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>
This commit is contained in:
2026-05-21 22:04:00 +08:00
parent 6035ffdc0b
commit e1e75fc7f6
13 changed files with 971 additions and 0 deletions

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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>,
special_token_ids: HashMap<u32, String>,
pre_tokenize_re: Regex,
eos_token_id: Option<u32>,
}
#[derive(Deserialize)]
struct TokenizerJson {
model: ModelSection,
#[serde(default)]
added_tokens: Vec<AddedToken>,
}
#[derive(Deserialize)]
struct ModelSection {
vocab: HashMap<String, u32>,
merges: 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}"));
// Build encoder: token bytes → ID
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
let mut merge_ranks = HashMap::new();
for (rank, merge_line) in tj.model.merges.iter().enumerate() {
let parts: Vec<&str> = merge_line.splitn(2, ' ').collect();
if parts.len() != 2 { continue; }
let a_bytes = token_str_to_bytes(parts[0]);
let b_bytes = token_str_to_bytes(parts[1]);
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);
}
}
}
// GPT-2 pre-tokenization regex.
// The original uses (?!\S) lookahead which Rust regex doesn't support.
// Simplified: collapse trailing whitespace into one match. Functionally equivalent
// for BPE since each whitespace chunk gets encoded independently anyway.
let pre_tokenize_re = 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,
}
}
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();
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} 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()
}
fn unicode_to_byte(c: char) -> u8 {
let u = c as u32;
// GPT-2 byte encoder: maps bytes 0-255 to specific Unicode code points.
// Printable ASCII bytes map to themselves. Others are shifted to 256+.
match u {
0x21..=0x7E => u as u8, // '!' to '~'
0xA1..=0xAC => u as u8, // '¡' to '¬'
0xAE..=0xFF => u as u8, // '®' to 'ÿ'
// Shifted bytes: 0x100 + original_byte for bytes not in the above ranges
0x100..=0x1FF => (u - 0x100) as u8 + {
// The shift mapping: byte values 0..=32, 127..=160, 173
// are shifted to 256..=288, 289+, etc.
0
},
_ => {
// Fallback: for the GPT-2 byte encoder, specific mappings
byte_from_unicode_gpt2(c)
}
}
}
fn byte_from_unicode_gpt2(c: char) -> u8 {
// Build the inverse of GPT-2's bytes_to_unicode mapping.
// The mapping assigns printable chars to themselves and shifts unprintable bytes.
let u = c as u32;
// Direct ASCII printable + Latin-1 supplement printable ranges map identity
if (0x21..=0x7E).contains(&u) { return u as u8; }
if (0xA1..=0xAC).contains(&u) { return u as u8; }
if (0xAE..=0xFF).contains(&u) { return u as u8; }
// Shifted range: the remaining 68 bytes (0-32, 127-160, 173) get mapped to 256..=323
static SHIFTED_BYTES: &[u8] = &[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,
137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
154, 155, 156, 157, 158, 159, 160, 173,
];
let shifted_start = 256u32;
if u >= shifted_start && u < shifted_start + SHIFTED_BYTES.len() as u32 {
return SHIFTED_BYTES[(u - shifted_start) as usize];
}
// Shouldn't reach here for valid GPT-2 tokenizer
c as u8
}

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pub mod bpe;
pub use bpe::Tokenizer;