mirror of
https://github.com/marian-nmt/marian.git
synced 2024-09-11 06:15:56 +03:00
Make Vocab const in beam search. Remove some trailing whitespace.
This commit is contained in:
Ulrich Germann
parent
cfdde151a1
commit
7228698b06
@ -16,7 +16,7 @@ private:
|
||||
Ptr<Options> options_;
|
||||
std::vector<Ptr<Scorer>> scorers_;
|
||||
size_t beamSize_;
|
||||
Ptr<Vocab> trgVocab_;
|
||||
Ptr<const Vocab> trgVocab_;
|
||||
|
||||
static constexpr auto INVALID_PATH_SCORE = -9999; // (@TODO: change to -9999.0 once C++ allows that)
|
||||
static constexpr auto PURGE_BATCH = true; // @TODO: diagnostic, to-be-removed once confirmed there are no issues.
|
||||
@ -24,7 +24,7 @@ private:
|
||||
public:
|
||||
BeamSearch(Ptr<Options> options,
|
||||
const std::vector<Ptr<Scorer>>& scorers,
|
||||
Ptr<Vocab> trgVocab)
|
||||
const Ptr<const Vocab> trgVocab)
|
||||
: options_(options),
|
||||
scorers_(scorers),
|
||||
beamSize_(options_->get<size_t>("beam-size")),
|
||||
@ -42,8 +42,8 @@ public:
|
||||
const std::vector<bool>& dropBatchEntries, // [origDimBatch] - empty source batch entries are marked with true, should be cleared after first use.
|
||||
const std::vector<IndexType>& batchIdxMap) const { // [origBatchIdx -> currentBatchIdx]
|
||||
std::vector<float> align; // collects alignment information from the last executed time step
|
||||
if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
|
||||
align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble,
|
||||
if(options_->hasAndNotEmpty("alignment") && factorGroup == 0)
|
||||
align = scorers_[0]->getAlignment(); // [beam depth * max src length * current batch size] -> P(s|t); use alignments from the first scorer, even if ensemble,
|
||||
|
||||
const auto origDimBatch = beams.size(); // see function search for definition of origDimBatch and currentDimBatch etc.
|
||||
Beams newBeams(origDimBatch); // return value of this function goes here. There are always origDimBatch beams.
|
||||
@ -56,7 +56,7 @@ public:
|
||||
reverseBatchIdxMap.resize(batchIdxMap.size()); // adjust size if doing batch purging.
|
||||
currentDimBatch = 0;
|
||||
for(int i = 0; i < batchIdxMap.size(); ++i) {
|
||||
reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one,
|
||||
reverseBatchIdxMap[batchIdxMap[i]] = i; // reverse batch index mapping, multiple occurences get overwritten with the last one,
|
||||
// which is expected due to down-shifting
|
||||
if(!beams[i].empty())
|
||||
currentDimBatch++;
|
||||
@ -143,12 +143,12 @@ public:
|
||||
auto lval = states[j]->getLogProbs().getFactoredLogitsTensor(factorGroup); // [maxBeamSize, 1, currentDimBatch, dimFactorVocab]
|
||||
// The flatting happens based on actual (current) batch size and batch index computed with batch-pruning as we are looking into the pruned tensor
|
||||
size_t flattenedLogitIndex = (beamHypIdx * currentDimBatch + currentBatchIdx) * vocabSize + wordIdx; // (beam idx, batch idx, word idx); note: beam and batch are transposed, compared to 'key'
|
||||
|
||||
|
||||
// @TODO: use a function on shape() to index, or new method val->at({i1, i2, i3, i4}) with broadcasting
|
||||
ABORT_IF(lval->shape() != Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}) &&
|
||||
(beamHypIdx == 0 && lval->shape() != Shape({1, 1, (int)currentDimBatch, (int)vocabSize})),
|
||||
"Unexpected shape of logits?? {} != {}", lval->shape(), Shape({(int)nBestBeamSize, 1, (int)currentDimBatch, (int)vocabSize}));
|
||||
|
||||
|
||||
breakDown[j] += lval->get(flattenedLogitIndex);
|
||||
}
|
||||
hyp->setScoreBreakdown(breakDown);
|
||||
@ -162,7 +162,7 @@ public:
|
||||
|
||||
newBeam.push_back(hyp);
|
||||
}
|
||||
|
||||
|
||||
// if factored vocab and this is not the first factor, we need to
|
||||
// also propagate factored hypotheses that do not get expanded in this step because they don't have this factor
|
||||
if (factorGroup > 0) {
|
||||
@ -214,7 +214,7 @@ public:
|
||||
// in a single beam, i.e.:
|
||||
// * [word1-batch1, word1-batch2, ..., word2-batch1, ...]
|
||||
//
|
||||
|
||||
|
||||
size_t origDimBatch = batch->size(); // number of sentences in batch
|
||||
size_t batchWidth = batch->width(); // max src length
|
||||
|
||||
@ -243,7 +243,7 @@ public:
|
||||
for(auto beam : beams) {
|
||||
Beam newBeam; // a beam of surviving hyps
|
||||
for(auto hyp : beam)
|
||||
if(hyp->getWord() != trgEosId) // if this hyp is not finished,
|
||||
if(hyp->getWord() != trgEosId) // if this hyp is not finished,
|
||||
newBeam.push_back(hyp); // move over to beam of surviving hyps
|
||||
|
||||
if(PURGE_BATCH)
|
||||
@ -298,8 +298,8 @@ public:
|
||||
// create one beam per batch entry with sentence-start hypothesis
|
||||
Beams beams(origDimBatch, Beam(beamSize_, Hypothesis::New())); // array [origDimBatch] of array [maxBeamSize] of Hypothesis, keeps full size through search.
|
||||
// batch purging is determined from an empty sub-beam.
|
||||
std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking.
|
||||
// By default that corresponds to position in array,
|
||||
std::vector<IndexType> batchIdxMap(origDimBatch); // Record at which batch entry a beam is looking.
|
||||
// By default that corresponds to position in array,
|
||||
// but shifts in the course of removing batch entries when they are finished.
|
||||
|
||||
const std::vector<bool> emptyBatchEntries; // used for recording if there are empty input batch entries
|
||||
@ -359,7 +359,7 @@ public:
|
||||
std::vector<IndexType> hypIndices; // [maxBeamSize, 1, currentDimBatch, 1] (flattened) tensor index ((beamHypIdx, batchIdx), flattened) of prev hyp that a hyp originated from
|
||||
std::vector<Word> prevWords; // [maxBeamSize, 1, currentDimBatch, 1] (flattened) word that a hyp ended in, for advancing the decoder-model's history
|
||||
Expr prevPathScores; // [maxBeamSize, 1, currentDimBatch, 1], path score that a hyp ended in (last axis will broadcast into vocab size when adding expandedPathScores)
|
||||
|
||||
|
||||
bool anyCanExpand = false; // stays false if all hyps are invalid factor expansions
|
||||
if(t == 0 && factorGroup == 0) { // no scores yet
|
||||
prevPathScores = graph->constant({1, 1, 1, 1}, inits::fromValue(0));
|
||||
@ -373,7 +373,7 @@ public:
|
||||
for(int currentBatchIdx = 0; currentBatchIdx < beams.size(); ++currentBatchIdx) // loop over batch entries (active sentences)
|
||||
if(!beams[currentBatchIdx].empty() || !PURGE_BATCH) // for each beam check
|
||||
batchIndices.push_back(prevBatchIdxMap[currentBatchIdx]); // which batch entries were active in previous step
|
||||
|
||||
|
||||
std::vector<float> prevScores;
|
||||
for(size_t beamHypIdx = 0; beamHypIdx < maxBeamSize; ++beamHypIdx) { // loop over globally maximal beam-size (maxBeamSize)
|
||||
for(int origBatchIdx = 0; origBatchIdx < origDimBatch; ++origBatchIdx) { // loop over all batch entries (active and inactive)
|
||||
@ -390,11 +390,11 @@ public:
|
||||
if(factorGroup == 0)
|
||||
currentBatchIdx = prevBatchIdxMap[origBatchIdx]; // subselection may happen for factorGroup == 0
|
||||
else
|
||||
currentBatchIdx = batchIdxMap[origBatchIdx]; // no subselection happens for factorGroup > 0,
|
||||
// but we treat it like a next step, since a step
|
||||
currentBatchIdx = batchIdxMap[origBatchIdx]; // no subselection happens for factorGroup > 0,
|
||||
// but we treat it like a next step, since a step
|
||||
// happened for factorGroup == 0
|
||||
}
|
||||
|
||||
|
||||
auto hypIndex = (IndexType)(hyp->getPrevStateIndex() * currentDimBatch + currentBatchIdx); // (beamHypIdx, batchIdx), flattened, for index_select() operation
|
||||
|
||||
hypIndices.push_back(hypIndex); // (beamHypIdx, batchIdx), flattened as said above.
|
||||
@ -409,7 +409,7 @@ public:
|
||||
}
|
||||
}
|
||||
}
|
||||
if(factorGroup == 0)
|
||||
if(factorGroup == 0)
|
||||
currentDimBatch = (IndexType) batchIndices.size(); // keep batch size constant for all factor groups in a time step
|
||||
prevPathScores = graph->constant({(int)maxBeamSize, 1, (int)currentDimBatch, 1}, inits::fromVector(prevScores));
|
||||
}
|
||||
@ -494,7 +494,7 @@ public:
|
||||
beams,
|
||||
states, // used for keeping track of per-ensemble-member path score
|
||||
batch, // only used for propagating alignment info
|
||||
factoredVocab, factorGroup,
|
||||
factoredVocab, factorGroup,
|
||||
emptyBatchEntries, // [origDimBatch] - empty source batch entries are marked with true
|
||||
batchIdxMap); // used to create a reverse batch index map to recover original batch indices for this step
|
||||
} // END FOR factorGroup = 0 .. numFactorGroups-1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user