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245 lines
7.5 KiB
245 lines
7.5 KiB
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "scorer.h"
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#include <unistd.h>
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#include <iostream>
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#include "lm/config.hh"
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#include "lm/model.hh"
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#include "lm/state.hh"
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#include "util/string_piece.hh"
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#include "util/tokenize_piece.hh"
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#include "decoder_utils.h"
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using namespace lm::ngram;
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Scorer::Scorer(double alpha,
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double beta,
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const std::string& lm_path,
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const std::vector<std::string>& vocab_list) {
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this->alpha = alpha;
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this->beta = beta;
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dictionary = nullptr;
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is_character_based_ = true;
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language_model_ = nullptr;
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max_order_ = 0;
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dict_size_ = 0;
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SPACE_ID_ = -1;
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setup(lm_path, vocab_list);
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}
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Scorer::~Scorer() {
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if (language_model_ != nullptr) {
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delete static_cast<lm::base::Model*>(language_model_);
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}
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if (dictionary != nullptr) {
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delete static_cast<fst::StdVectorFst*>(dictionary);
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}
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}
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void Scorer::setup(const std::string& lm_path,
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const std::vector<std::string>& vocab_list) {
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// load language model
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load_lm(lm_path);
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// set char map for scorer
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set_char_map(vocab_list);
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// fill the dictionary for FST
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if (!is_character_based()) {
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fill_dictionary(true);
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}
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}
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void Scorer::load_lm(const std::string& lm_path) {
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const char* filename = lm_path.c_str();
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VALID_CHECK_EQ(access(filename, F_OK), 0, "Invalid language model path");
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RetriveStrEnumerateVocab enumerate;
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lm::ngram::Config config;
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config.enumerate_vocab = &enumerate;
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language_model_ = lm::ngram::LoadVirtual(filename, config);
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max_order_ = static_cast<lm::base::Model*>(language_model_)->Order();
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vocabulary_ = enumerate.vocabulary;
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for (size_t i = 0; i < vocabulary_.size(); ++i) {
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if (is_character_based_ && vocabulary_[i] != UNK_TOKEN &&
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vocabulary_[i] != START_TOKEN && vocabulary_[i] != END_TOKEN &&
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get_utf8_str_len(enumerate.vocabulary[i]) > 1) {
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is_character_based_ = false;
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}
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}
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}
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double Scorer::get_log_cond_prob(const std::vector<std::string>& words) {
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lm::base::Model* model = static_cast<lm::base::Model*>(language_model_);
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double cond_prob;
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lm::ngram::State state, tmp_state, out_state;
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// avoid to inserting <s> in begin
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model->NullContextWrite(&state);
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for (size_t i = 0; i < words.size(); ++i) {
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lm::WordIndex word_index = model->BaseVocabulary().Index(words[i]);
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// encounter OOV
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if (word_index == 0) {
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return OOV_SCORE;
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}
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cond_prob = model->BaseScore(&state, word_index, &out_state);
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tmp_state = state;
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state = out_state;
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out_state = tmp_state;
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}
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// return log10 prob
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return cond_prob;
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}
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double Scorer::get_sent_log_prob(const std::vector<std::string>& words) {
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std::vector<std::string> sentence;
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if (words.size() == 0) {
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for (size_t i = 0; i < max_order_; ++i) {
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sentence.push_back(START_TOKEN);
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}
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} else {
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for (size_t i = 0; i < max_order_ - 1; ++i) {
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sentence.push_back(START_TOKEN);
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}
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sentence.insert(sentence.end(), words.begin(), words.end());
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}
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sentence.push_back(END_TOKEN);
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return get_log_prob(sentence);
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}
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double Scorer::get_log_prob(const std::vector<std::string>& words) {
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assert(words.size() > max_order_);
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double score = 0.0;
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for (size_t i = 0; i < words.size() - max_order_ + 1; ++i) {
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std::vector<std::string> ngram(words.begin() + i,
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words.begin() + i + max_order_);
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score += get_log_cond_prob(ngram);
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}
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return score;
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}
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void Scorer::reset_params(float alpha, float beta) {
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this->alpha = alpha;
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this->beta = beta;
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}
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std::string Scorer::vec2str(const std::vector<int>& input) {
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std::string word;
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for (auto ind : input) {
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word += char_list_[ind];
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}
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return word;
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}
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std::vector<std::string> Scorer::split_labels(const std::vector<int>& labels) {
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if (labels.empty()) return {};
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std::string s = vec2str(labels);
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std::vector<std::string> words;
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if (is_character_based_) {
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words = split_utf8_str(s);
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} else {
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words = split_str(s, " ");
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}
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return words;
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}
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void Scorer::set_char_map(const std::vector<std::string>& char_list) {
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char_list_ = char_list;
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char_map_.clear();
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// Set the char map for the FST for spelling correction
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for (size_t i = 0; i < char_list_.size(); i++) {
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if (char_list_[i] == kSPACE) {
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SPACE_ID_ = i;
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}
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// The initial state of FST is state 0, hence the index of chars in
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// the FST should start from 1 to avoid the conflict with the initial
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// state, otherwise wrong decoding results would be given.
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char_map_[char_list_[i]] = i + 1;
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}
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}
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std::vector<std::string> Scorer::make_ngram(PathTrie* prefix) {
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std::vector<std::string> ngram;
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PathTrie* current_node = prefix;
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PathTrie* new_node = nullptr;
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for (int order = 0; order < max_order_; order++) {
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std::vector<int> prefix_vec;
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if (is_character_based_) {
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new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_, 1);
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current_node = new_node;
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} else {
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new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_);
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current_node = new_node->parent; // Skipping spaces
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}
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// reconstruct word
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std::string word = vec2str(prefix_vec);
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ngram.push_back(word);
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if (new_node->character == -1) {
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// No more spaces, but still need order
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for (int i = 0; i < max_order_ - order - 1; i++) {
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ngram.push_back(START_TOKEN);
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}
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break;
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}
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}
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std::reverse(ngram.begin(), ngram.end());
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return ngram;
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}
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void Scorer::fill_dictionary(bool add_space) {
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fst::StdVectorFst dictionary;
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// For each unigram convert to ints and put in trie
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int dict_size = 0;
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for (const auto& word : vocabulary_) {
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bool added = add_word_to_dictionary(
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word, char_map_, add_space, SPACE_ID_ + 1, &dictionary);
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dict_size += added ? 1 : 0;
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}
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dict_size_ = dict_size;
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/* Simplify FST
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* This gets rid of "epsilon" transitions in the FST.
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* These are transitions that don't require a string input to be taken.
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* Getting rid of them is necessary to make the FST determinisitc, but
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* can greatly increase the size of the FST
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*/
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fst::RmEpsilon(&dictionary);
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fst::StdVectorFst* new_dict = new fst::StdVectorFst;
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/* This makes the FST deterministic, meaning for any string input there's
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* only one possible state the FST could be in. It is assumed our
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* dictionary is deterministic when using it.
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* (lest we'd have to check for multiple transitions at each state)
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*/
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fst::Determinize(dictionary, new_dict);
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/* Finds the simplest equivalent fst. This is unnecessary but decreases
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* memory usage of the dictionary
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*/
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fst::Minimize(new_dict);
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this->dictionary = new_dict;
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}
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