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4fa9a6ab81
- gen_shader_profiles.py + shader-profiles/: Iris per-pack option files resolved from each pack's profile.* lines (Iris has no profile key) - bench.py: flag rows shader_not_loaded when Iris falls back to vanilla rendering (analyze.py treats it as invalid) - matrix: shader_options session key, trial jar globs pinned to the downloaded Modrinth builds, post-join settle 45 s (B0 v1 showed rep 1 still warming at 10 s)
199 lines
8.7 KiB
Python
199 lines
8.7 KiB
Python
#!/usr/bin/env python3
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"""Summarise bench.py session rows and apply the BENCH-MATRIX win rule.
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analyze.py [--file ~/onion-bench/results/session.jsonl] [--label B2]
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[--baseline C0 --noise B0] [--include-invalid] [--exclude-backup]
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Groups windows by (label, variant, scene). Each session contributes its median p99 and
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median 1 % low (frametime-based); a group's value is the median of its session medians
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and its spread is max - min of those. With --baseline/--noise, every non-baseline group
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is compared with the baseline variant on the same scene: same label if present, else the
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same block (a tagged label 'B3.dh-off' looks in 'B3', 'B3.baseline', then all 'B3.*'
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pooled), else all labels pooled. A tagged session of the baseline variant itself is
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compared with 'B3' / 'B3.baseline' when one exists: win = median p99 improves by more than the noise label's p99 spread
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AND 1 % low does not worsen by more than its 1 % low spread; loss = p99 worsens beyond
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the spread or 1 % low drops beyond it; otherwise tie (ties keep upstream defaults).
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Rows flagged crashed / timeout / no_data / no_markers / low_n / vsync_suspect are
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excluded unless --include-invalid. Stdlib only.
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"""
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import argparse
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import json
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import statistics
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import sys
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from pathlib import Path
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INVALID = {"crashed", "timeout", "no_data", "no_markers", "low_n", "vsync_suspect",
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"shader_not_loaded"}
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DEFAULT_FILE = Path.home() / "onion-bench" / "results" / "session.jsonl"
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def load_rows(path):
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rows = []
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for line in Path(path).read_text().splitlines():
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line = line.strip()
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if line:
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rows.append(json.loads(line))
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return [r for r in rows if r.get("kind", "window") == "window"]
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def is_valid(row, exclude_backup=False):
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flags = set(row.get("flags") or [])
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if not row.get("metrics") or flags & INVALID:
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return False
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return not (exclude_backup and "backup_in_window" in flags)
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def summarise(rows):
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"""{(label, variant, scene): stats} from valid window rows."""
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by_session = {}
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for r in rows:
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key = (r.get("label"), r["variant"], r["scene"])
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by_session.setdefault(key, {}).setdefault(r["session"], []).append(r["metrics"])
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out = {}
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for key, sessions in by_session.items():
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p99s = [statistics.median(m["p99_ms"] for m in ms) for ms in sessions.values()]
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lows = [statistics.median(m["one_low_fps"] for m in ms) for ms in sessions.values()]
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fps = [statistics.median(m["avg_fps"] for m in ms) for ms in sessions.values()]
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out[key] = {
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"sessions": len(sessions),
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"windows": sum(len(ms) for ms in sessions.values()),
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"p99": statistics.median(p99s), "p99_spread": max(p99s) - min(p99s),
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"low": statistics.median(lows), "low_spread": max(lows) - min(lows),
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"fps": statistics.median(fps),
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}
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return out
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def noise_spread(summary, noise_label):
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"""(p99 spread, 1 % low spread) of the noise block, worst case across its groups."""
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groups = [v for (lab, _, _), v in summary.items() if lab == noise_label]
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if not groups:
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return None
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return max(g["p99_spread"] for g in groups), max(g["low_spread"] for g in groups)
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def verdict(base, cand, spread_p99, spread_low):
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gain = base["p99"] - cand["p99"] # > 0: candidate's p99 is lower (better)
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low_drop = base["low"] - cand["low"] # > 0: candidate's 1 % low is worse
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if gain > spread_p99 and low_drop <= spread_low:
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return "win"
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if gain < -spread_p99 or low_drop > spread_low:
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return "loss"
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return "tie"
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def block_of(label):
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"""'B3.dh-off' -> 'B3'; untagged labels (and None) are their own block."""
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return label.split(".", 1)[0] if label else label
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def _pooled(rows, labels, baseline, scene, name):
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pooled = [r for r in rows if r["variant"] == baseline and r["scene"] == scene
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and (labels is None or r.get("label") in labels)]
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if not pooled:
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return None
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return summarise([dict(r, label=name) for r in pooled]).get((name, baseline, scene))
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def baseline_for(summary, rows, label, scene, baseline):
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"""(baseline stats, where-label) for one candidate group, or (None, None).
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Order: the candidate's own label; then its block - the untagged block label
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('B3', for tagged candidates), '<block>.baseline', then every '<block>.*' label pooled; then all labels
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pooled. A tagged session of the baseline variant itself (e.g. 'B3.dh-off' running
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C1 against baseline C1) is compared only with 'B3' or 'B3.baseline'."""
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block = block_of(label)
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tagged = bool(label) and block != label
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order = [label] + ([block] if tagged else []) + ([f"{block}.baseline"] if block else [])
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for lab in order:
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if (lab, baseline, scene) in summary:
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return summary[(lab, baseline, scene)], lab
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if block:
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labels = {r.get("label") for r in rows
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if r.get("label") == block or str(r.get("label")).startswith(block + ".")}
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b = _pooled(rows, labels, baseline, scene, f"{block}.*")
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if b is not None:
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return b, f"{block}.*"
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b = _pooled(rows, None, baseline, scene, "*")
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return (b, "*") if b is not None else (None, None)
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def self_baseline(summary, label, scene, baseline):
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"""Baseline for a tagged group that runs the baseline variant itself, or None
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(then the group *is* the baseline)."""
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block = block_of(label)
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if not label or block == label:
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return None, None
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for lab in (block, f"{block}.baseline"):
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if lab != label and (lab, baseline, scene) in summary:
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return summary[(lab, baseline, scene)], lab
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return None, None
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def table(summary, rows, baseline=None, noise=None):
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spreads = noise_spread(summary, noise) if noise else None
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full = summarise(rows) # baselines may live in labels filtered out of the table
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hdr = ["label", "variant", "scene", "sess", "win#", "p99 ms", "p99 spr", "1%low",
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"1%low spr", "avg fps"]
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if baseline:
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hdr += ["vs", "Δp99 ms", "Δ1%low", "verdict"]
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out = ["| " + " | ".join(hdr) + " |", "|" + "---|" * len(hdr)]
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for key in sorted(summary, key=lambda k: (str(k[0]), k[2], k[1])):
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lab, var, scene = key
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s = summary[key]
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cells = [str(lab), var, scene, str(s["sessions"]), str(s["windows"]),
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f"{s['p99']:.2f}", f"{s['p99_spread']:.2f}", f"{s['low']:.1f}",
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f"{s['low_spread']:.1f}", f"{s['fps']:.0f}"]
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if baseline:
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b, blab = (self_baseline(full, lab, scene, baseline) if var == baseline
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else baseline_for(full, rows, lab, scene, baseline))
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if var == baseline and b is None:
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cells += ["—", "", "", "baseline"]
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else:
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if b is None:
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cells += ["—", "", "", "no baseline"]
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elif spreads is None:
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cells += [f"{baseline}@{blab}", f"{b['p99'] - s['p99']:+.2f}",
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f"{s['low'] - b['low']:+.1f}", "no noise data"]
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else:
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cells += [f"{baseline}@{blab}", f"{b['p99'] - s['p99']:+.2f}",
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f"{s['low'] - b['low']:+.1f}", verdict(b, s, *spreads)]
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out.append("| " + " | ".join(cells) + " |")
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if baseline and noise:
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out.append("")
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out.append(f"noise ({noise}): " + (
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f"p99 spread {spreads[0]:.2f} ms, 1%low spread {spreads[1]:.1f} fps"
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if spreads else "no rows - verdicts unavailable"))
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return "\n".join(out)
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def main(argv=None):
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ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
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ap.add_argument("--file", default=str(DEFAULT_FILE))
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ap.add_argument("--label", action="append", help="only these labels (repeatable); "
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"the noise label is always kept")
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ap.add_argument("--baseline", help="baseline variant, e.g. C0")
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ap.add_argument("--noise", help="label whose spread is the win threshold, e.g. B0")
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ap.add_argument("--include-invalid", action="store_true")
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ap.add_argument("--exclude-backup", action="store_true")
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a = ap.parse_args(argv)
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rows = load_rows(a.file)
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total = len(rows)
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if not a.include_invalid:
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rows = [r for r in rows if is_valid(r, a.exclude_backup)]
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warn = sum(1 for r in rows if "backup_in_window" in (r.get("flags") or []))
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shown = rows
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if a.label:
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keep = set(a.label) | ({a.noise} if a.noise else set())
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shown = [r for r in rows if r.get("label") in keep]
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if not shown:
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print("no valid rows", file=sys.stderr)
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return 1
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print(table(summarise(shown), rows, a.baseline, a.noise))
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print(f"\n{len(rows)}/{total} windows valid; {warn} with backup_in_window (warn only)")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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