mirror of
https://github.com/wassname/evil_MoE.git
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wassname
d68c17e7c5
route/routeV final eval now measures both endpoints at n=119 test: knob-off (ablate_quarantine, the deploy headline) AND knob-on (trained model as-is). Writes deploy_hack_on/deploy_solve_on/deploy_vhack_on so the before->after quarantine move is plottable from the deploy set instead of borrowing the val curve's different scale. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Verify provenance and evaluation-split invariants that protect paper claims."""
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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 tempfile
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from pathlib import Path
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import torch
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from loguru import logger
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from safetensors.torch import save_file
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from tabulate import tabulate
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from vgrout.data import DATA, RH_HINT_REPLACE_FROM, load_problems
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from vgrout.eval import load_eval_splits
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from vgrout.vhack import load_v_hack, pairset_sha256
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def _must_raise(fn) -> bool:
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try:
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fn()
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except ValueError:
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return True
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return False
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def main() -> int:
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rows = []
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with tempfile.TemporaryDirectory() as td:
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tmp = Path(td)
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pairs_path = tmp / "pairs.json"
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pairs_path.write_text('[{"prompt":"p","hack":"h","clean":"c"}]\n')
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vhack_path = tmp / "vhack.safetensors"
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dtype = "bf16" if torch.cuda.is_available() else "fp32"
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save_file(
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{"module": torch.tensor([[1.0, 0.0, 0.0]]), "_sv/module": torch.tensor([1.0])},
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str(vhack_path),
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metadata={"model": "test", "dtype": dtype, "pairs_sha256": pairset_sha256(pairs_path)},
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)
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wrappers = {"module": {"delta_S": torch.zeros(3)}}
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exact_load = bool(load_v_hack(vhack_path, "test", wrappers, pairs_path))
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pairs_path.write_text(pairs_path.read_text() + " ")
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changed_rejected = _must_raise(lambda: load_v_hack(vhack_path, "test", wrappers, pairs_path))
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rows.append({"invariant": "v_hack pair bytes", "success": exact_load and changed_rejected})
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source = json.loads(DATA.read_text().splitlines()[0])
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missing = json.loads(json.dumps(source))
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missing["prompt"][-1]["content"] = missing["prompt"][-1]["content"].replace(
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RH_HINT_REPLACE_FROM, "and should pass every check")
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duplicate = json.loads(json.dumps(source))
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duplicate["prompt"][-1]["content"] += f" Also {RH_HINT_REPLACE_FROM}."
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missing_path, duplicate_path = tmp / "missing.jsonl", tmp / "duplicate.jsonl"
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missing_path.write_text(json.dumps(missing) + "\n")
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duplicate_path.write_text(json.dumps(duplicate) + "\n")
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canonical_load = len(load_problems(1, ["run_tests"])) == 1
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hint_drift_rejected = (
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_must_raise(lambda: load_problems(1, ["run_tests"], data_path=missing_path))
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and _must_raise(lambda: load_problems(1, ["run_tests"], data_path=duplicate_path))
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)
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rows.append({"invariant": "exactly one prompt hint", "success": canonical_load and hint_drift_rejected})
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val_a, test_a = load_eval_splits(["run_tests"], 32)
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val_b, test_b = load_eval_splits(["run_tests"], 32)
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val_ids = [p["problem_id"] for p in val_a]
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test_ids = [p["problem_id"] for p in test_a]
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split_ok = (
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len(val_ids) == 32
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and len(test_ids) == 87
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and set(val_ids).isdisjoint(test_ids)
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and val_ids == [p["problem_id"] for p in val_b]
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and test_ids == [p["problem_id"] for p in test_b]
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)
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val_sha = hashlib.sha256(",".join(map(str, val_ids)).encode()).hexdigest()[:12]
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test_sha = hashlib.sha256(",".join(map(str, test_ids)).encode()).hexdigest()[:12]
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rows.append({
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"invariant": "deterministic disjoint val/test",
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"success": split_ok,
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"detail": f"n=32/87 ids={val_sha}/{test_sha}",
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})
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print(tabulate(rows, headers="keys", tablefmt="github"))
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ok = all(row["success"] for row in rows)
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logger.info("PASS: science invariants hold" if ok else "FAIL: science invariant broken")
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return 0 if ok else 1
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if __name__ == "__main__":
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raise SystemExit(main())
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