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5000a45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
from dataclasses import dataclass
import sys
from pathlib import Path
from typing import Any, Dict, List
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT))
from server.softmax_surrogate_environment import SoftmaxSurrogateEnvironment
@dataclass
class RunRecord:
task_id: str
episode: int
best_latency_ms: float
best_config: Dict[str, int]
final_validation_mse: float
final_state: Dict[str, Any]
final_regret: float
history: List[Dict[str, Any]]
def _aggregate_metrics(episode_records: List[Dict[str, Any]], budget: int) -> Dict[str, Any]:
ks = sorted(set(k for k in (1, 3, 5, budget) if k <= budget))
regrets_by_k: Dict[int, List[float]] = {k: [] for k in ks}
auc_regrets: List[float] = []
for episode in episode_records:
regrets = [float(step["regret"]) for step in episode["history"]]
if regrets:
auc_regrets.append(float(sum(regrets) / len(regrets)))
for k in ks:
if len(regrets) >= k:
regrets_by_k[k].append(regrets[k - 1])
return {
"mean_regret_at": {
str(k): float(sum(vals) / len(vals)) for k, vals in regrets_by_k.items() if vals
},
"median_regret_at": {
str(k): float(np.median(np.asarray(vals, dtype=np.float32))) for k, vals in regrets_by_k.items() if vals
},
"mean_auc_regret": float(sum(auc_regrets) / len(auc_regrets)) if auc_regrets else None,
"oracle_hit_rate_final": float(
sum(1 for episode in episode_records if float(episode["final_regret"]) == 0.0) / len(episode_records)
) if episode_records else None,
}
def _pick_task_from_input(args: argparse.Namespace) -> str:
if args.task:
return args.task
env = SoftmaxSurrogateEnvironment(
measurement_path=args.measurement_path,
budget=args.budget,
seed=args.seed,
)
return env.reset()["observation"]["task_id"]
def run_random_baseline(
task: str,
episodes: int,
budget: int,
seed: int,
measurement_path: str,
) -> Dict[str, Any]:
rng = np.random.default_rng(seed)
best_overall: Dict[str, Any] = {"latency_ms": float("inf"), "config": None, "task_id": task}
episode_records: List[Dict[str, Any]] = []
env = SoftmaxSurrogateEnvironment(
measurement_path=measurement_path,
budget=budget,
seed=seed,
)
for episode in range(episodes):
env.reset(task=task, seed=seed + episode)
done = False
episode_best = float("inf")
episode_best_cfg: Dict[str, int] | None = None
history: List[Dict[str, Any]] = []
while not done:
unseen = [config_id for config_id in env.available_config_ids() if config_id not in env.seen_config_ids()]
choice_pool = unseen if unseen else env.available_config_ids()
config_id = int(rng.choice(choice_pool))
step_out = env.step({"config_id": config_id})
obs = step_out["observation"]
trial = obs["last_trial"]
history.append(
{
"config_id": config_id,
"latency_ms": trial["latency_ms"],
"config": trial["config"],
"reward": step_out["reward"],
"regret": step_out["info"]["current_regret"],
"validation_mse": step_out["info"]["validation_mse"],
}
)
if obs["best_so_far_ms"] < episode_best:
episode_best = obs["best_so_far_ms"]
best_id = env.seen_config_ids()[int(np.argmin([env.measured_latency_ms(cid) for cid in env.seen_config_ids()]))]
episode_best_cfg = env.config_info(best_id)
done = bool(step_out["done"])
if episode_best < best_overall["latency_ms"]:
best_overall = {
"latency_ms": float(episode_best),
"config": episode_best_cfg,
"task_id": task,
}
diagnostics = env.diagnostics()
episode_records.append(
RunRecord(
task_id=task,
episode=episode,
best_latency_ms=float(episode_best),
best_config=episode_best_cfg or {},
final_validation_mse=float(diagnostics["validation_mse"]),
final_state=env.state(),
final_regret=float(diagnostics["current_regret"]),
history=history,
).__dict__
)
return {
"task": task,
"method": "random",
"episodes": episodes,
"budget": budget,
"seed": seed,
"oracle_best_ms": env.oracle_best()["median_ms"],
"best_overall": best_overall,
"aggregate_metrics": _aggregate_metrics(episode_records, budget),
"episodes_summary": episode_records,
}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Random baseline for surrogate environment.")
parser.add_argument("--task", default=None, help="Task ID (e.g., softmax_m4096_n2048)")
parser.add_argument("--episodes", type=int, default=20)
parser.add_argument("--budget", type=int, default=6)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument(
"--measurement-path",
type=str,
default="data/autotune_measurements.csv",
)
parser.add_argument(
"--output",
type=Path,
default=Path("outputs/random_baseline.json"),
)
return parser.parse_args()
def main() -> None:
args = parse_args()
task = _pick_task_from_input(args)
summary = run_random_baseline(
task=task,
episodes=args.episodes,
budget=args.budget,
seed=args.seed,
measurement_path=args.measurement_path,
)
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("w", encoding="utf-8") as f:
json.dump(summary, f, indent=2)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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