Description
Hi there,
test_linspace is failing for the jax backend for ivy, and hypothesis picks up two distinct failures:
AssertionError: out[0]=ivy.array(nan), but should be 9.9792015476736e+291 [linspace(9.9792015476736e+291, -1.7976931348623157e+308, 2)]
- raised on line
- Falsifying example:
linspace(9.9792015476736e+291, -1.7976931348623157e+308, num =2, dtype= 'float64', endpoint=False) - The output for the same inputs in numpy is:
ivy.array([[ nan, -inf]])(i.e.out[0] = nan, the same as in jax)
AssertionError: out[0]=ivy.array(9.97920155e+291), should be ivy.array(nan) [linspace()]
- raised on line:
ph.assert_array_elements("linspace", out, expected) - Falsifying example:
linspace(9.9792015476736e+291, -1.7976931348623157e+308, num =1, dtype= 'float64', endpoint=False) - The output for the same inputs in numpy is:
ivy.array([[ nan]])(i.e.out[0] = nan, different from the jax output)
I went about fixing the second failure as jax was diverging from numpy. However, I then (locally) got:
AssertionError: out[0]=ivy.array(nan), but should be 9.9792015476736e+291 [linspace(9.9792015476736e+291, -1.7976931348623157e+308, 1)].
Summary: the expected value for linspace seems to vary from the numpy value when start=9.9792015476736e+291 and stop= -1.7976931348623157e+308 (close to float64 min value) with num=1/2, causing the test to fail for jax.
I can't reproduce these inputs for numpy test_linspace, so it is unclear if it would also fail for numpy or any other backend - jax is the only one that has raised this issue.
Reference stack trace from ivy CI (click Run Array API Tests'