File size: 21,424 Bytes
7a31b53 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 | # """
# Custom Chess Tokenizer for the Chess Challenge.
# This tokenizer treats each move as a single token using the extended UCI notation
# from the Lichess dataset (e.g., WPe2e4, BNg8f6).
# The dataset format uses:
# - W/B prefix for White/Black
# - Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
# - Source and destination squares (e.g., e2e4)
# - Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
# """
#
# from __future__ import annotations
# import json
# import os
# from pathlib import Path
# from typing import Dict, List, Optional
# from transformers import PreTrainedTokenizer
# class ChessTokenizer(PreTrainedTokenizer):
# """
# A custom tokenizer for chess moves using extended UCI notation.
# This tokenizer maps each possible chess move to a unique token ID.
# The vocabulary is built from the training dataset to ensure all moves
# encountered during training have a corresponding token.
# Example:
# >>> tokenizer = ChessTokenizer()
# >>> tokenizer.encode("WPe2e4 BPe7e5")
# [1, 42, 87, 2] # [BOS, e2e4, e7e5, EOS]
# """
# 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: Additional arguments passed to PreTrainedTokenizer.
# """
# # 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
# # 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
# # The full vocabulary should be built from the dataset
# self._vocab = self._create_default_vocab()
# # Create reverse mapping
# self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
# # 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]:
# """
# Create a minimal default vocabulary with just special tokens.
# For the full vocabulary, use `build_vocab_from_dataset()`.
# This minimal vocab is just a placeholder - you should build from data.
# """
# 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 tokenizer vocabulary from an iterator of game strings.
# # Args:
# # iterator: An iterator yielding game strings (space-separated moves).
# # min_frequency: Minimum frequency for a token to be included.
# # Returns:
# # A ChessTokenizer with the built vocabulary.
# # """
# # from collections import Counter
# # token_counts = Counter()
# # for game in iterator:
# # moves = game.strip().split()
# # token_counts.update(moves)
# # # Filter by frequency
# # tokens = [
# # token for token, count in token_counts.items()
# # if count >= min_frequency
# # ]
# # # Sort for reproducibility
# # tokens = sorted(tokens)
# # # Build vocabulary
# # 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)
# @classmethod
# def build_vocab_from_iterator(
# cls,
# iterator,
# vocab_size: int = 1200,
# min_frequency: int = 1,
# ) -> "ChessTokenizer":
# """
# Build a tokenizer vocabulary from an iterator of game strings.
# - Controls final vocab size explicitly via vocab_size.
# - Keeps the most frequent move tokens (best coverage).
# - Uses min_frequency as a floor, but vocab_size is the main control.
# """
# from collections import Counter
# token_counts = Counter()
# for game in iterator:
# moves = game.strip().split()
# token_counts.update(moves)
# # Filter by min_frequency first
# items = [(tok, cnt) for tok, cnt in token_counts.items() if cnt >= min_frequency]
# # Sort by frequency desc, then token for determinism
# items.sort(key=lambda x: (-x[1], x[0]))
# special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
# max_move_tokens = max(0, vocab_size - len(special_tokens))
# move_tokens = [tok for tok, _ in items[:max_move_tokens]]
# vocab = {token: idx for idx, token in enumerate(special_tokens + move_tokens)}
# return cls(vocab=vocab)
# # @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 tokenizer vocabulary from a Hugging Face dataset.
# # Args:
# # dataset_name: Name of the dataset on Hugging Face Hub.
# # split: Dataset split to use.
# # column: Column containing the game strings.
# # min_frequency: Minimum frequency for a token to be included (default: 500).
# # max_samples: Maximum number of samples to process (default: 100k).
# # Returns:
# # A ChessTokenizer 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_vocab_from_dataset(
# cls,
# dataset_name: str = "dlouapre/lichess_2025-01_1M",
# split: str = "train",
# column: str = "text",
# vocab_size: int = 1200,
# min_frequency: int = 1,
# max_samples: Optional[int] = 200000,
# ) -> "ChessTokenizer":
# """
# Build a tokenizer vocabulary from a Hugging Face dataset.
# Args:
# vocab_size: Final vocab size INCLUDING special tokens.
# min_frequency: Minimum count to consider a move (usually 1 is fine).
# max_samples: How many games to scan to build vocab.
# """
# from datasets import load_dataset
# dataset = load_dataset(dataset_name, split=split)
# # if max_samples is not None: # v0&1
# # dataset = dataset.select(range(min(max_samples, len(dataset))))
# if max_samples is not None: # v2
# n = min(max_samples, len(dataset))
# dataset = dataset.shuffle(seed=42).select(range(n))
# def game_iterator():
# for example in dataset:
# yield example[column]
# return cls.build_vocab_from_iterator(
# game_iterator(),
# vocab_size=vocab_size,
# min_frequency=min_frequency,
# )
# @property
# def vocab_size(self) -> int:
# """Return the size of the vocabulary."""
# return len(self._vocab)
# def get_vocab(self) -> Dict[str, int]:
# """Return the vocabulary as a dictionary."""
# return dict(self._vocab)
# def _tokenize(self, text: str) -> List[str]:
# """
# Tokenize a string of moves into a list of tokens.
# Args:
# text: A string of space-separated moves.
# Returns:
# List of move tokens.
# """
# return text.strip().split()
# def _convert_token_to_id(self, token: str) -> int:
# """Convert a token to its ID."""
# return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
# def _convert_id_to_token(self, index: int) -> str:
# """Convert an ID to its token."""
# return self._ids_to_tokens.get(index, self.UNK_TOKEN)
# def convert_tokens_to_string(self, tokens: List[str]) -> str:
# """Convert a list of tokens back to a string."""
# # 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:
# """
# Save the vocabulary to a JSON file.
# Args:
# save_directory: Directory to save the vocabulary.
# filename_prefix: Optional prefix for the filename.
# Returns:
# Tuple containing the path to the saved vocabulary file.
# """
# 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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
# # if token_ids_1 is not None:
# # # Not expected here, but handle gracefully
# # token_ids = token_ids_0 + token_ids_1
# # else:
# # token_ids = token_ids_0
# # return [self.bos_token_id] + token_ids + [self.eos_token_id]
# # def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
# # if already_has_special_tokens:
# # return [1 if t in (self.pad_token_id, self.bos_token_id, self.eos_token_id, self.unk_token_id) else 0 for t in token_ids_0]
# # if token_ids_1 is not None:
# # token_ids = token_ids_0 + token_ids_1
# # else:
# # token_ids = token_ids_0
# # return [1] + [0] * len(token_ids) + [1]
# 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).
# Args:
# dataset_name: Name of the dataset on Hugging Face Hub.
# split: Dataset split to use.
# column: Column containing the game strings.
# max_samples: Maximum number of samples to process.
# Returns:
# Dictionary mapping tokens to their frequencies.
# """
# 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)
"""
Grammar-aware Chess Tokenizer for the Chess Challenge.
Goal: maximize legal move extraction in evaluate.py which searches for
two square patterns ([a-h][1-8]) in the generated text and takes the first two.
Strategy:
- Decompose each move into structured tokens:
- CP_<color><piece> (e.g., CP_WP, CP_BN)
- SQ_<square> (e.g., SQ_e2, SQ_e4)
- EV_<event> (e.g., EV_NONE, EV_X, EV_PLUS, EV_MATE, EV_PROMO_Q, ...)
- SEP (end-of-move marker, decoded as a space)
- Deterministic vocab: no dataset-dependent OOV -> UNK for rare full moves disappears.
"""
from __future__ import annotations
import json
import os
import re
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids", "attention_mask"]
vocab_files_names = {"vocab_file": "vocab.json"}
PAD_TOKEN = "[PAD]"
BOS_TOKEN = "[BOS]"
EOS_TOKEN = "[EOS]"
UNK_TOKEN = "[UNK]"
SEP_TOKEN = "[SEP]" # end-of-move marker (decoded as a space)
_SQUARE_RE = re.compile(r"^[a-h][1-8]$") # positions are in the format xY where x is in [a-h], y in [1-8]
def __init__(
self,
vocab_file: Optional[str] = None,
vocab: Optional[Dict[str, int]] = None,
**kwargs,
):
self._pad_token = self.PAD_TOKEN
self._bos_token = self.BOS_TOKEN
self._eos_token = self.EOS_TOKEN
self._unk_token = self.UNK_TOKEN
self._sep_token = self.SEP_TOKEN
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
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:
self._vocab = self._create_default_vocab()
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
super().__init__(
pad_token=self._pad_token,
bos_token=self._bos_token,
eos_token=self._eos_token,
unk_token=self._unk_token,
**kwargs,
)
#### Vocab
def _create_default_vocab(self) -> Dict[str, int]:
special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.SEP_TOKEN]
# Color+piece (12 tokens)
cp = [f"CP_{c}{p}" for c in "WB" for p in "PNBRQK"]
# Squares (64 tokens)
squares = [f"SQ_{f}{r}" for f in "abcdefgh" for r in "12345678"]
# Events: keep small & canonical (you can extend later)
events = [
"EV_NONE",
"EV_X",
"EV_PLUS",
"EV_MATE",
"EV_XPLUS",
"EV_XMATE",
"EV_O", # kingside castle
"EV_OO", # queenside castle
"EV_PROMO_N",
"EV_PROMO_B",
"EV_PROMO_R",
"EV_PROMO_Q",
"EV_XPROMO_N",
"EV_XPROMO_B",
"EV_XPROMO_R",
"EV_XPROMO_Q",
]
vocab_list = special + cp + squares + events # this vocabulary has size 12 + 64 + 16 + 5 = 97 tokens
return {tok: i for i, tok in enumerate(vocab_list)}
@property
def vocab_size(self) -> int:
return len(self._vocab)
def get_vocab(self) -> Dict[str, int]:
return dict(self._vocab)
#### Core tokenization
def _tokenize(self, text: str) -> List[str]:
"""
Input is a space-separated list of moves in extended UCI, e.g.
"WPe2e4 BPe7e5 ..."
Output is a sequence of structured tokens:
CP_WP SQ_e2 SQ_e4 EV_NONE [SEP] ...
"""
moves = text.strip().split()
tokens: List[str] = []
for mv in moves:
toks = self._tokenize_one_move(mv)
tokens.extend(toks)
tokens.append(self.SEP_TOKEN)
return tokens
def _tokenize_one_move(self, mv: str) -> List[str]:
# Minimal sanity: needs at least "WPe2e4" length 6
if len(mv) < 6:
return [self.UNK_TOKEN]
color = mv[0] # W/B
piece = mv[1] # P/N/B/R/Q/K
from_sq = mv[2:4]
to_sq = mv[4:6]
suffix = mv[6:] # can include capture/check/mate/castle/promo etc. => cf events tokens
cp_tok = f"CP_{color}{piece}"
from_tok = f"SQ_{from_sq}"
to_tok = f"SQ_{to_sq}"
if cp_tok not in self._vocab or from_tok not in self._vocab or to_tok not in self._vocab:
return [self.UNK_TOKEN]
ev_tok = self._event_token(piece, from_sq, to_sq, suffix)
return [cp_tok, from_tok, to_tok, ev_tok]
def _event_token(self, piece: str, from_sq: str, to_sq: str, suffix: str) -> str:
"""
Canonicalize suffix into one of EV_* tokens.
Keep it simple: evaluator does not need these, but they help learning.
"""
# Castling (dataset uses (o)/(O))
if "(o)" in suffix: # kingside
return "EV_O"
if "(O)" in suffix: # queenside
return "EV_OO"
capture = "(x" in suffix # covers (x), (x+), (x+*), (x+) etc.
mate = "+*" in suffix
check = "(+)" in suffix or "(x+)" in suffix or "(+)" in suffix # tolerant
promo = None
m = re.search(r"=([NBRQ])", suffix)
if m:
promo = m.group(1)
if promo is not None:
base = f"EV_PROMO_{promo}"
if capture:
base = f"EV_XPROMO_{promo}"
return base if base in self._vocab else "EV_NONE"
if capture and mate:
return "EV_XMATE"
if capture and check:
return "EV_XPLUS"
if capture:
return "EV_X"
if mate:
return "EV_MATE"
if check:
return "EV_PLUS"
return "EV_NONE"
#### Conversions
def _convert_token_to_id(self, token: str) -> int:
return self._vocab.get(token, self._vocab[self.UNK_TOKEN])
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:
"""
Decode to a string that contains squares early and clearly.
We intentionally emit raw squares like "e2" "e4" separated by spaces,
so evaluate.py will reliably extract them.
"""
out: List[str] = []
special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
for tok in tokens:
if tok in special:
continue
if tok == self.SEP_TOKEN:
out.append(" ")
continue
if tok.startswith("SQ_"):
out.append(tok[3:]) # "SQ_e2" -> "e2"
out.append(" ")
continue
if tok.startswith("CP_"):
# Optional: keep CP to help model conditioning; does not hurt extraction
out.append(tok[3:]) # "CP_WP" -> "WP"
out.append(" ")
continue
if tok.startswith("EV_"):
# Optional: keep events; ensure no squares are embedded here
out.append(tok[3:]) # "EV_X" -> "X"
out.append(" ")
continue
# fallback
out.append(tok)
out.append(" ")
return "".join(out).strip()
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,)
|