comments and code style

src/ngraph/runtime/gpu/gpu_cuda_kernel_emitters.cpp

@@ -74,10 +74,10 @@ namespace ngraph
                     std::string kernel;
                     std::string data_type("float");
 
-                                    kernel = R"(
+                    kernel = R"(
 extern "C" __global__
-void cuda_)" + name + "(" +
-                         data_type + "* in, " + data_type + "* out, size_t m, size_t k, size_t n)\n" + R"(
+void cuda_)" + name + "(" + data_type +
+                             "* in, " + data_type + "* out, size_t m, size_t k, size_t n)\n" + R"(
 {
     size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
     if(tid < n)

src/ngraph/runtime/gpu/gpu_emitter.cpp

@@ -462,6 +462,7 @@ void runtime::gpu::GPU_Emitter::EmitBroadcast(
     auto result_shape = out[0].get_shape();
 
     auto& axes = broadcast->get_broadcast_axes();
+    //broadcast axes is empty, do a copy
     if (axes.empty())
     {
         writer << "{   // " << n->get_name() << " \n";
@@ -473,8 +474,10 @@ void runtime::gpu::GPU_Emitter::EmitBroadcast(
         return;
     }
 
+    //broadcast axes size is 1, or can be group to 1 (serveral continuous axes, like 01 or 12 or 123 etc)
     vector<int> axes_v;
     std::copy(axes.begin(), axes.end(), std::back_inserter(axes_v));
+    std::sort(axes_v.begin(), axes_v.end());
     bool is_one_axes = true;
     if (axes.size() != 1)
     {
@@ -490,13 +493,13 @@ void runtime::gpu::GPU_Emitter::EmitBroadcast(
     if (is_one_axes)
     {
         int repeat_times = 1;
-        for (int i = 0; i < axes.size(); i++)
+        for (int i = 0; i < axes_v.size(); i++)
         {
             repeat_times *= result_shape[axes_v[i]];
         }
 
         int repeat_size = 1;
-        for (int i = *axes.rbegin(); i < result_shape.size(); i++)
+        for (int i = *axes_v.rbegin() + 1; i < result_shape.size(); i++)
         {
             repeat_size *= result_shape[i];
         }