ENH: torch dtype promo
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Pull Request Overview
This PR enhances dtype promotion behavior in PyTorch compatibility by supporting additional unsigned integer types and standardizing deep copy semantics for sum and prod operations when axis is an empty tuple.
- Updated the promotion table to incorporate uint16, uint32, and uint64 on PyTorch ≥2.3.
- Introduced a new internal helper (_sum_prod_no_axis) to implement deep-copy behavior for sum and prod when axis=().
Comments suppressed due to low confidence (2)
array_api_compat/torch/_aliases.py:280
- Consider adding unit tests to explicitly verify the behavior of _sum_prod_no_axis across different dtypes, including the cases with and without _HAS_LARGE_UINT enabled.
def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array:
array_api_compat/torch/_aliases.py:280
- [nitpick] Consider renaming _sum_prod_no_axis to a more descriptive name that conveys its role in returning a deep copy when no axis is provided, for example, _cast_and_clone_no_axis.
def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array:
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#253 seems to have run into a wall with uint promotions in pytorch, did something change |
No, nothing has changed on the torch side AFAIK.
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You lost me here :-). So is it basically "let's add something because we want it, but not test it on CI because we know it'll break"? |
Well, we will need this and the other PR eventually when torch introduces support. |
The latter we definitely do not want to do. |
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I've updated this to incorporate the latest changes and added the contents of #253, which can now be closed. |
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result_typesumandprodwill now promote uint8, uint16 and uint32 to uint64.