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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
- Promotion: (Q)=queen, (R)=rook, (B)=bishop, (N)=knight
New token strategy:
- we only retain the squares involved in the move and the promotion piece if any
- everything else (piece type, capture flag, check flag, etc.) is discarded
- the vocabulary size is thus minimal (72 tokens): 64 squares + 4 promotion pieces + 4 special tokens
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Dict, List, Optional
import re
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids", "attention_mask"]
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,
):
self._pad_token = self.PAD_TOKEN
self._bos_token = self.BOS_TOKEN
self._eos_token = self.EOS_TOKEN
self._unk_token = self.UNK_TOKEN
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
self.token_pattern = re.compile(r'[a-h][1-8]|[qrbn]')
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,
)
def _create_default_vocab(self) -> Dict[str, int]:
special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
vocab = {token: idx for idx, token in enumerate(special_tokens)}
idx = len(vocab)
for f in 'abcdefgh':
for r in '12345678':
vocab[f"{f}{r}"] = idx
idx += 1
for p in ['q', 'r', 'b', 'n']:
vocab[p] = idx
idx += 1
return vocab
def _tokenize(self, text: str) -> List[str]:
text = (text.replace("(Q)", "q")
.replace("(R)", "r")
.replace("(B)", "b")
.replace("(N)", "n"))
return self.token_pattern.findall(text)
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:
special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
clean_tokens = [t for t in tokens if t not in special]
output = []
for token in clean_tokens:
if token in ['q', 'r', 'b', 'n'] and output:
output[-1] += token
elif output and len(output[-1]) == 2 and output[-1][0] in 'abcdefgh':
output[-1] += token
else:
output.append(token)
return " ".join(output)
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,)
@classmethod
def build_vocab_from_iterator(cls, iterator, min_frequency=1):
return cls()
@classmethod
def build_vocab_from_dataset(cls, **kwargs):
return cls()
@property
def vocab_size(self) -> int:
return len(self._vocab)
def get_vocab(self) -> Dict[str, int]:
return dict(self._vocab) |