/usr/include/fst/extensions/linear/linear-fst-data.h is in libfst-dev 1.6.3-2.
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// finite-state transducer library.
//
// Data structures for storing and looking up the actual feature weights.
#ifndef FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_
#define FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#include <fst/compat.h>
#include <fst/fst.h>
#include <fst/extensions/linear/trie.h>
namespace fst {
// Forward declarations
template <class A>
class LinearFstDataBuilder;
template <class A>
class FeatureGroup;
// Immutable data storage of the feature weights in a linear
// model. Produces state tuples that represent internal states of a
// LinearTaggerFst. Object of this class can only be constructed via
// either `LinearFstDataBuilder::Dump()` or `LinearFstData::Read()`
// and usually used as refcount'd object shared across mutiple
// `LinearTaggerFst` copies.
//
// TODO(wuke): more efficient trie implementation
template <class A>
class LinearFstData {
public:
friend class LinearFstDataBuilder<A>; // For builder access
typedef typename A::Label Label;
typedef typename A::Weight Weight;
// Sentence boundary labels. Both of them are negative labels other
// than `kNoLabel`.
static const Label kStartOfSentence;
static const Label kEndOfSentence;
// Constructs empty data; for non-trivial ways of construction see
// `Read()` and `LinearFstDataBuilder`.
LinearFstData()
: max_future_size_(0), max_input_label_(1), input_attribs_(1) {}
// Appends the state tuple of the start state to `output`, where
// each tuple holds the node ids of a trie for each feature group.
void EncodeStartState(std::vector<Label> *output) const {
for (int i = 0; i < NumGroups(); ++i) output->push_back(GroupStartState(i));
}
// Takes a transition from the trie states stored in
// `(trie_state_begin, trie_state_end)` with input label `ilabel`
// and output label `olabel`; appends the destination state tuple to
// `next` and multiplies the weight of the transition onto
// `weight`. `next` should be the shifted input buffer of the caller
// in `LinearTaggerFstImpl` (i.e. of size `LinearTaggerFstImpl::delay_`;
// the last element is `ilabel`).
template <class Iterator>
void TakeTransition(Iterator buffer_end, Iterator trie_state_begin,
Iterator trie_state_end, Label ilabel, Label olabel,
std::vector<Label> *next, Weight *weight) const;
// Returns the final weight of the given trie state sequence.
template <class Iterator>
Weight FinalWeight(Iterator trie_state_begin, Iterator trie_state_end) const;
// Returns the start trie state of the given group.
Label GroupStartState(int group_id) const {
return groups_[group_id]->Start();
}
// Takes a transition only within the given group. Returns the
// destination trie state and multiplies the weight onto `weight`.
Label GroupTransition(int group_id, int trie_state, Label ilabel,
Label olabel, Weight *weight) const;
// Returns the final weight of the given trie state in the given group.
Weight GroupFinalWeight(int group_id, int trie_state) const {
return groups_[group_id]->FinalWeight(trie_state);
}
Label MinInputLabel() const { return 1; }
Label MaxInputLabel() const { return max_input_label_; }
// Returns the maximum future size of all feature groups. Future is
// the look-ahead window of a feature, e.g. if a feature looks at
// the next 2 words after the current input, then the future size is
// 2. There is no look-ahead for output. Features inside a single
// `FeatureGroup` must have equal future size.
size_t MaxFutureSize() const { return max_future_size_; }
// Returns the number of feature groups
size_t NumGroups() const { return groups_.size(); }
// Returns the range of possible output labels for an input label.
std::pair<typename std::vector<Label>::const_iterator,
typename std::vector<Label>::const_iterator>
PossibleOutputLabels(Label word) const;
static LinearFstData<A> *Read(std::istream &strm); // NOLINT
std::ostream &Write(std::ostream &strm) const; // NOLINT
private:
// Offsets in `output_pool_`
struct InputAttribute {
size_t output_begin, output_length;
std::istream &Read(std::istream &strm); // NOLINT
std::ostream &Write(std::ostream &strm) const; // NOLINT
};
// Mapping from input label to per-group feature label
class GroupFeatureMap;
// Translates the input label into input feature label of group
// `group`; returns `kNoLabel` when there is no feature for that
// group.
Label FindFeature(size_t group, Label word) const;
size_t max_future_size_;
Label max_input_label_;
std::vector<std::unique_ptr<const FeatureGroup<A>>> groups_;
std::vector<InputAttribute> input_attribs_;
std::vector<Label> output_pool_, output_set_;
GroupFeatureMap group_feat_map_;
LinearFstData(const LinearFstData &) = delete;
LinearFstData &operator=(const LinearFstData &) = delete;
};
template <class A>
const typename A::Label LinearFstData<A>::kStartOfSentence = -3;
template <class A>
const typename A::Label LinearFstData<A>::kEndOfSentence = -2;
template <class A>
template <class Iterator>
void LinearFstData<A>::TakeTransition(Iterator buffer_end,
Iterator trie_state_begin,
Iterator trie_state_end, Label ilabel,
Label olabel, std::vector<Label> *next,
Weight *weight) const {
DCHECK_EQ(trie_state_end - trie_state_begin, groups_.size());
DCHECK(ilabel > 0 || ilabel == kEndOfSentence);
DCHECK(olabel > 0 || olabel == kStartOfSentence);
size_t group_id = 0;
for (Iterator it = trie_state_begin; it != trie_state_end; ++it, ++group_id) {
size_t delay = groups_[group_id]->Delay();
// On the buffer, there may also be `kStartOfSentence` from the
// initial empty buffer.
Label real_ilabel = delay == 0 ? ilabel : *(buffer_end - delay);
next->push_back(
GroupTransition(group_id, *it, real_ilabel, olabel, weight));
}
}
template <class A>
typename A::Label LinearFstData<A>::GroupTransition(int group_id,
int trie_state,
Label ilabel, Label olabel,
Weight *weight) const {
Label group_ilabel = FindFeature(group_id, ilabel);
return groups_[group_id]->Walk(trie_state, group_ilabel, olabel, weight);
}
template <class A>
template <class Iterator>
inline typename A::Weight LinearFstData<A>::FinalWeight(
Iterator trie_state_begin, Iterator trie_state_end) const {
DCHECK_EQ(trie_state_end - trie_state_begin, groups_.size());
size_t group_id = 0;
Weight accum = Weight::One();
for (Iterator it = trie_state_begin; it != trie_state_end; ++it, ++group_id)
accum = Times(accum, GroupFinalWeight(group_id, *it));
return accum;
}
template <class A>
inline std::pair<typename std::vector<typename A::Label>::const_iterator,
typename std::vector<typename A::Label>::const_iterator>
LinearFstData<A>::PossibleOutputLabels(Label word) const {
const InputAttribute &attrib = input_attribs_[word];
if (attrib.output_length == 0)
return std::make_pair(output_set_.begin(), output_set_.end());
else
return std::make_pair(
output_pool_.begin() + attrib.output_begin,
output_pool_.begin() + attrib.output_begin + attrib.output_length);
}
template <class A>
inline LinearFstData<A> *LinearFstData<A>::Read(std::istream &strm) { // NOLINT
std::unique_ptr<LinearFstData<A>> data(new LinearFstData<A>());
ReadType(strm, &(data->max_future_size_));
ReadType(strm, &(data->max_input_label_));
// Feature groups
size_t num_groups = 0;
ReadType(strm, &num_groups);
data->groups_.resize(num_groups);
for (size_t i = 0; i < num_groups; ++i)
data->groups_[i].reset(FeatureGroup<A>::Read(strm));
// Other data
ReadType(strm, &(data->input_attribs_));
ReadType(strm, &(data->output_pool_));
ReadType(strm, &(data->output_set_));
ReadType(strm, &(data->group_feat_map_));
if (strm) {
return data.release();
} else {
return nullptr;
}
}
template <class A>
inline std::ostream &LinearFstData<A>::Write(
std::ostream &strm) const { // NOLINT
WriteType(strm, max_future_size_);
WriteType(strm, max_input_label_);
// Feature groups
WriteType(strm, groups_.size());
for (size_t i = 0; i < groups_.size(); ++i) {
groups_[i]->Write(strm);
}
// Other data
WriteType(strm, input_attribs_);
WriteType(strm, output_pool_);
WriteType(strm, output_set_);
WriteType(strm, group_feat_map_);
return strm;
}
template <class A>
typename A::Label LinearFstData<A>::FindFeature(size_t group,
Label word) const {
DCHECK(word > 0 || word == kStartOfSentence || word == kEndOfSentence);
if (word == kStartOfSentence || word == kEndOfSentence)
return word;
else
return group_feat_map_.Find(group, word);
}
template <class A>
inline std::istream &LinearFstData<A>::InputAttribute::Read(
std::istream &strm) { // NOLINT
ReadType(strm, &output_begin);
ReadType(strm, &output_length);
return strm;
}
template <class A>
inline std::ostream &LinearFstData<A>::InputAttribute::Write(
std::ostream &strm) const { // NOLINT
WriteType(strm, output_begin);
WriteType(strm, output_length);
return strm;
}
// Forward declaration
template <class A>
class FeatureGroupBuilder;
// An immutable grouping of features with similar context shape. Like
// `LinearFstData`, this can only be constructed via `Read()` or
// via its builder.
//
// Internally it uses a trie to store all feature n-grams and their
// weights. The label of a trie edge is a pair (feat, olabel) of
// labels. They can be either positive (ordinary label), `kNoLabel`,
// `kStartOfSentence`, or `kEndOfSentence`. `kNoLabel` usually means
// matching anything, with one exception: from the root of the trie,
// there is a special (kNoLabel, kNoLabel) that leads to the implicit
// start-of-sentence state. This edge is never actually matched
// (`FindFirstMatch()` ensures this).
template <class A>
class FeatureGroup {
public:
friend class FeatureGroupBuilder<A>; // for builder access
typedef typename A::Label Label;
typedef typename A::Weight Weight;
int Start() const { return start_; }
// Finds destination node from `cur` by consuming `ilabel` and
// `olabel`. The transition weight is multiplied onto `weight`.
int Walk(int cur, Label ilabel, Label olabel, Weight *weight) const;
// Returns the final weight of the current trie state. Only valid if
// the state is already known to be part of a final state (see
// `LinearFstData<>::CanBeFinal()`).
Weight FinalWeight(int trie_state) const {
return trie_[trie_state].final_weight;
}
static FeatureGroup<A> *Read(std::istream &strm) { // NOLINT
size_t delay;
ReadType(strm, &delay);
int start;
ReadType(strm, &start);
Trie trie;
ReadType(strm, &trie);
std::unique_ptr<FeatureGroup<A>> ret(new FeatureGroup<A>(delay, start));
ret->trie_.swap(trie);
ReadType(strm, &ret->next_state_);
if (strm) {
return ret.release();
} else {
return nullptr;
}
}
std::ostream &Write(std::ostream &strm) const { // NOLINT
WriteType(strm, delay_);
WriteType(strm, start_);
WriteType(strm, trie_);
WriteType(strm, next_state_);
return strm;
}
size_t Delay() const { return delay_; }
string Stats() const;
private:
// Label along the arcs on the trie. `kNoLabel` means anything
// (non-negative label) can match; both sides holding `kNoLabel`
// is not allow; otherwise the label is > 0 (enforced by
// `LinearFstDataBuilder::AddWeight()`).
struct InputOutputLabel;
struct InputOutputLabelHash;
// Data to be stored on the trie
struct WeightBackLink {
int back_link;
Weight weight, final_weight;
WeightBackLink()
: back_link(kNoTrieNodeId),
weight(Weight::One()),
final_weight(Weight::One()) {}
std::istream &Read(std::istream &strm) { // NOLINT
ReadType(strm, &back_link);
ReadType(strm, &weight);
ReadType(strm, &final_weight);
return strm;
}
std::ostream &Write(std::ostream &strm) const { // NOLINT
WriteType(strm, back_link);
WriteType(strm, weight);
WriteType(strm, final_weight);
return strm;
}
};
typedef FlatTrieTopology<InputOutputLabel, InputOutputLabelHash> Topology;
typedef MutableTrie<InputOutputLabel, WeightBackLink, Topology> Trie;
explicit FeatureGroup(size_t delay, int start)
: delay_(delay), start_(start) {}
// Finds the first node with an arc with `label` following the
// back-off chain of `parent`. Returns the node index or
// `kNoTrieNodeId` when not found.
int FindFirstMatch(InputOutputLabel label, int parent) const;
size_t delay_;
int start_;
Trie trie_;
// Where to go after hitting this state. When we reach a state with
// no child and with no additional final weight (i.e. its final
// weight is the same as its back-off), we can immediately go to its
// back-off state.
std::vector<int> next_state_;
FeatureGroup(const FeatureGroup &) = delete;
FeatureGroup &operator=(const FeatureGroup &) = delete;
};
template <class A>
struct FeatureGroup<A>::InputOutputLabel {
Label input, output;
InputOutputLabel(Label i = kNoLabel, Label o = kNoLabel)
: input(i), output(o) {}
bool operator==(InputOutputLabel that) const {
return input == that.input && output == that.output;
}
std::istream &Read(std::istream &strm) { // NOLINT
ReadType(strm, &input);
ReadType(strm, &output);
return strm;
}
std::ostream &Write(std::ostream &strm) const { // NOLINT
WriteType(strm, input);
WriteType(strm, output);
return strm;
}
};
template <class A>
struct FeatureGroup<A>::InputOutputLabelHash {
size_t operator()(InputOutputLabel label) const {
return static_cast<size_t>(label.input * 7853 + label.output);
}
};
template <class A>
int FeatureGroup<A>::Walk(int cur, Label ilabel, Label olabel,
Weight *weight) const {
// Note: user of this method need to ensure `ilabel` and `olabel`
// are valid (e.g. see DCHECKs in
// `LinearFstData<>::TakeTransition()` and
// `LinearFstData<>::FindFeature()`).
int next;
if (ilabel == LinearFstData<A>::kStartOfSentence) {
// An observed start-of-sentence only occurs in the beginning of
// the input, when this feature group is delayed (i.e. there is
// another feature group with a larger future size). The actual
// input hasn't arrived so stay at the start state.
DCHECK_EQ(cur, start_);
next = start_;
} else {
// First, try exact match
next = FindFirstMatch(InputOutputLabel(ilabel, olabel), cur);
// Then try with don't cares
if (next == kNoTrieNodeId)
next = FindFirstMatch(InputOutputLabel(ilabel, kNoLabel), cur);
if (next == kNoTrieNodeId)
next = FindFirstMatch(InputOutputLabel(kNoLabel, olabel), cur);
// All failed, go to empty context
if (next == kNoTrieNodeId) next = trie_.Root();
*weight = Times(*weight, trie_[next].weight);
next = next_state_[next];
}
return next;
}
template <class A>
inline int FeatureGroup<A>::FindFirstMatch(InputOutputLabel label,
int parent) const {
if (label.input == kNoLabel && label.output == kNoLabel)
return kNoTrieNodeId; // very important; see class doc.
for (; parent != kNoTrieNodeId; parent = trie_[parent].back_link) {
int next = trie_.Find(parent, label);
if (next != kNoTrieNodeId) return next;
}
return kNoTrieNodeId;
}
template <class A>
inline string FeatureGroup<A>::Stats() const {
std::ostringstream strm;
int num_states = 2;
for (int i = 2; i < next_state_.size(); ++i)
num_states += i == next_state_[i];
strm << trie_.NumNodes() << " node(s); " << num_states << " state(s)";
return strm.str();
}
template <class A>
class LinearFstData<A>::GroupFeatureMap {
public:
GroupFeatureMap() {}
void Init(size_t num_groups, size_t num_words) {
num_groups_ = num_groups;
pool_.clear();
pool_.resize(num_groups * num_words, kNoLabel);
}
Label Find(size_t group_id, Label ilabel) const {
return pool_[IndexOf(group_id, ilabel)];
}
bool Set(size_t group_id, Label ilabel, Label feat) {
size_t i = IndexOf(group_id, ilabel);
if (pool_[i] != kNoLabel && pool_[i] != feat) {
FSTERROR() << "Feature group " << group_id
<< " already has feature for word " << ilabel;
return false;
}
pool_[i] = feat;
return true;
}
std::istream &Read(std::istream &strm) { // NOLINT
ReadType(strm, &num_groups_);
ReadType(strm, &pool_);
return strm;
}
std::ostream &Write(std::ostream &strm) const { // NOLINT
WriteType(strm, num_groups_);
WriteType(strm, pool_);
return strm;
}
private:
size_t IndexOf(size_t group_id, Label ilabel) const {
return ilabel * num_groups_ + group_id;
}
size_t num_groups_;
// `pool_[ilabel * num_groups_ + group_id]` is the feature active
// for group `group_id` with input `ilabel`
std::vector<Label> pool_;
};
} // namespace fst
#endif // FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_H_
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