A Scheme to Unlambda compiler
Scheme Makefile Other
Switch branches/tags
Nothing to show
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



Unlambda is an obfuscated functional programming language. This is a compiler for a small, pure subset of the Scheme programming language, which generates Unlambda code. In addition, implemented in this Scheme subset is an interpreter for a very simple Lambda-calculus-like virtual machine, which makes it possible to overcome the exponential code growth resulting from abstraction elimination. The following is a description of the basic concepts of this Compiler/Interpreter pair:

The basic building blocks in the Unlambda programming language are the functions S, K, I, and the application operator ` (backtick). Translated to Scheme, the function I is

(lambda (x) x)

i.e., the identity function. The function K takes two arguments (all functions in Unlambda are curried, which means that K really takes one argument and returns a function which takes a second argument) and returns the first one:

(lambda (x y) x)

S is a little bit more complicated. It takes three arguments X, Y, Z and applies X to Z, then Y to Z and then the result of the former application to the result of the latter, i.e:

(lambda (x y z) ((x z) (y z)))

The application operator ` uses an infix syntax. It takes a function, an argument (which is, like every value in Unlambda, a function as well) and applies the function to the argument, i.e., `XY is the same in Unlambda as (X Y) is in Scheme.

Unlambda provides other primitives as well, which are mainly useful for I/O and as shortcuts for more complicated constructs.

One thing Unlambda completely lacks is a concept of variables. There is no lambda or let operator or anything else that would give a name (or anything similar) to a value. Luckily, it's possible to "emulate" the lambda operator with the functions S, K, and I, by a process called "Abstraction Elimination". It is described on the Unlambda Home Page, so I won't repeat it here. What I will repeat, though, is that it has the unfortunate side effect of blowing a program up by a factor roughly proportional to the deepest nesting depth of lambda operators, which is obviously a big problem. We will see later how it is possible to overcome it.

Abstraction elimination gives us the lambda operator with one argument, but apart from that, we have very little yet. The first thing we can do is to extend lambda to support more than one argument, like in Scheme. To do this, we simply curry all functions, i.e., transform them to functions with one argument, like this:

(lambda (x y z) body) => (lambda (x) (lambda (y) (lambda (z) body)))

Function calls must be transformed similarly:

(f x y z) => (((f x) y) z)

Next on the list is the let operator, which gives names to values. The let operator can be transformed into a lambda operator and an application:

(let ((x x-val) (y y-val)) body) => ((lambda (x y) body) x-val y-val)

The letrec operator allows the definition of recursive functions. Because its implementation is a bit unwieldy, I'll first discuss the simpler lambda* operator, which can be used to generate a single anonymous recursive function (lambda* is not a part of the Scheme programming language, but a feature of this dialect). As an example, here's a function for computing the factorial of a number:

(lambda* fac (n)
  (if (<= n 2)
	(* n (fac (- n 1))))))

The name fac is only visible within the function itself. This function can be transformed to eliminate the lambda* operator to this semantically equivalent function:

((lambda (x) (x x))
 (lambda (fac n)
   (if (<= n 2)
	 (* n (fac fac (- n 1)))))))

Note that the upper lambda expression applies the function below to itself, thereby giving it a way to refer to itself. Also note that when the function calls itself recursively, it must again pass itself as an argument to itself. Note even further that this example would not run in standard Scheme, because Scheme does not curry. In Scheme, the function would have to look like this:

((lambda (x) (x x))
 (lambda (fac)
   (lambda (n)
	 (if (<= n 2)
	   (* n ((fac fac) (- n 1))))))))

The main difference between lambda* and letrec is that the latter can introduce more than one function, each of which can reference each other. The principle for translation is the same as with lambda*, only that here each function must be passed all functions (including itself) as arguments. For example, the expression

(letrec ((fa (lambda (i)
			   (fb (- i 1))))
	 (fb (lambda (i)
		   (fa (+ i 1)))))
  (fa 0))

is semantically equivalent to

(let ((fa (lambda (fa fb i)
			(fb fa fb (- i 1))))
	  (fb (lambda (fa fb i)
			(fa fa fb (- i 1)))))
  (fa fa fb 0))

The resulting let can be translated to a lambda as described above.

The most important language part that remains is the conditional operator if and the booleans #t and #f. Unlambda's "native" booleans are the function I for true and V, which takes argument and always returns itself, for false. The Unlambda home page presents an if function for these booleans, which takes three arguments, the first one a boolean, i.e., either I or V. If it is true, the function returns the second argument, otherwise the third.

In a strict language like Scheme, as opposed to a lazy one like Haskell, if is not a function, however. Its second argument is only evaluated if the condition is true, the third only if it is false. To accomplish this, we have to delay these arguments and force only the resulting one. Thus, we transform the expression

(if condition consequent alternative)


((*if* condition
   (lambda (dummy) consequent)
   (lambda (dummy) alternative))

whereas *if* is the Unlambda if function and *I* the function I.

That's pretty much all there is to the language and the compiler. As an example of how complex data structures can be represented, I'll discuss how lists can be implemented. Lists in Scheme are constructed out of so called "cons" cells. Each such cell is a tuple with two elements, which are called the "car" and the "cdr". The function cons takes two arguments and constructs such a cell. It can be implemented like this:

(lambda (car cdr)
  (lambda (f)
	(f car cdr)))

In this implementation, a cell is nothing more than a function which takes another function and applies it to the car and the cdr. This is taken advantage of in the implementation of the functions car and cdr, each of which take a cell and return its car or cdr, respectively. Here is the function car:

(lambda (cell)
  (cell (lambda (car cdr)

What's left is a way of representing the empty list (), which is not a cons cell. Applying car or cdr to it is illegal, so it is not supposed to give meaningful results in those cases (we don't do error handling). However, it can be tested for with the function null?, which returns #t if applied to the empty list, and #f if applied to a cons cell. Here is the empty list:

(lambda (f)

The reason for this implementation only becomes apparent when seen in conjunction with the function null?:

(lambda (cell)
  (cell (lambda (car cdr)

If the argument cell is the empty list, applying it to whatever argument will return #t, which is exactly what we want. If it is a cell, however, the inner lambda takes care of returning the result #f.

Given bits (booleans) and a way of putting them together (lists) it's now easy to represent numbers, or any other data structure desired, so I'll not go into the details of how this can be done.

We now have a compiler translating a reasonable subset of the Scheme programming language to Unlambda. In theory, we can now program to our heart's delight, compile the program to Unlambda and see it run. In practice, however, we still have one serious problem: The Unlambda programs get much too big. A simple program which reads two binary numbers, represents them as lists of booleans (in binary representation), adds them, and outputs the resulting binary number, compiles to an Unlambda program about 18MB in size. The original Scheme code is less than 50 lines. Running the program on my 1GB RAM machine is impossible because it uses too much memory. Clearly something must be done.

As I have already mentioned in the introduction, the result of this code growth is the process of abstraction elimination. For every single increase in nested lambda depth, the generated code grows by a factor of about three, and not very much can be done about it, except to keep lambdas shallow.

An important observation about abstraction elimination is that if one uses only some pre-fabricated functions and just applies them to each other, the lambda depth does not increase beyond that of those functions. Another observation is that constructing lists (via cons) only uses pre-fabricated functions which are applied to each other. In other words, we can construct lists of arbitrary length and depth without going beyond a fixed, very shallow, lambda depth. The result is that Unlambda programs representing lists only grow linearly with relation to the size of the list, not exponentially.

Since a Scheme program is nothing but a list, we could in theory write a Scheme interpreter in Scheme, compile it to Unlambda, and then have a way of executing arbitrary Scheme programs in Unlambda without exponential growth. Of course, a full Scheme interpreter - even for our limited subset - would be far too big as an Unlambda program, but it is possible to translate Scheme to a very simple list representation, which can then be interpreted by an Unlambda program.

I have discussed above how to translate the most important Scheme language features into pure lambda calculus. Now I'll discuss how to transform lambda calculus into something more appropriate for direct interpretation.

An expression in pure lambda calculus can be of one out of three types:

  • An application. In Scheme syntax: (F A). F if the expression which evaluates to the function, which is applied to the result of the evaluation of A.

  • A lambda expression (with one argument), which constructs a function. In Scheme: (lambda (X) B). X is the name of the argument, B is the body expression.

  • A variable reference. The variable must be the argument of some enclosing lambda expression.

The main problem with making this work in an interpreter is representing and looking up the lambda argument names. Fortunately, it is easy to recognize that the names can be done away with completely, if environments are represented in a list form, like in the Scheme in OCaml interpreter discussed above. In that case, the only piece of information that is necessary for a variable reference is how many lambda expressions outside of the reference the name was introduced. For example, in

(lambda (x) (lambda (y) (lambda (z) y)))

we can do away with the names altogether and replace the reference to y by the number 2, since y was introduced by the second lambda expression outward of the reference, giving

(lambda (lambda (lambda 2)))

Of course, my interpreter does not represent a lambda expression with the symbol lambda. Instead, each expression is represented as a cons cell (compiled to Unlambda functions, as detailed above), the car of which gives the type of the expression. To that end, it is a cons cell itself whose car and cdr are booleans, which encode the following expression types:

  • (#t . #t): lambda expression

  • (#t . #f): application

  • (#f . #t): variable reference

  • (#f . #f): a native function

The first thing the eval function does is check of which type the expression it is fed is:

(lambda* eval (env expr)
  (if (car (car expr))
	(if (cdr (car expr))

In the case of a lambda expression, the cdr of the expression cell gives the body. All we have to do is construct a closure with this body and the current environment. My interpreter represents a closure as a cons cell whose car is the environment and whose cdr is the body, so the code for interpreting a lambda expression is simply

      (cons env (cdr expr))

Note that we do not have to tag this closure for being a closure, because in this interpreter, every value is a closure.

Next on the list are applications. The cdr of an application's expression cell is a cons cell whose car is the function and whose cdr is the argument, both of which must of course be evaluated. Hence, the first thing we do with an application is evaluate the function:

      (let ((fun (eval env (car (cdr expr)))))

The result (now in fun) is a closure. When applying a function to an argument, we must call eval with the function body and with an environment which includes the new argument. The rest of the environment is that of the closure in fun. The code looks like this:

        (eval (cons (eval env (cdr (cdr expr)))
					(car fun))
			  (cdr fun))))

What this code does is first evaluate the application argument (via the inner call to eval), then cons the result together with the function's environment to get the new environment, and then evaluate the function's body with this new environment.

The last lambda calculus expression type is the variable reference. As outlined above, a variable reference contains a number giving the index of the variable to get in the current environment list. Representing numbers as lists of booleans involves using the if special form, which adds lambda depth and blows up the interpreter. Encoding the number in the length of the list would result in additional size in the generated code, especially when dealing with deep lambda nesting, which is not uncommon.

The solution I chose encodes numbers in binary representation, but instead of using #t for one and #f for zero, I use the function cdr for one and I for zero. In other words, applying a digit to a list returns either the list, if the digit is "zero", or the list advanced by one element if the digit is "one". Applying such a binary number list to an environment list entails - if the number list is not empty - applying the first, least significant, "digit" to the list, and then applying the rest of the number list to the result twice, or the other way around. The code for the recursive function get, which performs this application, is this:

(lambda* get (env pos)
  (if (null? pos)
	((car pos) (get (get env (cdr pos)) (cdr pos)))))

Now that we have this function, implementing a variable reference is very easy. The cdr of the expression cell contains the number list, so all we have to do is supply the current environment and this list to get and take the car, i.e., the first list element, of the result:

    (if (cdr (car expr))
      (car (get env (cdr expr)))

Don't mind the if line - it checks for the second boolean in the expression type cell, in case the first boolean turns out to be false (this is the alternative path of the outermost if.

Last, but not least, we come to the native functions, the purpose of which I have not yet mentioned. While the first three expression types are sufficient to implement any function, the interpreter still lacks the ability to do input and output. Instead of putting this functionality directly into the interpreter, for example by creating a new expression type for each input/output function, and thereby blowing up the interpreter by a considerable amount, I decided to simply add the functionality to call a "native", i.e., non interpreted, function, which is directly embedded in the intermediate code. The code for this expression type looks a bit complicated, but it is really quite simple:

      (cons () ((cdr expr) (cons (cons #t #t) (cons (cons #f #t) ())))))))

The cdr of the expression cell contains the native function, so we apply it to some argument. Since most I/O function in Unlambda behave - apart from their side effect - like the identity function, and even a native function must return a result in the form of an interpretable closure, we pass the native function an expression cell for an interpretable identity function, namely the cell ((#t . #t) . ((#f . #t) . ())), which is what the function (lambda (x) x) looks like to the interpreter. Of course, the native function must not necessarily return this expression cell, but most do. Those which don't, which are functions returning booleans, checking, for example, for end-of-file, must nevertheless return valid expression cells. The outermost cons creates a closure from the expression cell returned by the native function, with an empty environment.

Finally, we're through. This minimalistic interpreter compiles to about 400KB of Unlambda code. The benefits are obvious when comparing the sizes of the directly compiled code versus interpreted code of more complicated programs. The abovementioned binary adding program, which is about 18MB in size when compiled directly, now comes to only 470KB, and that includes the interpreter. Furthermore, it consumes little memory and runs nicely - albeit slowly - on my machine.


You'll need the Bigloo Scheme system to compile the compiler. You can get it from the Bigloo homepage.

Of course, you might also want to get an Unlambda interpreter to execute the Unlambda programs. You can get the official Unlambda distribution from the Unlambda homepage.

A much more efficient Unlambda interpreter is available here.





To compile a Scheme program directly to Unlambda (without the interpreter), use

./unlcomp -c FILENAME

The Unlambda code is written to stdout.

To compile with the interpreter, use

./compile FILENAME

The resulting Unlambda code will be in the file FILENAME.unl.


This program is licenced under the GNU General Public License. See the file COPYING for details.

Mark Probst mark.probst@gmail.com