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Phase 6: Evaluate the GRPO-trained agent on the test split.
Loads:
- base agent: Qwen/Qwen2.5-1.5B-Instruct (frozen weights)
- LoRA adapter: from --adapter (local dir or HF model repo id)
For each test task:
1. build agent input (same as training)
2. agent generates a candidate system prompt
3. env runs LLM-under-test with that prompt; verify; reward
4. up to --max-turns retries with the previous attempt visible
Outputs results/trained_agent.json in the same shape as run_baseline.py.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from pathlib import Path
from typing import Any, Dict, List
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from src.envs.promptops_arena.server.environment import PromptOpsArenaEnvironment
from src.envs.promptops_arena.tasks import load_tasks
from src.envs.promptops_arena import llm_under_test
from scripts.train_grpo import build_agent_input # reuse the exact prompt template
def _load_agent(base_model: str, adapter: str | None):
import torch # type: ignore
from transformers import AutoModelForCausalLM, AutoTokenizer # type: ignore
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32
tok = AutoTokenizer.from_pretrained(base_model)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
mdl = AutoModelForCausalLM.from_pretrained(
base_model, torch_dtype=dtype, device_map=device,
)
if adapter:
from peft import PeftModel # type: ignore
mdl = PeftModel.from_pretrained(mdl, adapter)
mdl.eval()
def gen(text: str, max_new_tokens: int = 300) -> str:
msgs = [
{"role": "system", "content": "You are a helpful prompt engineer."},
{"role": "user", "content": text},
]
encoded = tok.apply_chat_template(
msgs, add_generation_prompt=True, return_tensors="pt",
)
if hasattr(encoded, "input_ids"):
ids = encoded.input_ids
elif isinstance(encoded, dict):
ids = encoded["input_ids"]
else:
ids = encoded
ids = ids.to(device)
with torch.no_grad():
out = mdl.generate(
input_ids=ids, max_new_tokens=max_new_tokens,
do_sample=False, pad_token_id=tok.eos_token_id,
)
return tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True).strip()
return gen
def _build_followup_input(task: dict, history: List[dict]) -> str:
"""Like build_agent_input but with prior attempts visible (refinement turn)."""
base = build_agent_input(task)
if not history:
return base
extra = ["", "PRIOR ATTEMPTS (yours, with what the small model produced):"]
for i, h in enumerate(history, 1):
extra.append(f"--- attempt {i} (reward={h['reward']:.2f}, correct={h['correct']}) ---")
extra.append(f"YOUR PROMPT: {h['prompt'][:400]}")
extra.append(f"MODEL OUTPUT: {h['completion'][:200]}")
extra.append("")
extra.append("Improve the system prompt. Output ONLY the new system prompt, no preamble.")
return base + "\n" + "\n".join(extra)
def evaluate_trained(env, task, agent_gen, max_turns: int = 3) -> Dict[str, Any]:
history: List[dict] = []
best_reward = -1.0
best_components: Dict[str, float] = {}
correct = False
edit_turns = 0
for turn in range(max_turns):
edit_turns = turn + 1
ai = build_agent_input(task) if turn == 0 else _build_followup_input(task, history)
sp = agent_gen(ai).strip() or "Solve this:"
res = env.execute_prompt(task, sp)
components = res["reward"]
total = components["total"]
is_correct = components["correctness"] >= 1.0
history.append({
"prompt": sp,
"completion": res["completion"],
"reward": total,
"correct": is_correct,
})
if total > best_reward:
best_reward = total
best_components = components
if is_correct:
correct = True
break
return {
"task_id": task["id"],
"task_type": task["type"],
"policy": "trained_agent",
"edit_turns": edit_turns,
"final_reward": best_reward,
"correct": correct,
"format_ok": best_components.get("format", 0.0) >= 1.0,
"components": best_components,
"trace": history,
}
def main():
p = argparse.ArgumentParser()
p.add_argument("--adapter", default=None,
help="Local dir or HF repo id of the LoRA adapter.")
p.add_argument("--base", default="Qwen/Qwen2.5-1.5B-Instruct")
p.add_argument("--split", default="test")
p.add_argument("--out", default="results/trained_agent.json")
p.add_argument("--limit", type=int, default=None)
p.add_argument("--per-type", type=int, default=None)
p.add_argument("--max-turns", type=int, default=3)
args = p.parse_args()
os.environ.setdefault("PROMPTOPS_LLM_BACKEND", "transformers")
tasks = load_tasks(split=args.split)
if args.per_type:
bucketed: Dict[str, List[dict]] = {}
for t in tasks:
bucketed.setdefault(t["type"], []).append(t)
sampled: List[dict] = []
for tt, lst in bucketed.items():
sampled.extend(lst[: args.per_type])
tasks = sampled
if args.limit:
tasks = tasks[: args.limit]
print(f"[eval_trained] adapter={args.adapter} base={args.base} "
f"split={args.split} n_tasks={len(tasks)} "
f"llm_backend={llm_under_test.backend_name()}")
env = PromptOpsArenaEnvironment(split=args.split, seed=0)
agent_gen = _load_agent(args.base, args.adapter)
rows: List[Dict[str, Any]] = []
t0 = time.time()
for i, task in enumerate(tasks):
row = evaluate_trained(env, task, agent_gen, max_turns=args.max_turns)
rows.append(row)
n_correct = sum(1 for r in rows if r["correct"])
print(f" [{i+1}/{len(tasks)}] {task['type']:5s} "
f"correct={n_correct}/{i+1} "
f"r={row['final_reward']:+.3f} elapsed={time.time()-t0:.1f}s")
by_type: Dict[str, Dict[str, int]] = {}
for r in rows:
d = by_type.setdefault(r["task_type"], {"n": 0, "correct": 0, "format": 0})
d["n"] += 1
d["correct"] += int(r["correct"])
d["format"] += int(r["format_ok"])
overall = {
"n": len(rows),
"correct": sum(1 for r in rows if r["correct"]),
"format": sum(1 for r in rows if r["format_ok"]),
"mean_reward": sum(r["final_reward"] for r in rows) / max(1, len(rows)),
}
out = {
"policy": "trained_agent",
"adapter": args.adapter,
"base_model": args.base,
"split": args.split,
"llm_backend": llm_under_test.backend_name(),
"by_type": by_type,
"overall": overall,
"rows": rows,
}
out_path = Path(args.out)
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
print(f"\n[eval_trained] wrote {out_path}")
print(f" overall: {overall['correct']}/{overall['n']} correct, "
f"mean_reward={overall['mean_reward']:.3f}")
for tt, d in by_type.items():
print(f" {tt:5s}: {d['correct']}/{d['n']} correct, format {d['format']}/{d['n']}")
if __name__ == "__main__":
main()
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