added zeta in paddle elementwise.py#13409
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MahmoudAshraf97
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don't forget the unsupported ditypes decorator, uint16 and bfloat16 are unsupported in all functions, and if there are more dtypes that you cant support, add them accordingly
@with_unsupported_device_and_dtypes(
{"2.4.2 and below": {"cpu": ("uint16", "bfloat16")}}, backend_version
)
also the result is different than the ground truth, this is also a problem in the numpy implementation where this is derived from, so you will need to rethink this again
E AssertionError: the results from backend paddle and ground truth framework tensorflow do not match
E [2.]!=[1.64493407]
E
E
E Falsifying example: test_zeta(
E dtype_and_x=(['float64', 'float64'], [array([2.]), array([1.])]),
| temp = ivy.logical_and(ivy.not_equal(ivy.remainder(x, 2), 0), ivy.greater(x, 1)) | ||
| temp = ivy.logical_and(temp, ivy.less_equal(q, 0)) | ||
| nan_indices = ivy.logical_or(temp, ivy.less(x, 1)) | ||
| n, res = 1, 1 / q**x |
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in this line and the following lop there are two issues:
qandxare tensors, so use ivy elementwise ops instead,ivy.addinstead of+for example- this implementation is extremely slow compared to other backends and I thinks it's because of this loop
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yes, I agree
I will make the changes.
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hi @MahmoudAshraf97 I have used jax implementation and also we need to update numpy backend zeta function. |
fix left over numpy syntax
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Apr 9, 2023
Co-authored-by: Mahmoud Ashraf <[email protected]>
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zeta #13408