blob: 6fc4c42bdd7180055f6a249616bcdb5d022eaa2b [file] [log] [blame]
//===- MLInlineAdvisor.cpp - machine learned InlineAdvisor ----------------===//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// This file implements the interface between the inliner and a learned model.
// It delegates model evaluation to either the AOT compiled model (the
// 'release' mode) or a runtime-loaded model (the 'development' case).
#include "llvm/Config/config.h"
#if defined(LLVM_HAVE_TF_AOT) || defined(LLVM_HAVE_TF_API)
#include <limits>
#include <unordered_map>
#include <unordered_set>
#include "llvm/ADT/SCCIterator.h"
#include "llvm/Analysis/CallGraph.h"
#include "llvm/Analysis/FunctionPropertiesAnalysis.h"
#include "llvm/Analysis/InlineCost.h"
#include "llvm/Analysis/MLInlineAdvisor.h"
#include "llvm/Analysis/MLModelRunner.h"
#include "llvm/Analysis/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/TargetLibraryInfo.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/IR/InstIterator.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/PassManager.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Path.h"
using namespace llvm;
#define DEBUG_TYPE "inline-ml"
static cl::opt<float> SizeIncreaseThreshold(
"ml-advisor-size-increase-threshold", cl::Hidden,
cl::desc("Maximum factor by which expected native size may increase before "
"blocking any further inlining."),
// clang-format off
const std::array<std::string, NumberOfFeatures> llvm::FeatureNameMap{
// InlineCost features - these must come first
// Non-cost features
// clang-format on
const char *const llvm::DecisionName = "inlining_decision";
const char *const llvm::DefaultDecisionName = "inlining_default";
const char *const llvm::RewardName = "delta_size";
CallBase *getInlinableCS(Instruction &I) {
if (auto *CS = dyn_cast<CallBase>(&I))
if (Function *Callee = CS->getCalledFunction()) {
if (!Callee->isDeclaration()) {
return CS;
return nullptr;
MLInlineAdvisor::MLInlineAdvisor(Module &M, ModuleAnalysisManager &MAM,
std::unique_ptr<MLModelRunner> Runner)
: InlineAdvisor(
M, MAM.getResult<FunctionAnalysisManagerModuleProxy>(M).getManager()),
ModelRunner(std::move(Runner)), CG(new CallGraph(M)),
InitialIRSize(getModuleIRSize()), CurrentIRSize(InitialIRSize) {
// Extract the 'call site height' feature - the position of a call site
// relative to the farthest statically reachable SCC node. We don't mutate
// this value while inlining happens. Empirically, this feature proved
// critical in behavioral cloning - i.e. training a model to mimic the manual
// heuristic's decisions - and, thus, equally important for training for
// improvement.
for (auto I = scc_begin(CG.get()); !I.isAtEnd(); ++I) {
const std::vector<CallGraphNode *> &CGNodes = *I;
unsigned Level = 0;
for (auto *CGNode : CGNodes) {
Function *F = CGNode->getFunction();
if (!F || F->isDeclaration())
for (auto &I : instructions(F)) {
if (auto *CS = getInlinableCS(I)) {
auto *Called = CS->getCalledFunction();
auto Pos = FunctionLevels.find(Called);
// In bottom up traversal, an inlinable callee is either in the
// same SCC, or to a function in a visited SCC. So not finding its
// level means we haven't visited it yet, meaning it's in this SCC.
if (Pos == FunctionLevels.end())
Level = std::max(Level, Pos->second + 1);
for (auto *CGNode : CGNodes) {
Function *F = CGNode->getFunction();
if (F && !F->isDeclaration())
FunctionLevels[F] = Level;
void MLInlineAdvisor::onPassEntry() {
// Function passes executed between InlinerPass runs may have changed the
// module-wide features.
if (!Invalid)
NodeCount = 0;
EdgeCount = 0;
for (auto &F : M)
if (!F.isDeclaration()) {
EdgeCount += getLocalCalls(F);
Invalid = false;
int64_t MLInlineAdvisor::getLocalCalls(Function &F) {
return FAM.getResult<FunctionPropertiesAnalysis>(F)
// Update the internal state of the advisor, and force invalidate feature
// analysis. Currently, we maintain minimal (and very simple) global state - the
// number of functions and the number of static calls. We also keep track of the
// total IR size in this module, to stop misbehaving policies at a certain bloat
// factor (SizeIncreaseThreshold)
void MLInlineAdvisor::onSuccessfulInlining(const MLInlineAdvice &Advice,
bool CalleeWasDeleted) {
Function *Caller = Advice.getCaller();
Function *Callee = Advice.getCallee();
// The caller features aren't valid anymore.
PreservedAnalyses PA = PreservedAnalyses::all();
FAM.invalidate(*Caller, PA);
int64_t IRSizeAfter =
getIRSize(*Caller) + (CalleeWasDeleted ? 0 : Advice.CalleeIRSize);
CurrentIRSize += IRSizeAfter - (Advice.CallerIRSize + Advice.CalleeIRSize);
if (CurrentIRSize > SizeIncreaseThreshold * InitialIRSize)
ForceStop = true;
// We can delta-update module-wide features. We know the inlining only changed
// the caller, and maybe the callee (by deleting the latter).
// Nodes are simple to update.
// For edges, we 'forget' the edges that the caller and callee used to have
// before inlining, and add back what they currently have together.
int64_t NewCallerAndCalleeEdges =
if (CalleeWasDeleted)
NewCallerAndCalleeEdges +=
EdgeCount += (NewCallerAndCalleeEdges - Advice.CallerAndCalleeEdges);
assert(CurrentIRSize >= 0 && EdgeCount >= 0 && NodeCount >= 0);
int64_t MLInlineAdvisor::getModuleIRSize() const {
int64_t Ret = 0;
for (auto &F : CG->getModule())
if (!F.isDeclaration())
Ret += getIRSize(F);
return Ret;
std::unique_ptr<InlineAdvice> MLInlineAdvisor::getAdviceImpl(CallBase &CB) {
auto &Caller = *CB.getCaller();
auto &Callee = *CB.getCalledFunction();
auto GetAssumptionCache = [&](Function &F) -> AssumptionCache & {
return FAM.getResult<AssumptionAnalysis>(F);
auto &TIR = FAM.getResult<TargetIRAnalysis>(Callee);
auto &ORE = FAM.getResult<OptimizationRemarkEmitterAnalysis>(Caller);
auto MandatoryKind = InlineAdvisor::getMandatoryKind(CB, FAM, ORE);
// If this is a "never inline" case, there won't be any changes to internal
// state we need to track, so we can just return the base InlineAdvice, which
// will do nothing interesting.
// Same thing if this is a recursive case.
if (MandatoryKind == InlineAdvisor::MandatoryInliningKind::Never ||
&Caller == &Callee)
return getMandatoryAdvice(CB, false);
bool Mandatory =
MandatoryKind == InlineAdvisor::MandatoryInliningKind::Always;
// If we need to stop, we won't want to track anymore any state changes, so
// we just return the base InlineAdvice, which acts as a noop.
if (ForceStop) {
ORE.emit([&] {
return OptimizationRemarkMissed(DEBUG_TYPE, "ForceStop", &CB)
<< "Won't attempt inlining because module size grew too much.";
return std::make_unique<InlineAdvice>(this, CB, ORE, Mandatory);
int CostEstimate = 0;
if (!Mandatory) {
auto IsCallSiteInlinable =
llvm::getInliningCostEstimate(CB, TIR, GetAssumptionCache);
if (!IsCallSiteInlinable) {
// We can't inline this for correctness reasons, so return the base
// InlineAdvice, as we don't care about tracking any state changes (which
// won't happen).
return std::make_unique<InlineAdvice>(this, CB, ORE, false);
CostEstimate = *IsCallSiteInlinable;
const auto CostFeatures =
llvm::getInliningCostFeatures(CB, TIR, GetAssumptionCache);
if (!CostFeatures) {
return std::make_unique<InlineAdvice>(this, CB, ORE, false);
if (Mandatory)
return getMandatoryAdvice(CB, true);
auto NrCtantParams = 0;
for (auto I = CB.arg_begin(), E = CB.arg_end(); I != E; ++I) {
NrCtantParams += (isa<Constant>(*I));
auto &CallerBefore = FAM.getResult<FunctionPropertiesAnalysis>(Caller);
auto &CalleeBefore = FAM.getResult<FunctionPropertiesAnalysis>(Callee);
ModelRunner->setFeature(FeatureIndex::NodeCount, NodeCount);
ModelRunner->setFeature(FeatureIndex::NrCtantParams, NrCtantParams);
ModelRunner->setFeature(FeatureIndex::EdgeCount, EdgeCount);
ModelRunner->setFeature(FeatureIndex::CallerUsers, CallerBefore.Uses);
ModelRunner->setFeature(FeatureIndex::CalleeUsers, CalleeBefore.Uses);
ModelRunner->setFeature(FeatureIndex::CostEstimate, CostEstimate);
// Add the cost features
for (size_t I = 0;
I < static_cast<size_t>(InlineCostFeatureIndex::NumberOfFeatures); ++I) {
return getAdviceFromModel(CB, ORE);
MLInlineAdvisor::getAdviceFromModel(CallBase &CB,
OptimizationRemarkEmitter &ORE) {
return std::make_unique<MLInlineAdvice>(this, CB, ORE, ModelRunner->run());
std::unique_ptr<InlineAdvice> MLInlineAdvisor::getMandatoryAdvice(CallBase &CB,
bool Advice) {
// Make sure we track inlinings in all cases - mandatory or not.
if (Advice && !ForceStop)
return getMandatoryAdviceImpl(CB);
// If this is a "never inline" case, there won't be any changes to internal
// state we need to track, so we can just return the base InlineAdvice, which
// will do nothing interesting.
// Same if we are forced to stop - we don't track anymore.
return std::make_unique<InlineAdvice>(this, CB, getCallerORE(CB), Advice);
MLInlineAdvisor::getMandatoryAdviceImpl(CallBase &CB) {
return std::make_unique<MLInlineAdvice>(this, CB, getCallerORE(CB), true);
void MLInlineAdvice::reportContextForRemark(
DiagnosticInfoOptimizationBase &OR) {
using namespace ore;
OR << NV("Callee", Callee->getName());
for (size_t I = 0; I < NumberOfFeatures; ++I)
OR << NV(FeatureNameMap[I], getAdvisor()->getModelRunner().getFeature(I));
OR << NV("ShouldInline", isInliningRecommended());
void MLInlineAdvice::recordInliningImpl() {
ORE.emit([&]() {
OptimizationRemark R(DEBUG_TYPE, "InliningSuccess", DLoc, Block);
return R;
getAdvisor()->onSuccessfulInlining(*this, /*CalleeWasDeleted*/ false);
void MLInlineAdvice::recordInliningWithCalleeDeletedImpl() {
ORE.emit([&]() {
OptimizationRemark R(DEBUG_TYPE, "InliningSuccessWithCalleeDeleted", DLoc,
return R;
getAdvisor()->onSuccessfulInlining(*this, /*CalleeWasDeleted*/ true);
void MLInlineAdvice::recordUnsuccessfulInliningImpl(
const InlineResult &Result) {
ORE.emit([&]() {
OptimizationRemarkMissed R(DEBUG_TYPE, "InliningAttemptedAndUnsuccessful",
DLoc, Block);
return R;
void MLInlineAdvice::recordUnattemptedInliningImpl() {
ORE.emit([&]() {
OptimizationRemarkMissed R(DEBUG_TYPE, "IniningNotAttempted", DLoc, Block);
return R;
#endif // defined(LLVM_HAVE_TF_AOT) || defined(LLVM_HAVE_TF_API)