[Numpy-discussion] Experimental `like=` attribute for array creation functions

Thu Aug 13 23:00:50 EDT 2020

Hello everyone again!

A few clarifications about my proposal of external peer review:

- Yes, all this work is public and announced on the mailing list. However, I don’t think there’s a single person in this discussion or even this whole ecosystem that does not have a more immediately-pressing and also virtually infinite to-do list, so it’s unreasonable to expect that generally they would do more than glance at the stuff in the mailing list. In the peer review analogy, the mailing list is like the arXiv or Biorxiv stream — yep, anyone can see the stuff on there and comment, but most people just don’t have the time or attention to grab onto that. The only reason I stopped to comment here is Sebastian’s “Imma merge, YOLO!”, which had me raising my eyebrows real high. 😂 Especially for something that would expand the NumPy API!

- So, my proposal is that there needs to be an *editor* of NEPs who takes responsibility, once they are themselves satisfied with the NEP, for seeking out external reviewers and pinging them individually and asking them if they would be ok to review.

- A good friend who does screenwriting once told me, “don’t use all your proofreaders at once”. You want to get feedback, improve things, then feedback from a *totally independent* new person who can see the document with fresh eyes.

Obviously, all of the above slows things down. But “alone we go fast, together we go far”. The point of a NEP is to document critical decisions for the long term health of the project. If the documentation is insufficient, it defeats the whole purpose. Might as well just implement stuff and skip the whole NEP process. (Side note: Stephan, I for one would definitely appreciate an update to existing NEPs if there’s obvious ways they can be improved!)

I do think that NEP templates should be strict, and I don’t think that is incompatible with plain, jargon-free text. The NEP template and guidelines should specify that, and that the motivation should be understandable by a casual NumPy user — the kind described by Ilhan, for whom bare NumPy actually meets all their needs. Maybe they’ve also used PyTorch but they’ve never really had cause to mix them or write a program that worked with both kinds of arrays.

Ditto for backwards compatibility — everyone should be clear when their existing code is going to be broken. Actually NEP18 broke so much of my code, but its Backward compatibility section basically says all good! https://numpy.org/neps/nep-0018-array-function-protocol.html#backward-compatibility <https://numpy.org/neps/nep-0018-array-function-protocol.html#backward-compatibility> 

Anywho, as always, none of this is criticism to work done — I thank you all, and am eternally grateful for all the hard work everyone is doing to keep the ecosystem from fragmenting. I’m just hoping that this discussion can improve the process going forward!

And, yes, apologies to Peter, I know from repeated personal experience how frustrating it can be to have last-minute drive-by objections after months of consensus building! But I think in the end every time that happened the end result was better — I hope the same is true here! And yes, I’ll reiterate Ralf’s point: my concerns are about the NEP process itself rather than this one. I’ll summarise my proposal:

- strict NEP template. NEPs with missing sections will not be accepted.
- sections Abstract, Motivation, and Backwards Compatibility should be understandable at a high level by casual users with ~zero background on the topic
- enforce the above with at least two independent rounds of coordinated peer review.

Thank you,

Juan.

> On 14 Aug 2020, at 5:29 am, Stephan Hoyer <shoyer at gmail.com> wrote:
> 
> On Thu, Aug 13, 2020 at 5:22 AM Ralf Gommers <ralf.gommers at gmail.com <mailto:ralf.gommers at gmail.com>> wrote:
> Thanks for raising these concerns Ilhan and Juan, and for answering Peter. Let me give my perspective as well.
> 
> To start with, this is not specifically about Peter's NEP and PR. NEP 35 simply follows the pattern set by previous PRs, and given its tight scope is less difficult to understand than other NEPs on such technical topics. Peter has done a lot of things right, and is close to the finish line.
> 
> 
> On Thu, Aug 13, 2020 at 12:02 PM Peter Andreas Entschev <peter at entschev.com <mailto:peter at entschev.com>> wrote:
> 
> > I think, arriving to an agreement would be much faster if there is an executive summary of who this is intended for and what the regular usage is. Because with no offense, all I see is "dispatch", "_array_function_" and a lot of technical details of which I am absolutely ignorant.
> 
> This is what I intended to do in the Usage Guidance [2] section. Could
> you elaborate on what more information you'd want to see there? Or is
> it just a matter of reorganizing the NEP a bit to try and summarize
> such things right at the top?
> 
> We adapted the NEP template [6] several times last year to try and improve this. And specified in there as well that NEP content set to the mailing list should only contain the sections: Abstract, Motivation and Scope, Usage and Impact, and Backwards compatibility. This to ensure we fully understand the "why" and "what" before the "how". Unfortunately that template and procedure hasn't been exercised much yet, only in NEP 38 [7] and partially in NEP 41 [8].
> 
> If we have long-time maintainers of SciPy (Ilhan and myself), scikit-image (Juan) and CuPy (Leo, on the PR review) all saying they don't understand the goals, relevance, target audience, or how they're supposed to use a new feature, that indicates that the people doing the writing and having the discussion are doing something wrong at a very fundamental level. 
> 
> At this point I'm pretty disappointed in and tired of how we write and discuss NEPs on technical topics like dispatching, dtypes and the like. People literally refuse to write down concrete motivations, goals and non-goals, code that's problematic now and will be better/working post-NEP and usage examples before launching into extensive discussion of the gory details of the internals. I'm not sure what to do about it. Completely separate API and behavior proposals from implementation proposals? Make separate "API" and "internals" teams with the likes of Juan, Ilhan and Leo on the API team which then needs to approve every API change in new NEPs? Offer to co-write NEPs if someone is willing but doesn't understand how to go about it? Keep the current structure/process but veto further approvals until NEP authors get it right?
> 
> I think the NEP template is great, and we should try to be more diligent about following it!
> 
> My own NEP 37 (__array_module__) is probably a good example of poor presentation due to not following the template structure. It goes pretty deep into low-level motivation and some implementation details before usage examples.
> 
> Speaking just for myself, I would have appreciated a friendly nudge to use the template. Certainly I think it would be fine to require using the template for newly submitted NEPs. I did not remember about it when I started drafting NEP 37, and it definitely would have helped. I may still try to do a revision at some point to use the template structure.
>  
> I want to make an exception for merging the current NEP, for which the plan is to merge it as experimental to try in downstream PRs and get more experience. That does mean that master will be in an unreleasable state by the way, which is unusual and it'd be nice to get Chuck's explicit OK for that. But after that, I think we need a change here. I would like to hear what everyone thinks is the shape that change should take - any of my above suggestions, or something else?
> 
>  
> > Finally as a minor point, I know we are mostly (ex-)academics but this necessity of formal language on NEPs is self-imposed (probably PEPs are to blame) and not quite helping. It can be a bit more descriptive in my external opinion.
> 
> TBH, I don't really know how to solve that point, so if you have any
> specific suggestions, that's certainly welcome. I understand the
> frustration for a reader trying to understand all the details, with
> many being only described in NEP-18 [3], but we also strive to avoid
> rewriting things that are written elsewhere, which would also
> overburden those who are aware of what's being discussed.
> 
> 
> > I also share Ilhan’s concern (and I mentioned this in a previous NEP discussion) that NEPs are getting pretty inaccessible. In a sense these are difficult topics and readers should be expected to have *some* familiarity with the topics being discussed, but perhaps more effort should be put into the context/motivation/background of a NEP before accepting it. One way to ensure this might be to require a final proofreading step by someone who has not been involved at all in the discussions, like peer review does for papers.
> 
> Some variant of this proposal would be my preference.
> 
> Cheers,
> Ralf
> 
> 
> [1] https://github.com/numpy/numpy/issues/14441#issuecomment-529969572 <https://github.com/numpy/numpy/issues/14441#issuecomment-529969572>
> [2] https://numpy.org/neps/nep-0035-array-creation-dispatch-with-array-function.html#usage-guidance <https://numpy.org/neps/nep-0035-array-creation-dispatch-with-array-function.html#usage-guidance>
> [3] https://numpy.org/neps/nep-0018-array-function-protocol.html <https://numpy.org/neps/nep-0018-array-function-protocol.html>
> [4] https://numpy.org/neps/nep-0000.html#nep-workflow <https://numpy.org/neps/nep-0000.html#nep-workflow>
> [5] https://mail.python.org/pipermail/numpy-discussion/2019-October/080176.html <https://mail.python.org/pipermail/numpy-discussion/2019-October/080176.html>
> 
> [6] https://github.com/numpy/numpy/blob/master/doc/neps/nep-template.rst <https://github.com/numpy/numpy/blob/master/doc/neps/nep-template.rst>
> [7] https://github.com/numpy/numpy/blob/master/doc/neps/nep-0038-SIMD-optimizations.rst <https://github.com/numpy/numpy/blob/master/doc/neps/nep-0038-SIMD-optimizations.rst>
> [8] https://github.com/numpy/numpy/blob/master/doc/neps/nep-0041-improved-dtype-support.rst <https://github.com/numpy/numpy/blob/master/doc/neps/nep-0041-improved-dtype-support.rst>
> 
> 
> 
> 
> 
> On Thu, Aug 13, 2020 at 3:44 AM Juan Nunez-Iglesias <jni at fastmail.com <mailto:jni at fastmail.com>> wrote:
> >
> > I’ve generally been on the “let the NumPy devs worry about it” side of things, but I do agree with Ilhan that `like=` is confusing and `typeof=` would be a much more appropriate name for that parameter.
> >
> > I do think library writers are NumPy users and so I wouldn’t really make that distinction, though. Users writing their own analysis code could very well be interested in writing code using numpy functions that will transparently work when the input is a CuPy array or whatever.
> >
> > I also share Ilhan’s concern (and I mentioned this in a previous NEP discussion) that NEPs are getting pretty inaccessible. In a sense these are difficult topics and readers should be expected to have *some* familiarity with the topics being discussed, but perhaps more effort should be put into the context/motivation/background of a NEP before accepting it. One way to ensure this might be to require a final proofreading step by someone who has not been involved at all in the discussions, like peer review does for papers.
> >
> > Food for thought.
> >
> > Juan.
> >
> > On 13 Aug 2020, at 9:24 am, Ilhan Polat <ilhanpolat at gmail.com <mailto:ilhanpolat at gmail.com>> wrote:
> >
> > For what is worth, as a potential consumer in SciPy, it really doesn't say anything (both in NEP and the PR) about how the regular users of NumPy will benefit from this. If only and only 3rd parties are going to benefit from it, I am not sure adding a new keyword to an already confusing function is the right thing to do.
> >
> > Let me clarify,
> >
> > - This is already a very (I mean extremely very) easy keyword name to confuse with ones_like, zeros_like and by its nature any other interpretation. It is not signalling anything about the functionality that is being discussed. I would seriously consider reserving such obvious names for really obvious tasks. Because you would also expect the shape and ndim would be mimicked by the "like"d argument but it turns out it is acting more like "typeof=" and not "like=" at all. Because if we follow the semantics it reads as "make your argument asarray like the other thing" but it is actually doing, "make your argument an array with the other thing's type" which might not be an array after all.
> >
> > - Again, if this is meant for downstream libraries (because that's what I got out of the PR discussion, cupy, dask, and JAX were the only examples I could read) then hiding it in another function and writing with capital letters "this is not meant for numpy users" would be a much more convenient way to separate the target audience and regular users. numpy.astypedarray([[some data], [...]], type_of=x) or whatever else it may be would be quite clean and to the point with no ambiguous keywords.
> >
> > I think, arriving to an agreement would be much faster if there is an executive summary of who this is intended for and what the regular usage is. Because with no offense, all I see is "dispatch", "_array_function_" and a lot of technical details of which I am absolutely ignorant.
> >
> > Finally as a minor point, I know we are mostly (ex-)academics but this necessity of formal language on NEPs is self-imposed (probably PEPs are to blame) and not quite helping. It can be a bit more descriptive in my external opinion.
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