Support duck arrays in numba gufuncs

There might be some room for improvement in handling numpy-like arrays in numba-generated gufuncs

I was quite happy to see this work well:

In [1]: from numba import vectorize, float64
   ...: 
   ...: @vectorize([float64(float64, float64)])
   ...: def f(x, y):
   ...:     return x + y
   ...: 

In [2]: import dask.array as da

In [3]: x = da.arange(10, chunks=(5,))

In [4]: f(x, x)  # hooray!
Out[4]: dask.array<f, shape=(10,), dtype=float64, chunksize=(5,)>

Unfortunately this didn’t fare as well. Am I using vectorize incorrectly or is this a feature request?

In [5]: @vectorize
   ...: def f(x, y):
   ...:     return x + y
   ...: 

In [6]: f(x, x)  # oh no!
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-4438cca8adef> in <module>()
----> 1 f(x, x)  # oh no!

~/Software/anaconda/lib/python3.6/site-packages/numba/npyufunc/dufunc.py in _compile_for_args(self, *args, **kws)
    162                 argtys.append(numpy_support.map_arrayscalar_type(arg))
    163             else:
--> 164                 argty = typeof(arg)
    165                 if isinstance(argty, types.Array):
    166                     argty = argty.dtype

~/Software/anaconda/lib/python3.6/site-packages/numba/typing/typeof.py in typeof(val, purpose)
     29     if ty is None:
     30         msg = "cannot determine Numba type of %r" % (type(val),)
---> 31         raise ValueError(msg)
     32     return ty
     33 

ValueError: cannot determine Numba type of <class 'dask.array.core.Array'>

[mrocklin]

Just to put a bit of motivation behind this feature request, I’d like to write a blogpost that shows that numba and dask.array are able to inter-operate over the gufunc protocol without explicit coordination.

[sklam]

@mrocklin, yes, your comment in https://github.com/sklam/numba/commit/34f9d48b10603948b1b345091b4d8175217614bc is exactly right. I have only done the minimal to see how to make it work and to see what code needed to be changed. If someone wanted to take on the work, they would need to:

• implement the ufunc protocol properly as per your comment. they can continue from where I made the changes.
• add test (presumably w/o using dask.array; using some made up type with __array_ufunc__).
• and docs.