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output all beam search results in layer group.

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revert-3824-remove_grad_op_type
caoying03 8 years ago
parent 82801f24e5
commit 56a722a1d0
5 changed files with 102 additions and 59 deletions
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104
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
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@ -1012,11 +1012,6 @@ void RecurrentGradientMachine::generateSequence() {
/* width */ resultNum,
false,
/* useGpu */ false);
Matrix::resizeOrCreate(generator_.outArg.value,
/* height */ maxGenWordCount,
/* width */ 1,
false,
/* useGpu */ false);
}
ICpuGpuVector::resizeOrCreate(generator_.outArg.sequenceStartPositions,
numSequences + 1,
@ -1026,7 +1021,7 @@ void RecurrentGradientMachine::generateSequence() {
} else {
oneWaySearch(numSequences);
}
if (dataArgsSize_) createDataOutlink(batchMachineIdVec_);
if (dataArgsSize_) createDataOutlink();
size_t size = generator_.ids.size();
generator_.outArg.ids->resize(size);
@ -1106,6 +1101,7 @@ void RecurrentGradientMachine::oneWaySearch(size_t batchSize) {
}
batchMachineIdVec_.clear();
batchMachineStartPos_.clear();
int* starts = generator_.outArg.sequenceStartPositions->getMutableData(false);
starts[0] = 0;
generator_.ids.clear();
@ -1312,13 +1308,20 @@ void RecurrentGradientMachine::fillGenOutputs() {
finalPaths_[i].resize(minFinalPathsSize);
}
batchMachineIdVec_.clear();
generator_.ids.clear();
int* starts = generator_.outArg.sequenceStartPositions->getMutableData(false);
starts[0] = 0;
if (numResults > 1) {
real* probs = generator_.outArg.in->getData();
int idsProbSaveSize = 0;
for (auto inSeq : finalPaths_) {
for (auto path : inSeq) idsProbSaveSize += path.ids.size();
idsProbSaveSize += inSeq.size();
}
Matrix::resizeOrCreate(
generator_.outArg.value, idsProbSaveSize, 1, false, false);
real* idsProb = generator_.outArg.value->getData();
real* probs = generator_.outArg.in->getData();
size_t curPos = 0;
for (size_t i = 0; i < finalPaths_.size(); ++i) {
for (size_t j = 0; j < finalPaths_[i].size(); ++j) {
@ -1333,24 +1336,16 @@ void RecurrentGradientMachine::fillGenOutputs() {
curPos += genLen;
idsProb[curPos++] = -1.0;
probs[i * numResults + j] = path.logProb;
if (!j && dataArgsSize_) {
// in beam search, here only reserved the top 1 generated result
// for out_links that are not the generated word indices.
batchMachineIdVec_.insert(batchMachineIdVec_.end(),
path.machineIdVec.begin(),
path.machineIdVec.end());
}
}
starts[i + 1] = generator_.ids.size();
}
} else {
for (size_t i = 0; i < finalPaths_.size(); ++i) {
CHECK(!finalPaths_[i].empty());
generator_.ids.insert(generator_.ids.begin(),
finalPaths_[i][0].ids.begin(),
finalPaths_[i][0].ids.end());
starts[i + 1] = starts[i] + finalPaths_[i][0].ids.size();
Path& path = finalPaths_[i][0];
generator_.ids.insert(
generator_.ids.begin(), path.ids.begin(), path.ids.end());
starts[i + 1] = starts[i] + path.ids.size();
}
}
}
@ -1364,25 +1359,70 @@ void RecurrentGradientMachine::copyDataOutlinkFrame(size_t machineCur) {
}
}
void RecurrentGradientMachine::createDataOutlink(
std::vector<int>& machineIdVec) {
size_t seqNum =
getBeamSize() > 1UL ? finalPaths_.size() : finalPaths_[0].size();
std::vector<int> starts(seqNum + 1, 0);
for (size_t i = 0; i < seqNum; ++i) {
size_t seqLen = getBeamSize() > 1UL ? finalPaths_[i][0].ids.size()
: finalPaths_[0][i].ids.size();
starts[i + 1] = starts[i] + seqLen;
void RecurrentGradientMachine::createDataOutlinkSelRowsInfo(
bool isSeq, std::vector<Argument>& outArgs) {
batchMachineIdVec_.clear();
size_t seqIdx = 0;
for (size_t i = 0; i < finalPaths_.size(); ++i) {
for (size_t j = 0; j < finalPaths_[i].size(); ++j) {
std::vector<int>& machineIdVec = finalPaths_[i][j].machineIdVec;
if (isSeq) {
for (size_t i = 0; i < machineIdVec.size(); ++i) {
size_t rowId = machineIdVec[i];
int* seqPos =
outArgs[i].sequenceStartPositions->getMutableData(false);
batchMachineIdVec_.push_back(seqPos[rowId]);
}
} else {
batchMachineIdVec_.insert(
batchMachineIdVec_.end(), machineIdVec.begin(), machineIdVec.end());
}
seqIdx++;
}
}
}
void RecurrentGradientMachine::createDataOutlinkCopySizeInfo(
bool isSeq, std::vector<Argument>& outArgs, std::vector<int>& copySize) {
size_t totalSeqNum = std::accumulate(
finalPaths_.begin(),
finalPaths_.end(),
0UL,
[](size_t a, const std::vector<Path>& b) { return a + b.size(); });
copySize.resize(totalSeqNum, 1);
batchMachineStartPos_.resize(totalSeqNum + 1, 0);
if (isSeq) {
ICpuGpuVectorPtr inputSeqStartPos = outArgs[0].sequenceStartPositions;
CHECK_EQ(inputSeqStartPos->getSize() - 1, finalPaths_.size());
int* starts = inputSeqStartPos->getMutableData(false);
int seqId = 0;
for (int i = 0; i < finalPaths_.size(); ++i) {
for (int j = 0; j < finalPaths_[i].size(); ++j) {
copySize[seqId] = starts[i + 1] - starts[i];
batchMachineStartPos_[seqId + 1] =
batchMachineStartPos_[seqId] + finalPaths_[i][j].ids.size();
seqId++;
}
}
}
}
void RecurrentGradientMachine::createDataOutlink() {
for (size_t i = 0; i < dataArgsSize_; i++) {
bool isSeq = dataArgsFrame_[i][0].hasSeq();
std::vector<int> copySize;
createDataOutlinkCopySizeInfo(isSeq, dataArgsFrame_[i], copySize);
createDataOutlinkSelRowsInfo(isSeq, dataArgsFrame_[i]);
dataArgs_[i].concat(dataArgsFrame_[i],
machineIdVec,
starts,
batchMachineIdVec_,
batchMachineStartPos_,
copySize,
useGpu_,
HPPL_STREAM_1,
PASS_TEST);
auto dataAgent =
dynamic_cast<DataLayer*>(outFrameLines_[i + 1].agentLayer.get());
CHECK_NOTNULL(dataAgent);

7
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
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@ -480,7 +480,11 @@ private:
* @param machineIdVec : select a row of output matrix in each frame
* that the generation process expanded.
*/
void createDataOutlink(std::vector<int>& machineIdVec);
void createDataOutlink();
void createDataOutlinkCopySizeInfo(bool isSeq,
std::vector<Argument>& outArgs,
std::vector<int>& copySize);
void createDataOutlinkSelRowsInfo(bool isSeq, std::vector<Argument>& outArgs);
/*
* @brief used in beam search, connect previous frame to form recurrent link
@ -543,6 +547,7 @@ private:
std::vector<int> topIds_;
std::vector<int> seqIds_;
std::vector<int> batchMachineIdVec_;
std::vector<int> batchMachineStartPos_;
std::vector<std::vector<Path>> finalPaths_;
std::vector<real> minFinalPathLogProb_;
BeamSearchControlCallbacks* beamSearchCtrlCallbacks_;

36
paddle/parameter/Argument.cpp
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@ -276,17 +276,21 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src,
void Argument::concat(const std::vector<Argument>& args,
const std::vector<int>& selectRows,
const std::vector<int>& seqStartPos,
const std::vector<int>& copySize,
bool useGpu,
hl_stream_t stream,
PassType passType) {
CHECK(!subSequenceStartPositions)
<< "undefined behavior for subsequence positions";
size_t batchSize = selectRows.size();
size_t batchSize = 0;
for (size_t i = 0; i < copySize.size(); ++i)
batchSize += copySize[i] * (seqStartPos[i + 1] - seqStartPos[i]);
auto copyArg = [batchSize, stream](MatrixPtr& dst,
MatrixPtr src,
int startRow,
int pos,
int desStartRow,
int srcStartRow,
int size,
bool useGpu) {
if (!src) {
@ -300,8 +304,8 @@ void Argument::concat(const std::vector<Argument>& args,
dst->resize(batchSize, width);
}
MatrixPtr tmpMatrix = dst->subMatrix(startRow, size);
tmpMatrix->copyFrom(*src->subMatrix(pos, size), stream);
MatrixPtr tmpMatrix = dst->subMatrix(desStartRow, size);
tmpMatrix->copyFrom(*src->subMatrix(srcStartRow, size), stream);
};
auto copyIds = [batchSize, stream](IVectorPtr& dst,
@ -339,24 +343,24 @@ void Argument::concat(const std::vector<Argument>& args,
dataId = args[0].dataId;
CHECK_NE(seqStartPos.size(), 0UL);
size_t sampleNum = seqStartPos.size() - 1;
for (size_t i = 0; i < sampleNum; ++i) {
int desStartRow = 0;
for (size_t i = 0; i < copySize.size(); ++i) {
int startPos = seqStartPos[i];
int endPos = seqStartPos[i + 1];
CHECK_GE(args.size(), static_cast<size_t>(endPos - startPos));
for (int j = startPos; j < endPos; ++j) {
const Argument& arg = args[j - startPos];
CHECK_EQ(arg.dataId, dataId) << "Arguments in concat should have"
<< " same dataId";
const int copySize = 1;
const int rowIdx = selectRows[j];
copyArg(in, arg.in, j, rowIdx, copySize, useGpu);
copyArg(value, arg.value, j, rowIdx, copySize, useGpu);
CHECK_EQ(arg.dataId, dataId) << "Arguments in concat should have the "
<< "same dataId";
const int srcStartRow = selectRows[j];
copyArg(in, arg.in, desStartRow, srcStartRow, copySize[i], useGpu);
copyArg(value, arg.value, desStartRow, srcStartRow, copySize[i], useGpu);
if (passType != PASS_TEST) {
copyArg(grad, arg.grad, j, rowIdx, copySize, useGpu);
copyArg(grad, arg.grad, desStartRow, srcStartRow, copySize[i], useGpu);
}
copyIds(ids, arg.ids, j, rowIdx, copySize, useGpu);
copyStrs(strs, arg.strs, j, rowIdx, copySize, useGpu);
copyIds(ids, arg.ids, desStartRow, srcStartRow, copySize[i], useGpu);
copyStrs(strs, arg.strs, desStartRow, srcStartRow, copySize[i], useGpu);
desStartRow += copySize[i];
}
}
ICpuGpuVector::resizeOrCreate(

1
paddle/parameter/Argument.h
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@ -240,6 +240,7 @@ struct Argument {
void concat(const std::vector<Argument>& args,
const std::vector<int>& selectRows,
const std::vector<int>& seqStartPos,
const std::vector<int>& copySize,
bool useGpu,
hl_stream_t stream,
PassType passType);

13
python/paddle/trainer_config_helpers/networks.py
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@ -1370,14 +1370,7 @@ def simple_attention(encoded_sequence,
param_attr=softmax_param_attr,
name="%s_softmax" % name,
bias_attr=False)
scaled = scaling_layer(
weight=attention_weight,
input=encoded_sequence,
name='%s_scaling' % name)
return pooling_layer(
input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name)
return attention_weight
def inputs(layers, *args):
@ -1395,7 +1388,7 @@ def inputs(layers, *args):
if len(args) != 0:
layers.extend(args)
Inputs(* [l.name for l in layers])
Inputs(*[l.name for l in layers])
def outputs(layers, *args):
@ -1438,7 +1431,7 @@ def outputs(layers, *args):
assert len(layers) > 0
if HasInputsSet(): # input already set
Outputs(* [l.name for l in layers])
Outputs(*[l.name for l in layers])
return # just return outputs.
if len(layers) != 1:

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