Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
提交 17994e38 编写于 作者: X xuwei06
浏览文件
操作

RecurrentGroup with mixed input sequence types 

No longer need to use SubsequenceInput. The framework will detect.
上级 14c0e71d
显示空白变更内容
内联 并排
Showing with 652 addition and 429 deletion
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @ 17994e38
......@@ -214,7 +214,6 @@ void RecurrentGradientMachine::init(
inFrameLines_[i].linkName = subModelConfig->in_links(i).link_name();
inFrameLines_[i].inLayer =
rootNetwork_->getLayer(subModelConfig->in_links(i).layer_name());
inFrameLines_[i].hasSubseq = subModelConfig->in_links(i).has_subseq();
}
outFrameLines_.resize(subModelConfig->out_links_size());
......@@ -241,11 +240,8 @@ void RecurrentGradientMachine::init(
rootNetwork_->getLayer(memoryConfig.boot_layer_name());
LayerConfig scatterConfig = *agentConfig;
memoryFrameLines_[i].is_sequence = memoryConfig.is_sequence();
memoryFrameLines_[i].rootAgent.reset(
memoryConfig.is_sequence()
? new SequenceScatterAgentLayer(scatterConfig)
: new ScatterAgentLayer(scatterConfig));
new ScatterAgentLayer(scatterConfig));
memoryFrameLines_[i].rootAgent->init(LayerMap(), parameterMap_);
memoryFrameLines_[i].bootLayer = memoryFrameLines_[i].rootAgent;
......@@ -267,9 +263,7 @@ void RecurrentGradientMachine::init(
if (subModelConfig->has_generator()) {
memoryFrameLines_[i].scatterAgents.resize(2);
for (auto& agent : memoryFrameLines_[i].scatterAgents) {
agent.reset(memoryConfig.is_sequence()
? new SequenceScatterAgentLayer(*agentConfig)
: new ScatterAgentLayer(*agentConfig));
agent.reset(new ScatterAgentLayer(*agentConfig));
agent->init(LayerMap(), parameterMap_);
}
}
......@@ -297,8 +291,6 @@ void RecurrentGradientMachine::init(
if (subModelConfig->evaluator_names_size() > 0) {
evaluator_.reset(frames_[0]->makeEvaluator());
}
targetInfoInlinkId_ = subModelConfig->target_inlinkid();
}
void RecurrentGradientMachine::resizeOrCreateFrames(int numFrames) {
......@@ -376,108 +368,102 @@ void RecurrentGradientMachine::prefetch(const std::vector<Argument>& inArgs) {
LOG(FATAL) << "should not use this function";
}
void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
std::vector<Argument>* outArgs,
PassType passType) {
if (inFrameLines_.empty() && passType == PASS_TEST) {
generateSequence();
return;
} // else forward..
const Argument& input = inFrameLines_[0].inLayer->getOutput();
CHECK(input.sequenceStartPositions);
int batchSize = input.getBatchSize();
size_t numSequences = input.getNumSequences();
const int* starts = input.sequenceStartPositions->getData(false);
bool hasSubseq = input.hasSubseq();
// In case of !hasSubseq or targetInfoInlinkId_ == -1, all inlinks share the
// same inframe info
bool shareInlinkInfo = !hasSubseq || targetInfoInlinkId_ == -1;
// Defaultly, share info with the first inlink
if (shareInlinkInfo) {
targetInfoInlinkId_ = 0;
void RecurrentGradientMachine::checkInputConsistency(
int inlinkId, const std::vector<Argument::SeqInfo>& seqInfo) {
if (commonSeqInfo_.empty()) {
commonSeqInfo_.resize(seqInfo.size());
for (size_t i = 0; i < seqInfo.size(); ++i) {
commonSeqInfo_[i].topLevelLength = seqInfo[i].topLevelLength;
commonSeqInfo_[i].seqId = seqInfo[i].seqId;
}
// check hasSubseq in both config and input are the same
CHECK_EQ(hasSubseq, inFrameLines_[0].hasSubseq);
CHECK_EQ(starts[numSequences], batchSize);
CHECK(input.sequenceStartPositions);
// check other inputs has same sequence length and start
for (size_t i = 1; i < inFrameLines_.size(); ++i) {
const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
CHECK_EQ((size_t)input1.getNumSequences(), numSequences);
// check all inputs should have same hasSubseq flag
CHECK_EQ(input.hasSubseq(), inFrameLines_[0].hasSubseq);
// if shareInlinkInfo, checks:
// 1. all inlinks have same number of total tokens
// 2. all inlinks have same number of tokens for each sentence of each
// sample. If hasSubseq, one sample has multiple sentence, else, one
// sample is one sentence
if (shareInlinkInfo) {
CHECK_EQ(input1.getBatchSize(), batchSize);
CHECK(std::equal(starts,
starts + numSequences + 1,
input1.sequenceStartPositions->getData(false)));
} else {
CHECK_EQ(commonSeqInfo_.size(), seqInfo.size())
<< " RecurrentGroup " << subModelName_ << " input " << inlinkId
<< " has mismatched number of sequences";
for (size_t i = 0; i < seqInfo.size(); ++i) {
CHECK_EQ(commonSeqInfo_[i].topLevelLength, seqInfo[i].topLevelLength)
<< " RecurrentGroup " << subModelName_ << " input " << inlinkId
<< " has mismatched sequence length";
CHECK_EQ(commonSeqInfo_[i].seqId, seqInfo[i].seqId)
<< " RecurrentGroup " << subModelName_ << " input " << inlinkId
<< " has mismatched sequence length";
}
}
}
if (hasSubseq) {
CHECK(input.subSequenceStartPositions);
size_t numSubSequences = input.getNumSubSequences();
const int* subStarts = input.subSequenceStartPositions->getData(false);
CHECK_EQ(subStarts[numSubSequences], batchSize);
// if hasSubseq, check other inputs has same sub-sequence and sub-start
for (size_t i = 1; i < inFrameLines_.size(); ++i) {
const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
CHECK_EQ((size_t)input1.getNumSubSequences(), numSubSequences);
if (shareInlinkInfo) {
CHECK(std::equal(subStarts,
subStarts + numSubSequences + 1,
input1.subSequenceStartPositions->getData(false)));
}
void RecurrentGradientMachine::calcNumSequencesAtEachStep() {
int numSequences = commonSeqInfo_.size();
numSeqs_.resize(maxSequenceLength_);
for (int i = 0; i < numSequences; ++i) {
for (int j = 0; j < commonSeqInfo_[i].topLevelLength; ++j) {
numSeqs_[j] = i + 1;
}
}
}
void RecurrentGradientMachine::reorganizeInput(PassType passType) {
info_.clear();
info_.resize(inFrameLines_.size());
commonSeqInfo_.clear();
seqInfos_.clear();
seqInfos_.resize(inFrameLines_.size());
{
AsyncGpuBlock asyncGpuBlock;
// if shareInlinkInfo, only calculate info of the first inlink
// else, calculate info for each inlink
if (shareInlinkInfo) {
input.getSeqInfo(&seqInfos_[0]);
maxSequenceLength_ = seqInfos_[0][0].topLevelLength;
createInFrameInfo(0, input, passType);
} else {
for (size_t i = 0; i < inFrameLines_.size(); i++) {
const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
input1.getSeqInfo(&seqInfos_[i]);
maxSequenceLength_ = seqInfos_[i][0].topLevelLength;
createInFrameInfo(i, input1, passType);
const Argument& input = inFrameLines_[i].inLayer->getOutput();
if (!input.hasSeq()) {
continue;
}
input.getSeqInfo(&seqInfos_[i]);
checkInputConsistency(i, seqInfos_[i]);
}
CHECK(!commonSeqInfo_.empty())
<< "At least one input needs to be sequence or subsequence";
maxSequenceLength_ = commonSeqInfo_[0].topLevelLength;
calcNumSequencesAtEachStep();
for (size_t i = 0; i < inFrameLines_.size(); ++i) {
const Argument& input = inFrameLines_[i].inLayer->getOutput();
if (!input.hasSeq()) {
seqInfos_[i] = commonSeqInfo_;
}
createInFrameInfo(i, input, passType);
}
{
AsyncGpuBlock asyncGpuBlock;
// inFrameLine select rows in real layer one time
for (size_t i = 0; i < inFrameLines_.size(); i++) {
int curInlinkId = shareInlinkInfo ? 0 : i;
selectRowsOneTime(inFrameLines_[i].inLayer,
info_[curInlinkId].allIds,
info_[i].allIds,
&(inFrameLines_[i].outArg),
passType);
}
}
resizeOrCreateFrames(maxSequenceLength_);
resizeBootFrame(numSequences);
}
void RecurrentGradientMachine::reorganizeOutput(PassType passType) {
calcSequenceStartPositions();
for (size_t i = 0; i < outFrameLines_.size(); ++i) {
Info info;
auto& outFrameLine = outFrameLines_[i];
ICpuGpuVectorPtr sequenceStartPositions;
ICpuGpuVectorPtr subSequenceStartPositions;
createOutFrameInfo(
outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
auto gatherAgent =
dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
CHECK_NOTNULL(gatherAgent);
gatherAgent->copyIdAndSequenceInfo(sequenceStartPositions,
subSequenceStartPositions,
info.allIds,
info.idIndex);
}
}
void RecurrentGradientMachine::connectFrames(PassType passType) {
for (auto& memoryFrameLine : memoryFrameLines_) {
if (memoryFrameLine.rootAgent) {
auto scatterAgent =
......@@ -487,8 +473,9 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
memoryFrameLine.outArg,
memoryFrameLine.allIds,
/* idIndex */ 0,
memoryFrameLine.allIds->getSize());
if (memoryFrameLine.is_sequence) { // memoryConfig is sequence
memoryFrameLine.allIds->getSize(),
/* handleBackward */ true);
if (memoryFrameLine.sequenceStartPositions) {
int size = memoryFrameLine.sequenceStartPositions->getSize();
scatterAgent->setSequenceStartPositions(
memoryFrameLine.sequenceStartPositions,
......@@ -501,28 +488,26 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
for (auto& outFrameLine : outFrameLines_) {
auto gatherAgent =
dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
CHECK_NOTNULL(gatherAgent);
gatherAgent->copyIdAndSequenceInfo(input,
info_[targetInfoInlinkId_].allIds,
info_[targetInfoInlinkId_].idIndex);
gatherAgent->clearRealLayers();
}
for (int i = 0; i < maxSequenceLength_; ++i) {
int idSize = 0;
// connect in_links
for (size_t j = 0; j < inFrameLines_.size(); ++j) {
Info& info = info_[shareInlinkInfo ? 0 : j];
Info& info = info_[j];
// idSize denotes the sum number of tokens in each length i
idSize = info.idIndex[i + 1] - info.idIndex[i];
int idIndex = info.idIndex.empty() ? 0 : info.idIndex[i];
int idSize = info.idIndex.empty() ? numSeqs_[i]
: info.idIndex[i + 1] - info.idIndex[i];
InFrameLine inFrameLine = inFrameLines_[j];
auto scatterAgent =
dynamic_cast<ScatterAgentLayer*>(inFrameLine.agents[i].get());
scatterAgent->setRealLayerAndOutput(inFrameLine.inLayer,
inFrameLine.outArg,
info.allIds,
info.idIndex[i],
idSize);
if (hasSubseq) {
idIndex,
idSize,
i == 0);
if (info.sequenceStartPositions) {
// size: the length of subsequence
int size = info.seqStartPosIndex[i + 1] - info.seqStartPosIndex[i];
scatterAgent->setSequenceStartPositions(
......@@ -536,11 +521,6 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
gatherAgent->addRealLayer(outFrameLine.frames[i]);
}
// connect memory links
// Adopt info_[0].idIndex because seq which has_subseq=True
// doesn't support Memory with !hasSubseq bootlayer;
// And inlinks that !hasSubSeq must have same inlink length.
idSize = info_[0].idIndex[i + 1] - info_[0].idIndex[i];
for (auto& memoryFrameLine : memoryFrameLines_) {
NeuralNetwork::connect(
memoryFrameLine.agents[i],
......@@ -548,6 +528,28 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
numSeqs_[i] /*height of agent*/);
}
}
}
void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
std::vector<Argument>* outArgs,
PassType passType) {
/* inArgs and outArgs are not used.
The inputs are inFrameLines_[i].inLayer.
The outputs are outFramesLines_[i].agentLayer
*/
if (inFrameLines_.empty() && passType == PASS_TEST) {
generateSequence();
return;
} // else forward..
reorganizeInput(passType);
int numSequences = commonSeqInfo_.size();
resizeOrCreateFrames(maxSequenceLength_);
resizeBootFrame(numSequences);
connectFrames(passType);
REGISTER_TIMER_INFO("RecurrentFwTime", "RecurrentFwTime");
// forward
......@@ -558,16 +560,12 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
const std::vector<Argument> inArgs;
std::vector<Argument> outArgs;
frames_[i]->forward(inArgs, &outArgs, passType);
if (hasSubseq) {
for (auto& outFrameLine : outFrameLines_) {
CHECK(outFrameLine.frames[i]->getOutput().sequenceStartPositions)
<< "In hierachical RNN, all out links should be from sequences.";
}
}
}
if (evaluator_ && passType == PASS_TEST) {
this->eval(evaluator_.get());
}
reorganizeOutput(passType);
}
void RecurrentGradientMachine::backward(const UpdateCallback& callback) {
......@@ -634,68 +632,221 @@ void RecurrentGradientMachine::removeBeamSearchStatisticsCallbacks() {
this->beamSearchStatistics_ = nullptr;
}
}
namespace {
void lenToStarts(std::vector<int>& starts) {
int pos = 0;
starts.back() = 0;
for (auto& start : starts) {
int tmp = start;
start = pos;
pos += tmp;
}
starts.back() = pos;
}
}
void RecurrentGradientMachine::calcSequenceStartPositions() {
std::vector<int> starts(commonSeqInfo_.size() + 1);
for (auto& seqInfo : commonSeqInfo_) {
starts[seqInfo.seqId] = seqInfo.topLevelLength;
}
lenToStarts(starts);
ICpuGpuVector::resizeOrCreate(sequenceStartPositions_, starts.size(), false);
std::copy(starts.begin(),
starts.end(),
sequenceStartPositions_->getMutableData(false));
}
void RecurrentGradientMachine::checkOutputConsistency(
OutFrameLine& outFrameLine) {
bool hasSeq = outFrameLine.frames[0]->getOutput().hasSeq();
for (int i = 0; i < maxSequenceLength_; ++i) {
LayerPtr frame = outFrameLine.frames[i];
CHECK_EQ(hasSeq, frame->getOutput().hasSeq());
int numSequences = frame->getOutput().getNumSequences();
CHECK_EQ(numSeqs_[i], numSequences);
}
}
void RecurrentGradientMachine::createOutFrameInfo(
OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions) {
checkOutputConsistency(outFrameLine);
if (!outFrameLine.frames[0]->getOutput().hasSeq()) {
createOutFrameInfo_seq(
outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
} else {
createOutFrameInfo_subseq(
outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
}
}
void RecurrentGradientMachine::createOutFrameInfo_seq(
OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions) {
std::vector<int> allIds;
info.idIndex.resize(1, 0); // first idIndex = 0
const int* starts = sequenceStartPositions_->getData(false);
for (int i = 0; i < maxSequenceLength_; ++i) {
LayerPtr frame = outFrameLine.frames[i];
size_t numSequences = frame->getOutput().getNumSequences();
for (size_t j = 0; j < numSequences; ++j) {
int seqStart = starts[commonSeqInfo_[j].seqId];
int seqLength = commonSeqInfo_[j].topLevelLength;
allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
: (seqStart + i));
}
info.idIndex.push_back(allIds.size());
}
sequenceStartPositions = sequenceStartPositions_;
copyScattedId(allIds, &info.allIds, allIds.size());
CHECK_EQ(info.idIndex.size(), static_cast<size_t>(maxSequenceLength_ + 1));
}
void RecurrentGradientMachine::createOutFrameInfo_subseq(
OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions) {
size_t numSequences = commonSeqInfo_.size();
std::vector<int> allIds;
info.idIndex.resize(1, 0); // first idIndex = 0
const int* starts = sequenceStartPositions_->getData(false);
std::vector<int> subStarts(starts[numSequences] + 1);
for (int i = 0; i < maxSequenceLength_; ++i) {
LayerPtr frame = outFrameLine.frames[i];
size_t numSequences = frame->getOutput().getNumSequences();
const int* seqStarts =
frame->getOutput().sequenceStartPositions->getData(false);
for (size_t j = 0; j < numSequences; ++j) {
subStarts[starts[commonSeqInfo_[j].seqId] + i] =
seqStarts[j + 1] - seqStarts[j];
}
}
lenToStarts(subStarts);
for (int i = 0; i < maxSequenceLength_; ++i) {
LayerPtr frame = outFrameLine.frames[i];
size_t numSequences = frame->getOutput().getNumSequences();
for (size_t j = 0; j < numSequences; ++j) {
int pos = starts[commonSeqInfo_[j].seqId] + i;
int subSeqStart = subStarts[pos];
int subSeqEnd = subStarts[pos + 1];
for (int k = subSeqStart; k < subSeqEnd; ++k) {
allIds.push_back(k);
}
}
info.idIndex.push_back(allIds.size());
}
ICpuGpuVector::resizeOrCreate(
subSequenceStartPositions, subStarts.size(), false);
int* cpuSubSequenceStartPositions =
subSequenceStartPositions->getMutableData(false);
std::copy(subStarts.begin(), subStarts.end(), cpuSubSequenceStartPositions);
ICpuGpuVector::resizeOrCreate(
sequenceStartPositions, numSequences + 1, false);
int* cpuSequenceStartPositions =
sequenceStartPositions->getMutableData(false);
for (size_t i = 0; i <= numSequences; ++i) {
cpuSequenceStartPositions[i] = subStarts[starts[i]];
}
copyScattedId(allIds, &info.allIds, allIds.size());
CHECK_EQ(info.idIndex.size(), static_cast<size_t>(maxSequenceLength_ + 1));
}
/* create scattered id infomation for all realLayer of inFrameLines one time.
* If hasSubseq, will also create scattered sequenceStartPositions infomation
* for all realLayer of inFrameLines one time.
*/
void RecurrentGradientMachine::createInFrameInfo(int inlinkId,
const Argument& input,
PassType passType) {
bool hasSubseq = input.hasSubseq();
// numSequences: # samples(sequences) in a batch
size_t numSequences = input.getNumSequences();
if (!input.hasSeq()) {
createInFrameInfo_nonseq(inlinkId, input, passType);
} else if (!input.hasSubseq()) {
createInFrameInfo_seq(inlinkId, input, passType);
} else {
createInFrameInfo_subseq(inlinkId, input, passType);
}
}
void RecurrentGradientMachine::createInFrameInfo_nonseq(int inlinkId,
const Argument& input,
PassType passType) {
std::vector<int> allIds;
auto& seqInfo = seqInfos_[inlinkId];
numSeqs_.clear();
Info* inlinkInfo = &info_[inlinkId];
inlinkInfo->idIndex.clear();
inlinkInfo->idIndex.push_back(0); // first idIndex = 0
for (size_t i = 0; i < seqInfo.size(); ++i) {
allIds.push_back(seqInfo[i].seqId);
}
// copy and check scatterId
copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
}
void RecurrentGradientMachine::createInFrameInfo_seq(int inlinkId,
const Argument& input,
PassType passType) {
std::vector<int> allIds;
auto& seqInfo = seqInfos_[inlinkId];
Info* inlinkInfo = &info_[inlinkId];
inlinkInfo->idIndex.resize(1, 0); // first idIndex = 0
for (int i = 0; i < maxSequenceLength_; ++i) {
for (int j = 0; j < numSeqs_[i]; ++j) {
int seqLength = seqInfo[j].topLevelLength;
int seqStart = seqInfo[j].seqStart;
allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
: (seqStart + i));
}
inlinkInfo->idIndex.push_back(allIds.size());
}
// copy and check scatterId
copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
CHECK_EQ(inlinkInfo->idIndex.size(),
static_cast<size_t>(maxSequenceLength_ + 1));
}
void RecurrentGradientMachine::createInFrameInfo_subseq(int inlinkId,
const Argument& input,
PassType passType) {
std::vector<int> allIds;
auto& seqInfo = seqInfos_[inlinkId];
Info* inlinkInfo = &info_[inlinkId];
inlinkInfo->idIndex.resize(1, 0); // first idIndex = 0
std::vector<int> sequenceStartPositions;
const int* subSequenceStartPositions = nullptr;
if (hasSubseq) { // for sequenceScatterAgentLayer
subSequenceStartPositions = input.subSequenceStartPositions->getData(false);
inlinkInfo->seqStartPosIndex.clear();
inlinkInfo->seqStartPosIndex.push_back(0); // first seqStartPosIndex = 0
}
// maxSequenceLength_: max topLevelLength in allsamples
for (int i = 0; i < maxSequenceLength_; ++i) {
if (hasSubseq) {
sequenceStartPositions.push_back(0); // first element = 0
}
int numSeqs = 0;
for (size_t j = 0; j < numSequences; ++j) {
int seqLength = seqInfo[j].topLevelLength;
if (i >= seqLength) {
break;
}
++numSeqs;
if (hasSubseq) {
for (int j = 0; j < numSeqs_[i]; ++j) {
int subSeqStart = subSequenceStartPositions[seqInfo[j].subSeqStart + i];
int subSeqEnd =
subSequenceStartPositions[seqInfo[j].subSeqStart + i + 1];
int subSeqEnd = subSequenceStartPositions[seqInfo[j].subSeqStart + i + 1];
for (int k = subSeqStart; k < subSeqEnd; ++k) {
allIds.push_back(k);
}
sequenceStartPositions.push_back(sequenceStartPositions.back() +
subSeqEnd - subSeqStart);
} else {
int seqStart = seqInfo[j].seqStart;
allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
: (seqStart + i));
}
}
inlinkInfo->idIndex.push_back(allIds.size());
numSeqs_.push_back(numSeqs);
if (hasSubseq) {
inlinkInfo->seqStartPosIndex.push_back(sequenceStartPositions.size());
}
}
if (hasSubseq) {
// inFrameLine create sequenceStartPositions one time
CHECK_EQ(
sequenceStartPositions.size(),
......@@ -703,7 +854,6 @@ void RecurrentGradientMachine::createInFrameInfo(int inlinkId,
CHECK_EQ(inlinkInfo->seqStartPosIndex.size(),
static_cast<size_t>(maxSequenceLength_ + 1));
createSeqPos(sequenceStartPositions, &inlinkInfo->sequenceStartPositions);
}
// copy and check scatterId
copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
......@@ -717,11 +867,11 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
const Argument& input = (*memoryFrameLine).rootLayer->getOutput();
size_t numSequences = input.getNumSequences();
std::vector<int> allIds;
bool seqFlag = (*memoryFrameLine).is_sequence;
bool seqFlag = input.hasSeq();
CHECK(!input.hasSubseq())
<< "Subsequence boot layer for memory is not supported";
if (seqFlag) { // for sequenceScatterAgentLayer
CHECK(input.sequenceStartPositions)
<< "boot layer must be a sequence when is_sequence = true";
std::vector<int> sequenceStartPositions;
sequenceStartPositions.push_back(0); // first element = 0
const int* starts = input.sequenceStartPositions->getData(false);
......@@ -804,8 +954,7 @@ size_t RecurrentGradientMachine::getGenBatchSize() {
for (auto& memoryFrameLine : memoryFrameLines_) {
if (!memoryFrameLine.rootLayer) continue;
Argument& bootArg = memoryFrameLine.rootLayer->getOutput();
size_t batchSize = memoryFrameLine.is_sequence ? bootArg.getNumSequences()
: bootArg.getBatchSize();
size_t batchSize = bootArg.getNumSequences();
if (numSequences) {
CHECK_EQ(numSequences, batchSize);
} else {
......@@ -845,12 +994,7 @@ void RecurrentGradientMachine::generateSequence() {
if (memoryFrameLine.rootAgent) {
auto scatterAgent =
dynamic_cast<ScatterAgentLayer*>(memoryFrameLine.rootAgent.get());
bool seqFlag = memoryFrameLine.is_sequence;
scatterAgent->setRealLayer(memoryFrameLine.rootLayer, ids, seqFlag);
if (seqFlag) {
CHECK(memoryFrameLine.rootLayer->getOutput().sequenceStartPositions)
<< "boot layer must be a sequence when is_sequence = true";
}
scatterAgent->setRealLayer(memoryFrameLine.rootLayer, ids);
}
NeuralNetwork::connect(
memoryFrameLine.agents[0], memoryFrameLine.bootLayer, ids.size());
......@@ -858,6 +1002,7 @@ void RecurrentGradientMachine::generateSequence() {
// boot layer forward
AsyncGpuBlock asyncGpuBlock;
for (auto& memoryFrameLine : memoryFrameLines_) {
memoryFrameLine.bootLayer->forward(PASS_TEST);
}
......@@ -930,8 +1075,7 @@ void RecurrentGradientMachine::oneWaySearch(size_t batchSize) {
auto scatterAgent = dynamic_cast<ScatterAgentLayer*>(
memoryFrameLine.scatterAgents[machineCur].get());
scatterAgent->setRealLayer(memoryFrameLine.frames[machinePrev],
scatterIds,
memoryFrameLine.is_sequence);
scatterIds);
scatterAgent->forward(PASS_TEST);
NeuralNetwork::connect(memoryFrameLine.agents[machineCur],
memoryFrameLine.scatterAgents[machineCur]);
......@@ -1003,8 +1147,7 @@ void RecurrentGradientMachine::connectPrevFrame(int stepId,
auto scatterAgent = dynamic_cast<ScatterAgentLayer*>(
memoryFrameLine.scatterAgents[machineCur].get());
scatterAgent->setRealLayer(memoryFrameLine.frames[machinePrev],
isOutIds ? topIds_ : machineIds_,
memoryFrameLine.is_sequence);
isOutIds ? topIds_ : machineIds_);
scatterAgent->forward(PASS_TEST);
NeuralNetwork::connect(memoryFrameLine.agents[machineCur],
memoryFrameLine.scatterAgents[machineCur]);
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
浏览文件 @ 17994e38
......@@ -284,6 +284,16 @@ public:
}
protected:
std::vector<Argument::SeqInfo> commonSeqInfo_;
ICpuGpuVectorPtr sequenceStartPositions_;
void calcSequenceStartPositions();
void checkInputConsistency(int inlinkId,
const std::vector<Argument::SeqInfo>& seqInfo);
void reorganizeInput(PassType passType);
void reorganizeOutput(PassType passType);
void connectFrames(PassType passType);
void calcNumSequencesAtEachStep();
void resizeOrCreateFrames(int numFrames);
void resizeBootFrame(int numSequences);
......@@ -295,7 +305,6 @@ protected:
std::string linkName;
LayerPtr inLayer;
std::vector<LayerPtr> agents; // Scatter Agents to reform batch input
bool hasSubseq;
Argument outArg; // scatter output argument
};
std::vector<InFrameLine> inFrameLines_;
......@@ -318,7 +327,6 @@ protected:
std::vector<LayerPtr> agents;
std::vector<LayerPtr> scatterAgents; // scatter agent used by beam search
Argument outArg; // scatter output argument
bool is_sequence;
// Different memoryFrameLine have different element as follows
IVectorPtr allIds; // scattered id of realLayer
ICpuGpuVectorPtr
......@@ -330,22 +338,27 @@ protected:
// and all outFrameLines(outlinks) share the info with one inFrameLine,
// which is assigned by targetInfoInlinkId_.
struct Info {
IVectorPtr allIds; // scattered id of realLayer
std::vector<int> idIndex; // index of allIds
// The original positions in the original batch
IVectorPtr allIds; // scattered id of realLayer [batchSize]
// index of allIds for each step [maxSequenceLength_]
// idIndex[i] is the total length of the first i sequences
std::vector<int> idIndex;
ICpuGpuVectorPtr
sequenceStartPositions; // scattered sequenceStartPositions
std::vector<int> seqStartPosIndex; // index of sequenceStartPositions
};
std::vector<Info> info_;
std::vector<Info> info_; // for input
// numSeqs_[i] is the number sequences which is longer than i (for sequence
// data) or has more than i subsequences (for subsequence data)
// Equivalently, numSeqs_[i] is the number of sequences at step i;
std::vector<int> numSeqs_;
std::vector<std::vector<Argument::SeqInfo>> seqInfos_;
// the id of inlink which share info with outlinks
int targetInfoInlinkId_;
void checkOutputConsistency(OutFrameLine& outFrameLine);
/* create scattered id infomation for all realLayer of inFrameLines one time.
* If hasSubseq, will also create scattered sequenceStartPositions infomation
......@@ -354,6 +367,28 @@ protected:
void createInFrameInfo(int inlinks_id,
const Argument& input,
PassType passType);
void createInFrameInfo_nonseq(int inlinks_id,
const Argument& input,
PassType passType);
void createInFrameInfo_seq(int inlinks_id,
const Argument& input,
PassType passType);
void createInFrameInfo_subseq(int inlinks_id,
const Argument& input,
PassType passType);
void createOutFrameInfo(OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions);
void createOutFrameInfo_seq(OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions);
void createOutFrameInfo_subseq(OutFrameLine& outFrameLine,
Info& info,
ICpuGpuVectorPtr& sequenceStartPositions,
ICpuGpuVectorPtr& subSequenceStartPositions);
void createMemoryFrameInfo(MemoryFrameLine* memoryFrameLine,
PassType passType);
......@@ -386,9 +421,7 @@ protected:
NeuralNetwork* rootNetwork_;
bool reversed_;
// if hasSubseq: max number of sentences(subseq)in batchsize samples
// else: max number of tokens in batchsize samples(sentences)
int maxSequenceLength_;
int maxSequenceLength_; // Max top-level length
bool useGpu_;
bool stopBeamSearch_;
......
paddle/gserver/layers/AgentLayer.cpp
浏览文件 @ 17994e38
......@@ -35,36 +35,15 @@ bool AgentLayer::init(const LayerMap& layerMap,
void AgentLayer::forward(PassType passType) {
Layer::forward(passType);
Argument& realOutput = realLayer_->getOutput();
int realHeight = realOutput.getBatchSize();
CHECK_LE(numSamples_, realHeight);
// get Arguments from real layers
if (numSamples_ > 0 && numSamples_ < realHeight) {
if (realOutput.ids) {
output_.ids =
IVector::create(realOutput.ids->getData(), numSamples_, useGpu_);
} else {
output_.subArgFrom(
realOutput, /* offset */ 0, numSamples_, getSize(), useGpu_);
}
} else {
output_ = realOutput;
}
}
void SequenceAgentLayer::forward(PassType passType) {
Layer::forward(passType);
Argument& realOutput = realLayer_->getOutput();
int realNumSequences = realOutput.getNumSequences();
CHECK_LE(numSamples_, realNumSequences);
// get Arguments from real layers
if (numSamples_ > 0 && numSamples_ < realNumSequences) {
if (realOutput.hasSeq()) {
int numRows =
realOutput.sequenceStartPositions->getData(false)[numSamples_];
CHECK(!realOutput.ids) << "Not supported";
output_.subArgFrom(realOutput,
/* offset */ 0,
numRows,
......@@ -74,13 +53,15 @@ void SequenceAgentLayer::forward(PassType passType) {
/* seqFlag */ true,
/* seqStart */ 0,
/* seqSize */ numSamples_ + 1);
} else {
output_.subArgFrom(
realOutput, /* offset */ 0, numSamples_, getSize(), useGpu_);
}
} else {
output_ = realOutput;
}
}
REGISTER_LAYER(sequence_agent, SequenceAgentLayer);
bool GatherAgentLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK_EQ(config_.inputs_size(), 0);
......@@ -91,18 +72,26 @@ bool GatherAgentLayer::init(const LayerMap& layerMap,
return true;
}
void GatherAgentLayer::copyIdAndSequenceInfo(const Argument& input,
void GatherAgentLayer::copyIdAndSequenceInfo(
ICpuGpuVectorPtr sequenceStartPositions,
ICpuGpuVectorPtr subSequenceStartPositions,
const IVectorPtr& ids,
const std::vector<int>& idIndex) {
output_.sequenceStartPositions = input.sequenceStartPositions;
output_.subSequenceStartPositions = input.subSequenceStartPositions;
realLayers_.clear();
output_.sequenceStartPositions = sequenceStartPositions;
output_.subSequenceStartPositions = subSequenceStartPositions;
allIds_ = ids;
idIndex_ = idIndex;
}
void GatherAgentLayer::forward(PassType passType) {
Layer::forward(passType);
forwardIds(passType);
forwardValue(passType);
}
void GatherAgentLayer::forwardValue(PassType passType) {
MatrixPtr valueReal = realLayers_[0]->getOutputValue();
if (!valueReal) return;
int height = allIds_->getSize();
int width = this->getSize();
......@@ -147,7 +136,9 @@ void ScatterAgentLayer::forward(PassType passType) {
CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
int width = this->getSize();
if (realOutArg_.value || realOutArg_.ids) {
if (realOutArg_.hasSeq()) {
forwardSequence(passType);
} else if (realOutArg_.value || realOutArg_.ids) {
output_.subArgFrom(
realOutArg_, /* offset */ idIndex_, idSize_, width, useGpu_);
} else { // used in generation
......@@ -174,7 +165,7 @@ void ScatterAgentLayer::backward(const UpdateCallback& callback) {
if (realGrad) {
// for agent in inFrameLines and memoryFrameLines,
// only first scatterAgentLayer should do addToRows in backward
if (idIndex_ == 0) {
if (handleBackward_) {
outputGrad->addToRows(*realGrad, *ids_);
}
}
......@@ -183,12 +174,14 @@ void ScatterAgentLayer::backward(const UpdateCallback& callback) {
REGISTER_LAYER(gather_agent, GatherAgentLayer);
REGISTER_LAYER(scatter_agent, ScatterAgentLayer);
void SequenceGatherAgentLayer::forward(PassType passType) {
Layer::forward(passType);
void GatherAgentLayer::forwardIds(PassType passType) {
int height = 0;
int* starts = output_.subSequenceStartPositions->getMutableData(false);
IVectorPtr idReal = realLayers_[0]->getOutputLabel();
if (idReal) {
if (!idReal) return;
if (output_.subSequenceStartPositions) {
int* starts = output_.subSequenceStartPositions->getMutableData(false);
// Gather generator.idsVec
// if is beam search generation result. Get first result.
if (idReal->getData()[idReal->getSize() - 1] == -1) {
......@@ -212,13 +205,11 @@ void SequenceGatherAgentLayer::forward(PassType passType) {
->copyFrom(*realLayers_[i]->getOutputLabel());
}
} else {
// Gather output.value, same as GatherAgentLayer
CHECK(output_.subSequenceStartPositions);
GatherAgentLayer::forward(passType);
LOG(FATAL) << "Not implemented";
}
}
void SequenceScatterAgentLayer::forward(PassType passType) {
void ScatterAgentLayer::forwardSequence(PassType passType) {
Layer::forward(passType);
CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
......@@ -241,6 +232,7 @@ void SequenceScatterAgentLayer::forward(PassType passType) {
/* seqStart */ seqStartPosIndex_,
/* seqSize */ numSequences_);
} else {
// Putting the generation logic here is really an ugly hack!
// used in generation
int height = 0;
size_t numSequences = ids_->getSize();
......@@ -284,7 +276,4 @@ void SequenceScatterAgentLayer::forward(PassType passType) {
}
}
REGISTER_LAYER(sequence_gather_agent, SequenceGatherAgentLayer);
REGISTER_LAYER(sequence_scatter_agent, SequenceScatterAgentLayer);
} // namespace paddle
paddle/gserver/layers/AgentLayer.h
浏览文件 @ 17994e38
......@@ -49,18 +49,6 @@ public:
void backward(const UpdateCallback& callback = nullptr) override {}
};
/**
* like AgentLayer, but use first *numSamples* sequences
*/
class SequenceAgentLayer : public AgentLayer {
public:
explicit SequenceAgentLayer(const LayerConfig& config) : AgentLayer(config) {}
~SequenceAgentLayer() {}
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override {}
};
/**
* Like AgentLayer, but it can gather many real layers. Each real
* layer give a few rows of a sequence, after gather all real layers,
......@@ -83,7 +71,10 @@ public:
const ParameterMap& parameterMap) override;
// call before addRealLayer
void copyIdAndSequenceInfo(const Argument& input,
void clearRealLayers() { realLayers_.clear(); }
void copyIdAndSequenceInfo(ICpuGpuVectorPtr sequenceStartPositions,
ICpuGpuVectorPtr subSequenceStartPositions,
const IVectorPtr& allIds,
const std::vector<int>& idIndex);
......@@ -92,24 +83,8 @@ public:
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
};
/**
* Like GatherAgentLayer, but select a few sequence in real layer.
* *ids* in addRealLayer() are the ids of selected sequence.
* It's used to reorder sequence output.
*/
class SequenceGatherAgentLayer : public GatherAgentLayer {
public:
explicit SequenceGatherAgentLayer(const LayerConfig& config)
: GatherAgentLayer(config) {}
virtual ~SequenceGatherAgentLayer() {}
void forward(PassType passType);
void backward(const UpdateCallback& callback) {
// same as GatherAgentLayer
GatherAgentLayer::backward(callback);
}
void forwardValue(PassType passType);
void forwardIds(PassType passType);
};
/**
......@@ -129,6 +104,11 @@ protected:
int idSize_;
int seqStartPosIndex_;
int numSequences_; // number of sequences in this scatterAgentLayer
bool handleBackward_;
// use to store expanded cpuStartPositions or subSequenceStartPositions
// of real layer.
ICpuGpuVectorPtr inputStartPos_;
public:
explicit ScatterAgentLayer(const LayerConfig& config) : Layer(config) {}
......@@ -147,13 +127,10 @@ public:
* false(default) in ScatterAgentLayer, and
* true in SequenceScatterAgentLayer.
*/
void setRealLayer(LayerPtr layer,
const std::vector<int>& ids,
bool copyId = false) {
void setRealLayer(LayerPtr layer, const std::vector<int>& ids) {
realLayer_ = layer;
IVector::resizeOrCreate(ids_, ids.size(), useGpu_);
ids_->copyFrom(ids.data(), ids.size());
if (copyId) {
if (useGpu_) {
IVector::resizeOrCreate(cpuIds_, ids.size(), false);
cpuIds_->copyFrom(ids.data(), ids.size());
......@@ -161,7 +138,6 @@ public:
cpuIds_ = ids_;
}
}
}
// set real layer and output, [idIndex, idIndex + idSize) of *ids*
// are selected row for realOutArg in realLayer
......@@ -169,12 +145,14 @@ public:
const Argument& outArg,
const IVectorPtr& ids,
int idIndex,
int idSize) {
int idSize,
bool handleBackward) {
realLayer_ = layer;
realOutArg_ = outArg;
ids_ = ids;
idIndex_ = idIndex;
idSize_ = idSize;
handleBackward_ = handleBackward;
}
void setSequenceStartPositions(const ICpuGpuVectorPtr& sequenceStartPositions,
......@@ -187,28 +165,8 @@ public:
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
};
/**
* Like ScatterAgentLayer, but select a few sequence in real layer.
* *ids* in setRealLayer() or setRealLayerAndOutput() are the ids of
* selected sequence. It's used to reorder sequence input.
*/
class SequenceScatterAgentLayer : public ScatterAgentLayer {
protected:
// use to store expanded cpuStartPositions or subSequenceStartPositions
// of real layer.
ICpuGpuVectorPtr inputStartPos_;
public:
explicit SequenceScatterAgentLayer(const LayerConfig& config)
: ScatterAgentLayer(config) {}
virtual ~SequenceScatterAgentLayer() {}
void forward(PassType passType);
void backward(const UpdateCallback& callback) {
ScatterAgentLayer::backward(callback);
}
void forwardSequence(PassType passType);
};
} // namespace paddle
paddle/gserver/layers/SequencePoolLayer.cpp
浏览文件 @ 17994e38
......@@ -46,6 +46,9 @@ void SequencePoolLayer::forward(PassType passType) {
Layer::forward(passType);
const Argument& input = getInput(0);
CHECK(input.hasSeq() || input.hasSubseq())
<< "Input should be a sequence or subsequence for layer " << getName();
newBatchSize_ = type_ ? input.getNumSubSequences() : input.getNumSequences();
size_t dim = getSize();
// check
......
paddle/gserver/tests/rnn_data_provider.py
浏览文件 @ 17994e38
......@@ -95,3 +95,22 @@ def process_unequalength_seq(settings, file_name):
words1 = reduce(lambda x, y: x + y, d[0])
words2 = reduce(lambda x, y: x + y, d[1])
yield words1, words2, d[2]
###########################################################
data3 = [
[[[1, 2], [4, 5, 2]], [1, 2], 0],
[[[0, 2], [2, 5], [0, 1, 2]], [2, 3, 0], 1],
]
# Used for sequence_nest_mixed_inputs.conf
@provider(
input_types=[
integer_value_sub_sequence(10), integer_value_sequence(10),
integer_value(2)
],
should_shuffle=False)
def process_mixed(settings, file_name):
for d in data3:
yield d
paddle/gserver/tests/sequence_nest_rnn_multi_input.conf
浏览文件 @ 17994e38
......@@ -19,7 +19,7 @@ from paddle.trainer_config_helpers import *
define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_subseq2')
obj='process_subseq')
settings(batch_size=2, learning_rate=0.01)
......
paddle/gserver/tests/sequence_rnn_matched_inputs.py 0 → 100644
浏览文件 @ 17994e38
# edit-mode: -*- python -*-
# 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_mixed')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 2
hidden_dim = 2
label_dim = 2
data1 = data_layer(name="word1", size=dict_dim)
data2 = data_layer(name="word2", size=dict_dim)
label = data_layer(name="label", size=label_dim)
encoding = embedding_layer(input=data2, size=word_dim)
subseq = embedding_layer(input=data1, size=word_dim)
seq = embedding_layer(input=data2, size=word_dim)
nonseq = embedding_layer(input=label, size=word_dim)
# This hierarchical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn_multi_unequalength_inputs.conf
def outer_step(subseq, seq, nonseq, encoding):
outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
def inner_step(subseq, seq, nonseq):
inner_mem = memory(
name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
out = fc_layer(
input=[subseq, seq, nonseq, inner_mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='inner_rnn_state')
return out
decoder = recurrent_group(
step=inner_step, name='inner', input=[subseq, seq, nonseq])
last = last_seq(name="outer_rnn_state", input=decoder)
context = simple_attention(
encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
return context
out = recurrent_group(
name="outer",
step=outer_step,
input=[
subseq, expand_layer(
seq, expand_as=subseq,
expand_level=ExpandLevel.FROM_SEQUENCE), expand_layer(
nonseq,
expand_as=subseq,
expand_level=ExpandLevel.FROM_NO_SEQUENCE),
StaticInput(encoding)
])
rep = last_seq(input=out)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(classification_cost(input=prob, label=label))
paddle/gserver/tests/sequence_rnn_mixed_inputs.py 0 → 100644
浏览文件 @ 17994e38
# edit-mode: -*- python -*-
# 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
define_py_data_sources2(
train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_mixed')
settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 2
hidden_dim = 2
label_dim = 2
data1 = data_layer(name="word1", size=dict_dim)
data2 = data_layer(name="word2", size=dict_dim)
label = data_layer(name="label", size=label_dim)
encoding = embedding_layer(input=data2, size=word_dim)
# This hierarchical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn_multi_unequalength_inputs.conf
def outer_step(subseq, seq, nonseq, encoding):
outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
def inner_step(data1, data2, label):
inner_mem = memory(
name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
subseq = embedding_layer(input=data1, size=word_dim)
seq = embedding_layer(input=data2, size=word_dim)
nonseq = embedding_layer(input=label, size=word_dim)
print_layer(input=[data1, seq, label, inner_mem])
out = fc_layer(
input=[subseq, seq, nonseq, inner_mem],
size=hidden_dim,
act=TanhActivation(),
bias_attr=True,
name='inner_rnn_state')
return out
decoder = recurrent_group(
step=inner_step, name='inner', input=[subseq, seq, nonseq])
last = last_seq(name="outer_rnn_state", input=decoder)
context = simple_attention(
encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
return context
out = recurrent_group(
name="outer",
step=outer_step,
input=[data1, data2, label, StaticInput(encoding)])
rep = last_seq(input=out)
prob = fc_layer(
size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
outputs(classification_cost(input=prob, label=label))
paddle/gserver/tests/sequence_rnn_multi_input.conf
浏览文件 @ 17994e38
......@@ -19,7 +19,7 @@ from paddle.trainer_config_helpers import *
define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
test_list=None,
module='rnn_data_provider',
obj='process_seq2')
obj='process_seq')
settings(batch_size=2, learning_rate=0.01)
......
paddle/gserver/tests/test_RecurrentGradientMachine.cpp
浏览文件 @ 17994e38
......@@ -155,6 +155,15 @@ TEST(RecurrentGradientMachine, rnn_multi_unequalength_input) {
}
}
TEST(RecurrentGradientMachine, rnn_mixed_input) {
for (bool useGpu : {false, true}) {
test("gserver/tests/sequence_rnn_mixed_inputs.py",
"gserver/tests/sequence_rnn_matched_inputs.py",
1e-6,
useGpu);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
......
paddle/math/Vector.cpp
浏览文件 @ 17994e38
......@@ -908,12 +908,13 @@ const T* CpuGpuVectorT<T>::getData(bool useGpu) const {
// Operation will change data and need to reset sync_ & syncFlag_.
#define MUTABLE_VECTOR_OP(OP, useGpu, args...) \
do { \
setSync(useGpu); \
if (useGpu) { \
copyToGpu(); \
setSync(useGpu); \
return gpuVectorT_->OP(args); \
} else { \
copyToCpu(); \
setSync(useGpu); \
return cpuVectorT_->OP(args); \
} \
} while (0)
......@@ -1030,7 +1031,7 @@ void CpuGpuVectorT<T>::copyToCpu() {
case DATA_AT_GPU:
CHECK(gpuVectorT_);
this->resizeOrCreate(gpuVectorT_->getSize(), false);
cpuVectorT_->copyFrom(*gpuVectorT_, HPPL_STREAM_DEFAULT);
cpuVectorT_->copyFrom(*gpuVectorT_);
setSync(SYNCED);
break;
case DATA_AT_CPU:
......@@ -1049,7 +1050,7 @@ void CpuGpuVectorT<T>::copyToGpu() {
case DATA_AT_CPU:
CHECK(cpuVectorT_);
this->resizeOrCreate(cpuVectorT_->getSize(), true);
gpuVectorT_->copyFrom(*cpuVectorT_, HPPL_STREAM_DEFAULT);
gpuVectorT_->copyFrom(*cpuVectorT_);
setSync(SYNCED);
break;
case DATA_AT_GPU:
......
paddle/parameter/Argument.h
浏览文件 @ 17994e38
......@@ -149,6 +149,7 @@ struct Argument {
: getBatchSize();
}
bool hasSeq() const { return sequenceStartPositions != nullptr; }
bool hasSubseq() const { return subSequenceStartPositions != nullptr; }
const int* getCpuStartPositions() const {
......
paddle/trainer/tests/test_recurrent_machine_generation.cpp
浏览文件 @ 17994e38
......@@ -124,6 +124,8 @@ TEST(RecurrentGradientMachine, test_generation) {
bool beam_search) {
FLAGS_config_args = beam_search ? "beam_search=1" : "beam_search=0";
for (auto useGpu : useGpuConfs) {
LOG(INFO) << configFile << " useGpu=" << useGpu
<< " beam_search=" << beam_search;
testGeneration(configFile, useGpu, hasSubseq, expRetFile);
}
};
......
python/paddle/trainer/config_parser.py
浏览文件 @ 17994e38
......@@ -333,48 +333,32 @@ def RecurrentLayerGroupWithoutOutLinksBegin(name,
for linkid, link in enumerate(in_links):
if isinstance(link, basestring):
name = link
has_subseq = False
else:
name = link.link_name
has_subseq = link.has_subseq
# assign target_inlinkid according to target_inlinkname
if target_inlinkname == name:
g_current_submodel.target_inlinkid = linkid
if in_links_count == 0:
in_links_has_subseq = has_subseq
else:
config_assert(
in_links_has_subseq == has_subseq,
"The sequence type of in_links should be the same in RecurrentLayerGroup"
)
in_links_count += 1
layer_name = MakeLayerNameInParentSubmodel(name)
layer = g_layer_map[layer_name]
if has_subseq:
SequenceScatterAgentLayer(name=name, size=layer.size)
else:
ScatterAgentLayer(name=name, size=layer.size)
pair = g_current_submodel.in_links.add()
pair.layer_name = layer_name
pair.link_name = MakeLayerNameInSubmodel(name)
pair.has_subseq = has_subseq
@config_func
def RecurrentLayerGroupSetOutLink(link):
if isinstance(link, basestring):
name = link
has_subseq = False
else:
name = link.link_name
has_subseq = link.has_subseq
layer_name = MakeLayerNameInParentSubmodel(name)
pair = g_current_submodel.out_links.add()
pair.layer_name = MakeLayerNameInSubmodel(name)
pair.link_name = layer_name
pair.has_subseq = has_subseq
def RecurrentLayerGroupSetGenerator(generator=None):
......@@ -425,8 +409,6 @@ def RecurrentLayerGroupEnd(name):
agent_name = GetLayerBaseName(pair.link_name)
if prev_submodel.HasField("generator"):
DataLayer(name=agent_name, size=layer.size)
elif pair.has_subseq:
SequenceGatherAgentLayer(name=agent_name, size=layer.size)
else:
GatherAgentLayer(name=agent_name, size=layer.size)
......@@ -2253,13 +2235,6 @@ class AgentLayer(LayerBase):
name, 'agent', size, inputs=[], device=device)
@config_layer('sequence_agent')
class SequenceAgentLayer(LayerBase):
def __init__(self, name, size, device=None):
super(SequenceAgentLayer, self).__init__(
name, 'sequence_agent', size, inputs=[], device=device)
@config_layer('gather_agent')
class GatherAgentLayer(LayerBase):
def __init__(self, name, size, device=None):
......@@ -2274,20 +2249,6 @@ class ScatterAgentLayer(LayerBase):
name, 'scatter_agent', size, inputs=[], device=device)
@config_layer('sequence_gather_agent')
class SequenceGatherAgentLayer(LayerBase):
def __init__(self, name, size, device=None):
super(SequenceGatherAgentLayer, self).__init__(
name, 'sequence_gather_agent', size, inputs=[], device=device)
@config_layer('sequence_scatter_agent')
class SequenceScatterAgentLayer(LayerBase):
def __init__(self, name, size, device=None):
super(SequenceScatterAgentLayer, self).__init__(
name, 'sequence_scatter_agent', size, inputs=[], device=device)
@config_layer('multiplex')
class MultiplexLayer(LayerBase):
def __init__(self, name, inputs, size, device=None):
......@@ -2303,12 +2264,12 @@ class MultiplexLayer(LayerBase):
@config_func
def Link(
name,
has_subseq=False, ):
def Link(name, has_subseq=False):
"""
Still keeping has_subseq for backward compatibility
"""
link_config = LinkConfig()
link_config.link_name = name
link_config.has_subseq = has_subseq
return link_config
......@@ -2341,12 +2302,6 @@ def Memory(name,
config_assert(name is not None, "name needs cannot be None")
memory_name = name + "+delay1"
agent_name = memory_name
if is_sequence:
config_assert(
boot_layer is not None,
"there must be boot_layer in network when is_sequence = True")
agent_layer = SequenceAgentLayer(agent_name, size)
else:
agent_layer = AgentLayer(agent_name, size)
config_assert(g_current_submodel.is_recurrent_layer_group,
'Memory should be used in recurrent layer group only')
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 17994e38
......@@ -3329,8 +3329,9 @@ class StaticInput(object):
input.size = size
class SubsequenceInput(object):
def SubsequenceInput(input):
"""
DEPRECATED.
Input sequence has sub-sequence, used in recurrent_group.
The example usage is:
......@@ -3339,11 +3340,7 @@ class SubsequenceInput(object):
input = SubsequenceInput(layer)
"""
def __init__(self, input):
assert isinstance(input, LayerOutput)
assert input.size is not None
self.input = input
return input
@wrap_name_default("recurrent_group")
......@@ -3407,7 +3404,8 @@ def recurrent_group(step,
input sequence in a reverse order.
:type reverse: bool
:param targetInlink: the input layer which share info with layer group's output
:param targetInlink: DEPRECATED.
The input layer which share info with layer group's output
Param input specifies multiple input layers. For
SubsequenceInput inputs, config should assign one input
......@@ -3429,46 +3427,21 @@ def recurrent_group(step,
model_type('recurrent_nn')
def is_single_input(x):
return isinstance(x, LayerOutput) or isinstance(x, StaticInput) \
or isinstance(x, SubsequenceInput)
return isinstance(x, LayerOutput) or isinstance(x, StaticInput)
if is_single_input(input):
input = [input]
assert isinstance(input, collections.Sequence)
def is_in_links(x):
return isinstance(x, LayerOutput) or isinstance(x, SubsequenceInput)
return isinstance(x, LayerOutput)
in_links = filter(is_in_links, input)
def targetInlink_in_inlinks():
for inlink in in_links:
if isinstance(inlink, SubsequenceInput):
if targetInlink == inlink.input:
return True
elif targetInlink == inlink:
return True
return False
assert (targetInlink == None or targetInlink_in_inlinks())
targetInlinkName = None if targetInlink == None \
else targetInlink.name if isinstance(targetInlink, LayerOutput) \
else targetInlink.input.name
contains_sub_seq = [False]
def map_in_links(x):
if isinstance(x, SubsequenceInput):
contains_sub_seq[0] = True
return Link(name=x.input.name, has_subseq=True)
else:
return x.name
RecurrentLayerGroupWithoutOutLinksBegin(
name=name,
in_links=map(map_in_links, in_links),
seq_reversed=reverse,
target_inlinkname=targetInlinkName)
in_links=map(lambda x: x.name, in_links),
seq_reversed=reverse)
in_args = []
has_LayerOutput = False
for each_input in input:
......@@ -3476,10 +3449,7 @@ def recurrent_group(step,
if isinstance(each_input, LayerOutput):
in_args.append(each_input)
has_LayerOutput = True
elif isinstance(each_input, SubsequenceInput):
in_args.append(each_input.input)
has_LayerOutput = True
else:
else: # StaticInput
mem_name = "__%s_memory__" % each_input.input.name
mem = memory(
name=mem_name,
......@@ -3503,9 +3473,6 @@ def recurrent_group(step,
for ot in layer_outs:
assert isinstance(ot, LayerOutput)
ot.reverse = reverse
if contains_sub_seq[0]:
RecurrentLayerGroupSetOutLink(Link(ot.name, has_subseq=True))
else:
RecurrentLayerGroupSetOutLink(ot.name)
RecurrentLayerGroupEnd(name=name)
......
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr
浏览文件 @ 17994e38
......@@ -261,12 +261,10 @@ sub_models {
in_links {
layer_name: "__simple_gru_0___transform"
link_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__simple_gru_0__@__simple_gru_0___recurrent_group"
link_name: "__simple_gru_0__"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -285,12 +283,10 @@ sub_models {
in_links {
layer_name: "__simple_gru_1___transform"
link_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__simple_gru_1__@__simple_gru_1___recurrent_group"
link_name: "__simple_gru_1__"
has_subseq: false
}
target_inlinkid: -1
}
......
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr
浏览文件 @ 17994e38
......@@ -351,12 +351,10 @@ sub_models {
in_links {
layer_name: "__mixed_0__"
link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -383,12 +381,10 @@ sub_models {
in_links {
layer_name: "__mixed_1__"
link_name: "__mixed_1__@__lstm_group_1___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1__"
has_subseq: false
}
target_inlinkid: -1
}
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr
浏览文件 @ 17994e38
......@@ -155,7 +155,7 @@ layers {
}
layers {
name: "sub_seq_input@__recurrent_group_2__"
type: "sequence_scatter_agent"
type: "scatter_agent"
size: 100
active_type: ""
}
......@@ -182,7 +182,7 @@ layers {
}
layers {
name: "rnn_subseq_forward"
type: "sequence_gather_agent"
type: "gather_agent"
size: 200
active_type: ""
}
......@@ -623,12 +623,10 @@ sub_models {
in_links {
layer_name: "seq_input"
link_name: "seq_input@__recurrent_group_0__"
has_subseq: false
}
out_links {
layer_name: "rnn_forward@__recurrent_group_0__"
link_name: "rnn_forward"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -647,12 +645,10 @@ sub_models {
in_links {
layer_name: "seq_input"
link_name: "seq_input@__recurrent_group_1__"
has_subseq: false
}
out_links {
layer_name: "rnn_back@__recurrent_group_1__"
link_name: "rnn_back"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -671,12 +667,10 @@ sub_models {
in_links {
layer_name: "sub_seq_input"
link_name: "sub_seq_input@__recurrent_group_2__"
has_subseq: true
}
out_links {
layer_name: "rnn_subseq_forward@__recurrent_group_2__"
link_name: "rnn_subseq_forward"
has_subseq: true
}
target_inlinkid: -1
}
......@@ -703,12 +697,10 @@ sub_models {
in_links {
layer_name: "__mixed_0__"
link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -727,12 +719,10 @@ sub_models {
in_links {
layer_name: "__mixed_1__"
link_name: "__mixed_1__@__gru_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__gru_group_0__@__gru_group_0___recurrent_group"
link_name: "__gru_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
......@@ -751,12 +741,10 @@ sub_models {
in_links {
layer_name: "seq_input"
link_name: "seq_input@__recurrent_group_3__"
has_subseq: false
}
out_links {
layer_name: "__fc_layer_0__@__recurrent_group_3__"
link_name: "__fc_layer_0__"
has_subseq: false
}
target_inlinkid: -1
}
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或