For a while, I had been meaning to write a nontrivial piece of code in Haskell, in order to gain a greater degree of familiarity with the language, and with functional programming in general. A few weeks ago I finished up an internship at Facebook (where they do have teams that code in Haskell, though I didn’t work on that), and there were still several weeks left before school starts up again, so I decided to use that time to work on a personal side project. I ended up writing a Lisp interpreter in Haskell, based on a friend’s suggestion.

This turned out to be an interesting suggestion, for a couple of reasons. I had often read that writing an interpreter for a programming language, usually some subset of Scheme, is something that everyone must do to be considered a “real programmer”. One of the classic SICP-style programming projects involves writing a Scheme interpreter in Scheme. I hadn’t written a full interpreter from scratch before, so this seemed like a good way to learn more about how interpreters actually work.

I had also heard that Haskell is supposed to be a good language for writing programming language interpreters and compilers. From what I can tell, Haskell seems to be pretty popular among programming languages people in general (or maybe they’ve all moved on to Idris or something else I don’t know). Haskell also seems popular for defining domain-specific languages, such as Forest, diagrams, and BASIC.

There seems to be a lot of Haskell tutorials online that teach the language by demonstrating how to write a Lisp interpreter. However, I wanted to figure out as much as possible on my own, and I resolved to check online tutorials only when I got stuck on some technical details. Because of this, I’m almost certain that there are places where I could have written more idiomatic Haskell code; I’ve already done some refactoring, and if I have time I’ll probably do a bit more. The intention of this post is not to be yet another Lisp-in-Haskell tutorial, but rather to give a high level overview of what I’ve worked on so far. You can see the code that I’ve written in the hasp GitHub repo. You can also check out the GitHub issues page for hasp to see what I plan to work on next.

The hasp language is inspired by the syntax and semantics of Lisp, mainly taking inspiration from Scheme, a language known for its elegance and simplicity. I wouldn’t call hasp a subset of Scheme, though, because I don’t have a formal specification for the language (I’m currently making it up as I go along), so I’m almost certain that it deviates, or will deviate, from Scheme in at least some respects. I’m hesitant even to call it a dialect of Lisp, since I’m not even sure what criteria a language must satisfy to be considered a Lisp. Nevertheless, it’s similar enough that the name hasp seemed appropriate for such a language.

## Writing the Parser

Generally, the first step in a compiler or interpreter is to parse input source code, a sequence of characters, and produce an abstract syntax tree, a data structure that encodes the syntactic structure of the input program. Most Haskell tutorials that involve writing an interpreter use libraries like Parsec and the machinery of parser combinators, which do most of the heavy lifting. Parsec is a very powerful library for writing parsers, and I’d probably appreciate its generality if I were to write a parser for a more syntactically-complex language. Instead, I decided to write the parser from scratch, as I figured I’d learn more that way. For a language like Lisp, this doesn’t end up being too complicated, since its syntax is very simple; the source code is basically the abstract syntax tree.

Even before we can run the parser though, we first need to execute a single pass through the source code with the tokenizer, or lexer. This simply involves splitting the source code, presented as a single string, into a sequence of “chunks” or tokens, with a specific meaning to the interpreter. For instance, it should turn this hasp source code

(+ 1 (length (list 2.3 "hello world")) 4)

into this list of tokens

["(", "+", "1", "(", "length", "(", "list", "2.3", "\"hello world\"", ")", ")", "4", ")"]

Essentially, the tokenizer is just a fancy string splitter. I originally used Haskell’s built-in string splitting functions to handle tokenization, however I quickly found that it would become too complicated (at least as far as I could tell) to specify all of the rules for determining which delimiters to keep, which to drop, dealing with string literals, and all that. So instead, I wrote my own simple tokenizer.

Its behaviour can be modeled as a simple finite state machine that passes through the input from left to right, switching “states” depending on the current character, with special rules, like what to do if we are currently in the middle of a string. I implemented the state machine as a series of Haskell functions that simply pass around a couple of accumulators to each other, and decide what to do based on pattern matching with the current character. In the code in Tokenizer.hs, you can probably guess how the state diagram for the tokenizer would look.

Once we have obtained a sequence of tokens from the input source code, the parser’s job is to convert the sequence of tokens into an abstract syntax tree. We can define a Haskell data structure that encodes the AST for a given hasp expression.

type Identifier = String
data Atomic = StringLiteral String
| IntLiteral Integer
| FloatLiteral Float
| BoolLiteral Bool
| Id Identifier
deriving (Show)

data Expr = Atom Atomic
| List [Expr]
deriving (Show)

Here, an Expr represents a hasp expression, which can either be an Atom (a single piece of data, like a string literal or a variable name), or a List of expressions. In the future one could also imagine adding quoted expressions to the list, but I haven’t implemented that yet. If you’re not familiar with Haskell, the deriving (Show) simply makes it so that expressions and atomic values can be represented as strings for output.

Now we want to take a list of tokens and produce a list of expressions (since the input source code might contain several expressions), that is, we want the type signature of the main parser function to be something like

parseExprs :: [Token] -> ThrowsError [Expr]

Here, Token is a type synonym for String, indicating that the data in the string specifically represents a token. The output type of parseExprs is actually ThrowsError [Expr] instead of simply [Expr], since it’s possible that it may throw an error, say when the input source code contains an unclosed parenthesis. In such a case, we want to use the ThrowsError monad for handling errors, which I will talk about more later.

Due to the simplicity of Lisp’s (and therefore hasp’s) syntax, the algorithm to parse hasp source code is almost as simple. Rather than put all the code for the parser, which you can find in Parser.hs, in this post, I figured it would be easier to describe the algorithm in English.

The algorithm makes a single pass from left to right over the sequence of tokens, maintaining a stack whose elements are lists of Exprs, as well as an accumulator of expressions that have been fully processed so far. The idea behind the stack is that it helps us keep track of our current level of nesting within the current expression being parsed.

At each token, the algorithm does the following:

1. If the current token is an opening parenthesis (, push a new empty list on top of the stack, indicating that we have reached a new level of nesting.

2. If the current token is a closing parenthesis ), squash the top two elements of the stack together, by taking the top list and appending it to the list immediately below it.

3. Otherwise, the current token is an atomic value, so we parse the token as an atom (using regular expressions to distinguish between identifiers, string literals, numbers, etc.) and append it to the list on top of the stack.

Once the stack is down to a single element and the algorithm encounters a closing parenthesis, we pop that value off of the stack and append it to the end of the accumulator. Once we have gone through all of the tokens, the algorithm outputs the contents of the accumulator. This description of the algorithm glosses over some details such as handling invalid syntax, but it should give you the general idea of how the parser works.

## Evaluating hasp Expressions

Once we have the AST generated from some hasp source code, we want to actually evaluate it and produce a result. That is, given the syntax of a hasp expression, we want to interpret its semantics.

### hasp Data Types

In a previous section, we defined the Expr data type, which represents the syntactic expressions that form a hasp source file. On the other hand, we can also define a data type (which I’ve called HData) that represent the actual results of hasp computations.

One of Haskell’s main features is its powerful static type system, which enables type checking to be done by the compiler. On the other hand, most Lisp dialects tend to be dynamically typed, that is, all type checking happens at runtime. One of the challenges I faced was trying to reconcile these two different type systems in a somewhat elegant way. This is one situation where static typing makes things a bit more difficult, since the nature of an interpreter is that the output depends on the results of computations whose type cannot be known at compile time (unless the interpreter only works for one specific program).

After several iterations, I settled on the following definition for HData.

data HNum = HInt Integer
| HFloat Float
deriving (Eq)

data HData = HN HNum
| HBool Bool
| HString String
| HList [HData]
| HQuote Expr
| HFunc Env (Env -> [HData] -> ThrowsError HData)

Another alternative that I had considered was to have HInt, HFloat, HBool, etc. all be their own types that are instances of a typeclass called HType, and then to define HData using existentially quantified types.

data HData = forall a. HType a => HData a

However, this approach ended up being difficult when it came to checking types later on, when I found out that you can’t pattern match on existentially quantified types in Haskell.

There’s more to the definition of HData in DataTypes.hs, such as making HNum an instance of Num (so that they support standard arithmetic operations like addition and multiplication), and making HData an instance of Eq and Show.

### Evaluation

Actually evaluating an expression in hasp follows the similar rules as in most Lisp dialects. When we are given an expression like

(a b c d)

First, the interpreter recursively evaluates the expressions a, b, c and d from left to right. (Actually, since hasp is a pure functional language with no side effects, they can be evaluated in any order, or even in parallel.) Then it treats the result of evaluating a as a function, and it evaluates it on the arguments obtained by evaluating b, c, and d.

In the definition of HData, you may have noticed the type constructor HFunc, which represents a hasp function, and includes a parameter of type Env. The Env data type is short for “environment”, and is essentially a mapping of variable names, or identifiers, to HData values. This is necessary so that hasp expressions can reference predefined name bindings and use their values in their own computations. The second argument of HFunc is the actual function itself: it takes an environment and a list of arguments, and outputs a value (or possibly an error).

The hasp interpreter includes a default global environment, which defines a basic set of built-in functions that are available to all hasp programs.

globalEnv :: Env
globalEnv = Env $Map.fromList [ ("+", numericFold (+) 0) , ("*", numericFold (*) 1) , ("-", minusHNum) , ("/", numericBinOp "/" divideHNum) , ("quotient", numericBinOp "quotient" divHNum) , ("modulo", numericBinOp "modulo" modHNum) , ("abs", numericUnaryOp "abs" abs) , ("sgn", numericUnaryOp "sgn" signum) , ("eq?", numericBinPred "eq?" (==)) , ("=", numericBinPred "=" (==)) , ("<", numericBinPred "<" (<)) , ("<=", numericBinPred "<=" (<=)) , (">", numericBinPred ">" (>)) , (">=", numericBinPred ">=" (>=)) , ("!=", numericBinPred "!=" (/=)) , ("list", list) , ("cons", cons) , ("car", car) , ("cdr", cdr) , ("empty?", testEmptyList) ] I’ve only included the bare minimum so far. The reason for this is that these functions, including basic arithmetic operations, comparison operators, and list operations, can only really be defined in Haskell and made available to hasp code. However, it can be a bit of a pain to define hasp functions in the interpreter itself because type checking needs to be done manually. For example, the function numericBinOp, which takes a Haskell numeric binary operation and converts it to the equivalent operation in hasp, has the following (rather inelegant) definition: numericBinOp :: String -> (HNum -> HNum -> ThrowsError HNum) -> HData numericBinOp opName op = HFunc emptyEnv$ \_ args ->
case args of
[HN x, HN y] -> do
result <- x op y
return $HN result [x, HN _] -> throw . errNotNum$ show x
[HN _, y] -> throw . errNotNum $show y [x, _] -> throw . errNotNum$ show x
_ -> throw $errNumArgs opName 2 (length args) Because of this, only a few built-in functions are defined directly in the interpreter’s Haskell code. However, once we have these basic functions (and assuming we have constructs like define, lambda and if), it’s possible to define standard library functions like map, filter and fold. For example, (define map (lambda (f lst) (if (empty? lst) () (cons (f (car lst)) (map f (cdr lst)))))) Currently hasp doesn’t do any sort of tail call elimination, so I haven’t bothered to define map in a tail recursive way. However if I do add that feature in the future, it would be simple to convert this definition into one that is tail recursive. Of course, language constructs like define, lambda and if can’t be defined as hasp functions in the usual sense. In the case of define, that is because its job is to mutate the current environment to define new name bindings. Similarly, lambda works with environments in a way that ordinary functions cannot, creating closures that capture the values of free identifiers from the context in which the lambda expression is evaluated. On the other hand, if is not a regular hasp function because it is short circuiting: in any computation, only one of the two branches of an if statement is ever evaluated. This violates the usual rules of function evaluation, which means that if needs to be implemented as a special syntactic keyword. ## Handling Errors In an earlier iteration of hasp, all of the functions that returned the type ThrowsError a originally returned Either Error a, and contained a lot of boilerplate code like define (Env envMap) [Atom (Id name), expr] = case evalExpr (Env envMap) expr of Left err -> Left err Right (result, _) -> Right (HList [], Env$ Map.insert name result envMap)

Here, the call to evalExpr is one that may return an error. If an error is returned, we propagate it forward, and if not, we make its result available to the rest of the computation. This pattern appears throughout the hasp interpreter, as well as any Haskell code that has to deal with errors.

In Haskell, one of the main benefits of monads is the ability to hide boilerplate code like in the above snippet. I defined a ThrowsError a data type to be a type synonym for Either Error a, and made it an instance of the Monad typeclass. The above code snippet can then be rewritten as

define (Env envMap) [Atom (Id name), expr] = do
(result, _) <- evalExpr (Env envMap) expr
return (HList [], Env \$ Map.insert name result envMap)

Most of the code that I used for error handling is from Bartosz Milewski’s post on error handling at the School of Haskell, which explains the basics of how to use monads for error handling.

## Conclusion and Next Steps

There are some details of hasp that I haven’t had time to cover in this post, and overall the project is not quite finished yet. There are several additional features that I plan to add, such as support for macros, struct data types, and a larger standard library. I also plan to do a lot of refactoring to make my Haskell code more idiomatic (which will probably involve some use of monad transformers), add unit tests and do some bug squashing.

The source code for the hasp interpreter is freely available on the hasp GitHub repo. If you are interested in contributing to hasp, feel free to fork the repo and submit a pull request. If you have any comments, questions, or suggestions, or if you give hasp a bug and you find a bug, feel free to email me, or create an issue on the hasp GitHub page.