PreservedAnalyses AlwaysInlinerPass::run(Module &M, ModuleAnalysisManager &) {
InlineFunctionInfo IFI;
SmallSetVector<CallSite, 16> Calls;
bool Changed = false;
SmallVector<Function *, 16> InlinedFunctions;
for (Function &F : M)
if (!F.isDeclaration() && F.hasFnAttribute(Attribute::AlwaysInline) &&
isInlineViable(F)) {
Calls.clear();
for (User *U : F.users())
if (auto CS = CallSite(U))
if (CS.getCalledFunction() == &F)
Calls.insert(CS);
for (CallSite CS : Calls)
// FIXME: We really shouldn't be able to fail to inline at this point!
// We should do something to log or check the inline failures here.
Changed |= InlineFunction(CS, IFI);
// Remember to try and delete this function afterward. This both avoids
// re-walking the rest of the module and avoids dealing with any iterator
// invalidation issues while deleting functions.
InlinedFunctions.push_back(&F);
}
// Remove any live functions.
erase_if(InlinedFunctions, [&](Function *F) {
F->removeDeadConstantUsers();
return !F->isDefTriviallyDead();
});
// Delete the non-comdat ones from the module and also from our vector.
auto NonComdatBegin = partition(
InlinedFunctions, [&](Function *F) { return F->hasComdat(); });
for (Function *F : make_range(NonComdatBegin, InlinedFunctions.end()))
M.getFunctionList().erase(F);
InlinedFunctions.erase(NonComdatBegin, InlinedFunctions.end());
if (!InlinedFunctions.empty()) {
// Now we just have the comdat functions. Filter out the ones whose comdats
// are not actually dead.
filterDeadComdatFunctions(M, InlinedFunctions);
// The remaining functions are actually dead.
for (Function *F : InlinedFunctions)
M.getFunctionList().erase(F);
}
return Changed ? PreservedAnalyses::none() : PreservedAnalyses::all();
}
PreservedAnalyses AlwaysInlinerPass::run(Module &M, ModuleAnalysisManager &) {
InlineFunctionInfo IFI;
SmallSetVector<CallSite, 16> Calls;
bool Changed = false;
SmallVector<Function *, 16> InlinedFunctions;
for (Function &F : M)
if (!F.isDeclaration() && F.hasFnAttribute(Attribute::AlwaysInline) &&
isInlineViable(F)) {
Calls.clear();
for (User *U : F.users())
if (auto CS = CallSite(U))
if (CS.getCalledFunction() == &F)
Calls.insert(CS);
for (CallSite CS : Calls)
// FIXME: We really shouldn't be able to fail to inline at this point!
// We should do something to log or check the inline failures here.
Changed |= InlineFunction(CS, IFI);
// Remember to try and delete this function afterward. This both avoids
// re-walking the rest of the module and avoids dealing with any iterator
// invalidation issues while deleting functions.
InlinedFunctions.push_back(&F);
}
// Now try to delete all the functions we inlined.
for (Function *InlinedF : InlinedFunctions) {
InlinedF->removeDeadConstantUsers();
// FIXME: We should use some utility to handle cases where we can
// completely remove the comdat.
if (InlinedF->isDefTriviallyDead() && !InlinedF->hasComdat())
M.getFunctionList().erase(InlinedF);
}
return Changed ? PreservedAnalyses::none() : PreservedAnalyses::all();
}
/// \brief Figure out if the loop is worth full unrolling.
///
/// Complete loop unrolling can make some loads constant, and we need to know
/// if that would expose any further optimization opportunities. This routine
/// estimates this optimization. It computes cost of unrolled loop
/// (UnrolledCost) and dynamic cost of the original loop (RolledDynamicCost). By
/// dynamic cost we mean that we won't count costs of blocks that are known not
/// to be executed (i.e. if we have a branch in the loop and we know that at the
/// given iteration its condition would be resolved to true, we won't add up the
/// cost of the 'false'-block).
/// \returns Optional value, holding the RolledDynamicCost and UnrolledCost. If
/// the analysis failed (no benefits expected from the unrolling, or the loop is
/// too big to analyze), the returned value is None.
Optional<EstimatedUnrollCost>
analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, ScalarEvolution &SE,
const TargetTransformInfo &TTI,
unsigned MaxUnrolledLoopSize) {
// We want to be able to scale offsets by the trip count and add more offsets
// to them without checking for overflows, and we already don't want to
// analyze *massive* trip counts, so we force the max to be reasonably small.
assert(UnrollMaxIterationsCountToAnalyze < (INT_MAX / 2) &&
"The unroll iterations max is too large!");
// Don't simulate loops with a big or unknown tripcount
if (!UnrollMaxIterationsCountToAnalyze || !TripCount ||
TripCount > UnrollMaxIterationsCountToAnalyze)
return None;
SmallSetVector<BasicBlock *, 16> BBWorklist;
DenseMap<Value *, Constant *> SimplifiedValues;
// The estimated cost of the unrolled form of the loop. We try to estimate
// this by simplifying as much as we can while computing the estimate.
unsigned UnrolledCost = 0;
// We also track the estimated dynamic (that is, actually executed) cost in
// the rolled form. This helps identify cases when the savings from unrolling
// aren't just exposing dead control flows, but actual reduced dynamic
// instructions due to the simplifications which we expect to occur after
// unrolling.
unsigned RolledDynamicCost = 0;
// Simulate execution of each iteration of the loop counting instructions,
// which would be simplified.
// Since the same load will take different values on different iterations,
// we literally have to go through all loop's iterations.
for (unsigned Iteration = 0; Iteration < TripCount; ++Iteration) {
SimplifiedValues.clear();
UnrolledInstAnalyzer Analyzer(Iteration, SimplifiedValues, L, SE);
BBWorklist.clear();
BBWorklist.insert(L->getHeader());
// Note that we *must not* cache the size, this loop grows the worklist.
for (unsigned Idx = 0; Idx != BBWorklist.size(); ++Idx) {
BasicBlock *BB = BBWorklist[Idx];
// Visit all instructions in the given basic block and try to simplify
// it. We don't change the actual IR, just count optimization
// opportunities.
for (Instruction &I : *BB) {
unsigned InstCost = TTI.getUserCost(&I);
// Visit the instruction to analyze its loop cost after unrolling,
// and if the visitor returns false, include this instruction in the
// unrolled cost.
if (!Analyzer.visit(I))
UnrolledCost += InstCost;
// Also track this instructions expected cost when executing the rolled
// loop form.
RolledDynamicCost += InstCost;
// If unrolled body turns out to be too big, bail out.
if (UnrolledCost > MaxUnrolledLoopSize)
return None;
}
// Add BB's successors to the worklist.
for (BasicBlock *Succ : successors(BB))
if (L->contains(Succ))
BBWorklist.insert(Succ);
}
// If we found no optimization opportunities on the first iteration, we
// won't find them on later ones too.
if (UnrolledCost == RolledDynamicCost)
return None;
}
return {{UnrolledCost, RolledDynamicCost}};
}
/// \brief Figure out if the loop is worth full unrolling.
///
/// Complete loop unrolling can make some loads constant, and we need to know
/// if that would expose any further optimization opportunities. This routine
/// estimates this optimization. It computes cost of unrolled loop
/// (UnrolledCost) and dynamic cost of the original loop (RolledDynamicCost). By
/// dynamic cost we mean that we won't count costs of blocks that are known not
/// to be executed (i.e. if we have a branch in the loop and we know that at the
/// given iteration its condition would be resolved to true, we won't add up the
/// cost of the 'false'-block).
/// \returns Optional value, holding the RolledDynamicCost and UnrolledCost. If
/// the analysis failed (no benefits expected from the unrolling, or the loop is
/// too big to analyze), the returned value is None.
static Optional<EstimatedUnrollCost>
analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
ScalarEvolution &SE, const TargetTransformInfo &TTI,
int MaxUnrolledLoopSize) {
// We want to be able to scale offsets by the trip count and add more offsets
// to them without checking for overflows, and we already don't want to
// analyze *massive* trip counts, so we force the max to be reasonably small.
assert(UnrollMaxIterationsCountToAnalyze < (INT_MAX / 2) &&
"The unroll iterations max is too large!");
// Only analyze inner loops. We can't properly estimate cost of nested loops
// and we won't visit inner loops again anyway.
if (!L->empty())
return None;
// Don't simulate loops with a big or unknown tripcount
if (!UnrollMaxIterationsCountToAnalyze || !TripCount ||
TripCount > UnrollMaxIterationsCountToAnalyze)
return None;
SmallSetVector<BasicBlock *, 16> BBWorklist;
SmallSetVector<std::pair<BasicBlock *, BasicBlock *>, 4> ExitWorklist;
DenseMap<Value *, Constant *> SimplifiedValues;
SmallVector<std::pair<Value *, Constant *>, 4> SimplifiedInputValues;
// The estimated cost of the unrolled form of the loop. We try to estimate
// this by simplifying as much as we can while computing the estimate.
int UnrolledCost = 0;
// We also track the estimated dynamic (that is, actually executed) cost in
// the rolled form. This helps identify cases when the savings from unrolling
// aren't just exposing dead control flows, but actual reduced dynamic
// instructions due to the simplifications which we expect to occur after
// unrolling.
int RolledDynamicCost = 0;
// We track the simplification of each instruction in each iteration. We use
// this to recursively merge costs into the unrolled cost on-demand so that
// we don't count the cost of any dead code. This is essentially a map from
// <instruction, int> to <bool, bool>, but stored as a densely packed struct.
DenseSet<UnrolledInstState, UnrolledInstStateKeyInfo> InstCostMap;
// A small worklist used to accumulate cost of instructions from each
// observable and reached root in the loop.
SmallVector<Instruction *, 16> CostWorklist;
// PHI-used worklist used between iterations while accumulating cost.
SmallVector<Instruction *, 4> PHIUsedList;
// Helper function to accumulate cost for instructions in the loop.
auto AddCostRecursively = [&](Instruction &RootI, int Iteration) {
assert(Iteration >= 0 && "Cannot have a negative iteration!");
assert(CostWorklist.empty() && "Must start with an empty cost list");
assert(PHIUsedList.empty() && "Must start with an empty phi used list");
CostWorklist.push_back(&RootI);
for (;; --Iteration) {
do {
Instruction *I = CostWorklist.pop_back_val();
// InstCostMap only uses I and Iteration as a key, the other two values
// don't matter here.
auto CostIter = InstCostMap.find({I, Iteration, 0, 0});
if (CostIter == InstCostMap.end())
// If an input to a PHI node comes from a dead path through the loop
// we may have no cost data for it here. What that actually means is
// that it is free.
continue;
auto &Cost = *CostIter;
if (Cost.IsCounted)
// Already counted this instruction.
continue;
// Mark that we are counting the cost of this instruction now.
Cost.IsCounted = true;
// If this is a PHI node in the loop header, just add it to the PHI set.
if (auto *PhiI = dyn_cast<PHINode>(I))
if (PhiI->getParent() == L->getHeader()) {
assert(Cost.IsFree && "Loop PHIs shouldn't be evaluated as they "
"inherently simplify during unrolling.");
if (Iteration == 0)
continue;
// Push the incoming value from the backedge into the PHI used list
// if it is an in-loop instruction. We'll use this to populate the
// cost worklist for the next iteration (as we count backwards).
if (auto *OpI = dyn_cast<Instruction>(
PhiI->getIncomingValueForBlock(L->getLoopLatch())))
if (L->contains(OpI))
PHIUsedList.push_back(OpI);
continue;
}
// First accumulate the cost of this instruction.
if (!Cost.IsFree) {
UnrolledCost += TTI.getUserCost(I);
DEBUG(dbgs() << "Adding cost of instruction (iteration " << Iteration
<< "): ");
DEBUG(I->dump());
}
// We must count the cost of every operand which is not free,
// recursively. If we reach a loop PHI node, simply add it to the set
// to be considered on the next iteration (backwards!).
for (Value *Op : I->operands()) {
// Check whether this operand is free due to being a constant or
// outside the loop.
auto *OpI = dyn_cast<Instruction>(Op);
if (!OpI || !L->contains(OpI))
continue;
// Otherwise accumulate its cost.
CostWorklist.push_back(OpI);
}
} while (!CostWorklist.empty());
if (PHIUsedList.empty())
// We've exhausted the search.
break;
assert(Iteration > 0 &&
"Cannot track PHI-used values past the first iteration!");
CostWorklist.append(PHIUsedList.begin(), PHIUsedList.end());
PHIUsedList.clear();
}
};
// Ensure that we don't violate the loop structure invariants relied on by
// this analysis.
assert(L->isLoopSimplifyForm() && "Must put loop into normal form first.");
assert(L->isLCSSAForm(DT) &&
"Must have loops in LCSSA form to track live-out values.");
DEBUG(dbgs() << "Starting LoopUnroll profitability analysis...\n");
// Simulate execution of each iteration of the loop counting instructions,
// which would be simplified.
// Since the same load will take different values on different iterations,
// we literally have to go through all loop's iterations.
for (unsigned Iteration = 0; Iteration < TripCount; ++Iteration) {
DEBUG(dbgs() << " Analyzing iteration " << Iteration << "\n");
// Prepare for the iteration by collecting any simplified entry or backedge
// inputs.
for (Instruction &I : *L->getHeader()) {
auto *PHI = dyn_cast<PHINode>(&I);
if (!PHI)
break;
// The loop header PHI nodes must have exactly two input: one from the
// loop preheader and one from the loop latch.
assert(
PHI->getNumIncomingValues() == 2 &&
"Must have an incoming value only for the preheader and the latch.");
Value *V = PHI->getIncomingValueForBlock(
Iteration == 0 ? L->getLoopPreheader() : L->getLoopLatch());
Constant *C = dyn_cast<Constant>(V);
if (Iteration != 0 && !C)
C = SimplifiedValues.lookup(V);
if (C)
SimplifiedInputValues.push_back({PHI, C});
}
// Now clear and re-populate the map for the next iteration.
SimplifiedValues.clear();
while (!SimplifiedInputValues.empty())
SimplifiedValues.insert(SimplifiedInputValues.pop_back_val());
UnrolledInstAnalyzer Analyzer(Iteration, SimplifiedValues, SE, L);
BBWorklist.clear();
BBWorklist.insert(L->getHeader());
// Note that we *must not* cache the size, this loop grows the worklist.
for (unsigned Idx = 0; Idx != BBWorklist.size(); ++Idx) {
BasicBlock *BB = BBWorklist[Idx];
// Visit all instructions in the given basic block and try to simplify
// it. We don't change the actual IR, just count optimization
// opportunities.
for (Instruction &I : *BB) {
// Track this instruction's expected baseline cost when executing the
// rolled loop form.
RolledDynamicCost += TTI.getUserCost(&I);
// Visit the instruction to analyze its loop cost after unrolling,
// and if the visitor returns true, mark the instruction as free after
// unrolling and continue.
bool IsFree = Analyzer.visit(I);
bool Inserted = InstCostMap.insert({&I, (int)Iteration,
(unsigned)IsFree,
/*IsCounted*/ false}).second;
(void)Inserted;
assert(Inserted && "Cannot have a state for an unvisited instruction!");
if (IsFree)
continue;
// If the instruction might have a side-effect recursively account for
// the cost of it and all the instructions leading up to it.
if (I.mayHaveSideEffects())
AddCostRecursively(I, Iteration);
// Can't properly model a cost of a call.
// FIXME: With a proper cost model we should be able to do it.
if(isa<CallInst>(&I))
return None;
// If unrolled body turns out to be too big, bail out.
if (UnrolledCost > MaxUnrolledLoopSize) {
DEBUG(dbgs() << " Exceeded threshold.. exiting.\n"
<< " UnrolledCost: " << UnrolledCost
<< ", MaxUnrolledLoopSize: " << MaxUnrolledLoopSize
<< "\n");
return None;
}
}
TerminatorInst *TI = BB->getTerminator();
// Add in the live successors by first checking whether we have terminator
// that may be simplified based on the values simplified by this call.
BasicBlock *KnownSucc = nullptr;
if (BranchInst *BI = dyn_cast<BranchInst>(TI)) {
if (BI->isConditional()) {
if (Constant *SimpleCond =
SimplifiedValues.lookup(BI->getCondition())) {
// Just take the first successor if condition is undef
if (isa<UndefValue>(SimpleCond))
KnownSucc = BI->getSuccessor(0);
else if (ConstantInt *SimpleCondVal =
dyn_cast<ConstantInt>(SimpleCond))
KnownSucc = BI->getSuccessor(SimpleCondVal->isZero() ? 1 : 0);
}
}
} else if (SwitchInst *SI = dyn_cast<SwitchInst>(TI)) {
if (Constant *SimpleCond =
SimplifiedValues.lookup(SI->getCondition())) {
// Just take the first successor if condition is undef
if (isa<UndefValue>(SimpleCond))
KnownSucc = SI->getSuccessor(0);
else if (ConstantInt *SimpleCondVal =
dyn_cast<ConstantInt>(SimpleCond))
KnownSucc = SI->findCaseValue(SimpleCondVal).getCaseSuccessor();
}
}
if (KnownSucc) {
if (L->contains(KnownSucc))
BBWorklist.insert(KnownSucc);
else
ExitWorklist.insert({BB, KnownSucc});
continue;
}
// Add BB's successors to the worklist.
for (BasicBlock *Succ : successors(BB))
if (L->contains(Succ))
BBWorklist.insert(Succ);
else
ExitWorklist.insert({BB, Succ});
AddCostRecursively(*TI, Iteration);
}
// If we found no optimization opportunities on the first iteration, we
// won't find them on later ones too.
if (UnrolledCost == RolledDynamicCost) {
DEBUG(dbgs() << " No opportunities found.. exiting.\n"
<< " UnrolledCost: " << UnrolledCost << "\n");
return None;
}
}
while (!ExitWorklist.empty()) {
BasicBlock *ExitingBB, *ExitBB;
std::tie(ExitingBB, ExitBB) = ExitWorklist.pop_back_val();
for (Instruction &I : *ExitBB) {
auto *PN = dyn_cast<PHINode>(&I);
if (!PN)
break;
Value *Op = PN->getIncomingValueForBlock(ExitingBB);
if (auto *OpI = dyn_cast<Instruction>(Op))
if (L->contains(OpI))
AddCostRecursively(*OpI, TripCount - 1);
}
}
DEBUG(dbgs() << "Analysis finished:\n"
<< "UnrolledCost: " << UnrolledCost << ", "
<< "RolledDynamicCost: " << RolledDynamicCost << "\n");
return {{UnrolledCost, RolledDynamicCost}};
}
bool MachineCopyPropagation::CopyPropagateBlock(MachineBasicBlock &MBB) {
SmallSetVector<MachineInstr*, 8> MaybeDeadCopies; // Candidates for deletion
DenseMap<unsigned, MachineInstr*> AvailCopyMap; // Def -> available copies map
DenseMap<unsigned, MachineInstr*> CopyMap; // Def -> copies map
SourceMap SrcMap; // Src -> Def map
bool Changed = false;
for (MachineBasicBlock::iterator I = MBB.begin(), E = MBB.end(); I != E; ) {
MachineInstr *MI = &*I;
++I;
if (MI->isCopy()) {
unsigned Def = MI->getOperand(0).getReg();
unsigned Src = MI->getOperand(1).getReg();
if (TargetRegisterInfo::isVirtualRegister(Def) ||
TargetRegisterInfo::isVirtualRegister(Src))
report_fatal_error("MachineCopyPropagation should be run after"
" register allocation!");
DenseMap<unsigned, MachineInstr*>::iterator CI = AvailCopyMap.find(Src);
if (CI != AvailCopyMap.end()) {
MachineInstr *CopyMI = CI->second;
if (!MRI->isReserved(Def) &&
(!MRI->isReserved(Src) || NoInterveningSideEffect(CopyMI, MI)) &&
isNopCopy(CopyMI, Def, Src, TRI)) {
// The two copies cancel out and the source of the first copy
// hasn't been overridden, eliminate the second one. e.g.
// %ECX<def> = COPY %EAX<kill>
// ... nothing clobbered EAX.
// %EAX<def> = COPY %ECX
// =>
// %ECX<def> = COPY %EAX
//
// Also avoid eliminating a copy from reserved registers unless the
// definition is proven not clobbered. e.g.
// %RSP<def> = COPY %RAX
// CALL
// %RAX<def> = COPY %RSP
// Clear any kills of Def between CopyMI and MI. This extends the
// live range.
for (MachineBasicBlock::iterator I = CopyMI, E = MI; I != E; ++I)
I->clearRegisterKills(Def, TRI);
removeCopy(MI);
Changed = true;
++NumDeletes;
continue;
}
}
// If Src is defined by a previous copy, it cannot be eliminated.
for (MCRegAliasIterator AI(Src, TRI, true); AI.isValid(); ++AI) {
CI = CopyMap.find(*AI);
if (CI != CopyMap.end())
MaybeDeadCopies.remove(CI->second);
}
// Copy is now a candidate for deletion.
MaybeDeadCopies.insert(MI);
// If 'Src' is previously source of another copy, then this earlier copy's
// source is no longer available. e.g.
// %xmm9<def> = copy %xmm2
// ...
// %xmm2<def> = copy %xmm0
// ...
// %xmm2<def> = copy %xmm9
SourceNoLongerAvailable(Def, SrcMap, AvailCopyMap);
// Remember Def is defined by the copy.
// ... Make sure to clear the def maps of aliases first.
for (MCRegAliasIterator AI(Def, TRI, false); AI.isValid(); ++AI) {
CopyMap.erase(*AI);
AvailCopyMap.erase(*AI);
}
CopyMap[Def] = MI;
AvailCopyMap[Def] = MI;
for (MCSubRegIterator SR(Def, TRI); SR.isValid(); ++SR) {
CopyMap[*SR] = MI;
AvailCopyMap[*SR] = MI;
}
// Remember source that's copied to Def. Once it's clobbered, then
// it's no longer available for copy propagation.
if (std::find(SrcMap[Src].begin(), SrcMap[Src].end(), Def) ==
SrcMap[Src].end()) {
SrcMap[Src].push_back(Def);
}
continue;
}
// Not a copy.
SmallVector<unsigned, 2> Defs;
int RegMaskOpNum = -1;
for (unsigned i = 0, e = MI->getNumOperands(); i != e; ++i) {
MachineOperand &MO = MI->getOperand(i);
if (MO.isRegMask())
RegMaskOpNum = i;
if (!MO.isReg())
continue;
unsigned Reg = MO.getReg();
if (!Reg)
continue;
if (TargetRegisterInfo::isVirtualRegister(Reg))
report_fatal_error("MachineCopyPropagation should be run after"
" register allocation!");
if (MO.isDef()) {
Defs.push_back(Reg);
continue;
}
// If 'Reg' is defined by a copy, the copy is no longer a candidate
// for elimination.
for (MCRegAliasIterator AI(Reg, TRI, true); AI.isValid(); ++AI) {
DenseMap<unsigned, MachineInstr*>::iterator CI = CopyMap.find(*AI);
if (CI != CopyMap.end())
MaybeDeadCopies.remove(CI->second);
}
}
// The instruction has a register mask operand which means that it clobbers
// a large set of registers. It is possible to use the register mask to
// prune the available copies, but treat it like a basic block boundary for
// now.
if (RegMaskOpNum >= 0) {
// Erase any MaybeDeadCopies whose destination register is clobbered.
const MachineOperand &MaskMO = MI->getOperand(RegMaskOpNum);
for (SmallSetVector<MachineInstr*, 8>::iterator
DI = MaybeDeadCopies.begin(), DE = MaybeDeadCopies.end();
DI != DE; ++DI) {
unsigned Reg = (*DI)->getOperand(0).getReg();
if (MRI->isReserved(Reg) || !MaskMO.clobbersPhysReg(Reg))
continue;
removeCopy(*DI);
Changed = true;
++NumDeletes;
}
// Clear all data structures as if we were beginning a new basic block.
MaybeDeadCopies.clear();
AvailCopyMap.clear();
CopyMap.clear();
SrcMap.clear();
continue;
}
for (unsigned i = 0, e = Defs.size(); i != e; ++i) {
unsigned Reg = Defs[i];
// No longer defined by a copy.
for (MCRegAliasIterator AI(Reg, TRI, true); AI.isValid(); ++AI) {
CopyMap.erase(*AI);
AvailCopyMap.erase(*AI);
}
// If 'Reg' is previously source of a copy, it is no longer available for
// copy propagation.
SourceNoLongerAvailable(Reg, SrcMap, AvailCopyMap);
}
}
// If MBB doesn't have successors, delete the copies whose defs are not used.
// If MBB does have successors, then conservative assume the defs are live-out
// since we don't want to trust live-in lists.
if (MBB.succ_empty()) {
for (SmallSetVector<MachineInstr*, 8>::iterator
DI = MaybeDeadCopies.begin(), DE = MaybeDeadCopies.end();
DI != DE; ++DI) {
if (!MRI->isReserved((*DI)->getOperand(0).getReg())) {
removeCopy(*DI);
Changed = true;
++NumDeletes;
}
}
}
return Changed;
}
PreservedAnalyses InlinerPass::run(LazyCallGraph::SCC &InitialC,
CGSCCAnalysisManager &AM, LazyCallGraph &CG,
CGSCCUpdateResult &UR) {
const ModuleAnalysisManager &MAM =
AM.getResult<ModuleAnalysisManagerCGSCCProxy>(InitialC, CG).getManager();
bool Changed = false;
assert(InitialC.size() > 0 && "Cannot handle an empty SCC!");
Module &M = *InitialC.begin()->getFunction().getParent();
ProfileSummaryInfo *PSI = MAM.getCachedResult<ProfileSummaryAnalysis>(M);
if (!ImportedFunctionsStats &&
InlinerFunctionImportStats != InlinerFunctionImportStatsOpts::No) {
ImportedFunctionsStats =
llvm::make_unique<ImportedFunctionsInliningStatistics>();
ImportedFunctionsStats->setModuleInfo(M);
}
// We use a single common worklist for calls across the entire SCC. We
// process these in-order and append new calls introduced during inlining to
// the end.
//
// Note that this particular order of processing is actually critical to
// avoid very bad behaviors. Consider *highly connected* call graphs where
// each function contains a small amonut of code and a couple of calls to
// other functions. Because the LLVM inliner is fundamentally a bottom-up
// inliner, it can handle gracefully the fact that these all appear to be
// reasonable inlining candidates as it will flatten things until they become
// too big to inline, and then move on and flatten another batch.
//
// However, when processing call edges *within* an SCC we cannot rely on this
// bottom-up behavior. As a consequence, with heavily connected *SCCs* of
// functions we can end up incrementally inlining N calls into each of
// N functions because each incremental inlining decision looks good and we
// don't have a topological ordering to prevent explosions.
//
// To compensate for this, we don't process transitive edges made immediate
// by inlining until we've done one pass of inlining across the entire SCC.
// Large, highly connected SCCs still lead to some amount of code bloat in
// this model, but it is uniformly spread across all the functions in the SCC
// and eventually they all become too large to inline, rather than
// incrementally maknig a single function grow in a super linear fashion.
SmallVector<std::pair<CallSite, int>, 16> Calls;
FunctionAnalysisManager &FAM =
AM.getResult<FunctionAnalysisManagerCGSCCProxy>(InitialC, CG)
.getManager();
// Populate the initial list of calls in this SCC.
for (auto &N : InitialC) {
auto &ORE =
FAM.getResult<OptimizationRemarkEmitterAnalysis>(N.getFunction());
// We want to generally process call sites top-down in order for
// simplifications stemming from replacing the call with the returned value
// after inlining to be visible to subsequent inlining decisions.
// FIXME: Using instructions sequence is a really bad way to do this.
// Instead we should do an actual RPO walk of the function body.
for (Instruction &I : instructions(N.getFunction()))
if (auto CS = CallSite(&I))
if (Function *Callee = CS.getCalledFunction()) {
if (!Callee->isDeclaration())
Calls.push_back({CS, -1});
else if (!isa<IntrinsicInst>(I)) {
using namespace ore;
ORE.emit([&]() {
return OptimizationRemarkMissed(DEBUG_TYPE, "NoDefinition", &I)
<< NV("Callee", Callee) << " will not be inlined into "
<< NV("Caller", CS.getCaller())
<< " because its definition is unavailable"
<< setIsVerbose();
});
}
}
}
if (Calls.empty())
return PreservedAnalyses::all();
// Capture updatable variables for the current SCC and RefSCC.
auto *C = &InitialC;
auto *RC = &C->getOuterRefSCC();
// When inlining a callee produces new call sites, we want to keep track of
// the fact that they were inlined from the callee. This allows us to avoid
// infinite inlining in some obscure cases. To represent this, we use an
// index into the InlineHistory vector.
SmallVector<std::pair<Function *, int>, 16> InlineHistory;
// Track a set vector of inlined callees so that we can augment the caller
// with all of their edges in the call graph before pruning out the ones that
// got simplified away.
SmallSetVector<Function *, 4> InlinedCallees;
// Track the dead functions to delete once finished with inlining calls. We
// defer deleting these to make it easier to handle the call graph updates.
SmallVector<Function *, 4> DeadFunctions;
// Loop forward over all of the calls. Note that we cannot cache the size as
// inlining can introduce new calls that need to be processed.
for (int i = 0; i < (int)Calls.size(); ++i) {
// We expect the calls to typically be batched with sequences of calls that
// have the same caller, so we first set up some shared infrastructure for
// this caller. We also do any pruning we can at this layer on the caller
// alone.
Function &F = *Calls[i].first.getCaller();
LazyCallGraph::Node &N = *CG.lookup(F);
if (CG.lookupSCC(N) != C)
continue;
if (F.hasFnAttribute(Attribute::OptimizeNone))
continue;
LLVM_DEBUG(dbgs() << "Inlining calls in: " << F.getName() << "\n");
// Get a FunctionAnalysisManager via a proxy for this particular node. We
// do this each time we visit a node as the SCC may have changed and as
// we're going to mutate this particular function we want to make sure the
// proxy is in place to forward any invalidation events. We can use the
// manager we get here for looking up results for functions other than this
// node however because those functions aren't going to be mutated by this
// pass.
FunctionAnalysisManager &FAM =
AM.getResult<FunctionAnalysisManagerCGSCCProxy>(*C, CG)
.getManager();
// Get the remarks emission analysis for the caller.
auto &ORE = FAM.getResult<OptimizationRemarkEmitterAnalysis>(F);
std::function<AssumptionCache &(Function &)> GetAssumptionCache =
[&](Function &F) -> AssumptionCache & {
return FAM.getResult<AssumptionAnalysis>(F);
};
auto GetBFI = [&](Function &F) -> BlockFrequencyInfo & {
return FAM.getResult<BlockFrequencyAnalysis>(F);
};
auto GetInlineCost = [&](CallSite CS) {
Function &Callee = *CS.getCalledFunction();
auto &CalleeTTI = FAM.getResult<TargetIRAnalysis>(Callee);
return getInlineCost(CS, Params, CalleeTTI, GetAssumptionCache, {GetBFI},
PSI, &ORE);
};
// Now process as many calls as we have within this caller in the sequnece.
// We bail out as soon as the caller has to change so we can update the
// call graph and prepare the context of that new caller.
bool DidInline = false;
for (; i < (int)Calls.size() && Calls[i].first.getCaller() == &F; ++i) {
int InlineHistoryID;
CallSite CS;
std::tie(CS, InlineHistoryID) = Calls[i];
Function &Callee = *CS.getCalledFunction();
if (InlineHistoryID != -1 &&
InlineHistoryIncludes(&Callee, InlineHistoryID, InlineHistory))
continue;
// Check if this inlining may repeat breaking an SCC apart that has
// already been split once before. In that case, inlining here may
// trigger infinite inlining, much like is prevented within the inliner
// itself by the InlineHistory above, but spread across CGSCC iterations
// and thus hidden from the full inline history.
if (CG.lookupSCC(*CG.lookup(Callee)) == C &&
UR.InlinedInternalEdges.count({&N, C})) {
LLVM_DEBUG(dbgs() << "Skipping inlining internal SCC edge from a node "
"previously split out of this SCC by inlining: "
<< F.getName() << " -> " << Callee.getName() << "\n");
continue;
}
Optional<InlineCost> OIC = shouldInline(CS, GetInlineCost, ORE);
// Check whether we want to inline this callsite.
if (!OIC)
continue;
// Setup the data structure used to plumb customization into the
// `InlineFunction` routine.
InlineFunctionInfo IFI(
/*cg=*/nullptr, &GetAssumptionCache, PSI,
&FAM.getResult<BlockFrequencyAnalysis>(*(CS.getCaller())),
&FAM.getResult<BlockFrequencyAnalysis>(Callee));
// Get DebugLoc to report. CS will be invalid after Inliner.
DebugLoc DLoc = CS->getDebugLoc();
BasicBlock *Block = CS.getParent();
using namespace ore;
if (!InlineFunction(CS, IFI)) {
ORE.emit([&]() {
return OptimizationRemarkMissed(DEBUG_TYPE, "NotInlined", DLoc, Block)
<< NV("Callee", &Callee) << " will not be inlined into "
<< NV("Caller", &F);
});
continue;
}
DidInline = true;
InlinedCallees.insert(&Callee);
ORE.emit([&]() {
bool AlwaysInline = OIC->isAlways();
StringRef RemarkName = AlwaysInline ? "AlwaysInline" : "Inlined";
OptimizationRemark R(DEBUG_TYPE, RemarkName, DLoc, Block);
R << NV("Callee", &Callee) << " inlined into ";
R << NV("Caller", &F);
if (AlwaysInline)
R << " with cost=always";
else {
R << " with cost=" << NV("Cost", OIC->getCost());
R << " (threshold=" << NV("Threshold", OIC->getThreshold());
R << ")";
}
return R;
});
// Add any new callsites to defined functions to the worklist.
if (!IFI.InlinedCallSites.empty()) {
int NewHistoryID = InlineHistory.size();
InlineHistory.push_back({&Callee, InlineHistoryID});
for (CallSite &CS : reverse(IFI.InlinedCallSites))
if (Function *NewCallee = CS.getCalledFunction())
if (!NewCallee->isDeclaration())
Calls.push_back({CS, NewHistoryID});
}
if (InlinerFunctionImportStats != InlinerFunctionImportStatsOpts::No)
ImportedFunctionsStats->recordInline(F, Callee);
// Merge the attributes based on the inlining.
AttributeFuncs::mergeAttributesForInlining(F, Callee);
// For local functions, check whether this makes the callee trivially
// dead. In that case, we can drop the body of the function eagerly
// which may reduce the number of callers of other functions to one,
// changing inline cost thresholds.
if (Callee.hasLocalLinkage()) {
// To check this we also need to nuke any dead constant uses (perhaps
// made dead by this operation on other functions).
Callee.removeDeadConstantUsers();
if (Callee.use_empty() && !CG.isLibFunction(Callee)) {
Calls.erase(
std::remove_if(Calls.begin() + i + 1, Calls.end(),
[&Callee](const std::pair<CallSite, int> &Call) {
return Call.first.getCaller() == &Callee;
}),
Calls.end());
// Clear the body and queue the function itself for deletion when we
// finish inlining and call graph updates.
// Note that after this point, it is an error to do anything other
// than use the callee's address or delete it.
Callee.dropAllReferences();
assert(find(DeadFunctions, &Callee) == DeadFunctions.end() &&
"Cannot put cause a function to become dead twice!");
DeadFunctions.push_back(&Callee);
}
}
}
// Back the call index up by one to put us in a good position to go around
// the outer loop.
--i;
if (!DidInline)
continue;
Changed = true;
// Add all the inlined callees' edges as ref edges to the caller. These are
// by definition trivial edges as we always have *some* transitive ref edge
// chain. While in some cases these edges are direct calls inside the
// callee, they have to be modeled in the inliner as reference edges as
// there may be a reference edge anywhere along the chain from the current
// caller to the callee that causes the whole thing to appear like
// a (transitive) reference edge that will require promotion to a call edge
// below.
for (Function *InlinedCallee : InlinedCallees) {
LazyCallGraph::Node &CalleeN = *CG.lookup(*InlinedCallee);
for (LazyCallGraph::Edge &E : *CalleeN)
RC->insertTrivialRefEdge(N, E.getNode());
}
// At this point, since we have made changes we have at least removed
// a call instruction. However, in the process we do some incremental
// simplification of the surrounding code. This simplification can
// essentially do all of the same things as a function pass and we can
// re-use the exact same logic for updating the call graph to reflect the
// change.
LazyCallGraph::SCC *OldC = C;
C = &updateCGAndAnalysisManagerForFunctionPass(CG, *C, N, AM, UR);
LLVM_DEBUG(dbgs() << "Updated inlining SCC: " << *C << "\n");
RC = &C->getOuterRefSCC();
// If this causes an SCC to split apart into multiple smaller SCCs, there
// is a subtle risk we need to prepare for. Other transformations may
// expose an "infinite inlining" opportunity later, and because of the SCC
// mutation, we will revisit this function and potentially re-inline. If we
// do, and that re-inlining also has the potentially to mutate the SCC
// structure, the infinite inlining problem can manifest through infinite
// SCC splits and merges. To avoid this, we capture the originating caller
// node and the SCC containing the call edge. This is a slight over
// approximation of the possible inlining decisions that must be avoided,
// but is relatively efficient to store.
// FIXME: This seems like a very heavyweight way of retaining the inline
// history, we should look for a more efficient way of tracking it.
if (C != OldC && llvm::any_of(InlinedCallees, [&](Function *Callee) {
return CG.lookupSCC(*CG.lookup(*Callee)) == OldC;
})) {
LLVM_DEBUG(dbgs() << "Inlined an internal call edge and split an SCC, "
"retaining this to avoid infinite inlining.\n");
UR.InlinedInternalEdges.insert({&N, OldC});
}
InlinedCallees.clear();
}
// Now that we've finished inlining all of the calls across this SCC, delete
// all of the trivially dead functions, updating the call graph and the CGSCC
// pass manager in the process.
//
// Note that this walks a pointer set which has non-deterministic order but
// that is OK as all we do is delete things and add pointers to unordered
// sets.
for (Function *DeadF : DeadFunctions) {
// Get the necessary information out of the call graph and nuke the
// function there. Also, cclear out any cached analyses.
auto &DeadC = *CG.lookupSCC(*CG.lookup(*DeadF));
FunctionAnalysisManager &FAM =
AM.getResult<FunctionAnalysisManagerCGSCCProxy>(DeadC, CG)
.getManager();
FAM.clear(*DeadF, DeadF->getName());
AM.clear(DeadC, DeadC.getName());
auto &DeadRC = DeadC.getOuterRefSCC();
CG.removeDeadFunction(*DeadF);
// Mark the relevant parts of the call graph as invalid so we don't visit
// them.
UR.InvalidatedSCCs.insert(&DeadC);
UR.InvalidatedRefSCCs.insert(&DeadRC);
// And delete the actual function from the module.
M.getFunctionList().erase(DeadF);
}
if (!Changed)
return PreservedAnalyses::all();
// Even if we change the IR, we update the core CGSCC data structures and so
// can preserve the proxy to the function analysis manager.
PreservedAnalyses PA;
PA.preserve<FunctionAnalysisManagerCGSCCProxy>();
return PA;
}
/// \brief Figure out if the loop is worth full unrolling.
///
/// Complete loop unrolling can make some loads constant, and we need to know
/// if that would expose any further optimization opportunities. This routine
/// estimates this optimization. It computes cost of unrolled loop
/// (UnrolledCost) and dynamic cost of the original loop (RolledDynamicCost). By
/// dynamic cost we mean that we won't count costs of blocks that are known not
/// to be executed (i.e. if we have a branch in the loop and we know that at the
/// given iteration its condition would be resolved to true, we won't add up the
/// cost of the 'false'-block).
/// \returns Optional value, holding the RolledDynamicCost and UnrolledCost. If
/// the analysis failed (no benefits expected from the unrolling, or the loop is
/// too big to analyze), the returned value is None.
static Optional<EstimatedUnrollCost>
analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
ScalarEvolution &SE, const TargetTransformInfo &TTI,
int MaxUnrolledLoopSize) {
// We want to be able to scale offsets by the trip count and add more offsets
// to them without checking for overflows, and we already don't want to
// analyze *massive* trip counts, so we force the max to be reasonably small.
assert(UnrollMaxIterationsCountToAnalyze < (INT_MAX / 2) &&
"The unroll iterations max is too large!");
// Don't simulate loops with a big or unknown tripcount
if (!UnrollMaxIterationsCountToAnalyze || !TripCount ||
TripCount > UnrollMaxIterationsCountToAnalyze)
return None;
SmallSetVector<BasicBlock *, 16> BBWorklist;
DenseMap<Value *, Constant *> SimplifiedValues;
SmallVector<std::pair<Value *, Constant *>, 4> SimplifiedInputValues;
// The estimated cost of the unrolled form of the loop. We try to estimate
// this by simplifying as much as we can while computing the estimate.
int UnrolledCost = 0;
// We also track the estimated dynamic (that is, actually executed) cost in
// the rolled form. This helps identify cases when the savings from unrolling
// aren't just exposing dead control flows, but actual reduced dynamic
// instructions due to the simplifications which we expect to occur after
// unrolling.
int RolledDynamicCost = 0;
// Ensure that we don't violate the loop structure invariants relied on by
// this analysis.
assert(L->isLoopSimplifyForm() && "Must put loop into normal form first.");
assert(L->isLCSSAForm(DT) &&
"Must have loops in LCSSA form to track live-out values.");
DEBUG(dbgs() << "Starting LoopUnroll profitability analysis...\n");
// Simulate execution of each iteration of the loop counting instructions,
// which would be simplified.
// Since the same load will take different values on different iterations,
// we literally have to go through all loop's iterations.
for (unsigned Iteration = 0; Iteration < TripCount; ++Iteration) {
DEBUG(dbgs() << " Analyzing iteration " << Iteration << "\n");
// Prepare for the iteration by collecting any simplified entry or backedge
// inputs.
for (Instruction &I : *L->getHeader()) {
auto *PHI = dyn_cast<PHINode>(&I);
if (!PHI)
break;
// The loop header PHI nodes must have exactly two input: one from the
// loop preheader and one from the loop latch.
assert(
PHI->getNumIncomingValues() == 2 &&
"Must have an incoming value only for the preheader and the latch.");
Value *V = PHI->getIncomingValueForBlock(
Iteration == 0 ? L->getLoopPreheader() : L->getLoopLatch());
Constant *C = dyn_cast<Constant>(V);
if (Iteration != 0 && !C)
C = SimplifiedValues.lookup(V);
if (C)
SimplifiedInputValues.push_back({PHI, C});
}
// Now clear and re-populate the map for the next iteration.
SimplifiedValues.clear();
while (!SimplifiedInputValues.empty())
SimplifiedValues.insert(SimplifiedInputValues.pop_back_val());
UnrolledInstAnalyzer Analyzer(Iteration, SimplifiedValues, SE);
BBWorklist.clear();
BBWorklist.insert(L->getHeader());
// Note that we *must not* cache the size, this loop grows the worklist.
for (unsigned Idx = 0; Idx != BBWorklist.size(); ++Idx) {
BasicBlock *BB = BBWorklist[Idx];
// Visit all instructions in the given basic block and try to simplify
// it. We don't change the actual IR, just count optimization
// opportunities.
for (Instruction &I : *BB) {
int InstCost = TTI.getUserCost(&I);
// Visit the instruction to analyze its loop cost after unrolling,
// and if the visitor returns false, include this instruction in the
// unrolled cost.
if (!Analyzer.visit(I))
UnrolledCost += InstCost;
else {
DEBUG(dbgs() << " " << I
<< " would be simplified if loop is unrolled.\n");
(void)0;
}
// Also track this instructions expected cost when executing the rolled
// loop form.
RolledDynamicCost += InstCost;
// If unrolled body turns out to be too big, bail out.
if (UnrolledCost > MaxUnrolledLoopSize) {
DEBUG(dbgs() << " Exceeded threshold.. exiting.\n"
<< " UnrolledCost: " << UnrolledCost
<< ", MaxUnrolledLoopSize: " << MaxUnrolledLoopSize
<< "\n");
return None;
}
}
TerminatorInst *TI = BB->getTerminator();
// Add in the live successors by first checking whether we have terminator
// that may be simplified based on the values simplified by this call.
if (BranchInst *BI = dyn_cast<BranchInst>(TI)) {
if (BI->isConditional()) {
if (Constant *SimpleCond =
SimplifiedValues.lookup(BI->getCondition())) {
BasicBlock *Succ = nullptr;
// Just take the first successor if condition is undef
if (isa<UndefValue>(SimpleCond))
Succ = BI->getSuccessor(0);
else
Succ = BI->getSuccessor(
cast<ConstantInt>(SimpleCond)->isZero() ? 1 : 0);
if (L->contains(Succ))
BBWorklist.insert(Succ);
continue;
}
}
} else if (SwitchInst *SI = dyn_cast<SwitchInst>(TI)) {
if (Constant *SimpleCond =
SimplifiedValues.lookup(SI->getCondition())) {
BasicBlock *Succ = nullptr;
// Just take the first successor if condition is undef
if (isa<UndefValue>(SimpleCond))
Succ = SI->getSuccessor(0);
else
Succ = SI->findCaseValue(cast<ConstantInt>(SimpleCond))
.getCaseSuccessor();
if (L->contains(Succ))
BBWorklist.insert(Succ);
continue;
}
}
// Add BB's successors to the worklist.
for (BasicBlock *Succ : successors(BB))
if (L->contains(Succ))
BBWorklist.insert(Succ);
}
// If we found no optimization opportunities on the first iteration, we
// won't find them on later ones too.
if (UnrolledCost == RolledDynamicCost) {
DEBUG(dbgs() << " No opportunities found.. exiting.\n"
<< " UnrolledCost: " << UnrolledCost << "\n");
return None;
}
}
DEBUG(dbgs() << "Analysis finished:\n"
<< "UnrolledCost: " << UnrolledCost << ", "
<< "RolledDynamicCost: " << RolledDynamicCost << "\n");
return {{UnrolledCost, RolledDynamicCost}};
}
