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"""Build and seal the private train/eval/import input archives on an HF CPU Job."""
from __future__ import annotations
import argparse
import json
import shutil
import sys
import tempfile
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
from repro_control.archives import create_deterministic_tar_gz
from repro_control.artifacts import finalize_attempt, verify_read_back
from repro_control.checkpoints import load_neutral_checkpoint
from repro_control.data import (
C5_SIZES,
build_mnist_mask_bank,
canonical_maze_identity,
generate_c5_size,
validate_smoke_fixture_registry,
write_mnist_mask_bank,
write_puzzle_split,
)
from repro_control.hashing import (
atomic_write_json,
canonical_root,
file_entry,
sha256_file,
)
from repro_control.heartbeat import heartbeat, marker
from repro_control.runtime import load_job_manifest, verify_science_spec
def _verify_handoff(handoff: Path, *, verify_checkpoints: bool) -> list[dict]:
package_manifest = json.loads((handoff / "PACKAGE-MANIFEST.json").read_text())
for row in package_manifest["entries"]:
path = handoff / row["path"]
if path.stat().st_size != row["bytes"] or sha256_file(path) != row["sha256"]:
raise SystemExit(f"handoff mismatch: {row['path']}")
imports = json.loads((handoff / "IMPORTS.json").read_text())
if len(imports) != 9:
raise SystemExit("IMPORTS.json must contain exactly nine rows")
validate_smoke_fixture_registry(handoff / "SMOKE-FIXTURES.json")
if verify_checkpoints:
for row in imports:
load_neutral_checkpoint(
handoff / "checkpoints" / row["neutral_alias"] / "checkpoint.pkl",
handoff / "configs" / f"{row['neutral_alias']}.json",
row,
)
return imports
def _tree_manifest(root: Path) -> dict:
entries = [
file_entry(path, relative_to=root)
for path in sorted(root.rglob("*"))
if path.is_file()
]
return {"format": 1, "entries": entries, "root_sha256": canonical_root(entries)}
def _copy_split(source_dataset: Path, split: str, target_dataset: Path) -> None:
target_dataset.mkdir(parents=True, exist_ok=True)
shutil.copytree(source_dataset / split, target_dataset / split)
for name in ("metadata.json", "config.json"):
source = source_dataset / name
if source.is_file():
shutil.copy2(source, target_dataset / name)
def _maze_training_identities(dataset_root: Path) -> frozenset[str]:
import numpy as np
inputs = np.load(dataset_root / "train/all__inputs.npy", mmap_mode="r")
labels = np.load(dataset_root / "train/all__labels.npy", mmap_mode="r")
return frozenset(
canonical_maze_identity(input_row, label_row)
for input_row, label_row in zip(inputs, labels, strict=True)
)
def _build_registered_inputs(
handoff: Path,
workspace: Path,
*,
sudoku_revision: str,
) -> dict:
from sheaf_admm.data.build_maze import MazeConfig
from sheaf_admm.data.build_maze import build as build_maze
from sheaf_admm.data.build_mnist import MNISTConfig
from sheaf_admm.data.build_mnist import build as build_mnist
from sheaf_admm.data.build_sudoku import SudokuConfig
from sheaf_admm.data.build_sudoku import build as build_sudoku
build_root = workspace / "build"
maze_root = build_root / "maze_std3_19px_10k"
mnist_root = build_root / "mnist"
sudoku_root = build_root / "sudoku_easy"
marker("HEARTBEAT", "CPU_IMPORT:build-maze")
build_maze(
MazeConfig(
height=19,
width=19,
train_size=10_000,
test_size=1_000,
min_path_length=18,
train_augment=True,
test_augment=False,
seed=0,
output_dir=maze_root,
),
ood_sizes=False,
)
marker("HEARTBEAT", "CPU_IMPORT:build-mnist")
build_mnist(
MNISTConfig(
padding=0,
seed=0,
normalize=True,
gen_robustness=False,
output_dir=mnist_root,
)
)
marker("HEARTBEAT", "CPU_IMPORT:build-sudoku")
build_sudoku(
SudokuConfig(
dataset_revision=sudoku_revision,
seed=0,
train_size=50_000,
test_size=2_000,
difficulty_max=2.0,
train_augment=True,
test_augment=False,
output_dir=sudoku_root,
)
)
train_root = workspace / "train"
eval_root = workspace / "eval"
imports_root = workspace / "imports"
_copy_split(maze_root, "train", train_root / "maze_std3_19px_10k")
_copy_split(mnist_root, "train", train_root / "mnist")
_copy_split(sudoku_root, "train", train_root / "sudoku_easy")
_copy_split(maze_root, "test", eval_root / "maze_std3_19px_10k")
_copy_split(mnist_root, "test", eval_root / "mnist")
_copy_split(sudoku_root, "test_hard", eval_root / "sudoku_easy")
test_count = 10_000
mask_bank = build_mnist_mask_bank(
f"ml-datasets-0.2.1:{sha256_file(mnist_root / 'test/images.npy')}",
range(test_count),
)
write_mnist_mask_bank(eval_root / "mnist/drop30-mask-bank.json", mask_bank)
marker("HEARTBEAT", "CPU_IMPORT:build-c5")
training_identities = _maze_training_identities(maze_root)
c5_receipts = {}
for size in C5_SIZES:
arrays, c5_manifest = generate_c5_size(
size,
training_identities=training_identities,
examples=1000,
)
c5_root = eval_root / "c5" / f"{size}x{size}"
write_puzzle_split(
c5_root,
"test",
arrays["inputs"],
arrays["labels"],
height=size,
width=size,
)
atomic_write_json(c5_root / "generation-manifest.json", c5_manifest)
c5_receipts[str(size)] = c5_manifest
shutil.copytree(handoff / "checkpoints", imports_root / "checkpoints")
shutil.copytree(handoff / "configs", imports_root / "configs")
shutil.copy2(handoff / "IMPORTS.json", imports_root / "IMPORTS.json")
shutil.copy2(handoff / "RIGHTS.json", imports_root / "RIGHTS.json")
manifests = {
"train": _tree_manifest(train_root),
"eval": _tree_manifest(eval_root),
"imports": _tree_manifest(imports_root),
}
for name, value in manifests.items():
atomic_write_json(workspace / f"{name}-tree-manifest.json", value)
return {
"tree_manifests": manifests,
"mnist_mask_bank_sha256": mask_bank["bank_sha256"],
"maze_training_identity_count": len(training_identities),
"c5": c5_receipts,
}
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--science-spec", type=Path, required=True)
parser.add_argument("--job-manifest", type=Path, required=True)
parser.add_argument("--handoff", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--sudoku-revision", required=True)
parser.add_argument("--verify-checkpoints", action="store_true")
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args()
spec = verify_science_spec(args.science_spec)
manifest = load_job_manifest(args.job_manifest)
if manifest["job_class"] != "CPU_IMPORT":
raise SystemExit("CPU import entrypoint requires a CPU_IMPORT manifest")
imports = _verify_handoff(args.handoff, verify_checkpoints=args.verify_checkpoints)
if args.dry_run:
print(
json.dumps(
{
"contract_verified": True,
"verified_import_rows": len(imports),
"outcomes": {},
}
)
)
return 0
if args.sudoku_revision != spec["data_rules"]["sudoku"]["revision"]:
raise SystemExit("Sudoku revision differs from the frozen science spec")
args.output_dir.mkdir(parents=True, exist_ok=False)
marker("CPU_READY", "CPU_IMPORT")
with (
heartbeat("CPU_IMPORT"),
tempfile.TemporaryDirectory(prefix="sheaf-import-") as temporary,
):
workspace = Path(temporary)
build_receipt = _build_registered_inputs(
args.handoff,
workspace,
sudoku_revision=args.sudoku_revision,
)
archive_hashes = {}
for name in ("train", "eval", "imports"):
archive = args.output_dir / f"{name}-data.tar.gz"
archive_hashes[name] = create_deterministic_tar_gz(workspace / name, archive)
shutil.copy2(
workspace / f"{name}-tree-manifest.json",
args.output_dir / f"{name}-tree-manifest.json",
)
receipt = {
"format": 1,
"logical_id": manifest["logical_id"],
"attempt_id": manifest["attempt_id"],
"verified_import_rows": len(imports),
"sudoku_revision": args.sudoku_revision,
"archives": archive_hashes,
"build": build_receipt,
"outcomes": {},
}
atomic_write_json(args.output_dir / "import-receipt.json", receipt)
finalize_attempt(
args.output_dir,
logical_id=manifest["logical_id"],
attempt_id=manifest["attempt_id"],
expected_outputs=manifest["expected_outputs"],
)
verify_read_back(
args.output_dir,
logical_id=manifest["logical_id"],
attempt_id=manifest["attempt_id"],
)
marker("DONE", f"{manifest['logical_id']} {manifest['attempt_id']}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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