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85 lines
3.2 KiB
85 lines
3.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/lod_rank_table.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class LoDRankTableOp : public framework::OperatorBase {
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public:
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LoDRankTableOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &dev_place) const override {
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auto x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
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auto *out =
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scope.FindVar(Output("Out"))->GetMutable<framework::LoDRankTable>();
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VLOG(10) << "Level = " << static_cast<size_t>(Attr<int>("level"));
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out->Reset(x.lod(), static_cast<size_t>(Attr<int>("level")));
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VLOG(10) << Input("X") << "'s lod information is " << *out;
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}
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};
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class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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LoDRankTableOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(LoDTensor) input lod tensor, must contain lod information.");
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AddOutput("Out", "(LoDRankTable) The rank table of specific level.");
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AddAttr<int>("level", "(int) the specific lod level to rank.")
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.SetDefault(0)
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.EqualGreaterThan(0);
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AddComment(R"DOC(Create LoDRanTable by LoDTensor
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LoD Rank Table stores the `level` of `lod` which is ordered by sequence
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length in descending order. It is useful when implement dynamic RNN and is
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shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice
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output operators.
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)DOC");
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}
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};
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class LoDRankTableInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE(context->HasInput("X"), "LoDRankTable must has input X");
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}
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};
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class LoDRankTableInferVarType : public framework::VarTypeInference {
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public:
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void operator()(const framework::OpDesc &op_desc,
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framework::BlockDesc *block) const override {
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for (auto &o : op_desc.Output("Out")) {
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block->FindRecursiveOrCreateVar(o).SetType(
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framework::proto::VarType::LOD_RANK_TABLE);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OPERATOR(lod_rank_table, paddle::operators::LoDRankTableOp,
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paddle::operators::LoDRankTableOpProtoMaker,
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paddle::operators::LoDRankTableInferShape,
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paddle::operators::LoDRankTableInferVarType,
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paddle::framework::EmptyGradOpMaker);
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