generate.py

"""Interactive generator for TinyLiquid.

Usage:
  .venv/bin/python generate.py --ckpt ckpt/forensic --persona analyst
  .venv/bin/python generate.py --ckpt ckpt/nlp --prompt "Once upon a time," --max-new 80
"""

import argparse
from pathlib import Path

import torch

from model.config import TinyLiquidConfig, CONFIGS
from model.utils import latest_ckpt
from model.tiny_liquid import TinyLiquid
from data.tokenizer import load_tokenizer

PERSONA_T = {"analyst": "<|analyst|>", "skeptic": "<|skeptic|>", "none": None}


def parse_args():
    ap = argparse.ArgumentParser()
    ap.add_argument("--ckpt", default="ckpt/forensic")
    ap.add_argument("--tok", default="data/tokenizer.json")
    ap.add_argument("--persona", default="analyst", choices=list(PERSONA_T))
    ap.add_argument("--prompt", default=None)
    ap.add_argument("--max-new", type=int, default=200)
    ap.add_argument("--temp", type=float, default=0.8)
    ap.add_argument("--topk", type=int, default=40)
    ap.add_argument("--threads", type=int, default=8)
    return ap.parse_args()


def main():
    args = parse_args()
    torch.set_num_threads(args.threads)
    tok = load_tokenizer(args.tok)
    ckpt = latest_ckpt(args.ckpt)
    assert ckpt, f"no checkpoints in {args.ckpt}"
    sd = torch.load(ckpt, map_location="cpu")
    cfg_dict = dict(sd.get("config", CONFIGS["tiny10m"]))
    cfg = TinyLiquidConfig(vocab_size=tok.get_vocab_size(), **{k: v for k, v in cfg_dict.items() if k != "vocab_size"})
    model = TinyLiquid(cfg)
    model.load_state_dict(sd["model"])
    model.eval()
    print(f"loaded {ckpt} (step {sd.get('step','?')})", flush=True)

    persona_id = {"none": 0, "analyst": 1, "skeptic": 2}[args.persona]
    p_token = PERSONA_T[args.persona]

    def respond(user_text, max_new=None, temp=None):
        mn = max_new or args.max_new
        t = temp or args.temp
        prompt = (p_token or "") + "<|user|>" + user_text + "<|assistant|>"
        ids = tok.encode(prompt).ids
        out = model.generate(tok, ids, persona_id=persona_id, max_new=mn,
                             temperature=t, top_k=args.topk, repetition_penalty=1.4, no_repeat_ngram_size=4)
        return tok.decode(out[len(ids):])

    if args.prompt:
        print(respond(args.prompt))
        return
    print("TinyLiquid chat. Persona:", args.persona, "| Ctrl-D to exit.")
    while True:
        try:
            line = input("you> ").strip()
        except (EOFError, KeyboardInterrupt):
            print()
            break
        if not line:
            continue
        print("model>", respond(line), flush=True)


if __name__ == "__main__":
    main()