model.py

"""
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)