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chess-mtanre-v2 / tokenizer.py
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Mtanre
Chess Challenge submission by Mtanre
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"""
Chess Move Tokenizer - Component-based approach.
This tokenizer decomposes chess moves into atomic components for efficient
representation. Each move is broken down into: color, piece type, source square,
destination square, and optional annotations (capture, check, promotion, etc.).
The vocabulary is built from atomic components rather than full moves, which
allows for better generalization and a smaller vocabulary size.
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Dict, List, Optional
import re
from transformers import PreTrainedTokenizer
# Regular expression to parse extended UCI move notation
# Format: [W|B][Piece][from_square][to_square][optional_suffixes]
MOVE_PATTERN = re.compile(
r"^(?P<side>[WB])"
r"(?P<piece>[PNBRQK])"
r"(?P<src>[a-h][1-8])"
r"(?P<dst>[a-h][1-8])"
r"(?P<suffix>.*)$"
)
class ChessTokenizer(PreTrainedTokenizer):
"""
Component-based chess move tokenizer.
Instead of treating each complete move as a single token, this tokenizer
breaks down moves into atomic components (color, piece, squares, annotations).
This approach results in a much smaller vocabulary while maintaining
the ability to represent all possible chess moves.
Example usage:
>>> tokenizer = ChessTokenizer()
>>> tokens = tokenizer._tokenize("WPe2e4 BPe7e5")
>>> # Returns: ['[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]"
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.
"""
# Set up special token strings
self._pad_token = self.PAD_TOKEN
self._bos_token = self.BOS_TOKEN
self._eos_token = self.EOS_TOKEN
self._unk_token = self.UNK_TOKEN
# Clean kwargs to prevent conflicts with special tokens
# This avoids errors when loading saved tokenizers
for token_key in ["pad_token", "bos_token", "eos_token", "unk_token"]:
kwargs.pop(token_key, None)
# Initialize vocabulary from provided source or create default
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:
# Fallback: create minimal vocabulary with component tokens
self._vocab = self._create_default_vocab()
# Build reverse lookup: token_id -> token_string
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]:
"""
Construct the default component-based vocabulary.
Creates a vocabulary from atomic chess move components:
- Special tokens (padding, start, end, unknown)
- Color indicators (White/Black)
- Piece types (Pawn, Knight, Bishop, Rook, Queen, King)
- Board squares (64 squares: a1-h8)
- Move annotations (capture, check, checkmate, castling, promotions)
"""
# Core special tokens
special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
# Player color indicators
color_tokens = ["[W]", "[B]"]
# Chess piece types (note: Bishop uses [BISHOP] to avoid conflict with [B])
piece_tokens = ["[P]", "[N]", "[BISHOP]", "[R]", "[Q]", "[K]"]
# All 64 chess board squares
square_tokens = [f"[{file}{rank}]" for rank in "12345678" for file in "abcdefgh"]
# Move annotations: capture, check, checkmate, castling, promotions
annotation_tokens = ["[x]", "[+]", "[#]", "[O-O]", "[O-O-O]",
"[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]"]
# Combine all components into vocabulary
all_tokens = special_tokens + color_tokens + piece_tokens + square_tokens + annotation_tokens
vocab = {token: idx for idx, token in enumerate(all_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.
"""
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 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.
"""
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]:
"""
Decompose chess moves into component tokens.
Parses each move and breaks it down into atomic components:
color, piece, source square, destination square, and annotations.
Args:
text: Space-separated sequence of moves in extended UCI format.
Returns:
List of component tokens representing the moves.
"""
token_list: List[str] = []
move_sequence = text.strip().split()
for move_str in move_sequence:
# Handle queenside castling (long castling)
if "O-O-O" in move_str:
player_color = "[W]" if move_str.startswith("W") else "[B]"
token_list.append(player_color)
token_list.append("[O-O-O]")
continue
# Handle kingside castling (short castling)
if "O-O" in move_str:
player_color = "[W]" if move_str.startswith("W") else "[B]"
token_list.append(player_color)
token_list.append("[O-O]")
continue
# Parse standard moves using regex
match = MOVE_PATTERN.match(move_str)
if not match:
token_list.append(self.UNK_TOKEN)
continue
# Extract move components
player_color = "[W]" if match.group("side") == "W" else "[B]"
piece_type = match.group("piece")
from_square = match.group("src")
to_square = match.group("dst")
move_annotations = match.group("suffix") or ""
# Add color and piece
token_list.append(player_color)
# Handle Bishop separately (B conflicts with Black)
if piece_type == "B":
token_list.append("[BISHOP]")
else:
token_list.append(f"[{piece_type}]")
# Add squares
token_list.append(f"[{from_square}]")
token_list.append(f"[{to_square}]")
# Process annotations
if "x" in move_annotations:
token_list.append("[x]") # Capture
# Check/checkmate (checkmate takes priority)
if "*" in move_annotations:
token_list.append("[#]") # Checkmate
elif "+" in move_annotations:
token_list.append("[+]") # Check
# Promotion
if "=" in move_annotations:
promo_idx = move_annotations.find("=")
if promo_idx != -1 and promo_idx + 1 < len(move_annotations):
promoted_piece = move_annotations[promo_idx + 1].upper()
if promoted_piece in ("Q", "R", "B", "N"):
token_list.append(f"[prom_{promoted_piece}]")
return token_list
def _convert_token_to_id(self, token: str) -> int:
"""Map token string to its vocabulary ID."""
return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
def _convert_id_to_token(self, index: int) -> str:
"""Map vocabulary ID back to token string."""
return self._ids_to_tokens.get(index, self.UNK_TOKEN)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Reconstruct string from token list, filtering special tokens."""
special_token_set = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
return " ".join(t for t in tokens if t not in special_token_set)
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]:
"""
Analyze token frequency distribution in the dataset.
Useful for understanding which components appear most frequently
and for vocabulary size planning.
Args:
dataset_name: HuggingFace dataset identifier.
split: Which dataset split to analyze.
column: Column name containing the game sequences.
max_samples: Limit number of samples for faster analysis.
Returns:
Frequency dictionary: token -> count.
"""
from collections import Counter
from datasets import load_dataset
# Load dataset
dataset = load_dataset(dataset_name, split=split)
# Limit samples if requested
if max_samples is not None:
dataset = dataset.select(range(min(max_samples, len(dataset))))
# Count component frequencies
tokenizer = ChessTokenizer()
frequency_counter = Counter()
for sample in dataset:
component_tokens = tokenizer._tokenize(sample[column])
frequency_counter.update(component_tokens)
return dict(frequency_counter)