A Gentle Intro to PLT

Programming Language Theory (PLT) is an extremely rich subject with a relatively high bar to entry. Most of the literature is written for a reader already well versed in the subject; it’s hard to find a tractable introduction. This post will take you through the construction of a simplistic toy programming language (and an interpreter for it) from first principles. I assume no knowledge on your part, aside from general programming experience.

What’s an interpreter? What’s a compiler?

These two terms are often bandied around but not usually rigorously defined. Both are programs that take as input programs written according to a given language specification. Both perform a series of transformations on that input to produce a final result. Both share many similar stages: for example (and as we will discuss soon), both will scan, parse, and typecheck the input. The difference is that a compiler translates the input code into code written in another language. For example, C compilers translate C code into assembly. Clojure and ClojureScript compile to JVM bytecode and JavaScript, respectively. In contrast, an interpreter directly executes the input code. Some popular interpreted languages are JavaScript, Python, and Ruby.

In this post, we will be building an interpreter. At least for the purposes of a first-principles introduction, building an interpreter will be a simpler task, because we only have to keep one language in mind.

Building a language

Before we build a toy interpreter, we need a toy language! In the world of PLT, languages are specified using a concrete syntax, which is a description of how syntactic terms in the language are constructed. This is also known as a grammar, a context-free grammar, or a BNF grammar (for Backus-Naur form). The grammar completely defines the set of terms in the language. Consider the following grammar:

t ::= true
    | false
    | 0
    | (succ t)
    | (pred t)
    | (not t)
    | (and t t)
    | (or t t)
    | (if t then t else t)

The grammar is defined recursively: you should read a t as “any term”. For example, we can write things like (succ (succ 0)), but we can also write (not (pred 0)). This may seem unintuitive at first: why would we allow our language to express nonsensical things? The answer is that the cost of catching nonsensical expressions at the level of the grammar is far too high - we will instead rely on the typechecker to catch such terms.

You may have also noticed that this is not a particularly powerful language. We can perform the basic boolean operations; we can take the succ (successor) and pred (predecessor) of only the natural numbers; and we have precisely one operation that can bridge across the two types (the if statement). This is deliberate and done for the sake of simplicity: many language features that we as programmers take for granted (e.g. variables and functions) turn out to be rather complex to implement.

As previously discussed, an interpreter is a program that takes as input code written according to a particular specification and evaluates it. There are many more steps between input and evaluation: a typical interpreter will include (at minimum) a scanner, a parser, a typechecker, and an evaluator. A real interpreter has many, many more phases - mostly to do with optimization - but we’ll ignore those for our purposes here. Let’s discuss what each phase entails, with an eye towards the type signatures of each function.

The scanner

The scanner takes as input the actual text of the program (i.e. a string), and outputs a list of tokens. A token is simply an in-program representation of each syntactic element in the program’s grammar. For our toy language, we might have the following declaration of the token type:

datatype token = LParen
               | RParen
               | True
               | False
               | Zero
               | Succ
               | Pred
               | Not
               | And
               | Or
               | If
               | Then
               | Else

Notice that certain elements of the grammar that don’t actually matter for the purposes of evaluation - e.g. Then and Else - are included in the token type: the job of the scanner is simply to translate a string into something simpler to work with.

Let’s consider a few examples:

Hopefully I’ve managed to communicate that the job of the scanner is quite simple; let’s now move on to the more complex parts of the interpreter.

The parser

The parser takes as input a list of tokens (constructed by the scanner), and outputs an abstract syntax tree, or AST. An AST is a complete representation of our program; it’s a structure that we can begin to evaluate.

The AST type might look something like this:

datatype ast = True
              | False
              | Zero
              | Succ of ast
              | Pred of ast
              | Not of ast
              | And of (ast * ast)
              | Or of (ast * ast)
              | If of (ast * ast * ast)

Throughout this post I’ve secretly been using Standard ML syntax to define these datatypes; hopefully it’s been transparent, but I think this definition bears some explaining. First of all, let’s examine Succ of ast: this means that the Succ term takes an additional term as an argument. Knowing that * is the same as the Cartesian cross product, we can see that And of (ast * ast) means that the And term takes a 2-tuple of terms.

Let’s run through some parsing examples:

The key point to understand here is that the parser transforms a token list into a single AST.

The typechecker

The typechecker sounds simple enough. It takes as input an AST, and returns… What, exactly? This is a rather confusing point. In the case of the typechecker, we don’t really care about the return value! The job of the typechecker is to try to assign a type to the program; we don’t care what that type is as long as no errors are raised.

It will be easier to first think about making a typeof function, that takes as input an AST and returns its type. We now need a type datatype (often called ty), which for our toy language is very short:

datatype ty = Nat
            | Bool

Type theory is an immensely rich subject. Parameterized types are extremely powerful; adding pair and list types to a language is a great introduction to more advanced topics in typing. But for our purposes, this will be enough complexity.

Now let’s run through some examples of typeof. Below I will simply write down programs in the concrete syntax, asking the reader to fill in the scanning and parsing phases:

This last error bears further discussion. You might be thinking, “well since the if condition is false, why can’t we just assign it the type of the false branch (i.e. Bool)? This is a common pitfall. The full answer is that doing so would require us to evaluate the conditional term during the typechecking phase. In our language this might be fine, but consider adding recursion or looping to our language: what happens if we construct a term like (if foo then 0 else true), where foo loops forever? If we used this condition-evaluation strategy, we would loop forever in the typechecking phase.

Finally, note that the actual typechecking function need not return the type of the program: the interpreter doesn’t care about the type of the program, it just cares that the program can be assigned a type. A common pattern is to have the typechecker return the original AST in the case that typeof succeeds, and to otherwise pass on any errors raised by typeof, like so:

fun typecheck t =
let
  val _ = typeof t
in
  t
end

Type soundness

Before moving on, let’s discuss type soundness. To prove that our language is type-sound, we must prove that it satisfies two properties:

  1. Preservation: if t is a term with type T and t steps to a term t', then t' has type T as well.
  2. Progress: if t is a term that can be assigned a type T and t is not a value, then there exists some term t' also of type T, such that t steps to t'.

Let’s unpack these two properties to examine what exactly type soundness gives us. First of all, there’s some new terminology to discuss:

Step refers to the small-step evaluation relation that we will develop in the next section. As an example, (not (not true)) would step to (not false), which would step to true.

Value refers to the concept of a value class, which we have not yet discussed. A value class is an arbitrarily-defined subset of the grammar which the language designer has declared to be acceptable results of evaluation. In our language, the logical value class is as follows:

  nv ::= 0
       | (succ nv)

  v ::= true
      | false
      | nv

So, both boolean constants are values, as well as any numeric value that is composed only of successors. We do this to avoid the ambiguity created by the fact that, say, (succ (succ 0)) and (pred (succ (succ (succ 0)))) are both valid representations of the number 3.

Two final pieces of vocabulary: a normal form is a term that cannot be stepped on. All values are normal forms, but so are things like (succ true). That’s what we call a stuck term; stuck terms are simply non-value normal forms. What’s important to note is that stuck terms actually represent runtime errors: (succ true) is not a valid return value for any program, yet if we come across it, we have no choice but to return it; it’s a runtime error.

Let’s now discuss each property of type soundness. Preservation tells us that terms cannot switch types on us in the course of evaluation; I think this is fairly intuitive. Progress tells us something very important: if a program passes the type checker, it evaluates to a value. Not a normal form, a value. This is extraordinarily significant: it means that any program that passes the typechecker is free of runtime errors (except logic errors like multiplying by 5 when you meant to add 5, etc).

This is a massive boon to the programmer. Think back, when have you ever worked in an environment like this? I know when I work in JavaScript, I have about 2% faith that any given piece of code I write will work as intended on the first run. In a language with a proven type soundness theorem, that percentage skyrockets, because most errors I make will be caught by the typechecker. As far as I know, Standard ML and Pony are the only quote-unquote “real” languages with proven type soundness theorems.

The evaluator

The evaluator, the final step in our pipeline, is where the proverbial action happens. It takes as input an AST, and returns another AST, representing the result of evaluating its input.

The evaluator uses a small-step evaluation relation, applied repeatedly, to arrive at its result. The step function takes in an AST, and returns an AST option. For those of you unfamiliar with the option type, it looks something like this:

datatype 'a option = Some of 'a
                   | None

Note that 'a is a Standard ML type variable, read as “alpha”. So for example, if we were dealing with an int option type, we could have Some 1, or None.

So, why does step return an ast option? We want some way for step to indicate that you have passed it a normal form, and that it has nowhere to go. So, in the case that step can actually take a step, it returns Some of the resultant term. In the case that step cannot step on the input, it returns None.

Evaluation, then, is just the repeated application of step until step returns None, at which point you just return the input of step.

Let’s run through some evaluation examples:

Putting it all together

Now that we have the phases of our interpreter built out, let’s take a moment to remind ourselves of their type signatures:

scan: string -> token list
parse: token list -> ast
typecheck: ast -> ast
eval: ast -> ast

Now, compile is basically trivial:

fun compile program = eval (typecheck (parse (scan program)))

Or, a bit more descriptively:

fun compile program =
let
  val tokens = scan program
  val ast = parse tokens
  val checked-ast = typecheck ast
  val result = eval checked-ast
in
  result
end

And that’s basically it! Of course, we’ve only scratched the surface of possibilities here. Stay tuned for a future post about using monadic programming to make this process more elegant. Discuss on lobste.rs. Thanks for reading!


Mark Cohen, June 10, 2018