/* 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/gather_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/op_version_registry.h"

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
                      platform::errors::InvalidArgument(
                          "Input(X) of GatherOp should not be null."));
    PADDLE_ENFORCE_EQ(ctx->HasInput("Index"), true,
                      platform::errors::InvalidArgument(
                          "Input(Index) of GatherOp should not be null."));
    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                      platform::errors::InvalidArgument(
                          "Output(Out) of GatherOp should not be null."));

    auto index_dims = ctx->GetInputDim("Index");

    if (index_dims.size() == 2) {
      PADDLE_ENFORCE_EQ(
          index_dims[1], 1,
          platform::errors::InvalidArgument(
              "The last dim of index should be 1 when it is 2D, but we get %d",
              index_dims[1]));
    } else {
      PADDLE_ENFORCE_EQ(
          index_dims.size(), 1,
          platform::errors::InvalidArgument(
              "The index should be 1D, when it is not 2D, but we get %d",
              index_dims.size()));
    }

    auto axis = ctx->Attrs().Get<int>("axis");
    auto input_dim = ctx->GetInputDim("X");
    if (ctx->HasInput("Axis") || axis == 0) {
      // if HasInput("Axis"), we can not obtain correct shape of output
      int batch_size = index_dims[0];
      framework::DDim output_dims(input_dim);
      output_dims[0] = batch_size;
      ctx->SetOutputDim("Out", output_dims);
      ctx->ShareLoD("X", /*->*/ "Out");
    } else {
      int index_size = index_dims[0];
      std::vector<int> out_dim_vec;
      for (int i = 0; i < axis; i++) {
        out_dim_vec.push_back(input_dim[i]);
      }
      out_dim_vec.push_back(index_size);
      for (int i = axis + 1; i < input_dim.size(); i++) {
        out_dim_vec.push_back(input_dim[i]);
      }
      auto output_dims = framework::make_ddim(out_dim_vec);
      ctx->SetOutputDim("Out", output_dims);
      ctx->ShareLoD("X", /*->*/ "Out");
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(
        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
        ctx.device_context());
  }
  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const framework::Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override {
    if (var_name == "Axis") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("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());
  }
  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const framework::Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override {
    if (var_name == "Axis") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

class GatherOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "The source input of gather op");
    AddInput("Index", "The index input of gather op");
    AddInput("Axis",
             "The Tensor which contains the axis that we do gather operation.")
        .AsDispensable();
    AddOutput("Out", "The output of gather op");
    AddAttr<bool>(
        "overwrite",
        "(bool, default: False) "
        "In backward process, calc the grad when has same index,"
        "If true, update the grad using the overwrite mode in same index,"
        "If false, using the accumulate mode in same index.")
        .SetDefault(true)
        .AsExtra();
    AddAttr<int>(
        "axis",
        "The Tensor which contains the axis that we do gather operation.")
        .SetDefault(0);
    AddComment(R"DOC(
Gather Operator.

$Out = X[Index]$

Out is obtained by gathering entries of the outer-most dimension
of X indexed by Index and concatenate them together.

Example:

X = [[1, 2],
     [3, 4],
     [5, 6]]

Index = [[1, 2]]

Then:

Out = [[3, 4],
       [5, 6]]

)DOC");
  }
};

template <typename T>
class GatherGradOpMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("gather_grad");
    op->SetInput("Index", this->Input("Index"));
    op->SetInput("Axis", this->Input("Axis"));

    op->SetInput("X", this->Input("X"));
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetAttrMap(this->Attrs());
  }
};

DECLARE_NO_NEED_BUFFER_VARS_INFERER(GatherGradNoNeedBufferVarInferer, "X");

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(gather, ops::GatherOp, ops::GatherOpMaker,
                  ops::GatherGradOpMaker<paddle::framework::OpDesc>,
                  ops::GatherGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(gather_grad, ops::GatherGradOp,
                  ops::GatherGradNoNeedBufferVarInferer);
REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel<float>,
                       ops::GatherOpKernel<double>, ops::GatherOpKernel<int>,
                       ops::GatherOpKernel<uint8_t>,
                       ops::GatherOpKernel<int64_t>);
REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel<float>,
                       ops::GatherGradientOpKernel<double>,
                       ops::GatherGradientOpKernel<int>,
                       ops::GatherGradientOpKernel<uint8_t>,
                       ops::GatherGradientOpKernel<int64_t>);
REGISTER_OP_VERSION(gather)
    .AddCheckpoint(R"ROC(upgrad gather, add a new input [Axis])ROC",
                   paddle::framework::compatible::OpVersionDesc().NewInput(
                       "Axis", "Specify the axis of gather operation."));
