Folding Cheat Sheet #6 - sixth in a series

Folding Cheat Sheet #6 - sixth in a series

• 1.
  CHEAT-SHEET Folding #6 ∶ / 𝒂𝟎 ∶ / 𝒂𝟏 ∶ / 𝒂𝟐 ∶ / 𝒂𝟑 𝒇 / 𝒂𝟎 𝒇 / 𝒂𝟏 𝒇 / 𝒂𝟐 𝒇 / 𝒂𝟑 𝒆 @philip_schwarz slides by https://fpilluminated.com/
• 2.
  𝑓𝑜𝑙𝑑𝑙 is tailrecursive. Folding a list (however large) with 𝑓𝑜𝑙𝑑𝑙 does not result in a stack overflow error. ‡ 𝑓𝑜𝑙𝑑𝑟 is not tail recursive. ☩ Folding a sufficiently large list with 𝑓𝑜𝑙𝑑𝑟 results in a stack overflow error. † † See slide after next for an exception in Scala ‡ See next slide for an exception in Haskell ☩ See slides five to nine for a refresher on tail recursion 𝑓𝑜𝑙𝑑𝑙 ∷ 𝛽 → 𝛼 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑓 𝑏 𝑥 𝑥𝑠 𝑓𝑜𝑙𝑑𝑟 ∷ 𝛼 → 𝛽 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓 𝑥 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥𝑠
• 3.
  𝑓𝑜𝑙𝑑𝑟 ∷ 𝛼→ 𝛽 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓 𝑥 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥𝑠 𝑓𝑜𝑙𝑑𝑙 ∷ 𝛽 → 𝛼 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑓 𝑏 𝑥 𝑥𝑠 > :{ | foldRight :: (α -> β -> β) -> β -> [α] -> β | foldRight f e [] = e | foldRight f e (x:xs) = f x (foldRight f e xs) | :} > foldRight (+) 0 [1..10000000] 50000005000000 > foldRight (+) 0 [1..100000000] *** Exception: stack overflow > -- same again but using built-in function > foldr (+) 0 [1..10000000] 50000005000000 > foldr (+) 0 [1..100000000] *** Exception: stack overflow > :{ | foldLeft :: (β -> α -> β) -> β -> [α] -> β | foldLeft f e [] = e | foldLeft f e (x:xs) = foldLeft f (f e x) xs | :} > foldLeft (+) 0 [1..10000000] 50000005000000 > foldLeft (+) 0 [1..100000000] *** Exception: stack overflow > -- same again but using built-in function > foldl (+) 0 [1..10000000] 50000005000000 > foldl (+) 0 [1..100000000] *** Exception: stack overflow > Data.List.foldl' (+) 0 [1..100000000] 5000000050000000 These stack overflows have to do with Haskell’s nonstrict evaluation, and are avoided using a strict left fold, called foldl’ (see final slides).
• 4.
  scala> def foldr[A,B](f:A=>B=>B)(e:B)(s:List[A]):B = | s match { case Nil => e | case x::xs => f(x)(foldr(f)(e)(xs)) } scala> def `(+)`: Long => Long => Long = | m => n => m + n scala> foldr(`(+)`)(0)(List(1,2,3,4)) val res1: Int = 10 scala> foldr(`(+)`)(0)(List.range(1,10_001)) val res2: Int = 50005000 scala> foldr(`(+)`)(0)(List.range(1,100_001)) java.lang.StackOverflowError scala> // same again but using built-in function scala> List.range(1,10_001).foldRight(0)(_+_) val res3: Int = 50005000 scala> List.range(1,100_001).foldRight(0L)(_+_) val res4: Long = 500000500000 scala> import scala.annotation.tailrec scala> @tailrec | def foldl[A,B](f: B=>A=>B)(e:B)(s:List[A]):B = | s match { case Nil => e | case x::xs => foldl(f)(f(e)(x))(xs) } scala> def `(+)`: Long => Long => Long = | m => n => m + n scala> foldl(`(+)`)(0)(List.range(1,10_001)) val res1: Int = 50005000 scala> foldl(`(+)`)(0)(List.range(1,100_001)) val res2: Int = 705082704 scala> // same again but using built-in function scala> List.range(1,10_001).foldLeft(0)(_+_) val res36: Int = 50005000 scala> List.range(1,100_001).foldLeft(0)(_+_) val res37: Long = 5000050000 The reason a stack overflow is not happening here is because built-in foldRight is defined in terms of foldLeft! (see cheat-sheet #7) 𝑓𝑜𝑙𝑑𝑟 ∷ 𝛼 → 𝛽 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓 𝑥 𝑓𝑜𝑙𝑑𝑟 𝑓 𝑏 𝑥𝑠 𝑓𝑜𝑙𝑑𝑙 ∷ 𝛽 → 𝛼 → 𝛽 → 𝛽 → 𝛼 → 𝛽 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 = 𝑏 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑏 𝑥: 𝑥𝑠 = 𝑓𝑜𝑙𝑑𝑙 𝑓 𝑓 𝑏 𝑥 𝑥𝑠
• 5.
  2.2.3 Tail recursion Thecode of lengthS will fail for large enough sequences. To see why, consider an inductive definition of the .length method as a function lengthS: def lengthS(s: Seq[Int]): Int = if (s.isEmpty) 0 else 1 + lengthS(s.tail) scala> lengthS((1 to 1000).toList) res0: Int = 1000 scala> val s = (1 to 100_000).toList s : List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, ... scala> lengthS(s) java.lang.StackOverflowError at .lengthS(<console>:12) at .lengthS(<console>:12) at .lengthS(<console>:12) at .lengthS(<console>:12) ... The problem is not due to insufficient main memory: we are able to compute and hold in memory the entire sequence s. The problem is with the code of the function lengthS. This function calls itself inside the expression 1 + lengthS(...). Let us visualize how the computer evaluates that code: lengthS(Seq(1, 2, ..., 100_000)) = 1 + lengthS(Seq(2, ..., 100_000)) = 1 + (1 + lengthS(Seq(3, ..., 100_000))) = ... Sergei Winitzki sergei-winitzki-11a6431
• 6.
  The code oflengthS will evaluate the inductive step, that is, the “else” part of the “if/else”, about 100,000 times. Each time, the intermediate sub-expression with nested computations 1+(1+(...)) will get larger. That sub-expression needs to be held somewhere in memory, until the function body goes into the base case with no more recursive calls. When that happens, the intermediate sub-expression will contain about 100_000_nested function calls still waiting to be evaluated. A special area of memory called stack memory is dedicated to storing the arguments for all not-yet-evaluated nested function calls. Due to the way computer memory is managed, the stack memory has a fixed size and cannot grow automatically. So, when the intermediate expression becomes large enough, it causes an overflow of the stack memory and crashes the program. One way to avoid stack overflows is to use a trick called tail recursion. Using tail recursion means rewriting the code so that all recursive calls occur at the end positions (at the “tails”) of the function body. In other words, each recursive call must be itself the last computation in the function body, rather than placed inside other computations. Here is an example of tail-recursive code: def lengthT(s: Seq[Int], res: Int): Int = if (s.isEmpty) res else lengthT(s.tail, res + 1) In this code, one of the branches of the if/else returns a fixed value without doing any recursive calls, while the other branch returns the result of a recursive call to lengthT(...). It is not a problem that the recursive call to lengthT has some sub-expressions such as res + 1 as its arguments, because all these sub-expressions will be computed before lengthT is recursively called. def lengthS(s: Seq[Int]): Int = if (s.isEmpty) 0 else 1 + lengthS(s.tail) lengthS(Seq(1, 2, ..., 100_000)) = 1 + lengthS(Seq(2, ..., 100_000)) = 1 + (1 + lengthS(Seq(3, ..., 100_000))) = ... Sergei Winitzki sergei-winitzki-11a6431
• 7.
  The recursive callto lengthT is the last computation performed by this branch of the if/else. A tail-recursive function can have many if/else or match/case branches, with or without recursive calls; but all recursive calls must be always the last expressions returned. The Scala compiler will always use tail recursion when possible. Additionally, Scala has a feature for verifying that a function’s code is tail-recursive: the tailrec annotation. If a function with a tailrec annotation is not tail-recursive (or is not recursive at all), the program will not compile. The code of lengthT with a tailrec annotation looks like this: import scala.annotation.tailrec @tailrec def lengthT(s: Seq[Int], res: Int): Int = if (s.isEmpty) res else lengthT(s.tail, res + 1) Let us trace the evaluation of this function on an example: lengthT(Seq(1,2,3), 0) = lengthT(Seq(2,3), 0 + 1) // = lengthT(Seq(2,3), 1) = lengthT(Seq(3), 1 + 1) // = lengthT(Seq(3), 2) = lengthT(Seq(), 2 + 1) // = lengthT(Seq(), 3) = 3 All sub-expressions such as 1 + 1 and 2 + 1 are computed before recursive calls to lengthT. Because of that, sub-expressions do not grow within the stack memory. This is the main benefit of tail recursion. How did we rewrite the code of lengthS into the tail-recursive code of lengthT? An important difference between lengthS and lengthT is the additional argument (res), called the accumulator argument. This argument is equal to an intermediate result of the computation. The next intermediate result (res + 1) is computed and passed on to the next recursive call via the accumulator argument. In the base case of the recursion, the function now returns the accumulated result (res) rather than 0, because at that time the computation is finished. Rewriting code by adding an accumulator argument to achieve tail recursion is called the accumulator technique or the “accumulator trick”. def lengthS(s: Seq[Int]): Int = if (s.isEmpty) 0 else 1 + lengthS(s.tail) Sergei Winitzki sergei-winitzki-11a6431
• 8.
  One consequence ofusing the accumulator trick is that the function lengthT now always needs a value for the accumulator argument. However, our goal is to implement a function such as length(s) with just one argument, s:Seq[Int]. We can define length(s) = lengthT(s, ???) if we supply an initial accumulator value. The correct initial value for the accumulator is 0, since in the base case (an empty sequence s) we need to return 0. It appears useful to define the helper function (lengthT) separately. Then length will just call lengthT and specify the initial value of the accumulator argument. To emphasize that lengthT is a helper function that is only used by length to achieve tail recursion, we define lengthT as a nested function inside the code of length: import scala.annotation.tailrec def length[A](xs: Seq[A]): Int = { @tailrec def lengthT(s: Seq[A], res: Int): Int = { if (s.isEmpty) res else lengthT(s.tail, res + 1) } lengthT(xs, 0) } When length is implemented like that, users will not be able to call lengthT directly, because lengthT is only visible within the body of the length function. Another possibility in Scala is to use a default value for the res argument: @tailrec def length(s: Seq[A], res: Int = 0): Int = if (s.isEmpty) res else length(s.tail, res + 1) Giving a default value for a function argument is the same as defining two functions: one with that argument and one without. For example, the syntax def f(x: Int, y: Boolean = false): Int = ... // Function body. Sergei Winitzki sergei-winitzki-11a6431
• 9.
  is equivalent todefining two functions with the same name but different numbers of arguments: def f(x: Int, y: Boolean) = ... // Define the function body here. def f(x: Int): Int = f(Int, false) // Call the function defined above. Using a default argument value, we can define the tail-recursive helper function and the main function at once, making the code shorter. The accumulator trick works in a large number of cases, but it may be not obvious how to introduce the accumulator argument, what its initial value must be, and how to define the inductive step for the accumulator. In the example with the lengthT function, the accumulator trick works because of the following mathematical property of the expression being computed: 1 + (1 + (1 + (... + 0))) = (((0 + 1) + 1) + ...) + 1 . This equation follows from the associativity law of addition. So, the computation can be rearranged to group all additions to the left. During the evaluation, the accumulator’s value corresponds to a certain number of left-grouped parentheses ((0 + 1) ...) + 1. In code, it means that intermediate expressions are fully computed before making recursive calls; So, recursive calls always occur outside all other sub-expressions - that is, in tail positions. There are no sub-expressions that need to be stored on the stack until all the recursive calls are complete. However, not all computations can be rearranged in that way. Even if a code rearrangement exists, it may not be immediately obvious how to find it. Sergei Winitzki sergei-winitzki-11a6431
• 10.
  Left Folds, Laziness,and Space Leaks To keep our initial discussion simple, we use foldl throughout most of this section. This is convenient for testing, but we will never use foldl in practice. The reason has to do with Haskell’s nonstrict evaluation. If we apply foldl (+) [1,2,3], it evaluates to the expression (((0 + 1) + 2) + 3). We can see this occur if we revisit the way in which the function gets expanded: foldl (+) 0 (1:2:3:[]) == foldl (+) (0 + 1) (2:3:[]) == foldl (+) ((0 + 1) + 2) (3:[]) == foldl (+) (((0 + 1) + 2) + 3) [] == (((0 + 1) + 2) + 3) The final expression will not be evaluated to 6 until its value is demanded. Before it is evaluated, it must be stored as a thunk. Not surprisingly, a thunk is more expensive to store than a single number, and the more complex the thunked expression, the more space it needs. For something cheap such as arithmetic, thunking an expression is more computationally expensive than evaluating it immediately. We thus end up paying both in space and in time. When GHC is evaluating a thunked expression, it uses an internal stack to do so. Because a thunked expression could potentially be infinitely large, GHC places a fixed limit on the maximum size of this stack. Thanks to this limit, we can try a large thunked expression in ghci without needing to worry that it might consume all the memory: ghci> foldl (+) 0 [1..1000] 500500 From looking at this expansion, we can surmise that this creates a thunk that consists of 1,000 integers and 999 applications of (+). That’s a lot of memory and effort to represent a single number! With a larger expression, although the size is still modest, the results are more dramatic: ghci> foldl (+) 0 [1..1000000] *** Exception: stack overflow On small expressions, foldl will work correctly but slowly, due to the thunking_overhead that it incurs. Bryan O’Sullivan John Goerzen Donald Bruce Stewart
• 11.
  We refer tothis invisible thunking as a space leak, because our code is operating normally, but it is using far more memory than it should. On larger expressions, code with a space leak will simply fail, as above. A space leak with foldl is a classic roadblock for new Haskell programmers. Fortunately, this is easy to avoid. The Data.List module defines a function named foldl' that is similar to foldl, but does not build up thunks. The difference in behavior between the two is immediately obvious: ghci> foldl (+) 0 [1..1000000] *** Exception: stack overflow ghci> :module +Data.List ghci> foldl' (+) 0 [1..1000000] 500000500000 Due to foldl’s thunking behavior, it is wise to avoid this function in real programs, even if it doesn’t fail outright, it will be unnecessarily inefficient. Instead, import Data.List and use foldl'. Bryan O’Sullivan John Goerzen Donald Bruce Stewart
• 12.
  https://fpilluminated.com/ based on