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Only require axis to be negative in vecdot and cross #740
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Nonnegative axes and negative axes less than the smaller of the two arrays are unspecified. This is because it is ambiguous in these cases whether the dimension should refer to the axis before or after broadcasting. Preciously, the spec stated it should refer to the dimension before broadcasting, but this deviates from NumPy gufunc behavior, and results in ambiguous and confusing situations, where, for instance, the result of a the function is different when the inputs are manually broadcasted together. Also clean up some of the cross text a little bit since the computed dimension must be exactly size 3. Fixes data-apis#724 Fixes data-apis#617 See the discussion in those issues for more details.
This also updates it to only test axes from [min(x1.ndim, x2.ndim), -1], as per data-apis/array-api#740
We could also allow nonnegative axis when the two input arrays have the same number of dimensions. In that case, there is no ambiguity for something like |
@kgryte could you remind me if this will be part of v2023? |
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This LGTM and is aligned with the discussion in the workgroup meeting on February 8, 2024. Thanks, @asmeurer!
This also updates it to only test axes from [min(x1.ndim, x2.ndim), -1], as per data-apis/array-api#740
Nonnegative axes and negative axes less than the smaller of the two arrays are unspecified.
This is because it is ambiguous in these cases whether the dimension should refer to the axis before or after broadcasting. Previously, the spec stated it should refer to the dimension before broadcasting, but this deviates from NumPy gufunc behavior, and results in ambiguous and confusing situations, where, for instance, the result of a the function is different when the inputs are manually broadcasted together.
Also clean up some of the cross text a little bit since the computed dimension must be exactly size 3.
Fixes #724
Fixes #617
See the discussion in those issues for more details.
I don't think this should be backported, since my vecdot implementation in np.array_api and array-api-compat already followed the spec behavior here, and will need to be updated.