/* 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/gru_op.h"

namespace paddle {
namespace operators {

using framework::Tensor;

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Input"),
                   "Input(%s) of GRUOp should not be null.", "Input");
    PADDLE_ENFORCE(ctx->HasInput("Weight"),
                   "Input(%s) of GRUOp should not be null.", "Weight");
    PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
                   "Output(%s) of GRUOp should not be null.", "BatchGate");
    PADDLE_ENFORCE(ctx->HasOutput("BatchResetHiddenPrev"),
                   "Output(%s) of GRUOp should not be null.",
                   "BatchResetHiddenPrev");
    PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"),
                   "Output(%s) of GRUOp should not be null.", "BatchHidden");
    PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
                   "Output(%s) of GRUOp should not be null.", "Hidden");
    auto input_dims = ctx->GetInputDim("Input");
    auto weight_dims = ctx->GetInputDim("Weight");
    int input_size = input_dims[1];
    int frame_size = weight_dims[0];
    PADDLE_ENFORCE_EQ(input_size, frame_size * 3,
                      "The input_size must be 3 times of frame_size in GRUOp.");
    PADDLE_ENFORCE_EQ(
        weight_dims[1], frame_size * 3,
        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
    auto h0 = Input("H0");
    if (h0 != framework::kEmptyVarName) {
      auto h0_dims = ctx->GetInputDim("H0");
      PADDLE_ENFORCE_EQ(h0_dims[1], frame_size,
                        "The width of H0 must be equal to frame_size.");
    }
    auto bias = Input("Bias");
    if (bias != framework::kEmptyVarName) {
      auto bias_dims = ctx->GetInputDim("Bias");
      int bias_height = bias_dims[0];
      int bias_width = bias_dims[1];
      PADDLE_ENFORCE_EQ(bias_height, 1,
                        "The shape of Bias must be [1, frame_size * 3].");
      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
                        "The shape of Bias must be [1, frame_size * 3].");
    }
    ctx->SetOutputDim("BatchGate", input_dims);
    ctx->SetOutputDim("BatchResetHiddenPrev", {input_dims[0], frame_size});
    ctx->SetOutputDim("BatchHidden", {input_dims[0], frame_size});
    ctx->SetOutputDim("Hidden", {input_dims[0], frame_size});
    // ctx->ShareLoD("Input", "Gate");
    // ctx->ShareLoD("Input", "ResetHiddenPrev");
    ctx->ShareLoD("Input", "Hidden");
  }
};

class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  GRUOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("Input",
             "(LoDTensor) the first input is a LodTensor, which support "
             "variable-time length input sequence. The underlying tensor in "
             "this LoDTenosr is a matrix with shape (T X 3D), where, T is the "
             "total time steps in this mini-batch, D is the hidden size.");
    AddInput("H0",
             "(Tensor, optional) the initial hidden state is an optional "
             "input. This is a tensor with shape (N x D), where N is the "
             "batch size, D is the hidden size.");
    AddInput(
        "Weight",
        "(Tensor) Weight matrix with shape [hidden_size, hidden_size * 3]. "
        "The elements continuous in memory can be divided into two parts. "
        "The first part are weights of the update gate and reset gate "
        "with shape [hidden_size, hidden_size * 2], and the second part are "
        "weights of output candidate with shape [hidden_size, hidden_size]");
    AddInput("Bias",
             "(Tensor) Bias vector with shape [1, hidden_size * 3] concating "
             "bias of the update gate, reset gate and output candidate.");
    AddOutput("BatchGate",
              "(LoDTensor) the update gata, reset gate and output candidate "
              "lod tensor of GRU operator. "
              "The shape and lod is the same with the `Input`.")
        .AsIntermediate();
    AddOutput(
        "BatchResetHiddenPrev",
        "(LoDTensor) the reseted hidden state lod tensor of GRU operator. "
        "The shape and lod is the same with the `Input`.")
        .AsIntermediate();
    AddOutput(
        "BatchHidden",
        "(LoDTensor) the reseted hidden state lod tensor of GRU operator. "
        "The shape and lod is the same with the `Input`.")
        .AsIntermediate();
    AddOutput("Hidden",
              "(LoDTensor) the hidden state lod tensor of GRU operator. "
              "The shape and lod is the same with the `Input`.");
    AddAttr<std::string>("activation",
                         "(string, default tanh) "
                         "The activation type used for output candidate {h}_t.")
        .SetDefault("tanh");
    AddAttr<std::string>(
        "gate_activation",
        "(string, default sigmoid) "
        "The activation type used in update gate and reset gate.")
        .SetDefault("sigmoid");
    AddAttr<bool>("is_reverse",
                  "(bool, defalut: False) "
                  "whether to compute reversed GRU.")
        .SetDefault(false);
    AddComment(R"DOC(
GRUOp implements part calculations of the GRU unit as following:
\f[
update \ gate: u_t = actGate(xu_t + W_u * hidden_prev + bias_u) \\
reset \ gate: r_t = actGate(xr_t + W_r * hidden_prev + bias_r)  \\
output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, hidden_prev) + bias_c) \\
output: h_t = dot((1-u_t), hidden_prev) + dot(u_t, {h}_t)
\f]
The rest of GRU unit can be completed by using FCOp's output as the input of GRUOp.
)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Input"),
                   "Input(%s) of GRUGradOp should not be null.", "Input");
    PADDLE_ENFORCE(ctx->HasInput("Weight"),
                   "Input(%s) of GRUGradOp should not be null.", "Weight");
    PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
                   "Input(%s) of GRUGradOp should not be null.", "BatchGate");
    PADDLE_ENFORCE(ctx->HasInput("BatchResetHiddenPrev"),
                   "Input(%s) of GRUGradOp should not be null.",
                   "BatchResetHiddenPrev");
    PADDLE_ENFORCE(ctx->HasInput("BatchHidden"),
                   "Input(%s) of GRUOp should not be null.", "BatchHidden");
    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
                   "Input(%s) of GRUGradOp should not be null.", "Hidden");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
                   "Input(%s@GRAD) of GRUGradOp should not be null.", "Hidden");
    auto input_dims = ctx->GetInputDim("Input");
    auto weight_dims = ctx->GetInputDim("Weight");
    int input_size = input_dims[1];
    int frame_size = weight_dims[0];
    int weight_height = weight_dims[0];
    int weight_width = weight_dims[1];
    PADDLE_ENFORCE_EQ(input_size, frame_size * 3,
                      "The input_size must be 3 times of frame_size in GRUOp.");
    PADDLE_ENFORCE_EQ(
        weight_height, frame_size,
        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
    PADDLE_ENFORCE_EQ(
        weight_width, frame_size * 3,
        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
    auto h0 = Input("H0");
    if (h0 != framework::kEmptyVarName) {
      auto h0_dims = ctx->GetInputDim("H0");
      PADDLE_ENFORCE_EQ(h0_dims[1], frame_size,
                        "The width of H0 must be equal to frame_size.");
      auto h0_grad_name = framework::GradVarName("H0");
      if (ctx->HasOutput(h0_grad_name))
        ctx->SetOutputDim(h0_grad_name, h0_dims);
    }
    auto bias = Input("Bias");
    if (bias != framework::kEmptyVarName) {
      auto bias_dims = ctx->GetInputDim("Bias");
      int bias_height = bias_dims[0];
      int bias_width = bias_dims[1];
      PADDLE_ENFORCE_EQ(bias_height, 1,
                        "The shape of Bias must be [1, frame_size * 3].");
      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
                        "The shape of Bias must be [1, frame_size * 3].");
      auto bias_grad_name = framework::GradVarName("Bias");
      if (ctx->HasOutput(bias_grad_name))
        ctx->SetOutputDim(bias_grad_name, bias_dims);
    }
    auto input_grad_name = framework::GradVarName("Input");
    if (ctx->HasOutput(input_grad_name))
      ctx->SetOutputDim(input_grad_name, input_dims);
    auto weight_grad_name = framework::GradVarName("Weight");
    if (ctx->HasOutput(weight_grad_name))
      ctx->SetOutputDim(weight_grad_name, weight_dims);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(gru, ops::GRUOp, ops::GRUOpMaker, gru_grad, ops::GRUGradOp);
REGISTER_OP_CPU_KERNEL(gru, ops::GRUKernel<paddle::platform::CPUPlace, float>,
                       ops::GRUKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(gru_grad,
                       ops::GRUGradKernel<paddle::platform::CPUPlace, float>,
                       ops::GRUGradKernel<paddle::platform::CPUPlace, double>);