wuyang
621 lines
21 KiB
Python
621 lines
21 KiB
Python
#!/usr/bin/env python3
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"""Build task-paired bootstrap and multi-tape diagnostics for Round 08."""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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from collections import Counter
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from pathlib import Path
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from statistics import mean, median
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from typing import Any
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import numpy as np
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PROTOCOL_ID = "llm-atlas-deepseek-chat-task-bootstrap-crn-v1"
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CONDITIONS = (
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"s0_eos",
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"s1_eos",
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"s0_period",
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"s1_period",
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)
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DOMAINS = ("code", "math")
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BOOTSTRAP_SEED = 1364512825
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BOOTSTRAP_RESAMPLES = 10000
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CONTRASTS = {
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"period_at_s0": ("s0_eos", "s0_period"),
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"period_at_s1": ("s1_eos", "s1_period"),
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"system_at_eos": ("s0_eos", "s1_eos"),
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"system_at_period": ("s0_period", "s1_period"),
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}
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METRICS = (
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"fixed_budget_success",
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"strict_complete_success",
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"natural_eos",
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"generated_tokens",
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument("--sampling-json", type=Path, required=True)
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parser.add_argument("--evaluation-json", type=Path, required=True)
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parser.add_argument("--reproduction-json", type=Path)
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parser.add_argument("--output", type=Path, required=True)
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return parser.parse_args()
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def sha256_file(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as handle:
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while chunk := handle.read(16 * 1024 * 1024):
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digest.update(chunk)
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return digest.hexdigest()
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def canonical_hash(value: Any) -> str:
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return hashlib.sha256(
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json.dumps(
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value,
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ensure_ascii=False,
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sort_keys=True,
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separators=(",", ":"),
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).encode()
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).hexdigest()
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def metric_value(row: dict[str, Any], metric: str) -> float:
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evaluation = row["task_evaluation"]
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if metric == "fixed_budget_success":
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key = (
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"fixed_budget_tests_pass"
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if row["domain"] == "code"
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else "fixed_budget_numeric_exact"
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)
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return float(evaluation[key])
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if metric == "strict_complete_success":
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key = (
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"strict_complete_tests_pass"
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if row["domain"] == "code"
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else "strict_complete_numeric_exact"
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)
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return float(evaluation[key])
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if metric == "natural_eos":
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return float(row["hit_eos"])
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if metric == "generated_tokens":
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return float(row["generated_tokens"])
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raise KeyError(metric)
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def summarize(values: list[float]) -> dict[str, Any]:
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return {
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"count": len(values),
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"mean": mean(values) if values else None,
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"median": median(values) if values else None,
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"min": min(values) if values else None,
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"max": max(values) if values else None,
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}
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def percentile_band(values: np.ndarray) -> dict[str, float]:
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quantiles = np.percentile(values, [2.5, 50.0, 97.5])
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return {
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"p2_5": float(quantiles[0]),
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"p50": float(quantiles[1]),
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"p97_5": float(quantiles[2]),
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}
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def direction_counts(values: list[float]) -> dict[str, int]:
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epsilon = 1e-12
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return {
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"positive": sum(value > epsilon for value in values),
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"zero": sum(abs(value) <= epsilon for value in values),
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"negative": sum(value < -epsilon for value in values),
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}
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def success_transition(
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left: list[float],
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right: list[float],
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) -> dict[str, int]:
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pairs = [(int(a), int(b)) for a, b in zip(left, right, strict=True)]
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return {
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"fail_to_fail": sum(a == 0 and b == 0 for a, b in pairs),
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"fail_to_pass": sum(a == 0 and b == 1 for a, b in pairs),
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"pass_to_fail": sum(a == 1 and b == 0 for a, b in pairs),
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"pass_to_pass": sum(a == 1 and b == 1 for a, b in pairs),
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}
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def common_prefix(left: list[int], right: list[int]) -> int:
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count = 0
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for left_id, right_id in zip(left, right):
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if left_id != right_id:
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break
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count += 1
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return count
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def tape_uint64(tape: str, source_id: str, step: int) -> int:
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payload = (
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f"{PROTOCOL_ID}\0uniform\0"
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f"{tape}\0{source_id}\0{step}"
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).encode()
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return int.from_bytes(hashlib.sha256(payload).digest()[:8], "big")
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def build_indices(
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sampling: dict[str, Any],
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evaluation: dict[str, Any],
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) -> tuple[
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dict[tuple[str, str, str], dict[str, Any]],
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dict[tuple[str, str, str], dict[str, Any]],
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]:
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sample_index = {
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(
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source["id"],
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run["tape_label"],
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output["condition"],
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): output
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for source in sampling["sources"]
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for run in source["runs"]
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for output in run["outputs"]
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}
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eval_index = {
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(
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row["source_id"],
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row["tape_label"],
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row["condition"],
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): row
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for row in evaluation["rows"]
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}
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if set(sample_index) != set(eval_index):
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raise RuntimeError("sampling/evaluation cell keys differ")
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return sample_index, eval_index
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def main_analysis(
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*,
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sampling: dict[str, Any],
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sample_index: dict[tuple[str, str, str], dict[str, Any]],
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eval_index: dict[tuple[str, str, str], dict[str, Any]],
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) -> dict[str, Any]:
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result = {}
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for domain in DOMAINS:
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sources = [
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source
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for source in sampling["sources"]
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if source["domain"] == domain
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]
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sources.sort(key=lambda row: row["within_domain_index"])
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if len(sources) != 32:
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raise RuntimeError(f"{domain}: main frame must have 32 tasks")
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source_ids = [source["id"] for source in sources]
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rng = np.random.default_rng(BOOTSTRAP_SEED)
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sampled_indices = rng.integers(
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0,
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len(source_ids),
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size=(BOOTSTRAP_RESAMPLES, len(source_ids)),
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endpoint=False,
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)
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conditions = {}
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for condition in CONDITIONS:
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rows = [
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eval_index[(source_id, "T0", condition)]
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for source_id in source_ids
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]
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conditions[condition] = {
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metric: summarize(
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[metric_value(row, metric) for row in rows]
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)
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for metric in METRICS
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} | {
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"task_outcomes": dict(
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sorted(
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Counter(row["task_outcome"] for row in rows).items()
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)
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)
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}
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contrasts = {}
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for name, (left_condition, right_condition) in CONTRASTS.items():
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metrics = {}
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for metric in METRICS:
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left = [
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metric_value(
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eval_index[(source_id, "T0", left_condition)],
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metric,
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)
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for source_id in source_ids
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]
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right = [
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metric_value(
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eval_index[(source_id, "T0", right_condition)],
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metric,
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)
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for source_id in source_ids
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]
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differences = np.asarray(right) - np.asarray(left)
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bootstrap_means = differences[
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sampled_indices
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].mean(axis=1)
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metric_result = {
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"right_minus_left_point": float(differences.mean()),
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"selected_task_resampling_band": percentile_band(
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bootstrap_means
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),
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"task_differences": summarize(
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differences.tolist()
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),
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"direction_counts": direction_counts(
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differences.tolist()
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),
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"by_source": [
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{
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"source_id": source_id,
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"domain_index": index,
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"left": float(left[index]),
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"right": float(right[index]),
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"right_minus_left": float(
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differences[index]
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),
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}
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for index, source_id in enumerate(source_ids)
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],
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}
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if metric in {
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"fixed_budget_success",
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"strict_complete_success",
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}:
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metric_result["transition"] = success_transition(
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left,
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right,
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)
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metrics[metric] = metric_result
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trajectory_rows = []
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crn_exact = 0
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for source_id in source_ids:
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left_output = sample_index[
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(source_id, "T0", left_condition)
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]
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right_output = sample_index[
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(source_id, "T0", right_condition)
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]
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left_ids = left_output["generated_token_ids"]
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right_ids = right_output["generated_token_ids"]
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shared_steps = min(len(left_ids), len(right_ids))
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expected_left_uniforms = [
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tape_uint64("T0", source_id, step)
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for step in range(
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left_output["uniform_steps_consumed"]
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)
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]
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expected_right_uniforms = [
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tape_uint64("T0", source_id, step)
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for step in range(
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right_output["uniform_steps_consumed"]
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)
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]
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left_uniform_exact = (
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canonical_hash(expected_left_uniforms)
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== left_output["uniform_uint64_prefix_sha256"]
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)
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right_uniform_exact = (
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canonical_hash(expected_right_uniforms)
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== right_output["uniform_uint64_prefix_sha256"]
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)
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crn_exact += left_uniform_exact and right_uniform_exact
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trajectory_rows.append(
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{
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"source_id": source_id,
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"shared_active_steps": shared_steps,
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"common_prefix_tokens": common_prefix(
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left_ids,
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right_ids,
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),
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"token_ids_exact": left_ids == right_ids,
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"shared_uniform_prefix_exact": (
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left_uniform_exact and right_uniform_exact
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),
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}
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)
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contrasts[name] = {
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"left": left_condition,
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"right": right_condition,
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"metrics": metrics,
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"trajectory": {
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"sources": len(trajectory_rows),
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"shared_uniform_prefix_exact": crn_exact,
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"exact_trajectories": sum(
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row["token_ids_exact"]
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for row in trajectory_rows
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),
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"common_prefix_tokens": summarize(
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[
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float(row["common_prefix_tokens"])
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for row in trajectory_rows
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]
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),
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"rows": trajectory_rows,
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},
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}
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result[domain] = {
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"tasks": len(source_ids),
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"source_ids": source_ids,
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"tape": "T0",
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"conditions": conditions,
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"contrasts": contrasts,
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}
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return result
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def tape_diagnostic(
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*,
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sampling: dict[str, Any],
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eval_index: dict[tuple[str, str, str], dict[str, Any]],
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) -> dict[str, Any]:
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result = {}
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for domain in DOMAINS:
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sources = [
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source
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for source in sampling["sources"]
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if source["domain"] == domain and len(source["runs"]) == 4
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]
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sources.sort(key=lambda row: row["within_domain_index"])
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if [row["within_domain_index"] for row in sources] != [
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0,
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8,
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16,
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24,
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]:
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raise RuntimeError(
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f"{domain}: diagnostic task indices differ"
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)
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source_ids = [row["id"] for row in sources]
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tapes = ["T0", "T1", "T2", "T3"]
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by_tape_condition = {
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tape: {
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condition: {
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metric: summarize(
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[
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metric_value(
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eval_index[
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(source_id, tape, condition)
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],
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metric,
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)
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for source_id in source_ids
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]
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)
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for metric in METRICS
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}
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for condition in CONDITIONS
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}
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for tape in tapes
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}
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contrasts = {}
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for name, (left_condition, right_condition) in CONTRASTS.items():
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metric_results = {}
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for metric in METRICS:
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matrix = np.asarray(
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[
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[
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metric_value(
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eval_index[
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(
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source_id,
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tape,
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right_condition,
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)
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],
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metric,
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)
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- metric_value(
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eval_index[
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(
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source_id,
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tape,
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left_condition,
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)
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],
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metric,
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)
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for tape in tapes
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]
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for source_id in source_ids
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],
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dtype=float,
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)
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tape_means = matrix.mean(axis=0)
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task_means = matrix.mean(axis=1)
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task_ranges_within_tape = (
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matrix.max(axis=0) - matrix.min(axis=0)
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)
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tape_ranges_within_task = (
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matrix.max(axis=1) - matrix.min(axis=1)
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)
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metric_results[metric] = {
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"matrix_task_by_tape": matrix.tolist(),
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"tape_means": {
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tape: float(tape_means[index])
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for index, tape in enumerate(tapes)
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},
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"task_means": {
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source_id: float(task_means[index])
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for index, source_id in enumerate(source_ids)
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},
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"direction_by_tape": {
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tape: direction_counts(
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matrix[:, index].tolist()
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)
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for index, tape in enumerate(tapes)
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},
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"task_range_within_tape": summarize(
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task_ranges_within_tape.tolist()
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),
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"tape_range_within_task": summarize(
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tape_ranges_within_task.tolist()
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),
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"grand_mean_descriptive": float(matrix.mean()),
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}
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contrasts[name] = {
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"left": left_condition,
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"right": right_condition,
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"metrics": metric_results,
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}
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result[domain] = {
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"tasks": len(source_ids),
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"source_ids": source_ids,
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"tapes": tapes,
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"by_tape_condition": by_tape_condition,
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"contrasts": contrasts,
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"independence_warning": (
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"The 4 tasks x 4 tapes are crossed repeated measures, "
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"not 16 independent tasks."
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),
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}
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return result
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|
|
|
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def main() -> None:
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args = parse_args()
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for path in (args.sampling_json, args.evaluation_json):
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if not path.is_file():
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raise FileNotFoundError(path)
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sampling = json.loads(
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args.sampling_json.read_text(encoding="utf-8")
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)
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evaluation = json.loads(
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args.evaluation_json.read_text(encoding="utf-8")
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)
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if (
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sampling["protocol_id"] != PROTOCOL_ID
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or evaluation["protocol_id"] != PROTOCOL_ID
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):
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raise RuntimeError("protocol ID differs")
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if sampling["summary"]["outputs"] != 352:
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raise RuntimeError("sampling formal output count differs")
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if len(evaluation["rows"]) != 352:
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raise RuntimeError("evaluation row count differs")
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sample_index, eval_index = build_indices(sampling, evaluation)
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reproduction = None
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if args.reproduction_json is not None:
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if not args.reproduction_json.is_file():
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raise FileNotFoundError(args.reproduction_json)
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reproduction_payload = json.loads(
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args.reproduction_json.read_text(encoding="utf-8")
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)
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if reproduction_payload["protocol_id"] != PROTOCOL_ID:
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raise RuntimeError("reproduction protocol ID differs")
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reproduction = {
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"path": str(args.reproduction_json),
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"sha256": sha256_file(args.reproduction_json),
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"summary": reproduction_payload["summary"],
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}
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result = {
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"schema_version": 1,
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"protocol_id": PROTOCOL_ID,
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"inputs": {
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"sampling": {
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"path": str(args.sampling_json),
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"sha256": sha256_file(args.sampling_json),
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"content_hash": sampling["content_hash"],
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},
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"evaluation": {
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"path": str(args.evaluation_json),
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"sha256": sha256_file(args.evaluation_json),
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"content_hash": evaluation["content_hash"],
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},
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"reproduction": reproduction,
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},
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"bootstrap_contract": {
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"resamples": BOOTSTRAP_RESAMPLES,
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"seed": BOOTSTRAP_SEED,
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"rng": "numpy.random.default_rng reset per domain",
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"unit": "selected task",
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"paired_conditions": True,
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"tape": "T0",
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"interval_label": (
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"selected-task resampling band for the fixed "
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"32-task frame and T0"
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),
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"not": [
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"benchmark-population confidence interval",
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"model-ability confidence interval",
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"generation-seed uncertainty interval",
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"causal-effect confidence interval",
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],
|
|
},
|
|
"main_t0_selected_task_analysis": main_analysis(
|
|
sampling=sampling,
|
|
sample_index=sample_index,
|
|
eval_index=eval_index,
|
|
),
|
|
"multi_tape_diagnostic": tape_diagnostic(
|
|
sampling=sampling,
|
|
eval_index=eval_index,
|
|
),
|
|
"claim_boundary": [
|
|
"HumanEval and GSM8K are analyzed separately.",
|
|
(
|
|
"T0 selected-task bands describe only this frozen "
|
|
"32-task frame."
|
|
),
|
|
(
|
|
"T1-T3 are sensitivity diagnostics and are not pooled "
|
|
"into the T0 primary success rates."
|
|
),
|
|
(
|
|
"Common random numbers align probability quantiles; "
|
|
"they do not force identical sampled tokens."
|
|
),
|
|
"Period prompts are counterfactual, not official-valid chats.",
|
|
],
|
|
}
|
|
result["content_hash"] = canonical_hash(
|
|
{
|
|
"bootstrap_contract": result["bootstrap_contract"],
|
|
"main": result["main_t0_selected_task_analysis"],
|
|
"diagnostic": result["multi_tape_diagnostic"],
|
|
}
|
|
)
|
|
args.output.parent.mkdir(parents=True, exist_ok=True)
|
|
args.output.write_text(
|
|
json.dumps(result, ensure_ascii=False, indent=2) + "\n",
|
|
encoding="utf-8",
|
|
)
|
|
payload = args.output.read_bytes()
|
|
compact = {
|
|
domain: {
|
|
name: {
|
|
metric: result[
|
|
"main_t0_selected_task_analysis"
|
|
][domain]["contrasts"][name]["metrics"][metric][
|
|
"right_minus_left_point"
|
|
]
|
|
for metric in METRICS
|
|
}
|
|
for name in CONTRASTS
|
|
}
|
|
for domain in DOMAINS
|
|
}
|
|
print(
|
|
json.dumps(
|
|
{
|
|
"output": str(args.output),
|
|
"sha256": hashlib.sha256(payload).hexdigest(),
|
|
"bytes": len(payload),
|
|
"main_contrast_points": compact,
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|