From 0a326e7afe3e690c0b4cb64bbd0ce4f0603a7d85 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 07 Aug 2018 23:45:47 +0000
Subject: [PATCH] XNOR-net on CPU AVX2
---
src/convolutional_layer.c | 217 ++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 203 insertions(+), 14 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 554bd42..bbc4807 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -9,7 +9,7 @@
#include <time.h>
#ifdef CUDNN
-#pragma comment(lib, "cudnn.lib")
+#pragma comment(lib, "cudnn.lib")
#endif
#ifdef AI2
@@ -141,7 +141,7 @@
{
#ifdef CUDNN_HALF
- // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
+ // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
// Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100
// PSEUDO_HALF_CONFIG is required for Tensor Cores - our case!
const cudnnDataType_t data_type = CUDNN_DATA_HALF;
@@ -161,7 +161,7 @@
cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
#endif
- // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported
+ // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported
// on architectures with DP4A support (compute capability 6.1 and later).
//cudnnDataType_t data_type = CUDNN_DATA_INT8;
@@ -188,7 +188,7 @@
int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
- if (cudnn_preference == cudnn_smallest)
+ if (cudnn_preference == cudnn_smallest)
{
forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
@@ -221,7 +221,7 @@
0,
&l->bf_algo);
- if (data_type == CUDNN_DATA_HALF)
+ if (data_type == CUDNN_DATA_HALF)
{
// HALF-16 if(data_type == CUDNN_DATA_HALF)
l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
@@ -249,8 +249,8 @@
if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2;
//printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n");
- if (fw == 2 && bd == 2 && bf == 2) printf("TF ");
- else if (fw == 1 && bd == 1 && bf == 1) printf("TH ");
+ //if (fw == 2 && bd == 2 && bf == 2) printf("TF ");
+ //else if (fw == 1 && bd == 1 && bf == 1) printf("TH ");
}
}
#endif
@@ -379,7 +379,7 @@
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
}
-#ifdef CUDNN
+#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.normDstTensorDesc);
cudnnCreateTensorDescriptor(&l.normDstTensorDescF16);
cudnnCreateTensorDescriptor(&l.normTensorDesc);
@@ -497,7 +497,7 @@
l->workspace_size = get_workspace_size(*l);
#ifdef CUDNN
- // check for excessive memory consumption
+ // check for excessive memory consumption
size_t free_byte;
size_t total_byte;
check_error(cudaMemGetInfo(&free_byte, &total_byte));
@@ -543,6 +543,85 @@
}
}
+void gemm_nn_custom(int M, int N, int K, float ALPHA,
+ float *A, int lda,
+ float *B, int ldb,
+ float *C, int ldc)
+{
+ int i, j, k;
+ for (i = 0; i < M; ++i) {
+ for (k = 0; k < K; ++k) {
+ register float A_PART = ALPHA*A[i*lda + k];
+ //printf("\n weight = %f \n", A_PART);
+ for (j = 0; j < N; ++j) {
+ C[i*ldc + j] += A_PART*B[k*ldb + j];
+ }
+ }
+ }
+}
+
+
+void get_mean_array(float *src, size_t size, size_t filters, float *mean_arr) {
+ size_t i, counter;
+ counter = 0;
+ for (i = 0; i < size; i += size / filters) {
+ mean_arr[counter++] = fabs(src[i]);
+ }
+}
+
+/*
+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, dst_i, dst_shift;
+ for (i = 0; i < size; ++i) {
+ if (src[i] > 0) set_bit(dst, i);
+ }
+}
+*/
+
+void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, float *mean_arr) {
+ memset(dst, 0, size *sizeof(float));
+ size_t i, src_i, src_shift;
+
+ for (i = 0; i < size; ++i) {
+ float mean_val = 1;
+ if(mean_arr != NULL) mean_val = fabs(mean_arr[i / (size / filters)]);
+ if(get_bit(src, i)) dst[i] = mean_val;
+ else dst[i] = -mean_val;
+ }
+}
+
+void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_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;
+
+ binarize_weights(l->weights, m, k, l->binary_weights);
+
+ size_t align_weights_size = new_ldb * 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));
+
+ size_t i, j;
+ // 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];
+ }
+ }
+ float_to_bit(align_weights, l->align_bit_weights, align_weights_size);
+
+ l->mean_arr = calloc(l->n, sizeof(float));
+ get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr);
+
+ free(align_weights);
+}
+
+
void forward_convolutional_layer(convolutional_layer l, network_state state)
{
int out_h = convolutional_out_height(l);
@@ -552,7 +631,10 @@
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
if(l.xnor){
- binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
+ if (!l.align_bit_weights) {
+ binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
+ //printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights);
+ }
swap_binary(&l);
binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
state.input = l.binary_input;
@@ -562,15 +644,122 @@
int k = l.size*l.size*l.c;
int n = out_h*out_w;
-
float *a = l.weights;
float *b = state.workspace;
float *c = l.output;
+ static int u = 0;
+ u++;
+
for(i = 0; i < l.batch; ++i){
- im2col_cpu(state.input, l.c, l.h, l.w,
+ im2col_cpu(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(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) {
+ size_t output_size = l.outputs;
+ //float *count_output = calloc(output_size, sizeof(float));
+ //size_t bit_output_size = output_size / 8 + 1;
+ //char *bit_output = calloc(bit_output_size, sizeof(char));
+
+ size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col()
+ size_t bit_input_size = intput_size / 8 + 1;
+ //char *bit_input = calloc(bit_input_size, sizeof(char));
+
+ size_t weights_size = k * m; //l.size*l.size*l.c*l.n;
+ size_t bit_weights_size = weights_size / 8 + 1;
+ //char *bit_weights = calloc(bit_weights_size, sizeof(char));
+ //float *mean_arr = calloc(l.n, sizeof(float));
+
+ // test: float->bit->float
+ //get_mean_array(l.weights, weights_size, l.n, mean_arr);
+ //float_to_bit(l.weights, bit_weights, weights_size);
+ //memset(l.weights, 0, weights_size * sizeof(float));
+ //bit_to_float(bit_weights, l.weights, weights_size, l.n, mean_arr); // just for test float->bit->float
+
+ //float_to_bit(b, bit_input, intput_size);
+ //memset(b, 0, intput_size * sizeof(float));
+ //bit_to_float(bit_input, b, intput_size, 1, NULL); // just for test float->bit->float
+
+ // 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;
+ 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;
+ for (i = 0; i < n; ++i) {
+ for (j = 0; j < k; ++j) {
+ t_input[i*new_ldb + j] = b[j*n + i];
+ }
+ }
+ float_to_bit(t_input, t_bit_input, t_intput_size);
+
+ if (!l.align_bit_weights)
+ {
+ size_t align_weights_size = new_ldb * 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));
+
+ // align A without transpose
+ for (i = 0; i < m; ++i) {
+ for (j = 0; j < k; ++j) {
+ align_weights[i*new_ldb + j] = a[i*k + j];
+ }
+ }
+ float_to_bit(align_weights, l.align_bit_weights, align_weights_size);
+
+ l.mean_arr = calloc(l.n, sizeof(float));
+ get_mean_array(align_weights, align_weights_size, l.n, l.mean_arr);
+
+ free(align_weights);
+ }
+
+ 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_bit_input);
+
+ //free(align_bit_weights);
+ }
+
+ // for bit_input: (k * n)
+ //if (u == 8) gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr); // last xnor layer
+ //else gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, NULL);
+
+ //gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr);
+
+ //printf("\n u = %d \n", u);
+
+ //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n);
+
+ //int j;
+ //if (u != 8) for (j = 0; j < l.n; ++j) l.biases[j] = l.biases[j] / (mean_arr[j]*2);
+
+ //free(count_output);
+ //free(bit_input);
+ //free(bit_weights);
+ //free(mean_arr);
+ }
+ else {
+ gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n);
+ // bit-count to float
+ }
c += n*m;
state.input += l.c*l.h*l.w;
}
@@ -606,7 +795,7 @@
float *im = state.input+i*l.c*l.h*l.w;
- im2col_cpu(im, l.c, l.h, l.w,
+ im2col_cpu(im, l.c, l.h, l.w,
l.size, l.stride, l.pad, b);
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
--
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