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Decomposed Chess Tokenizer.
This tokenizer decomposes each move into 3-4 tokens:
- color+piece token (e.g., "WP", "BN")
- from-square token with suffix "_f" (e.g., "e2_f")
- to-square token with suffix "_t" (e.g., "e4_t")
- optional promotion token (one of "q", "r", "b", "n")
This avoids UNKs for rare moves and makes legality learning easier because the model
always emits explicit squares.
"""
from __future__ import annotations
import json
import os
import re
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessDecomposedTokenizer(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]"
_MOVE_RE = re.compile(r"^[WB][PNBRQK][a-h][1-8][a-h][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
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_full_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,
)
@staticmethod
def _create_full_vocab() -> Dict[str, int]:
special_tokens = [
ChessDecomposedTokenizer.PAD_TOKEN,
ChessDecomposedTokenizer.BOS_TOKEN,
ChessDecomposedTokenizer.EOS_TOKEN,
ChessDecomposedTokenizer.UNK_TOKEN,
]
pieces = ["P", "N", "B", "R", "Q", "K"]
colors = ["W", "B"]
piece_tokens = [f"{c}{p}" for c in colors for p in pieces]
files = "abcdefgh"
ranks = "12345678"
squares = [f"{f}{r}" for f in files for r in ranks]
from_tokens = [f"{sq}_f" for sq in squares]
to_tokens = [f"{sq}_t" for sq in squares]
promo_tokens = ["q", "r", "b", "n"]
tokens = special_tokens + piece_tokens + from_tokens + to_tokens + promo_tokens
return {tok: idx for idx, tok in enumerate(tokens)}
@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]:
raw = text.strip()
if not raw:
return []
parts = raw.split()
out: List[str] = []
for part in parts:
if part in {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}:
out.append(part)
continue
if not self._MOVE_RE.match(part):
out.append(self.UNK_TOKEN)
continue
color = part[0]
piece = part[1]
from_sq = part[2:4]
to_sq = part[4:6]
out.append(f"{color}{piece}")
out.append(f"{from_sq}_f")
out.append(f"{to_sq}_t")
if "=" in part:
promo_idx = part.find("=")
if promo_idx != -1 and promo_idx + 1 < len(part):
promo = part[promo_idx + 1].lower()
if promo in {"q", "r", "b", "n"}:
out.append(promo)
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:
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,)
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