MIR optimizations

MIR optimizations are optimizations run on the MIR to produce better MIR before codegen. This is important for two reasons: first, it makes the final generated executable code better, and second, it means that LLVM has less work to do, so compilation is faster. Note that since MIR is generic (not monomorphized yet), these optimizations are particularly effective; we can optimize the generic version, so all of the monomorphizations are cheaper!

MIR optimizations run after borrow checking. We run a series of optimization passes over the MIR to improve it. Some passes are required to run on all code, some passes don't actually do optimizations but only check stuff, and some passes are only turned on in release mode.

The optimized_mir query is called to produce the optimized MIR for a given DefId. This query makes sure that the borrow checker has run and that some validation has occurred. Then, it steals the MIR, optimizes it, and returns the improved MIR.

Quickstart for adding a new optimization

  1. Make a Rust source file in src/test/mir-opt that shows the code you want to optimize. This should be kept simple, so avoid println! or other formatting code if it's not necessary for the optimization. The reason for this is that println!, format!, etc. generate a lot of MIR that can make it harder to understand what the optimization does to the test.

  2. Run ./x.py test --bless src/test/mir-opt/<your-test>.rs to generate a MIR dump. Read this README for instructions on how to dump things.

  3. Commit the current working directory state. The reason you should commit the test output before you implement the optimization is so that you (and your reviewers) can see a before/after diff of what the optimization changed.

  4. Implement a new optimization in compiler/rustc_mir/src/transform. The fastest and easiest way to do this is to

    1. pick a small optimization (such as no_landing_pads) and copy it to a new file,
    2. add your optimization to one of the lists in the run_optimization_passes() function,
    3. and then start modifying the copied optimization.
  5. Rerun ./x.py test --bless src/test/mir-opt/<your-test>.rs to regenerate the MIR dumps. Look at the diffs to see if they are what you expect.

  6. Run ./x.py test src/test/ui to see if your optimization broke anything.

  7. If there are issues with your optimization, experiment with it a bit and repeat steps 5 and 6.

  8. Commit and open a PR. You can do this at any point, even if things aren't working yet, so that you can ask for feedback on the PR. Open a "WIP" PR (just prefix your PR title with [WIP] or otherwise note that it is a work in progress) in that case.

    Make sure to commit the blessed test output as well! It's necessary for CI to pass and it's very helpful to reviewers.

If you have any questions along the way, feel free to ask in #t-compiler/wg-mir-opt on Zulip.

Defining optimization passes

The list of passes run and the order in which they are run is defined by the run_optimization_passes function. It contains an array of passes to run. Each pass in the array is a struct that implements the MirPass trait. The array is an array of &dyn MirPass trait objects. Typically, a pass is implemented in its own submodule of the rustc_mir::transform module.

Some examples of passes are:

  • CleanupNonCodegenStatements: remove some of the info that is only needed for analyses, rather than codegen.
  • ConstProp: Does constant propagation

You can see the "Implementors" section of the MirPass rustdocs for more examples.

MIR optimization levels

MIR optimizations can come in various levels of readiness. Experimental optimizations may cause miscompilations, or slow down compile times. These passes are still included in nightly builds to gather feedback and make it easier to modify the pass. To enable working with slow or otherwise experimental optimization passes, you can specify the -Z mir-opt-level debug flag. You can find the definitions of the levels in the compiler MCP. If you are developing a MIR pass and want to query whether your optimization pass should run, you can check the current level using tcx.sess.opts.debugging_opts.mir_opt_level.

Optimization fuel

Optimization fuel is a compiler option (-Z fuel=<crate>=<value>) that allows for fine grained control over which optimizations can be applied during compilation: each optimization reduces fuel by 1, and when fuel reaches 0 no more optimizations are applied. The primary use of fuel is debugging optimizations that may be incorrect or misapplied. By changing the fuel value, you can bisect a compilation session down to the exact incorrect optimization (this behaves like a kind of binary search through the optimizations).

MIR optimizations respect fuel, and in general each pass should check fuel by calling tcx.consider_optimizing and skipping the optimization if fuel is empty. There are a few considerations:

  1. If the pass is considered "guaranteed" (for example, it should always be run because it is needed for correctness), then fuel should not be used. An example of this is PromoteTemps.
  2. In some cases, an initial pass is performed to gather candidates, which are then iterated to perform optimizations. In these situations, we should allow for the initial gathering pass and then check fuel as close to the mutation as possible. This allows for the best debugging experience, because we can determine where in the list of candidates an optimization may have been misapplied. Examples of this are InstCombine and ConstantPropagation.