/* 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/lookup_table_op.h"
#include "paddle/fluid/framework/var_type_inference.h"

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("W"),
                   "Input(W) of LookupTableOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Ids"),
                   "Input(Ids) of LookupTableOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of LookupTableOp should not be null.");

    auto table_dims = ctx->GetInputDim("W");
    auto ids_dims = ctx->GetInputDim("Ids");

    auto ids_var_type = ctx->GetInputsVarType("Ids").front();
    // lookup_table and concat_rows use the same InferShape, for lookup_table,
    // ids_var_type should be LoDTensor, for concat_rows, it should be
    // SelectedRows.
    if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
      PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
      PADDLE_ENFORCE_EQ(ids_dims[1], 1);
    }

    ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
    ctx->ShareLoD("Ids", /*->*/ "Out");
  }

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

class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  LookupTableOpMaker(OpProto* proto, OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("W",
             "An input represents embedding tensors, "
             "which is a learnable parameter.");
    AddInput("Ids",
             "An input with type int32 or int64 "
             "contains the ids to be looked up in W. "
             "Ids must be a column vector with rank = 2. "
             "The 2nd dimension size must be 1.");
    AddOutput("Out", "The lookup results, which have the same type as W.");
    AddAttr<bool>("is_sparse",
                  "(boolean, default false) "
                  "Sparse update")
        .SetDefault(false);
    AddAttr<int64_t>("padding_idx",
                     "(int64, default -1) "
                     "If the value is -1, it makes no effect to lookup. "
                     "Otherwise the given value indicates padding the output "
                     "with zeros whenever lookup encounters it in Ids.")
        .SetDefault(-1);
    AddComment(R"DOC(
Lookup Table Operator.

This operator is used to perform lookups on the parameter W,
then concatenated into a dense tensor.

The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.

)DOC");
  }
};

class ConcatRowsOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ConcatRowsOpMaker(OpProto* proto, OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("W",
             "(Tensor) The input tensor of concat_rows operator. "
             "The rank of this tensor is 2.");
    AddInput(
        "Ids",
        "(SelectedRows) The rows of Ids contains the index to be looked up "
        "in W.");
    AddOutput("Out",
              "(SelectedRows or Tensor) The result of concatenating, which "
              "have the same type as W.");
    AddAttr<bool>("is_sparse",
                  "(boolean, default true) This attribution is invalid, it's "
                  "only used by `Lookup Table Operator`.")
        .SetDefault(true);
    AddAttr<int64_t>("padding_idx",
                     "(int64, default -1) "
                     "If the value is -1, it makes no effect to lookup. "
                     "Otherwise the given value indicates padding the output "
                     "with zeros whenever lookup encounters it in Ids.")
        .SetDefault(-1);

    AddComment(R"DOC(
ConcatRows Operator.

This operator is used to perform lookups on the W(dense tensor) according to
rows contained by Idx(sparse tensor), then concatenates them into a sparse
tensor or dense tensor.

The type of Ids(Input) is SelectedRows.

)DOC");
  }
};

class LookupTableOpGradDescMaker
    : public framework::DefaultGradOpDescMaker<true> {
  using ::paddle::framework::DefaultGradOpDescMaker<
      true>::DefaultGradOpDescMaker;

 protected:
  virtual std::string GradOpType() const { return "lookup_table_grad"; }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    auto table_dims = ctx->GetInputDim("W");
    ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
  }

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

class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
 public:
  void operator()(const framework::OpDesc& op_desc,
                  framework::BlockDesc* block) const override {
    auto out_var_name = op_desc.Output(framework::GradVarName("W")).front();
    auto attr = op_desc.GetAttr("is_sparse");
    bool is_sparse = boost::get<bool>(attr);
    if (is_sparse) {
      VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
              << " is set to SelectedRows";
      block->Var(out_var_name)
          ->SetType(framework::proto::VarType::SELECTED_ROWS);
    } else {
      VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
              << " is set to LoDTensor";
      block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(lookup_table, ops::LookupTableOp,
                  ops::LookupTableOpGradDescMaker, ops::LookupTableOpMaker);
REGISTER_OPERATOR(lookup_table_grad, ops::LookupTableOpGrad,
                  ops::LookupTableOpGradVarTypeInference);

REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,
                       ops::LookupTableKernel<double>);
REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>,
                       ops::LookupTableGradKernel<double>);

// concat_rows is used by regularization and it doesn't have gradient operation.
REGISTER_OPERATOR(concat_rows, ops::LookupTableOp, ops::ConcatRowsOpMaker);
REGISTER_OP_CPU_KERNEL(concat_rows, ops::LookupTableKernel<float>,
                       ops::LookupTableKernel<double>);