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clang-r547379/include/llvm/Transforms/Utils/SampleProfileLoaderBaseImpl.h
Ryan Prichard 6024e5c395 Update prebuilt Clang to r547379 (20.0.0). 
clang 20.0.0 (based on r547379) from build 12806354.

Bug: http://b/379133546
Test: N/A
Change-Id: I2eb8938af55d809de674be63cb30cf27e801862b

Upstream-Commit: ad834e67b1105d15ef907f6255d4c96e8e733f57
2025-11-26 14:59:46 -05:00

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////===- SampleProfileLoadBaseImpl.h - Profile loader base impl --*- C++-*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
/// \file
/// This file provides the interface for the sampled PGO profile loader base
/// implementation.
//
//===----------------------------------------------------------------------===//
#ifndef LLVM_TRANSFORMS_UTILS_SAMPLEPROFILELOADERBASEIMPL_H
#define LLVM_TRANSFORMS_UTILS_SAMPLEPROFILELOADERBASEIMPL_H
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/ADT/IntrusiveRefCntPtr.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Analysis/LazyCallGraph.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/PostDominators.h"
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/CFG.h"
#include "llvm/IR/DebugInfoMetadata.h"
#include "llvm/IR/DebugLoc.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/Instruction.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/PseudoProbe.h"
#include "llvm/ProfileData/SampleProf.h"
#include "llvm/ProfileData/SampleProfReader.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/GenericDomTree.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Utils/SampleProfileInference.h"
#include "llvm/Transforms/Utils/SampleProfileLoaderBaseUtil.h"
namespace llvm {
using namespace sampleprof;
using namespace sampleprofutil;
using ProfileCount = Function::ProfileCount;
namespace vfs {
class FileSystem;
} // namespace vfs
#define DEBUG_TYPE "sample-profile-impl"
namespace afdo_detail {
template <typename BlockT> struct IRTraits;
template <> struct IRTraits<BasicBlock> {
using InstructionT = Instruction;
using BasicBlockT = BasicBlock;
using FunctionT = Function;
using BlockFrequencyInfoT = BlockFrequencyInfo;
using LoopT = Loop;
using LoopInfoPtrT = std::unique_ptr<LoopInfo>;
using DominatorTreePtrT = std::unique_ptr<DominatorTree>;
using PostDominatorTreeT = PostDominatorTree;
using PostDominatorTreePtrT = std::unique_ptr<PostDominatorTree>;
using OptRemarkEmitterT = OptimizationRemarkEmitter;
using OptRemarkAnalysisT = OptimizationRemarkAnalysis;
using PredRangeT = pred_range;
using SuccRangeT = succ_range;
static Function &getFunction(Function &F) { return F; }
static const BasicBlock *getEntryBB(const Function *F) {
return &F->getEntryBlock();
}
static pred_range getPredecessors(BasicBlock *BB) { return predecessors(BB); }
static succ_range getSuccessors(BasicBlock *BB) { return successors(BB); }
};
} // end namespace afdo_detail
// This class serves sample counts correlation for SampleProfileLoader by
// analyzing pseudo probes and their function descriptors injected by
// SampleProfileProber.
class PseudoProbeManager {
DenseMap<uint64_t, PseudoProbeDescriptor> GUIDToProbeDescMap;
public:
PseudoProbeManager(const Module &M) {
if (NamedMDNode *FuncInfo =
M.getNamedMetadata(PseudoProbeDescMetadataName)) {
for (const auto *Operand : FuncInfo->operands()) {
const auto *MD = cast<MDNode>(Operand);
auto GUID = mdconst::dyn_extract<ConstantInt>(MD->getOperand(0))
->getZExtValue();
auto Hash = mdconst::dyn_extract<ConstantInt>(MD->getOperand(1))
->getZExtValue();
GUIDToProbeDescMap.try_emplace(GUID, PseudoProbeDescriptor(GUID, Hash));
}
}
}
const PseudoProbeDescriptor *getDesc(uint64_t GUID) const {
auto I = GUIDToProbeDescMap.find(GUID);
return I == GUIDToProbeDescMap.end() ? nullptr : &I->second;
}
const PseudoProbeDescriptor *getDesc(StringRef FProfileName) const {
return getDesc(Function::getGUID(FProfileName));
}
const PseudoProbeDescriptor *getDesc(const Function &F) const {
return getDesc(Function::getGUID(FunctionSamples::getCanonicalFnName(F)));
}
bool profileIsHashMismatched(const PseudoProbeDescriptor &FuncDesc,
const FunctionSamples &Samples) const {
return FuncDesc.getFunctionHash() != Samples.getFunctionHash();
}
bool moduleIsProbed(const Module &M) const {
return M.getNamedMetadata(PseudoProbeDescMetadataName);
}
bool profileIsValid(const Function &F, const FunctionSamples &Samples) const {
const auto *Desc = getDesc(F);
bool IsAvailableExternallyLinkage =
GlobalValue::isAvailableExternallyLinkage(F.getLinkage());
// Always check the function attribute to determine checksum mismatch for
// `available_externally` functions even if their desc are available. This
// is because the desc is computed based on the original internal function
// and it's substituted by the `available_externally` function during link
// time. However, when unstable IR or ODR violation issue occurs, the
// definitions of the same function across different translation units could
// be different and result in different checksums. So we should use the
// state from the new (available_externally) function, which is saved in its
// attribute.
// TODO: If the function's profile only exists as nested inlinee profile in
// a different module, we don't have the attr mismatch state(unknown), we
// need to fix it later.
if (IsAvailableExternallyLinkage || !Desc)
return !F.hasFnAttribute("profile-checksum-mismatch");
return Desc && !profileIsHashMismatched(*Desc, Samples);
}
};
extern cl::opt<bool> SampleProfileUseProfi;
static inline bool skipProfileForFunction(const Function &F) {
return F.isDeclaration() || !F.hasFnAttribute("use-sample-profile");
}
static inline void
buildTopDownFuncOrder(LazyCallGraph &CG,
std::vector<Function *> &FunctionOrderList) {
CG.buildRefSCCs();
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs()) {
for (LazyCallGraph::SCC &C : RC) {
for (LazyCallGraph::Node &N : C) {
Function &F = N.getFunction();
if (!skipProfileForFunction(F))
FunctionOrderList.push_back(&F);
}
}
}
std::reverse(FunctionOrderList.begin(), FunctionOrderList.end());
}
template <typename FT> class SampleProfileLoaderBaseImpl {
public:
SampleProfileLoaderBaseImpl(std::string Name, std::string RemapName,
IntrusiveRefCntPtr<vfs::FileSystem> FS)
: Filename(Name), RemappingFilename(RemapName), FS(std::move(FS)) {}
void dump() { Reader->dump(); }
using NodeRef = typename GraphTraits<FT *>::NodeRef;
using BT = std::remove_pointer_t<NodeRef>;
using InstructionT = typename afdo_detail::IRTraits<BT>::InstructionT;
using BasicBlockT = typename afdo_detail::IRTraits<BT>::BasicBlockT;
using BlockFrequencyInfoT =
typename afdo_detail::IRTraits<BT>::BlockFrequencyInfoT;
using FunctionT = typename afdo_detail::IRTraits<BT>::FunctionT;
using LoopT = typename afdo_detail::IRTraits<BT>::LoopT;
using LoopInfoPtrT = typename afdo_detail::IRTraits<BT>::LoopInfoPtrT;
using DominatorTreePtrT =
typename afdo_detail::IRTraits<BT>::DominatorTreePtrT;
using PostDominatorTreePtrT =
typename afdo_detail::IRTraits<BT>::PostDominatorTreePtrT;
using PostDominatorTreeT =
typename afdo_detail::IRTraits<BT>::PostDominatorTreeT;
using OptRemarkEmitterT =
typename afdo_detail::IRTraits<BT>::OptRemarkEmitterT;
using OptRemarkAnalysisT =
typename afdo_detail::IRTraits<BT>::OptRemarkAnalysisT;
using PredRangeT = typename afdo_detail::IRTraits<BT>::PredRangeT;
using SuccRangeT = typename afdo_detail::IRTraits<BT>::SuccRangeT;
using BlockWeightMap = DenseMap<const BasicBlockT *, uint64_t>;
using EquivalenceClassMap =
DenseMap<const BasicBlockT *, const BasicBlockT *>;
using Edge = std::pair<const BasicBlockT *, const BasicBlockT *>;
using EdgeWeightMap = DenseMap<Edge, uint64_t>;
using BlockEdgeMap =
DenseMap<const BasicBlockT *, SmallVector<const BasicBlockT *, 8>>;
protected:
~SampleProfileLoaderBaseImpl() = default;
friend class SampleCoverageTracker;
Function &getFunction(FunctionT &F) {
return afdo_detail::IRTraits<BT>::getFunction(F);
}
const BasicBlockT *getEntryBB(const FunctionT *F) {
return afdo_detail::IRTraits<BT>::getEntryBB(F);
}
PredRangeT getPredecessors(BasicBlockT *BB) {
return afdo_detail::IRTraits<BT>::getPredecessors(BB);
}
SuccRangeT getSuccessors(BasicBlockT *BB) {
return afdo_detail::IRTraits<BT>::getSuccessors(BB);
}
unsigned getFunctionLoc(FunctionT &Func);
virtual ErrorOr<uint64_t> getInstWeight(const InstructionT &Inst);
ErrorOr<uint64_t> getInstWeightImpl(const InstructionT &Inst);
virtual ErrorOr<uint64_t> getProbeWeight(const InstructionT &Inst);
ErrorOr<uint64_t> getBlockWeight(const BasicBlockT *BB);
mutable DenseMap<const DILocation *, const FunctionSamples *>
DILocation2SampleMap;
virtual const FunctionSamples *
findFunctionSamples(const InstructionT &I) const;
void printEdgeWeight(raw_ostream &OS, Edge E);
void printBlockWeight(raw_ostream &OS, const BasicBlockT *BB) const;
void printBlockEquivalence(raw_ostream &OS, const BasicBlockT *BB);
bool computeBlockWeights(FunctionT &F);
void findEquivalenceClasses(FunctionT &F);
void findEquivalencesFor(BasicBlockT *BB1,
ArrayRef<BasicBlockT *> Descendants,
PostDominatorTreeT *DomTree);
void propagateWeights(FunctionT &F);
void applyProfi(FunctionT &F, BlockEdgeMap &Successors,
BlockWeightMap &SampleBlockWeights,
BlockWeightMap &BlockWeights, EdgeWeightMap &EdgeWeights);
uint64_t visitEdge(Edge E, unsigned *NumUnknownEdges, Edge *UnknownEdge);
void buildEdges(FunctionT &F);
bool propagateThroughEdges(FunctionT &F, bool UpdateBlockCount);
void clearFunctionData(bool ResetDT = true);
void computeDominanceAndLoopInfo(FunctionT &F);
bool
computeAndPropagateWeights(FunctionT &F,
const DenseSet<GlobalValue::GUID> &InlinedGUIDs);
void initWeightPropagation(FunctionT &F,
const DenseSet<GlobalValue::GUID> &InlinedGUIDs);
void
finalizeWeightPropagation(FunctionT &F,
const DenseSet<GlobalValue::GUID> &InlinedGUIDs);
void emitCoverageRemarks(FunctionT &F);
/// Map basic blocks to their computed weights.
///
/// The weight of a basic block is defined to be the maximum
/// of all the instruction weights in that block.
BlockWeightMap BlockWeights;
/// Map edges to their computed weights.
///
/// Edge weights are computed by propagating basic block weights in
/// SampleProfile::propagateWeights.
EdgeWeightMap EdgeWeights;
/// Set of visited blocks during propagation.
SmallPtrSet<const BasicBlockT *, 32> VisitedBlocks;
/// Set of visited edges during propagation.
SmallSet<Edge, 32> VisitedEdges;
/// Equivalence classes for block weights.
///
/// Two blocks BB1 and BB2 are in the same equivalence class if they
/// dominate and post-dominate each other, and they are in the same loop
/// nest. When this happens, the two blocks are guaranteed to execute
/// the same number of times.
EquivalenceClassMap EquivalenceClass;
/// Dominance, post-dominance and loop information.
DominatorTreePtrT DT;
PostDominatorTreePtrT PDT;
LoopInfoPtrT LI;
/// Predecessors for each basic block in the CFG.
BlockEdgeMap Predecessors;
/// Successors for each basic block in the CFG.
BlockEdgeMap Successors;
/// Profile coverage tracker.
SampleCoverageTracker CoverageTracker;
/// Profile reader object.
std::unique_ptr<SampleProfileReader> Reader;
/// Synthetic samples created by duplicating the samples of inlined functions
/// from the original profile as if they were top level sample profiles.
/// Use std::map because insertion may happen while its content is referenced.
std::map<SampleContext, FunctionSamples> OutlineFunctionSamples;
// A pseudo probe helper to correlate the imported sample counts.
std::unique_ptr<PseudoProbeManager> ProbeManager;
/// Samples collected for the body of this function.
FunctionSamples *Samples = nullptr;
/// Name of the profile file to load.
std::string Filename;
/// Name of the profile remapping file to load.
std::string RemappingFilename;
/// VirtualFileSystem to load profile files from.
IntrusiveRefCntPtr<vfs::FileSystem> FS;
/// Profile Summary Info computed from sample profile.
ProfileSummaryInfo *PSI = nullptr;
/// Optimization Remark Emitter used to emit diagnostic remarks.
OptRemarkEmitterT *ORE = nullptr;
};
/// Clear all the per-function data used to load samples and propagate weights.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::clearFunctionData(bool ResetDT) {
BlockWeights.clear();
EdgeWeights.clear();
VisitedBlocks.clear();
VisitedEdges.clear();
EquivalenceClass.clear();
if (ResetDT) {
DT = nullptr;
PDT = nullptr;
LI = nullptr;
}
Predecessors.clear();
Successors.clear();
CoverageTracker.clear();
}
#ifndef NDEBUG
/// Print the weight of edge \p E on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param E Edge to print.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::printEdgeWeight(raw_ostream &OS, Edge E) {
OS << "weight[" << E.first->getName() << "->" << E.second->getName()
<< "]: " << EdgeWeights[E] << "\n";
}
/// Print the equivalence class of block \p BB on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param BB Block to print.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::printBlockEquivalence(
raw_ostream &OS, const BasicBlockT *BB) {
const BasicBlockT *Equiv = EquivalenceClass[BB];
OS << "equivalence[" << BB->getName()
<< "]: " << ((Equiv) ? EquivalenceClass[BB]->getName() : "NONE") << "\n";
}
/// Print the weight of block \p BB on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param BB Block to print.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::printBlockWeight(
raw_ostream &OS, const BasicBlockT *BB) const {
const auto &I = BlockWeights.find(BB);
uint64_t W = (I == BlockWeights.end() ? 0 : I->second);
OS << "weight[" << BB->getName() << "]: " << W << "\n";
}
#endif
/// Get the weight for an instruction.
///
/// The "weight" of an instruction \p Inst is the number of samples
/// collected on that instruction at runtime. To retrieve it, we
/// need to compute the line number of \p Inst relative to the start of its
/// function. We use HeaderLineno to compute the offset. We then
/// look up the samples collected for \p Inst using BodySamples.
///
/// \param Inst Instruction to query.
///
/// \returns the weight of \p Inst.
template <typename BT>
ErrorOr<uint64_t>
SampleProfileLoaderBaseImpl<BT>::getInstWeight(const InstructionT &Inst) {
if (FunctionSamples::ProfileIsProbeBased)
return getProbeWeight(Inst);
return getInstWeightImpl(Inst);
}
template <typename BT>
ErrorOr<uint64_t>
SampleProfileLoaderBaseImpl<BT>::getInstWeightImpl(const InstructionT &Inst) {
const FunctionSamples *FS = findFunctionSamples(Inst);
if (!FS)
return std::error_code();
const DebugLoc &DLoc = Inst.getDebugLoc();
if (!DLoc)
return std::error_code();
const DILocation *DIL = DLoc;
uint32_t LineOffset = FunctionSamples::getOffset(DIL);
uint32_t Discriminator;
if (EnableFSDiscriminator)
Discriminator = DIL->getDiscriminator();
else
Discriminator = DIL->getBaseDiscriminator();
ErrorOr<uint64_t> R = FS->findSamplesAt(LineOffset, Discriminator);
if (R) {
bool FirstMark =
CoverageTracker.markSamplesUsed(FS, LineOffset, Discriminator, R.get());
if (FirstMark) {
ORE->emit([&]() {
OptRemarkAnalysisT Remark(DEBUG_TYPE, "AppliedSamples", &Inst);
Remark << "Applied " << ore::NV("NumSamples", *R);
Remark << " samples from profile (offset: ";
Remark << ore::NV("LineOffset", LineOffset);
if (Discriminator) {
Remark << ".";
Remark << ore::NV("Discriminator", Discriminator);
}
Remark << ")";
return Remark;
});
}
LLVM_DEBUG(dbgs() << " " << DLoc.getLine() << "." << Discriminator << ":"
<< Inst << " (line offset: " << LineOffset << "."
<< Discriminator << " - weight: " << R.get() << ")\n");
}
return R;
}
template <typename BT>
ErrorOr<uint64_t>
SampleProfileLoaderBaseImpl<BT>::getProbeWeight(const InstructionT &Inst) {
assert(FunctionSamples::ProfileIsProbeBased &&
"Profile is not pseudo probe based");
std::optional<PseudoProbe> Probe = extractProbe(Inst);
// Ignore the non-probe instruction. If none of the instruction in the BB is
// probe, we choose to infer the BB's weight.
if (!Probe)
return std::error_code();
const FunctionSamples *FS = findFunctionSamples(Inst);
if (!FS) {
// If we can't find the function samples for a probe, it could be due to the
// probe is later optimized away or the inlining context is mismatced. We
// treat it as unknown, leaving it to profile inference instead of forcing a
// zero count.
return std::error_code();
}
auto R = FS->findSamplesAt(Probe->Id, Probe->Discriminator);
if (R) {
uint64_t Samples = R.get() * Probe->Factor;
bool FirstMark = CoverageTracker.markSamplesUsed(FS, Probe->Id, 0, Samples);
if (FirstMark) {
ORE->emit([&]() {
OptRemarkAnalysisT Remark(DEBUG_TYPE, "AppliedSamples", &Inst);
Remark << "Applied " << ore::NV("NumSamples", Samples);
Remark << " samples from profile (ProbeId=";
Remark << ore::NV("ProbeId", Probe->Id);
if (Probe->Discriminator) {
Remark << ".";
Remark << ore::NV("Discriminator", Probe->Discriminator);
}
Remark << ", Factor=";
Remark << ore::NV("Factor", Probe->Factor);
Remark << ", OriginalSamples=";
Remark << ore::NV("OriginalSamples", R.get());
Remark << ")";
return Remark;
});
}
LLVM_DEBUG({dbgs() << " " << Probe->Id;
if (Probe->Discriminator)
dbgs() << "." << Probe->Discriminator;
dbgs() << ":" << Inst << " - weight: " << R.get()
<< " - factor: " << format("%0.2f", Probe->Factor) << ")\n";});
return Samples;
}
return R;
}
/// Compute the weight of a basic block.
///
/// The weight of basic block \p BB is the maximum weight of all the
/// instructions in BB.
///
/// \param BB The basic block to query.
///
/// \returns the weight for \p BB.
template <typename BT>
ErrorOr<uint64_t>
SampleProfileLoaderBaseImpl<BT>::getBlockWeight(const BasicBlockT *BB) {
uint64_t Max = 0;
bool HasWeight = false;
for (auto &I : *BB) {
const ErrorOr<uint64_t> &R = getInstWeight(I);
if (R) {
Max = std::max(Max, R.get());
HasWeight = true;
}
}
return HasWeight ? ErrorOr<uint64_t>(Max) : std::error_code();
}
/// Compute and store the weights of every basic block.
///
/// This populates the BlockWeights map by computing
/// the weights of every basic block in the CFG.
///
/// \param F The function to query.
template <typename BT>
bool SampleProfileLoaderBaseImpl<BT>::computeBlockWeights(FunctionT &F) {
bool Changed = false;
LLVM_DEBUG(dbgs() << "Block weights\n");
for (const auto &BB : F) {
ErrorOr<uint64_t> Weight = getBlockWeight(&BB);
if (Weight) {
BlockWeights[&BB] = Weight.get();
VisitedBlocks.insert(&BB);
Changed = true;
}
LLVM_DEBUG(printBlockWeight(dbgs(), &BB));
}
return Changed;
}
/// Get the FunctionSamples for an instruction.
///
/// The FunctionSamples of an instruction \p Inst is the inlined instance
/// in which that instruction is coming from. We traverse the inline stack
/// of that instruction, and match it with the tree nodes in the profile.
///
/// \param Inst Instruction to query.
///
/// \returns the FunctionSamples pointer to the inlined instance.
template <typename BT>
const FunctionSamples *SampleProfileLoaderBaseImpl<BT>::findFunctionSamples(
const InstructionT &Inst) const {
const DILocation *DIL = Inst.getDebugLoc();
if (!DIL)
return Samples;
auto it = DILocation2SampleMap.try_emplace(DIL, nullptr);
if (it.second) {
it.first->second = Samples->findFunctionSamples(DIL, Reader->getRemapper());
}
return it.first->second;
}
/// Find equivalence classes for the given block.
///
/// This finds all the blocks that are guaranteed to execute the same
/// number of times as \p BB1. To do this, it traverses all the
/// descendants of \p BB1 in the dominator or post-dominator tree.
///
/// A block BB2 will be in the same equivalence class as \p BB1 if
/// the following holds:
///
/// 1- \p BB1 is a descendant of BB2 in the opposite tree. So, if BB2
/// is a descendant of \p BB1 in the dominator tree, then BB2 should
/// dominate BB1 in the post-dominator tree.
///
/// 2- Both BB2 and \p BB1 must be in the same loop.
///
/// For every block BB2 that meets those two requirements, we set BB2's
/// equivalence class to \p BB1.
///
/// \param BB1 Block to check.
/// \param Descendants Descendants of \p BB1 in either the dom or pdom tree.
/// \param DomTree Opposite dominator tree. If \p Descendants is filled
/// with blocks from \p BB1's dominator tree, then
/// this is the post-dominator tree, and vice versa.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::findEquivalencesFor(
BasicBlockT *BB1, ArrayRef<BasicBlockT *> Descendants,
PostDominatorTreeT *DomTree) {
const BasicBlockT *EC = EquivalenceClass[BB1];
uint64_t Weight = BlockWeights[EC];
for (const auto *BB2 : Descendants) {
bool IsDomParent = DomTree->dominates(BB2, BB1);
bool IsInSameLoop = LI->getLoopFor(BB1) == LI->getLoopFor(BB2);
if (BB1 != BB2 && IsDomParent && IsInSameLoop) {
EquivalenceClass[BB2] = EC;
// If BB2 is visited, then the entire EC should be marked as visited.
if (VisitedBlocks.count(BB2)) {
VisitedBlocks.insert(EC);
}
// If BB2 is heavier than BB1, make BB2 have the same weight
// as BB1.
//
// Note that we don't worry about the opposite situation here
// (when BB2 is lighter than BB1). We will deal with this
// during the propagation phase. Right now, we just want to
// make sure that BB1 has the largest weight of all the
// members of its equivalence set.
Weight = std::max(Weight, BlockWeights[BB2]);
}
}
const BasicBlockT *EntryBB = getEntryBB(EC->getParent());
if (EC == EntryBB) {
BlockWeights[EC] = Samples->getHeadSamples() + 1;
} else {
BlockWeights[EC] = Weight;
}
}
/// Find equivalence classes.
///
/// Since samples may be missing from blocks, we can fill in the gaps by setting
/// the weights of all the blocks in the same equivalence class to the same
/// weight. To compute the concept of equivalence, we use dominance and loop
/// information. Two blocks B1 and B2 are in the same equivalence class if B1
/// dominates B2, B2 post-dominates B1 and both are in the same loop.
///
/// \param F The function to query.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::findEquivalenceClasses(FunctionT &F) {
SmallVector<BasicBlockT *, 8> DominatedBBs;
LLVM_DEBUG(dbgs() << "\nBlock equivalence classes\n");
// Find equivalence sets based on dominance and post-dominance information.
for (auto &BB : F) {
BasicBlockT *BB1 = &BB;
// Compute BB1's equivalence class once.
if (EquivalenceClass.count(BB1)) {
LLVM_DEBUG(printBlockEquivalence(dbgs(), BB1));
continue;
}
// By default, blocks are in their own equivalence class.
EquivalenceClass[BB1] = BB1;
// Traverse all the blocks dominated by BB1. We are looking for
// every basic block BB2 such that:
//
// 1- BB1 dominates BB2.
// 2- BB2 post-dominates BB1.
// 3- BB1 and BB2 are in the same loop nest.
//
// If all those conditions hold, it means that BB2 is executed
// as many times as BB1, so they are placed in the same equivalence
// class by making BB2's equivalence class be BB1.
DominatedBBs.clear();
DT->getDescendants(BB1, DominatedBBs);
findEquivalencesFor(BB1, DominatedBBs, &*PDT);
LLVM_DEBUG(printBlockEquivalence(dbgs(), BB1));
}
// Assign weights to equivalence classes.
//
// All the basic blocks in the same equivalence class will execute
// the same number of times. Since we know that the head block in
// each equivalence class has the largest weight, assign that weight
// to all the blocks in that equivalence class.
LLVM_DEBUG(
dbgs() << "\nAssign the same weight to all blocks in the same class\n");
for (auto &BI : F) {
const BasicBlockT *BB = &BI;
const BasicBlockT *EquivBB = EquivalenceClass[BB];
if (BB != EquivBB)
BlockWeights[BB] = BlockWeights[EquivBB];
LLVM_DEBUG(printBlockWeight(dbgs(), BB));
}
}
/// Visit the given edge to decide if it has a valid weight.
///
/// If \p E has not been visited before, we copy to \p UnknownEdge
/// and increment the count of unknown edges.
///
/// \param E Edge to visit.
/// \param NumUnknownEdges Current number of unknown edges.
/// \param UnknownEdge Set if E has not been visited before.
///
/// \returns E's weight, if known. Otherwise, return 0.
template <typename BT>
uint64_t SampleProfileLoaderBaseImpl<BT>::visitEdge(Edge E,
unsigned *NumUnknownEdges,
Edge *UnknownEdge) {
if (!VisitedEdges.count(E)) {
(*NumUnknownEdges)++;
*UnknownEdge = E;
return 0;
}
return EdgeWeights[E];
}
/// Propagate weights through incoming/outgoing edges.
///
/// If the weight of a basic block is known, and there is only one edge
/// with an unknown weight, we can calculate the weight of that edge.
///
/// Similarly, if all the edges have a known count, we can calculate the
/// count of the basic block, if needed.
///
/// \param F Function to process.
/// \param UpdateBlockCount Whether we should update basic block counts that
/// has already been annotated.
///
/// \returns True if new weights were assigned to edges or blocks.
template <typename BT>
bool SampleProfileLoaderBaseImpl<BT>::propagateThroughEdges(
FunctionT &F, bool UpdateBlockCount) {
bool Changed = false;
LLVM_DEBUG(dbgs() << "\nPropagation through edges\n");
for (const auto &BI : F) {
const BasicBlockT *BB = &BI;
const BasicBlockT *EC = EquivalenceClass[BB];
// Visit all the predecessor and successor edges to determine
// which ones have a weight assigned already. Note that it doesn't
// matter that we only keep track of a single unknown edge. The
// only case we are interested in handling is when only a single
// edge is unknown (see setEdgeOrBlockWeight).
for (unsigned i = 0; i < 2; i++) {
uint64_t TotalWeight = 0;
unsigned NumUnknownEdges = 0, NumTotalEdges = 0;
Edge UnknownEdge, SelfReferentialEdge, SingleEdge;
if (i == 0) {
// First, visit all predecessor edges.
NumTotalEdges = Predecessors[BB].size();
for (auto *Pred : Predecessors[BB]) {
Edge E = std::make_pair(Pred, BB);
TotalWeight += visitEdge(E, &NumUnknownEdges, &UnknownEdge);
if (E.first == E.second)
SelfReferentialEdge = E;
}
if (NumTotalEdges == 1) {
SingleEdge = std::make_pair(Predecessors[BB][0], BB);
}
} else {
// On the second round, visit all successor edges.
NumTotalEdges = Successors[BB].size();
for (auto *Succ : Successors[BB]) {
Edge E = std::make_pair(BB, Succ);
TotalWeight += visitEdge(E, &NumUnknownEdges, &UnknownEdge);
}
if (NumTotalEdges == 1) {
SingleEdge = std::make_pair(BB, Successors[BB][0]);
}
}
// After visiting all the edges, there are three cases that we
// can handle immediately:
//
// - All the edge weights are known (i.e., NumUnknownEdges == 0).
// In this case, we simply check that the sum of all the edges
// is the same as BB's weight. If not, we change BB's weight
// to match. Additionally, if BB had not been visited before,
// we mark it visited.
//
// - Only one edge is unknown and BB has already been visited.
// In this case, we can compute the weight of the edge by
// subtracting the total block weight from all the known
// edge weights. If the edges weight more than BB, then the
// edge of the last remaining edge is set to zero.
//
// - There exists a self-referential edge and the weight of BB is
// known. In this case, this edge can be based on BB's weight.
// We add up all the other known edges and set the weight on
// the self-referential edge as we did in the previous case.
//
// In any other case, we must continue iterating. Eventually,
// all edges will get a weight, or iteration will stop when
// it reaches SampleProfileMaxPropagateIterations.
if (NumUnknownEdges <= 1) {
uint64_t &BBWeight = BlockWeights[EC];
if (NumUnknownEdges == 0) {
if (!VisitedBlocks.count(EC)) {
// If we already know the weight of all edges, the weight of the
// basic block can be computed. It should be no larger than the sum
// of all edge weights.
if (TotalWeight > BBWeight) {
BBWeight = TotalWeight;
Changed = true;
LLVM_DEBUG(dbgs() << "All edge weights for " << BB->getName()
<< " known. Set weight for block: ";
printBlockWeight(dbgs(), BB););
}
} else if (NumTotalEdges == 1 &&
EdgeWeights[SingleEdge] < BlockWeights[EC]) {
// If there is only one edge for the visited basic block, use the
// block weight to adjust edge weight if edge weight is smaller.
EdgeWeights[SingleEdge] = BlockWeights[EC];
Changed = true;
}
} else if (NumUnknownEdges == 1 && VisitedBlocks.count(EC)) {
// If there is a single unknown edge and the block has been
// visited, then we can compute E's weight.
if (BBWeight >= TotalWeight)
EdgeWeights[UnknownEdge] = BBWeight - TotalWeight;
else
EdgeWeights[UnknownEdge] = 0;
const BasicBlockT *OtherEC;
if (i == 0)
OtherEC = EquivalenceClass[UnknownEdge.first];
else
OtherEC = EquivalenceClass[UnknownEdge.second];
// Edge weights should never exceed the BB weights it connects.
if (VisitedBlocks.count(OtherEC) &&
EdgeWeights[UnknownEdge] > BlockWeights[OtherEC])
EdgeWeights[UnknownEdge] = BlockWeights[OtherEC];
VisitedEdges.insert(UnknownEdge);
Changed = true;
LLVM_DEBUG(dbgs() << "Set weight for edge: ";
printEdgeWeight(dbgs(), UnknownEdge));
}
} else if (VisitedBlocks.count(EC) && BlockWeights[EC] == 0) {
// If a block Weights 0, all its in/out edges should weight 0.
if (i == 0) {
for (auto *Pred : Predecessors[BB]) {
Edge E = std::make_pair(Pred, BB);
EdgeWeights[E] = 0;
VisitedEdges.insert(E);
}
} else {
for (auto *Succ : Successors[BB]) {
Edge E = std::make_pair(BB, Succ);
EdgeWeights[E] = 0;
VisitedEdges.insert(E);
}
}
} else if (SelfReferentialEdge.first && VisitedBlocks.count(EC)) {
uint64_t &BBWeight = BlockWeights[BB];
// We have a self-referential edge and the weight of BB is known.
if (BBWeight >= TotalWeight)
EdgeWeights[SelfReferentialEdge] = BBWeight - TotalWeight;
else
EdgeWeights[SelfReferentialEdge] = 0;
VisitedEdges.insert(SelfReferentialEdge);
Changed = true;
LLVM_DEBUG(dbgs() << "Set self-referential edge weight to: ";
printEdgeWeight(dbgs(), SelfReferentialEdge));
}
if (UpdateBlockCount && !VisitedBlocks.count(EC) && TotalWeight > 0) {
BlockWeights[EC] = TotalWeight;
VisitedBlocks.insert(EC);
Changed = true;
}
}
}
return Changed;
}
/// Build in/out edge lists for each basic block in the CFG.
///
/// We are interested in unique edges. If a block B1 has multiple
/// edges to another block B2, we only add a single B1->B2 edge.
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::buildEdges(FunctionT &F) {
for (auto &BI : F) {
BasicBlockT *B1 = &BI;
// Add predecessors for B1.
SmallPtrSet<BasicBlockT *, 16> Visited;
if (!Predecessors[B1].empty())
llvm_unreachable("Found a stale predecessors list in a basic block.");
for (auto *B2 : getPredecessors(B1))
if (Visited.insert(B2).second)
Predecessors[B1].push_back(B2);
// Add successors for B1.
Visited.clear();
if (!Successors[B1].empty())
llvm_unreachable("Found a stale successors list in a basic block.");
for (auto *B2 : getSuccessors(B1))
if (Visited.insert(B2).second)
Successors[B1].push_back(B2);
}
}
/// Propagate weights into edges
///
/// The following rules are applied to every block BB in the CFG:
///
/// - If BB has a single predecessor/successor, then the weight
/// of that edge is the weight of the block.
///
/// - If all incoming or outgoing edges are known except one, and the
/// weight of the block is already known, the weight of the unknown
/// edge will be the weight of the block minus the sum of all the known
/// edges. If the sum of all the known edges is larger than BB's weight,
/// we set the unknown edge weight to zero.
///
/// - If there is a self-referential edge, and the weight of the block is
/// known, the weight for that edge is set to the weight of the block
/// minus the weight of the other incoming edges to that block (if
/// known).
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::propagateWeights(FunctionT &F) {
// Flow-based profile inference is only usable with BasicBlock instantiation
// of SampleProfileLoaderBaseImpl.
if (SampleProfileUseProfi) {
// Prepare block sample counts for inference.
BlockWeightMap SampleBlockWeights;
for (const auto &BI : F) {
ErrorOr<uint64_t> Weight = getBlockWeight(&BI);
if (Weight)
SampleBlockWeights[&BI] = Weight.get();
}
// Fill in BlockWeights and EdgeWeights using an inference algorithm.
applyProfi(F, Successors, SampleBlockWeights, BlockWeights, EdgeWeights);
} else {
bool Changed = true;
unsigned I = 0;
// If BB weight is larger than its corresponding loop's header BB weight,
// use the BB weight to replace the loop header BB weight.
for (auto &BI : F) {
BasicBlockT *BB = &BI;
LoopT *L = LI->getLoopFor(BB);
if (!L) {
continue;
}
BasicBlockT *Header = L->getHeader();
if (Header && BlockWeights[BB] > BlockWeights[Header]) {
BlockWeights[Header] = BlockWeights[BB];
}
}
// Propagate until we converge or we go past the iteration limit.
while (Changed && I++ < SampleProfileMaxPropagateIterations) {
Changed = propagateThroughEdges(F, false);
}
// The first propagation propagates BB counts from annotated BBs to unknown
// BBs. The 2nd propagation pass resets edges weights, and use all BB
// weights to propagate edge weights.
VisitedEdges.clear();
Changed = true;
while (Changed && I++ < SampleProfileMaxPropagateIterations) {
Changed = propagateThroughEdges(F, false);
}
// The 3rd propagation pass allows adjust annotated BB weights that are
// obviously wrong.
Changed = true;
while (Changed && I++ < SampleProfileMaxPropagateIterations) {
Changed = propagateThroughEdges(F, true);
}
}
}
template <typename FT>
void SampleProfileLoaderBaseImpl<FT>::applyProfi(
FunctionT &F, BlockEdgeMap &Successors, BlockWeightMap &SampleBlockWeights,
BlockWeightMap &BlockWeights, EdgeWeightMap &EdgeWeights) {
auto Infer = SampleProfileInference<FT>(F, Successors, SampleBlockWeights);
Infer.apply(BlockWeights, EdgeWeights);
}
/// Generate branch weight metadata for all branches in \p F.
///
/// Branch weights are computed out of instruction samples using a
/// propagation heuristic. Propagation proceeds in 3 phases:
///
/// 1- Assignment of block weights. All the basic blocks in the function
/// are initial assigned the same weight as their most frequently
/// executed instruction.
///
/// 2- Creation of equivalence classes. Since samples may be missing from
/// blocks, we can fill in the gaps by setting the weights of all the
/// blocks in the same equivalence class to the same weight. To compute
/// the concept of equivalence, we use dominance and loop information.
/// Two blocks B1 and B2 are in the same equivalence class if B1
/// dominates B2, B2 post-dominates B1 and both are in the same loop.
///
/// 3- Propagation of block weights into edges. This uses a simple
/// propagation heuristic. The following rules are applied to every
/// block BB in the CFG:
///
/// - If BB has a single predecessor/successor, then the weight
/// of that edge is the weight of the block.
///
/// - If all the edges are known except one, and the weight of the
/// block is already known, the weight of the unknown edge will
/// be the weight of the block minus the sum of all the known
/// edges. If the sum of all the known edges is larger than BB's weight,
/// we set the unknown edge weight to zero.
///
/// - If there is a self-referential edge, and the weight of the block is
/// known, the weight for that edge is set to the weight of the block
/// minus the weight of the other incoming edges to that block (if
/// known).
///
/// Since this propagation is not guaranteed to finalize for every CFG, we
/// only allow it to proceed for a limited number of iterations (controlled
/// by -sample-profile-max-propagate-iterations).
///
/// FIXME: Try to replace this propagation heuristic with a scheme
/// that is guaranteed to finalize. A work-list approach similar to
/// the standard value propagation algorithm used by SSA-CCP might
/// work here.
///
/// \param F The function to query.
///
/// \returns true if \p F was modified. Returns false, otherwise.
template <typename BT>
bool SampleProfileLoaderBaseImpl<BT>::computeAndPropagateWeights(
FunctionT &F, const DenseSet<GlobalValue::GUID> &InlinedGUIDs) {
bool Changed = (InlinedGUIDs.size() != 0);
// Compute basic block weights.
Changed |= computeBlockWeights(F);
if (Changed) {
// Initialize propagation.
initWeightPropagation(F, InlinedGUIDs);
// Propagate weights to all edges.
propagateWeights(F);
// Post-process propagated weights.
finalizeWeightPropagation(F, InlinedGUIDs);
}
return Changed;
}
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::initWeightPropagation(
FunctionT &F, const DenseSet<GlobalValue::GUID> &InlinedGUIDs) {
// Add an entry count to the function using the samples gathered at the
// function entry.
// Sets the GUIDs that are inlined in the profiled binary. This is used
// for ThinLink to make correct liveness analysis, and also make the IR
// match the profiled binary before annotation.
getFunction(F).setEntryCount(
ProfileCount(Samples->getHeadSamples() + 1, Function::PCT_Real),
&InlinedGUIDs);
if (!SampleProfileUseProfi) {
// Compute dominance and loop info needed for propagation.
computeDominanceAndLoopInfo(F);
// Find equivalence classes.
findEquivalenceClasses(F);
}
// Before propagation starts, build, for each block, a list of
// unique predecessors and successors. This is necessary to handle
// identical edges in multiway branches. Since we visit all blocks and all
// edges of the CFG, it is cleaner to build these lists once at the start
// of the pass.
buildEdges(F);
}
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::finalizeWeightPropagation(
FunctionT &F, const DenseSet<GlobalValue::GUID> &InlinedGUIDs) {
// If we utilize a flow-based count inference, then we trust the computed
// counts and set the entry count as computed by the algorithm. This is
// primarily done to sync the counts produced by profi and BFI inference,
// which uses the entry count for mass propagation.
// If profi produces a zero-value for the entry count, we fallback to
// Samples->getHeadSamples() + 1 to avoid functions with zero count.
if (SampleProfileUseProfi) {
const BasicBlockT *EntryBB = getEntryBB(&F);
ErrorOr<uint64_t> EntryWeight = getBlockWeight(EntryBB);
if (BlockWeights[EntryBB] > 0) {
getFunction(F).setEntryCount(
ProfileCount(BlockWeights[EntryBB], Function::PCT_Real),
&InlinedGUIDs);
}
}
}
template <typename BT>
void SampleProfileLoaderBaseImpl<BT>::emitCoverageRemarks(FunctionT &F) {
// If coverage checking was requested, compute it now.
const Function &Func = getFunction(F);
if (SampleProfileRecordCoverage) {
unsigned Used = CoverageTracker.countUsedRecords(Samples, PSI);
unsigned Total = CoverageTracker.countBodyRecords(Samples, PSI);
unsigned Coverage = CoverageTracker.computeCoverage(Used, Total);
if (Coverage < SampleProfileRecordCoverage) {
Func.getContext().diagnose(DiagnosticInfoSampleProfile(
Func.getSubprogram()->getFilename(), getFunctionLoc(F),
Twine(Used) + " of " + Twine(Total) + " available profile records (" +
Twine(Coverage) + "%) were applied",
DS_Warning));
}
}
if (SampleProfileSampleCoverage) {
uint64_t Used = CoverageTracker.getTotalUsedSamples();
uint64_t Total = CoverageTracker.countBodySamples(Samples, PSI);
unsigned Coverage = CoverageTracker.computeCoverage(Used, Total);
if (Coverage < SampleProfileSampleCoverage) {
Func.getContext().diagnose(DiagnosticInfoSampleProfile(
Func.getSubprogram()->getFilename(), getFunctionLoc(F),
Twine(Used) + " of " + Twine(Total) + " available profile samples (" +
Twine(Coverage) + "%) were applied",
DS_Warning));
}
}
}
/// Get the line number for the function header.
///
/// This looks up function \p F in the current compilation unit and
/// retrieves the line number where the function is defined. This is
/// line 0 for all the samples read from the profile file. Every line
/// number is relative to this line.
///
/// \param F Function object to query.
///
/// \returns the line number where \p F is defined. If it returns 0,
/// it means that there is no debug information available for \p F.
template <typename BT>
unsigned SampleProfileLoaderBaseImpl<BT>::getFunctionLoc(FunctionT &F) {
const Function &Func = getFunction(F);
if (DISubprogram *S = Func.getSubprogram())
return S->getLine();
if (NoWarnSampleUnused)
return 0;
// If the start of \p F is missing, emit a diagnostic to inform the user
// about the missed opportunity.
Func.getContext().diagnose(DiagnosticInfoSampleProfile(
"No debug information found in function " + Func.getName() +
": Function profile not used",
DS_Warning));
return 0;
}
#undef DEBUG_TYPE
} // namespace llvm
#endif // LLVM_TRANSFORMS_UTILS_SAMPLEPROFILELOADERBASEIMPL_H
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