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951f760 | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
from typing import Any, Callable
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
LEDGER_TEMPLATE_PATH = REPO_ROOT / "artifacts" / "benchmark_ledger.template.json"
from scripts.hydra_generation import build_hydra_generator
from scripts.benchmark_datasets import resolve_benchmark_dataset as resolve_canonical_dataset
from scripts.benchmark_suite import build_prompt, validate_sample
def load_jsonl_samples(path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
for line in path.read_text(encoding="utf-8").splitlines():
if line.strip():
rows.append(json.loads(line))
return rows
def _score_mbpp(samples: list[dict[str, Any]], generate_fn: Callable[[str], str]) -> float:
passed = 0
for sample in samples:
validate_sample("MBPP", sample)
code = generate_fn(build_prompt("MBPP", sample))
namespace: dict[str, Any] = {}
exec(code, namespace, namespace)
for test in sample["tests"]:
exec(test, namespace, namespace)
passed += 1
return passed / len(samples) if samples else 0.0
def _extract_last_number(text: str) -> str | None:
matches = re.findall(r"-?\d+(?:\.\d+)?", text)
return matches[-1] if matches else None
def _score_gsm8k(samples: list[dict[str, Any]], generate_fn: Callable[[str], str]) -> float:
passed = 0
for sample in samples:
validate_sample("GSM8K", sample)
output = generate_fn(build_prompt("GSM8K", sample))
pred = _extract_last_number(output)
if pred is not None and pred == str(sample["answer"]):
passed += 1
return passed / len(samples) if samples else 0.0
def _score_humaneval(samples: list[dict[str, Any]], generate_fn: Callable[[str], str]) -> float:
passed = 0
for sample in samples:
validate_sample("HumanEval", sample)
code = generate_fn(build_prompt("HumanEval", sample))
namespace: dict[str, Any] = {}
exec(code, namespace, namespace)
exec(sample["test"], namespace, namespace)
passed += 1
return passed / len(samples) if samples else 0.0
def _score_arc(samples: list[dict[str, Any]], generate_fn: Callable[[str], str]) -> float:
passed = 0
for sample in samples:
validate_sample("ARC-Challenge", sample)
output = generate_fn(build_prompt("ARC-Challenge", sample)).strip()
if output == str(sample["answer"]):
passed += 1
return passed / len(samples) if samples else 0.0
def run_benchmark(benchmark_name: str, path: Path, generate_fn: Callable[[str], str]) -> dict[str, Any]:
samples = load_jsonl_samples(path)
if benchmark_name == "MBPP":
return {
"benchmark": "MBPP",
"primary_metric": "pass_at_1",
"score": _score_mbpp(samples, generate_fn),
"n_samples": len(samples),
}
if benchmark_name == "GSM8K":
return {
"benchmark": "GSM8K",
"primary_metric": "exact_match",
"score": _score_gsm8k(samples, generate_fn),
"n_samples": len(samples),
}
if benchmark_name == "HumanEval":
return {
"benchmark": "HumanEval",
"primary_metric": "pass_at_1",
"score": _score_humaneval(samples, generate_fn),
"n_samples": len(samples),
}
if benchmark_name == "ARC-Challenge":
return {
"benchmark": "ARC-Challenge",
"primary_metric": "accuracy",
"score": _score_arc(samples, generate_fn),
"n_samples": len(samples),
}
raise ValueError(f"Unsupported runnable benchmark: {benchmark_name}")
def write_benchmark_result(path: Path, payload: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
def append_benchmark_run_record(
ledger_path: Path,
result: dict[str, Any],
*,
benchmark_name: str,
variant: str,
seed: int,
samples_path: Path,
) -> None:
if not ledger_path.exists():
ledger_path.parent.mkdir(parents=True, exist_ok=True)
ledger_path.write_text(LEDGER_TEMPLATE_PATH.read_text(encoding="utf-8"), encoding="utf-8")
payload = json.loads(ledger_path.read_text(encoding="utf-8"))
run_records = payload.setdefault("run_records", [])
if len(run_records) == 1 and run_records[0].get("run_id") == "example-run-0001":
run_records.clear()
run_records.append(
{
"run_id": result.get("run_id", f"{benchmark_name.lower()}-{seed}"),
"commit": "HEAD",
"model_family": "hydra",
"variant": variant,
"seed": seed,
"hardware": {
"hardware_class": payload.get("benchmark_cycle", {}).get("hardware_class", "unknown"),
},
"budget": {
"budget_mode": payload.get("benchmark_cycle", {}).get("budget_modes", [None])[0],
},
"capability": {
"coding_score": result["score"] if benchmark_name in {"MBPP", "HumanEval"} else None,
"reasoning_score": result["score"] if benchmark_name in {"GSM8K", "ARC-Challenge"} else None,
},
"artifacts": {
"samples_path": str(samples_path),
},
}
)
ledger_path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
def resolve_samples_path(benchmark_name: str, samples: Path | None, suite_path: Path) -> Path:
if samples is not None:
return samples
payload = json.loads(suite_path.read_text(encoding="utf-8"))
for section in ("coding_benchmarks", "reasoning_benchmarks"):
if section not in payload:
continue
for slot in ("fast_iteration", "milestone"):
entry = payload[section].get(slot)
if isinstance(entry, dict) and entry.get("name") == benchmark_name and "sample_path" in entry:
return Path(entry["sample_path"])
try:
return resolve_canonical_dataset(benchmark_name, None)
except ValueError:
raise ValueError(f"No sample path found for benchmark: {benchmark_name}")
def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run a local benchmark against JSONL samples")
parser.add_argument("--benchmark", required=True, choices=["MBPP", "GSM8K", "HumanEval", "ARC-Challenge"])
parser.add_argument("--samples", type=Path)
parser.add_argument("--suite", type=Path, default=REPO_ROOT / "artifacts" / "benchmark_suite.cycle1.json")
parser.add_argument("--out", type=Path)
parser.add_argument("--ledger", type=Path)
parser.add_argument("--variant", default="hydra_full")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--generator-mode", choices=["stub", "hydra"], default="stub")
parser.add_argument("--checkpoint", type=Path)
parser.add_argument("--device")
parser.add_argument("--max-new-tokens", type=int, default=256)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top-p", type=float, default=0.95)
return parser.parse_args(argv)
def main(argv: list[str] | None = None) -> int:
args = parse_args(argv)
sample_path = resolve_samples_path(args.benchmark, args.samples, args.suite)
try:
if args.generator_mode == "hydra":
generator = build_hydra_generator(
checkpoint_path=args.checkpoint,
device=args.device,
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
top_p=args.top_p,
)
else:
def generator(prompt: str) -> str:
return prompt
result = run_benchmark(args.benchmark, sample_path, generator)
exit_code = 0
except FileNotFoundError as exc:
result = {
"benchmark": args.benchmark,
"status": "failed",
"failure_type": "missing_checkpoint",
"error": str(exc),
"n_samples": 0,
}
exit_code = 1
except Exception as exc: # noqa: BLE001
result = {
"benchmark": args.benchmark,
"status": "failed",
"failure_type": type(exc).__name__,
"error": str(exc),
"n_samples": 0,
}
exit_code = 1
if args.out is not None:
write_benchmark_result(args.out, result)
if args.ledger is not None and exit_code == 0:
append_benchmark_run_record(
args.ledger,
result,
benchmark_name=args.benchmark,
variant=args.variant,
seed=args.seed,
samples_path=sample_path,
)
print(json.dumps(result, indent=2, sort_keys=True))
return exit_code
if __name__ == "__main__":
raise SystemExit(main())
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