Introducing MIR

We are in the final stages of a grand transformation on the Rust compiler internals. Over the past year or so, we have been steadily working on a plan to change our internal compiler pipeline, as shown here:

Introducing MIR

That is, we are introducing a new intermediate representation (IR) of your program that we call MIR : MIR stands for mid-level IR, because the MIR comes between the existing HIR (“high-level IR”, roughly an abstract syntax tree ) and LLVM (the “low-level” IR). Previously, the “translation” phase in the compiler would convert from full-blown Rust into machine-code-like LLVM in one rather large step. But now, it will do its work in two phases, with a vastly simplified version of Rust – MIR – standing in the middle.

If you’re not a compiler enthusiast, this all might seem arcane and unlikely to affect you directly. But in reality, MIR is the key to ticking off a number of our highest priorities for Rust:

  • Faster compilation time. We are working to make Rust’s compilation incremental , so that when you re -compile code, the compiler recomputes only what it has to. MIR has been designed from the start with this use-case in mind, so it’s much easier for us to save and reload, even if other parts of the program have changed in the meantime.

    MIR also provides a foundation for more efficient data structures and removal of redundant work in the compiler, both of which should speed up compilation across the board.

  • Faster execution time. You may have noticed that in the new compiler pipeline, optimization appears twice. That’s no accident: previously, the compiler relied solely on LLVM to perform optimizations, but with MIR, we can do some Rust-specific optimizations before ever hitting LLVM – or, for that matter, before monomorphizing code. Rust’s rich type system should provide fertile ground for going beyond LLVM’s optimizations.

    In addition, MIR will uncork some longstanding performance improvements to the code Rust generates, like “non-zeroing” drop .

  • More precise type checking. Today’s Rust compiler imposes some artificial restrictions on borrowing , restrictions which largely stem from the way the compiler currently represents programs. MIR will enable much more flexible borrowing , which will in turn improve Rust’s ergonomics and learning curve.

Beyond these banner user-facing improvements, MIR also has substantial engineering benefits for the compiler :

  • Eliminating redundancy.Currently, because we write all of our passes in terms of the full Rust language, there is quite a lot of duplication. For example, both the safety analyses and the backend which produces LLVM IR must agree about how to translate drops, or the precise order in which match expression arms will be tested and executed (which can get quite complex). With MIR, all of that logic is centralized in MIR construction, and the later passes can just rely on that.

  • Raising ambitions.In addition to being more DRY , working with MIR is just plain easier , because it contains a much more primitive set of operations than ordinary Rust. This simplification enables us to do a lot of things that were forbodingly complex before. We’ll look at one such case in this post – non-zeroing drop – but as we’ll see at the end, there are already many others in the pipeline.

Needless to say, we’re excited, and the Rust community has stepped up in a big way to make MIR a reality. The compiler can bootstrap and run its test suite using MIR, and these tests have to pass on every new commit. Once we’re able to run Crater with MIR enabled and see no regressions across the entire crates.io ecosystem, we’ll turn it on by default (or, you’ll forgive a terrible (wonderful) pun, launch MIR into orbit ).

This blog post begins with an overview of MIR’s design, demonstrating some of the ways that MIR is able to abstract away the full details of the Rust language. Next, we look at how MIR will help with implementing non-zeroing drops , a long-desired optimization. If after this post you find you are hungry for more, have a look at the RFC that introduced MIR , or jump right into the code . (Compiler buffs may be particularly interested in the alternatives section , which discusses certain design choices in detail, such as why MIR does not currently use SSA.)

Reducing Rust to a simple core

MIR reduces Rust down to a simple core, removing almost all of the Rust syntax that you use every day, such as for loops, match expressions, and even method calls. Instead, those constructs are translated to a small set of primitives. This does not mean that MIR is a subset of Rust. As we’ll see, many of these primitives operations are not available in real Rust. This is because those primitives could be misused to write unsafe or undesirable programs.

The simple core language that MIR supports is not something you would want to program in. In fact, it makes things almost painfully explicit. But it’s great if you want to write a type-checker or generate assembly code, as you now only have to handle the core operations that remain after MIR translation.

To see what I mean, let’s start by simplifying a fragment of Rust code. At first, we’ll just break the Rust down into “simpler Rust”, but eventually we’ll step away from Rust altogether and into MIR code.

Our Rust example starts out as this simple for loop, which iterates over all the elements in a vector and processes them one by one:

for elem in vec {     process(elem); } 

Rust itself offers three kinds of loops: for loops, like this one; while and while let loops, that iterate until some condition is met; and finally the simple loop , which just iterates until you break out of it. Each of these kinds of loops encapsulates a particular pattern, so they are quite useful when writing code. But for MIR, we’d like to reduce all of these into one core concept.

A for loop in Rust works by converting a value into an iterator and then repeatedly calling next on that iterator. That means that we can rewrite the for loop we saw before into a while let loop that looks like this:

let mut iterator = vec.into_iter(); while let Some(elem) = iterator.next() {     process(elem); } 

By applying this rewriting, we can remove all for loops, but that still leaves multiple kinds of loops. So next we can imagine rewrite all while let loops into a simple loop combined with a match :

let mut iterator = vec.into_iter(); loop {     match iterator.next() {         Some(elem) => process(elem),         None => break,     } } 

We’ve already eliminated two constructs ( for loops and while loops), but we can go further still. Let’s turn from loops for a bit to look at the method calls that we see. In Rust, method calls like vec.into_iter() and iterator.next() are also a kind of syntactic sugar . These particular methods are defined in traits, which are basically pre-defined interfaces. For example, into_iter is a method in the IntoIterator trait. Types which can be converted into iterators implement that trait and define how the into_iter method works for them. Similarly, next is defined in the Iterator trait. When you write a method call like iterator.next() , the Rust compiler automatically figures out which trait the method belongs to based on the type of the iterator and the set of traits in scope. But if we prefer to be more explicit, we could instead invoke the methods in the trait directly, using function call syntax:

// Rather than `vec.into_iter()`, we are calling // the function `IntoIterator::into_iter`. This is // exactly equivalent, just more explicit. let mut iterator = IntoIterator::into_iter(vec); loop {     // Similarly, `iterator.next()` can be rewritten     // to make clear which trait the `next` method     // comes from. We see here that the `.` notation     // was also adding an implicit mutable reference,     // which is now made explicit.     match Iterator::next(&mut iterator) {         Some(elem) => process(elem),         None => break,     } } 

At this point, we’ve managed to reduce the set of language features for our little fragment quite a bit: we now only use loop loops and we don’t use method calls. But we could reduce the set of concepts further if were moved away from loop and break and towards something more fundamental: goto . Using goto we could transform the previous code example into something like this:

    let mut iterator = IntoIterator::into_iter(vec);  loop:     match Iterator::next(&mut iterator) {         Some(elem) => { process(elem); goto loop; }         None => { goto break; }     }  break:     ... 

We’ve gotten pretty far in breaking our example down into simpler constructs. We’re not quite done yet, but before we go further it’s worth stepping back a second to make a few observations:

Some MIR primitives are more powerful than the structured construct they replace.Introducing the goto keyword is a big simplification in one sense: it unifies and replaces a large number of control-flow keywords. goto completely replaces loop , break , continue , but it also allows us to simplify if and match as well (we’ll see more on match in particular in a bit). However, this simplification is only possible because goto is a more general construct than loop , and it’s something we would not want to introduce into the language proper, because we don’t want people to be able to write spaghetti-like-code with complex control-flow that is hard to read and follow later. But it’s fine to have such a construct in MIR, because we know that it will only be used in particular ways, such as to express a loop or a break .

MIR construction is type-driven.We saw that all method calls like iterator.next() can be desugared into fully qualified function calls like Iterator::next(&mut iterator) . However, doing this rewrite is only possible with full type information, since we must (for example) know the type of iterator to determine which trait the next method comes from. In general, constructing MIR is only possible after type-checking is done.

MIR makes all types explicit.Since we are constructing MIR after the main type-checking is done, MIR can include full type information. This is useful for analyses like the borrow checker, which require the types of local variables and so forth to operate, but also means we can run the type-checker periodically as a kind of sanity check to ensure that the MIR is well-formed.

Control-flow graphs

In the previous section, I presented a gradual “deconstruction” of a Rust program into something resembling MIR, but we stayed in textual form. Internally to the compiler, though, we represent MIR as a control-flow graph (CFG) . If you’ve ever used a flow-chart, then the concept of a control-flow graph will be pretty familiar to you. It’s a representation of your program that exposes the underlying control flow in a very clear way.

A control-flow graph is structured as a set of basic blocks connected by edges. Each basic block contains a sequence of statements and ends in a terminator , which defines how the blocks are connected to one another. When using a control-flow graph, a loop simply appears as a cycle in the graph, and the break keyword translates into a path out of that cycle.

Here is the running example from the previous section, expressed as a control-flow graph:

Introducing MIR

Building a control-flow graph is typically a first step for any kind of flow-sensitive analysis. It’s also a natural match for LLVM IR, which is also structured into control-flow graph form. The fact that MIR and LLVM correspond to one another fairly closely makes translation quite straight-forward. It also eliminates a vector for bugs: in today’s compiler, the control-flow graph used for analyses is not necessarily the same as the one which results from LLVM construction, which can lead to incorrect programs being accepted.

Simplifying match expressions

The example in the previous section showed how we can reduce all of Rust’s loops into, effectively, gotos in the MIR and how we can remove methods calls in favor of calls to explicit calls to trait functions. But it glossed over one detail: match expressions .

One of the big goals in MIR was to simplify match expressions into a very small core of operations. We do this by introducing two constructs that the main language does not include: switches and variant downcasts . Like goto , these are things that we would not want in the base language, because they can be misused to write bad code; but they are perfectly fine in MIR.

It’s probably easiest to explain match handling by example. Let’s consider the match expression we saw in the previous section:

match Iterator::next(&mut iterator) {     Some(elem) => process(elem),     None => break, } 

Here, the result of calling next is of type Option<T> , where T is the type of the elements. The match expression is thus doing two things: first, it is determining whether this Option was a value with the Some or None variant. Then, in the case of the Some variant, it is extracting the value elem out.

In normal Rust, these two operations are intentionally coupled, because we don’t want you to read the data from an Option unless it has the Some variant (to do otherwise would be effectively a C union, where reads are not checked for correctness).

In MIR, though, we separate the checking of the variant from the extracting of the data. I’m going to give the equivalent of MIR here first in a kind of pseudo-code, since there is no actual Rust syntax for these operations:

loop:     // Put the value we are matching on into a temporary variable.     let tmp = Iterator::next(&mut iterator);      // Next, we "switch" on the value to determine which it has.     switch tmp {         Some => {             // If this is a sum, we can extract the element out             // by "downcasting", this effectively asserts that             // the value `tmp` is of the Some variant.             let elem = (tmp as Some).0;              // The user's original code:             process(elem);              goto loop;         }         None => {             goto break;         }     }  break:     .... 

Of course, the actual MIR is based on a control-flow-graph, so it would look something like this:

Introducing MIR

Explicit drops and panics

So now we’ve seen how we can remove loops, method calls, and matches out of the MIR, and replace them with simpler equivalents. But there is still one key area that we can simplify. Interestingly, it’s something that happens almost invisibly in the code today: running destructors and cleanup in the case of a panic.

In the example control-flow-graph we saw before, we were assuming that all of the code would execute successfully. But in reality, we can’t know that. For example, any of the function calls that we see could panic, which would trigger the start of unwinding. As we unwind the stack, we would have to run destructors for any values we find. Figuring out precisely which local variables should be freed at each point of panic is actually somewhat complex, so we would like to make it explicit in the MIR: this way, MIR construction has to figure it out, but later passes can just rely on the MIR.

The way we do this is two-fold. First, we make drops explicit in the MIR. Drop is the term we use for running the destructor on a value. In MIR, whenever control-flow passes a point where a value should be dropped, we add in a special drop(...) operation. Second, we add explicit edges in the control-flow graph to represent potential panics, and the cleanup that we have to do.

Let’s look at the explicit drops first. If you recall, we started with an example that was just a for loop:

for elem in vec {     process(elem); } 

We then transformed this for loop to explicitly invoke IntoIterator::into_iter(vec) , yielding a value iterator , from which we extract the various elements. Well, this value iterator actually has a destructor, and it will need to be freed (in this case, its job is to free the memory that was used by the vector vec ; this memory is no longer needed, since we’ve finished iterating over the vector). Using the drop operation, we can adjust our MIR control-flow-graph to show explicitly where the iterator value gets freed. Take a look at the new graph, and in particular what happens when a None variant is found:

Introducing MIR

Here we see that, when the loop exits normally, we will drop the iterator once it has finished. But what about if a panic occurs? Any of the function calls we see here could panic, after all. To account for that, we introduce panic edges into the graph:

Introducing MIR

Here we have introduced panic edges onto each of the function calls. By looking at these edges, you can see that the if the call to next or process should panic, then we will drop the variable iterator ; but if the call to into_iter panics, the the iterator hasn’t been initialized yet, so it should not be dropped.

One interesting wrinkle: we recently approved RFC 1513 , which allows an application to specify that panics should be treated as calls to abort , rather than triggering unwinding. If the program is being compiled with “panic as abort” semantics, then this too would be reflected in the MIR, as the panic edges and handling would simply be absent from the graph.

Viewing MIR on play

At this point, we’ve reduced our example into something fairly close to what MIR actually looks like. If you’d like to see for yourself, you can view the MIR for our example onplay.rust-lang.org. Just follow this link and then press the “MIR” button along the top. You’ll wind up seeing the MIR for several functions, so you have to search through to find the start of the example fn. (I won’t reproduce the output here, as it is fairly lengthy.) In the compiler itself, you can also enable graphviz output.

Drops and stack flags

By now I think you have a feeling for how MIR represents a simplified Rust. Let’s look at one example of where MIR will allow us to implement a long-awaited improvement to Rust: the shift to non-zeroing drop. This is a change to how we detect when destructors must execute, particularly when values are only sometimes moved. This move was proposed (and approved) in RFC 320 , but it has yet to be implemented. This is primarily because doing it on the pre-MIR compiler was architecturally challenging.

To better understand what the feature is, consider this function send_if , which conditionally sends a vector to another thread:

fn send_if(data: Vec<Data>) {     // If `some_condition` returns *true*, then ownership of `data`     // moves into the `send_to_other_thread` function, and hence     // we should not free it (the other thread will free it).     if some_condition(&data) {         send_to_other_thread(data);     }      post_send();      // If `some_condition` returned *false*, the ownership of `data`     // remains with `send_if`, which means that the `data` vector     // should be freed here, when we return. } 

The key point, as indicated in the comments, is that we can’t know statically whether we ought to free data or not. It depends on whether we entered the if or not.

To handle this scenario today, the compiler uses zeroing . Or, more accurately, overwriting . What this means is that, if ownership of data is moved, we will overwrite the stack slot for data with a specific, distinctive bit pattern that is not a valid pointer (we used to use zeroes, so we usually call this zeroing, but we’ve since shifted to something different). Then, when it’s time to free data , we check whether it was overwritten. (As an aside, this is roughly the same thing that the equivalent C++ code would do.)

But we’d like to do better than that. What we would like to do is to use boolean flags on the stack that tell us what needs to be freed. So that might look something like this:

fn send_if(data: Vec<Data>) {     let mut data_is_owned = true;      if some_condition(&data) {         send_to_other_thread(data);         data_is_owned = false;     }      post_send();      // Free `data`, but only if we still own it:     if data_is_owned {         mem::drop(data);     } } 

Of course, you couldn’t write code like this in Rust. You’re not allowed to acccess the variable data after the if , since it might have been moved. (This is yet another example of where we can do things in MIR that we would not want to allow in full Rust.)

Using boolean stack flags like this has a lot of advantages. For one, it’s more efficient: instead of overwriting the entire vector, we only have to set the one flag. But also, it’s easier to optimize: imagine that, through inlining or some other means, the compiler was able to determine that some_condition would always be true. In that case, standard constant propagation techniques would tell us that data_is_owned is always false, and hence we can just optimize away the entire call to mem::drop , resulting in tighter code. See RFC 320 for more details on that.

However, implementing this optimization properly on the current compiler architecture is quite difficult. With MIR, it becomes relatively straightforward. The MIR control-flow-graph tells us explicitly where values will be dropped and when. When MIR is first generated, we assume that dropping moved data has no effect – roughly like the current overwriting semantics. So this means that the MIR for send_if might look like this (for simplicity, I’ll ignore unwinding edges).

Introducing MIR

We can then transform this graph by identifying each place where data is moved or dropped and checking whether any of those places can reach one another. In this case, the send_to_other_thread(data) block can reach drop(data) . This indicates that we will need to introduce a flag, which can be done rather mechanically:

Introducing MIR

Finally, we can apply standard compiler techniques to optimize this flag (but in this case, the flag is needed, and so the final result would be the same).

Just to drive home why MIR is useful, let’s consider a variation on the send_if function called send_if2 . This variation checks some condition and, if it is met, sends the data to another thread for processing. Otherwise, it processes it locally:

fn send_if2(data: Vec<Data>) {     if some_condition(&data) {         send_to_other_thread(data);         return;     }      process(&data); } 

This would generate MIR like:

Introducing MIR

As before, we still generate the drops of data in all cases, at least to start. Since there are still moves that can later reach a drop, we could now introduce a stack flag variable, just as before:

Introducing MIR

But in this case, if we apply constant propagation, we can see that at each point where we test data_is_owned , we know statically whether it is true or false, which would allow us to remove the stack flag and optimize the graph above, yielding this result:

Introducing MIR


I expect the use of MIR to be quite transformative in terms of what the compiler can accomplish. By reducing the language to a core set of primitives, MIR opens the door to a number of language improvements. We looked at drop flags in this post. Another example is improving Rust’s lifetime system to leverage the control-flow-graph for better precision . But I think there will be many applications that we haven’t foreseen. In fact, one such example has already arisen: Scott Olson has been making great strides developing a MIR interpreter miri , and the techniques it is exploring may well form the basis for a more powerful constant evaluator in the compiler itself.

The transition to MIR in the compiler is not yet complete, but it’s getting quite close. Special thanks go out to Simonas Kazlauskas ( nagisa ) and Eduard-Mihai Burtescu ( eddyb ), who have both had a particularly large impact on pushing MIR towards the finish line. Our initial goal is to switch our LLVM generation to operate exclusively from the MIR. Work is also proceeding on porting the borrow checker. After that, I expect we will port a number of other pieces on the compiler that are currently using the HIR. If you’d be interested in contributing, look for issues tagged with A-mir or ask around in the #rustc channel on IRC .

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