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Using GHC’s RebindableSyntax for a Dynamic State “Fauxnad”

I just learned about GHC’s RebindableSyntax extension from chrisdoner’s thread on reddit and wanted to play around with scratching a couple itches I’ve had. In this post I’ll illustrate using RebindableSyntax to allow us to use haskell’s do notation in a State-monad-like construction, in which our state type is allowed to change (I’ve played with this idea previously).

The dynamic state construction looks like the traditional State, but with separate types for input and output state:

{-# LANGUAGE DeriveFunctor, MultiParamTypeClasses, FunctionalDependencies, FlexibleInstances #-}
newtype DynState s1 s2 a = DynState { runState :: s1 -> (a,s2) }
    deriving Functor

get :: DynState s s s
get = DynState $ \s-> (s,s)

put :: s -> DynState x s ()
put = DynState . const . (,) ()

modify :: (s1 -> s2) -> DynState s1 s2 ()
modify = DynState . fmap ((,) ())

We can compose these by defining a (for the moment, very verbose) function dynStateBind. Interestingly, this is actually easier to understand IMHO than the State monad because the type signature makes explicit the fact that our lambda-bound state s1 is the initial state value that we run the construction on:

infixl 1 `dynStateBind` 
dynStateBind :: DynState s1 s2 a -> (a -> DynState s2 s3 b) -> DynState s1 s3 b
dynStateBind x f = DynState $ \s1-> let ~(a,s2) = runState x s1
                                     in runState (f a) s2

We also need stand-ins for (>>) and return:

-- It would be nice if >> inherited default instances in terms of bind, as in
-- an instance declaration
infixl 1 `dynThen`
dynThen :: DynState s1 s2 a -> DynState s2 s3 b -> DynState s1 s3 b
dynThen x y = x `dynStateBind` \_ ->  y

dynReturn :: a -> DynState s s a
dynReturn a = DynState $ \s-> (a,s)

So then we can use all the nonsense above as follows:

{-# LANGUAGE RebindableSyntax #-}
module Main

import Prelude -- since RebindableSyntax implies NoImplicitPrelude
import DynState

-- avoid lame "Not in scope: `ifThenElse'"
ifThenElse b x y | b = x
                 | otherwise = y
-- oops, infinite loop fail!
--ifThenElse b x y = if b then x else y 

test :: DynState Int Int String
test = let (>>=)  = dynStateBind
           (>>)   = dynThen
           return = dynReturn
        in do i <- get             -- state :: Int
              put (even i, show i) -- state :: (Bool, String)
              (b,i_str) <- get
              put (i+1)            -- state :: Int
              if b then return "uh oh, even!"
                   else return $ "pre-incremented: "++i_str

If we want to we can even enrich our bind and add some machinery to support composing traditional StateT computations with our dynamic state:

-- | a silly class to support comosing regular State monad computations with
-- dynamic state computations.
class Stateful n s1 s2 | n -> s1 s2 where
    expand :: n a -> DynState s1 s2 a

instance Stateful (DynState s1 s2) s1 s2 where
    expand = id

instance Stateful (M.StateT s Identity) s s where
    expand = DynState . M.runState

-- category with bind sort of situation...
polyDynStateBind :: (Stateful x s1 s2, Stateful y s2 s3)=> x a -> (a -> y b) -> DynState s1 s3 b
polyDynStateBind x f = expand x `dynStateBind` fmap expand f

-- | convert a dynamic stateful computation into the usual State monad, so we
-- can compose it with normal state computations
flatten :: DynState s s a -> M.State s a
flatten = M.state . runState 


At some point while working on zippo (you
should check out this instead) I made up detailed notes on a DSL for zipper operations that I wished I could shoe-horn into do or Arrow notation, but never quite could.

Lost the notes, but the idea would be to have bind expose the “focus” of the zipper and allow motions down and up to be sequenced nicely with minimal boilerplate, as in the state monad. Maybe I’ll find those and fill in this section properly.

CRRVA Poster Designs

A couple posters for the Classical Revolution RVA events at Balliceaux in November and December. Great programs both.

Poster for Nov 11

These both play with some photograms of found glasses I’d made prior.

Poster for Dec 2

dilly.js: A Library for Loops With Delays in JavaScript

I’ve released a new library for writing sort of idiomatic-looking loops with delays in javascript. This was motivated by this work in which I was implementing an algorithm/simulation; I wanted the code to be readable as straighforward loops, but I needed a delay for the visual portion.

Here is an example of its usage:

    .for("x",1,2)                // range: 1, 2
        .for("y",1,3 , 8)        // range(with step of 2): 1, 3, 5, 7
            .for("z",['a','b'])  // foreach: 'a', 'b'

The inner do block runs with a one-second delay between executions.

You can pick up the code with

$ git clone

The design is actually much closer conceptually to list comprehensions. Also, I’m fairly certain that the approach I took is far messier than necessary, but I was suffering from elegant coder’s block.

Send me any pull requests or bug reports you have.

Linocut Coasters

My first linocut. Two-color stencil with Krylon fluorescent paint, under linocut block-printed text and outline. Then I quickly painted highlights with a paint pen on each. The adorable font “Leckerli” was designed by Gesine Todt.


Promotion for Richmond Symphony Orchestra players’ website, and fun party favor for their show tonight at Balliceaux.

Making Your Zipper Disappear With Zippo

I’ve finally had a chance to look at how my new lens-based, power-packed, minimal zipper library zippo looks when compiled, and initial results are pleasing!

For example, here is a silly zipper operation that increments the nodes of a sort-of tree:

module Main where

import Control.Category((>>>))
import Data.Lens.Zipper
import Data.Yall.Lens

data Tree a = Br { _lBranch :: NodeBr a
                 , _rBranch :: NodeBr a }
            deriving Show

data NodeBr a = NodeBr { _node :: a }
    deriving Show

lBranch = lens _lBranch (\(Br _ r) l-> Br l r)
rBranch = lens _rBranch (\(Br l _) r-> Br l r)
node = lens _node $ const NodeBr

-- example data:
t = Br (NodeBr 1 ) (NodeBr 3 )

-- zipper operations
incNode = moveP node >>> modf (+1) >>> moveUp
zops = zipper >>> moveP lBranch >>> incNode >>> moveUp >>> 
        moveP rBranch >>> incNode >>> close

main = print $ zops t

When compiled with

ghc --make -fforce-recomp -ddump-simpl -dsuppress-all -O2 SimpleExample.hs

this produces the following beautiful core:

zops =
  \ w_s2uY ->
    case w_s2uY of _ { Br ww_s2v0 ww1_s2v1 ->
         (case ww_s2v0 of _ { NodeBr ds_d16N ->
          plusInteger ds_d16N incNode2
         (case ww1_s2v1 of _ { NodeBr ds_d16N ->
          plusInteger ds_d16N incNode2

Alakazam! Some more complex examples also produced nice code with performance identical to the most straightforward equivalent definition.


The above gave me an opportunity to test a handful of rewrite rules which I’m excited about, but first I need to tie up some loose ends to get this useful:

  1. create some combinators that together with (>>>) form a nice DSL for zipper operations, including nice support for partial lens “Alternative-like” statements
  2. get the underlying lens library situation sorted out:
    • either add automatic lens deriving TH to yall (not looking forward to this), or…
    • see if I can convince ekmett to modify lens in some clever way to support my use case (not optimistic about this)
    • use data-lens now that it does “partial lenses”
  3. look at some of Oleg’s zipper stuff for actual applications of zippers to guide development

Leave a note if you have questions or ideas for me.

Simple-actors 0.4.0: A Few Interesting Design Bits

simple-actors is my library for more structured concurrent programming based on the Actor model. It’s been a fun vehicle for exploring concurrent semantics, and an opportunity to solve some tricky API design problems in pretty clever ways. I want to present a handful of these problems, and the solutions I came up with, below.

Short digression: I love distributed systems, but this library has nothing to do with distributed programming. Also performance.

Goals and Constraints

To frame things, these were more or less the goals of the library design:

  • create a friendly, light-weight (hopefully intuitive), non-leaky, non-brittle eDSL for concurrent algorithms
  • base functionality on existing typeclasses and abstractions wherever possible
  • employ an economy of concepts; avoid creating new things requiring names and explanations, unless absolutely necessary

And here are the three little case-studies.

Eschew channel abstraction

The first challenge was how to keep the abstraction for message routing as minimal as possible. Ideally we would like our spawn function to return a token (we call it a “Mailbox”) that is used by other actors as a reference for sending messages to the spawn-ed actor:

spawn :: Behavior a -> Action (Mailbox a)

But then how do you handle two actors sending messages to each other? Or an actor sending itself a message for that matter?

do a <- spawn $ senderTo b
   b <- spawn $ senderTo a
   c <- spawn $ senderTo c
   send a ...

The sequencing of monadic actions in a do block make scoping an issue here, so what can we do?

Should we insist that actors like a and b pass their mailboxes explicitly in messages? But then we’d have to build implicit access to an actor’s own Mailbox into our Behavior abstraction since we can’t even define a Behavior closed over it’s own Mailbox, as in c above.

What about separating channel creation and spawning into two distinct functions, where we spawn a Behavior listening on a channel? But then we have to decide what should happen when two actors are spawned on the same channel, etc. Let’s just not do any of that.

The solution turns out to be a matter of knowing the right classes, in particular the exotic MonadFix (read up on it here). Combined with GHC’s lovely DoRec extension, our crisis resolves itself, allowing:

{-# LANGUAGE DoRec #-}
do rec a <- spawn $ senderTo b
       b <- spawn $ senderTo a
       c <- spawn $ senderTo c
   send a ...

IMHO this looks much tastier than what erlang has to offer.

Mailbox should support a rich set of transformations

A second design challenge has been to try to realize the full potential of our internal “chan” pair type (of which Mailbox only is exposed) in terms of CT-ish transformations supported.

Some background: a concurrent Chan separated into a pair of “read” and “write” sides is attractive, because it suggests the possibility for the “read” end to have a Functor instance, while the “write” side suggests it could be a contravariant functor, supporting an operation:

contramap :: (Contravariant f)=> (b -> a) -> f a -> f b

Consider how Control.Concurrent.Chan doesn’t permit that possibility.

Initial versions of chan-split (used internally) defined Functor and Contravariant instances, supported with a clumsy GADT representation which looked like, e.g.

data InChan i where
    InChan :: (i -> a) -> C.Chan a -> InChan i

This was removed when I realized I could support the operations (“read” / “write”) and powerful transformations that weren’t possible before, by defining Mailbox as simply a wrapper around writeChan c itself. Likewise, our internal “write end” becomes a wrapped readChan c action:

newtype Mailbox a = Mailbox (a -> IO ())
newtype Messages a = Messages (IO a)

Here are the nice transformations we’ve defined so far; more are possible. N.B. that these don’t add anything in terms of expressiveness to the actor model, i.e. we could envision doing the same sort of thing trivially with actors.

Getting more expressive with join patterns

The final and most recent design bit I wanted to share address the problem of “synchronization”.

Consider how you would go about trying to definean actor that “pairs up” inputs received from a pair of actors; your solution would involve an actor that kept a possibly-ever-expanding buffer of unmatched messages. This is a limitation inherent in the actor model, and leads to needless tragedy like erlang’s “selective receive”.

What we want is to be able to define an actor that can block on multiple channels. How do we do that without weird channel abstractions creeping into our API?

I’d been mulling around the idea of creating a completely new beast, sort of dual to an Actor that could read from arbitrary chans, and feed inputs one-at-a-time to an actor. A “Reducer” or something.

Luckily I had a better idea while reading about process calculi, in particular the join calculus. The solution makes the library formally more expressive while removing complexity from the UI and leaving the semantics of behaviors and message-passing unchanged!

The trick was to come up with a class that would allow the spawn function to introduce assymetry between spawned Behavior inputs and Mailboxes, i.e. we create a spawn that can return multiple Mailboxes which are “joined” to a single Behavior input in the background:

sumTuple :: Behavior (Int, Int)

do b <- spawn sumTuple
   send b (4, 1) 
   -- or. like magic...
   (b1, b2) <- spawn sumTuple
   send b1 4
   send b2 1

See the docs for a few other examples of the new behavior of spawn. The Sources class works using TypeFamilies and an associated Joined type that is a function of the return type of spawn. Use the source.


Is this sort of post interesting, or is this kind of case study too esoteric or domain-specific to be useful? Let me know. And if you have ideas of your own or want to help with performance testing, do a

git clone

and play with it.