mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
XNOR-net on CPU AVX2
This commit is contained in:
@ -9,7 +9,7 @@
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#include <time.h>
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#include <time.h>
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#ifdef CUDNN
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#ifdef CUDNN
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#pragma comment(lib, "cudnn.lib")
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#pragma comment(lib, "cudnn.lib")
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#endif
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#endif
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#ifdef AI2
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#ifdef AI2
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@ -141,7 +141,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
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{
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{
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#ifdef CUDNN_HALF
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#ifdef CUDNN_HALF
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// TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
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// TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0):
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// Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100
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// Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100
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// PSEUDO_HALF_CONFIG is required for Tensor Cores - our case!
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// PSEUDO_HALF_CONFIG is required for Tensor Cores - our case!
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const cudnnDataType_t data_type = CUDNN_DATA_HALF;
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const cudnnDataType_t data_type = CUDNN_DATA_HALF;
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@ -161,7 +161,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
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cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
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cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
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#endif
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#endif
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// INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported
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// INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported
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// on architectures with DP4A support (compute capability 6.1 and later).
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// on architectures with DP4A support (compute capability 6.1 and later).
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//cudnnDataType_t data_type = CUDNN_DATA_INT8;
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//cudnnDataType_t data_type = CUDNN_DATA_INT8;
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@ -188,7 +188,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
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int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
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int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
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int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
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int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
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int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
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int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
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if (cudnn_preference == cudnn_smallest)
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if (cudnn_preference == cudnn_smallest)
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{
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{
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forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
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forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
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backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
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backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
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@ -221,7 +221,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
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0,
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0,
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&l->bf_algo);
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&l->bf_algo);
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if (data_type == CUDNN_DATA_HALF)
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if (data_type == CUDNN_DATA_HALF)
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{
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{
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// HALF-16 if(data_type == CUDNN_DATA_HALF)
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// HALF-16 if(data_type == CUDNN_DATA_HALF)
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l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
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l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
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@ -249,8 +249,8 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
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if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2;
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if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2;
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//printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n");
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//printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n");
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if (fw == 2 && bd == 2 && bf == 2) printf("TF ");
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//if (fw == 2 && bd == 2 && bf == 2) printf("TF ");
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else if (fw == 1 && bd == 1 && bf == 1) printf("TH ");
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//else if (fw == 1 && bd == 1 && bf == 1) printf("TH ");
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}
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}
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}
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}
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#endif
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#endif
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@ -379,7 +379,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
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l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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}
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}
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#ifdef CUDNN
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#ifdef CUDNN
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cudnnCreateTensorDescriptor(&l.normDstTensorDesc);
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cudnnCreateTensorDescriptor(&l.normDstTensorDesc);
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cudnnCreateTensorDescriptor(&l.normDstTensorDescF16);
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cudnnCreateTensorDescriptor(&l.normDstTensorDescF16);
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cudnnCreateTensorDescriptor(&l.normTensorDesc);
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cudnnCreateTensorDescriptor(&l.normTensorDesc);
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@ -497,7 +497,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
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l->workspace_size = get_workspace_size(*l);
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l->workspace_size = get_workspace_size(*l);
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#ifdef CUDNN
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#ifdef CUDNN
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// check for excessive memory consumption
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// check for excessive memory consumption
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size_t free_byte;
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size_t free_byte;
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size_t total_byte;
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size_t total_byte;
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check_error(cudaMemGetInfo(&free_byte, &total_byte));
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check_error(cudaMemGetInfo(&free_byte, &total_byte));
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@ -543,6 +543,85 @@ void backward_bias(float *bias_updates, float *delta, int batch, int n, int size
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}
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}
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}
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}
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void gemm_nn_custom(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 *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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register float A_PART = ALPHA*A[i*lda + k];
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//printf("\n weight = %f \n", A_PART);
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for (j = 0; j < N; ++j) {
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C[i*ldc + j] += A_PART*B[k*ldb + j];
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}
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}
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}
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}
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void get_mean_array(float *src, size_t size, size_t filters, float *mean_arr) {
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size_t i, counter;
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counter = 0;
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for (i = 0; i < size; i += size / filters) {
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mean_arr[counter++] = fabs(src[i]);
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}
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}
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/*
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void float_to_bit(float *src, unsigned char *dst, size_t size) {
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size_t dst_size = size / 8 + 1;
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memset(dst, 0, dst_size);
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size_t i, dst_i, dst_shift;
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for (i = 0; i < size; ++i) {
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if (src[i] > 0) set_bit(dst, i);
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}
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}
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*/
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void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, float *mean_arr) {
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memset(dst, 0, size *sizeof(float));
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size_t i, src_i, src_shift;
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for (i = 0; i < size; ++i) {
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float mean_val = 1;
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if(mean_arr != NULL) mean_val = fabs(mean_arr[i / (size / filters)]);
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if(get_bit(src, i)) dst[i] = mean_val;
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else dst[i] = -mean_val;
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}
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}
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void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align)
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{
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int m = l->n;
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int k = l->size*l->size*l->c;
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size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
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binarize_weights(l->weights, m, k, l->binary_weights);
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size_t align_weights_size = new_ldb * m;
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size_t align_bit_weights_size = align_weights_size / 8;// +1;
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float *align_weights = calloc(align_weights_size, sizeof(float));
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l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char));
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size_t i, j;
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// align A without transpose
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for (i = 0; i < m; ++i) {
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for (j = 0; j < k; ++j) {
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align_weights[i*new_ldb + j] = l->binary_weights[i*k + j];
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}
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}
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float_to_bit(align_weights, l->align_bit_weights, align_weights_size);
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l->mean_arr = calloc(l->n, sizeof(float));
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get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr);
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free(align_weights);
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}
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void forward_convolutional_layer(convolutional_layer l, network_state state)
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void forward_convolutional_layer(convolutional_layer l, network_state state)
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{
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{
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int out_h = convolutional_out_height(l);
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int out_h = convolutional_out_height(l);
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@ -552,7 +631,10 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
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fill_cpu(l.outputs*l.batch, 0, l.output, 1);
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fill_cpu(l.outputs*l.batch, 0, l.output, 1);
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if(l.xnor){
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if(l.xnor){
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binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
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if (!l.align_bit_weights) {
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binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
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//printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights);
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}
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swap_binary(&l);
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swap_binary(&l);
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binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
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binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
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state.input = l.binary_input;
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state.input = l.binary_input;
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@ -562,15 +644,122 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
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int k = l.size*l.size*l.c;
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int k = l.size*l.size*l.c;
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int n = out_h*out_w;
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int n = out_h*out_w;
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float *a = l.weights;
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float *a = l.weights;
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float *b = state.workspace;
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float *b = state.workspace;
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float *c = l.output;
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float *c = l.output;
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static int u = 0;
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u++;
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for(i = 0; i < l.batch; ++i){
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for(i = 0; i < l.batch; ++i){
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im2col_cpu(state.input, l.c, l.h, l.w,
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im2col_cpu(state.input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, b);
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l.size, l.stride, l.pad, b);
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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//gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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//gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n);
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if (l.xnor) {
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size_t output_size = l.outputs;
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//float *count_output = calloc(output_size, sizeof(float));
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//size_t bit_output_size = output_size / 8 + 1;
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//char *bit_output = calloc(bit_output_size, sizeof(char));
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size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col()
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size_t bit_input_size = intput_size / 8 + 1;
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//char *bit_input = calloc(bit_input_size, sizeof(char));
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size_t weights_size = k * m; //l.size*l.size*l.c*l.n;
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size_t bit_weights_size = weights_size / 8 + 1;
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//char *bit_weights = calloc(bit_weights_size, sizeof(char));
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//float *mean_arr = calloc(l.n, sizeof(float));
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// test: float->bit->float
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//get_mean_array(l.weights, weights_size, l.n, mean_arr);
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//float_to_bit(l.weights, bit_weights, weights_size);
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//memset(l.weights, 0, weights_size * sizeof(float));
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//bit_to_float(bit_weights, l.weights, weights_size, l.n, mean_arr); // just for test float->bit->float
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//float_to_bit(b, bit_input, intput_size);
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//memset(b, 0, intput_size * sizeof(float));
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//bit_to_float(bit_input, b, intput_size, 1, NULL); // just for test float->bit->float
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// transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits)
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{
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size_t ldb_align = 256;// 8;
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size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
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size_t t_intput_size = new_ldb * n;
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size_t t_bit_input_size = t_intput_size / 8;// +1;
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float *t_input = calloc(t_intput_size, sizeof(float));
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char *t_bit_input = calloc(t_bit_input_size, sizeof(char));
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//printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n);
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//printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb);
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//printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k);
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//printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb);
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// transpose and align B
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int i, j;
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for (i = 0; i < n; ++i) {
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for (j = 0; j < k; ++j) {
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t_input[i*new_ldb + j] = b[j*n + i];
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}
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}
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float_to_bit(t_input, t_bit_input, t_intput_size);
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if (!l.align_bit_weights)
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{
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size_t align_weights_size = new_ldb * m;
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size_t align_bit_weights_size = align_weights_size / 8;// +1;
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float *align_weights = calloc(align_weights_size, sizeof(float));
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l.align_bit_weights = calloc(align_bit_weights_size, sizeof(char));
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// align A without transpose
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for (i = 0; i < m; ++i) {
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for (j = 0; j < k; ++j) {
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align_weights[i*new_ldb + j] = a[i*k + j];
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}
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}
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float_to_bit(align_weights, l.align_bit_weights, align_weights_size);
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l.mean_arr = calloc(l.n, sizeof(float));
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get_mean_array(align_weights, align_weights_size, l.n, l.mean_arr);
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free(align_weights);
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}
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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);
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//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);
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free(t_input);
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free(t_bit_input);
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//free(align_bit_weights);
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}
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// for bit_input: (k * n)
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//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
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//else gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, NULL);
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||||||
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//gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr);
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||||||
|
//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;
|
c += n*m;
|
||||||
state.input += l.c*l.h*l.w;
|
state.input += l.c*l.h*l.w;
|
||||||
}
|
}
|
||||||
@ -606,7 +795,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
|
|||||||
|
|
||||||
float *im = state.input+i*l.c*l.h*l.w;
|
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);
|
l.size, l.stride, l.pad, b);
|
||||||
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
|
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
|
||||||
|
|
||||||
|
@ -35,6 +35,8 @@ void binarize_weights(float *weights, int n, int size, float *binary);
|
|||||||
void swap_binary(convolutional_layer *l);
|
void swap_binary(convolutional_layer *l);
|
||||||
void binarize_weights2(float *weights, int n, int size, char *binary, float *scales);
|
void binarize_weights2(float *weights, int n, int size, char *binary, float *scales);
|
||||||
|
|
||||||
|
void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align);
|
||||||
|
|
||||||
void backward_convolutional_layer(convolutional_layer layer, network_state state);
|
void backward_convolutional_layer(convolutional_layer layer, network_state state);
|
||||||
|
|
||||||
void add_bias(float *output, float *biases, int batch, int n, int size);
|
void add_bias(float *output, float *biases, int batch, int n, int size);
|
||||||
|
@ -146,6 +146,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int
|
|||||||
}
|
}
|
||||||
//set_batch_network(&net, 1);
|
//set_batch_network(&net, 1);
|
||||||
fuse_conv_batchnorm(net);
|
fuse_conv_batchnorm(net);
|
||||||
|
calculate_binary_weights(net);
|
||||||
srand(2222222);
|
srand(2222222);
|
||||||
|
|
||||||
if(filename){
|
if(filename){
|
||||||
|
@ -568,6 +568,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
|||||||
}
|
}
|
||||||
//set_batch_network(&net, 1);
|
//set_batch_network(&net, 1);
|
||||||
fuse_conv_batchnorm(net);
|
fuse_conv_batchnorm(net);
|
||||||
|
calculate_binary_weights(net);
|
||||||
srand(time(0));
|
srand(time(0));
|
||||||
|
|
||||||
list *plist = get_paths(valid_images);
|
list *plist = get_paths(valid_images);
|
||||||
@ -1094,6 +1095,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
|
|||||||
}
|
}
|
||||||
//set_batch_network(&net, 1);
|
//set_batch_network(&net, 1);
|
||||||
fuse_conv_batchnorm(net);
|
fuse_conv_batchnorm(net);
|
||||||
|
calculate_binary_weights(net);
|
||||||
if (net.layers[net.n - 1].classes != names_size) {
|
if (net.layers[net.n - 1].classes != names_size) {
|
||||||
printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
|
printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
|
||||||
name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
|
name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
|
||||||
|
475
src/gemm.c
475
src/gemm.c
@ -5,8 +5,8 @@
|
|||||||
#include <stdio.h>
|
#include <stdio.h>
|
||||||
#include <math.h>
|
#include <math.h>
|
||||||
|
|
||||||
void gemm_bin(int M, int N, int K, float ALPHA,
|
void gemm_bin(int M, int N, int K, float ALPHA,
|
||||||
char *A, int lda,
|
char *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
{
|
{
|
||||||
@ -62,8 +62,8 @@ void time_random_matrix(int TA, int TB, int m, int k, int n)
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
@ -71,6 +71,234 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
|||||||
gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
|
gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
//--------------------------------------------
|
||||||
|
// XNOR bitwise GEMM for binary neural network
|
||||||
|
//--------------------------------------------
|
||||||
|
|
||||||
|
#include <stdint.h>
|
||||||
|
|
||||||
|
static inline unsigned char xnor(unsigned char a, unsigned char b) {
|
||||||
|
//return a == b;
|
||||||
|
return !(a^b);
|
||||||
|
}
|
||||||
|
|
||||||
|
// INT-32
|
||||||
|
static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) {
|
||||||
|
size_t src_i = index / 32;
|
||||||
|
int src_shift = index % 32;
|
||||||
|
unsigned char val = (src[src_i] & (1 << src_shift)) > 0;
|
||||||
|
return val;
|
||||||
|
}
|
||||||
|
|
||||||
|
static inline uint32_t xnor_int32(uint32_t a, uint32_t b) {
|
||||||
|
return ~(a^b);
|
||||||
|
}
|
||||||
|
|
||||||
|
static inline uint64_t xnor_int64(uint64_t a, uint64_t b) {
|
||||||
|
return ~(a^b);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
static inline uint32_t fill_bit_int32(char src) {
|
||||||
|
if (src == 0) return 0x00000000;
|
||||||
|
else return 0xFFFFFFFF;
|
||||||
|
}
|
||||||
|
|
||||||
|
static inline uint64_t fill_bit_int64(char src) {
|
||||||
|
if (src == 0) return 0x0000000000000000;
|
||||||
|
else return 0xFFFFFFFFFFFFFFFF;
|
||||||
|
}
|
||||||
|
|
||||||
|
void binary_int32_printf(uint32_t src) {
|
||||||
|
int i;
|
||||||
|
for (i = 0; i < 32; ++i) {
|
||||||
|
if (src & 1) printf("1");
|
||||||
|
else printf("0");
|
||||||
|
src = src >> 1;
|
||||||
|
}
|
||||||
|
printf("\n");
|
||||||
|
}
|
||||||
|
|
||||||
|
void binary_int64_printf(uint64_t src) {
|
||||||
|
int i;
|
||||||
|
for (i = 0; i < 64; ++i) {
|
||||||
|
if (src & 1) printf("1");
|
||||||
|
else printf("0");
|
||||||
|
src = src >> 1;
|
||||||
|
}
|
||||||
|
printf("\n");
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
unsigned char *A, int lda,
|
||||||
|
unsigned char *B, int ldb,
|
||||||
|
float *C, int ldc, float *mean_arr)
|
||||||
|
{
|
||||||
|
int *count_arr = calloc(M*N, sizeof(int));
|
||||||
|
|
||||||
|
int i, j, k;
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
char a_bit = get_bit(A, i*lda + k);
|
||||||
|
|
||||||
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
char b_bit = get_bit(B, k*ldb + j);
|
||||||
|
count_arr[i*ldc + j] += xnor(a_bit, b_bit);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
for (i = 0; i < M; ++i) {
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
for (j = 0; j < N; ++j) {
|
||||||
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
free(count_arr);
|
||||||
|
}
|
||||||
|
*/
|
||||||
|
|
||||||
|
/*
|
||||||
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
unsigned char *A, int lda,
|
||||||
|
unsigned char *B, int ldb,
|
||||||
|
float *C, int ldc, float *mean_arr)
|
||||||
|
{
|
||||||
|
int *count_arr = calloc(M*N, sizeof(int));
|
||||||
|
|
||||||
|
int i, j, k;
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
char a_bit = get_bit(A, i*lda + k);
|
||||||
|
char b_bit = get_bit(B, j*ldb + k);
|
||||||
|
count_arr[i*ldc + j] += xnor(a_bit, b_bit);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
for (i = 0; i < M; ++i) {
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
for (j = 0; j < N; ++j) {
|
||||||
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
free(count_arr);
|
||||||
|
}
|
||||||
|
*/
|
||||||
|
|
||||||
|
/*
|
||||||
|
void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
unsigned char *A, int lda,
|
||||||
|
unsigned char *B, int ldb,
|
||||||
|
float *C, int ldc, float *mean_arr)
|
||||||
|
{
|
||||||
|
int *count_arr = calloc(M*N, sizeof(int));
|
||||||
|
|
||||||
|
int i, j, k, h;
|
||||||
|
|
||||||
|
#pragma omp parallel for
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
const char a_bit = get_bit(A, i*lda + k);
|
||||||
|
uint64_t a_bit64 = fill_bit_int64(a_bit);
|
||||||
|
int k_ldb = k*ldb;
|
||||||
|
|
||||||
|
for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
if ((N - j > 64) && (k_ldb % 8 == 0)) {
|
||||||
|
uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8));
|
||||||
|
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
|
||||||
|
//printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc
|
||||||
|
printf("\n %d \n", __popcnt64(c_bit64)); // msvs
|
||||||
|
|
||||||
|
int h;
|
||||||
|
for (h = 0; h < 64; ++h)
|
||||||
|
if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1;
|
||||||
|
|
||||||
|
//binary_int64_printf(a_bit64);
|
||||||
|
//binary_int64_printf(b_bit64);
|
||||||
|
//binary_int64_printf(c_bit64);
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
char b_bit = get_bit(B, k_ldb + j);
|
||||||
|
if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (mean_arr) {
|
||||||
|
//int K_2 = K / 2;
|
||||||
|
for (i = 0; i < M; ++i) {
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
//float mean_val2 = 2 * mean_val;
|
||||||
|
for (j = 0; j < N; ++j) {
|
||||||
|
C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val;
|
||||||
|
//C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
for (i = 0; i < M; ++i) {
|
||||||
|
for (j = 0; j < N; ++j) {
|
||||||
|
C[i*ldc + j] = count_arr[i*ldc + j] - K / 2;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
free(count_arr);
|
||||||
|
|
||||||
|
//getchar();
|
||||||
|
}
|
||||||
|
*/
|
||||||
|
|
||||||
|
|
||||||
|
/*
|
||||||
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
unsigned char *A, int lda,
|
||||||
|
unsigned char *B, int ldb,
|
||||||
|
float *C, int ldc, float *mean_arr)
|
||||||
|
{
|
||||||
|
int i, j, k, h;
|
||||||
|
|
||||||
|
#pragma omp parallel for
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
|
||||||
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
int count = 0;
|
||||||
|
|
||||||
|
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8));
|
||||||
|
uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
|
||||||
|
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
|
||||||
|
|
||||||
|
#ifdef WIN32
|
||||||
|
int tmp_count = __popcnt64(c_bit64);
|
||||||
|
#else
|
||||||
|
int tmp_count = __builtin_popcountll(c_bit64);
|
||||||
|
#endif
|
||||||
|
|
||||||
|
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
|
||||||
|
count += tmp_count;
|
||||||
|
//binary_int64_printf(c_bit64);
|
||||||
|
//printf(", count = %d \n\n", tmp_count);
|
||||||
|
}
|
||||||
|
|
||||||
|
C[i*ldc + j] = (2 * count - K) * mean_val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
*/
|
||||||
|
|
||||||
|
//----------------------------
|
||||||
|
|
||||||
|
|
||||||
#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64)
|
#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64)
|
||||||
|
|
||||||
#define OSXSAVEFlag (1UL<<27)
|
#define OSXSAVEFlag (1UL<<27)
|
||||||
@ -79,8 +307,6 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
|||||||
#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag)
|
#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag)
|
||||||
#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag)
|
#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag)
|
||||||
|
|
||||||
#include <stdint.h>
|
|
||||||
|
|
||||||
#ifdef _WIN64
|
#ifdef _WIN64
|
||||||
#include <intrin.h>
|
#include <intrin.h>
|
||||||
#include <ammintrin.h>
|
#include <ammintrin.h>
|
||||||
@ -196,6 +422,97 @@ void gemm_nn(int M, int N, int K, float ALPHA,
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// http://graphics.stanford.edu/~seander/bithacks.html
|
||||||
|
// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register
|
||||||
|
|
||||||
|
// 2 x faster than popcnt: https://arxiv.org/pdf/1611.07612.pdf
|
||||||
|
|
||||||
|
static inline int popcnt128(__m128i n) {
|
||||||
|
const __m128i n_hi = _mm_unpackhi_epi64(n, n);
|
||||||
|
#ifdef _MSC_VER
|
||||||
|
return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi));
|
||||||
|
#else
|
||||||
|
return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi));
|
||||||
|
#endif
|
||||||
|
}
|
||||||
|
|
||||||
|
static inline int popcnt256(__m256i n) {
|
||||||
|
return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1));
|
||||||
|
}
|
||||||
|
|
||||||
|
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)
|
||||||
|
{
|
||||||
|
__m256i all_1 = _mm256_set1_epi8(255);
|
||||||
|
int i, j, k, h;
|
||||||
|
|
||||||
|
#pragma omp parallel for
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
|
||||||
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
int count = 0;
|
||||||
|
const int bit_step = 256;
|
||||||
|
|
||||||
|
for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
|
||||||
|
//__m128i a_bit128 = _mm_loadu_si128((__m128i *)(A + (i*lda + k) / 8));
|
||||||
|
//__m128i b_bit128 = _mm_loadu_si128((__m128i *)(B + (j*ldb + k) / 8));
|
||||||
|
//__m128i xor128 = _mm_xor_si128(a_bit128, b_bit128);
|
||||||
|
//__m128i c_bit128 = _mm_andnot_si128(xor128, all_1);
|
||||||
|
//int tmp_count = popcnt128(c_bit128);
|
||||||
|
|
||||||
|
__m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8));
|
||||||
|
__m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8));
|
||||||
|
__m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256);
|
||||||
|
__m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); //we can do NOT for wegihts once and do not do this NOT
|
||||||
|
int tmp_count = popcnt256(c_bit256);
|
||||||
|
|
||||||
|
if (K - k < bit_step) tmp_count = tmp_count - (bit_step - (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 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;
|
||||||
|
__m128i all128_0 = _mm_set_epi32(0, 0, 0, 0);
|
||||||
|
__m256 all256_0 = _mm256_set1_ps(0);
|
||||||
|
__m256i bits_asc = _mm256_set_epi32(1, 2, 4, 8, 16, 32, 64, 128);
|
||||||
|
//for(i = 0; i < 8; ++i) bits_asc.m256i_i32[i] = 1 << i;
|
||||||
|
|
||||||
|
for (i = 0; i < size; i+=8)
|
||||||
|
{
|
||||||
|
__m256 src256 = _mm256_loadu_ps((__m256i *)(&src[i])); // load 256 bits
|
||||||
|
__m256 result256 = _mm256_cmp_ps(src256, all256_0, _CMP_GT_OS); // compare dst[i] = (float[i] > 0)
|
||||||
|
|
||||||
|
__m256i bits256 = _mm256_castps_si256(result256); // floats to ints32
|
||||||
|
__m256i and256 = _mm256_and_si256(bits256, bits_asc); // bitwise and
|
||||||
|
|
||||||
|
// sum all elements from single and256
|
||||||
|
__m128i tmp128 = _mm_hadd_epi32(_mm256_extractf128_si256(and256, 0), _mm256_extractf128_si256(and256, 1));
|
||||||
|
tmp128 = _mm_hadd_epi32(tmp128, all128_0);
|
||||||
|
tmp128 = _mm_hadd_epi32(tmp128, all128_0);
|
||||||
|
|
||||||
|
dst[i / 8] = tmp128.m128i_i32[0];
|
||||||
|
}
|
||||||
|
// int _mm256_movemask_epi8 (__m256i a)
|
||||||
|
}
|
||||||
|
|
||||||
#else
|
#else
|
||||||
|
|
||||||
void gemm_nn(int M, int N, int K, float ALPHA,
|
void gemm_nn(int M, int N, int K, float ALPHA,
|
||||||
@ -213,10 +530,76 @@ void gemm_nn(int M, int N, int K, float ALPHA,
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
unsigned char *A, int lda,
|
||||||
|
unsigned char *B, int ldb,
|
||||||
|
float *C, int ldc, float *mean_arr)
|
||||||
|
{
|
||||||
|
int i, j, k, h;
|
||||||
|
|
||||||
|
#pragma omp parallel for
|
||||||
|
for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024]
|
||||||
|
float mean_val = mean_arr[i];
|
||||||
|
|
||||||
|
for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056]
|
||||||
|
int count = 0;
|
||||||
|
|
||||||
|
for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216]
|
||||||
|
uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8));
|
||||||
|
uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8));
|
||||||
|
uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64);
|
||||||
|
|
||||||
|
#ifdef WIN32
|
||||||
|
int tmp_count = __popcnt64(c_bit64);
|
||||||
|
#else
|
||||||
|
int tmp_count = __builtin_popcountll(c_bit64);
|
||||||
|
#endif
|
||||||
|
|
||||||
|
if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits
|
||||||
|
count += tmp_count;
|
||||||
|
//binary_int64_printf(c_bit64);
|
||||||
|
//printf(", count = %d \n\n", tmp_count);
|
||||||
|
}
|
||||||
|
|
||||||
|
C[i*ldc + j] = (2 * count - K) * mean_val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void float_to_bit(float *src, unsigned char *dst, size_t size)
|
||||||
|
{
|
||||||
|
size_t dst_size = size / 8 + 1;
|
||||||
|
memset(dst, 0, dst_size);
|
||||||
|
|
||||||
|
size_t i;
|
||||||
|
char *byte_arr = calloc(size, sizeof(char));
|
||||||
|
for (i = 0; i < size; ++i) {
|
||||||
|
if (src[i] > 0) byte_arr[i] = 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
//for (i = 0; i < size; ++i) {
|
||||||
|
// dst[i / 8] |= byte_arr[i] << (i % 8);
|
||||||
|
//}
|
||||||
|
|
||||||
|
for (i = 0; i < size; i += 8) {
|
||||||
|
char dst_tmp = 0;
|
||||||
|
dst_tmp |= byte_arr[i + 0] << 0;
|
||||||
|
dst_tmp |= byte_arr[i + 1] << 1;
|
||||||
|
dst_tmp |= byte_arr[i + 2] << 2;
|
||||||
|
dst_tmp |= byte_arr[i + 3] << 3;
|
||||||
|
dst_tmp |= byte_arr[i + 4] << 4;
|
||||||
|
dst_tmp |= byte_arr[i + 5] << 5;
|
||||||
|
dst_tmp |= byte_arr[i + 6] << 6;
|
||||||
|
dst_tmp |= byte_arr[i + 7] << 7;
|
||||||
|
dst[i / 8] = dst_tmp;
|
||||||
|
}
|
||||||
|
free(byte_arr);
|
||||||
|
}
|
||||||
#endif // __x86_64
|
#endif // __x86_64
|
||||||
|
|
||||||
void gemm_nt(int M, int N, int K, float ALPHA,
|
void gemm_nt(int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
{
|
{
|
||||||
@ -232,8 +615,8 @@ void gemm_nt(int M, int N, int K, float ALPHA,
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
void gemm_tn(int M, int N, int K, float ALPHA,
|
void gemm_tn(int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
{
|
{
|
||||||
@ -248,8 +631,8 @@ void gemm_tn(int M, int N, int K, float ALPHA,
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
void gemm_tt(int M, int N, int K, float ALPHA,
|
void gemm_tt(int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
{
|
{
|
||||||
@ -266,8 +649,8 @@ void gemm_tt(int M, int N, int K, float ALPHA,
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
@ -300,21 +683,21 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
|||||||
|
|
||||||
#include <math.h>
|
#include <math.h>
|
||||||
|
|
||||||
void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A_gpu, int lda,
|
float *A_gpu, int lda,
|
||||||
float *B_gpu, int ldb,
|
float *B_gpu, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C_gpu, int ldc)
|
float *C_gpu, int ldc)
|
||||||
{
|
{
|
||||||
cublasHandle_t handle = blas_handle();
|
cublasHandle_t handle = blas_handle();
|
||||||
cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream());
|
cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream());
|
||||||
cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N),
|
cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N),
|
||||||
(TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc);
|
(TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc);
|
||||||
check_error(status);
|
check_error(status);
|
||||||
}
|
}
|
||||||
|
|
||||||
void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc)
|
float *C, int ldc)
|
||||||
@ -435,38 +818,38 @@ void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
|
|||||||
int test_gpu_blas()
|
int test_gpu_blas()
|
||||||
{
|
{
|
||||||
/*
|
/*
|
||||||
test_gpu_accuracy(0,0,10,576,75);
|
test_gpu_accuracy(0,0,10,576,75);
|
||||||
|
|
||||||
test_gpu_accuracy(0,0,17,10,10);
|
test_gpu_accuracy(0,0,17,10,10);
|
||||||
test_gpu_accuracy(1,0,17,10,10);
|
test_gpu_accuracy(1,0,17,10,10);
|
||||||
test_gpu_accuracy(0,1,17,10,10);
|
test_gpu_accuracy(0,1,17,10,10);
|
||||||
test_gpu_accuracy(1,1,17,10,10);
|
test_gpu_accuracy(1,1,17,10,10);
|
||||||
|
|
||||||
test_gpu_accuracy(0,0,1000,10,100);
|
test_gpu_accuracy(0,0,1000,10,100);
|
||||||
test_gpu_accuracy(1,0,1000,10,100);
|
test_gpu_accuracy(1,0,1000,10,100);
|
||||||
test_gpu_accuracy(0,1,1000,10,100);
|
test_gpu_accuracy(0,1,1000,10,100);
|
||||||
test_gpu_accuracy(1,1,1000,10,100);
|
test_gpu_accuracy(1,1,1000,10,100);
|
||||||
|
|
||||||
test_gpu_accuracy(0,0,10,10,10);
|
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(0,0,64,2916,363);
|
||||||
time_ongpu(0,0,64,2916,363);
|
time_ongpu(0,0,64,2916,363);
|
||||||
time_ongpu(0,0,192,729,1600);
|
time_ongpu(0,0,192,729,1600);
|
||||||
time_ongpu(0,0,384,196,1728);
|
time_ongpu(0,0,384,196,1728);
|
||||||
time_ongpu(0,0,256,196,3456);
|
time_ongpu(0,0,256,196,3456);
|
||||||
time_ongpu(0,0,256,196,2304);
|
time_ongpu(0,0,256,196,2304);
|
||||||
time_ongpu(0,0,128,4096,12544);
|
time_ongpu(0,0,128,4096,12544);
|
||||||
time_ongpu(0,0,128,4096,4096);
|
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(0,0,64,75,12544);
|
||||||
time_ongpu(0,0,64,75,12544);
|
time_ongpu(0,0,64,75,12544);
|
||||||
time_ongpu(0,0,64,576,12544);
|
time_ongpu(0,0,64,576,12544);
|
||||||
time_ongpu(0,0,256,2304,784);
|
time_ongpu(0,0,256,2304,784);
|
||||||
time_ongpu(1,1,2304,256,784);
|
time_ongpu(1,1,2304,256,784);
|
||||||
time_ongpu(0,0,512,4608,196);
|
time_ongpu(0,0,512,4608,196);
|
||||||
time_ongpu(1,1,4608,512,196);
|
time_ongpu(1,1,4608,512,196);
|
||||||
|
|
||||||
return 0;
|
return 0;
|
||||||
}
|
}
|
||||||
|
50
src/gemm.h
50
src/gemm.h
@ -1,32 +1,60 @@
|
|||||||
#ifndef GEMM_H
|
#ifndef GEMM_H
|
||||||
#define GEMM_H
|
#define GEMM_H
|
||||||
|
|
||||||
void gemm_bin(int M, int N, int K, float ALPHA,
|
static inline void set_bit(unsigned char *const dst, size_t index) {
|
||||||
char *A, int lda,
|
size_t dst_i = index / 8;
|
||||||
|
int dst_shift = index % 8;
|
||||||
|
dst[dst_i] |= 1 << dst_shift;
|
||||||
|
}
|
||||||
|
|
||||||
|
static inline unsigned char get_bit(unsigned char const*const src, size_t index) {
|
||||||
|
size_t src_i = index / 8;
|
||||||
|
int src_shift = index % 8;
|
||||||
|
unsigned char val = (src[src_i] & (1 << src_shift)) > 0;
|
||||||
|
return val;
|
||||||
|
}
|
||||||
|
|
||||||
|
void float_to_bit(float *src, unsigned char *dst, size_t size);
|
||||||
|
|
||||||
|
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);
|
||||||
|
|
||||||
|
|
||||||
|
//void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED,
|
||||||
|
//unsigned char *A, int lda,
|
||||||
|
//unsigned char *B, int ldb,
|
||||||
|
//float *C, int ldc, float *mean_arr)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
void gemm_bin(int M, int N, int K, float ALPHA,
|
||||||
|
char *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float *C, int ldc);
|
float *C, int ldc);
|
||||||
|
|
||||||
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc);
|
float *C, int ldc);
|
||||||
|
|
||||||
void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc);
|
float *C, int ldc);
|
||||||
|
|
||||||
#ifdef GPU
|
#ifdef GPU
|
||||||
void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A_gpu, int lda,
|
float *A_gpu, int lda,
|
||||||
float *B_gpu, int ldb,
|
float *B_gpu, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C_gpu, int ldc);
|
float *C_gpu, int ldc);
|
||||||
|
|
||||||
void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
|
||||||
float *A, int lda,
|
float *A, int lda,
|
||||||
float *B, int ldb,
|
float *B, int ldb,
|
||||||
float BETA,
|
float BETA,
|
||||||
float *C, int ldc);
|
float *C, int ldc);
|
||||||
|
@ -33,6 +33,8 @@ void free_layer(layer l)
|
|||||||
if (l.scale_updates) free(l.scale_updates);
|
if (l.scale_updates) free(l.scale_updates);
|
||||||
if (l.weights) free(l.weights);
|
if (l.weights) free(l.weights);
|
||||||
if (l.weight_updates) free(l.weight_updates);
|
if (l.weight_updates) free(l.weight_updates);
|
||||||
|
if (l.weights) free(l.align_bit_weights);
|
||||||
|
if (l.weights) free(l.mean_arr);
|
||||||
if (l.delta) free(l.delta);
|
if (l.delta) free(l.delta);
|
||||||
if (l.output) free(l.output);
|
if (l.output) free(l.output);
|
||||||
if (l.squared) free(l.squared);
|
if (l.squared) free(l.squared);
|
||||||
|
@ -179,6 +179,9 @@ struct layer{
|
|||||||
float *weights;
|
float *weights;
|
||||||
float *weight_updates;
|
float *weight_updates;
|
||||||
|
|
||||||
|
char *align_bit_weights;
|
||||||
|
float *mean_arr;
|
||||||
|
|
||||||
float *col_image;
|
float *col_image;
|
||||||
int * input_layers;
|
int * input_layers;
|
||||||
int * input_sizes;
|
int * input_sizes;
|
||||||
|
@ -222,7 +222,7 @@ float *get_network_output(network net)
|
|||||||
{
|
{
|
||||||
#ifdef GPU
|
#ifdef GPU
|
||||||
if (gpu_index >= 0) return get_network_output_gpu(net);
|
if (gpu_index >= 0) return get_network_output_gpu(net);
|
||||||
#endif
|
#endif
|
||||||
int i;
|
int i;
|
||||||
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
|
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
|
||||||
return net.layers[i].output;
|
return net.layers[i].output;
|
||||||
@ -366,7 +366,7 @@ void set_batch_network(network *net, int b)
|
|||||||
/*
|
/*
|
||||||
layer *l = net->layers + i;
|
layer *l = net->layers + i;
|
||||||
cudnn_convolutional_setup(l, cudnn_fastest);
|
cudnn_convolutional_setup(l, cudnn_fastest);
|
||||||
// check for excessive memory consumption
|
// check for excessive memory consumption
|
||||||
size_t free_byte;
|
size_t free_byte;
|
||||||
size_t total_byte;
|
size_t total_byte;
|
||||||
check_error(cudaMemGetInfo(&free_byte, &total_byte));
|
check_error(cudaMemGetInfo(&free_byte, &total_byte));
|
||||||
@ -520,7 +520,7 @@ void visualize_network(network net)
|
|||||||
if(l.type == CONVOLUTIONAL){
|
if(l.type == CONVOLUTIONAL){
|
||||||
prev = visualize_convolutional_layer(l, buff, prev);
|
prev = visualize_convolutional_layer(l, buff, prev);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
void top_predictions(network net, int k, int *index)
|
void top_predictions(network net, int k, int *index)
|
||||||
@ -684,7 +684,7 @@ matrix network_predict_data_multi(network net, data test, int n)
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
free(X);
|
free(X);
|
||||||
return pred;
|
return pred;
|
||||||
}
|
}
|
||||||
|
|
||||||
matrix network_predict_data(network net, data test)
|
matrix network_predict_data(network net, data test)
|
||||||
@ -707,7 +707,7 @@ matrix network_predict_data(network net, data test)
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
free(X);
|
free(X);
|
||||||
return pred;
|
return pred;
|
||||||
}
|
}
|
||||||
|
|
||||||
void print_network(network net)
|
void print_network(network net)
|
||||||
@ -749,7 +749,7 @@ void compare_networks(network n1, network n2, data test)
|
|||||||
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
|
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
|
||||||
float num = pow((abs(b - c) - 1.), 2.);
|
float num = pow((abs(b - c) - 1.), 2.);
|
||||||
float den = b + c;
|
float den = b + c;
|
||||||
printf("%f\n", num/den);
|
printf("%f\n", num/den);
|
||||||
}
|
}
|
||||||
|
|
||||||
float network_accuracy(network net, data d)
|
float network_accuracy(network net, data d)
|
||||||
@ -847,3 +847,25 @@ void fuse_conv_batchnorm(network net)
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
void calculate_binary_weights(network net)
|
||||||
|
{
|
||||||
|
int j;
|
||||||
|
for (j = 0; j < net.n; ++j) {
|
||||||
|
layer *l = &net.layers[j];
|
||||||
|
|
||||||
|
if (l->type == CONVOLUTIONAL) {
|
||||||
|
//printf(" Merges Convolutional-%d and batch_norm \n", j);
|
||||||
|
|
||||||
|
if (l->xnor) {
|
||||||
|
//printf("\n %d \n", j);
|
||||||
|
size_t ldb_align = 256; // 256bit for AVX2
|
||||||
|
binary_transpose_align_weights(l, ldb_align);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
//printf("\n calculate_binary_weights Done! \n");
|
||||||
|
|
||||||
|
}
|
@ -151,6 +151,7 @@ YOLODLL_API void optimize_picture(network *net, image orig, int max_layer, float
|
|||||||
int get_network_nuisance(network net);
|
int get_network_nuisance(network net);
|
||||||
int get_network_background(network net);
|
int get_network_background(network net);
|
||||||
YOLODLL_API void fuse_conv_batchnorm(network net);
|
YOLODLL_API void fuse_conv_batchnorm(network net);
|
||||||
|
YOLODLL_API void calculate_binary_weights(network net);
|
||||||
|
|
||||||
#ifdef __cplusplus
|
#ifdef __cplusplus
|
||||||
}
|
}
|
||||||
|
Reference in New Issue
Block a user