Chess Challenge submission by swdo

config.json

@@ -3,8 +3,8 @@
     "ChessTRMForCausalLM"
   ],
   "auto_map": {
-    "AutoConfig": "model.ChessConfig",
-    "AutoModelForCausalLM": "model.ChessForCausalLM"
+    "AutoConfig": "model.ChessTRMConfig",
+    "AutoModelForCausalLM": "model.ChessTRMForCausalLM"
   },
   "bos_token_id": 1,
   "dropout": 0.1,

tokenizer.py

@@ -1,91 +1,63 @@
 """
-Custom Chess Tokenizer for the Chess Challenge.
+Decomposed Chess Tokenizer.
 
-This tokenizer treats each move as a single token using the extended UCI notation
-from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+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")
 
-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
+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
-from pathlib import Path
+import re
 from typing import Dict, List, Optional
 
 from transformers import PreTrainedTokenizer
 
 
-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]
-    """
-    
+class ChessDecomposedTokenizer(PreTrainedTokenizer):
     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]"
-    
+
+    _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,
     ):
-        """
-        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._vocab = self._create_full_vocab()
+
         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,
@@ -93,186 +65,93 @@ class ChessTokenizer(PreTrainedTokenizer):
             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
+
+    @staticmethod
+    def _create_full_vocab() -> Dict[str, int]:
+        special_tokens = [
+            ChessDecomposedTokenizer.PAD_TOKEN,
+            ChessDecomposedTokenizer.BOS_TOKEN,
+            ChessDecomposedTokenizer.EOS_TOKEN,
+            ChessDecomposedTokenizer.UNK_TOKEN,
         ]
-        
-        # 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, cache_dir=os.environ["HF_HOME"], 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)
-    
+
+        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 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()
-    
+        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:
-        """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.
-        """
+
+    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,)
 
+        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, cache_dir=os.environ["HF_HOME"], 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)