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"""Build a run-level WandB/profiling inventory from benchmark metadata.
This intentionally inventories links and run summaries, not trace blobs. The
large profiler outputs stay in their original artifact stores; the CSV gives the
paper/class-grading path a compact "what exists and where" surface.
"""
from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
from urllib.parse import urlparse
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_BENCHMARKS = ROOT / "benchmarks"
DEFAULT_SCORES = ROOT / "results" / "metrics" / "scenario_scores.jsonl"
DEFAULT_REGISTRY = ROOT / "results" / "metrics" / "evidence_registry.csv"
DEFAULT_OUT = ROOT / "results" / "metrics" / "profiling_inventory.csv"
OUTPUT_COLUMNS = [
"benchmark_run_dir",
"run_name",
"benchmark_cell_dir",
"experiment_cell",
"orchestration_mode",
"mcp_mode",
"model_id",
"git_sha",
"registry_status",
"include_in_paper",
"registry_reason",
"run_status",
"started_at",
"finished_at",
"total_runs",
"pass",
"fail",
"has_wandb",
"wandb_run_id",
"wandb_run_url",
"has_profiling",
"profiling_dir",
"profiling_artifact",
"has_profiling_summary",
"profiling_gpu_util_mean",
"profiling_gpu_util_max",
"profiling_gpu_mem_used_mib_mean",
"profiling_gpu_mem_used_mib_max",
"profiling_power_draw_w_mean",
"profiling_power_draw_w_max",
"profiling_nvidia_smi_samples",
"notes",
]
CELL_DIR_FALLBACKS = {
"cell_A_direct": "A",
"cell_B_mcp_baseline": "B",
"cell_C_mcp_optimized": "C",
"cell_D": "D",
"cell_ZSD": "ZSD",
}
def load_json(path: Path) -> dict:
with path.open(encoding="utf-8") as f:
return json.load(f)
def load_score_lookup(path: Path) -> dict[str, dict]:
if not path.exists():
return {}
lookup: dict[str, dict] = {}
with path.open(encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
row = json.loads(line)
run_name = row.get("run_name")
if run_name and run_name not in lookup:
lookup[run_name] = {
"experiment_cell": row.get("experiment_cell"),
"orchestration_mode": row.get("orchestration_mode"),
"mcp_mode": row.get("mcp_mode"),
"model_id": row.get("model_id"),
"wandb_run_url": row.get("wandb_run_url"),
}
return lookup
def load_registry(path: Path) -> dict[str, dict[str, str]]:
if not path.exists():
return {}
with path.open(encoding="utf-8", newline="") as f:
return {row["run_name"]: row for row in csv.DictReader(f)}
def infer_cell(cell_dir: str, run_name: str) -> str:
if cell_dir in CELL_DIR_FALLBACKS:
return CELL_DIR_FALLBACKS[cell_dir]
if cell_dir == "cell_Y_plan_execute":
return "YS" if "self_ask" in run_name else "Y"
if cell_dir == "cell_Z_hybrid":
return "ZS" if "self_ask" in run_name else "Z"
if cell_dir.startswith("cell_"):
return cell_dir.removeprefix("cell_")
return ""
def infer_orchestration(cell: str, run_name: str) -> str:
if cell in {"A", "B", "C", "D"} or cell.endswith("70B"):
return "agent_as_tool"
if cell.startswith("Y"):
return "plan_execute"
if cell.startswith("Z"):
return "verified_pe"
if "verified_pe" in run_name:
return "verified_pe"
if "pe_" in run_name:
return "plan_execute"
if "aat" in run_name:
return "agent_as_tool"
return ""
def infer_mcp_mode(cell: str, run_name: str) -> str:
if cell == "A" or cell == "A70B" or "_aat_direct" in run_name:
return "direct"
if "optimized" in run_name or cell in {"C", "D", "ZSD"}:
return "optimized"
if "baseline" in run_name or cell in {"B", "Y", "YS", "Z", "ZS"}:
return "baseline"
return ""
def wandb_run_id(url: str) -> str:
if not url:
return ""
path = urlparse(url).path.rstrip("/")
return path.rsplit("/", 1)[-1] if path else ""
def flatten_summary(summary: dict | None, key: str) -> object:
if not isinstance(summary, dict):
return ""
return summary.get(key, "")
def bool_s(value: object) -> str:
return "true" if bool(value) else "false"
def build_rows(
benchmarks_dir: Path,
scores_path: Path,
registry_path: Path,
) -> list[dict[str, object]]:
score_lookup = load_score_lookup(scores_path)
registry = load_registry(registry_path)
rows: list[dict[str, object]] = []
for meta_path in sorted(benchmarks_dir.glob("*/raw/*/meta.json")):
meta = load_json(meta_path)
run_dir = meta_path.parent
cell_dir = run_dir.parents[1].name
run_name = meta.get("run_name") or run_dir.name
score = score_lookup.get(run_name, {})
reg = registry.get(run_name, {})
inferred_cell = infer_cell(cell_dir, run_name)
experiment_cell = (
meta.get("experiment_cell") or inferred_cell or score.get("experiment_cell")
)
orchestration_mode = (
meta.get("orchestration_mode")
or score.get("orchestration_mode")
or infer_orchestration(experiment_cell, run_name)
)
mcp_mode = (
meta.get("mcp_mode")
or score.get("mcp_mode")
or infer_mcp_mode(experiment_cell, run_name)
)
wandb_url = meta.get("wandb_run_url") or score.get("wandb_run_url") or ""
profiling_dir = meta.get("profiling_dir") or ""
profiling_artifact = meta.get("profiling_artifact") or ""
profiling_summary = meta.get("profiling_summary")
has_profiling_summary = isinstance(profiling_summary, dict) and bool(
profiling_summary
)
has_profiling = bool(
profiling_dir or profiling_artifact or has_profiling_summary
)
notes = ""
if has_profiling_summary:
notes = "profiling summary present in meta.json"
elif has_profiling:
notes = "profiling artifact linked; summary stats absent from meta.json"
elif wandb_url:
notes = "wandb only"
rows.append(
{
"benchmark_run_dir": run_dir.relative_to(ROOT).as_posix(),
"run_name": run_name,
"benchmark_cell_dir": cell_dir,
"experiment_cell": experiment_cell,
"orchestration_mode": orchestration_mode,
"mcp_mode": mcp_mode,
"model_id": meta.get("model_id") or score.get("model_id") or "",
"git_sha": meta.get("git_sha", ""),
"registry_status": reg.get("status", ""),
"include_in_paper": reg.get("include_in_paper", ""),
"registry_reason": reg.get("reason", ""),
"run_status": meta.get("run_status", ""),
"started_at": meta.get("started_at", ""),
"finished_at": meta.get("finished_at", ""),
"total_runs": meta.get("total_runs", ""),
"pass": meta.get("pass", ""),
"fail": meta.get("fail", ""),
"has_wandb": bool_s(wandb_url),
"wandb_run_id": wandb_run_id(wandb_url),
"wandb_run_url": wandb_url,
"has_profiling": bool_s(has_profiling),
"profiling_dir": profiling_dir,
"profiling_artifact": profiling_artifact,
"has_profiling_summary": bool_s(has_profiling_summary),
"profiling_gpu_util_mean": flatten_summary(
profiling_summary, "profiling/gpu_util_mean"
),
"profiling_gpu_util_max": flatten_summary(
profiling_summary, "profiling/gpu_util_max"
),
"profiling_gpu_mem_used_mib_mean": flatten_summary(
profiling_summary, "profiling/gpu_mem_used_mib_mean"
),
"profiling_gpu_mem_used_mib_max": flatten_summary(
profiling_summary, "profiling/gpu_mem_used_mib_max"
),
"profiling_power_draw_w_mean": flatten_summary(
profiling_summary, "profiling/power_draw_w_mean"
),
"profiling_power_draw_w_max": flatten_summary(
profiling_summary, "profiling/power_draw_w_max"
),
"profiling_nvidia_smi_samples": flatten_summary(
profiling_summary, "profiling/nvidia_smi_samples"
),
"notes": notes,
}
)
rows.sort(
key=lambda row: (
row["experiment_cell"] or "",
row["run_name"] or "",
row["benchmark_run_dir"] or "",
)
)
return rows
def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8", newline="") as f:
writer = csv.DictWriter(f, fieldnames=OUTPUT_COLUMNS, lineterminator="\n")
writer.writeheader()
writer.writerows(rows)
def display_path(path: Path) -> str:
resolved = path.resolve()
try:
return resolved.relative_to(ROOT).as_posix()
except ValueError:
return resolved.as_posix()
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--benchmarks", type=Path, default=DEFAULT_BENCHMARKS)
parser.add_argument("--scores", type=Path, default=DEFAULT_SCORES)
parser.add_argument("--registry", type=Path, default=DEFAULT_REGISTRY)
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
args = parser.parse_args()
rows = build_rows(args.benchmarks, args.scores, args.registry)
write_csv(args.out, rows)
print(f"wrote {len(rows)} rows to {display_path(args.out)}")
print(
"coverage: "
f"{sum(r['has_wandb'] == 'true' for r in rows)} W&B-linked, "
f"{sum(r['has_profiling'] == 'true' for r in rows)} profiler-linked, "
f"{sum(r['has_profiling_summary'] == 'true' for r in rows)} with summary stats"
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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