/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. 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. */ #ifndef HL_SEQUENCE_H_ #define HL_SEQUENCE_H_ #include "hl_base.h" /** * @brief Maximum sequence forward. * * @param[in] input each sequence contains some instances. * @param[in] sequence sequence index.. * @param[out] output max instance in this sequence. * @param[out] index index of max instance. * @param[in] numSequences size of sequence[in]. * @param[in] dim input dimension. * */ extern void hl_max_sequence_forward(real* input, const int* sequence, real* output, int* index, int numSequences, int dim); /** * @brief Maximum sequence backward. * * @param[in] outputGrad output gradient. * @param[in] index index of max instance. * @param[out] inputGrad input gradient. * @param[in] numSequences size of sequence[in]. * @param[in] dim input dimension. * */ extern void hl_max_sequence_backward( real* outputGrad, int* index, real* inputGrad, int numSequences, int dim); /** * @brief Context projection forward. * * @param[in] input input sequence. * @param[in] sequence sequence index. * @param[in] weightData padding data. * @param[out] output output sequence. * @param[in] numSequences number of sequences. * @param[in] inputDim input sequence dimension. * @param[in] contextLength context length. * @param[in] contextStart context start. * @param[in] beginPad number of extra timesteps added at the * beginning. * @param[in] isPadding trainable padding. * */ extern void hl_context_projection_forward(real* input, const int* sequence, real* weightData, real* output, int numSequences, int inputDim, int contextLength, int contextStart, int beginPad, bool isPadding); /** * @brief Context projection backward data. * * @param[in] outputGrad output gradient. * @param[in] sequence sequence index. * @param[out] inputGrad input gradient. * @param[in] numSequences number of sequences. * @param[in] inputDim input sequence dimension. * @param[in] contextLength context length. * @param[in] contextStart context start. * */ extern void hl_context_projection_backward_data(real* outputGrad, const int* sequence, real* inputGrad, int numSequences, int inputDim, int contextLength, int contextStart); /** * @brief Context projection backward weight. * * @param[in] outputGrad output gradient. * @param[in] sequence sequence index. * @param[out] weightGrad weight gradient. * @param[in] numSequences number of sequences. * @param[in] weightDim input sequence dimension. * @param[in] totalPad number of extra timesteps. * @param[in] contextLength context length. * @param[in] contextStart context start. * @param[in] beginPad number of extra timesteps added at the * beginning. * */ extern void hl_context_projection_backward_weight(real* outputGrad, const int* sequence, real* weightGrad, int numSequences, int weightDim, int totalPad, int contextLength, int contextStart, int beginPad); /** * @brief Memory copy from sequence to batch. * * if seq2batch == true * * copy from sequence to batch: batch[i] = sequence[batchIndex[i]]. * * if seq2batch == false * * copy from batch to sequence: sequence[batchIndex[i]] = batch[i]. * * @param[in,out] batch batch matrix. * @param[in,out] sequence equence matrix. * @param[in] batchIndex index vector. * @param[in] seqWidth width of sequence. * @param[in] batchCount number of batchIndex. * @param[in] seq2batch copy direction. * */ extern void hl_sequence2batch_copy(real* batch, real* sequence, const int* batchIndex, int seqWidth, int batchCount, bool seq2batch); /** * @brief Add sequence to batch. * * if seq2batch == true * * add sequence to batch: batch[i] = sequence[batchIndex[i]]. * * if seq2batch == false * * add batch to sequence: sequence[batchIndex[i]] = batch[i]. * * @param[in,out] batch batch matrix. * @param[in,out] sequence equence matrix. * @param[in] batchIndex index vector. * @param[in] seqWidth width of sequence. * @param[in] batchCount number of batchIndex. * @param[in] seq2batch copy direction. * */ extern void hl_sequence2batch_add(real* batch, real* sequence, int* batchIndex, int seqWidth, int batchCount, bool seq2batch); /** * @brief Memory copy from sequence to batch, * while padding all sequences to the same length. * * if seq2batch == true * * copy from sequence to batch: * batch[i] = sequence[sequenceStartPositions[i]] * * if seq2batch == false * * copy from batch to sequence: * sequence[sequenceStartPositions[i]] = batch[i] * * @param[in,out] batch batch matrix. * @param[in,out] sequence sequence matrix. * @param[in] sequenceStartPositions index vector. * @param[in] sequenceWidth width of sequence. * @param[in] maxSequenceLength maximum length of sequences. * @param[in] numSequences number of sequences. * @param[in] normByTimes whether dividing sequence's length. * @param[in] seq2batch copy direction. * */ extern void hl_sequence2batch_copy_padding(real* batch, real* sequence, const int* sequenceStartPositions, const size_t sequenceWidth, const size_t maxSequenceLength, const size_t numSequences, bool normByTimes, bool seq2batch); /** * @brief dst = Op(src), src is sequence. * * mode = 0, Op is average. * * mode = 1, Op is sum. * * mode = 2, Op is sum(src)/sqrt(N), N is sequence length. * * @param[in,out] dst destination data. * @param[in] src source data. * @param[in] starts sequence start positions. * @param[in] height height of dst data. * @param[in] width width of dst data. * @param[in] mode 0: avreage, * 1: sum, * 2: divide by square root * of sequenceLength */ extern void hl_sequence_avg_forward(real* dst, real* src, const int* starts, int height, int width, const int mode); #endif /* HL_SEQUENCE_H_ */