Chess Challenge submission by Vadim38

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-gpt-char-level-v7
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Vadim38](https://huggingface.co/Vadim38)
+- **Parameters**: 997,764
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 30
+- **Embedding dim**: 128
+- **Layers**: 7
+- **Heads**: 8

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 28,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 29,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 1024,
+  "n_embd": 128,
+  "n_head": 8,
+  "n_inner": 220,
+  "n_layer": 7,
+  "pad_token_id": 27,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 30
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:722049aec3c84823bdd7cb2ed42a8a48961951518bc8fd79564f9b2e5a1a0507
+size 3998520

tokenizer.py

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+from __future__ import annotations
+import torch
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+from transformers import PreTrainedTokenizer
+
+
+
+
+
+
+
+"""
+Custom Chess Tokenizer (Character Level)
+Compatible with HF Trainer & Evaluator (BatchEncoding support)
+"""
+
+class BatchEncoding(dict):
+    """
+    Sert à envelopper le dictionnaire de sortie pour qu'il accepte la méthode .to(device).
+    """
+    def to(self, device):
+        new_obj = BatchEncoding()
+        for k, v in self.items():
+            if hasattr(v, "to"):
+                new_obj[k] = v.to(device)
+            else:
+                new_obj[k] = v
+        return new_obj
+
+class ChessTokenizer:
+    def __init__(self):
+        # Vocabulaire statique
+        self.chars = list("abcdefgh12345678PRNBQKxoO-=") + ["<pad>", "<s>", "</s>"]
+        
+        self.vocab = {ch: i for i, ch in enumerate(self.chars)}
+        self.id_to_char = {i: ch for i, ch in enumerate(self.chars)}
+        
+        # Attributs spéciaux
+        self.pad_token = "<pad>"
+        self.bos_token = "<s>"
+        self.eos_token = "</s>"
+        self.unk_token = "<pad>"
+        
+        self.pad_token_id = self.vocab["<pad>"]
+        self.bos_token_id = self.vocab["<s>"]
+        self.eos_token_id = self.vocab["</s>"]
+        self.vocab_size = len(self.vocab)
+
+        self.model_max_length = 1024
+        self.padding_side = "right"
+
+    @classmethod
+    def build_vocab_from_dataset(cls, *args, **kwargs):
+        return cls()
+
+    def encode(self, text):
+        return [self.vocab.get(c, self.pad_token_id) for c in text]
+
+    # --- CORRECTION ICI : Ajout de **kwargs pour accepter 'skip_special_tokens' ---
+    def decode(self, token_ids, skip_special_tokens=False, **kwargs):
+        if isinstance(token_ids, torch.Tensor):
+            token_ids = token_ids.tolist()
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        
+        tokens = [self.id_to_char.get(i, "") for i in token_ids]
+        # On nettoie toujours les tokens spéciaux, peu importe l'argument
+        return "".join(tokens).replace("<pad>", "").replace("<s>", "").replace("</s>", "")
+
+    def __call__(self, text, max_length=None, padding=False, truncation=False, return_tensors=None, **kwargs):
+        # 1. Encodage
+        ids = self.encode(text)
+        
+        # 2. Truncation
+        if truncation and max_length is not None:
+            ids = ids[:max_length]
+            
+        # 3. Padding + Mask
+        attention_mask = [1] * len(ids)
+        
+        if padding == "max_length" and max_length is not None:
+            if len(ids) < max_length:
+                pad_len = max_length - len(ids)
+                ids = ids + [self.pad_token_id] * pad_len
+                attention_mask = attention_mask + [0] * pad_len
+                
+        # 4. Retour intelligent (BatchEncoding)
+        if return_tensors == "pt":
+            return BatchEncoding({
+                "input_ids": torch.tensor([ids], dtype=torch.long),
+                "attention_mask": torch.tensor([attention_mask], dtype=torch.long)
+            })
+            
+        return {
+            "input_ids": ids,
+            "attention_mask": attention_mask
+        }
+
+    def save_pretrained(self, save_directory):
+        pass
+
+    @classmethod
+    def from_pretrained(cls, save_directory):
+        return cls()
+
+
+
+
+
+
+
+
+
+
+
+
+
+'''"""
+Custom Chess Tokenizer (Character Level) - Fully Compatible with HF Trainer
+"""
+
+class ChessTokenizer:
+    def __init__(self):
+        # Vocabulaire statique
+        self.chars = list("abcdefgh12345678PRNBQKxoO-=") + ["<pad>", "<s>", "</s>"]
+        
+        self.vocab = {ch: i for i, ch in enumerate(self.chars)}
+        self.id_to_char = {i: ch for i, ch in enumerate(self.chars)}
+        
+        # Attributs spéciaux (version texte)
+        self.pad_token = "<pad>"
+        self.bos_token = "<s>"
+        self.eos_token = "</s>"
+        self.unk_token = "<pad>"
+        
+        # Attributs spéciaux (version ID)
+        self.pad_token_id = self.vocab["<pad>"]
+        self.bos_token_id = self.vocab["<s>"]
+        self.eos_token_id = self.vocab["</s>"]
+        self.vocab_size = len(self.vocab)
+
+        # Config par défaut
+        self.model_max_length = 1024
+        self.padding_side = "right"
+
+    @classmethod
+    def build_vocab_from_dataset(cls, *args, **kwargs):
+        print("⚡ Utilisation du Tokenizer 'Char-Level' (Vocabulaire statique) ⚡")
+        return cls()
+
+    def encode(self, text):
+        return [self.vocab.get(c, self.pad_token_id) for c in text]
+
+    def decode(self, token_ids):
+        if isinstance(token_ids, torch.Tensor):
+            token_ids = token_ids.tolist()
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        
+        tokens = [self.id_to_char.get(i, "") for i in token_ids]
+        return "".join(tokens).replace("<pad>", "").replace("<s>", "").replace("</s>", "")
+
+    def __call__(self, text, max_length=None, padding=False, truncation=False, return_tensors=None, **kwargs):
+        """
+        Cette méthode est le coeur du problème. Elle imite le comportement
+        d'un tokenizer Hugging Face standard (Padding, Truncation, Tensors).
+        """
+        # 1. Encodage brut
+        ids = self.encode(text)
+        
+        # 2. Truncation (Couper si trop long)
+        if truncation and max_length is not None:
+            ids = ids[:max_length]
+            
+        # 3. Padding (Remplir si trop court)
+        # On calcule le masque d'attention en même temps (1 pour les vrais tokens, 0 pour le padding)
+        attention_mask = [1] * len(ids)
+        
+        if padding == "max_length" and max_length is not None:
+            if len(ids) < max_length:
+                pad_len = max_length - len(ids)
+                ids = ids + [self.pad_token_id] * pad_len
+                attention_mask = attention_mask + [0] * pad_len
+                
+        # 4. Conversion en Tenseurs PyTorch
+        if return_tensors == "pt":
+            # data.py s'attend à une dimension de batch [1, seq_len] pour pouvoir faire .squeeze(0)
+            return {
+                "input_ids": torch.tensor([ids], dtype=torch.long),
+                "attention_mask": torch.tensor([attention_mask], dtype=torch.long)
+            }
+            
+        # Fallback (liste simple)
+        return {
+            "input_ids": ids,
+            "attention_mask": attention_mask
+        }
+
+    def save_pretrained(self, save_directory):
+        pass
+
+    @classmethod
+    def from_pretrained(cls, save_directory):
+        return cls()
+'''
+
+
+
+
+
+"""
+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
+"""
+
+
+
+'''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)