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/**
* Copyright (c) 2016-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "dictionary.h"
#include <assert.h>
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <iterator>
#include <stdexcept>
namespace fasttext {
const std::string Dictionary::EOS = "</s>";
const std::string Dictionary::BOW = "<";
const std::string Dictionary::EOW = ">";
Dictionary::Dictionary(std::shared_ptr<Args> args)
: args_(args),
word2int_(MAX_VOCAB_SIZE, -1),
size_(0),
nwords_(0),
nlabels_(0),
ntokens_(0),
pruneidx_size_(-1) {}
Dictionary::Dictionary(std::shared_ptr<Args> args, std::istream& in)
: args_(args),
size_(0),
nwords_(0),
nlabels_(0),
ntokens_(0),
pruneidx_size_(-1) {
load(in);
}
int32_t Dictionary::find(const std::string& w) const {
return find(w, hash(w));
}
int32_t Dictionary::find(const std::string& w, uint32_t h) const {
int32_t word2intsize = word2int_.size();
int32_t id = h % word2intsize;
while (word2int_[id] != -1 && words_[word2int_[id]].word != w) {
id = (id + 1) % word2intsize;
}
return id;
}
void Dictionary::add(const std::string& w) {
int32_t h = find(w);
ntokens_++;
if (word2int_[h] == -1) {
entry e;
e.word = w;
e.count = 1;
e.type = getType(w);
words_.push_back(e);
word2int_[h] = size_++;
} else {
words_[word2int_[h]].count++;
}
}
int32_t Dictionary::nwords() const {
return nwords_;
}
int32_t Dictionary::nlabels() const {
return nlabels_;
}
int64_t Dictionary::ntokens() const {
return ntokens_;
}
const std::vector<int32_t>& Dictionary::getSubwords(int32_t i) const {
assert(i >= 0);
assert(i < nwords_);
return words_[i].subwords;
}
const std::vector<int32_t> Dictionary::getSubwords(
const std::string& word) const {
int32_t i = getId(word);
if (i >= 0) {
return getSubwords(i);
}
std::vector<int32_t> ngrams;
if (word != EOS) {
computeSubwords(BOW + word + EOW, ngrams);
}
return ngrams;
}
void Dictionary::getSubwords(
const std::string& word,
std::vector<int32_t>& ngrams,
std::vector<std::string>& substrings) const {
int32_t i = getId(word);
ngrams.clear();
substrings.clear();
if (i >= 0) {
ngrams.push_back(i);
substrings.push_back(words_[i].word);
}
if (word != EOS) {
computeSubwords(BOW + word + EOW, ngrams, &substrings);
}
}
bool Dictionary::discard(int32_t id, real rand) const {
assert(id >= 0);
assert(id < nwords_);
if (args_->model == model_name::sup) {
return false;
}
return rand > pdiscard_[id];
}
int32_t Dictionary::getId(const std::string& w, uint32_t h) const {
int32_t id = find(w, h);
return word2int_[id];
}
int32_t Dictionary::getId(const std::string& w) const {
int32_t h = find(w);
return word2int_[h];
}
entry_type Dictionary::getType(int32_t id) const {
assert(id >= 0);
assert(id < size_);
return words_[id].type;
}
entry_type Dictionary::getType(const std::string& w) const {
return (w.find(args_->label) == 0) ? entry_type::label : entry_type::word;
}
std::string Dictionary::getWord(int32_t id) const {
assert(id >= 0);
assert(id < size_);
return words_[id].word;
}
// The correct implementation of fnv should be:
// h = h ^ uint32_t(uint8_t(str[i]));
// Unfortunately, earlier version of fasttext used
// h = h ^ uint32_t(str[i]);
// which is undefined behavior (as char can be signed or unsigned).
// Since all fasttext models that were already released were trained
// using signed char, we fixed the hash function to make models
// compatible whatever compiler is used.
uint32_t Dictionary::hash(const std::string& str) const {
uint32_t h = 2166136261;
for (size_t i = 0; i < str.size(); i++) {
h = h ^ uint32_t(int8_t(str[i]));
h = h * 16777619;
}
return h;
}
void Dictionary::computeSubwords(
const std::string& word,
std::vector<int32_t>& ngrams,
std::vector<std::string>* substrings) const {
for (size_t i = 0; i < word.size(); i++) {
std::string ngram;
if ((word[i] & 0xC0) == 0x80) {
continue;
}
for (size_t j = i, n = 1; j < word.size() && n <= args_->maxn; n++) {
ngram.push_back(word[j++]);
while (j < word.size() && (word[j] & 0xC0) == 0x80) {
ngram.push_back(word[j++]);
}
if (n >= args_->minn && !(n == 1 && (i == 0 || j == word.size()))) {
int32_t h = hash(ngram) % args_->bucket;
pushHash(ngrams, h);
if (substrings) {
substrings->push_back(ngram);
}
}
}
}
}
void Dictionary::initNgrams() {
for (size_t i = 0; i < size_; i++) {
std::string word = BOW + words_[i].word + EOW;
words_[i].subwords.clear();
words_[i].subwords.push_back(i);
if (words_[i].word != EOS) {
computeSubwords(word, words_[i].subwords);
}
}
}
bool Dictionary::readWord(std::istream& in, std::string& word) const {
int c;
std::streambuf& sb = *in.rdbuf();
word.clear();
while ((c = sb.sbumpc()) != EOF) {
if (c == ' ' || c == '\n' || c == '\r' || c == '\t' || c == '\v' ||
c == '\f' || c == '\0') {
if (word.empty()) {
if (c == '\n') {
word += EOS;
return true;
}
continue;
} else {
if (c == '\n')
sb.sungetc();
return true;
}
}
word.push_back(c);
}
// trigger eofbit
in.get();
return !word.empty();
}
void Dictionary::readFromFile(std::istream& in) {
std::string word;
int64_t minThreshold = 1;
while (readWord(in, word)) {
add(word);
if (ntokens_ % 1000000 == 0 && args_->verbose > 1) {
std::cerr << "\rRead " << ntokens_ / 1000000 << "M words" << std::flush;
}
if (size_ > 0.75 * MAX_VOCAB_SIZE) {
minThreshold++;
threshold(minThreshold, minThreshold);
}
}
threshold(args_->minCount, args_->minCountLabel);
initTableDiscard();
initNgrams();
if (args_->verbose > 0) {
std::cerr << "\rRead " << ntokens_ / 1000000 << "M words" << std::endl;
std::cerr << "Number of words: " << nwords_ << std::endl;
std::cerr << "Number of labels: " << nlabels_ << std::endl;
}
if (size_ == 0) {
throw std::invalid_argument(
"Empty vocabulary. Try a smaller -minCount value.");
}
}
void Dictionary::threshold(int64_t t, int64_t tl) {
sort(words_.begin(), words_.end(), [](const entry& e1, const entry& e2) {
if (e1.type != e2.type) {
return e1.type < e2.type;
}
return e1.count > e2.count;
});
words_.erase(
remove_if(
words_.begin(),
words_.end(),
[&](const entry& e) {
return (e.type == entry_type::word && e.count < t) ||
(e.type == entry_type::label && e.count < tl);
}),
words_.end());
words_.shrink_to_fit();
size_ = 0;
nwords_ = 0;
nlabels_ = 0;
std::fill(word2int_.begin(), word2int_.end(), -1);
for (auto it = words_.begin(); it != words_.end(); ++it) {
int32_t h = find(it->word);
word2int_[h] = size_++;
if (it->type == entry_type::word) {
nwords_++;
}
if (it->type == entry_type::label) {
nlabels_++;
}
}
}
void Dictionary::initTableDiscard() {
pdiscard_.resize(size_);
for (size_t i = 0; i < size_; i++) {
real f = real(words_[i].count) / real(ntokens_);
pdiscard_[i] = std::sqrt(args_->t / f) + args_->t / f;
}
}
std::vector<int64_t> Dictionary::getCounts(entry_type type) const {
std::vector<int64_t> counts;
for (auto& w : words_) {
if (w.type == type) {
counts.push_back(w.count);
}
}
return counts;
}
void Dictionary::addWordNgrams(
std::vector<int32_t>& line,
const std::vector<int32_t>& hashes,
int32_t n) const {
for (int32_t i = 0; i < hashes.size(); i++) {
uint64_t h = hashes[i];
for (int32_t j = i + 1; j < hashes.size() && j < i + n; j++) {
h = h * 116049371 + hashes[j];
pushHash(line, h % args_->bucket);
}
}
}
void Dictionary::addSubwords(
std::vector<int32_t>& line,
const std::string& token,
int32_t wid) const {
if (wid < 0) { // out of vocab
if (token != EOS) {
computeSubwords(BOW + token + EOW, line);
}
} else {
if (args_->maxn <= 0) { // in vocab w/o subwords
line.push_back(wid);
} else { // in vocab w/ subwords
const std::vector<int32_t>& ngrams = getSubwords(wid);
line.insert(line.end(), ngrams.cbegin(), ngrams.cend());
}
}
}
void Dictionary::reset(std::istream& in) const {
if (in.eof()) {
in.clear();
in.seekg(std::streampos(0));
}
}
int32_t Dictionary::getLine(
std::istream& in,
std::vector<int32_t>& words,
std::minstd_rand& rng) const {
std::uniform_real_distribution<> uniform(0, 1);
std::string token;
int32_t ntokens = 0;
reset(in);
words.clear();
while (readWord(in, token)) {
int32_t h = find(token);
int32_t wid = word2int_[h];
if (wid < 0) {
continue;
}
ntokens++;
if (getType(wid) == entry_type::word && !discard(wid, uniform(rng))) {
words.push_back(wid);
}
if (ntokens > MAX_LINE_SIZE || token == EOS) {
break;
}
}
return ntokens;
}
int32_t Dictionary::getLine(
std::istream& in,
std::vector<int32_t>& words,
std::vector<int32_t>& labels) const {
std::vector<int32_t> word_hashes;
std::string token;
int32_t ntokens = 0;
reset(in);
words.clear();
labels.clear();
while (readWord(in, token)) {
uint32_t h = hash(token);
int32_t wid = getId(token, h);
entry_type type = wid < 0 ? getType(token) : getType(wid);
ntokens++;
if (type == entry_type::word) {
addSubwords(words, token, wid);
word_hashes.push_back(h);
} else if (type == entry_type::label && wid >= 0) {
labels.push_back(wid - nwords_);
}
if (token == EOS) {
break;
}
}
addWordNgrams(words, word_hashes, args_->wordNgrams);
return ntokens;
}
void Dictionary::pushHash(std::vector<int32_t>& hashes, int32_t id) const {
if (pruneidx_size_ == 0 || id < 0) {
return;
}
if (pruneidx_size_ > 0) {
if (pruneidx_.count(id)) {
id = pruneidx_.at(id);
} else {
return;
}
}
hashes.push_back(nwords_ + id);
}
std::string Dictionary::getLabel(int32_t lid) const {
if (lid < 0 || lid >= nlabels_) {
throw std::invalid_argument(
"Label id is out of range [0, " + std::to_string(nlabels_) + "]");
}
return words_[lid + nwords_].word;
}
void Dictionary::save(std::ostream& out) const {
out.write((char*)&size_, sizeof(int32_t));
out.write((char*)&nwords_, sizeof(int32_t));
out.write((char*)&nlabels_, sizeof(int32_t));
out.write((char*)&ntokens_, sizeof(int64_t));
out.write((char*)&pruneidx_size_, sizeof(int64_t));
for (int32_t i = 0; i < size_; i++) {
entry e = words_[i];
out.write(e.word.data(), e.word.size() * sizeof(char));
out.put(0);
out.write((char*)&(e.count), sizeof(int64_t));
out.write((char*)&(e.type), sizeof(entry_type));
}
for (const auto pair : pruneidx_) {
out.write((char*)&(pair.first), sizeof(int32_t));
out.write((char*)&(pair.second), sizeof(int32_t));
}
}
void Dictionary::load(std::istream& in) {
words_.clear();
in.read((char*)&size_, sizeof(int32_t));
in.read((char*)&nwords_, sizeof(int32_t));
in.read((char*)&nlabels_, sizeof(int32_t));
in.read((char*)&ntokens_, sizeof(int64_t));
in.read((char*)&pruneidx_size_, sizeof(int64_t));
for (int32_t i = 0; i < size_; i++) {
char c;
entry e;
while ((c = in.get()) != 0) {
e.word.push_back(c);
}
in.read((char*)&e.count, sizeof(int64_t));
in.read((char*)&e.type, sizeof(entry_type));
words_.push_back(e);
}
pruneidx_.clear();
for (int32_t i = 0; i < pruneidx_size_; i++) {
int32_t first;
int32_t second;
in.read((char*)&first, sizeof(int32_t));
in.read((char*)&second, sizeof(int32_t));
pruneidx_[first] = second;
}
initTableDiscard();
initNgrams();
int32_t word2intsize = std::ceil(size_ / 0.7);
word2int_.assign(word2intsize, -1);
for (int32_t i = 0; i < size_; i++) {
word2int_[find(words_[i].word)] = i;
}
}
void Dictionary::init() {
initTableDiscard();
initNgrams();
}
void Dictionary::prune(std::vector<int32_t>& idx) {
std::vector<int32_t> words, ngrams;
for (auto it = idx.cbegin(); it != idx.cend(); ++it) {
if (*it < nwords_) {
words.push_back(*it);
} else {
ngrams.push_back(*it);
}
}
std::sort(words.begin(), words.end());
idx = words;
if (ngrams.size() != 0) {
int32_t j = 0;
for (const auto ngram : ngrams) {
pruneidx_[ngram - nwords_] = j;
j++;
}
idx.insert(idx.end(), ngrams.begin(), ngrams.end());
}
pruneidx_size_ = pruneidx_.size();
std::fill(word2int_.begin(), word2int_.end(), -1);
int32_t j = 0;
for (int32_t i = 0; i < words_.size(); i++) {
if (getType(i) == entry_type::label ||
(j < words.size() && words[j] == i)) {
words_[j] = words_[i];
word2int_[find(words_[j].word)] = j;
j++;
}
}
nwords_ = words.size();
size_ = nwords_ + nlabels_;
words_.erase(words_.begin() + size_, words_.end());
initNgrams();
}
void Dictionary::dump(std::ostream& out) const {
out << words_.size() << std::endl;
for (auto it : words_) {
std::string entryType = "word";
if (it.type == entry_type::label) {
entryType = "label";
}
out << it.word << " " << it.count << " " << entryType << std::endl;
}
}
} // namespace fasttext