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, List

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.
    """
    
    model_type = "chess_transformer"
    
    def __init__(
        self,
        vocab_size: int = 200, # Approx size for component vocab
        n_embd: int = 120,    # Reduced to be divisible by heads and fit budget
        n_layer: int = 6,
        n_head: int = 4,
        n_ctx: int = 250,     # Max moves (not tokens)
        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
        self.dropout = dropout
        self.layer_norm_epsilon = layer_norm_epsilon
        self.tie_weights = tie_weights
        self.tie_word_embeddings = bool(tie_weights)


class MultiHeadAttention(nn.Module):
    def __init__(self, config: ChessConfig):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.head_dim = config.n_embd // config.n_head
        
        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)
        
        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, attention_mask=None):
        batch_size, seq_len, _ = x.size()
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        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)
        
        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        
        causal_mask = self.bias[:, :, :seq_len, :seq_len]
        attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
        
        if attention_mask is not None:
            # Mask should be broadcastable
            attn_weights = attn_weights + attention_mask
        
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_weights = self.dropout(attn_weights)
        
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.transpose(1, 2).contiguous().view(
            batch_size, seq_len, self.n_embd
        )
        return self.c_proj(attn_output)


class FeedForward(nn.Module):
    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):
        x = self.c_fc(x)
        x = F.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x


class TransformerBlock(nn.Module):
    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, attention_mask=None):
        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
        x = x + self.mlp(self.ln_2(x))
        return x


class ChessForCausalLM(PreTrainedModel):
    config_class = ChessConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    
    def __init__(self, config: ChessConfig):
        super().__init__(config)
        
        # Component embeddings (Color, Piece, Src, Dst, Suffix)
        self.wte_color = nn.Embedding(config.vocab_size, config.n_embd)
        self.wte_piece = nn.Embedding(config.vocab_size, config.n_embd)
        self.wte_src = nn.Embedding(config.vocab_size, config.n_embd)
        self.wte_dst = nn.Embedding(config.vocab_size, config.n_embd)
        self.wte_suf = nn.Embedding(config.vocab_size, config.n_embd)
        
        self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
        self.drop = nn.Dropout(config.dropout)
        
        self.h = nn.ModuleList([
            TransformerBlock(config) for _ in range(config.n_layer)
        ])
        
        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        
        # 5 Heads for predicting next components
        # We model p(NextMove | History). 
        # Components of NextMove are predicted conditionally independent given History (simplification)
        # or we could make them autoregressive within the move. 
        # For "product encoding", parallel prediction is natural.
        self.head_color = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.head_piece = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.head_src = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.head_dst = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.head_suf = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        
        self.post_init()

    def _init_weights(self, module):
        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 get_input_embeddings(self):
        # Return first embedding as proxy, though we have multiple
        return self.wte_color

    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]:
        
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        batch_size, seq_len = input_ids.size()
        
        # Ensure sequence length is multiple of 5
        if seq_len % 5 != 0:
            # Pad or truncate? For training we expect aligned batches
            # Truncate to nearest multiple of 5
            new_len = (seq_len // 5) * 5
            input_ids = input_ids[:, :new_len]
            if labels is not None:
                labels = labels[:, :new_len]
            if attention_mask is not None:
                attention_mask = attention_mask[:, :new_len]
            seq_len = new_len
            
        num_moves = seq_len // 5
        
        # Reshape to (B, L, 5)
        # Components: 0=Color, 1=Piece, 2=Src, 3=Dst, 4=Suf
        reshaped_ids = input_ids.view(batch_size, num_moves, 5)
        
        # Product Embedding
        emb_c = self.wte_color(reshaped_ids[:, :, 0])
        emb_p = self.wte_piece(reshaped_ids[:, :, 1])
        emb_s = self.wte_src(reshaped_ids[:, :, 2])
        emb_d = self.wte_dst(reshaped_ids[:, :, 3])
        emb_f = self.wte_suf(reshaped_ids[:, :, 4])
        
        # Element-wise product
        token_embeds = emb_c * emb_p * emb_s * emb_d * emb_f
        
        # Position Embeddings
        device = input_ids.device
        if position_ids is None:
            position_ids = torch.arange(num_moves, device=device).unsqueeze(0)
        
        position_embeds = self.wpe(position_ids)
        hidden_states = self.drop(token_embeds + position_embeds)
        
        # Attention mask adaptation
        # input mask is (B, 5L). We need (B, L).
        # We consider a move valid if ALL components are valid? Or ANY?
        # Typically padding is consistent.
        if attention_mask is not None:
             # Take every 5th element or min/max
             reshaped_mask = attention_mask.view(batch_size, num_moves, 5)
             # If any part is unmasked (1), keep it? 
             # Usually PAD=0. If all are PAD, then 0.
             chess_mask = reshaped_mask.all(dim=-1).float() # (B, L)
             # Standard broadcast for attention: (B, 1, 1, L)
             extended_attention_mask = (1.0 - chess_mask) * -10000.0
             extended_attention_mask = extended_attention_mask.unsqueeze(1).unsqueeze(2)
        else:
             extended_attention_mask = None
        
        # Transformer
        for block in self.h:
            hidden_states = block(hidden_states, attention_mask=extended_attention_mask)
            
        hidden_states = self.ln_f(hidden_states)
        
        # Output Heads (Predicting Next Move Components)
        logits_c = self.head_color(hidden_states)
        logits_p = self.head_piece(hidden_states)
        logits_s = self.head_src(hidden_states)
        logits_d = self.head_dst(hidden_states)
        logits_f = self.head_suf(hidden_states)
        
        # Stack logits: (B, L, 5, V)
        logits_stacked = torch.stack([logits_c, logits_p, logits_s, logits_d, logits_f], dim=2)
        
        # Compute Loss
        loss = None
        if labels is not None:
            # Reshape labels: (B, L, 5)
            labels_reshaped = labels.view(batch_size, num_moves, 5)
            
            # Shift: Hidden[t] predicts Labels[t+1]
            shift_logits = logits_stacked[:, :-1, :, :].contiguous()
            shift_labels = labels_reshaped[:, 1:, :].contiguous()
            
            # Flatten
            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(
                shift_logits.view(-1, self.config.vocab_size),
                shift_labels.view(-1)
            )
            
        # Return structured output
        # To satisfy Trainer, we might need to return (B, 5L, V) logits?
        # But we produced (B, L, 5, V). Flattening gives (B, 5L, V).
        # Trainer expects logits matching input length usually, or labels length.
        
        flat_logits = logits_stacked.view(batch_size, -1, self.config.vocab_size)
        
        if not return_dict:
            output = (flat_logits,)
            return ((loss,) + output) if loss is not None else output
            
        return CausalLMOutputWithPast(
            loss=loss,
            logits=flat_logits,
        )

    @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,
    ) -> List[int]:
        """
        Generate the next move (5 tokens).
        """
        self.eval()
        
        # Forward pass
        # input_ids (1, 5L)
        outputs = self(input_ids)
        # Logits: (1, 5L, V)
        # We want the last move prediction.
        # The logits for the NEXT move are at the very end.
        # Specifically, the last block of 5 logits corresponds to predictions from the last hidden state.
        
        # Check dimensions
        next_move_logits = outputs.logits[:, -5:, :] # (1, 5, V)
        
        generated = []
        for i in range(5):
            logits = next_move_logits[:, i, :] / temperature
            # Apply filtering
            if top_k is not None:
                v, _ = torch.topk(logits, top_k)
                logits[logits < v[:, [-1]]] = -float('Inf')
            
            probs = F.softmax(logits, dim=-1)
            next_token = torch.multinomial(probs, num_samples=1)
            generated.append(next_token.item())
            
        return generated

# Register
from transformers import AutoConfig, AutoModelForCausalLM
AutoConfig.register("chess_transformer", ChessConfig)
AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)