PLAI §7 (done with Haskell)
For this part, we will use a new language, Lazy Racket.
As the name suggests, this is a version of the normal (untyped) Racket language that is lazy.
First of all, let’s verify that this is indeed a lazy language:
That went without a problem — the argument expression was indeed not
evaluated. In this language, you can treat all expressions as future
promises to evaluate. There are certain points where such promises
are actually forced, all of these stem from some need to print a
resulting value, in our case, it’s the REPL that prints such values:
The expression by itself only generates a promise, but when we want to print it, this promise is forced to evaluate — this forces the addition, which forces its arguments (plain values rather than computation promises), and at this stage we get an error. (If we never want to see any results, then the language will never do anything at all.) So a promise is forced either when a value printout is needed, or if it is needed to recursively compute a value to print:
Note that the error was raised by the internal expression: the outer
expression uses *, and + requires actual values not promises.
Another example, which is now obvious, is that we can now define an if
function:
Actually, in this language if, and, and or are all function values
instead of special forms:
(By now, you should know that these have no value in Racket — using
them like this in plain will lead to syntax errors.) There are some
primitives that do not force their arguments. Constructors fall in this
category, for example cons and list:
Nothing — the definition simply worked, but that’s expected, since nothing is printed. If we try to inspect this value, we can get some of its parts, provided we do not force the bogus one:
The same holds for cons:
Now if this is the case, then how about this:
Everything is fine, as expected — but what is the value of ones now?
Clearly, it is a list that has 1 as its first element:
But what do we have in the tail of this list? We have ones which we
already know is a list that has 1 in its first place — so following
Racket’s usual rules, it means that the second element of ones is,
again, 1. If we continue this, we can see that ones is, in fact, an
infinite list of 1s:
In this sense, the way define behaves is that it defines a true
equation: if ones is defined as (cons 1 ones), then the real value does
satisfy
which means that the value is the fixpoint of the defined expression.
We can use append in a similar way:
This looks like it has some common theme with the discussion of
implementing recursive environments — it actually demonstrates that in
this language, letrec can be used for simple values too. First of
all, a side note — here an expression that indicated a bug in our
substituting evaluator:
When our evaluator returned 1 for this, we noticed that this was a
bug: it does not obey the lexical scoping rules. As seen above, Lazy
Racket is correctly using lexical scope. Now we can go back to the use
of letrec — what do we get by this definition:
we get an error about xs being undefined.
xs is unbound because of the usual scope that let uses. How can we
make this work? — We simply use letrec:
As expected, if we try to print an infinite list will cause an infinite loop, which DrRacket catches and prints in that weird way:
How would we inspect an infinite list? We write a function that returns part of it:
Dealing with infinite lists can lead to lots of interesting things, for example:
To see how it works, see what you know about fibs[n] which will be our
notation for the nth element of fibs (starting from 1):
and for all n>2:
so it follows the exact definition of Fibonacci numbers.
Note that the list examples demonstrate that laziness applies to nested values (actually, nested computations) too: a value that is not needed is not computed, even if it is contained in a value that is needed. For example, in:
the if needs to know only whether its first argument (note: it is an
argument, since this if is a function) is #f or not. Once it is
determined that it is a pair (a cons cell), there is no need to
actually look at the values inside the pair, and therefore (+ 1 x)
(and more specifically, x) is never evaluated and we see no error.
There are a few issues that we need to be aware of when we’re dealing with a lazy language. First of all, remember that our previous attempt at lazy evaluation has made
evaluate to 1, which does not follow the rules of lexical scope. This is not a problem with lazy evaluation, but rather a problem with our naive implementation. We will shortly see a way to resolve this problem. In the meanwhile, remember that when we try the same in Lazy Racket we do get the expected error:
A second issue is a subtle point that you might have noticed when we
played with Lazy Racket: for some of the list values we have see a “.”
printed. This is part of the usual way Racket displays an improper
list — any list that does not terminate with a null value. For
example, in plain Racket:
In the dialect that we’re using in this course, this is not possible.
The secret is that the cons that we use first checks that its second
argument is a proper list, and it will raise an error if not. So how
come Lazy Racket’s cons is not doing the same thing? The problem is
that to know that something is a proper list, we will have to force it,
which will make it not behave like a constructor.
As a side note, we can achieve some of this protection if we don’t insist on immediately checking the second argument completely, and instead we do the check when needed — lazily:
(define (safe-cons x l)
(cons x (if (pair? l) l (error "poof"))))
Finally, there are two consequences of using a lazy language that make it more difficult to debug (or at lease take some time to get used to). First of all, control tends to flow in surprising ways. For example, enter the following into DrRacket, and run it in the normal language level for the course:
In the normal language level, we get an error, and red arrows that show
us how where in the computation the error was raised. The arrows are
all expected, except that foo2 is not in the path — why is that?
Remember the discussion about tail-calls and how they are important in
Racket since they are the only tool to generate loops? This is what
we’re seeing here: foo2 calls foo3 in a tail position, so there is
no need to keep the foo2 context anymore — it is simply replaced by
foo3. (Incidentally, there is also no arrow that goes through foo1:
Racket does some smart inlining, and it figures out that foo0+foo1
are simply returning the same value, so it skips foo1.)
Now switch to Lazy Racket and re-run — you’ll see no arrows at all.
What’s the problem? The call of foo0 creates a promise that is forced
in the top-level expression, that simply returns the first of the
list that foo1 created — and all of that can be done without
forcing the foo2 call. Going this way, the computation is finally
running into an error after the calls to foo0, foo1, and foo2
are done — so we get the seemingly out-of-context error.
To follow what’s happening here, we need to follow how promise are forced: when we have code like
then the foo call is a strict point, since we need an actual value
to display on the REPL. Since it’s in a strict position, we do the
call, but when we’re in the function there is no need to compute the
division result — so it is returned as a lazy promise value back to
the toplevel. It is only then that we continue the process of getting
an actual value, which leads to trying to compute the division and get
the error.
Finally, there are also potential problems when you’re not careful about memory use. A common technique when using a lazy language is to generate an infinite list and pull out its Nth element. For example, to compute the Nth Fibonacci number, we’ve seen how we can do this:
and we can also do this (reminder: letrec is the same as an internal
definition):
but the problem here is that when list-ref is making its way down the
list, it might still hold a reference to fibs, which means that as the
list is forced, all intermediate values are held in memory. In the
first of these two, this is guaranteed to happen since we have a binding
that points at the head of the fibs list. With the second form things
can be confusing: it might be that our language implementation is smart
enough to see that fibs is not really needed anymore and release the
offending reference. If it isn’t, then we’d have to do something like
to eliminate it. But even if the implementation does know that there is no need for that reference, there are other tricky situations that are hard to avoid.
Side note: Racket didn’t use to do this optimization, but now it does, and the lazy language helped in clarifying more cases where references should be released. To check that, consider these two variants:
If we try to use them with a big input:
then fib1 would work fine, whereas fib2 will likely run into
DrRacket’s memory limit and the computation will be terminated. The
problem is that fib2 uses the fibs2 value after the list-ref
call, which will make a reference to the head of the list, preventing it
from being garbage-collected while list-ref is cdr-ing down the
list and making more cons cells materialize.
There is a very simple and elegant principle in shell programming — we get a single data type, a character stream, and many small functions, each doing a single simple job. With these small building blocks, we can construct more sequences that achieve more complex tasks, for example — a sorted frequency table of lines in a file:
This is very much like a programming language — we get small blocks,
and build stuff out of them. Of course there are swiss army knives like
awk that try to do a whole bunch of stuff, (the same attitude that
brought Perl to the world…) and even these respect the “stream” data
type. For example, a simple { print $1 } statement will work over all
lines, one by one, making it a program over an infinite input stream,
which is what happens in reality in something like:
But there is something else in shell programming that makes so effective: it is implementing a sort of a lazy evaluation. For example, compare this:
to:
Each element in the pipe is doing its own small job, and it is always doing just enough to feed its output. Each basic block is designed to work even on infinite inputs! (Even sort works on unlimited inputs…) (Soon we will see a stronger connection with lazy evaluation.)
Side note: (Alan Perlis) “It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures”… But the uniformity comes at a price: the biggest problem shells have is in their lack of a recursive structure, contaminating the world with way too many hacked up solutions. More than that, it is extremely inefficient and usually leads to data being re-parsed over and over and over — each small Unix command needs to always output stuff that is human readable, but the next command in the pipe will need to re-parse that, eg, rereading decimal numbers. If you look at pipelines as composing functions, then a pipe of numeric commands translates to something like:
itoa(baz(atoi(itoa(bar(atoi(itoa(foo(atoi(inp)))))))))and it is impossible to get rid of the redundant
atoi(itoa(...))s.
We already know that when we use lazy evaluation, we are guaranteed to have more robust programs. For example, a function like:
is completely useless in Racket because all functions are eager, but in a lazy language, it would behave exactly like the real if. Note that we still need some primitive conditional, but this primitive can be a function (and it is, in Lazy Racket).
But we get more than that. If we have a lazy language, then computations are pushed around as if they were values (computations, because these are expressions that are yet to be evaluated). In fact, there is no distinction between computations and values, it just happens that some values contain “computational promises”, things that will do something in the future.
To see how this happens, we write a simple program to compute the (infinite) list of prime numbers using the sieve of Eratosthenes. To do this, we begin by defining the list of all natural numbers:
And now define a sift function: it receives an integer n and an
infinite list of integers l, and returns a list without the numbers
that can be divided by n. This is simple to write using filter:
and it requires a definition for divides? — we use Racket’s modulo
for this:
Now, a sieve is a function that consumes a list that begins with a
prime number, and returns the prime numbers from this list. To do this,
it returns a list that has the same first number, and for its tail it
sifts out numbers that are divisible by the first from the original
list’s tail, and calls itself recursively on the result:
Finally, the list of prime numbers is the result of applying sieve on
the list of numbers from 2. The whole program is now:
To see how this runs, we trace modulo to see which tests are being
used. The effect of this is that each time divides? is actually
required to return a value, we will see a line with its inputs, and its
output. This output looks quite tricky — things are computed only on
a “need to know” basis, meaning that debugging lazy programs can be
difficult, since things happen when they are needed which takes time to
get used to. However, note that the program actually performs the same
tests that you’d do using any eager-language implementation of the sieve
of Eratosthenes, and the advantage is that we don’t need to decide in
advance how many values we want to compute — all values will be
computed when you want to see the corresponding result. Implementing
this behavior in an eager language is more difficult than a simple
program, yet we don’t need such complex code when we use lazy
evaluation.
Note that if we trace divides? we see results that are some promise
struct — these are unevaluated expressions, and they point at the fact
that when divides? is used, it doesn’t really force its arguments —
this happens later when these results are forced.
The analogy with shell programming using pipes should be clear now — for example, we have seen this:
The last head -5 means that no computation is done on parts of the
original file that are not needed. It is similar to a (take 5 l)
expression in Lazy Racket.
Using infinite lists is similar to using channels — a tool for
synchronizing threads and (see a Rob Pike’s talk), and
generators (as they exist in Python). Here are examples of both, note
how similar they both are, and how similar they are to the above
definition of primes. (But note that there is an important
difference, can you see it? It has to be with whether a stream is
reusable or not.)
First, the threads & channels version:
And here is the generator version:
Finally, note that on requiring different parts of the primes, the
same calls are not repeated. This indicates that our language
implements “call by need” rather than “call by name”: once an expression
is forced, its value is remembered, so subsequent usages of this value
do not require further computations.
Using “call by name” means that we actually use expressions which can lead to confusing code. An old programming language that used this is Algol. A confusing example that demonstrates this evaluation strategy is:
x and i are arguments that are passed by name, which means that they
can use the same memory location. This is called aliasing, a problem
that happens when pointers are involved (eg, pointers in C and
reference arguments in C++).
Another interesting behavior that we can now observe, is that the TOY
evaluation rule for with:
is specifying an eager evaluator only if the language that this rule is written in is itself eager. Indeed, if we run the TOY interpreter in Lazy Racket (or other interpreters we have implemented), we can verify that running:
is perfectly fine — the call to Racket’s division is done in the evaluation of the TOY division expression, but since Lazy Racket is lazy, then if this value is never used then we never get to do this division! On the other hand, if we evaluate
we do get an error when DrRacket tries to display the result, which
forces strictness. Note how the arrows in DrRacket that show where the
computation is are quite confusing: the computation seem to go directly
to the point of the arithmetic operations (arith-op) since the rest of
the evaluation that the evaluator performed was already done, and
succeeded. The actual failure happens when we try to force the
resulting promise which contains only the strict points in our code.
Generally, we know how lazy evaluation works when we use the substitution model. We even know that if we have:
then the result should be an error because we cannot substitute the y
expression in because it will capture the y — changing the binding
structure. As an indication, the original expression contains a free
reference to y, which is exactly why we shouldn’t substitute it. But
what about:
Evaluating this eagerly returns 18, we therefore expect any other evaluation (eager or lazy, using substitutions or environments) to return 18 too, because any of these options should not change the meaning of numbers, of addition, or of the scoping rules. (And we know that no matter what evaluation strategy we choose, if we get to a value (no infinite loop or exception) then it’ll always be the same value.) For example, try using lazy evaluation with substitutions:
And what about lazy evaluation using environments:
We have a problem! This problem should be familiar now, it is very similar to the problem that led us down the mistaken path of dynamic scoping when we tried to have first-class functions. In both cases, substitution always worked, and it looks like in both cases the problem is that we don’t remember the environment of an expression: in the case of functions, it is the environment at the time of creating the closure that we want to capture and use when we go back later to evaluate the body of the function. Here we have a similar situation, except that we don’t need a function to defer computation: most expressions get evaluated at some time in the future, so every time we defer such a computation we need to remember the lexical environment of the expression.
This is the major point that will make things work again: every expression creates something like a closure — an object that closes over an expression and an environment at the (lexical) place where that expression was used, and when we actually want to evaluate it later, we need to do it in the right lexical context. So it is like a closure except it doesn’t need to be applied, and there are no arguments. In fact it is also a form of a closure — instead of closing over a function body and an environment, it closes over any expression and an environment. (As we shall see, lazy evaluation is tightly related to using nullary functions: thunks.)
So we implement this by creating such closure values for all expressions that are not evaluated right now. We begin with the Toy language, and rename it to “Sloth”. We then add one more case to the data type of values which implements the new kind of expression closures, which contains the expression and its environment:
(Intuition#1: ExprV is a delayed evaluation and therefore it has the
two values that are ultimately passed to eval. Intuition#2: laziness
can be implemented with thunks, so we hold the same information as a
FunV does, only there’s no need for the argument names.)
Where should we use the new ExprV? — At any place where we want to
be lazy and defer evaluating an expression for later. The two places in
the interpreter where we want to delay evaluation are the named
expressions in a bind form and the argument expressions in a function
application. Both of these cases use the helper eval* function to do
their evaluations, for example:
To delay these evaluations, we need to change eval* so it returns an
expression closure instead of actually doing the evaluation — change:
to:
Note how simple this change is — instead of an eval function call,
we create a value that contains the parts that would have been used in
the eval function call. This value serves as a promise to do this
evaluation (the eval call) later, if needed. (This is exactly why a
Lazy Racket would make this a lazy evaluator: in it, all function
calls are promises.)
Side note: this can be used in any case when you’re using an eager language, and you want to delay some function call — all you need to do is replace (using a C-ish syntax)
with
now all calls to foo return a delayed_foo instance instead of an
integer. Whenever we want to force the delayed promise, we can use this
function:
You might even want to make sure that each such promise is evaluated exactly once — this is simple to achieve by adding a cache field to the struct:
As we will see shortly, this corresponds to switching from a call-by-name lazy language to a call-by-need one.
Back to our Sloth interpreter — given the eval* change, we expect
that eval-uating:
will return:
and the same goes for eval-uating
Similarly, evaluating
should return
But what about evaluating an expression like this one:
?
Using what we have so far, we will get to evaluate the body, which is a
(Call …) expression, but when we evaluate the arguments for this
function call, we will get ExprV values — so we will not be able to
perform the addition. Instead, we will get an error from the function
that racket-func->prim-val creates, due to the value being an ExprV
instead of a RktV.
What we really want is to actually add two values, not promises. So
maybe distinguish the two applications — treat PrimV differently
from FunV closures?
This still doesn’t work — the problem is that the function now gets a
bunch of values, where some of these can still be ExprVs because the
evaluation itself can return such values… Another way to see this
problem is to consider the code for evaluating an If conditional
expression:
…we need to take care of a possible ExprV here. What should we do?
The obvious solution is to use eval if we get an ExprV value:
Note how this translates back the data structure that represents a
delayed eval promise back into a real eval call…
Going back to our code for Call, there is a problem with it — the
will indeed evaluate the expression instead of lazily deferring this to the future, but this evaluation might itself return such lazy values. So we need to inspect the resulting value again, forcing the promise if needed:
But we still have a problem — programs can get an arbitrarily long
nested chains of ExprVs that get forced to other ExprVs.
What we really need is to write a loop that keeps forcing promises over
and over until it gets a proper non-ExprV value.
Note that it’s close to real-eval*, but there’s no need to mix it with
eval. The recursive call is important: we can never be sure that
eval didn’t return an ExprV promise, so we have to keep looping
until we get a “real” value.
Now we can change the evaluation of function calls to something more manageable:
The code is fairly similar to what we had previously — the only
difference is that we wrap a strict call where a proper value is
needed — the function value itself, and arguments to primitive
functions.
The If case is similar (note that it doesn’t matter if strict is
used with the result of eval or eval* (which returns an ExprV)):
Note that, like before, we always return #t for non-RktV values —
this is because we know that the value there is never an ExprV. All
we need now to get a working evaluator, is one more strictness point:
the outermost point that starts our evaluation — run — needs to
use strict to get a proper result value.
With this, all of the tests that we took from the Toy evaluator run successfully. To make sure that the interpreter is lazy, we can add a test that will fail if the language is strict:
[In fact, we can continue and replace all eval calls with ExprV,
leaving only the one call in strict. This doesn’t make any
difference, because the resulting promises will eventually be forced by
strict anyway.]