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Description
The below example works differently depending on the device:
import dpctl.tensor as dpt
a = dpt.asarray([0], dtype='c16', device='gpu')
dpt.pow(a,1)
# usm_ndarray([0.+0.j])
a = dpt.asarray([0], dtype='c16', device='cpu')
dpt.pow(a,1)
# usm_ndarray([nan+nanj])
with dtype = 'c8' returns the same result for different devices
import dpctl.tensor as dpt
a = dpt.asarray([0], dtype='c8',device='gpu')
dpt.pow(a ,1)
# usm_ndarray([0.+0.j], dtype=complex64)
a = dpt.asarray([0], dtype='c8',device='cpu')
dpt.pow(a,1)
# usm_ndarray([0.+0.j], dtype=complex64)
I also noticed that dpt.pow
works correctly when the input array size is between 2 and 7 for dtype c16
.
import dpctl.tensor as dpt
a = dpt.zeros((2,), dtype='c16', device='cpu')
dpt.pow(a,1)
# usm_ndarray([0.+0.j, 0.+0.j])
a = dpt.zeros((7,), dtype='c16', device='cpu')
dpt.pow(a,1)
# usm_ndarray([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
a = dpt.zeros((8,), dtype='c16', device='cpu')
dpt.pow(a,1)
# usm_ndarray([ 0. +0.j, 0. +0.j, 0. +0.j, 0. +0.j, nan+nanj, nan+nanj,
nan+nanj, nan+nanj])
Besides this there is an interesting case when x2
(scalar) is numpy dtype
Then dpt.pow
with input array with data type c8
returns nans
too
import dpctl.tensor as dpt
import numpy
a = dpt.zeros((8,), dtype='c8', device='cpu')
dpt.pow(a, 1)
# usm_ndarray([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,
0.+0.j], dtype=complex64)
dpt.pow(a, numpy.int32(1))
# usm_ndarray([ 0. +0.j, 0. +0.j, 0. +0.j, 0. +0.j, nan+nanj, nan+nanj,
nan+nanj, nan+nanj])
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