wuyang
679 lines
22 KiB
Python
679 lines
22 KiB
Python
#!/usr/bin/env python3
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"""Build source-blocked summaries for the Round 07 sampling grid.
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The primary unit is the preregistered source. Seed-level outputs remain
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within-source repeats and are never counted as independent benchmark tasks.
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No p-values or population confidence intervals are produced from four sources
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per domain.
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"""
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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, Callable
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PROTOCOL_ID = "llm-atlas-deepseek-chat-cross-source-sampling-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 = ("english", "chinese", "code", "math")
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TASK_DOMAINS = ("code", "math")
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METRICS = (
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"natural_eos_rate",
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"mean_generated_tokens",
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"fixed_budget_success_rate",
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"strict_complete_success_rate",
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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 summarize(values: list[float | int]) -> 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 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 row_success(row: dict[str, Any], strict: bool) -> int | None:
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evaluation = row["task_evaluation"]
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if row["domain"] == "math":
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key = (
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"strict_complete_numeric_exact"
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if strict
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else "fixed_budget_numeric_exact"
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)
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return int(evaluation[key])
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if row["domain"] == "code":
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key = (
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"strict_complete_tests_pass"
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if strict
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else "fixed_budget_tests_pass"
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)
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return int(evaluation[key])
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return None
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def cell_summary(rows: list[dict[str, Any]]) -> dict[str, Any]:
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if not rows:
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raise ValueError("cell has no rows")
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fixed = [
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value
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for row in rows
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if (value := row_success(row, strict=False)) is not None
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]
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strict = [
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value
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for row in rows
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if (value := row_success(row, strict=True)) is not None
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]
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lengths = [row["generated_tokens"] for row in rows]
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hashes = {
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row["generated_token_ids_sha256"] for row in rows
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}
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result = {
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"outputs": len(rows),
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"natural_eos": sum(row["hit_eos"] for row in rows),
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"natural_eos_rate": mean(
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int(row["hit_eos"]) for row in rows
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),
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"budget_truncated": sum(
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row["stopped_at_max_new_tokens"] for row in rows
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),
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"generated_tokens": summarize(lengths),
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"generated_token_seed_range": max(lengths) - min(lengths),
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"unique_trajectory_hashes": len(hashes),
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"completion_classes": dict(
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Counter(row["completion_class"] for row in rows)
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),
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"fixed_budget_success": sum(fixed) if fixed else None,
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"fixed_budget_success_rate": mean(fixed) if fixed else None,
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"strict_complete_success": sum(strict) if strict else None,
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"strict_complete_success_rate": (
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mean(strict) if strict else None
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),
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"observed_any_fixed_budget_pass": (
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any(fixed) if fixed else None
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),
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"observed_any_strict_complete_pass": (
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any(strict) if strict else None
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),
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"seed_success_range": (
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max(fixed) - min(fixed) if fixed else None
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),
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}
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if rows[0]["domain"] == "math":
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answers = Counter(
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row["task_evaluation"]["predicted_final"]
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for row in rows
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if row["task_evaluation"]["predicted_final"] is not None
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)
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largest = max(answers.values(), default=0)
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modes = sorted(
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answer
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for answer, count in answers.items()
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if count == largest
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)
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result["math_answers"] = {
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"frequencies": dict(answers),
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"modes": modes,
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"unique_absolute_majority": (
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modes[0]
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if len(modes) == 1 and largest > len(rows) / 2
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else None
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),
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"gold": rows[0]["task_evaluation"]["gold_final"],
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}
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if rows[0]["domain"] == "code":
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result["code"] = {
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"ast_parse": sum(
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row["task_evaluation"]["python_ast_parse"]
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for row in rows
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),
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"executed": sum(
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row["task_evaluation"]["execution"]["status"]
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!= "not_run"
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for row in rows
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),
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"execution_statuses": dict(
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Counter(
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row["task_evaluation"]["execution"]["status"]
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for row in rows
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)
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),
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}
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return result
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def contrast(
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cells: dict[str, dict[str, Any]],
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metric: str,
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) -> dict[str, float] | None:
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def metric_value(cell: dict[str, Any]) -> float | None:
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if metric == "mean_generated_tokens":
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return cell["generated_tokens"]["mean"]
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return cell[metric]
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values = {
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condition: metric_value(cells[condition])
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for condition in CONDITIONS
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}
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if any(value is None for value in values.values()):
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return None
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system = (
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(values["s1_eos"] + values["s1_period"]) / 2
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- (values["s0_eos"] + values["s0_period"]) / 2
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)
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boundary = (
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(values["s0_period"] + values["s1_period"]) / 2
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- (values["s0_eos"] + values["s1_eos"]) / 2
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)
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interaction = (
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values["s1_period"]
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- values["s1_eos"]
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- values["s0_period"]
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+ values["s0_eos"]
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)
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return {
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"system_main": system,
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"boundary_main_period_minus_eos": boundary,
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"interaction": interaction,
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"period_minus_eos_s0": (
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values["s0_period"] - values["s0_eos"]
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),
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"period_minus_eos_s1": (
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values["s1_period"] - values["s1_eos"]
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),
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"system_on_minus_off_eos": (
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values["s1_eos"] - values["s0_eos"]
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),
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"system_on_minus_off_period": (
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values["s1_period"] - values["s0_period"]
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),
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}
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def domain_condition_summary(
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*,
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domain: str,
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condition: str,
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source_ids: list[str],
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source_cells: dict[str, dict[str, dict[str, Any]]],
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rows: list[dict[str, Any]],
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) -> dict[str, Any]:
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subset = [
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row
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for row in rows
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if row["domain"] == domain
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and row["condition"] == condition
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]
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cells = [source_cells[source_id][condition] for source_id in source_ids]
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fixed_rates = [
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cell["fixed_budget_success_rate"]
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for cell in cells
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if cell["fixed_budget_success_rate"] is not None
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]
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strict_rates = [
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cell["strict_complete_success_rate"]
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for cell in cells
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if cell["strict_complete_success_rate"] is not None
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]
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source_mean_lengths = [
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cell["generated_tokens"]["mean"] for cell in cells
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]
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return {
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"sources": len(source_ids),
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"outputs": len(subset),
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"micro": {
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"natural_eos": sum(row["hit_eos"] for row in subset),
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"natural_eos_rate": mean(
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int(row["hit_eos"]) for row in subset
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),
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"budget_truncated": sum(
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row["stopped_at_max_new_tokens"]
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for row in subset
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),
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"mean_generated_tokens": mean(
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row["generated_tokens"] for row in subset
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),
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"fixed_budget_success": (
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sum(
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value
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for row in subset
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if (
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value := row_success(row, strict=False)
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)
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is not None
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)
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if fixed_rates
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else None
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),
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"strict_complete_success": (
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sum(
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value
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for row in subset
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if (
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value := row_success(row, strict=True)
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)
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is not None
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)
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if strict_rates
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else None
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),
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},
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"source_macro": {
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"natural_eos_rate": summarize(
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[cell["natural_eos_rate"] for cell in cells]
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),
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"mean_generated_tokens": summarize(source_mean_lengths),
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"fixed_budget_success_rate": summarize(fixed_rates),
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"strict_complete_success_rate": summarize(strict_rates),
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},
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"variability": {
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"within_source_seed_length_range": summarize(
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[
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cell["generated_token_seed_range"]
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for cell in cells
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]
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),
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"between_source_mean_length_range": (
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max(source_mean_lengths) - min(source_mean_lengths)
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),
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"within_source_seed_success_range": summarize(
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[
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cell["seed_success_range"]
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for cell in cells
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if cell["seed_success_range"] is not None
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]
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),
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"between_source_success_rate_range": (
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max(fixed_rates) - min(fixed_rates)
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if fixed_rates
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else None
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),
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},
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}
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def build_source_cells(
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rows: list[dict[str, Any]],
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source_order: list[str],
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) -> dict[str, dict[str, dict[str, Any]]]:
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result: dict[str, dict[str, dict[str, Any]]] = {}
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for source_id in source_order:
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result[source_id] = {}
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for condition in CONDITIONS:
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subset = [
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row
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for row in rows
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if row["source_id"] == source_id
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and row["condition"] == condition
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]
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if len(subset) != 4:
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raise RuntimeError(
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f"{source_id}/{condition} has {len(subset)} rows; "
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"expected 4"
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)
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result[source_id][condition] = cell_summary(subset)
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return result
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def load_json(path: Path) -> dict[str, Any]:
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if not path.is_file():
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raise FileNotFoundError(path)
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return json.loads(path.read_text(encoding="utf-8"))
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def main() -> None:
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args = parse_args()
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sampling = load_json(args.sampling_json)
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evaluation = load_json(args.evaluation_json)
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if sampling["protocol_id"] != PROTOCOL_ID:
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raise RuntimeError("sampling protocol ID differs")
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if evaluation["protocol_id"] != PROTOCOL_ID:
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raise RuntimeError("evaluation protocol ID differs")
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if evaluation["input"]["sampling_sha256"] != sha256_file(
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args.sampling_json
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):
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raise RuntimeError("evaluation does not reference this sampling file")
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if tuple(
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sampling["seed_contract"]["condition_row_order"]
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) != CONDITIONS:
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raise RuntimeError("condition order differs from preregistration")
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if len(
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sampling["seed_contract"]["executed_base_seeds"]
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) != 4:
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raise RuntimeError("formal grid must contain four seeds")
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source_metadata = {
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source["id"]: {
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"domain": source["domain"],
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"within_domain_index": source["within_domain_index"],
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"selection_rank": source["selection_rank"],
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}
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for source in sampling["sources"]
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}
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source_order = [source["id"] for source in sampling["sources"]]
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if len(source_order) != 16 or len(set(source_order)) != 16:
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raise RuntimeError("formal grid must contain 16 unique sources")
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by_domain = {
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domain: [
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source_id
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for source_id in source_order
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if source_metadata[source_id]["domain"] == domain
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]
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for domain in DOMAINS
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}
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if any(len(source_ids) != 4 for source_ids in by_domain.values()):
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raise RuntimeError("each domain must contain four sources")
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rows = evaluation["rows"]
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if len(rows) != 256:
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raise RuntimeError(f"expected 256 evaluated rows; got {len(rows)}")
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grid_keys = {
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(
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row["source_id"],
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row["base_seed"],
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row["condition"],
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)
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for row in rows
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}
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if len(grid_keys) != 256:
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raise RuntimeError("evaluation grid has duplicate or missing cells")
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source_cells = build_source_cells(rows, source_order)
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source_contrasts = {
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source_id: {
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metric: contrast(source_cells[source_id], metric)
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for metric in METRICS
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}
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for source_id in source_order
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}
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domain_conditions = {
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domain: {
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condition: domain_condition_summary(
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domain=domain,
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condition=condition,
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source_ids=by_domain[domain],
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source_cells=source_cells,
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rows=rows,
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)
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for condition in CONDITIONS
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}
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for domain in DOMAINS
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}
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domain_contrasts: dict[str, Any] = {}
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for domain in DOMAINS:
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domain_contrasts[domain] = {}
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for metric in METRICS:
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available = {
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source_id: source_contrasts[source_id][metric]
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for source_id in by_domain[domain]
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if source_contrasts[source_id][metric] is not None
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}
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domain_contrasts[domain][metric] = {}
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for contrast_name in (
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"system_main",
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"boundary_main_period_minus_eos",
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"interaction",
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"period_minus_eos_s0",
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"period_minus_eos_s1",
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"system_on_minus_off_eos",
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"system_on_minus_off_period",
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):
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values = [
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value[contrast_name]
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for value in available.values()
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]
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domain_contrasts[domain][metric][contrast_name] = {
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**summarize(values),
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"directions": direction_counts(values),
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"by_source": {
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source_id: value[contrast_name]
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for source_id, value in available.items()
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},
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}
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task_matrix = {
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domain: [
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{
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"source_id": source_id,
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"within_domain_index": source_metadata[source_id][
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"within_domain_index"
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],
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"conditions": {
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condition: {
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key: source_cells[source_id][condition][key]
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for key in (
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"fixed_budget_success",
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"strict_complete_success",
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"observed_any_fixed_budget_pass",
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"observed_any_strict_complete_pass",
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"natural_eos",
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"generated_tokens",
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)
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}
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for condition in CONDITIONS
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},
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}
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for source_id in by_domain[domain]
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]
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for domain in TASK_DOMAINS
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}
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reproduction = None
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if args.reproduction_json is not None:
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reproduction_payload = load_json(args.reproduction_json)
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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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if reproduction_payload["formal"]["sha256"] != sha256_file(
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args.sampling_json
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):
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raise RuntimeError(
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"reproduction does not reference this formal file"
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)
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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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"contract": {
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"source_is_primary_coverage_unit": True,
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"seed_is_within_source_repeat": True,
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"sources": len(source_order),
|
|
"sources_per_domain": 4,
|
|
"seeds_per_source_condition": 4,
|
|
"conditions": list(CONDITIONS),
|
|
"outputs": len(rows),
|
|
"source_order": source_order,
|
|
"sources_by_domain": by_domain,
|
|
"no_population_confidence_intervals": True,
|
|
"no_p_values": True,
|
|
},
|
|
"overall": {
|
|
"outputs": len(rows),
|
|
"natural_eos": sum(row["hit_eos"] for row in rows),
|
|
"budget_truncated": sum(
|
|
row["stopped_at_max_new_tokens"] for row in rows
|
|
),
|
|
"unique_trajectory_hashes": len(
|
|
{
|
|
row["generated_token_ids_sha256"]
|
|
for row in rows
|
|
}
|
|
),
|
|
"math": {
|
|
"outputs": sum(
|
|
row["domain"] == "math" for row in rows
|
|
),
|
|
"fixed_budget_success": sum(
|
|
row_success(row, strict=False) or 0
|
|
for row in rows
|
|
if row["domain"] == "math"
|
|
),
|
|
"strict_complete_success": sum(
|
|
row_success(row, strict=True) or 0
|
|
for row in rows
|
|
if row["domain"] == "math"
|
|
),
|
|
},
|
|
"code": {
|
|
"outputs": sum(
|
|
row["domain"] == "code" for row in rows
|
|
),
|
|
"fixed_budget_success": sum(
|
|
row_success(row, strict=False) or 0
|
|
for row in rows
|
|
if row["domain"] == "code"
|
|
),
|
|
"strict_complete_success": sum(
|
|
row_success(row, strict=True) or 0
|
|
for row in rows
|
|
if row["domain"] == "code"
|
|
),
|
|
},
|
|
},
|
|
"source_metadata": source_metadata,
|
|
"source_condition_cells": source_cells,
|
|
"domain_condition_summary": domain_conditions,
|
|
"source_contrasts": source_contrasts,
|
|
"domain_contrasts": domain_contrasts,
|
|
"task_matrix": task_matrix,
|
|
"prior_direction_check": {
|
|
domain: {
|
|
"period_shortens_mean_tokens": {
|
|
"by_source": {
|
|
source_id: (
|
|
source_contrasts[source_id][
|
|
"mean_generated_tokens"
|
|
][
|
|
"boundary_main_period_minus_eos"
|
|
]
|
|
< 0
|
|
)
|
|
for source_id in by_domain[domain]
|
|
},
|
|
},
|
|
"system_on_raises_natural_eos_rate": {
|
|
"by_source": {
|
|
source_id: (
|
|
source_contrasts[source_id][
|
|
"natural_eos_rate"
|
|
]["system_main"]
|
|
> 0
|
|
)
|
|
for source_id in by_domain[domain]
|
|
},
|
|
},
|
|
}
|
|
for domain in DOMAINS
|
|
},
|
|
"claim_boundary": [
|
|
"Four sources per domain are not full benchmark estimates.",
|
|
"Seed-level repeats are not independent task observations.",
|
|
"Source-blocked contrasts are descriptive and have no p-values.",
|
|
"Observed any-pass is not standard HumanEval pass@4.",
|
|
"Natural EOS, evaluator coverage, and correctness remain separate.",
|
|
"The period condition is a counterfactual, not an official-valid chat.",
|
|
"Round 06 motivated the period contrast, so this is a directed follow-up.",
|
|
],
|
|
}
|
|
result["content_hash"] = canonical_hash(
|
|
{
|
|
"protocol_id": result["protocol_id"],
|
|
"contract": result["contract"],
|
|
"source_condition_cells": result[
|
|
"source_condition_cells"
|
|
],
|
|
"source_contrasts": result["source_contrasts"],
|
|
"task_matrix": result["task_matrix"],
|
|
}
|
|
)
|
|
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",
|
|
)
|
|
print(
|
|
json.dumps(
|
|
{
|
|
"output": str(args.output),
|
|
"sha256": sha256_file(args.output),
|
|
"bytes": args.output.stat().st_size,
|
|
"overall": result["overall"],
|
|
"reproduction": reproduction,
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|