tree things, tree stuff

Makefile

@@ -10,7 +10,7 @@ EXEC=darknet
 OBJDIR=./obj/
 
 CC=gcc
-NVCC=nvcc
+NVCC=nvcc 
 OPTS=-Ofast
 LDFLAGS= -lm -pthread 
 COMMON= 

src/blas.h

@@ -77,7 +77,7 @@ void mult_add_into_gpu(int num, float *a, float *b, float *c);
 
 void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out);
 
-void softmax_gpu(float *input, int n, int groups, float temp, float *output, cudaStream_t stream);
+void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output);
 
 #endif
 #endif

src/blas_kernels.cu

@@ -693,31 +693,35 @@ extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c)
 }
 
 
-__global__ void softmax_kernel(int n, int batch, float *input, float temp, float *output)
+__device__ void softmax_device(int n, float *input, float temp, float *output)
 {
-    int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
-    if(b >= batch) return;
-
     int i;
     float sum = 0;
     float largest = -INFINITY;
     for(i = 0; i < n; ++i){
-        int val = input[i+b*n];
+        int val = input[i];
         largest = (val>largest) ? val : largest;
     }
     for(i = 0; i < n; ++i){
-        sum += exp(input[i+b*n]/temp-largest/temp);
+        sum += exp(input[i]/temp-largest/temp);
     }
     sum = (sum != 0) ? largest/temp+log(sum) : largest-100;
     for(i = 0; i < n; ++i){
-        output[i+b*n] = exp(input[i+b*n]/temp-sum);
+        output[i] = exp(input[i]/temp-sum);
     }
 }
 
-extern "C" void softmax_gpu(float *input, int n, int groups, float temp, float *output, cudaStream_t stream)
+__global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output)
+{
+    int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(b >= batch) return;
+    softmax_device(n, input + b*offset, temp, output + b*offset);
+}
+
+extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output)
 {
     int inputs = n;
     int batch = groups;
-    softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, stream>>>(inputs, batch, input, temp, output);
+    softmax_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, offset, batch, input, temp, output);
     check_error(cudaPeekAtLastError());
 }

src/network_kernels.cu

@@ -134,6 +134,7 @@ void *train_thread(void *ptr)
     free(ptr);
     cuda_set_device(args.net.gpu_index);
     *args.err = train_network(args.net, args.d);
+    printf("%d\n", args.net.gpu_index);
     return 0;
 }
 
@@ -359,11 +360,14 @@ float train_networks(network *nets, int n, data d, int interval)
         //printf("%f\n", errors[i]);
         sum += errors[i];
     }
+    //cudaDeviceSynchronize();
     if (get_current_batch(nets[0]) % interval == 0) {
         printf("Syncing... ");
+        fflush(stdout);
         sync_nets(nets, n, interval);
         printf("Done!\n");
     }
+    //cudaDeviceSynchronize();
     free(threads);
     free(errors);
     return (float)sum/(n);

src/softmax_layer.c

@@ -73,37 +73,16 @@ void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
 {
     int inputs = l.inputs / l.groups;
     int batch = l.batch * l.groups;
-    int b;
     if(l.softmax_tree){
-        if(0){
-            float *buff = calloc(inputs * batch, sizeof(float));
-            cuda_pull_array(state.input, buff, batch * inputs);
-            state.input = buff;
-            forward_softmax_layer(l, state);
-            cuda_push_array(l.output_gpu, l.output, batch*inputs);
-            free(buff);
-        } else {
-            int i;
-            const int nstreams = 32;
-            cudaStream_t streams[nstreams];
-            for (i = 0; i < nstreams; ++i) {
-                cudaStreamCreate(&streams[i]);
-            }
-            for (b = 0; b < batch; ++b) {
-                int i;
-                int count = 0;
-                for (i = 0; i < l.softmax_tree->groups; ++i) {
-                    int group_size = l.softmax_tree->group_size[i];
-                    softmax_gpu(state.input+b*inputs + count, group_size, 1, l.temperature, l.output_gpu+b*inputs + count, streams[(b*l.softmax_tree->groups + i) % nstreams]);
-                    count += group_size;
-                }
-            }
-            for(i = 0; i < nstreams; ++i){
-                cudaStreamDestroy(streams[i]);
-            }
+        int i;
+        int count = 0;
+        for (i = 0; i < l.softmax_tree->groups; ++i) {
+            int group_size = l.softmax_tree->group_size[i];
+            softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count);
+            count += group_size;
         }
     } else {
-        softmax_gpu(state.input, inputs, batch, l.temperature, l.output_gpu, 0);
+        softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu);
     }
 }