Arch-dependent failures of test_minimize_ipopt_jac_with_scipy_methods[cobyla] #237
Description
While working on updating the python-cyipopt package in Fedora Linux to 1.3.0, I observed consistent failures of of test_minimize_ipopt_jac_with_scipy_methods[cobyla] on ppc64le and s390x.
In five scratch-builds, I observed:
x86_64: 2/5 builds failedaarch64: 0/5 builds failedppc64le: 5/5 builds faileds390x: 5/5 builds failed
The ppc64le and s390x failures all looked like this, with identical numeric values:
=================================== FAILURES ===================================
______________ test_minimize_ipopt_jac_with_scipy_methods[cobyla] ______________
method = 'cobyla'
@pytest.mark.skipif("scipy" not in sys.modules,
reason="Test only valid if Scipy available.")
@pytest.mark.parametrize('method', [None] + MINIMIZE_METHODS)
def test_minimize_ipopt_jac_with_scipy_methods(method):
x0 = [0] * 4
a0, b0, c0, d0 = 1, 2, 3, 4
atol, rtol = 5e-5, 5e-5
def fun(x, a=0, e=0, b=0):
assert a == a0
assert b == b0
fun.count += 1
return (x[0] - a) ** 2 + (x[1] - b) ** 2 + x[2] ** 2 + x[3] ** 2
def grad(x, a=0, e=0, b=0):
assert a == a0
assert b == b0
grad.count += 1
return [2 * (x[0] - a), 2 * (x[1] - b), 2 * x[2], 2 * x[3]]
def hess(x, a=0, e=0, b=0):
assert a == a0
assert b == b0
hess.count += 1
return 2 * np.eye(4)
def hessp(x, p, a=0, e=0, b=0):
assert a == a0
assert b == b0
hessp.count += 1
return 2 * np.eye(4) @ p
def fun_constraint(x, c=0, e=0, d=0):
assert c == c0
assert d == d0
fun_constraint.count += 1
return [(x[2] - c) ** 2, (x[3] - d) ** 2]
def grad_constraint(x, c=0, e=0, d=0):
assert c == c0
assert d == d0
grad_constraint.count += 1
return np.hstack((np.zeros((2, 2)),
np.diag([2 * (x[2] - c), 2 * (x[3] - d)])))
def callback(*args, **kwargs):
callback.count += 1
constr = {
"type": "eq",
"fun": fun_constraint,
"jac": grad_constraint,
"args": (c0,),
"kwargs": {'d': d0},
}
kwargs = {}
jac_methods = {'cg', 'bfgs', 'newton-cg', 'l-bfgs-b', 'tnc', 'slsqp,',
'dogleg', 'trust-ncg', 'trust-krylov', 'trust-exact',
'trust-constr'}
hess_methods = {'newton-cg', 'dogleg', 'trust-ncg', 'trust-krylov',
'trust-exact', 'trust-constr'}
hessp_methods = hess_methods - {'dogleg', 'trust-exact'}
constr_methods = {'slsqp', 'trust-constr'}
if method in jac_methods:
kwargs['jac'] = grad
if method in hess_methods:
kwargs['hess'] = hess
if method in constr_methods:
kwargs['constraints'] = constr
if method in MINIMIZE_METHODS:
kwargs['callback'] = callback
fun.count = 0
grad.count = 0
hess.count = 0
hessp.count = 0
fun_constraint.count = 0
grad_constraint.count = 0
callback.count = 0
res = cyipopt.minimize_ipopt(fun, x0, method=method, args=(a0,),
kwargs={'b': b0}, **kwargs)
assert res.success
np.testing.assert_allclose(res.x[:2], [a0, b0], rtol=rtol)
# confirm that the test covers what we think it does: all the functions
# that we provide are actually being executed; that is, the assertions
# are *passing*, not being skipped
assert fun.count > 0
if method in MINIMIZE_METHODS:
assert callback.count > 0
if method in jac_methods:
assert grad.count > 0
if method in hess_methods:
assert hess.count > 0
if method in constr_methods:
assert fun_constraint.count > 0
assert grad_constraint.count > 0
np.testing.assert_allclose(res.x[2:], [c0, d0], rtol=rtol)
else:
> np.testing.assert_allclose(res.x[2:], 0, atol=atol)
cyipopt/tests/unit/test_scipy_optional.py:308:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (<function assert_allclose.<locals>.compare at 0x7ffcf5691260>, array([-6.84786337e-05, -2.23338511e-05]), array(0))
kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-07, atol=5e-05', 'verbose': True}
@wraps(func)
def inner(*args, **kwds):
with self._recreate_cm():
> return func(*args, **kwds)
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=5e-05
E
E Mismatched elements: 1 / 2 (50%)
E Max absolute difference: 6.84786337e-05
E Max relative difference: inf
E x: array([-6.847863e-05, -2.233385e-05])
E y: array(0)
/usr/lib64/python3.12/contextlib.py:81: AssertionError
The “flaky” x86_64 failures are in TestSLSQP::test_minimize_unbounded_approximated; I’ll report more detail on them in a separate issue.
The scipy package is at version 1.11.1. I’m happy to run any tests or add any other details that might be helpful.