Related
I'm trying to get some basic information on the performance characteristics of branches in SBV.
Let's suppose I have an SInt16 and a very sparse lookup table Map Int16 a. I can implement the lookup with nested ite:
sCase :: (Mergeable a) => SInt16 -> a -> Map Int16 a -> a
sCase x def = go . toList
where
go [] = def
go ((k,v):kvs) = ite (x .== literal k) v (go kvs)
However, this means the generated tree will be very deep.
Does that matter?
If yes, is it better to instead generate a balanced tree of branches, effectively mirroring the Map's structure? Or is there some other scheme that would give even better performance?
If there are less than 256 entries in the map, would it change anything to "compress" it so that sCase works on an SInt8 and a Map Int8 a?
Is there some built-in SBV combinator for this use case that works better than iterated ite?
EDIT: It turns out that it matters a lot what my a is, so let me add some more detail to that. I am currently using sCase to branch in a stateful computation modeled as an RWS r w s a, with the following instances:
instance forall a. Mergeable a => Mergeable (Identity a) where
symbolicMerge force cond thn els = Identity $ symbolicMerge force cond (runIdentity thn) (runIdentity els)
instance (Mergeable s, Mergeable w, Mergeable a, forall a. Mergeable a => Mergeable (m a)) => Mergeable (RWST r w s m a) where
symbolicMerge force cond thn els = Lazy.RWST $
symbolicMerge force cond (runRWST thn) (runRWST els)
So stripping away all the newtypes, I'd like to branch into something of type r -> s -> (a, s, w) s.t. Mergeable s, Mergeable w and Mergeable a.
Symbolic look-ups are expensive
Symbolic array lookup will be expensive regardless of what data-structure you use. It boils down to the fact that there's no information available to the symbolic execution engine to cut-down on the state-space, so it ends up doing more or less what you coded yourself.
SMTLib Arrays
However, the best solution in these cases is to actually use SMT's support for arrays: http://smtlib.cs.uiowa.edu/theories-ArraysEx.shtml
SMTLib arrays are different than what you'd consider as an array in a regular programming language: It does not have bounds. In that sense, it's more of a map from inputs to outputs, spanning the entire domain. (i.e., they are equivalent to functions.) But SMT has custom theories to deal with arrays and thus they can handle problems involving arrays much more efficiently. (On the down-side, there's no notion of index-out-of-bounds or somehow controlling the range of elements you can access. You can code those up yourself on top of the abstraction though, leaving it up to you to decide how you want to handle such invalid accesses.)
If you are interested in learning more about how SMT solvers deal with arrays, the classic reference is: http://theory.stanford.edu/~arbrad/papers/arrays.pdf
Arrays in SBV
SBV supports arrays, through the SymArray class: https://hackage.haskell.org/package/sbv-8.7/docs/Data-SBV.html#t:SymArray
The SFunArray type actually does not use SMTLib arrays. This was designed to support solvers that didn't understand Arrays, such as ABC: https://hackage.haskell.org/package/sbv-8.7/docs/Data-SBV.html#t:SFunArray
The SArray type fully supports SMTLib arrays: https://hackage.haskell.org/package/sbv-8.7/docs/Data-SBV.html#t:SArray
There are some differences between these types, and the above links describe them. However, for most purposes, you can use them interchangeably.
Converting a Haskell map to an SBV array
Going back to your original question, I'd be tempted to use an SArray to model such a look up. I'd code it as:
{-# LANGUAGE ScopedTypeVariables #-}
import Data.SBV
import qualified Data.Map as M
import Data.Int
-- Fill an SBV array from a map
mapToSArray :: (SymArray array, SymVal a, SymVal b) => M.Map a (SBV b) -> array a b -> array a b
mapToSArray m a = foldl (\arr (k, v) -> writeArray arr (literal k) v) a (M.toList m)
And use it as:
g :: Symbolic SBool
g = do let def = 0
-- get a symbolic array, initialized with def
arr <- newArray "myArray" (Just def)
let m :: M.Map Int16 SInt16
m = M.fromList [(5, 2), (10, 5)]
-- Fill the array from the map
let arr' :: SArray Int16 Int16 = mapToSArray m arr
-- A simple problem:
idx1 <- free "idx1"
idx2 <- free "idx2"
pure $ 2 * readArray arr' idx1 + 1 .== readArray arr' idx2
When I run this, I get:
*Main> sat g
Satisfiable. Model:
idx1 = 5 :: Int16
idx2 = 10 :: Int16
You can run it as satWith z3{verbose=True} g to see the SMTLib output it generates, which avoids costly lookups by simply delegating those tasks to the backend solver.
Efficiency
The question of whether this will be "efficient" really depends on how many elements your map has that you're constructing the array from. The larger the number of elements and the trickier the constraints, the less efficient it will be. In particular, if you ever write to an index that is symbolic, I'd expect slow-downs in solving time. If they're all constants, it should be relatively performant. As is usual in symbolic programming, it's really hard to predict any performance without seeing the actual problem and experimenting with it.
Arrays in the query context
The function newArray works in the symbolic context. If you're in a query context, instead use freshArray: https://hackage.haskell.org/package/sbv-8.7/docs/Data-SBV-Control.html#v:freshArray
I need a second order function pairApply that applies a binary function f to all unique pairs of a list-like structure and then combines them somehow. An example / sketch:
pairApply (+) f [a, b, c] = f a b + f a c + f b c
Some research leads me to believe that Data.Vector.Unboxed probably will have good performance (I will also need fast access to specific elements); also it necessary for Statistics.Sample, which would come in handy further down the line.
With this in mind I have the following, which almost compiles:
import qualified Data.Vector.Unboxed as U
pairElement :: (U.Unbox a, U.Unbox b)
=> (U.Vector a)
-> (a -> a -> b)
-> Int
-> a
-> (U.Vector b)
pairElement v f idx el =
U.map (f el) $ U.drop (idx + 1) v
pairUp :: (U.Unbox a, U.Unbox b)
=> (a -> a -> b)
-> (U.Vector a)
-> (U.Vector (U.Vector b))
pairUp f v = U.imap (pairElement v f) v
pairApply :: (U.Unbox a, U.Unbox b)
=> (b -> b -> b)
-> b
-> (a -> a -> b)
-> (U.Vector a)
-> b
pairApply combine neutral f v =
folder $ U.map folder (pairUp f v) where
folder = U.foldl combine neutral
The reason this doesn't compile is that there is no Unboxed instance of a U.Vector (U.Vector a)). I have been able to create new unboxed instances in other cases using Data.Vector.Unboxed.Deriving, but I'm not sure it would be so easy in this case (transform it to a tuple pair where the first element is all the inner vectors concatenated and the second is the length of the vectors, to know how to unpack?)
My question can be stated in two parts:
Does the above implementation make sense at all or is there some quick library function magic etc that could do it much easier?
If so, is there a better way to make an unboxed vector of vectors than the one sketched above?
Note that I'm aware that foldl is probably not the best choice; once I've got the implementation sorted I plan to benchmark with a few different folds.
There is no way to define a classical instance for Unbox (U.Vector b), because that would require preallocating a memory area in which each element (i.e. each subvector!) has the same fixed amount of space. But in general, each of them may be arbitrarily big, so that's not feasible at all.
It might in principle be possible to define that instance by storing only a flattened form of the nested vector plus an extra array of indices (where each subvector starts). I once briefly gave this a try; it actually seems somewhat promising as far as immutable vectors are concerned, but a G.Vector instance also requires a mutable implementation, and that's hopeless for such an approach (because any mutation that changes the number of elements in one subvector would require shifting everything behind it).
Usually, it's just not worth it, because if the individual element vectors aren't very small the overhead of boxing them won't matter, i.e. often it makes sense to use B.Vector (U.Vector b).
For your application however, I would not do that at all – there's no need to ever wrap the upper element-choices in a single triangular array. (And it would be really bad for performance to do that, because it make the algorithm take O (n²) memory rather than O (n) which is all that's needed.)
I would just do the following:
pairApply combine neutral f v
= U.ifoldl' (\acc i p -> U.foldl' (\acc' q -> combine acc' $ f p q)
acc
(U.drop (i+1) v) )
neutral v
This corresponds pretty much to the obvious nested-loops imperative implementation
pairApply(combine, b, f, v):
for(i in 0..length(v)-1):
for(j in i+1..length(v)-1):
b = combine(b, f(v[i], v[j]);
return b;
My answer is basically the same as leftaroundabout's nested-loops imperative implementation:
pairApply :: (Int -> Int -> Int) -> Vector Int -> Int
pairApply f v = foldl' (+) 0 [f (v ! i) (v ! j) | i <- [0..(n-1)], j <- [(i+1)..(n-1)]]
where n = length v
As far as I know, I do not see any performance issue with this implementation.
Non-polymorphic for simplicity.
In my humble opinion the answers to the famous question "What is a monad?", especially the most voted ones, try to explain what is a monad without clearly explaining why monads are really necessary. Can they be explained as the solution to a problem?
Why do we need monads?
We want to program only using functions. ("functional programming (FP)" after all).
Then, we have a first big problem. This is a program:
f(x) = 2 * x
g(x,y) = x / y
How can we say what is to be executed first? How can we form an ordered sequence of functions (i.e. a program) using no more than functions?
Solution: compose functions. If you want first g and then f, just write f(g(x,y)). This way, "the program" is a function as well: main = f(g(x,y)). OK, but ...
More problems: some functions might fail (i.e. g(2,0), divide by 0). We have no "exceptions" in FP (an exception is not a function). How do we solve it?
Solution: Let's allow functions to return two kind of things: instead of having g : Real,Real -> Real (function from two reals into a real), let's allow g : Real,Real -> Real | Nothing (function from two reals into (real or nothing)).
But functions should (to be simpler) return only one thing.
Solution: let's create a new type of data to be returned, a "boxing type" that encloses maybe a real or be simply nothing. Hence, we can have g : Real,Real -> Maybe Real. OK, but ...
What happens now to f(g(x,y))? f is not ready to consume a Maybe Real. And, we don't want to change every function we could connect with g to consume a Maybe Real.
Solution: let's have a special function to "connect"/"compose"/"link" functions. That way, we can, behind the scenes, adapt the output of one function to feed the following one.
In our case: g >>= f (connect/compose g to f). We want >>= to get g's output, inspect it and, in case it is Nothing just don't call f and return Nothing; or on the contrary, extract the boxed Real and feed f with it. (This algorithm is just the implementation of >>= for the Maybe type). Also note that >>= must be written only once per "boxing type" (different box, different adapting algorithm).
Many other problems arise which can be solved using this same pattern: 1. Use a "box" to codify/store different meanings/values, and have functions like g that return those "boxed values". 2. Have a composer/linker g >>= f to help connecting g's output to f's input, so we don't have to change any f at all.
Remarkable problems that can be solved using this technique are:
having a global state that every function in the sequence of functions ("the program") can share: solution StateMonad.
We don't like "impure functions": functions that yield different output for same input. Therefore, let's mark those functions, making them to return a tagged/boxed value: IO monad.
Total happiness!
The answer is, of course, "We don't". As with all abstractions, it isn't necessary.
Haskell does not need a monad abstraction. It isn't necessary for performing IO in a pure language. The IO type takes care of that just fine by itself. The existing monadic desugaring of do blocks could be replaced with desugaring to bindIO, returnIO, and failIO as defined in the GHC.Base module. (It's not a documented module on hackage, so I'll have to point at its source for documentation.) So no, there's no need for the monad abstraction.
So if it's not needed, why does it exist? Because it was found that many patterns of computation form monadic structures. Abstraction of a structure allows for writing code that works across all instances of that structure. To put it more concisely - code reuse.
In functional languages, the most powerful tool found for code reuse has been composition of functions. The good old (.) :: (b -> c) -> (a -> b) -> (a -> c) operator is exceedingly powerful. It makes it easy to write tiny functions and glue them together with minimal syntactic or semantic overhead.
But there are cases when the types don't work out quite right. What do you do when you have foo :: (b -> Maybe c) and bar :: (a -> Maybe b)? foo . bar doesn't typecheck, because b and Maybe b aren't the same type.
But... it's almost right. You just want a bit of leeway. You want to be able to treat Maybe b as if it were basically b. It's a poor idea to just flat-out treat them as the same type, though. That's more or less the same thing as null pointers, which Tony Hoare famously called the billion-dollar mistake. So if you can't treat them as the same type, maybe you can find a way to extend the composition mechanism (.) provides.
In that case, it's important to really examine the theory underlying (.). Fortunately, someone has already done this for us. It turns out that the combination of (.) and id form a mathematical construct known as a category. But there are other ways to form categories. A Kleisli category, for instance, allows the objects being composed to be augmented a bit. A Kleisli category for Maybe would consist of (.) :: (b -> Maybe c) -> (a -> Maybe b) -> (a -> Maybe c) and id :: a -> Maybe a. That is, the objects in the category augment the (->) with a Maybe, so (a -> b) becomes (a -> Maybe b).
And suddenly, we've extended the power of composition to things that the traditional (.) operation doesn't work on. This is a source of new abstraction power. Kleisli categories work with more types than just Maybe. They work with every type that can assemble a proper category, obeying the category laws.
Left identity: id . f = f
Right identity: f . id = f
Associativity: f . (g . h) = (f . g) . h
As long as you can prove that your type obeys those three laws, you can turn it into a Kleisli category. And what's the big deal about that? Well, it turns out that monads are exactly the same thing as Kleisli categories. Monad's return is the same as Kleisli id. Monad's (>>=) isn't identical to Kleisli (.), but it turns out to be very easy to write each in terms of the other. And the category laws are the same as the monad laws, when you translate them across the difference between (>>=) and (.).
So why go through all this bother? Why have a Monad abstraction in the language? As I alluded to above, it enables code reuse. It even enables code reuse along two different dimensions.
The first dimension of code reuse comes directly from the presence of the abstraction. You can write code that works across all instances of the abstraction. There's the entire monad-loops package consisting of loops that work with any instance of Monad.
The second dimension is indirect, but it follows from the existence of composition. When composition is easy, it's natural to write code in small, reusable chunks. This is the same way having the (.) operator for functions encourages writing small, reusable functions.
So why does the abstraction exist? Because it's proven to be a tool that enables more composition in code, resulting in creating reusable code and encouraging the creation of more reusable code. Code reuse is one of the holy grails of programming. The monad abstraction exists because it moves us a little bit towards that holy grail.
Benjamin Pierce said in TAPL
A type system can be regarded as calculating a kind of static
approximation to the run-time behaviours of the terms in a program.
That's why a language equipped with a powerful type system is strictly more expressive, than a poorly typed language. You can think about monads in the same way.
As #Carl and sigfpe point, you can equip a datatype with all operations you want without resorting to monads, typeclasses or whatever other abstract stuff. However monads allow you not only to write reusable code, but also to abstract away all redundant detailes.
As an example, let's say we want to filter a list. The simplest way is to use the filter function: filter (> 3) [1..10], which equals [4,5,6,7,8,9,10].
A slightly more complicated version of filter, that also passes an accumulator from left to right, is
swap (x, y) = (y, x)
(.*) = (.) . (.)
filterAccum :: (a -> b -> (Bool, a)) -> a -> [b] -> [b]
filterAccum f a xs = [x | (x, True) <- zip xs $ snd $ mapAccumL (swap .* f) a xs]
To get all i, such that i <= 10, sum [1..i] > 4, sum [1..i] < 25, we can write
filterAccum (\a x -> let a' = a + x in (a' > 4 && a' < 25, a')) 0 [1..10]
which equals [3,4,5,6].
Or we can redefine the nub function, that removes duplicate elements from a list, in terms of filterAccum:
nub' = filterAccum (\a x -> (x `notElem` a, x:a)) []
nub' [1,2,4,5,4,3,1,8,9,4] equals [1,2,4,5,3,8,9]. A list is passed as an accumulator here. The code works, because it's possible to leave the list monad, so the whole computation stays pure (notElem doesn't use >>= actually, but it could). However it's not possible to safely leave the IO monad (i.e. you cannot execute an IO action and return a pure value — the value always will be wrapped in the IO monad). Another example is mutable arrays: after you have leaved the ST monad, where a mutable array live, you cannot update the array in constant time anymore. So we need a monadic filtering from the Control.Monad module:
filterM :: (Monad m) => (a -> m Bool) -> [a] -> m [a]
filterM _ [] = return []
filterM p (x:xs) = do
flg <- p x
ys <- filterM p xs
return (if flg then x:ys else ys)
filterM executes a monadic action for all elements from a list, yielding elements, for which the monadic action returns True.
A filtering example with an array:
nub' xs = runST $ do
arr <- newArray (1, 9) True :: ST s (STUArray s Int Bool)
let p i = readArray arr i <* writeArray arr i False
filterM p xs
main = print $ nub' [1,2,4,5,4,3,1,8,9,4]
prints [1,2,4,5,3,8,9] as expected.
And a version with the IO monad, which asks what elements to return:
main = filterM p [1,2,4,5] >>= print where
p i = putStrLn ("return " ++ show i ++ "?") *> readLn
E.g.
return 1? -- output
True -- input
return 2?
False
return 4?
False
return 5?
True
[1,5] -- output
And as a final illustration, filterAccum can be defined in terms of filterM:
filterAccum f a xs = evalState (filterM (state . flip f) xs) a
with the StateT monad, that is used under the hood, being just an ordinary datatype.
This example illustrates, that monads not only allow you to abstract computational context and write clean reusable code (due to the composability of monads, as #Carl explains), but also to treat user-defined datatypes and built-in primitives uniformly.
I don't think IO should be seen as a particularly outstanding monad, but it's certainly one of the more astounding ones for beginners, so I'll use it for my explanation.
Naïvely building an IO system for Haskell
The simplest conceivable IO system for a purely-functional language (and in fact the one Haskell started out with) is this:
main₀ :: String -> String
main₀ _ = "Hello World"
With lazyness, that simple signature is enough to actually build interactive terminal programs – very limited, though. Most frustrating is that we can only output text. What if we added some more exciting output possibilities?
data Output = TxtOutput String
| Beep Frequency
main₁ :: String -> [Output]
main₁ _ = [ TxtOutput "Hello World"
-- , Beep 440 -- for debugging
]
cute, but of course a much more realistic “alterative output” would be writing to a file. But then you'd also want some way to read from files. Any chance?
Well, when we take our main₁ program and simply pipe a file to the process (using operating system facilities), we have essentially implemented file-reading. If we could trigger that file-reading from within the Haskell language...
readFile :: Filepath -> (String -> [Output]) -> [Output]
This would use an “interactive program” String->[Output], feed it a string obtained from a file, and yield a non-interactive program that simply executes the given one.
There's one problem here: we don't really have a notion of when the file is read. The [Output] list sure gives a nice order to the outputs, but we don't get an order for when the inputs will be done.
Solution: make input-events also items in the list of things to do.
data IO₀ = TxtOut String
| TxtIn (String -> [Output])
| FileWrite FilePath String
| FileRead FilePath (String -> [Output])
| Beep Double
main₂ :: String -> [IO₀]
main₂ _ = [ FileRead "/dev/null" $ \_ ->
[TxtOutput "Hello World"]
]
Ok, now you may spot an imbalance: you can read a file and make output dependent on it, but you can't use the file contents to decide to e.g. also read another file. Obvious solution: make the result of the input-events also something of type IO, not just Output. That sure includes simple text output, but also allows reading additional files etc..
data IO₁ = TxtOut String
| TxtIn (String -> [IO₁])
| FileWrite FilePath String
| FileRead FilePath (String -> [IO₁])
| Beep Double
main₃ :: String -> [IO₁]
main₃ _ = [ TxtIn $ \_ ->
[TxtOut "Hello World"]
]
That would now actually allow you to express any file operation you might want in a program (though perhaps not with good performance), but it's somewhat overcomplicated:
main₃ yields a whole list of actions. Why don't we simply use the signature :: IO₁, which has this as a special case?
The lists don't really give a reliable overview of program flow anymore: most subsequent computations will only be “announced” as the result of some input operation. So we might as well ditch the list structure, and simply cons a “and then do” to each output operation.
data IO₂ = TxtOut String IO₂
| TxtIn (String -> IO₂)
| Terminate
main₄ :: IO₂
main₄ = TxtIn $ \_ ->
TxtOut "Hello World"
Terminate
Not too bad!
So what has all of this to do with monads?
In practice, you wouldn't want to use plain constructors to define all your programs. There would need to be a good couple of such fundamental constructors, yet for most higher-level stuff we would like to write a function with some nice high-level signature. It turns out most of these would look quite similar: accept some kind of meaningfully-typed value, and yield an IO action as the result.
getTime :: (UTCTime -> IO₂) -> IO₂
randomRIO :: Random r => (r,r) -> (r -> IO₂) -> IO₂
findFile :: RegEx -> (Maybe FilePath -> IO₂) -> IO₂
There's evidently a pattern here, and we'd better write it as
type IO₃ a = (a -> IO₂) -> IO₂ -- If this reminds you of continuation-passing
-- style, you're right.
getTime :: IO₃ UTCTime
randomRIO :: Random r => (r,r) -> IO₃ r
findFile :: RegEx -> IO₃ (Maybe FilePath)
Now that starts to look familiar, but we're still only dealing with thinly-disguised plain functions under the hood, and that's risky: each “value-action” has the responsibility of actually passing on the resulting action of any contained function (else the control flow of the entire program is easily disrupted by one ill-behaved action in the middle). We'd better make that requirement explicit. Well, it turns out those are the monad laws, though I'm not sure we can really formulate them without the standard bind/join operators.
At any rate, we've now reached a formulation of IO that has a proper monad instance:
data IO₄ a = TxtOut String (IO₄ a)
| TxtIn (String -> IO₄ a)
| TerminateWith a
txtOut :: String -> IO₄ ()
txtOut s = TxtOut s $ TerminateWith ()
txtIn :: IO₄ String
txtIn = TxtIn $ TerminateWith
instance Functor IO₄ where
fmap f (TerminateWith a) = TerminateWith $ f a
fmap f (TxtIn g) = TxtIn $ fmap f . g
fmap f (TxtOut s c) = TxtOut s $ fmap f c
instance Applicative IO₄ where
pure = TerminateWith
(<*>) = ap
instance Monad IO₄ where
TerminateWith x >>= f = f x
TxtOut s c >>= f = TxtOut s $ c >>= f
TxtIn g >>= f = TxtIn $ (>>=f) . g
Obviously this is not an efficient implementation of IO, but it's in principle usable.
Monads serve basically to compose functions together in a chain. Period.
Now the way they compose differs across the existing monads, thus resulting in different behaviors (e.g., to simulate mutable state in the state monad).
The confusion about monads is that being so general, i.e., a mechanism to compose functions, they can be used for many things, thus leading people to believe that monads are about state, about IO, etc, when they are only about "composing functions".
Now, one interesting thing about monads, is that the result of the composition is always of type "M a", that is, a value inside an envelope tagged with "M". This feature happens to be really nice to implement, for example, a clear separation between pure from impure code: declare all impure actions as functions of type "IO a" and provide no function, when defining the IO monad, to take out the "a" value from inside the "IO a". The result is that no function can be pure and at the same time take out a value from an "IO a", because there is no way to take such value while staying pure (the function must be inside the "IO" monad to use such value). (NOTE: well, nothing is perfect, so the "IO straitjacket" can be broken using "unsafePerformIO : IO a -> a" thus polluting what was supposed to be a pure function, but this should be used very sparingly and when you really know to be not introducing any impure code with side-effects.
Monads are just a convenient framework for solving a class of recurring problems. First, monads must be functors (i.e. must support mapping without looking at the elements (or their type)), they must also bring a binding (or chaining) operation and a way to create a monadic value from an element type (return). Finally, bind and return must satisfy two equations (left and right identities), also called the monad laws. (Alternatively one could define monads to have a flattening operation instead of binding.)
The list monad is commonly used to deal with non-determinism. The bind operation selects one element of the list (intuitively all of them in parallel worlds), lets the programmer to do some computation with them, and then combines the results in all worlds to single list (by concatenating, or flattening, a nested list). Here is how one would define a permutation function in the monadic framework of Haskell:
perm [e] = [[e]]
perm l = do (leader, index) <- zip l [0 :: Int ..]
let shortened = take index l ++ drop (index + 1) l
trailer <- perm shortened
return (leader : trailer)
Here is an example repl session:
*Main> perm "a"
["a"]
*Main> perm "ab"
["ab","ba"]
*Main> perm ""
[]
*Main> perm "abc"
["abc","acb","bac","bca","cab","cba"]
It should be noted that the list monad is in no way a side effecting computation. A mathematical structure being a monad (i.e. conforming to the above mentioned interfaces and laws) does not imply side effects, though side-effecting phenomena often nicely fit into the monadic framework.
You need monads if you have a type constructor and functions that returns values of that type family. Eventually, you would like to combine these kind of functions together. These are the three key elements to answer why.
Let me elaborate. You have Int, String and Real and functions of type Int -> String, String -> Real and so on. You can combine these functions easily, ending with Int -> Real. Life is good.
Then, one day, you need to create a new family of types. It could be because you need to consider the possibility of returning no value (Maybe), returning an error (Either), multiple results (List) and so on.
Notice that Maybe is a type constructor. It takes a type, like Int and returns a new type Maybe Int. First thing to remember, no type constructor, no monad.
Of course, you want to use your type constructor in your code, and soon you end with functions like Int -> Maybe String and String -> Maybe Float. Now, you can't easily combine your functions. Life is not good anymore.
And here's when monads come to the rescue. They allow you to combine that kind of functions again. You just need to change the composition . for >==.
Why do we need monadic types?
Since it was the quandary of I/O and its observable effects in nonstrict languages like Haskell that brought the monadic interface to such prominence:
[...] monads are used to address the more general problem of computations (involving state, input/output, backtracking, ...) returning values: they do not solve any input/output-problems directly but rather provide an elegant and flexible abstraction of many solutions to related problems. [...] For instance, no less than three different input/output-schemes are used to solve these basic problems in Imperative functional programming, the paper which originally proposed `a new model, based on monads, for performing input/output in a non-strict, purely functional language'. [...]
[Such] input/output-schemes merely provide frameworks in which side-effecting operations can safely be used with a guaranteed order of execution and without affecting the properties of the purely functional parts of the language.
Claus Reinke (pages 96-97 of 210).
(emphasis by me.)
[...] When we write effectful code – monads or no monads – we have to constantly keep in mind the context of expressions we pass around.
The fact that monadic code ‘desugars’ (is implementable in terms of) side-effect-free code is irrelevant. When we use monadic notation, we program within that notation – without considering what this notation desugars into. Thinking of the desugared code breaks the monadic abstraction. A side-effect-free, applicative code is normally compiled to (that is, desugars into) C or machine code. If the desugaring argument has any force, it may be applied just as well to the applicative code, leading to the conclusion that it all boils down to the machine code and hence all programming is imperative.
[...] From the personal experience, I have noticed that the mistakes I make when writing monadic code are exactly the mistakes I made when programming in C. Actually, monadic mistakes tend to be worse, because monadic notation (compared to that of a typical imperative language) is ungainly and obscuring.
Oleg Kiselyov (page 21 of 26).
The most difficult construct for students to understand is the monad. I introduce IO without mentioning monads.
Olaf Chitil.
More generally:
Still, today, over 25 years after the introduction of the concept of monads to the world of functional programming, beginning functional programmers struggle to grasp the concept of monads. This struggle is exemplified by the numerous blog posts about the effort of trying to learn about monads. From our own experience we notice that even at university level, bachelor level students often struggle to comprehend monads and consistently score poorly on monad-related exam questions.
Considering that the concept of monads is not likely to disappear from the functional programming landscape any time soon, it is vital that we, as the functional programming community, somehow overcome the problems novices encounter when first studying monads.
Tim Steenvoorden, Jurriën Stutterheim, Erik Barendsen and Rinus Plasmeijer.
If only there was another way to specify "a guaranteed order of execution" in Haskell, while keeping the ability to separate regular Haskell definitions from those involved in I/O (and its observable effects) - translating this variation of Philip Wadler's echo:
val echoML : unit -> unit
fun echoML () = let val c = getcML () in
if c = #"\n" then
()
else
let val _ = putcML c in
echoML ()
end
fun putcML c = TextIO.output1(TextIO.stdOut,c);
fun getcML () = valOf(TextIO.input1(TextIO.stdIn));
...could then be as simple as:
echo :: OI -> ()
echo u = let !(u1:u2:u3:_) = partsOI u in
let !c = getChar u1 in
if c == '\n' then
()
else
let !_ = putChar c u2 in
echo u3
where:
data OI -- abstract
foreign import ccall "primPartOI" partOI :: OI -> (OI, OI)
⋮
foreign import ccall "primGetCharOI" getChar :: OI -> Char
foreign import ccall "primPutCharOI" putChar :: Char -> OI -> ()
⋮
and:
partsOI :: OI -> [OI]
partsOI u = let !(u1, u2) = partOI u in u1 : partsOI u2
How would this work? At run-time, Main.main receives an initial OI pseudo-data value as an argument:
module Main(main) where
main :: OI -> ()
⋮
...from which other OI values are produced, using partOI or partsOI. All you have to do is ensure each new OI value is used at most once, in each call to an OI-based definition, foreign or otherwise. In return, you get back a plain ordinary result - it isn't e.g. paired with some odd abstract state, or requires the use of a callback continuation, etc.
Using OI, instead of the unit type () like Standard ML does, means we can avoid always having to use the monadic interface:
Once you're in the IO monad, you're stuck there forever, and are reduced to Algol-style imperative programming.
Robert Harper.
But if you really do need it:
type IO a = OI -> a
unitIO :: a -> IO a
unitIO x = \ u -> let !_ = partOI u in x
bindIO :: IO a -> (a -> IO b) -> IO b
bindIO m k = \ u -> let !(u1, u2) = partOI u in
let !x = m u1 in
let !y = k x u2 in
y
⋮
So, monadic types aren't always needed - there are other interfaces out there:
LML had a fully fledged implementation of oracles running of a multi-processor (a Sequent Symmetry) back in ca 1989. The description in the Fudgets thesis refers to this implementation. It was fairly pleasant to work with and quite practical.
[...]
These days everything is done with monads so other solutions are sometimes forgotten.
Lennart Augustsson (2006).
Wait a moment: since it so closely resembles Standard ML's direct use of effects, is this approach and its use of pseudo-data referentially transparent?
Absolutely - just find a suitable definition of "referential transparency"; there's plenty to choose from...
In an object-oriented language when I need to cache/memoize the results of a function for a known life-time I'll generally follow this pattern:
Create a new class
Add to the class a data member and a method for each function result I want to cache
Implement the method to first check to see if the result has been stored in the data member. If so, return that value; else call the function (with the appropriate arguments) and store the returned result in the data member.
Objects of this class will be initialized with values that are needed for the various function calls.
This object-based approach is very similar to the function-based memoization pattern described here: http://www.bardiak.com/2012/01/javascript-memoization-pattern.html
The main benefit of this approach is that the results are kept around only for the life time of the cache object. A common use case is in the processing of a list of work items. For each work item one creates the cache object for that item, processes the work item with that cache object then discards the work item and cache object before proceeding to the next work item.
What are good ways to implement short-lived memoization in Haskell? And does the answer depend on if the functions to be cached are pure or involve IO?
Just to reiterate - it would be nice to see solutions for functions which involve IO.
Let's use Luke Palmer's memoization library: Data.MemoCombinators
import qualified Data.MemoCombinators as Memo
import Data.Function (fix) -- we'll need this too
I'm going to define things slightly different from how his library does, but it's basically the same (and furthermore, compatible). A "memoizable" thing takes itself as input, and produces the "real" thing.
type Memoizable a = a -> a
A "memoizer" takes a function and produces the memoized version of it.
type Memoizer a b = (a -> b) -> a -> b
Let's write a little function to put these two things together. Given a Memoizable function and a Memoizer, we want the resultant memoized function.
runMemo :: Memoizer a b -> Memoizable (a -> b) -> a -> b
runMemo memo f = fix (f . memo)
This is a little magic using the fixpoint combinator (fix). Never mind that; you can google it if you are interested.
So let's write a Memoizable version of the classic fib example:
fib :: Memoizable (Integer -> Integer)
fib self = go
where go 0 = 1
go 1 = 1
go n = self (n-1) + self (n-2)
Using a self convention makes the code straightforward. Remember, self is what we expect to be the memoized version of this very function, so recursive calls should be on self. Now fire up ghci.
ghci> let fib' = runMemo Memo.integral fib
ghci> fib' 10000
WALL OF NUMBERS CRANKED OUT RIDICULOUSLY FAST
Now, the cool thing about runMemo is you can create more than one freshly memoized version of the same function, and they will not share memory banks. That means that I can write a function that locally creates and uses fib', but then as soon as fib' falls out of scope (or earlier, depending on the intelligence of the compiler), it can be garbage collected. It doesn't have to be memoized at the top level. This may or may not play nicely with memoization techniques that rely on unsafePerformIO. Data.MemoCombinators uses a pure, lazy Trie, which fits perfectly with runMemo. Rather than creating an object which essentially becomes a memoization manager, you can simply create memoized functions on demand. The catch is that if your function is recursive, it must be written as Memoizable. The good news is you can plug in any Memoizer that you wish. You could even use:
noMemo :: Memoizer a b
noMemo f = f
ghci> let fib' = runMemo noMemo fib
ghci> fib' 30 -- wait a while; it's computing stupidly
1346269
Lazy-Haskell programming is, in a way, the memoization paradigm taken to a extreme. Also, whatever you do in an imperative language is possible in Haskell, using either IO monad, the ST monad, monad transformers, arrows, or you name what.
The only problem is that these abstraction devices are much more complicated than the imperative equivalent that you mentioned, and they need a pretty deep mind-rewiring.
I believe the above answers are both more complex than necessary, although they might be more portable than what I'm about to describe.
As I understand it, there is a rule in ghc that each value is computed exactly once when it's enclosing lambda expression is entered. You may thus create exactly your short lived memoization object as follows.
import qualified Data.Vector as V
indexerVector :: (t -> Int) -> V.Vector t -> Int -> [t]
indexerVector idx vec = \e -> tbl ! e
where m = maximum $ map idx $ V.toList vec
tbl = V.accumulate (flip (:)) (V.replicate m [])
(V.map (\v -> (idx v, v)) vec)
What does this do? It groups all the elements in the Data.Vector t passed as it's second argument vec according to integer computed by it's first argument idx, retaining their grouping as a Data.Vector [t]. It returns a function of type Int -> [t] which looks up this grouping by this pre-computed index value.
Our compiler ghc has promised that tbl shall only be thunked once when we invoke indexerVector. We may therefore assign the lambda expression \e -> tbl ! e returned by indexVector to another value, which we may use repeatedly without fear that tbl ever gets recomputed. You may verify this by inserting a trace on tbl.
In short, your caching object is exactly this lambda expression.
I've found that almost anything you can accomplish with a short term object can be better accomplished by returning a lambda expression like this.
You can use very same pattern in haskell too. Lazy evaluation will take care of checking whether value is evaluated already. It has been mentioned mupltiple times already but code example could be useful. In example below memoedValue will calculated only once when it is demanded.
data Memoed = Memoed
{ value :: Int
, memoedValue :: Int
}
memo :: Int -> Memoed
memo i = Memoed
{ value = i
, memoedValue = expensiveComputation i
}
Even better you can memoize values which depend on other memoized values. You shoud avoid dependecy loops. They can lead to nontermination
data Memoed = Memoed
{ value :: Int
, memoedValue1 :: Int
, memoedValue2 :: Int
}
memo :: Int -> Memoed
memo i = r
where
r = Memoed
{ value = i
, memoedValue1 = expensiveComputation i
, memoedValue2 = anotherComputation (memoedValue1 r)
}
Here's a simple, barebones example of how the code that I'm trying to do would look in C++.
while (state == true) {
a = function1();
b = function2();
state = function3();
}
In the program I'm working on, I have some functions that I need to loop through until bool state equals false (or until one of the variables, let's say variable b, equals 0).
How would this code be done in Haskell? I've searched through here, Google, and even Bing and haven't been able to find any clear, straight forward explanations on how to do repetitive actions with functions.
Any help would be appreciated.
Taking Daniels comment into account, it could look something like this:
f = loop init_a init_b true
where
loop a b True = loop a' b' (fun3 a' b')
where
a' = fun1 ....
b' = fun2 .....
loop a b False = (a,b)
Well, here's a suggestion of how to map the concepts here:
A C++ loop is some form of list operation in Haskell.
One iteration of the loop = handling one element of the list.
Looping until a certain condition becomes true = base case of a function that recurses on a list.
But there is something that is critically different between imperative loops and functional list functions: loops describe how to iterate; higher-order list functions describe the structure of the computation. So for example, map f [a0, a1, ..., an] can be described by this diagram:
[a0, a1, ..., an]
| | |
f f f
| | |
v v v
[f a0, f a1, ..., f an]
Note that this describes how the result is related to the arguments f and [a0, a1, ..., an], not how the iteration is performed step by step.
Likewise, foldr f z [a0, a1, ..., an] corresponds to this:
f a0 (f a1 (... (f an z)))
filter doesn't quite lend itself to diagramming, but it's easy to state many rules that it satisfies:
length (filter pred xs) <= length xs
For every element x of filter pred xs, pred x is True.
If x is an element of filter pred xs, then x is an element of xs
If x is not an element of xs, then x is not an element of filter pred xs
If x appears before x' in filter pred xs, then x appears before x' in xs
If x appears before x' in xs, and both x and x' appear in filter pred xs, then x appears before x' in filter pred xs
In a classic imperative program, all three of these cases are written as loops, and the difference between them comes down to what the loop body does. Functional programming, on the contrary, insists that this sort of structural pattern does not belong in "loop bodies" (the functions f and pred in these examples); rather, these patterns are best abstracted out into higher-order functions like map, foldr and filter. Thus, every time you see one of these list functions you instantly know some important facts about how the arguments and the result are related, without having to read any code; whereas in a typical imperative program, you must read the bodies of loops to figure this stuff out.
So the real answer to your question is that it's impossible to offer an idiomatic translation of an imperative loop into functional terms without knowing what the loop body is doing—what are the preconditions supposed to be before the loop runs, and what the postconditions are supposed to be when the loop finishes. Because that loop body that you only described vaguely is going to determine what the structure of the computation is, and different such structures will call for different higher-order functions in Haskell.
First of all, let's think about a few things.
Does function1 have side effects?
Does function2 have side effects?
Does function3 have side effects?
The answer to all of these is a resoundingly obvious YES, because they take no inputs, and presumably there are circumstances which cause you to go around the while loop more than once (rather than def function3(): return false). Now let's remodel these functions with explicit state.
s = initialState
sentinel = true
while(sentinel):
a,b,s,sentinel = function1(a,b,s,sentinel)
a,b,s,sentinel = function2(a,b,s,sentinel)
a,b,s,sentinel = function3(a,b,s,sentinel)
return a,b,s
Well that's rather ugly. We know absolutely nothing about what inputs each function draws from, nor do we know anything about how these functions might affect the variables a, b, and sentinel, nor "any other state" which I have simply modeled as s.
So let's make a few assumptions. Firstly, I am going to assume that these functions do not directly depend on nor affect in any way the values of a, b, and sentinel. They might, however, change the "other state". So here's what we get:
s = initState
sentinel = true
while (sentinel):
a,s2 = function1(s)
b,s3 = function2(s2)
sentinel,s4 = function(s3)
s = s4
return a,b,s
Notice I've used temporary variables s2, s3, and s4 to indicate the changes that the "other state" goes through. Haskell time. We need a control function to behave like a while loop.
myWhile :: s -- an initial state
-> (s -> (Bool, a, s)) -- given a state, produces a sentinel, a current result, and the next state
-> (a, s) -- the result, plus resultant state
myWhile s f = case f s of
(False, a, s') -> (a, s')
(True, _, s') -> myWhile s' f
Now how would one use such a function? Well, given we have the functions:
function1 :: MyState -> (AType, MyState)
function2 :: MyState -> (BType, MyState)
function3 :: MyState -> (Bool, MyState)
We would construct the desired code as follows:
thatCodeBlockWeAreTryingToSimulate :: MyState -> ((AType, BType), MyState)
thatCodeBlockWeAreTryingToSimulate initState = myWhile initState f
where f :: MyState -> (Bool, (AType, BType), MyState)
f s = let (a, s2) = function1 s
(b, s3) = function2 s2
(sentinel, s4) = function3 s3
in (sentinel, (a, b), s4)
Notice how similar this is to the non-ugly python-like code given above.
You can verify that the code I have presented is well-typed by adding function1 = undefined etc for the three functions, as well as the following at the top of the file:
{-# LANGUAGE EmptyDataDecls #-}
data MyState
data AType
data BType
So the takeaway message is this: in Haskell, you must explicitly model the changes in state. You can use the "State Monad" to make things a little prettier, but you should first understand the idea of passing state around.
Lets take a look at your C++ loop:
while (state == true) {
a = function1();
b = function2();
state = function3();
}
Haskell is a pure functional language, so it won't fight us as much (and the resulting code will be more useful, both in itself and as an exercise to learn Haskell) if we try to do this without side effects, and without using monads to make it look like we're using side effects either.
Lets start with this structure
while (state == true) {
<<do stuff that updates state>>
}
In Haskell we're obviously not going to be checking a variable against true as the loop condition, because it can't change its value[1] and we'd either evaluate the loop body forever or never. So instead, we'll want to be evaluating a function that returns a boolean value on some argument:
while (check something == True) {
<<do stuff that updates state>>
}
Well, now we don't have a state variable, so that "do stuff that updates state" is looking pretty pointless. And we don't have a something to pass to check. Lets think about this a bit more. We want the something to be checked to depend on what the "do stuff" bit is doing. We don't have side effects, so that means something has to be (or be derived from) returned from the "do stuff". "do stuff" also needs to take something that varies as an argument, or it'll just keep returning the same thing forever, which is also pointless. We also need to return a value out all this, otherwise we're just burning CPU cycles (again, with no side effects there's no point running a function if we don't use its output in some way, and there's even less point running a function repeatedly if we never use its output).
So how about something like this:
while check func state =
let next_state = func state in
if check next_state
then while check func next_state
else next_state
Lets try it in GHCi:
*Main> while (<20) (+1) 0
20
This is the result of applying (+1) repeatedly while the result is less than 20, starting from 0.
*Main> while ((<20) . length) (++ "zob") ""
"zobzobzobzobzobzobzob"
This is the result of concatenating "zob" repeatedly while the result's length is less than 20, starting from the empty string.
So you can see I've defined a function that is (sort of a bit) analogous to a while loop from imperative languages. We didn't even need dedicated loop syntax for it! (which is the real reason Haskell has no such syntax; if you need this kind of thing you can express it as a function). It's not the only way to do so, and experienced Haskell programmers would probably use other standard library functions to do this kind of job, rather than writing while.
But I think it's useful to see how you can express this kind of thing in Haskell. It does show that you can't translate things like imperative loops directly into Haskell; I didn't end up translating your loop in terms of my while because it ends up pretty pointless; you never use the result of function1 or function2, they're called with no arguments so they'd always return the same thing in every iteration, and function3 likewise always returns the same thing, and can only return true or false to either cause while to keep looping or stop, with no information resulting.
Presumably in the C++ program they're all using side effects to actually get some work done. If they operate on in-memory things then you need to translate a bigger chunk of your program at once to Haskell for the translation of this loop to make any sense. If those functions are doing IO then you'll need to do this in the IO monad in Haskell, for which my while function doesn't work, but you can do something similar.
[1] As an aside, it's worth trying to understand that "you can't change variables" in Haskell isn't just an arbitrary restriction, nor is it just an acceptable trade off for the benefits of purity, it is a concept that doesn't make sense the way Haskell wants you to think about Haskell code. You're writing down expressions that result from evaluating functions on certain arguments: in f x = x + 1 you're saying that f x is x + 1. If you really think of it that way rather than thinking "f takes x, then adds one to it, then returns the result" then the concept of "having side effects" doesn't even apply; how could something existing and being equal to something else somehow change a variable, or have some other side effect?
You should write a solution to your problem in a more functional approach.
However, some code in haskell works a lot like imperative looping, take for example state monads, terminal recursivity, until, foldr, etc.
A simple example is the factorial. In C, you would write a loop where in haskell you can for example write fact n = foldr (*) 1 [2..n].
If you've two functions f :: a -> b and g :: b -> c where a, b, and c are types like String or [Int] then you can compose them simply by writing f . b.
If you wish them to loop over a list or vector you could write map (f . g) or V.map (f . g), assuming you've done Import qualified Data.Vector as V.
Example : I wish to print a list of markdown headings like ## <number>. <heading> ## but I need roman numerals numbered from 1 and my list headings has type type [(String,Double)] where the Double is irrelevant.
Import Data.List
Import Text.Numeral.Roman
let fun = zipWith (\a b -> a ++ ". " ++ b ++ "##\n") (map toRoman [1..]) . map fst
fun [("Foo",3.5),("Bar",7.1)]
What the hell does this do?
toRoman turns a number into a string containing the roman numeral. map toRoman does this to every element of a loop. map toRoman [1..] does it to every element of the lazy infinite list [1,2,3,4,..], yielding a lazy infinite list of roman numeral strings
fst :: (a,b) -> a simply extracts the first element of a tuple. map fst throws away our silly Meow information along the entire list.
\a b -> "##" ++ show a ++ ". " ++ b ++ "##" is a lambda expression that takes two strings and concatenates them together within the desired formatting strings.
zipWith :: (a -> b -> c) -> [a] -> [b] -> [c] takes a two argument function like our lambda expression and feeds it pairs of elements from it's own second and third arguments.
You'll observe that zip, zipWith, etc. only read as much of the lazy infinite list of Roman numerals as needed for the list of headings, meaning I've number my headings without maintaining any counter variable.
Finally, I have declared fun without naming it's argument because the compiler can figure it out from the fact that map fst requires one argument. You'll notice that put a . before my second map too. I could've written (map fst h) or $ map fst h instead if I'd written fun h = ..., but leaving the argument off fun meant I needed to compose it with zipWith after applying zipWith to two arguments of the three arguments zipWith wants.
I'd hope the compiler combines the zipWith and maps into one single loop via inlining.