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#include "ff_klm.h"
#include <cstring>
#include "hg.h"
#include "tdict.h"
#include "lm/enumerate_vocab.hh"
using namespace std;
template <class Model>
string KLanguageModel<Model>::usage(bool /*param*/,bool /*verbose*/) {
return "KLanguageModel";
}
struct VMapper : public lm::ngram::EnumerateVocab {
VMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
void Add(lm::WordIndex index, const StringPiece &str) {
const WordID cdec_id = TD::Convert(str.as_string());
if (cdec_id >= out_->size())
out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
(*out_)[cdec_id] = index;
}
vector<lm::WordIndex>* out_;
const lm::WordIndex kLM_UNKNOWN_TOKEN;
};
template <class Model>
class KLanguageModelImpl {
// returns the number of unscored words at the left edge of a span
inline int UnscoredSize(const void* state) const {
return *(static_cast<const char*>(state) + unscored_size_offset_);
}
inline void SetUnscoredSize(int size, void* state) const {
*(static_cast<char*>(state) + unscored_size_offset_) = size;
}
static inline const lm::ngram::State& RemnantLMState(const void* state) {
return *static_cast<const lm::ngram::State*>(state);
}
inline void SetRemnantLMState(const lm::ngram::State& lmstate, void* state) const {
// if we were clever, we could use the memory pointed to by state to do all
// the work, avoiding this copy
memcpy(state, &lmstate, ngram_->StateSize());
}
lm::WordIndex IthUnscoredWord(int i, const void* state) const {
const lm::WordIndex* const mem = reinterpret_cast<const lm::WordIndex*>(static_cast<const char*>(state) + unscored_words_offset_);
return mem[i];
}
void SetIthUnscoredWord(int i, lm::WordIndex index, void *state) const {
lm::WordIndex* mem = reinterpret_cast<lm::WordIndex*>(static_cast<char*>(state) + unscored_words_offset_);
mem[i] = index;
}
bool HasFullContext(const void *state) const {
return *(static_cast<const char*>(state) + is_complete_offset_);
}
void SetHasFullContext(bool flag, void *state) const {
*(static_cast<char*>(state) + is_complete_offset_) = flag;
}
public:
double LookupWords(const TRule& rule, const vector<const void*>& ant_states, double* pest_sum, void* remnant) {
double sum = 0.0;
double est_sum = 0.0;
int num_scored = 0;
int num_estimated = 0;
lm::ngram::State state = ngram_->NullContextState();
const vector<WordID>& e = rule.e();
bool context_complete = false;
for (int j = 0; j < e.size(); ++j) {
if (e[j] < 1) {
const void* astate = (ant_states[-e[j]]);
int unscored_ant_len = UnscoredSize(astate);
for (int k = 0; k < unscored_ant_len; ++k) {
const lm::WordIndex cur_word = IthUnscoredWord(k, astate);
const lm::ngram::State scopy(state);
const double p = ngram_->Score(scopy, cur_word, state);
++num_scored;
if (!context_complete) {
if (num_scored >= order_) context_complete = true;
}
if (context_complete) {
sum += p;
} else {
if (remnant)
SetIthUnscoredWord(num_estimated, cur_word, remnant);
++num_estimated;
est_sum += p;
}
}
if (HasFullContext(astate)) { // this is equivalent to the "star" in Chiang 2007
state = RemnantLMState(astate);
context_complete = true;
}
} else {
const lm::WordIndex cur_word = MapWord(e[j]);
const lm::ngram::State scopy(state);
const double p = ngram_->Score(scopy, cur_word, state);
++num_scored;
if (!context_complete) {
if (num_scored >= order_) context_complete = true;
}
if (context_complete) {
sum += p;
} else {
if (remnant)
SetIthUnscoredWord(num_estimated, cur_word, remnant);
++num_estimated;
est_sum += p;
}
}
}
if (pest_sum) *pest_sum = est_sum;
if (remnant) {
state.ZeroRemaining();
SetRemnantLMState(state, remnant);
SetUnscoredSize(num_estimated, remnant);
SetHasFullContext(context_complete || (num_scored >= order_), remnant);
}
return sum;
}
//FIXME: this assumes no target words on final unary -> goal rule. is that ok?
// for <s> (n-1 left words) and (n-1 right words) </s>
double FinalTraversalCost(const void* state) {
SetRemnantLMState(ngram_->BeginSentenceState(), dummy_state_);
SetHasFullContext(1, dummy_state_);
SetUnscoredSize(0, dummy_state_);
dummy_ants_[1] = state;
return LookupWords(*dummy_rule_, dummy_ants_, NULL, NULL);
}
lm::WordIndex MapWord(WordID w) const {
if (w >= map_.size())
return 0;
else
return map_[w];
}
public:
KLanguageModelImpl(const std::string& param) {
lm::ngram::Config conf;
VMapper vm(&map_);
conf.enumerate_vocab = &vm;
ngram_ = new Model(param.c_str(), conf);
order_ = ngram_->Order();
cerr << "Loaded " << order_ << "-gram KLM from " << param << " (MapSize=" << map_.size() << ")\n";
state_size_ = ngram_->StateSize() + 2 + (order_ - 1) * sizeof(lm::WordIndex);
unscored_size_offset_ = ngram_->StateSize();
is_complete_offset_ = unscored_size_offset_ + 1;
unscored_words_offset_ = is_complete_offset_ + 1;
// special handling of beginning / ending sentence markers
dummy_state_ = new char[state_size_];
dummy_ants_.push_back(dummy_state_);
dummy_ants_.push_back(NULL);
dummy_rule_.reset(new TRule("[DUMMY] ||| [BOS] [DUMMY] ||| [1] [2] </s> ||| X=0"));
}
~KLanguageModelImpl() {
delete ngram_;
delete[] dummy_state_;
}
int ReserveStateSize() const { return state_size_; }
private:
Model* ngram_;
int order_;
int state_size_;
int unscored_size_offset_;
int is_complete_offset_;
int unscored_words_offset_;
char* dummy_state_;
vector<const void*> dummy_ants_;
vector<lm::WordIndex> map_;
TRulePtr dummy_rule_;
};
template <class Model>
KLanguageModel<Model>::KLanguageModel(const string& param) {
pimpl_ = new KLanguageModelImpl<Model>(param);
fid_ = FD::Convert("LanguageModel");
SetStateSize(pimpl_->ReserveStateSize());
}
template <class Model>
Features KLanguageModel<Model>::features() const {
return single_feature(fid_);
}
template <class Model>
KLanguageModel<Model>::~KLanguageModel() {
delete pimpl_;
}
template <class Model>
void KLanguageModel<Model>::TraversalFeaturesImpl(const SentenceMetadata& /* smeta */,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
double est = 0;
features->set_value(fid_, pimpl_->LookupWords(*edge.rule_, ant_states, &est, state));
estimated_features->set_value(fid_, est);
}
template <class Model>
void KLanguageModel<Model>::FinalTraversalFeatures(const void* ant_state,
SparseVector<double>* features) const {
features->set_value(fid_, pimpl_->FinalTraversalCost(ant_state));
}
// instantiate templates
template class KLanguageModel<lm::ngram::ProbingModel>;
template class KLanguageModel<lm::ngram::SortedModel>;
template class KLanguageModel<lm::ngram::TrieModel>;
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