diff --git a/build.rs b/build.rs index d170572..d5bd33a 100644 --- a/build.rs +++ b/build.rs @@ -3,14 +3,10 @@ feature = "default-medium", feature = "default-large" ))] -use lz4_flex::block::compress_prepend_size; - -#[cfg(any( - feature = "default-small", - feature = "default-medium", - feature = "default-large" -))] -use std::{collections::HashMap, env, fs, path::PathBuf}; +use { + lz4_flex::block::compress_prepend_size, + std::{collections::HashMap, env, fs, path::PathBuf}, +}; fn main() { #[cfg(feature = "default-small")] diff --git a/src/constants.rs b/src/constants.rs new file mode 100644 index 0000000..ad60ea7 --- /dev/null +++ b/src/constants.rs @@ -0,0 +1,11 @@ +/// The character used to denote word breaks in the tokenized output. +pub(crate) const WORD_BREAK_CHAR: &str = "▁"; + +/// The token used to mark the start of a sentence. +pub(crate) const SENTENCE_START_TOKEN: &str = ""; + +/// The token used to mark the end of a sentence. +pub(crate) const SENTENCE_END_TOKEN: &str = ""; + +/// The token used to represent unknown words or subwords. +pub(crate) const UNKNOWN_TOKEN: &str = ""; diff --git a/src/default_vocabs.rs b/src/default_vocabs.rs new file mode 100644 index 0000000..3fe92a9 --- /dev/null +++ b/src/default_vocabs.rs @@ -0,0 +1,84 @@ +use crate::{BytePairEncoder, BytePairEncoderError}; + +#[cfg(any( + feature = "default-small", + feature = "default-medium", + feature = "default-large" +))] +use {lz4_flex::decompress_size_prepended, std::collections::HashMap}; + +#[allow(dead_code)] +#[derive(Debug)] +pub(crate) enum DefaultVocab { + Small, + Medium, + Large, +} + +#[cfg(feature = "default-small")] +const DEFAULT_SMALL_DATA: &[u8] = include_bytes!(concat!( + env!("OUT_DIR"), + "/", + "multi.wiki.bpe.vs100000.vocab.hashmap.bincode.lz4" +)); + +#[allow(dead_code)] +#[cfg(not(feature = "default-small"))] +const DEFAULT_SMALL_DATA: &[u8] = &[]; + +#[cfg(feature = "default-medium")] +const DEFAULT_MEDIUM_DATA: &[u8] = include_bytes!(concat!( + env!("OUT_DIR"), + "/", + "multi.wiki.bpe.vs320000.vocab.hashmap.bincode.lz4" +)); + +#[allow(dead_code)] +#[cfg(not(feature = "default-medium"))] +const DEFAULT_MEDIUM_DATA: &[u8] = &[]; + +#[cfg(feature = "default-large")] +const DEFAULT_LARGE_DATA: &[u8] = include_bytes!(concat!( + env!("OUT_DIR"), + "/", + "multi.wiki.bpe.vs1000000.vocab.hashmap.bincode.lz4" +)); + +#[allow(dead_code)] +#[cfg(not(feature = "default-large"))] +const DEFAULT_LARGE_DATA: &[u8] = &[]; + +// The helper function to initialize a BytePairEncoder from a compressed vocabulary dataset. +// This doesn't need to be public, so we use `pub(crate)` or leave it private entirely. +#[cfg(any( + feature = "default-small", + feature = "default-medium", + feature = "default-large" +))] +pub(crate) fn new_default(vocab: DefaultVocab) -> Result { + let data = match vocab { + DefaultVocab::Small => DEFAULT_SMALL_DATA, + DefaultVocab::Medium => DEFAULT_MEDIUM_DATA, + DefaultVocab::Large => DEFAULT_LARGE_DATA, + }; + + // Decompress the LZ4 binary data. + let uncompressed = decompress_size_prepended(data) + .map_err(|e| BytePairEncoderError::DecompressionError(e.to_string()))?; + + // Deserialize the uncompressed data into a HashMap. + let tokens: HashMap = bincode::deserialize(&uncompressed) + .map_err(|e| BytePairEncoderError::DeserializationError(e.to_string()))?; + + // Return the BytePairEncoder. + Ok(BytePairEncoder { tokens }) +} + +#[cfg(not(any( + feature = "default-small", + feature = "default-medium", + feature = "default-large" +)))] +pub(crate) fn new_default(_vocab: DefaultVocab) -> Result { + Err(BytePairEncoderError::NoDefaultVocabFeature) +} diff --git a/src/errors.rs b/src/errors.rs new file mode 100644 index 0000000..2ef0006 --- /dev/null +++ b/src/errors.rs @@ -0,0 +1,25 @@ +use thiserror::Error; + +/// Represents errors that can occur during BPE tokenization operations. +#[derive(Error, Debug, Clone, PartialEq, Eq)] +pub enum BytePairEncoderError { + /// Indicates an error occurred while reading the vocabulary file. + #[error("Error reading file: {0}")] + InvalidFile(String), + + /// Indicates that the vocabulary input was invalid or could not be parsed correctly. + #[error("Invalid vocabulary input: Could not parse vocabulary file.")] + InvalidVocabularyInput, + + /// Indicates an error occurred during decompression of the vocabulary data. + #[error("Error decompressing vocabulary data: {0}")] + DecompressionError(String), + + /// Indicates an error occurred during deserialization of the vocabulary data. + #[error("Error deserializing vocabulary data: {0}")] + DeserializationError(String), + + // Indicates attempt to use a default vocabulary without enabling its Cargo feature. + #[error("Error, must enable defualt-small, default-medium, and/or default-large feature(s) to use default vocabulary.")] + NoDefaultVocabFeature, +} diff --git a/src/lib.rs b/src/lib.rs index cb99352..e51d11e 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -129,1157 +129,15 @@ //! [`BytePairEncoder::new_default_large`]) become available for constructing a `BytePairEncoder`. //! Only enable the features that you need to ensure minimized memory and binary size. -use std::{collections::HashMap, fs, iter}; - -use thiserror::Error; -use unicode_segmentation::UnicodeSegmentation; - -#[cfg(any( - feature = "default-small", - feature = "default-medium", - feature = "default-large" -))] -use lz4_flex::decompress_size_prepended; - -/// Represents errors that can occur during BPE tokenization operations. -#[derive(Error, Debug, Clone, PartialEq, Eq)] -pub enum BytePairEncoderError { - /// Indicates an error occurred while reading the vocabulary file. - #[error("Error reading file: {0}")] - InvalidFile(String), - - /// Indicates that the vocabulary input was invalid or could not be parsed correctly. - #[error("Invalid vocabulary input: Could not parse vocabulary file.")] - InvalidVocabularyInput, - - /// Indicates an error occurred during decompression of the vocabulary data. - #[error("Error decompressing vocabulary data: {0}")] - DecompressionError(String), - - /// Indicates an error occurred during deserialization of the vocabulary data. - #[error("Error deserializing vocabulary data: {0}")] - DeserializationError(String), -} - -/// The character used to denote word breaks in the tokenized output. -const WORD_BREAK_CHAR: &str = "▁"; - -/// The token used to mark the start of a sentence. -const SENTENCE_START_TOKEN: &str = ""; - -/// The token used to mark the end of a sentence. -const SENTENCE_END_TOKEN: &str = ""; - -/// The token used to represent unknown words or subwords. -const UNKNOWN_TOKEN: &str = ""; - -/// Token vocabulary data for the default small model. -#[cfg(feature = "default-small")] -const DEFAULT_SMALL_DATA: &[u8] = include_bytes!(concat!( - env!("OUT_DIR"), - "/", - "multi.wiki.bpe.vs100000.vocab.hashmap.bincode.lz4" -)); - -/// Token vocabulary data for the default small model. -#[cfg(feature = "default-medium")] -const DEFAULT_MEDIUM_DATA: &[u8] = include_bytes!(concat!( - env!("OUT_DIR"), - "/", - "multi.wiki.bpe.vs320000.vocab.hashmap.bincode.lz4" -)); - -/// Token vocabulary data for the default small model. -#[cfg(feature = "default-large")] -const DEFAULT_LARGE_DATA: &[u8] = include_bytes!(concat!( - env!("OUT_DIR"), - "/", - "multi.wiki.bpe.vs1000000.vocab.hashmap.bincode.lz4" -)); - -/// # Represents a Byte Pair Encoding (BPE) vocabulary used for tokenization. -/// -/// This struct holds the mapping of tokens to their respective scores (or IDs) -/// and provides methods for tokenizing text using the BPE algorithm. -/// -/// The vocabulary is typically loaded from a file or string where each line -/// contains a token and its score, separated by a tab character. -/// -/// ## Example -/// -/// ``` -/// use bpe_tokenizer::BytePairEncoder; -/// -/// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); -/// let tokenized = vocab.tokenize("Hello, world!"); -/// ``` -#[derive(Debug, Clone, PartialEq, Eq)] -pub struct BytePairEncoder { - /// # A mapping of tokens to their respective scores (or IDs). - /// - /// In BPE, tokens with lower scores (or IDs) are typically more common - /// and are preferred during the tokenization process. - tokens: HashMap, -} - -impl BytePairEncoder { - /// # Creates a new `BytePairEncoder` from a file containing token-score pairs. - /// - /// This function reads the contents of the file specified by `file_path` and constructs - /// a `BytePairEncoder` from it. The file should contain token-score pairs, with each pair - /// on a separate line and the token and score separated by a tab character (`\t`). - /// - /// ## Input Format - /// - /// The file is expected to follow this format: - /// - /// ```text - /// \t\n - /// ``` - /// - /// Each line should consist of: - /// * A token (a string) followed by a tab character (`\t`) - /// * A score (an integer) as either a positive or negative value. - /// - /// Example lines from the file: - /// - /// ```text - /// 0 - /// ▁t -0 - /// ▁the -4 - /// ``` - /// - /// ## Arguments - /// - /// * `file_path` - A string slice that holds the path to the file containing token-score pairs. - /// - /// ## Returns - /// - /// * `Result` - A Result containing the created `BytePairEncoder` if successful, - /// or a `BytePairEncoderError` if there was an error reading the file or parsing its contents. - /// - /// ## Errors - /// - /// This function will return an error if: - /// * The file cannot be read (returns `BytePairEncoderError::InvalidFile`) - /// * The file contents are not in the expected format (returns `BytePairEncoderError::InvalidVocabularyInput`) - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let vocab = BytePairEncoder::new_from_file("path/to/vocabulary/file.txt"); - /// ``` - pub fn new_from_file(file_path: &str) -> Result { - Self::new_from_str( - fs::read_to_string(file_path) - .map_err(|_| BytePairEncoderError::InvalidFile(file_path.to_string()))? - .as_ref(), - ) - } - - /// # Creates a new `BytePairEncoder` from a string containing token-score pairs. - /// - /// This function parses the input string to construct a `BytePairEncoder`. The input should - /// contain token-score pairs, with each pair on a separate line and the token and score - /// separated by a tab character (`\t`). - /// - /// ## Input Format - /// - /// The string must follow this format: - /// - /// ```text - /// \t\n - /// ``` - /// - /// Each line in the string should consist of: - /// * A token (a string) followed by a tab character (`\t`) - /// * A score (an integer) as either a positive or negative value. - /// - /// For example: - /// - /// ```text - /// hello 1 - /// world 2 - /// ▁the -4 - /// ``` - /// - /// ## Arguments - /// - /// * `input` - A string slice that holds the token-score pairs. - /// - /// ## Returns - /// - /// * `Result` - A Result containing the created `BytePairEncoder` if successful, - /// or a `BytePairEncoderError` if there was an error parsing the input. - /// - /// ## Errors - /// - /// This function will return `BytePairEncoderError::InvalidVocabularyInput` if: - /// * A line doesn't contain a tab character to separate token and score. - /// * The score cannot be parsed as an `isize`. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let input = "hello\t1\nworld\t2"; - /// let vocab = BytePairEncoder::new_from_str(input).unwrap(); - /// ``` - pub fn new_from_str(input: &str) -> Result { - let mut tokens = HashMap::new(); - - for line in input.lines() { - let (token, score_str) = match line.split_once('\t') { - Some(pair) => pair, - None => return Err(BytePairEncoderError::InvalidVocabularyInput), - }; - let score = match score_str.parse::() { - Ok(score) => score, - Err(_) => return Err(BytePairEncoderError::InvalidVocabularyInput), - }; - tokens.insert(token.to_string(), score); - } - - Ok(BytePairEncoder { tokens }) - } - - /// # Initializes a new `BytePairEncoder` from given compressed vocabulary data. - /// - /// This private function decompresses and deserializes a pre-trained multilingual vocabulary that supports - /// 275 languages. The compressed vocabulary is expected to be in LZ4 format. It is then decompressed and - /// deserialized into a `HashMap`, which is used to tokenize text. - /// - /// The function handles any decompression or deserialization failures by returning a `BytePairEncoderError`. - /// - /// ## Arguments - /// - /// * `data` - A reference to a binary slice holding compressed LZ4 vocabulary data. - /// - /// ## Returns - /// - /// A `Result` where on success, a `BytePairEncoder` instance is returned, and - /// on failure, a `BytePairEncoderError` indicates what went wrong. - /// - /// ## Errors - /// - /// * `DecompressionError`: Returned if decompression of the LZ4-based vocabulary data fails. - /// * `DeserializationError`: Returned if deserialization of the decompressed data fails. - #[cfg(any( - feature = "default-small", - feature = "default-medium", - feature = "default-large" - ))] - fn new_default(data: &'static [u8]) -> Result { - // Decompress the binary data - let uncompressed = decompress_size_prepended(data) - .map_err(|e| BytePairEncoderError::DecompressionError(e.to_string()))?; - - // Deserialize the uncompressed data into a HashMap - let tokens: HashMap = bincode::deserialize(&uncompressed) - .map_err(|e| BytePairEncoderError::DeserializationError(e.to_string()))?; - - // Successfully create a BytePairEncoder - Ok(Self { tokens }) - } - - /// # Creates a new `BytePairEncoder` with a default small vocabulary size (100,000 tokens). - /// - /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary - /// that supports 275 languages. The vocabulary is sourced from the - /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The small-sized - /// vocabulary file consists of 100,000 tokens, allowing for highly compressed tokenization - /// suitable for tasks with limited memory constraints. - /// - /// ## Returns - /// - /// A `Result`, constructing the `BytePairEncoder` on successful - /// vocabulary loading, or a corresponding error if initialization fails. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let encoder = BytePairEncoder::new_default_small().unwrap(); - /// ``` - /// - /// ## Note - /// - /// This is only enabled when the `default-small` feature is enabled in Cargo.toml. - /// - /// ```toml - /// [dependencies] - /// bpe-tokenizer = { version = " Result { - Self::new_default(DEFAULT_SMALL_DATA) - } - - /// # Creates a new `BytePairEncoder` with a default medium vocabulary size (320,000 tokens). - /// - /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary - /// that supports 275 languages. The vocabulary is sourced from the - /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The - /// medium-sized vocabulary file consists of 320,000 tokens, offering a balance between token - /// coverage and memory efficiency, making it suitable for a wide variety of NLP tasks. - /// - /// ## Returns - /// - /// A `Result`, constructing the `BytePairEncoder` on successful - /// vocabulary loading, or a corresponding error if initialization fails. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let encoder = BytePairEncoder::new_default_medium().unwrap(); - /// ``` - /// - /// ## Note - /// - /// This is only enabled when the `default-medium` feature is enabled in Cargo.toml. - /// - /// ```toml - /// [dependencies] - /// bpe-tokenizer = { version = " Result { - Self::new_default(DEFAULT_MEDIUM_DATA) - } - - /// # Creates a new `BytePairEncoder` with a default large vocabulary size (1,000,000 tokens). - /// - /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary - /// that supports 275 languages. The vocabulary is sourced from the - /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The large-sized - /// vocabulary consists of 1,000,000 tokens, providing maximum coverage for detailed language - /// representation, especially useful in applications requiring high granularity. - /// - /// ## Returns - /// - /// A `Result`, constructing the `BytePairEncoder` on successful - /// vocabulary loading, or a corresponding error if initialization fails. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let encoder = BytePairEncoder::new_default_large().unwrap(); - /// ``` - /// - /// ## Note - /// - /// This is only enabled when the `default-large` feature is enabled in Cargo.toml. - /// - /// ```toml - /// [dependencies] - /// bpe-tokenizer = { version = " Result { - Self::new_default(DEFAULT_LARGE_DATA) - } - - /// # Tokenizes a text into sentences, then words, and finally into BPE tokens. - /// - /// This function takes a string of text and returns an iterator that yields - /// vectors of tokens, where each vector represents a tokenized sentence. - /// - /// ## Arguments - /// - /// * `text` - A string slice containing the text to be tokenized. - /// - /// ## Returns - /// - /// An iterator that yields `Vec`, where each `Vec` represents - /// a tokenized sentence. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); - /// let text = "Hello, world! How are you?"; - /// let tokenized: Vec> = vocab - /// .tokenize_sentences_iter(text) - /// .map(|sentence_iter| sentence_iter.collect()) // Collect each inner iterator into a Vec - /// .collect(); // Then collect everything into Vec> - /// ``` - /// - /// ## Notes - /// - /// - This function uses Unicode-aware sentence and word segmentation. - /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. - /// - Words are prefixed with the word break character (`▁`). - /// - Unknown tokens are replaced with the `` token. - pub fn tokenize_sentences_iter<'a>( - &'a self, - text: &'a str, - ) -> impl Iterator + 'a> + 'a { - UnicodeSegmentation::unicode_sentences(text) - .map(move |sentence| self.tokenize_with_sentence_markers_iter(sentence)) - } - - /// # Tokenizes a text into a flat sequence of BPE tokens. - /// - /// This function takes a string of text and returns an iterator that yields - /// individual tokens. It first tokenizes the text into sentences, then words, - /// and finally into BPE tokens, flattening the result into a single sequence. - /// - /// ## Arguments - /// - /// * `text` - A string slice containing the text to be tokenized. - /// - /// ## Returns - /// - /// An iterator that yields `String`, where each `String` represents a token. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); - /// let text = "Hello, world! How are you?"; - /// let tokenized: Vec = vocab.tokenize_iter(text).collect(); - /// ``` - /// - /// ## Notes - /// - /// - This function uses Unicode-aware sentence and word segmentation. - /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. - /// - Words are prefixed with the word break character (`▁`). - /// - Unknown tokens are replaced with the `` token. - pub fn tokenize_iter<'a>(&'a self, text: &'a str) -> impl Iterator + 'a { - self.tokenize_sentences_iter(text).flatten() - } - - /// # Tokenizes a text into sentences, then words, and finally into BPE tokens. - /// - /// This function takes a string of text and returns a vector of tokenized sentences, - /// where each sentence is represented as a vector of tokens. - /// - /// ## Arguments - /// - /// * `text` - A string slice containing the text to be tokenized. - /// - /// ## Returns - /// - /// A `Vec>`, where each inner `Vec` represents a tokenized sentence. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); - /// let text = "Hello, world! How are you?"; - /// let tokenized = vocab.tokenize_sentences(text); - /// ``` - /// - /// ## Notes - /// - /// - This function uses Unicode-aware sentence and word segmentation. - /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. - /// - Words are prefixed with the word break character (`▁`). - /// - Unknown tokens are replaced with the `` token. - pub fn tokenize_sentences(&self, text: &str) -> Vec> { - self.tokenize_sentences_iter(text) - .map(|sentence_iter| sentence_iter.collect()) - .collect() - } - - /// # Tokenizes a text into a flat sequence of BPE tokens. - /// - /// This function takes a string of text and returns a vector of tokens. - /// It first tokenizes the text into sentences, then words, and finally into BPE tokens, - /// flattening the result into a single sequence. - /// - /// ## Arguments - /// - /// * `text` - A string slice containing the text to be tokenized. - /// - /// ## Returns - /// - /// A `Vec`, where each `String` represents a token. - /// - /// ## Example - /// - /// ``` - /// use bpe_tokenizer::BytePairEncoder; - /// - /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); - /// let text = "Hello, world! How are you?"; - /// let tokenized = vocab.tokenize(text); - /// ``` - /// - /// ## Notes - /// - /// - This function uses Unicode-aware sentence and word segmentation. - /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. - /// - Words are prefixed with the word break character (`▁`). - /// - Unknown tokens are replaced with the `` token. - pub fn tokenize(&self, text: &str) -> Vec { - self.tokenize_iter(text).collect() - } - - /// # Tokenizes a single sentence, adding sentence start and end markers. - /// - /// This function breaks down the tokenization process for a single sentence: - /// 1. Adds a sentence start token. - /// 2. Splits the sentence into words using Unicode-aware word segmentation. - /// 3. Prepends each word with the word break character. - /// 4. Tokenizes each word using the BPE vocabulary. - /// 5. Adds a sentence end token. - /// - /// ## Arguments - /// - /// * `sentence` - A string slice containing a single sentence to be tokenized. - /// - /// ## Returns - /// - /// An iterator that yields `String`s representing the tokenized sentence, - /// including start and end markers. - /// - /// ## Implementation Notes - /// - /// - Uses `unicode_words` for word segmentation to handle various Unicode scripts correctly. - /// - Converts words to lowercase before tokenization to match the vocabulary. - /// - Returns an iterator instead of a fully collected `Vec` to allow for - /// more efficient tokenization and processing. - fn tokenize_with_sentence_markers_iter<'a>( - &'a self, - sentence: &'a str, - ) -> impl Iterator + 'a { - iter::once(SENTENCE_START_TOKEN.to_string()) - .chain(sentence.unicode_words().flat_map(move |word| { - self.tokenize_word(&format!("{}{}", WORD_BREAK_CHAR, word.to_lowercase())) - })) - .chain(iter::once(SENTENCE_END_TOKEN.to_string())) - } - - /// # Tokenizes a single word using the Byte Pair Encoding (BPE) algorithm. - /// - /// This function implements the core BPE tokenization logic: - /// 1. If the word is empty, return an empty vector. - /// 2. Convert the word to a vector of Unicode characters. - /// 3. Iterate through possible substrings of the word, from longest to shortest. - /// 4. For each substring length, find all matching tokens in the vocabulary. - /// 5. Choose the matching token with the highest score in the vocabulary. - /// 6. Split the word at the chosen token and recursively tokenize the parts before and after. - /// 7. If no match is found, return the unknown token. - /// - /// ## Arguments - /// - /// * `text` - A string slice containing a single word to be tokenized. - /// - /// ## Returns - /// - /// A `Vec` containing the BPE tokens for the input word. - /// - /// ## Implementation Notes - /// - /// - The algorithm prioritizes longer matches over shorter ones. - /// - In case of multiple matches of the same length, it chooses the one with the highest score. - /// - The function is recursive, handling subwords created by splitting at a matched token. - /// - If no match is found in the vocabulary, it returns the unknown token. - fn tokenize_word(&self, text: &str) -> Vec { - // Base case: If the input is empty, return an empty vector - if text.is_empty() { - return vec![]; - } - - // Convert the `text` to a Vec of `char`s to index by character rather than byte - let word: Vec = text.chars().collect(); - - // Look for the longest matching token in the vocabulary - for len in (1..=word.len()).rev() { - let mut matches = vec![]; - // Iterate over each possible start position for substrings of length `len` - for start in 0..=(word.len() - len) { - let end = start + len; - - // Extract candidate substring (convert chars[start..end] back to a &str) - let candidate = &word[start..end].iter().collect::(); - - // If we have an exact match, just store it for now - if self.tokens.contains_key(candidate) { - matches.push((candidate.to_string(), start, end)); - } - } - - // If we got matches, choose the one with the highest score - if !matches.is_empty() { - let (candidate, start, end) = matches - .into_iter() - .max_by_key(|(candidate, _, _)| { - self.tokens.get(candidate).copied().unwrap_or(isize::MIN) - }) - .unwrap(); - - // Recursively process the left part (before the match) - let left: String = word[..start].iter().collect(); - let left_tokens = self.tokenize_word(&left); - - // The middle part is the matched token - let middle = vec![candidate]; - - // Recursively process the right part (after the match) - let right: String = word[end..].iter().collect(); - let right_tokens = self.tokenize_word(&right); - - // Concatenate the result of left, middle, and right - return [left_tokens, middle, right_tokens].concat(); - } - } - - // If no match is found, return for the whole text - vec![UNKNOWN_TOKEN.to_string()] - } -} +mod constants; +mod default_vocabs; +mod errors; +mod tokenizer; +// tests #[cfg(test)] -mod tests { - use super::*; - use std::fs::File; - use std::io::Write; +mod tests; - #[test] - fn test_new_valid_file() { - // Create a temporary file with valid content - let file_path = "test_vocab.txt"; - let mut file = File::create(file_path).unwrap(); - file.write_all(b"hello\t1\nworld\t2").unwrap(); - - // Test the new function - let result = BytePairEncoder::new_from_file(file_path); - assert!(result.is_ok()); - - let vocab = result.unwrap(); - assert_eq!(vocab.tokens.len(), 2); - assert_eq!(vocab.tokens.get("hello"), Some(&1)); - assert_eq!(vocab.tokens.get("world"), Some(&2)); - - // Clean up the temporary file - std::fs::remove_file(file_path).unwrap(); - } - - #[test] - fn test_new_invalid_file() { - // Test with a non-existent file - let result = BytePairEncoder::new_from_file("non_existent_file.txt"); - assert!(result.is_err()); - assert!(matches!( - result.unwrap_err(), - BytePairEncoderError::InvalidFile(_) - )); - } - - #[test] - fn test_new_from_str_valid_input() { - let input = "hello\t1\nworld\t2\ntest\t3"; - let result = BytePairEncoder::new_from_str(input); - - assert!(result.is_ok()); - let vocab = result.unwrap(); - - assert_eq!(vocab.tokens.len(), 3); - assert_eq!(vocab.tokens.get("hello"), Some(&1)); - assert_eq!(vocab.tokens.get("world"), Some(&2)); - assert_eq!(vocab.tokens.get("test"), Some(&3)); - } - - #[test] - fn test_new_from_str_empty_input() { - let input = ""; - let result = BytePairEncoder::new_from_str(input); - - assert!(result.is_ok()); - let vocab = result.unwrap(); - - assert_eq!(vocab.tokens.len(), 0); - } - - #[test] - fn test_new_from_str_invalid_format() { - let input = "hello 1\nworld\t2"; - let result = BytePairEncoder::new_from_str(input); - - assert!(result.is_err()); - assert_eq!( - result.unwrap_err(), - BytePairEncoderError::InvalidVocabularyInput - ); - } - - #[test] - fn test_new_from_str_invalid_score() { - let input = "hello\t1\nworld\tabc"; - let result = BytePairEncoder::new_from_str(input); - - assert!(result.is_err()); - assert_eq!( - result.unwrap_err(), - BytePairEncoderError::InvalidVocabularyInput - ); - } - - #[test] - #[cfg(feature = "default-small")] - fn test_new_default_small_with_tokenization() { - // Initialize the BytePairEncoder with the default small vocabulary - let result = BytePairEncoder::new_default_small(); - assert!(result.is_ok()); - - let vocab = result.unwrap(); - assert!(!vocab.tokens.is_empty()); - - // Test tokenizing a phrase - let text = "This is a test sentence."; - let tokenized = vocab.tokenize(text); - - // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. - let expected_tokens = vec![ - "".to_string(), // Sentence start - "▁this".to_string(), // Word break for 'This' - "▁is".to_string(), // Word break for 'This' - "▁a".to_string(), // Word break for 'This' - "▁test".to_string(), // Word break for 'This' - "▁sent".to_string(), // Word break for 'This' - "ence".to_string(), // Word break for 'This' - "".to_string(), // Sentence end - ]; - - assert_eq!(tokenized, expected_tokens); - } - - #[test] - #[cfg(feature = "default-medium")] - fn test_new_default_medium_with_tokenization() { - // Initialize the BytePairEncoder with the default medium vocabulary - let result = BytePairEncoder::new_default_medium(); - assert!(result.is_ok()); - - let vocab = result.unwrap(); - assert!(!vocab.tokens.is_empty()); - - // Test tokenizing a phrase - let text = "This is a test sentence."; - let tokenized = vocab.tokenize(text); - - // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. - let expected_tokens = vec![ - "".to_string(), // Sentence start - "▁this".to_string(), // Word break for 'This' - "▁is".to_string(), // Word break for 'This' - "▁a".to_string(), // Word break for 'This' - "▁test".to_string(), // Word break for 'This' - "▁sentence".to_string(), // Word break for 'This' - "".to_string(), // Sentence end - ]; - - assert_eq!(tokenized, expected_tokens); - } - - #[test] - #[cfg(feature = "default-large")] - fn test_new_default_large_with_tokenization() { - // Initialize the BytePairEncoder with the default large vocabulary - let result = BytePairEncoder::new_default_large(); - assert!(result.is_ok()); - - let vocab = result.unwrap(); - assert!(!vocab.tokens.is_empty()); - - // Test tokenizing a phrase - let text = "This is a test sentence."; - let tokenized = vocab.tokenize(text); - - // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. - let expected_tokens = vec![ - "".to_string(), // Sentence start - "▁this".to_string(), // Word break for 'This' - "▁is".to_string(), // Word break for 'This' - "▁a".to_string(), // Word break for 'This' - "▁test".to_string(), // Word break for 'This' - "▁sentence".to_string(), // Word break for 'This' - "".to_string(), // Sentence end - ]; - - assert_eq!(tokenized, expected_tokens); - } - - #[test] - fn test_tokenize_sentences_iter() { - let vocab_str = "hello\t1\nworld\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "Hello, world! How are you?"; - let tokenized: Vec> = vocab - .tokenize_sentences_iter(text) - .map(|sentence_iter| sentence_iter.collect()) - .collect(); - - assert_eq!(tokenized.len(), 2); - - assert_eq!( - tokenized[0], - vec![ - "".to_string(), - "▁".to_string(), - "hello".to_string(), - "▁".to_string(), - "world".to_string(), - "".to_string(), - ] - ); - - assert_eq!( - tokenized[1], - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_sentences_iter_empty_input() { - let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); - let text = ""; - let tokenized: Vec> = vocab - .tokenize_sentences_iter(text) - .map(|sentence_iter| sentence_iter.collect()) - .collect(); - - assert_eq!(tokenized.len(), 0); - } - - #[test] - fn test_tokenize_sentences_iter_unicode() { - let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "こんにちは、世界！お元気ですか？"; - let tokenized: Vec> = vocab - .tokenize_sentences_iter(text) - .map(|sentence_iter| sentence_iter.collect()) - .collect(); - - assert_eq!(tokenized.len(), 2); - - assert_eq!( - tokenized[0], - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - - assert_eq!( - tokenized[1], - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_iter() { - let vocab_str = "hello\t1\nworld\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "Hello, world! How are you?"; - let tokenized: Vec = vocab.tokenize_iter(text).collect(); - - assert_eq!( - tokenized, - vec![ - "".to_string(), - "▁".to_string(), - "hello".to_string(), - "▁".to_string(), - "world".to_string(), - "".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_iter_empty_input() { - let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); - let text = ""; - let tokenized: Vec = vocab.tokenize_iter(text).collect(); - - assert_eq!(tokenized.len(), 0); - } - - #[test] - fn test_tokenize_iter_unicode() { - let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "こんにちは、世界！お元気ですか？"; - let tokenized: Vec = vocab.tokenize_iter(text).collect(); - - assert_eq!( - tokenized, - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_sentences() { - let vocab_str = "hello\t1\nworld\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "Hello, world! How are you?"; - let tokenized = vocab.tokenize_sentences(text); - - assert_eq!(tokenized.len(), 2); - assert_eq!( - tokenized[0], - vec![ - "".to_string(), - "▁".to_string(), - "hello".to_string(), - "▁".to_string(), - "world".to_string(), - "".to_string(), - ] - ); - assert_eq!( - tokenized[1], - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize() { - let vocab_str = "hello\t1\nworld\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let text = "Hello, world! How are you?"; - let tokenized = vocab.tokenize(text); - - assert_eq!( - tokenized, - vec![ - "".to_string(), - "▁".to_string(), - "hello".to_string(), - "▁".to_string(), - "world".to_string(), - "".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_empty_input() { - let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); - let text = ""; - - assert_eq!(vocab.tokenize_sentences(text), Vec::>::new()); - assert_eq!(vocab.tokenize(text), Vec::::new()); - } - - #[test] - fn test_tokenize_with_sentence_markers() { - let vocab_str = "hello\t1\nworld\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let sentence = "Hello, World!"; - let tokenized: Vec = vocab - .tokenize_with_sentence_markers_iter(sentence) - .collect(); - - assert_eq!( - tokenized, - vec![ - "".to_string(), - "▁".to_string(), - "hello".to_string(), - "▁".to_string(), - "world".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_with_sentence_markers_unicode() { - let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - let sentence = "こんにちは、世界！"; - let tokenized: Vec = vocab - .tokenize_with_sentence_markers_iter(sentence) - .collect(); - - assert_eq!( - tokenized, - vec![ - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "▁".to_string(), - "".to_string(), - "".to_string(), - ] - ); - } - - #[test] - fn test_tokenize_word() { - let vocab_str = "hell\t1\no\t2\nwo\t3\nrld\t4\n▁\t5"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - assert_eq!( - vocab.tokenize_word("▁hello"), - vec!["▁".to_string(), "hell".to_string(), "o".to_string()] - ); - assert_eq!( - vocab.tokenize_word("▁world"), - vec!["▁".to_string(), "wo".to_string(), "rld".to_string()] - ); - assert_eq!( - vocab.tokenize_word("▁unknown"), - vec![ - "▁".to_string(), - "".to_string(), - "o".to_string(), - "".to_string() - ] - ); - } - - #[test] - fn test_tokenize_word_empty() { - let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); - assert_eq!(vocab.tokenize_word(""), Vec::::new()); - } - - #[test] - fn test_tokenize_word_partial_match() { - let vocab_str = "partial\t1\npar\t2\ntial\t3\n▁\t4"; - let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); - - assert_eq!( - vocab.tokenize_word("▁partial"), - vec!["▁".to_string(), "partial".to_string()] - ); - assert_eq!( - vocab.tokenize_word("▁partially"), - vec!["▁".to_string(), "partial".to_string(), "".to_string()] - ); - } -} +// re-exports +pub use errors::BytePairEncoderError; +pub use tokenizer::BytePairEncoder; diff --git a/src/tests.rs b/src/tests.rs new file mode 100644 index 0000000..6eb962f --- /dev/null +++ b/src/tests.rs @@ -0,0 +1,533 @@ +use std::{fs::File, io::Write}; + +use crate::{BytePairEncoder, BytePairEncoderError}; + +#[test] +fn test_new_valid_file() { + // Create a temporary file with valid content + let file_path = "test_vocab.txt"; + let mut file = File::create(file_path).unwrap(); + file.write_all(b"hello\t1\nworld\t2").unwrap(); + + // Test the new function + let result = BytePairEncoder::new_from_file(file_path); + assert!(result.is_ok()); + + let vocab = result.unwrap(); + assert_eq!(vocab.tokens.len(), 2); + assert_eq!(vocab.tokens.get("hello"), Some(&1)); + assert_eq!(vocab.tokens.get("world"), Some(&2)); + + // Clean up the temporary file + std::fs::remove_file(file_path).unwrap(); +} + +#[test] +fn test_new_invalid_file() { + // Test with a non-existent file + let result = BytePairEncoder::new_from_file("non_existent_file.txt"); + assert!(result.is_err()); + assert!(matches!( + result.unwrap_err(), + BytePairEncoderError::InvalidFile(_) + )); +} + +#[test] +fn test_new_from_str_valid_input() { + let input = "hello\t1\nworld\t2\ntest\t3"; + let result = BytePairEncoder::new_from_str(input); + + assert!(result.is_ok()); + let vocab = result.unwrap(); + + assert_eq!(vocab.tokens.len(), 3); + assert_eq!(vocab.tokens.get("hello"), Some(&1)); + assert_eq!(vocab.tokens.get("world"), Some(&2)); + assert_eq!(vocab.tokens.get("test"), Some(&3)); +} + +#[test] +fn test_new_from_str_empty_input() { + let input = ""; + let result = BytePairEncoder::new_from_str(input); + + assert!(result.is_ok()); + let vocab = result.unwrap(); + + assert_eq!(vocab.tokens.len(), 0); +} + +#[test] +fn test_new_from_str_invalid_format() { + let input = "hello 1\nworld\t2"; + let result = BytePairEncoder::new_from_str(input); + + assert!(result.is_err()); + assert_eq!( + result.unwrap_err(), + BytePairEncoderError::InvalidVocabularyInput + ); +} + +#[test] +fn test_new_from_str_invalid_score() { + let input = "hello\t1\nworld\tabc"; + let result = BytePairEncoder::new_from_str(input); + + assert!(result.is_err()); + assert_eq!( + result.unwrap_err(), + BytePairEncoderError::InvalidVocabularyInput + ); +} + +#[test] +#[cfg(feature = "default-small")] +fn test_new_default_small_with_tokenization() { + // Initialize the BytePairEncoder with the default small vocabulary + let result = BytePairEncoder::new_default_small(); + assert!(result.is_ok()); + + let vocab = result.unwrap(); + assert!(!vocab.tokens.is_empty()); + + // Test tokenizing a phrase + let text = "This is a test sentence."; + let tokenized = vocab.tokenize(text); + + // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. + let expected_tokens = vec![ + "".to_string(), // Sentence start + "▁this".to_string(), // Word break for 'This' + "▁is".to_string(), // Word break for 'This' + "▁a".to_string(), // Word break for 'This' + "▁test".to_string(), // Word break for 'This' + "▁sent".to_string(), // Word break for 'This' + "ence".to_string(), // Word break for 'This' + "".to_string(), // Sentence end + ]; + + assert_eq!(tokenized, expected_tokens); +} + +#[test] +#[cfg(feature = "default-medium")] +fn test_new_default_medium_with_tokenization() { + // Initialize the BytePairEncoder with the default medium vocabulary + let result = BytePairEncoder::new_default_medium(); + assert!(result.is_ok()); + + let vocab = result.unwrap(); + assert!(!vocab.tokens.is_empty()); + + // Test tokenizing a phrase + let text = "This is a test sentence."; + let tokenized = vocab.tokenize(text); + + // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. + let expected_tokens = vec![ + "".to_string(), // Sentence start + "▁this".to_string(), // Word break for 'This' + "▁is".to_string(), // Word break for 'This' + "▁a".to_string(), // Word break for 'This' + "▁test".to_string(), // Word break for 'This' + "▁sentence".to_string(), // Word break for 'This' + "".to_string(), // Sentence end + ]; + + assert_eq!(tokenized, expected_tokens); +} + +#[test] +#[cfg(feature = "default-large")] +fn test_new_default_large_with_tokenization() { + // Initialize the BytePairEncoder with the default large vocabulary + let result = BytePairEncoder::new_default_large(); + assert!(result.is_ok()); + + let vocab = result.unwrap(); + assert!(!vocab.tokens.is_empty()); + + // Test tokenizing a phrase + let text = "This is a test sentence."; + let tokenized = vocab.tokenize(text); + + // Ensure we get the correct tokens. Since the vocabulary is pre-trained, ensure it returns sensible results. + let expected_tokens = vec![ + "".to_string(), // Sentence start + "▁this".to_string(), // Word break for 'This' + "▁is".to_string(), // Word break for 'This' + "▁a".to_string(), // Word break for 'This' + "▁test".to_string(), // Word break for 'This' + "▁sentence".to_string(), // Word break for 'This' + "".to_string(), // Sentence end + ]; + + assert_eq!(tokenized, expected_tokens); +} + +#[test] +fn test_tokenize_sentences_iter() { + let vocab_str = "hello\t1\nworld\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "Hello, world! How are you?"; + let tokenized: Vec> = vocab + .tokenize_sentences_iter(text) + .map(|sentence_iter| sentence_iter.collect()) + .collect(); + + assert_eq!(tokenized.len(), 2); + + assert_eq!( + tokenized[0], + vec![ + "".to_string(), + "▁".to_string(), + "hello".to_string(), + "▁".to_string(), + "world".to_string(), + "".to_string(), + ] + ); + + assert_eq!( + tokenized[1], + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_sentences_iter_empty_input() { + let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); + let text = ""; + let tokenized: Vec> = vocab + .tokenize_sentences_iter(text) + .map(|sentence_iter| sentence_iter.collect()) + .collect(); + + assert_eq!(tokenized.len(), 0); +} + +#[test] +fn test_tokenize_sentences_iter_unicode() { + let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "こんにちは、世界！お元気ですか？"; + let tokenized: Vec> = vocab + .tokenize_sentences_iter(text) + .map(|sentence_iter| sentence_iter.collect()) + .collect(); + + assert_eq!(tokenized.len(), 2); + + assert_eq!( + tokenized[0], + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); + + assert_eq!( + tokenized[1], + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_iter() { + let vocab_str = "hello\t1\nworld\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "Hello, world! How are you?"; + let tokenized: Vec = vocab.tokenize_iter(text).collect(); + + assert_eq!( + tokenized, + vec![ + "".to_string(), + "▁".to_string(), + "hello".to_string(), + "▁".to_string(), + "world".to_string(), + "".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_iter_empty_input() { + let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); + let text = ""; + let tokenized: Vec = vocab.tokenize_iter(text).collect(); + + assert_eq!(tokenized.len(), 0); +} + +#[test] +fn test_tokenize_iter_unicode() { + let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "こんにちは、世界！お元気ですか？"; + let tokenized: Vec = vocab.tokenize_iter(text).collect(); + + assert_eq!( + tokenized, + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_sentences() { + let vocab_str = "hello\t1\nworld\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "Hello, world! How are you?"; + let tokenized = vocab.tokenize_sentences(text); + + assert_eq!(tokenized.len(), 2); + assert_eq!( + tokenized[0], + vec![ + "".to_string(), + "▁".to_string(), + "hello".to_string(), + "▁".to_string(), + "world".to_string(), + "".to_string(), + ] + ); + assert_eq!( + tokenized[1], + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize() { + let vocab_str = "hello\t1\nworld\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let text = "Hello, world! How are you?"; + let tokenized = vocab.tokenize(text); + + assert_eq!( + tokenized, + vec![ + "".to_string(), + "▁".to_string(), + "hello".to_string(), + "▁".to_string(), + "world".to_string(), + "".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_empty_input() { + let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); + let text = ""; + + assert_eq!(vocab.tokenize_sentences(text), Vec::>::new()); + assert_eq!(vocab.tokenize(text), Vec::::new()); +} + +#[test] +fn test_tokenize_with_sentence_markers() { + let vocab_str = "hello\t1\nworld\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let sentence = "Hello, World!"; + let tokenized: Vec = vocab + .tokenize_with_sentence_markers_iter(sentence) + .collect(); + + assert_eq!( + tokenized, + vec![ + "".to_string(), + "▁".to_string(), + "hello".to_string(), + "▁".to_string(), + "world".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_with_sentence_markers_unicode() { + let vocab_str = "こんにちは\t1\n世界\t2\n▁\t3"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + let sentence = "こんにちは、世界！"; + let tokenized: Vec = vocab + .tokenize_with_sentence_markers_iter(sentence) + .collect(); + + assert_eq!( + tokenized, + vec![ + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "▁".to_string(), + "".to_string(), + "".to_string(), + ] + ); +} + +#[test] +fn test_tokenize_word() { + let vocab_str = "hell\t1\no\t2\nwo\t3\nrld\t4\n▁\t5"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + assert_eq!( + vocab.tokenize_word("▁hello"), + vec!["▁".to_string(), "hell".to_string(), "o".to_string()] + ); + assert_eq!( + vocab.tokenize_word("▁world"), + vec!["▁".to_string(), "wo".to_string(), "rld".to_string()] + ); + assert_eq!( + vocab.tokenize_word("▁unknown"), + vec![ + "▁".to_string(), + "".to_string(), + "o".to_string(), + "".to_string() + ] + ); +} + +#[test] +fn test_tokenize_word_empty() { + let vocab = BytePairEncoder::new_from_str("test\t1").unwrap(); + assert_eq!(vocab.tokenize_word(""), Vec::::new()); +} + +#[test] +fn test_tokenize_word_partial_match() { + let vocab_str = "partial\t1\npar\t2\ntial\t3\n▁\t4"; + let vocab = BytePairEncoder::new_from_str(vocab_str).unwrap(); + + assert_eq!( + vocab.tokenize_word("▁partial"), + vec!["▁".to_string(), "partial".to_string()] + ); + assert_eq!( + vocab.tokenize_word("▁partially"), + vec!["▁".to_string(), "partial".to_string(), "".to_string()] + ); +} diff --git a/src/tokenizer.rs b/src/tokenizer.rs new file mode 100644 index 0000000..135f307 --- /dev/null +++ b/src/tokenizer.rs @@ -0,0 +1,522 @@ +use std::{collections::HashMap, fs, iter}; + +use unicode_segmentation::UnicodeSegmentation; + +use crate::{ + constants::*, + default_vocabs::{new_default, DefaultVocab}, + BytePairEncoderError, +}; + +/// # Represents a Byte Pair Encoding (BPE) vocabulary used for tokenization. +/// +/// This struct holds the mapping of tokens to their respective scores and provides methods for +/// tokenizing text using the BPE algorithm. +/// +/// The vocabulary is typically loaded from a file or string where each line +/// contains a token and its score, separated by a tab character. +/// +/// ## Example +/// +/// ``` +/// use bpe_tokenizer::BytePairEncoder; +/// +/// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); +/// let tokenized = vocab.tokenize("Hello, world!"); +/// ``` +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct BytePairEncoder { + /// # A mapping of tokens to their respective scores. + /// + /// In BPE, tokens with higher scores are typically more common and are preferred during the + /// tokenization process. + pub(crate) tokens: HashMap, +} + +impl BytePairEncoder { + /// # Creates a new `BytePairEncoder` from a file containing token-score pairs. + /// + /// This function reads the contents of the file specified by `file_path` and constructs + /// a `BytePairEncoder` from it. The file should contain token-score pairs, with each pair + /// on a separate line and the token and score separated by a tab character (`\t`). + /// + /// ## Input Format + /// + /// The file is expected to follow this format: + /// + /// ```text + /// \t\n + /// ``` + /// + /// Each line should consist of: + /// * A token (a string) followed by a tab character (`\t`) + /// * A score (an integer) as either a positive or negative value. + /// + /// Example lines from the file: + /// + /// ```text + /// 0 + /// ▁t -0 + /// ▁the -4 + /// ``` + /// + /// ## Arguments + /// + /// * `file_path` - A string slice that holds the path to the file containing token-score pairs. + /// + /// ## Returns + /// + /// * `Result` - A Result containing the created `BytePairEncoder` if successful, + /// or a `BytePairEncoderError` if there was an error reading the file or parsing its contents. + /// + /// ## Errors + /// + /// This function will return an error if: + /// * The file cannot be read (returns `BytePairEncoderError::InvalidFile`) + /// * The file contents are not in the expected format (returns `BytePairEncoderError::InvalidVocabularyInput`) + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let vocab = BytePairEncoder::new_from_file("path/to/vocabulary/file.txt"); + /// ``` + pub fn new_from_file(file_path: &str) -> Result { + Self::new_from_str( + fs::read_to_string(file_path) + .map_err(|_| BytePairEncoderError::InvalidFile(file_path.to_string()))? + .as_ref(), + ) + } + + /// # Creates a new `BytePairEncoder` from a string containing token-score pairs. + /// + /// This function parses the input string to construct a `BytePairEncoder`. The input should + /// contain token-score pairs, with each pair on a separate line and the token and score + /// separated by a tab character (`\t`). + /// + /// ## Input Format + /// + /// The string must follow this format: + /// + /// ```text + /// \t\n + /// ``` + /// + /// Each line in the string should consist of: + /// * A token (a string) followed by a tab character (`\t`) + /// * A score (an integer) as either a positive or negative value. + /// + /// For example: + /// + /// ```text + /// hello 1 + /// world 2 + /// ▁the -4 + /// ``` + /// + /// ## Arguments + /// + /// * `input` - A string slice that holds the token-score pairs. + /// + /// ## Returns + /// + /// * `Result` - A Result containing the created `BytePairEncoder` if successful, + /// or a `BytePairEncoderError` if there was an error parsing the input. + /// + /// ## Errors + /// + /// This function will return `BytePairEncoderError::InvalidVocabularyInput` if: + /// * A line doesn't contain a tab character to separate token and score. + /// * The score cannot be parsed as an `isize`. + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let input = "hello\t1\nworld\t2"; + /// let vocab = BytePairEncoder::new_from_str(input).unwrap(); + /// ``` + pub fn new_from_str(input: &str) -> Result { + let mut tokens = HashMap::new(); + + for line in input.lines() { + let (token, score_str) = match line.split_once('\t') { + Some(pair) => pair, + None => return Err(BytePairEncoderError::InvalidVocabularyInput), + }; + let score = match score_str.parse::() { + Ok(score) => score, + Err(_) => return Err(BytePairEncoderError::InvalidVocabularyInput), + }; + tokens.insert(token.to_string(), score); + } + + Ok(BytePairEncoder { tokens }) + } + + /// # Creates a new `BytePairEncoder` with a default small vocabulary size (100,000 tokens). + /// + /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary + /// that supports 275 languages. The vocabulary is sourced from the + /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The small-sized + /// vocabulary file consists of 100,000 tokens, allowing for highly compressed tokenization + /// suitable for tasks with limited memory constraints. + /// + /// ## Returns + /// + /// A `Result`, constructing the `BytePairEncoder` on successful + /// vocabulary loading, or a corresponding error if initialization fails. + /// + /// ## Example + /// + /// ``` + /// # #[cfg(feature = "default-small")] { + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let encoder = BytePairEncoder::new_default_small().unwrap(); + /// # } + /// ``` + /// + /// ## Note + /// + /// This is only enabled when the `default-small` feature is enabled in Cargo.toml. + /// + /// ```toml + /// [dependencies] + /// bpe-tokenizer = { version = " Result { + new_default(DefaultVocab::Small) + } + + /// # Creates a new `BytePairEncoder` with a default medium vocabulary size (320,000 tokens). + /// + /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary + /// that supports 275 languages. The vocabulary is sourced from the + /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The + /// medium-sized vocabulary file consists of 320,000 tokens, offering a balance between token + /// coverage and memory efficiency, making it suitable for a wide variety of NLP tasks. + /// + /// ## Returns + /// + /// A `Result`, constructing the `BytePairEncoder` on successful + /// vocabulary loading, or a corresponding error if initialization fails. + /// + /// ## Example + /// + /// ``` + /// # #[cfg(feature = "default-medium")] { + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let encoder = BytePairEncoder::new_default_medium().unwrap(); + /// # } + /// ``` + /// + /// ## Note + /// + /// This is only enabled when the `default-medium` feature is enabled in Cargo.toml. + /// + /// ```toml + /// [dependencies] + /// bpe-tokenizer = { version = " Result { + new_default(DefaultVocab::Medium) + } + + /// # Creates a new `BytePairEncoder` with a default large vocabulary size (1,000,000 tokens). + /// + /// This function constructs a `BytePairEncoder` using a pre-trained multilingual vocabulary + /// that supports 275 languages. The vocabulary is sourced from the + /// [BPEmb](https://github.com/bheinzerling/bpemb) project, licensed under MIT. The large-sized + /// vocabulary consists of 1,000,000 tokens, providing maximum coverage for detailed language + /// representation, especially useful in applications requiring high granularity. + /// + /// ## Returns + /// + /// A `Result`, constructing the `BytePairEncoder` on successful + /// vocabulary loading, or a corresponding error if initialization fails. + /// + /// ## Example + /// + /// ``` + /// # #[cfg(feature = "default-large")] { + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let encoder = BytePairEncoder::new_default_large().unwrap(); + /// # } + /// ``` + /// + /// ## Note + /// + /// This is only enabled when the `default-large` feature is enabled in Cargo.toml. + /// + /// ```toml + /// [dependencies] + /// bpe-tokenizer = { version = " Result { + new_default(DefaultVocab::Large) + } + + /// # Tokenizes a text into sentences, then words, and finally into BPE tokens. + /// + /// This function takes a string of text and returns an iterator that yields + /// vectors of tokens, where each vector represents a tokenized sentence. + /// + /// ## Arguments + /// + /// * `text` - A string slice containing the text to be tokenized. + /// + /// ## Returns + /// + /// An iterator that yields `Vec`, where each `Vec` represents + /// a tokenized sentence. + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); + /// let text = "Hello, world! How are you?"; + /// let tokenized: Vec> = vocab + /// .tokenize_sentences_iter(text) + /// .map(|sentence_iter| sentence_iter.collect()) // Collect each inner iterator into a Vec + /// .collect(); // Then collect everything into Vec> + /// ``` + /// + /// ## Notes + /// + /// - This function uses Unicode-aware sentence and word segmentation. + /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. + /// - Words are prefixed with the word break character (`▁`). + /// - Unknown tokens are replaced with the `` token. + pub fn tokenize_sentences_iter<'a>( + &'a self, + text: &'a str, + ) -> impl Iterator + 'a> + 'a { + UnicodeSegmentation::unicode_sentences(text) + .map(move |sentence| self.tokenize_with_sentence_markers_iter(sentence)) + } + + /// # Tokenizes a text into a flat sequence of BPE tokens. + /// + /// This function takes a string of text and returns an iterator that yields + /// individual tokens. It first tokenizes the text into sentences, then words, + /// and finally into BPE tokens, flattening the result into a single sequence. + /// + /// ## Arguments + /// + /// * `text` - A string slice containing the text to be tokenized. + /// + /// ## Returns + /// + /// An iterator that yields `String`, where each `String` represents a token. + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); + /// let text = "Hello, world! How are you?"; + /// let tokenized: Vec = vocab.tokenize_iter(text).collect(); + /// ``` + /// + /// ## Notes + /// + /// - This function uses Unicode-aware sentence and word segmentation. + /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. + /// - Words are prefixed with the word break character (`▁`). + /// - Unknown tokens are replaced with the `` token. + pub fn tokenize_iter<'a>(&'a self, text: &'a str) -> impl Iterator + 'a { + self.tokenize_sentences_iter(text).flatten() + } + + /// # Tokenizes a text into sentences, then words, and finally into BPE tokens. + /// + /// This function takes a string of text and returns a vector of tokenized sentences, + /// where each sentence is represented as a vector of tokens. + /// + /// ## Arguments + /// + /// * `text` - A string slice containing the text to be tokenized. + /// + /// ## Returns + /// + /// A `Vec>`, where each inner `Vec` represents a tokenized sentence. + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); + /// let text = "Hello, world! How are you?"; + /// let tokenized = vocab.tokenize_sentences(text); + /// ``` + /// + /// ## Notes + /// + /// - This function uses Unicode-aware sentence and word segmentation. + /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. + /// - Words are prefixed with the word break character (`▁`). + /// - Unknown tokens are replaced with the `` token. + pub fn tokenize_sentences(&self, text: &str) -> Vec> { + self.tokenize_sentences_iter(text) + .map(|sentence_iter| sentence_iter.collect()) + .collect() + } + + /// # Tokenizes a text into a flat sequence of BPE tokens. + /// + /// This function takes a string of text and returns a vector of tokens. + /// It first tokenizes the text into sentences, then words, and finally into BPE tokens, + /// flattening the result into a single sequence. + /// + /// ## Arguments + /// + /// * `text` - A string slice containing the text to be tokenized. + /// + /// ## Returns + /// + /// A `Vec`, where each `String` represents a token. + /// + /// ## Example + /// + /// ``` + /// use bpe_tokenizer::BytePairEncoder; + /// + /// let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap(); + /// let text = "Hello, world! How are you?"; + /// let tokenized = vocab.tokenize(text); + /// ``` + /// + /// ## Notes + /// + /// - This function uses Unicode-aware sentence and word segmentation. + /// - Each sentence is wrapped with sentence start (``) and end (``) tokens. + /// - Words are prefixed with the word break character (`▁`). + /// - Unknown tokens are replaced with the `` token. + pub fn tokenize(&self, text: &str) -> Vec { + self.tokenize_iter(text).collect() + } + + /// # Tokenizes a single sentence, adding sentence start and end markers. + /// + /// This function breaks down the tokenization process for a single sentence: + /// 1. Adds a sentence start token. + /// 2. Splits the sentence into words using Unicode-aware word segmentation. + /// 3. Prepends each word with the word break character. + /// 4. Tokenizes each word using the BPE vocabulary. + /// 5. Adds a sentence end token. + /// + /// ## Arguments + /// + /// * `sentence` - A string slice containing a single sentence to be tokenized. + /// + /// ## Returns + /// + /// An iterator that yields `String`s representing the tokenized sentence, + /// including start and end markers. + /// + /// ## Implementation Notes + /// + /// - Uses `unicode_words` for word segmentation to handle various Unicode scripts correctly. + /// - Converts words to lowercase before tokenization to match the vocabulary. + /// - Returns an iterator instead of a fully collected `Vec` to allow for + /// more efficient tokenization and processing. + pub(crate) fn tokenize_with_sentence_markers_iter<'a>( + &'a self, + sentence: &'a str, + ) -> impl Iterator + 'a { + iter::once(SENTENCE_START_TOKEN.to_string()) + .chain(sentence.unicode_words().flat_map(move |word| { + self.tokenize_word(&format!("{}{}", WORD_BREAK_CHAR, word.to_lowercase())) + })) + .chain(iter::once(SENTENCE_END_TOKEN.to_string())) + } + + /// # Tokenizes a single word using the Byte Pair Encoding (BPE) algorithm. + /// + /// This function implements the core BPE tokenization logic: + /// 1. If the word is empty, return an empty vector. + /// 2. Convert the word to a vector of Unicode characters. + /// 3. Iterate through possible substrings of the word, from longest to shortest. + /// 4. For each substring length, find all matching tokens in the vocabulary. + /// 5. Choose the matching token with the highest score in the vocabulary. + /// 6. Split the word at the chosen token and recursively tokenize the parts before and after. + /// 7. If no match is found, return the unknown token. + /// + /// ## Arguments + /// + /// * `text` - A string slice containing a single word to be tokenized. + /// + /// ## Returns + /// + /// A `Vec` containing the BPE tokens for the input word. + /// + /// ## Implementation Notes + /// + /// - The algorithm prioritizes longer matches over shorter ones. + /// - In case of multiple matches of the same length, it chooses the one with the highest score. + /// - The function is recursive, handling subwords created by splitting at a matched token. + /// - If no match is found in the vocabulary, it returns the unknown token. + pub(crate) fn tokenize_word(&self, text: &str) -> Vec { + // Base case: If the input is empty, return an empty vector + if text.is_empty() { + return vec![]; + } + + // Convert the `text` to a Vec of `char`s to index by character rather than byte + let word: Vec = text.chars().collect(); + + // Look for the longest matching token in the vocabulary + for len in (1..=word.len()).rev() { + let mut matches = vec![]; + // Iterate over each possible start position for substrings of length `len` + for start in 0..=(word.len() - len) { + let end = start + len; + + // Extract candidate substring (convert chars[start..end] back to a &str) + let candidate = &word[start..end].iter().collect::(); + + // If we have an exact match, just store it for now + if self.tokens.contains_key(candidate) { + matches.push((candidate.to_string(), start, end)); + } + } + + // If we got matches, choose the one with the highest score + if !matches.is_empty() { + let (candidate, start, end) = matches + .into_iter() + .max_by_key(|(candidate, _, _)| { + self.tokens.get(candidate).copied().unwrap_or(isize::MIN) + }) + .unwrap(); + + // Recursively process the left part (before the match) + let left: String = word[..start].iter().collect(); + let left_tokens = self.tokenize_word(&left); + + // The middle part is the matched token + let middle = vec![candidate]; + + // Recursively process the right part (after the match) + let right: String = word[end..].iter().collect(); + let right_tokens = self.tokenize_word(&right); + + // Concatenate the result of left, middle, and right + return [left_tokens, middle, right_tokens].concat(); + } + } + + // If no match is found, return for the whole text + vec![UNKNOWN_TOKEN.to_string()] + } +}