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"""Sweep single-node CPU generation chunk sizes.
This script calls the existing cpu_generation_benchmark runner for material
counts 32, 48, 64, ... and stops when wall_seconds / num_materials first rises.
"""
from __future__ import annotations
import argparse
import json
import os
import subprocess
import sys
import time
from pathlib import Path
import pandas as pd
ROOT = Path("/workspace/mp_20_pxrdnet")
MODEL_PATH = ROOT / "hydra/singlerun/2026-07-20/pxrdgen_raw512_run02"
TEST_PATH = ROOT / "data/mp_20_pxrdgen_xrd90_0p1_raw512/test.csv"
DEFAULT_OUTPUT_ROOT = (
ROOT / "paper_results_pxrdgen_match_only/cpu_multinode/single_node_sweep"
)
def load_summary(output_dir: Path) -> dict:
summary_path = output_dir / "timing" / "summary.json"
if not summary_path.exists():
raise FileNotFoundError(f"Missing summary: {summary_path}")
return json.loads(summary_path.read_text())
def run_one(args: argparse.Namespace, n_materials: int) -> dict:
output_dir = Path(args.output_root) / f"n_{n_materials:05d}"
output_dir.mkdir(parents=True, exist_ok=True)
command = [
sys.executable,
"-W",
"ignore",
str(ROOT / "cpu_generation_benchmark/run_cpu_generation_benchmark.py"),
"--model-path",
str(MODEL_PATH),
"--output-dir",
str(output_dir),
"--first-idx",
str(args.first_idx),
"--num-materials",
str(n_materials),
"--num-starting-points",
str(args.num_starting_points),
"--workers",
str(args.cpu_workers),
"--torch-threads-per-worker",
str(args.torch_threads_per_worker),
"--num-gradient-steps",
str(args.num_gradient_steps),
"--n-step-each",
str(args.n_step_each),
"--progress-log-interval",
str(args.progress_log_interval),
"--progress-mininterval",
str(args.progress_mininterval),
]
if args.rebuild_limited_data:
command.append("--rebuild-limited-data")
env = os.environ.copy()
env.update(
{
"PROJECT_ROOT": str(ROOT),
"HYDRA_JOBS": str(ROOT / "hydra"),
"WABDB_DIR": str(ROOT / "wabdb"),
"PYTHONPATH": f"{ROOT}:{ROOT / 'scripts'}:{env.get('PYTHONPATH', '')}",
"WANDB_MODE": "disabled",
"CUDA_VISIBLE_DEVICES": "",
"PIP_CACHE_DIR": env.get("PIP_CACHE_DIR", "/workspace/.cache/pip"),
"TMPDIR": env.get("TMPDIR", "/workspace/tmp"),
}
)
record = {
"num_materials": n_materials,
"output_dir": str(output_dir),
"command": command,
"started_at_unix": time.time(),
}
if args.dry_run:
record.update(
{
"status": "dry_run",
"wall_seconds": None,
"avg_wall_seconds_per_material": None,
}
)
return record
log_path = output_dir / "benchmark.log"
started = time.perf_counter()
completed = subprocess.run(
command,
cwd=str(ROOT),
env=env,
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
elapsed = time.perf_counter() - started
log_path.write_text(completed.stdout)
record["returncode"] = completed.returncode
record["elapsed_seconds_by_driver"] = elapsed
if completed.returncode != 0:
record.update({"status": "failed", "log_path": str(log_path)})
return record
summary = load_summary(output_dir)
wall_seconds = float(summary["wall_seconds"])
record.update(
{
"status": "ok",
"summary": summary,
"wall_seconds": wall_seconds,
"avg_wall_seconds_per_material": wall_seconds / n_materials,
"avg_wall_minutes_per_material": wall_seconds / n_materials / 60.0,
"materials_per_wall_second": n_materials / wall_seconds,
"log_path": str(log_path),
}
)
return record
def write_outputs(output_root: Path, records: list[dict]) -> None:
output_root.mkdir(parents=True, exist_ok=True)
ok_records = [r for r in records if r.get("status") == "ok"]
best = None
if ok_records:
best = min(ok_records, key=lambda r: r["avg_wall_seconds_per_material"])
payload = {
"model_path": str(MODEL_PATH),
"test_path": str(TEST_PATH),
"test_rows": int(len(pd.read_pickle(TEST_PATH))),
"best_num_materials": best["num_materials"] if best else None,
"best_avg_wall_seconds_per_material": best["avg_wall_seconds_per_material"]
if best
else None,
"records": records,
}
(output_root / "sweep_summary.json").write_text(
json.dumps(payload, indent=2) + "\n"
)
with (output_root / "sweep_summary.tsv").open("w") as f:
f.write(
"num_materials\tstatus\twall_seconds\tavg_wall_seconds_per_material\tavg_wall_minutes_per_material\tmaterials_per_wall_second\toutput_dir\n"
)
for r in records:
f.write(
f"{r.get('num_materials')}\t{r.get('status')}\t{r.get('wall_seconds')}\t"
f"{r.get('avg_wall_seconds_per_material')}\t{r.get('avg_wall_minutes_per_material')}\t"
f"{r.get('materials_per_wall_second')}\t{r.get('output_dir')}\n"
)
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--first-idx", type=int, default=int(os.environ.get("FIRST_IDX", 0)))
parser.add_argument("--start-materials", type=int, default=int(os.environ.get("START_MATERIALS", 32)))
parser.add_argument("--step-materials", type=int, default=int(os.environ.get("STEP_MATERIALS", 16)))
parser.add_argument("--max-materials", type=int, default=int(os.environ.get("MAX_MATERIALS", 256)))
parser.add_argument("--increase-tolerance", type=float, default=float(os.environ.get("INCREASE_TOLERANCE", 0.0)))
parser.add_argument("--num-starting-points", type=int, default=int(os.environ.get("NUM_STARTING_POINTS", 1)))
parser.add_argument("--cpu-workers", type=int, default=int(os.environ.get("CPU_WORKERS", 0)))
parser.add_argument("--torch-threads-per-worker", type=int, default=int(os.environ.get("TORCH_THREADS_PER_WORKER", 0)))
parser.add_argument("--num-gradient-steps", type=int, default=int(os.environ.get("NUM_GRADIENT_STEPS", 5000)))
parser.add_argument("--n-step-each", type=int, default=int(os.environ.get("N_STEP_EACH", 100)))
parser.add_argument("--progress-log-interval", type=int, default=int(os.environ.get("PROGRESS_LOG_INTERVAL", 500)))
parser.add_argument("--progress-mininterval", type=float, default=float(os.environ.get("PROGRESS_MININTERVAL", 10)))
parser.add_argument("--output-root", default=os.environ.get("OUTPUT_ROOT", str(DEFAULT_OUTPUT_ROOT)))
parser.add_argument("--rebuild-limited-data", action="store_true")
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args()
if args.start_materials <= 0 or args.step_materials <= 0:
raise ValueError("start-materials and step-materials must be positive.")
if args.max_materials < args.start_materials:
raise ValueError("max-materials must be >= start-materials.")
output_root = Path(args.output_root)
records: list[dict] = []
previous_avg = None
for n_materials in range(
args.start_materials, args.max_materials + 1, args.step_materials
):
print(f"==> Running single-node CPU generation benchmark: N={n_materials}", flush=True)
record = run_one(args, n_materials)
records.append(record)
write_outputs(output_root, records)
print(json.dumps({k: record.get(k) for k in [
"num_materials",
"status",
"wall_seconds",
"avg_wall_seconds_per_material",
"avg_wall_minutes_per_material",
"materials_per_wall_second",
]}, indent=2), flush=True)
if record.get("status") == "dry_run":
previous_avg = previous_avg
continue
if record.get("status") != "ok":
print("Stopping because benchmark failed.", flush=True)
break
current_avg = float(record["avg_wall_seconds_per_material"])
if previous_avg is not None and current_avg > previous_avg * (1.0 + args.increase_tolerance):
print(
"Stopping because average time per material increased for the first time.",
flush=True,
)
break
previous_avg = current_avg
return 0
if __name__ == "__main__":
raise SystemExit(main())
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