/* 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/framework/lod_rank_table.h" #include "paddle/fluid/framework/op_registry.h" namespace paddle { namespace operators { class LoDRankTableOp : public framework::OperatorBase { public: LoDRankTableOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) : OperatorBase(type, inputs, outputs, attrs) {} private: void RunImpl(const framework::Scope &scope, const platform::Place &dev_place) const override { auto x = scope.FindVar(Input("X"))->Get(); auto *out = scope.FindVar(Output("Out"))->GetMutable(); VLOG(10) << "Level = " << static_cast(Attr("level")); out->Reset(x.lod(), static_cast(Attr("level"))); VLOG(10) << Input("X") << "'s lod information is " << *out; } }; class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker { public: LoDRankTableOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "(LoDTensor) input lod tensor, must contain lod information."); AddOutput("Out", "(LoDRankTable) The rank table of specific level."); AddAttr("level", "(int) the specific lod level to rank.") .SetDefault(0) .EqualGreaterThan(0); AddComment(R"DOC(Create LoDRanTable by LoDTensor LoD Rank Table stores the `level` of `lod` which is ordered by sequence length in descending order. It is useful when implement dynamic RNN and is shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice output operators. )DOC"); } }; class LoDRankTableInferShape : public framework::InferShapeBase { public: void operator()(framework::InferShapeContext *context) const override { PADDLE_ENFORCE(context->HasInput("X"), "LoDRankTable must has input X"); } }; class LoDRankTableInferVarType : public framework::VarTypeInference { public: void operator()(const framework::OpDesc &op_desc, framework::BlockDesc *block) const override { for (auto &o : op_desc.Output("Out")) { block->FindRecursiveOrCreateVar(o).SetType( framework::proto::VarType::LOD_RANK_TABLE); } } }; } // namespace operators } // namespace paddle REGISTER_OPERATOR(lod_rank_table, paddle::operators::LoDRankTableOp, paddle::operators::LoDRankTableOpProtoMaker, paddle::operators::LoDRankTableInferShape, paddle::operators::LoDRankTableInferVarType, paddle::framework::EmptyGradOpMaker);