File size: 8,603 Bytes
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Uncensored: no content filtering. Vocab size auto-adjusts per hardware tier.
Encodes/decodes text <-> token IDs. Save/load to disk.
"""
from __future__ import annotations
import json
import logging
import os
import re
from collections import Counter, defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
logger = logging.getLogger(__name__)
# Special tokens
PAD_TOKEN = "<pad>"
EOS_TOKEN = "<eos>"
BOS_TOKEN = "<bos>"
UNK_TOKEN = "<unk>"
SPECIAL_TOKENS = [PAD_TOKEN, BOS_TOKEN, EOS_TOKEN, UNK_TOKEN]
PAD_ID = 0
BOS_ID = 1
EOS_ID = 2
UNK_ID = 3
class BPETokenizer:
"""Byte-Pair Encoding tokenizer trained from raw text.
Training is simple and fast:
1. Split text into words (whitespace + punctuation)
2. Start with single characters as tokens
3. Repeatedly merge most frequent adjacent pairs
4. Stop when vocab_size reached or no more merges
No content filtering — uncensored by design.
"""
def __init__(self, vocab_size: int = 4096) -> None:
self.target_vocab_size = vocab_size
self.vocab: Dict[str, int] = {}
self.inv_vocab: Dict[int, str] = {}
self.merges: List[Tuple[str, str]] = []
self._word_cache: Dict[str, List[int]] = {}
# Initialize with special tokens
for i, tok in enumerate(SPECIAL_TOKENS):
self.vocab[tok] = i
self.inv_vocab[i] = tok
@property
def actual_vocab_size(self) -> int:
return len(self.vocab)
def _tokenize_words(self, text: str) -> List[str]:
"""Split text into word-level tokens with punctuation attached."""
return re.findall(r"\S+|\s+", text)
def _get_pairs(self, word_tokens: List[str]) -> List[Tuple[str, str]]:
"""Get all adjacent pairs in a word's token list."""
return [(word_tokens[i], word_tokens[i + 1]) for i in range(len(word_tokens) - 1)]
def train(self, texts: List[str] | str, verbose: bool = False) -> None:
"""Train BPE tokenizer on raw text.
Args:
texts: list of text strings or a single string
verbose: print progress
"""
if isinstance(texts, str):
texts = [texts]
# Build word frequency counter
word_freq: Counter = Counter()
for text in texts:
words = self._tokenize_words(text)
word_freq.update(words)
# Initialize each word as a sequence of characters
word_splits: Dict[str, List[str]] = {}
for word in word_freq:
word_splits[word] = list(word)
# Build initial vocab from characters
char_set: set[str] = set()
for word in word_splits:
char_set.update(word_splits[word])
for ch in sorted(char_set):
if ch not in self.vocab:
idx = len(self.vocab)
self.vocab[ch] = idx
self.inv_vocab[idx] = ch
# BPE merge loop
num_merges = self.target_vocab_size - len(self.vocab)
if num_merges <= 0:
logger.info("Vocab size already at target (%d), no merges needed", len(self.vocab))
return
for merge_idx in range(num_merges):
# Count pair frequencies
pair_freq: Counter = Counter()
for word, freq in word_freq.items():
tokens = word_splits[word]
for pair in self._get_pairs(tokens):
pair_freq[pair] += freq
if not pair_freq:
break
# Find most frequent pair
best_pair = pair_freq.most_common(1)[0][0]
best_freq = pair_freq[best_pair]
if best_freq < 2:
break
# Create merged token
merged = best_pair[0] + best_pair[1]
if merged in self.vocab:
break
idx = len(self.vocab)
self.vocab[merged] = idx
self.inv_vocab[idx] = merged
self.merges.append(best_pair)
# Apply merge to all words
for word in word_splits:
tokens = word_splits[word]
new_tokens: List[str] = []
i = 0
while i < len(tokens):
if i < len(tokens) - 1 and tokens[i] == best_pair[0] and tokens[i + 1] == best_pair[1]:
new_tokens.append(merged)
i += 2
else:
new_tokens.append(tokens[i])
i += 1
word_splits[word] = new_tokens
if verbose and (merge_idx + 1) % 100 == 0:
logger.info("BPE merge %d/%d: '%s' (freq=%d), vocab=%d",
merge_idx + 1, num_merges, merged, best_freq, len(self.vocab))
# Build word cache for fast encoding
self._rebuild_cache(word_splits)
logger.info("BPE training complete: %d tokens, %d merges", len(self.vocab), len(self.merges))
def _rebuild_cache(self, word_splits: Dict[str, List[str]]) -> None:
"""Build encoding cache from trained word splits."""
self._word_cache.clear()
for word, tokens in word_splits.items():
ids = [self.vocab.get(t, UNK_ID) for t in tokens]
self._word_cache[word] = ids
def encode(self, text: str, add_bos: bool = False, add_eos: bool = False) -> List[int]:
"""Encode text to token IDs."""
ids: List[int] = []
if add_bos:
ids.append(BOS_ID)
words = self._tokenize_words(text)
for word in words:
if word in self._word_cache:
ids.extend(self._word_cache[word])
else:
# Apply merges greedily
tokens = list(word)
changed = True
while changed and len(tokens) > 1:
changed = False
best_idx = -1
best_rank = len(self.merges)
for i in range(len(tokens) - 1):
pair = (tokens[i], tokens[i + 1])
if pair in self._merge_ranks:
rank = self._merge_ranks[pair]
if rank < best_rank:
best_rank = rank
best_idx = i
if best_idx >= 0:
merged = tokens[best_idx] + tokens[best_idx + 1]
tokens = tokens[:best_idx] + [merged] + tokens[best_idx + 2:]
changed = True
ids.extend(self.vocab.get(t, UNK_ID) for t in tokens)
self._word_cache[word] = [self.vocab.get(t, UNK_ID) for t in tokens]
if add_eos:
ids.append(EOS_ID)
return ids
@property
def _merge_ranks(self) -> Dict[Tuple[str, str], int]:
if not hasattr(self, '_merge_ranks_cache'):
self._merge_ranks_cache = {pair: i for i, pair in enumerate(self.merges)}
return self._merge_ranks_cache
def decode(self, ids: List[int]) -> str:
"""Decode token IDs back to text."""
tokens = []
for tid in ids:
if tid in (PAD_ID, BOS_ID, EOS_ID):
continue
tokens.append(self.inv_vocab.get(tid, UNK_TOKEN))
return "".join(tokens)
def save(self, path: str) -> None:
"""Save tokenizer to disk."""
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
data = {
"vocab_size": len(self.vocab),
"target_vocab_size": self.target_vocab_size,
"vocab": self.vocab,
"merges": [[a, b] for a, b in self.merges],
}
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False)
logger.info("Tokenizer saved to %s (%d tokens)", path, len(self.vocab))
@classmethod
def load(cls, path: str) -> "BPETokenizer":
"""Load tokenizer from disk."""
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
tok = cls(vocab_size=data["target_vocab_size"])
tok.vocab = {k: int(v) for k, v in data["vocab"].items()}
tok.inv_vocab = {v: k for k, v in tok.vocab.items()}
tok.merges = [(a, b) for a, b in data["merges"]]
tok._merge_ranks_cache = {pair: i for i, pair in enumerate(tok.merges)}
logger.info("Tokenizer loaded from %s (%d tokens)", path, len(tok.vocab))
return tok
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