follow comments and refine code

paddle/fluid/operators/concat_op.h

@@ -33,6 +33,7 @@ class ConcatKernel : public framework::OpKernel<T> {
     auto place = ctx.GetPlace();
     out->mutable_data<T>(place);
 
+    // TODO(zcd): Sometimes direct copies will be faster
     std::vector<framework::Tensor> inputs(ins.size());
     for (size_t j = 0; j < ins.size(); ++j) {
       inputs[j] = *ins[j];
@@ -51,6 +52,7 @@ class ConcatGradKernel : public framework::OpKernel<T> {
     auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
     int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
 
+    // TODO(zcd): Sometimes direct copies will be faster
     std::vector<framework::Tensor> outputs(outs.size());
     for (size_t j = 0; j < outs.size(); ++j) {
       outs[j]->mutable_data<T>(ctx.GetPlace());

paddle/fluid/operators/math/concat.cc

@@ -19,7 +19,8 @@ namespace operators {
 namespace math {
 
 /*
- * All tensors' dimension should be the same.
+ * All tensors' dimension should be the same and the values of
+ * each dimension are the same, except the axis dimension.
  */
 template <typename T>
 class ConcatFunctor<platform::CPUDeviceContext, T> {
@@ -27,12 +28,9 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& context,
                   const std::vector<framework::Tensor>& input, const int axis,
                   framework::Tensor* output) {
-    // assume the the max size of input is less than 8 and see the performance
-    // save origin dim
+    // TODO(zcd): Add input data validity checking
     int num = input.size();
-    std::vector<paddle::framework::DDim> origin_dim(num);
 
-    // get the matrix size
     int rows = 1;
     auto dim_0 = input[0].dims();
     for (int i = 0; i < axis; ++i) {
@@ -40,7 +38,6 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
     }
     int out_rows = rows, out_cols = 0;
 
-    // get input's cols
     std::vector<int64_t> input_cols(input.size());
     for (int i = 0; i < num; ++i) {
       int t_cols = input[i].numel() / rows;
@@ -64,18 +61,19 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
   }
 };
 
+/*
+ * All tensors' dimension should be the same and the values of
+ * each dimension are the same, except the axis dimension.
+ */
 template <typename T>
 class ConcatGradFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::Tensor& input, const int axis,
                   std::vector<framework::Tensor>& outputs) {
-    // assume the the max size of input is less than 8 and see the performance
-    // save origin dim
+    // TODO(zcd): Add input data validity checking
     int num = outputs.size();
-    std::vector<paddle::framework::DDim> origin_dim(num);
 
-    // get the matrix size
     int input_rows = 1;
     auto dim_0 = outputs[0].dims();
     for (int i = 0; i < axis; ++i) {
@@ -83,7 +81,6 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
     }
     int input_cols = 0;
 
-    // get outputs' cols
     std::vector<int64_t> output_cols(outputs.size());
     for (int i = 0; i < num; ++i) {
       int t_cols = outputs[i].numel() / input_rows;

paddle/fluid/operators/math/concat.h

@@ -20,7 +20,16 @@ namespace operators {
 namespace math {
 
 /*
+ * \brief Concatenate the input tensors along the dimension axis.
+ *  TODO(zcd): maybe it needs to be more detailed.
+ *  Examples:
+ *     Input[0] = [[1,2],[3,4]]
+ *     Input[1] = [[5,6]]
+ *     axis = 0
  *
+ *     Output = [[1,2],
+ *               [3,4],
+ *               [5,6]]
  */
 template <typename DeviceContext, typename T>
 class ConcatFunctor {
@@ -30,6 +39,18 @@ class ConcatFunctor {
                   framework::Tensor* output);
 };
 
+/*
+ * \brief Split the input tensors along the dimension axis into outputs.
+ *  TODO(zcd): maybe it needs to be more detailed.
+ *  Examples:
+ *     Input = [[1,2],
+ *              [3,4],
+ *              [5,6]]
+ *     axis = 0
+ *
+ *     Output[0] = [[1,2],[3,4]]
+ *     Output[1] = [[5,6]]
+ */
 template <typename DeviceContext, typename T>
 class ConcatGradFunctor {
  public: