File size: 3,898 Bytes
661b19b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | #!/usr/bin/env python3
"""CPU audit of the released Figure 3 heatmap artifacts.
The paper archive contains the two rendered 32B heatmaps but no checkpoint.
This audit extracts the figures, renders them at a fixed resolution, samples
all 20 x 10 cells, and calibrates each cell against the figure's own 0--1
colorbar. It is deliberately an artifact audit, not a substituted model run.
"""
from __future__ import annotations
import argparse
import json
import subprocess
import tarfile
import tempfile
from pathlib import Path
FIGURES = {
"baseline": "figs/needle_haystack_heatmap_chinese_32b_baseline.pdf",
"ours": "figs/needle_haystack_heatmap_chinese_32b_ours.pdf",
}
WIDTH, HEIGHT = 1666, 870
GRID_X = [185 + 64 * i for i in range(20)]
GRID_Y = [62 + 70 * i for i in range(10)]
BAR_X = 1530
BAR_Y = list(range(27, 728))
def read_ppm(path: Path) -> tuple[int, int, bytes]:
raw = path.read_bytes()
tokens = []
at = 0
while len(tokens) < 4:
while raw[at] in b" \t\r\n":
at += 1
end = at
while raw[end] not in b" \t\r\n":
end += 1
tokens.append(raw[at:end])
at = end
magic, width, height, maxval = tokens
if magic != b"P6" or maxval != b"255":
raise ValueError("expected an 8-bit binary PPM")
while raw[at] in b" \t\r\n":
at += 1
return int(width), int(height), raw[at:]
def rgb(data: bytes, width: int, x: int, y: int) -> tuple[int, int, int]:
at = (y * width + x) * 3
return tuple(data[at : at + 3])
def extract(tar: tarfile.TarFile, name: str, destination: Path) -> None:
for member in tar:
if member.name.endswith(name):
handle = tar.extractfile(member)
if handle is None:
break
destination.write_bytes(handle.read())
return
raise FileNotFoundError(name)
def audit_figure(pdf: Path, scratch: Path) -> dict:
prefix = scratch / pdf.stem
subprocess.run(
["pdftoppm", "-r", "150", "-singlefile", str(pdf), str(prefix)],
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
width, height, data = read_ppm(prefix.with_suffix(".ppm"))
if (width, height) != (WIDTH, HEIGHT):
raise AssertionError((width, height))
bar = [rgb(data, width, BAR_X, y) for y in BAR_Y]
scores = []
color_rows = []
for y in GRID_Y:
row = []
for x in GRID_X:
observed = rgb(data, width, x, y)
nearest = min(
range(len(bar)),
key=lambda j: sum((observed[k] - bar[j][k]) ** 2 for k in range(3)),
)
score = round(1.0 - nearest / (len(bar) - 1), 1)
row.append(score)
scores.append(score)
color_rows.append(row)
return {
"cells": len(scores),
"mean": sum(scores) / len(scores),
"mean_percent": 100.0 * sum(scores) / len(scores),
"histogram": {str(value): scores.count(value) for value in sorted(set(scores))},
"rows": color_rows,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--archive", default="source/2603.21719v1.tar")
args = parser.parse_args()
with tempfile.TemporaryDirectory(prefix="tablelong-heatmap-") as directory:
scratch = Path(directory)
results = {}
with tarfile.open(args.archive, "r:*") as tar:
for label, name in FIGURES.items():
pdf = scratch / f"{label}.pdf"
extract(tar, name, pdf)
results[label] = audit_figure(pdf, scratch)
if results["baseline"]["mean_percent"] != 87.95:
raise AssertionError(results)
if results["ours"]["mean_percent"] != 99.4:
raise AssertionError(results)
print(json.dumps(results, sort_keys=True))
if __name__ == "__main__":
main()
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