Chess Challenge submission by ilanou20

README.md

@@ -0,0 +1,15 @@
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-ilan-v7
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+- Submitted by: ilanou20
+- Parameters: 708,992

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 128,
+  "n_head": 4,
+  "n_inner": 384,
+  "n_layer": 4,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 113
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09addb055f16697ed37bcc0d1c14daecc0b7b24a5026cb3bd6884d09604cc6f2
+size 2840368

tokenizer.py

@@ -0,0 +1,468 @@
+"""
+Custom Chess Tokenizer for the Chess Challenge.
+We build a vocabulary with:
+- W/B prefix for White/Black
+- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+- Source and rank and file: e.g e 2
+- Destination and rank and file: e.g e 4
+- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+import shutil
+import inspect
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+from datasets import load_dataset
+
+
+class ChessTokenizer(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]"
+    SEP_TOKEN = "[SEP]"
+    
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+       
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+        self._sep_token = self.SEP_TOKEN
+
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+        kwargs.pop("sep_token", None)
+        
+        print("Initializing ChessTokenizer")
+        print(f"  vocab_file: {vocab_file}")
+        print(f"  vocab provided: {vocab is not None}")
+        print(f"  vocab: {vocab}")
+        
+        print(os.listdir("."))
+        
+        vocab = {
+            "[PAD]": 0,
+            "[BOS]": 1,
+            "[EOS]": 2,
+            "[UNK]": 3,
+            "[SEP]": 4,
+            "(+)": 5,
+            "(+*)": 6,
+            "(+*B)": 7,
+            "(+*N)": 8,
+            "(+*Q)": 9,
+            "(+*R)": 10,
+            "(+B)": 11,
+            "(+N)": 12,
+            "(+Q)": 13,
+            "(+R)": 14,
+            "(B)": 15,
+            "(N)": 16,
+            "(O)": 17,
+            "(O+)": 18,
+            "(O+*)": 19,
+            "(Q)": 20,
+            "(R)": 21,
+            "(o)": 22,
+            "(o+)": 23,
+            "(o+*)": 24,
+            "(x)": 25,
+            "(x+)": 26,
+            "(x+*)": 27,
+            "(x+*B)": 28,
+            "(x+*Q)": 29,
+            "(x+*R)": 30,
+            "(x+B)": 31,
+            "(x+N)": 32,
+            "(x+Q)": 33,
+            "(x+R)": 34,
+            "(xB)": 35,
+            "(xE)": 36,
+            "(xE+)": 37,
+            "(xE+*)": 38,
+            "(xN)": 39,
+            "(xQ)": 40,
+            "(xR)": 41,
+            "B": 42,
+            "K": 43,
+            "N": 44,
+            "P": 45,
+            "Q": 46,
+            "R": 47,
+            "W": 48,
+            "a1": 49,
+            "a2": 50,
+            "a3": 51,
+            "a4": 52,
+            "a5": 53,
+            "a6": 54,
+            "a7": 55,
+            "a8": 56,
+            "b1": 57,
+            "b2": 58,
+            "b3": 59,
+            "b4": 60,
+            "b5": 61,
+            "b6": 62,
+            "b7": 63,
+            "b8": 64,
+            "c1": 65,
+            "c2": 66,
+            "c3": 67,
+            "c4": 68,
+            "c5": 69,
+            "c6": 70,
+            "c7": 71,
+            "c8": 72,
+            "d1": 73,
+            "d2": 74,
+            "d3": 75,
+            "d4": 76,
+            "d5": 77,
+            "d6": 78,
+            "d7": 79,
+            "d8": 80,
+            "e1": 81,
+            "e2": 82,
+            "e3": 83,
+            "e4": 84,
+            "e5": 85,
+            "e6": 86,
+            "e7": 87,
+            "e8": 88,
+            "f1": 89,
+            "f2": 90,
+            "f3": 91,
+            "f4": 92,
+            "f5": 93,
+            "f6": 94,
+            "f7": 95,
+            "f8": 96,
+            "g1": 97,
+            "g2": 98,
+            "g3": 99,
+            "g4": 100,
+            "g5": 101,
+            "g6": 102,
+            "g7": 103,
+            "g8": 104,
+            "h1": 105,
+            "h2": 106,
+            "h3": 107,
+            "h4": 108,
+            "h5": 109,
+            "h6": 110,
+            "h7": 111,
+            "h8": 112
+            }
+        
+        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:
+            print("No vocabulary provided; creating default minimal vocab.")
+            self._vocab = self._create_default_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,
+            sep_token=self._sep_token,
+            **kwargs,
+        )
+    
+    def _create_default_vocab(self) -> Dict[str, int]:
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.SEP_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens)}
+        return vocab
+    
+    
+    @classmethod
+    def build_vocab_from_dataset(
+        cls,
+        dataset_name: str = "dlouapre/lichess_2025-01_1M",
+        split: str = "train",
+        column: str = "text",
+        min_frequency: Optional[int] = 1,
+        max_samples: Optional[int] = None,
+        save_path: Optional[str] = None,
+    ) -> "ChessTokenizer":
+        
+        
+
+        if save_path is None:
+            cwd = os.getcwd()
+            save_path = os.path.join(cwd, "chess_tokenizer_vocab.json")
+
+        if os.path.exists(save_path):
+            try:
+                with open(save_path, "r", encoding="utf-8") as f:
+                    print("Loading existing tokenizer vocab from", save_path)
+                    vocab = json.load(f)
+                return cls(vocab=vocab)
+            except Exception:
+                pass
+
+        dataset = load_dataset(dataset_name, split=split)
+
+        samples = dataset[column]
+
+        tokens = set()
+
+        for game in samples:
+            if not isinstance(game, str):
+                continue
+            moves = game.strip().split()
+            for move in moves:
+                if len(move) < 2:
+                    continue
+                color = move[0]
+                piece = move[1]
+                from_square = move[2:4] if len(move) >= 4 else ''
+                to_square = move[4:6] if len(move) >= 6 else ''
+                suffix = move[6:] if len(move) > 6 else ''
+                
+                tokens.add(color)
+                tokens.add(piece)
+                tokens.add(from_square)
+                tokens.add(to_square)
+                if suffix:
+                    tokens.add(suffix)   
+
+        tokens = sorted(tokens)
+
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN, cls.SEP_TOKEN]
+
+        vocab: Dict[str, int] = {}
+        idx = 0
+        for st in special_tokens:
+            vocab[st] = idx
+            idx += 1
+
+        for t in tokens:
+            if t in vocab:
+                continue
+            vocab[t] = idx
+            idx += 1
+
+        tokenizer = cls(vocab=vocab)
+
+        try:
+            if save_path is None:
+                cwd = os.getcwd()
+                save_path = os.path.join(cwd, "chess_tokenizer_vocab.json")
+
+            tmp_path = save_path + ".tmp"
+            with open(tmp_path, "w", encoding="utf-8") as f:
+                json.dump(vocab, f, ensure_ascii=False, indent=2)
+            os.replace(tmp_path, save_path)
+        except Exception:
+            # Non-fatal: ignore save errors but don't leave temp files behind.
+            try:
+                if 'tmp_path' in locals() and os.path.exists(tmp_path):
+                    os.remove(tmp_path)
+            except Exception:
+                pass
+
+        return tokenizer
+    
+    @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.
+        """
+        tokens: List[str] = []
+        for move in text.strip().split():
+            if len(move) < 2:
+                continue
+            color, piece, from_square, to_square, suffix = self._decompose_move(move)
+            tokens.append(color)
+            tokens.append(piece)
+            tokens.append(from_square)
+            tokens.append(to_square)
+            if suffix:
+                tokens.append(suffix)
+
+            tokens.append(self._sep_token)
+
+        return tokens[:-1]  # Remove last SEP token
+
+    @staticmethod
+    def _decompose_move(move: str):
+        """Decompose a move string into components: color, piece, from_square, to_square, suffix.
+        Returns a 5-tuple of strings (empty strings for missing parts).
+        """
+        color = move[0]
+        piece = move[1] if len(move) >= 2 else ''
+        from_square = move[2:4] if len(move) >= 4 else ''
+        to_square = move[4:6] if len(move) >= 6 else ''
+        suffix = move[6:] if len(move) > 6 else ''
+        return color, piece, from_square, to_square, suffix
+    
+    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 decode(self, token_ids: List[int], skip_special_tokens: bool = True) -> str:
+        """Decode a list of token IDs back to a string."""
+        tokens = [self._convert_id_to_token(int(tid)) for tid in token_ids]
+        if skip_special_tokens:
+            special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+            # SEP token should be replace by space
+            tokens = [t if t != self.SEP_TOKEN else " " for t in tokens if t not in special]
+        return "".join(tokens)
+
+    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 save_pretrained(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+        save_tokenizer_code: bool = True,
+    ) -> None:
+        """Save tokenizer files to a directory in a HF-compatible layout.
+        This writes the vocab JSON (via `save_vocabulary`), a small
+        `tokenizer_config.json` describing special tokens and the vocab
+        filename, and optionally copies the tokenizer module source file
+        into the directory so others can import the implementation.
+        """
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+
+        # Save the vocabulary file
+        vocab_file_tuple = self.save_vocabulary(save_directory, filename_prefix)
+        vocab_file = vocab_file_tuple[0]
+
+        # Write a minimal tokenizer config
+        config = {
+            "tokenizer_class": self.__class__.__name__,
+            "vocab_file": os.path.basename(vocab_file),
+            "pad_token": self.PAD_TOKEN,
+            "bos_token": self.BOS_TOKEN,
+            "eos_token": self.EOS_TOKEN,
+            "unk_token": self.UNK_TOKEN,
+        }
+        config_path = os.path.join(save_directory, "tokenizer_config.json")
+        with open(config_path, "w", encoding="utf-8") as f:
+            json.dump(config, f, ensure_ascii=False, indent=2)
+
+        # Optionally copy this module file so the tokenizer class implementation
+        # is available alongside the saved vocab/config. This helps when
+        # transferring the saved tokenizer to another environment.
+        if save_tokenizer_code:
+            try:
+                src_file = Path(inspect.getsourcefile(self.__class__))
+                dst_file = Path(save_directory) / src_file.name
+                shutil.copy2(src_file, dst_file)
+            except Exception:
+                # Non-fatal; we still saved vocab and config
+                pass
+
+
+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))))
+    
+    tokenizer = ChessTokenizer()
+    token_counts = Counter()
+    
+    for example in dataset:
+        token_counts.update(tokenizer._tokenize(example[column]))
+    
+    return dict(token_counts)

tokenizer_config.json

@@ -0,0 +1,8 @@
+{
+  "tokenizer_class": "ChessTokenizer",
+  "vocab_file": "vocab.json",
+  "pad_token": "[PAD]",
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,115 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "[SEP]": 4,
+  "(+)": 5,
+  "(+*)": 6,
+  "(+*B)": 7,
+  "(+*N)": 8,
+  "(+*Q)": 9,
+  "(+*R)": 10,
+  "(+B)": 11,
+  "(+N)": 12,
+  "(+Q)": 13,
+  "(+R)": 14,
+  "(B)": 15,
+  "(N)": 16,
+  "(O)": 17,
+  "(O+)": 18,
+  "(O+*)": 19,
+  "(Q)": 20,
+  "(R)": 21,
+  "(o)": 22,
+  "(o+)": 23,
+  "(o+*)": 24,
+  "(x)": 25,
+  "(x+)": 26,
+  "(x+*)": 27,
+  "(x+*B)": 28,
+  "(x+*Q)": 29,
+  "(x+*R)": 30,
+  "(x+B)": 31,
+  "(x+N)": 32,
+  "(x+Q)": 33,
+  "(x+R)": 34,
+  "(xB)": 35,
+  "(xE)": 36,
+  "(xE+)": 37,
+  "(xE+*)": 38,
+  "(xN)": 39,
+  "(xQ)": 40,
+  "(xR)": 41,
+  "B": 42,
+  "K": 43,
+  "N": 44,
+  "P": 45,
+  "Q": 46,
+  "R": 47,
+  "W": 48,
+  "a1": 49,
+  "a2": 50,
+  "a3": 51,
+  "a4": 52,
+  "a5": 53,
+  "a6": 54,
+  "a7": 55,
+  "a8": 56,
+  "b1": 57,
+  "b2": 58,
+  "b3": 59,
+  "b4": 60,
+  "b5": 61,
+  "b6": 62,
+  "b7": 63,
+  "b8": 64,
+  "c1": 65,
+  "c2": 66,
+  "c3": 67,
+  "c4": 68,
+  "c5": 69,
+  "c6": 70,
+  "c7": 71,
+  "c8": 72,
+  "d1": 73,
+  "d2": 74,
+  "d3": 75,
+  "d4": 76,
+  "d5": 77,
+  "d6": 78,
+  "d7": 79,
+  "d8": 80,
+  "e1": 81,
+  "e2": 82,
+  "e3": 83,
+  "e4": 84,
+  "e5": 85,
+  "e6": 86,
+  "e7": 87,
+  "e8": 88,
+  "f1": 89,
+  "f2": 90,
+  "f3": 91,
+  "f4": 92,
+  "f5": 93,
+  "f6": 94,
+  "f7": 95,
+  "f8": 96,
+  "g1": 97,
+  "g2": 98,
+  "g3": 99,
+  "g4": 100,
+  "g5": 101,
+  "g6": 102,
+  "g7": 103,
+  "g8": 104,
+  "h1": 105,
+  "h2": 106,
+  "h3": 107,
+  "h4": 108,
+  "h5": 109,
+  "h6": 110,
+  "h7": 111,
+  "h8": 112
+}