From b270eb58e22deb9f6a1c5342db41d531232ded0d Mon Sep 17 00:00:00 2001
From: yurenh <blackhao0426@gmail.com>
Date: Mon, 31 Aug 2026 18:34:12 -0500
Subject: results flow: collect.py (node-side bundle) + plot_ladder.py
 (gap-vs-scale analysis)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GkgLsACEF6CCP7EUfA5fZe
---
 scripts/plot_ladder.py | 42 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 scripts/plot_ladder.py

(limited to 'scripts/plot_ladder.py')

diff --git a/scripts/plot_ladder.py b/scripts/plot_ladder.py
new file mode 100644
index 0000000..4473e02
--- /dev/null
+++ b/scripts/plot_ladder.py
@@ -0,0 +1,42 @@
+"""Paper-side analysis from collected results: gap-vs-scale table + curves.
+    python scripts/plot_ladder.py --results results/h200node1 --fig results/h200node1/fig_ladder.png"""
+import os, json, glob, argparse
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+p = argparse.ArgumentParser()
+p.add_argument("--results", required=True)
+p.add_argument("--fig", default=None)
+a = p.parse_args()
+summary = json.load(open(os.path.join(a.results, "summary.json")))
+sizes = sorted({k.rsplit("_", 2)[0] if "_zbp_" in k else k.split("_")[0] for k in summary})
+print(f"{'run':22s} {'params':>10s} {'steps':>7s} {'val_loss':>9s} {'tok/s':>9s}")
+for k, v in sorted(summary.items()):
+    print(f"{k:22s} {v['params'] or 0:>10d} {v['steps']:>7d} {v['val_loss'] or float('nan'):>9.3f} {v['tok_per_s'] or 0:>9.0f}")
+fig, axes = plt.subplots(1, 2, figsize=(10, 3.8))
+for k in sorted(summary):
+    rows = [json.loads(l) for l in open(os.path.join(a.results, f"{k}.jsonl"))]
+    ev = [(r["step"], r["val_loss"]) for r in rows if r.get("kind") == "eval" and r["step"] > 0]
+    if ev:
+        axes[0].plot(*zip(*ev), label=k, lw=1.4)
+axes[0].set_xlabel("step"); axes[0].set_ylabel("val loss"); axes[0].legend(fontsize=6); axes[0].grid(alpha=0.3)
+# gap vs scale: for each size, zbp - bp final
+gaps = {}
+for k, v in summary.items():
+    if v["val_loss"] is None: continue
+    size = k.split("_")[0]
+    gaps.setdefault(size, {})[k[len(size) + 1:]] = (v["params"], v["val_loss"])
+xs, arms = [], {}
+for size, d in sorted(gaps.items(), key=lambda kv: (kv[1].get("bp") or (0, 0))[0]):
+    if "bp" not in d: continue
+    for arm, (params, loss) in d.items():
+        if arm == "bp": continue
+        arms.setdefault(arm, []).append((params, loss - d["bp"][1]))
+for arm, pts in arms.items():
+    pts.sort()
+    axes[1].plot(*zip(*pts), "o-", label=f"{arm} − bp")
+axes[1].set_xscale("log"); axes[1].axhline(0, color="k", lw=0.8)
+axes[1].set_xlabel("params"); axes[1].set_ylabel("val-loss gap vs BP"); axes[1].legend(fontsize=7); axes[1].grid(alpha=0.3)
+fig.tight_layout(); out = a.fig or os.path.join(a.results, "fig_ladder.png")
+fig.savefig(out, dpi=150); print("saved", out)
-- 
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