Implement asynchronous fill method using dpctl kernels

[ndgrigorian]

This PR proposes a change to dpnp_array.fill method which leverages dpctl kernels to make fill asynchronous and more efficient, avoiding repeated calls to index the array and copying scalars to the device for each element.

Shows significant performance gains on Iris Xe in WSL

Before

In [1]: import dpnp as dnp

In [2]: x_dnp = dnp.empty(10000, dtype="c8")

In [3]: %timeit x_dnp.fill(10)
1.25 s ± 47.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [4]: %timeit x_dnp.fill(10)
1.26 s ± 27.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

After

In [8]: %timeit x_dnp.fill(10); q.wait()
229 μs ± 37.8 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

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[ndgrigorian]

@antonwolfy @vtavana @vlad-perevezentsev
I've added a commit to skip test_fill_with_numpy_scalar_ndarray from the CuPy tests.

dpnp.fill_diagonal does not permit NumPy arrays, so it made sense to do the same here. If it would be preferred to keep this feature, it can be implemented, but I would argue that for consistency, the two should behave the same.

[antonwolfy]

@ndgrigorian, thank you for implementing so great improvement.
Please find some comments below.

[antonwolfy]

Could you please update docstring a bit, like:

        """
        Fill the array with a scalar value.

        For full documentation refer to :obj:`numpy.ndarray.fill`.

        Parameters
        ----------
        value : {dpnp.ndarray, usm_ndarray, scalar}
            All elements of `a` will be assigned this value.

        Examples
        --------
        >>> import numpy as np
        >>> a = np.array([1, 2])
        >>> a.fill(0)
        >>> a
        array([0, 0])
        >>> a = np.empty(2)
        >>> a.fill(1)
        >>> a
        array([1.,  1.])

        """

[antonwolfy]

Could you please add tests to cover lines: 50, 71, 73?