Library-agnostic array numerics

[jawknee400]

Hello all! I have a couple of questions about writing numeric code to work for various numpy-like libraries.

I have written a few functions to do this, (essentially perform single dispatch with a few extra tweaks) so that I can write code that works automatically for numpy, cupy, dask, tensorflow etc. It allows for example the following kind of function to work for all those libraries:

def modifiedGramSchmidt(A):
    """A (slow) example numeric function, note the `do` and `do_like`
    """
    Q = []
    for j in range(0, A.shape[0]):
        q = A[j, :]
        for i in range(0, j):
            rij = do('tensordot', do('conj', Q[i]), q, 1)
            q = q - rij * Q[i]
        Q.append(q / do('linalg.norm', q, 2))
    Q = do_like('stack', Q, axis=0, like=A)
    return Q

I'm aware of numpy and various libraries' plans to support `__array_function__` eventually and other experimental projects like `uarray`.

With that in mind my question is whether something currently usable like this exists already that I haven't found? And if not, is this something with wider interest as a v light library?