Using the New Pass Manager¶

Overview
Just Tell Me How To Run The Default Optimization Pipeline With The New Pass Manager
Adding Passes to a Pass Manager
Inserting Passes into Default Pipelines
Using Analyses
Implementing Analysis Invalidation
Invoking opt
Status of the New and Legacy Pass Managers

Overview ¶

For an overview of the new pass manager, see the blog post.

Just Tell Me How To Run The Default Optimization Pipeline With The New Pass Manager ¶

// Create the analysis managers.
LoopAnalysisManager LAM;
FunctionAnalysisManager FAM;
CGSCCAnalysisManager CGAM;
ModuleAnalysisManager MAM;

// Create the new pass manager builder.
// Take a look at the PassBuilder constructor parameters for more
// customization, e.g. specifying a TargetMachine or various debugging
// options.
PassBuilder PB;

// Register all the basic analyses with the managers.
PB.registerModuleAnalyses(MAM);
PB.registerCGSCCAnalyses(CGAM);
PB.registerFunctionAnalyses(FAM);
PB.registerLoopAnalyses(LAM);
PB.crossRegisterProxies(LAM, FAM, CGAM, MAM);

// Create the pass manager.
// This one corresponds to a typical -O2 optimization pipeline.
ModulePassManager MPM = PB.buildPerModuleDefaultPipeline(OptimizationLevel::O2);

// Optimize the IR!
MPM.run(MyModule, MAM);

The C API also supports most of this, see llvm-c/Transforms/PassBuilder.h.

Adding Passes to a Pass Manager ¶

For how to write a new PM pass, see this page.

To add a pass to a new PM pass manager, the important thing is to match the pass type and the pass manager type. For example, a FunctionPassManager can only contain function passes:

FunctionPassManager FPM;
// InstSimplifyPass is a function pass
FPM.addPass(InstSimplifyPass());

If you want to add a loop pass that runs on all loops in a function to a FunctionPassManager, the loop pass must be wrapped in a function pass adaptor that goes through all the loops in the function and runs the loop pass on each one.

FunctionPassManager FPM;
// LoopRotatePass is a loop pass
FPM.addPass(createFunctionToLoopPassAdaptor(LoopRotatePass()));

The IR hierarchy in terms of the new PM is Module -> (CGSCC ->) Function -> Loop, where going through a CGSCC is optional.

FunctionPassManager FPM;
// loop -> function
FPM.addPass(createFunctionToLoopPassAdaptor(LoopFooPass()));

CGSCCPassManager CGPM;
// loop -> function -> cgscc
CGPM.addPass(createCGSCCToFunctionPassAdaptor(createFunctionToLoopPassAdaptor(LoopFooPass())));
// function -> cgscc
CGPM.addPass(createCGSCCToFunctionPassAdaptor(FunctionFooPass()));

ModulePassManager MPM;
// loop -> function -> module
MPM.addPass(createModuleToFunctionPassAdaptor(createFunctionToLoopPassAdaptor(LoopFooPass())));
// function -> module
MPM.addPass(createModuleToFunctionPassAdaptor(FunctionFooPass()));

// loop -> function -> cgscc -> module
MPM.addPass(createModuleToCGSCCPassAdaptor(createCGSCCToFunctionPassAdaptor(createFunctionToLoopPassAdaptor(LoopFooPass()))));
// function -> cgscc -> module
MPM.addPass(createModuleToCGSCCPassAdaptor(createCGSCCToFunctionPassAdaptor(FunctionFooPass())));

A pass manager of a specific IR unit is also a pass of that kind. For example, a FunctionPassManager is a function pass, meaning it can be added to a ModulePassManager:

ModulePassManager MPM;

FunctionPassManager FPM;
// InstSimplifyPass is a function pass
FPM.addPass(InstSimplifyPass());

MPM.addPass(createModuleToFunctionPassAdaptor(std::move(FPM)));

Generally you want to group CGSCC/function/loop passes together in a pass manager, as opposed to adding adaptors for each pass to the containing upper level pass manager. For example,

ModulePassManager MPM;
MPM.addPass(createModuleToFunctionPassAdaptor(FunctionPass1()));
MPM.addPass(createModuleToFunctionPassAdaptor(FunctionPass2()));
MPM.run();

will run FunctionPass1 on each function in a module, then run FunctionPass2 on each function in the module. In contrast,

ModulePassManager MPM;

FunctionPassManager FPM;
FPM.addPass(FunctionPass1());
FPM.addPass(FunctionPass2());

MPM.addPass(createModuleToFunctionPassAdaptor(std::move(FPM)));

will run FunctionPass1 and FunctionPass2 on the first function in a module, then run both passes on the second function in the module, and so on. This is better for cache locality around LLVM data structures. This similarly applies for the other IR types, and in some cases can even affect the quality of optimization. For example, running all loop passes on a loop may cause a later loop to be able to be optimized more than if each loop pass were run separately.

Inserting Passes into Default Pipelines ¶

Rather than manually adding passes to a pass manager, the typical way of creating a pass manager is to use a PassBuilder and call something like PassBuilder::buildPerModuleDefaultPipeline() which creates a typical pipeline for a given optimization level.

Sometimes either frontends or backends will want to inject passes into the pipeline. For example, frontends may want to add instrumentation, and target backends may want to add passes that lower custom intrinsics. For these cases, PassBuilder exposes callbacks that allow injecting passes into certain parts of the pipeline. For example,

PassBuilder PB;
PB.registerPipelineStartEPCallback([&](ModulePassManager &MPM,
                                       PassBuilder::OptimizationLevel Level) {
    MPM.addPass(FooPass());
};

will add FooPass near the very beginning of the pipeline for pass managers created by that PassBuilder. See the documentation for PassBuilder for the various places that passes can be added.

If a PassBuilder has a corresponding TargetMachine for a backend, it will call TargetMachine::registerPassBuilderCallbacks() to allow the backend to inject passes into the pipeline. This is equivalent to the legacy PM’s TargetMachine::adjustPassManager().

Clang’s BackendUtil.cpp shows examples of a frontend adding (mostly sanitizer) passes to various parts of the pipeline. AMDGPUTargetMachine::registerPassBuilderCallbacks() is an example of a backend adding passes to various parts of the pipeline.

Using Analyses ¶

LLVM provides many analyses that passes can use, such as a dominator tree. Calculating these can be expensive, so the new pass manager has infrastructure to cache analyses and reuse them when possible.

When a pass runs on some IR, it also receives an analysis manager which it can query for analyses. Querying for an analysis will cause the manager to check if it has already computed the result for the requested IR. If it already has and the result is still valid, it will return that. Otherwise it will construct a new result by calling the analysis’s run() method, cache it, and return it. You can also ask the analysis manager to only return an analysis if it’s already cached.

The analysis manager only provides analysis results for the same IR type as what the pass runs on. For example, a function pass receives an analysis manager that only provides function-level analyses. This works for many passes which work on a fixed scope. However, some passes want to peek up or down the IR hierarchy. For example, an SCC pass may want to look at function analyses for the functions inside the SCC. Or it may want to look at some immutable global analysis. In these cases, the analysis manager can provide a proxy to an outer or inner level analysis manager. For example, to get a FunctionAnalysisManager from a CGSCCAnalysisManager, you can call

FunctionAnalysisManager &FAM =
    AM.getResult<FunctionAnalysisManagerCGSCCProxy>(InitialC, CG)
        .getManager();

and use FAM as a typical FunctionAnalysisManager that a function pass would have access to. To get access to an outer level IR analysis, you can call

const auto &MAMProxy =
    AM.getResult<ModuleAnalysisManagerCGSCCProxy>(InitialC, CG);
FooAnalysisResult *AR = MAMProxy.getCachedResult<FooAnalysis>(M);

Asking for a cached and immutable outer level IR analysis works via getCachedResult(), but getting direct access to an outer level IR analysis manager to compute an outer level IR analysis is not allowed. This is for a couple reasons.

The first reason is that running analyses across outer level IR in inner level IR passes can result in quadratic compile time behavior. For example, a module analysis often scans every function and allowing function passes to run a module analysis may cause us to scan functions a quadratic number of times. If passes could keep outer level analyses up to date rather than computing them on demand this wouldn’t be an issue, but that would be a lot of work to ensure every pass updates all outer level analyses, and so far this hasn’t been necessary and there isn’t infrastructure for this (aside from function analyses in loop passes as described below). Self-updating analyses that gracefully degrade also handle this problem (e.g. GlobalsAA), but they run into the issue of having to be manually recomputed somewhere in the optimization pipeline if we want precision, and they block potential future concurrency.

The second reason is to keep in mind potential future pass concurrency, for example parallelizing function passes over different functions in a CGSCC or module. Since passes can ask for a cached analysis result, allowing passes to trigger outer level analysis computation could result in non-determinism if concurrency was supported. A related limitation is that outer level IR analyses that are used must be immutable, or else they could be invalidated by changes to inner level IR. Outer analyses unused by inner passes can and often will be invalidated by changes to inner level IR. These invalidations happen after the inner pass manager finishes, so accessing mutable analyses would give invalid results.

The exception to not being able to access outer level analyses is accessing function analyses in loop passes. Loop passes often use function analyses such as the dominator tree. Loop passes inherently require modifying the function the loop is in, and that includes some function analyses the loop analyses depend on. This discounts future concurrency over separate loops in a function, but that’s a tradeoff due to how tightly a loop and its function are coupled. To make sure the function analyses that loop passes use are valid, they are manually updated in the loop passes to ensure that invalidation is not necessary. There is a set of common function analyses that loop passes and analyses have access to which is passed into loop passes as a LoopStandardAnalysisResults parameter. Other mutable function analyses are not accessible from loop passes.

As with any caching mechanism, we need some way to tell analysis managers when results are no longer valid. Much of the analysis manager complexity comes from trying to invalidate as few analysis results as possible to keep compile times as low as possible.

There are two ways to deal with potentially invalid analysis results. One is to simply force clear the results. This should generally only be used when the IR that the result is keyed on becomes invalid. For example, a function is deleted, or a CGSCC has become invalid due to call graph changes.

The typical way to invalidate analysis results is for a pass to declare what types of analyses it preserves and what types it does not. When transforming IR, a pass either has the option to update analyses alongside the IR transformation, or tell the analysis manager that analyses are no longer valid and should be invalidated. If a pass wants to keep some specific analysis up to date, such as when updating it would be faster than invalidating and recalculating it, the analysis itself may have methods to update it for specific transformations, or there may be helper updaters like DomTreeUpdater for a DominatorTree. Otherwise to mark some analysis as no longer valid, the pass can return a PreservedAnalyses with the proper analyses invalidated.

// We've made no transformations that can affect any analyses.
return PreservedAnalyses::all();

// We've made transformations and don't want to bother to update any analyses.
return PreservedAnalyses::none();

// We've specifically updated the dominator tree alongside any transformations, but other analysis results may be invalid.
PreservedAnalyses PA;
PA.preserve<DominatorAnalysis>();
return PA;

// We haven't made any control flow changes, any analyses that only care about the control flow are still valid.
PreservedAnalyses PA;
PA.preserveSet<CFGAnalyses>();
return PA;

The pass manager will call the analysis manager’s invalidate() method with the pass’s returned PreservedAnalyses. This can be also done manually within the pass:

FooModulePass::run(Module& M, ModuleAnalysisManager& AM) {
  auto &FAM = AM.getResult<FunctionAnalysisManagerModuleProxy>(M).getManager();

  // Invalidate all analysis results for function F1.
  FAM.invalidate(F1, PreservedAnalyses::none());

  // Invalidate all analysis results across the entire module.
  AM.invalidate(M, PreservedAnalyses::none());

  // Clear the entry in the analysis manager for function F2 if we've completely removed it from the module.
  FAM.clear(F2);

  ...
}

One thing to note when accessing inner level IR analyses is cached results for deleted IR. If a function is deleted in a module pass, its address is still used as the key for cached analyses. Take care in the pass to either clear the results for that function or not use inner analyses at all.

AM.invalidate(M, PreservedAnalyses::none()); will invalidate the inner analysis manager proxy which will clear all cached analyses, conservatively assuming that there are invalid addresses used as keys for cached analyses. However, if you’d like to be more selective about which analyses are cached/invalidated, you can mark the analysis manager proxy as preserved, essentially saying that all deleted entries have been taken care of manually. This should only be done with measurable compile time gains as it can be tricky to make sure all the right analyses are invalidated.

Implementing Analysis Invalidation ¶

By default, an analysis is invalidated if PreservedAnalyses says that analyses on the IR unit it runs on are not preserved (see AnalysisResultModel::invalidate()). An analysis can implement invalidate() to be more conservative when it comes to invalidation. For example,

bool FooAnalysisResult::invalidate(Function &F, const PreservedAnalyses &PA,
                                   FunctionAnalysisManager::Invalidator &) {
  auto PAC = PA.getChecker<FooAnalysis>();
  // the default would be:
  // return !(PAC.preserved() || PAC.preservedSet<AllAnalysesOn<Function>>());
  return !(PAC.preserved() || PAC.preservedSet<AllAnalysesOn<Function>>()
      || PAC.preservedSet<CFGAnalyses>());
}

says that if the PreservedAnalyses specifically preserves FooAnalysis, or if PreservedAnalyses preserves all analyses (implicit in PAC.preserved()), or if PreservedAnalyses preserves all function analyses, or PreservedAnalyses preserves all analyses that only care about the CFG, the FooAnalysisResult should not be invalidated.

If an analysis is stateless and generally shouldn’t be invalidated, use the following:

bool FooAnalysisResult::invalidate(Function &F, const PreservedAnalyses &PA,
                                   FunctionAnalysisManager::Invalidator &) {
  // Check whether the analysis has been explicitly invalidated. Otherwise, it's
  // stateless and remains preserved.
  auto PAC = PA.getChecker<FooAnalysis>();
  return !PAC.preservedWhenStateless();
}

If an analysis depends on other analyses, those analyses also need to be checked if they are invalidated:

bool FooAnalysisResult::invalidate(Function &F, const PreservedAnalyses &PA,
                                   FunctionAnalysisManager::Invalidator &) {
  auto PAC = PA.getChecker<FooAnalysis>();
  if (!PAC.preserved() && !PAC.preservedSet<AllAnalysesOn<Function>>())
    return true;

  // Check transitive dependencies.
  return Inv.invalidate<BarAnalysis>(F, PA) ||
        Inv.invalidate<BazAnalysis>(F, PA);
}

Combining invalidation and analysis manager proxies results in some complexity. For example, when we invalidate all analyses in a module pass, we have to make sure that we also invalidate function analyses accessible via any existing inner proxies. The inner proxy’s invalidate() first checks if the proxy itself should be invalidated. If so, that means the proxy may contain pointers to IR that is no longer valid, meaning that the inner proxy needs to completely clear all relevant analysis results. Otherwise the proxy simply forwards the invalidation to the inner analysis manager.

Generally for outer proxies, analysis results from the outer analysis manager should be immutable, so invalidation shouldn’t be a concern. However, it is possible for some inner analysis to depend on some outer analysis, and when the outer analysis is invalidated, we need to make sure that dependent inner analyses are also invalidated. This actually happens with alias analysis results. Alias analysis is a function-level analysis, but there are module-level implementations of specific types of alias analysis. Currently GlobalsAA is the only module-level alias analysis and it generally is not invalidated so this is not so much of a concern. See OuterAnalysisManagerProxy::Result::registerOuterAnalysisInvalidation() for more details.

Invoking `opt`¶

To use the legacy pass manager:

$ opt -enable-new-pm=0 -pass1 -pass2 /tmp/a.ll -S

This will be removed once the legacy pass manager is deprecated and removed for the optimization pipeline.

To use the new PM:

$ opt -passes='pass1,pass2' /tmp/a.ll -S

The new PM typically requires explicit pass nesting. For example, to run a function pass, then a module pass, we need to wrap the function pass in a module adaptor:

$ opt -passes='function(no-op-function),no-op-module' /tmp/a.ll -S

A more complete example, and -debug-pass-manager to show the execution order:

$ opt -passes='no-op-module,cgscc(no-op-cgscc,function(no-op-function,loop(no-op-loop))),function(no-op-function,loop(no-op-loop))' /tmp/a.ll -S -debug-pass-manager

Improper nesting can lead to error messages such as

$ opt -passes='no-op-function,no-op-module' /tmp/a.ll -S
opt: unknown function pass 'no-op-module'

The nesting is: module (-> cgscc) -> function -> loop, where the CGSCC nesting is optional.

There are a couple of special cases for easier typing:

If the first pass is not a module pass, a pass manager of the first pass is implicitly created
- For example, the following are equivalent

$ opt -passes='no-op-function,no-op-function' /tmp/a.ll -S
$ opt -passes='function(no-op-function,no-op-function)' /tmp/a.ll -S

If there is an adaptor for a pass that lets it fit in the previous pass manager, that is implicitly created
- For example, the following are equivalent

$ opt -passes='no-op-function,no-op-loop' /tmp/a.ll -S
$ opt -passes='no-op-function,loop(no-op-loop)' /tmp/a.ll -S

For a list of available passes and analyses, including the IR unit (module, CGSCC, function, loop) they operate on, run

$ opt --print-passes

or take a look at PassRegistry.def.

To make sure an analysis named foo is available before a pass, add require<foo> to the pass pipeline. This adds a pass that simply requests that the analysis is run. This pass is also subject to proper nesting. For example, to make sure some function analysis is already computed for all functions before a module pass:

$ opt -passes='function(require<my-function-analysis>),my-module-pass' /tmp/a.ll -S

Status of the New and Legacy Pass Managers ¶

LLVM currently contains two pass managers, the legacy PM and the new PM. The optimization pipeline (aka the middle-end) works with both the legacy PM and the new PM, whereas the backend target-dependent code generation only works with the legacy PM.

For the optimization pipeline, the new PM is the default PM. Using the legacy PM for the optimization pipeline is deprecated and there are ongoing efforts to remove its usage.

Some IR passes are considered part of the backend codegen pipeline even if they are LLVM IR passes (whereas all MIR passes are codegen passes). This includes anything added via TargetPassConfig hooks, e.g. TargetPassConfig::addCodeGenPrepare(). As mentioned before, passes added in TargetMachine::adjustPassManager() are part of the optimization pipeline, and should have a corresponding line in TargetMachine::registerPassBuilderCallbacks().

Currently there are efforts to make the codegen pipeline work with the new PM.

Documentation

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