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Lisp in a weekend

Working as a professional software engineer can be tiring: you're often working on a small part of a larger system, making small changes and fixing bugs. Over time you also learn to rigidly follow best practises: write code that is efficient, document your functions, always check error codes, etc. It's easy to forget the fun of just programming and the satisfaction of creating something.

I set out to try to reclaim some of that fun by setting myself a challenge: implement a Lisp interpreter in a weekend. How much could I achieve? I wasn't sure but I figured it should be achievable. The nice thing about Lisp is the lack of syntax, meaning that parsing of the program text isn't difficult. But there are still lots of other things to take into account. I was also curious as to how small (in terms of lines of code) a Lisp interpreter could be.

There are several languages I could have chosen to do this, and some would have been easier than others. A Lisp interpreter in Python, for example, would probably have been fairly trivial. In the end I opted for C, just for the challenge - I'd have to implement my own garbage collector rather than "cheating" by using a higher level language!

In the end, I was quite happy with the result. I started on the Friday night and by the end of Sunday I had a complete, functioning interpreter. It includes most of the staple Lisp features: a read-eval-print loop, first-class functions with lexical closures, garbage collection and recursion with tail call optimization. The source code is less than 900 lines, though that doesn't include a separate file with a small "standard library" of common Lisp functions.

I cut a lot of corners to save time. The code is much more sparsely commented than my normal standard and error reporting is non-existent. In spite of that, it's still fairly readable. There's very little in the way of input checking: for example, syntax errors will often cause the interpreter to crash due to a null pointer dereference.

Below is an annotated timeline of my experience, linking to the commits I made over that weekend. I've made a few changes to the code since then but in general it's largely unchanged.

I name the resulting language Yoctolisp (yocto- is the SI prefix for 10-24, and all the preceding "small" prefixes have already been used). It's reminiscent of Scheme, mostly because Scheme is the only dialect of Lisp that I know to any practical extent.


Lisp programs are made up of S-expressions, so the first step to writing an interpreter is to write some code to parse S-expressions.

commit ebaff9

S-expression lexing

The first step to writing a parser is to write a Lexer to tokenize the input, so that's what I've done here. First we need some data structures.

	typedef enum {
	} YLispToken;

These are the token types I start off by defining (these change a bit later). It's then relatively simple to hand-roll a lexer. YLispLexer is a structure holding the current state of the lexer, which is initialized by ylisp_init_lexer. Then ylisp_read_token reads the next token from the input data. We can tell the type of the token we've found by looking at the first character we encounter - for example '#' means we've found a boolean constant, and so on.

One thing where Lisp is quite different to other languages is in symbol names. Languages like C only allow a very limited subset of names for variables (alphanumeric and underscore). Scheme allows any characters except for a small subset. I abuse C's string functions to implement this in a compact way.

	static int is_sym_char(unsigned char c)
		return !isspace(c) && strchr("()[]{}\",'`;#|\\", c) == NULL;

The lexing has a couple of big holes. Firstly, negative numbers aren't supported. Secondly, strings don't support escape characters. A proper lexer ought to do both these things.

Data structures

One thing that's needed already at this early stage is a data structure to represent the various Lisp data types. These are: integer, string, symbol, and cons cell. There were two possible ways I could have approached this. One is to use a "base" and "child" structures and cast between them, which would work like this:

	typedef struct {
	} YLispValue;
	typedef struct {
		YLispValue parent;
		char *s;
	} YLispString;

The other is to use a single union data structure to hold all the different types. I debated both approaches, and ultimately went with the latter. The former approach is undoubtedly a better approach, and would be what I would probably use if I were trying to write "proper" maintainable code. But it means having to constantly cast between types and ultimately more complicated code. Here's the union-based structure I went with.

	typedef enum {
	} YLispValueType;
	typedef struct _YLispValue {
		YLispValueType type;
		union {
			unsigned int i;
			unsigned char *s;
			struct {
				struct _YLispValue *cdr, *car;
			} cell;
			struct _YLispValue *symname;
		} v;
		struct _YLispValue *next;
	} YLispValue;

The "next" pointer is there for garbage collection. I defined ylisp_value as a convenience function to allocate new YLispValues of a particular type:

	YLispValue *ylisp_value(YLispValueType type)
		YLispValue *result = malloc(sizeof(YLispValue));
		assert(result != NULL);
		result->type = type;
		result->next = values; values = result;
		return result;

As well as allocating and initializing a new YLispValue, it also adds the new value onto a linked list of allocated values. Later on I'll use this for the sweep stage of the garbage collector.

One other thing: some will rightly criticize the use of a heap-allocated structure just to hold a simple integer value. Proper virtual machines use a trick called pointer tagging that makes use of the fact that heap-allocated pointers are aligned on word boundaries and therefore have their lower bits set to zero. Again, for simplicity I'm avoiding special cases.

commit d53138


Having written the lexer, the next stage is to actually parse S-expressions. The function ylisp_parse is the entrypoint into parsing: it takes a string as input and returns a parsed expression. Parsing S-expressions is pretty easy: parse_from_token interprets a new YLispValue from the lexer's input, branching off to parse_list when an open parenthesis is found. This then recurses back to parse_from_token to read the individual items in the list. Special attention is needed when parsing the quote symbol. This needs to be translated into a Lisp quote expression, for example:

	'(1 2 3 4)


	(quote (1 2 3 4))


To test out the parsing code, I wrote a simple "main" function that would parse an S-expression given on the command line. But to test it properly required some code to print out the parsed expression. I already wrote some code in the previous commit to do some basic printing of YLispValue structures, but expanded this into ylisp_print, which will recursively print S-expressions. This function becomes a lot more important later when I want a read-eval-print loop.

commit 1addbb

In this commit I add some much-needed convenience functions and macros. Firstly I add CAR() and CDR() for accessing those fields in a cons cell. This proves essential later when evaluation uses long chains of them. Defining them as preprocessor macros allows them to be used as lvalues in assignments, which is a further convenience.

I also add ylisp_cons which performs the equivalent to Lisp's cons function, allocating and assigning a new cons cell.

### commit [e86411](

When I first wrote the lexer, I defined separate token types for symbols, numbers, strings and booleans. These can be collapsed down into a single type, TOKEN_LITERAL, as the end result is the same regardless - a literal value has been read.


Having a functioning S-expression parser to work with, on Saturday I wrote the evaluation code. This was all contained in a single commit, and probably should have been split across several commits. The end result, though, was code capable of evaluating basic expressions.

commit a5f8f6


Certain symbols are interpreted by Lisp as "keywords" - examples are if, cond, and define. It's helpful to be able to access the symbols representing these keywords without having to do an expensive lookup. I therefore define an enum of the keywords I want; these get looked up on startup and stored in an array.

	enum {

	static const char *keyword_names[] = {
		"if", "cond", "quote", "lambda", "let", "define"
	static YLispValue *keywords[NUM_KEYWORDS];

It's then trivial to check for particular keywords, like on this line:

	} else if (first == keywords[KWD_IF]) {

Variable contexts

In imperative languages like C, it's not possible to access the variables of a function after it has returned. But in Lisp, it's possible to create a function within a function that can access the variables of its parent even after that parent has returned. For example:

	(define create-adder
	  (lambda (x)
	    (lambda (y)
	      (+ x y))))
	(define add-1 (create-adder 1))

	(print (add-1 2))

To achieve this, the variables of the parent function have to be stored in a context - a heap object that is garbage collected. So I define YLISP_CONTEXT as a new type of YLispValue, with these fields:

	struct {
		struct _YLispValue *parent, *vars;
	} context;

The parent field points to another context that is the parent of this one (or NULL for the top-level context). More interesting is vars, which stores a list of cons cells, one for each variable in that context. The car is the symbol representing the variable name, while the cdr is the value. So for example, if a = 1 and b = "hello world", then vars is:

	((a . 1) (b . "hello world"))

To look up a variable's value from its name (symbol), eval_variable simply walks the list of variables, then walks up to the parent context and repeats until the variable is found.

This is obviously very inefficient: to be efficient, at the very least we should use some kind of fast mapping structure rather than performing a linear scan of the variable list. It would be easier to optimize variable lookups if the code was being properly compiled into an internal representation. As we're interpreting S-expressions directly though, it's difficult to do better. The original Lisp paper did something similar (association lists).

If we were writing a compiler, it would be possible to do much more efficient optimizations: we could analyze the variables used within each function and construct a carefully packed memory layout to store each variable (conceptually the same as how a C compiler defines a stack frame). However, Yoctolisp is an interpreter and (aside from parsing S-expressions) does no kind of analysis of program structure at all! Because of this, the options available for doing this efficiently are very limited.


Having defined how variables are stored, I define functions as another type of YLispValue (YLISP_FUNCTION). Functions have a pointer to the context where they were defined, and the code to execute when they are called:

	struct {
		struct _YLispValue *context, *code;
	} func;

A typical Lisp lambda expression looks like:

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

The code part that's needed to be executed is simply the cdr of this. So the C code to handle a Lambda expression looks like this:

	} else if (first == keywords[KWD_LAMBDA]) {
		YLispValue *result = ylisp_value(YLISP_FUNCTION);
		result->v.func.context = context;
		result->v.func.code = CDR(code);
		return result;

When we want to invoke a function, we create a new context object, point its parent to the one from the function, populate its variables from the arguments given to it, and then start executing the statements from the function body (see eval_func_call).


The main entry point to evaluating an expression is ylisp_eval. This takes two arguments: a context in which evaluation is occurring, and the actual code to execute. There are three possibilities for what we might do:

  1. We have a symbol, in which case we need to do a variable lookup
  2. We have a literal value, like 123 or "hello world". These just evaluate to themselves.
  3. We have a cons cell (ie. a list), in which case we need to do something more complex.

The eval_list function handles evaluation of list expressions. There are special case checks for handling if, quote, lambda and define, which must be treated specially. The handling for each of these is fairly simple. There are other keywords that aren't handled yet. If it isn't a keyword, the list is interpreted as a function call, and invoked as previously described.


To be able to do anything interesting, there needs to be a way to call into native code. To do this, I first define a function signature for native C functions:

	typedef struct _YLispValue *(*YLispBuiltin)(struct _YLispValue *args);

A native function simply takes a linked list of YLispValues. For example, here's the definition for cons:

	static YLispValue *builtin_cons(YLispValue *args)
		return ylisp_cons(CAR(args), CAR(CDR(args)));

Then I define a new YLispValue type (YLISP_BUILTIN) to hold a pointer to a native function. The function call code (eval_func_call) invokes the native function by evaluating the arguments, building up the linked list and calling it. The built-in functions get added into the root context at startup.


At this point I have the basic foundations in place and can evaluate simple expressions by providing them on the command line. From this point on, what remains is a matter of fleshing out what's in place and polishing.

commit 6fc255

Garbage collection

A simple garbage collector works by marking all the active objects, then freeing the objects that have not been marked. This is essentially the approach I took here. A counter (values_alloc_count) tracks the number of allocated objects and runs the garbage collector when a threshold number is reached (I arbitrarily chose 100).

The code so far has all been written with the assumption that a garbage collector would eventually be present, so I haven't made any effort to track the allocated YLispValue structures. This adds extra complexity because it is necessary to be able to reason about when the garbage collector can run: if a variable references an allocated YLispValue, that variable must be marked when the collector runs, or the value might be freed.

The approach I took was to invoke the collector at the start of ylisp_eval. It is then a matter of verifying each location where it is invoked, and the evaluation code is the only part that needs to be checked.

There are several locations where variables must be "pinned" - their values marked when the garbage collector is run. A simple example is that the context for a function must be pinned while it executes. I therefore declare two functions to start and stop tracking a variable:

	static void pin_variable(YLispValue **variable)
	static void unpin_variable(YLispValue **variable)

There are several locations where these pin/unpin pairs are needed. Getting this right is particularly tricky, as a missing pin can mean a value being freed unexpectedly. I eventually had to track down a couple of bugs related to this before I got it right. To make bugs more obvious, values are memset() to clear their contents before they are freed.

The garbage collector is very crude. One problem is that it just uses C's native allocator and doesn't make any attempt at managing memory. Classic Lisp used [copying garbage collection]( schemes, which have the advantage that surviving memory is compacted as part of the process. If we wanted to be really elaborate we'd use generational garbage collection, which can help to reduce pauses, but that's more than is probably feasible in a weekend.

commit c5cc40


A read-eval-print loop is a program that reads an expression from the console, evaluates it and prints the result. The pieces are now all in place to do this. This commit adds the read, eval and print builtin functions, which are essentially wrappers around the equivalent C functions. The program is still executing expressions from the command line at this point, so a simple demonstration is:

	$ ./yoctolisp "(print (eval (read)))"
	> (+ 1 2 3)

commit 865363

Tail-call optimization

Functional programming languages like Lisp prefer recursion to iteration. Iterating over lists, for example, is performed by a function calling itself until the end of the list is reached:

	(define (foldl func acc list)
	  (if (null? list)
	    (foldl func
	           (func acc (car list))
	           (cdr list))))

In an imperative language like C this would be a problem, as a long list could cause the stack to overflow. At the very least it could cause large amounts of temporary memory to be used until the function has completed. For this reason, Lisp-like languages implement tail-call optimization. If a function ends in a function call (ie. it returns the result of a function call), then its stack frame can be replaced by that call.

This optimization is important enough that some versions of Lisp (eg. Scheme) require that implementations include it. It was therefore desireable to include it in Yoctolisp.

In a compiled implementation it would be possible to analyze the program structure during the compilation phase and detect tail calls. Because we are just evaluating S-expressions it's more difficult. Instead, I came up with an alternate scheme where the last function call in a function can be "deferred".

I define a new type of YLispValue (YLISP_DEFERRED) that can be returned when evaluating a list. This is only used internally to the evaluation code and never returned from ylisp_eval. The function body code is then changed so that the last expression to be evaluated is deferred:

	if (CDR(code) == NULL) {
		return defer_eval(context, CAR(code));
	} else {
		ylisp_eval(context, CAR(code));

The list evaluation code is then wrapped so that deferred calls are unwrapped and re-evaluated, until a real value is returned.

Several days after I finished the project I discovered a simpler way of doing this. We only ever pass the deferred call up the call chain, so only one YLISP_DEFERRED object can ever exist. It's therefore possible to avoid allocating a new YLispValue each time and just use a static one that gets reused (see commit cec04b).

commit 9f1936

Up until now, all expressions are still being evaluated from the command line arguments. This changes the program to read from a file specified on the command line instead. This requires some changes to the way the parsing code works, so that multiple S-expressions can be read. I also add a simple program to run a read-eval-print loop.

commit 47dfb1

This fixes a bug in the string lexing code that I've missed until now; most of the expressions I've been using as test input so far have just been using integers. I only realised that the string code was broken when I tried to write a "hello world" example!

commit ad78dc

The builtin comparison function (=) so far only handles integers. This commit extends it to work properly with other types; it's particularly important to be able to compare against empty lists so that recursive functions can be written for processing lists.

commit 6031b3

This fixes a subtle bug due to a missing GC pin, caused by the tail-call optimization. It shows how tricky it is to write a good garbage collector. I add some conditional code to display values as they are freed, to assist with debugging future GC bugs.

commit f39910

This commit adds less-than, greater-than and negation builtin functions, and also adds handling for the begin keyword. The latter turns out to be easy to handle as the function call body code can simply be reused.

After the weekend was over I later came back and simplified how the comparison operators are implemented. I moved much of the logic for the comparison operators into Lisp code: all comparisons can be defined in terms of the = and < comparisons.

commit 6238da

Following the previous commit, I add support for the let keyword. This works essentially like an inner function; a new context is created inside the current context, and the body of the let expression evaluated like a function body.

The addition of the begin and let keywords means that the read-eval-print loop example can terminate when an empty line is typed.

commit 9508f3

At this point the interpreter has now reached a level of completeness that it is possible to write fairly complex programs. There are lots of standard functions still missing, but rather than implement them as built-ins, they can be implemented from the primitives already available. I add a standard library file read on startup and containing implementations of some well-known Lisp functions.

commit 1405b4

Defining functions requires using a lambda expression, for example:

	(define map
	  (lambda (callback list)

Having started writing a significant number of functions with the introduction of the standard library, this is becoming increasingly tedious. I therefore add support for Scheme's function declaration syntax, ie.:

	(define (map callback list)

commit 7d9592

Variadic functions

The function processing code assumes that functions have a fixed number of arguments, but there are some Lisp functions that operate on a variable number of arguments. An example is sum:

	(sum 1 2 3 4 5)

Functions take variable lists of arguments by using the "dot" notation to specify a variable. For example, this is what the definition of sum looks like:

	(define (sum . list) (foldl + 0 list))

This commit adds support for this syntax, and opens the door for more complicated functions to be added to the standard library.

commit d5e454

Following the previous commit, this adds some more functions to the standard library. It also adds foldl, which allows various functions to be defined tersely.

commit 7021cb

I finish off the weekend's work by implementing cond, which I defined as a keyword much earlier but never implemented.