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from __future__ import annotations
import argparse
import json
import os
import queue
import shutil
import subprocess
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from llm_memory_editability.grok_depth import utc, write_json
from llm_memory_editability.storage_composition import file_hash
CONFIG = Path("configs/depth-step-development-v1.json")
ARTIFACTS = Path("docs/development-artifacts/depth-step-v1")
RESULTS = Path("results/depth-step-v1")
DESIGN = ARTIFACTS / "design.md"
SOURCES = [
"src/llm_memory_editability/depth_step.py",
"src/llm_memory_editability/bios_model.py",
"src/llm_memory_editability/grok_depth.py",
"src/llm_memory_editability/grok_depth_data.py",
"src/llm_memory_editability/grok_multihop_data.py",
"src/llm_memory_editability/grok_loop_data.py",
"src/llm_memory_editability/grok_loop_model.py",
"src/llm_memory_editability/storage_composition.py",
"src/llm_memory_editability/storage_frontier.py",
"src/llm_memory_editability/latent_scaling.py",
"src/llm_memory_editability/text_pretrain.py",
"scripts/run_depth_step.py",
"scripts/execute_depth_step.py",
"scripts/report_depth_step.py",
"tests/test_depth_step.py",
"tests/test_depth_step_report.py",
]
def specifications():
base = {
"world_seed": 751011,
"entities": 64,
"relations": 4,
"degree": 4,
"phi": 4.0,
"id_fraction": 0.75,
"id_test_fraction": 0.2,
"evaluation_size": 512,
"width": 128,
"heads": 4,
"dropout": 0.0,
"initialization": 752011,
"stream_seed": 753011,
"model_initialization": "scaled_effective",
"batch_size": 128,
"steps": 64000,
"nodes": [0, 256, 512, 1000, 2000, 4000, 8000, 16000, 32000, 64000],
"checkpoint_nodes": [0, 8000, 32000, 64000],
"lr": 0.001,
"weight_decay": 0.01,
"warmup": 200,
"schedule": "cosine",
"min_lr_ratio": 0.1,
"clip": 1.0,
}
architectures = [(layers, 1) for layers in (1, 2, 3, 4, 6)]
architectures += [(1, repeats) for repeats in (2, 3, 4, 6)]
return [{**base, "layers": layers, "repeats": repeats} for layers, repeats in architectures]
def prepare():
from llm_memory_editability.depth_step import build_world, construct
if CONFIG.exists():
raise FileExistsError(CONFIG)
specs = specifications()
world = build_world(specs[0])
for spec in specs:
spec["frozen_data_sha256"] = world["metadata"]["dataset_sha256"]
data = {key: len(value) for key, value in world.items() if key != "metadata"}
source = {path: file_hash(path) for path in SOURCES}
initial_models = []
for spec in specs:
model = construct(spec, "cpu")
from llm_memory_editability.latent_scaling import model_digest
initial_models.append(
{
"layers": spec["layers"],
"repeats": spec["repeats"],
"parameters": sum(p.numel() for p in model.parameters()),
"initial_model_sha256": model_digest(model),
}
)
config = {
"created_utc": utc(),
"phase": "single-world development; no new-world confirmation",
"specs": specs,
"source": source,
"design_sha256": file_hash(DESIGN),
"data_sizes": data,
"metadata": world["metadata"],
"initial_models": initial_models,
"git_commit": subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
"primary": "Fixed-endpoint familiar/strict accuracies for k=2/3/4 and all trajectories",
"analysis_unit": (
"One development world and one initialization; "
"layers/steps/queries are repeated measurements"
),
"budget": {
"runs": len(specs),
"updates": sum(s["steps"] for s in specs),
"learning_nodes": sum(len(s["nodes"]) for s in specs),
},
}
write_json(CONFIG, config)
for path in SOURCES:
dest = ARTIFACTS / "source" / path
dest.parent.mkdir(parents=True, exist_ok=True)
shutil.copy2(path, dest)
shutil.copy2(CONFIG, ARTIFACTS / "frozen-config.json")
print(json.dumps({"data_sizes": data, "budget": config["budget"]}), flush=True)
def check_config():
config = json.loads(CONFIG.read_text())
for path, digest in config["source"].items():
if file_hash(path) != digest:
raise RuntimeError(f"Frozen source changed: {path}")
if file_hash(DESIGN) != config["design_sha256"]:
raise RuntimeError("Frozen design changed")
return config
def run_one(spec, source, folder, gpu):
folder.mkdir(parents=True, exist_ok=True)
if (folder / "complete.json").exists():
raise FileExistsError(folder)
write_json(folder / "input-spec.json", {"spec": spec, "source": source})
env = dict(os.environ, OMP_NUM_THREADS="1", MKL_NUM_THREADS="1")
statuses = []
for stage in ("train", "audit"):
command = [
sys.executable,
"scripts/run_depth_step.py",
stage,
"--out",
str(folder),
"--device",
f"cuda:{gpu}",
]
if stage == "train":
command.extend(["--spec-file", str(folder / "input-spec.json")])
started = time.perf_counter()
with (folder / f"{stage}-process.log").open("w") as log:
result = subprocess.run(command, stdout=log, stderr=subprocess.STDOUT, env=env)
status = {
"stage": stage,
"gpu": gpu,
"returncode": result.returncode,
"seconds": time.perf_counter() - started,
}
statuses.append(status)
write_json(folder / f"{stage}-process-status.json", status)
if result.returncode:
raise RuntimeError(f"{stage} failed: {folder}")
return {"run": folder.name, "stages": statuses}
def preflight(gpu):
config = check_config()
records = []
for layers, repeats in ((2, 1), (1, 2)):
spec = next(
s for s in config["specs"] if (s["layers"], s["repeats"]) == (layers, repeats)
).copy()
spec.update(steps=8, nodes=[0, 8], checkpoint_nodes=[0, 8])
records.append(
run_one(spec, config["source"], RESULTS / f"engineering-l{layers}-r{repeats}", gpu)
)
write_json(
ARTIFACTS / "preflight.json",
{
"scope": "8-step engineering, no performance selection",
"passed": True,
"records": records,
},
)
def execute(gpus):
from llm_memory_editability.depth_step import run_name
config = check_config()
if not json.loads((ARTIFACTS / "preflight.json").read_text())["passed"]:
raise RuntimeError("Engineering preflight required")
if not gpus or len(set(gpus)) != len(gpus):
raise ValueError("GPU slots must be nonempty and distinct")
slots = queue.Queue()
for gpu in gpus:
slots.put(gpu)
records = []
started = time.perf_counter()
write_json(
ARTIFACTS / "launch.json",
{
"started_utc": utc(),
"gpus": gpus,
"config_sha256": file_hash(CONFIG),
"runs": len(config["specs"]),
},
)
def launch(spec):
gpu = slots.get()
try:
record = run_one(spec, config["source"], RESULTS / run_name(spec), gpu)
print(json.dumps(record), flush=True)
return record
finally:
slots.put(gpu)
with ThreadPoolExecutor(max_workers=len(gpus)) as pool:
for record in pool.map(launch, config["specs"]):
records.append(record)
write_json(
ARTIFACTS / "execution.json",
{"finished_utc": utc(), "wall_seconds": time.perf_counter() - started, "records": records},
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("stage", choices=["prepare", "preflight", "execute"])
parser.add_argument("--gpus", default="0,1,2,3,4,5,6,7")
args = parser.parse_args()
gpus = [int(s) for s in args.gpus.split(",")]
if args.stage == "prepare":
prepare()
elif args.stage == "preflight":
preflight(gpus[0])
else:
execute(gpus)
if __name__ == "__main__":
main()
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