From 73f7aacf35ec9b1d0f9de9ddf38af0889f213e99 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 20 Sep 2016 18:34:49 +0000
Subject: [PATCH] better multigpu
---
src/network_kernels.cu | 243 +++++++++++++++++++++++++++++++++++-------------
1 files changed, 178 insertions(+), 65 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 3e0c2b6..b7d1d2b 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -219,34 +219,32 @@
typedef struct {
network net;
- float *X;
- float *y;
+ data d;
+ float *err;
} 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);
+ cuda_set_device(args.net.gpu_index);
+ *args.err = train_network(args.net, args.d);
return 0;
}
-pthread_t train_network_in_thread(network net, float *X, float *y)
+pthread_t train_network_in_thread(network net, data d, float *err)
{
pthread_t thread;
train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
ptr->net = net;
- ptr->X = X;
- ptr->y = y;
+ ptr->d = d;
+ ptr->err = err;
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);
@@ -255,12 +253,10 @@
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);
@@ -269,9 +265,95 @@
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 update_layer(layer l, network net)
+{
+ int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
+ 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 == RNN){
+ update_rnn_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 == 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 merge_weights(layer l, layer base)
+{
+ if (l.type == CONVOLUTIONAL) {
+ axpy_cpu(l.n, 1, l.biases, 1, base.biases, 1);
+ axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weights, 1, base.weights, 1);
+ if (l.scales) {
+ axpy_cpu(l.n, 1, l.scales, 1, base.scales, 1);
+ }
+ } else if(l.type == CONNECTED) {
+ axpy_cpu(l.outputs, 1, l.biases, 1, base.biases, 1);
+ axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, base.weights, 1);
+ }
+}
+
+void scale_weights(layer l, float s)
+{
+ if (l.type == CONVOLUTIONAL) {
+ scal_cpu(l.n, s, l.biases, 1);
+ scal_cpu(l.n*l.size*l.size*l.c, s, l.weights, 1);
+ if (l.scales) {
+ scal_cpu(l.n, s, l.scales, 1);
+ }
+ } else if(l.type == CONNECTED) {
+ scal_cpu(l.outputs, s, l.biases, 1);
+ scal_cpu(l.outputs*l.inputs, s, l.weights, 1);
+ }
+}
+
+
+void pull_weights(layer l)
+{
+ if(l.type == CONVOLUTIONAL){
+ cuda_pull_array(l.biases_gpu, l.biases, l.n);
+ cuda_pull_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c);
+ if(l.scales) cuda_pull_array(l.scales_gpu, l.scales, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
+ cuda_pull_array(l.weights_gpu, l.weights, l.outputs*l.inputs);
+ }
+}
+
+void push_weights(layer l)
+{
+ if(l.type == CONVOLUTIONAL){
+ cuda_push_array(l.biases_gpu, l.biases, l.n);
+ cuda_push_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c);
+ if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+ cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs);
+ }
+}
+
+void distribute_weights(layer l, layer base)
+{
+ if(l.type == CONVOLUTIONAL){
+ cuda_push_array(l.biases_gpu, base.biases, l.n);
+ cuda_push_array(l.weights_gpu, base.weights, l.n*l.size*l.size*l.c);
+ if(base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_push_array(l.biases_gpu, base.biases, l.outputs);
+ cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs);
+ }
+}
+
+
void merge_updates(layer l, layer base)
{
if (l.type == CONVOLUTIONAL) {
@@ -288,79 +370,110 @@
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);
+ if(l.type == CONVOLUTIONAL){
+ cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n);
+ cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.n*l.size*l.size*l.c);
+ if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs);
+ cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs);
}
}
-void sync_updates(network *nets, int n)
+void sync_layer(network *nets, int n, int j)
{
- int i,j;
- int layers = nets[0].n;
+ //printf("Syncing layer %d\n", j);
+ int i;
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);
+ layer base = net.layers[j];
+ cuda_set_device(net.gpu_index);
+ pull_weights(base);
+ for (i = 1; i < n; ++i) {
+ cuda_set_device(nets[i].gpu_index);
+ layer l = nets[i].layers[j];
+ pull_weights(l);
+ merge_weights(l, base);
}
+ scale_weights(base, 1./n);
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]);
+ layer l = nets[i].layers[j];
+ distribute_weights(l, base);
}
+ //printf("Done syncing layer %d\n", j);
}
-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));
+typedef struct{
+ network *nets;
+ int n;
+ int j;
+} sync_args;
+void *sync_layer_thread(void *ptr)
+{
+ sync_args args = *(sync_args*)ptr;
+ sync_layer(args.nets, args.n, args.j);
+ free(ptr);
+ return 0;
+}
+
+pthread_t sync_layer_in_thread(network *nets, int n, int j)
+{
+ pthread_t thread;
+ sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args));
+ ptr->nets = nets;
+ ptr->n = n;
+ ptr->j = j;
+ if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed");
+ return thread;
+}
+
+void sync_nets(network *nets, int n, int interval)
+{
+ int j;
+ int layers = nets[0].n;
+ pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t));
+
+ *nets[0].seen += interval * (n-1) * nets[0].batch * nets[0].subdivisions;
+ for (j = 0; j < n; ++j){
+ *nets[j].seen = *nets[0].seen;
+ }
+ for (j = 0; j < layers; ++j) {
+ threads[j] = sync_layer_in_thread(nets, n, j);
+ }
+ for (j = 0; j < layers; ++j) {
+ pthread_join(threads[j], 0);
+ }
+ free(threads);
+}
+
+float train_networks(network *nets, int n, data d, int interval)
+{
int i;
+ int batch = nets[0].batch;
+ int subdivisions = nets[0].subdivisions;
+ assert(batch * subdivisions * n == d.X.rows);
+ pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t));
+ float *errors = (float *) calloc(n, sizeof(float));
+
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]);
+ data p = get_data_part(d, i, n);
+ threads[i] = train_network_in_thread(nets[i], p, errors + 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]);
+ printf("%f\n", errors[i]);
+ sum += errors[i];
}
- if (((*nets[0].seen) / nets[0].batch) % nets[0].subdivisions == 0) sync_updates(nets, n);
- free(X);
- free(y);
+ if (get_current_batch(nets[0]) % interval == 0) {
+ printf("Syncing... ");
+ sync_nets(nets, n, interval);
+ printf("Done!\n");
+ }
free(threads);
- return (float)sum/(n*batch);
+ free(errors);
+ return (float)sum/(n);
}
float *get_network_output_layer_gpu(network net, int i)
--
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