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148 lines
4.2 KiB
148 lines
4.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#pragma once
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#include "paddle/framework/operator.h"
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#include "paddle/operators/rnn/recurrent_op_utils.h"
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namespace paddle {
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namespace operators {
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// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
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// TODO(Yan Chunwei):
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// 1. No-padding computing for sequences with indifinite length in one batch.
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// 2. Hierarchical RNN for sequence with sub-sequence.
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// 3. Internal Memory.
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// 4. More Complex RNN architecture, such as Gated Feedback RNN.
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// Refer to: https://arxiv.org/pdf/1502.02367.pdf
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class RecurrentAlgorithm {
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public:
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void Run(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const;
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void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
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/**
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* InferShape must be called before Run.
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*/
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void InferShape(const framework::Scope& scope) const;
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protected:
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/*
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* The step scopes will be stored in the father scope as a variable.
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*
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* NOTE the scopes are reused in both the forward and backward, so just
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* create once and expand its size if more steps need.
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*/
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void CreateScopes(const framework::Scope& scope) const;
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const std::vector<framework::Scope*>& GetStepScopes(
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const framework::Scope& scope) const {
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return *scope.FindVar(arg_->step_scopes)
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->GetMutable<std::vector<framework::Scope*>>();
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}
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void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
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private:
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std::unique_ptr<rnn::Argument> arg_;
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mutable size_t seq_len_;
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};
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class RecurrentGradientAlgorithm {
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/**
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* RNN's backward alogorithm.
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*
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* To accelerate the development of RecurrentGradientOp, we decouple RNN's
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* algorithm and `OperatorBase`'s implementation, the former contains the core
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* implementation of a RNN, and will keep stable even if the framework changes
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* a
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* lot, and the latter is a wrapper acts like an dapter for it to make RNN an
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* operator.
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*/
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public:
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void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
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void Run(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const;
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void LinkBootMemoryGradients(framework::Scope* step_scopes,
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bool infer_shape_mode) const;
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/**
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* InferShape must be called before Run.
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*/
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void InferShape(const framework::Scope& scope) const;
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protected:
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inline const std::vector<framework::Scope*>& GetStepScopes(
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const framework::Scope& scope) const {
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return *scope.FindVar(arg_->step_scopes)
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->GetMutable<std::vector<framework::Scope*>>();
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}
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private:
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std::unique_ptr<rnn::Argument> arg_;
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mutable size_t seq_len_;
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};
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class RecurrentOp final : public framework::OperatorBase {
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public:
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void Init() override;
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/**
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* InferShape must be called before Run.
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*/
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void InferShape(const framework::Scope& scope) const override {
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alg_.InferShape(scope);
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}
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void Run(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const override {
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alg_.Run(scope, dev_ctx);
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}
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static const rnn::ArgumentName kArgName;
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private:
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RecurrentAlgorithm alg_;
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};
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class RecurrentGradientOp final : public framework::OperatorBase {
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public:
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void Init() override;
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/**
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* InferShape must be called before Run.
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*/
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void InferShape(const framework::Scope& scope) const override {
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alg_.InferShape(scope);
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}
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void Run(const framework::Scope& scope,
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const platform::DeviceContext& dev_ctx) const override {
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alg_.Run(scope, dev_ctx);
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}
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static const rnn::ArgumentName kArgName;
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private:
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RecurrentGradientAlgorithm alg_;
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};
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} // namespace operators
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} // namespace paddle
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