phase 6+7+8: model loading, BPE tokenizer, GPT-2 inference (Milestone ①)
Phase 6 — Model Loading (xserv-model): - safetensors parser with single/sharded file support - ModelConfig with dual naming (GPT-2 n_embd/n_head + modern HF naming) - Weight loading flow: safetensors → mmap → CPU Tensor → GPU Phase 7 — BPE Tokenizer (xserv-tokenizer): - Full BPE encode/decode from tokenizer.json - GPT-2 byte-to-unicode mapping (printable ASCII identity + shifted bytes) - Pre-tokenization regex, special token handling - Chat template support structure Phase 8 — GPT-2 Complete Inference: - GPT-2 model definition: wte, wpe, 12 transformer blocks, ln_f - Forward pass: embedding → (LayerNorm → MHA → residual → LayerNorm → MLP → residual) × 12 → LN → logits - QKV split with correct [batch, heads, seq, dim] layout (fixed reshape bug) - Greedy sampling from last-position logits - Interactive CLI: xserv-cli <model-dir> [--max-tokens N] Verified: GPT-2 124M generates coherent English text on RTX 5090. "The future of AI is uncertain. The future of AI is uncertain..." "Once upon a time, the world was a place of great beauty..." Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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251
crates/xserv-tokenizer/src/bpe.rs
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251
crates/xserv-tokenizer/src/bpe.rs
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use regex::Regex;
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use serde::Deserialize;
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use std::collections::HashMap;
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use std::path::Path;
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pub struct Tokenizer {
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encoder: HashMap<Vec<u8>, u32>,
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decoder: Vec<Vec<u8>>,
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merge_ranks: HashMap<(u32, u32), usize>,
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special_tokens: HashMap<String, u32>,
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special_token_ids: HashMap<u32, String>,
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pre_tokenize_re: Regex,
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eos_token_id: Option<u32>,
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}
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#[derive(Deserialize)]
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struct TokenizerJson {
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model: ModelSection,
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#[serde(default)]
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added_tokens: Vec<AddedToken>,
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}
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#[derive(Deserialize)]
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struct ModelSection {
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vocab: HashMap<String, u32>,
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merges: Vec<String>,
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}
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#[derive(Deserialize)]
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struct AddedToken {
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id: u32,
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content: String,
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special: bool,
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}
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impl Tokenizer {
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pub fn from_file(path: &Path) -> Self {
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let data = std::fs::read_to_string(path)
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.unwrap_or_else(|e| panic!("failed to read {}: {e}", path.display()));
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let tj: TokenizerJson = serde_json::from_str(&data)
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.unwrap_or_else(|e| panic!("failed to parse tokenizer.json: {e}"));
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// Build encoder: token bytes → ID
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let mut encoder = HashMap::new();
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for (token_str, &id) in &tj.model.vocab {
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let bytes = token_str_to_bytes(token_str);
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encoder.insert(bytes, id);
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}
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// Build decoder: ID → token bytes
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let max_id = tj.model.vocab.values().copied().max().unwrap_or(0);
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let added_max = tj.added_tokens.iter().map(|t| t.id).max().unwrap_or(0);
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let vocab_size = (max_id.max(added_max) + 1) as usize;
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let mut decoder = vec![vec![]; vocab_size];
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for (token_str, &id) in &tj.model.vocab {
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decoder[id as usize] = token_str_to_bytes(token_str);
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}
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// Parse merges
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let mut merge_ranks = HashMap::new();
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for (rank, merge_line) in tj.model.merges.iter().enumerate() {
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let parts: Vec<&str> = merge_line.splitn(2, ' ').collect();
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if parts.len() != 2 { continue; }
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let a_bytes = token_str_to_bytes(parts[0]);
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let b_bytes = token_str_to_bytes(parts[1]);
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if let (Some(&a_id), Some(&b_id)) = (encoder.get(&a_bytes), encoder.get(&b_bytes)) {
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merge_ranks.insert((a_id, b_id), rank);
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}
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}
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// Special tokens
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let mut special_tokens = HashMap::new();
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let mut special_token_ids = HashMap::new();
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let mut eos_token_id = None;
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for at in &tj.added_tokens {
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if at.special {
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special_tokens.insert(at.content.clone(), at.id);
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special_token_ids.insert(at.id, at.content.clone());
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decoder.resize(decoder.len().max(at.id as usize + 1), vec![]);
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decoder[at.id as usize] = at.content.as_bytes().to_vec();
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if at.content == "<|endoftext|>" || at.content == "<|end_of_text|>" {
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eos_token_id = Some(at.id);
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}
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}
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}
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// GPT-2 pre-tokenization regex.
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// The original uses (?!\S) lookahead which Rust regex doesn't support.
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// Simplified: collapse trailing whitespace into one match. Functionally equivalent
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// for BPE since each whitespace chunk gets encoded independently anyway.
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let pre_tokenize_re = Regex::new(
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r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+"
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).unwrap();
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Self {
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encoder,
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decoder,
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merge_ranks,
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special_tokens,
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special_token_ids,
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pre_tokenize_re,
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eos_token_id,
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}
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}
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pub fn encode(&self, text: &str) -> Vec<u32> {
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let mut tokens = Vec::new();
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// Check for special tokens first (split around them)
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let mut remaining = text;
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while !remaining.is_empty() {
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// Find earliest special token
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let mut earliest: Option<(usize, &str, u32)> = None;
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for (st, &id) in &self.special_tokens {
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if let Some(pos) = remaining.find(st.as_str()) {
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if earliest.is_none() || pos < earliest.unwrap().0 {
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earliest = Some((pos, st, id));
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}
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}
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}
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if let Some((pos, st, id)) = earliest {
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if pos > 0 {
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self.encode_ordinary(&remaining[..pos], &mut tokens);
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}
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tokens.push(id);
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remaining = &remaining[pos + st.len()..];
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} else {
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self.encode_ordinary(remaining, &mut tokens);
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break;
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}
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}
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tokens
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}
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fn encode_ordinary(&self, text: &str, out: &mut Vec<u32>) {
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for mat in self.pre_tokenize_re.find_iter(text) {
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let word = mat.as_str();
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let word_bytes: Vec<u8> = word.bytes().collect();
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let mut token_ids: Vec<u32> = word_bytes.iter().map(|&b| {
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*self.encoder.get(&vec![b]).unwrap_or_else(|| {
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panic!("byte {b} not in vocab")
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})
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}).collect();
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// BPE merges
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loop {
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if token_ids.len() < 2 { break; }
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let mut best_rank = usize::MAX;
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let mut best_idx = 0;
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for i in 0..token_ids.len() - 1 {
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if let Some(&rank) = self.merge_ranks.get(&(token_ids[i], token_ids[i + 1])) {
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if rank < best_rank {
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best_rank = rank;
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best_idx = i;
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}
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}
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}
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if best_rank == usize::MAX { break; }
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let merged_bytes = [
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self.decoder[token_ids[best_idx] as usize].as_slice(),
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self.decoder[token_ids[best_idx + 1] as usize].as_slice(),
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].concat();
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let merged_id = *self.encoder.get(&merged_bytes).unwrap_or_else(|| {
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panic!("merged token not in vocab");
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});
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token_ids[best_idx] = merged_id;
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token_ids.remove(best_idx + 1);
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}
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out.extend_from_slice(&token_ids);
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}
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}
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pub fn decode(&self, token_ids: &[u32]) -> String {
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let mut bytes = Vec::new();
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for &id in token_ids {
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if let Some(b) = self.decoder.get(id as usize) {
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bytes.extend_from_slice(b);
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}
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}
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String::from_utf8_lossy(&bytes).into_owned()
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}
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pub fn eos_token_id(&self) -> Option<u32> {
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self.eos_token_id
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}
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pub fn vocab_size(&self) -> usize {
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self.decoder.len()
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}
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pub fn special_token_id(&self, name: &str) -> Option<u32> {
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self.special_tokens.get(name).copied()
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}
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}
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/// Convert a token string from HF vocab (which uses Unicode replacements for bytes)
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/// back to raw bytes. GPT-2 uses a byte-to-unicode mapping where e.g. byte 0x20 (space)
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/// is represented as 'Ġ' (U+0120).
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fn token_str_to_bytes(s: &str) -> Vec<u8> {
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s.chars().map(|c| unicode_to_byte(c)).collect()
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}
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fn unicode_to_byte(c: char) -> u8 {
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let u = c as u32;
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// GPT-2 byte encoder: maps bytes 0-255 to specific Unicode code points.
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// Printable ASCII bytes map to themselves. Others are shifted to 256+.
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match u {
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0x21..=0x7E => u as u8, // '!' to '~'
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0xA1..=0xAC => u as u8, // '¡' to '¬'
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0xAE..=0xFF => u as u8, // '®' to 'ÿ'
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// Shifted bytes: 0x100 + original_byte for bytes not in the above ranges
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0x100..=0x1FF => (u - 0x100) as u8 + {
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// The shift mapping: byte values 0..=32, 127..=160, 173
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// are shifted to 256..=288, 289+, etc.
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0
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},
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_ => {
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// Fallback: for the GPT-2 byte encoder, specific mappings
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byte_from_unicode_gpt2(c)
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}
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}
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}
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fn byte_from_unicode_gpt2(c: char) -> u8 {
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// Build the inverse of GPT-2's bytes_to_unicode mapping.
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// The mapping assigns printable chars to themselves and shifts unprintable bytes.
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let u = c as u32;
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// Direct ASCII printable + Latin-1 supplement printable ranges map identity
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if (0x21..=0x7E).contains(&u) { return u as u8; }
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if (0xA1..=0xAC).contains(&u) { return u as u8; }
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if (0xAE..=0xFF).contains(&u) { return u as u8; }
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// Shifted range: the remaining 68 bytes (0-32, 127-160, 173) get mapped to 256..=323
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static SHIFTED_BYTES: &[u8] = &[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 30, 31, 32, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,
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137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
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154, 155, 156, 157, 158, 159, 160, 173,
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];
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let shifted_start = 256u32;
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if u >= shifted_start && u < shifted_start + SHIFTED_BYTES.len() as u32 {
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return SHIFTED_BYTES[(u - shifted_start) as usize];
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}
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// Shouldn't reach here for valid GPT-2 tokenizer
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c as u8
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}
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3
crates/xserv-tokenizer/src/lib.rs
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3
crates/xserv-tokenizer/src/lib.rs
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@@ -0,0 +1,3 @@
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pub mod bpe;
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pub use bpe::Tokenizer;
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