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import json
import os
import warnings
import tempfile
from functools import wraps
import numpy as np
import numpy.array_api
import numpy.testing as npt
import pytest
import hypothesis
from scipy._lib._fpumode import get_fpu_mode
from scipy._lib._testutils import FPUModeChangeWarning
from scipy._lib import _pep440
from scipy._lib._array_api import SCIPY_ARRAY_API, SCIPY_DEVICE
def pytest_configure(config):
config.addinivalue_line("markers",
"slow: Tests that are very slow.")
config.addinivalue_line("markers",
"xslow: mark test as extremely slow (not run unless explicitly requested)")
config.addinivalue_line("markers",
"xfail_on_32bit: mark test as failing on 32-bit platforms")
try:
import pytest_timeout # noqa:F401
except Exception:
config.addinivalue_line(
"markers", 'timeout: mark a test for a non-default timeout')
def _get_mark(item, name):
if _pep440.parse(pytest.__version__) >= _pep440.Version("3.6.0"):
mark = item.get_closest_marker(name)
else:
mark = item.get_marker(name)
return mark
def pytest_runtest_setup(item):
mark = _get_mark(item, "xslow")
if mark is not None:
try:
v = int(os.environ.get('SCIPY_XSLOW', '0'))
except ValueError:
v = False
if not v:
pytest.skip("very slow test; "
"set environment variable SCIPY_XSLOW=1 to run it")
mark = _get_mark(item, 'xfail_on_32bit')
if mark is not None and np.intp(0).itemsize < 8:
pytest.xfail(f'Fails on our 32-bit test platform(s): {mark.args[0]}')
# Older versions of threadpoolctl have an issue that may lead to this
# warning being emitted, see gh-14441
with npt.suppress_warnings() as sup:
sup.filter(pytest.PytestUnraisableExceptionWarning)
try:
from threadpoolctl import threadpool_limits
HAS_THREADPOOLCTL = True
except Exception: # observed in gh-14441: (ImportError, AttributeError)
# Optional dependency only. All exceptions are caught, for robustness
HAS_THREADPOOLCTL = False
if HAS_THREADPOOLCTL:
# Set the number of openmp threads based on the number of workers
# xdist is using to prevent oversubscription. Simplified version of what
# sklearn does (it can rely on threadpoolctl and its builtin OpenMP helper
# functions)
try:
xdist_worker_count = int(os.environ['PYTEST_XDIST_WORKER_COUNT'])
except KeyError:
# raises when pytest-xdist is not installed
return
if not os.getenv('OMP_NUM_THREADS'):
max_openmp_threads = os.cpu_count() // 2 # use nr of physical cores
threads_per_worker = max(max_openmp_threads // xdist_worker_count, 1)
try:
threadpool_limits(threads_per_worker, user_api='blas')
except Exception:
# May raise AttributeError for older versions of OpenBLAS.
# Catch any error for robustness.
return
@pytest.fixture(scope="function", autouse=True)
def check_fpu_mode(request):
"""
Check FPU mode was not changed during the test.
"""
old_mode = get_fpu_mode()
yield
new_mode = get_fpu_mode()
if old_mode != new_mode:
warnings.warn(f"FPU mode changed from {old_mode:#x} to {new_mode:#x} during "
"the test",
category=FPUModeChangeWarning, stacklevel=0)
# Array API backend handling
xp_available_backends = {'numpy': np}
if SCIPY_ARRAY_API and isinstance(SCIPY_ARRAY_API, str):
# fill the dict of backends with available libraries
xp_available_backends.update({'numpy.array_api': numpy.array_api})
try:
import torch # type: ignore[import]
xp_available_backends.update({'pytorch': torch})
# can use `mps` or `cpu`
torch.set_default_device(SCIPY_DEVICE)
except ImportError:
pass
try:
import cupy # type: ignore[import]
xp_available_backends.update({'cupy': cupy})
except ImportError:
pass
# by default, use all available backends
if SCIPY_ARRAY_API.lower() not in ("1", "true"):
SCIPY_ARRAY_API_ = json.loads(SCIPY_ARRAY_API)
if 'all' in SCIPY_ARRAY_API_:
pass # same as True
else:
# only select a subset of backend by filtering out the dict
try:
xp_available_backends = {
backend: xp_available_backends[backend]
for backend in SCIPY_ARRAY_API_
}
except KeyError:
msg = f"'--array-api-backend' must be in {xp_available_backends.keys()}"
raise ValueError(msg)
if 'cupy' in xp_available_backends:
SCIPY_DEVICE = 'cuda'
array_api_compatible = pytest.mark.parametrize("xp", xp_available_backends.values())
skip_if_array_api = pytest.mark.skipif(
SCIPY_ARRAY_API,
reason="do not run with Array API on",
)
def skip_if_array_api_gpu(func):
reason = "do not run with Array API on and not on CPU"
# method gets there as a function so we cannot use inspect.ismethod
if '.' in func.__qualname__:
@wraps(func)
def wrapped(self, *args, **kwargs):
xp = kwargs["xp"]
if SCIPY_ARRAY_API and SCIPY_DEVICE != 'cpu':
if xp.__name__ == 'cupy':
pytest.skip(reason=reason)
elif xp.__name__ == 'torch':
if 'cpu' not in torch.empty(0).device.type:
pytest.skip(reason=reason)
return func(self, *args, **kwargs)
else:
@wraps(func)
def wrapped(*args, **kwargs):
xp = kwargs["xp"]
if SCIPY_ARRAY_API and SCIPY_DEVICE != 'cpu':
if xp.__name__ == 'cupy':
pytest.skip(reason=reason)
elif xp.__name__ == 'torch':
if 'cpu' not in torch.empty(0).device.type:
pytest.skip(reason=reason)
return func(*args, **kwargs)
return wrapped
def skip_if_array_api_backend(backend):
def wrapper(func):
reason = (
f"do not run with Array API backend: {backend}"
)
# method gets there as a function so we cannot use inspect.ismethod
if '.' in func.__qualname__:
@wraps(func)
def wrapped(self, *args, **kwargs):
xp = kwargs["xp"]
if xp.__name__ == backend:
pytest.skip(reason=reason)
return func(self, *args, **kwargs)
else:
@wraps(func)
def wrapped(*args, **kwargs):
xp = kwargs["xp"]
if xp.__name__ == backend:
pytest.skip(reason=reason)
return func(*args, **kwargs)
return wrapped
return wrapper
# Following the approach of NumPy's conftest.py...
# Use a known and persistent tmpdir for hypothesis' caches, which
# can be automatically cleared by the OS or user.
hypothesis.configuration.set_hypothesis_home_dir(
os.path.join(tempfile.gettempdir(), ".hypothesis")
)
# We register two custom profiles for SciPy - for details see
# https://hypothesis.readthedocs.io/en/latest/settings.html
# The first is designed for our own CI runs; the latter also
# forces determinism and is designed for use via scipy.test()
hypothesis.settings.register_profile(
name="nondeterministic", deadline=None, print_blob=True,
)
hypothesis.settings.register_profile(
name="deterministic",
deadline=None, print_blob=True, database=None, derandomize=True,
suppress_health_check=list(hypothesis.HealthCheck),
)
# Profile is currently set by environment variable `SCIPY_HYPOTHESIS_PROFILE`
# In the future, it would be good to work the choice into dev.py.
SCIPY_HYPOTHESIS_PROFILE = os.environ.get("SCIPY_HYPOTHESIS_PROFILE",
"deterministic")
hypothesis.settings.load_profile(SCIPY_HYPOTHESIS_PROFILE)
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