[MXNET-257] Do not copy when casting as same type

[ThomasDelteil]

MXNET-257

Description

Currently there is a copy even when the data has already the correct type when calling 'astype'.
I am not sure why, so if it's on purpose please enlighten me 😄

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

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

Comments

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

[piiswrong]

Numpy always copies. We should stay consistent with numpy

>>> import numpy as np
>>> a = np.zeros((10,))
>>> c = a.astype(a.dtype)
>>> a[0] = 1
>>> a
array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])
>>> c
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

[ThomasDelteil]

@piiswrong should then we introduce a flag copy=True to follow the numpy API?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html
I am not sure I agree with the default value, but at least that should keep things backward compatible

[piiswrong]

Sure we can add copy flag

[szha]

It's unclear what the dtype requirement here is from the doc. Please fix.

[ThomasDelteil]

I agree. However I used the exact same wording as the numpy docs: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html

copy : bool, optional: By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.

I suggest switching to:

If this is set to False, and the dtype requested is the same as the array's dtype,

[szha]

Looks good