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| """Local CPU-only microbenchmark for scoring, not model-serving QPS. | |
| Run: python bench_controller.py --requests 100000 --actions 20 | |
| """ | |
| import argparse | |
| import statistics | |
| import time | |
| from service.policy import Candidate,Limits,select | |
| p=argparse.ArgumentParser() | |
| p.add_argument("--requests",type=int,default=50000) | |
| p.add_argument("--actions",type=int,default=20) | |
| a=p.parse_args() | |
| assert a.actions>0 and a.requests>0 | |
| options=[Candidate(f"a{i}",.65+i%6/100,.0002+i*.0001,600+i*30,1+(i%2)) for i in range(a.actions)] | |
| lim=Limits(max_expected_cost_usd=.02,deadline_ms=4000,max_llm_calls=2,price_per_usd=3,price_per_second=.03) | |
| lat=[] | |
| for i in range(a.requests): | |
| start=time.perf_counter_ns() | |
| result=select(options,lim) | |
| lat.append((time.perf_counter_ns()-start)/1e6) | |
| assert result["decision_llm_calls"]==0 | |
| lat.sort() | |
| print({"calls":a.requests,"actions_per_call":a.actions,"mean_ms":round(statistics.mean(lat),6), | |
| "p50_ms":round(lat[int(.5*len(lat))],6),"p95_ms":round(lat[int(.95*len(lat))],6), | |
| "p99_ms":round(lat[int(.99*len(lat))],6),"controller_llm_calls":0, | |
| "note":"Local CPU-only numerical benchmark; does not test provider throughput."}) | |