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User:Benmachine/Non-strict semantics

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An expression language is said to have non-strict semantics if expressions can have a value even if some of their subexpressions do not. Haskell is one of the few modern languages to have non-strict semantics by default: nearly every other language has strict semantics, in which if any subexpression fails to have a value, the whole expression fails with it.

1 What?

Any sufficiently capable programming language is non-total, which is to say you can write expressions that do not produce a value: common examples are an infinite loop or unproductive recursion, e.g. the following definition in Haskell:

noreturn :: Integer -> Integer
noreturn x = negate (noreturn x)

or the following Python function:

def noreturn(x):
    while True:
        x = -x

    return x # not reached

both fail to produce a value when executed. We say that noreturn x is undefined, and write noreturn x = .

In Python the following expression:

{7: noreturn(5), 2: 0}[2]

also fails to have a value, because in order to construct the dictionary, the interpreter tries to work out noreturn(5), which of course doesn't return a value. This is called innermost-first evaluation: in order to call a function with some arguments, you first have to calculate what all the arguments are, starting from the innermost function call and working outwards. The result is that Python is strict, in the sense that calling any function with an undefined argument produces an undefined value, i.e. f(⊥) = ⊥.

In Haskell, an analogous expression:

lookup 2 [(7, noreturn 5), (2, 0)]
in fact has the value
Just 0
. The program does not try to compute
noreturn 5
because it is irrelevant to the overall value of the computation: only the values that are necessary to the result are computed. This is called outermost-first evaluation because you first look at
to see if it needs to use its arguments, and only if it does do you look at what those arguments are. The upshot is that you can give a function an argument that it doesn't look at, and it doesn't matter if it's
or not, the function will return a value anyway. Such functions are not strict, i.e. they satisfy
f ⊥ ≠ ⊥
. Practically, this means that Haskell functions need not completely compute their arguments before using them, which is why e.g.
take 3 [1..]
can produce
even though it is given a conceptually infinite list.

2 Why?

The important thing to understand about non-strict semantics is that it is not a performance feature. Non-strict semantics means that only the things that are needed for the answer are evaluated, but if you write your programs carefully, you'll only compute what is absolutely necessary anyway, so the extra time spent working out what should and shouldn't be evaluated is time wasted. For this reason, a very well-optimised strict program will frequently outperform even the fastest non-strict program.

However, the real and major advantage that non-strictness gives you over strict languages is you get to write cleaner and more composable code. In particular, you can separate production and consumption of data: don't know how many prime numbers you're going to need? Just make `primes` a list of all prime numbers, and then which ones actually get generated depends on how you use them in the rest of your code. By contrast, writing code in a strict language that constructs a data structure in response to demand usually will require first-class functions and/or a lot of manual hoop-jumping to make it all behave itself.

What this means in practice is that in Haskell you can often write your function as a simple composition of other general functions, and still get the behaviour you need, e.g:

any :: (a -> Bool) -> [a] -> Bool
any p = or . map p
uses non-strictness to stop at the first
in the input,
doesn't even need to know that only the first half of the list might be needed. We can write
in the completely straightforward and obviously correct way, and still have it interact well with
in this way. In a strict langauge, you'd have to write the recursion out manually:
any p [] = False
any p (x:xs)
  | p x = True
  | otherwise = xs
since in strict languages only builtin control structures can decide whether some bit of code gets executed or not: ordinary functions like
can't. It's this additional power that Haskell has that leads people to say you can define your own control structures as normal Haskell functions, that makes lists act as for loops, which you can break or continue as necessary, doing no more work than required.

3 How do I stop it?

As mentioned above, non-strictness can hurt performance: if a result is definitely going to be needed later, you might as well evaluate it now, to avoid having to hold on to all the data that goes into it. Fortunately, the Haskell designers were aware of these problems and introduced a loophole or two so that we could force our programs to be strict when necessary.

The most basic method of strictness introduction is the seq function. seq takes two arguments of any type, and returns the second. However, it also has the important property that it is magically strict in its first argument; in essence seq is defined by the following two equations:

⊥ `seq` b = ⊥
a `seq` b = b

The important thing to notice about seq is that it doesn't evaluate anything just by virtue of being in the code. All it does is introduce an artificial dependency of one value on another: when the result of seq is evaluated, the first argument must also be evaluated. As an example, suppose x :: Integer, then seq x b is essentially a bit like if x == 0 then b else b - unconditionally equal to b, but forcing x along the way. In particular, the expression x `seq` x is completely redundant, and always has exactly the same effect as just writing x.

Strictly speaking, the two equations of
are all it must satisfy, and if the compiler can statically prove that the first argument is not ⊥, it doesn't have to evaluate it to meet its obligations. In practice, this almost never happens, and would probably be considered highly counterintuitive behaviour on the part of GHC (or whatever else you use to run your code). However, it is the case that evaluating
and then
, then returning
is a perfectly legitimate thing to do; it is to prevent this ambiguity that
was invented, but that's another story. Note that
is the only way to force evaluation of a value with a function type.