On Tue, Sep 10, 2019 at 10:59 AM Stephan Hoyer <shoyer@gmail.com> wrote:

On Tue, Sep 10, 2019 at 6:06 AM Hameer Abbasi <einstein.edison@gmail.com> wrote:On 10.09.19 05:32, Stephan Hoyer wrote:

On Mon, Sep 9, 2019 at 6:27 PM Ralf Gommers <ralf.gommers@gmail.com> wrote:

I think we've chosen to try the former - dispatch on functions so we can reuse the NumPy API. It could work out well, it could give some long-term maintenance issues, time will tell. The question is now if and how to plug the gap that __array_function__ left. It's main limitation is "doesn't work for functions that don't have an array-like input" - that left out ~10-20% of functions. So now we have a proposal for a structural solution to that last 10-20%. It seems logical to want that gap plugged, rather than go back and say "we shouldn't have gone for the first 80%, so let's go no further".

I'm excited about solving the remaining 10-20% of use cases for flexible array dispatching,

Great! I think most (but not all) of us are on the same page here. Actually now that Peter came up with the `like=` keyword idea for array creation functions I'm very interested in seeing that worked out, feels like that could be a nice solution for part of that 10-20% that did look pretty bad before.

but the unumpy interface suggested here (numpy.overridable) feels like a redundant redo of __array_function__ and __array_ufunc__.

A bit of context: a big part of the reason I advocated for numpy.overridable is that library authors can use it *only* for the parts not already covered by the protocols we already have. If there's overlap there's several ways to deal with that, including only including part of the unumpy API surface. It does plug all the holes in one go (although you can then indeed argue it does too much), and there is no other coherent proposal/vision yet that does this. What you wrote below comes closest, and I'd love to see that worked out (e.g. the like= argument for array creation). What I don't like is an ad-hoc plugging of one hole at a time without visibility on how many more protocols and new workaround functions in the API we would need. So hopefully we can come to an apples-to-apples comparison of two design alternatives.

Also, we just discussed this whole thread in the community call, and it's clear that it's a complex matter with many different angles. It's very hard to get a full overview. Our conclusion in the call was that this will benefit from an in-person discussion. The sprint in November may be a really good opportunity for that.

In the meantime we can of course keep working out ideas/docs. For now I think it's clear that we (the NEP authors) have some homework to do - that may take some time.

I would much rather continue to develop specialized protocols for the remaining usecases. Summarizing those I've seen in this thread, these include:1. Overrides for customizing array creation and coercion.2. Overrides to implement operations for new dtypes.3. Overriding implementations of NumPy functions, e.g., FFT and ufuncs with MKL.

(1) could mostly be solved by adding np.duckarray() and another function for duck array coercion. There is still the matter of overriding np.zeros and the like, which perhaps justifies another new protocol, but in my experience the use-cases for truly an array from scratch are quite rare.While they're rare for libraries like XArray; CuPy, Dask and PyData/Sparse need these.

We already have a split in the type system with e.g. Cython's buffers, Numba's parallel type system. This is a different issue altogether, e.g. allowing a unyt dtype to spawn a unyt array, rather than forcing a re-write of unyt to cooperate with NumPy's new dtype system.

(2) should be tackled as part of overhauling NumPy's dtype system to better support user defined dtypes. But it should definitely be in the form of specialized protocols, e.g., which pass in preallocated arrays to into ufuncs for a new dtype. By design, new dtypes should not be able to customize the semantics of array *structure*.I guess you're proposing that operations like np.sum(numpy_array, dtype=other_dtype) could rely on other_dtype for the implementation and potentially return a non-NumPy array? I'm not sure this is well motivated -- it would be helpful to discuss actual use-cases.The most commonly used NumPy functionality related to dtypes can be found only in methods on np.ndarray, e.g., astype() and view(). But I don't think there's any proposal to change that.4. Having default implementations that allow overrides of a large part of the API while defining only a small part. This holds for e.g. transpose/concatenate.

I'm not sure how unumpy solve the problems we encountered when trying to do this with __array_function__ -- namely the way that it exposes all of NumPy's internals, or requires rewriting a lot of internal NumPy code to ensure it always casts inputs with asarray().I think it would be useful to expose default implementations of NumPy operations somewhere to make it easier to implement __array_function__, but it doesn't make much sense to couple this to user facing overrides. These can be exposed as a separate package or numpy module (e.g., numpy.default_implementations) that uses np.duckarray(), which library authors can make use of by calling inside their __aray_function__ methods.5. Generation of Random numbers (overriding

RandomState). CuPy has its own implementation which would be nice to override.I'm not sure that NumPy's random state objects make sense for duck arrays. Because these are stateful objects, they are pretty coupled to NumPy's implementation -- you cannot store any additional state on RandomState objects that might be needed for a new implementation. At a bare minimum, you will loss the reproducibility of random seeds, though this may be less of a concern with the new random API.Backend switching needs global context, in any case. There isn't a good way around that other than the class dundermethods outlined in another thread, which would require rewrites of large amounts of code.I also share Nathaniel's concern that the overrides in unumpy are too powerful, by allowing for control from arbitrary function arguments and even *non-local* control (i.e., global variables) from context managers. This level of flexibility can make code very hard to debug, especially in larger codebases.Do we really need to support robust backend switching in NumPy? I'm not strongly opposed, but what use cases does it actually solve to be able to override np.fft.fft rather than using a new function?

I don't know, but that feels like an odd question. We wanted an FFT backend system. Now applying __array_function__ to numpy.fft happened without a real discussion, but as a backend system I don't think it would have met the criteria. Something that works for CuPy, Dask and Xarray, but not for Pyfftw or mkl_fft is only half a solution.

Cheers,

Ralf