File size: 11,381 Bytes
f3100e5 | 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 | """
Custom Chess Tokenizer for the Chess Challenge.
This tokenizer supports TWO tokenization modes:
1) tokenization_mode="move" (original)
- Each move is a single token using the extended UCI notation
from the Lichess dataset (e.g., WPe2e4, BNg8f6, WPe7e8=Q(x+), ...).
- Vocabulary is usually built from the dataset (frequency threshold).
2) tokenization_mode="uci_square" (recommended for good legal-move performance with small vocab)
- Each move is decomposed into 3 tokens:
[from_square, to_square, promotion_or_-]
Example:
"WPe2e4" -> ["e2", "e4", "-"]
"WPe7e8=Q(+)" -> ["e7", "e8", "q"]
- Fixed vocabulary that can express ANY UCI move:
specials (4) + squares (64) + promo tokens (5) = 73 tokens.
Why uci_square helps:
- You can keep vocab tiny (70-150 range) WITHOUT losing expressivity,
so the model can still output any move.
"""
from __future__ import annotations
import json
import os
import re
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
"""
A custom tokenizer for chess moves.
- "move" mode: extended-uci move tokens like "WPe2e4"
- "uci_square" mode: squares + promotion tokens
"""
model_input_names = ["input_ids", "attention_mask"]
vocab_files_names = {"vocab_file": "vocab.json"}
# Special tokens
PAD_TOKEN = "[PAD]"
BOS_TOKEN = "[BOS]"
EOS_TOKEN = "[EOS]"
UNK_TOKEN = "[UNK]"
def __init__(
self,
vocab_file: Optional[str] = None,
vocab: Optional[Dict[str, int]] = None,
**kwargs,
):
"""
Initialize the chess tokenizer.
Args:
vocab_file: Path to a JSON file containing the vocabulary mapping.
vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
kwargs:
- tokenization_mode: "move" (default) or "uci_square"
- plus usual HF tokenizer kwargs
"""
# Initialize special tokens
self._pad_token = self.PAD_TOKEN
self._bos_token = self.BOS_TOKEN
self._eos_token = self.EOS_TOKEN
self._unk_token = self.UNK_TOKEN
# Read tokenization_mode from kwargs (and keep it for save/load)
tokenization_mode = kwargs.pop("tokenization_mode", "move")
if tokenization_mode not in ("move", "uci_square"):
raise ValueError(f"Unknown tokenization_mode={tokenization_mode!r}")
self.tokenization_mode = tokenization_mode
# Remove any duplicate special-token entries passed through kwargs
# to avoid "multiple values for keyword" errors when loading from disk.
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
# Load or create vocabulary
if vocab is not None:
self._vocab = vocab
elif vocab_file is not None and os.path.exists(vocab_file):
with open(vocab_file, "r", encoding="utf-8") as f:
self._vocab = json.load(f)
else:
# Create a minimal vocabulary with just special tokens
# (you should build from dataset or use build_uci_square_vocab)
self._vocab = self._create_default_vocab()
# Create reverse mapping
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
# Ensure tokenization_mode is saved in tokenizer_config.json
kwargs["tokenization_mode"] = self.tokenization_mode
# Call parent init AFTER setting up vocab
super().__init__(
pad_token=self._pad_token,
bos_token=self._bos_token,
eos_token=self._eos_token,
unk_token=self._unk_token,
**kwargs,
)
def _create_default_vocab(self) -> Dict[str, int]:
"""
Minimal default vocabulary with just special tokens.
"""
special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
vocab = {token: idx for idx, token in enumerate(special_tokens)}
return vocab
@classmethod
def build_vocab_from_iterator(
cls,
iterator,
min_frequency: int = 1,
) -> "ChessTokenizer":
"""
Build a "move" tokenizer vocabulary from an iterator of game strings.
Args:
iterator: yields game strings (space-separated moves).
min_frequency: minimum frequency for a token to be included.
Returns:
ChessTokenizer(tokenization_mode="move") with the built vocabulary.
"""
from collections import Counter
token_counts = Counter()
for game in iterator:
moves = game.strip().split()
token_counts.update(moves)
tokens = [token for token, count in token_counts.items() if count >= min_frequency]
tokens = sorted(tokens)
special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
return cls(vocab=vocab, tokenization_mode="move")
@classmethod
def build_vocab_from_dataset(
cls,
dataset_name: str = "dlouapre/lichess_2025-01_1M",
split: str = "train",
column: str = "text",
min_frequency: int = 500,
max_samples: Optional[int] = 100000,
) -> "ChessTokenizer":
"""
Build a "move" tokenizer vocabulary from a Hugging Face dataset.
Args:
dataset_name: dataset on HF Hub.
split: dataset split.
column: column containing game strings.
min_frequency: minimum frequency for a token to be included.
max_samples: max number of samples to process.
Returns:
ChessTokenizer(tokenization_mode="move") with the built vocabulary.
"""
from datasets import load_dataset
dataset = load_dataset(dataset_name, split=split)
if max_samples is not None:
dataset = dataset.select(range(min(max_samples, len(dataset))))
def game_iterator():
for example in dataset:
yield example[column]
return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
@classmethod
def build_uci_square_vocab(cls) -> "ChessTokenizer":
"""
Build a fixed tiny vocab that can express ANY UCI move using 3 tokens:
[from_square, to_square, promotion_or_-].
Vocab:
- 4 specials
- 64 squares (a1..h8)
- 5 promo tokens: "-", "q", "r", "b", "n"
Total = 73 tokens.
"""
special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
files = "abcdefgh"
ranks = "12345678"
squares = [f"{f}{r}" for r in ranks for f in files] # 64
promo = ["-", "q", "r", "b", "n"] # 5
vocab = {tok: i for i, tok in enumerate(special + squares + promo)}
return cls(vocab=vocab, tokenization_mode="uci_square")
@property
def vocab_size(self) -> int:
return len(self._vocab)
def get_vocab(self) -> Dict[str, int]:
return dict(self._vocab)
def _tokenize(self, text: str) -> List[str]:
"""
Tokenize a string.
- mode="move": split on spaces (original dataset tokens like "WPe2e4").
- mode="uci_square": each dataset move token -> [from_sq, to_sq, promo_or_-]
Example: "WPe2e4" -> ["e2", "e4", "-"]
"WPe7e8=Q" -> ["e7", "e8", "q"]
"""
tokens = text.strip().split()
if self.tokenization_mode != "uci_square":
return tokens
out: List[str] = []
for tok in tokens:
# Keep special tokens as-is if they appear in text
if tok in self._vocab:
out.append(tok)
continue
# Typical dataset format:
# [W|B][Piece][from_sq][to_sq]... possibly "(x)" "(+)" "(o)" "=Q" etc.
# Examples:
# WPe2e4
# BNg8f6
# WPe7e8=Q(+)
# WPe5d6(x)
if len(tok) >= 6 and tok[0] in ("W", "B"):
from_sq = tok[2:4]
to_sq = tok[4:6]
if re.fullmatch(r"[a-h][1-8]", from_sq) and re.fullmatch(r"[a-h][1-8]", to_sq):
promo = "-"
if "=" in tok:
i = tok.index("=")
if i + 1 < len(tok):
p = tok[i + 1].lower()
if p in ("q", "r", "b", "n"):
promo = p
out.extend([from_sq, to_sq, promo])
continue
# Fallback: find two squares anywhere in token
squares = re.findall(r"[a-h][1-8]", tok)
if len(squares) >= 2:
promo = "-"
m = re.search(r"[=]?([qrbnQRBN])", tok)
if m:
promo = m.group(1).lower()
out.extend([squares[0], squares[1], promo])
else:
out.append(self.UNK_TOKEN)
return out
def _convert_token_to_id(self, token: str) -> int:
return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
def _convert_id_to_token(self, index: int) -> str:
return self._ids_to_tokens.get(index, self.UNK_TOKEN)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
# Filter out special tokens for cleaner output
special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
return " ".join(t for t in tokens if t not in special)
def save_vocabulary(
self,
save_directory: str,
filename_prefix: Optional[str] = None,
) -> tuple:
if not os.path.isdir(save_directory):
os.makedirs(save_directory, exist_ok=True)
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + "vocab.json",
)
with open(vocab_file, "w", encoding="utf-8") as f:
json.dump(self._vocab, f, ensure_ascii=False, indent=2)
return (vocab_file,)
def count_vocab_from_dataset(
dataset_name: str = "dlouapre/lichess_2025-01_1M",
split: str = "train",
column: str = "text",
max_samples: Optional[int] = 10000,
) -> Dict[str, int]:
"""
Count token frequencies in a dataset (useful for vocabulary analysis).
"""
from collections import Counter
from datasets import load_dataset
dataset = load_dataset(dataset_name, split=split)
if max_samples is not None:
dataset = dataset.select(range(min(max_samples, len(dataset))))
token_counts = Counter()
for example in dataset:
moves = example[column].strip().split()
token_counts.update(moves)
return dict(token_counts)
|