/*
|
void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
|
unsigned char *B, int ldb,
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float *C, int ldc, float *mean_arr)
|
{
|
int *count_arr = calloc(M*N, sizeof(int));
|
|
int i, j, k, h;
|
|
#pragma omp parallel for
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for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
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for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
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const char a_bit = get_bit(A, i*lda + k);
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uint64_t a_bit64 = fill_bit_int64(a_bit);
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int k_ldb = k*ldb;
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|
for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056]
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if ((N - j > 64) && (k_ldb % 8 == 0)) {
|
uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8));
|
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
|
//printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc
|
printf("\n %d \n", __popcnt64(c_bit64)); // msvs
|
|
int h;
|
for (h = 0; h < 64; ++h)
|
if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1;
|
|
//binary_int64_printf(a_bit64);
|
//binary_int64_printf(b_bit64);
|
//binary_int64_printf(c_bit64);
|
}
|
else {
|
for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
char b_bit = get_bit(B, k_ldb + j);
|
if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1;
|
}
|
}
|
|
}
|
}
|
}
|
|
if (mean_arr) {
|
//int K_2 = K / 2;
|
for (i = 0; i < M; ++i) {
|
float mean_val = mean_arr[i];
|
//float mean_val2 = 2 * mean_val;
|
for (j = 0; j < N; ++j) {
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
//C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2;
|
}
|
}
|
}
|
else {
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for (i = 0; i < M; ++i) {
|
for (j = 0; j < N; ++j) {
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C[i*ldc + j] = count_arr[i*ldc + j] - K / 2;
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}
|
}
|
}
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|
free(count_arr);
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|
//getchar();
|
}
|
*/
|
|
|
/*
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
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unsigned char *B, int ldb,
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float *C, int ldc, float *mean_arr)
|
{
|
int i, j, k, h;
|
|
#pragma omp parallel for
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for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
float mean_val = mean_arr[i];
|
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
int count = 0;
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|
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
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uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8));
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uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
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uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
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|
#ifdef WIN32
|
int tmp_count = __popcnt64(c_bit64);
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#else
|
int tmp_count = __builtin_popcountll(c_bit64);
|
#endif
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|
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
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count += tmp_count;
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//binary_int64_printf(c_bit64);
|
//printf(", count = %d \n\n", tmp_count);
|
}
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|
C[i*ldc + j] = (2 * count - K) * mean_val;
|
}
|
}
|
}
|
*/
|
|
//----------------------------
|
|
|
#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64)
|
|
#define OSXSAVEFlag (1UL<<27)
|
#define AVXFlag ((1UL<<28)|OSXSAVEFlag)
|
#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag)
|
#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag)
|
#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag)
|
|
#ifdef _WIN64
|
#include <intrin.h>
|
#include <ammintrin.h>
|
#include <immintrin.h>
|
#include <smmintrin.h>
|
|
#if defined(_MSC_VER) && _MSC_VER <= 1900
|
static inline __int32 _mm256_extract_epi64(__m256i a, const int index) {
|
return a.m256i_i64[index];
|
}
|
|
static inline __int32 _mm256_extract_epi32(__m256i a, const int index) {
|
return a.m256i_i32[index];
|
}
|
|
#endif
|
|
static inline float _castu32_f32(uint32_t a) {
|
return *((float *)&a);
|
}
|
|
static inline float _mm256_extract_float32(__m256 a, const int index) {
|
return a.m256_f32[index];
|
}
|
|
#else // Linux GCC/Clang
|
#include <x86intrin.h>
|
#include <ammintrin.h>
|
#include <immintrin.h>
|
#include <smmintrin.h>
|
#include <cpuid.h>
|
|
static inline float _castu32_f32(uint32_t a) {
|
return *((float *)&a);
|
}
|
|
static inline float _mm256_extract_float32(__m256 a, const int index) {
|
return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index));
|
}
|
|
void asm_cpuid(uint32_t* abcd, uint32_t eax)
|
{
|
uint32_t ebx = 0, edx = 0, ecx = 0;
|
|
// EBX is saved to EDI and later restored
|
__asm__("movl %%ebx, %%edi;"
|
"cpuid;"
|
"xchgl %%ebx, %%edi;"
|
: "=D"(ebx),
|
"+a"(eax), "+c"(ecx), "=d"(edx));
|
|
abcd[0] = eax;
|
abcd[1] = ebx;
|
abcd[2] = ecx;
|
abcd[3] = edx;
|
}
|
|
#endif
|
|
int simd_detect_x86(unsigned int idFeature)
|
{
|
uint32_t regs[4]; // EAX, EBX, ECX, EDX;
|
#ifdef _WIN32
|
__cpuid(regs, 0);
|
if (regs[0] > 1U) __cpuid(regs, 1);
|
#else
|
__get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]);
|
if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]);
|
#endif
|
|
if ((regs[2] & idFeature) != idFeature)
|
return 0;
|
return 1;
|
}
|
|
int is_fma_avx() {
|
static int result = -1;
|
if (result == -1) {
|
result = simd_detect_x86(AVXFlag);
|
if (result == 1) printf(" Used AVX \n");
|
else printf(" Not used AVX \n");
|
}
|
return result;
|
}
|
|
// https://software.intel.com/sites/landingpage/IntrinsicsGuide
|
void gemm_nn(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i, j, k;
|
if (is_fma_avx() == 1) { // AVX
|
for (i = 0; i < M; ++i) {
|
for (k = 0; k < K; ++k) {
|
float A_PART = ALPHA*A[i*lda + k];
|
__m256 a256, b256, c256, result256; // AVX
|
a256 = _mm256_set1_ps(A_PART);
|
for (j = 0; j < N - 8; j += 8) {
|
b256 = _mm256_loadu_ps(&B[k*ldb + j]);
|
c256 = _mm256_loadu_ps(&C[i*ldc + j]);
|
// FMA - Intel Haswell (2013), AMD Piledriver (2012)
|
//result256 = _mm256_fmadd_ps(a256, b256, c256);
|
result256 = _mm256_mul_ps(a256, b256);
|
result256 = _mm256_add_ps(result256, c256);
|
_mm256_storeu_ps(&C[i*ldc + j], result256);
|
}
|
|
int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8;
|
for (j = prev_end; j < N; ++j)
|
C[i*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
}
|
}
|
else {
|
for (i = 0; i < M; ++i) {
|
for (k = 0; k < K; ++k) {
|
register float A_PART = ALPHA*A[i*lda + k];
|
for (j = 0; j < N; ++j) {
|
C[i*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
/* // SSE
|
__m128 a128, b128, c128, result128; // SSE
|
a128 = _mm_set1_ps(A_PART);
|
for (j = 0; j < N - 4; j += 4) {
|
b128 = _mm_loadu_ps(&B[k*ldb + j]);
|
c128 = _mm_loadu_ps(&C[i*ldc + j]);
|
//result128 = _mm_fmadd_ps(a128, b128, c128);
|
result128 = _mm_mul_ps(a128, b128);
|
result128 = _mm_add_ps(result128, c128);
|
_mm_storeu_ps(&C[i*ldc + j], result128);
|
}
|
|
int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4;
|
for (j = prev_end; j < N; ++j){
|
C[i*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
*/
|
}
|
}
|
}
|
}
|
|
|
void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride,
|
float *weights, float *input, float *output)
|
{
|
int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1
|
int i, f, j;
|
|
int fil;
|
// filter index
|
#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP
|
for (fil = 0; fil < n; ++fil) {
|
int chan, y, x, f_y, f_x;
|
// channel index
|
for (chan = 0; chan < c; ++chan)
|
// input - y
|
for (y = 0; y < h; ++y)
|
// input - x
|
for (x = 0; x < w; ++x)
|
{
|
int const output_index = fil*w*h + y*w + x;
|
int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize;
|
int const input_pre_index = chan*w*h;
|
float sum = 0;
|
|
// filter - y
|
for (f_y = 0; f_y < ksize; ++f_y)
|
{
|
int input_y = y + f_y - pad;
|
// filter - x
|
for (f_x = 0; f_x < ksize; ++f_x)
|
{
|
int input_x = x + f_x - pad;
|
if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;
|
|
int input_index = input_pre_index + input_y*w + input_x;
|
int weights_index = weights_pre_index + f_y*ksize + f_x;
|
|
sum += input[input_index] * weights[weights_index];
|
}
|
}
|
// l.output[filters][width][height] +=
|
// state.input[channels][width][height] *
|
// l.weights[filters][channels][filter_width][filter_height];
|
output[output_index] += sum;
|
}
|
}
|
}
|
|
void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride,
|
float *weights, float *input, float *output, float *mean)
|
{
|
int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1
|
int i, f, j;
|
|
#if defined(_OPENMP)
|
static int max_num_threads = 0;
|
if (max_num_threads == 0) {
|
max_num_threads = omp_get_max_threads();
|
omp_set_num_threads(4);// max_num_threads / 2);
|
}
|
#endif
|
|
//convolution_2d_old(w, h, ksize, n, c, pad, stride, weights, input, output);
|
|
__m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000);
|
for (i = 0; i < ksize*ksize*n*c; i+=8) {
|
*((__m256*)&weights[i]) = _mm256_and_ps(*((__m256*)&weights[i]), _mm256_castsi256_ps(all256_sing1));
|
}
|
|
for (i = 0; i < w*h*c; i += 8) {
|
//*((__m256*)&input[i]) = _mm256_and_ps(*((__m256*)&input[i]), _mm256_castsi256_ps(all256_sing1));
|
}
|
|
|
//__m256i all256_last_zero = _mm256_set1_epi32(0xFFFFFFFF);
|
//all256_last_zero.m256i_i32[7] = 0;
|
__m256i all256_last_zero =
|
_mm256_set_epi32(0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0x0);
|
|
__m256i idx256 = _mm256_set_epi32(0, 7, 6, 5, 4, 3, 2, 1);
|
//__m256 all256_sing1 = _mm256_set1_ps(0x80000000);
|
__m256 all256_one = _mm256_set1_ps(1);
|
__m256i all256i_one = _mm256_set1_epi32(1);
|
|
///__m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i]));
|
///__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats
|
|
int fil;
|
// filter index
|
#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP
|
for (fil = 0; fil < n; ++fil) {
|
int chan, y, x, f_y, f_x;
|
float cur_mean = fabs(mean[fil]);
|
__m256 mean256 = _mm256_set1_ps(cur_mean);
|
// channel index
|
//for (chan = 0; chan < c; ++chan)
|
// input - y
|
for (y = 0; y < h; ++y)
|
// input - x
|
for (x = 0; x < w-8; x+=8)
|
{
|
int const output_index = fil*w*h + y*w + x;
|
float sum = 0;
|
__m256 sum256 = _mm256_set1_ps(0);
|
|
for (chan = 0; chan < c; ++chan) {
|
int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize;
|
int const input_pre_index = chan*w*h;
|
|
|
// filter - y
|
for (f_y = 0; f_y < ksize; ++f_y)
|
{
|
int input_y = y + f_y - pad;
|
//__m256 in = *((__m256*)&input[input_pre_index + input_y*w]);
|
if (input_y < 0 || input_y >= h) continue;
|
//__m256 in = _mm256_loadu_ps(&input[input_pre_index + input_y*w + x - pad]);
|
|
// filter - x
|
for (f_x = 0; f_x < ksize; ++f_x)
|
{
|
int input_x = x + f_x - pad;
|
//if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;
|
|
int input_index = input_pre_index + input_y*w + input_x;
|
int weights_index = weights_pre_index + f_y*ksize + f_x;
|
//if (input_y < 0 || input_y >= h) continue;
|
|
//sum += input[input_index] * weights[weights_index];
|
|
__m256 in = *((__m256*)&input[input_index]);
|
__m256 w = _mm256_set1_ps(weights[weights_index]);
|
//__m256 w_sign = _mm256_and_ps(w, _mm256_castsi256_ps(all256_sing1)); // check sign in 8 x 32-bit floats
|
__m256 xor256 = _mm256_xor_ps(w, in);
|
//printf("\n xor256_1 = %f, xor256_2 = %f \n", xor256.m256_f32[0], xor256.m256_f32[1]);
|
//printf("\n in = %f, w = %f, xor256 = %f \n", in.m256_f32[0], w_sign.m256_f32[0], xor256.m256_f32[0]);
|
|
//__m256 pn1 = _mm256_and_ps(_mm256_castsi256_ps(all256i_one), xor256);
|
|
|
//sum256 = xor256;
|
sum256 = _mm256_add_ps(xor256, sum256);
|
//printf("\n --- \n");
|
//printf("\n 0 = %f, 1 = %f, 2 = %f, 3 = %f, 4 = %f, 5 = %f, 6 = %f, 7 = %f \n", in.m256_f32[0], in.m256_f32[1], in.m256_f32[2], in.m256_f32[3], in.m256_f32[4], in.m256_f32[5], in.m256_f32[6], in.m256_f32[7]);
|
|
if (f_x < ksize-1) {
|
//in = _mm256_permutevar8x32_ps(in, idx256);
|
//in = _mm256_and_ps(in, _mm256_castsi256_ps(all256_last_zero));
|
}
|
}
|
}
|
}
|
// l.output[filters][width][height] +=
|
// state.input[channels][width][height] *
|
// l.weights[filters][channels][filter_width][filter_height];
|
//output[output_index] += sum;
|
|
sum256 = _mm256_mul_ps(sum256, mean256);
|
//printf("\n cur_mean = %f, sum256 = %f, sum256 = %f, in = %f \n",
|
// cur_mean, sum256.m256_f32[0], sum256.m256_f32[1], input[input_pre_index]);
|
|
//__m256 out = *((__m256*)&output[output_index]);
|
//out = _mm256_add_ps(out, sum256);
|
//*((__m256*)&output[output_index]) = out;
|
*((__m256*)&output[output_index]) = sum256;
|
|
//_mm256_storeu_ps(&C[i*ldc + j], result256);
|
}
|
}
|
}
|
|
|
|
// http://graphics.stanford.edu/~seander/bithacks.html
|
// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register
|
// https://arxiv.org/pdf/1611.07612.pdf
|
|
static inline int popcnt128(__m128i n) {
|
const __m128i n_hi = _mm_unpackhi_epi64(n, n);
|
#ifdef _MSC_VER
|
return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi));
|
#else
|
return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi));
|
#endif
|
}
|
|
static inline int popcnt256(__m256i n) {
|
return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1));
|
}
|
|
static inline __m256i count256(__m256i v) {
|
__m256i lookup =
|
_mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2,
|
2, 3, 2, 3, 3, 4, 0, 1, 1, 2, 1, 2, 2, 3,
|
1, 2, 2, 3, 2, 3, 3, 4);
|
|
__m256i low_mask = _mm256_set1_epi8(0x0f);
|
|
__m256i lo = _mm256_and_si256(v, low_mask);
|
__m256i hi = _mm256_and_si256(_mm256_srli_epi32(v, 4), low_mask);
|
__m256i popcnt1 = _mm256_shuffle_epi8(lookup, lo);
|
__m256i popcnt2 = _mm256_shuffle_epi8(lookup, hi);
|
__m256i total = _mm256_add_epi8(popcnt1, popcnt2);
|
|
return _mm256_sad_epu8(total, _mm256_setzero_si256());
|
}
|
|
static inline int popcnt256_custom(__m256i n) {
|
__m256i val = count256(n);
|
|
//return val.m256i_i64[0] +
|
//val.m256i_i64[1] +
|
//val.m256i_i64[2] +
|
//val.m256i_i64[3];
|
return _mm256_extract_epi64(val, 0)
|
+ _mm256_extract_epi64(val, 1)
|
+ _mm256_extract_epi64(val, 2)
|
+ _mm256_extract_epi64(val, 3);
|
}
|
|
// 5x times faster than gemm()-float32
|
// further optimizations: do mean-mult only for the last layer
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
|
unsigned char *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int i;
|
|
#if defined(_OPENMP)
|
static int max_num_threads = 0;
|
if (max_num_threads == 0) {
|
max_num_threads = omp_get_max_threads();
|
//omp_set_num_threads(max_num_threads / 2);
|
}
|
#endif
|
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i)
|
{ // l.n - filters [16 - 55 - 1024]
|
float mean_val = mean_arr[i];
|
int j, k;
|
__m256i all_1 = _mm256_set1_epi8(255);
|
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
int count = 0;
|
const int bit_step = 256;
|
__m256i count_sum = _mm256_set1_epi8(0);
|
|
for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
__m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8));
|
__m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8));
|
__m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b))
|
__m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); // can be optimized - we can do other NOT for wegihts once and do not do this NOT
|
|
count_sum = _mm256_add_epi64(count256(c_bit256), count_sum); // MulaÂ’s algorithm
|
|
//count += popcnt256(c_bit256);
|
|
//binary_int64_printf(c_bit64);
|
//printf(", count = %d \n\n", tmp_count);
|
}
|
|
// count of 1 bits
|
//count = count_sum.m256i_i64[0] +
|
// count_sum.m256i_i64[1] +
|
// count_sum.m256i_i64[2] +
|
// count_sum.m256i_i64[3];
|
count = _mm256_extract_epi64(count_sum, 0)
|
+ _mm256_extract_epi64(count_sum, 1)
|
+ _mm256_extract_epi64(count_sum, 2)
|
+ _mm256_extract_epi64(count_sum, 3);
|
|
int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step));
|
count = count - f1; // remove extra bits (from empty space for align only)
|
|
C[i*ldc + j] = (2 * count - K) * mean_val;
|
}
|
}
|
}
|
|
|
static inline float im2col_get_pixel(float *im, int height, int width, int channels,
|
int row, int col, int channel, int pad)
|
{
|
row -= pad;
|
col -= pad;
|
|
if (row < 0 || col < 0 ||
|
row >= height || col >= width) return 0;
|
return im[col + width*(row + height*channel)];
|
}
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom_transpose(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col, int ldb_align)
|
{
|
int c, h, w;
|
int height_col = (height + 2 * pad - ksize) / stride + 1;
|
int width_col = (width + 2 * pad - ksize) / stride + 1;
|
int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1)
|
{
|
#pragma omp parallel for
|
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 = pad; h < height_col - pad; ++h) {
|
for (w = pad; w < width_col - pad - 4; w+=8) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
//int col_index = (c * height_col + h) * width_col + w;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
|
//data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
__m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)]));
|
data_col[col_index + ldb_align * 0] = _mm256_extract_float32(src256, 0);// src256.m256_f32[0];
|
data_col[col_index + ldb_align * 1] = _mm256_extract_float32(src256, 1);// src256.m256_f32[1];
|
data_col[col_index + ldb_align * 2] = _mm256_extract_float32(src256, 2);// src256.m256_f32[2];
|
data_col[col_index + ldb_align * 3] = _mm256_extract_float32(src256, 3);// src256.m256_f32[3];
|
data_col[col_index + ldb_align * 4] = _mm256_extract_float32(src256, 4);// src256.m256_f32[4];
|
data_col[col_index + ldb_align * 5] = _mm256_extract_float32(src256, 5);// src256.m256_f32[5];
|
data_col[col_index + ldb_align * 6] = _mm256_extract_float32(src256, 6);// src256.m256_f32[6];
|
data_col[col_index + ldb_align * 7] = _mm256_extract_float32(src256, 7);// src256.m256_f32[7];
|
|
//_mm256_storeu_ps(&data_col[col_index], src256);
|
}
|
|
for (; w < width_col - pad; ++w) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
}
|
}
|
|
{
|
w = 0;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
w = width_col - 1;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = 0;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = height_col - 1;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
}
|
|
}
|
else {
|
#pragma omp parallel for
|
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) {
|
int im_row = h_offset + h * stride;
|
int im_col = w_offset + w * stride;
|
|
int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
}
|
}
|
}
|
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col)
|
{
|
|
int c, h, w;
|
int height_col = (height + 2 * pad - ksize) / stride + 1;
|
int width_col = (width + 2 * pad - ksize) / stride + 1;
|
int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx())
|
{
|
#pragma omp parallel for
|
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 = pad; h < height_col-pad; ++h) {
|
for (w = pad; w < width_col-pad-8; w += 8) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
int col_index = (c * height_col + h) * width_col + w;
|
|
//data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
__m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)]));
|
_mm256_storeu_ps(&data_col[col_index], src256);
|
}
|
|
for (; w < width_col - pad; ++w) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
int col_index = (c * height_col + h) * width_col + w;
|
|
data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
}
|
}
|
|
{
|
w = 0;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
w = width_col-1;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = 0;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = height_col-1;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
}
|
|
}
|
else {
|
//printf("\n Error: is no non-optimized version \n");
|
im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col);
|
}
|
}
|
|
void transpose_8x8_bits(unsigned char A[8], unsigned char B[8], int m, int n)
|
{
|
unsigned x, y, t;
|
|
// Load the array and pack it into x and y.
|
|
x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m];
|
y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m];
|
|
t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7);
|
t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7);
|
|
t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14);
|
t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14);
|
|
t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F);
|
y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F);
|
x = t;
|
|
B[0] = x >> 24; B[n] = x >> 16; B[2 * n] = x >> 8; B[3 * n] = x;
|
B[4 * n] = y >> 24; B[5 * n] = y >> 16; B[6 * n] = y >> 8; B[7 * n] = y;
|
}
|
|
void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a)
|
{
|
int i = 0;
|
if (a == LINEAR)
|
{}
|
else if (a == LEAKY)
|
{
|
if (is_fma_avx()) {
|
__m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000);
|
__m256 all256_01 = _mm256_set1_ps(0.1F);
|
|
for (i = 0; i < n - 8; i += 8) {
|
//x[i] = (x[i]>0) ? x[i] : .1*x[i];
|
|
__m256 src256 = _mm256_loadu_ps(&x[i]);
|
__m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1
|
|
__m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats
|
|
__m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult;
|
_mm256_storeu_ps(&x[i], result256);
|
}
|
}
|
|
for (; i < n; ++i) {
|
x[i] = (x[i]>0) ? x[i] : .1*x[i];
|
}
|
}
|
else {
|
for (i = 0; i < n; ++i) {
|
x[i] = activate(x[i], a);
|
}
|
}
|
}
|
|
void float_to_bit(float *src, unsigned char *dst, size_t size)
|
{
|
size_t dst_size = size / 8 + 1;
|
memset(dst, 0, dst_size);
|
|
size_t i;
|
__m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000);
|
|
for (i = 0; i < size; i+=8)
|
{
|
__m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i]));
|
__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats
|
|
uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1
|
mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0
|
|
dst[i / 8] = mask;
|
}
|
}
|
|
static inline void transpose4x4_SSE(float *A, float *B, const int lda, const int ldb)
|
{
|
__m128 row1 = _mm_loadu_ps(&A[0 * lda]);
|
__m128 row2 = _mm_loadu_ps(&A[1 * lda]);
|
__m128 row3 = _mm_loadu_ps(&A[2 * lda]);
|
__m128 row4 = _mm_loadu_ps(&A[3 * lda]);
|
_MM_TRANSPOSE4_PS(row1, row2, row3, row4);
|
_mm_storeu_ps(&B[0 * ldb], row1);
|
_mm_storeu_ps(&B[1 * ldb], row2);
|
_mm_storeu_ps(&B[2 * ldb], row3);
|
_mm_storeu_ps(&B[3 * ldb], row4);
|
}
|
|
void transpose_block_SSE4x4(float *A, float *B, const int n, const int m,
|
const int lda, const int ldb, const int block_size)
|
{
|
int i;
|
#pragma omp parallel for
|
for (i = 0; i < n; i += block_size) {
|
int j, i2, j2;
|
//int max_i2 = (i + block_size < n) ? (i + block_size) : n;
|
if (i + block_size < n) {
|
int max_i2 = i + block_size;
|
for (j = 0; j < m; j += block_size) {
|
//int max_j2 = (j + block_size < m) ? (j + block_size) : m;
|
if (j + block_size < m) {
|
int max_j2 = j + block_size;
|
for (i2 = i; i2 < max_i2; i2 += 4) {
|
for (j2 = j; j2 < max_j2; j2 += 4) {
|
transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb);
|
}
|
}
|
}
|
else {
|
for (i2 = i; i2 < max_i2; ++i2) {
|
for (j2 = j; j2 < m; ++j2) {
|
B[j2*ldb + i2] = A[i2*lda + j2];
|
}
|
}
|
}
|
}
|
}
|
else {
|
for (i2 = i; i2 < n; ++i2) {
|
for (j2 = 0; j2 < m; ++j2) {
|
B[j2*ldb + i2] = A[i2*lda + j2];
|
}
|
}
|
}
|
}
|
}
|
|
|
#else
|
|
void gemm_nn(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i, j, k;
|
for (i = 0; i < M; ++i) {
|
for (k = 0; k < K; ++k) {
|
register float A_PART = ALPHA*A[i*lda + k];
|
for (j = 0; j < N; ++j) {
|
C[i*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
}
|
}
|
}
|
|
|
void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride,
|
float *weights, float *input, float *output, float *mean)
|
{
|
int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1
|
int i, f, j;
|
|
int fil;
|
// filter index
|
#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP
|
for (fil = 0; fil < n; ++fil) {
|
int chan, y, x, f_y, f_x;
|
// channel index
|
for (chan = 0; chan < c; ++chan)
|
// input - y
|
for (y = 0; y < h; ++y)
|
// input - x
|
for (x = 0; x < w; ++x)
|
{
|
int const output_index = fil*w*h + y*w + x;
|
int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize;
|
int const input_pre_index = chan*w*h;
|
float sum = 0;
|
|
// filter - y
|
for (f_y = 0; f_y < ksize; ++f_y)
|
{
|
int input_y = y + f_y - pad;
|
// filter - x
|
for (f_x = 0; f_x < ksize; ++f_x)
|
{
|
int input_x = x + f_x - pad;
|
if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;
|
|
int input_index = input_pre_index + input_y*w + input_x;
|
int weights_index = weights_pre_index + f_y*ksize + f_x;
|
|
sum += input[input_index] * weights[weights_index];
|
}
|
}
|
// l.output[filters][width][height] +=
|
// state.input[channels][width][height] *
|
// l.weights[filters][channels][filter_width][filter_height];
|
output[output_index] += sum;
|
}
|
}
|
}
|
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
unsigned char *A, int lda,
|
unsigned char *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int i, j, k, h;
|
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
float mean_val = mean_arr[i];
|
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
int count = 0;
|
|
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8));
|
uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
|
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
|
|
#ifdef WIN32
|
int tmp_count = __popcnt64(c_bit64);
|
#else
|
int tmp_count = __builtin_popcountll(c_bit64);
|
#endif
|
|
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
|
count += tmp_count;
|
//binary_int64_printf(c_bit64);
|
//printf(", count = %d \n\n", tmp_count);
|
}
|
|
C[i*ldc + j] = (2 * count - K) * mean_val;
|
}
|
}
|
}
|
|
void im2col_cpu_custom_transpose(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col, int ldb_align)
|
{
|
printf("\n im2col_cpu_custom_transpose() isn't implemented without AVX \n");
|
}
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col)
|
{
|
|
int c, h, w;
|
int height_col = (height + 2 * pad - ksize) / stride + 1;
|
int width_col = (width + 2 * pad - ksize) / stride + 1;
|
int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1)
|
{
|
#pragma omp parallel for
|
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 = pad; h < height_col - pad; ++h) {
|
for (w = pad; w < width_col - pad; ++w) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
int col_index = (c * height_col + h) * width_col + w;
|
|
data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
}
|
|
for (; w < width_col - pad; ++w) {
|
int im_row = h_offset + h - pad;
|
int im_col = w_offset + w - pad;
|
int col_index = (c * height_col + h) * width_col + w;
|
|
data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
}
|
}
|
|
{
|
w = 0;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
w = width_col - 1;
|
for (h = 0; h < height_col; ++h) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = 0;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
|
{
|
h = height_col - 1;
|
for (w = 0; w < width_col; ++w) {
|
int im_row = h_offset + h;
|
int im_col = w_offset + w;
|
int col_index = (c * height_col + h) * width_col + w;
|
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
|
im_row, im_col, c_im, pad);
|
}
|
}
|
}
|
|
}
|
else {
|
//printf("\n Error: is no non-optimized version \n");
|
im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col);
|
}
|
}
|
|
void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a)
|
{
|
int i;
|
if (a == LINEAR)
|
{
|
}
|
else if (a == LEAKY)
|
{
|
for (i = 0; i < n; ++i) {
|
x[i] = (x[i]>0) ? x[i] : .1*x[i];
|
}
|
}
|
else {
|
for (i = 0; i < n; ++i) {
|
x[i] = activate(x[i], a);
|
}
|
}
|
}
|
|
void float_to_bit(float *src, unsigned char *dst, size_t size)
|
{
|
size_t dst_size = size / 8 + 1;
|
memset(dst, 0, dst_size);
|
|
size_t i;
|
char *byte_arr = calloc(size, sizeof(char));
|
for (i = 0; i < size; ++i) {
|
if (src[i] > 0) byte_arr[i] = 1;
|
}
|
|
//for (i = 0; i < size; ++i) {
|
// dst[i / 8] |= byte_arr[i] << (i % 8);
|
//}
|
|
for (i = 0; i < size; i += 8) {
|
char dst_tmp = 0;
|
dst_tmp |= byte_arr[i + 0] << 0;
|
dst_tmp |= byte_arr[i + 1] << 1;
|
dst_tmp |= byte_arr[i + 2] << 2;
|
dst_tmp |= byte_arr[i + 3] << 3;
|
dst_tmp |= byte_arr[i + 4] << 4;
|
dst_tmp |= byte_arr[i + 5] << 5;
|
dst_tmp |= byte_arr[i + 6] << 6;
|
dst_tmp |= byte_arr[i + 7] << 7;
|
dst[i / 8] = dst_tmp;
|
}
|
free(byte_arr);
|
}
|
|
static inline void transpose_scalar_block(float *A, float *B, const int lda, const int ldb, const int block_size)
|
{
|
int i, j;
|
//#pragma omp parallel for
|
for (i = 0; i<block_size; i++) {
|
for (j = 0; j<block_size; j++) {
|
B[j*ldb + i] = A[i*lda + j];
|
}
|
}
|
}
|
|
void transpose_block_SSE4x4(float *A, float *B, const int n, const int m,
|
const int lda, const int ldb, const int block_size)
|
{
|
int i;
|
#pragma omp parallel for
|
for (i = 0; i < n; i += block_size) {
|
int j, i2, j2;
|
for (j = 0; j < m; j += block_size) {
|
int max_i2 = i + block_size < n ? i + block_size : n;
|
int max_j2 = j + block_size < m ? j + block_size : m;
|
for (i2 = i; i2 < max_i2; ++i2) {
|
for (j2 = j; j2 < max_j2; ++j2) {
|
B[j2*ldb + i2] = A[i2*lda + j2];
|
}
|
}
|
}
|
}
|
}
|
#endif // __x86_64
|
|
void gemm_nt(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i,j,k;
|
for(i = 0; i < M; ++i){
|
for(j = 0; j < N; ++j){
|
register float sum = 0;
|
for(k = 0; k < K; ++k){
|
sum += ALPHA*A[i*lda+k]*B[j*ldb + k];
|
}
|
C[i*ldc+j] += sum;
|
}
|
}
|
}
|
|
void gemm_tn(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i,j,k;
|
for(i = 0; i < M; ++i){
|
for(k = 0; k < K; ++k){
|
register float A_PART = ALPHA*A[k*lda+i];
|
for(j = 0; j < N; ++j){
|
C[i*ldc+j] += A_PART*B[k*ldb+j];
|
}
|
}
|
}
|
}
|
|
void gemm_tt(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i,j,k;
|
for(i = 0; i < M; ++i){
|
for(j = 0; j < N; ++j){
|
register float sum = 0;
|
for(k = 0; k < K; ++k){
|
sum += ALPHA*A[i+k*lda]*B[k+j*ldb];
|
}
|
C[i*ldc+j] += sum;
|
}
|
}
|
}
|
|
|
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)
|
{
|
//printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc);
|
if (BETA != 1){
|
int i, j;
|
for(i = 0; i < M; ++i){
|
for(j = 0; j < N; ++j){
|
C[i*ldc + j] *= BETA;
|
}
|
}
|
}
|
|
int t;
|
#pragma omp parallel for
|
for (t = 0; t < M; ++t) {
|
if (!TA && !TB)
|
gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc);
|
else if (TA && !TB)
|
gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc);
|
else if (!TA && TB)
|
gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc);
|
else
|
gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc);
|
}
|
}
|
|
#ifdef GPU
|
|
#include <math.h>
|
|
void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
float *A_gpu, int lda,
|
float *B_gpu, int ldb,
|
float BETA,
|
float *C_gpu, int ldc)
|
{
|
cublasHandle_t handle = blas_handle();
|
cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream());
|
cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N),
|
(TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc);
|
check_error(status);
|
}
|
|
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)
|
{
|
float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M));
|
float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K));
|
float *C_gpu = cuda_make_array(C, ldc*M);
|
|
gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc);
|
|
cuda_pull_array(C_gpu, C, ldc*M);
|
cuda_free(A_gpu);
|
cuda_free(B_gpu);
|
cuda_free(C_gpu);
|
}
|
|
#include <stdio.h>
|
#include <stdlib.h>
|
#include <string.h>
|
#include <time.h>
|
|
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<32; ++i){
|
gemm_gpu(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 s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC);
|
free(a);
|
free(b);
|
free(c);
|
}
|
|
void time_ongpu(int TA, int TB, int m, int k, int n)
|
{
|
int iter = 10;
|
float *a = random_matrix(m,k);
|
float *b = random_matrix(k,n);
|
|
int lda = (!TA)?k:m;
|
int ldb = (!TB)?n:k;
|
|
float *c = random_matrix(m,n);
|
|
float *a_cl = cuda_make_array(a, m*k);
|
float *b_cl = cuda_make_array(b, k*n);
|
float *c_cl = cuda_make_array(c, m*n);
|
|
int i;
|
clock_t start = clock(), end;
|
for(i = 0; i<iter; ++i){
|
gemm_ongpu(TA,TB,m,n,k,1,a_cl,lda,b_cl,ldb,1,c_cl,n);
|
cudaThreadSynchronize();
|
}
|
double flop = ((double)m)*n*(2.*k + 2.)*iter;
|
double gflop = flop/pow(10., 9);
|
end = clock();
|
double seconds = sec(end-start);
|
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s, %lf GFLOPS\n",m,k,k,n, TA, TB, seconds, gflop/seconds);
|
cuda_free(a_cl);
|
cuda_free(b_cl);
|
cuda_free(c_cl);
|
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);
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gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c_gpu,n);
|
//printf("GPU\n");
|
//pm(m, n, c_gpu);
|
|
gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
|
//printf("\n\nCPU\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 SSE\n",m,k,k,n, TA, TB, sse/(m*n));
|
free(a);
|
free(b);
|
free(c);
|
free(c_gpu);
|
}
|
|
int test_gpu_blas()
|
{
|
/*
|
test_gpu_accuracy(0,0,10,576,75);
|
|
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);
|
|
test_gpu_accuracy(0,0,10,10,10);
|
|
time_ongpu(0,0,64,2916,363);
|
time_ongpu(0,0,64,2916,363);
|
time_ongpu(0,0,64,2916,363);
|
time_ongpu(0,0,192,729,1600);
|
time_ongpu(0,0,384,196,1728);
|
time_ongpu(0,0,256,196,3456);
|
time_ongpu(0,0,256,196,2304);
|
time_ongpu(0,0,128,4096,12544);
|
time_ongpu(0,0,128,4096,4096);
|
*/
|
time_ongpu(0,0,64,75,12544);
|
time_ongpu(0,0,64,75,12544);
|
time_ongpu(0,0,64,75,12544);
|
time_ongpu(0,0,64,576,12544);
|
time_ongpu(0,0,256,2304,784);
|
time_ongpu(1,1,2304,256,784);
|
time_ongpu(0,0,512,4608,196);
|
time_ongpu(1,1,4608,512,196);
|
|
return 0;
|
}
|
#endif
|
