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Array API tests fixes #636

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merged 3 commits into from
Dec 5, 2024
Merged

Array API tests fixes #636

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e88ce8f
Handle complex dtype in full
tomwhite Dec 4, 2024
9f543ad
Exclude test_getitem from array api tests since negative step sizes a…
tomwhite Dec 4, 2024
bee866c
Fix broadcast_to when target shape has size 0
tomwhite Dec 5, 2024
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2 changes: 2 additions & 0 deletions .github/workflows/array-api-tests.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,8 @@ jobs:
# edge case failures (https://github.com/cubed-dev/cubed/issues/420)
array_api_tests/test_linalg.py::test_tensordot
array_api_tests/test_linalg.py::test_vecdot
# (getitem with negative step size is not implemented)
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:) we actually rewrite such queries in Xarray for Zarr

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Oh interesting! How do you do that?

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@dcherian dcherian Dec 4, 2024 •
edited
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https://github.com/pydata/xarray/blob/99ee8c6ca54057a9b994d7685f36236f2d5a69d9/xarray/core/indexing.py#L1084 and friends. We rewrite the query to normal slice with +ve stride, then reverse in-memory after read :)

This is one of Xarray's magic tricks that very few people know about. We guarantee consistent indexing API over any array for the most part.

In pydata/xarray#8667, I suggested reusing this machinery for cubed.

EDIT: actually my comment is here: pydata/xarray#8834 (comment)

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Very cool. Would be great to reuse that. BTW Cubed flip is implemented in a similar way, but it's only step=-1 of course.

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Opened #637 to track

array_api_tests/test_array_object.py::test_getitem

# not implemented
array_api_tests/test_array_object.py::test_setitem
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2 changes: 2 additions & 0 deletions cubed/array_api/creation_functions.py
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Expand Up @@ -145,6 +145,8 @@ def full(
dtype = nxp.int64
elif isinstance(fill_value, float):
dtype = nxp.float64
elif isinstance(fill_value, complex):
dtype = nxp.complex128
else:
raise TypeError("Invalid input to full")
chunksize = to_chunksize(normalize_chunks(chunks, shape=shape, dtype=dtype))
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10 changes: 6 additions & 4 deletions cubed/array_api/manipulation_functions.py
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Expand Up @@ -47,14 +47,16 @@ def broadcast_to(x, /, shape, *, chunks=None):
):
raise ValueError(f"cannot broadcast shape {x.shape} to shape {shape}")

# TODO: fix case where shape has a dimension of size zero

if chunks is None:
# New dimensions and broadcast dimensions have chunk size 1
# This behaviour differs from dask where it is the full dimension size
xchunks = normalize_chunks(x.chunks, x.shape, dtype=x.dtype)
chunks = tuple((1,) * s for s in shape[:ndim_new]) + tuple(
bd if old > 1 else ((1,) * new if new > 0 else (0,))

def chunklen(shapelen):
return (1,) * shapelen if shapelen > 0 else (0,)

chunks = tuple(chunklen(s) for s in shape[:ndim_new]) + tuple(
bd if old > 1 else chunklen(new)
for bd, old, new in zip(xchunks, x.shape, shape[ndim_new:])
)
else:
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3 changes: 2 additions & 1 deletion cubed/tests/test_array_api.py
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Expand Up @@ -474,7 +474,8 @@ def test_broadcast_arrays(executor):
@pytest.mark.parametrize(
"shape, chunks, new_shape, new_chunks, new_chunks_expected",
[
# ((5, 1, 6), (3, 1, 3), (5, 0, 6), None, ((3, 2), (0,), (3, 3))), # fails
((), (), (0,), None, ((0,),)),
((5, 1, 6), (3, 1, 3), (5, 0, 6), None, ((3, 2), (0,), (3, 3))),
((5, 1, 6), (3, 1, 3), (5, 4, 6), None, ((3, 2), (1, 1, 1, 1), (3, 3))),
((5, 1, 6), (3, 1, 3), (2, 5, 1, 6), None, ((1, 1), (3, 2), (1,), (3, 3))),
((5, 1, 6), (3, 1, 3), (5, 3, 6), (3, 3, 3), ((3, 2), (3,), (3, 3))),
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