File size: 8,577 Bytes
a537615 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | """Submission-root inference entrypoint for OpenEnv gates."""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from dataclasses import dataclass
from pathlib import Path
from statistics import mean
from typing import Any, Mapping, Sequence, TextIO
from urllib.parse import urlparse
ENV_ROOT = Path(__file__).resolve().parent
PARENT = ENV_ROOT.parent
if str(PARENT) not in sys.path:
sys.path.insert(0, str(PARENT))
from openai import OpenAI
from legacy_cobol_env.eval.model_rollout import run_model_repair_rollout, run_model_rollout
from legacy_cobol_env.eval.providers import StaticResponseProvider, TextProvider
from legacy_cobol_env.server.task_bank import TaskInstance, all_tasks, load_task
VALID_MARKERS = {"START", "STEP", "END"}
STATIC_RESPONSE = '{"code": "def migrate(input_record: str) -> str:\\n return input_record\\n"}'
@dataclass(frozen=True)
class RuntimeConfig:
api_base_url: str
model_name: str
hf_token: str
mode: str
api_version: str = "2024-12-01-preview"
def load_runtime_config(env: Mapping[str, str] | None = None, mode: str | None = None) -> RuntimeConfig:
values = os.environ if env is None else env
selected_mode = mode or values.get("INFERENCE_MODE") or values.get("MODE") or "live"
if selected_mode in {"static", "mock"}:
return RuntimeConfig(
api_base_url=values.get("API_BASE_URL", ""),
model_name=values.get("MODEL_NAME", "static"),
hf_token=values.get("HF_TOKEN", ""),
mode=selected_mode,
api_version=values.get(
"API_VERSION",
values.get(
"OPENAI_API_VERSION",
values.get("AZURE_OPENAI_API_VERSION", "2024-12-01-preview"),
),
),
)
required = ["API_BASE_URL", "MODEL_NAME", "HF_TOKEN"]
missing = [key for key in required if not values.get(key)]
if missing:
raise ValueError(f"missing inference configuration: {', '.join(missing)}")
return RuntimeConfig(
api_base_url=values["API_BASE_URL"],
model_name=values["MODEL_NAME"],
hf_token=values["HF_TOKEN"],
mode=selected_mode,
api_version=values.get(
"API_VERSION",
values.get("OPENAI_API_VERSION", values.get("AZURE_OPENAI_API_VERSION", "2024-12-01-preview")),
),
)
def format_event(marker: str, payload: Mapping[str, object]) -> str:
if marker not in VALID_MARKERS:
raise ValueError(f"invalid log marker: {marker}")
data = json.dumps(dict(payload), sort_keys=True, separators=(",", ":"))
return f"[{marker}] {data}"
def build_openai_client(config: RuntimeConfig) -> OpenAI:
base_url = config.api_base_url.rstrip("/")
if _is_azure_endpoint(base_url):
deployment_base = (
base_url
if "/openai/deployments/" in base_url
else f"{base_url}/openai/deployments/{config.model_name}"
)
return OpenAI(
base_url=deployment_base,
api_key=config.hf_token,
default_headers={"api-key": config.hf_token},
default_query={"api-version": config.api_version},
timeout=60.0,
)
return OpenAI(base_url=base_url, api_key=config.hf_token, timeout=60.0)
class OpenAITextProvider:
name = "openai-client"
def __init__(self, client: OpenAI, model_name: str) -> None:
self._client = client
self._model_name = model_name
def generate(self, prompt: str) -> str:
response = self._client.chat.completions.create(
model=self._model_name,
messages=[
{
"role": "system",
"content": "Return only JSON with a single code field containing a Python migrate function.",
},
{"role": "user", "content": prompt},
],
temperature=0,
)
return response.choices[0].message.content or ""
def build_provider(config: RuntimeConfig) -> TextProvider:
if config.mode in {"static", "mock"}:
return StaticResponseProvider(config.mode, STATIC_RESPONSE)
return OpenAITextProvider(build_openai_client(config), config.model_name)
def run_inference(task_id: str | None, max_repairs: int, config: RuntimeConfig) -> dict[str, object]:
tasks = [load_task(task_id=task_id)] if task_id else all_tasks()
provider = build_provider(config)
results = [_run_task(task, provider, max_repairs) for task in tasks]
return {
"mode": config.mode,
"model_name": config.model_name,
"max_repairs": max_repairs,
"task_count": len(results),
"mean_public_score": mean(item["score"] for item in results) if results else 0.0,
"accepted_count": sum(1 for item in results if item["accepted"]),
"results": results,
}
def _run_task(task: TaskInstance, provider: TextProvider, max_repairs: int) -> dict[str, Any]:
started = time.perf_counter()
try:
trajectory = (
run_model_repair_rollout(task=task, provider=provider, max_repairs=max_repairs)
if max_repairs > 0
else run_model_rollout(task=task, provider=provider)
)
except Exception as exc:
return {
"task_id": task.task_id,
"family_id": task.family_id,
"difficulty": task.metadata["difficulty"],
"score": 0.0,
"accepted": False,
"duration_s": round(time.perf_counter() - started, 3),
"error": f"{type(exc).__name__}: {exc}",
}
final = trajectory["final"]
return {
"task_id": task.task_id,
"family_id": task.family_id,
"difficulty": task.metadata["difficulty"],
"score": final["public_score"],
"accepted": final["accepted"],
"visible_pass_rate": trajectory["visible"]["pass_rate"],
"duration_s": round(time.perf_counter() - started, 3),
"trajectory": trajectory,
}
def write_output(path: str | None, payload: Mapping[str, object]) -> None:
if not path:
return
output_path = Path(path)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(dict(payload), sort_keys=True, indent=2) + "\n", encoding="utf-8")
def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run OpenEnv submission inference gate.")
parser.add_argument("--task-id")
parser.add_argument("--max-repairs", type=int, default=0)
parser.add_argument("--output")
parser.add_argument("--mode", choices=["live", "static", "mock"])
return parser.parse_args(argv)
def main(
argv: Sequence[str] | None = None,
env: Mapping[str, str] | None = None,
stdout: TextIO | None = None,
) -> int:
args = parse_args(argv)
stream = sys.stdout if stdout is None else stdout
config = load_runtime_config(env, mode=args.mode)
print(
format_event(
"START",
{
"mode": config.mode,
"model_name": config.model_name,
"task_id": args.task_id or "all",
"max_repairs": args.max_repairs,
},
),
file=stream,
)
result = run_inference(args.task_id, args.max_repairs, config)
for index, task_result in enumerate(result["results"], start=1):
step_payload = {
"index": index,
"task_id": task_result["task_id"],
"family_id": task_result["family_id"],
"difficulty": task_result["difficulty"],
"score": task_result["score"],
"accepted": task_result["accepted"],
}
if "error" in task_result:
step_payload["error"] = task_result["error"]
print(format_event("STEP", step_payload), file=stream)
write_output(args.output, result)
print(
format_event(
"END",
{
"mode": config.mode,
"task_count": result["task_count"],
"mean_public_score": result["mean_public_score"],
"accepted_count": result["accepted_count"],
},
),
file=stream,
)
return 0
def _is_azure_endpoint(base_url: str) -> bool:
host = urlparse(base_url).netloc.lower()
return "openai.azure.com" in host or "cognitiveservices.azure.com" in host
if __name__ == "__main__":
raise SystemExit(main())
|