/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

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/fluid/operators/fused_elemwise_activation_op.h"
#include <string>
#include <vector>

namespace paddle {
namespace operators {

/*
 * Whether the compound function is Unary(Binary(X, Y)).
 * For Unary(Binary(X, Y)), the intermediate_out's shape is the same the final
 * out.
 */
static bool IsUnaryCompound(const std::vector<std::string> &functor_list) {
  PADDLE_ENFORCE_EQ(functor_list.size(), 2);
  static std::unordered_set<std::string> binary_fun = {
      "elementwise_add", "elementwise_mul", "elementwise_add_grad",
      "elementwise_mul_grad"};
  return binary_fun.count(functor_list[1]) != 0;
}

/*
 * Whether the Input(X) could be absent.
 */
static bool InputXCanBeAbsent(const std::vector<std::string> &functor_list) {
  PADDLE_ENFORCE_EQ(functor_list.size(), 2);
  static std::unordered_set<std::string> binary_fun = {"elementwise_add_grad"};
  return binary_fun.count(functor_list[0]) != 0 ||
         binary_fun.count(functor_list[1]) != 0;
}

/*
 * Whether the compound function is supported.
 * For Unary(Binary(X, Y)), the intermediate_out's shape is the same the final
 * out.
 */
static bool IsSupportedCompound(const std::vector<std::string> &functors) {
  static std::unordered_set<std::string> unary_fun = {"scale", "relu"};
  static std::unordered_set<std::string> binary_fun = {"elementwise_add",
                                                       "elementwise_mul"};

  std::string unary_fun_str;
  if (binary_fun.count(functors[0])) {
    unary_fun_str = functors[1];
  } else if (binary_fun.count(functors[1])) {
    unary_fun_str = functors[0];
  } else {
    PADDLE_THROW("%s and %s are not included in fused_list.", functors[0],
                 functors[1]);
  }
  PADDLE_ENFORCE_EQ(unary_fun.count(unary_fun_str), 1,
                    "%s is not included in fused_list.", unary_fun_str);
  return true;
}

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

  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE(
        ctx->HasInput("X"),
        "Input(X) of FusedElemwiseActivationOp op should not be null.");
    PADDLE_ENFORCE(
        ctx->HasInput("Y"),
        "Input(Y) of FusedElemwiseActivationOp op should not be null.");
    PADDLE_ENFORCE(
        ctx->HasOutput("Out"),
        "Output(Out) of FusedElemwiseActivationOp op should not be null.");

    auto x_dim = ctx->GetInputDim("X");
    auto y_dim = ctx->GetInputDim("Y");

    // Whether the shape of Y is a continuous subsequence of X,
    // For more information please refer to the op's introduction.
    bool bcast_y = x_dim.size() >= y_dim.size();
    if (x_dim.size() == y_dim.size()) {
      for (int i = 0; i < x_dim.size(); ++i) {
        if (x_dim[i] < y_dim[i]) {
          bcast_y = false;
          break;
        }
      }
    }

    auto &out_dim = bcast_y ? x_dim : y_dim;
    std::string out_lod = bcast_y ? "X" : "Y";

    if (ctx->Attrs().Get<bool>("keep_intermediate_value")) {
      PADDLE_ENFORCE(ctx->HasOutput("IntermediateOut"),
                     "Output(IntermediateOut) of FusedElemwiseActivationOp "
                     "should not be null.");

      if (IsUnaryCompound(
              ctx->Attrs().Get<std::vector<std::string>>("functor_list"))) {
        // for Unary(Binary(X, Y)), the shape and lod of out and
        // intermediate_out are the same.
        ctx->SetOutputDim("IntermediateOut", out_dim);
        // set the lod of intermediate_out
        ctx->ShareLoD(out_lod, /*->*/ "IntermediateOut");
      } else {
        // for Binary(X, Unary(Y)), the shape and lod of Y and
        // intermediate_out are the same.
        ctx->SetOutputDim("IntermediateOut", y_dim);
        // set the lod of intermediate_out
        ctx->ShareLoD("Y", /*->*/ "IntermediateOut");
      }
    }
    ctx->SetOutputDim("Out", out_dim);
    ctx->ShareLoD(out_lod, /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    PADDLE_ENFORCE_EQ(ctx.Input<framework::Tensor>("X")->type(),
                      ctx.Input<framework::Tensor>("Y")->type(),
                      "The element's type of input should be the same.");
    auto input_data_type =
        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type());
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }
};

class FusedElemwiseActivationMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput(
        "X",
        "(Tensor) The input tensor of fused_elemwise_activation operator.");
    AddInput(
        "Y",
        "(Tensor) The input tensor of fused_elemwise_activation operator.");
    AddOutput("Out",
              "vector<Tensor> The output tensor of fused_elemwise_activation "
              "operator.");
    AddOutput("IntermediateOut",
              "Tensor The IntermediateOut tensor of fused_elemwise_activation "
              "operator.")
        .AsIntermediate();
    AddAttr<int>("axis",
                 "axis is used by elementwise_op, the default value is -1.")
        .SetDefault(-1);
    AddAttr<float>("scale",
                   "scale is used by scale_op, the default value is 0.0.")
        .SetDefault(0.0);
    AddAttr<bool>(
        "recomputation",
        "Whether to recompute the Out."
        "The computation of fused_elemwise_activation_grad has two methods to "
        "get the dx and dy, one is to use the 'Out', and the other is not. "
        "The former method will save the time of recomputing the 'Out', but it "
        "must occupy the memory to store the 'out'. While, the later method "
        "can avoid occupying the memory, but it must recompute the 'Out'. "
        "It is useful for Unary(Binary(X, Y)). The default value is true.")
        .SetDefault(true);
    AddAttr<bool>("keep_intermediate_value",
                  "Whether to save the intermediate_out.")
        .SetDefault(false);
    AddAttr<std::vector<std::string>>("functor_list",
                                      "The functors that should be fused.")
        .AddCustomChecker([&](const std::vector<std::string> &functor_list) {
          PADDLE_ENFORCE(IsSupportedCompound(functor_list));
        });

    AddComment(R"DOC(
FusedElemwiseActivation Operator.

At present, FusedElemwiseActivation only supports Two kinds of compound
operators (elementwise_op and activation_op):

    Z = Binary(X, Unary(Y))
    Z = Unary(Binary(X, Y))

There are two cases for this operator:

1. The shape of $Y$ and $X$ is the same.
2. The shape of $Y$ is a continuous subsequence of $X$ or the shape of $X$ is a continuous subsequence of $Y$.

For case 2 (assume that the shape of $Y$ is a continuous subsequence of $X$ ):

1. Broadcast $Y$ to match the shape of $X$, where $axis$ is the start dimension index
   for broadcasting $Y$ onto $X$.
2. If $axis$ is -1 (default), $axis = rank(X) - rank(Y)$.
3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of
   subsequence, such as shape(Y) = (2, 1) => (2).

For example:

  .. code-block:: python

    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
    shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
    shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
    shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
    shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0


The inputs $X$ and $Y$ can carry the different LoD information.
But the output only shares the LoD information with the one whose shape is the same with Out.
The attributions of activation_op can be get from fused_elemwise_activation_op's.
The functor_list records the functions to be fused, for example
["scale", "elementwise_add"].

)DOC");
  }
};

class FusedElemwiseActivationGradMaker
    : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  std::unique_ptr<framework::OpDesc> Apply() const override {
    auto *op_desc_ptr = new framework::OpDesc();
    op_desc_ptr->SetType(this->ForwardOpType() + "_grad");

    for (auto &input_param : this->InputNames()) {
      op_desc_ptr->SetInput(input_param, this->Input(input_param));
      op_desc_ptr->SetOutput(framework::GradVarName(input_param),
                             this->InputGrad(input_param, true));
    }

    for (auto &output_param : this->OutputNames()) {
      op_desc_ptr->SetInput(output_param, this->Output(output_param));
      op_desc_ptr->SetInput(framework::GradVarName(output_param),
                            this->OutputGrad(output_param));
    }

    op_desc_ptr->SetAttrMap(this->Attrs());

    std::vector<std::string> functor_names =
        boost::get<std::vector<std::string>>(
            op_desc_ptr->GetAttr("functor_list"));
    functor_names[0] += "_grad";
    functor_names[1] += "_grad";
    op_desc_ptr->SetAttr("functor_list", functor_names);
    return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
  }
};

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

  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Input(Out@Grad) should not be null");
    if (ctx->Attrs().Get<bool>("keep_intermediate_value")) {
      PADDLE_ENFORCE(ctx->HasInput("IntermediateOut"),
                     "Input(IntermediateOut) should not be null");
    } else {
      PADDLE_ENFORCE_EQ(ctx->Inputs(framework::GradVarName("Out")).size(), 1);
    }

    auto funtor_list =
        ctx->Attrs().Get<std::vector<std::string>>("functor_list");
    auto x_grad_name = framework::GradVarName("X");
    auto y_grad_name = framework::GradVarName("Y");

    if (ctx->HasOutput(x_grad_name)) {
      if (ctx->HasInputs("X")) {
        ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
        ctx->ShareLoD("X", x_grad_name);
      } else {
        // Node: If "X" is absence, the shape of Y should be a continuous
        // subsequence of X, if not, we could not infer the shape of dx.

        // Currently, only when Binary is elementwise_add or elementwise_sub,
        // the "X" could be absent.
        PADDLE_ENFORCE(InputXCanBeAbsent(funtor_list),
                       "Only when BinaryFunctor is elementwise_add, the 'X' "
                       "could be absent.");

        // For Unary(Binary(X, Y)), IntermediateOut should not be empty.
        if (IsUnaryCompound(funtor_list)) {
          PADDLE_ENFORCE(
              ctx->HasInputs("IntermediateOut"),
              "If the compound_functor is Unary(Binary(X, Y)) and Binary "
              "is elementwise_add, the intermediate_out must be not absent.");
        }

        ctx->SetOutputDim(x_grad_name,
                          ctx->GetInputDim(framework::GradVarName("Out")));
        ctx->ShareLoD(framework::GradVarName("Out"), x_grad_name);
      }
    }
    if (ctx->HasOutput(y_grad_name)) {
      PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
      ctx->SetOutputDim(y_grad_name, ctx->GetInputDim("Y"));
      ctx->ShareLoD("Y", y_grad_name);
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    //    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
    auto input_data_type_index = ctx.Input<framework::Tensor>("Y")->type();
    auto input_data_type = framework::ToDataType(input_data_type_index);
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }
};
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(fused_elemwise_activation, ops::FusedElemwiseActivationOp,
                  ops::FusedElemwiseActivationMaker,
                  ops::FusedElemwiseActivationGradMaker);
REGISTER_OPERATOR(fused_elemwise_activation_grad,
                  ops::FusedElemwiseActivationOpGrad);

REGISTER_OP_CPU_KERNEL(
    fused_elemwise_activation,
    ops::FusedElemwiseActivationKernel<paddle::platform::CPUDeviceContext,
                                       float>,
    ops::FusedElemwiseActivationKernel<paddle::platform::CPUDeviceContext,
                                       double>);

REGISTER_OP_CPU_KERNEL(
    fused_elemwise_activation_grad,
    ops::FusedElemwiseActivationGradKernel<paddle::platform::CPUDeviceContext,
                                           float>,
    ops::FusedElemwiseActivationGradKernel<paddle::platform::CPUDeviceContext,
                                           double>);