// Copyright (c) 2021 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/optimizers/sparse_momentum_op.h"

#include "paddle/fluid/framework/op_version_registry.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

class SparseMomentumOpInferVarType : public framework::VarTypeInference {
 public:
  void operator()(framework::InferVarTypeContext* ctx) const override {
    auto in_var_type = ctx->GetInputType("Param");
    PADDLE_ENFORCE_EQ(in_var_type == framework::proto::VarType::LOD_TENSOR,
                      true,
                      platform::errors::InvalidArgument(
                          "Only support LodTensor, Unexpected Input Type."));

    ctx->SetOutputType("ParamOut", in_var_type, framework::ALL_ELEMENTS);
  }
};

void SparseMomentumOpMaker::Make() {
  AddInput("Param",
           "(Tensor, default Tensor<float>) "
           "Input parameter that has to be updated");
  AddInput("Grad",
           "(Tensor, default Tensor<float>) "
           "Input gradient of the parameter");
  AddInput("Velocity",
           "(Tensor, default Tensor<float>) "
           "Input velocity (corresponding to the parameter) "
           "that has to be updated");
  AddInput("Index",
           "(Tensor, default Tensor<int>) "
           "Input index of Param to do update operation");
  AddInput("Axis",
           "The Tensor which contains the axis that we do update operation.")
      .AsDispensable();
  AddInput("LearningRate",
           "(Tensor, default Tensor<float>) "
           "Input learning rate");
  AddInput("MasterParam", "FP32 master weight for AMP.").AsDispensable();
  AddOutput("ParamOut",
            "(Tensor) This output is updated parameter. "
            "It shared memory with Input(Param).");
  AddOutput("VelocityOut",
            "(Tensor) This output is updated velocity. "
            "It shared memory with Input(Velocity).");
  AddOutput("MasterParamOut",
            "The updated FP32 master weight for AMP. "
            "It shared memory with Input(MasterParam).")
      .AsDispensable();

  AddAttr<float>("mu", "(float) Momentum coefficient");
  AddAttr<bool>("use_nesterov",
                "(bool, default false) "
                "Use Nesterov Momentum")
      .SetDefault(false);
  AddAttr<std::string>(
      "regularization_method",
      "(string) regularization_method, right now only support l2decay or none")
      .SetDefault("");
  AddAttr<float>("regularization_coeff", "(float) regularization_coeff")
      .SetDefault(0.0f);
  AddAttr<bool>("multi_precision",
                "(bool, default false) "
                "Whether to use multi-precision during weight updating.")
      .SetDefault(false);
  AddAttr<float>(
      "rescale_grad",
      "(float, default 1.0) Multiply the gradient with `rescale_grad`"
      "before updating. Often choose to be `1.0/batch_size`.")
      .SetDefault(1.0f);
  AddAttr<int>("axis",
               "(int, default 0) The integer which specific the axis that we "
               "do update operation.")
      .SetDefault(0);

  AddComment(R"DOC(
Sparse Momentum Optimizer.

This optimizer has a flag for Nestrov Momentum.
The update equations are as follows:

$$
velocity = mu * velocity + gradient \\
if (use\_nesterov):   \\
  param = param - (gradient + mu * velocity) * learning\_rate \\
else:   \\
  param = param - learning\_rate * velocity. \\
$$

)DOC");
}

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(
    sparse_momentum,
    ops::SparseMomentumOp,
    ops::SparseMomentumOpMaker,
    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
    ops::SparseMomentumOpInferVarType);
REGISTER_OP_CPU_KERNEL(
    sparse_momentum,
    ops::SparseMomentumOpKernel<paddle::platform::CPUDeviceContext, float>,
    ops::SparseMomentumOpKernel<paddle::platform::CPUDeviceContext, double>);
