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"""Run actual isolated CUDA preflights and read-only ordinary endpoint checks."""
from __future__ import annotations
import argparse
import hashlib
import json
import subprocess
from pathlib import Path
from execute_learning_use import claim_gpu
from run_memory_interface_next import now, write
def validate(config_path):
config_path = Path(config_path).resolve()
config = json.loads(config_path.read_text())
project = Path(__file__).resolve().parents[1]
artifact = project / "docs/development-artifacts" / config["batch"]
source = Path(config.get("source_root", project))
jobs = [
(spec["name"], index, gpu, False)
for index, (spec, gpu) in enumerate(zip(config["specs"], config["gpus"], strict=True))
]
jobs.append(("reused-ordinary", None, 4, True))
locks, workers = [], []
try:
for name, index, gpu, reused in jobs:
lock = claim_gpu(gpu)
if lock is None:
raise RuntimeError(f"GPU {gpu} is occupied; no other experiment is preempted")
locks.append(lock)
out = artifact / "preflight" / name
out.mkdir(parents=True, exist_ok=False)
container = "lm-moe-preflight-" + hashlib.sha256(str(out).encode()).hexdigest()[:16]
command = [
"docker",
"--context",
"lm-memory",
"run",
"--name",
container,
"--label",
"project=llm-memory-editability",
"--label",
"batch=" + config["batch"],
"--network=none",
"--gpus",
f"device={gpu}",
"--cpus",
"4",
"--memory",
"32g",
"--read-only",
"--tmpfs",
"/tmp:rw,size=2g",
"--shm-size=1g",
"--mount",
f"type=bind,src={project},dst={project},readonly",
"--mount",
f"type=bind,src={out},dst={out}",
"--workdir",
str(project),
"--env",
"MEMORY_INTERFACE_CONTAINER=1",
"--env",
"PYTHONDONTWRITEBYTECODE=1",
"--env",
"OMP_NUM_THREADS=1",
"--env",
"PYTHONPATH=" + str(source / "src"),
config["runtime"]["image"],
config["runtime"]["python"],
"-u",
"-m",
"llm_memory_editability.moe_sequential",
"--config",
str(config_path),
"--out",
str(out),
"--device",
"cuda:0",
]
if reused:
command.append("--verify-reused")
else:
command += ["--preflight", "--run", config["specs"][index]["name"]]
write(out / "command.json", {"command": command, "physical_gpu": gpu})
log = (out / "output.log").open("w")
process = subprocess.Popen(command, stdout=log, stderr=subprocess.STDOUT)
workers.append((name, out, process, log, reused))
results = []
for name, out, process, log, reused in workers:
returncode = process.wait()
log.close()
path = out / ("reuse-validation.json" if reused else "preflight.json")
result = json.loads(path.read_text()) if path.exists() else {}
passed = returncode == 0 and result.get("passed") is True
results.append(
{"name": name, "passed": passed, "returncode": returncode, "evidence": str(path)}
)
# Check real-sized ordinary, expert and wide parameter counts and common tensors.
ledgers = {}
for entry in config["reused_runs"]:
ledger = artifact / "preflight/reused-ordinary" / entry["name"] / "architecture.json"
ledgers.setdefault("D4", json.loads(ledger.read_text()))
for spec in config["specs"]:
ledger = artifact / "preflight" / spec["name"] / "architecture.json"
if ledger.exists():
ledgers.setdefault(spec["architecture"], json.loads(ledger.read_text()))
counts = {
arm: {
key: ledger[key]
for key in ("total_parameters", "nominal_parameters_selected_per_token")
}
for arm, ledger in ledgers.items()
}
matching = False
if set(ledgers) == {"D4", "M4", "W4"}:
m, w, d = (ledgers[arm] for arm in ("M4", "W4", "D4"))
matching = (
abs(m["total_parameters"] - w["total_parameters"]) < 0.001 * m["total_parameters"]
)
matching &= (
abs(m["nominal_parameters_selected_per_token"] - d["total_parameters"])
< 0.001 * d["total_parameters"]
)
dense = {
key: value
for key, value in d["initialization"]["shared_tensor_sha256"].items()
if ".mlp." not in key
}
matching &= dense == m["initialization"]["shared_tensor_sha256"]
matching &= dense == w["initialization"]["shared_tensor_sha256"]
summary = {
"passed": matching and all(row["passed"] for row in results),
"jobs": results,
"real_size_parameter_ledger": counts,
"paired_common_tensors_and_parameter_matching": matching,
"created_utc": now(),
}
write(artifact / "preflight-summary.json", summary)
print(json.dumps(summary), flush=True)
if not summary["passed"]:
raise SystemExit(1)
finally:
# Running isolated preflights are preserved if orchestration fails.
for _, _, process, log, _ in workers:
if process.poll() is not None:
log.close()
for lock in locks:
lock.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, required=True)
validate(parser.parse_args().config)