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README
The typeparams library
This library provides a lenslike interface for working with type parameters. In the code:
data Example p1 (p2::Config Nat) (p3::Constraint) = Example
p1
, p2
, and p3
are the type parameters. The tutorial below uses unboxed vectors to demonstrate some of the library's capabilities. In particular, we'll see:
A type safe way to unbox your unboxed vectors. This technique gives a 25% speed improvement on nearest neighbor queries. The standard
Vector
class provided inData.Vector.Generic
can be used, so we retain all the stream fusion goodness.A simple interface for supercompilation. In the example below, we combine this library and the fastmath library to get up to a 40x speed improvement when calculating the Lp distance between vectors.
Further documentation can be found on hackage, and examples with nonvector data types can be found in the examples folder. You can download the library from github directly, or via cabal:
cabal update
cabal install typeparams
Tutorial: unbox your unboxed vectors!
The remainder of this README is a literate haskell file. Please follow along yourself!
> import Control.Category
> import Data.Params
> import Data.Params.Vector.Unboxed
> import qualified Data.Vector.Generic as VG
> import Prelude hiding ((.),id)
The Data.Params.Vector.Unboxed
module contains the following definition for our vectors:
data family Vector (len::Config Nat) elem
mkParams ''Vector
mkParams
is a template haskell function that generates a number of useful functions and classes that will be described below. The len
type param lets us statically enforce the size of a vector as follows:
> v1 = VG.fromList [1..10] :: Vector (Static 10) Float
Here, Static
means that the parameter is known statically at compile time. If we don't know in advance the size of our vectors, however, we can set len
to Automatic
:
> v2 = VG.fromList [1..10] :: Vector Automatic Float
v2
will behave exactly like the unboxed vectors in the vector
package.
The Config
param generalizes the concept of implicit configurations introduced by this functional pearl by Oleg Kiselyov and Chungchieh Shan. (See also the ImplicitParams GHC extension.) It can take on types of Static x
, Automatic
, or RunTime
. This tutorial will begin by working through the capabilities of the Static
configurations before discussing the other options.
From type params to values
We can get access to the value of the len
parameter using the function:
viewParam :: ViewParam p t => TypeLens Base p > t > ParamType p
The singleton type TypeLens Base p
identifies which parameter we are viewing in type t
. The type lens we want is _len :: TypeLens Base Param_len
. The value _len
and type Param_len
were created by the mkParams
function above. The significance of Base
will be explained in a subsequent section.
All together, we use it as:
ghci> viewParam _len v1
10
The viewParam
function does not evaluate its arguments, so we could also call the function as:
ghci> viewParam _len (undefined::Vector (Static 10) Float)
10
We cannot use ViewParam
if the length is being managed automatically. Vector Automatic Float
is not an instance of the ViewParam
type class, so the type checker enforces this restriction automatically.
Unboxing the vector
If we know a vector's size at compile time, then the compiler has all the information it needs to unbox the vector. Therefore, we can construct a 2d unboxed vector by:
> vv1 :: Vector (Static 2) (Vector (Static 10) Float)
> vv1 = VG.fromList [VG.fromList [1..10], VG.fromList [21..30]]
or even a 3d vector by:
> vvv1 :: Vector (Static 20) (Vector (Static 2) (Vector (Static 10) Float))
> vvv1 = VG.replicate 20 vv1
In general, there are no limits to the depth the vectors can be nested.
Viewing nested parameters
What if we want to view the length of a nested inner vector? The value _elem :: TypeLens p (Param_elem p)
gives us this capability. It composes with _len
to give the type:
_elem._len :: TypeLens Base (Param_elem Param_len)
_elem
and Param_elem
were also created by mkParams
. In general, mkParams
will generate these type lenses for every type param of its argument. If the type param p1
has kind *
, then the type lens will have type _p1 :: TypeLens p (Param_p1 p)
and the class will have kind Param_p1 :: (* > Constraint) > * > Constraint
. If the type param has any other kind (e.g. Config Nat
), then mkParams
will generate _p1 :: TypeLens Base Param_p1
and Param_p1 :: * > Constraint
.
The type of _elem
allows us to combine it with _len
to view the inner parameters of a type. Using the vectors we created above, we can view their parameters with:
ghci> viewParam _len vv1
2
ghci> viewParam (_elem._len) vv1
10
ghci> viewParam _len vvv1
20
ghci> viewParam (_elem._len) vvv1
2
ghci> viewParam (_elem._elem._len) vvv1
10
Lensing into giant types
What if instead of having a Vector
of Vector
s, we have some other data type of Vectors
? For example, what if we have a Maybe (Vector len elem)
. Now, how can we get access to the length of the vector?
Consider the definition of Maybe
:
data Maybe a = Nothing  Just a
If we run the following template haskell:
> mkParams ''Maybe
then we will generate the type lens _a :: TypeLens p (Param_a p)
which will give us the desired capability:
ghci> viewParam (_a._len) (undefined :: Maybe (Vector (Static 10) Int))
10
We can do the same process for any data type, even if the names of their type params overlap. For example, we can run:
> mkparams ''Either
This will reuse the already created _a
type lens (which corresponds to the left component of Either
) and generate the type lens _b :: TypeLens p (Param_b p)
(which corresponds to the right component).
We can use type lenses in this fashion to extract parameters from truly monstrous types. For example, given the type:
> type Monster a = Either
> (Maybe (Vector (Static 34) Float))
> (Either
> a
> (Either
> (Vector (Static 2) (Vector (Static 10) Double))
> (Vector (Static 1) Int)
> )
> )
We can do:
ghci> viewParam (_a._a._len) (undefined::Monster Int)
34
ghci> viewParam (_b._b._a._elem._len) (undefined::Monster Float)
10
No matter how large the type is, we can compose TypeLens
es to access any configuration parameter.
It would be nice if the type lenses for these built in data types had more meaningful names (like _just
,_left
, and _right
), but this would require a change to base.
From values back to type params
That's cool, but it's not super useful if we have to know the values of all our configurations at compile time. The RunTime
and Automatic
Config
values give us more flexibility. We will see that the RunTime
method is powerful but cumbersome, and the Automatic
method will provide a much simpler interface that wraps the RunTime
method.
(The RunTime
configurations use the magic of the reflection package. The internal code is based off of Austin Seipp's excellent reflection tutorial.)
Whenever we need to specify a RunTime
param, we use the function:
with1Param ::
( ParamIndex p
) => TypeLens Base p > ParamType p > ((ApplyConstraint p m) => m) > m
For example, we can specify the length of the innermost vector as follows:
> vvv2 :: Vector (Static 1) (Vector (Static 1) (Vector RunTime Float))
> vvv2 = with1Param (_elem._elem._len) 10 $ VG.singleton $ VG.singleton $ VG.fromList [1..10]
Or we can specify the length of all vectors:
> vvv3 :: Vector RunTime (Vector RunTime (Vector RunTime Float))
> vvv3 = with1Param (_elem._elem._len) 10
> $ with1Param (_elem._len) 1
> $ with1Param _len 1
> $ VG.singleton $ VG.singleton $ VG.fromList [1..10]
But wait! If we try to show
either of these variables, we get an error message:
ghci> show vvv2
<interactive>:19:1:
No instance for (Param_len (Vector 'RunTime Float))
arising from a use of ‘print’
In a stmt of an interactive GHCi command: print it
This is because RunTime
configurations don't remember what value they were set to. Every time we use a variable with a RunTime
configuration, we must manually specify the value.
The with1Param
function is only useful when we pass parameters to the output of whatever function we are calling. In the example of show
, however, we need to pass parameters to the input of the function. We do this using the function:
apWith1Param ::
( ValidIndex p
) => TypeLens Base p
> ParamType p
> ((ApplyConstraint p m) => m > n)
> ((ApplyConstraint p m) => m)
> n
Similar functions exist for passing more than one parameter. These functions let us specify configurations to the arguments of a function. So if we want to show our vectors, we could call:
ghci> apWith1Param (_elem._elem._len) 10 show vvv2
"fromList [fromList [fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]]]"
ghci> apWith3Param (_elem._elem._len) 10 (_elem._len) 1 _len 1 show vvv3
"fromList [fromList [fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]]]"
A bug in GHC!
Unfortunately, due to a bug in GHC 7.8.2's typechecker, the above code doesn't typecheck. We must explicitly specify the specialized type of apWithNParam
for it to work. This is syntactically very awkward. As a temporary workaround, the library provides the function:
apWith1Param' :: m > (
( ParamIndex p
) => TypeLens Base p
> ParamType p
> (ApplyConstraint p m => m > n)
> (ApplyConstraint p m => m)
> n
)
The only difference is that the unconstrained type m
is passed as the first argument, which causes the apWith1Param'
function's type signature to be specialized for us correctly. We can use this function like:
ghci> apWith1Param' vvv2 (_elem._elem._len) 10 show vvv2 :: String
"fromList [fromList [fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]]]"
ghci> apWith3Param vvv3 (_elem._elem._len) 10 (_elem._len) 1 _len 1 show vvv3 :: String
"fromList [fromList [fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]]]"
Notice that we can use the same variable as both the first and last parameter. This gives us a useable workaround in the presence of the GHC bug.
Lying to the RunTime
We can specify any value we want to a RunTime
configuration. We can even change the value throughout the course of the program. For our Vector
type, this will change the shape with no runtime overhead. For example:
ghci> apWith3Param' vvv3 (_elem._elem._len) 2 (_elem._len) 5 _len 1 show vvv3 :: String
fromList [fromList [fromList [1.0,2.0]
,fromList [3.0,4.0]
,fromList [5.0,6.0]
,fromList [7.0,8.0]
,fromList [9.0,10.0]
]]
Of course, we must be careful. If we specify lengths that cause the size of the result to exceed the allocated ByteArray
, then we will get undefined results:
ghci> apWith3Param vvv3 (_elem._elem._len) 2 (_elem._len) 5 _len 2 show vvv3 :: String
fromList [fromList [fromList [1.0,2.0]
,fromList [3.0,4.0]
,fromList [5.0,6.0]
,fromList [7.0,8.0]
,fromList [9.0,10.0]
]]
,fromList [fromList [1.7796708,4.5566e41]
,fromList [1.46142,4.5566e41]
,fromList [1.5570038e7,4.5566e41]
,fromList [1.701097e5,4.5566e41]
,fromList [1.23e43,0.0]]
]
(I've manually reformatted the output of show to make it easier to read.)
Making it Automatic
Let's recap... Static
configurations are easy to work with but less flexible, whereas RunTime
configurations are a flexible pain in the butt. We get the best of both worlds with Automatic
configurations.
With one dimensional vectors, making the length automatic is as easy as specifying the type signature:
> v3 :: Vector Automatic Float
> v3 = VG.fromList [1..5]
Now, we can use v3
just like we would use any vector from the vector
package.
With multiple dimensions, we must explicitly specify the inner dimensions like so:
> vvv4 :: Vector Automatic (Vector Automatic (Vector Automatic Float))
> vvv4 = with1ParamAutomatic (_elem._elem._len) 5
> $ with1ParamAutomatic (_elem._len) 2
> $ VG.singleton $ VG.replicate 2 $ VG.fromList [1..5]
This is required so that the vectors can enforce that every inner vector at the same level has the correct size. For example, the following code will give a run time error:
> vvv5 :: Vector Automatic (Vector Automatic (Vector Automatic Float))
> vvv5 = with1ParamAutomatic (_elem._elem._len) 5
> $ with1ParamAutomatic (_elem._len) 2
> $ VG.singleton $ VG.fromList [VG.fromList [1..5], VG.fromList [1..4]]
Using vvv4
is as convenient as using any vectors from the vector
package that can be nested. For example:
ghci> show vvv4
fromList [fromList [fromList [1.0,2.0,3.0,4.0,5.0],fromList [1.0,2.0,3.0,4.0,5.0]]]
ghci> vvv4 VG.! 0 VG.! 1 VG.! 3
4.0
ghci> VG.foldl1' (VG.zipWith (+)) $ vvv4 VG.! 0
fromList [2.0,4.0,6.0,8.0,10.0]
When using Automatic
parameters, there is no need for the apWithNParam
family of functions. Internally, the type will store the value of the configuration. Whenever the value is needed, apWith1Param
is called for us automatically.
So how much faster?!
The file examples/criterion.hs contains some run time experiments that show just how fast the unboxed unboxed vectors are. In one test, it uses the naive O(n^{2)} algorithm to perform nearest neighbor searches. The results are shown below:
The green line uses vectors provided in the Data.Params.Vector.Unboxed
module of type Vector Automatic (Vector Automatic Float)
; and the red line uses standard vectors from the vector
package of type Data.Vector.Vector (Data.Vector.Unboxed.Vector Float)
. In both cases, the number of dimensions of the data points was 400.
Switching to unboxed unboxed vectors yields a nice performance boost of about 25%. The best part is that we barely have to change existing code at all. The only difference between the interface for a boxed unboxed vector and an unboxed unboxed vector is the initial construction. If you have code that creates boxed unboxed vectors, you should get a similar performance gain switching over to this library.
Lebesgue or not to beg, that is the supercompilation
If we combine this typeparams package with the fastmath package, we get a very simple form of supercompilation. To demonstrate how this works, we will use the example of distance calculations in arbitrary Lebesgue (Lp) spaces. For a given value p
, the Lp norm is defined as:
In haskell code we can create a newtype
that will encode the value of p
by:
newtype Lebesgue (p::Config Frac) (vec :: * > *) elem = Lebesgue (vec elem)
instance VG.Vector vec elem => VG.Vector (Lebesgue p vec) elem where
{ ... }
The Frac
kind is similar to the Nat
kind, except it represents any positive fraction at the type level. The file src/Data/Params/Frac.hs contains the implementation of Frac
. The file examples/supercomplebesgue.hs for contains the implementation details of the Lebesgue
example.
We can then define a generic distance function over any Lp space as:
lp_distance ::
( VG.Vector vec elem
, Floating elem
, ViewParam Param_p (Lebesgue p vec elem)
) => Lebesgue p vec elem > Lebesgue p vec elem > elem
lp_distance !v1 !v2 = (go 0 (VG.length v11))**(1/p)
where
p = viewParam _p v1
go tot (1) = tot
go tot i = go (tot+diff1**p) (i1)
where
diff1 = abs $ v1 `VG.unsafeIndex` iv2 `VG.unsafeIndex` i
The value of p
can now be set at compile time or at run time using the typeparams machinery. If we know the value at compile time, however, GHC can perform a number of optimizations:
The most important optimization is that the value of
p
never has to be stored in memory or even in a register. The resulting assembly uses what is called immediate instructions. These assembly instructions are very fast in inner loops, and make the code run about 2x faster no matter what the value ofp
is. (The example examples/coretest.hs provides a minimal code sample that facilitates inspecting the effect of different parameters on the core code and resulting assembly.)For specific values of
p
, we can optimize the formula of the Lp distance considerably. For example, exponentiation is very slow on x86 CPUs. Instead of evaluatingx**2
, it is much cheaper to evaluatex*x
. Similarly, instead of evaluatingx**(1/2)
, it is cheaper to evaluatesqrt x
. These optimizations are not safe for floating point numbers (small amounts of precision can be lost), so GHC doesn't perform them by default. The fastmath library is needed to cause these optimizations.
The plot below shows the resulting run times:
The green values are the run times of the lp_distance
function where p
is specified using Static
; the red for when p
is specified using RunTime
; and the blue for handoptimized routines. Hashed columns indicate the test was run with the Numeric.FastMath
import. All code was compiled using llvm and the optimization flags: optlo O3 optlo enableunsafefpmath
. Notice that there are some cases where the fastmath library is able to perform optimizations that llvm's enableunsafefpmath
flag cannot.
By using the generic lp_distance
function, we get all the speed advantages of handoptimized code, but we still have the flexibility of having users enter whatever p
value they want to compute. We also avoid the need to manually write many handtuned distance functions.
Thoughts for the road
It is popular to think of these type level configurations as "lightweight dependent types." The traditional use for dependent types is to make programs safer... but maybe they can make our programs faster too!? Exploring both of these possibilities is the goal of typeparams
library.
There's still a couple of warts in the library:
The classes in the vector library were never meant to be abused in this way, and so there are a small number of edge cases where this framework does not work. For example, you cannot call
slice
on aStatic
length vector. This throws a run time error. Fixing this would require rewriting the vector library, which is a MAJOR undertaking.The
mkParams
template haskell function currently only makes the necessary instances forStatic
andRunTime
configurations. The infrastructure forAutomatic
configurations must be done manually. It is possible to automatically produce the required infrastructure forAutomatic
configurations as well, but I haven't figured out a way to do it without introducing overhead that's usually unnecessary.For simplicity, this package only currently implements unboxed vectors in this framework. There is no reason, however, that boxed vectors and storable vectors could not be implemented as well. This would allow storable storable vectors using all the same techniques as above.
Please report any bugs/issues/feature requests!