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"""
Custom Chess Tokenizer for the Chess Challenge.
This tokenizer uses a STRUCTURED approach to tokenize chess moves, breaking down
each move into its components to help the model learn legal chess patterns.
The dataset format uses extended UCI notation:
- 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
Instead of treating each move as a single token (which creates thousands of tokens),
we tokenize the COMPONENTS:
- Color tokens: W, B
- Piece tokens: P, N, B, R, Q, K
- Square tokens: a1, a2, ..., h8 (64 squares)
- Suffix tokens: (x), (+), (+*), (o), (O), =Q, =R, =B, =N
This gives ~80 tokens total, helping the model learn:
1. Valid squares on the board
2. Which pieces can make which types of moves
3. The structure of legal chess moves
"""
from __future__ import annotations
import json
import os
import re
from pathlib import Path
from typing import Dict, List, Optional, Tuple
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
"""
A structured tokenizer for chess moves using component-based tokenization.
Instead of treating each move as a single token, this tokenizer breaks moves
into their structural components (color, piece, from-square, to-square, suffix).
This smaller vocabulary helps the model learn valid chess patterns.
Vocabulary (~80 tokens):
- Special: [PAD], [BOS], [EOS], [UNK]
- Colors: W, B
- Pieces: P, N, B, R, Q, K
- Squares: a1-h8 (64 tokens)
- Suffixes: (x), (+), (+*), (o), (O), =Q, =R, =B, =N
Example:
>>> tokenizer = ChessTokenizer()
>>> tokens = tokenizer.tokenize("WPe2e4 BPe7e5")
>>> print(tokens)
['W', 'P', 'e2', 'e4', 'B', 'P', 'e7', 'e5']
"""
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]"
# Chess components
COLORS = ["W", "B"]
PIECES = ["P", "N", "B", "R", "Q", "K"]
FILES = ["a", "b", "c", "d", "e", "f", "g", "h"]
RANKS = ["1", "2", "3", "4", "5", "6", "7", "8"]
SQUARES = [f + r for f in ["a", "b", "c", "d", "e", "f", "g", "h"]
for r in ["1", "2", "3", "4", "5", "6", "7", "8"]] # a1, a2, ..., h8
SUFFIXES = ["(x)", "(+)", "(+*)", "(o)", "(O)", "=Q", "=R", "=B", "=N"]
# Regex pattern to parse extended UCI moves
# Format: [W|B][Piece][from_sq][to_sq][optional: =PromoPiece][optional: suffix]
MOVE_PATTERN = re.compile(
r'^([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(=[QRBN])?(\([xo+*O]+\))?$'
)
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 the structured vocabulary
self._vocab = self._create_structured_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_structured_vocab(self) -> Dict[str, int]:
"""
Create the structured vocabulary with all chess components.
This creates a fixed vocabulary of ~85 tokens covering all possible
chess move components.
"""
tokens = []
# Special tokens first
tokens.extend([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
# Colors
tokens.extend(self.COLORS)
# Pieces
tokens.extend(self.PIECES)
# Squares (64 tokens)
tokens.extend(self.SQUARES)
# Suffixes
tokens.extend(self.SUFFIXES)
# Build vocabulary
vocab = {token: idx for idx, token in enumerate(tokens)}
return vocab
def _create_default_vocab(self) -> Dict[str, int]:
"""Alias for _create_structured_vocab for compatibility."""
return self._create_structured_vocab()
def _parse_move(self, move: str) -> List[str]:
"""
Parse a single move into its component tokens.
Args:
move: A move in extended UCI format (e.g., "WPe2e4", "BNg8f6(x)").
Returns:
List of component tokens.
"""
move = move.strip()
if not move:
return []
# Handle special tokens
if move in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
return [move]
# Try to match the move pattern
match = self.MOVE_PATTERN.match(move)
if match:
color, piece, from_sq, to_sq, promotion, suffix = match.groups()
tokens = [color, piece, from_sq, to_sq]
if promotion:
tokens.append(promotion)
if suffix:
tokens.append(suffix)
return tokens
# If pattern doesn't match, try to extract what we can
# This handles edge cases and malformed moves gracefully
tokens = []
i = 0
# Color (W or B)
if i < len(move) and move[i] in self.COLORS:
tokens.append(move[i])
i += 1
# Piece (P, N, B, R, Q, K)
if i < len(move) and move[i] in self.PIECES:
tokens.append(move[i])
i += 1
# From square (e.g., e2)
if i + 1 < len(move) and move[i:i+2] in self.SQUARES:
tokens.append(move[i:i+2])
i += 2
# To square (e.g., e4)
if i + 1 < len(move) and move[i:i+2] in self.SQUARES:
tokens.append(move[i:i+2])
i += 2
# Promotion (e.g., =Q)
if i + 1 < len(move) and move[i:i+2] in self.SUFFIXES:
tokens.append(move[i:i+2])
i += 2
# Suffix (e.g., (x), (+), (+*), (o), (O))
remaining = move[i:]
if remaining in self.SUFFIXES:
tokens.append(remaining)
elif remaining:
# Try to find a matching suffix
for suffix in self.SUFFIXES:
if remaining.startswith(suffix):
tokens.append(suffix)
break
# If we couldn't parse anything, return UNK
if not tokens:
return [self.UNK_TOKEN]
return tokens
@classmethod
def build_vocab_from_iterator(
cls,
iterator,
min_frequency: int = 1,
) -> "ChessTokenizer":
"""
Build a tokenizer (for compatibility - vocab is fixed).
The structured tokenizer has a fixed vocabulary, so this method
simply returns a new tokenizer instance.
Args:
iterator: An iterator yielding game strings (ignored for structured vocab).
min_frequency: Minimum frequency (ignored for structured vocab).
Returns:
A ChessTokenizer with the structured vocabulary.
"""
return cls()
@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 (for compatibility - vocab is fixed).
The structured tokenizer has a fixed vocabulary covering all valid
chess move components, so no dataset scanning is needed.
Args:
dataset_name: Name of the dataset (ignored).
split: Dataset split (ignored).
column: Column name (ignored).
min_frequency: Minimum frequency (ignored).
max_samples: Maximum samples (ignored).
Returns:
A ChessTokenizer with the structured vocabulary.
"""
return cls()
@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 component tokens.
Args:
text: A string of space-separated moves.
Returns:
List of component tokens.
"""
tokens = []
moves = text.strip().split()
for move in moves:
move_tokens = self._parse_move(move)
tokens.extend(move_tokens)
return tokens
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 move string.
Reconstructs moves from component tokens by grouping them appropriately.
"""
# Filter out special tokens
special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
tokens = [t for t in tokens if t not in special]
if not tokens:
return ""
# Reconstruct moves from components
result = []
current_move = []
for token in tokens:
# Start of a new move (color token)
if token in self.COLORS:
if current_move:
result.append("".join(current_move))
current_move = [token]
else:
current_move.append(token)
# Don't forget the last move
if current_move:
result.append("".join(current_move))
return " ".join(result)
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 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).
With the structured tokenizer, this counts component frequencies.
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
tokenizer = ChessTokenizer()
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:
tokens = tokenizer._tokenize(example[column])
token_counts.update(tokens)
return dict(token_counts)
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