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-rw-r--r--training/crf/mpi_adagrad_optimize.cc14
-rw-r--r--training/dtrain/dtrain.cc32
-rw-r--r--training/dtrain/dtrain.h2
-rw-r--r--training/dtrain/examples/standard/cdec.ini1
-rw-r--r--training/dtrain/examples/standard/dtrain.ini2
-rw-r--r--training/dtrain/examples/standard/expected-output116
-rw-r--r--training/dtrain/pairsampling.h1
-rw-r--r--training/dtrain/score.cc18
-rw-r--r--training/dtrain/score.h18
9 files changed, 109 insertions, 95 deletions
diff --git a/training/crf/mpi_adagrad_optimize.cc b/training/crf/mpi_adagrad_optimize.cc
index 39bd763e..bac57324 100644
--- a/training/crf/mpi_adagrad_optimize.cc
+++ b/training/crf/mpi_adagrad_optimize.cc
@@ -157,11 +157,11 @@ struct TrainingObserver : public DecoderObserver {
void GetGradient(SparseVector<double>* g) const {
g->clear();
-#if HAVE_CXX11
+#if HAVE_CXX11 && (__GNUC_MINOR__ > 4 || __GNUC__ > 4)
for (auto& gi : acc_grad) {
#else
for (FastSparseVector<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it) {
- pair<unsigned, double>& gi = *it;
+ const pair<unsigned, prob_t>& gi = *it;
#endif
g->set_value(gi.first, -gi.second.as_float());
}
@@ -190,7 +190,7 @@ class AdaGradOptimizer {
G() {}
void update(const SparseVector<double>& g, vector<double>* x, SparseVector<double>* sx) {
if (x->size() > G.size()) G.resize(x->size(), 0.0);
-#if HAVE_CXX11
+#if HAVE_CXX11 && (__GNUC_MINOR__ > 4 || __GNUC__ > 4)
for (auto& gi : g) {
#else
for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it) {
@@ -220,7 +220,7 @@ class AdaGradL1Optimizer {
G.resize(x->size(), 0.0);
u.resize(x->size(), 0.0);
}
-#if HAVE_CXX11
+#if HAVE_CXX11 && (__GNUC_MINOR__ > 4 || __GNUC__ > 4)
for (auto& gi : g) {
#else
for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it) {
@@ -236,11 +236,11 @@ class AdaGradL1Optimizer {
// compute updates (avoid invalidating iterators by putting them all
// in the vector vupdate and applying them after this)
vector<pair<unsigned, double>> vupdate;
-#if HAVE_CXX11
+#if HAVE_CXX11 && (__GNUC_MINOR__ > 4 || __GNUC__ > 4)
for (auto& xi : *sx) {
#else
- for (SparseVector<double>::const_iterator it = sx->begin(); it != sx->end(); ++it) {
- const pair<unsigned,double>& gi = *it;
+ for (SparseVector<double>::iterator it = sx->begin(); it != sx->end(); ++it) {
+ const pair<unsigned,double>& xi = *it;
#endif
double z = fabs(u[xi.first] / t) - lambda;
double s = 1;
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 0a27a068..b01cf421 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -44,7 +44,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate")
("batch", po::value<bool>()->zero_tokens(), "do batch optimization")
("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times")
- //("test-k-best", po::value<bool>()->zero_tokens(), "check if optimization works (use repeat >= 2)")
+ ("check", po::value<bool>()->zero_tokens(), "produce list of loss differentials")
("noup", po::value<bool>()->zero_tokens(), "do not update weights");
po::options_description cl("Command Line Options");
cl.add_options()
@@ -130,8 +130,8 @@ main(int argc, char** argv)
const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
int repeat = cfg["repeat"].as<unsigned>();
- //bool test_k_best = false;
- //if (cfg.count("test-k-best")) test_k_best = true;
+ bool check = false;
+ if (cfg.count("check")) check = true;
weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
bool batch = false;
if (cfg.count("batch")) batch = true;
@@ -412,27 +412,38 @@ main(int argc, char** argv)
int cur_npairs = pairs.size();
npairs += cur_npairs;
- score_t kbest_loss_first, kbest_loss_last = 0.0;
+ score_t kbest_loss_first = 0.0, kbest_loss_last = 0.0;
+
+ if (check) repeat = 2;
+ vector<float> losses; // for check
for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
it != pairs.end(); it++) {
score_t model_diff = it->first.model - it->second.model;
- kbest_loss_first += max(0.0, -1.0 * model_diff);
+ score_t loss = max(0.0, -1.0 * model_diff);
+ losses.push_back(loss);
+ kbest_loss_first += loss;
}
+ score_t kbest_loss = 0.0;
for (int ki=0; ki < repeat; ki++) {
- score_t kbest_loss = 0.0; // test-k-best
SparseVector<weight_t> lambdas_copy; // for l1 regularization
SparseVector<weight_t> sum_up; // for pclr
if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas;
+ unsigned pair_idx = 0; // for check
for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
it != pairs.end(); it++) {
score_t model_diff = it->first.model - it->second.model;
+ score_t loss = max(0.0, -1.0 * model_diff);
+
+ if (check && ki == 1) cout << losses[pair_idx] - loss << endl;
+ pair_idx++;
+
if (repeat > 1) {
model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f);
- kbest_loss += max(0.0, -1.0 * model_diff);
+ kbest_loss += loss;
}
bool rank_error = false;
score_t margin;
@@ -449,7 +460,7 @@ main(int argc, char** argv)
if (rank_error || margin < loss_margin) {
SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
if (batch) {
- batch_loss += max(0., -1.0*model_diff);
+ batch_loss += max(0., -1.0 * model_diff);
batch_updates += diff_vec;
continue;
}
@@ -529,9 +540,8 @@ main(int argc, char** argv)
if (ki==repeat-1) { // done
kbest_loss_last = kbest_loss;
if (repeat > 1) {
- score_t best_score = -1.;
score_t best_model = -std::numeric_limits<score_t>::max();
- unsigned best_idx;
+ unsigned best_idx = 0;
for (unsigned i=0; i < samples->size(); i++) {
score_t s = lambdas.dot((*samples)[i].f);
if (s > best_model) {
@@ -634,6 +644,8 @@ main(int argc, char** argv)
Weights::WriteToFile(w_fn, decoder_weights, true);
}
+ if (check) cout << "---" << endl;
+
} // outer loop
if (average) w_average /= (weight_t)T;
diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h
index ccb5ad4d..eb23b813 100644
--- a/training/dtrain/dtrain.h
+++ b/training/dtrain/dtrain.h
@@ -64,7 +64,7 @@ struct LocalScorer
vector<score_t> w_;
virtual score_t
- Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0;
+ Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0;
virtual void Reset() {} // only for ApproxBleuScorer, LinearBleuScorer
diff --git a/training/dtrain/examples/standard/cdec.ini b/training/dtrain/examples/standard/cdec.ini
index e1edc68d..6cba9e1e 100644
--- a/training/dtrain/examples/standard/cdec.ini
+++ b/training/dtrain/examples/standard/cdec.ini
@@ -21,6 +21,7 @@ feature_function=RuleIdentityFeatures
feature_function=RuleSourceBigramFeatures
feature_function=RuleTargetBigramFeatures
feature_function=RuleShape
+feature_function=RuleWordAlignmentFeatures
#feature_function=SourceSpanSizeFeatures
#feature_function=SourceWordPenalty
#feature_function=SpanFeatures
diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini
index fc83f08e..a515db02 100644
--- a/training/dtrain/examples/standard/dtrain.ini
+++ b/training/dtrain/examples/standard/dtrain.ini
@@ -2,7 +2,7 @@
#refs=./nc-wmt11.en.gz
bitext=./nc-wmt11.gz
output=- # a weights file (add .gz for gzip compression) or STDOUT '-'
-select_weights=VOID # output average (over epochs) weight vector
+select_weights=avg # output average (over epochs) weight vector
decoder_config=./cdec.ini # config for cdec
# weights for these features will be printed on each iteration
print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output
index 75f47337..fa831221 100644
--- a/training/dtrain/examples/standard/expected-output
+++ b/training/dtrain/examples/standard/expected-output
@@ -4,7 +4,7 @@ Reading ./nc-wmt11.en.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Example feature: Shape_S00000_T00000
-Seeding random number sequence to 3751911392
+Seeding random number sequence to 4138446869
dtrain
Parameters:
@@ -22,7 +22,7 @@ Parameters:
pairs 'XYX'
hi lo 0.1
pair threshold 0
- select weights 'VOID'
+ select weights 'avg'
l1 reg 0 'none'
pclr no
max pairs 4294967295
@@ -36,87 +36,87 @@ Iteration #1 of 3.
. 10
Stopping after 10 input sentences.
WEIGHTS
- Glue = -110
- WordPenalty = -8.2082
- LanguageModel = -319.91
- LanguageModel_OOV = -19.2
- PhraseModel_0 = +312.82
- PhraseModel_1 = -161.02
- PhraseModel_2 = -433.65
- PhraseModel_3 = +291.03
- PhraseModel_4 = +252.32
- PhraseModel_5 = +50.6
- PhraseModel_6 = +146.7
- PassThrough = -38.7
+ Glue = -80.3
+ WordPenalty = -51.247
+ LanguageModel = +282.46
+ LanguageModel_OOV = -85.8
+ PhraseModel_0 = -100.06
+ PhraseModel_1 = -98.692
+ PhraseModel_2 = -9.4958
+ PhraseModel_3 = +18.535
+ PhraseModel_4 = +62.35
+ PhraseModel_5 = +7
+ PhraseModel_6 = +31.4
+ PassThrough = -126.5
---
- 1best avg score: 0.16966 (+0.16966)
- 1best avg model score: 29874 (+29874)
- avg # pairs: 906.3
+ 1best avg score: 0.25631 (+0.25631)
+ 1best avg model score: -4843.6 (-4843.6)
+ avg # pairs: 744.4
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 832
+ non0 feature count: 1274
avg list sz: 91.3
- avg f count: 139.77
-(time 0.35 min, 2.1 s/S)
+ avg f count: 143.72
+(time 0.4 min, 2.4 s/S)
Iteration #2 of 3.
. 10
WEIGHTS
- Glue = -122.1
- WordPenalty = +83.689
- LanguageModel = +233.23
- LanguageModel_OOV = -145.1
- PhraseModel_0 = +150.72
- PhraseModel_1 = -272.84
- PhraseModel_2 = -418.36
- PhraseModel_3 = +181.63
- PhraseModel_4 = -289.47
- PhraseModel_5 = +140.3
- PhraseModel_6 = +3.5
- PassThrough = -109.7
+ Glue = -117.4
+ WordPenalty = -99.584
+ LanguageModel = +395.05
+ LanguageModel_OOV = -136.8
+ PhraseModel_0 = +40.614
+ PhraseModel_1 = -123.29
+ PhraseModel_2 = -152
+ PhraseModel_3 = -161.13
+ PhraseModel_4 = -76.379
+ PhraseModel_5 = +39.1
+ PhraseModel_6 = +137.7
+ PassThrough = -162.1
---
- 1best avg score: 0.17399 (+0.004325)
- 1best avg model score: 4936.9 (-24937)
- avg # pairs: 662.4
+ 1best avg score: 0.26751 (+0.011198)
+ 1best avg model score: -10061 (-5216.9)
+ avg # pairs: 639.1
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 1240
+ non0 feature count: 1845
avg list sz: 91.3
- avg f count: 125.11
-(time 0.27 min, 1.6 s/S)
+ avg f count: 139.88
+(time 0.35 min, 2.1 s/S)
Iteration #3 of 3.
. 10
WEIGHTS
- Glue = -157.4
- WordPenalty = -1.7372
- LanguageModel = +686.18
- LanguageModel_OOV = -399.7
- PhraseModel_0 = -39.876
- PhraseModel_1 = -341.96
- PhraseModel_2 = -318.67
- PhraseModel_3 = +105.08
- PhraseModel_4 = -290.27
- PhraseModel_5 = -48.6
- PhraseModel_6 = -43.6
- PassThrough = -298.5
+ Glue = -101.1
+ WordPenalty = -139.97
+ LanguageModel = +327.98
+ LanguageModel_OOV = -234.7
+ PhraseModel_0 = -144.49
+ PhraseModel_1 = -263.88
+ PhraseModel_2 = -149.25
+ PhraseModel_3 = -38.805
+ PhraseModel_4 = +50.575
+ PhraseModel_5 = -52.4
+ PhraseModel_6 = +41.6
+ PassThrough = -230.2
---
- 1best avg score: 0.30742 (+0.13343)
- 1best avg model score: -15393 (-20329)
- avg # pairs: 623.8
+ 1best avg score: 0.36222 (+0.094717)
+ 1best avg model score: -17416 (-7355.5)
+ avg # pairs: 661.2
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 1776
+ non0 feature count: 2163
avg list sz: 91.3
- avg f count: 118.58
-(time 0.28 min, 1.7 s/S)
+ avg f count: 132.53
+(time 0.33 min, 2 s/S)
Writing weights file to '-' ...
done
---
-Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.30742].
-This took 0.9 min.
+Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.36222].
+This took 1.0833 min.
diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
index 3f67e209..1a3c498c 100644
--- a/training/dtrain/pairsampling.h
+++ b/training/dtrain/pairsampling.h
@@ -112,6 +112,7 @@ _PRO_cmp_pair_by_diff_d(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b
inline void
PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool _unused=false, float _also_unused=0)
{
+ sort(s->begin(), s->end(), cmp_hyp_by_score_d);
unsigned max_count = 5000, count = 0, sz = s->size();
bool b = false;
for (unsigned i = 0; i < sz-1; i++) {
diff --git a/training/dtrain/score.cc b/training/dtrain/score.cc
index 96d6e10a..127f34d2 100644
--- a/training/dtrain/score.cc
+++ b/training/dtrain/score.cc
@@ -32,7 +32,7 @@ BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref
}
score_t
-BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+BleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -52,7 +52,7 @@ BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* NOTE: 0 iff no 1gram match ('grounded')
*/
score_t
-StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+StupidBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -81,7 +81,7 @@ StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* (Nakov et al. '12)
*/
score_t
-FixedStupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+FixedStupidBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -112,7 +112,7 @@ FixedStupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* NOTE: max is 0.9375 (with N=4)
*/
score_t
-SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+SmoothBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -143,7 +143,7 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* sum up Ngram precisions
*/
score_t
-SumBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+SumBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -167,7 +167,7 @@ SumBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* sum up exp(Ngram precisions)
*/
score_t
-SumExpBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+SumExpBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -191,7 +191,7 @@ SumExpBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* sum up exp(weight * log(Ngram precisions))
*/
score_t
-SumWhateverBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+SumWhateverBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned /*rank*/, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -224,7 +224,7 @@ SumWhateverBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
* No scaling by src len.
*/
score_t
-ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+ApproxBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned rank, const unsigned src_len)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
@@ -255,7 +255,7 @@ ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
*
*/
score_t
-LinearBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
+LinearBleuScorer::Score(const vector<WordID>& hyp, const vector<WordID>& ref,
const unsigned rank, const unsigned /*src_len*/)
{
unsigned hyp_len = hyp.size(), ref_len = ref.size();
diff --git a/training/dtrain/score.h b/training/dtrain/score.h
index 53e970ba..1cdd3fa9 100644
--- a/training/dtrain/score.h
+++ b/training/dtrain/score.h
@@ -138,43 +138,43 @@ make_ngram_counts(const vector<WordID>& hyp, const vector<WordID>& ref, const un
struct BleuScorer : public LocalScorer
{
score_t Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len);
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct StupidBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct FixedStupidBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct SmoothBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct SumBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct SumExpBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {}
};
struct SumWhateverBleuScorer : public LocalScorer
{
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/);
void Reset() {};
};
@@ -194,7 +194,7 @@ struct ApproxBleuScorer : public BleuScorer
glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0.;
}
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned src_len);
};
struct LinearBleuScorer : public BleuScorer
@@ -207,7 +207,7 @@ struct LinearBleuScorer : public BleuScorer
onebest_counts_.One();
}
- score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned /*src_len*/);
+ score_t Score(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned rank, const unsigned /*src_len*/);
inline void Reset() {
onebest_len_ = 1;
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-06 11:35:11 +0000