model.py

"""GPT Model Architecture for Thirukkural Training."""

from dataclasses import dataclass

import torch
import torch.nn as nn


@dataclass
class GPTConfig:
    """Configuration for GPT model."""

    vocab_size: int = 65  # Will be set dynamically based on dataset
    block_size: int = 256  # Max sequence length
    n_layer: int = 6  # Number of transformer blocks
    n_head: int = 6  # Number of attention heads
    n_embd: int = 384  # Embedding dimension


class CausalSelfAttention(nn.Module):
    """Multi-head causal self-attention layer."""

    def __init__(self, config: GPTConfig):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.n_head = config.n_head
        self.n_embd = config.n_embd

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, T, C = x.shape
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)

        head_dim = C // self.n_head
        q = q.view(B, T, self.n_head, head_dim).transpose(1, 2)
        k = k.view(B, T, self.n_head, head_dim).transpose(1, 2)
        v = v.view(B, T, self.n_head, head_dim).transpose(1, 2)

        y = torch.nn.functional.scaled_dot_product_attention(
            q, k, v, is_causal=True
        )

        y = y.transpose(1, 2).contiguous().view(B, T, C)
        return self.c_proj(y)


class MLP(nn.Module):
    """Feed-forward network with GELU activation."""

    def __init__(self, config: GPTConfig):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.gelu = nn.GELU(approximate="tanh")
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.c_fc(x)
        x = self.gelu(x)
        return self.c_proj(x)


class Block(nn.Module):
    """Transformer block with attention and MLP."""

    def __init__(self, config: GPTConfig):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp = MLP(config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class GPT(nn.Module):
    """GPT language model."""

    def __init__(self, config: GPTConfig):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict(
            dict(
                wte=nn.Embedding(config.vocab_size, config.n_embd),
                wpe=nn.Embedding(config.block_size, config.n_embd),
                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
                ln_f=nn.LayerNorm(config.n_embd),
            )
        )
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight

    def forward(
        self, idx: torch.Tensor, targets: torch.Tensor | None = None
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        B, T = idx.shape
        pos = torch.arange(0, T, device=idx.device)

        tok_emb = self.transformer.wte(idx)
        pos_emb = self.transformer.wpe(pos)
        x = tok_emb + pos_emb

        for block in self.transformer.h:
            x = block(x)

        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)

        loss = None
        if targets is not None:
            loss = nn.functional.cross_entropy(
                logits.view(-1, logits.size(-1)), targets.view(-1)
            )
        return logits, loss