27. 10. 2023 William Calliari Development

Taking RPITIT ― Rust’s Shiny New Feature ― for a Test Ride with Type-system-level Lambda Calculus.

With the upcoming Rust version 1.75.0, two important new features will be stabilized. These features go by the names return-position impl Trait in trait (RPITIT) and async fn in trait (AFIT).

Let me take a moment here to congratulate and thank everyone working on these features for their incredible work. This really is a big step in the Rust ecosystem. While the latter one is certainly the one more await-ed by most people, I will today focus on RPITIT, which is also the foundation for AFIT.

I’ll provide some code samples, but for the sake of brevity and to not repeat myself, not all of the code is explicitly written out. If you want to take a look at the full version, I’ve uploaded it to my GitHub. Since the feature is not out right now, it has to be compiled with the nightly: $ cargo +nightly run

impl Trait in the Return Type

So what exactly is RPITIT? Rust has had the possibility to specify the trait bound in the return type instead of the actual type since version 1.26.0. However, one of the limitations was that this syntax couldn’t be used in traits. With the release of Rust 1.75.0 this restriction will be lifted.

But what do we even need this for in the first place? Couldn’t this just be accomplished with generics? To answer that question, let’s first take a look at the function declaration of serde_json’s from_str:

pub fn from_str<'a, T: Deserialize<'a>>(s: &'a str) -> Result<T>

As you can see, this function is generic over T. But the actual concrete type of T is in the hands of the end user. But what if we want to return a value of a distinct type that implements a trait, but that shouldn’t be left up to the user? Then we need to use the impl syntax.

If you think it’s just syntactic sugar to not spell out the whole name of the type, you’re at least partially right… if there weren’t Voldemort types. Voldemort types (types which must not be named) are types that you can’t name in a program. In Rust, both closures and functions are such Voldemort types. Each function or closure has its own unique type determined during compile-time.

This means you can’t know the concrete type before the program actually compiles, and therefore can’t use it to refer to them. But with the return type impl syntax, you can tell the compiler to figure the type out for you. Let’s look at a quick example:

fn get_iter(start: i32, end: i32) -> impl Iterator<Item = (usize, i32)> {
    (start..end).into_iter().map(|x| x * 2 + 1).filter(|x| *x % 3 == 0).enumerate()

Besides the obvious error with the Voldemort types of the two closures, this function could have the return type:

Enumerate<Filter<Map<Range<i32>, {{closure}}>, {{closure}}>>

But that wouldn’t really give the user of our function any additional, useful information. And if we ever decided to change the function, we would have to change the return type along with it. With this new feature however, we can pass a lot of the work onto the compiler, so we can be faster when programming.

return-position impl Trait in Trait

Now with Rust 1.75.0 this feature is finally also available for Traits. Now you can specify a trait with impl return types. Each implementation of the trait can then return a different concrete type without violating the Trait bounds. The compiler then figures out (based on the implementation of the Trait) the concrete type that is returned. Since we need to know the concrete implementation at compile-time in order to know the type that is returned, this obviously makes the whole Trait no longer object-safe, meaning we cannot create a Trait object Box<dyn Trait> with it.

Lambda Calculus

Before we can implement Lambda Calculus on the type system, we first need to understand what it actually is. Lambda Calculus is a model of computation introduced in the 1930s by mathematician Alonzo Church. It follows a few simple rules:

  1. The only thing that exists in the basic calculus are functions.
  2. Each function takes exactly one value as an argument and returns exactly one value.
  3. In the function body, we can apply functions to other functions, or construct new functions.

The simplest function like this is the identity function: λx . x which is a function that just returns its input. This function however isn’t very helpful.

We can also apply functions, for example the function λx. x x applies the function x once to itself. Multiple arguments can be modeled by returning a function that binds the first argument. With that we can construct new functions. λx . λy . x returns a function that takes one argument y and completely ignores it, returning x instead.

In the standard notation, lambda calculus is evaluated from left to right, so the function λx . x x x is equivalent to λx . (x x) x. Calculation is then performed by applying the functions until it’s in its minimal form and can no longer be reduced.

Putting Everything Together

So how can we use this to actually calculate things in Rust? We know that we need to store state to return constructed functions, so each function will be a struct. Then, we need to define the common interface for our functions. As we just established, the function needs to take one argument and return one argument, both of which are functions. The function is also allowed a reference to itself, which does not count as a function argument.

trait Lambda: Copy {
    fn eval<T: Lambda>(self, lambda: T) -> impl Lambda;

And here we can already see RPITIT in action. We know that each function will return a struct that has the lambda trait and we know that that function will be a concrete type. So we leave the concrete type details up to the compiler.

Boolean Logic

Let’s warm up to the concept by starting from the very basics of computer science: true and false. We can’t have truth values, as lambda calculus only allows for functions to exist, so we have to encode these two as functions, e.g. types that implement Lambda. If we think about how we use true and false, it’s to compare them and change the control flow accordingly. So our two values will simply be functions that take two inputs and call the respective function: True = λx. λy . x and False = λx. λy . y. In plain English: If the value is true, the first function is called, if the value is false, the second function will be called instead.

We also know that we need to store some state here, so we need a second struct to hold it. We will name each function that carries state for its parent function with the same name as the parent function, followed by an underscore, so we’ll know it represents a partial computation. When implemented, this logic would look like this:

#[derive(Copy, Clone)]
struct True;

#[derive(Copy, Clone)]
struct True_<T: Lambda>(T);

impl Lambda for True {
    fn eval<T: Lambda>(self, lambda: T) -> impl Lambda {

impl<T: Lambda> Lambda for True_<T> {
    fn eval<U: Lambda>(self, lambda: U) -> impl Lambda {

The same (just in reverse) is also true for our False implementation. As you can see, the only thing we are doing in these functions is either creating a type or returning a type. The return type of True_<T> will always be T which will be known at compile-time. So no matter which two values we initially put into True, the resulting type will be known at compile-time.

With our boolean values in place, let’s do some logic with logical or and and. If we write them out in lambda calculus, the implementation will also be immediately clear: lor = λx . λy . x x y. If x is true, then we can return x knowing that it’s true, otherwise the operation can only be true if y is true, so we return y. The same goes for logical and: land = λx . λy . x y x. If x is false, we can return x, if it’s true, then the expression is true if and only if y is true. Lastly to have a full logic system we’re missing the not operator, which is probably the simplest to understand: λx . x False True, if it’s true, return false, else return true.

When implemented, it would look something like:

impl Lambda for Lor {
    fn eval<T: Lambda>(self, lambda: T) -> impl Lambda {

impl<T: Lambda> Lambda for Lor_<T> {
    fn eval<U: Lambda>(self, lambda: U) -> impl Lambda {
        let first = self.0;


And similarly for Land and Not.

The attentive ones of you might have noticed that the lor implementation is overly complex. Since we can just partially apply functions, we can reduce the implementation to λx . x x if we so choose. This will return a function with the first field bound, and it can equally be applied to the next function. In Haskell this is also called point-free programming.

Helper Functions

Now we want to actually get our hands on the result of the calculations we’ve performed so far. Since the structs / types ARE our data, we can simply print out the type name of the final value to get our answers. Rust provides the std::any::type_name() function to get the string for a type. However to get that, we would have to know that type, which we explicitly don’t want to know ourselves. We can easily build around this though by creating a helper function that does the job for us:

fn type_name<T>(_: &T) -> &'static str {

fn size_of<T>(_: &T) -> usize {

println!("{}", type_name(&Lor.eval(True).eval(False)));

With this we delegate the work to actually know the type we want to print out to the compiler. We can do the same to check the size of the type so we know how much memory it occupies.

Church Numerals

The last thing I want to touch on are numbers. How can we represent numbers in this system? In a system where all you can do is apply functions to other functions, numbers can be represented as a number of function applications. Zero obviously applies the function zero times. This could also be defined as False, e.g. return the last parameter ignoring the first, or as Zero returning the identity function zero = λx . id. Each function greater than zero is then represented as the successor to the previous number. So one is the successor of zero and two is the successor of one (or, the successor of the successor of zero).

The application of successor is then a bit more involved: succ = λx . λy . λz . y (x y z), i.e. we apply the function y once to the result of x applications of y to z. So each successor applies the function once until it hits zero which does not apply the function, but returns its initial value.

Addition then can be defined as the repeated application of the successor function to a value: add = λx . λy . x succ y. So we are basically adding one x times to y which is functionally the same as x + y.

Remember the size_of function from before? Now let’s see how much memory our church numeral takes up if we, for example calculate 2 + 3:

let result = Add.eval(Succ.eval(Succ.eval(Zero))).eval(Succ.eval(Succ.eval(Succ.eval(Zero))));
println!("{}", type_name(&result)); // Succ_<Succ_<Succ_<Succ_<Succ_<Zero>>>>>
println!("{}", size_of(&result)); // 0

Yes, that’s right. Our church numeral does not take up any memory at all! It’s all just type information at compile-time. And the addition we have defined is just type resolution at compile-time. If we execute $ strings on our final binary, we can even see the result of the type name already lying there. So theoretically we have created the perfect program. It computes everything in 0 seconds (not counting compile-time). However all information must also be known at compile-time, and we have no further control at runtime, so it’s probably not the most flexible program ever written.

These Solutions are Engineered by Humans

Did you find this article interesting? Does it match your skill set? Programming is at the heart of how we develop customized solutions. In fact, we’re currently hiring for roles just like this and others here at Würth Phoenix.

William Calliari

William Calliari


William Calliari

Leave a Reply

Your email address will not be published. Required fields are marked *