220 lines
7.0 KiB
Rust
220 lines
7.0 KiB
Rust
use super::traits::{Decoder, Encoder, Encoding, SpecialTokens, Tokenizer as TokenizerTrait};
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use crate::metrics::TokenizerMetrics;
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use anyhow::{Error, Result};
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use std::collections::HashMap;
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use std::time::Instant;
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use tokenizers::tokenizer::Tokenizer as HfTokenizer;
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/// HuggingFace tokenizer wrapper
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pub struct HuggingFaceTokenizer {
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tokenizer: HfTokenizer,
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special_tokens: SpecialTokens,
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vocab: HashMap<String, u32>,
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reverse_vocab: HashMap<u32, String>,
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}
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impl HuggingFaceTokenizer {
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/// Create a tokenizer from a HuggingFace tokenizer JSON file
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pub fn from_file(file_path: &str) -> Result<Self> {
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let tokenizer = HfTokenizer::from_file(file_path)
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.map_err(|e| Error::msg(format!("Failed to load tokenizer: {}", e)))?;
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// Extract special tokens
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let special_tokens = Self::extract_special_tokens(&tokenizer);
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// Build vocab mappings
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let vocab = tokenizer.get_vocab(false);
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let reverse_vocab: HashMap<u32, String> = vocab
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.iter()
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.map(|(token, &id)| (id, token.clone()))
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.collect();
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Ok(HuggingFaceTokenizer {
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tokenizer,
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special_tokens,
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vocab,
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reverse_vocab,
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})
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}
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/// Create from an existing HuggingFace tokenizer
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pub fn from_tokenizer(tokenizer: HfTokenizer) -> Self {
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let special_tokens = Self::extract_special_tokens(&tokenizer);
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let vocab = tokenizer.get_vocab(false);
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let reverse_vocab: HashMap<u32, String> = vocab
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.iter()
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.map(|(token, &id)| (id, token.clone()))
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.collect();
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HuggingFaceTokenizer {
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tokenizer,
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special_tokens,
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vocab,
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reverse_vocab,
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}
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}
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/// Extract special tokens from the tokenizer
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fn extract_special_tokens(tokenizer: &HfTokenizer) -> SpecialTokens {
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// Try to get special tokens from the tokenizer
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// This is a simplified version - actual implementation would need to handle various formats
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let vocab = tokenizer.get_vocab(true);
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let find_token = |patterns: &[&str]| -> Option<String> {
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for pattern in patterns {
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if vocab.contains_key(*pattern) {
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return Some(pattern.to_string());
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}
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}
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None
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};
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SpecialTokens {
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bos_token: find_token(&["<s>", "<|startoftext|>", "<BOS>", "[CLS]"]),
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eos_token: find_token(&["</s>", "<|endoftext|>", "<EOS>", "[SEP]"]),
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unk_token: find_token(&["<unk>", "<UNK>", "[UNK]"]),
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sep_token: find_token(&["[SEP]", "<sep>", "<SEP>"]),
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pad_token: find_token(&["<pad>", "<PAD>", "[PAD]"]),
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cls_token: find_token(&["[CLS]", "<cls>", "<CLS>"]),
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mask_token: find_token(&["[MASK]", "<mask>", "<MASK>"]),
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additional_special_tokens: vec![],
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}
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}
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/// Apply chat template if available
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pub fn apply_chat_template(&self, messages: &[ChatMessage]) -> Result<String> {
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// This is a placeholder - actual implementation would handle templates
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let mut result = String::new();
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for msg in messages {
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result.push_str(&format!("{}: {}\n", msg.role, msg.content));
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}
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Ok(result)
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}
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}
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impl Encoder for HuggingFaceTokenizer {
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fn encode(&self, input: &str) -> Result<Encoding> {
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let start = Instant::now();
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TokenizerMetrics::record_encode_request("huggingface");
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TokenizerMetrics::record_chars_per_encode(input.len());
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self.tokenizer
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.encode(input, false)
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.map_err(|e| {
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TokenizerMetrics::record_encode_error("encoding_failed");
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Error::msg(format!("Encoding failed: {}", e))
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})
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.map(|encoding| {
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TokenizerMetrics::record_tokens_per_encode(encoding.get_ids().len());
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TokenizerMetrics::record_encode_duration(start.elapsed());
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Encoding::Hf(Box::new(encoding))
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})
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}
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fn encode_batch(&self, inputs: &[&str]) -> Result<Vec<Encoding>> {
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let start = Instant::now();
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let encodings = self
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.tokenizer
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.encode_batch(inputs.to_vec(), false)
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.map_err(|e| {
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TokenizerMetrics::record_encode_error("batch_encoding_failed");
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Error::msg(format!("Batch encoding failed: {}", e))
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})?;
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TokenizerMetrics::record_encode_batch_duration(start.elapsed(), inputs.len());
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Ok(encodings
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.into_iter()
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.map(|e| Encoding::Hf(Box::new(e)))
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.collect())
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}
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}
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impl Decoder for HuggingFaceTokenizer {
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fn decode(&self, token_ids: &[u32], skip_special_tokens: bool) -> Result<String> {
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let start = Instant::now();
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TokenizerMetrics::record_decode_request("huggingface");
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TokenizerMetrics::record_tokens_per_decode(token_ids.len());
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self.tokenizer
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.decode(token_ids, skip_special_tokens)
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.map_err(|e| {
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TokenizerMetrics::record_decode_error("decoding_failed");
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Error::msg(format!("Decoding failed: {}", e))
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})
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.inspect(|_| {
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TokenizerMetrics::record_decode_duration(start.elapsed());
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})
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}
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}
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impl TokenizerTrait for HuggingFaceTokenizer {
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fn vocab_size(&self) -> usize {
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self.tokenizer.get_vocab_size(false)
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}
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fn get_special_tokens(&self) -> &SpecialTokens {
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&self.special_tokens
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}
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fn token_to_id(&self, token: &str) -> Option<u32> {
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self.vocab.get(token).copied()
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}
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fn id_to_token(&self, id: u32) -> Option<String> {
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self.reverse_vocab.get(&id).cloned()
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}
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}
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/// Represents a chat message for template application
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#[derive(Debug, Clone)]
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pub struct ChatMessage {
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pub role: String,
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pub content: String,
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}
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impl ChatMessage {
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pub fn new(role: impl Into<String>, content: impl Into<String>) -> Self {
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ChatMessage {
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role: role.into(),
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content: content.into(),
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}
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}
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pub fn system(content: impl Into<String>) -> Self {
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Self::new("system", content)
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}
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pub fn user(content: impl Into<String>) -> Self {
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Self::new("user", content)
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}
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pub fn assistant(content: impl Into<String>) -> Self {
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Self::new("assistant", content)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_chat_message_creation() {
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let msg = ChatMessage::system("You are a helpful assistant");
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assert_eq!(msg.role, "system");
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assert_eq!(msg.content, "You are a helpful assistant");
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let user_msg = ChatMessage::user("Hello!");
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assert_eq!(user_msg.role, "user");
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let assistant_msg = ChatMessage::assistant("Hi there!");
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assert_eq!(assistant_msg.role, "assistant");
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}
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// Note: Actual tokenizer tests would require a real tokenizer file
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// These would be integration tests rather than unit tests
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}
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