Writing a self-hosting compiler for Inko

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About a year ago I wrote "Inko: a brief introduction", and later published the Inko website. Since then, I made a lot of progress towards making it useful for everyday use. Some recent milestones include:

The next milestone for Inko is having a self-hosting compiler. But why would one want to write a self-hosting compiler? Why not use an already established language? What are the benefits of writing a self-hosting compiler? Let's find out!

The first compiler

When creating a language, you need a way to compile its source code. But we can't use our own language, since we are still developing it. To deal with this, developers use a different language for the first compiler. Two examples of this are Rust and Go. The first compiler for Rust was written in OCaml, and the first compiler for Go was written in C.

For Inko's current compiler we use Ruby. Before writing the compiler in Ruby I made an attempt at writing it in Rust. Inko's Virtual Machine is also written in Rust, so using Rust for the compiler made sense at the time. Writing the compiler in Rust turned out to be frustrating, as I kept running into minor issues along the way. After about a month, I decided to cut my losses and use Ruby instead. Using Ruby allowed me to deliver a working compiler faster.

There were also two others reasons for using Ruby instead of Rust:

  1. The compiler would one day be rewritten in Inko. This meant that quality was not the focus of the first compiler. Instead, it had to focus getting enough done so I could start building the standard library.
  2. Ruby is closer to Inko than Rust is, which makes it easier to port code to the new compiler.

Rust tends to be an unforgiving language, at least it feels that way. This makes sense when you are writing production-ready software, but can slow you down when trying to prototype a compiler.

Benefits of a self-hosting compiler

If we have to use a different language for our first compiler, why not keep using this compiler? Why should one spend the extra time and effort on making their compiler self-hosting?

A typical compiler consist of different components, such as:

To write our compiler in our own language, the language must provide the necessary features. Such features might be:

Adding these features to the standard library benefits all users of the language. We could come up with a list of features to add, without a reference program. But it can be difficult to come up with every possible feature, before there is a use case for them. Worse, we may end up adding features that turn out to not be useful once actually used.

Performance is also important for a programming language. Your language can have all the features in the world, but users will not use it if the language is too slow. To ensure our language performs well, we need a way to measure and improve its performance. One way of doing this is by writing synthetic benchmarks. While useful for measuring specific sections of code, they are not useful for determining the impact of a change on a larger program.

A more realistic way of measuring performance is using a program with users. Compilers are an excellent reference. For example, a lexer operates on sequences of characters or bytes, executing code for every value in the sequence. Without any optimisations, such code could be slow. By writing our compiler in our own language, we have a program to measure the performance impact of changes made to the language.

While not a benefit per se, making the compiler self-hosting is a way of showing the capabilities of the language. If you can write the language's compiler in the language itself, you can write any other program in the language.

Towards a self-hosting Inko compiler

The first step towards a self-hosting compiler was to simplify the syntax in various places. For example, Inko allowed you to implement a trait in two different ways: when defining an object, or separately. Implementing a trait when defining an object looked like this:

object Person impl ToString {
  # ...

The alternative is to implement the trait separately:

impl ToString for Person {
  # ...

I added support for both so that object definitions and trait implementations were closer together. This complicates various parts of the compiler. In practise I also found it not to be as useful as anticipated.

Another syntax change is the removal of support for unicode identifiers. Being able to use unicode identifiers could be useful, but it complicates the lexer. I also doubt it will see much use in the coming years.

With the syntax simplified, I started implementing the lexer. The merge request tracking progress is "Implement Inko's lexer in Inko itself".

Implementing Inko's lexer in Inko

As I work on the compiler I will write about the progress made, starting with the lexer. After all, talking about the compiler and not showing anything would be boring.

The basic idea of a lexer is simple: take a sequence of bytes or characters, and produce one or more "tokens". A token is some sort of object containing at least two values: a type indicator of some sort, and a value. The type indicator could be a string, integer, enum, or something else. The value is typically a string.

Inko uses an object called Token for tokens, defined as follows (excluding methods not relevant for this example):

object Token {
  def init(type: String, value: String, location: SourceLocation) {
    let @type = type
    let @value = value
    let @location = location

For those unfamiliar with Inko's syntax, this defines and object called Token and its constructor method init. The init method takes three arguments:

  1. type: the type name of the token, such as 'integer' or 'comma'.
  2. value: the value of the token, such as '10' for an integer.
  3. location: an object that contains source location information, such as the line range and column number.

The init method sets three instance attributes: @type, @value, and @location.

For the lexer, Inko uses an object called Lexer. Showing all the lexer's source code would be a bit much. Instead, we'll highlight some interesting parts. The constructor of the lexer is as follows:

object Lexer {
  def init(input: ToByteArray, file: ToPath) {
    let @input = input.to_byte_array
    let @file = file.to_path

    # ...

ToByteArray is a trait that provides the method to_byte_array, for converting a type to a ByteArray. When reading data from a file, Inko will read it into a ByteArray. Converting this to a String requires allocating an extra object, and twice the memory. The type ByteArray also implements the ToByteArray trait. This allows lexing of files, without allocating a String:

ToPath is a trait that provides the method to_path, for converting a type to a Path. Path is a type that represents file paths, providing a more pleasant interface compared to using a String. Using this trait allows one to supply either a String or a Path as the file argument:

import std::compiler::lexer::Lexer
import std::fs::path::Path

Lexer.new(input: '10', file: 'test.inko')
Lexer.new(input: '10', file: Path.new('test.inko'))

The Lexer type is an iterator, allowing the user to retrieve tokens one by one:

import std::compiler::lexer::Lexer

let lexer = Lexer.new(input: '10', file: 'test.inko')
let token = lexer.next

token.type  # => 'integer'
token.value # => '10'

To determine what token to produce, a Lexer will look at the current byte in the input. Based on the current byte, next sends different messages to the Lexer. The implementation of next is a bit much to cover, but more or less looks as follows:

def next -> ?Token {
  let current = current_byte

  current == A
    .if_true {
      return foo

  current == B
    .if_true {
      return bar


The return type here is ?Token, meaning it may return a Token or Nil.

Inko does not have a match or switch statement, instead we compare objects for equality and use block returns. In the above example, if current == A evaluates to true we return the result of foo, skipping the code that follows it. Reading the above code, one might think that the code is incorrect. In most languages, this code:

A == B

Is parsed as this:

A == (B.foo)

In Inko this is not the case. If the message that follows a binary operation (A == B) is on a new line, it's sent to the result. This means it's parsed as follows:

(A == B).foo

This allows one to write this:

A == B
  .and { C }
  .if_true {
    # ...

Instead of this:

(A == B)
  .and { C }
  .if_true {
    # ...

For certain tokens we need to perform more complex checks. For example, for integers we can not compare for equality because an integer can start with different values (0, 1, etc). Instead, we use Inko's range type like so:

INTEGER_DIGIT_RANGE.cover?(current).if_true {
  return number

Here INTEGER_DIGIT_RANGE is a range (using the Range type) covering the digits 0 to 9. The method cover? checks if its argument is contained in the range, without evaluating all values in the range.

The implementations of the methods that produce tokens vary. Some are simple, others are more complex. Strings in particular are tricky, as they can contain escaped quotes and escape sequences (\n, \r, etc).

Numbers are also tricky, as there are different number types and formats:

The difficulty here is that the type is not known until reaching a certain character, such as . or x.

Covering all this would be far too much, so I recommend taking a closer look at the merge request "Implement Inko's lexer in Inko itself".

Work after the lexer

After finishing work on the lexer, the parser is next. After that, I will have to spend some time planning what steps would be next. I would like for the compiler to be parallel and incremental, but I do not yet have an idea on how to implement this. I also need to revisit the type system, as certain parts feel a bit hacky.

Determining how long all this takes is difficult. After implementing the parser I will have a better estimate. I expect it will take between three and six months. I do have a three week vacation in a couple of weeks, and I tend to be productive during my vacations. Perhaps a bit too productive.