rust_tokenizers 8.0.0

// Copyright 2018 The Open AI Team Authors
// Copyright 2018 The HuggingFace Inc. team.
// Copyright 2019-2020 Guillaume Becquin
// Copyright 2020 Maarten van Gompel
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//     http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

use crate::error::TokenizerError;
use crate::tokenizer::tokenization_utils::{openai_gpt_bpe, split_on_bpe_pairs, BpeCache};
use crate::tokenizer::{BaseTokenizer, MultiThreadedTokenizer, Tokenizer};
use crate::vocab::bpe_vocab::BpePairVocab;
use crate::vocab::{OpenAiGptVocab, Vocab};
use crate::{Mask, Token, TokenRef};
use std::collections::HashMap;
use std::path::Path;
use std::sync::RwLock;

/// # GPT tokenizer
/// GPT tokenizer performing:
/// - BaseTokenizer tokenization (see `BaseTokenizer` for more details)
/// - BPE tokenization
pub struct OpenAiGptTokenizer {
    vocab: OpenAiGptVocab,
    base_tokenizer: BaseTokenizer<OpenAiGptVocab>,
    bpe_ranks: BpePairVocab,
    cache: BpeCache,
}

impl OpenAiGptTokenizer {
    /// Create a new instance of a `OpenAiGptTokenizer`
    /// Expects a vocabulary flat file and merges file as an input.
    ///
    /// # Parameters
    /// - vocab_path (`&str`): path to the vocabulary file
    /// - merges_path (`&str`): path to the merges file (use as part of the BPE encoding process)
    /// - lower_case (`bool`): flag indicating if the text should be lower-cased as part of the tokenization
    ///
    /// # Example
    ///
    /// ```no_run
    /// use rust_tokenizers::tokenizer::{OpenAiGptTokenizer, Tokenizer};
    /// let lower_case = false;
    /// let tokenizer =
    ///     OpenAiGptTokenizer::from_file("path/to/vocab/file", "path/to/merges/file", lower_case)
    ///         .unwrap();
    /// ```
    pub fn from_file<P: AsRef<Path>, M: AsRef<Path>>(
        vocab_path: P,
        merges_path: M,
        lower_case: bool,
    ) -> Result<OpenAiGptTokenizer, TokenizerError> {
        let vocab = OpenAiGptVocab::from_file(vocab_path)?;
        let base_tokenizer = BaseTokenizer::from_existing_vocab(vocab.clone(), lower_case, true);
        let bpe_ranks = BpePairVocab::from_file(merges_path)?;
        let cache = RwLock::new(HashMap::new());
        Ok(OpenAiGptTokenizer {
            vocab,
            base_tokenizer,
            bpe_ranks,
            cache,
        })
    }

    /// Create a new instance of a `OpenAiGptTokenizer`
    /// Expects a vocabulary flat file and merges file and special token mapping file as inputs.
    ///
    /// # Parameters
    /// - vocab_path (`&str`): path to the vocabulary file
    /// - merges_path (`&str`): path to the merges file (use as part of the BPE encoding process)
    /// - lower_case (`bool`): flag indicating if the text should be lower-cased as part of the tokenization
    /// - special_token_mapping_path (`&str`): path to a special token mapping file to overwrite default special tokens
    ///
    /// # Example
    ///
    /// ```no_run
    /// use rust_tokenizers::tokenizer::{OpenAiGptTokenizer, Tokenizer};
    /// let lower_case = false;
    /// let tokenizer = OpenAiGptTokenizer::from_file_with_special_token_mapping(
    ///     "path/to/vocab/file",
    ///     "path/to/merges/file",
    ///     lower_case,
    ///     "path/to/special/token/mapping/file",
    /// )
    /// .unwrap();
    /// ```
    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<OpenAiGptTokenizer, TokenizerError> {
        let vocab = OpenAiGptVocab::from_file_with_special_token_mapping(
            vocab_path,
            special_token_mapping_path,
        )?;
        let base_tokenizer = BaseTokenizer::from_existing_vocab(vocab.clone(), lower_case, true);
        let bpe_ranks = BpePairVocab::from_file(merges_path)?;
        let cache = RwLock::new(HashMap::new());
        Ok(OpenAiGptTokenizer {
            vocab,
            base_tokenizer,
            bpe_ranks,
            cache,
        })
    }

    /// Create a new instance of a `OpenAiGptTokenizer` from an existing vocabulary and merges
    ///
    /// # Parameters
    /// - vocab (`Gpt2Vocab`): GPT-like vocabulary
    /// - merges (`BpePairVocab`): BPE pairs vocabulary
    /// - lower_case (`bool`): flag indicating if the text should be lower-cased as part of the tokenization
    ///
    /// # Example
    ///
    /// ```no_run
    /// use rust_tokenizers::tokenizer::{OpenAiGptTokenizer, Tokenizer};
    /// use rust_tokenizers::vocab::{BpePairVocab, OpenAiGptVocab, Vocab};
    /// let lower_case = false;
    /// let vocab = OpenAiGptVocab::from_file("path/to/vocab/file").unwrap();
    /// let merges = BpePairVocab::from_file("path/to/merges/file").unwrap();
    ///
    /// let tokenizer = OpenAiGptTokenizer::from_existing_vocab_and_merges(vocab, merges, lower_case);
    /// ```
    pub fn from_existing_vocab_and_merges(
        vocab: OpenAiGptVocab,
        merges: BpePairVocab,
        lower_case: bool,
    ) -> OpenAiGptTokenizer {
        let base_tokenizer = BaseTokenizer::from_existing_vocab(vocab.clone(), lower_case, true);
        let cache = RwLock::new(HashMap::new());
        OpenAiGptTokenizer {
            vocab,
            base_tokenizer,
            bpe_ranks: merges,
            cache,
        }
    }
}

impl Tokenizer<OpenAiGptVocab> for OpenAiGptTokenizer {
    fn vocab(&self) -> &OpenAiGptVocab {
        &self.vocab
    }

    fn tokenize_to_tokens(&self, initial_token: TokenRef) -> Vec<Token> {
        let tokens: Vec<Token> = self
            .base_tokenizer
            .tokenize_to_tokens(initial_token)
            .into_iter()
            .flat_map(|token| {
                if token.mask != Mask::Special && token.mask != Mask::Unknown {
                    split_on_bpe_pairs(
                        token.as_ref(),
                        openai_gpt_bpe,
                        &self.bpe_ranks,
                        &self.cache,
                        false,
                    )
                } else {
                    vec![token]
                }
            })
            .collect();

        tokens
    }

    fn convert_tokens_to_string(&self, tokens: Vec<String>) -> String {
        tokens.join("").replace("</w>", " ").trim().to_owned()
    }
}

impl MultiThreadedTokenizer<OpenAiGptVocab> for OpenAiGptTokenizer {}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tokenizer::base_tokenizer::{Offset, TokenizedInput, TruncationStrategy};
    use crate::vocab::base_vocab::{swap_key_values, SpecialTokenMap};
    use crate::vocab::OpenAiGptVocab;
    use itertools::Itertools;
    use std::collections::HashMap;

    fn generate_test_vocab() -> OpenAiGptVocab {
        let values: HashMap<String, i64> = [
            ("t".to_owned(), 0),
            ("h".to_owned(), 1),
            ("a</w>".to_owned(), 2),
            ("n".to_owned(), 3),
            ("the".to_owned(), 4),
            ("Ġ".to_owned(), 5),
            ("<unk>".to_owned(), 6),
            ("o</w>".to_owned(), 7),
            ("the</w>".to_owned(), 8),
            ("rth</w>".to_owned(), 9),
            ("ea".to_owned(), 10),
        ]
        .iter()
        .cloned()
        .collect();

        let special_token_map = SpecialTokenMap {
            unk_token: "<unk>".to_string(),
            pad_token: None,
            bos_token: None,
            sep_token: None,
            cls_token: None,
            eos_token: None,
            mask_token: None,
            additional_special_tokens: None,
        };

        let special_values: HashMap<String, i64> =
            [("<unk>".to_owned(), 6)].iter().cloned().collect();

        let indices = swap_key_values(&values);
        let special_indices = swap_key_values(&special_values);

        OpenAiGptVocab {
            values,
            indices,
            special_token_map,
            special_values,
            special_indices,
        }
    }

    fn generate_test_merges() -> BpePairVocab {
        let values: HashMap<(String, String), i64> = [
            (("4".to_owned(), "t".to_owned()), 0),
            (("2".to_owned(), "n".to_owned()), 1),
            (("r".to_owned(), "th</w>".to_owned()), 2),
            (("t".to_owned(), "he</w>".to_owned()), 3),
            (("h".to_owned(), "e".to_owned()), 4),
            (("t".to_owned(), "h</w>".to_owned()), 5),
            (("t".to_owned(), "h".to_owned()), 6),
            (("th".to_owned(), "e</w>".to_owned()), 7),
            (("e".to_owned(), "a".to_owned()), 8),
        ]
        .iter()
        .cloned()
        .collect();

        BpePairVocab { values }
    }

    #[test]
    fn test_openai_gpt_tokenizer() {
        //        Given
        let vocab = generate_test_vocab();
        let merges = generate_test_merges();
        let openai_gpt_tokenizer: OpenAiGptTokenizer =
            OpenAiGptTokenizer::from_existing_vocab_and_merges(vocab, merges, true);
        let test_tuples = [
            ("The earth", vec!["the</w>", "ea", "rth</w>"]),
            ("", vec![]),
            (" ", vec![]),
            (" \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();

        //        When & Then
        for (source_text, expected_result) in test_tuples.iter() {
            assert_eq!(openai_gpt_tokenizer.tokenize(source_text), *expected_result);
        }

        assert_eq!(
            MultiThreadedTokenizer::tokenize_list(&openai_gpt_tokenizer, &source_texts),
            expected_results
        );
    }

    #[test]
    fn test_openai_gpt_tokenizer_no_lower_casing() {
        //        Given
        let vocab = generate_test_vocab();
        let merges = generate_test_merges();
        let openai_gpt_tokenizer: OpenAiGptTokenizer =
            OpenAiGptTokenizer::from_existing_vocab_and_merges(vocab, merges, false);
        let test_tuples = [
            ("The Earth", vec!["T", "h", "e</w>", "E", "a", "rth</w>"]),
            ("", vec![]),
            (" ", vec![]),
            (" \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();

        //        When & Then
        for (source_text, expected_result) in test_tuples.iter() {
            assert_eq!(openai_gpt_tokenizer.tokenize(source_text), *expected_result);
        }

        assert_eq!(
            MultiThreadedTokenizer::tokenize_list(&openai_gpt_tokenizer, &source_texts),
            expected_results
        );
    }

    #[test]
    fn test_encode() {
        //        Given
        let vocab = generate_test_vocab();
        let merges = generate_test_merges();
        let openai_gpt_tokenizer: OpenAiGptTokenizer =
            OpenAiGptTokenizer::from_existing_vocab_and_merges(vocab, merges, true);
        let truncation_strategy = TruncationStrategy::LongestFirst;
        let test_tuples = [
            (
                "the earth",
                TokenizedInput {
                    token_ids: vec![8, 10, 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: 4, end: 6 }),
                        Some(Offset { begin: 6, end: 9 }),
                    ],
                    reference_offsets: vec![vec![0, 1, 2], vec![4, 5], vec![6, 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();

        //        When & Then
        for (source_text, expected_result) in test_tuples.iter() {
            assert_eq!(
                openai_gpt_tokenizer.encode(source_text, None, 128, &truncation_strategy, 0),
                *expected_result
            );
        }
        assert_eq!(
            MultiThreadedTokenizer::encode_list(
                &openai_gpt_tokenizer,
                &source_texts,
                128,
                &truncation_strategy,
                0
            ),
            expected_results
        );
    }

    #[test]
    fn test_decode() {
        //        Given
        let vocab = generate_test_vocab();
        let merges = generate_test_merges();
        let openai_gpt_tokenizer: OpenAiGptTokenizer =
            OpenAiGptTokenizer::from_existing_vocab_and_merges(vocab, merges, true);
        let skip_special_tokens = false;
        let clean_up_tokenization_spaces = false;
        let test_tuples = [(vec![8, 10, 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();

        //        When & Then
        for (source_ids, expected_result) in test_tuples.iter() {
            assert_eq!(
                openai_gpt_tokenizer.decode(
                    source_ids,
                    skip_special_tokens,
                    clean_up_tokenization_spaces
                ),
                *expected_result
            );
        }
        assert_eq!(
            Tokenizer::decode_list(
                &openai_gpt_tokenizer,
                &source_ids,
                skip_special_tokens,
                clean_up_tokenization_spaces
            ),
            expected_results
        );
    }
}