code/scripts/generate_gcd.py

#!/usr/bin/env python
"""
Inference with optional Grammar-Constrained Decoding (GCD) for valid SMILES.
Load encoder + LLM (with LoRA), run on spectra (JSONL or single), output SMILES.
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

import torch

from spec_rag.spectra_reason_encoder import build_spectra_reason_encoder
from spec_rag.gcd_inference import predict_smiles_from_spectrum, get_smiles_constraint


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Generate SMILES from spectra (with optional GCD).")
    p.add_argument("--input-jsonl", default=None, help="JSONL with 'peaks' per line")
    p.add_argument("--output-jsonl", default=None, help="Same + 'predicted_smiles'")
    p.add_argument("--encoder-dir", default=None, help="Dir with encoder state (perceiver.pt, etc.)")
    p.add_argument("--model-dir", required=True, help="Dir with LoRA + tokenizer (from Stage 2)")
    p.add_argument("--llm-name", default="meta-llama/Meta-Llama-3-8B-Instruct", help="Base LLM (for loading LoRA)")
    p.add_argument("--dreams-ckpt", default=None)
    p.add_argument("--specbridge-ckpt", default=None)
    p.add_argument("--max-peaks", type=int, default=60)
    p.add_argument("--max-new-tokens", type=int, default=200)
    p.add_argument("--no-gcd", action="store_true", help="Disable grammar constraint")
    p.add_argument("--grammar", default=None, help="Path to smiles.ebnf")
    p.add_argument("--device", default="cuda")
    p.add_argument("--batch-size", type=int, default=1)
    return p.parse_args()


def load_encoder(args, device: torch.device):
    encoder = build_spectra_reason_encoder(
        llm_dim=4096,
        num_latents=64,
        dreams_ckpt=args.dreams_ckpt,
        specbridge_ckpt=args.specbridge_ckpt,
        device=str(device),
    )
    if args.encoder_dir:
        p = Path(args.encoder_dir)
        if (p / "perceiver.pt").exists():
            encoder.perceiver.load_state_dict(
                torch.load(p / "perceiver.pt", map_location=device),
            )
        if (p / "encoder_last.pt").exists():
            encoder.load_state_dict(
                torch.load(p / "encoder_last.pt", map_location=device),
                strict=False,
            )
    return encoder.to(device).eval()


def main() -> None:
    args = parse_args()
    if args.device == "cuda" and not torch.cuda.is_available():
        args.device = "cpu"
    device = torch.device(args.device)
    grammar_path = Path(args.grammar) if args.grammar else (ROOT / "grammars" / "smiles.ebnf")

    from transformers import AutoModelForCausalLM, AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained(args.model_dir)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    base = AutoModelForCausalLM.from_pretrained(
        args.llm_name,
        torch_dtype=torch.float16 if device.type == "cuda" else torch.float32,
        device_map=str(device),
    )
    try:
        from peft import PeftModel
        model = PeftModel.from_pretrained(base, args.model_dir)
    except Exception:
        model = base
    model.eval()

    encoder = load_encoder(args, device)

    if not args.input_jsonl:
        print("No --input-jsonl; run with --input-jsonl and --output-jsonl to process a file.")
        return

    results = []
    with open(args.input_jsonl) as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            obj = json.loads(line)
            peaks_list = obj.get("peaks", [])
            peaks_list = sorted(peaks_list, key=lambda x: float(x[1]), reverse=True)[:args.max_peaks]
            arr = torch.zeros(args.max_peaks, 2, dtype=torch.float32)
            for j, (mz, i) in enumerate(peaks_list):
                arr[j, 0] = float(mz)
                arr[j, 1] = float(i)
            arr = arr.unsqueeze(0).to(device)
            pred = predict_smiles_from_spectrum(
                encoder,
                model,
                tokenizer,
                arr,
                device,
                max_new_tokens=args.max_new_tokens,
                use_gcd=not args.no_gcd,
                grammar_path=grammar_path if grammar_path.exists() else None,
            )
            obj["predicted_smiles"] = pred
            results.append(obj)

    if args.output_jsonl:
        with open(args.output_jsonl, "w") as out:
            for obj in results:
                out.write(json.dumps(obj) + "\n")
        print(f"Wrote {len(results)} predictions to {args.output_jsonl}")
    else:
        for obj in results:
            print(json.dumps(obj))


if __name__ == "__main__":
    main()