From 809f924db2823b9e1eaf3efb9370380edc1f76ed Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 23 Jan 2015 00:38:24 +0000
Subject: [PATCH] CUDA so fast
---
src/network_kernels.cu | 82 ++++++++++++++++++++---------------------
1 files changed, 40 insertions(+), 42 deletions(-)
diff --git a/src/network_gpu.c b/src/network_kernels.cu
similarity index 77%
rename from src/network_gpu.c
rename to src/network_kernels.cu
index c958056..a009174 100644
--- a/src/network_gpu.c
+++ b/src/network_kernels.cu
@@ -1,3 +1,4 @@
+extern "C" {
#include <stdio.h>
#include <time.h>
@@ -15,12 +16,12 @@
#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
+}
-#ifdef GPU
-cl_mem get_network_output_cl_layer(network net, int i);
-cl_mem get_network_delta_cl_layer(network net, int i);
+extern "C" float * get_network_output_gpu_layer(network net, int i);
+extern "C" float * get_network_delta_gpu_layer(network net, int i);
-void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
+void forward_network_gpu(network net, float * input, float * truth, int train)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -28,7 +29,7 @@
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
forward_convolutional_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
else if(net.types[i] == COST){
cost_layer layer = *(cost_layer *)net.layers[i];
@@ -37,47 +38,46 @@
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
forward_maxpool_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
else if(net.types[i] == DROPOUT){
if(!train) continue;
dropout_layer layer = *(dropout_layer *)net.layers[i];
forward_dropout_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer_gpu(layer, input);
- input = layer.output_cl;
+ input = layer.output_gpu;
}
- check_error(cl);
//printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
}
}
-void backward_network_gpu(network net, cl_mem input)
+void backward_network_gpu(network net, float * input)
{
int i;
- cl_mem prev_input;
- cl_mem prev_delta;
+ float * prev_input;
+ float * prev_delta;
for(i = net.n-1; i >= 0; --i){
//clock_t time = clock();
if(i == 0){
prev_input = input;
prev_delta = 0;
}else{
- prev_input = get_network_output_cl_layer(net, i-1);
- prev_delta = get_network_delta_cl_layer(net, i-1);
+ prev_input = get_network_output_gpu_layer(net, i-1);
+ prev_delta = get_network_delta_gpu_layer(net, i-1);
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -103,7 +103,6 @@
softmax_layer layer = *(softmax_layer *)net.layers[i];
backward_softmax_layer_gpu(layer, prev_delta);
}
- check_error(cl);
//printf("Backward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
}
}
@@ -123,54 +122,54 @@
}
}
-cl_mem get_network_output_cl_layer(network net, int i)
+float * get_network_output_gpu_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
} else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *)net.layers[i];
- return layer.output_cl;
+ return layer.output_gpu;
}
return 0;
}
-cl_mem get_network_delta_cl_layer(network net, int i)
+float * get_network_delta_gpu_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.delta_cl;
+ return layer.delta_gpu;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.delta_cl;
+ return layer.delta_gpu;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.delta_cl;
+ return layer.delta_gpu;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.delta_cl;
+ return layer.delta_gpu;
} else if(net.types[i] == DROPOUT){
if(i == 0) return 0;
- return get_network_delta_cl_layer(net, i-1);
+ return get_network_delta_gpu_layer(net, i-1);
}
return 0;
}
@@ -179,15 +178,15 @@
{
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
- if(!*net.input_cl){
- *net.input_cl = cl_make_array(x, x_size);
- *net.truth_cl = cl_make_array(y, y_size);
+ if(!*net.input_gpu){
+ *net.input_gpu = cuda_make_array(x, x_size);
+ *net.truth_gpu = cuda_make_array(y, y_size);
}else{
- cl_write_array(*net.input_cl, x, x_size);
- cl_write_array(*net.truth_cl, y, y_size);
+ cuda_push_array(*net.input_gpu, x, x_size);
+ cuda_push_array(*net.truth_gpu, y, y_size);
}
- forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
- backward_network_gpu(net, *net.input_cl);
+ forward_network_gpu(net, *net.input_gpu, *net.truth_gpu, 1);
+ backward_network_gpu(net, *net.input_gpu);
update_network_gpu(net);
float error = get_network_cost(net);
return error;
@@ -201,7 +200,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- cl_read_array(layer.output_cl, layer.output, layer.outputs*layer.batch);
+ cuda_pull_array(layer.output_gpu, layer.output, layer.outputs*layer.batch);
return layer.output;
}
else if(net.types[i] == MAXPOOL){
@@ -227,11 +226,10 @@
{
int size = get_network_input_size(net) * net.batch;
- cl_mem input_cl = cl_make_array(input, size);
- forward_network_gpu(net, input_cl, 0, 0);
+ float * input_gpu = cuda_make_array(input, size);
+ forward_network_gpu(net, input_gpu, 0, 0);
float *out = get_network_output_gpu(net);
- clReleaseMemObject(input_cl);
+ cuda_free(input_gpu);
return out;
}
-#endif
--
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