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#!/usr/bin/env python3
"""Render the audited RRM-3 endpoint-alignment failure diagnostic."""
import argparse
import hashlib
import json
import math
import os
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.ticker import LogLocator
PDF_METADATA = {
"Creator": "SDIL audited figure pipeline",
"Producer": "Matplotlib",
"CreationDate": None,
"ModDate": None,
}
def sha256(path):
digest = hashlib.sha256()
with open(path, "rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--record", default="results/residual_mirror_full/rrm.json")
parser.add_argument(
"--gate", default="results/residual_mirror_full_gate.json")
parser.add_argument(
"--out", default="results/figs/figureS_tracking_failure")
args = parser.parse_args()
with open(args.record) as handle:
record = json.load(handle)
with open(args.gate) as handle:
gate = json.load(handle)
if gate.get("protocol") != "residual_response_mirror_full_v1":
raise ValueError("unexpected RRM full protocol")
if gate.get("status") != "failed":
raise ValueError("tracking-failure figure requires the failed frozen gate")
if gate["metrics"]["source_commit"] != record["provenance"]["git_commit"]:
raise ValueError("record/gate provenance mismatch")
if len(record["epochs"]) != 200:
raise ValueError("RRM trajectory is incomplete")
epochs = [int(row["epoch"]) for row in record["epochs"]]
losses = [float(row["train_loss"]) for row in record["epochs"]]
if not all(math.isfinite(value) and value > 0 for value in losses):
raise ValueError("training-loss trajectory is invalid")
metrics = gate["metrics"]
values = [
float(metrics["accuracy"]),
float(metrics["early_third_alignment"]),
float(metrics["all_layer_alignment"]),
float(metrics["mean_feedback_forward_cosine"]),
]
references = [float(metrics["bp_accuracy"]), 1.0, 1.0, 1.0]
plt.rcParams.update({
"font.family": "DejaVu Sans",
"font.size": 10,
"axes.titleweight": "bold",
"axes.spines.top": False,
"axes.spines.right": False,
})
fig, (left, right) = plt.subplots(
1, 2, figsize=(10.4, 4.25), gridspec_kw={"width_ratios": [1.32, 1]})
red = "#C44E52"
charcoal = "#3D3D3D"
pale_red = "#F5DDDE"
pale_blue = "#E6F0F7"
left.axvspan(1, 100, color=pale_red, alpha=0.65, linewidth=0)
left.axvspan(100, 150, color="#F7E9D2", alpha=0.65, linewidth=0)
left.axvspan(150, 200, color=pale_blue, alpha=0.75, linewidth=0)
left.semilogy(epochs, losses, color=red, linewidth=2.2, zorder=3)
left.scatter([epochs[0]], [losses[0]], color=charcoal, s=26, zorder=4)
peak_index = max(range(len(losses)), key=losses.__getitem__)
left.scatter([epochs[peak_index]], [losses[peak_index]], color=red,
edgecolor="white", linewidth=0.8, s=45, zorder=4)
left.annotate(
f"peak {losses[peak_index]:.2e}\n(epoch {epochs[peak_index]})",
xy=(epochs[peak_index], losses[peak_index]), xytext=(114, 3.0e15),
arrowprops={"arrowstyle": "-", "color": red, "linewidth": 1},
color=red, fontsize=9, ha="left")
left.annotate(
f"final train loss\n{losses[-1]:.2e}",
xy=(epochs[-1], losses[-1]), xytext=(155, 1.5e11),
arrowprops={"arrowstyle": "-", "color": charcoal, "linewidth": 1},
fontsize=9, ha="left")
left.text(48, 5.0, "LR 0.1", ha="center", color="#87383B", fontsize=9)
left.text(125, 5.0, "LR 0.01", ha="center", color="#8A632B", fontsize=9)
left.text(175, 5.0, "LR 0.001", ha="center", color="#35698C", fontsize=9)
left.set_xlim(1, 200)
left.set_ylim(1, 4e16)
left.yaxis.set_major_locator(LogLocator(base=10, numticks=7))
left.grid(axis="y", which="major", color="#DDDDDD", linewidth=0.7)
left.set_xlabel("Training epoch")
left.set_ylabel("Mean cross-entropy (log scale)")
left.set_title("a The destructive trajectory", loc="left", pad=10)
positions = list(range(4))
width = 0.34
right.bar([value - width / 2 for value in positions], references,
width=width, color="#B8B8B8", label="BP / ideal reference")
bars = right.bar([value + width / 2 for value in positions], values,
width=width, color=red, label="RRM-3 endpoint")
labels = ["Validation\naccuracy", "Early\nalignment",
"All-layer\nalignment", "Q/W\ncosine"]
right.set_xticks(positions, labels)
right.set_ylim(0, 1.08)
right.set_ylabel("Normalized endpoint metric")
right.grid(axis="y", color="#E2E2E2", linewidth=0.7)
right.set_title("b The reassuring endpoint snapshot", loc="left", pad=10)
right.legend(frameon=False, loc="lower right", fontsize=8.5)
for bar, value in zip(bars, values):
right.text(bar.get_x() + bar.get_width() / 2, value + 0.025,
f"{value:.3f}", ha="center", va="bottom",
color=red, fontsize=8.5, fontweight="bold")
right.text(
0.98, 0.49,
"Final validation loss: NaN\nQ/W tracking recovered; task learning did not",
transform=right.transAxes, ha="right", va="center", fontsize=9,
color="#6F2B2E",
bbox={"boxstyle": "round,pad=0.45", "facecolor": pale_red,
"edgecolor": "none"})
fig.suptitle(
"A quiet tail repairs the tracker, not the learner",
x=0.5, y=1.015, fontsize=14, fontweight="bold")
fig.tight_layout(w_pad=2.7)
os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
png = args.out + ".png"
pdf = args.out + ".pdf"
fig.savefig(png, dpi=240, bbox_inches="tight", facecolor="white")
fig.savefig(pdf, bbox_inches="tight", metadata=PDF_METADATA,
facecolor="white")
plt.close(fig)
output = {
"script": os.path.relpath(__file__),
"script_sha256": sha256(__file__),
"record": args.record,
"record_sha256": sha256(args.record),
"gate": args.gate,
"gate_sha256": sha256(args.gate),
"png": png,
"png_sha256": sha256(png),
"pdf": pdf,
"pdf_sha256": sha256(pdf),
}
manifest = args.out + "_manifest.json"
output["manifest"] = manifest
with open(manifest, "w") as handle:
json.dump(output, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps(output, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
|
