File size: 8,678 Bytes
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from __future__ import annotations
import argparse
import hashlib
import json
import shutil
from pathlib import Path
from h3_mlx.compare import make_comparison
LABELS = {
"native": "BF16 native / six passes",
"tuned": "BF16 / fused AdaLN / tile batch 8",
"fused": "BF16 / fused AdaLN + QK-RoPE / tile batch 8",
"q8": "DiT Q8 / six passes",
"q4": "DiT Q4 / six passes",
"preview": "BF16 preview / steps 0,2,4",
"q8-g32": "DiT Q8 / group 32 / six passes",
"chunked": "BF16 / MLP chunks 1024",
}
def main():
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--runs", default="outputs/suite")
p.add_argument("--output", default="comparisons")
p.add_argument(
"--cuda-reference-root",
help="Optional saved single-bf16-stream CUDA run; quality=lossless",
)
a = p.parse_args()
runs, output = Path(a.runs), Path(a.output)
records = []
for case in sorted(d for d in runs.iterdir() if d.is_dir()):
files = sorted(case.glob("*.png"))
native = sorted(case.glob("native-*.png"))
if not native:
continue
reference = native[-1]
candidates = []
labels = [LABELS["native"]]
for name, label in LABELS.items():
if name == "native":
continue
selected = sorted(case.glob(f"{name}-*.png"))
if selected:
candidates.append(selected[-1])
labels.append(label)
if candidates:
crop = (1024, 384, 1536, 896) if case.name == "portrait" else None
make_comparison(
reference, candidates, output / case.name, labels=labels, crop=crop
)
for file in files:
record = json.loads(file.with_suffix(".json").read_text())
record["case"] = case.name
record["profile"] = file.stem.rsplit("-", 1)[0]
record["trial"] = int(file.stem.rsplit("-", 1)[1])
records.append(record)
target = (
Path("reports/inference") / case.name / file.with_suffix(".json").name
)
target.parent.mkdir(parents=True, exist_ok=True)
shutil.copyfile(file.with_suffix(".json"), target)
if a.cuda_reference_root:
source = Path(a.cuda_reference_root)
image = source / f"{case.name}.png"
if image.exists():
rows = [
json.loads(line)
for line in (source / "results.jsonl").read_text().splitlines()
]
original = next(
r for r in rows if r["case"] == case.name and r["trial"] == 1
)
if hashlib.sha256(image.read_bytes()).hexdigest() != original["sha256"]:
raise ValueError(f"CUDA source hash mismatch: {image}")
native_record = json.loads(reference.with_suffix(".json").read_text())
if tuple(map(int, original["size"].split("x"))) != tuple(
native_record["size"]
):
continue
target = output / "cuda-reference" / f"{case.name}.png"
target.parent.mkdir(parents=True, exist_ok=True)
shutil.copyfile(image, target)
r = {
k: native_record[k]
for k in (
"prompt",
"seed",
"size",
"canvas",
"rng",
"sigmas",
"steps",
)
}
# The original request script explicitly used CPU random noise.
r.update(
output=str(target),
timings={"total_s": original["wall_seconds"]},
environment={
"device": "RTX 5090",
"backend": "SGLang CUDA",
"quality": original["quality"],
},
source_record=original,
reference_images=[],
reference_sha256=[],
)
r["dit"] = {"precision": "BF16"}
r["vae"] = {"quality": original["quality"]}
target.with_suffix(".json").write_text(json.dumps(r, indent=2) + "\n")
make_comparison(
target,
[reference],
output / f"{case.name}-cuda",
labels=[
"CUDA BF16 / upstream lossless quality",
"MLX BF16 / native tile recipe",
],
)
Path("reports").mkdir(exist_ok=True)
Path("reports/image-trials.json").write_text(json.dumps(records, indent=2) + "\n")
plot(records, output)
def plot(records, output):
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from PIL import Image
from h3_mlx.compare import image_metrics
product = [
r for r in records if r["case"] == "product" and r.get("dit_was_resident")
]
if not product:
return
order = [n for n in LABELS if any(r["profile"] == n for r in product)]
selected = [
max((r for r in product if r["profile"] == n), key=lambda r: r["trial"])
for n in order
]
baseline = max(
(r for r in records if r["case"] == "product" and r["profile"] == "native"),
key=lambda r: r["trial"],
)
ref = Image.open(baseline["output"])
scores = [image_metrics(ref, Image.open(r["output"])) for r in selected]
summary = []
for row, score in zip(selected, scores, strict=True):
summary.append(
{
"profile": row["profile"],
"passes": len(row["steps"]),
"denoise_s": sum(
v for k, v in row["timings"].items() if k.startswith("step_")
),
"fresh_end_to_end_s": row["timings"].get("total_s"),
"peak_mlx_gib": row["memory"]["peak_gb"],
**score,
}
)
Path("reports/tradeoffs.json").write_text(json.dumps(summary, indent=2) + "\n")
table = [
"| Profile | Passes | DiT only | Fresh end-to-end | Recorded peak MLX | RGB SSIM | RGB MAE / 255 |",
"|---|---:|---:|---:|---:|---:|---:|",
]
for row in summary:
e2e = (
"—"
if row["fresh_end_to_end_s"] is None
else f"{row['fresh_end_to_end_s']:.2f} s"
)
table.append(
f"| {row['profile']} | {row['passes']} | {row['denoise_s']:.2f} s | {e2e} | {row['peak_mlx_gib']:.2f} GiB | {row['ssim_rgb']:.4f} | {row['mae_8bit']:.3f} |"
)
result_path = Path("reports/RESULTS.md")
if result_path.exists():
content = result_path.read_text()
begin, end = "<!-- PRODUCT_TABLE_START -->", "<!-- PRODUCT_TABLE_END -->"
if begin in content and end in content:
before, rest = content.split(begin, 1)
_, after = rest.split(end, 1)
result_path.write_text(
before + begin + "\n\n" + "\n".join(table) + "\n\n" + end + after
)
fig, axes = plt.subplots(1, 3, figsize=(14, 5), layout="constrained")
names = [r["profile"] for r in selected]
values = [
[
sum(v for k, v in r["timings"].items() if k.startswith("step_"))
for r in selected
],
[r["memory"]["peak_gb"] for r in selected],
[m["ssim_rgb"] for m in scores],
]
colors = [
"#456dba" if n in ("native", "tuned", "fused") else "#d28c43" for n in names
]
for ax, vs, title in zip(
axes,
values,
[
"DiT denoising only: seconds",
"Recorded peak MLX allocation: GiB",
"RGB SSIM to MLX native",
],
strict=True,
):
bars = ax.bar(names, vs, color=colors)
ax.set_title(title)
ax.tick_params(axis="x", rotation=45)
ax.spines[["top", "right"]].set_visible(False)
ax.bar_label(bars, fmt="%.3g", padding=3, fontsize=9)
ax.set_ylim(0, max(vs) * 1.2)
fig.suptitle(
"M5 Pro 64 GB · 2048×2048 product · recorded DiT stages, corrected decoder for every image",
fontsize=12,
)
fig.savefig(Path("reports") / "tradeoffs.png", dpi=160)
plt.close(fig)
if __name__ == "__main__":
main()
|