mirror of
https://github.com/storytold/cloud-worker.git
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Hanashi
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""Summarize benchmark results across GPUs and weight families.
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Reads bench/results.csv (B200) plus bench/4090/results.csv and
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bench/4000/results.csv when present, groups repeated configs, and prints
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mean +/- spread tables (markdown, padded columns) for docs/BENCHMARKS.md.
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"""
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import csv
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import json
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import statistics
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from collections import defaultdict
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from pathlib import Path
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HERE = Path(__file__).parent
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SOURCES = [
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("B200", HERE / "results.csv"),
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("RTX 4090", HERE / "4090" / "results.csv"),
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("RTX 4090 (patched)", HERE / "4090c" / "results.csv"),
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("RTX 5090", HERE / "5090" / "results.csv"),
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("RTX PRO 4000", HERE / "4000" / "results.csv"),
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]
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FAMILY_PREFIXES = [
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("pi8", "pruned int8"), ("i8", "int8"), ("b16", "bf16"),
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("prunedint8", "pruned int8"), ("int8", "int8"), ("bf16", "bf16"),
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("g4090_pi8", "pruned int8"), ("g4000_pi8", "pruned int8"),
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]
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def family_of(row):
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m = row["model_file"]
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if "pruned" in m:
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return "pruned int8"
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if "int8" in m:
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return "int8"
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if "bf16" in m:
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return "bf16"
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return m
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def load(path):
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jl = path.with_suffix(".jsonl")
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if jl.exists():
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rows = [json.loads(l) for l in jl.open() if l.strip()]
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return [{k: str(v) if v is not None else "" for k, v in r.items()}
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for r in rows if r.get("ok") is True]
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if not path.exists():
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return []
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return [r for r in csv.DictReader(path.open()) if r.get("ok") == "True"]
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def key_of(row):
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return (row["task"], f'{row["width"]}x{row["height"]}', row["seconds"],
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row.get("ref_count") or "1", row.get("ref_image_size") or "match",
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row["steps"])
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def fmt_stats(vals):
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if len(vals) == 1:
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return f"{vals[0]:.0f} s"
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return f"{statistics.mean(vals):.0f} ± {statistics.stdev(vals):.0f} s (n={len(vals)})"
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def pad_table(rows):
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widths = [max(len(r[c]) for r in rows) for c in range(len(rows[0]))]
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out = []
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for i, r in enumerate(rows):
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out.append("| " + " | ".join(v.ljust(w) for v, w in zip(r, widths)) + " |")
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if i == 0:
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out.append("|" + "|".join("-" * (w + 2) for w in widths) + "|")
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return "\n".join(out)
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def main():
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for gpu, path in SOURCES:
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rows = load(path)
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if not rows:
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continue
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# group warm runs only (exclude first-load runs marked warm=False)
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groups = defaultdict(lambda: defaultdict(list))
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vram = defaultdict(lambda: defaultdict(list))
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for r in rows:
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if r.get("warm") == "False":
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continue
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fam = family_of(r)
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try:
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t = float(r["exec_s"])
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except (ValueError, TypeError):
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continue
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groups[key_of(r)][fam].append(t)
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try:
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vram[key_of(r)][fam].append(float(r["peak_vram_gb"]))
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except (ValueError, TypeError):
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pass
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fams = sorted({f for g in groups.values() for f in g},
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key=lambda f: ["pruned int8", "int8", "bf16"].index(f)
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if f in ["pruned int8", "int8", "bf16"] else 9)
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print(f"\n## {gpu}\n")
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header = ["task", "canvas", "sec", "refs", "sizing"] + \
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[f"{f} (exec)" for f in fams] + [f"{f} (peak GB)" for f in fams]
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table = [header]
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for k in sorted(groups):
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task, wh, sec, refs, sizing, steps = k
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row = [task, wh, sec, refs, sizing]
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for f in fams:
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row.append(fmt_stats(groups[k][f]) if groups[k][f] else "—")
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for f in fams:
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row.append(f"{max(vram[k][f]):.0f}" if vram[k][f] else "—")
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table.append(row)
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print(pad_table(table))
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if __name__ == "__main__":
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main()
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