[Numpy-discussion] NEP 31 — Context-local and global overrides of the NumPy API

Hameer Abbasi einstein.edison at gmail.com
Tue Sep 10 09:05:55 EDT 2019


On 10.09.19 05:32, Stephan Hoyer wrote:
> On Mon, Sep 9, 2019 at 6:27 PM Ralf Gommers <ralf.gommers at gmail.com 
> <mailto:ralf.gommers at 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, but the unumpy interface suggested here 
> (numpy.overridable) feels like a redundant redo of __array_function__ 
> and __array_ufunc__.
>
> 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.

>
> (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*.
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.
>
> (3) could potentially motivate a new solution, but it should exist 
> *inside* of select existing NumPy implementations, after checking for 
> overrides with __array_function__. If the only option NumPy provides 
> for overriding np.fft is to implement np.overrideable.fft, I doubt 
> that would suffice to convince MKL developers from monkey patching it 
> -- they already decided that a separate namespace is not good enough 
> for them.

That has already been addressed by Ralf in another email. We're 
proposing to merge that into NumPy proper.

Also, you're missing a few:

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.

5. Generation of Random numbers (overriding RandomState). CuPy has its 
own implementation which would be nice to override.

>
> 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.
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.
>
> Best,
> Stephan
>
>
>
>
>
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