Chess Challenge submission by nathanael-fijalkow

README.md

@@ -26,10 +26,6 @@ model = AutoModelForCausalLM.from_pretrained("LLM-course/my-chess-model", trust_
 tokenizer = AutoTokenizer.from_pretrained("LLM-course/my-chess-model", trust_remote_code=True)
 ```
 
-## Model Details
+## Evaluation
 
-- **Architecture**: Chess Transformer (GPT-style)
-- **Vocab size**: 1682
-- **Embedding dim**: 128
-- **Layers**: 4
-- **Heads**: 4
+This model is evaluated at the [Chess Challenge Arena](https://huggingface.co/spaces/LLM-course/Chess1MChallenge).

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "ChessForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  },
   "bos_token_id": 1,
   "dropout": 0.1,
   "dtype": "float32",

data.py

@@ -0,0 +1,253 @@
+"""
+Data loading utilities for the Chess Challenge.
+
+This module provides functions to load and process chess game data
+from the Lichess dataset on Hugging Face.
+"""
+
+from __future__ import annotations
+
+from typing import Dict, Iterator, List, Optional
+
+import torch
+from torch.utils.data import Dataset
+
+
+class ChessDataset(Dataset):
+    """
+    PyTorch Dataset for chess games.
+    
+    This dataset loads games from a Hugging Face dataset and prepares
+    them for language modeling training.
+    
+    Each game is tokenized and truncated/padded to max_length.
+    The labels are shifted by one position for next-token prediction.
+    
+    Example:
+        >>> from tokenizer import ChessTokenizer
+        >>> tokenizer = ChessTokenizer.build_vocab_from_dataset()
+        >>> dataset = ChessDataset(tokenizer, max_length=256)
+        >>> sample = dataset[0]
+        >>> print(sample["input_ids"].shape)  # (256,)
+    """
+    
+    def __init__(
+        self,
+        tokenizer,
+        dataset_name: str = "dlouapre/lichess_2025-01_1M",
+        split: str = "train",
+        column: str = "text",
+        max_length: int = 256,
+        max_samples: Optional[int] = None,
+    ):
+        """
+        Initialize the chess dataset.
+        
+        Args:
+            tokenizer: The chess tokenizer to use.
+            dataset_name: Name of the dataset on Hugging Face Hub.
+            split: Dataset split to use.
+            column: Column containing the game strings.
+            max_length: Maximum sequence length.
+            max_samples: Maximum number of samples to load.
+        """
+        from datasets import load_dataset
+        
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.column = column
+        
+        # Load dataset
+        dataset = load_dataset(dataset_name, split=split)
+        
+        if max_samples is not None:
+            dataset = dataset.select(range(min(max_samples, len(dataset))))
+        
+        self.data = dataset
+    
+    def __len__(self) -> int:
+        return len(self.data)
+    
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        game = self.data[idx][self.column]
+        
+        # Prepend BOS token for proper language modeling
+        game_with_bos = self.tokenizer.bos_token + " " + game
+        
+        # Tokenize
+        encoding = self.tokenizer(
+            game_with_bos,
+            truncation=True,
+            max_length=self.max_length,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        
+        # Squeeze batch dimension
+        input_ids = encoding["input_ids"].squeeze(0)
+        attention_mask = encoding["attention_mask"].squeeze(0)
+        
+        # Labels are the same as input_ids (model will shift internally)
+        labels = input_ids.clone()
+        
+        # Set padding tokens to -100 to ignore in loss
+        labels[attention_mask == 0] = -100
+        
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class ChessDataCollator:
+    """
+    Data collator for chess games.
+    
+    This collator pads sequences to the same length within a batch
+    and creates the appropriate attention masks.
+    """
+    
+    def __init__(self, tokenizer, max_length: int = 256):
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+    
+    def __call__(self, features: List[Dict]) -> Dict[str, torch.Tensor]:
+        # Stack tensors
+        input_ids = torch.stack([f["input_ids"] for f in features])
+        attention_mask = torch.stack([f["attention_mask"] for f in features])
+        labels = torch.stack([f["labels"] for f in features])
+        
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+def create_train_val_datasets(
+    tokenizer,
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    max_length: int = 256,
+    train_samples: Optional[int] = None,
+    val_samples: int = 5000,
+    val_ratio: float = 0.05,
+):
+    """
+    Create training and validation datasets.
+    
+    Args:
+        tokenizer: The chess tokenizer.
+        dataset_name: Name of the dataset.
+        max_length: Maximum sequence length.
+        train_samples: Maximum training samples (None for all).
+        val_samples: Number of validation samples.
+        val_ratio: Ratio of validation samples (used if train_samples is None).
+    
+    Returns:
+        Tuple of (train_dataset, val_dataset).
+    """
+    from datasets import load_dataset
+    
+    # Load full dataset
+    full_dataset = load_dataset(dataset_name, split="train")
+    
+    # Determine split sizes
+    total = len(full_dataset)
+    
+    if train_samples is not None:
+        n_train = min(train_samples, total - val_samples)
+    else:
+        n_train = int(total * (1 - val_ratio))
+    
+    n_val = min(val_samples, total - n_train)
+    
+    # Split dataset
+    train_data = full_dataset.select(range(n_train))
+    val_data = full_dataset.select(range(n_train, n_train + n_val))
+    
+    # Create dataset objects
+    train_dataset = ChessDataset(
+        tokenizer=tokenizer,
+        dataset_name=dataset_name,
+        max_length=max_length,
+    )
+    train_dataset.data = train_data
+    
+    val_dataset = ChessDataset(
+        tokenizer=tokenizer,
+        dataset_name=dataset_name,
+        max_length=max_length,
+    )
+    val_dataset.data = val_data
+    
+    return train_dataset, val_dataset
+
+
+def stream_games(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    split: str = "train",
+    column: str = "text",
+) -> Iterator[str]:
+    """
+    Stream games from the dataset for memory-efficient processing.
+    
+    Args:
+        dataset_name: Name of the dataset on Hugging Face Hub.
+        split: Dataset split to use.
+        column: Column containing the game strings.
+    
+    Yields:
+        Game strings one at a time.
+    """
+    from datasets import load_dataset
+    
+    dataset = load_dataset(dataset_name, split=split, streaming=True)
+    
+    for example in dataset:
+        yield example[column]
+
+
+def analyze_dataset_statistics(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    max_samples: int = 10000,
+) -> Dict:
+    """
+    Analyze statistics of the chess dataset.
+    
+    Args:
+        dataset_name: Name of the dataset.
+        max_samples: Maximum number of samples to analyze.
+    
+    Returns:
+        Dictionary containing dataset statistics.
+    """
+    from collections import Counter
+    from datasets import load_dataset
+    
+    dataset = load_dataset(dataset_name, split="train")
+    dataset = dataset.select(range(min(max_samples, len(dataset))))
+    
+    game_lengths = []
+    move_counts = Counter()
+    opening_moves = Counter()
+    
+    for example in dataset:
+        moves = example["text"].strip().split()
+        game_lengths.append(len(moves))
+        move_counts.update(moves)
+        
+        # Track common openings (first 4 moves)
+        if len(moves) >= 4:
+            opening = " ".join(moves[:4])
+            opening_moves[opening] += 1
+    
+    return {
+        "total_games": len(dataset),
+        "avg_game_length": sum(game_lengths) / len(game_lengths),
+        "min_game_length": min(game_lengths),
+        "max_game_length": max(game_lengths),
+        "unique_moves": len(move_counts),
+        "most_common_moves": move_counts.most_common(20),
+        "most_common_openings": opening_moves.most_common(10),
+    }

model.py

@@ -0,0 +1,438 @@
+"""
+Chess Transformer Model for the Chess Challenge.
+
+This module provides a simple GPT-style transformer architecture
+designed to fit within the 1M parameter constraint.
+
+Key components:
+- ChessConfig: Configuration class for model hyperparameters
+- ChessForCausalLM: The main model class for next-move prediction
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class ChessConfig(PretrainedConfig):
+    """
+    Configuration class for the Chess Transformer model.
+    
+    This configuration is designed for a ~1M parameter model.
+    Students can adjust these values to explore different architectures.
+    
+    Parameter budget breakdown (with default values):
+    - Embeddings (vocab): 1200 x 128 = 153,600
+    - Position Embeddings: 256 x 128 = 32,768
+    - Transformer Layers: 6 x ~120,000 = ~720,000
+    - LM Head (with weight tying): 0 (shared with embeddings)
+    - Total: ~906,000 parameters
+    
+    Attributes:
+        vocab_size: Size of the vocabulary (number of unique moves).
+        n_embd: Embedding dimension (d_model).
+        n_layer: Number of transformer layers.
+        n_head: Number of attention heads.
+        n_ctx: Maximum sequence length (context window).
+        n_inner: Feed-forward inner dimension (default: 3 * n_embd).
+        dropout: Dropout probability.
+        layer_norm_epsilon: Epsilon for layer normalization.
+        tie_weights: Whether to tie embedding and output weights.
+    """
+    
+    model_type = "chess_transformer"
+    
+    def __init__(
+        self,
+        vocab_size: int = 1200,
+        n_embd: int = 128,
+        n_layer: int = 6,
+        n_head: int = 4,
+        n_ctx: int = 256,
+        n_inner: Optional[int] = None,
+        dropout: float = 0.1,
+        layer_norm_epsilon: float = 1e-5,
+        tie_weights: bool = True,
+        pad_token_id: int = 0,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_ctx = n_ctx
+        self.n_inner = n_inner if n_inner is not None else 3 * n_embd  # Reduced from 4x to 3x
+        self.dropout = dropout
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.tie_weights = tie_weights
+        # Inform HF base class about tying behavior
+        self.tie_word_embeddings = bool(tie_weights)
+
+
+class MultiHeadAttention(nn.Module):
+    """
+    Multi-head self-attention module.
+    
+    This is a standard scaled dot-product attention implementation
+    with causal masking for autoregressive generation.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        assert config.n_embd % config.n_head == 0, \
+            f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
+        
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.head_dim = config.n_embd // config.n_head
+        
+        # Combined QKV projection for efficiency
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        
+        self.dropout = nn.Dropout(config.dropout)
+        
+        # Causal mask (will be created on first forward pass)
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(
+                1, 1, config.n_ctx, config.n_ctx
+            ),
+            persistent=False,
+        )
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = x.size()
+        
+        # Compute Q, K, V
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        
+        # Reshape for multi-head attention
+        q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
+        
+        # Scaled dot-product attention
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        # Apply causal mask
+        causal_mask = self.bias[:, :, :seq_len, :seq_len]
+        attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
+        
+        # Apply attention mask (for padding)
+        if attention_mask is not None:
+            # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len)
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf"))
+        
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        # Apply attention to values
+        attn_output = torch.matmul(attn_weights, v)
+        
+        # Reshape back
+        attn_output = attn_output.transpose(1, 2).contiguous().view(
+            batch_size, seq_len, self.n_embd
+        )
+        
+        # Output projection
+        attn_output = self.c_proj(attn_output)
+        
+        return attn_output
+
+
+class FeedForward(nn.Module):
+    """
+    Feed-forward network (MLP) module.
+    
+    Standard two-layer MLP with GELU activation.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        self.c_fc = nn.Linear(config.n_embd, config.n_inner)
+        self.c_proj = nn.Linear(config.n_inner, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.c_fc(x)
+        x = F.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+class TransformerBlock(nn.Module):
+    """
+    A single transformer block with attention and feed-forward layers.
+    
+    Uses pre-normalization (LayerNorm before attention/FFN) for better
+    training stability.
+    """
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+        
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = MultiHeadAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.mlp = FeedForward(config)
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # Pre-norm attention
+        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
+        # Pre-norm FFN
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class ChessForCausalLM(PreTrainedModel):
+    """
+    Chess Transformer for Causal Language Modeling (next-move prediction).
+    
+    This model is designed to predict the next chess move given a sequence
+    of previous moves. It uses a GPT-style architecture with:
+    - Token embeddings for chess moves
+    - Learned positional embeddings
+    - Stacked transformer blocks
+    - Linear head for next-token prediction
+    
+    The model supports weight tying between the embedding layer and the
+    output projection to save parameters.
+    
+    Example:
+        >>> config = ChessConfig(vocab_size=1200, n_embd=128, n_layer=6)
+        >>> model = ChessForCausalLM(config)
+        >>> inputs = {"input_ids": torch.tensor([[1, 42, 87]])}
+        >>> outputs = model(**inputs)
+        >>> next_move_logits = outputs.logits[:, -1, :]
+    """
+    
+    config_class = ChessConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    # Suppress missing-key warning for tied lm_head when loading
+    keys_to_ignore_on_load_missing = ["lm_head.weight"]
+    
+    def __init__(self, config: ChessConfig):
+        super().__init__(config)
+        
+        # Token and position embeddings
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
+        
+        self.drop = nn.Dropout(config.dropout)
+        
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            TransformerBlock(config) for _ in range(config.n_layer)
+        ])
+        
+        # Final layer norm
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        
+        # Output head
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        # Declare tied weights for proper serialization
+        if config.tie_weights:
+            self._tied_weights_keys = ["lm_head.weight"]
+        
+        # Initialize weights
+        self.post_init()
+        
+        # Tie weights if configured
+        if config.tie_weights:
+            self.tie_weights()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings: nn.Module):
+        self.wte = new_embeddings
+        if getattr(self.config, "tie_weights", False):
+            self.tie_weights()
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def tie_weights(self):
+        # Use HF helper to tie or clone depending on config
+        if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False):
+            self._tie_or_clone_weights(self.lm_head, self.wte)
+    
+    def _init_weights(self, module: nn.Module):
+        """Initialize weights following GPT-2 style."""
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            torch.nn.init.ones_(module.weight)
+            torch.nn.init.zeros_(module.bias)
+    
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Forward pass of the model.
+        
+        Args:
+            input_ids: Token IDs of shape (batch_size, seq_len).
+            attention_mask: Attention mask of shape (batch_size, seq_len).
+            position_ids: Position IDs of shape (batch_size, seq_len).
+            labels: Labels for language modeling loss.
+            return_dict: Whether to return a ModelOutput object.
+        
+        Returns:
+            CausalLMOutputWithPast containing loss (if labels provided) and logits.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        batch_size, seq_len = input_ids.size()
+        device = input_ids.device
+        
+        # Create position IDs if not provided
+        if position_ids is None:
+            position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
+        
+        # Get embeddings
+        token_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = self.drop(token_embeds + position_embeds)
+        
+        # Pass through transformer blocks
+        for block in self.h:
+            hidden_states = block(hidden_states, attention_mask=attention_mask)
+        
+        # Final layer norm
+        hidden_states = self.ln_f(hidden_states)
+        
+        # Get logits
+        logits = self.lm_head(hidden_states)
+        
+        # Compute loss if labels are provided
+        loss = None
+        if labels is not None:
+            # Shift logits and labels for next-token prediction
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            
+            # Flatten for cross-entropy
+            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+            # loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1),
+            )
+        
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=None,
+            hidden_states=None,
+            attentions=None,
+        )
+    
+    @torch.no_grad()
+    def generate_move(
+        self,
+        input_ids: torch.LongTensor,
+        temperature: float = 1.0,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+    ) -> int:
+        """
+        Generate the next move given a sequence of moves.
+        
+        Args:
+            input_ids: Token IDs of shape (1, seq_len).
+            temperature: Sampling temperature (1.0 = no change).
+            top_k: If set, only sample from top k tokens.
+            top_p: If set, use nucleus sampling with this threshold.
+        
+        Returns:
+            The token ID of the predicted next move.
+        """
+        self.eval()
+        
+        # Get logits for the last position
+        outputs = self(input_ids)
+        logits = outputs.logits[:, -1, :] / temperature
+        
+        # Apply top-k filtering
+        if top_k is not None:
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = float("-inf")
+        
+        # Apply top-p (nucleus) filtering
+        if top_p is not None:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+            
+            # Remove tokens with cumulative probability above the threshold
+            sorted_indices_to_remove = cumulative_probs > top_p
+            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+            sorted_indices_to_remove[..., 0] = 0
+            
+            indices_to_remove = sorted_indices_to_remove.scatter(
+                dim=-1, index=sorted_indices, src=sorted_indices_to_remove
+            )
+            logits[indices_to_remove] = float("-inf")
+        
+        # Sample from the distribution
+        probs = F.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+        
+        return next_token.item()
+
+
+# Register the model with Auto classes for easy loading
+from transformers import AutoConfig, AutoModelForCausalLM
+
+AutoConfig.register("chess_transformer", ChessConfig)
+AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)

tokenizer.py

@@ -0,0 +1,278 @@
+"""
+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
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+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]"
+    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)

tokenizer_config.json

@@ -33,6 +33,12 @@
       "special": true
     }
   },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      "tokenizer.ChessTokenizer"
+    ]
+  },
   "bos_token": "[BOS]",
   "clean_up_tokenization_spaces": false,
   "eos_token": "[EOS]",
@@ -41,4 +47,4 @@
   "pad_token": "[PAD]",
   "tokenizer_class": "ChessTokenizer",
   "unk_token": "[UNK]"
-}
+}

train.py

@@ -0,0 +1,290 @@
+"""
+Training script for the Chess Challenge.
+
+This script provides a complete training pipeline using the Hugging Face Trainer.
+Students can modify this script to experiment with different training strategies.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import warnings
+from pathlib import Path
+
+# Suppress warnings from third-party libraries (multiprocess has Python 3.14 compat issues)
+warnings.filterwarnings("ignore", message="'return' in a 'finally' block")
+
+import torch
+from transformers import (
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+
+from data import ChessDataCollator, create_train_val_datasets
+from model import ChessConfig, ChessForCausalLM
+from tokenizer import ChessTokenizer
+
+
+def count_parameters(model, trainable_only=True):
+    """Count the number of parameters in a model."""
+    if trainable_only:
+        return sum(p.numel() for p in model.parameters() if p.requires_grad)
+    return sum(p.numel() for p in model.parameters())
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Train a chess-playing language model"
+    )
+    
+    # Model arguments
+    parser.add_argument(
+        "--n_embd", type=int, default=128,
+        help="Embedding dimension"
+    )
+    parser.add_argument(
+        "--n_layer", type=int, default=4,
+        help="Number of transformer layers"
+    )
+    parser.add_argument(
+        "--n_head", type=int, default=4,
+        help="Number of attention heads"
+    )
+    parser.add_argument(
+        "--n_ctx", type=int, default=256,
+        help="Maximum context length"
+    )
+    parser.add_argument(
+        "--n_inner", type=int, default=None,
+        help="Feed-forward inner dimension (default: 4 * n_embd)"
+    )
+    parser.add_argument(
+        "--dropout", type=float, default=0.1,
+        help="Dropout probability"
+    )
+    parser.add_argument(
+        "--no_tie_weights", action="store_true",
+        help="Disable weight tying between embedding and output layers"
+    )
+    
+    # Data arguments
+    parser.add_argument(
+        "--dataset_name", type=str, default="dlouapre/lichess_2025-01_1M",
+        help="Name of the dataset on Hugging Face Hub"
+    )
+    parser.add_argument(
+        "--max_train_samples", type=int, default=None,
+        help="Maximum number of training samples"
+    )
+    parser.add_argument(
+        "--val_samples", type=int, default=5000,
+        help="Number of validation samples"
+    )
+    
+    # Training arguments
+    parser.add_argument(
+        "--output_dir", type=str, default="./output",
+        help="Output directory for model and logs"
+    )
+    parser.add_argument(
+        "--num_train_epochs", type=int, default=3,
+        help="Number of training epochs"
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size", type=int, default=32,
+        help="Training batch size per device"
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size", type=int, default=64,
+        help="Evaluation batch size per device"
+    )
+    parser.add_argument(
+        "--learning_rate", type=float, default=5e-4,
+        help="Learning rate"
+    )
+    parser.add_argument(
+        "--weight_decay", type=float, default=0.01,
+        help="Weight decay"
+    )
+    parser.add_argument(
+        "--warmup_ratio", type=float, default=0.1,
+        help="Warmup ratio"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42,
+        help="Random seed"
+    )
+    
+    # Logging arguments
+    parser.add_argument(
+        "--logging_steps", type=int, default=100,
+        help="Logging frequency"
+    )
+    parser.add_argument(
+        "--eval_steps", type=int, default=500,
+        help="Evaluation frequency"
+    )
+    parser.add_argument(
+        "--save_steps", type=int, default=1000,
+        help="Checkpoint saving frequency"
+    )
+    
+    return parser.parse_args()
+
+
+def main():
+    """Main training function."""
+    args = parse_args()
+    
+    # Set seed for reproducibility
+    set_seed(args.seed)
+    
+    print("=" * 60)
+    print("CHESS CHALLENGE - TRAINING")
+    print("=" * 60)
+    
+    # Build tokenizer from dataset
+    print("\nBuilding tokenizer from dataset...")
+    tokenizer = ChessTokenizer.build_vocab_from_dataset(
+        dataset_name=args.dataset_name,
+        min_frequency=500,  # Only keep moves that appear at least 500 times
+        max_samples=100000,  # Use 100k games to build vocabulary
+    )
+    print(f"   Vocabulary size: {tokenizer.vocab_size}")
+    
+    # Use the vocab size from tokenizer (override args if provided)
+    actual_vocab_size = tokenizer.vocab_size
+    
+    # Create model configuration
+    print("\nCreating model configuration...")
+    config = ChessConfig(
+        vocab_size=actual_vocab_size,
+        n_embd=args.n_embd,
+        n_layer=args.n_layer,
+        n_head=args.n_head,
+        n_ctx=args.n_ctx,
+        n_inner=args.n_inner,
+        dropout=args.dropout,
+        tie_weights=not args.no_tie_weights,
+        pad_token_id=tokenizer.pad_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    
+    # Print configuration
+    print(f"\nModel configuration:")
+    print(f"  vocab_size: {config.vocab_size}")
+    print(f"  n_embd: {config.n_embd}")
+    print(f"  n_layer: {config.n_layer}")
+    print(f"  n_head: {config.n_head}")
+    print(f"  tie_weights: {config.tie_weights}")
+    
+    # Create model
+    print("\nCreating model...")
+    model = ChessForCausalLM(config)
+    n_params = count_parameters(model)
+    print(f"   Total parameters: {n_params:,}")
+    
+    if n_params > 1_000_000:
+        print("WARNING: Model exceeds 1M parameter limit!")
+    else:
+        print("OK: Model is within 1M parameter limit")
+    
+    # Load datasets
+    print("\nLoading datasets...")
+    train_dataset, val_dataset = create_train_val_datasets(
+        tokenizer=tokenizer,
+        dataset_name=args.dataset_name,
+        max_length=args.n_ctx,
+        train_samples=args.max_train_samples,
+        val_samples=args.val_samples,
+    )
+    print(f"   Training samples: {len(train_dataset):,}")
+    print(f"   Validation samples: {len(val_dataset):,}")
+    
+    # Create data collator
+    data_collator = ChessDataCollator(tokenizer, max_length=args.n_ctx)
+    
+    # Training arguments
+    training_args = TrainingArguments(
+        output_dir=args.output_dir,
+        num_train_epochs=args.num_train_epochs,
+        per_device_train_batch_size=args.per_device_train_batch_size,
+        per_device_eval_batch_size=args.per_device_eval_batch_size,
+        learning_rate=args.learning_rate,
+        weight_decay=args.weight_decay,
+        warmup_ratio=args.warmup_ratio,
+        logging_dir=os.path.join(args.output_dir, "logs"),
+        logging_steps=args.logging_steps,
+        eval_strategy="epoch",
+        save_strategy="epoch",
+        save_total_limit=3,
+        load_best_model_at_end=True,
+        metric_for_best_model="eval_loss",
+        greater_is_better=False,
+        seed=args.seed,
+        bf16=torch.cuda.is_available() and torch.cuda.is_bf16_supported(),
+        report_to=["none"],
+    )
+    
+    # Create trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=val_dataset,
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+    )
+    
+    # Train
+    print("\nStarting training...")
+    trainer.train()
+    
+    # Save final model
+    print("\nSaving final model...")
+    final_model_dir = os.path.join(args.output_dir, "final_model")
+    trainer.save_model(final_model_dir)
+    tokenizer.save_pretrained(final_model_dir)
+    
+    # Copy model.py and tokenizer.py for trust_remote_code loading
+    import shutil
+    import json
+    script_dir = Path(__file__).parent
+    shutil.copy(script_dir / "model.py", final_model_dir)
+    shutil.copy(script_dir / "tokenizer.py", final_model_dir)
+    print("   Copied model.py and tokenizer.py")
+    
+    # Add auto_map to config.json for AutoModelForCausalLM
+    config_path = os.path.join(final_model_dir, "config.json")
+    with open(config_path) as f:
+        config_dict = json.load(f)
+    config_dict["auto_map"] = {
+        "AutoConfig": "model.ChessConfig",
+        "AutoModelForCausalLM": "model.ChessForCausalLM",
+    }
+    with open(config_path, "w") as f:
+        json.dump(config_dict, f, indent=2)
+    print("   Added auto_map to config.json")
+    
+    # Add auto_map to tokenizer_config.json for AutoTokenizer
+    tokenizer_config_path = os.path.join(final_model_dir, "tokenizer_config.json")
+    with open(tokenizer_config_path) as f:
+        tokenizer_dict = json.load(f)
+    tokenizer_dict["auto_map"] = {
+        "AutoTokenizer": ["tokenizer.ChessTokenizer", None],
+    }
+    with open(tokenizer_config_path, "w") as f:
+        json.dump(tokenizer_dict, f, indent=2)
+    print("   Added auto_map to tokenizer_config.json")
+    
+    print("\nTraining complete!")
+    print(f"   Model saved to: {final_model_dir}")
+    print("   Ready for submission with: python submit.py --model_path " + final_model_dir)
+
+
+if __name__ == "__main__":
+    main()