Fixed CUDA-implementation of back-propagation for [scale_channels]-layer

src/blas_kernels.cu

@@ -1119,17 +1119,45 @@ extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size,
 }
 
 
+__inline__ __device__
+float warpAllReduceSum(float val) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2)
+#if CUDART_VERSION >= 9000
+        val += __shfl_xor_sync(0xffffffff, val, mask);
+#else
+        val += __shfl_xor(val, mask);
+#endif
+    return val;
+}
+
 __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size,
     float *in_scales_c, float *out_from_delta,
     float *in_from_output, float *out_state_delta)
 {
     const int index = blockIdx.x*blockDim.x + threadIdx.x;
-    if (index < size) {
+    int osd_index = index / channel_size;
-        out_state_delta[index / channel_size] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from  (should be divided by channel_size?)
-        out_from_delta[index] += in_scales_c[index / channel_size] * in_w_h_c_delta[index]; // input * l.delta
 
-        //out_state_delta[index / channel_size] += in_w_h_c_delta[index] / channel_size;
+    if (index < size) {
-        //out_from_delta[index] = in_w_h_c_delta[index];
+        //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from  (should be divided by channel_size?)
+
+        int warp_id = index / 32;
+        int index_warp_start = warp_id * 32;
+        int osd_index_warp_start = index_warp_start / channel_size;
+        int osd_index_warp_end = (index_warp_start + 31) / channel_size;
+
+        if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel
+        {
+            float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from
+            if (threadIdx.x % 32 == 0) {
+                atomicAdd(&out_state_delta[osd_index], sum);
+                //out_state_delta[osd_index] += sum;
+            }
+        }
+        else {
+            atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from
+        }
+
+        out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta  // atomic isn't required here
     }
 }
 

src/scale_channels_layer.c

@@ -86,9 +86,9 @@ void backward_scale_channels_layer(const layer l, network_state state)
     int i;
     #pragma omp parallel for
     for (i = 0; i < size; ++i) {
-        state.delta[i / channel_size] += l.delta[i] * from_output[i]; // l.delta * from  (should be divided by channel_size?)
+        state.delta[i / channel_size] += l.delta[i] * from_output[i] / channel_size; // l.delta * from  (should be divided by channel_size?)
 
-        from_delta[i] = state.input[i / channel_size] * l.delta[i]; // input * l.delta
+        from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta
     }
 }
 
@@ -112,7 +112,6 @@ void backward_scale_channels_layer_gpu(const layer l, network_state state)
     float *from_output = state.net.layers[l.index].output_gpu;
     float *from_delta = state.net.layers[l.index].delta_gpu;
 
-
     backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta);
 }
 #endif