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Custom Chess Tokenizer for the Chess Challenge with TRM optimizations.
This tokenizer treats each move as a single token using the extended UCI notation.
Optimized for smaller vocabulary size to work better with TRM's tiny networks.
"""
from __future__ import annotations
import json
import os
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
"""
A custom tokenizer for chess moves using extended UCI notation.
Optimized for TRM with aggressive vocabulary pruning.
"""
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."""
# Remove special token kwargs if they exist (to avoid duplicates)
kwargs.pop('pad_token', None)
kwargs.pop('bos_token', None)
kwargs.pop('eos_token', None)
kwargs.pop('unk_token', None)
self._pad_token = self.PAD_TOKEN
self._bos_token = self.BOS_TOKEN
self._eos_token = self.EOS_TOKEN
self._unk_token = self.UNK_TOKEN
# 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:
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."""
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."""
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_dataset(
cls,
dataset_name: str = "dlouapre/lichess_2025-01_1M",
split: str = "train",
column: str = "text",
min_frequency: int = 1000, # Increased for TRM (smaller vocab)
max_samples: Optional[int] = 200000, # More samples for better coverage
) -> "ChessTokenizer":
"""
Build tokenizer vocabulary optimized for TRM.
Uses higher min_frequency to create a smaller, more focused vocabulary.
"""
from datasets import load_dataset
dataset = load_dataset(dataset_name, split=split, cache_dir=os.environ["HF_DATASETS_CACHE"])
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
)
@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."""
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."""
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."""
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,) |