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Fixed race condition in server

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This commit is contained in:
Joseph Redmon 2014-12-08 11:48:57 -08:00
parent d81ab1942b
commit cb1f33c6ae
13 changed files with 56 additions and 41 deletions
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2
src/activations.c
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@ -116,7 +116,6 @@ cl_kernel get_activation_kernel()
void activate_array_ongpu(cl_mem x, int n, ACTIVATION a)
{
cl_setup();
cl_kernel kernel = get_activation_kernel();
cl_command_queue queue = cl.queue;
@ -145,7 +144,6 @@ cl_kernel get_gradient_kernel()
void gradient_array_ongpu(cl_mem x, int n, ACTIVATION a, cl_mem delta)
{
cl_setup();
cl_kernel kernel = get_gradient_kernel();
cl_command_queue queue = cl.queue;

3
src/axpy.c
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@ -70,7 +70,6 @@ void axpy_ongpu(int N, float ALPHA, cl_mem X, int INCX, cl_mem Y, int INCY)
void axpy_ongpu_offset(int N, float ALPHA, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY)
{
cl_setup();
cl_kernel kernel = get_axpy_kernel();
cl_command_queue queue = cl.queue;
@ -97,7 +96,6 @@ void copy_ongpu(int N, cl_mem X, int INCX, cl_mem Y, int INCY)
}
void copy_ongpu_offset(int N, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY, int INCY)
{
cl_setup();
cl_kernel kernel = get_copy_kernel();
cl_command_queue queue = cl.queue;
@ -118,7 +116,6 @@ void copy_ongpu_offset(int N, cl_mem X, int OFFX, int INCX, cl_mem Y, int OFFY,
}
void scal_ongpu(int N, float ALPHA, cl_mem X, int INCX)
{
cl_setup();
cl_kernel kernel = get_scal_kernel();
cl_command_queue queue = cl.queue;

37
src/cnn.c
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@ -666,6 +666,28 @@ void train_nist()
}
}
void train_nist_distributed(char *address)
{
srand(time(0));
network net = parse_network_cfg("cfg/nist.client");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
normalize_data_rows(train);
//normalize_data_rows(test);
int count = 0;
int iters = 50000/net.batch;
iters = 1000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd_gpu(net, train, iters);
client_update(net, address);
end = clock();
//float test_acc = network_accuracy_gpu(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
}
void test_ensemble()
{
int i;
@ -875,7 +897,7 @@ void test_correct_alexnet()
void run_server()
{
srand(time(0));
network net = parse_network_cfg("cfg/alexnet.server");
network net = parse_network_cfg("cfg/nist.server");
server_update(net);
}
void test_client()
@ -891,12 +913,23 @@ void test_client()
printf("Transfered: %lf seconds\n", sec(clock()-time));
}
int find_int_arg(int argc, char* argv[], char *arg)
{
int i;
for(i = 0; i < argc-1; ++i) if(0==strcmp(argv[i], arg)) return atoi(argv[i+1]);
return 0;
}
int main(int argc, char *argv[])
{
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
int index = find_int_arg(argc, argv, "-i");
#ifdef GPU
cl_setup(index);
#endif
if(0==strcmp(argv[1], "train")) train_imagenet();
else if(0==strcmp(argv[1], "detection")) train_detection_net();
else if(0==strcmp(argv[1], "asirra")) train_asirra();
@ -912,7 +945,7 @@ int main(int argc, char *argv[])
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
else if(0==strcmp(argv[1], "client")) train_nist_distributed(argv[2]);
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
fprintf(stderr, "Success!\n");

1
src/col2im.c
View File

@ -61,7 +61,6 @@ void col2im_ongpu(cl_mem data_col, int offset,
int channels, int height, int width,
int ksize, int stride, int pad, cl_mem data_im)
{
cl_setup();
cl_kernel kernel = get_col2im_kernel();
cl_command_queue queue = cl.queue;

2
src/convolutional_layer.c
View File

@ -280,7 +280,6 @@ void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
{
int size = convolutional_out_height(layer) * convolutional_out_width(layer);
cl_setup();
cl_kernel kernel = get_convolutional_learn_bias_kernel();
cl_command_queue queue = cl.queue;
@ -315,7 +314,6 @@ void bias_output_gpu(const convolutional_layer layer)
int out_w = convolutional_out_width(layer);
int size = out_h*out_w;
cl_setup();
cl_kernel kernel = get_convolutional_bias_kernel();
cl_command_queue queue = cl.queue;

1
src/cost_layer.c
View File

@ -75,7 +75,6 @@ cl_kernel get_mask_kernel()
void mask_ongpu(int n, cl_mem x, cl_mem mask, int mod)
{
cl_setup();
cl_kernel kernel = get_mask_kernel();
cl_command_queue queue = cl.queue;

3
src/gemm.c
View File

@ -178,14 +178,12 @@ void gemm_ongpu_offset(int TA, int TB, int M, int N, int K, float ALPHA,
cl_mem C_gpu, int c_off, int ldc)
{
#ifdef CLBLAS
cl_setup();
cl_command_queue queue = cl.queue;
cl_event event;
cl.error = clblasSgemm(clblasRowMajor, TA?clblasTrans:clblasNoTrans, TB?clblasTrans:clblasNoTrans,M, N, K,ALPHA, A_gpu, a_off, lda,B_gpu, b_off, ldb,BETA, C_gpu, c_off, ldc,1, &queue, 0, NULL, &event);
check_error(cl);
#else
//printf("gpu: %d %d %d %d %d\n",TA, TB, M, N, K);
cl_setup();
cl_kernel gemm_kernel = get_gemm_kernel();
if(!TA && !TB) gemm_kernel = get_gemm_nn_kernel();
if(!TA && TB) gemm_kernel = get_gemm_nt_kernel();
@ -225,7 +223,6 @@ void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA,
float BETA,
float *C, int ldc)
{
cl_setup();
cl_context context = cl.context;
cl_command_queue queue = cl.queue;

2
src/im2col.c
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@ -75,7 +75,6 @@ void im2col_ongpu(cl_mem data_im, int offset,
int channels, int height, int width,
int ksize, int stride, int pad, cl_mem data_col)
{
cl_setup();
int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1;
@ -113,7 +112,6 @@ void im2col_ongpu(cl_mem data_im, int offset,
int channels, int height, int width,
int ksize, int stride, int pad, float *data_col)
{
cl_setup();
cl_context context = cl.context;
cl_command_queue queue = cl.queue;

2
src/maxpool_layer.c
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@ -115,7 +115,6 @@ void forward_maxpool_layer_gpu(maxpool_layer layer, cl_mem input)
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
int c = layer.c;
cl_setup();
cl_kernel kernel = get_forward_kernel();
cl_command_queue queue = cl.queue;
@ -149,7 +148,6 @@ cl_kernel get_backward_kernel()
void backward_maxpool_layer_gpu(maxpool_layer layer, cl_mem delta)
{
cl_setup();
cl_kernel kernel = get_backward_kernel();
cl_command_queue queue = cl.queue;

15
src/opencl.c
View File

@ -27,7 +27,7 @@ void check_error(cl_info info)
#define MAX_DEVICES 10
cl_info cl_init()
cl_info cl_init(int index)
{
cl_info info;
info.initialized = 0;
@ -87,8 +87,7 @@ cl_info cl_init()
printf(" DEVICE_MAX_WORK_ITEM_SIZES = %u / %u / %u \n", (unsigned int)workitem_size[0], (unsigned int)workitem_size[1], (unsigned int)workitem_size[2]);
}
int index = getpid()%num_devices;
index = 1;
index = index%num_devices;
printf("%d rand, %d devices, %d index\n", getpid(), num_devices, index);
info.device = devices[index];
fprintf(stderr, "Found %d device(s)\n", num_devices);
@ -135,17 +134,16 @@ cl_program cl_fprog(char *filename, char *options, cl_info info)
return prog;
}
void cl_setup()
void cl_setup(int index)
{
if(!cl.initialized){
printf("initializing\n");
cl = cl_init();
cl = cl_init(index);
}
}
cl_kernel get_kernel(char *filename, char *kernelname, char *options)
{
cl_setup();
cl_program prog = cl_fprog(filename, options, cl);
cl_kernel kernel=clCreateKernel(prog, kernelname, &cl.error);
check_error(cl);
@ -154,7 +152,6 @@ cl_kernel get_kernel(char *filename, char *kernelname, char *options)
void cl_read_array(cl_mem mem, float *x, int n)
{
cl_setup();
cl.error = clEnqueueReadBuffer(cl.queue, mem, CL_TRUE, 0, sizeof(float)*n,x,0,0,0);
check_error(cl);
}
@ -171,14 +168,12 @@ float cl_checksum(cl_mem mem, int n)
void cl_write_array(cl_mem mem, float *x, int n)
{
cl_setup();
cl.error = clEnqueueWriteBuffer(cl.queue, mem, CL_TRUE, 0,sizeof(float)*n,x,0,0,0);
check_error(cl);
}
void cl_copy_array(cl_mem src, cl_mem dst, int n)
{
cl_setup();
cl.error = clEnqueueCopyBuffer(cl.queue, src, dst, 0, 0, sizeof(float)*n,0,0,0);
check_error(cl);
}
@ -196,7 +191,6 @@ cl_mem cl_sub_array(cl_mem src, int offset, int size)
cl_mem cl_make_array(float *x, int n)
{
cl_setup();
cl_mem mem = clCreateBuffer(cl.context,
CL_MEM_READ_WRITE|CL_MEM_COPY_HOST_PTR,
sizeof(float)*n, x, &cl.error);
@ -207,7 +201,6 @@ cl_mem cl_make_array(float *x, int n)
cl_mem cl_make_int_array(int *x, int n)
{
cl_setup();
cl_mem mem = clCreateBuffer(cl.context,
CL_MEM_READ_WRITE|CL_MEM_COPY_HOST_PTR,
sizeof(int)*n, x, &cl.error);

2
src/opencl.h
View File

@ -19,7 +19,7 @@ typedef struct {
extern cl_info cl;
void cl_setup();
void cl_setup(int index);
void check_error(cl_info info);
cl_kernel get_kernel(char *filename, char *kernelname, char *options);
void cl_read_array(cl_mem mem, float *x, int n);

26
src/server.c
View File

@ -51,20 +51,22 @@ typedef struct{
void read_all(int fd, char *buffer, size_t bytes)
{
//printf("Want %d\n", bytes);
size_t n = 0;
while(n < bytes){
int next = read(fd, buffer + n, bytes-n);
if(next < 0) error("read failed");
if(next <= 0) error("read failed");
n += next;
}
}
void write_all(int fd, char *buffer, size_t bytes)
{
//printf("Writ %d\n", bytes);
size_t n = 0;
while(n < bytes){
int next = write(fd, buffer + n, bytes-n);
if(next < 0) error("write failed");
if(next <= 0) error("write failed");
n += next;
}
}
@ -79,8 +81,9 @@ void read_and_add_into(int fd, float *a, int n)
void handle_connection(void *pointer)
{
printf("New Connection\n");
connection_info info = *(connection_info *) pointer;
free(pointer);
printf("New Connection\n");
int fd = info.fd;
network net = info.net;
int i;
@ -117,8 +120,6 @@ void handle_connection(void *pointer)
}
printf("Received updates\n");
close(fd);
++*(info.counter);
if(*(info.counter)%10==0) save_network(net, "/home/pjreddie/imagenet_backup/alexnet.part");
}
void server_update(network net)
@ -128,14 +129,16 @@ void server_update(network net)
listen(fd, 10);
struct sockaddr_in client; /* remote address */
socklen_t client_size = sizeof(client); /* length of addresses */
connection_info info;
info.net = net;
info.counter = &counter;
while(1){
connection_info *info = calloc(1, sizeof(connection_info));
info->net = net;
info->counter = &counter;
pthread_t worker;
int connection = accept(fd, (struct sockaddr *) &client, &client_size);
info.fd = connection;
pthread_create(&worker, NULL, (void *) &handle_connection, &info);
info->fd = connection;
pthread_create(&worker, NULL, (void *) &handle_connection, info);
++counter;
if(counter%1000==0) save_network(net, "cfg/nist.part");
}
}
@ -166,6 +169,7 @@ void client_update(network net, char *address)
/* send a message to the server */
int i;
//printf("Sending\n");
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
@ -183,6 +187,7 @@ void client_update(network net, char *address)
memset(layer.weight_updates, 0, layer.inputs*layer.outputs*sizeof(float));
}
}
//printf("Sent\n");
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
@ -203,5 +208,6 @@ void client_update(network net, char *address)
push_connected_layer(layer);
}
}
//printf("Updated\n");
close(fd);
}

1
src/softmax_layer.c
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@ -69,7 +69,6 @@ cl_kernel get_softmax_forward_kernel()
void forward_softmax_layer_gpu(const softmax_layer layer, cl_mem input)
{
cl_setup();
cl_kernel kernel = get_softmax_forward_kernel();
cl_command_queue queue = cl.queue;