text: /* Copyright (c) 2016 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/sequence_ops/sequence_pool_op.h"
  #include <memory>
  #include <string>
  
  namespace paddle {
  namespace operators {
  
  class SequencePoolOp : public framework::OperatorWithKernel {
   public:
    using framework::OperatorWithKernel::OperatorWithKernel;
  
    void InferShape(framework::InferShapeContext* ctx) const override {
      PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
                        "Input(X) of SequencePoolOp should not be null.");
      PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                        "Output(Out) of SequencePoolOp should not be null.");
  
      if (!ctx->IsRuntime()) {
        // Check the lod_level for compile-time.
        framework::VarDesc* x_desc =
            boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
        PADDLE_ENFORCE_GT(
            x_desc->GetLoDLevel(), 0,
            "The LoD level Input(X) of sequence_pool should be larger than 0");
      }
  
      ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
      if (ctx->Attrs().Get<std::string>("pooltype") == "MAX") {
        PADDLE_ENFORCE_EQ(
            ctx->HasOutput("MaxIndex"), true,
            "Output(MaxIndex) of SequencePoolOp should not be null.");
        ctx->SetOutputDim("MaxIndex", ctx->GetInputDim("X"));
      }
    }
  };
  
  class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
   public:
    void Make() override {
      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<bool>("is_test",
                    "(bool, default false) Set to true for inference only, false "
                    "for training. Some layers may run faster when this is true.")
          .SetDefault(false);
      AddAttr<std::string>(
          "pooltype",
          "(string, default 'AVERAGE') the pooling pooltype of SequencePoolOp.")
          .SetDefault("AVERAGE")
          .InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
      AddAttr<float>("pad_value",
                     "(float, default 0.0) The value to pad for empty sequence.")
          .SetDefault(0.0);
      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:     $$Out[i] = max(X_i)$$
  
  and for the empty sequence Out[i] = attr(pad_value).
  
  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_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
                        "Gradient of Out should not be null.");
      PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
                        "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->ShareDim("X", /*->*/ framework::GradVarName("X"));
      ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
    }
  
   protected:
    framework::OpKernelType GetExpectedKernelType(
        const framework::ExecutionContext& ctx) const override {
      return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
                                         ctx, framework::GradVarName("Out")),
                                     ctx.device_context());
    }
  };
  
  template <typename T>
  class SequencePoolGradOpMaker : public framework::SingleGradOpMaker<T> {
   public:
    using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
  
   protected:
    std::unique_ptr<T> Apply() const override {
      auto* op_desc_ptr = new T();
      op_desc_ptr->SetType("sequence_pool_grad");
      op_desc_ptr->SetInput("X", this->Input("X"));
      if (boost::get<std::string>(this->GetAttr("pooltype")) == "MAX") {
        op_desc_ptr->SetInput("MaxIndex", this->Output("MaxIndex"));
      }
      op_desc_ptr->SetInput(framework::GradVarName("Out"),
                            this->OutputGrad("Out"));
      op_desc_ptr->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
      op_desc_ptr->SetAttrMap(this->Attrs());
      return std::unique_ptr<T>(op_desc_ptr);
    }
  };
  
  DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
      SequencePoolGradOpNoNeedBufferVarsInference, "X");
  
  }  // namespace operators
  }  // namespace paddle
  
  namespace ops = paddle::operators;
  REGISTER_OPERATOR(sequence_pool, ops::SequencePoolOp, ops::SequencePoolOpMaker,
                    ops::SequencePoolGradOpMaker<paddle::framework::OpDesc>,
                    ops::SequencePoolGradOpMaker<paddle::imperative::OpBase>);
  REGISTER_OPERATOR(sequence_pool_grad, ops::SequencePoolGradOp,
                    ops::SequencePoolGradOpNoNeedBufferVarsInference);
  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>);
  