Bug fixes. Tested im2col_cpu_custom_transpose - bad way.

AlexeyAB

2018-08-10 a9fef1bd66e6b2c40c344c1bdcd33bb1d209461c

Bug fixes. Tested im2col_cpu_custom_transpose - bad way.

 src/convolutional_layer.c

@@ -593,11 +593,11 @@
    }
}

void binary_align_weights(convolutional_layer *l, size_t lda_align)
void binary_align_weights(convolutional_layer *l)
{
    int m = l->n;
    int k = l->size*l->size*l->c;
    size_t new_lda = k + (lda_align - k%lda_align); // (k / 8 + 1) * 8;
    size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8;

    binarize_weights(l->weights, m, k, l->binary_weights);

@@ -680,7 +680,17 @@

    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_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b);

        //float *t_input = NULL;
        //if (l.xnor) {
        //    size_t new_ldb = k + (l.lda_align - k%l.lda_align);
        //    size_t t_intput_size = new_ldb * n;
        //    t_input = calloc(t_intput_size, sizeof(float));
        //    im2col_cpu_custom_transpose(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, t_input, new_ldb);
        //}
        //else
            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);
@@ -760,19 +770,28 @@
                    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);
                /*
                if (l.size == 3 && l.stride == 1 && l.pad == 1) {
                    convolution_2d(l.w, l.h, l.size, l.n, l.c, l.pad, l.stride,
                        l.weights, state.input, l.output);
                }
                else {
                */
                    //size_t ldb_align = 256; // 256 bit for AVX2
                    int ldb_align = l.lda_align;
                    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, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);
                    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);

                //free(t_input);
                free(t_bit_input);
                    //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);

                //free(align_bit_weights);
                    //free(t_input);
                    free(t_bit_input);
                //}

            }

            // for bit_input: (k * n)

 src/convolutional_layer.h

@@ -35,7 +35,7 @@
void swap_binary(convolutional_layer *l);
void binarize_weights2(float *weights, int n, int size, char *binary, float *scales);

void binary_align_weights(convolutional_layer *l, size_t ldb_align);
void binary_align_weights(convolutional_layer *l);

void backward_convolutional_layer(convolutional_layer layer, network_state state);


 src/gemm.c

@@ -429,6 +429,56 @@
}


void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride,
    float *weights, float *input, float *output)
{
    int out_h = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1
    int out_w = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1
    int i, f, j;

    int fil;
    // filter index
#pragma omp parallel for      // "omp parallel for" - automatic parallelization of loop by using OpenMP
    for (fil = 0; fil < n; ++fil) {
        int chan, y, x, f_y, f_x;
        // channel index
        for (chan = 0; chan < c; ++chan)
            // input - y
            for (y = 0; y < h; ++y)
                // input - x
                for (x = 0; x < w; ++x)
                {
                    int const output_index = fil*w*h + y*w + x;
                    int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize;
                    int const input_pre_index = chan*w*h;
                    float sum = 0;

                    // filter - y
                    for (f_y = 0; f_y < ksize; ++f_y)
                    {
                        int input_y = y + f_y - pad;
                        // filter - x
                        for (f_x = 0; f_x < ksize; ++f_x)
                        {
                            int input_x = x + f_x - pad;
                            if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;

                            int input_index = input_pre_index + input_y*w + input_x;
                            int weights_index = weights_pre_index + f_y*ksize + f_x;

                            sum += input[input_index] * weights[weights_index];
                        }
                    }
                    // l.output[filters][width][height] +=
                    //        state.input[channels][width][height] *
                    //        l.weights[filters][channels][filter_width][filter_height];
                    output[output_index] += sum;
                }
    }
}



// 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
@@ -541,6 +591,121 @@

//From Berkeley Vision's Caffe!
//https://github.com/BVLC/caffe/blob/master/LICENSE
void im2col_cpu_custom_transpose(float* data_im,
    int channels, int height, int width,
    int ksize, int stride, int pad, float* data_col, int ldb_align)
{
    int c, h, w;
    int height_col = (height + 2 * pad - ksize) / stride + 1;
    int width_col = (width + 2 * pad - ksize) / stride + 1;
    int channels_col = channels * ksize * ksize;

    // optimized version
    if (height_col == height && width_col == width && stride == 1 && pad == 1)
    {
#pragma omp parallel for
        for (c = 0; c < channels_col; ++c) {
            int w_offset = c % ksize;
            int h_offset = (c / ksize) % ksize;
            int c_im = c / ksize / ksize;
            for (h = pad; h < height_col - pad; ++h) {
                for (w = pad; w < width_col - pad - 4; w+=8) {
                    int im_row = h_offset + h - pad;
                    int im_col = w_offset + w - pad;
                    //int col_index = (c * height_col + h) * width_col + w;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned

                    //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
                    __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)]));
                    data_col[col_index + ldb_align * 0] = src256.m256_f32[0];
                    data_col[col_index + ldb_align * 1] = src256.m256_f32[1];
                    data_col[col_index + ldb_align * 2] = src256.m256_f32[2];
                    data_col[col_index + ldb_align * 3] = src256.m256_f32[3];
                    data_col[col_index + ldb_align * 4] = src256.m256_f32[4];
                    data_col[col_index + ldb_align * 5] = src256.m256_f32[5];
                    data_col[col_index + ldb_align * 6] = src256.m256_f32[6];
                    data_col[col_index + ldb_align * 7] = src256.m256_f32[7];

                    //_mm256_storeu_ps(&data_col[col_index], src256);
                }

                for (; w < width_col - pad; ++w) {
                    int im_row = h_offset + h - pad;
                    int im_col = w_offset + w - pad;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)];
                }
            }

            {
                w = 0;
                for (h = 0; h < height_col; ++h) {
                    int im_row = h_offset + h;
                    int im_col = w_offset + w;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
                        im_row, im_col, c_im, pad);
                }
            }

            {
                w = width_col - 1;
                for (h = 0; h < height_col; ++h) {
                    int im_row = h_offset + h;
                    int im_col = w_offset + w;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
                        im_row, im_col, c_im, pad);
                }
            }

            {
                h = 0;
                for (w = 0; w < width_col; ++w) {
                    int im_row = h_offset + h;
                    int im_col = w_offset + w;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
                        im_row, im_col, c_im, pad);
                }
            }

            {
                h = height_col - 1;
                for (w = 0; w < width_col; ++w) {
                    int im_row = h_offset + h;
                    int im_col = w_offset + w;
                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
                        im_row, im_col, c_im, pad);
                }
            }
        }

    }
    else {
        #pragma omp parallel for
        for (c = 0; c < channels_col; ++c) {
            int w_offset = c % ksize;
            int h_offset = (c / ksize) % ksize;
            int c_im = c / ksize / ksize;
            for (h = 0; h < height_col; ++h) {
                for (w = 0; w < width_col; ++w) {
                    int im_row = h_offset + h * stride;
                    int im_col = w_offset + w * stride;

                    int col_index = (h * width_col + w)*ldb_align + c;   // transposed & aligned
                    data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
                        im_row, im_col, c_im, pad);
                }
            }
        }
    }
}


//From Berkeley Vision's Caffe!
//https://github.com/BVLC/caffe/blob/master/LICENSE
void im2col_cpu_custom(float* data_im,
    int channels, int height, int width,
    int ksize, int stride, int pad, float* data_col)
@@ -641,7 +806,7 @@
        __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; i += 8) {
        for (i = 0; i < n-8; i += 8) {
            //x[i] = (x[i]>0) ? x[i] : .1*x[i];

            __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i]));
@@ -755,6 +920,55 @@
    }
}


void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride,
    float *weights, float *input, float *output)
{
    int out_h = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1
    int out_w = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1
    int i, f, j;

    int fil;
    // filter index
#pragma omp parallel for      // "omp parallel for" - automatic parallelization of loop by using OpenMP
    for (fil = 0; fil < n; ++fil) {
        int chan, y, x, f_y, f_x;
        // channel index
        for (chan = 0; chan < c; ++chan)
            // input - y
            for (y = 0; y < h; ++y)
                // input - x
                for (x = 0; x < w; ++x)
                {
                    int const output_index = fil*w*h + y*w + x;
                    int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize;
                    int const input_pre_index = chan*w*h;
                    float sum = 0;

                    // filter - y
                    for (f_y = 0; f_y < ksize; ++f_y)
                    {
                        int input_y = y + f_y - pad;
                        // filter - x
                        for (f_x = 0; f_x < ksize; ++f_x)
                        {
                            int input_x = x + f_x - pad;
                            if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue;

                            int input_index = input_pre_index + input_y*w + input_x;
                            int weights_index = weights_pre_index + f_y*ksize + f_x;

                            sum += input[input_index] * weights[weights_index];
                        }
                    }
                    // l.output[filters][width][height] +=
                    //        state.input[channels][width][height] *
                    //        l.weights[filters][channels][filter_width][filter_height];
                    output[output_index] += sum;
                }
    }
}

void 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,
@@ -791,6 +1005,13 @@
    }
}

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,

 src/gemm.h

@@ -4,6 +4,9 @@
#include <stdint.h>
#include <stddef.h>

void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride,
    float *weights, float *input, float *output);

static inline void set_bit(unsigned char *const dst, size_t index) {
    size_t dst_i = index / 8;
    int dst_shift = index % 8;
@@ -31,6 +34,10 @@
    int channels, int height, int width,
    int ksize, int stride, int pad, float* data_col);

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);

void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a);



 src/layer.h

@@ -181,6 +181,7 @@

    char *align_bit_weights;
    float *mean_arr;
    int lda_align;

    float *col_image;
    int   * input_layers;

 src/network.c

@@ -861,9 +861,9 @@

            if (l->xnor) {
                //printf("\n %d \n", j);
                size_t ldb_align = 256; // 256bit for AVX2
                l->lda_align = 256; // 256bit for AVX2

                binary_align_weights(l, ldb_align);
                binary_align_weights(l);
            }
        }
    }