[numpy] fix mix numpy scalar and MXNet numpy ndarray

[yijunc]

Description

Attempt to resolve issue mentioned in #16743
It is done by casting the inputs into mx_np or np.
Current conversion rules:

Expression	a type	b type	out type
a += b	onp	mx_np	onp
a += b	mx_np	onp	mx_np
c = a + b	onp	mx_np	onp
c = a + b	mx_np	onp	mx_np

However, the example code in the issue still can't run due to type issue.

import mxnet as mx
import numpy as np
mx.npx.set_np()
a = np.array(1) // the type in np is int64
a += mx.np.array(1) // the type in mx_np is float32

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

• Feature1, tests, (and when applicable, API doc)
• Feature2, tests, (and when applicable, API doc)

Comments

• If this change is a backward incompatible change, why must this change be made.
• Interesting edge cases to note here

[mxnet-bot]

Hey @BenjaminCHEN2016 , Thanks for submitting the PR
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[hgt312]

@yzhliu @sxjscience Any thoughts on the conversion rules?

[sxjscience]

import jax.numpy as jnp
import numpy as onp
# jnp += onp
a = jnp.ones((10,))
b = onp.ones((10,))
a += b
print(type(a))

# onp += jnp
a = onp.ones((10,))
b = jnp.ones((10,))
a += b
print(type(a))

Output:

<class 'jax.interpreters.xla.DeviceArray'>
<class 'jax.interpreters.xla.DeviceArray'>

[sxjscience]

In Jax, if any ndarray is a Jax ndarray, the result will also be a Jax ndarray.

[hgt312]

Jax does not implement inplace op overload like __iadd__. It is not a real inplace operation.
In my opinion, inplace operation should keep its type, while the other should return a mx ndarray.

[sxjscience]

Jax does not implement inplace op overload like __iadd__. It is not a real inplace operation.
In my opinion, inplace operation should keep its type, while the other should return a mx ndarray.

I agree with that.

[yijunc]

Conversion rules:

Expression	a type	b type	out type
a += b	onp	mx_np	onp
a += b	mx_np	onp	mx_np
c = a + b	onp	mx_np	mx_np
c = a + b	mx_np	onp	mx_np

[mxnet-bot]

Undefined action detected.
Permissible actions are : run ci [all], run ci [job1, job2]
Example : @mxnet-bot run ci [all]
Example : @mxnet-bot run ci [centos-cpu, clang]

[mxnet-bot]

Undefined action detected.
Permissible actions are : run ci [all], run ci [job1, job2]
Example : @mxnet-bot run ci [all]
Example : @mxnet-bot run ci [centos-cpu, clang]

[yijunc]

@mxnet-bot run ci [miscellaneous, unix-cpu]

[mxnet-bot]

Jenkins CI successfully triggered : [miscellaneous, unix-cpu]

[yijunc]

@mxnet-bot run ci [windows-cpu, windows-gpu, centos-cpu]

[mxnet-bot]

Jenkins CI successfully triggered : [centos-cpu, windows-gpu, windows-cpu]

[hgt312]

LGTM now. @yzhliu

[yijunc]

@mxnet-bot run ci [centos-cpu]

[mxnet-bot]

Jenkins CI successfully triggered : [centos-cpu]

[yijunc]

@mxnet-bot run ci [unix-gpu]

[mxnet-bot]

Jenkins CI successfully triggered : [unix-gpu]

[yijunc]

@mxnet-bot run ci [unix-gpu]

[mxnet-bot]

Jenkins CI successfully triggered : [unix-gpu]

[yijunc]

@sxjscience Any thoughts on this pr?