tensor-tang
7 years ago
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/* 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 <gflags/gflags.h>
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#include "Layer.h"
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#include "SequenceToBatch.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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/**
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* @brief RecurrentLayer takes 1 input layer. The output size is the same with
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* input layer.
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* For each sequence [start, end] it performs the following computation:
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* \f[
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* out_{i} = act(in_{i}) \ \ \text{for} \ i = start \\
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* out_{i} = act(in_{i} + out_{i-1} * W) \ \ \text{for} \ start < i <= end
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*
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* \f]
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* If reversed is true, the order is reversed:
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* \f[
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* out_{i} = act(in_{i}) \ \ \text{for} \ i = end \\
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* out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end
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* \f]
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* There are two methods to calculate rnn. One way is to compute rnn one
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* sequence by one sequence. The other way is to reorganize the input
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* into batches, then compute rnn one batch by one batch. Users can select
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* them by rnn_use_batch flag.
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*/
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class RecurrentLayer : public Layer {
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public:
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explicit RecurrentLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback) override;
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void resetState() override;
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void setState(LayerStatePtr state) override;
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LayerStatePtr getState() override;
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protected:
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/**
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* @brief If user do not set --rnn_use_batch=true, it will
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* compute rnn forward one sequence by one sequence in default.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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void forwardSequence(int batchSize, size_t numSequences, const int* starts);
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/**
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* @brief Compute rnn forward by one sequence.
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* @param start The start position of this sequence (or sample).
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* @param length The length of this sequence (or sample), namely the words
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* number of this sequence.
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*/
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void forwardOneSequence(int start, int length);
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/**
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* @brief Compute rnn backward one sequence by onesequence.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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void backwardSequence(int batchSize, size_t numSequences, const int* starts);
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/**
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* @brief Compute rnn backward by one sequence.
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* @param start The start position of this sequence (or sample).
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* @param length The length of this sequence (or sample), namely the words
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* number of this sequence.
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*/
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void backwardOneSequence(int start, int length);
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/**
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* @brief Reorganize input into batches and compute rnn forward batch
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* by batch. It will convert batch shape to sequence after finishing forward.
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* The batch info can refer to SequenceToBatch class.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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virtual void forwardBatch(int batchSize,
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size_t numSequences,
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const int* starts);
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/**
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* @brief Reorganize input into batches and compute rnn forward batch
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* by batch.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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virtual void backwardBatch(int batchSize,
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size_t numSequences,
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const int* starts);
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protected:
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std::unique_ptr<Weight> weight_;
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std::unique_ptr<Weight> bias_;
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/// frameOutput_[i] is used to hold the i-th sample of output_
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std::vector<Argument> frameOutput_;
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MatrixPtr prevOutput_;
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/// Whether compute rnn by reverse.
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bool reversed_;
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/// If compute batch by batch, batchValue_ will be used to save the
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/// reorganized input value.
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std::unique_ptr<SequenceToBatch> batchValue_;
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/// If compute batch by batch, batchGrad_ will be used to save the
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/// gradient with respect to reorganized input value.
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std::unique_ptr<SequenceToBatch> batchGrad_;
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};
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} // namespace paddle
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