Chess Challenge submission by Chiensaucisse67

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "ChessForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  },
   "bos_token_id": 1,
   "dropout": 0.1,
   "dtype": "float32",

tokenizer.py

@@ -0,0 +1,565 @@
+"""
+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
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from token import OP
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+import re
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    A custom tokenizer for chess moves using extended UCI notation.
+    
+    This tokenizer maps each possible chess move to a unique token ID.
+    The vocabulary is built from the training dataset to ensure all moves
+    encountered during training have a corresponding token.
+    
+    Example:
+        >>> tokenizer = ChessTokenizer()
+        >>> tokenizer.encode("WPe2e4 BPe7e5")
+        [1, 42, 87, 2]  # [BOS, e2e4, e7e5, EOS]
+    """
+    
+    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.
+        """
+        # 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 a minimal vocabulary with just special tokens
+            # The full vocabulary should be built from the dataset
+            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.
+        
+        For the full vocabulary, use `build_vocab_from_dataset()`.
+        This minimal vocab is just a placeholder - you should build from data.
+        """
+        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.
+        
+        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.
+        """
+        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 = 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.
+        """
+        from datasets import load_dataset
+        
+        dataset = load_dataset(dataset_name, split=split)
+        
+        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.
+        
+        Args:
+            text: A string of space-separated moves.
+        
+        Returns:
+            List of move 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."""
+        # Filter out special tokens for cleaner output
+        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.
+        
+        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).
+    
+    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
+    
+    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:
+        moves = example[column].strip().split()
+        token_counts.update(moves)
+    
+    return dict(token_counts)
+
+
+
+class CoordinateTokenizer(ChessTokenizer):
+    def __init__(self, **kwargs):
+        squares = [f"{f}{r}" for f in "abcdefgh" for r in "12345678"]
+        promotions = ["q", "r", "b", "n"]
+        control = ["[PAD]", "[BOS]", "[EOS]", "[UNK]"]
+        vocab_list = control + squares + promotions
+        self._vocab = {t: i for i, t in enumerate(vocab_list)}
+        self._ids_to_token = {i: t for t, i in self._vocab.items()}
+
+        super().__init__(
+            vocab=self._vocab, 
+            pad_token="[PAD]", 
+            bos_token="[BOS]", 
+            eos_token="[EOS]", 
+            unk_token="[UNK]",
+            truncation_side="left",
+            **kwargs
+        )
+
+    def _tokenize(self, text: str) -> List[str]:
+        raw_moves = text.strip().split()
+        tokens = []
+        for raw_move in raw_moves:
+            squares = re.findall(r'[a-h][1-8]', raw_move)
+            tokens.extend(squares)
+            if "=" in raw_move:
+                idx = raw_move.index("=")
+                if idx + 1 < len(raw_move):
+                    tokens.append(raw_move[idx+1].lower())
+            elif "q" in raw_move[-2:].lower():
+                tokens.append(raw_move[-1].lower())
+        return tokens
+
+
+class CoordinateChessTokenizer(PreTrainedTokenizer):
+    """
+    Tokenizer that decomposes chess moves into coordinate components.
+    
+    Example:
+        WPe2e4 -> ['e2', 'e4']
+        WPa7a8q -> ['a7', 'a8', 'q']  # pawn promotion
+    
+    Vocabulary size: 72 tokens
+    - 64 squares (a1-h8)
+    - 4 promotions (q, r, b, n)
+    - 4 special tokens
+    """
+    
+    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]"
+    
+    # Regex to extract from-square, to-square, and optional promotion
+    MOVE_PATTERN = re.compile(r'([a-h][1-8])([a-h][1-8])([qrbn])?')
+    
+    def __init__(self, vocab_file: Optional[str] = None, **kwargs):
+        # Remove duplicate special token kwargs
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+        
+        # Build fixed vocabulary
+        if 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_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_vocab(self) -> Dict[str, int]:
+        """Create fixed vocabulary of 72 tokens."""
+        tokens = [
+            self.PAD_TOKEN,
+            self.BOS_TOKEN,
+            self.EOS_TOKEN,
+            self.UNK_TOKEN,
+        ]
+        
+        # Add all 64 squares
+        for file in 'abcdefgh':
+            for rank in '12345678':
+                tokens.append(f"{file}{rank}")
+        
+        # Add promotion pieces
+        tokens.extend(['q', 'r', 'b', 'n'])
+        
+        return {token: idx for idx, token 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]:
+        """
+        Tokenize move string into coordinate components.
+        
+        Args:
+            text: Space-separated moves like "WPe2e4 BNg8f6"
+        
+        Returns:
+            List of coordinate tokens: ['e2', 'e4', 'g8', 'f6']
+        """
+        tokens = []
+        raw_moves = text.strip().split()
+        
+        for move in raw_moves:
+            match = self.MOVE_PATTERN.search(move)
+            if match:
+                from_sq, to_sq, promotion = match.groups()
+                tokens.append(from_sq)
+                tokens.append(to_sq)
+                if promotion:
+                    tokens.append(promotion)
+        
+        return tokens
+    
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab[self.UNK_TOKEN])
+    
+    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:
+        """Reconstruct moves from coordinate tokens."""
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        clean = [t for t in tokens if t not in special]
+        
+        # Group into moves (2 or 3 tokens per move)
+        moves = []
+        i = 0
+        while i < len(clean):
+            if i + 1 < len(clean):
+                move = clean[i] + clean[i + 1]
+                i += 2
+                # Check for promotion
+                if i < len(clean) and clean[i] in ['q', 'r', 'b', 'n']:
+                    move += clean[i]
+                    i += 1
+                moves.append(move)
+            else:
+                i += 1
+        
+        return " ".join(moves)
+    
+    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,)
+
+
+class EnhancedCoordinateTokenizer(CoordinateChessTokenizer):
+    """
+    Extended version that preserves piece information as optional metadata.
+    Vocabulary: 76 tokens (adds W, B, P, N, B, R, Q, K but makes them optional)
+    
+    Use this if you want to preserve color/piece info with minimal vocab growth.
+    """
+    
+    def _create_vocab(self) -> Dict[str, int]:
+        vocab = super()._create_vocab()
+        
+        # Add optional color and piece tokens
+        piece_tokens = ['W', 'B', 'P', 'N', 'R', 'Q', 'K']  # Note: B appears in both contexts
+        
+        next_id = len(vocab)
+        for token in piece_tokens:
+            if token not in vocab:
+                vocab[token] = next_id
+                next_id += 1
+        
+        return vocab
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Optionally include piece info: WPe2e4 -> ['W', 'P', 'e2', 'e4']
+        Or strip it for minimal version: WPe2e4 -> ['e2', 'e4']
+        """
+        tokens = []
+        raw_moves = text.strip().split()
+        
+        for move in raw_moves:
+            # Extract color and piece if present
+            if len(move) >= 2 and move[0] in 'WB' and move[1] in 'PNBRQK':
+                # Uncomment to include piece info (increases sequence length):
+                # tokens.extend([move[0], move[1]])
+                pass
+            
+            # Extract coordinates
+            match = self.MOVE_PATTERN.search(move)
+            if match:
+                from_sq, to_sq, promotion = match.groups()
+                tokens.append(from_sq)
+                tokens.append(to_sq)
+                if promotion:
+                    tokens.append(promotion)
+        
+        return tokens
+    
+
+
+class SanitizedChessTokenizer(ChessTokenizer):
+    
+    # Strategy:
+    # 1. Strip suffixes: (, ), x, +, *, o, O, E
+    # 2. Strip prefixes: W or B followed by P, N, B, R, Q, K
+    #    Regex: ^[WB][PNBRQK] matches the start of the string
+    
+    # We can use a single regex to find the "Pure Move" part.
+    # We look for the square-to-square pattern (e.g., e2e4) and optional promotion (q,r,b,n)
+    # This is safer than stripping because it ignores all noise around the move.
+    MOVE_PATTERN = re.compile(r'([a-h][1-8][a-h][1-8][qrbn]?)')
+
+    def _sanitize(self, text: str) -> str:
+        # Extract just the move part (e.g., "WPe2e4(x)" -> "e2e4")
+        match = self.MOVE_PATTERN.search(text)
+        if match:
+            return match.group(1)
+        return self.unk_token # Fallback if no valid move found
+
+    def _tokenize(self, text: str) -> List[str]:
+        # Tokenize by splitting space, then extracting the move
+        tokens = []
+        for t in text.strip().split():
+            clean = self._sanitize(t)
+            if clean != self.unk_token:
+                tokens.append(clean)
+        return tokens
+
+    @classmethod
+    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "SanitizedChessTokenizer":
+        from collections import Counter
+        
+        token_counts = Counter()
+        
+        for game in iterator:
+            moves = game.strip().split()
+            # Extract only the Pure UCI part
+            clean_moves = []
+            for m in moves:
+                match = cls.MOVE_PATTERN.search(m)
+                if match:
+                    clean_moves.append(match.group(1))
+            
+            token_counts.update(clean_moves)
+        
+        # Filter by frequency
+        tokens = [
+            token for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+        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)