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import copy
from pathlib import Path
import subprocess
import sys
import pytest
import yaml
from dlb.data import (
build_data_manifest,
build_processing_contract,
disk_preflight_evidence,
processing_contract_sha256,
publication_is_reusable,
publish_staged_output,
recover_incomplete_publication,
validate_download_manifest,
validate_manifest_contract,
verify_processed_dataset,
)
from dlb.io import atomic_json_write
class TinySplit:
def __init__(self, rows):
self._rows = rows
def __len__(self):
return len(self._rows)
def __getitem__(self, index):
return self._rows[index]
class TinyTokenizer:
def decode(self, token_ids, skip_special_tokens=False):
assert skip_special_tokens is False
return " ".join(str(token_id) for token_id in token_ids)
def _test_contract(
*,
dataset,
dataset_id,
source_revision,
tokenizer_id,
tokenizer_revision,
sequence_length,
split_expression,
document_counts,
vocab_size,
materialization_revision=None,
):
return {
"schema_version": 1,
"dataset": dataset,
"dataset_id": dataset_id,
"source_revision": source_revision,
"source_materialization_revision": materialization_revision,
"tokenizer_id": tokenizer_id,
"tokenizer_revision": tokenizer_revision,
"sequence_length": sequence_length,
"split_expression": split_expression,
"expected_document_counts": document_counts,
"vocab_size": vocab_size,
"packing": {
"algorithm": "document_eos_continuous_bos_eos",
"version": 1,
},
}
def test_manifest_records_reproducibility_fields_and_file_digests(tmp_path):
"""Catch manifests that cannot identify and integrity-check their data cache."""
output_dir = tmp_path / "processed"
output_dir.mkdir()
(output_dir / "train.arrow").write_bytes(b"train rows")
(output_dir / "validation.arrow").write_bytes(b"validation rows")
contract = _test_contract(
dataset="owt",
dataset_id="openwebtext",
source_revision="a" * 40,
tokenizer_id="gpt2",
tokenizer_revision="b" * 40,
sequence_length=1024,
split_expression={
"train": "train[:-100000]",
"validation": "train[-100000:]",
},
document_counts={"train": 7_913_769, "validation": 100_000},
vocab_size=50_257,
)
manifest = build_data_manifest(
dataset="owt",
dataset_id="openwebtext",
source_revision="a" * 40,
tokenizer_id="gpt2",
tokenizer_revision="b" * 40,
sequence_length=1024,
split_expression={
"train": "train[:-100000]",
"validation": "train[-100000:]",
},
document_counts={"train": 7_913_769, "validation": 100_000},
packed_sequence_counts={"train": 2, "validation": 1},
vocab_size=50_257,
min_token_id=1,
max_token_id=50_256,
output_dir=output_dir,
root=tmp_path,
processing_contract=contract,
)
assert manifest["source_revision"] == "a" * 40
assert manifest["tokenizer_revision"] == "b" * 40
assert manifest["split_expression"]["validation"] == "train[-100000:]"
assert manifest["document_counts"] == {"train": 7_913_769, "validation": 100_000}
assert manifest["packed_sequence_counts"] == {"train": 2, "validation": 1}
assert manifest["vocabulary"] == {
"size": 50_257,
"min_token_id": 1,
"max_token_id": 50_256,
}
assert manifest["created_at"].endswith("Z")
assert manifest["processed_path"] == "processed"
assert [entry["path"] for entry in manifest["files"]] == [
"train.arrow",
"validation.arrow",
]
assert all(len(entry["sha256"]) == 64 for entry in manifest["files"])
assert manifest["verified"] is False
assert manifest["processing_contract"] == contract
assert manifest["processing_contract_sha256"] == processing_contract_sha256(contract)
def test_verifier_checks_lengths_bounds_counts_digests_and_decoding(tmp_path):
"""Catch a verifier that trusts manifest claims without reading processed rows."""
output_dir = tmp_path / "processed"
output_dir.mkdir()
(output_dir / "state.json").write_text("complete\n", encoding="utf-8")
dataset = {
"train": TinySplit([{"input_ids": [101, 10, 11, 102]}]),
"validation": TinySplit([{"input_ids": [101, 12, 13, 102]}]),
}
contract = _test_contract(
dataset="lm1b",
dataset_id="lm1b",
source_revision="c" * 40,
tokenizer_id="bert-base-uncased",
tokenizer_revision="d" * 40,
sequence_length=4,
split_expression={"train": "train", "validation": "test"},
document_counts={"train": 2, "validation": 1},
vocab_size=30_522,
)
manifest = build_data_manifest(
dataset="lm1b",
dataset_id="lm1b",
source_revision="c" * 40,
tokenizer_id="bert-base-uncased",
tokenizer_revision="d" * 40,
sequence_length=4,
split_expression={"train": "train", "validation": "test"},
document_counts={"train": 2, "validation": 1},
packed_sequence_counts={"train": 1, "validation": 1},
vocab_size=30_522,
min_token_id=10,
max_token_id=102,
output_dir=output_dir,
root=tmp_path,
processing_contract=contract,
)
result = verify_processed_dataset(
dataset=dataset,
manifest=manifest,
output_dir=output_dir,
tokenizer=TinyTokenizer(),
expected_contract=contract,
)
assert result["verified"] is True
assert result["verification"]["checked_sequences"] == 2
assert result["verification"]["decoded_samples"] == 2
def test_verifier_rejects_out_of_range_tokens(tmp_path):
"""Catch processed IDs that exceed the pinned tokenizer vocabulary."""
output_dir = tmp_path / "processed"
output_dir.mkdir()
(output_dir / "state.json").write_text("complete\n", encoding="utf-8")
contract = _test_contract(
dataset="tiny",
dataset_id="tiny",
source_revision="e" * 40,
tokenizer_id="tiny",
tokenizer_revision="f" * 40,
sequence_length=4,
split_expression={"train": "train", "validation": "validation"},
document_counts={"train": 1, "validation": 1},
vocab_size=20,
)
manifest = build_data_manifest(
dataset="tiny",
dataset_id="tiny",
source_revision="e" * 40,
tokenizer_id="tiny",
tokenizer_revision="f" * 40,
sequence_length=4,
split_expression={"train": "train", "validation": "validation"},
document_counts={"train": 1, "validation": 1},
packed_sequence_counts={"train": 1, "validation": 1},
vocab_size=20,
min_token_id=1,
max_token_id=19,
output_dir=output_dir,
root=tmp_path,
processing_contract=contract,
)
dataset = {
"train": TinySplit([{"input_ids": [1, 2, 20, 3]}]),
"validation": TinySplit([{"input_ids": [1, 2, 3, 4]}]),
}
with pytest.raises(ValueError, match="outside vocabulary"):
verify_processed_dataset(
dataset=dataset,
manifest=manifest,
output_dir=output_dir,
tokenizer=TinyTokenizer(),
expected_contract=contract,
)
def test_verified_manifest_is_atomically_persistable(tmp_path):
"""Catch verification results that cannot be stored as valid JSON metadata."""
path = tmp_path / "manifest.json"
value = {"dataset": "tiny", "verified": True, "verification": {"ok": True}}
atomic_json_write(path, value)
assert json.loads(path.read_text(encoding="utf-8")) == value
def test_data_configuration_pins_exact_revisions_and_semantics():
"""Catch moving Hub references or accidental changes to split/tokenizer semantics."""
root = Path(__file__).parents[1]
configuration = yaml.safe_load((root / "artifacts" / "data.yaml").read_text())
assert configuration["datasets"]["lm1b"] == {
"repo_id": "billion-word-benchmark/lm1b",
"revision": "35161838ea9e05371a25a8db037f94fcae4c2064",
"parquet_revision": "8d52bfd3cc2819fc1166dbb8144c328e2690de3e",
"config": "plain_text",
"splits": {"train": "train", "validation": "test"},
"sequence_length": 128,
"tokenizer": "bert-base-uncased",
}
assert configuration["datasets"]["owt"] == {
"repo_id": "Skylion007/openwebtext",
"revision": "79d93d786212f7344586290adb811d4ae6a1762c",
"config": "plain_text",
"documents": 8_013_769,
"splits": {
"train": "train[:-100000]",
"validation": "train[-100000:]",
},
"sequence_length": 1024,
"tokenizer": "gpt2",
}
assert configuration["models"] == {
"bert-base-uncased": "86b5e0934494bd15c9632b12f734a8a67f723594",
"gpt2": "607a30d783dfa663caf39e06633721c8d4cfcd7e",
"gpt2-large": "32b71b12589c2f8d625668d2335a01cac3249519",
}
assert configuration["lm1b_archive"] == {
"url": "http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz",
"size_bytes": 1_792_209_805,
"sha256": "01ba60381110baf7f189dfd2b8374de371e8c9a340835793f190bdae9e90a34e",
"train_documents": 30_301_028,
"test_documents": 306_688,
}
def test_fetch_dry_run_lists_every_target_without_writing_data(tmp_path):
"""Catch dry-run mode that omits a required snapshot or creates cache files."""
root = Path(__file__).parents[1]
data_path = tmp_path / "data"
result = subprocess.run(
[
sys.executable,
str(root / "scripts" / "fetch_data.py"),
"--root",
str(tmp_path),
"--config",
str(root / "artifacts" / "data.yaml"),
"--dry-run",
],
check=False,
capture_output=True,
text=True,
)
assert result.returncode == 0, result.stderr
assert result.stdout.splitlines() == [
"DATASET lm1b billion-word-benchmark/lm1b@35161838ea9e05371a25a8db037f94fcae4c2064",
"DATASET owt Skylion007/openwebtext@79d93d786212f7344586290adb811d4ae6a1762c",
"MODEL bert-base-uncased@86b5e0934494bd15c9632b12f734a8a67f723594",
"MODEL gpt2@607a30d783dfa663caf39e06633721c8d4cfcd7e",
"MODEL gpt2-large@32b71b12589c2f8d625668d2335a01cac3249519",
]
assert not data_path.exists()
def test_owt_disk_preflight_requires_55_gib_unless_explicitly_overridden():
"""Catch starting the large OWT fetch on unsafe disk without explicit consent."""
required = 55 * 1024**3
with pytest.raises(RuntimeError, match="at least 55 GiB"):
disk_preflight_evidence(required - 1, allow_low_disk=False)
def test_owt_disk_preflight_records_requested_and_actual_override_separately():
"""Catch metadata claiming a bypass when the flag was unnecessary."""
required = 55 * 1024**3
sufficient = disk_preflight_evidence(required, allow_low_disk=True)
low = disk_preflight_evidence(required - 1, allow_low_disk=True)
assert sufficient == {
"observed_free_bytes": required,
"required_free_bytes": required,
"below_threshold": False,
"override_requested": True,
"override_used": False,
}
assert low == {
"observed_free_bytes": required - 1,
"required_free_bytes": required,
"below_threshold": True,
"override_requested": True,
"override_used": True,
}
def _configuration():
root = Path(__file__).parents[1]
return yaml.safe_load((root / "artifacts" / "data.yaml").read_text())
def _valid_downloads(configuration):
lm1b = configuration["datasets"]["lm1b"]
owt = configuration["datasets"]["owt"]
return {
"schema_version": 1,
"datasets": {
"lm1b": {
"repo_id": lm1b["repo_id"],
"source_revision": lm1b["revision"],
"materialization_revision": lm1b["parquet_revision"],
"split_rows": {
"train": configuration["lm1b_archive"]["train_documents"],
"test": configuration["lm1b_archive"]["test_documents"],
},
"dataset_snapshot": (
"data/raw/huggingface/hub/datasets--lm1b/snapshots/"
+ lm1b["parquet_revision"]
),
"source_metadata_snapshot": (
"data/raw/huggingface/hub/datasets--lm1b/snapshots/"
+ lm1b["revision"]
),
"cache_files": {"train": ["train.arrow"], "test": ["test.arrow"]},
},
"owt": {
"repo_id": owt["repo_id"],
"source_revision": owt["revision"],
"materialization_revision": None,
"split_rows": {"train": owt["documents"]},
"dataset_snapshot": None,
"cache_files": {"train": ["owt.arrow"]},
},
},
"models": {
name: {
"repo_id": name,
"revision": revision,
"snapshot_path": (
f"data/raw/huggingface/hub/models--{name}/snapshots/{revision}"
),
}
for name, revision in configuration["models"].items()
},
}
@pytest.mark.parametrize(
("field", "bad_value"),
[
("repo_id", "wrong/lm1b"),
("source_revision", "0" * 40),
("materialization_revision", "1" * 40),
("split_rows", {"train": 1, "test": 1}),
],
)
def test_preprocessing_rejects_stale_lm1b_download_provenance(field, bad_value):
"""Catch preprocessing data that does not match its claimed immutable source."""
configuration = _configuration()
downloads = _valid_downloads(configuration)
downloads["datasets"]["lm1b"][field] = bad_value
with pytest.raises(ValueError, match=field):
validate_download_manifest(configuration, downloads, "lm1b")
def test_preprocessing_validates_owt_rows_and_tokenizer_snapshot_revision():
"""Catch truncated OWT caches or a tokenizer snapshot from another revision."""
configuration = _configuration()
downloads = _valid_downloads(configuration)
downloads["datasets"]["owt"]["split_rows"] = {"train": 8_000_000}
with pytest.raises(ValueError, match="split_rows"):
validate_download_manifest(configuration, downloads, "owt")
downloads = _valid_downloads(configuration)
downloads["models"]["gpt2"]["revision"] = "2" * 40
with pytest.raises(ValueError, match="tokenizer revision"):
validate_download_manifest(configuration, downloads, "owt")
def test_preprocessing_rejects_snapshot_paths_for_other_immutable_revisions():
"""Catch correct labels pointing preprocessing at stale snapshot content."""
configuration = _configuration()
downloads = _valid_downloads(configuration)
downloads["datasets"]["lm1b"]["dataset_snapshot"] = (
"data/raw/huggingface/hub/datasets--lm1b/snapshots/" + "4" * 40
)
with pytest.raises(ValueError, match="dataset_snapshot"):
validate_download_manifest(configuration, downloads, "lm1b")
@pytest.mark.parametrize(
("path", "bad_value"),
[
(("sequence_length",), 999),
(("split_expression", "validation"), "train[:100000]"),
(("tokenizer_id",), "wrong-tokenizer"),
(("source_materialization_revision",), "3" * 40),
(("packing", "version"), 999),
],
)
def test_manifest_contract_rejects_processing_semantic_mutations(path, bad_value):
"""Catch reuse or verification under stale processing semantics."""
expected = build_processing_contract(_configuration(), "lm1b")
manifest_contract = copy.deepcopy(expected)
target = manifest_contract
for key in path[:-1]:
target = target[key]
target[path[-1]] = bad_value
manifest = {
"processing_contract": manifest_contract,
"processing_contract_sha256": processing_contract_sha256(manifest_contract),
}
with pytest.raises(ValueError, match="processing contract"):
validate_manifest_contract(manifest, expected)
def test_restart_recovers_final_output_missing_manifest_and_publishes_cleanly(tmp_path):
"""Catch a crash-after-rename state requiring manual deletion before rerun."""
configuration = _configuration()
contract = build_processing_contract(configuration, "lm1b")
final = tmp_path / "lm1b-bert-128"
final.mkdir()
(final / "interrupted.arrow").write_bytes(b"incomplete")
manifest_path = tmp_path / "lm1b.json"
assert recover_incomplete_publication(final, manifest_path, contract) is False
assert not final.exists()
staged = tmp_path / "lm1b-bert-128.staging"
staged.mkdir()
(staged / "train.arrow").write_bytes(b"complete")
manifest = build_data_manifest(
dataset="lm1b",
dataset_id=contract["dataset_id"],
source_revision=contract["source_revision"],
tokenizer_id=contract["tokenizer_id"],
tokenizer_revision=contract["tokenizer_revision"],
sequence_length=contract["sequence_length"],
split_expression=contract["split_expression"],
document_counts=contract["expected_document_counts"],
packed_sequence_counts={"train": 1, "validation": 1},
vocab_size=contract["vocab_size"],
min_token_id=1,
max_token_id=102,
output_dir=staged,
root=tmp_path,
processing_contract=contract,
published_output_dir=final,
)
publish_staged_output(staged, final, manifest_path, manifest)
assert publication_is_reusable(final, manifest_path, contract)
assert json.loads(manifest_path.read_text())["processed_path"] == "lm1b-bert-128"
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