[BUG] arithmetic operations with numpy arrays are not commutative

[lkarthee]

Describe the bug mx array being first operand in arithmetic op is being converted complex numbers

To Reproduce

Include code snippet

import numpy as np
import mlx.core as mx

mx.array(1) + np.array(2)
x = mx.array(1)
y = np.array(2)
x + y 
# >>> array(3+0j, dtype=complex64)
x * y
# >>> array(2+0j, dtype=complex64)
y + x
# >>> 3
type(y + x)
# >>> <class 'numpy.int64'>
type(x + y)
# >>> <class 'mlx.core.array'>

Expected behavior Conversion to complex should not happen. Arithmetic ops should be commutative (ignoring type like ndarray or mx).

Desktop (please complete the following information):

• OS Version: [e.g. MacOS 14.1.2] MacOS 14.1.1
• Version [e.g. 0.7.0] 0.11

Additional context Add any other context about the problem here.

[awni]

That looks like a bug 🤔

[lkarthee]

I am not sure if this is related - I am adding it here. Let me know if its not related, i will log another issue.

np.ndarray of bfloat16 using ml_dtypes is being interpreted as complex64 by mlx.

>>> from ml_dtypes import bfloat16
>>> import numpy as np
>>> x = np.array(1., dtype=bfloat16)
>>> import mlx.core as mx
>>> mx.array(x)
array(1+0j, dtype=complex64)
>>> x = np.array(1)
>>> mx.array(x)
array(1, dtype=int64)
>>> x = np.array(1., dtype=bfloat16)
>>> x.dtype
dtype(bfloat16)
>>> type(x.dtype)
<class 'numpy.dtype[bfloat16]'>
>>>

[awni]

The bfloat thing is a different issue. I will send a fix for the add shortly. Could put the bfloat problem in a separate issue as it might be harder to fix / require some changes in Nanobind (still investigating).