Maintainership roundtable and discussion

[DeliciousHair]

I'm just looking at the activity level in terms of PRs being merged, wondering if this project is still a thing?

[nilgoyette]

Last time I heard about this problem, both maintainers were too busy. IIRC, one is a teacher and has several projects going on and the other has just finished his phd and started working. xd009642 was added as a maintainer in 2020.

See this issue for more information.

[bluss]

I'd like to give away more of "my role" since I don't have the bandwidth unfortunately. That means bringing on permanent maintainers.

I maybe want to keeping going working on lower level stuff, like I have with matrixmultiply now and would maybe with numerical simd for other blas-like operations that could benefit ndarray.

I guess it's a bit of a pickle now that the organization is bigger than just one repo but low activity across the board. I can't necessarily do everything without asking others.

The status of the code is "not great" in terms of how easy it is to maintain and change (me knows most of the internals, some lack of abstractions for internals, lots of unsafe code that works just because of careful contributors, easy to mess it up).

What do @termoshtt @nilgoyette @adamreichold @jturner314 @LukeMathWalker think about this? What's the direction for ndarray (there's a lot that can be done - modernisation using const generics)? Are there other projects that we should emulate? Or that have made ndarray redundant?

[ZuseZ4]

@bluss Since you mentioned matrixmultiply and simd in the other post, did you see the work from sarah (faer-rs)? She does seem to have extremely competitive performance when compared to openBLAS, Eigen and Matrixmultiply. I generally liked the design of ndarray a touch more than that of nalgebra due to the data I work with. If I can add a wish for the direction of ndarray, I'd love to see ndarray using faer for more of it's operations.

[antimora]

@bluss Thanks for the update because the community has been worried about the maintenance of such important Rust library. Many projects rely on its existence and can't find any drop in replacement. I hope the ndarray creators and maintainers can come up with a long term solution. I am sure there are people who would be happy very least to review PRs.

[DeliciousHair]

@bluss good to see this is not abandoned! I also really appreciate the lack-of-bandwidth problem, suffering from it myself regularly.

The issue of how to bring on "permanent" maintainers is an ongoing problem though, at least for open-source projects like this as they often live and die at the bandwidth (or interest) of a small handful of people. Given that this project does not seem to have the corporate backing that tends to address this particular problem via financial incentives, one possible option is that maintenance is handled via committee, with membership that can be changed. This would require first setting up a contributing guideline and a code of conduct that would enable said committee to exist, but it may allow progress to actually progress without the time-poor bottleneck in the mix.

Just my two cents, really happy to see this conversation happening :-)

[adamreichold]

What do you think about this?

I think bringing in more people to share the load is a good idea. It can still fail as volunteers sometimes just do not have any time to contribute. For example, we do have multiple active maintainers who continue the work independently at PyO3. But currently, our active phases almost never overlap which makes small changes slow and it often feels impossible to obtain the necessary consensus for large changes.

As for actually doing it, I see two options: You give some people you are able to trust somewhat access and let it run living with the likely but hopefully temporary breakage resulting from that. Or you increase your time investment for a while to actively guide new people into reviewing PR and making releases but I am not sure if that is possible at all.

What's the direction for ndarray (there's a lot that can be done - modernisation using const generics)?

For me personally, with my rust-numpy hat on, this crate is mainly a fundamental data structure for scientific computing and hence I would - also in consideration of the bandwidth issue - prefer if it would focus on that. So yes, modernisation but also simplification, i.e. trying to move even more towards an ecosystem model like ndarray-stat and ndarray-linalg where most operations live outside of ndarray itself. If the whole can of worms of accelerated subroutines via BLAS et al. could be simplified or moved into separate add-on crates, the would be great as well.

Are there other projects that we should emulate? Or that have made ndarray redundant?

I do not know of any with the same "fundamental data structure" focus as ndarray.

Is the NumFOCUS organisation something you could see yourself contacting and asking for (monetary) support? Would money alone actually solve anything?

[DeliciousHair]

Would money alone actually solve anything?

I think the thing it does help with is that funding enables somebody to justify prioritizing their time to doing maintenance should there be conflicting pressures on them as well. I mean it's far from a perfect solution, but life is expensive so unless one can afford to volunteer their time to an open-source software project (and many people can and do, don't get me wrong) then if there is demand X that pays the rent vs. really-intersting-project Y, then X will usually win. A financial incentive simply helps to level this field a bit.

As for directions / applications / focus, it occurs to me that a selling point that could be used to attract some funding (I don't know how any of this stuff works, outside my realm of experience) is that using these shiny-new rust implementations of ubiquitous python libraries does have massive market appeal--just look at how hot polars has become, partially because it stomps all over pandas in many ways. Thus, rather than taking the view that ndarray is a sort of numpy-for-rust, in light of projects like faer and rapl, it may be better to view the ecosystem as the new rust-backed numpy, assuming said ecosystem would include some python wrappers of course.

Dispensing with BLAS/LAPACK and gaining out-of-the-box parallelization has immeasurable value to many industries and use-cases after all.

[adamreichold]

I think the thing it does help with is that funding enables somebody to justify prioritizing their time to doing maintenance should there be conflicting pressures on them as well. I mean it's far from a perfect solution, but life is expensive so unless one can afford to volunteer their time to an open-source software project (and many people can and do, don't get me wrong) then if there is demand X that pays the rent vs. really-intersting-project Y, then X will usually win. A financial incentive simply helps to level this field a bit.

I do not disagree, but I would like to add that this reasoning is limited to situations where one works on a project basis. If you have a steady job and obligations to a family, funding for individual projects does not change how much time one has for FOSS work.

[nilgoyette]

The status of the code is "not great" in terms of how easy it is to maintain and change (me knows most of the internals, some lack of abstractions for internals, lots of unsafe code that works just because of careful contributors, easy to mess it up).

That's my main problem with this crate if I'm going to help maintain it. When I open the internals, I don't understand what I'm reading. I'm usually able to add a method and whatnot, but I don't feel knowledgeable enough for "more complex" stuff.

• "lack of abstractions for internals" @bluss Is there a list somewhere? Did you have ideas on how to solve them but never had the time to do it? Can you share those ideas?
• IIRC, @jturner314 had a PoC for a new iteration management. I don't remember the details, but he claimed that it was, at least, faster. This is super interesting. Can we know the status of this project? Once we have more details, maybe someone will be able to finish it?

So yes, modernisation but also simplification, i.e. trying to move even more towards an ecosystem model like ndarray-stat and ndarray-linalg where most operations live outside of ndarray itself.

This is an excellent idea and this is already what's going on. I created ndarray-ndimage for that reason. ndarray should probably be kept "small" and clean, then let others build on it.

ndarray is a sort of numpy-for-rust

This is exactly my opinion. I don't think ndarray is redundant. nalgebra certainly share some features, but I do not use them in the same ways nor with the same goals. At least for now, ndarray has a reason to live!

[antimora]

@nilgoyette

This is exactly my opinion. I don't think ndarray is redundant. nalgebra certainly share some features, but I do not use them in the same ways nor with the same goals. At least for now, ndarray has a reason to live!

Just to highlight the importance of this library. We use NDArray as one of our backends for Burn's deep learning framework.

[jturner314]

Personally, now that I'm no longer a student, am working full-time, and have more responsibilities, I have less time and energy to devote to FOSS. And, unfortunately, I don't have much need for ndarray at work, so it's hard to justify spending time on it at work. (I still use ndarray for a few things, such as interacting with NumPy and conveniently parallelizing things in some cases, but nalgebra is usually a better fit for the things I'm working on now.)

I do think that an n-dimensional array type is very important; while nalgebra is nicely polished, the 1-D and 2-D vectors and matrices provided by nalgebra don't satisfy all use cases.

It would be great to bring on more people to take over the maintenance. I'd also be happy to move my ndarray-npy crate to the rust-ndarray organization; I haven't had the energy to really maintain it properly by myself.

As far as improvements go, I think that it would be possible to simplify ndarray's internals and public API by taking advantage of const generics and GATs and reworking the API in terms of traits (instead of the generic ArrayBase type). By simplifying the implementation (using better abstractions which enforce correctness) and making the public API easier to use, I'd hope that more people would use and contribute to ndarray.

I have some ideas for how to update the internals and API using traits, GATs, and const generics, but I doubt I'll find the time to implement it all myself. If someone is interested on working on it, I'd be willing to chat about it.

IIRC, @jturner314 had a PoC for a new iteration management. I don't remember the details, but he claimed that it was, at least, faster. This is super interesting. Can we know the status of this project? Once we have more details, maybe someone will be able to finish it?

Yeah, ndarray currently has optimal iteration only in a few special cases (all arrays standard layout or all Fortran layout). By reordering iteration to be as close to memory order as possible, flattening nested loops where possible, and tiling where necessary, it would be possible to improve the iteration performance over arrays of dissimilar or non-standard layouts. I put together an initial prototype of the reordering and loop-flattening pieces at https://github.com/jturner314/nditer. I also worked on automatic tiling but haven't pushed that to the repo. The primary thing that blocked me from finalizing that project was testing -- the code is complicated in some places, so I really wanted to implement proptest support for generating and testing with arrays of arbitrary shapes and layouts. I didn't get a chance to finish it. Another way to improve performance would be to take better advantage of SIMD, but I didn't work on that.

[bluss]

Great input from everyone. I wasn't fully aware of faer-rs, no, so thanks for the pointer.

I would like to invite those participating in the discussion here to become collaborators in ndarray. It's unfortunately not realistic for me to take on a greater responsibility now, so that is not going to be the outcome of the discussion, even though one could reasonably wish for it. I want to leave the way open for others to develop ndarray without having me as gatekeeper.

Can I for example ask @adamreichold, are you interested? Do you have any contacts that are?

[adamreichold]

Can I for example ask @adamreichold, are you interested? Do you have any contacts that are?

Took me a while to consider the commitment but yes, I am interested. I would be glad if I could help with maintenance and eventually further development.

I do think my own time budget and my inexperience in maintaining this particular project imply that I could not immediately tackle any large charges. On contrary, in the beginning I would deliberately limit myself to building and packaging issues and reviewing contributions with the aim of producing point releases and hopefully eventually a 0.16.0 release. Ideally, I will be able to learn enough to do more in the future.

(I also do not want to give a wrong impression, I do not consider myself well-networked and have few contacts beyond direct collaboration via the FOSS projects. I will ask the one acquaintance who I think could be in a position to contribute though.)

[nilgoyette]

I would like to invite those participating in the discussion here to become collaborators in ndarray.

I find myself in jturner's situation (less/no more ndarray at work, for a while), but I really love ndarray and I can at least

• check the issues page and answer, when I'm able.
  • We currently have 213 of those. Can I (we) clean it? I offer myself to read all of them and close them when they are no longer relevant. Can I have the right to do so?
• try to review the PR when I do understand what's going on

[bluss]

Awesome, I've added you on this repo, but there is more admin to do - the whole org - which we will get to

[DeliciousHair]

Thought I'd chime in here that I'd be happy to put my hand up to volunteer for some sort of maintainer / reviewer status. At the moment I'm also trying to contribute to rapl so I've at least got my mind in the correct linear algebra / tensor space to be thinking about this. Work schedule is a bit up and down, rather "up" at the moment so free time is at a premium and contributions will be slim for the next month or so. However, I do have enough availability to do reviews most any time, and am happy to participate in any planning where my input may be of value.

[bluthej]

Hey! I was looking to start contributing to ndarray and found this thread.

If my understanding is correct it sounds like going through the good first issues and sending PRs might not be the most useful thing to do right now? If I'm wrong I'd be happy to start contributing!

In any case, it might be worth updating the status section of the readme to make the information readily available 🙂 Especially for people who would like to depend on this crate or contribute to it.

[nilgoyette]

@bluthej This project is nor dead nor actively maintained. It seems that there has been as much action in the 2 last months than in the 3 least years, which is kinda promising. Look at the commits to get a better understanding.

As a side note, we built an important part of our main project (medical imaging company) on ndarray and we do not regret it at all. Could it be better? Yes, of course. Does it offer everything we needed? Yep.

I can't answer your specific question (Should I contribute?), but I can at least say that your issues/discussions will be answered and your MR will be read.

[grothesque]

It seems to me that the apparent stagnation of this project is only partially explained by the original contributors being too busy. This is normal, but it could be expected that new contributors show up for such an important or even foundational (for numerical Rust code) crate. I suspect that the very high level of complexity of this library plays a role in limiting contributions. And my gut feeling is that a fair share of this complexity is due to overengineering and could be removed.

Are there people who would be interested in discussing the feasibility of a radically simplified and modernized "next generation" ndarray? This could eventually lead to a prototype that could eventually either be absorbed into ndarray proper, or the old ndarray could eventually be deprecated, or remain as a compatibility layer, or whatever. I believe that work on such a simplified and modernized ndarray could be a way to revitalize numerics in Rust.

There seems to be consensus that if ndarray was started today with the benefit of hindsight (and with current rustc), its design would be quite different:

• The core library would be limited to essential functionality that is required to safely work with n-dimensional arrays. Any operations that can be efficiently implemented externally would be provided by traits in separate crates. (See https://github.com/rust-ndarray/ndarray/issues/1272#issuecomment-1534061816.)

• Most array functionality would be implemented for a reference type. This would simplify the API and reduce monomorphization bloat.

• The library would provide a few array types (most importantly an owned and a view variant in addition to the reference type mentioned above). Most essential functionality would be available through the reference type to which these types would dereference. Functionality that needs to take ownership would be available through one or a few common traits: see section "Future possibilities" of https://github.com/rust-ndarray/ndarray/issues/879 and https://github.com/rust-ndarray/ndarray/issues/1272#issuecomment-1539249191.

While the above does not seem to be controversial, here are some additional ideas for further streamlining:

• With the new design, the job of the array types (e.g. Array for an owned array) would be to manage memory. So it seems that all the RawData, Data, etc. would no longer be necessary.

• Same for the various Repr variants like OwnedRepr. These could be dropped and the necessary logic incorporated into the array types. ArcArray and CowArray would be replaced by specific array types as well (or perhaps removed or delegated to a separate crate).

• Given that ndarray is focused on dynamically sized arrays (i.e. the shape is dynamic), is there actually much value in supporting a static number of dimensions (i.e. ndim can be static)? It is true that in current ndarray IxDyn is slow, but looking at the example given in the issue, this is not because of the extra storage/allocation required (this should be negligible), but because of inefficient code.

  I believe that with a clever implementation the number of extra allocations required for an dynamical ndim could be reduced almost to zero. Currently each IxDyn instance (there are two per array, one for the shape, and one for the strides) holds one allocation (if ndim > 4). It should be possible to unite both in a single allocation or even in one allocation together with the data. In this way, a single extra allocation would be needed only for views that change stride/shape and have ndim > 4 (or some other threshold).

• RawArrayView and similar structs are the equivalent of raw pointers. Do they actually have convincing use cases? They do have a cost in terms of code and API complexity, so perhaps they could be simply left out?

---

With all or most of the above, a new ndarray should be radically simpler and smaller, both in terms of implementation and API. User code that reads any array of f64 would simply take &ArrayRef<f64>, for example.

[nilgoyette]

I suspect that the very high level of complexity of this library plays a role in limiting contributions.

I tried contributing to this crate and everything non-trivial was/is too complex for me. As a professional Rust/C++/Python programmer, I'm not particularly proud of writing this, but it is the way it is. So, yes, I totally agree with your sentence. Now that I've said that I'm ignorant about the internals, here's my opinion :) on some of your points

• Having external crates to handle the complexity seems like a good idea, but then we have several unmaintained projects (i.e. ndarray-stats). This might be an entirely different problem and we can't do much about it in the issue... I think that, if we remove something in this crate, there might not be another crate to offer it.
• The reference type RFC. I never took the time to read it before, but now that I did, it seems logical to respect the way other Rust objects work. I'm the maintainer of ndarray-ndimage crate and I often wondered what kind of parameters I should accept.
• I always thought IxDyn was slow because - well, it's dynamic. We need to allocate more and we have more branch prediction errors. If it's possible to make the dynamic arrays as fast as the static ones, it's perfect. However, how will we link some methods with arrays of a specific size, like some of the constructors for 2D arrays (identity and from_diag)?
• I used ndarray for several years and I never saw a use-case for ArcArray and CowArray. Of course this is not a reason to remove them :) but if there's not much demand for those and supporting them is actually complex, then they should probably be removed.

[akern40]

I suspect that the very high level of complexity of this library plays a role in limiting contributions. And my gut feeling is that a fair share of this complexity is due to overengineering and could be removed.

Agreed! I had the same gut impression when I came to the library. However, after working intensively on the array reference RFC, I can say that much of the complexity is more warranted than it first appears. I'd want to be careful not to confuse "unneeded complexity" with "undocumented complexity". Still, I think there are cleaner designs that could keep the same capability with clearer abstractions.

Are there people who would be interested in discussing the feasibility of a radically simplified and modernized "next generation" ndarray? This could eventually lead to a prototype that could eventually either be absorbed into ndarray proper...

I am interested in this, but I'd strongly encourage this effort to be done under the ndarray name. The Rust multi-dim array ecosystem is already quite difficult for newcomers to navigate, having to find, understand, and differentiate between ndarray and the strongly-supported nalgebra with its focus on static-sized 2D matrices (not to include other participants, such as faer-rs). Since ndarray never hit 1.0 and therefore can still use breaking changes, I think it's likely that a path forward could be found that slowly weans users off of the current design via deprecation while building a stronger foundation for new users. I'd think of it more like aiming to bring ndarray to v1.0.

• Given that ndarray is focused on dynamically sized arrays (i.e. the shape is dynamic), is there actually much value in supporting a static number of dimensions (i.e. ndim can be static)?

I believe if the library is designed carefully, we can leave the door open to (and maybe should provide) statically-dimensioned arrays. I think this for a few reasons: firstly, if you look at projects like numpy, PyTorch, or jax, they have spent years trying to build up type annotation systems that can at least indicate the dimensionality (if not shape) of their arrays. While this effort is easier in Rust (without the limitations of Python's type hint system), I think we should take a strong hint from efforts in other languages that knowing the dimensionality of an array in your type system is a pretty important capability. The second reason is that it opens the door to potentially-powerful optimizations, like things that you can do if you absolutely know that your array is just a 2D matrix, or even having some fancy "layout" type that just represents diagonal matrices; focusing only on dynamic-sized arrays may blind us to the designs necessary to make these things happen.

• RawArrayView and similar structs are the equivalent of raw pointers. Do they actually have convincing use cases?

I think this is a bit of a misconception about the job of Raw... and its role in creating complexity. I don't think maintaining a "raw pointer" type in ndarray is particularly difficult (see my comment on the array reference RFC), and keeping the capability for low-level, unsafe, or other advanced usage seems important to such a fundamental crate.

• Same for the various Repr variants like OwnedRepr. These could be dropped and the necessary logic incorporated into the array types. ArcArray and CowArray would be replaced by specific array types as well (or perhaps removed or delegated to a separate crate).

With all or most of the above, a new ndarray should be radically simpler and smaller, both in terms of implementation and API. User code that reads any array of f64 would simply take &ArrayRef<f64>, for example.

Interesting note on these two: I've been thinking it over a lot, and I think generics in this case are very hard to avoid as cleanly as the above example. Obviously you need one for the type of the element. Given that I'm arguing for keeping statically-dimensioned arrays (and opening the door to fancy other layouts), that requires a second generic. And after a lot of thought, I think that you essentially have two options for things like Arc and Cow at the data-level (keeping in mind you could always wrap an entire array reference in an Arc or Cow or whatever, as long as you're chill with having changes to the array's layout also being included in the reference count or clone-on-write capability).

1. Option 1: pointer wrappers that aren't included in the type system. Bury the wrappers in an enum, build that enum into the basic ndarray array type, and voila, you can get away without a third generic. The costs here are pretty bad, though. Including Cow as an option suddenly requires every ndarray to include only elements that are Clone. It also precludes users (and us) from reasoning about the various shared array types without runtime inspection. And finally, that runtime inspection (and runtime access to the underlying pointer) may have performance implications that are very difficult to benchmark.
2. Option 2: include the pointer wrappers in the type system. This is what ndarray does right now, and it requries a third generic: Rust just can't reason about non-concrete types without them. You also can't maintain ArcArray and CowArray as completely separate types while writing a non-generic function for them.

Sorry if those explanations kinda suck; I'm having trouble explaining clearly what I think is a fundamental trade off. Also, a disclaimer: it may be possible to still do this by managing to write a Deref from something like ArcArrayRef to ArrayRef, where ArcArrayRef carries an Arc<T> and ArrayRef carries a NonNull<T>, but I haven't been able to figure out how to do that.

I used ndarray for several years and I never saw a use-case for ArcArray and CowArray. Of course this is not a reason to remove them :) but if there's not much demand for those and supporting them is actually complex, then they should probably be removed.

Music to my ears.

Finally, a few thoughts that aren't included in the above conversation:

1. If we're considering a redesign, I'd make a shortlist of capabilities we'd like to support. For example, GPU arrays are probably out (see this Reddit comment on why), but I'd bet we want to support sparse arrays. Special layouts (e.g., diagonals) may also be of interest. What needs to be considered in the design to make that possible?
2. Despite my first bullet point, I'd encourage us to choose a first step and make that happen; I can imagine a goal like "redesign the internals to support all future use cases" as the sort of project that never gets to a release (speaking from personal experience here 😅). In addition, an increasing cadence of releases and contributions will give users trust in the vitality of the crate, hopefully drawing even more contributors.
3. I'd also consider "competitive advantage": what can ndarray do that others can't, and what are we leaving to others? For example, nalgebra seems to have statically-shaped and stack-allocated matrices down pretty pat. Seems like we shouldn't focus on that use case?
4. On that note, let's consider compatibility, with nalgebra but also with PyO3, numpy, and other efforts like faer-rs and other accelerator efforts.

[bluss]

Great to see your interest for this @akern40 and @grothesque!

IxDyn is slow because when I've been working on it, it's a feature that's an extra, along for the ride, and has not been designed for. Its purpose has been to help encapsulate data, not be used for numerical operations.

I like the analysis done here, and the complexity level in ndarray is high like you say. The internal model of ndarray is not too complicated, but the knowledge of it is used in too many places. So refactoring that would definitely be welcome.

(To some extent RawArrayView is an example of finding a more basic common building block for some operations - but its reason to exist is to handle uninitialized array elements correctly.)

And yes, the focus should (ideally..) be on continuous delivery (making releases). That's how we make real contributions to the ecosystem. This is also maybe the hardest thing to recruit, someone (or a group) who can take over driving making releases. :slightly_smiling_face:

User code that reads any array of f64 would simply take &ArrayRef, for example.

I agree with the general push towards this ideal, without saying anything now about if dimensionality information should be static or not, if it should be a concrete type or a trait based interface or not.

[akern40]

Thanks for the feedback, @bluss! Can you chime in at all about your opinions on keeping the data-level Arc and Cow? It feels to me like that's a big design decision at the center of a refactor, and would love to hear some input from someone with a lot more knowledge of the library.

As for pushing releases, I'd be happy to be part of a team that works on this. I'm a relative newcomer, but I'm strongly interested and have an ok understanding of the array part of the codebase (I'm a little shakier on iteration and dimensionality, but those will come with time). I've also got the time right now in a way that others with more life responsibilities may not. Happy to talk offline if that's of interest.

[daniellga]

* I used `ndarray` for several years and I never saw a use-case for `ArcArray` and `CowArray`. Of course this is not a reason to remove them :) but if there's not much demand for those and supporting them is actually complex, then they should probably be removed.

It's funny I stumbled into a use case today, the same day I am reading your comment. I also use ArcArray in my far less important crate for the COW behaviour and for being Send and Sync. IxDyn seems to be the only option if I want to implement a port of ndarray in other languages, like python, for example. So I think it's really important to keep it.

Thanks you all for your work!

[grothesque]

Thanks for your comments! I will reply to all the points, but this may take me some time.

@akern40 wrote:

I am interested in this, but I'd strongly encourage this effort to be done under the ndarray name. The Rust multi-dim array ecosystem is already quite difficult for newcomers to navigate, having to find, understand, and differentiate between ndarray and the strongly-supported nalgebra with its focus on static-sized 2D matrices (not to include other participants, such as faer-rs). Since ndarray never hit 1.0 and therefore can still use breaking changes, I think it's likely that a path forward could be found that slowly weans users off of the current design via deprecation while building a stronger foundation for new users. I'd think of it more like aiming to bring ndarray to v1.0.

I fully agree about avoiding to split the ecosystem. What I tried to suggest is that there might be value to explore the viability of a radically simplified ndarray foundation in a separate crate. That may give clarity about what is feasible without having to consider current users of ndarray.

Once it is clear what is feasible, the necessary changes could be added to ndarray proper in a way that gives users and depending crates time to adapt. But without knowing what is feasible, it seems difficult to justify bold changes.

[grothesque]

Now for IxDyn. Would love to hear whether you agree or rather think that I get carried away in my argumentation.

@akern40 wrote:

I believe if the library is designed carefully, we can leave the door open to (and maybe should provide) statically-dimensioned arrays. I think this for a few reasons: firstly, if you look at projects like numpy, PyTorch, or jax, they have spent years trying to build up type annotation systems that can at least indicate the dimensionality (if not shape) of their arrays. While this effort is easier in Rust (without the limitations of Python's type hint system), I think we should take a strong hint from efforts in other languages that knowing the dimensionality of an array in your type system is a pretty important capability.

Sure, these typing systems are useful, but they are not just about ndim, but also about shapes (like fixing the length of some axes, or expressing that these two axes have the same length or that "unit"). Is anyone capable of doing such checking at compile time in any language?

Already a Rust array library where ndim and all elements of shape are static but fully generic would be very cool. However I am not sure whether technically the language is ripe even for this. (The nalgebra crate might be doing just what is feasible right now.)

Since ndarray’s core business is dynamically shaped arrays (as in BLAS/LAPACK-style linear algebra and not in 3d vector linear algebra), adding partially static shapes to this would only increase complexity: Given that already a purely static-shaped array library would be technically very difficult, going beyond that seems even more so.

Case in point: ndarray’s current model (dynamic shape/strides with optionally static ndim) is very limited compared to jaxtyping, but it’s already responsible for a fair share of API and implementation complexity (without much gain to show for it as I try to demonstrate below). My impression is that the price for the small gain in static checking is too high.

The second reason is that it opens the door to potentially-powerful optimizations, like things that you can do if you absolutely know that your array is just a 2D matrix, or even having some fancy "layout" type that just represents diagonal matrices; focusing only on dynamic-sized arrays may blind us to the designs necessary to make these things happen.

I would like to argue that it is incoherent to pair static ndim with dynamic shape and strides. Shape is a more low-level property in the sense that it matters in the innermost loops, while ndim is a more high-level property that rather determines the number of loops. (For optimizing inner loops knowing statically the innermost element of shape and strides would be more useful.) Moreover there are typically "few" possible values for ndim, so even if it is dynamic, it can be still dealt with efficiently:

• To emit really efficient code (say SIMD-optimized code for 4x4x4 arrays), shape and strides would have to be static as well. Should knowing ndim statically matter, one is free to emit separate code paths for different ndim values and branch/check at runtime. In most cases where static ndim is applicable, the runtime cost of dynamic ndin should be negligible thanks to the CPU’s branch prediction (see also https://github.com/rust-ndarray/ndarray/issues/1340).

• Currently IxDyn means two extra allocations when ndim > 4. I believe these could be easily reduced to a single one, or even zero if one is ready to share a single allocation for shape, strides and data - this should be good for cache locality as well.

It seems to me that the only real advantage of static ndim is the ability of the compiler to catch some errors at compile time. But note that ndim is just one among several potentially useful properties that could be encoded in the type (at least in principle). Singling out ndim, a property that can be checked at runtime at negligible cost, seems to me a needless complication for a library focused on rather large dynamically shaped arrays.

@nilgoyette wrote:

I always thought IxDyn was slow because - well, it's dynamic. We need to allocate more and we have more branch prediction errors. If it's possible to make the dynamic arrays as fast as the static ones, it's perfect. However, how will we link some methods with arrays of a specific size, like some of the constructors for 2D arrays (identity and from_diag)?

It would no longer be possible to express in the type system that identity emits a 2D array, but I do not think that this is a big deal. After all, identity emits a square array, and this is not encoded as well. The function identity would simply return a dynamically sized array that happens to be 2D and square.

@bluss wrote:

IxDyn is slow because when I've been working on it, it's a feature that's an extra, along for the ride, and has not been designed for. Its purpose has been to help encapsulate data, not be used for numerical operations.

But IxDyn is relevant. For example what brought me to ndarray is my work on a Rust library for tensor network computations (a library similar to https://itensor.org/). There, one has to deal with networks that contain a mixture of arrays of variable ndim all the time.

[akern40]

It's funny I stumbled into a use case today, the same day I am reading your comment.

@daniellga funny how those things happen! Question about that usage: is it important for your use that the data specifically is Arc, separate from the shape/strides? Would it be ok if the data and shapes/strides were all atomically reference counted together? Also, I notice that you use IxDyn for that implementation, then wrap that with a usize const generic. Two questions: why did you opt for const generic sizes for your tensors, and if there were a const-generic implementation of dimensions (it's been mentioned in a few comments and issues here), would that be of interest?

@grothesque:

Once it is clear what is feasible, the necessary changes could be added to ndarray proper in a way that gives users and depending crates time to adapt. But without knowing what is feasible, it seems difficult to justify bold changes.

Agreed. I actually have a repo that I was using for mocking up the array reference RFC; if that's a good place, happy to use its issues/PRs/codebase as a place for people to do some design concepts.

On the note of IxDyn, I think you make strong points. I'll try to be brief in my responses, but here's my top-line: dynamic-dimensional arrays should be possible, easy, and just as fast as static-dimensional arrays (should the latter exist). Ergonomic aliases should also exist to easily employ dynamic-dimensional arrays without worrying quite so much about generics.

only real advantage of static ndim is the ability of the compiler to catch some errors at compile time

Even if this I the only advantage, that seems worth it to me? I also think it aligns with a Rust ethos: if we can get the compiler to check it for us, let's do that.

But note that ndim is just one among several potentially useful properties that could be encoded in the type

It would no longer be possible to express in the type system that identity emits a 2D array, but I do not think that this is a big deal. After all, identity emits a square array, and this is not encoded as well

I actually think this is the stronger argument for maintaining genericity in some "layout" parameter. Seems like it would be better to build in a generic that lets us (and others) play with layouts (fixed dim, fixed stride, diagonal, sparse(?), etc), then expose that as a lower-level API. Maybe identity should indicate that it returns a square array. When users come to learn ndarray, present them first with a simple dynamic-dimensional &ArrayRef<f64>, then let them discover/learn the more complex API as needed. I think it would also make ndarray a stronger candidate as a base library for others to build on for specialized or niche tasks.

focused on rather large dynamically shaped arrays

Just to throw my two cents in here, I came to ndarray because I was working on an application that specifically deals with 1D, 2D, and 3D state matrices - matrices that are often (3,), (N,3), or (N,3,3). Frankly, I started with nalgebra, because their fixed-shapes implementation is particularly powerful for state matrices. However, they have no 3D support (nor does the C++ Eigen library, officially), so I moved to ndarray instead. But I still liked specifically knowing the dimensionality of the arrays.

[bluss]

Thanks for the feedback, @bluss! Can you chime in at all about your opinions on keeping the data-level Arc and Cow? It feels to me like that's a big design decision at the center of a refactor, and would love to hear some input from someone with a lot more knowledge of the library.

I was thinking - as long as there is more than one kind of array type (i.e both owned and view types) then having ArcArray is a relatively minor addition. Maybe that's not quite right, not sure? Copy on write data is wanted but not strictly necessary (both Arc and Cow in this crate are some variant of that) and the ArcArray kind is the more important IMO. (Yes, I do not engage so much with the design discussion - please keep it going as you are if you want - I just don't have the bandwidth.)

As for pushing releases, I'd be happy to be part of a team that works on this. I'm a relative newcomer, but I'm strongly interested and have an ok understanding of the array part of the codebase (I'm a little shakier on iteration and dimensionality, but those will come with time). I've also got the time right now in a way that others with more life responsibilities may not. Happy to talk offline if that's of interest.

I would be happy to add you to the team.

My responsibility is I think - still - to get 0.16.0 out (with more or less the current master). Of course we could start collaborating already there. It's more or less just the changelog that's needed and I've been meaning to write it.

[daniellga]

@daniellga funny how those things happen! Question about that usage: is it important for your use that the data specifically is Arc, separate from the shape/strides? Would it be ok if the data and shapes/strides were all atomically reference counted together? Also, I notice that you use IxDyn for that implementation, then wrap that with a usize const generic. Two questions: why did you opt for const generic sizes for your tensors, and if there were a const-generic implementation of dimensions (it's been mentioned in a few comments and issues here), would that be of interest?

Sorry I am not a developer of that crate so I can't answer these questions. It just happened that I was peeking at that project at the same time I saw this thread.

[akern40]

I would be happy to add you to the team.

My responsibility is I think - still - to get 0.16.0 out (with more or less the current master). Of course we could start collaborating already there. It's more or less just the changelog that's needed and I've been meaning to write it.

Happy to join. Seems to me like we might need a communication channel that's not GitHub issues, though 😅. I know you're low bandwidth, but in the hopes of a smooth transition to less work, can you (or I) set up a Zulip/Slack/Discord to communicate?