model.py

"""GPT model definition and checkpoint loading for exported smartwatch LM."""

from __future__ import annotations

import math
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F
from tokenizers import Tokenizer

import config as cfg


class CausalSelfAttention(nn.Module):
    def __init__(self, n_head: int, n_embd: int, block_size: int, dropout: float, bias: bool):
        super().__init__()
        assert n_embd % n_head == 0
        self.n_head = n_head
        self.n_embd = n_embd
        self.head_dim = n_embd // n_head
        self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=bias)
        self.c_proj = nn.Linear(n_embd, n_embd, bias=bias)
        self.attn_dropout = nn.Dropout(dropout)
        self.resid_dropout = nn.Dropout(dropout)
        self.register_buffer(
            "bias",
            torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b, t, c = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
        q = q.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
        v = v.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
        att = att.masked_fill(self.bias[:, :, :t, :t] == 0, float("-inf"))
        att = F.softmax(att, dim=-1)
        att = self.attn_dropout(att)
        y = att @ v
        y = y.transpose(1, 2).contiguous().view(b, t, c)
        return self.resid_dropout(self.c_proj(y))


class MLP(nn.Module):
    def __init__(self, n_embd: int, dropout: float, bias: bool):
        super().__init__()
        self.c_fc = nn.Linear(n_embd, 4 * n_embd, bias=bias)
        self.gelu = nn.GELU()
        self.c_proj = nn.Linear(4 * n_embd, n_embd, bias=bias)
        self.dropout = nn.Dropout(dropout)

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


class Block(nn.Module):
    def __init__(self, n_head: int, n_embd: int, block_size: int, dropout: float, bias: bool):
        super().__init__()
        self.ln1 = nn.LayerNorm(n_embd)
        self.attn = CausalSelfAttention(n_head, n_embd, block_size, dropout, bias)
        self.ln2 = nn.LayerNorm(n_embd)
        self.mlp = MLP(n_embd, dropout, bias)

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


class GPT(nn.Module):
    def __init__(
        self,
        vocab_size: int,
        n_layer: int,
        n_head: int,
        n_embd: int,
        block_size: int,
        dropout: float,
        bias: bool,
    ):
        super().__init__()
        self.block_size = block_size
        self.transformer = nn.ModuleDict(
            {
                "wte": nn.Embedding(vocab_size, n_embd),
                "wpe": nn.Embedding(block_size, n_embd),
                "drop": nn.Dropout(dropout),
                "h": nn.ModuleList(
                    [Block(n_head, n_embd, block_size, dropout, bias) for _ in range(n_layer)]
                ),
                "ln_f": nn.LayerNorm(n_embd),
            }
        )
        self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)

    def _init_weights(self, module: nn.Module) -> None:
        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, idx: torch.Tensor, targets=None):
        b, t = idx.size()
        assert t <= self.block_size
        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)
        x = self.transformer.drop(
            self.transformer.wte(idx) + self.transformer.wpe(pos)
        )
        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 = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    @torch.no_grad()
    def generate(self, idx: torch.Tensor, max_new_tokens: int, temperature: float = 1.0, top_k=None):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.block_size :]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / max(temperature, 1e-8)
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float("Inf")
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx


def resolve_checkpoint_paths(
    checkpoint_path: Path | None = None,
    tokenizer_path: Path | None = None,
) -> tuple[Path, Path]:
    ckpt = checkpoint_path or cfg.OUTPUT_DIR / "checkpoint.pt"
    tok = tokenizer_path or cfg.OUTPUT_DIR / "tokenizer.json"
    if not ckpt.is_file():
        raise FileNotFoundError(
            f"Checkpoint not found at {ckpt}. Ensure checkpoint.pt is in this model folder."
        )
    if not tok.is_file():
        raise FileNotFoundError(
            f"Tokenizer not found at {tok}. Ensure tokenizer.json is in this model folder."
        )
    return ckpt, tok


def load_model(
    checkpoint_path: Path | None = None,
    tokenizer_path: Path | None = None,
    device: str | None = None,
) -> tuple[GPT, Tokenizer, str]:
    ckpt_path, tok_path = resolve_checkpoint_paths(checkpoint_path, tokenizer_path)
    dev = device or ("cuda" if torch.cuda.is_available() else "cpu")

    tokenizer = Tokenizer.from_file(str(tok_path))
    checkpoint = torch.load(ckpt_path, map_location=dev, weights_only=False)
    model_config = checkpoint["model_config"]

    model = GPT(
        vocab_size=model_config["vocab_size"],
        n_layer=model_config["n_layer"],
        n_head=model_config["n_head"],
        n_embd=model_config["n_embd"],
        block_size=model_config["block_size"],
        dropout=model_config["dropout"],
        bias=model_config["bias"],
    )
    model.load_state_dict(checkpoint["model_state_dict"])
    model.to(dev)
    model.eval()

    return model, tokenizer, dev