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ENH: test vecdot values, incl complex conj #314

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Merged
merged 2 commits into from
Nov 23, 2024
Merged

ENH: test vecdot values, incl complex conj #314

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fe3e05c
ENH: test vecdot values, incl complex conj
ev-br Nov 20, 2024
6ea8ae2
MAINT: inline the check for complex dtypes
ev-br Nov 20, 2024
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23 changes: 18 additions & 5 deletions array_api_tests/test_linalg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@
from . import _array_module as xp
from ._array_module import linalg


def assert_equal(x, y, msg_extra=None):
extra = '' if not msg_extra else f' ({msg_extra})'
if x.dtype in dh.all_float_dtypes:
Expand All @@ -60,6 +61,7 @@ def assert_equal(x, y, msg_extra=None):
else:
assert_exactly_equal(x, y, msg_extra=msg_extra)


def _test_stacks(f, *args, res=None, dims=2, true_val=None,
matrix_axes=(-2, -1),
res_axes=None,
Expand Down Expand Up @@ -106,6 +108,7 @@ def _test_stacks(f, *args, res=None, dims=2, true_val=None,
if true_val:
assert_equal(decomp_res_stack, true_val(*x_stacks, **kw), msg_extra)


def _test_namedtuple(res, fields, func_name):
"""
Test that res is a namedtuple with the correct fields.
Expand All @@ -121,6 +124,7 @@ def _test_namedtuple(res, fields, func_name):
assert hasattr(res, field), f"{func_name}() result namedtuple doesn't have the '{field}' field"
assert res[i] is getattr(res, field), f"{func_name}() result namedtuple '{field}' field is not in position {i}"


@pytest.mark.unvectorized
@pytest.mark.xp_extension('linalg')
@given(
Expand Down Expand Up @@ -901,6 +905,15 @@ def true_trace(x_stack, offset=0):

_test_stacks(linalg.trace, x, **kw, res=res, dims=0, true_val=true_trace)


def _conj(x):
"""Work around xp.conj rejecting floats."""
if xp.isdtype(x.dtype, 'complex floating'):
return xp.conj(x)
else:
return x


def _test_vecdot(namespace, x1, x2, data):
vecdot = namespace.vecdot
broadcasted_shape = sh.broadcast_shapes(x1.shape, x2.shape)
Expand All @@ -925,11 +938,8 @@ def _test_vecdot(namespace, x1, x2, data):
ph.assert_result_shape("vecdot", in_shapes=[x1.shape, x2.shape],
out_shape=res.shape, expected=expected_shape)

if x1.dtype in dh.int_dtypes:
def true_val(x, y, axis=-1):
return xp.sum(xp.multiply(x, y), dtype=res.dtype)
else:
true_val = None
def true_val(x, y, axis=-1):
return xp.sum(xp.multiply(_conj(x), y), dtype=res.dtype)
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Unfortunately, if you look here, there is no approximate testing done at all for floating-point values https://github.com/data-apis/array-api-tests/pull/314/files?diff=unified#diff-6056c0b3af9cd3ba66387432a17f5f36bbd54220419656441a8b01bcdc4df44bR57.

We should probably add a flag to that helper to allow approximate testing to be enabled. Some functions are impossible to do approximate testing for because they don't even have a single possible output (e.g., eigh could pick completely different eigenvectors and still be correct).

There are helpers used in the elementwise functions that could be reused here for testing floating-point (and complex) closeness. Basically, they test with very large epsilons. Even that would be enough to detect that a library isn't conjugating, which is the real concern for this test specifically.

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@ev-br ev-br Nov 26, 2024 •
edited
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Wait, what: https://github.com/data-apis/array-api-tests/blob/master/array_api_tests/test_linalg.py#L109 and I was sure this PR switches on equality testing. But assert_equal does not do what name the implies:
https://github.com/data-apis/array-api-tests/blob/master/array_api_tests/test_linalg.py#L49

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Yes, I originally had it there but later had to comment it out when I found out that you fundamentally can't compare floating point stacks for some functions like eigh because sometimes implementations (like CuPy) use different algorithms that give different (but still mathematically correct) results. #101 (comment) I never renamed the functions. While I did turn the floating-point checks off completely, and they don't make sense for some functions like eigh, they do make sense for functions like vecdot and others where the mathematical answer is has a single well-defined value. We just need to be careful about loss of significance, especially for the tensor contraction functions (vecdot, matmul, tensordot, etc.) that involve additions.


_test_stacks(vecdot, x1, x2, res=res, dims=0,
matrix_axes=(axis,), true_val=true_val)
Expand All @@ -944,6 +954,7 @@ def true_val(x, y, axis=-1):
def test_linalg_vecdot(x1, x2, data):
_test_vecdot(linalg, x1, x2, data)


@pytest.mark.unvectorized
@given(
*two_mutual_arrays(dh.numeric_dtypes, mutually_broadcastable_shapes(2, min_dims=1)),
Expand All @@ -952,10 +963,12 @@ def test_linalg_vecdot(x1, x2, data):
def test_vecdot(x1, x2, data):
_test_vecdot(_array_module, x1, x2, data)


# Insanely large orders might not work. There isn't a limit specified in the
# spec, so we just limit to reasonable values here.
max_ord = 100


@pytest.mark.unvectorized
@pytest.mark.xp_extension('linalg')
@given(
Expand Down
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