/* 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/sequence_pool_op.h"

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of SequencePoolOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of SequencePoolOp should not be null.");
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    if (ctx->Attrs().Get<std::string>("pooltype") == "MAX") {
      PADDLE_ENFORCE(ctx->HasOutput("MaxIndex"),
                     "Output(MaxIndex) of SequencePoolOp should not be null.");
      ctx->SetOutputDim("MaxIndex", ctx->GetInputDim("X"));
    }
  }
};

class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  SequencePoolOpMaker(OpProto* proto, OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "(LoDTensor) The variable-length input of SequencePoolOp");
    AddOutput("Out",
              "(Tensor) The output of SequencePoolOp does not contain LoD "
              "infomation.");
    AddOutput("MaxIndex",
              "(Tensor<int>) This tensor is used for the sequence max-pooling "
              "to record the max indexes.")
        .AsIntermediate();
    AddAttr<std::string>(
        "pooltype",
        "(int, default AVERAGE) the pooling pooltype of SequencePoolOp.")
        .SetDefault("AVERAGE")
        .InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
    AddComment(R"DOC(
Sequence Pool Operator.

The SequencePoolOp pools features of all time-steps of each instance.
It supports six pooling types:
1. AVERAGE: $$Out[i] = \frac{\sum_i X_i}{N}$$
2. SUM:     $$Out[i] = \sum_jX_{ij}$$
3. SQRT:    $$Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}$$
4. LAST:    Out[i] = last instance in i-th sequence X[i]
5. FIRST:   Out[i] = first instance in i-th sequence X[i]
6. MAX:     $$Out[i] = max(X_i)$$

The following example explains how this works:
For a mini-batch of 3 variable-length sentences,
containing 2, 3, and 2 time-steps:

Assume X is a [7,M,N] LoDTensor, and X->lod()[0] = [0, 2, 5, 7], 7=2+3+2.
Besides, for the sake of simplicity, we assume M=1 and N=1,
and the value of X = [[1, 3], [2, 4, 6], [5, 1]].

Thus, Out is a [3,1,1] Tensor without LoD infomation.
And for different pooltype, the value of Out is as follows:

- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
- SUM: [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
- SQRT: [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
           6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
- MAX: [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
- LAST: [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
- FIRST: [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)

    )DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Gradient of Out should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
    auto og_dims = ctx->GetInputDim(framework::GradVarName("Out"));
    auto x_dims = ctx->GetInputDim("X");
    PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
                      "The rank of output grad must equal to Input(X).");
    for (int64_t i = 1; i < og_dims.size(); ++i) {
      PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
    }
    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
    ctx->ShareLoD("X", framework::GradVarName("X"));
  }

 protected:
  framework::OpKernelType GetActualKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(
        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
        ctx.device_context());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(sequence_pool, ops::SequencePoolOp, ops::SequencePoolOpMaker,
            sequence_pool_grad, ops::SequencePoolGradOp);
REGISTER_OP_CPU_KERNEL(
    sequence_pool,
    ops::SequencePoolKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
    sequence_pool_grad,
    ops::SequencePoolGradKernel<paddle::platform::CPUDeviceContext, float>);