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Usage:
# accumulate val batches (safe to re-run; resumes from state)
.venv/bin/python eval/bench2.py --mode val --max-iters 6
.venv/bin/python eval/bench2.py --mode probe --idx 0 # one probe
.venv/bin/python eval/bench2.py --mode speed
.venv/bin/python eval/bench2.py --mode sample --idx 0 # one sample
.venv/bin/python eval/bench2.py --mode finish # write metrics.json
"""
import argparse, json, time
from pathlib import Path
import numpy as np, torch
from model.config import TinyLiquidConfig
from model.tiny_liquid import TinyLiquid
from data.tokenizer import load_tokenizer
PROBES = [
("Discrepancy", "Two accounts describe the same event. Account A: 'The meeting started at 9am and ended at 11am.' Account B: 'The meeting started at 9am and ran until noon.' Find discrepancies.", "end time"),
("Contradiction", "Account A: 'No officials were present.' Account B: 'An official arrived later.' What can you conclude?", "not necessarily a contradiction"),
("Overclaim", "Evaluate: 'Crime doubled last year because of the new policy.' Incidents rose 1,000 to 2,000 while reporting methods changed.", "overclaim"),
("Pattern", "What are the weak links in a theory claiming one actor caused three unrelated disasters?", "no direct evidence"),
("Verification", "Verify: 'The building was built in 2015 and never renovated.' Assessor record: built 2015, renovation permit 2019.", "false"),
("Timeline", "Witness saw a van at 3:30pm; outage reported 3:40pm; inspection 6pm; no report before 5pm. Reconstruct.", "gap"),
("History", "2019: 'officials rule out sabotage.' 2021 filing: a second investigation opened the same week. What changed?", "parallel investigation hidden"),
("Politics", "Statement: 'We balanced the budget without raising taxes.' Budget includes a reassessment raising collections 9%.", "misleading"),
("Source chain", "A claim rests on: company blog, a wire story repeating it, an analyst note quoting the wire. Rate the evidence.", "single chain"),
]
SAMPLE_PROMPTS = [
"<|analyst|><|user|>Verify: 'The bridge was painted in 2019 and never repainted.' Records show a 2022 repaint permit.<|assistant|>",
"<|analyst|><|user|>What's the most common mistake you see in research?<|assistant|>",
"<|skeptic|><|user|>Attack this conclusion: 'Three failures in one week with vans nearby is deliberate.'<|assistant|>",
]
def load(tok_path, ckpt, threads):
torch.set_num_threads(threads)
tok = load_tokenizer(tok_path)
sd = torch.load(ckpt, map_location="cpu")
cfg = TinyLiquidConfig(vocab_size=tok.get_vocab_size(),
**{k: v for k, v in sd["config"].items() if k != "vocab_size"})
model = TinyLiquid(cfg)
model.load_state_dict(sd["model"])
model.eval()
return tok, model
def read_state(p):
if p.exists():
return json.loads(p.read_text())
return {"val_total": 0.0, "val_count": 0, "val_iters": 0,
"probes": {}, "speed": None, "samples": []}
def write_state(p, st):
p.parent.mkdir(parents=True, exist_ok=True)
p.write_text(json.dumps(st), encoding="utf-8")
def val_batch(model, tok, arr_t, batch, seq, seed):
n = (len(arr_t) - 1) // seq
rng = np.random.RandomState(seed)
s = int(rng.randint(0, n - batch))
idx = torch.arange(s * seq, (s + batch) * seq, dtype=torch.long)
buf = torch.stack([torch.from_numpy(arr_t[int(i): int(i) + seq].astype(np.int64)) for i in idx])
x, y = buf[:, :-1], buf[:, 1:]
logits = model(x)
loss = torch.nn.functional.cross_entropy(
logits.view(-1, logits.size(-1)), y.reshape(-1))
return float(loss.item()) * y.numel(), y.numel()
@torch.no_grad()
def generate(model, tok, ids, persona_id, max_new, temperature, top_k,
repetition_penalty, no_repeat_ngram_size):
return model.generate(tok, ids, persona_id=persona_id, max_new=max_new,
temperature=temperature, top_k=top_k,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--mode", required=True)
ap.add_argument("--ckpt", default="ckpt/dpo/model_final.pt")
ap.add_argument("--tok", default="data/tokenizer.json")
ap.add_argument("--val", default="data/valid.bin")
ap.add_argument("--out", default="bench/metrics.json")
ap.add_argument("--state", default="bench/state.json")
ap.add_argument("--val-batches", type=int, default=16)
ap.add_argument("--max-iters", type=int, default=6)
ap.add_argument("--batch", type=int, default=4)
ap.add_argument("--seq", type=int, default=64)
ap.add_argument("--idx", type=int, default=-1)
ap.add_argument("--threads", type=int, default=1)
ap.add_argument("--seed", type=int, default=0)
args = ap.parse_args()
tok, model = load(args.tok, args.ckpt, args.threads)
print('model loaded', flush=True)
st = read_state(Path(args.state))
if args.mode == "val":
mm = np.memmap(args.val, dtype=np.uint16, mode='r')
print('val data loaded', flush=True)
t = mm
n_iters = min(args.max_iters, args.val_batches - st["val_iters"])
for i in range(n_iters):
seed = args.seed * 1000 + st["val_iters"]
tot, cnt = val_batch(model, tok, t, args.batch, args.seq, seed)
st["val_total"] += tot; st["val_count"] += cnt; st["val_iters"] += 1
write_state(Path(args.state), st)
print(f"val iter {st['val_iters']}/{args.val_batches} "
f"partial_ppl={np.exp(st['val_total']/st['val_count']):.3f} "
f"tokens={st['val_count']}", flush=True)
print("val done", flush=True)
elif args.mode == "probe":
idx = int(args.idx)
q = PROBES[idx][1]; want = PROBES[idx][2]
prompt = "<|analyst|><|user|>" + q + "<|assistant|>"
ids = tok.encode(prompt).ids
out = tok.decode(generate(model, tok, ids, persona_id=1, max_new=60,
temperature=0.4, top_k=40,
repetition_penalty=1.5,
no_repeat_ngram_size=4)[len(ids):]).lower()
hit = any(w in out for w in want.split())
st["probes"][str(idx)] = {"hit": hit, "out": out[:200]}
write_state(Path(args.state), st)
print(f"probe {idx} {PROBES[idx][0]}: hit={hit}", flush=True)
elif args.mode == "speed":
ids = tok.encode("<|analyst|><|user|>Evaluate this claim: 'X caused Y.'<|assistant|>").ids
t0 = time.time()
generate(model, tok, ids, persona_id=1, max_new=40, temperature=0.6,
top_k=40, repetition_penalty=1.4, no_repeat_ngram_size=4)
dt = time.time() - t0
st["speed"] = round(40.0 / dt, 1)
write_state(Path(args.state), st)
print(f"speed {st['speed']} tok/s", flush=True)
elif args.mode == "sample":
idx = int(args.idx)
p = SAMPLE_PROMPTS[idx]
persona = 2 if p.startswith("<|skeptic|>") else 1
ids = tok.encode(p).ids
out = tok.decode(generate(model, tok, ids, persona_id=persona,
max_new=80, temperature=0.6, top_k=40,
repetition_penalty=1.4,
no_repeat_ngram_size=4)[len(ids):]).strip()
st["samples"].append({"idx": idx,
"prompt": p.split("<|user|>")[1].split("<|assistant|>")[0],
"persona": "skeptic" if persona == 2 else "analyst",
"output": out})
write_state(Path(args.state), st)
print(f"sample {idx} done ({len(out)} chars)", flush=True)
elif args.mode == "finish":
assert st["val_iters"] >= args.val_batches, \
f"val incomplete {st['val_iters']}/{args.val_batches}"
ppl = np.exp(st["val_total"] / st["val_count"])
hits = sum(v["hit"] for v in st["probes"].values())
total = len(PROBES)
st["samples"] = sorted(st["samples"], key=lambda s: s["idx"])
metrics = {
"checkpoint": args.ckpt,
"params": 7788288,
"val_loss": round(float(np.log(ppl)), 4),
"val_ppl": round(float(ppl), 4),
"val_tokens": int(st["val_count"]),
"probe_hits": f"{hits}/{total}",
"probe_accuracy": round(hits / total, 3),
"gen_speed_tok_per_s": st["speed"],
"hardware": "8-core ARM, no GPU",
"samples": st["samples"],
}
out = Path(args.out)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(metrics, indent=2, ensure_ascii=False), encoding="utf-8")
print(json.dumps(metrics, indent=2, ensure_ascii=False), flush=True)
print("metrics written", flush=True)
if __name__ == "__main__":
main()
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