Add ops required for gradient checks #11
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hameerabbasi
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hameerabbasi
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Sep 3, 2025
| def register_binary_nonlinear(op: OpType) -> Callable: | ||
| def impl(lhs: ComplexTensor, rhs: ComplexTensor, *args, **kwargs) -> ComplexTensor: | ||
| a_r, a_i = split_complex_tensor(lhs) | ||
| a_r, a_i = split_complex_arg(lhs) |
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This is needed as any of lhs and rhs may be a ComplexTensor, but it isn't guaranteed which one.
hameerabbasi
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Sep 3, 2025
| if alpha is not None: | ||
| return impl_with_alpha(lhs, rhs, *args, alpha=alpha, **kwargs) | ||
| a_r, a_i = split_complex_tensor(lhs) | ||
| a_r, a_i = split_complex_arg(lhs) |
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Same as #11 (comment).
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cc @amjames Ready for review -- each commit can be reviewed separately. |
benjaminglass1
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Sep 11, 2025
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@hameerabbasi Overall this looks good to me! I had a couple comments on de-duplicating some logic, and I didn't spend a lot of time looking at the actual numerical logic of the implemented functions (I'm trusting the tests to catch issues there).
I assume the stuff you need to push upstream includes the functions that are skipping __torch_dispatch__?
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