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Optimized on CPU: gemm_bin, im2col, activation, transpose

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This commit is contained in:
AlexeyAB
2018-08-08 19:28:39 +03:00
parent a284a7da8d
commit d6162af210
6 changed files with 450 additions and 126 deletions
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78
src/convolutional_layer.c
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@ -593,15 +593,15 @@ void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, f
}
}
void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align)
void binary_align_weights(convolutional_layer *l, size_t lda_align)
{
int m = l->n;
int k = l->size*l->size*l->c;
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
size_t new_lda = k + (lda_align - k%lda_align); // (k / 8 + 1) * 8;
binarize_weights(l->weights, m, k, l->binary_weights);
size_t align_weights_size = new_ldb * m;
size_t align_weights_size = new_lda * m;
size_t align_bit_weights_size = align_weights_size / 8;// +1;
float *align_weights = calloc(align_weights_size, sizeof(float));
l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char));
@ -610,7 +610,7 @@ void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align)
// align A without transpose
for (i = 0; i < m; ++i) {
for (j = 0; j < k; ++j) {
align_weights[i*new_ldb + j] = l->binary_weights[i*k + j];
align_weights[i*new_lda + j] = l->binary_weights[i*k + j];
}
}
float_to_bit(align_weights, l->align_bit_weights, align_weights_size);
@ -622,6 +622,56 @@ void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align)
}
size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align)
{
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
size_t t_intput_size = new_ldb * n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
float *t_input = calloc(t_intput_size, sizeof(float));
//char *
*t_bit_input = calloc(t_bit_input_size, sizeof(char));
//printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n);
//printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb);
//printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k);
//printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb);
// transpose and align B
int i, j;
//#pragma omp parallel for
/*
for (i = 0; i < n; ++i) {
for (j = 0; j < k; ++j) {
t_input[i*new_ldb + j] = b[j*n + i];
}
}*/
//transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size)
//transpose_block(b, t_input, k, n, n, new_ldb, 16);
int blocksize = 1;
int mod_k = 1, mod_n = 1;
for (i = 2; i < 256; i *= 2)
if (k % i == 0) mod_k = i;
for (i = 2; i < 256; i *= 2)
if (n % i == 0) mod_n = i;
blocksize = (mod_k < mod_n) ? mod_k : mod_n;
transpose_block_SSE4x4(b, t_input, k, n, n, new_ldb, blocksize);
//transpose_block(b, t_input, k, n, n, new_ldb, blocksize);
//printf("\n blocksize = %d \n", blocksize);
float_to_bit(t_input, *t_bit_input, t_intput_size);
free(t_input);
return t_intput_size;
}
void forward_convolutional_layer(convolutional_layer l, network_state state)
{
int out_h = convolutional_out_height(l);
@ -652,8 +702,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
u++;
for(i = 0; i < l.batch; ++i){
im2col_cpu(state.input, l.c, l.h, l.w,
l.size, l.stride, l.pad, b);
//im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b);
im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b);
//gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
//gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n);
if (l.xnor) {
@ -683,8 +734,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
// transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits)
{
/*
size_t ldb_align = 256;// 8;
if (k > 4096)ldb_align = 4096;
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
size_t t_intput_size = new_ldb * n;
@ -709,6 +760,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
}
float_to_bit(t_input, t_bit_input, t_intput_size);
if (!l.align_bit_weights)
{
size_t align_weights_size = new_ldb * m;
@ -729,12 +782,17 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
free(align_weights);
}
*/
size_t ldb_align = 256; // 256 bit for AVX2
size_t new_ldb = k + (ldb_align - k%ldb_align);
char *t_bit_input = NULL;
size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align);
gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr);
//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);
free(t_input);
//free(t_input);
free(t_bit_input);
//free(align_bit_weights);
@ -771,7 +829,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
}
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
activate_array(l.output, m*n*l.batch, l.activation);
//activate_array(l.output, m*n*l.batch, l.activation);
activate_array_cpu_custom(l.output, m*n*l.batch, l.activation);
if(l.binary || l.xnor) swap_binary(&l);
}