/* Copyright (c) 2020 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 "mpc_adam_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include <string>

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

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

  void InferShape(framework::InferShapeContext* ctx) const override;
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override;
  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const framework::Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override;
};

void MpcAdamOp::InferShape(framework::InferShapeContext *ctx) const {
  PADDLE_ENFORCE_EQ(
      ctx->HasInput("Param"), true,
      platform::errors::NotFound("Input(Param) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(
      ctx->HasInput("Grad"), true,
      platform::errors::NotFound("Input(Grad) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasInput("Moment1"), true,
                    platform::errors::NotFound(
                        "Input(Moment1) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasInput("Moment2"), true,
                    platform::errors::NotFound(
                        "Input(Moment2) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasInput("LearningRate"), true,
                    platform::errors::NotFound(
                        "Input(LearningRate) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasInput("Beta1Pow"), true,
                    platform::errors::NotFound(
                        "Input(Beta1Pow) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasInput("Beta2Pow"), true,
                    platform::errors::NotFound(
                        "Input(Beta2Pow) of AdamOp should not be null."));

  PADDLE_ENFORCE_EQ(ctx->HasOutput("ParamOut"), true,
                    platform::errors::NotFound(
                        "Output(ParamOut) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment1Out"), true,
                    platform::errors::NotFound(
                        "Output(Moment1Out) of AdamOp should not be null."));
  PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment2Out"), true,
                    platform::errors::NotFound(
                        "Output(Moment2Out) of AdamOp should not be null."));

  auto lr_dims = ctx->GetInputDim("LearningRate");
  PADDLE_ENFORCE_NE(
      framework::product(lr_dims), 0,
      platform::errors::InvalidArgument(
          "The number of LearningRate shall not be 0, but received %d. Maybe "
          "the Input variable LearningRate has not "
          "been initialized. You may need to confirm "
          "if you put exe.run(startup_program) "
          "after optimizer.minimize function.",
          framework::product(lr_dims)));
  PADDLE_ENFORCE_EQ(
      framework::product(lr_dims), 1,
      platform::errors::InvalidArgument(
          "Learning rate should have 1 dimension, but received %d",
          framework::product(lr_dims)));
  auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
  VLOG(3) << "dims of Beta1Pow : [" << beta1_pow_dims << "]";
  PADDLE_ENFORCE_GE(framework::product(beta1_pow_dims), 1,
                    platform::errors::InvalidArgument(
                        "The size of Beta1 power accumulator should be greater "
                        "than 0, but received %d.",
                        framework::product(beta1_pow_dims)));
  auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
  VLOG(3) << "dims of Beta2Pow : [" << beta2_pow_dims << "]";
  PADDLE_ENFORCE_GE(framework::product(beta2_pow_dims), 1,
                    platform::errors::InvalidArgument(
                        "The size of Beta2 power accumulator should be greater "
                        "than 0, but received %d.",
                        framework::product(beta2_pow_dims)));

  auto param_dims = ctx->GetInputDim("Param");
  if (ctx->GetInputsVarType("Grad")[0] ==
      framework::proto::VarType::LOD_TENSOR) {
    PADDLE_ENFORCE_EQ(
        param_dims, ctx->GetInputDim("Grad"),
        platform::errors::InvalidArgument(
            "Param and Grad input of AdamOp should have same dimension. But "
            "received Param dims: [%s], Grad dims: [%s].",
            param_dims, ctx->GetInputDim("Grad")));
  }
  PADDLE_ENFORCE_EQ(
      param_dims, ctx->GetInputDim("Moment1"),
      platform::errors::InvalidArgument(
          "Param and Moment1 input of AdamOp should have same dimension. But "
          "received Param dims: [%s], Moment1 dims: [%s].",
          param_dims, ctx->GetInputDim("Moment1")));
  PADDLE_ENFORCE_EQ(
      param_dims, ctx->GetInputDim("Moment2"),
      platform::errors::InvalidArgument(
          "Param and Moment2 input of AdamOp should have same dimension. But "
          "received Param dims: [%s], Moment2 dims: [%s].",
          param_dims, ctx->GetInputDim("Moment2")));

  ctx->SetOutputDim("ParamOut", param_dims);
  ctx->SetOutputDim("Moment1Out", param_dims);
  ctx->SetOutputDim("Moment2Out", param_dims);
  ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
  ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims);
}

framework::OpKernelType MpcAdamOp::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "Param");
  return framework::OpKernelType(input_data_type, ctx.GetPlace());
}

framework::OpKernelType MpcAdamOp::GetKernelTypeForVar(
    const std::string &var_name, const framework::Tensor &tensor,
    const framework::OpKernelType &expected_kernel_type) const {
  if (var_name == "Beta1Pow" || var_name == "Beta2Pow") {
    return expected_kernel_type;
  } else {
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
}

class MpcAdamOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("Param", "(Tensor) Input parameter");
    AddInput("Grad", "(Tensor) Input gradient");
    AddInput("LearningRate", "(Tensor) Learning rate");
    AddInput("Moment1", "(Tensor) Input first moment");
    AddInput("Moment2", "(Tensor) Input second moment");
    AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
    AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");

    AddInput("Beta1Tensor",
             "(Tensor<float32>, optional) If provided, Adam will use this "
             "as beta1, this has a higher priority than attr(beta1), the "
             "shape of this tensor MUST BE [1].")
        .AsDispensable();
    AddInput("Beta2Tensor",
             "(Tensor<float32>, optional) If provided, Adam will use this "
             "as beta2, this has a higher priority than attr(beta2), the "
             "shape of this tensor MUST BE [1].")
        .AsDispensable();

    AddOutput("ParamOut", "(Tensor) Output parameter");
    AddOutput("Moment1Out", "(Tensor) Output first moment");
    AddOutput("Moment2Out", "(Tensor) Output second moment");
    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
    AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator");

    AddAttr<float>("beta1",
                   "(float, default 0.9) "
                   "Exponential decay rate for the "
                   "first moment estimates.")
        .SetDefault(0.9f);
    AddAttr<float>("beta2",
                   "(float, default 0.999) "
                   "exponential decay rate for the "
                   "second moment estimates.")
        .SetDefault(0.999f);
    AddAttr<float>("epsilon",
                   "(float, default 1.0e-4) "
                   "Constant for numerical stability")
        .SetDefault(1.0e-4f);

    AddComment(R"DOC(
Adam Optimizer.

This implements the Adam optimizer from Section 2 of the Adam
paper : https://arxiv.org/abs/1412.6980.
Adam is a first-order gradient-based optimization method based on
adaptive estimates of lower-order moments.

Adam updates:

$$
moment\_1\_out = \beta_1 * moment\_1 + (1 - \beta_1) * grad \\
moment\_2_\out = \beta_2 * moment\_2 + (1 - \beta_2) * grad * grad \\
learning\_rate = learning\_rate *
                  \frac{\sqrt{1 - \beta_{2\_pow}}}{1 - \beta_{1\_pow}} \\
param\_out = param - learning\_rate * \frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
$$

)DOC");
  }
};

template <typename T>
struct GetAttrFromTensor<platform::CPUDeviceContext, T> {
    T operator()(const framework::Tensor* t) {
        return t->data<T>()[0];
    }
};

template <typename T>
struct SetAttrToTensor<platform::CPUDeviceContext, T> {
    void operator()(framework::Tensor* t, T val) {
        t->data<T>()[0] = val;
        return;
    }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(
    mpc_adam, ops::MpcAdamOp, ops::MpcAdamOpMaker);

REGISTER_OP_CPU_KERNEL(
    mpc_adam,
    ops::MpcAdamOpKernel<paddle::platform::CPUDeviceContext, int64_t, float>);
