"""`fig_mergebench_scale.png` -- MergeBench at scale, all eight base families. Two heatmaps, families x domains: (a) the published five-expert (Model soup / Weight Avg) merge score -- MergeBench's number, not ours; every published MergeBench score is a five-expert merge, which is why the outcome unit is the family and not the pair. (b) mean qmd_raw over the four within-family pairs that CONTAIN each domain, x10^3 -- ours. Design is pinned to the version already in the manuscript so a regenerated file drops in unchanged: viridis for (a) with a 0-90 scale, magma_r for (b), value annotations, `n/a` in italic grey for any cell without data, two-line suptitle, row labels `family\\n(params)`. PYTHONPATH=src python scripts/make_fig_mergebench_scale.py """ from __future__ import annotations import glob import os import sys import numpy as np import pandas as pd sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from mergeschool import paths RESULTS = paths.RESULTS / "mergebench" OUT_DIRS = [paths.ROOT / "paper" / "overleaf_iclr", paths.FIGURES / "mergebench"] NAME = "fig_mergebench_scale" FAMILIES = [("gemma-2-2b", "2.6B"), ("gemma-2-2b-it", "2.6B"), ("Llama-3.2-3B", "3.2B"), ("Llama-3.2-3B-Instruct", "3.2B"), ("Llama-3.1-8B", "8.0B"), ("Llama-3.1-8B-Instruct", "8.0B"), ("gemma-2-9b", "9.2B"), ("gemma-2-9b-it", "9.2B")] # figure column order, and the task label each maps to in MergeBench's published table DOMAINS = [("math", "Math"), ("safety", "Safety"), ("coding", "Coding"), ("multilingual", "Multilingual"), ("instruction", "Instruction following")] METHOD = "Model soup" def load(): pairs = pd.concat([pd.read_csv(f) for f in glob.glob(str(RESULTS / "pairs_w*.csv"))], ignore_index=True).drop_duplicates("pair_id") pub = pd.read_csv(RESULTS / "mergebench_published_scores.csv") pub = pub[pub.method == METHOD] S = np.full((len(FAMILIES), len(DOMAINS)), np.nan) # published merge score Q = np.full((len(FAMILIES), len(DOMAINS)), np.nan) # mean qmd_raw x10^3 for i, (fam, _p) in enumerate(FAMILIES): sub = pub[pub.family == fam] g = pairs[pairs.family == fam] for j, (dom, task) in enumerate(DOMAINS): v = sub[sub.task == task]["score"] if len(v): S[i, j] = float(v.iloc[0]) # the four pairs of this family that contain this domain m = g[(g.domain_a == dom) | (g.domain_b == dom)]["qmd_raw"].dropna() if len(m): Q[i, j] = float(m.mean()) * 1e3 return S, Q, pairs def panel(ax, M, cmap, title, cbar_label, vmin=None, vmax=None, log=False): # LOG COLOUR SCALE for the quotient-distance panel, forced by the data rather than chosen for # looks. With all eight families present qmd_raw spans 9.65 to 70.3 -- the 8B/9B experts drift # 3-5x further from each other than the 2B/3B ones -- so on a linear scale the four small # families all sit in the bottom tenth of the range and render as one flat pale block, losing # exactly the within-family structure the original figure showed. A log scale keeps both reads: # the cross-scale growth AND the ordering inside each family. norm = matplotlib.colors.LogNorm(vmin=np.nanmin(M), vmax=np.nanmax(M)) if log else None im = ax.imshow(np.ma.masked_invalid(M), cmap=cmap, aspect="auto", norm=norm, **({} if log else {"vmin": vmin, "vmax": vmax})) im.cmap.set_bad("white") ax.set_xticks(range(len(DOMAINS))) ax.set_xticklabels([d for d, _t in DOMAINS], rotation=35, ha="right", rotation_mode="anchor") ax.set_yticks(range(len(FAMILIES))) ax.set_yticklabels([f"{f}\n({p})" for f, p in FAMILIES], fontsize=9) ax.set_title(title, fontsize=11, pad=10) lo, hi = np.nanmin(M), np.nanmax(M) for i in range(M.shape[0]): for j in range(M.shape[1]): v = M[i, j] if not np.isfinite(v): ax.text(j, i, "n/a", ha="center", va="center", fontsize=9, style="italic", color="#9A9A9A") continue # white on dark cells, black on light -- judged against this panel's own range frac = ((np.log(v) - np.log(lo)) / max(np.log(hi) - np.log(lo), 1e-12)) if log \ else (v - lo) / max(hi - lo, 1e-12) dark = frac > 0.55 if cmap.endswith("_r") else frac < 0.55 ax.text(j, i, f"{v:.1f}" if cmap == "viridis" else f"{v:.2f}", ha="center", va="center", fontsize=9, color="white" if dark else "#1A1A1A") cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.03) cb.set_label(cbar_label, fontsize=9) cb.ax.tick_params(labelsize=8) if log: # matplotlib's default log locator adds 6x10^1-style minor labels that collide with the # round ticks the reader wants; silence both locators and set the ticks explicitly. cb.ax.yaxis.set_minor_locator(matplotlib.ticker.NullLocator()) cb.ax.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter()) ticks = [t for t in (10, 15, 20, 30, 50, 70) if np.nanmin(M) <= t <= np.nanmax(M)] cb.set_ticks(ticks) cb.ax.yaxis.set_major_formatter(matplotlib.ticker.FixedFormatter([str(t) for t in ticks])) return im def main(): S, Q, pairs = load() n_fam = int(np.isfinite(S).any(axis=1).sum()) n_pairs = int(len(pairs)) missing = [f for i, (f, _p) in enumerate(FAMILIES) if not np.isfinite(Q[i]).any()] fig, axes = plt.subplots(1, 2, figsize=(20, 8)) panel(axes[0], S, "viridis", "(a) Published 5-expert merge score (Model soup)", "norm. task score", vmin=0, vmax=90) panel(axes[1], Q, "magma_r", "(b) Mean quotient dist. qmd$_\\mathrm{raw}$ ($\\times10^{3}$, log scale)", "qmd$_\\mathrm{raw}$ $\\times$ 10$^{3}$ (log)", log=True) sub = (f"(all {n_fam} families measured, {n_pairs}/80 pairs; " f"MergeBench publishes only 5-expert merges, n={n_fam} families)" if not missing else f"({n_fam}/8 families measured; {', '.join(missing)} pending, n/a; " f"MergeBench publishes only 5-expert merges)") fig.suptitle("MergeBench at scale — 8 base families (2.6B / 3.2B / 8.0B / 9.2B) × 5 domains\n" + sub, fontsize=13) fig.tight_layout(rect=(0, 0, 1, 0.94)) for d in OUT_DIRS: d.mkdir(parents=True, exist_ok=True) for ext in ("png", "pdf"): fig.savefig(d / f"{NAME}.{ext}", dpi=200, bbox_inches="tight") print(f" wrote {d / (NAME + '.png')}") plt.close(fig) print(f" families with data: {n_fam}/8 | pairs: {n_pairs}/80 | " f"pending: {missing or 'none'}") return S, Q if __name__ == "__main__": main()