| """Render recorded sample timings; requires matplotlib (no model weights).""" |
|
|
| import json |
| from pathlib import Path |
|
|
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
|
|
| def main(): |
| root = Path(__file__).resolve().parents[1] |
| plt.rcParams.update( |
| { |
| "font.family": "DejaVu Sans", |
| "font.size": 11, |
| "svg.fonttype": "none", |
| "axes.spines.top": False, |
| "axes.spines.right": False, |
| } |
| ) |
| fig, axes = plt.subplots(1, 2, figsize=(11.6, 4.5), sharey=True) |
| fig.patch.set_facecolor("#f7fafc") |
| colors = ["#8296ac", "#137f82"] |
| for ax, storage, title in zip( |
| axes, ["bf16", "packed12"], ["BF16", "Lossless packed BF16"], strict=True |
| ): |
| ax.set_facecolor("#f7fafc") |
| summary = json.loads( |
| (root / f"benchmarks/data/{storage}/summary.json").read_text() |
| ) |
| comparison = json.loads( |
| (root / f"benchmarks/data/{storage}/comparison.json").read_text() |
| ) |
| for case_index, case in enumerate(["product", "portrait"]): |
| for index, (variant, label) in enumerate( |
| [("control", "Control"), ("qk-fc2-native-norm", "Tuned")] |
| ): |
| values = summary["cases"][case][variant]["samples_s"] |
| x = case_index + (-0.16 if index == 0 else 0.16) |
| ax.scatter( |
| x + np.array([-0.045, 0, 0.045]), |
| values, |
| color=colors[index], |
| s=35, |
| zorder=3, |
| label=label if case_index == 0 else None, |
| ) |
| median = float(np.median(values)) |
| ax.plot( |
| [x - 0.11, x + 0.11], [median, median], color=colors[index], lw=2.3 |
| ) |
| ax.annotate( |
| f"{median:.2f}", |
| (x, median), |
| xytext=(0, -17 if index else 12), |
| textcoords="offset points", |
| ha="center", |
| color=colors[index], |
| fontsize=10, |
| fontweight="bold", |
| ) |
| before = comparison["baseline_summary"]["geomean_seconds"] |
| after = comparison["candidate_summary"]["geomean_seconds"] |
| ax.set_title( |
| f"{title}\n{before:.2f} s → {after:.2f} s (−{comparison['latency_reduction_pct']:.2f}%)", |
| loc="left", |
| fontsize=13, |
| fontweight="bold", |
| pad=18, |
| ) |
| ax.set_xticks([0, 1], ["Product · seed 731", "Portrait · seed 1234"]) |
| ax.set_xlim(-0.48, 1.48) |
| ax.set_ylim(58.5, 69) |
| ax.grid(axis="y", color="#dce5ed", lw=0.8, zorder=0) |
| ax.legend(frameon=False, loc="lower left", ncols=2) |
| ax.spines["left"].set_color("#b7c8d6") |
| ax.spines["bottom"].set_color("#b7c8d6") |
| axes[0].set_ylabel("Warm complete generation · seconds") |
| fig.suptitle( |
| "2048 × 2048 · six steps · M5 Pro / 64 GiB · MLX 0.32.4", |
| fontsize=14, |
| fontweight="bold", |
| y=0.98, |
| ) |
| fig.text( |
| 0.5, |
| 0.025, |
| "Dots: all three repeats. Lines: medians. Cached conditioning and resident weights; separate paired campaigns.\nHeadline values: geometric mean of case medians. Startup/downloads excluded; trace and PNG save included.", |
| ha="center", |
| fontsize=9, |
| color="#465d70", |
| ) |
| fig.tight_layout(rect=(0, 0.12, 1, 0.9), w_pad=3) |
| target = root / "assets/benchmarks.svg" |
| fig.savefig(target, facecolor=fig.get_facecolor()) |
| fig.savefig( |
| "/tmp/minimax-benchmarks-preview.png", dpi=140, facecolor=fig.get_facecolor() |
| ) |
| print(target) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
