Incomplete tests

[asmeurer]

This is a tracking issue for tests that are currently incomplete. These are mostly notated in the code by TODO comments, but it's useful to note them all here too. I haven't checked everything throughly so if there is something missing that isn't noted in a comment it might not be listed here.

Note there are also some functions that aren't yet tested at all, but those are covered in separate issues (e.g., #248 #299). I also am not including tests that are currently skipped entirely as flaky (https://github.com/data-apis/array-api-tests/issues/300).

test_array_object.py

• test_getitem_masking.py (key.ndim < x.ndim not tested)

test_creation_functions.py

The device keyword is never tested (see below)

test_data_type_functions.py

• test_astype (no complex types, no values testing, no test for copy)
• test_broadcast_arrays (no values testing)
• test_broadcast_to (no values testing)
• test_can_cast (no complex types)
• test_finfo (no values testing)
• test_iinfo (no values testing)

test_fft.py

• test_irfftn (no shape tests)

test_has_names.py

• Several names are not tested for (see https://github.com/data-apis/array-api-tests/issues/224)

test_indexing.py

• test_take (negative axis, negative indices, different dtypes for indices, axis=None https://github.com/data-apis/array-api-tests/issues/191)

test_inspection_functions.py

Basically none of the inspection APIs are currently tested, beyond the basic signature tests. Note that for many of these functions, we may not even need to use hypothesis. Just a basic test that the function outputs the right thing and a smoke test that the data outputted is correct.

test_linalg.py

(note: values testing here might be hard. Values testing isn't as high priority for functions that already generally work)

• test_det (no values testing)
• test_eigh (no values testing)
• test_eigvalsh (no values testing)
• test_inv (no values testing)
• test_matmul (the @ __matmul__ operator methods are not tested)
• test_matrix_norm (no values testing)
• test_qr (no test that Q is orthonormal)
• test_slogdet (no values testing)
• test_svdvals (no values testing; no test that it agrees with svd())
• test_trace (need to update dtype assertion, this should match test_sum)
• test_vector_norm (no values testing)

Additionally, there are some limitations in the hypothesis strategies used by some of the functions. These should be made more general so that the functions are actually tested on a full gamut of possible inputs:

• symmetric_matrices (does not generate complex Hermitian matrices
• positive_definite_matrices (only generates stacks of identity matrices
• invertable_matrices (only generates stacks of diagonal matrices)

test_manipulation_functions.py

• test_tile (no shapes or values testing)

test_operators_and_elementwise_functions.py

• test_divide (no complex numbers testing)
• test_negative (there is a TODO about uints)

test_signatures.py

Several issues tracked in other issues: https://github.com/data-apis/array-api-tests/issues/256, https://github.com/data-apis/array-api-tests/issues/171

test_sorting_functions.py

• test_argsort (no testing of signed zeros or nans)
• test_sort (no testing of signed zeros or nans)

test_special_cases.py

Many special cases are not tested. This is tracked at https://github.com/data-apis/array-api-tests/issues/284

test_statistical_functions.py

See also https://github.com/data-apis/array-api-tests/issues/167

• test_mean (no values testing)
• test_std (no values testing)
• test_var (no values testing)

OTHER

• Tests for device support https://github.com/data-apis/array-api-tests/issues/302
• Tests for dlpack

[ev-br]

test_negative (there is a TODO about uints)

The TODO is # TODO: clarify if uints are acceptable, adjust accordingly and apparently negative(uint) wraps around in at least several libraries:

In [1]: import torch

In [2]: torch.negative
Out[2]: <function torch._VariableFunctionsClass.negative>

In [3]: torch.negative(torch.as_tensor(3, dtype=torch.uint8))
Out[3]: tensor(253, dtype=torch.uint8)

In [4]: import jax.numpy as jnp

In [5]: jnp.negative(jnp.asarray(3, dtype=jnp.uint8))
Out[5]: Array(253, dtype=uint8)

In [6]: import numpy as np

In [7]: np.negative(np.asarray(3, dtype=np.uint8))
Out[7]: np.uint8(253)

In [8]: np.__version__
Out[8]: '2.1.0'

In [9]: import array_api_strict as xp

In [10]: xp.negative(xp.asarray(3, dtype=xp.uint8))
Out[10]: Array(253, dtype=array_api_strict.uint8)

The spec does not seem to fix the behavior for unsigned ints: https://data-apis.org/array-api/latest/API_specification/generated/array_api.negative.html

So maybe a first action item is to clarify the spec?