model.py

"""
Chess Transformer Model for the Chess Challenge.

This module provides a modular 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

Modular options:
- Attention: MHA (standard), GQA (grouped query), MQA (multi-query)
- Position encoding: learned, rope (rotary), alibi
- FFN activation: gelu, swiglu
"""

from __future__ import annotations

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union, Literal

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
try:
    from transformers.generation.utils import GenerationMixin
except ImportError:  # Fallback for older transformers
    from transformers import GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast

# Type aliases for configuration options
AttentionType = Literal["mha", "gqa", "mqa"]
PositionEncoding = Literal["learned", "rope", "alibi"]
FFNType = Literal["gelu", "swiglu"]


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 (0 with rope/alibi)
    - 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_kv_heads: Number of key-value heads (for GQA/MQA). None = same as n_head.
        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.
        attention_type: Type of attention mechanism ("mha", "gqa", "mqa").
        pos_encoding: Type of position encoding ("learned", "rope", "alibi").
        ffn_type: Type of FFN activation ("gelu", "swiglu").
        rope_theta: Base frequency for RoPE (default 10000.0).
        legal_loss_weight: Auxiliary legal-move loss weight (default 0.0).
    """

    model_type = "chess_transformer"

    def __init__(
        self,
        vocab_size: int = 1200,
        n_embd: int = 128,
        n_layer: int = 6,
        n_head: int = 4,
        n_kv_heads: Optional[int] = None,
        n_ctx: int = 256,
        n_inner: Optional[int] = None,
        dropout: float = 0.1,
        layer_norm_epsilon: float = 1e-5,
        tie_weights: bool = True,
        # New modular options
        attention_type: AttentionType = "mha",
        pos_encoding: PositionEncoding = "learned",
        ffn_type: FFNType = "gelu",
        rope_theta: float = 10000.0,
        legal_loss_weight: float = 0.0,
        # Token IDs
        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
        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)

        # Modular architecture options
        self.attention_type = attention_type
        self.pos_encoding = pos_encoding
        self.ffn_type = ffn_type
        self.rope_theta = rope_theta
        self.legal_loss_weight = legal_loss_weight

        # Handle n_kv_heads based on attention type
        if n_kv_heads is None:
            if attention_type == "mqa":
                self.n_kv_heads = 1
            elif attention_type == "gqa":
                # Default to n_head // 2 for GQA, but at least 1
                self.n_kv_heads = max(1, n_head // 2)
            else:  # mha
                self.n_kv_heads = n_head
        else:
            self.n_kv_heads = n_kv_heads

        # Validation
        assert n_embd % n_head == 0, f"n_embd ({n_embd}) must be divisible by n_head ({n_head})"
        assert n_head % self.n_kv_heads == 0, f"n_head ({n_head}) must be divisible by n_kv_heads ({self.n_kv_heads})"
        assert attention_type in ("mha", "gqa", "mqa"), f"Invalid attention_type: {attention_type}"
        assert pos_encoding in ("learned", "rope", "alibi"), f"Invalid pos_encoding: {pos_encoding}"
        assert ffn_type in ("gelu", "swiglu"), f"Invalid ffn_type: {ffn_type}"


# ==============================================================================
# Position Encoding Modules
# ==============================================================================


class RotaryEmbedding(nn.Module):
    """
    Rotary Position Embedding (RoPE).

    Applies rotary embeddings to queries and keys, encoding position
    information through rotation in the complex plane. This allows
    relative position information without explicit position embeddings.

    Reference: https://arxiv.org/abs/2104.09864
    """

    def __init__(self, dim: int, max_seq_len: int = 256, theta: float = 10000.0):
        super().__init__()
        self.dim = dim
        self.max_seq_len = max_seq_len
        self.theta = theta

        # Precompute frequency bands
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Precompute sin/cos for all positions
        self._build_cache(max_seq_len)

    def _build_cache(self, seq_len: int):
        """Build sin/cos cache for given sequence length."""
        positions = torch.arange(seq_len, dtype=torch.float32)
        freqs = torch.outer(positions, self.inv_freq)
        # Create [cos, sin] interleaved for rotation
        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]:
        """Return cos and sin for the given sequence length."""
        if seq_len > self.max_seq_len:
            self._build_cache(seq_len)
            self.max_seq_len = seq_len
        return (
            self.cos_cached[:seq_len].to(x.dtype),
            self.sin_cached[:seq_len].to(x.dtype),
        )


def rotate_half(x: torch.Tensor) -> torch.Tensor:
    """Rotate half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat([-x2, x1], dim=-1)


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 embedding to queries and keys.

    Args:
        q: Query tensor of shape (batch, n_heads, seq_len, head_dim)
        k: Key tensor of shape (batch, n_kv_heads, seq_len, head_dim)
        cos: Cosine of rotation angles
        sin: Sine of rotation angles

    Returns:
        Rotated q and k tensors
    """
    # cos/sin shape: (seq_len, head_dim) -> (1, 1, seq_len, head_dim)
    cos = cos.unsqueeze(0).unsqueeze(0)
    sin = sin.unsqueeze(0).unsqueeze(0)

    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def build_alibi_slopes(n_heads: int) -> torch.Tensor:
    """
    Build ALiBi slopes for attention bias.

    ALiBi adds a linear bias to attention scores based on position distance.
    The slope decreases geometrically for each head.

    Reference: https://arxiv.org/abs/2108.12409
    """
    def get_slopes_power_of_2(n: int) -> list:
        start = 2 ** (-(2 ** -(math.log2(n) - 3)))
        ratio = start
        return [start * (ratio ** i) for i in range(n)]

    if math.log2(n_heads).is_integer():
        slopes = get_slopes_power_of_2(n_heads)
    else:
        # For non-power-of-2, use closest power of 2 and interpolate
        closest_power_of_2 = 2 ** math.floor(math.log2(n_heads))
        slopes = get_slopes_power_of_2(closest_power_of_2)
        extra_slopes = get_slopes_power_of_2(2 * closest_power_of_2)
        slopes = slopes + extra_slopes[0::2][: n_heads - closest_power_of_2]

    return torch.tensor(slopes, dtype=torch.float32)


def build_alibi_bias(seq_len: int, slopes: torch.Tensor) -> torch.Tensor:
    """
    Build the ALiBi attention bias matrix.

    Args:
        seq_len: Sequence length
        slopes: ALiBi slopes tensor of shape (n_heads,)

    Returns:
        Bias tensor of shape (1, n_heads, seq_len, seq_len)
    """
    # Create distance matrix: distance[i, j] = j - i (negative for causal)
    positions = torch.arange(seq_len)
    distance = positions.unsqueeze(0) - positions.unsqueeze(1)  # (seq_len, seq_len)

    # Apply slopes: (n_heads, 1, 1) * (seq_len, seq_len) -> (n_heads, seq_len, seq_len)
    alibi = slopes.unsqueeze(1).unsqueeze(1) * distance.unsqueeze(0)

    return alibi.unsqueeze(0)  # (1, n_heads, seq_len, seq_len)


# ==============================================================================
# Attention Modules
# ==============================================================================


class Attention(nn.Module):
    """
    Unified attention module supporting MHA, GQA, and MQA.

    Supports multiple position encoding methods:
    - learned: Standard learned position embeddings (handled externally)
    - rope: Rotary Position Embeddings (applied to Q and K)
    - alibi: Attention with Linear Biases (added to attention scores)

    Architecture variants:
    - MHA (Multi-Head Attention): n_kv_heads == n_head
    - GQA (Grouped Query Attention): n_kv_heads < n_head, n_head % n_kv_heads == 0
    - MQA (Multi-Query Attention): n_kv_heads == 1
    """

    def __init__(self, config: ChessConfig):
        super().__init__()

        self.n_head = config.n_head
        self.n_kv_heads = config.n_kv_heads
        self.n_embd = config.n_embd
        self.head_dim = config.n_embd // config.n_head
        self.n_rep = config.n_head // config.n_kv_heads  # Repetition factor for GQA/MQA
        self.pos_encoding = config.pos_encoding

        # Compute projection sizes
        # Q: n_head * head_dim = n_embd
        # K, V: n_kv_heads * head_dim (smaller for GQA/MQA)
        self.q_proj = nn.Linear(config.n_embd, config.n_head * self.head_dim, bias=False)
        self.k_proj = nn.Linear(config.n_embd, config.n_kv_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(config.n_embd, config.n_kv_heads * self.head_dim, bias=False)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)

        self.dropout = nn.Dropout(config.dropout)

        # Position encoding components
        if config.pos_encoding == "rope":
            self.rotary_emb = RotaryEmbedding(
                dim=self.head_dim,
                max_seq_len=config.n_ctx,
                theta=config.rope_theta,
            )
        elif config.pos_encoding == "alibi":
            # Precompute ALiBi slopes
            slopes = build_alibi_slopes(config.n_head)
            self.register_buffer("alibi_slopes", slopes, persistent=False)

        # Causal mask
        self.register_buffer(
            "causal_mask",
            torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(
                1, 1, config.n_ctx, config.n_ctx
            ),
            persistent=False,
        )

    def _repeat_kv(self, x: torch.Tensor) -> torch.Tensor:
        """
        Repeat KV heads to match the number of query heads.

        For GQA/MQA, we need to expand K and V to match Q's head count.
        Input shape: (batch, n_kv_heads, seq_len, head_dim)
        Output shape: (batch, n_head, seq_len, head_dim)
        """
        if self.n_rep == 1:
            return x
        batch, n_kv_heads, seq_len, head_dim = x.shape
        x = x.unsqueeze(2).expand(batch, n_kv_heads, self.n_rep, seq_len, head_dim)
        return x.reshape(batch, n_kv_heads * self.n_rep, seq_len, head_dim)

    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        batch_size, seq_len, _ = x.size()

        # Compute Q, K, V projections
        q = self.q_proj(x)
        k = self.k_proj(x)
        v = self.v_proj(x)

        # 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_kv_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.n_kv_heads, self.head_dim).transpose(1, 2)

        # Apply rotary embeddings if using RoPE
        if self.pos_encoding == "rope":
            cos, sin = self.rotary_emb(q, seq_len)
            q, k = apply_rotary_pos_emb(q, k, cos, sin)

        # Repeat K and V for GQA/MQA
        k = self._repeat_kv(k)
        v = self._repeat_kv(v)

        # Scaled dot-product attention
        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)

        # Apply ALiBi bias if using ALiBi
        if self.pos_encoding == "alibi":
            alibi_bias = build_alibi_bias(seq_len, self.alibi_slopes.to(x.device))
            attn_weights = attn_weights + alibi_bias.to(attn_weights.dtype)

        # Apply causal mask
        causal_mask = self.causal_mask[:, :, :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 = 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


# Alias for backward compatibility
MultiHeadAttention = Attention


# ==============================================================================
# Feed-Forward Modules
# ==============================================================================


class FeedForward(nn.Module):
    """
    Feed-forward network (MLP) module with configurable activation.

    Supports:
    - gelu: Standard GELU activation (2 weight matrices)
    - swiglu: SwiGLU activation (3 weight matrices, better performance)

    For SwiGLU, the hidden dimension is adjusted to keep parameter count similar:
    - GELU: 2 * n_embd * n_inner parameters
    - SwiGLU: 3 * n_embd * n_inner_swiglu parameters
    To match, n_inner_swiglu = 2/3 * n_inner
    """

    def __init__(self, config: ChessConfig):
        super().__init__()

        self.ffn_type = config.ffn_type

        if config.ffn_type == "swiglu":
            # SwiGLU uses 3 projections, so reduce hidden dim to compensate
            # Adjust n_inner for SwiGLU to maintain similar parameter count
            hidden_dim = int(2 * config.n_inner / 3)
            # Round to nearest multiple of 8 for efficiency
            hidden_dim = ((hidden_dim + 7) // 8) * 8

            self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=False)  # Gate
            self.w2 = nn.Linear(config.n_embd, hidden_dim, bias=False)  # Up
            self.w3 = nn.Linear(hidden_dim, config.n_embd, bias=False)  # Down
        else:  # gelu
            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:
        if self.ffn_type == "swiglu":
            # SwiGLU: Swish(W1*x) * W2*x, then W3
            gate = F.silu(self.w1(x))  # Swish activation
            up = self.w2(x)
            x = gate * up
            x = self.w3(x)
            x = self.dropout(x)
        else:  # gelu
            x = self.c_fc(x)
            x = F.gelu(x)
            x = self.c_proj(x)
            x = self.dropout(x)
        return x


# ==============================================================================
# Transformer Block
# ==============================================================================


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 = Attention(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


# ==============================================================================
# Main Model
# ==============================================================================


class ChessForCausalLM(PreTrainedModel, GenerationMixin):
    """
    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 modular GPT-style architecture with:
    - Token embeddings for chess moves
    - Configurable positional embeddings (learned/RoPE/ALiBi)
    - Stacked transformer blocks with configurable attention (MHA/GQA/MQA)
    - Configurable FFN activation (GELU/SwiGLU)
    - Linear head for next-token prediction

    The model supports weight tying between the embedding layer and the
    output projection to save parameters.

    Example:
        >>> # Baseline configuration
        >>> config = ChessConfig(vocab_size=1200, n_embd=128, n_layer=6)
        >>> model = ChessForCausalLM(config)

        >>> # GQA with RoPE (saves parameters, allows more layers)
        >>> config = ChessConfig(
        ...     vocab_size=1200, n_embd=128, n_layer=8,
        ...     attention_type="gqa", n_kv_heads=2,
        ...     pos_encoding="rope"
        ... )
        >>> model = ChessForCausalLM(config)
    """

    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)

        self.pos_encoding = config.pos_encoding

        # Token embeddings (always needed)
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)

        # Position embeddings (only for learned position encoding)
        if config.pos_encoding == "learned":
            self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
        else:
            # RoPE and ALiBi don't need position embeddings
            self.wpe = None

        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 prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Optional[Tuple] = None,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> dict:
        # No KV-cache support; fall back to full forward each step.
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]
        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past_key_values,
        }
    
    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,
        legal_token_ids: Optional[List[List[int]]] = 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

        # Get token embeddings
        hidden_states = self.wte(input_ids)

        # Add position embeddings only for learned encoding
        if self.pos_encoding == "learned":
            if position_ids is None:
                position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
            position_embeds = self.wpe(position_ids)
            hidden_states = hidden_states + position_embeds

        # Apply dropout
        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 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 self.config.legal_loss_weight > 0 and legal_token_ids:
                aux_loss = self._legal_move_loss(logits, labels, legal_token_ids)
                if aux_loss is not None:
                    loss = loss + self.config.legal_loss_weight * aux_loss
        
        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,
        )

    def _legal_move_loss(
        self,
        logits: torch.Tensor,
        labels: torch.Tensor,
        legal_token_ids: List[List[int]],
    ) -> Optional[torch.Tensor]:
        batch_size = logits.size(0)
        total_loss = logits.new_tensor(0.0)
        count = 0

        for batch_idx in range(batch_size):
            if batch_idx >= len(legal_token_ids):
                continue
            legal_ids = legal_token_ids[batch_idx]
            if not legal_ids:
                continue

            label_row = labels[batch_idx]
            valid_mask = label_row != -100
            for special_id in (
                getattr(self.config, "pad_token_id", None),
                getattr(self.config, "bos_token_id", None),
                getattr(self.config, "eos_token_id", None),
            ):
                if special_id is not None:
                    valid_mask = valid_mask & (label_row != int(special_id))

            valid_positions = valid_mask.nonzero(as_tuple=False)
            if valid_positions.numel() == 0:
                continue

            last_pos = int(valid_positions[-1].item())
            pred_pos = last_pos - 1
            if pred_pos < 0:
                continue

            logits_slice = logits[batch_idx, pred_pos]
            legal_logits = logits_slice.index_select(
                0,
                torch.tensor(legal_ids, device=logits_slice.device, dtype=torch.long),
            )

            loss = torch.logsumexp(logits_slice, dim=-1) - torch.logsumexp(legal_logits, dim=-1)
            total_loss = total_loss + loss
            count += 1

        if count == 0:
            return None
        return total_loss / count
    
    @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)