The example 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
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.)
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:
Static semantics: If
lazy e : u Lazy.t.
lazy edoes not evaluate
eto a value. Instead it produced a delayed value aka lazy value that, when later forced, will evaluate
eto a value
v. Moreover, that delayed value remembers that
vis its forced value. And if the delayed value is ever forced again, it immediately returns
vinstead 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
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
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.
Here's a representation for infinite lists using lazy values:
type 'a lazystream = Cons of 'a * 'a lazystream Lazy.t
We've gotten rid of the thunk, and instead are using a lazy value as the tail of the lazy stream. If we ever want that tail to be computed, we force it.
Streams vs. Lazy Streams
The following two modules implement the Fibonacci sequence with streams, then with lazy streams. Try computing the 30th Fibonacci number with both modules, and you'll see that the lazy streams implementation is much faster than the standard streams.
module StreamFibs = struct type 'a stream = | Cons of 'a * (unit -> 'a stream) let hd : 'a stream -> 'a = fun (Cons (h, _)) -> h let tl : 'a stream -> 'a stream = fun (Cons (_, t)) -> t () let rec take_aux n (Cons (h, t)) lst = if n = 0 then lst else take_aux (n-1) (t ()) (h::lst) let take : int -> 'a stream -> 'a list = fun n s -> List.rev (take_aux n s ) let nth : int -> 'a stream -> 'a = fun n s -> List.hd (take_aux (n+1) s ) let rec sum : int stream -> int stream -> int stream = fun (Cons (h_a, t_a)) (Cons (h_b, t_b)) -> Cons (h_a + h_b, fun () -> sum (t_a ()) (t_b ())) let rec fibs = Cons(1, fun () -> Cons(1, fun () -> sum (tl fibs) fibs)) let nth_fib n = nth n fibs end module LazyFibs = struct type 'a lazystream = | Cons of 'a * 'a lazystream Lazy.t let hd : 'a lazystream -> 'a = fun (Cons (h, _)) -> h let tl : 'a lazystream -> 'a lazystream = fun (Cons (_, t)) -> Lazy.force t let rec take_aux n (Cons (h, t)) lst = if n = 0 then lst else take_aux (n-1) (Lazy.force t) (h::lst) let take : int -> 'a lazystream -> 'a list = fun n s -> List.rev (take_aux n s ) let nth : int -> 'a lazystream -> 'a = fun n s -> List.hd (take_aux (n+1) s ) let rec sum : int lazystream -> int lazystream -> int lazystream = fun (Cons (h_a, t_a)) (Cons (h_b, t_b)) -> Cons (h_a + h_b, lazy (sum (Lazy.force t_a) (Lazy.force t_b))) let rec fibs = Cons(1, lazy ( Cons(1, lazy ( sum (tl fibs) fibs)))) let nth_fib n = nth n fibs end
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.