Fix model loading with auto_map

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors.backup filter=lfs diff=lfs merge=lfs -text

__init__.py

@@ -0,0 +1,22 @@
+"""Chess Challenge source module."""
+
+from .model import ChessConfig, ChessForCausalLM
+from .tokenizer import ChessTokenizer
+
+# Lazy import for evaluate to avoid RuntimeWarning when running as module
+def __getattr__(name):
+    if name == "ChessEvaluator":
+        from .evaluate import ChessEvaluator
+        return ChessEvaluator
+    if name == "load_model_from_hub":
+        from .evaluate import load_model_from_hub
+        return load_model_from_hub
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+__all__ = [
+    "ChessConfig",
+    "ChessForCausalLM", 
+    "ChessTokenizer",
+    "ChessEvaluator",
+    "load_model_from_hub",
+]

config.json

@@ -17,5 +17,9 @@
   "rope_base": 10000,
   "tie_weights": true,
   "transformers_version": "4.57.0",
-  "vocab_size": 1682
-}
+  "vocab_size": 1682,
+  "auto_map": {
+    "AutoConfig": "model.ChessConfig",
+    "AutoModelForCausalLM": "model.ChessForCausalLM"
+  }
+}

model.py

@@ -0,0 +1,559 @@
+"""
+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 RMSNorm(nn.Module):
+    """
+    Root Mean Square Layer Normalization.
+
+    RMSNorm is more efficient than LayerNorm as it:
+    - Does not subtract mean (re-centering)
+    - Does not have a bias parameter
+    - Only has scale parameter (weight)
+
+    This saves computation and parameters while maintaining performance.
+    Paper: https://arxiv.org/abs/1910.07467
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x: torch.Tensor) -> torch.Tensor:
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+class RotaryPositionalEmbedding(nn.Module):
+    """
+    Rotary Position Embedding (RoPE).
+
+    RoPE encodes absolute positional information with rotation matrices
+    and incorporates relative positional information naturally.
+
+    Advantages over learned embeddings:
+    - No additional parameters (saves n_ctx * n_embd parameters)
+    - Better extrapolation to longer sequences
+    - Encodes relative positions naturally
+
+    Paper: https://arxiv.org/abs/2104.09864
+    """
+
+    def __init__(self, dim: int, max_seq_len: int = 2048, base: int = 10000):
+        super().__init__()
+        self.dim = dim
+        self.max_seq_len = max_seq_len
+        self.base = base
+
+        # Compute inverse frequencies
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Pre-compute frequencies for max_seq_len
+        self._set_cos_sin_cache(max_seq_len)
+
+    def _set_cos_sin_cache(self, seq_len: int):
+        self.max_cached_len = seq_len
+        t = torch.arange(seq_len, device=self.inv_freq.device).type_as(self.inv_freq)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but uses a different permutation to get same result
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        # x: [batch_size, seq_len, n_head, head_dim]
+        if seq_len > self.max_cached_len:
+            self._set_cos_sin_cache(seq_len)
+
+        return (
+            self.cos_cached[:seq_len, ...].to(x.device),
+            self.sin_cached[:seq_len, ...].to(x.device),
+        )
+
+
+def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary position embeddings to query and key tensors.
+
+    Args:
+        q: Query tensor of shape [batch_size, seq_len, n_head, head_dim]
+        k: Key tensor of shape [batch_size, seq_len, n_head, head_dim]
+        cos: Cosine values of shape [seq_len, head_dim]
+        sin: Sine values of shape [seq_len, head_dim]
+
+    Returns:
+        Tuple of rotated (q, k) tensors
+    """
+    # Reshape cos and sin for broadcasting
+    cos = cos.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, head_dim]
+    sin = sin.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, head_dim]
+
+    # Rotate half
+    def rotate_half(x):
+        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+        return torch.cat((-x2, x1), dim=-1)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    return q_embed, k_embed
+
+
+class ChessConfig(PretrainedConfig):
+    """
+    Configuration class for the Chess Transformer model.
+
+    This configuration is designed for a ~1M parameter model.
+    Optimizations applied:
+    - RMSNorm instead of LayerNorm (fewer params, same performance)
+    - RoPE instead of learned positional embeddings (saves n_ctx * n_embd params)
+    - Weight tying between embeddings and output (saves vocab_size * n_embd params)
+
+    Parameter budget breakdown (with default values):
+    - Token Embeddings: 1200 x 128 = 153,600
+    - Position Embeddings: 0 (using RoPE - no parameters)
+    - Transformer Layers: 6 x ~115,000 = ~690,000 (RMSNorm saves params)
+    - LM Head (with weight tying): 0 (shared with embeddings)
+    - Total: ~843,000 parameters (157k saved vs LayerNorm + learned pos embeddings)
+
+    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.
+        rms_norm_eps: Epsilon for RMS normalization.
+        tie_weights: Whether to tie embedding and output weights.
+        rope_base: Base for RoPE frequency calculation.
+    """
+    
+    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,
+        rms_norm_eps: float = 1e-6,
+        tie_weights: bool = True,
+        rope_base: int = 10000,
+        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.rms_norm_eps = rms_norm_eps
+        self.tie_weights = tie_weights
+        self.rope_base = rope_base
+        # Inform HF base class about tying behavior
+        self.tie_word_embeddings = bool(tie_weights)
+
+
+class MultiHeadAttention(nn.Module):
+    """
+    Multi-head self-attention module with RoPE.
+
+    This implementation uses:
+    - Rotary Position Embeddings (RoPE) for position encoding
+    - Causal masking for autoregressive generation
+    - Scaled dot-product attention
+    """
+
+    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)
+
+        # RoPE for positional encoding
+        self.rotary_emb = RotaryPositionalEmbedding(
+            dim=self.head_dim,
+            max_seq_len=config.n_ctx,
+            base=config.rope_base,
+        )
+
+        # Causal mask
+        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: [batch, seq_len, n_head, head_dim]
+        q = q.view(batch_size, seq_len, self.n_head, self.head_dim)
+        k = k.view(batch_size, seq_len, self.n_head, self.head_dim)
+        v = v.view(batch_size, seq_len, self.n_head, self.head_dim)
+
+        # Apply RoPE to Q and K
+        cos, sin = self.rotary_emb(q, seq_len)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+
+        # Transpose for attention: [batch, n_head, seq_len, head_dim]
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.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 (RMSNorm before attention/FFN) for better
+    training stability and parameter efficiency.
+    """
+
+    def __init__(self, config: ChessConfig):
+        super().__init__()
+
+        self.rms_1 = RMSNorm(config.n_embd, eps=config.rms_norm_eps)
+        self.attn = MultiHeadAttention(config)
+        self.rms_2 = RMSNorm(config.n_embd, eps=config.rms_norm_eps)
+        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.rms_1(x), attention_mask=attention_mask)
+        # Pre-norm FFN
+        x = x + self.mlp(self.rms_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 an optimized GPT-style architecture with:
+    - Token embeddings for chess moves
+    - RoPE (Rotary Position Embeddings) - no learned positional embeddings
+    - RMSNorm for efficient normalization
+    - 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.
+
+    Optimizations:
+    - RoPE saves n_ctx * n_embd parameters vs learned embeddings
+    - RMSNorm saves parameters vs LayerNorm (no bias, no mean centering)
+    - Weight tying saves vocab_size * n_embd 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 embeddings (no positional embeddings - using RoPE instead)
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+
+        self.drop = nn.Dropout(config.dropout)
+
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            TransformerBlock(config) for _ in range(config.n_layer)
+        ])
+
+        # Final RMS norm
+        self.rms_f = RMSNorm(config.n_embd, eps=config.rms_norm_eps)
+
+        # 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 with adaptations for RMSNorm."""
+        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, RMSNorm):
+            torch.nn.init.ones_(module.weight)
+    
+    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: Not used (kept for compatibility). Position encoding via RoPE.
+            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
+
+        # Get token embeddings (no positional embeddings - using RoPE in attention)
+        hidden_states = self.wte(input_ids)
+        hidden_states = self.drop(hidden_states)
+
+        # Pass through transformer blocks
+        for block in self.h:
+            hidden_states = block(hidden_states, attention_mask=attention_mask)
+
+        # Final RMS norm
+        hidden_states = self.rms_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 = 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

@@ -1,3 +1,3 @@
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model.safetensors.backup

@@ -0,0 +1,3 @@
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tokenizer_config.json

@@ -33,12 +33,6 @@
       "special": true
     }
   },
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenizer.ChessTokenizer",
-      null
-    ]
-  },
   "bos_token": "[BOS]",
   "clean_up_tokenization_spaces": false,
   "eos_token": "[EOS]",

training_args.bin

@@ -0,0 +1,3 @@
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