/usr/include/fst/extensions/linear/linear-fst-data-builder.h is in libfst-dev 1.6.3-2.
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// finite-state transducer library.
#ifndef FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_BUILDER_H_
#define FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_BUILDER_H_
#include <map>
#include <queue>
#include <set>
#include <sstream>
#include <stack>
#include <string>
#include <vector>
#include <fst/compat.h>
#include <fst/log.h>
#include <fst/fst.h>
#include <fst/symbol-table.h>
#include <fst/util.h>
#include <fst/extensions/linear/linear-fst-data.h>
namespace fst {
// Forward declaration
template <class A>
class FeatureGroupBuilder;
// For logging purposes
inline string TranslateLabel(int64 label, const SymbolTable *syms);
template <class Iterator>
string JoinLabels(Iterator begin, Iterator end, const SymbolTable *syms);
template <class Label>
string JoinLabels(const std::vector<Label> &labels, const SymbolTable *syms);
// Guesses the appropriate boundary label (start- or end-of-sentence)
// for all labels equal to `boundary` and modifies the `sequence`
// in-place. Returns the number of positions that are still uncertain.
template <class A>
typename A::Label GuessStartOrEnd(std::vector<typename A::Label> *sequence,
typename A::Label boundary);
// Builds a `LinearFstData` object by adding words and feature
// weights. A few conventions:
//
// - Input labels forms a dense non-empty range from 1 to `MaxInputLabel()`.
// - Feature labels, output labels are > 0.
// - Being a discriminative linear model, it only makes sense to use tropical
// semirings.
template <class A>
class LinearFstDataBuilder {
public:
typedef typename A::Label Label;
typedef typename A::Weight Weight;
// Constructs a builder with associated symbol tables for diagonstic
// output. Each of these symbol tables may also be nullptr.
explicit LinearFstDataBuilder(const SymbolTable *isyms = nullptr,
const SymbolTable *fsyms = nullptr,
const SymbolTable *osyms = nullptr)
: error_(false),
max_future_size_(0),
max_input_label_(1),
isyms_(isyms),
fsyms_(fsyms),
osyms_(osyms) {}
// Tests whether the builder has encountered any error. No operation
// is valid if the builder is already at error state. All other
// public methods should check this before any actual operations.
bool Error() const { return error_; }
// Adds a word and its feature labels to the vocabulary; this
// version allows the word to have any output label. Returns true
// iff the word is added.
//
// This may fail if the word is added twice or if the feature labels
// are non-positive.
bool AddWord(Label word, const std::vector<Label> &features);
// Adds a word and its feature labels to the vocabulary; this
// version puts constraint on possible output labels the word can
// have. `possible_output` must not be empty. Returns true iff the
// word is added.
//
// In addition to the reasons above in the two-parameter version,
// this may also fail if `possible_output` is empty or any output
// label in it is non-positive.
bool AddWord(Label word, const std::vector<Label> &word_features,
const std::vector<Label> &possible_output);
// Creates a new feature group with specified future size (size of
// the look-ahead window), returns the group id to be used for
// adding actual feature weights or a negative number when called at
// error state.
//
// This does not fail unless called at error state.
int AddGroup(size_t future_size);
// Adds an instance of feature weight to the specified feature
// group. If some weight has already been added with the same
// feature, the product of the old and new weights are
// stored. Returns true iff the weight is added. A weight is not
// added when the context has ill-formed context involving start-,
// end-of-sentence marks.
//
// For two features to be within the same group, it must satisfy
// that (1) they have the same future size; (2) the two either have
// disjoint context or one is the back-off context of the
// other. Furthermore, for all features in a single group, there
// must be one and only one other context (not necessarily an active
// feature) that the feature immediately backs off to (i.e. there is
// no other context that is the back-off of the first and backs off
// to the second).
//
// Consider for example features with zero look-ahead of the form
// (input, OUTPUT).
//
// - The following two features can be put in the same group because
// their context is disjoint: (a a a, A A), (b, B B);
//
// - The following two features can be put in the same group because
// one is the back-off context of the other: (a a a, A A), (a a, A
// A);
//
// - The following two features can NOT be put in the same group
// because there is overlap but neither is the other's back-off: (a
// a a, A), (a a, A A);
//
// - Finally, the following three features cannot be in a same group
// because the first one can immediately back off to either of the
// rest: (a a a, A A), (a a, A A), (a a a, A).
//
// The easiest way to satisfy the constraints is to create a feature
// group for each feature template. However, better feature grouping
// may help improve speed.
//
// This may fail if any of input or output labels are non-positive,
// or if any call to `FeatureGroupBuilder<>::AddWeight()` fails.
bool AddWeight(size_t group, const std::vector<Label> &input,
const std::vector<Label> &output, Weight weight);
// Returns a newly created `LinearFstData` object or nullptr in case
// of failure. The caller takes the ownership of the memory. No
// other methods shall be called after this --- this is enforced by
// putting the builder at error state, even when a
// `LinearFstData<>` object is successfully built.
//
// This may fail if the call to any `FeatureGroupBuilder<>::Dump()`
// fails.
LinearFstData<A> *Dump();
private:
bool error_;
CompactSet<Label, kNoLabel> all_output_labels_;
std::map<Label, std::set<Label>> word_output_map_, word_feat_map_;
std::map<Label, std::set<size_t>> feat_groups_;
std::vector<std::unique_ptr<FeatureGroupBuilder<A>>> groups_;
size_t max_future_size_;
Label max_input_label_;
const SymbolTable *isyms_, *fsyms_, *osyms_;
LinearFstDataBuilder(const LinearFstDataBuilder &) = delete;
LinearFstDataBuilder &operator=(const LinearFstDataBuilder &) = delete;
};
// Builds a LinearFstData tailored for a LinearClassifierFst. The
// major difference between an ordinary LinearFstData that works on
// taggers and a LinearFstData that works on classifiers is that
// feature groups are divided into sections by the prediction class
// label. For a prediction label `pred` and a logical group id
// `group`, the actual group id is `group * num_classes + pred -
// 1`.
//
// This layout saves us from recording output labels in each single
// FeatureGroup. Because there is no need for any delaying, stripping
// the output allows features with different shapes but using the same
// set of feature label mapping to reside in a single FeatureGroup.
template <class A>
class LinearClassifierFstDataBuilder {
public:
typedef typename A::Label Label;
typedef typename A::Weight Weight;
// Constructs a builder for a `num_classes`-class classifier,
// optinally with associated symbol tables for diagnostic
// output. The output labels (i.e. prediction) must be in the range
// of [1, num_classes].
explicit LinearClassifierFstDataBuilder(size_t num_classes,
const SymbolTable *isyms = nullptr,
const SymbolTable *fsyms = nullptr,
const SymbolTable *osyms = nullptr)
: error_(false),
num_classes_(num_classes),
num_groups_(0),
builder_(isyms, fsyms, osyms) {}
// Tests whether the builder has encountered any error. Similar to
// LinearFstDataBuilder<>::Error().
bool Error() const { return error_; }
// Same as LinearFstDataBuilder<>::AddWord().
bool AddWord(Label word, const std::vector<Label> &features);
// Adds a logical feature group. Similar to
// LinearFstDataBuilder<>::AddGroup(), with the exception that the
// returned group id is the logical group id. Also there is no need
// for "future" in a classifier.
int AddGroup();
// Adds an instance of feature weight to the specified logical
// feature group. Instead of a vector of output, only a single
// prediction is needed as the output.
//
// This may fail if `pred` is not in the range of [1, num_classes_].
bool AddWeight(size_t group, const std::vector<Label> &input, Label pred,
Weight weight);
// Returns a newly created `LinearFstData` object or nullptr in case of
// failure.
LinearFstData<A> *Dump();
private:
std::vector<Label> empty_;
bool error_;
size_t num_classes_, num_groups_;
LinearFstDataBuilder<A> builder_;
};
// Builds a single feature group. Usually used in
// `LinearFstDataBuilder::AddWeight()`. See that method for the
// constraints on grouping features.
template <class A>
class FeatureGroupBuilder {
public:
typedef typename A::Label Label;
typedef typename A::Weight Weight;
// Constructs a builder with the given future size. All features
// added to the group will have look-ahead windows of this size.
FeatureGroupBuilder(size_t future_size, const SymbolTable *fsyms,
const SymbolTable *osyms)
: error_(false), future_size_(future_size), fsyms_(fsyms), osyms_(osyms) {
// This edge is special; see doc of class `FeatureGroup` on the
// details.
start_ = trie_.Insert(trie_.Root(), InputOutputLabel(kNoLabel, kNoLabel));
}
// Tests whether the builder has encountered any error. No operation
// is valid if the builder is already at error state. All other
// public methods should check this before any actual operations.
bool Error() const { return error_; }
// Adds a feature weight with the given context. Returns true iff
// the weight is added. A weight is not added if it has ill-formed
// context involving start-, end-of-sentence marks.
//
// Note: `input` is the sequence of input
// features, instead of input labels themselves. `input` must be at
// least as long as `future_size`; `output` may be empty, but
// usually should be non-empty because an empty output context is
// useless in discriminative modelling. All labels in both `input`
// and `output` must be > 0 (this is checked in
// `LinearFstDataBuilder::AddWeight()`). See
// LinearFstDataBuilder<>::AddWeight for more details.
//
// This may fail if the input is smaller than the look-ahead window.
bool AddWeight(const std::vector<Label> &input,
const std::vector<Label> &output, Weight weight);
// Creates an actual FeatureGroup<> object. Connects back-off links;
// pre-accumulates weights from back-off features. Returns nullptr if
// there is any violation in unique immediate back-off
// constraints.
//
// Regardless of whether the call succeeds or not, the error flag is
// always set before this returns, to prevent repeated dumping.
//
// TODO(wuke): check overlapping top-level contexts (see
// `DumpOverlappingContext()` in tests).
FeatureGroup<A> *Dump(size_t max_future_size);
private:
typedef typename FeatureGroup<A>::InputOutputLabel InputOutputLabel;
typedef typename FeatureGroup<A>::InputOutputLabelHash InputOutputLabelHash;
typedef typename FeatureGroup<A>::WeightBackLink WeightBackLink;
// Nested trie topology uses more memory but we can traverse a
// node's children easily, which is required in `BuildBackLinks()`.
typedef NestedTrieTopology<InputOutputLabel, InputOutputLabelHash> Topology;
typedef MutableTrie<InputOutputLabel, WeightBackLink, Topology> Trie;
// 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. The number of hops is stored in
// `hop` when it is not `nullptr`.
//
// This does not fail.
int FindFirstMatch(InputOutputLabel label, int parent, int *hop) const;
// Links each node to its immediate back-off. root is linked to -1.
//
// This may fail when the unique immediate back-off constraint is
// violated.
void BuildBackLinks();
// Traces back on the back-chain for each node to multiply the
// weights from back-offs to the node itself.
//
// This does not fail.
void PreAccumulateWeights();
// Reconstruct the path from trie root to given node for logging.
bool TrieDfs(const Topology &topology, int cur, int target,
std::vector<InputOutputLabel> *path) const;
string TriePath(int node, const Topology &topology) const;
bool error_;
size_t future_size_;
Trie trie_;
int start_;
const SymbolTable *fsyms_, *osyms_;
FeatureGroupBuilder(const FeatureGroupBuilder &) = delete;
FeatureGroupBuilder &operator=(const FeatureGroupBuilder &) = delete;
};
//
// Implementation of methods in `LinearFstDataBuilder`
//
template <class A>
bool LinearFstDataBuilder<A>::AddWord(Label word,
const std::vector<Label> &features) {
if (error_) {
FSTERROR() << "Calling LinearFstDataBuilder<>::AddWord() at error state";
return false;
}
if (word == LinearFstData<A>::kStartOfSentence ||
word == LinearFstData<A>::kEndOfSentence) {
LOG(WARNING) << "Ignored: adding boundary label: "
<< TranslateLabel(word, isyms_)
<< "(start-of-sentence=" << LinearFstData<A>::kStartOfSentence
<< ", end-of-sentence=" << LinearFstData<A>::kEndOfSentence
<< ")";
return false;
}
if (word <= 0) {
error_ = true;
FSTERROR() << "Word label must be > 0; got " << word;
return false;
}
if (word > max_input_label_) max_input_label_ = word;
// Make sure the word hasn't been added before
if (word_feat_map_.find(word) != word_feat_map_.end()) {
error_ = true;
FSTERROR() << "Input word " << TranslateLabel(word, isyms_)
<< " is added twice";
return false;
}
// Store features
std::set<Label> *feats = &word_feat_map_[word];
for (size_t i = 0; i < features.size(); ++i) {
Label feat = features[i];
if (feat <= 0) {
error_ = true;
FSTERROR() << "Feature label must be > 0; got " << feat;
return false;
}
feats->insert(feat);
}
return true;
}
template <class A>
bool LinearFstDataBuilder<A>::AddWord(
Label word, const std::vector<Label> &word_features,
const std::vector<Label> &possible_output) {
if (error_) {
FSTERROR() << "Calling LinearFstDataBuilder<>::AddWord() at error state";
return false;
}
if (!AddWord(word, word_features)) return false;
// Store possible output constraint
if (possible_output.empty()) {
error_ = true;
FSTERROR() << "Empty possible output constraint; "
<< "use the two-parameter version if no constraint is need.";
return false;
}
std::set<Label> *outputs = &word_output_map_[word];
for (size_t i = 0; i < possible_output.size(); ++i) {
Label output = possible_output[i];
if (output == LinearFstData<A>::kStartOfSentence ||
output == LinearFstData<A>::kEndOfSentence) {
LOG(WARNING) << "Ignored: word = " << TranslateLabel(word, isyms_)
<< ": adding boundary label as possible output: " << output
<< "(start-of-sentence="
<< LinearFstData<A>::kStartOfSentence
<< ", end-of-sentence=" << LinearFstData<A>::kEndOfSentence
<< ")";
continue;
}
if (output <= 0) {
error_ = true;
FSTERROR() << "Output label must be > 0; got " << output;
return false;
}
outputs->insert(output);
all_output_labels_.Insert(output);
}
return true;
}
template <class A>
inline int LinearFstDataBuilder<A>::AddGroup(size_t future_size) {
if (error_) {
FSTERROR() << "Calling LinearFstDataBuilder<>::AddGroup() at error state";
return -1;
}
size_t ret = groups_.size();
groups_.emplace_back(new FeatureGroupBuilder<A>(future_size, fsyms_, osyms_));
if (future_size > max_future_size_) max_future_size_ = future_size;
return ret;
}
template <class A>
bool LinearFstDataBuilder<A>::AddWeight(size_t group,
const std::vector<Label> &input,
const std::vector<Label> &output,
Weight weight) {
if (error_) {
FSTERROR() << "Calling LinearFstDataBuilder<>::AddWeight() at error state";
return false;
}
// Check well-formedness of boundary marks on the input.
{
bool start_in_middle = false, end_in_middle = false;
for (int i = 1; i < input.size(); ++i) {
if (input[i] == LinearFstData<A>::kStartOfSentence &&
input[i - 1] != LinearFstData<A>::kStartOfSentence)
start_in_middle = true;
if (input[i - 1] == LinearFstData<A>::kEndOfSentence &&
input[i] != LinearFstData<A>::kEndOfSentence)
end_in_middle = true;
}
if (start_in_middle) {
LOG(WARNING) << "Ignored: start-of-sentence in the middle of the input!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
if (end_in_middle) {
LOG(WARNING) << "Ignored: end-of-sentence in the middle of the input!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
}
// Check well-formedness of boundary marks on the output.
{
bool non_first_start = false, non_last_end = false;
for (int i = 1; i < output.size(); ++i) {
if (output[i] == LinearFstData<A>::kStartOfSentence)
non_first_start = true;
if (output[i - 1] == LinearFstData<A>::kEndOfSentence)
non_last_end = true;
}
if (non_first_start) {
LOG(WARNING) << "Ignored: start-of-sentence not appearing "
<< "as the first label in the output!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
if (non_last_end) {
LOG(WARNING) << "Ignored: end-of-sentence not appearing "
<< "as the last label in the output!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
}
for (size_t i = 0; i < input.size(); ++i) {
Label feat = input[i];
if (feat != LinearFstData<A>::kStartOfSentence &&
feat != LinearFstData<A>::kEndOfSentence && feat <= 0) {
error_ = true;
FSTERROR() << "Feature label must be > 0; got " << feat;
return false;
}
feat_groups_[feat].insert(group);
}
for (size_t i = 0; i < output.size(); ++i) {
Label label = output[i];
if (label != LinearFstData<A>::kStartOfSentence &&
label != LinearFstData<A>::kEndOfSentence && label <= 0) {
error_ = true;
FSTERROR() << "Output label must be > 0; got " << label;
return false;
}
if (label != LinearFstData<A>::kStartOfSentence &&
label != LinearFstData<A>::kEndOfSentence)
all_output_labels_.Insert(label);
}
// Everything looks good at this point (more checks on the way in
// the feature group). Add this feature weight.
bool added = groups_[group]->AddWeight(input, output, weight);
if (groups_[group]->Error()) {
error_ = true;
FSTERROR() << "FeatureGroupBuilder<>::AddWeight() failed";
return false;
}
return added;
}
template <class A>
LinearFstData<A> *LinearFstDataBuilder<A>::Dump() {
if (error_) {
FSTERROR() << "Calling LinearFstDataBuilder<>::Dump() at error state";
return nullptr;
}
std::unique_ptr<LinearFstData<A>> data(new LinearFstData<A>());
data->max_future_size_ = max_future_size_;
data->max_input_label_ = max_input_label_;
// Feature groups; free builders after it's dumped.
data->groups_.resize(groups_.size());
for (int group = 0; group != groups_.size(); ++group) {
FeatureGroup<A> *new_group = groups_[group]->Dump(max_future_size_);
if (new_group == nullptr) {
error_ = true;
FSTERROR() << "Error in dumping group " << group;
return nullptr;
}
data->groups_[group].reset(new_group);
groups_[group].reset();
VLOG(1) << "Group " << group << ": " << new_group->Stats();
}
// Per-group feature mapping
data->group_feat_map_.Init(data->NumGroups(), max_input_label_ + 1);
for (Label word = 1; word <= max_input_label_; ++word) {
typename std::map<Label, std::set<Label>>::const_iterator it =
word_feat_map_.find(word);
if (it == word_feat_map_.end()) continue;
for (typename std::set<Label>::const_iterator oit = it->second.begin();
oit != it->second.end(); ++oit) {
Label feat = *oit;
typename std::map<Label, std::set<size_t>>::const_iterator jt =
feat_groups_.find(feat);
if (jt == feat_groups_.end()) continue;
for (std::set<size_t>::const_iterator git = jt->second.begin();
git != jt->second.end(); ++git) {
size_t group_id = *git;
if (!data->group_feat_map_.Set(group_id, word, feat)) {
error_ = true;
return nullptr;
}
}
}
}
// Possible output labels
{
std::vector<typename LinearFstData<A>::InputAttribute> *input_attribs =
&data->input_attribs_;
std::vector<Label> *output_pool = &data->output_pool_;
input_attribs->resize(max_input_label_ + 1);
for (Label word = 0; word <= max_input_label_; ++word) {
typename std::map<Label, std::set<Label>>::const_iterator it =
word_output_map_.find(word);
if (it == word_output_map_.end()) {
(*input_attribs)[word].output_begin = 0;
(*input_attribs)[word].output_length = 0;
} else {
(*input_attribs)[word].output_begin = output_pool->size();
(*input_attribs)[word].output_length = it->second.size();
for (typename std::set<Label>::const_iterator oit = it->second.begin();
oit != it->second.end(); ++oit) {
Label olabel = *oit;
output_pool->push_back(olabel);
}
}
}
}
for (typename CompactSet<Label, kNoLabel>::const_iterator it =
all_output_labels_.Begin();
it != all_output_labels_.End(); ++it)
data->output_set_.push_back(*it);
error_ = true; // prevent future calls on this object
return data.release();
}
//
// Implementation of methods in `LinearClassifierFstDataBuilder`
//
template <class A>
inline bool LinearClassifierFstDataBuilder<A>::AddWord(
Label word, const std::vector<Label> &features) {
if (error_) {
FSTERROR() << "Calling LinearClassifierFstDataBuilder<>::AddWord() at "
"error state";
return false;
}
bool added = builder_.AddWord(word, features);
if (builder_.Error()) error_ = true;
return added;
}
template <class A>
inline int LinearClassifierFstDataBuilder<A>::AddGroup() {
if (error_) {
FSTERROR() << "Calling LinearClassifierFstDataBuilder<>::AddGroup() at "
"error state";
return -1;
}
for (int i = 0; i < num_classes_; ++i) builder_.AddGroup(0);
if (builder_.Error()) {
error_ = true;
return -1;
}
return num_groups_++;
}
template <class A>
inline bool LinearClassifierFstDataBuilder<A>::AddWeight(
size_t group, const std::vector<Label> &input, Label pred, Weight weight) {
if (error_) {
FSTERROR() << "Calling LinearClassifierFstDataBuilder<>::AddWeight() at "
"error state";
return false;
}
if (pred <= 0 || pred > num_classes_) {
FSTERROR() << "Out-of-range prediction label: " << pred
<< " (num classes = " << num_classes_ << ")";
error_ = true;
return false;
}
size_t real_group = group * num_classes_ + pred - 1;
bool added = builder_.AddWeight(real_group, input, empty_, weight);
if (builder_.Error()) error_ = true;
return added;
}
template <class A>
inline LinearFstData<A> *LinearClassifierFstDataBuilder<A>::Dump() {
if (error_) {
FSTERROR()
<< "Calling LinearClassifierFstDataBuilder<>::Dump() at error state";
return nullptr;
}
LinearFstData<A> *data = builder_.Dump();
error_ = true;
return data;
}
//
// Implementation of methods in `FeatureGroupBuilder`
//
template <class A>
bool FeatureGroupBuilder<A>::AddWeight(const std::vector<Label> &input,
const std::vector<Label> &output,
Weight weight) {
if (error_) {
FSTERROR() << "Calling FeatureGroupBuilder<>::AddWeight() at error state";
return false;
}
// `LinearFstDataBuilder<>::AddWeight()` ensures prefix/suffix
// properties for us. We can directly count.
int num_input_start = 0;
while (num_input_start < input.size() &&
input[num_input_start] == LinearFstData<A>::kStartOfSentence)
++num_input_start;
int num_output_start = 0;
while (num_output_start < output.size() &&
output[num_output_start] == LinearFstData<A>::kStartOfSentence)
++num_output_start;
int num_input_end = 0;
for (int i = input.size() - 1;
i >= 0 && input[i] == LinearFstData<A>::kEndOfSentence; --i)
++num_input_end;
int num_output_end = 0;
for (int i = output.size() - 1;
i >= 0 && output[i] == LinearFstData<A>::kEndOfSentence; --i)
++num_output_end;
DCHECK_LE(num_output_end, 1);
if (input.size() - num_input_start < future_size_) {
LOG(WARNING) << "Ignored: start-of-sentence in the future!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, fsyms_);
return false;
}
if (num_input_start > 0 &&
input.size() - future_size_ - num_input_start <
output.size() - num_output_start) {
LOG(WARNING) << "Ignored: matching start-of-sentence with actual output!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
if (num_output_start > 0 &&
input.size() - future_size_ - num_input_start >
output.size() - num_output_start) {
LOG(WARNING) << "Ignored: matching start-of-sentence with actual input!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
// The following two require `num_output_end` <= 1.
if (num_input_end > future_size_ && num_input_end - future_size_ != 1) {
LOG(WARNING) << "Ignored: matching end-of-sentence with actual output!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
if (num_output_end > 0 &&
((input.size() == future_size_ && future_size_ != num_input_end) ||
(input.size() > future_size_ &&
num_input_end != future_size_ + num_output_end))) {
LOG(WARNING) << "Ignored: matching end-of-sentence with actual input!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
// Check if the context has no other labels than boundary marks
// (such features are useless).
if (num_input_start + num_input_end == input.size() &&
num_output_start + num_output_end == output.size()) {
LOG(WARNING)
<< "Ignored: feature context consisting of only boundary marks!";
LOG(WARNING) << "\tInput: " << JoinLabels(input, fsyms_);
LOG(WARNING) << "\tOutput: " << JoinLabels(output, osyms_);
return false;
}
// Start point for insertion in the trie. Insert at `start_` iff the
// beginning of the context is non-consumed start-of-sentence.
int cur = (num_input_start == 0 && num_output_start <= future_size_)
? trie_.Root()
: start_;
// Skip all input start-of-sentence marks
size_t ipos = num_input_start;
// Skip to keep at most `future_size_` start-of-sentence marks
size_t opos =
num_output_start <= future_size_ ? 0 : num_output_start - future_size_;
// Skip `num_output_end` end-of-sentence marks on both input and output
size_t iend = !input.empty() ? input.size() - num_output_end : 0,
oend = output.size() - num_output_end;
// Further, when output is empty, keep at most `future_size_`
// end-of-sentence marks on input.
if (output.empty() && num_input_end > future_size_)
iend = input.size() - num_input_end + future_size_;
// Actual feature context is (input[ipos:iend], output[opos:oend]).
// Pad `kNoLabel` as don't cares on the shorter of actual `input`
// and `output`.
const size_t effective_input_size = iend - ipos,
effective_output_size = oend - opos;
if (effective_input_size > effective_output_size) {
for (size_t pad = effective_input_size - effective_output_size; pad != 0;
--pad, ++ipos)
cur = trie_.Insert(cur, InputOutputLabel(input[ipos], kNoLabel));
} else if (effective_input_size < effective_output_size) {
for (size_t pad = effective_output_size - effective_input_size; pad != 0;
--pad, ++opos)
cur = trie_.Insert(cur, InputOutputLabel(kNoLabel, output[opos]));
}
CHECK_EQ(iend - ipos, oend - opos);
for (; ipos != iend; ++ipos, ++opos)
cur = trie_.Insert(cur, InputOutputLabel(input[ipos], output[opos]));
// We only need to attach final weight when there is an output
// end-of-sentence. When there is only end-of-sentence on the input,
// they are all consumed as the end-of-sentence paddings from
// `LinearFstImpl<>::ShiftBuffer()`. `LinearFstImpl<>::Expand()`
// and `LinearFstImpl<>::MatchInput()` ensures no other
// transition takes place after consuming the padding.
if (num_output_end > 0 || (output.empty() && num_input_end > future_size_))
trie_[cur].final_weight = Times(trie_[cur].final_weight, weight);
else
trie_[cur].weight = Times(trie_[cur].weight, weight);
return true;
}
template <class A>
FeatureGroup<A> *FeatureGroupBuilder<A>::Dump(size_t max_future_size) {
if (error_) {
FSTERROR() << "Calling FeatureGroupBuilder<>::PreAccumulateWeights() "
<< "at error state";
return nullptr;
}
if (max_future_size < future_size_) {
error_ = true;
FSTERROR() << "max_future_size (= " << max_future_size
<< ") is smaller the builder's future_size (= " << future_size_
<< ")";
return nullptr;
}
BuildBackLinks();
if (error_) return nullptr;
PreAccumulateWeights(); // does not fail
FeatureGroup<A> *ret =
new FeatureGroup<A>(max_future_size - future_size_, start_);
// Walk around the trie to compute next states
ret->next_state_.resize(trie_.NumNodes());
const Topology &topology = trie_.TrieTopology();
for (int i = 0; i < topology.NumNodes(); ++i) {
int next = i;
while (next != topology.Root() && topology.ChildrenOf(next).empty() &&
trie_[next].final_weight ==
trie_[trie_[next].back_link].final_weight)
next = trie_[next].back_link;
ret->next_state_[i] = next;
}
// Copy the trie
typename FeatureGroup<A>::Trie store_trie(trie_);
ret->trie_.swap(store_trie);
// Put the builder at error state to prevent repeated call of `Dump()`.
error_ = true;
return ret;
}
template <class A>
int FeatureGroupBuilder<A>::FindFirstMatch(InputOutputLabel label, int parent,
int *hop) const {
int hop_count = 0;
int ret = kNoTrieNodeId;
for (; parent >= 0; parent = trie_[parent].back_link, ++hop_count) {
int next = trie_.Find(parent, label);
if (next != kNoTrieNodeId) {
ret = next;
break;
}
}
if (hop != nullptr) *hop = hop_count;
return ret;
}
template <class A>
void FeatureGroupBuilder<A>::BuildBackLinks() {
// Breadth first search from the root. In the case where we only
// have the input label, the immedate back-off is simply the longest
// suffix of the current node that is also in the trie. For a node
// reached from its parent with label L, we can simply walk through
// the parent's back-off chain to find the first state with an arc
// of the same label L. The uniqueness is always
// guanranteed. However, in the case with both input and output
// labels, it is possible to back off by removing first labels from
// either side, which in general causes non-uniqueness.
const Topology &topology = trie_.TrieTopology();
std::queue<int> q; // all enqueued or visited nodes have known links
// Note: nodes have back link initialized to -1 in their
// constructor.
q.push(trie_.Root());
while (!error_ && !q.empty()) {
int parent = q.front();
q.pop();
// Find links for every child
const typename Topology::NextMap &children = topology.ChildrenOf(parent);
for (typename Topology::NextMap::const_iterator eit = children.begin();
eit != children.end(); ++eit) {
const std::pair<InputOutputLabel, int> &edge = *eit;
InputOutputLabel label = edge.first;
int child = edge.second;
if (label.input == kNoLabel || label.output == kNoLabel) {
// Label pairs from root to here all have one and only one
// `kNoLabel` on the same side; equivalent to the
// "longest-suffix" case.
trie_[child].back_link =
FindFirstMatch(label, trie_[parent].back_link, nullptr);
} else {
// Neither side is `kNoLabel` at this point, there are
// three possible ways to back-off: if the parent backs
// off to some context with only one side non-empty, the
// empty side may remain empty; or else an exact match of
// both sides is needed. Try to find all three possible
// backs and look for the closest one (in terms of hops
// along the parent's back-off chain).
int only_input_hop, only_output_hop, full_hop;
int only_input_link =
FindFirstMatch(InputOutputLabel(label.input, kNoLabel), parent,
&only_input_hop),
only_output_link =
FindFirstMatch(InputOutputLabel(kNoLabel, label.output), parent,
&only_output_hop),
full_link =
FindFirstMatch(label, trie_[parent].back_link, &full_hop);
if (only_input_link != -1 && only_output_link != -1) {
error_ = true;
FSTERROR() << "Branching back-off chain:\n"
<< "\tnode " << child << ": " << TriePath(child, topology)
<< "\n"
<< "\tcan back-off to node " << only_input_link << ": "
<< TriePath(only_input_link, topology) << "\n"
<< "\tcan back-off to node " << only_output_link << ": "
<< TriePath(only_output_link, topology);
return;
} else if (full_link != -1) {
++full_hop;
if (full_hop <= only_input_hop && full_hop <= only_output_hop) {
trie_[child].back_link = full_link;
} else {
error_ = true;
int problem_link = only_input_link != kNoTrieNodeId
? only_input_link
: only_output_link;
CHECK_NE(problem_link, kNoTrieNodeId);
FSTERROR() << "Branching back-off chain:\n"
<< "\tnode " << child << ": "
<< TriePath(child, topology) << "\n"
<< "\tcan back-off to node " << full_link << ": "
<< TriePath(full_link, topology) << "\n"
<< "tcan back-off to node " << problem_link << ": "
<< TriePath(problem_link, topology);
return;
}
} else {
trie_[child].back_link =
only_input_link != -1 ? only_input_link : only_output_link;
}
}
if (error_) break;
// Point to empty context (root) when no back-off can be found
if (trie_[child].back_link == -1) trie_[child].back_link = 0;
q.push(child);
}
}
}
template <class A>
void FeatureGroupBuilder<A>::PreAccumulateWeights() {
std::vector<bool> visited(trie_.NumNodes(), false);
visited[trie_.Root()] = true;
for (size_t i = 0; i != trie_.NumNodes(); ++i) {
std::stack<int> back_offs;
for (int j = i; !visited[j]; j = trie_[j].back_link) back_offs.push(j);
while (!back_offs.empty()) {
int j = back_offs.top();
back_offs.pop();
WeightBackLink &node = trie_[j];
node.weight = Times(node.weight, trie_[node.back_link].weight);
node.final_weight =
Times(node.final_weight, trie_[node.back_link].final_weight);
visited[j] = true;
}
}
}
template <class A>
bool FeatureGroupBuilder<A>::TrieDfs(
const Topology &topology, int cur, int target,
std::vector<InputOutputLabel> *path) const {
if (cur == target) return true;
const typename Topology::NextMap &children = topology.ChildrenOf(cur);
for (typename Topology::NextMap::const_iterator eit = children.begin();
eit != children.end(); ++eit) {
const std::pair<InputOutputLabel, int> &edge = *eit;
path->push_back(edge.first);
if (TrieDfs(topology, edge.second, target, path)) return true;
path->pop_back();
}
return false;
}
template <class A>
string FeatureGroupBuilder<A>::TriePath(int node,
const Topology &topology) const {
std::vector<InputOutputLabel> labels;
TrieDfs(topology, topology.Root(), node, &labels);
bool first = true;
std::ostringstream strm;
for (typename std::vector<InputOutputLabel>::const_iterator it =
labels.begin();
it != labels.end(); ++it) {
InputOutputLabel i = *it;
if (first)
first = false;
else
strm << ", ";
strm << "(" << TranslateLabel(i.input, fsyms_) << ", "
<< TranslateLabel(i.output, osyms_) << ")";
}
return strm.str();
}
inline string TranslateLabel(int64 label, const SymbolTable *syms) {
string ret;
if (syms != nullptr) ret += syms->Find(label);
if (ret.empty()) {
std::ostringstream strm;
strm << '<' << label << '>';
ret = strm.str();
}
return ret;
}
template <class Iterator>
string JoinLabels(Iterator begin, Iterator end, const SymbolTable *syms) {
if (begin == end) return "<empty>";
std::ostringstream strm;
bool first = true;
for (Iterator it = begin; it != end; ++it) {
if (first)
first = false;
else
strm << '|';
strm << TranslateLabel(*it, syms);
}
return strm.str();
}
template <class Label>
string JoinLabels(const std::vector<Label> &labels, const SymbolTable *syms) {
return JoinLabels(labels.begin(), labels.end(), syms);
}
template <class A>
typename A::Label GuessStartOrEnd(std::vector<typename A::Label> *sequence,
typename A::Label boundary) {
const size_t length = sequence->size();
std::vector<bool> non_boundary_on_left(length, false),
non_boundary_on_right(length, false);
for (size_t i = 1; i < length; ++i) {
non_boundary_on_left[i] =
non_boundary_on_left[i - 1] || (*sequence)[i - 1] != boundary;
non_boundary_on_right[length - 1 - i] = non_boundary_on_right[length - i] ||
(*sequence)[length - i] != boundary;
}
int unresolved = 0;
for (size_t i = 0; i < length; ++i) {
if ((*sequence)[i] != boundary) continue;
const bool left = non_boundary_on_left[i], right = non_boundary_on_right[i];
if (left && right) {
// Boundary in the middle
LOG(WARNING) << "Boundary label in the middle of the sequence! position: "
<< i << "; boundary: " << boundary
<< "; sequence: " << JoinLabels(*sequence, nullptr);
LOG(WARNING)
<< "This is an invalid sequence anyway so I will set it to start.";
(*sequence)[i] = LinearFstData<A>::kStartOfSentence;
} else if (left && !right) {
// Can only be end
(*sequence)[i] = LinearFstData<A>::kEndOfSentence;
} else if (!left && right) {
// Can only be start
(*sequence)[i] = LinearFstData<A>::kStartOfSentence;
} else {
// !left && !right; can't really tell
++unresolved;
}
}
return unresolved;
}
} // namespace fst
#endif // FST_EXTENSIONS_LINEAR_LINEAR_FST_DATA_BUILDER_H_
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