Files
abap-llm/train/baseline.py

64 lines
3.2 KiB
Python

"""Stage 1 eval runs on the fixed subset (train/subset.json) with a local OpenAI-compatible server.
train/.venv not needed: python3 train/baseline.py --label baseline [--only T01] [--run-base 20000]
One task at a time. Results: runs/stage1/<label>.json (written after each task).
"""
import argparse
import json
import os
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, ROOT)
from harness.adt_client import load_env # noqa: E402
from harness.agents import LlmAgent # noqa: E402
from harness.runner import Runner # noqa: E402
MODEL = os.path.expanduser("~/models/Qwen3.8-27B-4bit")
BASE_URL = "http://127.0.0.1:8080/v1"
MAX_TOKENS = 16384 # per turn; sent in each request (the server limit stays 32768). Same for before/after.
def main():
load_env(os.path.join(ROOT, ".env"))
ap = argparse.ArgumentParser()
ap.add_argument("--label", default="baseline")
ap.add_argument("--only", nargs="*")
ap.add_argument("--run-base", type=int, default=20000)
a = ap.parse_args()
subset = json.load(open(os.path.join(ROOT, "train", "subset.json")))["tasks"]
out_path = os.path.join(ROOT, "runs", "stage1", f"{a.label}.json")
os.makedirs(os.path.dirname(out_path), exist_ok=True)
res = json.load(open(out_path)) if os.path.exists(out_path) else {"model": MODEL, "tasks": {}}
res["settings"] = {"max_tokens": MAX_TOKENS, "reasoning_effort": "medium", "temperature": 0.2, "tool_call_budget": 60,
"subset": "train/subset.json", "date": "2026-10-03"}
runs_root = os.path.join(ROOT, "runs", "stage1", a.label)
for i, t in enumerate(subset):
tid = t["id"]
if (a.only and tid not in a.only) or tid in res["tasks"]:
continue
pool = os.path.join(ROOT, "tasks") if tid.startswith("T") else os.path.join(ROOT, "tasks_gen", "eval")
agent = LlmAgent(MODEL, BASE_URL, max_tokens=MAX_TOKENS)
try:
rep, run_dir = Runner(pool, runs_root).run(tid, agent, a.run_base + i)
except Exception as e: # noqa: BLE001 one broken run must not stop the series
res["tasks"][tid] = {"error": str(e)[:300]}
json.dump(res, open(out_path, "w"), indent=1)
print(json.dumps({"task": tid, "error": str(e)[:300]}), flush=True)
continue
h = rep.get("hidden_tests") or {}
res["tasks"][tid] = {"category": t["category"], "object_type": t["object_type"],
"score": (rep.get("score") or {}).get("total"), "parts": rep.get("score"),
"gates": rep.get("gates"), "hidden": f"{h.get('passed')}/{h.get('total')}",
"tool_calls": rep.get("tool_calls"), "seconds": rep.get("seconds"),
"agent_seconds": rep.get("agent_seconds"), "final": (rep.get("final_report") or "")[:300],
"run_dir": os.path.relpath(run_dir, ROOT)}
json.dump(res, open(out_path, "w"), indent=1)
print(json.dumps({"task": tid, "score": res["tasks"][tid]["score"], "hidden": res["tasks"][tid]["hidden"],
"tool_calls": rep.get("tool_calls"), "seconds": rep.get("seconds")}), flush=True)
if __name__ == "__main__":
main()