A year ago I published Agda code for “Why dependent types matter” paper by Thorsten Altenkirch, Conor McBride and James McKinna. Now I rewrote that code in Haskell. This work is similar to my recent conversion of weight-biased leftist heaps from Agda to Haskell so I won’t go into technical details. As usual you can view the code on Github.
In January I announced my implementation of weight-biased leftist heaps verified with dependent types in Agda. This was part of my work on a paper submitted to CAV’14 conference. The paper got rejected and I decided not to resubmit it anywhere else. At this year’s ICFP listening to Stephanie Weirich’s keynote speech motivated me to finally port that implementation to Haskell, something that I had planned for a couple of months now. You can take a look at the result on github. Here I want to share some of my experiences and insights.
My overall impression is that porting from Agda to Haskell turned out to be fairly straightforward. It was definitely not a complete rewrite. More like syntax adjustments here and there. There were of course some surprises and bumps along the way but nothing too problematic. More precise details are given in the code comments.
Agda beats Haskell
When it comes to programming with dependent types Agda, being a fully-fledged dependently-typed language, beats Haskell in many aspects:
- Agda has the same language for terms and types. Haskell separates these languages, which means that if I want to have addition for natural numbers then I need to have two separate definitions for terms and types. Moreover, to tie types and terms together I need singleton types. And once I have singleton types then I need to write third definition of addition that works on singletons. All of this is troublesome to write and use. (This tedious process can be automated by using singletons package.)
- interactive agda-mode for Emacs makes writing code much simpler in Agda. Here I was porting code that was already written so having an interactive Emacs mode for Haskell was not at all important. But if I were to write all that dependently-typed code from scratch in Haskell this would be painful. We definitely need better tools for dependently-typed programming in Haskell.
- Agda admits Unicode identifiers. This allows me to have type constructors like
≥or variables like
p≥b. In Haskell I have
pgeb, respectively. I find that less readable. (This is very subjective.)
- Agda has implicit arguments that can be deduced from types. Haskell does not, which makes some function calls more difficult. Surprisingly that was not as huge problem as I initially thought it will be.
- Agda is total, while Haskell is not. Since there are bottoms in Haskell it is not sound as a logic. In other words we can prove false eg. by using undefined.
Haskell beats Agda
The list is noticeably shorter:
- Haskell has much better term-level syntax. In many places this resulted in significantly shorter code than in Agda.
- Haskell is not total. As stated earlier this has its drawbacks but it also has a good side: we don’t need to struggle with convincing the termination checker that our code does actually terminate. This was painful in Agda since it required using sized types.
gcastWithfunction is much better than Agda’s
subst. Both these functions allow type-safe casts given the proof that the cast is safe. The difference is that Agda’s
substrequires more explicit arguments (as I noted earlier the opposite is usually the case) and restricts the cast to the last type parameter (Haskell allows cast for any type parameter).
While the list of wins is longer for Agda than it is for Haskell I’m actually very happy with Haskell’s performance in this task. The verification in Haskell is as powerful as it is in Agda. No compromises required.
It’s worth remarking that my implementation works with GHC 7.6, so you don’t need the latest fancy type-level features like closed type families. The really essential part are the promoted data types.
It’s been very quiet on the blog these past few months not because I’m spending less time on functional programming but precisely for the opposite reason. Since January I’ve been working together with Richard Eisenberg to extend his
singletons library. This work was finished in June and last Friday I gave a talk about our research on Haskell Symposium 2014. This was the first time I’ve been to the ICFP and Haskell Symposium. It was pretty cool to finally meet all these people I know only from IRC. I also admit that the atmosphere of the conference quite surprised me as it often felt like some sort of fan convention rather than the biggest event in the field of functional programming.
The paper Richard and I published is titled “Promoting Functions to Type Families in Haskell”. This work is based on Richard’s earlier paper “Dependently typed programming with singletons” presented two years ago on Haskell Symposium. Back then Richard presented the
singletons library that uses Template Haskell to generate singleton types and functions that operate on them. Singleton types are types that have only one value (aside from bottom) which allows to reason about runtime values during compilation (some introduction to singletons can be found in this post on Richard’s blog). This smart encoding allows to simulate some of the features of dependent types in Haskell. In our current work we extended promotion capabilities of the library. Promotion is only concerned with generating type-level definitions from term-level ones. Type-level language in GHC has become quite expressive during the last couple of years but it is still missing many features available in the term-level language. Richard and I have found ways to encode almost all of these missing features using the already existing type-level language features. What this means is that you can write normal term-level definition and then our library will automatically generate an equivalent type family. You’re only forbidden from using infinite terms, the
do-notation, and decomposing
String literals to
Chars. Numeric literals are also very problematic and the support is very limited but some of the issues can be worked around. What is really cool is that our library allows you to have partial application at the type level, which GHC normally prohibits.
You can learn more by watching my talk on YouTube, reading the paper or the
singletons documentation. Here I’d like to add a few more information that are not present in the paper. So first of all the paper was concerned only with promotion and didn’t say anything about singletonization. But as we enabled more and more language constructs to be promoted we also made them singletonizable. So almost everything that can be promoted can also be singletonized. The most notable exception to this rule are type classes, which are not yet implemented at the moment.
An interesting issue was raised by Adam Gundry in a question after the talk: what about difference between lazy term-level semantics and strict type-level semantics? You can listen to my answer in the video but I’ll elaborate some more on this here. At one point during our work we were wondering about this issue and decided to demonstrate an example of an algorithm that crucially relies on laziness to work, ie. fails to work with strict semantics. I think it’s not straightforward to come up with such an algorithm but luckily I recalled the backwards state monad from Philip Wadler’s paper “The essence of functional programming”1. Bind operator of that monad looks like this (definition copied from the paper):
m `bindS` k = \s2 -> let (a,s0) = m s1 (b,s1) = k a s2 in (b,s0)
The tricky part here is that the output of call to
m becomes input to call to
k, while the output of call to
k becomes the input of
m. Implementing this in a strict language does not at all look straightforward. So I promoted that definition expecting it to fail spectacularly but to my surprised it worked perfectly fine. After some investigation I understood what’s going on. Type-level computations performed by GHC are about constraint solving. It turns out that GHC is able to figure out in which order to solve these constraints and get the result. It’s exactly analogous to what happens with the term-level version at runtime: we have an order of dependencies between the closures and there is a way in which we can run these closures to get the final result.
All of this work is a small part of a larger endeavour to push Haskell’s type system towards dependent types. With singletons you can write type-level functions easily by writing their definitions using the term-level language and then promoting these definitions. And then you can singletonize your functions to work on singleton types. There were two other talks about dependent types during the conference: Stephanie Weirich’s “Depending on Types” keynote lecture during ICPF and Richard’s “Dependent Haskell” talk during Haskell Implementators Workshop. I encourage everyone interested in Haskell’s type system to watch both of these talks.