Support buffer protocol objects as advanced index keys

[vlad-perevezentsev]

This PR proposes by adding support for buffer protocol objects (array.array, memoryview etc.) as advanced index keys
These changes were proposed in #2872 as an extension to the advanced indexing support.

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[github-actions]

Array API standard conformance tests for dpnp=0.21.0dev0=py313h509198e_25 ran successfully.
Passed: 1358
Failed: 2
Skipped: 16

[antonwolfy]

Can all that use cases covered converting x to numpy.ndarray?
Do we need so complicated if-logic?

[ndgrigorian]

here is CuPy implementation of a similar idea for a reference:
https://github.com/cupy/cupy/blob/main/cupy/_core/_routines_indexing.pyx#L247

can take a similar approach too in some cases (best to leave converting to usm_ndarray to the tensor layer, though, for queue propagation reasons)

[antonwolfy]

I don't think we need to fully follow the same CuPy implementation in _prepare_slice_list, because it seems CuPy mimics faulty NumPy behavior sometimes.

Not sure if I got the comment fully correctly, but in general I meant something like:

    if x is None or x is Ellipsis or isinstance(x, (dpt.usm_ndarray, slice, numpy.ndarray)):
        return x
    if isinstance(x, dpnp_array):
        return x.get_array()

    try:
        x = numpy.asarray(x)
    except ... :
        ...
        raise ...
    ...
    return x

[ndgrigorian]

yep, agreed mostly, though problem is, we don't really want to send integers or boolean Python scalars to numpy arrays, which is why we may need something like:

        elif numpy.isscalar(s):
            if not isinstance(s, (bool, numpy.bool_)):
                # keep scalar int
                continue

which is in cupy implementation. We could handle range, list, tuple through numpy though

[vlad-perevezentsev]

I don not think try/except around numpy.asarray is necessary because invalid index types either raise naturally or produce arrays with unsupported dtypes that the tensor layer rejects with IndexError

Regarding elif numpy.isscalar(s) I replaced it with isinstance(x, (int, numpy.generic)) because np.isscalar(memoryview(array.array("l", [0, 1, 2]))) returns True
bool check is not needed since bool is subclass of int

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/2889/index.html

[antonwolfy]

Why do we need to explicitly check arr.dtype.kind == "f"?