src/model.py

"""
model.py — Transformer Decoder-Only (estilo GPT) implementado do zero em PyTorch.

Arquitetura:
  Embedding de tokens + Positional Encoding absoluto aprendido
  → N x (Causal Self-Attention + FFN com residual + LayerNorm pré-norma)
  → Linear head projetando de d_model → vocab_size

Não usa nenhum peso externo. Todos os pesos são inicializados aleatoriamente
e aprendidos do zero durante o treino.
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint

from config import BabelConfig


# ── Bloco de Atenção Causal ────────────────────────────────────────────────────

class CausalSelfAttention(nn.Module):
    """
    Multi-head self-attention com máscara causal (cada token só enxerga
    os tokens anteriores e a si mesmo — nunca os futuros).
    """

    def __init__(self, config: BabelConfig):
        super().__init__()
        assert config.d_model % config.n_heads == 0, (
            f"d_model ({config.d_model}) deve ser divisível por n_heads ({config.n_heads})"
        )

        self.n_heads = config.n_heads
        self.d_head = config.d_model // config.n_heads
        self.d_model = config.d_model
        self.dropout = config.dropout

        self.qkv_proj = nn.Linear(config.d_model, 3 * config.d_model, bias=False)
        self.out_proj = nn.Linear(config.d_model, config.d_model, bias=False)
        self.resid_dropout = nn.Dropout(config.dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, T, C = x.shape

        qkv = self.qkv_proj(x)
        q, k, v = qkv.split(self.d_model, dim=2)

        q = q.view(B, T, self.n_heads, self.d_head).transpose(1, 2)
        k = k.view(B, T, self.n_heads, self.d_head).transpose(1, 2)
        v = v.view(B, T, self.n_heads, self.d_head).transpose(1, 2)

        # Flash Attention via PyTorch 2.x — is_causal=True substitui a máscara manual
        dropout_p = self.dropout if self.training else 0.0
        out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p, is_causal=True)

        out = out.transpose(1, 2).contiguous().view(B, T, C)
        return self.resid_dropout(self.out_proj(out))


# ── Feed-Forward Network ───────────────────────────────────────────────────────

class FeedForward(nn.Module):
    """
    FFN com dois lineares e GELU. d_ff = 4 * d_model por convenção GPT.
    """

    def __init__(self, config: BabelConfig):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.d_model, config.d_ff, bias=False),
            nn.GELU(),
            nn.Linear(config.d_ff, config.d_model, bias=False),
            nn.Dropout(config.dropout),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


# ── Bloco Transformer ──────────────────────────────────────────────────────────

class TransformerBlock(nn.Module):
    """
    Bloco padrão: pré-norma → atenção → residual → pré-norma → FFN → residual.

    Pré-norma (LayerNorm antes das sub-camadas) é mais estável em treinos
    do zero do que a pós-norma do paper original.
    """

    def __init__(self, config: BabelConfig):
        super().__init__()
        self.ln1 = nn.LayerNorm(config.d_model)
        self.attn = CausalSelfAttention(config)
        self.ln2 = nn.LayerNorm(config.d_model)
        self.ffn = FeedForward(config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln1(x))
        x = x + self.ffn(self.ln2(x))
        return x


# ── Modelo Completo ────────────────────────────────────────────────────────────

class BabelTransformer(nn.Module):
    """
    Transformer decoder-only treinado do zero.

    Uso:
        config = get_config("babel_25M")
        model = BabelTransformer(config)
        logits, loss = model(input_ids, targets)
    """

    def __init__(self, config: BabelConfig):
        super().__init__()
        self.config = config

        self.token_emb = nn.Embedding(config.vocab_size, config.d_model)
        self.pos_emb = nn.Embedding(config.context_length, config.d_model)
        self.emb_dropout = nn.Dropout(config.dropout)

        self.blocks = nn.ModuleList(
            [TransformerBlock(config) for _ in range(config.n_layers)]
        )

        self.ln_final = nn.LayerNorm(config.d_model)

        # Projeção final: d_model → vocab_size (sem bias, como GPT-2)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        # Weight tying: token_emb e lm_head compartilham os mesmos pesos.
        # Reduz parâmetros e melhora qualidade (Press & Wolf, 2017).
        self.lm_head.weight = self.token_emb.weight

        self._init_weights()

    def _init_weights(self):
        """Inicialização normal com std 0.02, igual ao GPT-2."""
        for module in self.modules():
            if isinstance(module, (nn.Linear, nn.Embedding)):
                nn.init.normal_(module.weight, mean=0.0, std=0.02)
                if isinstance(module, nn.Linear) and module.bias is not None:
                    nn.init.zeros_(module.bias)
            elif isinstance(module, nn.LayerNorm):
                nn.init.ones_(module.weight)
                nn.init.zeros_(module.bias)

    def forward(
        self,
        input_ids: torch.Tensor,                  # (B, T)
        targets: torch.Tensor | None = None,      # (B, T) — None em inferência
    ) -> tuple[torch.Tensor, torch.Tensor | None]:

        B, T = input_ids.shape
        assert T <= self.config.context_length, (
            f"Sequência de tamanho {T} excede context_length={self.config.context_length}"
        )

        positions = torch.arange(T, device=input_ids.device).unsqueeze(0)  # (1, T)
        x = self.emb_dropout(self.token_emb(input_ids) + self.pos_emb(positions))

        for block in self.blocks:
            if self.config.gradient_checkpointing and self.training:
                x = checkpoint(block, x, use_reentrant=False)
            else:
                x = block(x)

        x = self.ln_final(x)
        logits = self.lm_head(x)   # (B, T, vocab_size)

        loss = None
        if targets is not None:
            # Cross-entropy: achata (B*T, V) vs (B*T,)
            loss = F.cross_entropy(
                logits.view(-1, self.config.vocab_size),
                targets.view(-1),
                ignore_index=-1,
            )

        return logits, loss

    @torch.no_grad()
    def generate(
        self,
        input_ids: torch.Tensor,   # (1, T) — um único prompt
        max_new_tokens: int = 200,
        temperature: float = 0.8,
        top_p: float = 0.9,
        repetition_penalty: float = 1.3,
    ) -> torch.Tensor:
        """
        Gera texto auto-regressivamente com top-p (nucleus) sampling.
        Retorna tensor (1, T + max_new_tokens).
        """
        self.eval()
        for _ in range(max_new_tokens):
            # Trunca se ultrapassar context_length
            ctx = input_ids[:, -self.config.context_length:]
            logits, _ = self(ctx)
            logits = logits[:, -1, :] / temperature   # apenas o último token

            # Penaliza tokens já gerados para reduzir repetição
            if repetition_penalty != 1.0:
                for token_id in input_ids[0].tolist():
                    if logits[0, token_id] > 0:
                        logits[0, token_id] /= repetition_penalty
                    else:
                        logits[0, token_id] *= repetition_penalty

            # Top-p sampling
            sorted_logits, sorted_idx = torch.sort(logits, descending=True)
            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
            # Remove tokens acima do threshold top_p
            sorted_logits[cumulative_probs - F.softmax(sorted_logits, dim=-1) > top_p] = float("-inf")
            probs = F.softmax(sorted_logits, dim=-1)
            next_token_sorted = torch.multinomial(probs, num_samples=1)
            next_token = sorted_idx.gather(-1, next_token_sorted)

            input_ids = torch.cat([input_ids, next_token], dim=1)

        return input_ids

    def count_parameters(self) -> int:
        return sum(p.numel() for p in self.parameters() if p.requires_grad)