InvokeAI/tests/test_model_hash.py
psychedelicious eb6e6548ed feat(mm): faster hashing for spinning disk HDDs
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31

- Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted.
- Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality.
- Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue
- Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3`
- Update all calls of the probe to use the app's configured hashing algorithm
- Update an external script that probes models
- Update tests
- Move ModelHash into its own module to avoid circuclar import issues
2024-03-14 15:54:42 +11:00

115 lines
3.7 KiB
Python

# pyright:reportPrivateUsage=false
from pathlib import Path
from typing import Iterable
import pytest
from blake3 import blake3
from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, MODEL_FILE_EXTENSIONS, ModelHash
test_cases: list[tuple[HASHING_ALGORITHMS, str]] = [
("md5", "a0cd925fc063f98dbf029eee315060c3"),
("sha1", "9e362940e5603fdc60566ea100a288ba2fe48b8c"),
("sha256", "6dbdb6a147ad4d808455652bf5a10120161678395f6bfbd21eb6fe4e731aceeb"),
(
"sha512",
"c4a10476b21e00042f638ad5755c561d91f2bb599d3504d25409495e1c7eda94543332a1a90fbb4efdaf9ee462c33e0336b5eae4acfb1fa0b186af452dd67dc6",
),
("blake3", "ce3f0c5f3c05d119f4a5dcaf209b50d3149046a0d3a9adee9fed4c83cad6b4d0"),
]
@pytest.mark.parametrize("algorithm,expected_hash", test_cases)
def test_model_hash_hashes_file(tmp_path: Path, algorithm: HASHING_ALGORITHMS, expected_hash: str):
file = Path(tmp_path / "test")
file.write_text("model data")
md5 = ModelHash(algorithm).hash(file)
assert md5 == expected_hash
@pytest.mark.parametrize("algorithm", ["md5", "sha1", "sha256", "sha512", "blake3"])
def test_model_hash_hashes_dir(tmp_path: Path, algorithm: HASHING_ALGORITHMS):
model_hash = ModelHash(algorithm)
files = [Path(tmp_path, f"{i}.bin") for i in range(5)]
for f in files:
f.write_text("data")
md5 = model_hash.hash(tmp_path)
# Manual implementation of composite hash - always uses BLAKE3
composite_hasher = blake3()
for f in files:
h = model_hash.hash(f)
composite_hasher.update(h.encode("utf-8"))
assert md5 == composite_hasher.hexdigest()
def test_model_hash_blake3_matches_blake3_single(tmp_path: Path):
model_hash = ModelHash("blake3")
model_hash_simple = ModelHash("blake3_single")
file = tmp_path / "test.bin"
file.write_text("model data")
assert model_hash.hash(file) == model_hash_simple.hash(file)
def test_model_hash_random_algorithm(tmp_path: Path):
model_hash = ModelHash("random")
file = tmp_path / "test.bin"
file.write_text("model data")
assert model_hash.hash(file) != model_hash.hash(file)
def test_model_hash_raises_error_on_invalid_algorithm():
with pytest.raises(ValueError, match="Algorithm invalid_algorithm not available"):
ModelHash("invalid_algorithm") # pyright: ignore [reportArgumentType]
def paths_to_str_set(paths: Iterable[Path]) -> set[str]:
return {str(p) for p in paths}
def test_model_hash_filters_out_non_model_files(tmp_path: Path):
model_files = {Path(tmp_path, f"{i}{ext}") for i, ext in enumerate(MODEL_FILE_EXTENSIONS)}
for i, f in enumerate(model_files):
f.write_text(f"data{i}")
assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
model_files
)
# Add file that should be ignored - hash should not change
file = tmp_path / "test.icecream"
file.write_text("data")
assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
model_files
)
# Add file that should not be ignored - hash should change
file = tmp_path / "test.bin"
file.write_text("more data")
model_files.add(file)
assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
model_files
)
def test_model_hash_uses_custom_filter(tmp_path: Path):
model_files = {Path(tmp_path, f"file{ext}") for ext in [".pickme", ".ignoreme"]}
for i, f in enumerate(model_files):
f.write_text(f"data{i}")
def file_filter(file_path: str) -> bool:
return file_path.endswith(".pickme")
assert {p.name for p in ModelHash._get_file_paths(tmp_path, file_filter)} == {"file.pickme"}