Chess Challenge submission by Br0wks

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+# chess_test
+Submitted by: Br0wks
+Parameters: 924,000

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 112,
+  "n_head": 8,
+  "n_inner": 336,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 1200
+}

model.py

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+"""
+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=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)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9de7e6548178446130b8f76b2cbd82b98c64822ee046afefb208b0e897e8da3
+size 3702448

special_tokens_map.json

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+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

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+"""
+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
+"""
+Custom Chess Tokenizer - Normalized Version
+"""
+import re
+
+# Regex pour extraire case départ, arrivée et promotion
+MOVE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])")
+PROMO_RE = re.compile(r"=([NBRQ])")
+
+def normalize_move(tok: str) -> str:
+    """Transforme 'WPe2e4(x)' en 'WPe2e4' pour réduire le vocabulaire."""
+    # 1. Garder les infos de base
+    m = MOVE_RE.search(tok)
+    if not m:
+        return tok # Fallback (sera probablement UNK)
+    
+    fr, to = m.group(1), m.group(2)
+    
+    # 2. Gérer la promotion
+    promo = ""
+    pm = PROMO_RE.search(tok)
+    if pm:
+        promo = "=" + pm.group(1)
+        
+    # 3. Reconstruire le token standardisé
+    # On garde le préfixe WP/BN (chars 0 et 1) pour garder l'info couleur/pièce
+    # mais on supprime les suffixes (x), (+), etc.
+    prefix = tok[:2] if len(tok) >= 2 else "WP"
+    return f"{prefix}{fr}{to}{promo}"
+
+class ChessTokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids", "attention_mask"]
+    
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+    
+    def __init__(self, vocab_file=None, vocab=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
+        
+        # Nettoyage kwargs
+        for t in ["pad_token", "bos_token", "eos_token", "unk_token"]:
+            kwargs.pop(t, None)
+            
+        if vocab:
+            self._vocab = vocab
+        elif vocab_file:
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            self._vocab = {t: i for i, t in enumerate([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])}
+            
+        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, **kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self._vocab)
+
+    def get_vocab(self):
+        return dict(self._vocab)
+
+    def _tokenize(self, text):
+        # C'est ICI que la magie opère : on normalise à la volée
+        return [normalize_move(t) for t in text.strip().split()]
+
+    def _convert_token_to_id(self, token):
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
+
+    def _convert_id_to_token(self, index):
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+
+    def convert_tokens_to_string(self, tokens):
+        return " ".join(t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
+    
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        if not os.path.exists(save_directory):
+            os.makedirs(save_directory)
+        path = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json")
+        with open(path, "w") as f:
+            json.dump(self._vocab, f, indent=2)
+        return (path,)
+
+    @classmethod
+    def build_vocab_from_dataset(cls, dataset_name, min_frequency=2, max_vocab_size=1200, **kwargs):
+        """Construit un vocabulaire compact et dense."""
+        from datasets import load_dataset
+        from collections import Counter
+        
+        # On charge en streaming pour aller vite
+        ds = load_dataset(dataset_name, split="train", streaming=True)
+        ds = ds.take(50000) # 50k parties suffisent pour voir tous les coups possibles
+        
+        counter = Counter()
+        for ex in ds:
+            # On normalise avant de compter !
+            moves = [normalize_move(t) for t in ex["text"].split()]
+            counter.update(moves)
+            
+        # On garde les tokens spéciaux + les N plus fréquents
+        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        most_common = counter.most_common(max_vocab_size - len(special))
+        
+        vocab = {t: i for i, t in enumerate(special + [t for t, c in most_common])}
+        return cls(vocab=vocab)

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 256,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]",
+  "vocab_file": "vocab.json",
+  "vocab_files_names": {
+    "vocab_file": "vocab.json"
+  },
+  "vocab_size": 1200,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ],
+    "AutoModelForCausalLM": [
+      "model.ChessForCausalLM",
+      null
+    ]
+  }
+}

utils.py

@@ -0,0 +1,305 @@
+"""
+Utility functions for the Chess Challenge.
+
+This module provides helper functions for:
+- Parameter counting and budget analysis
+- Model registration with Hugging Face
+- Move validation with python-chess
+"""
+
+from __future__ import annotations
+
+from typing import Dict, Optional, TYPE_CHECKING
+
+import torch.nn as nn
+
+if TYPE_CHECKING:
+    from src.model import ChessConfig
+
+
+def count_parameters(model: nn.Module, trainable_only: bool = True) -> int:
+    """
+    Count the number of parameters in a model.
+    
+    Args:
+        model: The PyTorch model.
+        trainable_only: If True, only count trainable parameters.
+    
+    Returns:
+        Total number of parameters.
+    """
+    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 count_parameters_by_component(model: nn.Module) -> Dict[str, int]:
+    """
+    Count parameters broken down by model component.
+    
+    Args:
+        model: The PyTorch model.
+    
+    Returns:
+        Dictionary mapping component names to parameter counts.
+    """
+    counts = {}
+    for name, module in model.named_modules():
+        if len(list(module.children())) == 0:  # Leaf module
+            param_count = sum(p.numel() for p in module.parameters(recurse=False))
+            if param_count > 0:
+                counts[name] = param_count
+    return counts
+
+
+def estimate_parameters(config: "ChessConfig") -> Dict[str, int]:
+    """
+    Estimate the parameter count for a given configuration.
+    
+    This is useful for planning your architecture before building the model.
+    
+    Args:
+        config: Model configuration.
+    
+    Returns:
+        Dictionary with estimated parameter counts by component.
+    """
+    V = config.vocab_size
+    d = config.n_embd
+    L = config.n_layer
+    n_ctx = config.n_ctx
+    n_inner = config.n_inner
+    
+    estimates = {
+        "token_embeddings": V * d,
+        "position_embeddings": n_ctx * d,
+        "attention_qkv_per_layer": 3 * d * d,
+        "attention_proj_per_layer": d * d,
+        "ffn_per_layer": 2 * d * n_inner,
+        "layernorm_per_layer": 4 * d,  # 2 LayerNorms, each with weight and bias
+        "final_layernorm": 2 * d,
+    }
+    
+    # Calculate totals
+    per_layer = (
+        estimates["attention_qkv_per_layer"] +
+        estimates["attention_proj_per_layer"] +
+        estimates["ffn_per_layer"] +
+        estimates["layernorm_per_layer"]
+    )
+    
+    estimates["total_transformer_layers"] = L * per_layer
+    
+    # LM head (tied with embeddings by default)
+    if config.tie_weights:
+        estimates["lm_head"] = 0
+        estimates["lm_head_note"] = "Tied with token embeddings"
+    else:
+        estimates["lm_head"] = V * d
+    
+    # Grand total
+    estimates["total"] = (
+        estimates["token_embeddings"] +
+        estimates["position_embeddings"] +
+        estimates["total_transformer_layers"] +
+        estimates["final_layernorm"] +
+        estimates["lm_head"]
+    )
+    
+    return estimates
+
+
+def print_parameter_budget(config: "ChessConfig", limit: int = 1_000_000) -> None:
+    """
+    Print a formatted parameter budget analysis.
+    
+    Args:
+        config: Model configuration.
+        limit: Parameter limit to compare against.
+    """
+    estimates = estimate_parameters(config)
+    
+    print("=" * 60)
+    print("PARAMETER BUDGET ANALYSIS")
+    print("=" * 60)
+    print(f"\nConfiguration:")
+    print(f"  vocab_size (V) = {config.vocab_size}")
+    print(f"  n_embd (d)     = {config.n_embd}")
+    print(f"  n_layer (L)    = {config.n_layer}")
+    print(f"  n_head         = {config.n_head}")
+    print(f"  n_ctx          = {config.n_ctx}")
+    print(f"  n_inner        = {config.n_inner}")
+    print(f"  tie_weights    = {config.tie_weights}")
+    
+    print(f"\nParameter Breakdown:")
+    print(f"  Token Embeddings:    {estimates['token_embeddings']:>10,}")
+    print(f"  Position Embeddings: {estimates['position_embeddings']:>10,}")
+    print(f"  Transformer Layers:  {estimates['total_transformer_layers']:>10,}")
+    print(f"  Final LayerNorm:     {estimates['final_layernorm']:>10,}")
+    
+    if config.tie_weights:
+        print(f"  LM Head:             {'(tied)':>10}")
+    else:
+        print(f"  LM Head:             {estimates['lm_head']:>10,}")
+    
+    print(f"  " + "-" * 30)
+    print(f"  TOTAL:               {estimates['total']:>10,}")
+    
+    print(f"\nBudget Status:")
+    print(f"  Limit:    {limit:>10,}")
+    print(f"  Used:     {estimates['total']:>10,}")
+    print(f"  Remaining:{limit - estimates['total']:>10,}")
+    
+    if estimates['total'] <= limit:
+        print(f"\n Within budget! ({estimates['total'] / limit * 100:.1f}% used)")
+    else:
+        print(f"\n OVER BUDGET by {estimates['total'] - limit:,} parameters!")
+    
+    print("=" * 60)
+
+
+def validate_move_with_chess(move: str, board_fen: Optional[str] = None) -> bool:
+    """
+    Validate a move using python-chess.
+    
+    This function converts the dataset's extended UCI format to standard UCI
+    and validates it against the current board state.
+    
+    Args:
+        move: Move in extended UCI format (e.g., "WPe2e4", "BNg8f6(x)").
+        board_fen: FEN string of the current board state (optional).
+    
+    Returns:
+        True if the move is legal, False otherwise.
+    """
+    try:
+        import chess
+    except ImportError:
+        raise ImportError("python-chess is required for move validation. "
+                         "Install it with: pip install python-chess")
+    
+    # Parse the extended UCI format
+    # Format: [W|B][Piece][from_sq][to_sq][suffix]
+    # Example: WPe2e4, BNg8f6(x), WKe1g1(o)
+    
+    if len(move) < 6:
+        return False
+    
+    # Extract components
+    color = move[0]  # W or B
+    piece = move[1]  # P, N, B, R, Q, K
+    from_sq = move[2:4]  # e.g., "e2"
+    to_sq = move[4:6]  # e.g., "e4"
+    
+    # Check for promotion
+    promotion = None
+    if "=" in move:
+        promo_idx = move.index("=")
+        promotion = move[promo_idx + 1].lower()
+    
+    # Create board
+    board = chess.Board(board_fen) if board_fen else chess.Board()
+    
+    # Build UCI move string
+    uci_move = from_sq + to_sq
+    if promotion:
+        uci_move += promotion
+    
+    try:
+        move_obj = chess.Move.from_uci(uci_move)
+        return move_obj in board.legal_moves
+    except (ValueError, chess.InvalidMoveError):
+        return False
+
+
+def convert_extended_uci_to_uci(move: str) -> str:
+    """
+    Convert extended UCI format to standard UCI format.
+    
+    Args:
+        move: Move in extended UCI format (e.g., "WPe2e4").
+    
+    Returns:
+        Move in standard UCI format (e.g., "e2e4").
+    """
+    if len(move) < 6:
+        return move
+    
+    # Extract squares
+    from_sq = move[2:4]
+    to_sq = move[4:6]
+    
+    # Check for promotion
+    promotion = ""
+    if "=" in move:
+        promo_idx = move.index("=")
+        promotion = move[promo_idx + 1].lower()
+    
+    return from_sq + to_sq + promotion
+
+
+def convert_uci_to_extended(
+    uci_move: str,
+    board_fen: str,
+) -> str:
+    """
+    Convert standard UCI format to extended UCI format.
+    
+    Args:
+        uci_move: Move in standard UCI format (e.g., "e2e4").
+        board_fen: FEN string of the current board state.
+    
+    Returns:
+        Move in extended UCI format (e.g., "WPe2e4").
+    """
+    try:
+        import chess
+    except ImportError:
+        raise ImportError("python-chess is required for move conversion.")
+    
+    board = chess.Board(board_fen)
+    move = chess.Move.from_uci(uci_move)
+    
+    # Get color
+    color = "W" if board.turn == chess.WHITE else "B"
+    
+    # Get piece
+    piece = board.piece_at(move.from_square)
+    piece_letter = piece.symbol().upper() if piece else "P"
+    
+    # Build extended UCI
+    from_sq = chess.square_name(move.from_square)
+    to_sq = chess.square_name(move.to_square)
+    
+    result = f"{color}{piece_letter}{from_sq}{to_sq}"
+    
+    # Add promotion
+    if move.promotion:
+        result += f"={chess.piece_symbol(move.promotion).upper()}"
+    
+    # Add suffix for captures
+    if board.is_capture(move):
+        result += "(x)"
+    
+    # Add suffix for check/checkmate
+    board.push(move)
+    if board.is_checkmate():
+        if "(x)" in result:
+            result = result.replace("(x)", "(x+*)")
+        else:
+            result += "(+*)"
+    elif board.is_check():
+        if "(x)" in result:
+            result = result.replace("(x)", "(x+)")
+        else:
+            result += "(+)"
+    board.pop()
+    
+    # Handle castling notation
+    if board.is_castling(move):
+        if move.to_square in [chess.G1, chess.G8]:  # Kingside
+            result = result.replace("(x)", "").replace("(+)", "") + "(o)"
+        else:  # Queenside
+            result = result.replace("(x)", "").replace("(+)", "") + "(O)"
+    
+    return result

vocab.json

@@ -0,0 +1,1202 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "WNg1f3": 4,
+  "BNg8f6": 5,
+  "WPe2e4": 6,
+  "WPd2d4": 7,
+  "WNb1c3": 8,
+  "WKe1g1": 9,
+  "BNb8c6": 10,
+  "BKe8g8": 11,
+  "BPd7d5": 12,
+  "BPe7e6": 13,
+  "BPe7e5": 14,
+  "BPd7d6": 15,
+  "WPh2h3": 16,
+  "WPc2c3": 17,
+  "BPg7g6": 18,
+  "BPc7c6": 19,
+  "BPh7h6": 20,
+  "BPc7c5": 21,
+  "BPa7a6": 22,
+  "WPc2c4": 23,
+  "WNf3e5": 24,
+  "BBf8e7": 25,
+  "WPa2a3": 26,
+  "WPg2g3": 27,
+  "WPe2e3": 28,
+  "WPf2f4": 29,
+  "BNb8d7": 30,
+  "WBf1c4": 31,
+  "WPd2d3": 32,
+  "WRf1e1": 33,
+  "WNb1d2": 34,
+  "BPb7b6": 35,
+  "WPe4e5": 36,
+  "WBf1d3": 37,
+  "BPf7f6": 38,
+  "BNf6e4": 39,
+  "WPb2b3": 40,
+  "BPb7b5": 41,
+  "WBc1g5": 42,
+  "WBc1e3": 43,
+  "BBc8g4": 44,
+  "WPf2f3": 45,
+  "BRf8e8": 46,
+  "WPe4d5": 47,
+  "BBf8g7": 48,
+  "WBf1e2": 49,
+  "BPf7f5": 50,
+  "WNf3d4": 51,
+  "WPb2b4": 52,
+  "WBc1f4": 53,
+  "BBc8b7": 54,
+  "BPc5d4": 55,
+  "BNc6d4": 56,
+  "WNc3d5": 57,
+  "WPg2g4": 58,
+  "BNf6d5": 59,
+  "WPa2a4": 60,
+  "BBf8d6": 61,
+  "BBc8d7": 62,
+  "BPa7a5": 63,
+  "WPh2h4": 64,
+  "WPd4d5": 65,
+  "BBc8e6": 66,
+  "WPd4e5": 67,
+  "BRa8c8": 68,
+  "WRa1d1": 69,
+  "BBf8c5": 70,
+  "WQd1e2": 71,
+  "BNc6e5": 72,
+  "BNg8e7": 73,
+  "BPh7h5": 74,
+  "WNc3e4": 75,
+  "BRa8d8": 76,
+  "BPe6e5": 77,
+  "BQd8e7": 78,
+  "BBc8f5": 79,
+  "BPe5d4": 80,
+  "WNf3g5": 81,
+  "WRa1c1": 82,
+  "WQd1f3": 83,
+  "BPd5e4": 84,
+  "WKe1c1": 85,
+  "WKg1h1": 86,
+  "WBf1b5": 87,
+  "BPg7g5": 88,
+  "WQd1d2": 89,
+  "BPc6c5": 90,
+  "WBc1d2": 91,
+  "BBf8b4": 92,
+  "WBf1g2": 93,
+  "BKg8h8": 94,
+  "BKg8g7": 95,
+  "WBc1b2": 96,
+  "WPc4d5": 97,
+  "BPe5e4": 98,
+  "BRa8b8": 99,
+  "WPh4h5": 100,
+  "BKe8c8": 101,
+  "BPe6d5": 102,
+  "BPd6d5": 103,
+  "BPd5d4": 104,
+  "BQd8c7": 105,
+  "WPc3c4": 106,
+  "WRa1b1": 107,
+  "BQd8f6": 108,
+  "BPb5b4": 109,
+  "BQd8d7": 110,
+  "BNf6g4": 111,
+  "BPd6e5": 112,
+  "WKg1g2": 113,
+  "WPf4f5": 114,
+  "WPg4g5": 115,
+  "BPc6d5": 116,
+  "BPb7c6": 117,
+  "WRf1d1": 118,
+  "BRf8d8": 119,
+  "WRa1e1": 120,
+  "WPe3e4": 121,
+  "WNg1e2": 122,
+  "WPc3d4": 123,
+  "WPb2c3": 124,
+  "BPa5a4": 125,
+  "WKg1h2": 126,
+  "BBg4f3": 127,
+  "WPh3h4": 128,
+  "BNd7e5": 129,
+  "BQd8b6": 130,
+  "BNf6d7": 131,
+  "WBg5f6": 132,
+  "BPc5c4": 133,
+  "WPd3d4": 134,
+  "BRa8e8": 135,
+  "BKg8h7": 136,
+  "BBe7f6": 137,
+  "BPg6g5": 138,
+  "WPc4c5": 139,
+  "WQd1c2": 140,
+  "WNd2f3": 141,
+  "WPa4a5": 142,
+  "WPb4b5": 143,
+  "BNd7f6": 144,
+  "BPh5h4": 145,
+  "BPa6a5": 146,
+  "BQd8d5": 147,
+  "WNc3e2": 148,
+  "WKg1f2": 149,
+  "BPg5g4": 150,
+  "WPg3g4": 151,
+  "WPf3f4": 152,
+  "BPf6f5": 153,
+  "BPh6h5": 154,
+  "BKg8f7": 155,
+  "WKg1f1": 156,
+  "WNc3b5": 157,
+  "BKg8f8": 158,
+  "BPd5c4": 159,
+  "BPf5f4": 160,
+  "BNf6h5": 161,
+  "WNd2e4": 162,
+  "WPd4c5": 163,
+  "WBe2f3": 164,
+  "WBb5c6": 165,
+  "WPf4e5": 166,
+  "BBb4c3": 167,
+  "BNc6e7": 168,
+  "WQd1d3": 169,
+  "BPb6b5": 170,
+  "WBc4b3": 171,
+  "WQd1b3": 172,
+  "BNc6b4": 173,
+  "WNf3d2": 174,
+  "WNf3h4": 175,
+  "WQd1d4": 176,
+  "WPa3a4": 177,
+  "BNe7f5": 178,
+  "BNd7c5": 179,
+  "WPb3b4": 180,
+  "WPg2f3": 181,
+  "BPg7f6": 182,
+  "BNc6a5": 183,
+  "BPf7e6": 184,
+  "BNe7g6": 185,
+  "WKc1b1": 186,
+  "BNd7b6": 187,
+  "WNe2g3": 188,
+  "BRf8f7": 189,
+  "BPe5f4": 190,
+  "BQd8d6": 191,
+  "BBg4h5": 192,
+  "WQd1h5": 193,
+  "WBg5h4": 194,
+  "WNd2c4": 195,
+  "BQd8a5": 196,
+  "WPe3d4": 197,
+  "BBd7c6": 198,
+  "BRh8g8": 199,
+  "WPe5f6": 200,
+  "WBd3e4": 201,
+  "WRf1f2": 202,
+  "BKc8b8": 203,
+  "BPf6e5": 204,
+  "WPe4f5": 205,
+  "BPh6g5": 206,
+  "BKe8d7": 207,
+  "BKe8f7": 208,
+  "WPe5e6": 209,
+  "WRd1d2": 210,
+  "WRd1e1": 211,
+  "WBe3d4": 212,
+  "WRa1f1": 213,
+  "WNd4c6": 214,
+  "BRf8c8": 215,
+  "WNc3a4": 216,
+  "BPf5e4": 217,
+  "BRa8f8": 218,
+  "WPf2e3": 219,
+  "BKe8d8": 220,
+  "WNd2b3": 221,
+  "BBe7d6": 222,
+  "WRh1g1": 223,
+  "WNe2f4": 224,
+  "BKe8e7": 225,
+  "WPd5d6": 226,
+  "WPh3g4": 227,
+  "BRd8d7": 228,
+  "WPd3e4": 229,
+  "WRe1e2": 230,
+  "BPa6b5": 231,
+  "WPh5h6": 232,
+  "BRd8e8": 233,
+  "WRe1d1": 234,
+  "BBd6e5": 235,
+  "BPa4a3": 236,
+  "WBf4g3": 237,
+  "WRf1c1": 238,
+  "BKf8e7": 239,
+  "BRe8e7": 240,
+  "WBc4d5": 241,
+  "WNe4f6": 242,
+  "WRh1e1": 243,
+  "WBf4e5": 244,
+  "WPa3b4": 245,
+  "WKf1e2": 246,
+  "WRd1d8": 247,
+  "WBd2c3": 248,
+  "BPh7g6": 249,
+  "BNe7d5": 250,
+  "BRd8d1": 251,
+  "BBc8a6": 252,
+  "BPd4d3": 253,
+  "BRh8e8": 254,
+  "BRh8f8": 255,
+  "WPa5a6": 256,
+  "WRe1e3": 257,
+  "BQd8h4": 258,
+  "BKe8f8": 259,
+  "WPf3e4": 260,
+  "BKg7f6": 261,
+  "WPa4b5": 262,
+  "WNe5c6": 263,
+  "WBe2d3": 264,
+  "WPe5d6": 265,
+  "WRf1f3": 266,
+  "WQd1g4": 267,
+  "BPe4e3": 268,
+  "BBc5b6": 269,
+  "WKe1d2": 270,
+  "BBe7g5": 271,
+  "WKe1d1": 272,
+  "WKe1e2": 273,
+  "WNe2d4": 274,
+  "WQd1a4": 275,
+  "BRe8d8": 276,
+  "BPa5b4": 277,
+  "BQd8g5": 278,
+  "BPe6f5": 279,
+  "WKg2f3": 280,
+  "BPf7g6": 281,
+  "BNb8a6": 282,
+  "WRd1d7": 283,
+  "WBc4d3": 284,
+  "BPb5c4": 285,
+  "WKf2e3": 286,
+  "BPh5g4": 287,
+  "BNg8h6": 288,
+  "BNe5f3": 289,
+  "BBh5g6": 290,
+  "BRe8e1": 291,
+  "WKh1g1": 292,
+  "BBg7e5": 293,
+  "BPh4h3": 294,
+  "WPd5c6": 295,
+  "BRf8f6": 296,
+  "WRe1e8": 297,
+  "BNd5c3": 298,
+  "BBe6d5": 299,
+  "BPd4c3": 300,
+  "WPh4g5": 301,
+  "WBg5e7": 302,
+  "BKf7e6": 303,
+  "WRh1f1": 304,
+  "BNe7c6": 305,
+  "WNe5d7": 306,
+  "WPf5f6": 307,
+  "BPg6f5": 308,
+  "WBh4g3": 309,
+  "WNg5e6": 310,
+  "BBg7f6": 311,
+  "BBf5g6": 312,
+  "WRe1e4": 313,
+  "WRe1e5": 314,
+  "BBe7c5": 315,
+  "WBd3e2": 316,
+  "BPb4b3": 317,
+  "WBd3c4": 318,
+  "WBe3c5": 319,
+  "WPc5c6": 320,
+  "WNb1a3": 321,
+  "WRe1e7": 322,
+  "BNe4c3": 323,
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