# Streams and Laziness * * * <i> Topics: * infinite data structures * streams * thunks * lazy evaluation </i> * * * ## Infinite data structures We already know that OCaml allows us to create recursive functions&mdash;that is, functions defined in terms of themselves. It turns out we can define other values in terms of themselves, too.  # let rec ones = 1::ones;; val ones : int list = [1; <cycle>] # let rec a = 0::b and b = 1::a;; val a : int list = [0; 1; <cycle>] val b : int list = [1; 0; <cycle>]  The expressions above create *recursive values*. The list ones contains an infinite sequence of 1, and the lists a and b alternate infinitely between 0 and 1. As the lists are infinite, the toplevel cannot print them in their entirety. Instead, it indicates a *cycle*: the list cycles back to its beginning. Even though these lists represent an infinite sequence of values, their representation in memory is finite: they are linked lists with back pointers that create those cycles. There are other kinds of infinite mathematical objects we might want to represent with finite data structures: * Infinite sequences, such as the sequence of all natural numbers, or the sequence of all primes, or the sequence of all Fibonacci numbers. * A stream of inputs read from a file, a network socket, or a user. All of these are unbounded in length, hence we can think of them as being infinite in length. In fact, many I/O libraries treat reaching the end of an I/O stream as an unexpected situation and raise an exception. * A *game tree* is a tree in which the positions of a game (e.g., chess or tic-tac-toe)_ are the nodes and the edges are possible moves. For some games this tree is in fact infinite (imagine, e.g., that the pieces on the board could chase each other around forever), and for other games, it's so deep that we would never want to manifest the entire tree, hence it is effectively infinite. Suppose we wanted to represent the first of those examples: the sequence of all natural numbers. Some of the obvious things we might try simply don't work:  # let rec from n = n :: from (n+1);; # let nats = from 0;; Stack overflow during evaluation (looping recursion?). # let rec nats = 0 :: List.map (fun x -> x+1) nats;; Error: This kind of expression is not allowed as right-hand side of let rec  The problem with the first attempt is that nats attempts to compute the entire infinite sequence of natural numbers. Because the function isn't tail recursive, it quickly overflows the stack. If it were tail recursive, it would go into an infinite loop. The second attempt doesn't work for a more subtle reason. In the definition of a recursive value, we are not permitted to use a value before it is finished being defined. The problem is that List.map is applied to nats, and therefore pattern matches to extract the head and tail of nats, but we are in the middle of defining nats, so that use of nats is not permitted. Let's find another way. ## Streams A *stream* is an infinite list. Sometimes these are also called sequences, delayed lists, or lazy lists. We can try to define a stream by analogy to how we can define (finite) lists. Recall that definition:  type 'a mylist = | Nil | Cons of 'a * 'a mylist  We could try to convert that into a definition for streams:  (* doesn't actually work *) type 'a stream = | Cons of 'a * 'a stream  Note that we got rid of the Nil constructor, because the empty list is finite, but we want only infinite lists. The problem with that definition is that it's really no better than the built-in list in OCaml, in that we still can't define nats:  # let rec from n = Cons (n, from (n+1));; # let nats = from 0;; Stack overflow during evaluation (looping recursion?).  As before, that definition attempts to go off and compute the entire infinite sequence of naturals. What we need is a way to *pause* evaluation, so that at any point in time, only a finite approximation to the infinite sequence has been computed. Fortunately, we already know how to do that! Consider the following definitions:  # let f1 = failwith "oops";; Exception: Failure "oops". # let f2 = fun x -> failwith "oops";; val f2 : 'a -> 'b = <fun> # f2 ();; Exception: Failure "oops".  The definition of f1 immediately raises an exception, whereas the definition of f2 does not. Why? Because f2 wraps the failwith inside an anonymous function. Recall that, according to the dynamic semantics of OCaml, **functions are already values**. So no computation is done inside the body of the function until it is applied. That's why f2 () raises an exception. We can use this property of evaluation&mdash;that functions delay evaluation&mdash;to our advantage in defining streams: let's wrap the tail of a stream inside a function. Since it doesn't really matter what argument that function takes, we might as well let it be unit. A function that is used just to delay computation, and in particular one that takes unit as input, is called a *thunk*.  (* An ['a stream] is an infinite list of values of type ['a]. * AF: [Cons (x, f)] is the stream whose head is [x] and tail is [f()]. * RI: none. *) type 'a stream = Cons of 'a * (unit -> 'a stream)  This definition turns out to work quite well. We can define nats, at last:  # let rec from n = Cons (n, fun () -> from (n+1));; val from : int -> int stream = <fun> # let nats = from 0;; val nats : int stream = Cons (0, <fun>)  We do not get an infinite loop or a stack overflow. The evaluation of nats has paused. Only the first element of it, 0, has been computed. The remaining elements will not be computed until they are requested. To do that, we can define functions to access parts of a stream, similarly to how we can access parts of a list:  (* [hd s] is the head of [s] *)   let hd (Cons (h, _)) = h (* [tl s] is the tail of [s] *) let tl (Cons (_, tf)) = tf ()    (* [take n s] is the list of the first [n] elements of [s] *) let rec take n s =   if n=0 then []   else hd s :: take (n-1) (tl s)    (* [drop n s] is all but the first [n] elements of [s] *) let rec drop n s =    if n = 0 then s   else drop (n-1) (tl s)  It is informative to observe the types of those functions:  val hd : 'a stream -> 'a val tl : 'a stream -> 'a stream val take : int -> 'a stream -> 'a list val drop : int -> 'a stream -> 'a stream  Note how, in the definition of tl, we must apply the function tf to () to obtain the tail of the stream. That is, we must *force* the thunk to evaluate at that point, rather than continue to delay its computation. We can use take to observe a finite prefix of a stream. For example:  # take 10 nats;; - : int list = [0; 1; 2; 3; 4; 5; 6; 7; 8; 9]  ## Programming with streams Let's write some functions that manipulate streams. It will help to have a notation for streams to use as part of documentation. Let's use <a; b; c; ...> to denote the stream that has elements a, b, and c at its head, followed by infinitely many other elements. Here are functions to square a stream, and to sum two streams:  (* [square <a;b;c;...>] is [<a*a;b*b;c*c;...]. *) let rec square (Cons (h, tf)) = Cons (h*h, fun () -> square (tf ())) (* [sum <a1;b1;c1;...> <a2;b2;c2;...>] is  * [<a1+b1;a2+b2;a3+b3;...>] *) let rec sum (Cons (h1, tf1)) (Cons (h2, tf2)) = Cons (h1+h2, fun () -> sum (tf1 ()) (tf2 ()))  Their types are:  val square : int stream -> int stream val sum : int stream -> int stream -> int stream  Note how the basic template for defining both functions is the same: * Pattern match against the input stream(s), which must be Cons of a head and a tail function (a thunk). * Construct a stream as the output, which must be Cons of a new head and a new tail function (a thunk). * In constructing the new tail function, delay the evaluation of the tail by immediately starting with fun () -> .... * Inside the body of that thunk, recursively apply the function being defined (square or sum) to the result of forcing a thunk (or thunks) to evaluate. Of course, squaring and summing are just two possible ways of mapping a function across a stream or streams. That suggests we could write a higher-order map function, much like for lists:  (* [map f <a;b;c;...>] is [<f a; f b; f c; ...>] *) let rec map f (Cons (h, tf)) = Cons (f h, fun () -> map f (tf ())) (* [map2 f <a1;b1;c1;...> <a2;b2;c2;...>] is  * [<f a1 b1; f a2 b2; f a3 b3; ...>] *) let rec map2 f (Cons (h1, tf1)) (Cons (h2, tf2)) = Cons (f h1 h2, fun () -> map2 f (tf1 ()) (tf2 ())) let square' = map (fun n -> n*n) let sum' = map2 (+)  And their types are as we would expect:  val map : ('a -> 'b) -> 'a stream -> 'b stream val map2 : ('a -> 'b -> 'c) -> 'a stream -> 'b stream -> 'c stream val square' : int stream -> int stream val sum' : int stream -> int stream -> int stream  Now that we have a map function for streams, we can successfully define nats in one of the clever ways we originally attempted:  # let rec nats = Cons(0, fun () -> map (fun x -> x+1) nats);; val nats : int stream = Cons (0, <fun>) # take 10 nats;; - : int list = [0; 1; 2; 3; 4; 5; 6; 7; 8; 9]  Why does this work? Intuitively, nats is <0; 1; 2; 3; ...>, so mapping the increment function over nats is <1; 2; 3; 4; ...>. If we cons 0 onto the beginning of <1; 2; 3; 4; ...>, we get <0; 1; 2; 3; ...>, as desired. The recursive value definition is permitted, because we never attempt to use nats until after its definition is finished. In particular, the thunk delays nats from being evaluated on the right-hand side of the definition. Here's another clever definition. Consider the Fibonacci sequence <1; 1; 2; 3; 5; 8; ...>. If we take the tail of it, we get <1; 2; 3; 5; 8; 13; ...>. If we sum those two streams, we get <2; 3; 5; 8; 13; 21; ...>. That's nothing other than the tail of the tail of the Fibonacci sequence. So if we were to prepend [1; 1] to it, we'd have the actual Fibonacci sequence. That's the intuition behind this definition:  let rec fibs = Cons(1, fun () -> Cons(1, fun () -> sum fibs (tl fibs)))  And it works!  # take 10 fibs;; - : int list = [1; 1; 2; 3; 5; 8; 13; 21; 34; 55]  Unfortunately, it's highly inefficient. Every time we force the computation of the next element, it required recomputing all the previous elements, twice: once for fibs and once for tl fibs in the last line of the definition. By the time we get up to the 30th number, the computation is noticeably slow; by the time of the 100th, it seems to last forever. Could we do better? Yes, with a little help from a new language feature. ## Laziness The example above with the Fibonacci sequence demonstrates that it would be useful if the computation of a thunk happened only once: when it is forced, the resulting value could be remembered, and if the thunk is ever forced again, that value could immediately be returned instead of recomputing it. That's the idea behind the OCaml Lazy module:  module Lazy : sig type 'a t = 'a lazy_t val force : 'a t -> 'a end  A value of type 'a Lazy.t is a value of type 'a whose computation has been delayed. Intuitively, the language is being *lazy* about evaluating it: it won't be computed until specifically demanded. The way that demand is expressed with by *forcing* the evaluation with Lazy.force, which takes the 'a Lazy.t and causes the 'a inside it to finally be produced. The first time a lazy value is forced, the computation might take a long time. But the result is *cached* aka *memoized*, and any subsequent time that lazy value is forced, the memoized result will be returned immediately. (By the way, "memoized" really is the correct spelling of this term. We didn't misspell "memorized", though it might look that way.) The Lazy module doesn't contain a function that produces a 'a Lazy.t. Instead, there is a keyword built-in to the OCaml syntax that does it: lazy e. * **Syntax:** lazy e * **Static semantics:** If e:u, then lazy e : u Lazy.t. * **Dynamic semantics:** lazy e does not evaluate e to a value. Instead it produced a *delayed value* aka *lazy value* that, when later forced, will evaluate e to a value v and return v. Moreover, that delayed value remembers that v is its forced value. And if the delayed value is ever forced again, it immediately returns v instead of recomputing it. To illustrate the use of lazy values, let's try computing the 30th Fibonacci number using the definition of fibs, which we repeat here for convenience:  let rec fibs = Cons(1, fun () -> Cons(1, fun () -> sum fibs (tl fibs)))  If we try to get the 30th Fibonacci number, it will take a long time to compute:  let fib30long = take 30 fibs |> List.rev |> List.hd  But if we wrap evaluation of that with lazy, it will return immediately, because the evaluation of that number has been delayed:  let fib30lazy = lazy (take 30 fibs |> List.rev |> List.hd)  Later on we could force the evaluation of that lazy value, and that will take a long time to compute, as did fib30long:  let fib30 = Lazy.force fib30lazy  But if we ever try to recompute that same lazy value, it will return immediately, because the result has been memoized:  let fib30fast = Lazy.force fib30lazy  (The above examples will make much more sense if you try them in utop rather than just reading these notes.) Nonetheless, we still haven't totally succeeded. That particular computation of the 30th Fibonacci number has been memoized, but if we later define some other computation of another it won't be sped up the first time it's computed:  (* slow, even if [fib30lazy] was already forced *) let fib29 = take 29 fibs |> List.rev |> List.hd  What we really want is to change the representation of streams itself to make use of lazy values. **Lazy lists.** Here's a representation for infinite lists using lazy values:  type 'a lazylist = Cons of 'a * 'a lazylist Lazy.t  We've gotten rid of the thunk, and instead are using a lazy value as the tail of the lazy list. If we ever want that tail to be computed, we force it. Now, assuming appropriate definitions for hd, tl, sum, and take (left as an exercise for the reader), we can define the Fibonacci sequence as a lazy list:  let rec fibs = Cons(1, lazy ( Cons(1, lazy ( sum (tl fibs) fibs)))) (* both fast *) let fib30lazyfast = take 30 fibs let fib29lazyfast = take 29 fibs  **Lazy vs. eager.** OCaml's usual evaluation strategy is *eager* aka *strict*: it always evaluate an argument before function application. If you want a value to be computed lazily, you must specifically request that with the lazy keyword. Other function languages, notably Haskell, are lazy by default. Laziness can be pleasant when programming with infinite data structures. But lazy evaluation makes it harder to reason about space and time, and it has bad interactions with side effects. That's one reason we use OCaml rather than Haskell in this course. ## Summary The stream data structure can be used to represent an infinite mathematical sequence, but with only a finite amount of memory. That's because the values of the sequence are not produced until they are specifically requested. The thunks used in streams are used to pause evaluation until such a request is made. Thunks are a way of implementing lazy evaluation, which OCaml also has available. The advantage of OCaml's built-in implementation is that it can memoize results, avoiding the need for recomputation. ## Terms and concepts * caching * cycle * delayed evaluation * eager * force * infinite data structure * lazy * memoization * thunk * recursive values * stream * strict ## Further reading * *More OCaml: Algorithms, Methods, and Diversions*, chapter 2, by John Whitington. This book is a sequel to *OCaml from the Very Beginning*.