Download source/scripts/calibrate.py from rca-umb/five-desks-control: direct link, hf CLI and curl.
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https://huggingface.co/datasets/rca-umb/five-desks-control/resolve/main/source/scripts/calibrate.py
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curl -L -o calibrate.py https://huggingface.co/datasets/rca-umb/five-desks-control/resolve/main/source/scripts/calibrate.py
10.2 kB
| """Play Qwen3.8-27B (thinking off) against a running server to measure per-task solve rates. | |
| The HF token is read inside this process via huggingface_hub (HF_TOKEN_PATH selects the file); | |
| it is never printed or written anywhere. | |
| Usage: HF_TOKEN_PATH=... python scripts/calibrate.py --url http://localhost:8000 --episodes 4 --out reports/calibration/run1 | |
| """ | |
| import argparse | |
| import json | |
| import random | |
| import re | |
| import sys | |
| import threading | |
| import time | |
| import urllib.error | |
| import urllib.request | |
| from concurrent.futures import ThreadPoolExecutor | |
| from pathlib import Path | |
| from statistics import mean | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[1])) | |
| sys.path.insert(0, str(Path(__file__).resolve().parent)) | |
| from huggingface_hub import get_token # noqa: E402 | |
| from pydantic import ValidationError # noqa: E402 | |
| from five_desks.models import DeskAction # noqa: E402 | |
| from five_desks.tasks import TASK_IDS # noqa: E402 | |
| from wsclient import Session # noqa: E402 | |
| ROUTER = "https://router.huggingface.co/v1/chat/completions" | |
| FINISH_ACTION = {"tool": "submit", "answer": {}} | |
| _decoder = json.JSONDecoder(strict=False) | |
| def system_prompt(action_schema: dict) -> str: | |
| return ( | |
| "You are an agent acting in an environment. Reply to every message with exactly one JSON object and " | |
| "nothing else: an action that matches the action schema below, or {\"finish\": true} to end the episode.\n" | |
| f"Action schema: {json.dumps(action_schema)}" | |
| ) | |
| def first_json_object(text: str): | |
| text = re.sub(r"<think>.*?</think>", "", text or "", flags=re.S) | |
| for match in re.finditer(r"\{", text): | |
| try: | |
| obj, _ = _decoder.raw_decode(text[match.start():]) | |
| if isinstance(obj, dict): | |
| return obj | |
| except json.JSONDecodeError: | |
| continue | |
| return None | |
| class Model: | |
| def __init__(self, model: str, max_tokens: int, temperature: float, thinking: bool = False): | |
| self.model, self.max_tokens, self.temperature, self.thinking = model, max_tokens, temperature, thinking | |
| self._token = get_token() | |
| if not self._token: | |
| raise SystemExit("No Hugging Face token found (set HF_TOKEN_PATH).") | |
| self.usage = {"prompt_tokens": 0, "completion_tokens": 0, "calls": 0} | |
| self._lock = threading.Lock() | |
| self.empty_examples = [] | |
| def chat(self, messages): | |
| # Some provider responses come back with no content at all; retry those rather than score them. | |
| for _ in range(3): | |
| content, used, finish = self._chat_once(messages) | |
| if content.strip() or finish == "length": | |
| break | |
| return content, used, finish | |
| def _chat_once(self, messages): | |
| body = {"model": self.model, "messages": messages, "max_tokens": self.max_tokens, | |
| "temperature": self.temperature, "chat_template_kwargs": {"enable_thinking": self.thinking}} | |
| for attempt in range(5): | |
| req = urllib.request.Request(ROUTER, data=json.dumps(body).encode(), headers={ | |
| "Authorization": f"Bearer {self._token}", "Content-Type": "application/json"}) | |
| try: | |
| with urllib.request.urlopen(req, timeout=180) as response: | |
| data = json.load(response) | |
| break | |
| except urllib.error.HTTPError as exc: | |
| if exc.code in (429, 500, 502, 503, 504) and attempt < 4: | |
| time.sleep(2 ** attempt * 3) | |
| continue | |
| raise RuntimeError(f"inference HTTP {exc.code}: {exc.read().decode()[:200]}") from None | |
| except (urllib.error.URLError, TimeoutError): # DNS hiccups and slow responses are retried too | |
| if attempt < 4: | |
| time.sleep(2 ** attempt * 3) | |
| continue | |
| raise | |
| usage = data.get("usage") or {} | |
| with self._lock: | |
| self.usage["prompt_tokens"] += usage.get("prompt_tokens", 0) | |
| self.usage["completion_tokens"] += usage.get("completion_tokens", 0) | |
| self.usage["calls"] += 1 | |
| choice = data["choices"][0] | |
| message = choice["message"] | |
| content = message.get("content") or "" | |
| if not content.strip(): | |
| with self._lock: | |
| self.usage["empty_content"] = self.usage.get("empty_content", 0) + 1 | |
| self.empty_examples.append({k: v for k, v in message.items() if v}) | |
| return content, usage.get("completion_tokens", 0), choice.get("finish_reason") | |
| def play(url, model, schema, task_id, completion_budget, context_budget): | |
| messages = [{"role": "system", "content": system_prompt(schema)}] | |
| episode = {"task_id": task_id, "turns": [], "end": None, "reward": 0.0} | |
| completion_used = 0 | |
| with Session(url) as session: | |
| episode["seed"] = random.SystemRandom().randrange(2**48) | |
| data = session.reset(task_id=task_id, seed=episode["seed"]) | |
| obs = data["observation"] | |
| while True: | |
| obs_text = json.dumps({k: v for k, v in obs.items() if k != "metadata" or v}) | |
| messages.append({"role": "user", "content": obs_text}) | |
| if len(messages) > 2: | |
| completion_used += len(obs_text) // 3 # later observations count against the budget | |
| if completion_used >= completion_budget or sum(len(m["content"]) for m in messages) // 3 >= context_budget: | |
| episode["end"] = "budget" | |
| data = session.step(FINISH_ACTION) | |
| break | |
| reply, used, finish_reason = model.chat(messages) | |
| completion_used += used | |
| messages.append({"role": "assistant", "content": reply}) | |
| episode["turns"].append({"obs": obs_text[:4000], "reply": reply[:2000], "finish_reason": finish_reason, "tokens": used}) | |
| if finish_reason == "length": | |
| episode["end"] = "truncated" | |
| data = session.step(FINISH_ACTION) | |
| break | |
| action = first_json_object(reply) | |
| if isinstance(action, dict) and set(action) == {"action"} and isinstance(action["action"], dict): | |
| action = action["action"] | |
| if action is None: | |
| episode["end"] = "empty_reply" if not reply.strip() else "invalid_json" | |
| data = session.step(FINISH_ACTION) | |
| break | |
| if action.get("finish") is True: | |
| episode["end"] = "finish" | |
| data = session.step(FINISH_ACTION) | |
| break | |
| try: | |
| DeskAction.model_validate(action) | |
| except ValidationError: | |
| episode["end"] = "schema_invalid" | |
| data = session.step(FINISH_ACTION) | |
| break | |
| data = session.step(action) | |
| obs = data["observation"] | |
| if data["done"]: | |
| episode["end"] = "submitted" if action.get("tool") == "submit" else "out_of_steps" | |
| episode["final"] = obs.get("output") | |
| break | |
| episode["reward"] = data.get("reward") or 0.0 | |
| episode["n_turns"] = len(episode["turns"]) | |
| episode["completion_tokens_est"] = completion_used | |
| return episode | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--url", default="http://localhost:8000") | |
| parser.add_argument("--model", default="Qwen/Qwen3.8-27B:deepinfra") | |
| parser.add_argument("--episodes", type=int, default=4) | |
| parser.add_argument("--tasks", default="", help="comma-separated task ids or prefixes (default: all)") | |
| parser.add_argument("--workers", type=int, default=8) | |
| parser.add_argument("--max-tokens", type=int, default=1024) | |
| parser.add_argument("--temperature", type=float, default=1.0) | |
| parser.add_argument("--completion-budget", type=int, default=8192) | |
| parser.add_argument("--context-budget", type=int, default=16384) | |
| parser.add_argument("--out", required=True) | |
| args = parser.parse_args() | |
| tasks = TASK_IDS | |
| if args.tasks: | |
| wanted = args.tasks.split(",") | |
| tasks = [t for t in TASK_IDS if any(t.startswith(w) for w in wanted)] | |
| with urllib.request.urlopen(f"{args.url}/schema", timeout=15) as response: | |
| schema = json.load(response)["action"] | |
| model = Model(args.model, args.max_tokens, args.temperature) | |
| out = Path(args.out) | |
| out.mkdir(parents=True, exist_ok=True) | |
| jobs = [t for t in tasks for _ in range(args.episodes)] | |
| started = time.monotonic() | |
| results = [] | |
| with ThreadPoolExecutor(args.workers) as pool, open(out / "episodes.jsonl", "w") as log: | |
| futures = [pool.submit(play, args.url, model, schema, t, args.completion_budget, args.context_budget) for t in jobs] | |
| for fut in futures: | |
| try: | |
| ep = fut.result() | |
| except Exception as exc: # infrastructure errors are excluded, not scored as 0 | |
| print("episode error:", exc, file=sys.stderr) | |
| continue | |
| results.append(ep) | |
| log.write(json.dumps(ep) + "\n") | |
| summary = {} | |
| for task_id in tasks: | |
| eps = [e for e in results if e["task_id"] == task_id] | |
| if not eps: | |
| continue | |
| rewards = [e["reward"] for e in eps] | |
| ends = {} | |
| for e in eps: | |
| ends[e["end"]] = ends.get(e["end"], 0) + 1 | |
| summary[task_id] = {"n": len(eps), "mean": round(mean(rewards), 3), "rewards": rewards, | |
| "mixed": len(set(rewards)) > 1, "ends": ends, | |
| "turns": round(mean(e["n_turns"] for e in eps), 1)} | |
| report = {"model": args.model, "episodes": len(results), "seconds": round(time.monotonic() - started), | |
| "usage": model.usage, "tasks": summary} | |
| (out / "summary.json").write_text(json.dumps(report, indent=2) + "\n") | |
| (out / "empty_examples.json").write_text(json.dumps(model.empty_examples[:20], indent=2) + "\n") | |
| print(f"{'task':24s} {'mean':>5s} rewards ends") | |
| for task_id, s in summary.items(): | |
| print(f"{task_id:24s} {s['mean']:5.2f} {str(s['rewards']):30s} {s['ends']}") | |
| print("usage:", model.usage, "seconds:", report["seconds"]) | |
| if __name__ == "__main__": | |
| main() | |