From 5c067dc44785a761a0243d8cd634e3ac17d548ad Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 12 Sep 2016 20:55:20 +0000
Subject: [PATCH] good chance I didn't break anything
---
src/network_kernels.cu | 112 +++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 106 insertions(+), 6 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 3e01019..3e0c2b6 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -209,6 +209,7 @@
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);
@@ -226,25 +227,115 @@
{
train_args args = *(train_args*)ptr;
- cudaError_t status = cudaSetDevice(args.net.gpu_index);
- check_error(status);
+ 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(train_args args)
+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 = 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));
@@ -255,11 +346,20 @@
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]);
- float err = train_network_datum(nets[i], X[i], y[i]);
- sum += err;
+ 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);
}
--
Gitblit v1.10.0