/* 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/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"

#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/phi/infermeta/multiary.h"

namespace paddle {
namespace operators {

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

 protected:
  phi::KernelKey GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "param"),
                          ctx.GetPlace());
  }
};

class AverageAccumulatesOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("param", "(Tensor), The parameter to be accumulated.");
    AddInput("in_sum_1",
             "(Tensor), A tensor used to store the parameter "
             "sums with the same shape as input(param).");
    AddInput("in_sum_2",
             "(Tensor), A auxiliary tensor to help "
             "accumulating sums of parameter values with the same shape as "
             "input(param). It is used to avoid loss of precision due to too "
             "many sums.");
    AddInput("in_sum_3",
             "(Tensor), A auxiliary tensor to help "
             "accumulating sums of parameter values with the same shape as "
             "input(param).");
    AddInput("in_num_accumulates",
             "(Tensor<int64_t>), The accumulating times of current window with "
             "shape [1].");
    AddInput(
        "in_old_num_accumulates",
        "(Tensor<int64_t>), The accumulating times of previous window with "
        "shape [1].");
    AddInput("in_num_updates",
             "(Tensor<int64_t>), The total number of batches used by training "
             "before this batch with shape [1].");

    AddOutput("out_sum_1",
              "(Tensor), A tensor used to store the "
              "parameter sums with the same shape as input(param).");
    AddOutput("out_sum_2",
              "(Tensor), A auxiliary tensor to help "
              "accumulating sums of parameter values with the same shape as "
              "input(param). It is used to avoid loss of precision due to too "
              "many sums.");
    AddOutput("out_sum_3",
              "(Tensor), A auxiliary tensor to help "
              "accumulating sums of parameter values with the same shape as "
              "input(param).");
    AddOutput(
        "out_num_accumulates",
        "(Tensor<int64_t>), The accumulating times of current window with "
        "shape [1].");
    AddOutput(
        "out_old_num_accumulates",
        "(Tensor<int64_t>) The accumulating times of previous window with "
        "shape [1].");
    AddOutput("out_num_updates",
              "(Tensor<int64_t>), The total number of batches used by training "
              "before this batch with shape [1].");

    AddAttr<float>("average_window",
                   "(float, default 0) "
                   "The rate of average window size relative to num_updates.")
        .SetDefault(0);
    AddAttr<int64_t>("max_average_window",
                     "(int64_t) "
                     "Maximum size of average window. It suggests that the "
                     "number of mini-batches "
                     "in one pass is appropriate value to set.");
    AddAttr<int64_t>("min_average_window",
                     "(int64_t, default 10000L) "
                     "Minimu size of average window.")
        .SetDefault(10000L);

    AddComment(R"DOC(
AverageAccumulates Operator.
Accumulate the sum of parameter within sliding window. The size of sliding window is
determined by 'average_window', 'max_average_window' and 'min_average_window'.
Memory was shared by Input(in_sum_1) and Output(out_sum_1) which acts as an accumulator 'sum_1'.
'sum_2', 'sum_3', 'num_accumulates', 'old_num_accumulates' and 'num_updates' were the same as 'sum_1'.

All the accumulators were inited to zero before training.

And for a mini-batch in training, accumulators were computed as below steps:
    num_updates += 1
    num_accumulates += 1
    sum_1 += param
    if num_updates % kMaxNumAccumulates == 0:
        sum_2 += sum_1
        sum_1 = 0
    if num_accumulates >= min_average_window && num_accumulates >= min(max_average_window, num_updates * average_window):
        sum_3 = sum_1 + sum_2
        sum_1 = 0
        sum_2 = 0
        old_num_accumulates = num_accumulates
        num_accumulates = 0

)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(average_accumulates,
                            AverageAccumulatesInferShapeFunctor,
                            PD_INFER_META(phi::AverageAccumulatesInferMeta));

REGISTER_OPERATOR(
    average_accumulates,
    ops::AverageAccumulatesOp,
    ops::AverageAccumulatesOpMaker,
    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
    AverageAccumulatesInferShapeFunctor);
