PL: Lecture #20  Tuesday, March 23rd
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Side Effects in a Lazy Language

We’ve seen that a lazy language without the call-by-need optimization is too slow to be practical, but the optimization makes using side-effects extremely confusing. Specifically, when we deal with side-effects (I/O, mutation, errors, etc) the order of evaluation matters, but in our interpreter expressions are getting evaluated as needed. (Remember tracing the prime-numbers code in Lazy Racket — numbers are tested as needed, not in order.) If we can’t do these things, the question is whether there is any point in using a purely functional lazy language at all — since computer programs often interact with an imperative world.

There is a solution for this: the lazy language does not have any (sane) facilities for doing things (like printf that prints something in plain Racket), but it can use a data structure that describes such operations. For example, in Lazy Racket we cannot print stuff sanely using printf, but we can construct a string using format (which is just like printf, except that it returns the formatted string instead of printing it). So (assuming Racket syntax for simplicity), instead of:

(define (foo n)
  (printf "~s + 1 = ~s\n" n (+ n 1)))

we will write:

(define (foo n)
  (format "~s + 1 = ~s\n" n (+ n 1)))

and get back a string. We can now change the way that our interpreter deals with the output value that it receives after evaluating a lazy expression: if it receives a string, then it can take that string as denoting a request for printout, and simply print it. Such an evaluator will do the printout when the lazy evaluation is done, and everything works fine because we don’t try to use any side-effects in the lazy language — we just describe the desired side-effects, and constructing such a description does not require performing side-effects.

But this only solves printing a single string, and nothing else. If we want to print two strings, then the only thing we can do is concatenate the two strings — but that is not only inefficient, it cannot describe infinite output (since we will not be able to construct the infinite string in memory). So we need a better way to chain several printout representations. One way to do so is to use a list of strings, but to make things a little easier to manage, we will create a type for I/O descriptions — and populate it with one variant holding a string (for plain printout) and one for holding a chain of two descriptions (which can be used to construct an arbitrarily long sequence of descriptions):

(define-type IO
  [Print  String]
  [Begin2 IO IO])

Now we can use this to chain any number of printout representations by turning them into a single Begin2 request, which is very similar to simply using a loop to print the list. For example, the eager printout code:

(: print-list : (Listof A) -> Void)
(define (print-list l)
  (if (null? l)
    (printf "\n")
    (begin (printf "~s " (first l))
          (print-list (rest l)))))

turns to the following code:

(: print-list : (Listof A) -> IO)
(define (print-list l)
  (if (null? l)
    (Print "\n")
    (Begin2 (Print (format "~s " (first l)))
            (print-list (rest l)))))

This will basically scan an input list like the eager version, but instead of printing the list, it will convert it into a single output request that forms a recipe for this printout. Note that within the lazy world, the result of print-list is just a value, there are no side effects involved. Turning this value into the actual printout is something that needs to be done on the eager side, which must be part of the implementation. In the case of Lazy Racket, we have no access to the implementation, but we can do so in our Sloth implementation: again, run will inspect the result and either print a given string (if it gets a Print value), or print two things recursively (if it gets a Begin2 value). (To implement this, we will add an IOV variant to the VAL type definition, and have it contain an IO description of the above type.)

Because the sequence is constructed in the lazy world, it will not require allocating the whole sequence in memory — it can be forced bits by bits (using strict) as the imperative back-end (the run part of the implementation) follows the instructions in the resulting IO description. More concretely, it will also work on an infinite list: the translation of an infinite-loop printout function will be one that returns an infinite IO description tree of Begin2 values. This loop will also force only what it needs to print and will go on recursively printing the whole sequence (possibly not terminating). For example (again, using Racket syntax), the infinite printout loop

(: print-loop : -> Void)
(define (print-loop)
  (printf "foo\n")
  (print-loop))

is translated into a function that returns an infinite tree of print operations:

(: print-loop : -> IO)
(define (print-loop)
  (Begin2 (Print "foo\n")
          (print-loop)))

When this tree is converted to actions, it will result in an infinite loop that produces the same output — it is essentially the same infinite loop, only now it’s derived by an infinite description rather than an infinite process.

Finally, how should we deal with inputs? We can add another variant to our type definition that represents a read-line operation, assuming that like read-line it does not require any arguments:

(define-type IO
  [Print    String]
  [ReadLine ]
  [Begin2  IO IO])

Now the eager implementation can invoke read-line when it encounters a ReadLine value — but what should it do with the resulting string? Even worse, naively binding a value to ReadLine

(let ([name (ReadLine)])
  (Print (format "Your name is ~a" name)))

doesn’t get us the string that is read — instead, the value is a description of a read operation, which is very different from the actual string value that we want in the binding.

The solution is to take the “code that acts on the string value” and make it be the argument to ReadLine. In the above example, that could would be the let expression without the (ReadLine) part — and as you rememebr from the time we introduced fun into WAE, taking away a named expression from a binding expression leads to a function. With this in mind, it makes sense to make ReadLine take a function value that represents what to do in the future, once the reading is actually done.

(ReadLine (lambda (name)
            (Print (format "Your name is ~a" name))))

This receiver value is a kind of a continuation of the computation, provided as a callback value — it will get the string that was read on the terminal, and will return a new description of side-effects that represents the rest of the process:

(define-type IO
  [Print    String]
  [ReadLine (String -> IO)]
  [Begin2  IO IO])

Now, when the eager side sees a ReadLine value, it will read a line, and invoke the callback function with the string that it has read. By doing this, the control goes back to the lazy world to process the value and get back another IO value to continue the processing. This results in a process where the lazy code generates some IO descriptions, then the imperative side will execute it and control goes back to the lazy code, then back to the imperative side, etc.

As a more verbose example of all of the above, this silly loop:

(: silly-loop : -> Void)
(define (silly-loop)
  (printf "What is your name? ")
  (let ([name (read-line)])
    (if (equal? name "quit")
      (printf "bye\n")
      (begin (printf "Your name is ~s\n" name)
            (silly-loop)))))

is now translated to:

(: silly-loop : -> IO)
(define (silly-loop)
  (Begin2 (Print "What is your name? ")
          (ReadLine
          (lambda (name)
            (if (equal? name "quit")
              (Print "bye\n")
              (Begin2 (Print (format "Your name is ~s\n" name))
                      (silly-loop)))))))

Using this strategy to implement side-effects is possible, and you will do that in the homework — some technical details are going to be different but the principle is the same as discussed above. The last problem is that the above code is difficult to work with — in the homework you will see how to use syntactic abstractions to make thing much simpler.

Designing Domain Specific Languages (DSLs)

PLAI §35

Programming languages differ in numerous ways:

  1. Each uses different notations for writing down programs. As we’ve observed, however, syntax is only partially interesting. (This is, however, less true of languages that are trying to mirror the notation of a particular domain.)

  2. Control constructs: for instance, early languages didn’t even support recursion, while most modern languages still don’t have continuations.

  3. The kinds of data they support. Indeed, sophisticated languages like Racket blur the distinction between control and data by making fragments of control into data values (such as first-class functions and continuations).

  4. The means of organizing programs: do they have functions, modules, classes, namespaces, …?

  5. Automation such as memory management, run-time safety checks, and so on.

Each of these items suggests natural questions to ask when you design your own languages in particular domains.

Obviously, there are a lot of domain specific languages these days — and that’s not new. For example, four of the oldest languages were conceived as domain specific languages:

Only in the late 60s / early 70s languages began to get free from their special purpose domain and become general purpose languages (GPLs). These days, we usually use some GPL for our programs and often come up with small domain specific languages (DSLs) for specific jobs. The problem is designing such a specific language. There are lots of decisions to make, and as should be clear now, many ways of shooting your self in the foot. You need to know:

And very important:

To clarify why this can be applicable in more situations than you think, consider what programming languages are used for. One example that should not be ignored is using a programming language to implement a programming language — for example, what we did so far (or any other interpreter or compiler). In the same way that some piece of code in a PL represent functions about the “real world”, there are other programs that represent things in a language — possibly even the same one. To make a side-effect-full example, the meaning of one-brick might abstract over laying a brick when making a wall — it abstracts all the little details into a function:

(define (one-brick wall brick-pile)
  (move-eye (location brick-pile))
  (let ([pos (find-available-brick-position brick-pile)])
    (move-hand pos)
    (grab-object))
  (move-eye wall)
  (let ([pos (find-next-brick-position wall)])
    (move-hand pos)
    (drop-object)))

and we can now write

(one-brick my-wall my-brick-pile)

instead of all of the above. We might use that in a loop:

(define (build-wall wall pile)
  (define (loop n)
    (when (< n 500)
      (one-brick wall pile)
      (loop (add1 n))))
  (loop 0))

This is a common piece of looping code that we’ve seen in many forms, and a common complaint of newcomers to functional languages is the lack of some kind of a loop. But once you know the template, writing such loops is easy — and in fact, you can write code that would take something like:

(define (build-wall wall pile)
  (loop-for i from 1 to 500
    (one-brick wall pile)))

and produce the previous code. Note the main point here: we switch from code that deals with bricks to code that deals with code.

Now, a viable option for implementing a new DSL is to do so by transforming it into an existing language. Such a process is usually tedious and error prone — tedious because you need to deal with the boring parts of a language (making a parser etc), and error prone because it’s easy to generate bad code (especially when you’re dealing with strings) and you get bad errors in terms of the translated code instead of the actual code, resorting to debugging the intermediate generated programs. Lisp languages traditionally have taken this idea one level further than other languages: instead of writing a new transformer for your language, you use the host language, but you extend and customize it by adding you own forms.

Syntax Transformations: Macros

PLAI §36

Macros are one of the biggest advantages of all Lisps, and specifically even more so an advantage of Scheme implementations, and yet more specifically, it is a major Racket feature: this section is therefore specific to Racket (which has this unique feature), although most of this is the same in most Schemes.

As we have previously seen, it is possible to implement one language construct using another. What we did could be described as bits of a compiler, since they translate one language to another.

We will see how this can be done in Racket: implementing some new linguistic forms in terms of ones that are already known. In essence, we will be translating Racket constructs to other Racket constructs — and all that is done in Racket, no need to go back to the language Racket was implemented in (C).

This is possible with a simple “trick”: the Racket implementation uses some syntax objects. These objects are implemented somehow inside Racket’s own source code. But these objects are also directly available for our use — part of the implementation is exposed to our level. This is quite similar to the way we have implemented pairs in our language — a TOY or a SLOTH pair is implemented using a Racket pair, so the same data object is available at both levels.

This is the big idea in Lisp, which Scheme (and therefore Racket) inherited from (to some extent): programs are made of numbers, strings, symbols and lists of these — and these are all used both at the meta-level as well as the user level. This means that instead of having no meta-language at all (locking away a lot of useful stuff), and instead of having some crippled half-baked meta language (CPP being both the most obvious (as well as the most popular) example for such a meta language), instead of all this we get exactly the same language at both levels.

How is this used? Well, the principle is simple. For example, say we want to write a macro that will evaluate two forms in sequence, but if the first one returns a result that is not false then it returns it instead of evaluating the second one too. This is exactly how or behaves, so pretend we don’t have it — call our version orelse:

(orelse <expr1> <expr2>)

in effect, we add a new special form to our language, with its own evaluation rule, only we know how to express this evaluation rule by translating it to things that are already part of our language.

We could do this as a simple function — only if we’re willing to explicitly delay the arguments with a lambda, and use the thunks in the function:

(define (orelse thunk1 thunk2)
  (if (thunk1)
    (thunk1) ; ignore the double evaluation for now
    (thunk2)))

or:

(define (orelse thunk1 thunk2)
  ((if (thunk1)
    thunk1
    thunk2)))

and using it like this:

(orelse (lambda () 1) (lambda () (error "boom")))

But this is clearly not the right way to do this: whoever uses this code must be aware of the need to send us thunks, and it’s verbose and inconvenient.

Note that this could be a feasible solution if there was a uniform way to have an easy syntactic way to say “a chunk of code” instead of immediately execute it — this is exactly what (lambda () ...) does. So we could, for example, make {...} be a shorthand for that, which is what Perl-6 is doing. However, we will soon see examples where we want more than just delay the evaluation of some code.

We want to translate

(orelse <expr1> <expr2>)

--to-->

(if <expr1>
  <expr1>
  <expr2>)

If we look at the code as an s-expression, then we can write the following function:

(define (translate-orelse l)
  (if (and (list? l)
          (= 3 (length l))
          (eq? 'orelse (first l)))
    (list 'if (second l) (second l) (third l))
    (error 'translate-orelse "bad input: ~s" l)))

We can now try it with a simple list:

(translate-orelse '(orelse foo1 foo2))

and note that the result is correct.

How is this used? Well, all we need is to hook our function into our implementation’s evaluator. In Lisp, we get a defmacro form for this, and many Schemes inherited it or something similar. In Racket, we need to

(require compatibility/defmacro)

but it requires the transformation to be a little different in a way that makes life easier: the above contains a lot of boilerplate code. Usually, we will require the input to be a list of some known length, the first element to be a symbol that specifies our form, and then do something with the other arguments. So we’d want to always follow a template that looks like:

(define (translate-??? exprs)
  (if (and (list? exprs)
          (= N (length exprs))
          (eq? '??? (car exprs)))
    (let ([E1 (cadr exprs)]
          [E2 (caddr exprs)]
          ...)
      ...make result expression...)
    (error ...)))

But this looks very similar to making sure that a function call is a specific function call (and for a good reason — macro usages look just like function calls). So make the translation function get a number of arguments one each for each part of the input, an s-expression. For example, the above translation and test become:

(define (translate-orelse <expr1> <expr2>)
  (list 'if <expr1> <expr1> <expr2>))

(translate-orelse 'foo1 'foo2)

The number of arguments is used to check the input (turning an arity error for the macro to an arity error for the translator function call), and we don’t need to “caddr our way” to arguments.

This gives us the simple definition — but what about the promised hook? — All we need is to use define-macro instead of define, and change the name to the name that will trigger this translation (providing the last missing test of the input):

(define-macro (orelse <expr1> <expr2>)
  (list 'if <expr1> <expr1> <expr2>))

and test it:

(orelse 1 (error "boom"))

Note that this is basically a (usually purely functional) lazy language of transformations which is built on top of Racket. It is possible for macros to generate pieces of code that contain references to these same macros, and they will be used to expand those instances again.

Now we start with the problems, one by one.