Chess Challenge submission by hugoper

README.md

@@ -14,13 +14,13 @@ Chess model submitted to the LLM Course Chess Challenge.
 ## Submission Info
 
 - **Submitted by**: [hugoper](https://huggingface.co/hugoper)
-- **Parameters**: 996,480
+- **Parameters**: 998,250
 - **Organization**: LLM-course
 
 ## Model Details
 
 - **Architecture**: Chess Transformer (GPT-style)
-- **Vocab size**: 3154
-- **Embedding dim**: 120
-- **Layers**: 4
-- **Heads**: 5
+- **Vocab size**: 93
+- **Embedding dim**: 128
+- **Layers**: 6
+- **Heads**: 4

config.json

@@ -8,13 +8,13 @@
   "eos_token_id": 2,
   "layer_norm_epsilon": 1e-05,
   "model_type": "chess_transformer",
-  "n_ctx": 300,
-  "n_embd": 120,
-  "n_head": 5,
-  "n_inner": 360,
-  "n_layer": 4,
+  "n_ctx": 1024,
+  "n_embd": 128,
+  "n_head": 4,
+  "n_inner": 295,
+  "n_layer": 6,
   "pad_token_id": 0,
   "tie_weights": true,
   "transformers_version": "4.57.5",
-  "vocab_size": 3154
+  "vocab_size": 93
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:37399f6409eabe0ea2e9ef791299a24fad590bdd31b1830edbb2237a8cd303d7
-size 3990320
+oid sha256:07f505da65b87fb41fce704e8fd3af76db335ec83fe6f4514f58e1d5e99f6578
+size 3999440

tokenizer.py

@@ -1,91 +1,74 @@
 """
 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
+Strategy: Semantic Split (Piece, Square, Suffix)
 """
 
 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]
-    """
-    
     model_input_names = ["input_ids", "attention_mask"]
-    vocab_files_names = {"vocab_file": "vocab.json"}
     
-    # Special tokens
+    # --- FIXED VOCABULARY ---
+    # 1. 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.
-        """
+    # 2. Pieces (Color + Role)
+    PIECES = [
+        "WP", "WN", "WB", "WR", "WQ", "WK",  # White
+        "BP", "BN", "BB", "BR", "BQ", "BK"   # Black
+    ]
+    
+    # 3. Squares (a1 to h8)
+    SQUARES = [f"{c}{r}" for c in "abcdefgh" for r in "12345678"]
+    
+    # 4. Suffixes (Capture, Check, Mate, Castling, Promotion)
+    # Note: We include standard promotion suffixes just in case (q,r,b,n)
+    SUFFIXES = [
+        "(x)", "(+)", "(+*)", "(o)", "(O)",  # Event suffixes
+        "q", "r", "b", "n", "Q", "R", "B", "N" # Promotions
+    ]
+
+    def __init__(self, **kwargs):
         # 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
+        # Clean kwargs
+        for token in ["pad_token", "bos_token", "eos_token", "unk_token"]:
+            kwargs.pop(token, None)
+        
+        # Build Fixed Vocabulary
+        self.all_tokens = (
+            [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN] +
+            self.PIECES +
+            self.SQUARES +
+            self.SUFFIXES
+        )
+        self._vocab = {token: idx for idx, token in enumerate(self.all_tokens)}
         self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
         
-        # Call parent init AFTER setting up vocab
+        # Compile Regex for Tokenization
+        # Logic: Match Piece OR Square OR Suffix
+        # We sort suffixes by length (descending) to match longest first (e.g. (+*) before (+))
+        escaped_suffixes = [re.escape(s) for s in self.SUFFIXES]
+        suffix_pattern = "|".join(sorted(escaped_suffixes, key=len, reverse=True))
+        
+        self.token_pattern = re.compile(
+            r'([WB][PNBRQK])|([a-h][1-8])|(' + suffix_pattern + r')'
+        )
+
         super().__init__(
             pad_token=self._pad_token,
             bos_token=self._bos_token,
@@ -94,185 +77,53 @@ class ChessTokenizer(PreTrainedTokenizer):
             **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.
+        Splits a game string using Regex.
+        Example: "WPe2e4" -> ["WP", "e2", "e4"]
         """
-        return text.strip().split()
+        # Find all matches. Each match is a tuple like ('WP', '', '') or ('', 'e2', '')
+        # We flatten this list and filter out empty strings
+        matches = self.token_pattern.findall(text)
+        tokens = [token for group in matches for token in group if token]
+        return tokens
     
     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))
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
     
     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
+        # Simple join, but we might want to group them back into moves for display
+        # For raw processing, space separation is fine
         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",
+            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
+    # --- Static/Class Methods Override ---
     
-    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)
+    @classmethod
+    def build_vocab_from_dataset(cls, *args, **kwargs) -> "ChessTokenizer":
+        """
+        Override: Returns a pre-initialized tokenizer with fixed vocab.
+        We don't need to scan the dataset because we know the rules of Chess.
+        """
+        print("Using fixed vocabulary (Pieces + Squares + Suffixes). No dataset scan needed.")
+        return cls()

vocab.json

@@ -3,3154 +3,93 @@
   "[BOS]": 1,
   "[EOS]": 2,
   "[UNK]": 3,
-  "BBa3b4": 4,
-  "BBa4b5": 5,
-  "BBa5b6": 6,
-  "BBa5c7": 7,
-  "BBa6b5": 8,
-  "BBa6b5(x)": 9,
-  "BBa6b7": 10,
-  "BBa6c4": 11,
-  "BBa6c4(x)": 12,
-  "BBa6c8": 13,
-  "BBa6d3(x)": 14,
-  "BBa6e2(x)": 15,
-  "BBa6f1(x)": 16,
-  "BBb2a1(x)": 17,
-  "BBb2c3": 18,
-  "BBb2d4": 19,
-  "BBb2e5": 20,
-  "BBb2g7": 21,
-  "BBb4a5": 22,
-  "BBb4c3": 23,
-  "BBb4c3(x)": 24,
-  "BBb4c3(x+)": 25,
-  "BBb4c5": 26,
-  "BBb4d2(x)": 27,
-  "BBb4d2(x+)": 28,
-  "BBb4d6": 29,
-  "BBb4e7": 30,
-  "BBb5c4": 31,
-  "BBb5c6": 32,
-  "BBb6a5": 33,
-  "BBb6a7": 34,
-  "BBb6c5": 35,
-  "BBb6c7": 36,
-  "BBb6d4": 37,
-  "BBb6d4(x)": 38,
-  "BBb6e3(x)": 39,
-  "BBb7a6": 40,
-  "BBb7a8": 41,
-  "BBb7a8(x)": 42,
-  "BBb7c6": 43,
-  "BBb7c6(x)": 44,
-  "BBb7c8": 45,
-  "BBb7d5": 46,
-  "BBb7d5(x)": 47,
-  "BBb7e4": 48,
-  "BBb7e4(x)": 49,
-  "BBb7f3(x)": 50,
-  "BBb7g2(x)": 51,
-  "BBc3a1(x)": 52,
-  "BBc3b4": 53,
-  "BBc3d4": 54,
-  "BBc4d5": 55,
-  "BBc4e6": 56,
-  "BBc5a7": 57,
-  "BBc5b4": 58,
-  "BBc5b4(+)": 59,
-  "BBc5b4(x)": 60,
-  "BBc5b6": 61,
-  "BBc5d4": 62,
-  "BBc5d4(x)": 63,
-  "BBc5d6": 64,
-  "BBc5e3": 65,
-  "BBc5e3(x)": 66,
-  "BBc5e3(x+)": 67,
-  "BBc5e7": 68,
-  "BBc5f2(x)": 69,
-  "BBc5f2(x+)": 70,
-  "BBc6b5": 71,
-  "BBc6b5(x)": 72,
-  "BBc6b7": 73,
-  "BBc6d5": 74,
-  "BBc6d5(x)": 75,
-  "BBc6d7": 76,
-  "BBc6e4": 77,
-  "BBc6e4(x)": 78,
-  "BBc6e8": 79,
-  "BBc6f3(x)": 80,
-  "BBc6g2(x)": 81,
-  "BBc7b6": 82,
-  "BBc7d6": 83,
-  "BBc7e5(x)": 84,
-  "BBc8a6": 85,
-  "BBc8b7": 86,
-  "BBc8b7(x)": 87,
-  "BBc8d7": 88,
-  "BBc8d7(x)": 89,
-  "BBc8e6": 90,
-  "BBc8e6(x)": 91,
-  "BBc8f5": 92,
-  "BBc8f5(x)": 93,
-  "BBc8g4": 94,
-  "BBc8g4(+)": 95,
-  "BBc8g4(x)": 96,
-  "BBc8h3": 97,
-  "BBc8h3(x)": 98,
-  "BBd3c4": 99,
-  "BBd3e4": 100,
-  "BBd4b2(x)": 101,
-  "BBd4b6": 102,
-  "BBd4c3(x)": 103,
-  "BBd4c5": 104,
-  "BBd4e3(x)": 105,
-  "BBd4e5": 106,
-  "BBd4f2(x+)": 107,
-  "BBd4f6": 108,
-  "BBd4g7": 109,
-  "BBd5b7": 110,
-  "BBd5c4": 111,
-  "BBd5c6": 112,
-  "BBd5e4": 113,
-  "BBd5e4(x)": 114,
-  "BBd5e6": 115,
-  "BBd5f3(x)": 116,
-  "BBd5g2(x)": 117,
-  "BBd6a3(x)": 118,
-  "BBd6b4": 119,
-  "BBd6b4(x)": 120,
-  "BBd6b8": 121,
-  "BBd6c5": 122,
-  "BBd6c5(+)": 123,
-  "BBd6c5(x)": 124,
-  "BBd6c7": 125,
-  "BBd6e5": 126,
-  "BBd6e5(x)": 127,
-  "BBd6e7": 128,
-  "BBd6f4": 129,
-  "BBd6f4(x)": 130,
-  "BBd6f8": 131,
-  "BBd6g3(x)": 132,
-  "BBd6h2(x+)": 133,
-  "BBd7a4": 134,
-  "BBd7a4(x)": 135,
-  "BBd7b5": 136,
-  "BBd7b5(x)": 137,
-  "BBd7c6": 138,
-  "BBd7c6(x)": 139,
-  "BBd7c8": 140,
-  "BBd7e6": 141,
-  "BBd7e6(x)": 142,
-  "BBd7e8": 143,
-  "BBd7f5": 144,
-  "BBd7f5(x)": 145,
-  "BBd7g4": 146,
-  "BBd7g4(x)": 147,
-  "BBd7h3": 148,
-  "BBd7h3(x)": 149,
-  "BBd8b6": 150,
-  "BBd8c7": 151,
-  "BBd8e7": 152,
-  "BBd8f6": 153,
-  "BBe3d4": 154,
-  "BBe4b7": 155,
-  "BBe4c6": 156,
-  "BBe4d3": 157,
-  "BBe4d5": 158,
-  "BBe4f3(x)": 159,
-  "BBe4f5": 160,
-  "BBe4g2(x)": 161,
-  "BBe4g6": 162,
-  "BBe5b2(x)": 163,
-  "BBe5c3(x)": 164,
-  "BBe5d4": 165,
-  "BBe5d4(+)": 166,
-  "BBe5d4(x)": 167,
-  "BBe5d6": 168,
-  "BBe5f4": 169,
-  "BBe5f4(x)": 170,
-  "BBe5f6": 171,
-  "BBe5g3(x)": 172,
-  "BBe5g7": 173,
-  "BBe6a2(x)": 174,
-  "BBe6b3(x)": 175,
-  "BBe6c4": 176,
-  "BBe6c4(x)": 177,
-  "BBe6c8": 178,
-  "BBe6d5": 179,
-  "BBe6d5(x)": 180,
-  "BBe6d7": 181,
-  "BBe6f5": 182,
-  "BBe6f5(x)": 183,
-  "BBe6f7": 184,
-  "BBe6g4": 185,
-  "BBe6g4(x)": 186,
-  "BBe6h3": 187,
-  "BBe6h3(x)": 188,
-  "BBe7a3(x)": 189,
-  "BBe7b4": 190,
-  "BBe7b4(x)": 191,
-  "BBe7c5": 192,
-  "BBe7c5(+)": 193,
-  "BBe7c5(x)": 194,
-  "BBe7d6": 195,
-  "BBe7d6(x)": 196,
-  "BBe7d8": 197,
-  "BBe7d8(x)": 198,
-  "BBe7f6": 199,
-  "BBe7f6(x)": 200,
-  "BBe7f8": 201,
-  "BBe7g5": 202,
-  "BBe7g5(x)": 203,
-  "BBe7h4": 204,
-  "BBe7h4(+)": 205,
-  "BBe7h4(x)": 206,
-  "BBe8d7": 207,
-  "BBe8f7": 208,
-  "BBf4d6": 209,
-  "BBf4e5": 210,
-  "BBf5b1(x)": 211,
-  "BBf5c2(x)": 212,
-  "BBf5d3": 213,
-  "BBf5d3(x)": 214,
-  "BBf5d7": 215,
-  "BBf5e4": 216,
-  "BBf5e4(x)": 217,
-  "BBf5e6": 218,
-  "BBf5g4": 219,
-  "BBf5g4(x)": 220,
-  "BBf5g6": 221,
-  "BBf5h3": 222,
-  "BBf5h3(x)": 223,
-  "BBf5h7": 224,
-  "BBf6b2(x)": 225,
-  "BBf6c3(x)": 226,
-  "BBf6d4": 227,
-  "BBf6d4(x)": 228,
-  "BBf6d8": 229,
-  "BBf6e5": 230,
-  "BBf6e5(x)": 231,
-  "BBf6e7": 232,
-  "BBf6g5": 233,
-  "BBf6g5(x)": 234,
-  "BBf6g7": 235,
-  "BBf6h4": 236,
-  "BBf6h4(x)": 237,
-  "BBf7g6": 238,
-  "BBf8a3(x)": 239,
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