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#!/usr/bin/env python3
"""Audit and mechanically select the frozen Transformer P1 rates."""
import argparse
import hashlib
import json
import math
import os
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, ROOT)
from experiments.transformer_crossover_grid import p1_jobs, registry_sha256
TRAIN_HASH = (
"6ec305602a99ac2802745a134e1f5e33e2231b4855525b00b9aebb730ac2626f")
VALIDATION_HASH = (
"d37d30cc0c8327c270d493299c3dca54135f6d5f1c9ef60cda78076e311204b1")
EXPECTED_TRAIN_TOKENS = 1000 * 32 * 64
EXPECTED_VALIDATION_TOKENS = 1742 * 64
def sha256(path):
digest = hashlib.sha256()
with open(path, "rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def read_json(path):
with open(path, encoding="utf-8") as handle:
return json.load(handle)
def audit_completed(job, manifest, expected_source):
record = read_json(job["output"])
assert manifest["output_sha256"] == sha256(job["output"])
assert record["provenance"]["git_commit"] == expected_source["git_commit"]
assert record["provenance"]["git_tracked_dirty"] is False
args = record["args"]
expected_args = {
"method": job["method"],
"depth": 4,
"width": 128,
"heads": 4,
"mlp_ratio": 4,
"context_length": 64,
"batch_size": 32,
"eval_batch_size": 32,
"train_steps": 1000,
"lr": job["rate"],
"schedule": "constant",
"weight_decay": 0.1,
"run_seed": 0,
"model_seed": 2027,
"loader_seed": 0,
"negative_seed": 5001,
"ep_sign_seed": 5002,
"traffic_ratio": 4.0,
"eval_every": 0,
"max_val_batches": 0,
}
for key, expected in expected_args.items():
assert args[key] == expected, (
f"{job['experiment_name']}: drift in {key}")
assert record["protocol_family"] == (
"transformer_local_learning_crossover")
assert record["dataset"]["train_sha256"] == TRAIN_HASH
assert record["dataset"]["validation_sha256"] == VALIDATION_HASH
assert record["architecture"]["forward_parameter_count"] == 813568
assert record["evaluation_protocol"]["split"] == "validation"
assert record["evaluation_protocol"]["test_evaluations"] == 0
assert record["evaluation_protocol"]["test_used_for_selection"] is False
assert record["first_nonfinite_step"] is None
assert record["work"]["ordinary_training_tokens"] == (
EXPECTED_TRAIN_TOKENS)
assert record["work"]["ordinary_validation_tokens"] == (
EXPECTED_VALIDATION_TOKENS)
assert record["work"]["completed_optimizer_steps"] == 1000
assert len(record["validation"]) == 1
assert record["final"]["tokens"] == EXPECTED_VALIDATION_TOKENS
assert record["final"]["step"] == 1000
assert record["hardware"]["peak_memory_allocated_bytes"] is not None
assert record["hardware"]["peak_memory_reserved_bytes"] is not None
nll = float(record["final"]["nll"])
finite = math.isfinite(nll)
return {
"finite": finite,
"final_validation_nll": nll,
"final_validation_perplexity":
float(record["final"]["perplexity"]),
"final_validation_accuracy":
float(record["final"]["accuracy"]),
"completed_optimizer_steps": 1000,
"peak_memory_allocated_bytes":
record["hardware"]["peak_memory_allocated_bytes"],
"total_wall_seconds": float(record["total_wall_seconds"]),
"work": record["work"],
}
def audit_job(job, expected_source):
manifest_path = job["output"] + ".manifest.json"
assert os.path.isfile(manifest_path), (
f"missing manifest for {job['experiment_name']}")
manifest = read_json(manifest_path)
for key in (
"stage", "method", "architecture", "rate", "experiment_name",
"output", "timeout_seconds", "command"):
assert manifest[key] == job[key], (
f"{job['experiment_name']}: drift in {key}")
assert manifest["source"] == expected_source
hardware = manifest["hardware_lock"]
assert hardware["physical_gpu_index"] in (5, 7)
assert hardware["physical_gpu_uuid"]
status = manifest["status"]
common = {
"method": job["method"],
"rate": job["rate"],
"experiment_name": job["experiment_name"],
"manifest": os.path.relpath(manifest_path, ROOT),
"status": status,
"driver_wall_seconds": manifest["driver_wall_seconds"],
"physical_gpu_index": hardware["physical_gpu_index"],
"physical_gpu_uuid": hardware["physical_gpu_uuid"],
"output_sha256": manifest["output_sha256"],
}
if status == "completed":
assert manifest["output_exists"] is True
return {
**common,
**audit_completed(job, manifest, expected_source),
}
assert status in {
"timeout", "nonzero_exit", "missing_output"}
return {
**common,
"finite": False,
"final_validation_nll": None,
"final_validation_perplexity": None,
"final_validation_accuracy": None,
"completed_optimizer_steps": None,
"peak_memory_allocated_bytes": None,
"total_wall_seconds": None,
"work": None,
}
def choose(candidates):
return min(candidates, key=lambda row: (
not row["finite"],
(
row["final_validation_nll"]
if row["final_validation_nll"] is not None else math.inf),
row["rate"],
))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--out",
default="results/transformer_crossover/p1_selector.json")
parser.add_argument("--allow-partial", action="store_true")
args = parser.parse_args()
jobs = p1_jobs()
manifest_paths = [
job["output"] + ".manifest.json" for job in jobs
if os.path.isfile(job["output"] + ".manifest.json")]
missing = [
job["experiment_name"] for job in jobs
if not os.path.isfile(job["output"] + ".manifest.json")]
if missing and not args.allow_partial:
raise AssertionError(f"missing P1 jobs: {missing}")
sources = [read_json(path)["source"] for path in manifest_paths]
if sources:
expected_source = sources[0]
assert all(source == expected_source for source in sources)
else:
expected_source = None
records = [
audit_job(job, expected_source) for job in jobs
if os.path.isfile(job["output"] + ".manifest.json")]
selected = {}
launch = None
if not missing:
launch_path = os.path.join(
ROOT, "results", "transformer_crossover", "p1_launch.json")
assert os.path.isfile(launch_path), "missing Transformer P1 launch lock"
launch = read_json(launch_path)
assert launch["stage"] == "p1"
assert launch["source"] == expected_source
assert launch["registry_sha256"] == registry_sha256(jobs)
assert launch["num_jobs"] == len(jobs)
assert launch["allowed_physical_gpus"] == [5, 7]
for method in dict((job["method"], None) for job in jobs):
winner = choose([
record for record in records
if record["method"] == method])
selected[method] = {
"rate": winner["rate"],
"finite": winner["finite"],
"final_validation_nll":
winner["final_validation_nll"],
"experiment_name": winner["experiment_name"],
}
report = {
"gate": "pass" if not missing else "partial",
"stage": "transformer_crossover_p1",
"selection_rule":
"minimum finite final validation NLL, then lower learning rate; "
"failed/timeout records rank after finite records",
"source": expected_source,
"registry_sha256": hashlib.sha256(json.dumps(
jobs, sort_keys=True).encode("utf-8")).hexdigest(),
"num_expected_records": len(jobs),
"num_audited_records": len(records),
"missing_experiments": missing,
"test_policy": "none",
"launch_lock": launch,
"records": records,
"selected": selected,
}
os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
with open(args.out, "w", encoding="utf-8") as handle:
json.dump(report, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps({
"gate": report["gate"],
"num_audited_records": len(records),
"missing_experiments": missing,
"selected": selected,
}, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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