np.full ignores keyword argument names

In np.full, numba ignores the named keyword arguments.

import numba
import numpy as np

@numba.jit(nopython=True)
def test1():
    x = np.full(shape=123, fill_value=456)
    print(x.shape)

@numba.jit(nopython=True)
def test2():
    x = np.full(fill_value=456, shape=123)
    print(x.shape)

test1()
test2()

outputs

(123,)
(456,)  # WRONG! The shape is given as `shape=123` in the function call!

Bug

Both test1 and test2 would be interpereted the same by numpy alone. The only difference between them is the order of the keyword arguments fill_value and shape. Numba seems to silently ignore this order. I am not sure this is a bug, but this just cost me nearly one hour of debugging. (I am still a very happy Numba user)

Numba version

numba 0.55.1

• I have tried using the latest released version of Numba (most recent is visible in the change log (https://github.com/numba/numba/blob/main/CHANGE_LOG).
• I have included a self contained code sample to reproduce the problem. i.e. it’s possible to run as ‘python bug.py’.

[stuartarchibald]

Stuart, does it make sense to start migrating old code to use overload, instead of attempt to fix the “overload glue” part?

@guilhermeleobas Probably something for discussion at the next triage meeting? I’ve got a patch that migrates np.empty, np.zeros and np.ones already. Translating these functions is quite a detail heavy task but potentially a better place to spend effort. I would hope once the common translation patterns become clear it can progress reasonably quickly and the shim code can be removed.

[stuartarchibald]

I’ve got a WIP patch to fix here https://github.com/numba/numba/pull/7996, needs some work to clean it up but as this is both a regression and a mis-compile, it’s going to be fixed for the upcoming 0.56 release.