/* 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/mul_op.h"

namespace paddle {
namespace operators {

using framework::Tensor;

class MulOpShapeInference : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of MulOp should not be null.");

    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");

    int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
    int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");

    VLOG(3) << "mul operator x.shape=" << x_dims << " y.shape=" << y_dims
            << " x_num_col_dims=" << x_num_col_dims
            << " y_num_col_dims=" << y_num_col_dims;

    PADDLE_ENFORCE_GT(
        x_dims.size(), x_num_col_dims,
        "The input tensor X's rank of MulOp should be larger than "
        "x_num_col_dims.");
    PADDLE_ENFORCE_GT(
        y_dims.size(), y_num_col_dims,
        "The input tensor Y's rank of MulOp should be larger than "
        "y_num_col_dims.");

    auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
    auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);

    PADDLE_ENFORCE_EQ(
        x_mat_dims[1], y_mat_dims[0],
        "First matrix's width must be equal with second matrix's height.");
    std::vector<int64_t> output_dims;
    output_dims.reserve(
        static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));

    for (int i = 0; i < x_num_col_dims; ++i) {
      output_dims.push_back(x_dims[i]);
    }

    for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
      output_dims.push_back(y_dims[i]);
    }

    ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
    ctx->ShareLoD("X", /*->*/ "Out");
  }
};

class MulOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  MulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "The first input tensor of the mul op.");
    AddInput("Y", "The second input tensor of the mul op.");
    AddOutput("Out", "The output tensor of the mul op.");
    AddAttr<int>(
        "x_num_col_dims",
        "(int, default 1) "
        R"DOC(The mul_op can take tensors with more than two dimensions as its
              inputs. If the input `X` is a tensor with more than two
              dimensions, `X` will be flatten into a two-dimensional matrix
              first. The flatten rule is: the first `num_col_dims` will be
              flatten to form the first dimension of the matrix (height of the
              matrix), and the rest `rank(X) - num_col_dims` dimensions are
             flattened to form the second dimension of the matrix (width of the
             matrix). As a result, height of the flattened matrix is equal to
             the product of `X`'s first `x_num_col_dims` dimensions' sizes,
             and width of the flattened matrix is equal to the product of `X`'s
             last `rank(x) - num_col_dims` dimensions' size.
             For example, suppose `X` is a 6-dimensional tensor with the shape
             [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Then, the flattened
             matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
        )DOC")
        .SetDefault(1)
        .EqualGreaterThan(1);
    AddAttr<int>(
        "y_num_col_dims",
        "(int, default 1) "
        R"DOC(The mul_op can take tensors with more than two dimensions as its
              inputs. If the input `Y` is a tensor with more than two
              dimensions, `Y` will be flatten into a two-dimensional matrix
              first. The attribute `y_num_col_dims` is used to flatten `Y` into
              a two-dimensional matrix. See the comments of `x_num_col_dims` for
              more details.
        )DOC")
        .SetDefault(1)
        .EqualGreaterThan(1);
    AddComment(R"DOC(
Mul Operator. 

This operator is used to perform matrix multiplication for input X and Y.

The equation is:

    $$Out = X * Y$$

Both the input `X` and `Y` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input `X`.

)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Input(Out@GRAD) should not be null");
    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");
    auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));

    auto x_mat_dims = framework::flatten_to_2d(
        x_dims, ctx->Attrs().Get<int>("x_num_col_dims"));
    auto y_mat_dims = framework::flatten_to_2d(
        y_dims, ctx->Attrs().Get<int>("y_num_col_dims"));

    auto x_grad_name = framework::GradVarName("X");
    auto y_grad_name = framework::GradVarName("Y");

    if (ctx->HasOutput(x_grad_name)) {
      ctx->SetOutputDim(x_grad_name, x_dims);
    }
    if (ctx->HasOutput(y_grad_name)) {
      ctx->SetOutputDim(y_grad_name, y_dims);
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(mul, paddle::framework::OperatorWithKernel, ops::MulOpMaker,
                  ops::MulOpShapeInference,
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(
    mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
    mul_grad, ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);