model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class RotaryPositionalEmbedding(nn.Module):
    """RoPE - Rotary Position Embedding"""
    
    def __init__(self, dim, max_seq_len=2048, base=10000):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)
        self.max_seq_len = max_seq_len
        
    def forward(self, seq_len, device):
        t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb.cos(), emb.sin()


def apply_rotary_pos_emb(q, k, cos, sin):
    """Aplica RoPE a queries y keys"""
    def rotate_half(x):
        x1, x2 = x.chunk(2, dim=-1)
        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 MultiHeadSelfAttention(nn.Module):
    """Multi-Head Self-Attention mejorado con RoPE"""
    
    def __init__(self, d_model, n_heads, dropout=0.1, max_seq_len=2048):
        super().__init__()
        assert d_model % n_heads == 0
        
        self.d_model = d_model
        self.n_heads = n_heads
        self.d_k = d_model // n_heads
        
        # Proyecciones Q, K, V (sin bias para mejor eficiencia)
        self.q_linear = nn.Linear(d_model, d_model, bias=False)
        self.k_linear = nn.Linear(d_model, d_model, bias=False)
        self.v_linear = nn.Linear(d_model, d_model, bias=False)
        self.out_linear = nn.Linear(d_model, d_model, bias=False)
        
        self.dropout = nn.Dropout(dropout)
        self.rope = RotaryPositionalEmbedding(self.d_k, max_seq_len)
        
        # Flash Attention si está disponible
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        
    def forward(self, x, mask=None):
        batch_size, seq_len, d_model = x.size()
        
        # Proyecciones lineales
        Q = self.q_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
        K = self.k_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
        V = self.v_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
        
        # Aplicar RoPE
        cos, sin = self.rope(seq_len, x.device)
        cos = cos[None, None, :, :]
        sin = sin[None, None, :, :]
        Q, K = apply_rotary_pos_emb(Q, K, cos, sin)
        
        # Attention con Flash Attention si está disponible
        if self.flash and mask is None:
            context = F.scaled_dot_product_attention(
                Q, K, V, 
                attn_mask=None,
                dropout_p=self.dropout.p if self.training else 0.0,
                is_causal=True
            )
        else:
            scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
            if mask is not None:
                scores = scores.masked_fill(mask == 0, float('-inf'))
            attn_weights = F.softmax(scores, dim=-1)
            attn_weights = self.dropout(attn_weights)
            context = torch.matmul(attn_weights, V)
        
        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, d_model)
        output = self.out_linear(context)
        return output


class SwiGLU(nn.Module):
    """SwiGLU activation - Mejor que GELU"""
    
    def __init__(self, d_model, d_ff, dropout=0.1):
        super().__init__()
        self.w1 = nn.Linear(d_model, d_ff, bias=False)
        self.w2 = nn.Linear(d_ff, d_model, bias=False)
        self.w3 = nn.Linear(d_model, d_ff, bias=False)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        return self.w2(self.dropout(F.silu(self.w1(x)) * self.w3(x)))


class FeedForward(nn.Module):
    """Feed-Forward con GELU (fallback compatible)"""
    
    def __init__(self, d_model, d_ff, dropout=0.1):
        super().__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        return self.linear2(self.dropout(F.gelu(self.linear1(x))))


class RMSNorm(nn.Module):
    """RMSNorm - Más eficiente que LayerNorm"""
    
    def __init__(self, dim, eps=1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        
    def forward(self, x):
        norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
        return x * norm * self.weight


class TransformerBlock(nn.Module):
    """Transformer Block mejorado con pre-norm"""
    
    def __init__(self, d_model, n_heads, d_ff, dropout=0.1, max_seq_len=2048, use_swiglu=True):
        super().__init__()
        self.attention = MultiHeadSelfAttention(d_model, n_heads, dropout, max_seq_len)
        
        # Usar SwiGLU o FeedForward estándar
        if use_swiglu:
            self.feed_forward = SwiGLU(d_model, d_ff, dropout)
        else:
            self.feed_forward = FeedForward(d_model, d_ff, dropout)
        
        self.norm1 = RMSNorm(d_model)
        self.norm2 = RMSNorm(d_model)
        
    def forward(self, x, mask=None):
        # Pre-norm architecture
        x = x + self.attention(self.norm1(x), mask)
        x = x + self.feed_forward(self.norm2(x))
        return x


class MTPMiniModel(nn.Module):
    """MTP Mini - Arquitectura mejorada compatible"""
    
    def __init__(self, vocab_size, d_model=256, n_layers=4, n_heads=4, 
                 d_ff=1024, max_seq_len=128, dropout=0.1, use_swiglu=False):
        super().__init__()
        
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.max_seq_len = max_seq_len
        
        # Token embeddings (sin positional, usamos RoPE)
        self.token_embedding = nn.Embedding(vocab_size, d_model)
        
        # Transformer blocks
        self.blocks = nn.ModuleList([
            TransformerBlock(d_model, n_heads, d_ff, dropout, max_seq_len, use_swiglu)
            for _ in range(n_layers)
        ])
        
        # Final norm
        self.norm_f = RMSNorm(d_model)
        
        # Output projection
        self.lm_head = nn.Linear(d_model, vocab_size, bias=False)
        
        # Weight tying
        self.lm_head.weight = self.token_embedding.weight
        
        self.dropout = nn.Dropout(dropout)
        
        # Mejor inicialización
        self.apply(self._init_weights)
        
    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)
    
    def forward(self, input_ids, targets=None):
        batch_size, seq_len = input_ids.size()
        
        # Máscara causal
        mask = torch.tril(torch.ones(seq_len, seq_len, device=input_ids.device)).view(1, 1, seq_len, seq_len)
        
        # Token embeddings (RoPE se aplica en attention)
        x = self.dropout(self.token_embedding(input_ids))
        
        # Transformer blocks
        for block in self.blocks:
            x = block(x, mask)
        
        # Final norm
        x = self.norm_f(x)
        
        # Logits
        logits = self.lm_head(x)
        
        # Loss con label smoothing
        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, self.vocab_size), 
                targets.view(-1),
                label_smoothing=0.1
            )
        
        return logits, loss
    
    def generate(self, input_ids, max_new_tokens=100, temperature=0.8, 
                 top_k=50, top_p=0.9, repetition_penalty=1.1):
        """Generación mejorada con repetition penalty"""
        self.eval()
        
        generated = input_ids.clone()
        
        with torch.no_grad():
            for _ in range(max_new_tokens):
                # Crop context
                input_ids_cond = generated if generated.size(1) <= self.max_seq_len else generated[:, -self.max_seq_len:]
                
                # Forward
                logits, _ = self(input_ids_cond)
                logits = logits[:, -1, :]
                
                # Repetition penalty
                if repetition_penalty != 1.0:
                    for token_id in set(generated[0].tolist()):
                        logits[0, token_id] /= repetition_penalty
                
                # Temperature
                logits = logits / temperature
                
                # Top-k
                if top_k > 0:
                    v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                    logits[logits < v[:, [-1]]] = float('-inf')
                
                # Top-p (nucleus)
                if top_p < 1.0:
                    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
                    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
                    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(1, sorted_indices, sorted_indices_to_remove)
                    logits[indices_to_remove] = float('-inf')
                
                # Sample
                probs = F.softmax(logits, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1)
                
                generated = torch.cat([generated, next_token], dim=1)
        
        return generated
    
    def count_parameters(self):
        return sum(p.numel() for p in self.parameters() if p.requires_grad)