monomorph.md

Monomorphization

As you probably know, rust has a very expressive type system that has extensive support for generic types. But of course, assembly is not generic, so we need to figure out the concrete types of all the generics before the code can execute.

Different languages handle this problem differently. For example, in some languages, such as Java, we may not know the most precise type of value until runtime. In the case of Java, this is ok because (almost) all variables are reference values anyway (i.e. pointers to a stack allocated object). This flexibility comes at the cost of performance, since all accesses to an object must dereference a pointer.

Rust takes a different approach: it monomorphizes all generic types. This means that compiler stamps out a different copy of the code of a generic function for each concrete type needed. For example, if I use a Vec<u64> and a Vec<String> in my code, then the generated binary will have two copies of the generated code for Vec: one for Vec<u64> and another for Vec<String>. The result is fast programs, but it comes at the cost of compile time (creating all those copies can take a while) and binary size (all those copies might take a lot of space).

Monomorphization is the first step in the backend of the rust compiler.

Collection

First, we need to figure out what concrete types we need for all the generic things in our program. This is called collection, and the code that does this is called the monomorphization collector.

Take this example:

fn banana() {
   peach::<u64>();
}

fn main() {
    banana();
}

The monomorphization collector will give you a list of [main, banana, peach::<u64>]. These are the functions that will have machine code generated for them. Collector will also add things like statics to that list.

See the collector rustdocs for more info.

The monomorphization collector is run just before MIR lowering and codegen. rustc_codegen_ssa::base::codegen_crate calls the collect_and_partition_mono_items query, which does monomorphization collection and then partitions them into codegen units.

Polymorphization

As mentioned above, monomorphization produces fast code, but it comes at the cost of compile time and binary size. MIR optimizations can help a bit with this. Another optimization currently under development is called polymorphization.

The general idea is that often we can share some code between monomorphized copies of code. More precisely, if a MIR block is not dependent on a type parameter, it may not need to be monomorphized into many copies. Consider the following example:

pub fn f() {
    g::<bool>();
    g::<usize>();
}

fn g<T>() -> usize {
    let n = 1;
    let closure = || n;
    closure()
}

In this case, we would currently collect [f, g::<bool>, g::<usize>, g::<bool>::{{closure}}, g::<usize>::{{closure}}], but notice that the two closures would be identical -- they don't depend on the type parameter T of function g. So we only need to emit one copy of the closure.

For more information, see this thread on github.