Files
xserv/crates/xserv-tokenizer/src/bpe.rs
Gahow Wang 246ae1c590 phase 10: Qwen3-8B support (Milestone ②)
Qwen3 model (qwen3.rs):
- RMSNorm + QK normalization (per-head q_norm/k_norm)
- GQA: 32 Q heads, 8 KV heads, repeat_kv for attention
- SwiGLU FFN: gate_proj → SiLU → * up_proj → down_proj
- RoPE with transpose for [1,H,S,D] ↔ [S,H,D] layout
- BF16 forward pass, [out,in] weight layout via linear_t
- No attention bias (attention_bias=false)

Tokenizer fixes:
- Fixed unicode_to_byte: shifted bytes now use correct inverse lookup table
- MergeEntry supports both string and array formats
- Both GPT-2 and Qwen3 tokenizers work correctly (English + Chinese)

KVCache refactored:
- Dtype-agnostic: stores raw bytes per-head, works for F32 and BF16
- append_kv_tensor/get_kv_tensors use Tensor directly

CLI updated:
- Auto-detects model type from config.json (gpt2 vs qwen3)
- Supports both GPT-2 (F32) and Qwen3 (BF16)

Verified: Qwen3-8B generates coherent English and Chinese on single RTX 5090.
61/61 tests pass, GPT-2 performance no regression.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-05-22 00:46:37 +08:00

268 lines
8.9 KiB
Rust

use regex::Regex;
use serde::Deserialize;
use std::collections::HashMap;
use std::path::Path;
pub struct Tokenizer {
encoder: HashMap<Vec<u8>, u32>,
decoder: Vec<Vec<u8>>,
merge_ranks: HashMap<(u32, u32), usize>,
special_tokens: HashMap<String, u32>,
#[allow(dead_code)]
special_token_ids: HashMap<u32, String>,
pre_tokenize_re: Regex,
eos_token_id: Option<u32>,
byte_fallback: bool,
}
#[derive(Deserialize)]
struct TokenizerJson {
model: ModelSection,
#[serde(default)]
added_tokens: Vec<AddedToken>,
}
#[derive(Deserialize)]
struct ModelSection {
vocab: HashMap<String, u32>,
merges: Vec<MergeEntry>,
#[serde(default)]
byte_fallback: bool,
}
#[derive(Deserialize)]
#[serde(untagged)]
enum MergeEntry {
Str(String),
Pair(Vec<String>),
}
#[derive(Deserialize)]
struct AddedToken {
id: u32,
content: String,
special: bool,
}
impl Tokenizer {
pub fn from_file(path: &Path) -> Self {
let data = std::fs::read_to_string(path)
.unwrap_or_else(|e| panic!("failed to read {}: {e}", path.display()));
let tj: TokenizerJson = serde_json::from_str(&data)
.unwrap_or_else(|e| panic!("failed to parse tokenizer.json: {e}"));
let byte_fallback = tj.model.byte_fallback;
// Build encoder: token bytes → ID
// All HF tokenizers use GPT-2 byte-to-unicode mapping for vocab keys.
let mut encoder = HashMap::new();
for (token_str, &id) in &tj.model.vocab {
let bytes = token_str_to_bytes(token_str);
encoder.insert(bytes, id);
}
// Build decoder: ID → token bytes
let max_id = tj.model.vocab.values().copied().max().unwrap_or(0);
let added_max = tj.added_tokens.iter().map(|t| t.id).max().unwrap_or(0);
let vocab_size = (max_id.max(added_max) + 1) as usize;
let mut decoder = vec![vec![]; vocab_size];
for (token_str, &id) in &tj.model.vocab {
decoder[id as usize] = token_str_to_bytes(token_str);
}
// Parse merges (supports both "a b" string format and ["a", "b"] array format)
let byte_fallback = tj.model.byte_fallback;
let mut merge_ranks = HashMap::new();
for (rank, entry) in tj.model.merges.iter().enumerate() {
let (a_str, b_str) = match entry {
MergeEntry::Str(s) => {
let parts: Vec<&str> = s.splitn(2, ' ').collect();
if parts.len() != 2 { continue; }
(parts[0].to_string(), parts[1].to_string())
}
MergeEntry::Pair(v) => {
if v.len() != 2 { continue; }
(v[0].clone(), v[1].clone())
}
};
let a_bytes = token_str_to_bytes(&a_str);
let b_bytes = token_str_to_bytes(&b_str);
if let (Some(&a_id), Some(&b_id)) = (encoder.get(&a_bytes), encoder.get(&b_bytes)) {
merge_ranks.insert((a_id, b_id), rank);
}
}
// Special tokens
let mut special_tokens = HashMap::new();
let mut special_token_ids = HashMap::new();
let mut eos_token_id = None;
for at in &tj.added_tokens {
if at.special {
special_tokens.insert(at.content.clone(), at.id);
special_token_ids.insert(at.id, at.content.clone());
decoder.resize(decoder.len().max(at.id as usize + 1), vec![]);
decoder[at.id as usize] = at.content.as_bytes().to_vec();
if at.content == "<|endoftext|>" || at.content == "<|end_of_text|>" {
eos_token_id = Some(at.id);
}
}
}
// Pre-tokenization regex
let pre_tokenize_re = if byte_fallback {
// Qwen-style: split on whitespace boundaries, keep Unicode words/numbers
Regex::new(r"[\p{L}\p{N}]+|[^\s\p{L}\p{N}]|\s+").unwrap()
} else {
// GPT-2 style
Regex::new(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+").unwrap()
};
Self {
encoder,
decoder,
merge_ranks,
special_tokens,
special_token_ids,
pre_tokenize_re,
eos_token_id,
byte_fallback,
}
}
pub fn encode(&self, text: &str) -> Vec<u32> {
let mut tokens = Vec::new();
// Check for special tokens first (split around them)
let mut remaining = text;
while !remaining.is_empty() {
// Find earliest special token
let mut earliest: Option<(usize, &str, u32)> = None;
for (st, &id) in &self.special_tokens {
if let Some(pos) = remaining.find(st.as_str()) {
if earliest.is_none() || pos < earliest.unwrap().0 {
earliest = Some((pos, st, id));
}
}
}
if let Some((pos, st, id)) = earliest {
if pos > 0 {
self.encode_ordinary(&remaining[..pos], &mut tokens);
}
tokens.push(id);
remaining = &remaining[pos + st.len()..];
} else {
self.encode_ordinary(remaining, &mut tokens);
break;
}
}
tokens
}
fn encode_ordinary(&self, text: &str, out: &mut Vec<u32>) {
for mat in self.pre_tokenize_re.find_iter(text) {
let word = mat.as_str();
// Try to encode the whole word first
if let Some(&id) = self.encoder.get(word.as_bytes()) {
out.push(id);
continue;
}
// Fall back to per-byte encoding
let word_bytes: Vec<u8> = word.bytes().collect();
let mut token_ids: Vec<u32> = word_bytes.iter().map(|&b| {
*self.encoder.get(&vec![b]).unwrap_or_else(|| {
panic!("byte {b} (0x{b:02X}) not in vocab")
})
}).collect();
// BPE merges
loop {
if token_ids.len() < 2 { break; }
let mut best_rank = usize::MAX;
let mut best_idx = 0;
for i in 0..token_ids.len() - 1 {
if let Some(&rank) = self.merge_ranks.get(&(token_ids[i], token_ids[i + 1])) {
if rank < best_rank {
best_rank = rank;
best_idx = i;
}
}
}
if best_rank == usize::MAX { break; }
let merged_bytes = [
self.decoder[token_ids[best_idx] as usize].as_slice(),
self.decoder[token_ids[best_idx + 1] as usize].as_slice(),
].concat();
let merged_id = *self.encoder.get(&merged_bytes).unwrap_or_else(|| {
panic!("merged token not in vocab");
});
token_ids[best_idx] = merged_id;
token_ids.remove(best_idx + 1);
}
out.extend_from_slice(&token_ids);
}
}
pub fn decode(&self, token_ids: &[u32]) -> String {
let mut bytes = Vec::new();
for &id in token_ids {
if let Some(b) = self.decoder.get(id as usize) {
bytes.extend_from_slice(b);
}
}
String::from_utf8_lossy(&bytes).into_owned()
}
pub fn eos_token_id(&self) -> Option<u32> {
self.eos_token_id
}
pub fn vocab_size(&self) -> usize {
self.decoder.len()
}
pub fn special_token_id(&self, name: &str) -> Option<u32> {
self.special_tokens.get(name).copied()
}
}
/// Convert a token string from HF vocab (which uses Unicode replacements for bytes)
/// back to raw bytes. GPT-2 uses a byte-to-unicode mapping where e.g. byte 0x20 (space)
/// is represented as 'Ġ' (U+0120).
fn token_str_to_bytes(s: &str) -> Vec<u8> {
s.chars().map(|c| unicode_to_byte(c)).collect()
}
/// Convert a Unicode char back to the byte it represents in GPT-2 encoding.
fn unicode_to_byte(c: char) -> u8 {
// Build the inverse map on first use
use std::sync::OnceLock;
static INV_MAP: OnceLock<HashMap<u32, u8>> = OnceLock::new();
let map = INV_MAP.get_or_init(|| {
let mut m = HashMap::new();
// Build GPT-2's bytes_to_unicode forward map, then invert
let mut n = 0u32;
for b in 0..=255u16 {
let byte = b as u8;
let unicode = match byte {
0x21..=0x7E | 0xA1..=0xAC | 0xAE..=0xFF => byte as u32,
_ => {
let u = 256 + n;
n += 1;
u
}
};
m.insert(unicode, byte);
}
m
});
*map.get(&(c as u32)).unwrap_or_else(|| {
panic!("unmapped unicode char U+{:04X} in tokenizer", c as u32)
})
}