#include "cuda_runtime.h"
|
#include "curand.h"
|
#include "cublas_v2.h"
|
|
extern "C" {
|
#include <stdio.h>
|
#include <time.h>
|
#include <assert.h>
|
|
#include "network.h"
|
#include "image.h"
|
#include "data.h"
|
#include "utils.h"
|
#include "parser.h"
|
|
#include "crop_layer.h"
|
#include "connected_layer.h"
|
#include "rnn_layer.h"
|
#include "gru_layer.h"
|
#include "crnn_layer.h"
|
#include "detection_layer.h"
|
#include "region_layer.h"
|
#include "convolutional_layer.h"
|
#include "activation_layer.h"
|
#include "deconvolutional_layer.h"
|
#include "maxpool_layer.h"
|
#include "reorg_layer.h"
|
#include "avgpool_layer.h"
|
#include "normalization_layer.h"
|
#include "batchnorm_layer.h"
|
#include "cost_layer.h"
|
#include "local_layer.h"
|
#include "softmax_layer.h"
|
#include "dropout_layer.h"
|
#include "route_layer.h"
|
#include "shortcut_layer.h"
|
#include "blas.h"
|
}
|
|
float * get_network_output_gpu_layer(network net, int i);
|
float * get_network_delta_gpu_layer(network net, int i);
|
float * get_network_output_gpu(network net);
|
|
void forward_network_gpu(network net, network_state state)
|
{
|
state.workspace = net.workspace;
|
int i;
|
for(i = 0; i < net.n; ++i){
|
state.index = i;
|
layer l = net.layers[i];
|
if(l.delta_gpu){
|
fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
|
}
|
if(l.type == CONVOLUTIONAL){
|
forward_convolutional_layer_gpu(l, state);
|
} else if(l.type == DECONVOLUTIONAL){
|
forward_deconvolutional_layer_gpu(l, state);
|
} else if(l.type == ACTIVE){
|
forward_activation_layer_gpu(l, state);
|
} else if(l.type == LOCAL){
|
forward_local_layer_gpu(l, state);
|
} else if(l.type == DETECTION){
|
forward_detection_layer_gpu(l, state);
|
} else if(l.type == REGION){
|
forward_region_layer_gpu(l, state);
|
} else if(l.type == CONNECTED){
|
forward_connected_layer_gpu(l, state);
|
} else if(l.type == RNN){
|
forward_rnn_layer_gpu(l, state);
|
} else if(l.type == GRU){
|
forward_gru_layer_gpu(l, state);
|
} else if(l.type == CRNN){
|
forward_crnn_layer_gpu(l, state);
|
} else if(l.type == CROP){
|
forward_crop_layer_gpu(l, state);
|
} else if(l.type == COST){
|
forward_cost_layer_gpu(l, state);
|
} else if(l.type == SOFTMAX){
|
forward_softmax_layer_gpu(l, state);
|
} else if(l.type == NORMALIZATION){
|
forward_normalization_layer_gpu(l, state);
|
} else if(l.type == BATCHNORM){
|
forward_batchnorm_layer_gpu(l, state);
|
} else if(l.type == MAXPOOL){
|
forward_maxpool_layer_gpu(l, state);
|
} else if(l.type == REORG){
|
forward_reorg_layer_gpu(l, state);
|
} else if(l.type == AVGPOOL){
|
forward_avgpool_layer_gpu(l, state);
|
} else if(l.type == DROPOUT){
|
forward_dropout_layer_gpu(l, state);
|
} else if(l.type == ROUTE){
|
forward_route_layer_gpu(l, net);
|
} else if(l.type == SHORTCUT){
|
forward_shortcut_layer_gpu(l, state);
|
}
|
state.input = l.output_gpu;
|
}
|
}
|
|
void backward_network_gpu(network net, network_state state)
|
{
|
state.workspace = net.workspace;
|
int i;
|
float * original_input = state.input;
|
float * original_delta = state.delta;
|
for(i = net.n-1; i >= 0; --i){
|
state.index = i;
|
layer l = net.layers[i];
|
if(i == 0){
|
state.input = original_input;
|
state.delta = original_delta;
|
}else{
|
layer prev = net.layers[i-1];
|
state.input = prev.output_gpu;
|
state.delta = prev.delta_gpu;
|
}
|
if(l.type == CONVOLUTIONAL){
|
backward_convolutional_layer_gpu(l, state);
|
} else if(l.type == DECONVOLUTIONAL){
|
backward_deconvolutional_layer_gpu(l, state);
|
} else if(l.type == ACTIVE){
|
backward_activation_layer_gpu(l, state);
|
} else if(l.type == LOCAL){
|
backward_local_layer_gpu(l, state);
|
} else if(l.type == MAXPOOL){
|
if(i != 0) backward_maxpool_layer_gpu(l, state);
|
} else if(l.type == REORG){
|
backward_reorg_layer_gpu(l, state);
|
} else if(l.type == AVGPOOL){
|
if(i != 0) backward_avgpool_layer_gpu(l, state);
|
} else if(l.type == DROPOUT){
|
backward_dropout_layer_gpu(l, state);
|
} else if(l.type == DETECTION){
|
backward_detection_layer_gpu(l, state);
|
} else if(l.type == REGION){
|
backward_region_layer_gpu(l, state);
|
} else if(l.type == NORMALIZATION){
|
backward_normalization_layer_gpu(l, state);
|
} else if(l.type == BATCHNORM){
|
backward_batchnorm_layer_gpu(l, state);
|
} else if(l.type == SOFTMAX){
|
if(i != 0) backward_softmax_layer_gpu(l, state);
|
} else if(l.type == CONNECTED){
|
backward_connected_layer_gpu(l, state);
|
} else if(l.type == RNN){
|
backward_rnn_layer_gpu(l, state);
|
} else if(l.type == GRU){
|
backward_gru_layer_gpu(l, state);
|
} else if(l.type == CRNN){
|
backward_crnn_layer_gpu(l, state);
|
} else if(l.type == COST){
|
backward_cost_layer_gpu(l, state);
|
} else if(l.type == ROUTE){
|
backward_route_layer_gpu(l, net);
|
} else if(l.type == SHORTCUT){
|
backward_shortcut_layer_gpu(l, state);
|
}
|
}
|
}
|
|
void update_network_gpu(network net)
|
{
|
int i;
|
int update_batch = net.batch*net.subdivisions;
|
float rate = get_current_rate(net);
|
for(i = 0; i < net.n; ++i){
|
layer l = net.layers[i];
|
if(l.type == CONVOLUTIONAL){
|
update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
} else if(l.type == DECONVOLUTIONAL){
|
update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
|
} else if(l.type == CONNECTED){
|
update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
} else if(l.type == GRU){
|
update_gru_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
} else if(l.type == RNN){
|
update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
} else if(l.type == CRNN){
|
update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
} else if(l.type == LOCAL){
|
update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
|
}
|
}
|
}
|
|
void forward_backward_network_gpu(network net, float *x, float *y)
|
{
|
network_state state;
|
state.index = 0;
|
state.net = net;
|
int x_size = get_network_input_size(net)*net.batch;
|
int y_size = get_network_output_size(net)*net.batch;
|
if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch;
|
if(!*net.input_gpu){
|
*net.input_gpu = cuda_make_array(x, x_size);
|
*net.truth_gpu = cuda_make_array(y, y_size);
|
}else{
|
cuda_push_array(*net.input_gpu, x, x_size);
|
cuda_push_array(*net.truth_gpu, y, y_size);
|
}
|
state.input = *net.input_gpu;
|
state.delta = 0;
|
state.truth = *net.truth_gpu;
|
state.train = 1;
|
forward_network_gpu(net, state);
|
backward_network_gpu(net, state);
|
}
|
|
float train_network_datum_gpu(network net, float *x, float *y)
|
{
|
*net.seen += net.batch;
|
forward_backward_network_gpu(net, x, y);
|
float error = get_network_cost(net);
|
if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
|
|
return error;
|
}
|
|
typedef struct {
|
network net;
|
float *X;
|
float *y;
|
} train_args;
|
|
void *train_thread(void *ptr)
|
{
|
train_args args = *(train_args*)ptr;
|
|
cuda_set_device(args.net.gpu_index);
|
forward_backward_network_gpu(args.net, args.X, args.y);
|
free(ptr);
|
return 0;
|
}
|
|
pthread_t train_network_in_thread(network net, float *X, float *y)
|
{
|
pthread_t thread;
|
train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
|
ptr->net = net;
|
ptr->X = X;
|
ptr->y = y;
|
if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
|
return thread;
|
}
|
|
void pull_updates(layer l)
|
{
|
#ifdef GPU
|
if(l.type == CONVOLUTIONAL){
|
cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
|
cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c);
|
if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
|
cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
|
}
|
#endif
|
}
|
|
void push_updates(layer l)
|
{
|
#ifdef GPU
|
if(l.type == CONVOLUTIONAL){
|
cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
|
cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c);
|
if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
|
cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
|
}
|
#endif
|
}
|
|
void merge_updates(layer l, layer base)
|
{
|
if (l.type == CONVOLUTIONAL) {
|
axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1);
|
axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weight_updates, 1, base.weight_updates, 1);
|
if (l.scale_updates) {
|
axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1);
|
}
|
} else if(l.type == CONNECTED) {
|
axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1);
|
axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1);
|
}
|
}
|
|
void distribute_updates(layer l, layer base)
|
{
|
if (l.type == CONVOLUTIONAL) {
|
copy_cpu(l.n, base.bias_updates, 1, l.bias_updates, 1);
|
copy_cpu(l.n*l.size*l.size*l.c, base.weight_updates, 1, l.weight_updates, 1);
|
if (l.scale_updates) {
|
copy_cpu(l.n, base.scale_updates, 1, l.scale_updates, 1);
|
}
|
} else if(l.type == CONNECTED) {
|
copy_cpu(l.outputs, base.bias_updates, 1, l.bias_updates, 1);
|
copy_cpu(l.outputs*l.inputs, base.weight_updates, 1, l.weight_updates, 1);
|
}
|
}
|
|
void sync_updates(network *nets, int n)
|
{
|
int i,j;
|
int layers = nets[0].n;
|
network net = nets[0];
|
for (j = 0; j < layers; ++j) {
|
layer base = net.layers[j];
|
cuda_set_device(net.gpu_index);
|
pull_updates(base);
|
for (i = 1; i < n; ++i) {
|
cuda_set_device(nets[i].gpu_index);
|
layer l = nets[i].layers[j];
|
pull_updates(l);
|
merge_updates(l, base);
|
}
|
for (i = 1; i < n; ++i) {
|
cuda_set_device(nets[i].gpu_index);
|
layer l = nets[i].layers[j];
|
distribute_updates(l, base);
|
push_updates(l);
|
}
|
cuda_set_device(net.gpu_index);
|
push_updates(base);
|
}
|
for (i = 0; i < n; ++i) {
|
cuda_set_device(nets[i].gpu_index);
|
if(i > 0) nets[i].momentum = 0;
|
update_network_gpu(nets[i]);
|
}
|
}
|
|
float train_networks(network *nets, int n, data d)
|
{
|
int batch = nets[0].batch;
|
assert(batch * n == d.X.rows);
|
assert(nets[0].subdivisions % n == 0);
|
float **X = (float **) calloc(n, sizeof(float *));
|
float **y = (float **) calloc(n, sizeof(float *));
|
pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t));
|
|
int i;
|
float sum = 0;
|
for(i = 0; i < n; ++i){
|
X[i] = (float *) calloc(batch*d.X.cols, sizeof(float));
|
y[i] = (float *) calloc(batch*d.y.cols, sizeof(float));
|
get_next_batch(d, batch, i*batch, X[i], y[i]);
|
threads[i] = train_network_in_thread(nets[i], X[i], y[i]);
|
}
|
for(i = 0; i < n; ++i){
|
pthread_join(threads[i], 0);
|
*nets[i].seen += n*nets[i].batch;
|
printf("%f\n", get_network_cost(nets[i]) / batch);
|
sum += get_network_cost(nets[i]);
|
free(X[i]);
|
free(y[i]);
|
}
|
if (((*nets[0].seen) / nets[0].batch) % nets[0].subdivisions == 0) sync_updates(nets, n);
|
free(X);
|
free(y);
|
free(threads);
|
return (float)sum/(n*batch);
|
}
|
|
float *get_network_output_layer_gpu(network net, int i)
|
{
|
layer l = net.layers[i];
|
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
|
return l.output;
|
}
|
|
float *get_network_output_gpu(network net)
|
{
|
int i;
|
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
|
return get_network_output_layer_gpu(net, i);
|
}
|
|
float *network_predict_gpu(network net, float *input)
|
{
|
int size = get_network_input_size(net) * net.batch;
|
network_state state;
|
state.index = 0;
|
state.net = net;
|
state.input = cuda_make_array(input, size);
|
state.truth = 0;
|
state.train = 0;
|
state.delta = 0;
|
forward_network_gpu(net, state);
|
float *out = get_network_output_gpu(net);
|
cuda_free(state.input);
|
return out;
|
}
|
