#include "gemm.h"
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#include "utils.h"
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#include "im2col.h"
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#include "dark_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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#include <float.h>
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#include <string.h>
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#include <stdint.h>
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#if defined(_OPENMP)
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#include <omp.h>
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#endif
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#if defined(_MSC_VER)
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#if defined(_M_ARM) || defined(_M_ARM64)
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static inline uint32_t popcnt(uint32_t v) {
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v = v - ((v >> 1) & 0x55555555);
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v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
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return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
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}
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#define POPCNT(x) popcnt((x))
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#define POPCNT64(x) (popcnt((unsigned)(x)) + popcnt((unsigned)((uint64_t)(x) >> 32)))
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#else
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#include <intrin.h>
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#ifdef _WIN64
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#define POPCNT(x) __popcnt(x)
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#define POPCNT64(x) __popcnt64(x)
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#else
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static inline int popcnt_64(uint64_t val64) {
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int tmp_count = __popcnt(val64);
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tmp_count += __popcnt(val64 >> 32);
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return tmp_count;
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}
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#define POPCNT(x) __popcnt(x)
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#define POPCNT64(x) popcnt_64(x)
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#endif
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#endif
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#elif defined(__GNUC__)
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#define POPCNT(x) __builtin_popcount(x)
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#define POPCNT64(x) __builtin_popcountll(x)
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#endif
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#define TILE_M 4 // 4 ops
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#define TILE_N 16 // AVX2 = 2 ops * 8 floats
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#define TILE_K 16 // loop
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#ifdef __cplusplus
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#define PUT_IN_REGISTER
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#else
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#define PUT_IN_REGISTER register
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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 = (float*)xcalloc(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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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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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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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;
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else return 0xFFFFFFFFFFFFFFFF;
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}
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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;
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}
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printf("\n");
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}
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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");
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else printf("0");
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src = src >> 1;
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}
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printf("\n");
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}
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/*
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void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
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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)
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{
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int *count_arr = xcalloc(M*N, sizeof(int));
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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);
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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);
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}
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}
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}
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for (i = 0; i < M; ++i) {
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float mean_val = mean_arr[i];
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for (j = 0; j < N; ++j) {
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C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
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}
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}
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free(count_arr);
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}
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*/
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/*
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void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
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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)
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{
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int *count_arr = xcalloc(M*N, sizeof(int));
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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 (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
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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);
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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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}
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}
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}
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for (i = 0; i < M; ++i) {
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float mean_val = mean_arr[i];
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for (j = 0; j < N; ++j) {
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C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
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}
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}
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free(count_arr);
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}
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*/
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/*
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void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
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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)
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{
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int *count_arr = xcalloc(M*N, sizeof(int));
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int i;
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#pragma omp parallel for
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for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
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int j, k, h;
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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)) {
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uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8));
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uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
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//printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc
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printf("\n %d \n", POPCNT64(c_bit64)); // msvs
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int h;
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for (h = 0; h < 64; ++h)
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if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1;
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//binary_int64_printf(a_bit64);
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//binary_int64_printf(b_bit64);
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//binary_int64_printf(c_bit64);
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}
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else {
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for (; 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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if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1;
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}
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}
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}
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}
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}
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if (mean_arr) {
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//int K_2 = K / 2;
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for (i = 0; i < M; ++i) {
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float mean_val = mean_arr[i];
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//float mean_val2 = 2 * mean_val;
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for (j = 0; j < N; ++j) {
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C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
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//C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2;
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}
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}
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}
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else {
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for (i = 0; i < M; ++i) {
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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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}
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}
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free(count_arr);
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//getchar();
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}
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*/
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/*
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void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
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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)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
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int j, k, h;
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float mean_val = mean_arr[i];
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for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
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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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int tmp_count = POPCNT64(c_bit64);
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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);
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//printf(", count = %d \n\n", tmp_count);
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}
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C[i*ldc + j] = (2 * count - K) * mean_val;
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}
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}
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}
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*/
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//----------------------------
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// is not used
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/*
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void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb)
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{
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unsigned int x, y;
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for (y = 0; y < 32; ++y) {
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for (x = 0; x < 32; ++x) {
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if (A[y * lda] & ((uint32_t)1 << x)) B[x * ldb] |= (uint32_t)1 << y;
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}
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}
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}
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*/
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#ifndef GPU
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uint8_t reverse_8_bit(uint8_t a) {
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return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16;
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}
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uint32_t reverse_32_bit(uint32_t a)
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{
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// unsigned int __rbit(unsigned int val) // for ARM //__asm__("rbit %0, %1\n" : "=r"(output) : "r"(input));
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return (reverse_8_bit(a >> 24) << 0) |
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(reverse_8_bit(a >> 16) << 8) |
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(reverse_8_bit(a >> 8) << 16) |
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(reverse_8_bit(a >> 0) << 24);
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}
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#define swap(a0, a1, j, m) t = (a0 ^ (a1 >>j)) & m; a0 = a0 ^ t; a1 = a1 ^ (t << j);
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void transpose32_optimized(uint32_t A[32]) {
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int j, k;
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unsigned m, t;
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//m = 0x0000FFFF;
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//for (j = 16; j != 0; j = j >> 1, m = m ^ (m << j)) {
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// for (k = 0; k < 32; k = (k + j + 1) & ~j) {
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// t = (A[k] ^ (A[k + j] >> j)) & m;
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// A[k] = A[k] ^ t;
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// A[k + j] = A[k + j] ^ (t << j);
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// }
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//}
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j = 16;
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m = 0x0000FFFF;
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for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); }
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j = 8;
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m = 0x00ff00ff;
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for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); }
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j = 4;
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m = 0x0f0f0f0f;
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for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); }
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j = 2;
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m = 0x33333333;
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for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); }
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j = 1;
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m = 0x55555555;
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for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); }
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// reverse Y
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for (j = 0; j < 16; ++j) {
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uint32_t tmp = A[j];
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A[j] = reverse_32_bit(A[31 - j]);
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A[31 - j] = reverse_32_bit(tmp);
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}
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}
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void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n)
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{
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unsigned A_tmp[32];
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int i;
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#pragma unroll
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for (i = 0; i < 32; ++i) A_tmp[i] = A[i * m];
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transpose32_optimized(A_tmp);
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#pragma unroll
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for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i];
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}
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void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb)
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{
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unsigned x, y;
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for (y = 0; y < 8; ++y) {
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for (x = 0; x < 8; ++x) {
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if (A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y;
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}
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}
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}
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unsigned char reverse_byte_1(char a)
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{
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return ((a & 0x1) << 7) | ((a & 0x2) << 5) |
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((a & 0x4) << 3) | ((a & 0x8) << 1) |
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((a & 0x10) >> 1) | ((a & 0x20) >> 3) |
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((a & 0x40) >> 5) | ((a & 0x80) >> 7);
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}
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unsigned char reverse_byte(unsigned char a)
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{
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return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16;
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}
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static unsigned char lookup[16] = {
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0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe,
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0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf, };
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unsigned char reverse_byte_3(unsigned char n) {
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// Reverse the top and bottom nibble then swap them.
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return (lookup[n & 0b1111] << 4) | lookup[n >> 4];
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}
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|
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void transpose8rS32_reversed_diagonale(unsigned char* A, unsigned char* B, int m, int n)
|
{
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unsigned x, y, t;
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x = y = 0;
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// Load the array and pack it into x and y.
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//x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m];
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//y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m];
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t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7);
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t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7);
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t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14);
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t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14);
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t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F);
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y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F);
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x = t;
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B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x);
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B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y);
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}
|
|
/*
|
// transpose by 8-bit
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void transpose_bin(char *A, char *B, const int n, const int m,
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const int lda, const int ldb, const int block_size)
|
{
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//printf("\n n = %d, ldb = %d \t\t m = %d, lda = %d \n", n, ldb, m, lda);
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; i += 8) {
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int j;
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for (j = 0; j < m; j += 8) {
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int a_index = i*lda + j;
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int b_index = j*ldb + i;
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//transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8);
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transpose8rS32_reversed_diagonale(&A[a_index / 8], &B[b_index / 8], lda / 8, ldb / 8);
|
}
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for (; j < m; ++j) {
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if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i);
|
}
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}
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}
|
*/
|
|
#endif
|
|
// transpose by 32-bit
|
void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m,
|
const int lda, const int ldb, const int block_size)
|
{
|
//printf("\n n = %d (n mod 32 = %d), m = %d (m mod 32 = %d) \n", n, n % 32, m, m % 32);
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//printf("\n lda = %d (lda mod 32 = %d), ldb = %d (ldb mod 32 = %d) \n", lda, lda % 32, ldb, ldb % 32);
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int i;
|
#pragma omp parallel for
|
for (i = 0; i < n; i += 32) {
|
int j;
|
for (j = 0; j < m; j += 32) {
|
int a_index = i*lda + j;
|
int b_index = j*ldb + i;
|
transpose_32x32_bits_reversed_diagonale(&A[a_index / 32], &B[b_index / 32], lda / 32, ldb / 32);
|
//transpose_32x32_bits_my(&A[a_index/32], &B[b_index/32], lda/32, ldb/32);
|
}
|
for (; j < m; ++j) {
|
if (get_bit((const unsigned char* const)A, i * lda + j)) set_bit((unsigned char* const)B, j * ldb + i);
|
}
|
}
|
}
|
|
#if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__) && !defined(_M_ARM64))
|
|
#if (defined(_WIN64) && !defined(__MINGW64__))
|
#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 _dn_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 _dn_castu32_f32(uint32_t a) {
|
return *((float *)&a);
|
}
|
|
static inline float _mm256_extract_float32(__m256 a, const int index) {
|
switch(index) {
|
case 0:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0));
|
case 1:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 1));
|
case 2:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 2));
|
case 3:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 3));
|
case 4:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 4));
|
case 5:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 5));
|
case 6:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 6));
|
case 7:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 7));
|
default:
|
return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0));
|
}
|
}
|
|
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
|
|
|
|
#ifdef _WIN32
|
// Windows
|
#define cpuid(info, x) __cpuidex(info, x, 0)
|
#else
|
// GCC Intrinsics
|
void cpuid(int info[4], int InfoType) {
|
__cpuid_count(InfoType, 0, info[0], info[1], info[2], info[3]);
|
}
|
#endif
|
|
|
// Misc.
|
static int HW_MMX, HW_x64, HW_RDRAND, HW_BMI1, HW_BMI2, HW_ADX, HW_PREFETCHWT1;
|
static int HW_ABM; // Advanced Bit Manipulation
|
|
// SIMD: 128-bit
|
static int HW_SSE, HW_SSE2, HW_SSE3, HW_SSSE3, HW_SSE41, HW_SSE42, HW_SSE4a, HW_AES, HW_SHA;
|
|
// SIMD: 256-bit
|
static int HW_AVX, HW_XOP, HW_FMA3, HW_FMA4, HW_AVX2;
|
|
// SIMD: 512-bit
|
static int HW_AVX512F; // AVX512 Foundation
|
static int HW_AVX512CD; // AVX512 Conflict Detection
|
static int HW_AVX512PF; // AVX512 Prefetch
|
static int HW_AVX512ER; // AVX512 Exponential + Reciprocal
|
static int HW_AVX512VL; // AVX512 Vector Length Extensions
|
static int HW_AVX512BW; // AVX512 Byte + Word
|
static int HW_AVX512DQ; // AVX512 Doubleword + Quadword
|
static int HW_AVX512IFMA; // AVX512 Integer 52-bit Fused Multiply-Add
|
static int HW_AVX512VBMI; // AVX512 Vector Byte Manipulation Instructions
|
|
// https://stackoverflow.com/questions/6121792/how-to-check-if-a-cpu-supports-the-sse3-instruction-set
|
void check_cpu_features(void) {
|
int info[4];
|
cpuid(info, 0);
|
int nIds = info[0];
|
|
cpuid(info, 0x80000000);
|
unsigned nExIds = info[0];
|
|
// Detect Features
|
if (nIds >= 0x00000001) {
|
cpuid(info, 0x00000001);
|
HW_MMX = (info[3] & ((uint32_t)1 << 23)) != 0;
|
HW_SSE = (info[3] & ((uint32_t)1 << 25)) != 0;
|
HW_SSE2 = (info[3] & ((uint32_t)1 << 26)) != 0;
|
HW_SSE3 = (info[2] & ((uint32_t)1 << 0)) != 0;
|
|
HW_SSSE3 = (info[2] & ((uint32_t)1 << 9)) != 0;
|
HW_SSE41 = (info[2] & ((uint32_t)1 << 19)) != 0;
|
HW_SSE42 = (info[2] & ((uint32_t)1 << 20)) != 0;
|
HW_AES = (info[2] & ((uint32_t)1 << 25)) != 0;
|
|
HW_AVX = (info[2] & ((uint32_t)1 << 28)) != 0;
|
HW_FMA3 = (info[2] & ((uint32_t)1 << 12)) != 0;
|
|
HW_RDRAND = (info[2] & ((uint32_t)1 << 30)) != 0;
|
}
|
if (nIds >= 0x00000007) {
|
cpuid(info, 0x00000007);
|
HW_AVX2 = (info[1] & ((uint32_t)1 << 5)) != 0;
|
|
HW_BMI1 = (info[1] & ((uint32_t)1 << 3)) != 0;
|
HW_BMI2 = (info[1] & ((uint32_t)1 << 8)) != 0;
|
HW_ADX = (info[1] & ((uint32_t)1 << 19)) != 0;
|
HW_SHA = (info[1] & ((uint32_t)1 << 29)) != 0;
|
HW_PREFETCHWT1 = (info[2] & ((uint32_t)1 << 0)) != 0;
|
|
HW_AVX512F = (info[1] & ((uint32_t)1 << 16)) != 0;
|
HW_AVX512CD = (info[1] & ((uint32_t)1 << 28)) != 0;
|
HW_AVX512PF = (info[1] & ((uint32_t)1 << 26)) != 0;
|
HW_AVX512ER = (info[1] & ((uint32_t)1 << 27)) != 0;
|
HW_AVX512VL = (info[1] & ((uint32_t)1 << 31)) != 0;
|
HW_AVX512BW = (info[1] & ((uint32_t)1 << 30)) != 0;
|
HW_AVX512DQ = (info[1] & ((uint32_t)1 << 17)) != 0;
|
HW_AVX512IFMA = (info[1] & ((uint32_t)1 << 21)) != 0;
|
HW_AVX512VBMI = (info[2] & ((uint32_t)1 << 1)) != 0;
|
}
|
if (nExIds >= 0x80000001) {
|
cpuid(info, 0x80000001);
|
HW_x64 = (info[3] & ((uint32_t)1 << 29)) != 0;
|
HW_ABM = (info[2] & ((uint32_t)1 << 5)) != 0;
|
HW_SSE4a = (info[2] & ((uint32_t)1 << 6)) != 0;
|
HW_FMA4 = (info[2] & ((uint32_t)1 << 16)) != 0;
|
HW_XOP = (info[2] & ((uint32_t)1 << 11)) != 0;
|
}
|
}
|
|
int is_avx() {
|
static int result = -1;
|
if (result == -1) {
|
check_cpu_features();
|
result = HW_AVX;
|
if (result == 1) printf(" Used AVX \n");
|
else printf(" Not used AVX \n");
|
}
|
return result;
|
}
|
|
int is_fma_avx2() {
|
static int result = -1;
|
if (result == -1) {
|
check_cpu_features();
|
result = HW_FMA3 && HW_AVX2;
|
if (result == 1) printf(" Used FMA & AVX2 \n");
|
else printf(" Not used FMA & AVX2 \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_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) {
|
PUT_IN_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 gemm_nn_fast(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i;
|
|
#pragma omp parallel for
|
for (i = 0; i < (M / TILE_M)*TILE_M; i += TILE_M)
|
{
|
int j, k;
|
int i_d, k_d;
|
|
for (k = 0; k < (K / TILE_K)*TILE_K; k += TILE_K)
|
{
|
for (j = 0; j < (N / TILE_N)*TILE_N; j += TILE_N)
|
{
|
// L1 - 6 bits tag [11:6] - cache size 32 KB, conflict for each 4 KB
|
// L2 - 9 bits tag [14:6] - cache size 256 KB, conflict for each 32 KB
|
// L3 - 13 bits tag [18:6] - cache size 8 MB, conflict for each 512 KB
|
|
__m256 result256;
|
__m256 a256_0, b256_0; // AVX
|
__m256 a256_1, b256_1; // AVX
|
__m256 a256_2;// , b256_2; // AVX
|
__m256 a256_3;// , b256_3; // AVX
|
__m256 c256_0, c256_1, c256_2, c256_3;
|
__m256 c256_4, c256_5, c256_6, c256_7;
|
|
c256_0 = _mm256_loadu_ps(&C[(0 + i)*ldc + (0 + j)]);
|
c256_1 = _mm256_loadu_ps(&C[(1 + i)*ldc + (0 + j)]);
|
c256_2 = _mm256_loadu_ps(&C[(0 + i)*ldc + (8 + j)]);
|
c256_3 = _mm256_loadu_ps(&C[(1 + i)*ldc + (8 + j)]);
|
|
c256_4 = _mm256_loadu_ps(&C[(2 + i)*ldc + (0 + j)]);
|
c256_5 = _mm256_loadu_ps(&C[(3 + i)*ldc + (0 + j)]);
|
c256_6 = _mm256_loadu_ps(&C[(2 + i)*ldc + (8 + j)]);
|
c256_7 = _mm256_loadu_ps(&C[(3 + i)*ldc + (8 + j)]);
|
|
|
for (k_d = 0; k_d < (TILE_K); ++k_d)
|
{
|
a256_0 = _mm256_set1_ps(ALPHA*A[(0 + i)*lda + (k_d + k)]);
|
a256_1 = _mm256_set1_ps(ALPHA*A[(1 + i)*lda + (k_d + k)]);
|
|
a256_2 = _mm256_set1_ps(ALPHA*A[(2 + i)*lda + (k_d + k)]);
|
a256_3 = _mm256_set1_ps(ALPHA*A[(3 + i)*lda + (k_d + k)]);
|
|
|
b256_0 = _mm256_loadu_ps(&B[(k_d + k)*ldb + (0 + j)]);
|
b256_1 = _mm256_loadu_ps(&B[(k_d + k)*ldb + (8 + j)]);
|
|
// FMA - Intel Haswell (2013), AMD Piledriver (2012)
|
//c256_0 = _mm256_fmadd_ps(a256_0, b256_0, c256_0);
|
//c256_1 = _mm256_fmadd_ps(a256_1, b256_0, c256_1);
|
//c256_2 = _mm256_fmadd_ps(a256_0, b256_1, c256_2);
|
//c256_3 = _mm256_fmadd_ps(a256_1, b256_1, c256_3);
|
|
//c256_4 = _mm256_fmadd_ps(a256_2, b256_0, c256_4);
|
//c256_5 = _mm256_fmadd_ps(a256_3, b256_0, c256_5);
|
//c256_6 = _mm256_fmadd_ps(a256_2, b256_1, c256_6);
|
//c256_7 = _mm256_fmadd_ps(a256_3, b256_1, c256_7);
|
|
result256 = _mm256_mul_ps(a256_0, b256_0);
|
c256_0 = _mm256_add_ps(result256, c256_0);
|
|
result256 = _mm256_mul_ps(a256_1, b256_0);
|
c256_1 = _mm256_add_ps(result256, c256_1);
|
|
result256 = _mm256_mul_ps(a256_0, b256_1);
|
c256_2 = _mm256_add_ps(result256, c256_2);
|
|
result256 = _mm256_mul_ps(a256_1, b256_1);
|
c256_3 = _mm256_add_ps(result256, c256_3);
|
|
|
result256 = _mm256_mul_ps(a256_2, b256_0);
|
c256_4 = _mm256_add_ps(result256, c256_4);
|
|
result256 = _mm256_mul_ps(a256_3, b256_0);
|
c256_5 = _mm256_add_ps(result256, c256_5);
|
|
result256 = _mm256_mul_ps(a256_2, b256_1);
|
c256_6 = _mm256_add_ps(result256, c256_6);
|
|
result256 = _mm256_mul_ps(a256_3, b256_1);
|
c256_7 = _mm256_add_ps(result256, c256_7);
|
}
|
_mm256_storeu_ps(&C[(0 + i)*ldc + (0 + j)], c256_0);
|
_mm256_storeu_ps(&C[(1 + i)*ldc + (0 + j)], c256_1);
|
_mm256_storeu_ps(&C[(0 + i)*ldc + (8 + j)], c256_2);
|
_mm256_storeu_ps(&C[(1 + i)*ldc + (8 + j)], c256_3);
|
|
_mm256_storeu_ps(&C[(2 + i)*ldc + (0 + j)], c256_4);
|
_mm256_storeu_ps(&C[(3 + i)*ldc + (0 + j)], c256_5);
|
_mm256_storeu_ps(&C[(2 + i)*ldc + (8 + j)], c256_6);
|
_mm256_storeu_ps(&C[(3 + i)*ldc + (8 + j)], c256_7);
|
}
|
|
for (j = (N / TILE_N)*TILE_N; j < N; ++j) {
|
for (i_d = i; i_d < (i + TILE_M); ++i_d)
|
{
|
for (k_d = k; k_d < (k + TILE_K); ++k_d)
|
{
|
PUT_IN_REGISTER float A_PART = ALPHA*A[i_d*lda + k_d];
|
C[i_d*ldc + j] += A_PART*B[k_d*ldb + j];
|
}
|
}
|
}
|
}
|
|
for (k = (K / TILE_K)*TILE_K; k < K; ++k)
|
{
|
for (i_d = i; i_d < (i + TILE_M); ++i_d)
|
{
|
PUT_IN_REGISTER float A_PART = ALPHA*A[i_d*lda + k];
|
for (j = 0; j < N; ++j) {
|
C[i_d*ldc + j] += A_PART*B[k*ldb + j];
|
}
|
}
|
}
|
}
|
|
for (i = (M / TILE_M)*TILE_M; i < M; ++i) {
|
int j, k;
|
for (k = 0; k < K; ++k) {
|
PUT_IN_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 gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA,
|
uint32_t *A, int lda,
|
uint32_t *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int i;
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) { // l.n
|
int j, s;
|
float mean_val = mean_arr[i];
|
//printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]);
|
for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c)
|
{
|
PUT_IN_REGISTER uint32_t A_PART = A[i*lda + s];
|
__m256i a256 = _mm256_set1_epi32(A_PART);
|
|
for (j = 0; j < N - 8; j += 8)
|
{
|
__m256i b256 = *((__m256i*)&B[s*ldb + j]);
|
__m256i xor256 = _mm256_xor_si256(a256, b256); // xnor = xor(a,b)
|
__m256i all_1 = _mm256_set1_epi8((char)255);
|
__m256i xnor256 = _mm256_andnot_si256(xor256, all_1); // xnor = not(xor(a,b))
|
|
// waiting for - CPUID Flags: AVX512VPOPCNTDQ: __m512i _mm512_popcnt_epi32(__m512i a)
|
__m256 count = _mm256_setr_ps(
|
POPCNT(_mm256_extract_epi32(xnor256, 0)),
|
POPCNT(_mm256_extract_epi32(xnor256, 1)),
|
POPCNT(_mm256_extract_epi32(xnor256, 2)),
|
POPCNT(_mm256_extract_epi32(xnor256, 3)),
|
POPCNT(_mm256_extract_epi32(xnor256, 4)),
|
POPCNT(_mm256_extract_epi32(xnor256, 5)),
|
POPCNT(_mm256_extract_epi32(xnor256, 6)),
|
POPCNT(_mm256_extract_epi32(xnor256, 7)));
|
|
__m256 val2 = _mm256_set1_ps(2);
|
count = _mm256_mul_ps(count, val2); // count * 2
|
|
__m256 val32 = _mm256_set1_ps(32);
|
count = _mm256_sub_ps(count, val32); // count - 32
|
|
__m256 mean256 = _mm256_set1_ps(mean_val);
|
count = _mm256_mul_ps(count, mean256); // count * mean_val
|
|
__m256 c256 = *((__m256*)&C[i*ldc + j]);
|
count = _mm256_add_ps(count, c256); // c = c + count
|
*((__m256*)&C[i*ldc + j]) = count;
|
}
|
|
for (; j < N; ++j) // out_h*out_w;
|
{
|
PUT_IN_REGISTER uint32_t B_PART = B[s*ldb + j];
|
uint32_t xnor_result = ~(A_PART ^ B_PART);
|
int32_t count = POPCNT(xnor_result); // must be Signed int
|
|
C[i*ldc + j] += (2 * count - 32) * mean_val;
|
}
|
}
|
}
|
}
|
|
void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride,
|
float *weights, float *input, float *output)
|
{
|
//const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
//const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1
|
|
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 i, f, j;
|
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)
|
{
|
//const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
//const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1
|
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
|
|
//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);
|
return POPCNT64(_mm_cvtsi128_si64(n)) + POPCNT64(_mm_cvtsi128_si64(n_hi));
|
}
|
|
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);
|
}
|
|
static inline void xnor_avx2_popcnt(__m256i a_bit256, __m256i b_bit256, __m256i *count_sum) {
|
__m256i c_bit256 = _mm256_set1_epi8((char)255);
|
|
__m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b))
|
c_bit256 = _mm256_andnot_si256(xor256, c_bit256); // 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); // 1st part - popcnt Mula's algorithm
|
}
|
|
// 2nd part - popcnt Mula's algorithm
|
static inline int get_count_mula(__m256i count_sum) {
|
return _mm256_extract_epi64(count_sum, 0)
|
+ _mm256_extract_epi64(count_sum, 1)
|
+ _mm256_extract_epi64(count_sum, 2)
|
+ _mm256_extract_epi64(count_sum, 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)
|
#pragma omp parallel for
|
for (i = 0; i < (M/2)*2; i += 2)
|
{ // l.n - filters [16 - 55 - 1024]
|
float mean_val_0 = mean_arr[i + 0];
|
float mean_val_1 = mean_arr[i + 1];
|
int j, k;
|
//__m256i all_1 = _mm256_set1_epi8(255);
|
|
//for (j = 0; j < N; ++j)
|
for (j = 0; j < (N/2)*2; j += 2)
|
{ // out_h*out_w - one channel output size [169 - 173056]
|
//int count = 0;
|
const int bit_step = 256;
|
__m256i count_sum_0 = _mm256_set1_epi8(0);
|
__m256i count_sum_1 = _mm256_set1_epi8(0);
|
__m256i count_sum_2 = _mm256_set1_epi8(0);
|
__m256i count_sum_3 = _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_0 = _mm256_loadu_si256((__m256i *)(A + ((i + 0)*lda + k) / 8));
|
__m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8));
|
|
__m256i a_bit256_1 = _mm256_loadu_si256((__m256i *)(A + ((i + 1)*lda + k) / 8));
|
__m256i b_bit256_1 = _mm256_loadu_si256((__m256i *)(B + ((j + 1)*ldb + k) / 8));
|
|
|
xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum_0);
|
xnor_avx2_popcnt(a_bit256_0, b_bit256_1, &count_sum_1);
|
|
xnor_avx2_popcnt(a_bit256_1, b_bit256_0, &count_sum_2);
|
xnor_avx2_popcnt(a_bit256_1, b_bit256_1, &count_sum_3);
|
|
//count += popcnt256(c_bit256);
|
//binary_int64_printf(c_bit64);
|
//printf(", count = %d \n\n", tmp_count);
|
}
|
|
int count_0 = get_count_mula(count_sum_0);
|
int count_1 = get_count_mula(count_sum_1);
|
int count_2 = get_count_mula(count_sum_2);
|
int count_3 = get_count_mula(count_sum_3);
|
|
const int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step));
|
count_0 = count_0 - f1; // remove extra bits (from empty space for align only)
|
count_1 = count_1 - f1;
|
count_2 = count_2 - f1;
|
count_3 = count_3 - f1;
|
C[i*ldc + (j + 0)] = (2 * count_0 - K) * mean_val_0;
|
C[i*ldc + (j + 1)] = (2 * count_1 - K) * mean_val_0;
|
C[(i + 1)*ldc + (j + 0)] = (2 * count_2 - K) * mean_val_1;
|
C[(i + 1)*ldc + (j + 1)] = (2 * count_3 - K) * mean_val_1;
|
}
|
|
int i_d;
|
for (i_d = 0; i_d < 2; ++i_d)
|
{
|
float mean_val = mean_arr[i + i_d];
|
for (j = (N / 2) * 2; j < N; j += 1)
|
{ // out_h*out_w - one channel output size [169 - 173056]
|
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_0 = _mm256_loadu_si256((__m256i *)(A + ((i + i_d + 0)*lda + k) / 8));
|
__m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8));
|
xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum);
|
}
|
int count = get_count_mula(count_sum);
|
const 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 + i_d)*ldc + j] = (2 * count - K) * mean_val;
|
}
|
}
|
}
|
|
for (i = (M / 2) * 2; i < M; i += 1)
|
{
|
float mean_val = mean_arr[i];
|
int j, k;
|
for (j = 0; j < N; j += 1)
|
{ // out_h*out_w - one channel output size [169 - 173056]
|
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_0 = _mm256_loadu_si256((__m256i *)(A + ((i + 0)*lda + k) / 8));
|
__m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8));
|
xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum);
|
}
|
int count = get_count_mula(count_sum);
|
const 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;
|
}
|
}
|
}
|
|
|
|
|
//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)
|
{
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const int channels_col = channels * ksize * ksize;
|
int c;
|
|
// 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 h, w;
|
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 h, w;
|
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;
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2())
|
{
|
#pragma omp parallel for
|
for (c = 0; c < channels_col; ++c) {
|
int h, w;
|
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);
|
}
|
}
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom_align(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col, int bit_align)
|
{
|
int c;
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2())
|
{
|
int new_ldb = bit_align;
|
|
#pragma omp parallel for
|
for (c = 0; c < channels_col; ++c) {
|
int h, w;
|
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;
|
int col_index = c * new_ldb + 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;
|
int col_index = c * new_ldb + 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;
|
int col_index = c * new_ldb + 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;
|
int col_index = c * new_ldb + 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;
|
int col_index = c * new_ldb + 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;
|
int col_index = c * new_ldb + 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); // must be aligned for transpose after float_to_bin
|
// float_to_bit(b, t_input, src_size);
|
// transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8);
|
}
|
}
|
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom_bin(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col, int bit_align)
|
{
|
int c;
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2())
|
{
|
__m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000);
|
__m256 float_zero256 = _mm256_set1_ps(0.00);
|
|
int new_ldb = bit_align;
|
|
#pragma omp parallel for
|
for (c = 0; c < channels_col; ++c) {
|
int h, w;
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//__m256i src256 = _mm256_loadu_si256((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)]));
|
//__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats
|
//uint16_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1
|
//mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0
|
|
__m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)]));
|
__m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS);
|
uint16_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1
|
|
uint16_t* dst_ptr = (uint16_t*)&((uint8_t*)data_col)[col_index / 8];
|
*dst_ptr |= (mask << (col_index % 8));
|
}
|
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
float val = data_im[im_col + width*(im_row + height*c_im)];
|
if (val > 0) set_bit((unsigned char* const)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char* const)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char* const)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char* const)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char* const)data_col, col_index);
|
}
|
}
|
}
|
|
}
|
else {
|
printf("\n Error: is no non-optimized version \n");
|
//im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); // must be aligned for transpose after float_to_bin
|
// float_to_bit(b, t_input, src_size);
|
// transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8);
|
}
|
}
|
|
|
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_avx2()) {
|
__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);
|
__m256 float_zero256 = _mm256_set1_ps(0.0);
|
|
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
|
|
__m256 src256 = _mm256_loadu_ps((float *)(&src[i]));
|
__m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS);
|
uint32_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1
|
|
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];
|
}
|
}
|
}
|
}
|
}
|
|
|
void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c,
|
int pad, int stride, int batch)
|
{
|
|
const int w_offset = -pad / 2;
|
const int h_offset = -pad / 2;
|
int b, k;
|
|
for (b = 0; b < batch; ++b) {
|
#pragma omp parallel for
|
for (k = 0; k < c; ++k) {
|
int i, j, m, n;
|
for (i = 0; i < out_h; ++i) {
|
//for (j = 0; j < out_w; ++j) {
|
j = 0;
|
|
if(stride == 1 && is_avx() == 1) {
|
for (j = 0; j < out_w - 8 - (size - 1); j += 8) {
|
int out_index = j + out_w*(i + out_h*(k + c*b));
|
__m256 max256 = _mm256_set1_ps(-FLT_MAX);
|
for (n = 0; n < size; ++n) {
|
for (m = 0; m < size; ++m) {
|
int cur_h = h_offset + i*stride + n;
|
int cur_w = w_offset + j*stride + m;
|
int index = cur_w + w*(cur_h + h*(k + b*c));
|
int valid = (cur_h >= 0 && cur_h < h &&
|
cur_w >= 0 && cur_w < w);
|
if (!valid) continue;
|
|
__m256 src256 = _mm256_loadu_ps(&src[index]);
|
max256 = _mm256_max_ps(src256, max256);
|
}
|
}
|
_mm256_storeu_ps(&dst[out_index], max256);
|
|
}
|
}
|
else if (size == 2 && stride == 2 && is_avx() == 1) {
|
for (j = 0; j < out_w - 4; j += 4) {
|
int out_index = j + out_w*(i + out_h*(k + c*b));
|
//float max = -FLT_MAX;
|
//int max_i = -1;
|
__m128 max128 = _mm_set1_ps(-FLT_MAX);
|
|
for (n = 0; n < size; ++n) {
|
//for (m = 0; m < size; ++m)
|
m = 0;
|
{
|
int cur_h = h_offset + i*stride + n;
|
int cur_w = w_offset + j*stride + m;
|
int index = cur_w + w*(cur_h + h*(k + b*c));
|
int valid = (cur_h >= 0 && cur_h < h &&
|
cur_w >= 0 && cur_w < w);
|
if (!valid) continue;
|
|
__m256 src256 = _mm256_loadu_ps(&src[index]);
|
__m256 src256_2 = _mm256_permute_ps(src256, (1 << 0) | (3 << 4));
|
__m256 max256 = _mm256_max_ps(src256, src256_2);
|
|
__m128 src128_0 = _mm256_extractf128_ps(max256, 0);
|
__m128 src128_1 = _mm256_extractf128_ps(max256, 1);
|
__m128 src128 = _mm_shuffle_ps(src128_0, src128_1, (2 << 2) | (2 << 6));
|
|
max128 = _mm_max_ps(src128, max128);
|
}
|
}
|
_mm_storeu_ps(&dst[out_index], max128);
|
}
|
}
|
|
for (; j < out_w; ++j) {
|
int out_index = j + out_w*(i + out_h*(k + c*b));
|
float max = -FLT_MAX;
|
int max_i = -1;
|
for (n = 0; n < size; ++n) {
|
for (m = 0; m < size; ++m) {
|
int cur_h = h_offset + i*stride + n;
|
int cur_w = w_offset + j*stride + m;
|
int index = cur_w + w*(cur_h + h*(k + b*c));
|
int valid = (cur_h >= 0 && cur_h < h &&
|
cur_w >= 0 && cur_w < w);
|
float val = (valid != 0) ? src[index] : -FLT_MAX;
|
max_i = (val > max) ? index : max_i;
|
max = (val > max) ? val : max;
|
}
|
}
|
dst[out_index] = max;
|
if (indexes) indexes[out_index] = max_i;
|
}
|
}
|
}
|
}
|
}
|
|
#else // AVX
|
|
int is_avx() {
|
return 0;
|
}
|
|
int is_fma_avx2() {
|
return 0;
|
}
|
|
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) {
|
PUT_IN_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 gemm_nn_fast(int M, int N, int K, float ALPHA,
|
float *A, int lda,
|
float *B, int ldb,
|
float *C, int ldc)
|
{
|
int i, j, k;
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) {
|
for (k = 0; k < K; ++k) {
|
PUT_IN_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 gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA,
|
uint32_t *A, int lda,
|
uint32_t *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int i;
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) { // l.n
|
int j, s;
|
float mean_val = mean_arr[i];
|
//printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]);
|
for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c)
|
{
|
//PUT_IN_REGISTER float A_PART = 1*a[i*k + s];
|
PUT_IN_REGISTER uint32_t A_PART = A[i * lda + s];
|
for (j = 0; j < N; ++j) // out_h*out_w;
|
{
|
//c[i*n + j] += A_PART*b[s*n + j];
|
PUT_IN_REGISTER uint32_t B_PART = B[s * ldb + j];
|
uint32_t xnor_result = ~(A_PART ^ B_PART);
|
//printf(" xnor_result = %d, ", xnor_result);
|
int32_t count = POPCNT(xnor_result); // must be Signed int
|
|
C[i*ldc + j] += (2 * count - 32) * mean_val;
|
//c[i*n + j] += count*mean;
|
}
|
}
|
}
|
}
|
|
|
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)
|
{
|
const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1
|
const 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;
|
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
int j, k;
|
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);
|
|
int tmp_count = POPCNT64(c_bit64);
|
|
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)
|
{
|
im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col);
|
return;
|
|
int c;
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const 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 h, w;
|
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);
|
}
|
}
|
|
|
//From Berkeley Vision's Caffe!
|
//https://github.com/BVLC/caffe/blob/master/LICENSE
|
void im2col_cpu_custom_bin(float* data_im,
|
int channels, int height, int width,
|
int ksize, int stride, int pad, float* data_col, int bit_align)
|
{
|
int c;
|
const int height_col = (height + 2 * pad - ksize) / stride + 1;
|
const int width_col = (width + 2 * pad - ksize) / stride + 1;
|
const int channels_col = channels * ksize * ksize;
|
|
// optimized version
|
if (height_col == height && width_col == width && stride == 1 && pad == 1)
|
{
|
int new_ldb = bit_align;
|
|
#pragma omp parallel for
|
for (c = 0; c < channels_col; ++c) {
|
int h, w;
|
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 += 1) {
|
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 = c * new_ldb + h * width_col + w;
|
|
float val = data_im[im_col + width*(im_row + height*c_im)];
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
|
float val = data_im[im_col + width*(im_row + height*c_im)];
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
}
|
|
{
|
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;
|
int col_index = c * new_ldb + h * width_col + w;
|
|
//data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad);
|
if (val > 0) set_bit((unsigned char*)data_col, col_index);
|
}
|
}
|
}
|
|
}
|
else {
|
printf("\n Error: is no non-optimized version \n");
|
//im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); // must be aligned for transpose after float_to_bin
|
// float_to_bit(b, t_input, src_size);
|
// transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8);
|
}
|
}
|
|
|
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 = (char*)xcalloc(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;
|
//#pragma omp parallel for
|
for (i = 0; i<block_size; i++) {
|
int j;
|
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];
|
}
|
}
|
}
|
}
|
}
|
|
void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c,
|
int pad, int stride, int batch)
|
{
|
int b, k;
|
const int w_offset = -pad / 2;
|
const int h_offset = -pad / 2;
|
|
for (b = 0; b < batch; ++b) {
|
#pragma omp parallel for
|
for (k = 0; k < c; ++k) {
|
int i, j, m, n;
|
for (i = 0; i < out_h; ++i) {
|
for (j = 0; j < out_w; ++j) {
|
int out_index = j + out_w*(i + out_h*(k + c*b));
|
float max = -FLT_MAX;
|
int max_i = -1;
|
for (n = 0; n < size; ++n) {
|
for (m = 0; m < size; ++m) {
|
int cur_h = h_offset + i*stride + n;
|
int cur_w = w_offset + j*stride + m;
|
int index = cur_w + w*(cur_h + h*(k + b*c));
|
int valid = (cur_h >= 0 && cur_h < h &&
|
cur_w >= 0 && cur_w < w);
|
float val = (valid != 0) ? src[index] : -FLT_MAX;
|
max_i = (val > max) ? index : max_i;
|
max = (val > max) ? val : max;
|
}
|
}
|
dst[out_index] = max;
|
if (indexes) indexes[out_index] = max_i;
|
}
|
}
|
}
|
}
|
}
|
|
#endif // AVX
|
|
|
// 32 channels -> 1 channel (with 32 floats)
|
// 256 channels -> 8 channels (with 32 floats)
|
void repack_input(float *input, float *re_packed_input, int w, int h, int c)
|
{
|
const int items_per_channel = w * h;
|
int chan, i;
|
for (chan = 0; chan < c; chan += 32)
|
{
|
for (i = 0; i < items_per_channel; ++i)
|
{
|
int c_pack;
|
for (c_pack = 0; c_pack < 32; ++c_pack) {
|
float src = input[(chan + c_pack)*items_per_channel + i];
|
|
re_packed_input[chan*items_per_channel + i * 32 + c_pack] = src;
|
}
|
}
|
}
|
}
|
|
void transpose_uint32(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align)
|
{
|
//l.bit_align - algined (n) by 32
|
//new_ldb - aligned (k) by 256
|
|
int i;
|
//#pragma omp parallel for
|
for (i = 0; i < src_h; i += 1) // l.size*l.size*l.c;
|
{
|
int j;
|
for (j = 0; j < src_w; j += 1) // out_h*out_w;
|
{
|
((uint32_t *)dst)[j*dst_align / 32 + i] = ((uint32_t *)src)[i*src_align + j];
|
}
|
}
|
}
|
|
void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA,
|
uint32_t *A, int lda,
|
uint32_t *B, int ldb,
|
float *C, int ldc, float *mean_arr)
|
{
|
int i;
|
#pragma omp parallel for
|
for (i = 0; i < M; ++i) { // l.n
|
int j, s;
|
float mean_val = mean_arr[i];
|
for (j = 0; j < N; ++j) // out_h*out_w;
|
{
|
float val = 0;
|
for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c)
|
{
|
PUT_IN_REGISTER uint32_t A_PART = ((uint32_t*)A)[i*lda + s];
|
PUT_IN_REGISTER uint32_t B_PART = ((uint32_t*)B)[j * ldb + s];
|
uint32_t xnor_result = ~(A_PART ^ B_PART);
|
int32_t count = POPCNT(xnor_result); // must be Signed int
|
|
val += (2 * count - 32) * mean_val;
|
}
|
C[i*ldc + j] += val;
|
}
|
}
|
}
|
|
void convolution_repacked(uint32_t *packed_input, uint32_t *packed_weights, float *output,
|
int w, int h, int c, int n, int size, int pad, int new_lda, float *mean_arr)
|
{
|
int fil;
|
// filter index
|
#pragma omp parallel for
|
for (fil = 0; fil < n; ++fil) {
|
float mean_val = mean_arr[fil];
|
int chan, y, x, f_y, f_x; // c_pack
|
// channel index
|
for (chan = 0; chan < c / 32; ++chan)
|
//for (chan = 0; chan < l.c; chan += 32)
|
//for (c_pack = 0; c_pack < 32; ++c_pack)
|
// 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;
|
float sum = 0;
|
|
// filter - y
|
for (f_y = 0; f_y < size; ++f_y)
|
{
|
int input_y = y + f_y - pad;
|
// filter - x
|
for (f_x = 0; f_x < size; ++f_x)
|
{
|
int input_x = x + f_x - pad;
|
if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;
|
|
// normal
|
//float input = state.input[(chan + c_pack)*l.w*l.h + input_y*l.w + input_x];
|
//float weight = l.weights[fil*l.c*l.size*l.size + (chan + c_pack)*l.size*l.size + f_y*l.size + f_x];
|
|
// packed
|
//float input = re_packed_input[chan*l.w*l.h + (input_y*l.w + input_x) * 32 + c_pack];
|
//float weight = l.weights[fil*l.c*l.size*l.size + chan*l.size*l.size + (f_y*l.size + f_x) * 32 + c_pack];
|
//sum += input * weight;
|
|
//float input = re_packed_input[chan*l.w*l.h + (input_y*l.w + input_x) * 32 + c_pack];
|
//float weight = l.weights[fil*l.c*l.size*l.size + chan*l.size*l.size + (f_y*l.size + f_x) * 32 + c_pack];
|
//uint32_t bit1 = input > 0;
|
//uint32_t bit2 = weight > 0;
|
//uint32_t count = (~(bit1 ^ bit2)) & 1;
|
//float result = (2 * (float)count - 1) * mean_val;
|
//printf("\n mul = %f, bit1 = %d, bit2 = %d, count = %d, mean = %f, result = %f ", input*weight, bit1, bit2, count, mean_val, result);
|
//sum += result;
|
|
uint32_t input = ((uint32_t *)packed_input)[chan*w*h + input_y*w + input_x];
|
//uint32_t weight = ((uint32_t *)l.align_bit_weights)[fil*l.c*l.size*l.size/32 + chan*l.size*l.size + f_y*l.size + f_x];
|
uint32_t weight = ((uint32_t *)packed_weights)[fil*new_lda / 32 + chan*size*size + f_y*size + f_x];
|
|
uint32_t xnor_result = ~(input ^ weight);
|
int32_t count = POPCNT(xnor_result); // mandatory Signed int
|
sum += (2 * count - 32) * mean_val;
|
}
|
}
|
// l.output[filters][width][height] +=
|
// state.input[channels][width][height] *
|
// l.weights[filters][channels][filter_width][filter_height];
|
output[output_index] += sum;
|
}
|
}
|
}
|
|
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){
|
PUT_IN_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){
|
PUT_IN_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){
|
PUT_IN_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;
|
}
|
}
|
}
|
|
is_avx(); // initialize static variable
|
if (is_fma_avx2() && !TA && !TB) {
|
gemm_nn_fast(M, N, K, ALPHA, A, lda, B, ldb, C, ldc);
|
}
|
else {
|
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 = (cudaError_t)cublasSetStream(handle, get_cuda_stream());
|
CHECK_CUDA(stream_status);
|
cudaError_t status = (cudaError_t)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_CUDA(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);
|
cudaDeviceSynchronize();
|
}
|
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);
|
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
|
|
|
|
void init_cpu() {
|
is_avx();
|
is_fma_avx2();
|
}
|
