498 lines
16 KiB
Python
498 lines
16 KiB
Python
"""
|
|
Test the hashing module.
|
|
"""
|
|
|
|
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
|
|
# Copyright (c) 2009 Gael Varoquaux
|
|
# License: BSD Style, 3 clauses.
|
|
|
|
import time
|
|
import hashlib
|
|
import sys
|
|
import os
|
|
import gc
|
|
import io
|
|
import collections
|
|
import itertools
|
|
import pickle
|
|
import random
|
|
from concurrent.futures import ProcessPoolExecutor
|
|
from decimal import Decimal
|
|
import pytest
|
|
|
|
from joblib.hashing import hash
|
|
from joblib.func_inspect import filter_args
|
|
from joblib.memory import Memory
|
|
from joblib.testing import raises, skipif, fixture, parametrize
|
|
from joblib.test.common import np, with_numpy
|
|
|
|
|
|
def unicode(s):
|
|
return s
|
|
|
|
|
|
###############################################################################
|
|
# Helper functions for the tests
|
|
def time_func(func, *args):
|
|
""" Time function func on *args.
|
|
"""
|
|
times = list()
|
|
for _ in range(3):
|
|
t1 = time.time()
|
|
func(*args)
|
|
times.append(time.time() - t1)
|
|
return min(times)
|
|
|
|
|
|
def relative_time(func1, func2, *args):
|
|
""" Return the relative time between func1 and func2 applied on
|
|
*args.
|
|
"""
|
|
time_func1 = time_func(func1, *args)
|
|
time_func2 = time_func(func2, *args)
|
|
relative_diff = 0.5 * (abs(time_func1 - time_func2)
|
|
/ (time_func1 + time_func2))
|
|
return relative_diff
|
|
|
|
|
|
class Klass(object):
|
|
|
|
def f(self, x):
|
|
return x
|
|
|
|
|
|
class KlassWithCachedMethod(object):
|
|
|
|
def __init__(self, cachedir):
|
|
mem = Memory(location=cachedir)
|
|
self.f = mem.cache(self.f)
|
|
|
|
def f(self, x):
|
|
return x
|
|
|
|
|
|
###############################################################################
|
|
# Tests
|
|
|
|
input_list = [1, 2, 1., 2., 1 + 1j, 2. + 1j,
|
|
'a', 'b',
|
|
(1,), (1, 1,), [1, ], [1, 1, ],
|
|
{1: 1}, {1: 2}, {2: 1},
|
|
None,
|
|
gc.collect,
|
|
[1, ].append,
|
|
# Next 2 sets have unorderable elements in python 3.
|
|
set(('a', 1)),
|
|
set(('a', 1, ('a', 1))),
|
|
# Next 2 dicts have unorderable type of keys in python 3.
|
|
{'a': 1, 1: 2},
|
|
{'a': 1, 1: 2, 'd': {'a': 1}}]
|
|
|
|
|
|
@parametrize('obj1', input_list)
|
|
@parametrize('obj2', input_list)
|
|
def test_trivial_hash(obj1, obj2):
|
|
"""Smoke test hash on various types."""
|
|
# Check that 2 objects have the same hash only if they are the same.
|
|
are_hashes_equal = hash(obj1) == hash(obj2)
|
|
are_objs_identical = obj1 is obj2
|
|
assert are_hashes_equal == are_objs_identical
|
|
|
|
|
|
def test_hash_methods():
|
|
# Check that hashing instance methods works
|
|
a = io.StringIO(unicode('a'))
|
|
assert hash(a.flush) == hash(a.flush)
|
|
a1 = collections.deque(range(10))
|
|
a2 = collections.deque(range(9))
|
|
assert hash(a1.extend) != hash(a2.extend)
|
|
|
|
|
|
@fixture(scope='function')
|
|
@with_numpy
|
|
def three_np_arrays():
|
|
rnd = np.random.RandomState(0)
|
|
arr1 = rnd.random_sample((10, 10))
|
|
arr2 = arr1.copy()
|
|
arr3 = arr2.copy()
|
|
arr3[0] += 1
|
|
return arr1, arr2, arr3
|
|
|
|
|
|
def test_hash_numpy_arrays(three_np_arrays):
|
|
arr1, arr2, arr3 = three_np_arrays
|
|
|
|
for obj1, obj2 in itertools.product(three_np_arrays, repeat=2):
|
|
are_hashes_equal = hash(obj1) == hash(obj2)
|
|
are_arrays_equal = np.all(obj1 == obj2)
|
|
assert are_hashes_equal == are_arrays_equal
|
|
|
|
assert hash(arr1) != hash(arr1.T)
|
|
|
|
|
|
def test_hash_numpy_dict_of_arrays(three_np_arrays):
|
|
arr1, arr2, arr3 = three_np_arrays
|
|
|
|
d1 = {1: arr1, 2: arr2}
|
|
d2 = {1: arr2, 2: arr1}
|
|
d3 = {1: arr2, 2: arr3}
|
|
|
|
assert hash(d1) == hash(d2)
|
|
assert hash(d1) != hash(d3)
|
|
|
|
|
|
@with_numpy
|
|
@parametrize('dtype', ['datetime64[s]', 'timedelta64[D]'])
|
|
def test_numpy_datetime_array(dtype):
|
|
# memoryview is not supported for some dtypes e.g. datetime64
|
|
# see https://github.com/joblib/joblib/issues/188 for more details
|
|
a_hash = hash(np.arange(10))
|
|
array = np.arange(0, 10, dtype=dtype)
|
|
assert hash(array) != a_hash
|
|
|
|
|
|
@with_numpy
|
|
def test_hash_numpy_noncontiguous():
|
|
a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
|
|
order='F')[:, :1, :]
|
|
b = np.ascontiguousarray(a)
|
|
assert hash(a) != hash(b)
|
|
|
|
c = np.asfortranarray(a)
|
|
assert hash(a) != hash(c)
|
|
|
|
|
|
@with_numpy
|
|
@parametrize('coerce_mmap', [True, False])
|
|
def test_hash_memmap(tmpdir, coerce_mmap):
|
|
"""Check that memmap and arrays hash identically if coerce_mmap is True."""
|
|
filename = tmpdir.join('memmap_temp').strpath
|
|
try:
|
|
m = np.memmap(filename, shape=(10, 10), mode='w+')
|
|
a = np.asarray(m)
|
|
are_hashes_equal = (hash(a, coerce_mmap=coerce_mmap) ==
|
|
hash(m, coerce_mmap=coerce_mmap))
|
|
assert are_hashes_equal == coerce_mmap
|
|
finally:
|
|
if 'm' in locals():
|
|
del m
|
|
# Force a garbage-collection cycle, to be certain that the
|
|
# object is delete, and we don't run in a problem under
|
|
# Windows with a file handle still open.
|
|
gc.collect()
|
|
|
|
|
|
@with_numpy
|
|
@skipif(sys.platform == 'win32', reason='This test is not stable under windows'
|
|
' for some reason')
|
|
def test_hash_numpy_performance():
|
|
""" Check the performance of hashing numpy arrays:
|
|
|
|
In [22]: a = np.random.random(1000000)
|
|
|
|
In [23]: %timeit hashlib.md5(a).hexdigest()
|
|
100 loops, best of 3: 20.7 ms per loop
|
|
|
|
In [24]: %timeit hashlib.md5(pickle.dumps(a, protocol=2)).hexdigest()
|
|
1 loops, best of 3: 73.1 ms per loop
|
|
|
|
In [25]: %timeit hashlib.md5(cPickle.dumps(a, protocol=2)).hexdigest()
|
|
10 loops, best of 3: 53.9 ms per loop
|
|
|
|
In [26]: %timeit hash(a)
|
|
100 loops, best of 3: 20.8 ms per loop
|
|
"""
|
|
rnd = np.random.RandomState(0)
|
|
a = rnd.random_sample(1000000)
|
|
|
|
def md5_hash(x):
|
|
return hashlib.md5(memoryview(x)).hexdigest()
|
|
|
|
relative_diff = relative_time(md5_hash, hash, a)
|
|
assert relative_diff < 0.3
|
|
|
|
# Check that hashing an tuple of 3 arrays takes approximately
|
|
# 3 times as much as hashing one array
|
|
time_hashlib = 3 * time_func(md5_hash, a)
|
|
time_hash = time_func(hash, (a, a, a))
|
|
relative_diff = 0.5 * (abs(time_hash - time_hashlib)
|
|
/ (time_hash + time_hashlib))
|
|
assert relative_diff < 0.3
|
|
|
|
|
|
def test_bound_methods_hash():
|
|
""" Make sure that calling the same method on two different instances
|
|
of the same class does resolve to the same hashes.
|
|
"""
|
|
a = Klass()
|
|
b = Klass()
|
|
assert (hash(filter_args(a.f, [], (1, ))) ==
|
|
hash(filter_args(b.f, [], (1, ))))
|
|
|
|
|
|
def test_bound_cached_methods_hash(tmpdir):
|
|
""" Make sure that calling the same _cached_ method on two different
|
|
instances of the same class does resolve to the same hashes.
|
|
"""
|
|
a = KlassWithCachedMethod(tmpdir.strpath)
|
|
b = KlassWithCachedMethod(tmpdir.strpath)
|
|
assert (hash(filter_args(a.f.func, [], (1, ))) ==
|
|
hash(filter_args(b.f.func, [], (1, ))))
|
|
|
|
|
|
@with_numpy
|
|
def test_hash_object_dtype():
|
|
""" Make sure that ndarrays with dtype `object' hash correctly."""
|
|
|
|
a = np.array([np.arange(i) for i in range(6)], dtype=object)
|
|
b = np.array([np.arange(i) for i in range(6)], dtype=object)
|
|
|
|
assert hash(a) == hash(b)
|
|
|
|
|
|
@with_numpy
|
|
def test_numpy_scalar():
|
|
# Numpy scalars are built from compiled functions, and lead to
|
|
# strange pickling paths explored, that can give hash collisions
|
|
a = np.float64(2.0)
|
|
b = np.float64(3.0)
|
|
assert hash(a) != hash(b)
|
|
|
|
|
|
def test_dict_hash(tmpdir):
|
|
# Check that dictionaries hash consistently, even though the ordering
|
|
# of the keys is not guaranteed
|
|
k = KlassWithCachedMethod(tmpdir.strpath)
|
|
|
|
d = {'#s12069__c_maps.nii.gz': [33],
|
|
'#s12158__c_maps.nii.gz': [33],
|
|
'#s12258__c_maps.nii.gz': [33],
|
|
'#s12277__c_maps.nii.gz': [33],
|
|
'#s12300__c_maps.nii.gz': [33],
|
|
'#s12401__c_maps.nii.gz': [33],
|
|
'#s12430__c_maps.nii.gz': [33],
|
|
'#s13817__c_maps.nii.gz': [33],
|
|
'#s13903__c_maps.nii.gz': [33],
|
|
'#s13916__c_maps.nii.gz': [33],
|
|
'#s13981__c_maps.nii.gz': [33],
|
|
'#s13982__c_maps.nii.gz': [33],
|
|
'#s13983__c_maps.nii.gz': [33]}
|
|
|
|
a = k.f(d)
|
|
b = k.f(a)
|
|
|
|
assert hash(a) == hash(b)
|
|
|
|
|
|
def test_set_hash(tmpdir):
|
|
# Check that sets hash consistently, even though their ordering
|
|
# is not guaranteed
|
|
k = KlassWithCachedMethod(tmpdir.strpath)
|
|
|
|
s = set(['#s12069__c_maps.nii.gz',
|
|
'#s12158__c_maps.nii.gz',
|
|
'#s12258__c_maps.nii.gz',
|
|
'#s12277__c_maps.nii.gz',
|
|
'#s12300__c_maps.nii.gz',
|
|
'#s12401__c_maps.nii.gz',
|
|
'#s12430__c_maps.nii.gz',
|
|
'#s13817__c_maps.nii.gz',
|
|
'#s13903__c_maps.nii.gz',
|
|
'#s13916__c_maps.nii.gz',
|
|
'#s13981__c_maps.nii.gz',
|
|
'#s13982__c_maps.nii.gz',
|
|
'#s13983__c_maps.nii.gz'])
|
|
|
|
a = k.f(s)
|
|
b = k.f(a)
|
|
|
|
assert hash(a) == hash(b)
|
|
|
|
|
|
def test_set_decimal_hash():
|
|
# Check that sets containing decimals hash consistently, even though
|
|
# ordering is not guaranteed
|
|
assert (hash(set([Decimal(0), Decimal('NaN')])) ==
|
|
hash(set([Decimal('NaN'), Decimal(0)])))
|
|
|
|
|
|
def test_string():
|
|
# Test that we obtain the same hash for object owning several strings,
|
|
# whatever the past of these strings (which are immutable in Python)
|
|
string = 'foo'
|
|
a = {string: 'bar'}
|
|
b = {string: 'bar'}
|
|
c = pickle.loads(pickle.dumps(b))
|
|
assert hash([a, b]) == hash([a, c])
|
|
|
|
|
|
@with_numpy
|
|
def test_numpy_dtype_pickling():
|
|
# numpy dtype hashing is tricky to get right: see #231, #239, #251 #1080,
|
|
# #1082, and explanatory comments inside
|
|
# ``joblib.hashing.NumpyHasher.save``.
|
|
|
|
# In this test, we make sure that the pickling of numpy dtypes is robust to
|
|
# object identity and object copy.
|
|
|
|
dt1 = np.dtype('f4')
|
|
dt2 = np.dtype('f4')
|
|
|
|
# simple dtypes objects are interned
|
|
assert dt1 is dt2
|
|
assert hash(dt1) == hash(dt2)
|
|
|
|
dt1_roundtripped = pickle.loads(pickle.dumps(dt1))
|
|
assert dt1 is not dt1_roundtripped
|
|
assert hash(dt1) == hash(dt1_roundtripped)
|
|
|
|
assert hash([dt1, dt1]) == hash([dt1_roundtripped, dt1_roundtripped])
|
|
assert hash([dt1, dt1]) == hash([dt1, dt1_roundtripped])
|
|
|
|
complex_dt1 = np.dtype(
|
|
[('name', np.str_, 16), ('grades', np.float64, (2,))]
|
|
)
|
|
complex_dt2 = np.dtype(
|
|
[('name', np.str_, 16), ('grades', np.float64, (2,))]
|
|
)
|
|
|
|
# complex dtypes objects are not interned
|
|
assert hash(complex_dt1) == hash(complex_dt2)
|
|
|
|
complex_dt1_roundtripped = pickle.loads(pickle.dumps(complex_dt1))
|
|
assert complex_dt1_roundtripped is not complex_dt1
|
|
assert hash(complex_dt1) == hash(complex_dt1_roundtripped)
|
|
|
|
assert hash([complex_dt1, complex_dt1]) == hash(
|
|
[complex_dt1_roundtripped, complex_dt1_roundtripped]
|
|
)
|
|
assert hash([complex_dt1, complex_dt1]) == hash(
|
|
[complex_dt1_roundtripped, complex_dt1]
|
|
)
|
|
|
|
|
|
@parametrize('to_hash,expected',
|
|
[('This is a string to hash',
|
|
'71b3f47df22cb19431d85d92d0b230b2'),
|
|
(u"C'est l\xe9t\xe9",
|
|
'2d8d189e9b2b0b2e384d93c868c0e576'),
|
|
((123456, 54321, -98765),
|
|
'e205227dd82250871fa25aa0ec690aa3'),
|
|
([random.Random(42).random() for _ in range(5)],
|
|
'a11ffad81f9682a7d901e6edc3d16c84'),
|
|
({'abcde': 123, 'sadfas': [-9999, 2, 3]},
|
|
'aeda150553d4bb5c69f0e69d51b0e2ef')])
|
|
def test_hashes_stay_the_same(to_hash, expected):
|
|
# We want to make sure that hashes don't change with joblib
|
|
# version. For end users, that would mean that they have to
|
|
# regenerate their cache from scratch, which potentially means
|
|
# lengthy recomputations.
|
|
# Expected results have been generated with joblib 0.9.2
|
|
assert hash(to_hash) == expected
|
|
|
|
|
|
@with_numpy
|
|
def test_hashes_are_different_between_c_and_fortran_contiguous_arrays():
|
|
# We want to be sure that the c-contiguous and f-contiguous versions of the
|
|
# same array produce 2 different hashes.
|
|
rng = np.random.RandomState(0)
|
|
arr_c = rng.random_sample((10, 10))
|
|
arr_f = np.asfortranarray(arr_c)
|
|
assert hash(arr_c) != hash(arr_f)
|
|
|
|
|
|
@with_numpy
|
|
def test_0d_array():
|
|
hash(np.array(0))
|
|
|
|
|
|
@with_numpy
|
|
def test_0d_and_1d_array_hashing_is_different():
|
|
assert hash(np.array(0)) != hash(np.array([0]))
|
|
|
|
|
|
@with_numpy
|
|
def test_hashes_stay_the_same_with_numpy_objects():
|
|
# Note: joblib used to test numpy objects hashing by comparing the produced
|
|
# hash of an object with some hard-coded target value to guarantee that
|
|
# hashing remains the same across joblib versions. However, since numpy
|
|
# 1.20 and joblib 1.0, joblib relies on potentially unstable implementation
|
|
# details of numpy to hash np.dtype objects, which makes the stability of
|
|
# hash values across different environments hard to guarantee and to test.
|
|
# As a result, hashing stability across joblib versions becomes best-effort
|
|
# only, and we only test the consistency within a single environment by
|
|
# making sure:
|
|
# - the hash of two copies of the same objects is the same
|
|
# - hashing some object in two different python processes produces the same
|
|
# value. This should be viewed as a proxy for testing hash consistency
|
|
# through time between Python sessions (provided no change in the
|
|
# environment was done between sessions).
|
|
|
|
def create_objects_to_hash():
|
|
rng = np.random.RandomState(42)
|
|
# Being explicit about dtypes in order to avoid
|
|
# architecture-related differences. Also using 'f4' rather than
|
|
# 'f8' for float arrays because 'f8' arrays generated by
|
|
# rng.random.randn don't seem to be bit-identical on 32bit and
|
|
# 64bit machines.
|
|
to_hash_list = [
|
|
rng.randint(-1000, high=1000, size=50).astype('<i8'),
|
|
tuple(rng.randn(3).astype('<f4') for _ in range(5)),
|
|
[rng.randn(3).astype('<f4') for _ in range(5)],
|
|
{
|
|
-3333: rng.randn(3, 5).astype('<f4'),
|
|
0: [
|
|
rng.randint(10, size=20).astype('<i8'),
|
|
rng.randn(10).astype('<f4')
|
|
]
|
|
},
|
|
# Non regression cases for
|
|
# https://github.com/joblib/joblib/issues/308
|
|
np.arange(100, dtype='<i8').reshape((10, 10)),
|
|
# Fortran contiguous array
|
|
np.asfortranarray(np.arange(100, dtype='<i8').reshape((10, 10))),
|
|
# Non contiguous array
|
|
np.arange(100, dtype='<i8').reshape((10, 10))[:, :2],
|
|
]
|
|
return to_hash_list
|
|
|
|
# Create two lists containing copies of the same objects. joblib.hash
|
|
# should return the same hash for to_hash_list_one[i] and
|
|
# to_hash_list_two[i]
|
|
to_hash_list_one = create_objects_to_hash()
|
|
to_hash_list_two = create_objects_to_hash()
|
|
|
|
e1 = ProcessPoolExecutor(max_workers=1)
|
|
e2 = ProcessPoolExecutor(max_workers=1)
|
|
|
|
try:
|
|
for obj_1, obj_2 in zip(to_hash_list_one, to_hash_list_two):
|
|
# testing consistency of hashes across python processes
|
|
hash_1 = e1.submit(hash, obj_1).result()
|
|
hash_2 = e2.submit(hash, obj_1).result()
|
|
assert hash_1 == hash_2
|
|
|
|
# testing consistency when hashing two copies of the same objects.
|
|
hash_3 = e1.submit(hash, obj_2).result()
|
|
assert hash_1 == hash_3
|
|
|
|
finally:
|
|
e1.shutdown()
|
|
e2.shutdown()
|
|
|
|
|
|
def test_hashing_pickling_error():
|
|
def non_picklable():
|
|
return 42
|
|
|
|
with raises(pickle.PicklingError) as excinfo:
|
|
hash(non_picklable)
|
|
excinfo.match('PicklingError while hashing')
|
|
|
|
|
|
def test_wrong_hash_name():
|
|
msg = "Valid options for 'hash_name' are"
|
|
with raises(ValueError, match=msg):
|
|
data = {'foo': 'bar'}
|
|
hash(data, hash_name='invalid')
|