use crate::error::TokenizerError;
use crate::tokenizer::constants::UNICODE_TO_BYTES;
use crate::tokenizer::tokenization_utils::{
bpe, fix_mask, split_on_bpe_pairs, split_on_regex_with_lookahead, split_on_special_tokens,
};
use crate::tokenizer::tokenization_utils::{lowercase, BpeCache};
use crate::tokenizer::{MultiThreadedTokenizer, Tokenizer};
use crate::vocab::bpe_vocab::BpePairVocab;
use crate::vocab::{Gpt2Vocab, Vocab};
use crate::{Mask, Token, TokenRef};
use itertools::Itertools;
use regex::Regex;
use std::collections::HashMap;
use std::iter::Iterator;
use std::path::Path;
use std::sync::RwLock;
pub struct Gpt2Tokenizer {
vocab: Gpt2Vocab,
bpe_ranks: BpePairVocab,
cache: BpeCache,
pattern_lookahead: Regex,
pattern_tokenization: Regex,
lower_case: bool,
}
impl Gpt2Tokenizer {
pub fn from_file<P: AsRef<Path>, M: AsRef<Path>>(
vocab_path: P,
merges_path: M,
lower_case: bool,
) -> Result<Gpt2Tokenizer, TokenizerError> {
let vocab = Gpt2Vocab::from_file(vocab_path)?;
let bpe_ranks = BpePairVocab::from_file(merges_path)?;
let cache = RwLock::new(HashMap::new());
let pattern_lookahead = Regex::new(r"\s+\S").unwrap();
let pattern_tokenization =
Regex::new(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+")
.unwrap();
Ok(Gpt2Tokenizer {
vocab,
bpe_ranks,
cache,
pattern_lookahead,
pattern_tokenization,
lower_case,
})
}
pub fn from_file_with_special_token_mapping<V: AsRef<Path>, M: AsRef<Path>, S: AsRef<Path>>(
vocab_path: V,
merges_path: M,
lower_case: bool,
special_token_mapping_path: S,
) -> Result<Gpt2Tokenizer, TokenizerError> {
let vocab = Gpt2Vocab::from_file_with_special_token_mapping(
vocab_path,
special_token_mapping_path,
)?;
let bpe_ranks = BpePairVocab::from_file(merges_path)?;
let cache = RwLock::new(HashMap::new());
let pattern_lookahead = Regex::new(r"\s+\S").unwrap();
let pattern_tokenization =
Regex::new(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+")
.unwrap();
Ok(Gpt2Tokenizer {
vocab,
bpe_ranks,
cache,
pattern_lookahead,
pattern_tokenization,
lower_case,
})
}
pub fn from_existing_vocab_and_merges(
vocab: Gpt2Vocab,
merges: BpePairVocab,
lower_case: bool,
) -> Gpt2Tokenizer {
let cache = RwLock::new(HashMap::new());
let pattern_lookahead = Regex::new(r"\s+\S").unwrap();
let pattern_tokenization =
Regex::new(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+")
.unwrap();
Gpt2Tokenizer {
vocab,
bpe_ranks: merges,
cache,
pattern_lookahead,
pattern_tokenization,
lower_case,
}
}
}
impl Tokenizer<Gpt2Vocab> for Gpt2Tokenizer {
fn vocab(&self) -> &Gpt2Vocab {
&self.vocab
}
fn tokenize_to_tokens(&self, initial_token: TokenRef) -> Vec<Token> {
let mut tokens = split_on_special_tokens(initial_token, &self.vocab)
.into_iter()
.map(|token| token.to_owned())
.collect::<Vec<Token>>();
let mut sub_tokens = Vec::new();
for token in tokens.iter_mut() {
if token.mask != Mask::Special && token.mask != Mask::Unknown {
if self.lower_case {
lowercase(token);
}
for token in split_on_regex_with_lookahead(
token.as_ref(),
&self.pattern_lookahead,
&self.pattern_tokenization,
) {
sub_tokens.extend(split_on_bpe_pairs(
token,
bpe,
&self.bpe_ranks,
&self.cache,
true,
));
}
} else {
sub_tokens.push(token.clone());
}
}
fix_mask(&mut sub_tokens);
sub_tokens
}
fn convert_tokens_to_string(&self, tokens: Vec<String>) -> String {
let tokens = tokens
.iter()
.join("")
.replace(" ##", "")
.trim()
.chars()
.map(|character| *UNICODE_TO_BYTES.get(&character).unwrap())
.collect::<Vec<u8>>();
String::from_utf8_lossy(tokens.as_slice()).to_string()
}
}
impl MultiThreadedTokenizer<Gpt2Vocab> for Gpt2Tokenizer {}
#[cfg(test)]
mod tests {
use super::*;
use crate::tokenizer::base_tokenizer::TruncationStrategy;
use crate::vocab::base_vocab::{swap_key_values, SpecialTokenMap};
use crate::vocab::Gpt2Vocab;
use crate::{Offset, TokenizedInput};
use std::collections::HashMap;
fn generate_test_vocab() -> Gpt2Vocab {
let values: HashMap<String, i64> = [
("t".to_owned(), 0),
("h".to_owned(), 1),
("a@@".to_owned(), 2),
("n".to_owned(), 3),
("the".to_owned(), 4),
("Ä ".to_owned(), 5),
("<|endoftext|>".to_owned(), 6),
("o@@".to_owned(), 7),
("Ä ear".to_owned(), 8),
("th".to_owned(), 9),
]
.iter()
.cloned()
.collect();
let special_token_map = SpecialTokenMap {
unk_token: "<|endoftext|>".to_string(),
pad_token: None,
bos_token: Some("<|endoftext|>".to_string()),
sep_token: None,
cls_token: None,
eos_token: Some("<|endoftext|>".to_string()),
mask_token: None,
additional_special_tokens: None,
};
let special_values: HashMap<String, i64> =
[("<|endoftext|>".to_owned(), 6)].iter().cloned().collect();
let indices = swap_key_values(&values);
let special_indices = swap_key_values(&special_values);
Gpt2Vocab {
values,
indices,
special_token_map,
special_values,
special_indices,
}
}
fn generate_test_merges() -> BpePairVocab {
let values: HashMap<(String, String), i64> = [
(("Ä ".to_owned(), "t".to_owned()), 0),
(("Ä ".to_owned(), "n".to_owned()), 1),
(("e".to_owned(), "e".to_owned()), 2),
(("Ä t".to_owned(), "he".to_owned()), 3),
(("h".to_owned(), "e".to_owned()), 4),
(("t".to_owned(), "h".to_owned()), 5),
(("t".to_owned(), "he".to_owned()), 6),
(("Ä ".to_owned(), "e".to_owned()), 7),
(("Ä e".to_owned(), "a".to_owned()), 8),
(("Ä ea".to_owned(), "r".to_owned()), 9),
]
.iter()
.cloned()
.collect();
BpePairVocab { values }
}
#[test]
fn test_gpt2_tokenizer() {
let vocab = generate_test_vocab();
let merges = generate_test_merges();
let gpt2_tokenizer: Gpt2Tokenizer =
Gpt2Tokenizer::from_existing_vocab_and_merges(vocab, merges, true);
let test_tuples = [
("the Earth", vec!["the", "Ä ear", "th"]),
("", vec![]),
(" ", vec![]),
(" t", vec!["Ä ", "Ä ", "Ä t"]),
("t ", vec!["t", "Ä "]),
(" \n ", vec![]),
];
let source_texts: Vec<&str> = test_tuples.iter().map(|v| v.0).collect();
let expected_results: Vec<Vec<&str>> = test_tuples.iter().map(|v| v.1.clone()).collect();
for (source_text, expected_result) in test_tuples.iter() {
assert_eq!(gpt2_tokenizer.tokenize(source_text), *expected_result);
}
assert_eq!(
MultiThreadedTokenizer::tokenize_list(&gpt2_tokenizer, &source_texts),
expected_results
);
}
#[test]
fn test_gpt2_tokenizer_no_lower_casing() {
let vocab = generate_test_vocab();
let merges = generate_test_merges();
let gpt2_tokenizer: Gpt2Tokenizer =
Gpt2Tokenizer::from_existing_vocab_and_merges(vocab, merges, false);
let test_tuples = [
("the Earth", vec!["the", "Ä ", "E", "a", "r", "th"]),
("", vec![]),
(" ", vec![]),
(" t", vec!["Ä ", "Ä ", "Ä t"]),
(" \n ", vec![]),
];
let source_texts: Vec<&str> = test_tuples.iter().map(|v| v.0).collect();
let expected_results: Vec<Vec<&str>> = test_tuples.iter().map(|v| v.1.clone()).collect();
for (source_text, expected_result) in test_tuples.iter() {
assert_eq!(gpt2_tokenizer.tokenize(source_text), *expected_result);
}
assert_eq!(
MultiThreadedTokenizer::tokenize_list(&gpt2_tokenizer, &source_texts),
expected_results
);
}
#[test]
fn test_encode() {
let vocab = generate_test_vocab();
let merges = generate_test_merges();
let gpt2_tokenizer: Gpt2Tokenizer =
Gpt2Tokenizer::from_existing_vocab_and_merges(vocab, merges, true);
let truncation_strategy = TruncationStrategy::LongestFirst;
let test_tuples = [
(
"the earth",
TokenizedInput {
token_ids: vec![4, 8, 9],
segment_ids: vec![0, 0, 0],
special_tokens_mask: vec![0, 0, 0],
overflowing_tokens: vec![],
num_truncated_tokens: 0,
token_offsets: vec![
Some(Offset { begin: 0, end: 3 }),
Some(Offset { begin: 3, end: 7 }),
Some(Offset { begin: 7, end: 9 }),
],
reference_offsets: vec![vec![0, 1, 2], vec![3, 4, 5, 6], vec![7, 8]],
mask: vec![Mask::None, Mask::Begin, Mask::Continuation],
},
),
(
" ",
TokenizedInput {
token_ids: vec![],
segment_ids: vec![],
special_tokens_mask: vec![],
overflowing_tokens: vec![],
num_truncated_tokens: 0,
token_offsets: vec![],
reference_offsets: vec![],
mask: vec![],
},
),
(
"",
TokenizedInput {
token_ids: vec![],
segment_ids: vec![],
special_tokens_mask: vec![],
overflowing_tokens: vec![],
num_truncated_tokens: 0,
token_offsets: vec![],
reference_offsets: vec![],
mask: vec![],
},
),
];
let source_texts: Vec<&str> = test_tuples.iter().map(|v| v.0).collect();
let expected_results: Vec<TokenizedInput> =
test_tuples.iter().map(|v| v.1.clone()).collect();
for (source_text, expected_result) in test_tuples.iter() {
assert_eq!(
gpt2_tokenizer.encode(source_text, None, 128, &truncation_strategy, 0),
*expected_result
);
}
assert_eq!(
MultiThreadedTokenizer::encode_list(
&gpt2_tokenizer,
&source_texts,
128,
&truncation_strategy,
0
),
expected_results
);
}
#[test]
fn test_decode() {
let vocab = generate_test_vocab();
let merges = generate_test_merges();
let gpt2_tokenizer: Gpt2Tokenizer =
Gpt2Tokenizer::from_existing_vocab_and_merges(vocab, merges, true);
let skip_special_tokens = false;
let clean_up_tokenization_spaces = false;
let test_tuples = [(vec![4, 8, 9], "the earth")];
let source_ids: Vec<Vec<i64>> = test_tuples.iter().map(|v| v.0.clone()).collect_vec();
let expected_results: Vec<&str> = test_tuples.iter().map(|v| v.1).collect_vec();
for (source_ids, expected_result) in test_tuples.iter() {
assert_eq!(
gpt2_tokenizer.decode(
source_ids,
skip_special_tokens,
clean_up_tokenization_spaces
),
*expected_result
);
}
assert_eq!(
Tokenizer::decode_list(
&gpt2_tokenizer,
&source_ids,
skip_special_tokens,
clean_up_tokenization_spaces
),
expected_results
);
}
}