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Make telemetry audit replay-phase aware

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Gahow Wang
2026-07-14 17:18:25 +08:00
parent 791f7a8889
commit 0515ad8ecc
9 changed files with 11780 additions and 1 deletions
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runs/intervention-response-v2/analyze_existing.py Normal file
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#!/usr/bin/env python3
"""Audit telemetry responses over every uncensored replay decile.
This corrective analysis keeps the frozen P1 pairs and thresholds, but replaces
the absolute 5/10-second cutoff with cumulative and non-overlapping 10%-of-trace
windows. It deliberately reports every common decile instead of selecting the
best-looking horizon.
"""
from __future__ import annotations
import argparse
import hashlib
import importlib.util
import json
import math
from pathlib import Path
from statistics import median
from typing import Any, Iterable, Mapping
HERE = Path(__file__).resolve().parent
P1_PATH = HERE.parent / "intervention-response-v0" / "analyze_p1.py"
SCHEMA = "intervention-response-phase-aware-existing-v2"
DECILE_FRACTION = 0.1
MAX_DECILES = 10
def _load_p1():
spec = importlib.util.spec_from_file_location("intervention_response_p1", P1_PATH)
module = importlib.util.module_from_spec(spec)
assert spec.loader is not None
spec.loader.exec_module(module)
return module
P1 = _load_p1()
def numeric(values: Iterable[float | int]) -> dict[str, Any]:
finite = [float(value) for value in values]
result = P1.V0.numeric(finite)
result["median"] = median(finite)
return result
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as source:
for chunk in iter(lambda: source.read(1 << 20), b""):
digest.update(chunk)
return digest.hexdigest()
def trial_directories(run_root: Path) -> list[Path]:
result = []
for cell in sorted((run_root / "cells").iterdir()):
if not cell.is_dir():
continue
for candidate in sorted(cell.iterdir()):
if candidate.is_dir() and P1.RUN_PATTERN.match(candidate.name):
result.append(candidate)
if not result:
raise ValueError("P1 run root contains no measured trial directories")
return result
def load_metadata(run_root: Path) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
metadata = []
streams = []
for cell in sorted((run_root / "cells").iterdir()):
if not cell.is_dir():
continue
stream_paths = sorted((cell / "opprof").glob("*.jsonl"))
if len(stream_paths) != 1:
raise ValueError(f"{cell}: expected one Layer-1 stream")
stream_path = stream_paths[0]
streams.append(
{
"cell": cell.name,
"path": str(stream_path.resolve()),
"sha256": sha256_file(stream_path),
"bytes": stream_path.stat().st_size,
}
)
for run_dir in trial_directories(run_root):
match = P1.RUN_PATTERN.match(run_dir.name)
assert match is not None
level, replicate_text = match.groups()
result_path = run_dir / "result.json"
requests_path = run_dir / "requests.jsonl"
result = json.loads(result_path.read_text(encoding="utf-8"))
selected = int(result["selection"]["count"])
offered = float(result["selection"]["offered_req_s"])
if selected <= 0 or offered <= 0.0:
raise ValueError(f"{result_path}: invalid selected count or offered rate")
metadata.append(
{
"trial_id": str(result_path.relative_to(run_root)),
"cell": str(result["cell"]),
"tp": int(result["tp"]),
"mns": int(result["mns"]),
"level": level,
"replicate": int(replicate_text),
"elapsed_s": float(result["interval"]["elapsed_s"]),
"trace_duration_s": round(selected / offered, 9),
"early_stopped": bool(result["early_stopped"]),
"request_count": selected,
"result_sha256": sha256_file(result_path),
"requests_sha256": sha256_file(requests_path),
}
)
return metadata, streams
def common_decile_fractions(
*, trace_duration_s: float, minimum_elapsed_s: float
) -> tuple[float, ...]:
if trace_duration_s <= 0.0 or minimum_elapsed_s <= 0.0:
raise ValueError("trace duration and elapsed time must be positive")
supported = min(
MAX_DECILES,
int(math.floor((minimum_elapsed_s / trace_duration_s) * 10.0 + 1e-12)),
)
return tuple(
round(index * DECILE_FRACTION, 10) for index in range(1, supported + 1)
)
def _trial_record(
*,
run_root: Path,
run_dir: Path,
result: Mapping[str, Any],
state: dict[str, float],
outcome: dict[str, float],
) -> dict[str, Any]:
match = P1.RUN_PATTERN.match(run_dir.name)
assert match is not None
level, replicate_text = match.groups()
result_path = run_dir / "result.json"
requests_path = run_dir / "requests.jsonl"
return {
"trial_id": str(result_path.relative_to(run_root)),
"cell": str(result["cell"]),
"tp": int(result["tp"]),
"mns": int(result["mns"]),
"level": level,
"replicate": int(replicate_text),
"offered_rate_per_gpu": float(
result["selection"]["offered_req_s_per_gpu"]
),
"request_hash": str(result["selection"]["request_id_order_sha256"]),
"request_count": int(result["selection"]["count"]),
"result_sha256": sha256_file(result_path),
"requests_sha256": sha256_file(requests_path),
"full_pass_rate": float(result["pass_rate"]),
"full_feasible": bool(result["feasible"]),
"early_stopped": bool(result["early_stopped"]),
"state": state,
"outcome": outcome,
}
def load_interval_trials(
run_root: Path,
intervals_s: tuple[tuple[float, float], ...],
) -> tuple[dict[tuple[float, float], list[dict[str, Any]]], list[dict[str, Any]]]:
by_interval = {interval: [] for interval in intervals_s}
stream_provenance = []
for cell in sorted((run_root / "cells").iterdir()):
if not cell.is_dir():
continue
stream_paths = sorted((cell / "opprof").glob("*.jsonl"))
if len(stream_paths) != 1:
raise ValueError(f"{cell}: expected one Layer-1 stream")
stream_path = stream_paths[0]
stream = P1.load_jsonl(stream_path)
stream_provenance.append(
{
"cell": cell.name,
"path": str(stream_path.resolve()),
"sha256": sha256_file(stream_path),
"bytes": stream_path.stat().st_size,
}
)
for run_dir in sorted(cell.iterdir()):
if not run_dir.is_dir() or P1.RUN_PATTERN.match(run_dir.name) is None:
continue
result_path = run_dir / "result.json"
requests_path = run_dir / "requests.jsonl"
result = json.loads(result_path.read_text(encoding="utf-8"))
requests = P1.load_jsonl(requests_path)
start_ns = int(result["interval"]["start_mono_ns"])
elapsed_s = float(result["interval"]["elapsed_s"])
for interval in intervals_s:
start_s, end_s = interval
if start_s < 0.0 or end_s <= start_s:
raise ValueError(f"invalid analysis interval: {interval}")
if elapsed_s + 1e-9 < end_s:
raise ValueError(
f"{result_path}: elapsed {elapsed_s} shorter than {end_s}s"
)
state = P1.V0.flatten_state(
P1.summarize_engine(
stream,
start_ns=start_ns + int(start_s * 1e9),
end_ns=start_ns + int(end_s * 1e9),
request_count=int(result["selection"]["count"]),
)
)
outcome = P1._prefix_outcome(result, requests, end_s)
by_interval[interval].append(
_trial_record(
run_root=run_root,
run_dir=run_dir,
result=result,
state=state,
outcome=outcome,
)
)
return by_interval, stream_provenance
def coverage(trials: list[dict[str, Any]]) -> dict[str, Any]:
admitted = [float(trial["outcome"]["admitted_fraction"]) for trial in trials]
completed = [
float(trial["outcome"]["admitted_fraction"])
* float(trial["outcome"]["completed_over_admitted"])
for trial in trials
]
return {
"admitted_fraction_of_total": numeric(admitted),
"completed_fraction_of_total": numeric(completed),
}
def slim_window_analysis(
trials: list[dict[str, Any]], *, start_s: float, end_s: float, fraction: float
) -> dict[str, Any]:
analysis = P1.analyze_horizon(trials, end_s)
return {
"start_s": start_s,
"end_s": end_s,
"end_fraction": fraction,
"coverage_at_end": coverage(trials),
"action_pairs": len(analysis["actions"]),
"repeat_pairs": len(analysis["repeats"]),
"response_statistics": analysis["response_statistics"],
"qualifying_response_features": analysis["qualifying_response_features"],
"efficacy": analysis["efficacy"],
"sanity": analysis["sanity"],
}
def _pearson(left: list[float], right: list[float]) -> float | None:
if len(left) != len(right) or not left:
raise ValueError("Pearson inputs must be non-empty and have equal length")
left_mean = sum(left) / len(left)
right_mean = sum(right) / len(right)
numerator = sum(
(x - left_mean) * (y - right_mean)
for x, y in zip(left, right, strict=True)
)
left_ss = sum((x - left_mean) ** 2 for x in left)
right_ss = sum((y - right_mean) ** 2 for y in right)
if left_ss == 0.0 or right_ss == 0.0:
return None
return numerator / math.sqrt(left_ss * right_ss)
def trajectory_summary(
block_trials: list[tuple[tuple[float, float], list[dict[str, Any]]]]
) -> dict[str, Any]:
if not block_trials:
raise ValueError("trajectory requires at least one block")
identities = []
states_by_block = []
for interval, trials in block_trials:
ordered = sorted(
trials,
key=lambda trial: (trial["cell"], trial["level"], trial["replicate"]),
)
current_identities = [
(trial["cell"], trial["level"], trial["replicate"]) for trial in ordered
]
if identities and current_identities != identities:
raise ValueError("trajectory blocks do not contain identical trials")
identities = current_identities
states_by_block.append((interval, [trial["state"] for trial in ordered]))
features = {}
for feature in P1.V0.ALL_FEATURES:
block_values = [
[float(state[feature]) for state in states]
for _interval, states in states_by_block
]
first = block_values[0]
last = block_values[-1]
delta = [right - left for left, right in zip(first, last, strict=True)]
features[feature] = {
"block_medians": [median(values) for values in block_values],
"first_to_last_delta": numeric(delta),
"first_to_last_abs_delta": numeric(abs(value) for value in delta),
"first_to_last_pearson": _pearson(first, last),
"changed_trials": sum(abs(value) > 1e-12 for value in delta),
}
return {
"trial_count": len(identities),
"blocks": [
{"start_s": interval[0], "end_s": interval[1]}
for interval, _states in states_by_block
],
"features": features,
}
def audit(*, run_root: Path, manifest_path: Path, output_path: Path) -> dict[str, Any]:
metadata, metadata_streams = load_metadata(run_root)
durations = [float(item["trace_duration_s"]) for item in metadata]
elapsed = [float(item["elapsed_s"]) for item in metadata]
duration = median(durations)
deciles = common_decile_fractions(
trace_duration_s=duration, minimum_elapsed_s=min(elapsed)
)
if not deciles:
raise ValueError("no complete replay decile is shared by all trials")
cumulative_intervals = tuple(
(0.0, round(duration * fraction, 9)) for fraction in deciles
)
block_intervals = tuple(
(
round(duration * (fraction - DECILE_FRACTION), 9),
round(duration * fraction, 9),
)
for fraction in deciles
)
all_intervals = tuple(dict.fromkeys([*cumulative_intervals, *block_intervals]))
trials_by_interval, streams = load_interval_trials(run_root, all_intervals)
manifest_validation = P1.validate_manifest(
trials_by_interval[cumulative_intervals[0]], manifest_path
)
cumulative = []
blocks = []
for fraction, cumulative_interval, block_interval in zip(
deciles, cumulative_intervals, block_intervals, strict=True
):
cumulative.append(
slim_window_analysis(
trials_by_interval[cumulative_interval],
start_s=cumulative_interval[0],
end_s=cumulative_interval[1],
fraction=fraction,
)
)
blocks.append(
slim_window_analysis(
trials_by_interval[block_interval],
start_s=block_interval[0],
end_s=block_interval[1],
fraction=fraction,
)
)
invariants = {
"expected_trial_count": len(metadata) == 36,
"trace_duration_consistent": max(durations) - min(durations) <= 1e-9,
"all_intervals_uncensored": all(
item["elapsed_s"] + 1e-9 >= cumulative_intervals[-1][1]
for item in metadata
),
"stream_provenance_consistent": metadata_streams == streams,
"manifest_trials_match": (
manifest_validation["expected_trials"]
== manifest_validation["matched_trials"]
== len(metadata)
),
"all_window_sanity_pass": all(
not item["sanity"]["red_flags"] for item in [*cumulative, *blocks]
),
}
red_flags = [name for name, passed in invariants.items() if not passed]
complete_full_trajectory = min(elapsed) + 1e-9 >= duration
if red_flags:
decision = "STOP_DATA_INVALID"
elif not complete_full_trajectory:
decision = "REQUIRES_UNCENSORED_PHASE_AWARE_PILOT"
else:
decision = "FULL_TRAJECTORY_AVAILABLE"
payload = {
"schema": SCHEMA,
"status": "COMPLETE",
"decision": decision,
"claim_boundary": (
"Post-hoc corrective audit over every common replay decile. It can "
"diagnose horizon sensitivity but cannot establish a held-out tuning claim."
),
"design": {
"decile_fraction": DECILE_FRACTION,
"available_deciles": list(deciles),
"trace_duration_s": duration,
"maximum_common_end_s": cumulative_intervals[-1][1],
"maximum_common_fraction": deciles[-1],
"select_best_horizon": False,
"cumulative_and_nonoverlapping_blocks": True,
},
"cumulative": cumulative,
"blocks": blocks,
"trajectory": trajectory_summary(
[(interval, trials_by_interval[interval]) for interval in block_intervals]
),
"provenance": {
"analysis_script": str(Path(__file__).resolve()),
"analysis_script_sha256": sha256_file(Path(__file__).resolve()),
"p1_analysis_script": str(P1_PATH.resolve()),
"p1_analysis_script_sha256": sha256_file(P1_PATH),
"run_root": str(run_root.resolve()),
"manifest": str(manifest_path.resolve()),
"manifest_sha256": sha256_file(manifest_path),
"manifest_validation": manifest_validation,
"streams": streams,
"trial_inputs": metadata,
},
"sanity": {
"trials": len(metadata),
"elapsed_s": numeric(elapsed),
"trace_duration_s": numeric(durations),
"early_stopped": sum(bool(item["early_stopped"]) for item in metadata),
"request_count": numeric(item["request_count"] for item in metadata),
"stream_bytes": numeric(item["bytes"] for item in streams),
"invariants": invariants,
"red_flags": red_flags,
},
}
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
return payload
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--run-root", type=Path, required=True)
parser.add_argument("--manifest", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
payload = audit(
run_root=args.run_root,
manifest_path=args.manifest,
output_path=args.output,
)
print(
json.dumps(
{
"decision": payload["decision"],
"design": payload["design"],
"sanity": payload["sanity"],
},
indent=2,
sort_keys=True,
)
)
if __name__ == "__main__":
main()