Specify zero division behavior

numpy and PyTorch diverge on the zero division behavior of integral dtype’s:

>>> a1 = np.array(1, dtype=np.int)
>>> a2 = np.array(0, dtype=np.int)
>>> a1 // a2
0
>>> a1 % a2
0
>>> t1 = torch.tensor(1, dtype=torch.int)
>>> t2 = torch.tensor(0, dtype=torch.int)
>>> t1 // t2
RuntimeError: ZeroDivisionError
>>> t1 % t2
RuntimeError: ZeroDivisionError

We should either specify the behavior or fix the test suite to exclude zero as second input when testing these functions. cc @asmeurer

[asmeurer]

Yes, just a note is what I’m expecting. For the test suite I am completely skipping testing zero inputs for x2 for floor_divide and remainder(). The spec says “Rounds the result of dividing each element x1_i of the input array x1 by the respective element x2_i of the input array x2 …” which is meaningless if x2_i is 0 and x2 has an integer dtype.

[rgommers]

The numpy code does give:

>>> a1 // a2
<ipython-input-42-136abf062072>:1: RuntimeWarning: divide by zero encountered in floor_divide
  a1 // a2
0
>>> a1 % a2
<ipython-input-43-bdabd79cdfbc>:1: RuntimeWarning: divide by zero encountered in remainder
  a1 % a2
0

There’s an issue about it here: https://github.com/numpy/numpy/issues/5150

I don’t think this should be specified in the array API spec, it falls under the warnings/exceptions behavior which is library-specific.