73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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#!/usr/bin/env python3
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"""Fail fast if a downloaded release is incomplete or has drifted."""
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from __future__ import annotations
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import hashlib
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import json
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import os
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from collections import Counter
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from pathlib import Path
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from datasets import load_from_disk
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RECIPE = Path(__file__).resolve().parents[1]
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REPO = RECIPE.parent
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def sha256(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as handle:
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for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
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digest.update(block)
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return digest.hexdigest()
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def read_jsonl(path: Path) -> list[dict]:
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return [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
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def main() -> None:
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manifest = json.loads((RECIPE / "manifest.json").read_text())
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checked = []
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for relative, expected in manifest["files"].items():
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path = (RECIPE / relative).resolve()
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if not path.exists():
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raise FileNotFoundError(path)
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if path.name == "model.safetensors" and os.environ.get("SKIP_LARGE_HASH") == "1":
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continue
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actual = sha256(path)
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if actual != expected:
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raise RuntimeError(f"SHA256 mismatch for {path}: {actual} != {expected}")
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checked.append(str(path.relative_to(REPO)))
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config = json.loads((REPO / "config.json").read_text())
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if config.get("model_type") != "qwen3":
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raise RuntimeError(f"unexpected tuned-vanilla model_type: {config.get('model_type')}")
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dataset = load_from_disk(str(RECIPE / "data/am_distilled_long_mix"))
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if len(dataset["train"]) != 1024:
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raise RuntimeError(f"expected 1024 training rows, found {len(dataset['train'])}")
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helmet = read_jsonl(RECIPE / "data/eval_inputs/helmet_icl_8k_n50_per_config.jsonl")
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mrcr = read_jsonl(RECIPE / "data/eval_inputs/mrcr_8k_2_4_8needle_n10_per_config.jsonl")
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helmet_counts = Counter(row["config"] for row in helmet)
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mrcr_counts = Counter(row["config"] for row in mrcr)
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if sorted(helmet_counts.values()) != [50] * 5:
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raise RuntimeError(f"unexpected HELMET counts: {helmet_counts}")
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if sorted(mrcr_counts.values()) != [10] * 3:
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raise RuntimeError(f"unexpected MRCR counts: {mrcr_counts}")
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print(json.dumps({
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"status": "ok",
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"checked_sha256": checked,
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"model_type": config["model_type"],
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"training_rows": len(dataset["train"]),
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"helmet_rows": len(helmet),
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"mrcr_rows": len(mrcr),
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}, indent=2))
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if __name__ == "__main__":
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main()
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