/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/operators/expand_op.h"

namespace paddle {
namespace operators {

using framework::Tensor;

class ExpandOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(const framework::InferShapeContext& ctx) const override {
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
    std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
    auto x_dims = ctx.Input<Tensor>("X")->dims();

    PADDLE_ENFORCE_EQ(x_dims.size(), expand_times.size(),
                      "The number of expandTimes's value must be equal "
                      "to the rank of X.");
    PADDLE_ENFORCE_LE(x_dims.size(), 6,
                      "The rank of X must not be greater than 6.");

    std::vector<int64_t> out_shape(x_dims.size());
    for (size_t i = 0; i < expand_times.size(); ++i) {
      PADDLE_ENFORCE_GE(expand_times[i], 1,
                        "Each value of expandTimes should not be "
                        "less than 1.");
      out_shape[i] = x_dims[i] * expand_times[i];
    }
    auto* out = ctx.Output<framework::LoDTensor>("Out");
    out->Resize(framework::make_ddim(out_shape));
  }
};

class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ExpandOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X",
             "The input tensor of expand op."
             "The rank of X should be between in 1 and 6.");
    AddOutput("Out",
              "Output tensor of expand op."
              "The rank of Out is same as X except that each dimension size "
              "of Out equals to corresponding dimension size of X multiplying "
              "corresponding value of expandTimes.");
    AddAttr<std::vector<int>>("expandTimes",
                              "Expand times number for each dimension.");
    AddComment(R"DOC(
Expand operator tiles the input by given times number. You should set times
number for each dimension by providing attribute 'expandTimes'. The rank of X
should be between in 1 and 6. Please notice that size of 'expandTimes' must be
same with X's rank.
)DOC");
  }
};

class ExpandGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(const framework::InferShapeContext& ctx) const override {
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                            "Input(Out@GRAD) should not be null.");
    auto x_dims = ctx.Input<Tensor>("X")->dims();
    std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
    auto out_dims =
        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
    auto* x_grad =
        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));

    for (size_t i = 0; i < expand_times.size(); ++i) {
      PADDLE_ENFORCE_EQ(x_dims[i] * expand_times[i], out_dims[i],
                        "Each dimension size of Input(Out@GRAD) should be "
                        "equal to multiplication of crroresponding dimension "
                        "size of Input(X) and expandTimes value.");
    }

    if (x_grad) x_grad->Resize(x_dims);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(expand, ops::ExpandOp, ops::ExpandOpMaker, expand_grad,
            ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(expand,
                       ops::ExpandKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
    expand_grad, ops::ExpandGradKernel<paddle::platform::CPUPlace, float>);