Chess Challenge submission by filomeneroquefort

README.md

@@ -10,11 +10,11 @@ license: mit
 Chess model submitted to the LLM Course Chess Challenge.
 ## Submission Info
 - **Submitted by**: [filomeneroquefort](https://huggingface.co/filomeneroquefort)
-- **Parameters**: 1,677,436
+- **Parameters**: 885,696
 - **Organization**: LLM-course
 ## Model Details
 - **Architecture**: Chess Transformer (GPT-style)
-- **Vocab size**: 1204
-- **Embedding dim**: 160
+- **Vocab size**: 90
+- **Embedding dim**: 112
 - **Layers**: 6
-- **Heads**: 4
+- **Heads**: 8

config.json

@@ -3,22 +3,18 @@
     "ChessForCausalLM"
   ],
   "bos_token_id": 1,
-  "dropout": 0.0,
+  "dropout": 0.1,
   "dtype": "float32",
   "eos_token_id": 2,
   "layer_norm_epsilon": 1e-05,
   "model_type": "chess_transformer",
-  "n_ctx": 256,
-  "n_embd": 160,
-  "n_head": 4,
-  "n_inner": 426,
-  "n_kv_head": 2,
+  "n_ctx": 1024,
+  "n_embd": 112,
+  "n_head": 8,
+  "n_inner": 336,
   "n_layer": 6,
   "pad_token_id": 0,
-  "rope_theta": 10000.0,
   "tie_weights": true,
   "transformers_version": "4.57.6",
-  "use_gqa": true,
-  "use_rope": true,
-  "vocab_size": 1204
+  "vocab_size": 90
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bfa57b78ceae0ce8f7653d2fca3abda1ec8ead5b2b85c9fec4ace6f6e2e0bd98
-size 6716208
+oid sha256:df7a86dd032d238f1187972c5a09513a33352263500b3e0537467c5263dc502b
+size 3549224

tokenizer.py

@@ -1,42 +1,29 @@
 """
-Custom Chess Tokenizer for the Chess Challenge.
+Atomic Chess Tokenizer.
 
-This tokenizer treats each move as a single token using the extended UCI notation
-from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+Decomposes chess moves into atomic components:
+[Piece] + [Source] + [Destination] + [Suffix]
 
-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
+Example: "WPe2e4(x)" -> ["WP", "e2", "e4", "(x)"]
+
+Benefits:
+- Drastically reduces vocab size (~1200 -> ~90)
+- Saves ~140k parameters in the embedding layer
+- Allows the model to learn spatial relationships (e2 is close to e3)
 """
 
 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
     PAD_TOKEN = "[PAD]"
@@ -44,48 +31,37 @@ class ChessTokenizer(PreTrainedTokenizer):
     EOS_TOKEN = "[EOS]"
     UNK_TOKEN = "[UNK]"
     
+    # Regex to parse the extended UCI format
+    # Groups: 1=Piece, 2=Source, 3=Dest, 4=Suffix
+    MOVE_REGEX = 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.
+        
+        # Clean kwargs
         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_atomic_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 +69,129 @@ class ChessTokenizer(PreTrainedTokenizer):
             unk_token=self._unk_token,
             **kwargs,
         )
-    
-    def _create_default_vocab(self) -> Dict[str, int]:
+
+    def _create_atomic_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.
+        Manually builds the vocabulary because we know the rules of Chess.
+        We don't need to learn this from the dataset.
         """
-        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
-        vocab = {token: idx for idx, token in enumerate(special_tokens)}
+        vocab = {}
+        idx = 0
+        
+        # 1. Special Tokens
+        for token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
+            vocab[token] = idx
+            idx += 1
+            
+        # 2. Pieces (Color + Type)
+        colors = ['W', 'B']
+        pieces = ['P', 'N', 'B', 'R', 'Q', 'K']
+        for c in colors:
+            for p in pieces:
+                vocab[f"{c}{p}"] = idx
+                idx += 1
+                
+        # 3. Squares (a1 to h8)
+        files = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
+        ranks = ['1', '2', '3', '4', '5', '6', '7', '8']
+        for f in files:
+            for r in ranks:
+                vocab[f"{f}{r}"] = idx
+                idx += 1
+                
+        # 4. Common Suffixes (derived from Lichess notation)
+        # (x)=capture, (+)=check, (#)=mate, (o)=castling
+        suffixes = ["(x)", "(+)", "(+*)", "(o)", "(O)", "=", "=Q", "=R", "=B", "=N"]
+        for s in suffixes:
+            vocab[s] = idx
+            idx += 1
+            
         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 string of moves into atomic tokens.
+        "WPe2e4" -> ["WP", "e2", "e4"]
         """
-        return text.strip().split()
-    
+        raw_moves = text.strip().split()
+        tokens = []
+        
+        for move in raw_moves:
+            match = self.MOVE_REGEX.match(move)
+            if match:
+                # Add piece, source, dest
+                tokens.extend([match.group(1), match.group(2), match.group(3)])
+                # Add suffix if it exists
+                suffix = match.group(4)
+                if suffix:
+                    tokens.append(suffix)
+            else:
+                # Fallback for weird formatting (or UNK)
+                tokens.append(move)
+                
+        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
-        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.
+        Reconstructs moves from atomic tokens.
+        This is tricky because we need to join them without spaces, 
+        but add spaces between actual moves.
+        """
+        out = []
+        current_move = []
         
-        Args:
-            save_directory: Directory to save the vocabulary.
-            filename_prefix: Optional prefix for the filename.
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
         
-        Returns:
-            Tuple containing the path to the saved vocabulary file.
-        """
+        for t in tokens:
+            if t in special:
+                continue
+            
+            current_move.append(t)
+            
+            # Logic to decide when a move ends
+            # A move usually ends after a Suffix OR after a Destination square if no suffix follows
+            # This heuristic is simple: if we have a piece, src, and dest, check next token
+            
+            # Simplified reconstruction:
+            # Just join everything and use a heuristic to insert spaces?
+            # Better: The model generates atomic tokens. 
+            # We know a move starts with [WB][PNBRQK].
+            
+        # Robust reconstruction approach:
+        full_str = "".join([t for t in tokens if t not in special])
+        
+        # Insert space before every Piece token (except the first one)
+        # Regex lookbehind isn't strictly necessary, we can just replace
+        formatted = re.sub(r'(?<!^)([WB][PNBRQK])', r' \1', full_str)
+        
+        return formatted
+
+    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
-    
-    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)
+    # We don't really need build_vocab_from_dataset anymore as we hardcoded the rules,
+    # but we keep the method signature to satisfy the template.
+    @classmethod
+    def build_vocab_from_dataset(cls, *args, **kwargs):
+        print("Note: Atomic tokenizer uses a static vocabulary rule set.")
+        return cls()

vocab.json

@@ -3,1204 +3,90 @@
   "[BOS]": 1,
   "[EOS]": 2,
   "[UNK]": 3,
-  "BBa5b6": 4,
-  "BBa6b7": 5,
-  "BBb4a5": 6,
-  "BBb4c3(x)": 7,
-  "BBb4c3(x+)": 8,
-  "BBb4c5": 9,
-  "BBb4d2(x+)": 10,
-  "BBb4d6": 11,
-  "BBb4e7": 12,
-  "BBb7a6": 13,
-  "BBb7c6": 14,
-  "BBb7c6(x)": 15,
-  "BBb7c8": 16,
-  "BBb7d5": 17,
-  "BBb7d5(x)": 18,
-  "BBb7e4(x)": 19,
-  "BBb7f3(x)": 20,
-  "BBb7g2(x)": 21,
-  "BBc5a7": 22,
-  "BBc5b4": 23,
-  "BBc5b6": 24,
-  "BBc5d4": 25,
-  "BBc5d4(x)": 26,
-  "BBc5d6": 27,
-  "BBc5e3(x)": 28,
-  "BBc5e7": 29,
-  "BBc5f2(x+)": 30,
-  "BBc8a6": 31,
-  "BBc8b7": 32,
-  "BBc8d7": 33,
-  "BBc8d7(x)": 34,
-  "BBc8e6": 35,
-  "BBc8e6(x)": 36,
-  "BBc8f5": 37,
-  "BBc8f5(x)": 38,
-  "BBc8g4": 39,
-  "BBc8g4(x)": 40,
-  "BBc8h3": 41,
-  "BBc8h3(x)": 42,
-  "BBd6b4": 43,
-  "BBd6c5": 44,
-  "BBd6c7": 45,
-  "BBd6e5": 46,
-  "BBd6e5(x)": 47,
-  "BBd6e7": 48,
-  "BBd6f4(x)": 49,
-  "BBd6g3(x)": 50,
-  "BBd7b5": 51,
-  "BBd7b5(x)": 52,
-  "BBd7c6": 53,
-  "BBd7c6(x)": 54,
-  "BBd7e6": 55,
-  "BBd7e8": 56,
-  "BBd7f5": 57,
-  "BBd7g4": 58,
-  "BBe6c4(x)": 59,
-  "BBe6d5": 60,
-  "BBe6d5(x)": 61,
-  "BBe6d7": 62,
-  "BBe6f5": 63,
-  "BBe6f7": 64,
-  "BBe6g4": 65,
-  "BBe7b4": 66,
-  "BBe7c5": 67,
-  "BBe7c5(x)": 68,
-  "BBe7d6": 69,
-  "BBe7d6(x)": 70,
-  "BBe7d8": 71,
-  "BBe7f6": 72,
-  "BBe7f6(x)": 73,
-  "BBe7f8": 74,
-  "BBe7g5": 75,
-  "BBe7g5(x)": 76,
-  "BBe7h4(x)": 77,
-  "BBf5c2(x)": 78,
-  "BBf5d3(x)": 79,
-  "BBf5e4": 80,
-  "BBf5e4(x)": 81,
-  "BBf5e6": 82,
-  "BBf5g4": 83,
-  "BBf5g6": 84,
-  "BBf6d4(x)": 85,
-  "BBf6e5(x)": 86,
-  "BBf6e7": 87,
-  "BBf6g5": 88,
-  "BBf6g7": 89,
-  "BBf8b4": 90,
-  "BBf8b4(+)": 91,
-  "BBf8c5": 92,
-  "BBf8c5(x)": 93,
-  "BBf8d6": 94,
-  "BBf8d6(x)": 95,
-  "BBf8e7": 96,
-  "BBf8g7": 97,
-  "BBf8h6": 98,
-  "BBg4d7": 99,
-  "BBg4e2(x)": 100,
-  "BBg4e6": 101,
-  "BBg4f3(x)": 102,
-  "BBg4f5": 103,
-  "BBg4h5": 104,
-  "BBg7b2(x)": 105,
-  "BBg7c3(x)": 106,
-  "BBg7d4(x)": 107,
-  "BBg7e5(x)": 108,
-  "BBg7f6": 109,
-  "BBg7f6(x)": 110,
-  "BBg7f8": 111,
-  "BBg7h6": 112,
-  "BBh5g6": 113,
-  "BKb8a7": 114,
-  "BKb8a8": 115,
-  "BKb8c7": 116,
-  "BKb8c8": 117,
-  "BKc5b4": 118,
-  "BKc6b5": 119,
-  "BKc6b6": 120,
-  "BKc6d5": 121,
-  "BKc6d6": 122,
-  "BKc7b6": 123,
-  "BKc7b7": 124,
-  "BKc7c6": 125,
-  "BKc7d6": 126,
-  "BKc7d7": 127,
-  "BKc8b7": 128,
-  "BKc8b8": 129,
-  "BKc8c7": 130,
-  "BKc8d7": 131,
-  "BKc8d8": 132,
-  "BKd5c4": 133,
-  "BKd6c5": 134,
-  "BKd6c6": 135,
-  "BKd6c7": 136,
-  "BKd6d5": 137,
-  "BKd6e5": 138,
-  "BKd6e6": 139,
-  "BKd6e7": 140,
-  "BKd7c6": 141,
-  "BKd7c7": 142,
-  "BKd7c8": 143,
-  "BKd7d6": 144,
-  "BKd7d8": 145,
-  "BKd7e6": 146,
-  "BKd7e7": 147,
-  "BKd7e8": 148,
-  "BKd8c7": 149,
-  "BKd8c8": 150,
-  "BKd8d7": 151,
-  "BKd8e7": 152,
-  "BKd8e8": 153,
-  "BKe5d4": 154,
-  "BKe6d5": 155,
-  "BKe6d6": 156,
-  "BKe6d7": 157,
-  "BKe6e5": 158,
-  "BKe6e7": 159,
-  "BKe6f5": 160,
-  "BKe6f6": 161,
-  "BKe6f7": 162,
-  "BKe7d6": 163,
-  "BKe7d7": 164,
-  "BKe7d8": 165,
-  "BKe7e6": 166,
-  "BKe7e8": 167,
-  "BKe7f6": 168,
-  "BKe7f7": 169,
-  "BKe7f8": 170,
-  "BKe8c8(O)": 171,
-  "BKe8d7": 172,
-  "BKe8d8": 173,
-  "BKe8d8(x)": 174,
-  "BKe8e7": 175,
-  "BKe8f7": 176,
-  "BKe8f7(x)": 177,
-  "BKe8f8": 178,
-  "BKe8g8(o)": 179,
-  "BKf5e4": 180,
-  "BKf5e6": 181,
-  "BKf5f4": 182,
-  "BKf5g4": 183,
-  "BKf5g6": 184,
-  "BKf6e5": 185,
-  "BKf6e6": 186,
-  "BKf6e7": 187,
-  "BKf6f5": 188,
-  "BKf6f7": 189,
-  "BKf6g5": 190,
-  "BKf6g6": 191,
-  "BKf6g7": 192,
-  "BKf7e6": 193,
-  "BKf7e7": 194,
-  "BKf7e8": 195,
-  "BKf7f6": 196,
-  "BKf7f8": 197,
-  "BKf7g6": 198,
-  "BKf7g7": 199,
-  "BKf7g8": 200,
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