#include "cuda_runtime.h" #include "curand.h" #include "cublas_v2.h" extern "C" { #include "convolutional_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" #include "col2im.h" #include "utils.h" #include "cuda.h" } __global__ void scale_bias_kernel(float *output, float *biases, int n, int size) { int offset = blockIdx.x * blockDim.x + threadIdx.x; int filter = blockIdx.y; int batch = blockIdx.z; if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter]; } void scale_bias_gpu(float *output, float *biases, int batch, int n, int size) { dim3 dimGrid((size-1)/BLOCK + 1, n, batch); dim3 dimBlock(BLOCK, 1, 1); scale_bias_kernel<<>>(output, biases, n, size); check_error(cudaPeekAtLastError()); } __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index]*x_norm[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i]; } } void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { backward_scale_kernel<<>>(x_norm, delta, batch, n, size, scale_updates); check_error(cudaPeekAtLastError()); } __global__ void add_bias_kernel(float *output, float *biases, int n, int size) { int offset = blockIdx.x * blockDim.x + threadIdx.x; int filter = blockIdx.y; int batch = blockIdx.z; if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter]; } void add_bias_gpu(float *output, float *biases, int batch, int n, int size) { dim3 dimGrid((size-1)/BLOCK + 1, n, batch); dim3 dimBlock(BLOCK, 1, 1); add_bias_kernel<<>>(output, biases, n, size); check_error(cudaPeekAtLastError()); } __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) { __shared__ float part[BLOCK]; int i,b; int filter = blockIdx.x; int p = threadIdx.x; float sum = 0; for(b = 0; b < batch; ++b){ for(i = 0; i < size; i += BLOCK){ int index = p + i + size*(filter + n*b); sum += (p+i < size) ? delta[index] : 0; } } part[p] = sum; __syncthreads(); if (p == 0) { for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i]; } } void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) { backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); check_error(cudaPeekAtLastError()); } void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { int i; int m = l.n; int k = l.size*l.size*l.c; int n = convolutional_out_height(l)* convolutional_out_width(l); fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); for(i = 0; i < l.batch; ++i){ im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); float * a = l.filters_gpu; float * b = l.col_image_gpu; float * c = l.output_gpu; gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); } if(l.batch_normalize){ if(state.train){ fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.variance_gpu); /* cuda_pull_array(l.variance_gpu, l.mean, 1); printf("%f\n", l.mean[0]); */ scal_ongpu(l.n, .95, l.rolling_mean_gpu, 1); axpy_ongpu(l.n, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1); scal_ongpu(l.n, .95, l.rolling_variance_gpu, 1); axpy_ongpu(l.n, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1); copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_h*l.out_w); copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); } else { normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.n, l.out_h*l.out_w); } scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w); } add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n); activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation); } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { int i; int m = l.n; int n = l.size*l.size*l.c; int k = convolutional_out_height(l)* convolutional_out_width(l); gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu); backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k); if(l.batch_normalize){ backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.scale_updates_gpu); scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w); fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.mean_delta_gpu); fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.variance_delta_gpu); normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.delta_gpu); } for(i = 0; i < l.batch; ++i){ float * a = l.delta_gpu; float * b = l.col_image_gpu; float * c = l.filter_updates_gpu; im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n); if(state.delta){ float * a = l.filters_gpu; float * b = l.delta_gpu; float * c = l.col_image_gpu; gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k); col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w); } } } void pull_convolutional_layer(convolutional_layer layer) { cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); if (layer.batch_normalize){ cuda_pull_array(layer.scales_gpu, layer.scales, layer.n); cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); } } void push_convolutional_layer(convolutional_layer layer) { cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); cuda_push_array(layer.biases_gpu, layer.biases, layer.n); cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); if (layer.batch_normalize){ cuda_push_array(layer.scales_gpu, layer.scales, layer.n); cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); } } void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) { int size = layer.size*layer.size*layer.c*layer.n; axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1); axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1); scal_ongpu(size, momentum, layer.filter_updates_gpu, 1); }