[ bsd3, control, library, monad ] [ Propose Tags ]

Memoization monad transformer supporting most of the standard monad transformers and a range of memoization cache types: from default pure maps to extremely fast mutable vectors

1) Add Control.Monad.Memo.memo combinator at the point when memoization is required (i.e. recursive call)

import Control.Monad.Memo

fibm 0 = return 0
fibm 1 = return 1
fibm n = do
n1 <- memo fibm (n-1)
n2 <- memo fibm (n-2)
return (n1+n2)

2) Use approprite *eval* or *run* function to evaluate resulting MonadMemo monad:

startEvalMemo (fibm 100)

See detailed description and examples: Control.Monad.Memo

Versions 0.1.0, 0.1.1, 0.2.0, 0.3.0, 0.4.0, 0.4.1, 0.5.0, 0.5.1 CHANGES array (>=0.3), base (>=3.0 && <=5.0), containers (>=0.3), primitive (>=0.3), transformers (>=0.2), transformers-compat (>=0.3), vector (>=0.7) [details] BSD-3-Clause Eduard Sergeev eduard.sergeev@gmail.com Control, Monad https://github.com/EduardSergeev/monad-memo head: git clone https://github.com/EduardSergeev/monad-memo.gitthis: git clone https://github.com/EduardSergeev/monad-memo.git(tag 0.5.0) by EduardSergeev at Fri Aug 31 06:16:54 UTC 2018 LTSHaskell:0.4.1, NixOS:0.5.1, Stackage:0.5.1 3030 total (131 in the last 30 days) (no votes yet) [estimated by rule of succession] λ λ λ Docs available Last success reported on 2018-08-31 Hackage Matrix CI

#### Maintainer's Corner

For package maintainers and hackage trustees

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## Memoization

Memoization is a well known way to speed up function evaluation by caching previously calculated results and reusing them whenever a memoized function is needed to be evaluated with the same arguments again. It is usually associated with dynamic programming techiques.

## Overview

Even though it is possible to manually add memoization to the code which would benefit from it, this ad-hoc approach has usual ad-hoc drawbacks: code pollution, bugs, resistance to changes. This package however encapsulates the underlying plumbing behind its simple monadic interface MonadMemo with a single combinator memo which, when applied to monadic function, turns it into "memoized" one.

The package offers various implementation of MonadMemo (which differs in terms of performance and requirements) and it is possible to choose/change the implementation without affecting the main function code. The range of supported implementations "out of box" is limited by the range of containers provided by the standard packages installed by Haskel Platform: from default pure "fit them all" Data.Map to very fast but limiting vector. It is also possible to plug-in a custom container (from a third-party library) and run existing monadic code with it.

The default implementation of MonadMemo is also monad transformer so it can be "mixed" with other monads. The package also provides the "memoized" versions of most standard monads found in mtl.

## Example of usage

A clasic example of function which greatelly benefits from memoization is a recursively defined Fibonacci number function. A plain version of this function can be written in the following way:

fib :: Integer -> Integer
fib 0 = 0
fib 1 = 1
fib n = fib (n-1) + fib (n-2)


which is very inefficient (impractical for n>40).

We can rewrite this definition as a monad:

fibm :: Monad m => Integer -> m Integer
fibm 0 = return 0
fibm 1 = return 1
fibm n = do
f1 <- fibm (n-1)
f2 <- fibm (n-2)
return (f1+f2)


and even run it with Identity monad with identical inefficiency:

evalFibmId :: Integer -> Integer
evalFibmId = runIdentity . fibm


But all we need to do to make this function "computable" for reasonable argument is to add memoization for both recursive branches with memo combinator:

fibm :: (MonadMemo Integer Integer m) => Integer -> m Integer
fibm 0 = return 0
fibm 1 = return 1
fibm n = do
f1 <- memo fibm (n-1)
f2 <- memo fibm (n-2)
return (f1+f2)


then, to evaluate it with default Data.Map based memoization cache we use the following "eval*" function:

evalFibm :: Integer -> Integer
evalFibm = startEvalMemo . fibm


Now the range of the arguments it can handle is limited only by Integer computation complexity and stack memory limit.

## More Examples

### Slightly more complicated recursive function

Well known Ackerman function is a two arguments function. To memoize two argument function for2 combinator can be used, giving the following generic code:

ackm :: (Num n, Ord n, MonadMemo (n, n) n m) => n -> n -> m n
ackm 0 n = return (n+1)
ackm m 0 = for2 memo ackm (m-1) 1
ackm m n = do
n1 <- for2 memo ackm m (n-1)    -- 'for2' adapts 'memo' for 2-argument 'ackm'
for2 memo ackm (m-1) n1

evalAckm :: (Num n, Ord n) => n -> n -> n
evalAckm n m = startEvalMemo $ackm n m  ### Mutually recursive function memoization This example is taken from paper "Monadic Memoization Mixins" by Daniel Brown and William R. Cook Given the following mutually recursive function definitions: -- 'f' depends on 'g' f :: Int -> (Int,String) f 0 = (1,"+") f (n+1) =(g(n,fst(f n)),"-" ++ snd(f n)) -- 'g' depends on 'f' g :: (Int, Int) -> Int g (0, m) = m + 1 g (n+1,m) = fst(f n)-g(n,m)  How can we memoize both functions? Lets try to just add memo for both functions: -- WRONG: Will NOT compile! fm 0 = return (1,"+") fm (n+1) = do fn <- memo fm n gn <- memo gm (n , fst fn) return (gn , "-" ++ snd fn) gm (0,m) = return (m+1) gm (n+1,m) = do fn <- memo fm n gn <- memo gm (n,m) return$ fst fn - gn


GHC complains:

"Occurs check: cannot construct the infinite type: t = (t, v)
Expected type: t

Inferred type: (t, v)"


which is understandable since we are trying to use the same cache for storing "key-value" pairs of the functions of different types (fm :: Int -> m (Int,String) and gm :: (Int, Int) -> m Int). Obviously, to cache both function we will need two caches (even if the types of the functions were identical, it's not very good idea to share the same cache). And this is precisely what we have to do - use two memoization caches! The way to achieve it is to use two MemoT monad transformers one nested in another:

-- Memo-cache for 'fm'
type MemoF = MemoT Int (Int,String)
-- Memo-cache for 'gm'
type MemoG = MemoT (Int,Int) Int

-- | Combined stack of caches (transformers)
-- Stacks two 'MemoT' transformers in one monad to be used in both 'gm' and 'fm' monadic functions
type MemoFG = MemoF (MemoG Identity)


NB As usually with Haskell it isn't necessary to specify types here (or restrict them to MemoT combinations for the given example).

Then, a little bit of complication, since we use two caches now (one from the current monad transformer and another from the next, nested in the current) we need to use memol_X_ set of functions: memol0, memol1 etc. Where X specifies "sequential number" of the transformer in stack for a given cache (starting from the current). Here we use the current (0) and the next (1) for fm and gm respectively:

fm :: Int -> MemoFG (Int,String)
fm 0 = return (1,"+")
fm (n+1) = do
fn <- memol0 fm n
gn <- memol1 gm (n , fst fn)
return (gn , "-" ++ snd fn)

gm :: (Int,Int) -> MemoFG Int
gm (0,m) = return (m+1)
gm (n+1,m) = do
fn <- memol0 fm n
gn <- memol1 gm (n,m)
return $fst fn - gn evalAll = startEvalMemo . startEvalMemoT -- | Function to run 'fm' computation evalFm :: Int -> (Int, String) evalFm = evalAll . fm -- | Function to run 'gm' computation evalGm :: (Int,Int) -> Int evalGm = evalAll . gm  In fact we can also define 'gm' function in curried form: fm2 :: Int -> MemoFG (Int,String) fm2 0 = return (1,"+") fm2 n = do fn <- memol0 fm2 (n-1) gn <- for2 memol1 gm2 (n-1) (fst fn) return (gn , "-" ++ snd fn) -- 2-argument function now gm2 :: Int -> Int -> MemoFG Int gm2 0 m = return (m+1) gm2 n m = do fn <- memol0 fm2 (n-1) gn <- for2 memol1 gm2 (n-1) m -- 'for2' adapts 'memol1' for 2-argument gm2 return$ fst fn - gn

evalFm2 :: Int -> (Int, String)
evalFm2 = evalAll . fm2

evalGm2 :: Int -> Int -> Int
evalGm2 n m = evalAll $gm2 n m  ### Combining MemoT with other monads Being monad transformer, memoization monad can be combined with most of existing monads. Here we mix it with MonadWriter: fibmw :: (Num n, MonadWriter String m, MonadMemo n n m) => n -> m n fibmw 0 = tell "0" >> return 0 fibmw 1 = tell "1" >> return 1 fibmw n = do f1 <- memo fibmw (n-1) f2 <- memo fibmw (n-2) tell$ show n
return (f1+f2)

-- To run combined monad we need to sequence both 'run' functions:
evalFibmw :: Integer -> (Integer, String)
evalFibmw = startEvalMemo . runWriterT . fibmw

res = evalFibmw 6  -- > produces (8,"1021310241021351021310246")


## Custom pure cache container

From monad-memo version 0.3.0 it is possible to replace default Data.Map with another (more efficient?) implementation of internal cache-container as long as there is an instance of Data.MapLike defined for this container. The package currently defines these instances for Data.Map and Data.IntMap only.

For instance, should we decide to use unordered-containers all we need to do is to define the following instance for our container:

import Data.Hashable
import qualified Data.HashMap.Strict as H

instance (Eq k, Hashable k) => MapLike (H.HashMap k v) k v where
lookup = H.lookup


then we just need to use (evalMemoStateH.empty) instead of startEvalMemo and our memoized function will be evaluated using Hashmap as an internal container hosted in MemoState. There is usually no need to do any modification to the memoized function itself.

## Mutable arrays and vectors as MonadCache

### Array-based memoization cache

version 0.4.0 adds ArrayCache: a new MonadCache implementation based on mutable arrays (inside IO or ST s monad). The main benefit of this MonadCache is its performance: it can be an order of magnitude faser than standard Data.Map-based cache. This is due to the fact that arrays have O(1) lookup time and in-place mutable arrays also have O(1) for updates (i.e. the cache add operation).

Unfortunatelly you cannot always use this MonadCache due to array's natural limitations:

• The key must be an instance of Ix typeclass
• The bounds of the array must be known (and specified) beforehand and array cannot be resized
• Array is a continious space of values, so if the key distribution is wide and sparse the memory will be wasted (or array may not even fit into memory)

But if the nature of your memoized function permits the usage of ArrayCache you can make your code much more faster by simply switching from Map-based MonadCache to ArrayCache especially if the value type of your function can be "unboxed" (i.e. it is one of primitive types like Int or Double). "Unboxed" values are packed in unboxed arrays UArray which offer even faster execution and are the most efficient in terms of memory usage. Normally you don't have to modify your monadic function definition to run ArrayCache-based memoization: just use appropriate eval* or run* function. For instance our canonical fibm function:

fibm 0 = return 0
fibm 1 = return 1
fibm n = do
n1 <- memo fibm (n-1)
n2 <- memo fibm (n-2)
return (n1+n2)


can be run using ST array of Integers with the following function:

evalFibmSTA :: Integer -> Integer
evalFibmSTA n = runST $evalArrayMemo (fibm n) (0,n)  here the (0,n) argument defines the bounds of cache array. Is it equally easy to use unboxed version of the array, but Integer cannot be unboxed (it isn't primitive type), so lets just use Double for our function result: evalFibmSTUA :: Integer -> Double evalFibmSTUA n = runST$ evalUArrayMemo (fibm n) (0,n)


Instead of ST you can use IO monad:

evalFibmIOA :: Integer -> IO Integer
evalFibmIOA n = evalArrayMemo (fibm n) (0,n)

evalFibmIOUA :: Integer -> IO Double
evalFibmIOUA n = evalUArrayMemo (fibm n) (0,n)


### Vector-based memoization cache

For even better performance use VectorCache and its flavours (unsafe version and dynamically expandable version) which are all based on very fast vector library.

Note however that this MonadCache is even more limiting that ArrayCache since vector supports only Int as an index.

The usage is very similar to ArrayCache, but instead of range we need to specify the length of the vector:

evalFibmSTV :: Int -> Integer
evalFibmSTV n = runST $evalVectorMemo (fibm n) n evalFibmIOUV :: Int -> IO Double evalFibmIOUV n = evalUVectorMemo (fibm n) n  Use "Expandable" version to avoid specifying length parameter: import qualified Control.Monad.Memo.Vector.Expandable as VE evalFibmSTVE :: Int -> Integer evalFibmSTVE n = runST$ VE.startEvalVectorMemo (fibm n)


## Performance of different MonadCache's:

The difference in performance for different MonadCache's with Fibonacci function is demonstrated by this criterion test. The test runs memoized Fibonacci function using the following caches:

• default Map-based
• State-based with Data.IntMap
• array and unboxed array based (Array and UArray)
• vector, unsafe vector and expandable vector (both boxed and unboxed vectors)

Full report can be found here.

## Custom mutable cache

It is also possible to use a mutable container as a MonadCache not defined here. For example if we wish to use mutable hash-table from hashtables package we can do so with the following code:

{-# LANGUAGE MultiParamTypeClasses, TypeSynonymInstances, FlexibleInstances #-}

import Data.Hashable
import qualified Data.HashTable.ST.Basic as H

newtype Container s k v = Container { toTable :: H.HashTable s k v }

type Cache s k v = ReaderCache (Container s k v)

instance (Eq k, Hashable k) => MonadMemo k v (Cache s k v (ST s)) where
{-# INLINE memo #-}
memo f k = do
c <- container
e <- lift $H.lookup (toTable c) k if isNothing e then do v <- f k lift$ H.insert (toTable c) k v
return v
else return (fromJust e)

{-# INLINE fib1 #-}
fibm 0 = return 0
fibm 1 = return 1
fibm n = do
f1 <- memo fibm (n-1)
f2 <- memo fibm (n-2)
return (f1+f2)

evalFib :: Int -> Int
evalFib n = runST \$ do
c <- H.new