| """Head-to-head benchmark: Groot vs Julia-1 vs Jev on identical requests. |
| |
| python scripts/benchmark.py build --test data/v1/test.jsonl --per-source 500 --sources typed_decisions agnews ... --out bench.jsonl |
| python scripts/benchmark.py run --rows bench.jsonl --groot runs/x/final --groot-set state_retrieval=true \ |
| --julia models/Julia-1 --jev --out bench_results.json |
| python scripts/benchmark.py table --results bench_results.json |
| |
| Accuracy is argmax agreement with the gold distribution's argmax; Brier is the |
| multiclass Brier score against the (possibly soft) gold distribution. Latency is |
| the median wall time of one request (one question). Groot and Julia run |
| in-process on the same GPU; Jev is called over its public HTTPS API, so its |
| latency includes the network round trip. |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import os |
| import random |
| import statistics |
| import time |
| from collections import defaultdict |
| from concurrent.futures import ThreadPoolExecutor |
| from pathlib import Path |
|
|
|
|
| def build(test: str, sources: list[str], per_source: int, out: str, seed: int = 0) -> None: |
| by: dict[str, list[dict]] = defaultdict(list) |
| for line in open(test, encoding="utf-8"): |
| r = json.loads(line) |
| if r["source"] in sources: |
| by[r["source"]].append(r) |
| rng = random.Random(seed) |
| rows, counts = [], {} |
| for s in sources: |
| xs = by.get(s, []) |
| rng.shuffle(xs) |
| take = xs if s == "typed_decisions" else xs[:per_source] |
| counts[s] = len(take) |
| rows += take |
| with open(out, "w", encoding="utf-8") as f: |
| for r in rows: |
| f.write(json.dumps(r, ensure_ascii=False) + "\n") |
| print(json.dumps(counts)) |
|
|
|
|
| def to_question(r: dict) -> tuple[dict, list[str]]: |
| if r["type"] == "choice": |
| crit = {} |
| for o in r["options"]: |
| name, sep, desc = o.partition(": ") |
| crit[name] = desc if sep else None |
| return {"type": "choice", "instructions": r["question"], "criteria": crit}, list(crit) |
| if r["type"] == "score": |
| levels = [o.split(": ", 1)[1] if o.startswith("level ") and ": " in o else o for o in r["options"]] |
| return {"type": "score", "instructions": r["question"], "criteria": levels}, [str(i) for i in range(len(levels))] |
| q = {"type": "noul", "instructions": r["question"]} |
| crit = {} |
| for o, k in zip(r["options"], ("false", "true")): |
| _, sep, desc = o.partition(": ") |
| if sep and desc: |
| crit[k] = desc |
| if crit: |
| q["criteria"] = crit |
| return q, ["false", "true"] |
|
|
|
|
| def probs_of(answer: dict, kind: str, keys: list[str]) -> list[float]: |
| if kind == "noul": |
| p = float(answer["noul"]) |
| return [1 - p, p] |
| pr = answer["probabilities"] |
| v = [float(pr.get(k, 0.0)) for k in keys] |
| s = sum(v) or 1.0 |
| return [x / s for x in v] |
|
|
|
|
| def score(r: dict, p: list[float]) -> tuple[bool, float]: |
| t = r["target"] |
| ok = max(range(len(p)), key=p.__getitem__) == max(range(len(t)), key=t.__getitem__) |
| return ok, sum((a - b) ** 2 for a, b in zip(p, t)) |
|
|
|
|
| def run_groot(rows, ckpt, overrides): |
| from groot1.config import parse_overrides |
| from groot1.schema import SystemOneRequest |
| from groot1.serve import GrootEngine |
|
|
| eng = GrootEngine(ckpt, overrides=parse_overrides(overrides)) |
| out = [] |
| for r in rows: |
| q, keys = to_question(r) |
| req = SystemOneRequest(state=r["state"], questions={"q": q}) |
| t0 = time.perf_counter() |
| a = eng.answer(req.state, req.questions).answers["q"].model_dump() |
| dt = (time.perf_counter() - t0) * 1000 |
| ok, br = score(r, probs_of(a, r["type"], keys)) |
| out.append({"ok": ok, "brier": br, "ms": dt}) |
| return out |
|
|
|
|
| def run_julia(rows, path, device="cuda"): |
| import julia |
|
|
| try: |
| import julia.router.encoder as je |
|
|
| je.specialize_decision_encoder = lambda model: False |
| except Exception: |
| pass |
| eng = julia.load_model(path, device=device, max_length=8192, head_length=512) |
| out = [] |
| for r in rows: |
| q, keys = to_question(r) |
| if r["type"] == "choice" and not 2 <= len(keys) <= 20: |
| out.append({"skip": f"{len(keys)} options > Julia's 20-option native limit"}) |
| continue |
| if q["type"] == "choice": |
| q = {**q, "criteria": {k: v or k.replace("_", " ") for k, v in q["criteria"].items()}} |
| try: |
| t0 = time.perf_counter() |
| res = eng.predict(state=r["state"], questions={"q": q}) |
| dt = (time.perf_counter() - t0) * 1000 |
| pr = res["answers"]["q"]["probabilities"] |
| probs = [float(pr.get(k, 0.0)) for k in keys] |
| s = sum(probs) or 1.0 |
| ok, br = score(r, [x / s for x in probs]) |
| out.append({"ok": ok, "brier": br, "ms": dt}) |
| except Exception as e: |
| out.append({"skip": f"error: {str(e)[:120]}"}) |
| return out |
|
|
|
|
| def run_jev(rows, workers=8): |
| import httpx |
|
|
| key = os.environ["TYPESAFE_API_KEY"] |
|
|
| def one(r): |
| q, keys = to_question(r) |
| body = {"model": "jev-latest", "state": r["state"], "questions": {"q": q}} |
| headers = {"Authorization": f"Bearer {key}"} |
| err = None |
| for attempt in range(5): |
| try: |
| t0 = time.perf_counter() |
| resp = httpx.post("https://api.typesafe.ai/v1/systemone", json=body, headers=headers, timeout=120) |
| dt = (time.perf_counter() - t0) * 1000 |
| if resp.status_code == 429 or resp.status_code >= 500: |
| time.sleep(2 ** attempt) |
| continue |
| if resp.status_code != 200: |
| return {"skip": f"{resp.status_code} {resp.text[:100]}"} |
| ok, br = score(r, probs_of(resp.json()["answers"]["q"], r["type"], keys)) |
| return {"ok": ok, "brier": br, "ms": dt} |
| except Exception as e: |
| err = str(e)[:100] |
| time.sleep(2 ** attempt) |
| return {"skip": f"error: {err}"} |
|
|
| with ThreadPoolExecutor(workers) as ex: |
| return list(ex.map(one, rows)) |
|
|
|
|
| def summarize(rows, res): |
| by = defaultdict(list) |
| for r, x in zip(rows, res): |
| by[r["source"]].append(x) |
| if r["source"] == "typed_decisions": |
| by["typed_decisions / " + r["type"]].append(x) |
| if r["source"].startswith("massive_"): |
| by[f"{r['source']} / {len(r['options'])} labels"].append(x) |
| out = {} |
| for k, xs in by.items(): |
| done = [x for x in xs if "skip" not in x] |
| if not done: |
| out[k] = {"n": 0, "skipped": len(xs), "reason": xs[0].get("skip")} |
| continue |
| out[k] = {"n": len(done), "correct": sum(x["ok"] for x in done), "acc": sum(x["ok"] for x in done) / len(done), |
| "brier": sum(x["brier"] for x in done) / len(done), "p50_ms": statistics.median(x["ms"] for x in done), |
| "skipped": len(xs) - len(done)} |
| return out |
|
|
|
|
| def table(results: dict) -> str: |
| systems = [s for s in ("groot", "julia", "jev") if s in results] |
| keys = sorted({k for s in systems for k in results[s]}, key=lambda k: (" / " in k, k)) |
| lines = ["| Benchmark | n | " + " | ".join(f"{s.capitalize()} acc | {s.capitalize()} Brier" for s in systems) + " |", |
| "|" + "---|" * (2 + 2 * len(systems))] |
| for k in keys: |
| n = max(results[s].get(k, {}).get("n", 0) for s in systems) |
| cells = [] |
| for s in systems: |
| v = results[s].get(k) |
| cells += [f"{v['acc']:.3f}", f"{v['brier']:.3f}"] if v and v.get("n") else ["n/a", "n/a"] |
| lines.append(f"| {k} | {n} | " + " | ".join(cells) + " |") |
| return "\n".join(lines) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| sub = ap.add_subparsers(dest="cmd", required=True) |
| b = sub.add_parser("build") |
| b.add_argument("--test", required=True) |
| b.add_argument("--sources", nargs="+", required=True) |
| b.add_argument("--per-source", type=int, default=500) |
| b.add_argument("--out", required=True) |
| r = sub.add_parser("run") |
| r.add_argument("--rows", required=True) |
| r.add_argument("--groot", default=None) |
| r.add_argument("--groot-set", action="append", default=[]) |
| r.add_argument("--julia", default=None) |
| r.add_argument("--jev", action="store_true") |
| r.add_argument("--out", required=True) |
| t = sub.add_parser("table") |
| t.add_argument("--results", required=True) |
| args = ap.parse_args() |
| if args.cmd == "build": |
| build(args.test, args.sources, args.per_source, args.out) |
| return |
| if args.cmd == "table": |
| print(table(json.loads(Path(args.results).read_text()))) |
| return |
| rows = [json.loads(l) for l in open(args.rows, encoding="utf-8")] |
| results = json.loads(Path(args.out).read_text()) if Path(args.out).exists() else {} |
| for name, fn in (("groot", lambda: run_groot(rows, args.groot, args.groot_set) if args.groot else None), |
| ("julia", lambda: run_julia(rows, args.julia) if args.julia else None), |
| ("jev", lambda: run_jev(rows) if args.jev else None)): |
| res = fn() |
| if res is None: |
| continue |
| results[name] = summarize(rows, res) |
| Path(args.out).write_text(json.dumps(results, indent=2)) |
| print(f"{name} done", flush=True) |
| print(table(results)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
