In Scrap your boilerplate reloaded, the authors describe a new presentation of Scrap Your Boilerplate, which is supposed to be equivalent to the original.
However, one difference is that they assume a finite, closed set of "base" types, encoded with a GADT
data Type :: * -> * where
Int :: Type Int
List :: Type a -> Type [a]
...
In the original SYB, type-safe cast is used, implemented using the Typeable class.
My questions are:
What is the relationship between these two approaches?
Why was the GADT representation chosen for the "SYB Reloaded" presentation?
[I am one of the authors of the "SYB Reloaded" paper.]
TL;DR We really just used it because it seemed more beautiful to us. The class-based Typeable approach is more practical. The Spine view can be combined with the Typeable class and does not depend on the Type GADT.
The paper states this in its conclusions:
Our implementation handles the two central ingredients of generic programming differently from the original SYB paper: we use overloaded functions with
explicit type arguments instead of overloaded functions based on a type-safe
cast 1 or a class-based extensible scheme [20]; and we use the explicit spine
view rather than a combinator-based approach. Both changes are independent
of each other, and have been made with clarity in mind: we think that the structure of the SYB approach is more visible in our setting, and that the relations
to PolyP and Generic Haskell become clearer. We have revealed that while the
spine view is limited in the class of generic functions that can be written, it is
applicable to a very large class of data types, including GADTs.
Our approach cannot be used easily as a library, because the encoding of
overloaded functions using explicit type arguments requires the extensibility of
the Type data type and of functions such as toSpine. One can, however, incorporate Spine into the SYB library while still using the techniques of the SYB
papers to encode overloaded functions.
So, the choice of using a GADT for type representation is one we made mainly for clarity. As Don states in his answer, there are some obvious advantages in this representation, namely that it maintains static information about what type a type representation is for, and that it allows us to implement cast without any further magic, and in particular without the use of unsafeCoerce. Type-indexed functions can also be implemented directly by using pattern matching on the type, and without falling back to various combinators such as mkQ or extQ.
Fact is that I (and I think the co-authors) simply were not very fond of the Typeable class. (In fact, I'm still not, although it is finally becoming a bit more disciplined now in that GHC adds auto-deriving for Typeable, makes it kind-polymorphic, and will ultimately remove the possibility to define your own instances.) In addition, Typeable wasn't quite as established and widely known as it is perhaps now, so it seemed appealing to "explain" it by using the GADT encoding. And furthermore, this was the time when we were also thinking about adding open datatypes to Haskell, thereby alleviating the restriction that the GADT is closed.
So, to summarize: If you actually need dynamic type information only for a closed universe, I'd always go for the GADT, because you can use pattern matching to define type-indexed functions, and you do not have to rely on unsafeCoerce nor advanced compiler magic. If the universe is open, however, which is quite common, certainly for the generic programming setting, then the GADT approach might be instructive, but isn't practical, and using Typeable is the way to go.
However, as we also state in the conclusions of the paper, the choice of Type over Typeable isn't a prerequisite for the other choice we're making, namely to use the Spine view, which I think is more important and really the core of the paper.
The paper itself shows (in Section 8) a variation inspired by the "Scrap your Boilerplate with Class" paper, which uses a Spine view with a class constraint instead. But we can also do a more direct development, which I show in the following. For this, we'll use Typeable from Data.Typeable, but define our own Data class which, for simplicity, just contains the toSpine method:
class Typeable a => Data a where
toSpine :: a -> Spine a
The Spine datatype now uses the Data constraint:
data Spine :: * -> * where
Constr :: a -> Spine a
(:<>:) :: (Data a) => Spine (a -> b) -> a -> Spine b
The function fromSpine is as trivial as with the other representation:
fromSpine :: Spine a -> a
fromSpine (Constr x) = x
fromSpine (c :<>: x) = fromSpine c x
Instances for Data are trivial for flat types such as Int:
instance Data Int where
toSpine = Constr
And they're still entirely straightforward for structured types such as binary trees:
data Tree a = Empty | Node (Tree a) a (Tree a)
instance Data a => Data (Tree a) where
toSpine Empty = Constr Empty
toSpine (Node l x r) = Constr Node :<>: l :<>: x :<>: r
The paper then goes on and defines various generic functions, such as mapQ. These definitions hardly change. We only get class constraints for Data a => where the paper has function arguments of Type a ->:
mapQ :: Query r -> Query [r]
mapQ q = mapQ' q . toSpine
mapQ' :: Query r -> (forall a. Spine a -> [r])
mapQ' q (Constr c) = []
mapQ' q (f :<>: x) = mapQ' q f ++ [q x]
Higher-level functions such as everything also just lose their explicit type arguments (and then actually look exactly the same as in original SYB):
everything :: (r -> r -> r) -> Query r -> Query r
everything op q x = foldl op (q x) (mapQ (everything op q) x)
As I said above, if we now want to define a generic sum function summing up all Int occurrences, we cannot pattern match anymore, but have to fall back to mkQ, but mkQ is defined purely in terms of Typeable and completely independent of Spine:
mkQ :: (Typeable a, Typeable b) => r -> (b -> r) -> a -> r
(r `mkQ` br) a = maybe r br (cast a)
And then (again exactly as in original SYB):
sum :: Query Int
sum = everything (+) sumQ
sumQ :: Query Int
sumQ = mkQ 0 id
For some of the stuff later in the paper (e.g., adding constructor information), a bit more work is needed, but it can all be done. So using Spine really does not depend on using Type at all.
Well, obviously the Typeable use is open -- new variants can be added after the fact, and without modifying the original definitions.
The important change though is that in that TypeRep is untyped. That is, there is no connection between the runtime type , TypeRep, and the static type it encodes. With the GADT approach we can encode the mapping between a type a and its Type, given by the GADT Type a.
We thus bake in evidence for the type rep being statically linked to its origin type, and can write statically typed dynamic application (for example) using Type a as evidence that we have a runtime a.
In the older TypeRep case, we have no such evidence and it comes down to runtime string equality, and a coerce and hope for the best through fromDynamic.
Compare the signatures:
toDyn :: Typeable a => a -> TypeRep -> Dynamic
versus GADT style:
toDyn :: Type a => a -> Type a -> Dynamic
I can't fake my type evidence, and I can use that later when reconstructing things, to e.g. lookup the type class instances for a when all I have is a Type a.
Related
I'm trying to achieve a deeper understanding of lens library, so I play around with the types it offers. I have already had some experience with lenses, and know how powerful and convenient they are. So I moved on to Prisms, and I'm a bit lost. It seems that prisms allow two things:
Determining if an entity belongs to a particular branch of a sum type, and if it does, capturing the underlying data in a tuple or a singleton.
Destructuring and reconstructing an entity, possibly modifying it in process.
The first point seems useful, but usually one doesn't need all the data from an entity, and ^? with plain lenses allows getting Nothing if the field in question doesn't belong to the branch the entity represents, just like it does with prisms.
The second point... I don't know, might have uses?
So the question is: what can I do with a Prism that I can't with other optics?
Edit: thank you everyone for excellent answers and links for further reading! I wish I could accept them all.
Lenses characterise the has-a relationship; Prisms characterise the is-a relationship.
A Lens s a says "s has an a"; it has methods to get exactly one a from an s and to overwrite exactly one a in an s. A Prism s a says "a is an s"; it has methods to upcast an a to an s and to (attempt to) downcast an s to an a.
Putting that intuition into code gives you the familiar "get-set" (or "costate comonad coalgebra") formulation of lenses,
data Lens s a = Lens {
get :: s -> a,
set :: a -> s -> s
}
and an "upcast-downcast" representation of prisms,
data Prism s a = Prism {
up :: a -> s,
down :: s -> Maybe a
}
up injects an a into s (without adding any information), and down tests whether the s is an a.
In lens, up is spelled review and down is preview. There’s no Prism constructor; you use the prism' smart constructor.
What can you do with a Prism? Inject and project sum types!
_Left :: Prism (Either a b) a
_Left = Prism {
up = Left,
down = either Just (const Nothing)
}
_Right :: Prism (Either a b) b
_Right = Prism {
up = Right,
down = either (const Nothing) Just
}
Lenses don't support this - you can't write a Lens (Either a b) a because you can't implement get :: Either a b -> a. As a practical matter, you can write a Traversal (Either a b) a, but that doesn't allow you to create an Either a b from an a - it'll only let you overwrite an a which is already there.
Aside: I think this subtle point about Traversals is the source of your confusion about partial record fields.
^? with plain lenses allows getting Nothing if the field in question doesn't belong to the branch the entity represents
Using ^? with a real Lens will never return Nothing, because a Lens s a identifies exactly one a inside an s.
When confronted with a partial record field,
data Wibble = Wobble { _wobble :: Int } | Wubble { _wubble :: Bool }
makeLenses will generate a Traversal, not a Lens.
wobble :: Traversal' Wibble Int
wubble :: Traversal' Wibble Bool
For an example of this how Prisms can be applied in practice, look to Control.Exception.Lens, which provides a collection of Prisms into Haskell's extensible Exception hierarchy. This lets you perform runtime type tests on SomeExceptions and inject specific exceptions into SomeException.
_ArithException :: Prism' SomeException ArithException
_AsyncException :: Prism' SomeException AsyncException
-- etc.
(These are slightly simplified versions of the actual types. In reality these prisms are overloaded class methods.)
Thinking at a higher level, certain whole programs can be thought of as being "basically a Prism". Encoding and decoding data is one example: you can always convert structured data to a String, but not every String can be parsed back:
showRead :: (Show a, Read a) => Prism String a
showRead = Prism {
up = show,
down = listToMaybe . fmap fst . reads
}
To summarise, Lenses and Prisms together encode the two core design tools of object-oriented programming: composition and subtyping. Lenses are a first-class version of Java's . and = operators, and Prisms are a first-class version of Java's instanceof and implicit upcasting.
One fruitful way of thinking about Lenses is that they give you a way of splitting up a composite s into a focused value a and some context c. Pseudocode:
type Lens s a = exists c. s <-> (a, c)
In this framework, a Prism gives you a way to look at an s as being either an a or some context c.
type Prism s a = exists c. s <-> Either a c
(I'll leave it to you to convince yourself that these are isomorphic to the simple representations I demonstrated above. Try implementing get/set/up/down for these types!)
In this sense a Prism is a co-Lens. Either is the categorical dual of (,); Prism is the categorical dual of Lens.
You can also observe this duality in the "profunctor optics" formulation - Strong and Choice are dual.
type Lens s t a b = forall p. Strong p => p a b -> p s t
type Prism s t a b = forall p. Choice p => p a b -> p s t
This is more or less the representation which lens uses, because these Lenses and Prisms are very composable. You can compose Prisms to get bigger Prisms ("a is an s, which is a p") using (.); composing a Prism with a Lens gives you a Traversal.
I just wrote a blog post, which might help build some intuition about Prisms: Prisms are constructors (Lenses are fields). http://oleg.fi/gists/posts/2018-06-19-prisms-are-constructors.html
Prisms could be introduced as first-class pattern matching, but that is a
one-sided view. I'd say they are generalised constructors, though maybe
more often used for pattern matching than for actual construction.
The important property of constructors (and lawful prisms), is their
injectivity. Though the usual prism laws don't state that directly,
injectivity property can be deduced.
To quote lens-library documentation, the prisms laws are:
First, if I review a value with a Prism and then preview, I will get it back:
preview l (review l b) ≡ Just b
Second, if you can extract a value a using a Prism l from a value s, then
the value s is completely described by l and a:
preview l s ≡ Just a ⇒ review l a ≡ s
In fact, the first law alone is enough to prove the injectivity of construction
via Prism:
review l x ≡ review l y ⇒ x ≡ y
The proof is straight-forward:
review l x ≡ review l y
-- x ≡ y -> f x ≡ f y
preview l (review l x) ≡ preview l (review l y)
-- rewrite both sides with the first law
Just x ≡ Just y
-- injectivity of Just
x ≡ y
We can use injectivity property as an additional tool in the equational
reasoning toolbox. Or we can use it as a easy property to check to decide
whether something is a lawful Prism. The check is easy as we only the
review side of Prism. Many smart constructors, which for example
normalise the input data, aren't lawful prisms.
An example using case-insensitive:
-- Bad!
_CI :: FoldCase s => Prism' (CI s) s
_CI = prism' ci (Just . foldedCase)
λ> review _CI "FOO" == review _CI "foo"
True
λ> "FOO" == "foo"
False
The first law is also violated:
λ> preview _CI (review _CI "FOO")
Just "foo"
In addition to the other excellent answers, I feel Isos provide a nice vantage point for considering this matter.
There being some i :: Iso' s a means if you have an s value you also (virtually) have an a value, and vice versa. The Iso' gives you two conversion functions, view i :: s -> a and review i :: a -> s which are both guaranteed to succeed and lossless.
There being some l :: Lens' s a means if you have an s you also have an a, but not vice versa. view l :: s -> a may drop information along the way, as the conversion isn't required to be lossless, and so you can't go the other way if all you have is an a (cf. set l :: a -> s -> s, which also requires an s in addition to the a value in order to provide the missing information).
There being some p :: Prism' s a means if you have an s value you might also have an a, but there are no guarantees. The conversion preview p :: s -> Maybe a is not guaranteed to succeed. Still, you do have the other direction, review p :: a -> s.
In other words, an Iso is invertible and always succeeds. If you drop the invertibility requirement, you get a Lens; if you drop the success guarantee, you get a Prism. If you drop both, you get an affine traversal (which is not in lens as a separate type), and if you go a step further and give up on having at most one target you end up with a Traversal. That is reflected in one of the diamonds of the lens subtype hierarchy:
Traversal
/ \
/ \
/ \
Lens Prism
\ /
\ /
\ /
Iso
I'm covering polymorphism and I'm trying to see the practical uses of such a feature.
My basic understanding of Rank 2 is:
type MyType = ∀ a. a -> a
subFunction :: a -> a
subFunction el = el
mainFunction :: MyType -> Int
mainFunction func = func 3
I understand that this is allowing the user to use a polymorphic function (subFunction) inside mainFunction and strictly specify it's output (Int). This seems very similar to GADT's:
data Example a where
ExampleInt :: Int -> Example Int
ExampleBool :: Bool -> Example Bool
1) Given the above, is my understanding of Rank 2 polymorphism correct?
2) What are the general situations where Rank 2 polymorphism can be used, as opposed to GADT's, for example?
If you pass a polymorphic function as and argument to a Rank2-polymorphic function, you're essentially passing not just one function but a whole family of functions – for all possible types that fulfill the constraints.
Typically, those forall quantifiers come with a class constraint. For example, I might wish to do number arithmetic with two different types simultaneously (for comparing precision or whatever).
data FloatCompare = FloatCompare {
singlePrecision :: Float
, doublePrecision :: Double
}
Now I might want to modify those numbers through some maths operation. Something like
modifyFloat :: (Num -> Num) -> FloatCompare -> FloatCompare
But Num is not a type, only a type class. I could of course pass a function that would modify any particular number type, but I couldn't use that to modify both a Float and a Double value, at least not without some ugly (and possibly lossy) converting back and forth.
Solution: Rank-2 polymorphism!
modifyFloat :: (∀ n . Num n => n -> n) -> FloatCompare -> FloatCompare
mofidyFloat f (FloatCompare single double)
= FloatCompare (f single) (f double)
The best single example of how this is useful in practice are probably lenses. A lens is a “smart accessor function” to a field in some larger data structure. It allows you to access fields, update them, gather results... while at the same time composing in a very simple way. How it works: Rank2-polymorphism; every lens is polymorphic, with the different instantiations corresponding to the “getter” / “setter” aspects, respectively.
The go-to example of an application of rank-2 types is runST as Benjamin Hodgson mentioned in the comments. This is a rather good example and there are a variety of examples using the same trick. For example, branding to maintain abstract data type invariants across multiple types, avoiding confusion of differentials in ad, a region-based version of ST.
But I'd actually like to talk about how Haskell programmers are implicitly using rank-2 types all the time. Every type class whose methods have universally quantified types desugars to a dictionary with a field with a rank-2 type. In practice, this is virtually always a higher-kinded type class* like Functor or Monad. I'll use a simplified version of Alternative as an example. The class declaration is:
class Alternative f where
empty :: f a
(<|>) :: f a -> f a -> f a
The dictionary representing this class would be:
data AlternativeDict f = AlternativeDict {
empty :: forall a. f a,
(<|>) :: forall a. f a -> f a -> f a }
Sometimes such an encoding is nice as it allows one to use different "instances" for the same type, perhaps only locally. For example, Maybe has two obvious instances of Alternative depending on whether Just a <|> Just b is Just a or Just b. Languages without type classes, such as Scala, do indeed use this encoding.
To connect to leftaroundabout's reference to lenses, you can view the hierarchy there as a hierarchy of type classes and the lens combinators as simply tools for explicitly building the relevant type class dictionaries. Of course, the reason it isn't actually a hierarchy of type classes is that we usually will have multiple "instances" for the same type. E.g. _head and _head . _tail are both "instances" of Traversal' s a.
* A higher-kinded type class doesn't necessarily lead to this, and it can happen for a type class of kind *. For example:
-- Higher-kinded but doesn't require universal quantification.
class Sum c where
sum :: c Int -> Int
-- Not higher-kinded but does require universal quantification.
class Length l where
length :: [a] -> l
If you are using modules in Haskell, you are already using Rank-2 types. Theoretically speaking, modules are records with rank-2 type properties.
For example, the Foo module below in Haskell ...
module Foo(id) where
id :: forall a. a -> a
id x = x
import qualified Foo
main = do
putStrLn (Foo.id "hello")
return ()
... can actually be thought as a record as follows:
type FooType = FooType {
id :: forall a. a -> a
}
Foo :: FooType
Foo = Foo {
id = \x -> x
}
P/S (unrelated this question): from a language design perspective, if you are going to support module system, then you might as well support higher-rank types (i.e. allow arbitrary quantification of type variables on any level) to reduce duplication of efforts (i.e. type checking a module should be almost the same as type checking a record with higher rank types).
I've just read this paper ("Type classes: an exploration of the design space" by Peyton Jones & Jones), which explains some challenges with the early typeclass system of Haskell, and how to improve it.
Many of the issues that they raise are related to context reduction which is a way to reduce the set of constraints over instance and function declarations by following the "reverse entailment" relationship.
e.g. if you have somewhere instance (Ord a, Ord b) => Ord (a, b) ... then within contexts, Ord (a, b) gets reduced to {Ord a, Ord b} (reduction does not always shrink the number of constrains).
I did not understand from the paper why this reduction was necessary.
Well, I gathered it was used to perform some form of type checking. When you have your reduced set of constraint, you can check that there exist some instance that can satisfy them, otherwise it's an error. I'm not too sure what the added value of that is, since you would notice the problem at the use site, but okay.
But even if you have to do that check, why use the result of reduction inside inferred types? The paper points out it leads to unintuitive inferred types.
The paper is quite ancient (1997) but as far as I can tell, context reduction is still an ongoing concern. The Haskell 2010 spec does mention the inference behaviour I explain above (link).
So, why do it this way?
I don't know if this is The Reason, necessarily, but it might be considered A Reason: in early Haskell, type signatures were only permitted to have "simple" constraints, namely, a type class name applied to a type variable. Thus, for example, all of these were okay:
Ord a => a -> a -> Bool
Eq a => a -> a -> Bool
Graph gr => gr n e -> [n]
But none of these:
Ord (Tree a) => Tree a -> Tree a -> Bool
Eq (a -> b) => (a -> b) -> (a -> b) -> Bool
Graph Gr => Gr n e -> [n]
I think there was a feeling then -- and still today, as well -- that allowing the compiler to infer a type which one couldn't write manually would be a bit unfortunate. Context reduction was a way of turning the above signatures either into ones that could be written by hand as well or an informative error. For example, since one might reasonably have
instance Ord a => Ord (Tree a)
in scope, we could turn the illegal signature Ord (Tree a) => ... into the legal signature Ord a => .... On the other hand, if we don't have any instance of Eq for functions in scope, one would report an error about the type which was inferred to require Eq (a -> b) in its context.
This has a couple of other benefits:
Intuitively pleasing. Many of the context reduction rules do not change whether the type is legal, but do reflect things humans would do when writing the type. I'm thinking here of the de-duplication and subsumption rules that let you turn, e.g. (Eq a, Eq a, Ord a) into just Ord a -- a transformation one definitely would want to do for readability.
This can frequently catch stupid errors; rather than inferring a type like Eq (Integer -> Integer) => Bool which can't be satisfied in a law-abiding way, one can report an error like Perhaps you did not apply a function to enough arguments?. Much friendlier!
It becomes the compiler's job to pinpoint what went wrong. Instead of inferring a complicated context like Eq (Tree (Grizwump a, [Flagle (Gr n e) (Gr n' e') c])) and complaining that the context is not satisfiable, it instead is forced to reduce this to the constituent constraints; it will instead complain that we couldn't determine Eq (Grizwump a) from the existing context -- a much more precise and actionable error.
I think this is indeed desirable in a dictionary passing implementation. In such an implementation, a "dictionary", that is, a tuple or record of functions is passed as implicit argument for every type class constraint in the type of the applied function.
Now, the question is simply when and how those dictionaries are created. Observe that for simple types like Int by necessity all dictionaries for whatever type class Int is an instance of will be a constant.
Not so in the case of parameterized types like lists, Maybe or tuples. It is clear that to show a tuple, for instance, the Show instances of the actual tuple elements need to be known. Hence such a polymorphic dictionary cannot be a constant.
It appears that the principle guiding the dictionary passing is such that only dictionaries for types that appear as type variables in the type of the applied function are passed. Or, to put it differently: no redundant information is replicated.
Consider this function:
f :: (Show a, Show b) => (a,b) -> Int
f ab = length (show ab)
The information that a tuple of show-able components is also showable, thus a constraint like Show (a,b) needs not to appear when we already know (Show a, Show b).
An alternative implementation would be possible, though, where the caller .would be responsible to create and pass dictionaries. This could work without context reduction, such that the type of f would look like:
f :: Show (a,b) => (a,b) -> Int
But this would mean that the code to create the tuple dictionary would have to be repeated on every call site. And it is easy to come up with examples where the number of necessary constraints actually increases, like in:
g :: (Show (a,a), Show(b,b), Show (a,b), Show (b, a)) => a -> b -> Int
g a b = maximum (map length [show (a,a), show (a,b), show (b,a), show(b,b)])
It is instructive to implement a type class/instance system with actual records that are explicitly passed. For example:
data Show' a = Show' { show' :: a -> String }
showInt :: Show' Int
showInt = Show' { show' = intshow } where
intshow :: Int -> String
intshow = show
Once you do this you will probably easily recognize the need for "context reduction".
In Haskell, there is a function called unsafeCoerce, that turns anything into any other type of thing. What exactly is this used for? Like, why we would you want to transform things into each other in such an "unsafe" way?
Provide an example of a way that unsafeCoerce is actually used. A link to Hackage would help. Example code in someones question would not.
unsafeCoerce lets you convince the type system of whatever property you like. It's thus only "safe" exactly when you can be completely certain that the property you're declaring is true. So, for instance:
unsafeCoerce True :: Int
is a violation and can lead to wonky, bad runtime behavior.
unsafeCoerce (3 :: Int) :: Int
is (obviously) fine and will not lead to runtime misbehavior.
So what's a non-trivial use of unsafeCoerce? Let's say we've got an typeclass-bound existential type
module MyClass ( SomethingMyClass (..), intSomething ) where
class MyClass x where {}
instance MyClass Int where {}
data SomethingMyClass = forall a. MyClass a => SomethingMyClass a
Let's also say, as noted here, that the typeclass MyClass is not exported and thus nobody else can ever create instances of it. Indeed, Int is the only thing that instantiates it and the only thing that ever will.
Now when we pattern match to destruct a value of SomethingMyClass we'll be able to pull a "something" out from inside
foo :: SomethingMyClass -> ...
foo (SomethingMyClass a) =
-- here we have a value `a` with type `exists a . MyClass a => a`
--
-- this is totally useless since `MyClass` doesn't even have any
-- methods for us to use!
...
Now, at this point, as the comment suggests, the value we've pulled out has no type information—it's been "forgotten" by the existential context. It could be absolutely anything which instantiates MyClass.
Of course, in this very particular situation we know that the only thing implementing MyClass is Int. So our value a must actually have type Int. We could never convince the typechecker that this is true, but due to an outside proof we know that it is.
Therefore, we can (very carefully)
intSomething :: SomethingMyClass -> Int
intSomething (SomethingMyClass a) = unsafeCoerce a -- shudder!
Now, hopefully I've suggested that this is a terrible, dangerous idea, but it also may give a taste of what kind of information we can take advantage of in order to know things that the typechecker cannot.
In non-pathological situations, this is rare. Even rarer is a situation where using something we know and the typechecker doesn't isn't itself pathological. In the above example, we must be completely certain that nobody ever extends our MyClass module to instantiate more types to MyClass otherwise our use of unsafeCoerce becomes instantly unsafe.
> instance MyClass Bool where {}
> intSomething (SomethingMyClass True)
6917529027658597398
Looks like our compiler internals are leaking!
A more common example where this sort of behavior might be valuable is when using newtype wrappers. It's a fairly common idea that we might wrap a type in a newtype wrapper in order to specialize its instance definitions.
For example, Int does not have a Monoid definition because there are two natural monoids over Ints: sums and products. Instead, we use newtype wrappers to be more explicit.
newtype Sum a = Sum { getSum :: a }
instance Num a => Monoid (Sum a) where
mempty = Sum 0
mappend (Sum a) (Sum b) = Sum (a+b)
Now, normally the compiler is pretty smart and recognizes that it can eliminate all of those Sum constructors in order to produce more efficient code. Sadly, there are times when it cannot, especially in highly polymorphic situations.
If you (a) know that some type a is actually just a newtype-wrapped b and (b) know that the compiler is incapable of deducing this itself, then you might want to do
unsafeCoerce (x :: a) :: b
for a slight efficiency gain. This, for instance, occurs frequently in lens and is expressed in the Data.Profunctor.Unsafe module of profunctors, a dependency of lens.
But let me again suggest that you really need to know what's going on before using unsafeCoerce like this is anything but highly unsafe.
One final thing to compare is the "typesafe cast" available in Data.Typeable. This function looks a bit like unsafeCoerce, but with much more ceremony.
unsafeCoerce :: a -> b
cast :: (Typeable a, Typeable b) => a -> Maybe b
Which, you might think of as being implemented using unsafeCoerce and a function typeOf :: Typeable a => a -> TypeRep where TypeRep are unforgeable, runtime tokens which reflect the type of a value. Then we have
cast :: (Typeable a, Typeable b) => a -> Maybe b
cast a = if (typeOf a == typeOf b) then Just b else Nothing
where b = unsafeCoerce a
Thus, cast is able to ensure that the types of a and b really are the same at runtime, and it can decide to return Nothing if they are not. As an example:
{-# LANGUAGE DeriveDataTypeable #-}
{-# LANGUAGE ExistentialQuantification #-}
data A = A deriving (Show, Typeable)
data B = B deriving (Show, Typeable)
data Forget = forall a . Typeable a => Forget a
getAnA :: Forget -> Maybe A
getAnA (Forget something) = cast something
which we can run as follows
> getAnA (Forget A)
Just A
> getAnA (Forget B)
Nothing
So if we compare this usage of cast with unsafeCoerce we see that it can achieve some of the same functionality. In particular, it allows us to rediscover information that may have been forgotten by ExistentialQuantification. However, cast manually checks the types at runtime to ensure that they are truly the same and thus cannot be used unsafely. To do this, it demands that both the source and target types allow for runtime reflection of their types via the Typeable class.
The only time I ever felt compelled to use unsafeCoerce was on finite natural numbers.
{-# LANGUAGE DataKinds, GADTs, TypeFamilies, StandaloneDeriving #-}
data Nat = Z | S Nat deriving (Eq, Show)
data Fin (n :: Nat) :: * where
FZ :: Fin (S n)
FS :: Fin n -> Fin (S n)
deriving instance Show (Fin n)
Fin n is a singly linked data structure that is statically ensured to be smaller than the n type level natural number by which it is parametrized.
-- OK, 1 < 2
validFin :: Fin (S (S Z))
validFin = FS FZ
-- type error, 2 < 2 is false
invalidFin :: Fin (S (S Z))
invalidFin = FS (FS FZ)
Fin can be used to safely index into various data structures. It's pretty standard in dependently typed languages, though not in Haskell.
Sometimes we want to convert a value of Fin n to Fin m where m is greater than n.
relaxFin :: Fin n -> Fin (S n)
relaxFin FZ = FZ
relaxFin (FS n) = FS (relaxFin n)
relaxFin is a no-op by definition, but traversing the value is still required for the types to check out. So we might just use unsafeCoerce instead of relaxFin. More pronounced gains in speed can result from coercing larger data structures that contain Fin-s (for example, you could have lambda terms with Fin-s as bound variables).
This is an admittedly exotic example, but I find it interesting in the sense that it's pretty safe: I can't really think of ways for external libraries or safe user code to mess this up. I might be wrong though and I'd be eager to hear about potential safety issues.
There is no use of unsafeCoerce I can really recommend, but I can see that in some cases such a thing might be useful.
The first use that springs to mind is the implementation of the Typeable-related routines. In particular cast :: (Typeable a, Typeable b) => a -> Maybe b achieves a type-safe behaviour, so it is safe to use, yet it has to play dirty tricks in its implementation.
Maybe unsafeCoerce can find some use when importing FFI subroutines to force types to match. After all, FFI already allows to import impure C functions as pure ones, so it is intrinsecally usafe. Note that "unsafe" does not mean impossible to use, but just "putting the burden of proof on the programmer".
Finally, pretend that sortBy did not exist. Consider then this example:
-- Like Int, but using the opposite ordering
newtype Rev = Rev { unRev :: Int }
instance Ord Rev where compare (Rev x) (Rev y) = compare y x
sortDescending :: [Int] -> [Int]
sortDescending = map unRev . sort . map Rev
The code above works, but feels silly IMHO. We perform two maps using functions such as Rev,unRev which we know to be no-ops at runtime. So we just scan the list twice for no reason, but that of convincing the compiler to use the right Ord instance.
The performance impact of these maps should be small since we also sort the list. Yet it is tempting to rewrite map Rev as unsafeCoerce :: [Int]->[Rev] and save some time.
Note that having a coercing function
castNewtype :: IsNewtype t1 t2 => f t2 -> f t1
where the constraint means that t1 is a newtype for t2 would help, but it would be quite dangerous. Consider
castNewtype :: Data.Set Int -> Data.Set Rev
The above would cause the data structure invariant to break, since we are changing the ordering underneath! Since Data.Set is implemented as a binary search tree, it would cause quite a large damage.
When metaprogramming, it may be useful (or necessary) to pass along to Haskell's type system information about types that's known to your program but not inferable in Hindley-Milner. Is there a library (or language extension, etc) that provides facilities for doing this—that is, programmatic type annotations—in Haskell?
Consider a situation where you're working with a heterogenous list (implemented using the Data.Dynamic library or existential quantification, say) and you want to filter the list down to a bog-standard, homogeneously typed Haskell list. You can write a function like
import Data.Dynamic
import Data.Typeable
dynListToList :: (Typeable a) => [Dynamic] -> [a]
dynListToList = (map fromJust) . (filter isJust) . (map fromDynamic)
and call it with a manual type annotation. For example,
foo :: [Int]
foo = dynListToList [ toDyn (1 :: Int)
, toDyn (2 :: Int)
, toDyn ("foo" :: String) ]
Here foo is the list [1, 2] :: [Int]; that works fine and you're back on solid ground where Haskell's type system can do its thing.
Now imagine you want to do much the same thing but (a) at the time you write the code you don't know what the type of the list produced by a call to dynListToList needs to be, yet (b) your program does contain the information necessary to figure this out, only (c) it's not in a form accessible to the type system.
For example, say you've randomly selected an item from your heterogenous list and you want to filter the list down by that type. Using the type-checking facilities supplied by Data.Typeable, your program has all the information it needs to do this, but as far as I can tell—this is the essence of the question—there's no way to pass it along to the type system. Here's some pseudo-Haskell that shows what I mean:
import Data.Dynamic
import Data.Typeable
randList :: (Typeable a) => [Dynamic] -> IO [a]
randList dl = do
tr <- randItem $ map dynTypeRep dl
return (dynListToList dl :: [<tr>]) -- This thing should have the type
-- represented by `tr`
(Assume randItem selects a random item from a list.)
Without a type annotation on the argument of return, the compiler will tell you that it has an "ambiguous type" and ask you to provide one. But you can't provide a manual type annotation because the type is not known at write-time (and can vary); the type is known at run-time, however—albeit in a form the type system can't use (here, the type needed is represented by the value tr, a TypeRep—see Data.Typeable for details).
The pseudo-code :: [<tr>] is the magic I want to happen. Is there any way to provide the type system with type information programatically; that is, with type information contained in a value in your program?
Basically I'm looking for a function with (pseudo-) type ??? -> TypeRep -> a that takes a value of a type unknown to Haskell's type system and a TypeRep and says, "Trust me, compiler, I know what I'm doing. This thing has the value represented by this TypeRep." (Note that this is not what unsafeCoerce does.)
Or is there something completely different that gets me the same place? For example, I can imagine a language extension that permits assignment to type variables, like a souped-up version of the extension enabling scoped type variables.
(If this is impossible or highly impractical,—e.g., it requires packing a complete GHCi-like interpreter into the executable—please try to explain why.)
No, you can't do this. The long and short of it is that you're trying to write a dependently-typed function, and Haskell isn't a dependently typed language; you can't lift your TypeRep value to a true type, and so there's no way to write down the type of your desired function. To explain this in a little more detail, I'm first going to show why the way you've phrased the type of randList doesn't really make sense. Then, I'm going to explain why you can't do what you want. Finally, I'll briefly mention a couple thoughts on what to actually do.
Existentials
Your type signature for randList can't mean what you want it to mean. Remembering that all type variables in Haskell are universally quantified, it reads
randList :: forall a. Typeable a => [Dynamic] -> IO [a]
Thus, I'm entitled to call it as, say, randList dyns :: IO [Int] anywhere I want; I must be able to provide a return value for all a, not simply for some a. Thinking of this as a game, it's one where the caller can pick a, not the function itself. What you want to say (this isn't valid Haskell syntax, although you can translate it into valid Haskell by using an existential data type1) is something more like
randList :: [Dynamic] -> (exists a. Typeable a => IO [a])
This promises that the elements of the list are of some type a, which is an instance of Typeable, but not necessarily any such type. But even with this, you'll have two problems. First, even if you could construct such a list, what could you do with it? And second, it turns out that you can't even construct it in the first place.
Since all that you know about the elements of the existential list is that they're instances of Typeable, what can you do with them? Looking at the documentation, we see that there are only two functions2 which take instances of Typeable:
typeOf :: Typeable a => a -> TypeRep, from the type class itself (indeed, the only method therein); and
cast :: (Typeable a, Typeable b) => a -> Maybe b (which is implemented with unsafeCoerce, and couldn't be written otherwise).
Thus, all that you know about the type of the elements in the list is that you can call typeOf and cast on them. Since we'll never be able to usefully do anything else with them, our existential might just as well be (again, not valid Haskell)
randList :: [Dynamic] -> IO [(TypeRep, forall b. Typeable b => Maybe b)]
This is what we get if we apply typeOf and cast to every element of our list, store the results, and throw away the now-useless existentially typed original value. Clearly, the TypeRep part of this list isn't useful. And the second half of the list isn't either. Since we're back to a universally-quantified type, the caller of randList is once again entitled to request that they get a Maybe Int, a Maybe Bool, or a Maybe b for any (typeable) b of their choosing. (In fact, they have slightly more power than before, since they can instantiate different elements of the list to different types.) But they can't figure out what type they're converting from unless they already know it—you've still lost the type information you were trying to keep.
And even setting aside the fact that they're not useful, you simply can't construct the desired existential type here. The error arises when you try to return the existentially-typed list (return $ dynListToList dl). At what specific type are you calling dynListToList? Recall that dynListToList :: forall a. Typeable a => [Dynamic] -> [a]; thus, randList is responsible for picking which a dynListToList is going to use. But it doesn't know which a to pick; again, that's the source of the question! So the type that you're trying to return is underspecified, and thus ambiguous.3
Dependent types
OK, so what would make this existential useful (and possible)? Well, we actually have slightly more information: not only do we know there's some a, we have its TypeRep. So maybe we can package that up:
randList :: [Dynamic] -> (exists a. Typeable a => IO (TypeRep,[a]))
This isn't quite good enough, though; the TypeRep and the [a] aren't linked at all. And that's exactly what you're trying to express: some way to link the TypeRep and the a.
Basically, your goal is to write something like
toType :: TypeRep -> *
Here, * is the kind of all types; if you haven't seen kinds before, they are to types what types are to values. * classifies types, * -> * classifies one-argument type constructors, etc. (For instance, Int :: *, Maybe :: * -> *, Either :: * -> * -> *, and Maybe Int :: *.)
With this, you could write (once again, this code isn't valid Haskell; in fact, it really bears only a passing resemblance to Haskell, as there's no way you could write it or anything like it within Haskell's type system):
randList :: [Dynamic] -> (exists (tr :: TypeRep).
Typeable (toType tr) => IO (tr, [toType tr]))
randList dl = do
tr <- randItem $ map dynTypeRep dl
return (tr, dynListToList dl :: [toType tr])
-- In fact, in an ideal world, the `:: [toType tr]` signature would be
-- inferable.
Now, you're promising the right thing: not that there exists some type which classifies the elements of the list, but that there exists some TypeRep such that its corresponding type classifies the elements of the list. If only you could do this, you would be set. But writing toType :: TypeRep -> * is completely impossible in Haskell: doing this requires a dependently-typed language, since toType tr is a type which depends on a value.
What does this mean? In Haskell, it's perfectly acceptable for values to depend on other values; this is what a function is. The value head "abc", for instance, depends on the value "abc". Similarly, we have type constructors, so it's acceptable for types to depend on other types; consider Maybe Int, and how it depends on Int. We can even have values which depend on types! Consider id :: a -> a. This is really a family of functions: id_Int :: Int -> Int, id_Bool :: Bool -> Bool, etc. Which one we have depends on the type of a. (So really, id = \(a :: *) (x :: a) -> x; although we can't write this in Haskell, there are languages where we can.)
Crucially, however, we can never have a type that depends on a value. We might want such a thing: imagine Vec 7 Int, the type of length-7 lists of integers. Here, Vec :: Nat -> * -> *: a type whose first argument must be a value of type Nat. But we can't write this sort of thing in Haskell.4 Languages which support this are called dependently-typed (and will let us write id as we did above); examples include Coq and Agda. (Such languages often double as proof assistants, and are generally used for research work as opposed to writing actual code. Dependent types are hard, and making them useful for everyday programming is an active area of research.)
Thus, in Haskell, we can check everything about our types first, throw away all that information, and then compile something that refers only to values. In fact, this is exactly what GHC does; since we can never check types at run-time in Haskell, GHC erases all the types at compile-time without changing the program's run-time behavior. This is why unsafeCoerce is easy to implement (operationally) and completely unsafe: at run-time, it's a no-op, but it lies to the type system. Consequently, something like toType is completely impossible to implement in the Haskell type system.
In fact, as you noticed, you can't even write down the desired type and use unsafeCoerce. For some problems, you can get away with this; we can write down the type for the function, but only implement it with by cheating. That's exactly how fromDynamic works. But as we saw above, there's not even a good type to give to this problem from within Haskell. The imaginary toType function allows you to give the program a type, but you can't even write down toType's type!
What now?
So, you can't do this. What should you do? My guess is that your overall architecture isn't ideal for Haskell, although I haven't seen it; Typeable and Dynamic don't actually show up that much in Haskell programs. (Perhaps you're "speaking Haskell with a Python accent", as they say.) If you only have a finite set of data types to deal with, you might be able to bundle things into a plain old algebraic data type instead:
data MyType = MTInt Int | MTBool Bool | MTString String
Then you can write isMTInt, and just use filter isMTInt, or filter (isSameMTAs randomMT).
Although I don't know what it is, there's probably a way you could unsafeCoerce your way through this problem. But frankly, that's not a good idea unless you really, really, really, really, really, really know what you're doing. And even then, it's probably not. If you need unsafeCoerce, you'll know, it won't just be a convenience thing.
I really agree with Daniel Wagner's comment: you're probably going to want to rethink your approach from scratch. Again, though, since I haven't seen your architecture, I can't say what that will mean. Maybe there's another Stack Overflow question in there, if you can distill out a concrete difficulty.
1 That looks like the following:
{-# LANGUAGE ExistentialQuantification #-}
data TypeableList = forall a. Typeable a => TypeableList [a]
randList :: [Dynamic] -> IO TypeableList
However, since none of this code compiles anyway, I think writing it out with exists is clearer.
2 Technically, there are some other functions which look relevant, such as toDyn :: Typeable a => a -> Dynamic and fromDyn :: Typeable a => Dynamic -> a -> a. However, Dynamic is more or less an existential wrapper around Typeables, relying on typeOf and TypeReps to know when to unsafeCoerce (GHC uses some implementation-specific types and unsafeCoerce, but you could do it this way, with the possible exception of dynApply/dynApp), so toDyn doesn't do anything new. And fromDyn doesn't really expect its argument of type a; it's just a wrapper around cast. These functions, and the other similar ones, don't provide any extra power that isn't available with just typeOf and cast. (For instance, going back to a Dynamic isn't very useful for your problem!)
3 To see the error in action, you can try to compile the following complete Haskell program:
{-# LANGUAGE ExistentialQuantification #-}
import Data.Dynamic
import Data.Typeable
import Data.Maybe
randItem :: [a] -> IO a
randItem = return . head -- Good enough for a short and non-compiling example
dynListToList :: Typeable a => [Dynamic] -> [a]
dynListToList = mapMaybe fromDynamic
data TypeableList = forall a. Typeable a => TypeableList [a]
randList :: [Dynamic] -> IO TypeableList
randList dl = do
tr <- randItem $ map dynTypeRep dl
return . TypeableList $ dynListToList dl -- Error! Ambiguous type variable.
Sure enough, if you try to compile this, you get the error:
SO12273982.hs:17:27:
Ambiguous type variable `a0' in the constraint:
(Typeable a0) arising from a use of `dynListToList'
Probable fix: add a type signature that fixes these type variable(s)
In the second argument of `($)', namely `dynListToList dl'
In a stmt of a 'do' block: return . TypeableList $ dynListToList dl
In the expression:
do { tr <- randItem $ map dynTypeRep dl;
return . TypeableList $ dynListToList dl }
But as is the entire point of the question, you can't "add a type signature that fixes these type variable(s)", because you don't know what type you want.
4 Mostly. GHC 7.4 has support for lifting types to kinds and for kind polymorphism; see section 7.8, "Kind polymorphism and promotion", in the GHC 7.4 user manual. This doesn't make Haskell dependently typed—something like TypeRep -> * example is still out5—but you will be able to write Vec by using very expressive types that look like values.
5 Technically, you could now write down something which looks like it has the desired type: type family ToType :: TypeRep -> *. However, this takes a type of the promoted kind TypeRep, and not a value of the type TypeRep; and besides, you still wouldn't be able to implement it. (At least I don't think so, and I can't see how you would—but I am not an expert in this.) But at this point, we're pretty far afield.
What you're observing is that the type TypeRep doesn't actually carry any type-level information along with it; only term-level information. This is a shame, but we can do better when we know all the type constructors we care about. For example, suppose we only care about Ints, lists, and function types.
{-# LANGUAGE GADTs, TypeOperators #-}
import Control.Monad
data a :=: b where Refl :: a :=: a
data Dynamic where Dynamic :: TypeRep a -> a -> Dynamic
data TypeRep a where
Int :: TypeRep Int
List :: TypeRep a -> TypeRep [a]
Arrow :: TypeRep a -> TypeRep b -> TypeRep (a -> b)
class Typeable a where typeOf :: TypeRep a
instance Typeable Int where typeOf = Int
instance Typeable a => Typeable [a] where typeOf = List typeOf
instance (Typeable a, Typeable b) => Typeable (a -> b) where
typeOf = Arrow typeOf typeOf
congArrow :: from :=: from' -> to :=: to' -> (from -> to) :=: (from' -> to')
congArrow Refl Refl = Refl
congList :: a :=: b -> [a] :=: [b]
congList Refl = Refl
eq :: TypeRep a -> TypeRep b -> Maybe (a :=: b)
eq Int Int = Just Refl
eq (Arrow from to) (Arrow from' to') = liftM2 congArrow (eq from from') (eq to to')
eq (List t) (List t') = liftM congList (eq t t')
eq _ _ = Nothing
eqTypeable :: (Typeable a, Typeable b) => Maybe (a :=: b)
eqTypeable = eq typeOf typeOf
toDynamic :: Typeable a => a -> Dynamic
toDynamic a = Dynamic typeOf a
-- look ma, no unsafeCoerce!
fromDynamic_ :: TypeRep a -> Dynamic -> Maybe a
fromDynamic_ rep (Dynamic rep' a) = case eq rep rep' of
Just Refl -> Just a
Nothing -> Nothing
fromDynamic :: Typeable a => Dynamic -> Maybe a
fromDynamic = fromDynamic_ typeOf
All of the above is pretty standard. For more on the design strategy, you'll want to read about GADTs and singleton types. Now, the function you want to write follows; the type is going to look a bit daft, but bear with me.
-- extract only the elements of the list whose type match the head
firstOnly :: [Dynamic] -> Dynamic
firstOnly [] = Dynamic (List Int) []
firstOnly (Dynamic rep v:xs) = Dynamic (List rep) (v:go xs) where
go [] = []
go (Dynamic rep' v:xs) = case eq rep rep' of
Just Refl -> v : go xs
Nothing -> go xs
Here we've picked a random element (I rolled a die, and it came up 1) and extracted only the elements that have a matching type from the list of dynamic values. Now, we could have done the same thing with regular boring old Dynamic from the standard libraries; however, what we couldn't have done is used the TypeRep in a meaningful way. I now demonstrate that we can do so: we'll pattern match on the TypeRep, and then use the enclosed value at the specific type the TypeRep tells us it is.
use :: Dynamic -> [Int]
use (Dynamic (List (Arrow Int Int)) fs) = zipWith ($) fs [1..]
use (Dynamic (List Int) vs) = vs
use (Dynamic Int v) = [v]
use (Dynamic (Arrow (List Int) (List (List Int))) f) = concat (f [0..5])
use _ = []
Note that on the right-hand sides of these equations, we are using the wrapped value at different, concrete types; the pattern match on the TypeRep is actually introducing type-level information.
You want a function that chooses a different type of values to return based on runtime data. Okay, great. But the whole purpose of a type is to tell you what operations can be performed on a value. When you don't know what type will be returned from a function, what do you do with the values it returns? What operations can you perform on them? There are two options:
You want to read the type, and perform some behaviour based on which type it is. In this case you can only cater for a finite list of types known in advance, essentially by testing "is it this type? then we do this operation...". This is easily possible in the current Dynamic framework: just return the Dynamic objects, using dynTypeRep to filter them, and leave the application of fromDynamic to whoever wants to consume your result. Moreover, it could well be possible without Dynamic, if you don't mind setting the finite list of types in your producer code, rather than your consumer code: just use an ADT with a constructor for each type, data Thing = Thing1 Int | Thing2 String | Thing3 (Thing,Thing). This latter option is by far the best if it is possible.
You want to perform some operation that works across a family of types, potentially some of which you don't know about yet, e.g. by using type class operations. This is trickier, and it's tricky conceptually too, because your program is not allowed to change behaviour based on whether or not some type class instance exists – it's an important property of the type class system that the introduction of a new instance can either make a program type check or stop it from type checking, but it can't change the behaviour of a program. Hence you can't throw an error if your input list contains inappropriate types, so I'm really not sure that there's anything you can do that doesn't essentially involve falling back to the first solution at some point.