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llvm-mirror/lib/Transforms/IPO/SampleProfile.cpp
Matthias Braun 882ae69776 Avoid overly large SmallPtrSet/SmallSet 
These sets perform linear searching in small mode so it is never a good
idea to use SmallSize/N bigger than 32.

llvm-svn: 259283
2016-01-30 01:24:31 +00:00

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//===- SampleProfile.cpp - Incorporate sample profiles into the IR --------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file implements the SampleProfileLoader transformation. This pass
// reads a profile file generated by a sampling profiler (e.g. Linux Perf -
// http://perf.wiki.kernel.org/) and generates IR metadata to reflect the
// profile information in the given profile.
//
// This pass generates branch weight annotations on the IR:
//
// - prof: Represents branch weights. This annotation is added to branches
// to indicate the weights of each edge coming out of the branch.
// The weight of each edge is the weight of the target block for
// that edge. The weight of a block B is computed as the maximum
// number of samples found in B.
//
//===----------------------------------------------------------------------===//
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/PostDominators.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/DebugInfo.h"
#include "llvm/IR/DiagnosticInfo.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/InstIterator.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/IR/MDBuilder.h"
#include "llvm/IR/Metadata.h"
#include "llvm/IR/Module.h"
#include "llvm/Pass.h"
#include "llvm/ProfileData/SampleProfReader.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ErrorOr.h"
#include "llvm/Support/Format.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/IPO.h"
#include "llvm/Transforms/Utils/Cloning.h"
#include <cctype>
using namespace llvm;
using namespace sampleprof;
#define DEBUG_TYPE "sample-profile"
// Command line option to specify the file to read samples from. This is
// mainly used for debugging.
static cl::opt<std::string> SampleProfileFile(
"sample-profile-file", cl::init(""), cl::value_desc("filename"),
cl::desc("Profile file loaded by -sample-profile"), cl::Hidden);
static cl::opt<unsigned> SampleProfileMaxPropagateIterations(
"sample-profile-max-propagate-iterations", cl::init(100),
cl::desc("Maximum number of iterations to go through when propagating "
"sample block/edge weights through the CFG."));
static cl::opt<unsigned> SampleProfileRecordCoverage(
"sample-profile-check-record-coverage", cl::init(0), cl::value_desc("N"),
cl::desc("Emit a warning if less than N% of records in the input profile "
"are matched to the IR."));
static cl::opt<unsigned> SampleProfileSampleCoverage(
"sample-profile-check-sample-coverage", cl::init(0), cl::value_desc("N"),
cl::desc("Emit a warning if less than N% of samples in the input profile "
"are matched to the IR."));
static cl::opt<double> SampleProfileHotThreshold(
"sample-profile-inline-hot-threshold", cl::init(0.1), cl::value_desc("N"),
cl::desc("Inlined functions that account for more than N% of all samples "
"collected in the parent function, will be inlined again."));
static cl::opt<double> SampleProfileGlobalHotThreshold(
"sample-profile-global-hot-threshold", cl::init(30), cl::value_desc("N"),
cl::desc("Top-level functions that account for more than N% of all samples "
"collected in the profile, will be marked as hot for the inliner "
"to consider."));
static cl::opt<double> SampleProfileGlobalColdThreshold(
"sample-profile-global-cold-threshold", cl::init(0.5), cl::value_desc("N"),
cl::desc("Top-level functions that account for less than N% of all samples "
"collected in the profile, will be marked as cold for the inliner "
"to consider."));
namespace {
typedef DenseMap<const BasicBlock *, uint64_t> BlockWeightMap;
typedef DenseMap<const BasicBlock *, const BasicBlock *> EquivalenceClassMap;
typedef std::pair<const BasicBlock *, const BasicBlock *> Edge;
typedef DenseMap<Edge, uint64_t> EdgeWeightMap;
typedef DenseMap<const BasicBlock *, SmallVector<const BasicBlock *, 8>>
BlockEdgeMap;
/// \brief Sample profile pass.
///
/// This pass reads profile data from the file specified by
/// -sample-profile-file and annotates every affected function with the
/// profile information found in that file.
class SampleProfileLoader : public ModulePass {
public:
// Class identification, replacement for typeinfo
static char ID;
SampleProfileLoader(StringRef Name = SampleProfileFile)
: ModulePass(ID), DT(nullptr), PDT(nullptr), LI(nullptr), Reader(),
Samples(nullptr), Filename(Name), ProfileIsValid(false),
TotalCollectedSamples(0) {
initializeSampleProfileLoaderPass(*PassRegistry::getPassRegistry());
}
bool doInitialization(Module &M) override;
void dump() { Reader->dump(); }
const char *getPassName() const override { return "Sample profile pass"; }
bool runOnModule(Module &M) override;
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.setPreservesCFG();
}
protected:
bool runOnFunction(Function &F);
unsigned getFunctionLoc(Function &F);
bool emitAnnotations(Function &F);
ErrorOr<uint64_t> getInstWeight(const Instruction &I) const;
ErrorOr<uint64_t> getBlockWeight(const BasicBlock *BB) const;
const FunctionSamples *findCalleeFunctionSamples(const CallInst &I) const;
const FunctionSamples *findFunctionSamples(const Instruction &I) const;
bool inlineHotFunctions(Function &F);
bool emitInlineHints(Function &F);
void printEdgeWeight(raw_ostream &OS, Edge E);
void printBlockWeight(raw_ostream &OS, const BasicBlock *BB) const;
void printBlockEquivalence(raw_ostream &OS, const BasicBlock *BB);
bool computeBlockWeights(Function &F);
void findEquivalenceClasses(Function &F);
void findEquivalencesFor(BasicBlock *BB1,
SmallVector<BasicBlock *, 8> Descendants,
DominatorTreeBase<BasicBlock> *DomTree);
void propagateWeights(Function &F);
uint64_t visitEdge(Edge E, unsigned *NumUnknownEdges, Edge *UnknownEdge);
void buildEdges(Function &F);
bool propagateThroughEdges(Function &F);
void computeDominanceAndLoopInfo(Function &F);
unsigned getOffset(unsigned L, unsigned H) const;
void clearFunctionData();
/// \brief 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;
/// \brief Map edges to their computed weights.
///
/// Edge weights are computed by propagating basic block weights in
/// SampleProfile::propagateWeights.
EdgeWeightMap EdgeWeights;
/// \brief Set of visited blocks during propagation.
SmallPtrSet<const BasicBlock *, 32> VisitedBlocks;
/// \brief Set of visited edges during propagation.
SmallSet<Edge, 32> VisitedEdges;
/// \brief 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;
/// \brief Dominance, post-dominance and loop information.
std::unique_ptr<DominatorTree> DT;
std::unique_ptr<DominatorTreeBase<BasicBlock>> PDT;
std::unique_ptr<LoopInfo> LI;
/// \brief Predecessors for each basic block in the CFG.
BlockEdgeMap Predecessors;
/// \brief Successors for each basic block in the CFG.
BlockEdgeMap Successors;
/// \brief Profile reader object.
std::unique_ptr<SampleProfileReader> Reader;
/// \brief Samples collected for the body of this function.
FunctionSamples *Samples;
/// \brief Name of the profile file to load.
StringRef Filename;
/// \brief Flag indicating whether the profile input loaded successfully.
bool ProfileIsValid;
/// \brief Total number of samples collected in this profile.
///
/// This is the sum of all the samples collected in all the functions executed
/// at runtime.
uint64_t TotalCollectedSamples;
};
class SampleCoverageTracker {
public:
SampleCoverageTracker() : SampleCoverage(), TotalUsedSamples(0) {}
bool markSamplesUsed(const FunctionSamples *FS, uint32_t LineOffset,
uint32_t Discriminator, uint64_t Samples);
unsigned computeCoverage(unsigned Used, unsigned Total) const;
unsigned countUsedRecords(const FunctionSamples *FS) const;
unsigned countBodyRecords(const FunctionSamples *FS) const;
uint64_t getTotalUsedSamples() const { return TotalUsedSamples; }
uint64_t countBodySamples(const FunctionSamples *FS) const;
void clear() {
SampleCoverage.clear();
TotalUsedSamples = 0;
}
private:
typedef std::map<LineLocation, unsigned> BodySampleCoverageMap;
typedef DenseMap<const FunctionSamples *, BodySampleCoverageMap>
FunctionSamplesCoverageMap;
/// Coverage map for sampling records.
///
/// This map keeps a record of sampling records that have been matched to
/// an IR instruction. This is used to detect some form of staleness in
/// profiles (see flag -sample-profile-check-coverage).
///
/// Each entry in the map corresponds to a FunctionSamples instance. This is
/// another map that counts how many times the sample record at the
/// given location has been used.
FunctionSamplesCoverageMap SampleCoverage;
/// Number of samples used from the profile.
///
/// When a sampling record is used for the first time, the samples from
/// that record are added to this accumulator. Coverage is later computed
/// based on the total number of samples available in this function and
/// its callsites.
///
/// Note that this accumulator tracks samples used from a single function
/// and all the inlined callsites. Strictly, we should have a map of counters
/// keyed by FunctionSamples pointers, but these stats are cleared after
/// every function, so we just need to keep a single counter.
uint64_t TotalUsedSamples;
};
SampleCoverageTracker CoverageTracker;
/// Return true if the given callsite is hot wrt to its caller.
///
/// Functions that were inlined in the original binary will be represented
/// in the inline stack in the sample profile. If the profile shows that
/// the original inline decision was "good" (i.e., the callsite is executed
/// frequently), then we will recreate the inline decision and apply the
/// profile from the inlined callsite.
///
/// To decide whether an inlined callsite is hot, we compute the fraction
/// of samples used by the callsite with respect to the total number of samples
/// collected in the caller.
///
/// If that fraction is larger than the default given by
/// SampleProfileHotThreshold, the callsite will be inlined again.
bool callsiteIsHot(const FunctionSamples *CallerFS,
const FunctionSamples *CallsiteFS) {
if (!CallsiteFS)
return false; // The callsite was not inlined in the original binary.
uint64_t ParentTotalSamples = CallerFS->getTotalSamples();
if (ParentTotalSamples == 0)
return false; // Avoid division by zero.
uint64_t CallsiteTotalSamples = CallsiteFS->getTotalSamples();
if (CallsiteTotalSamples == 0)
return false; // Callsite is trivially cold.
double PercentSamples =
(double)CallsiteTotalSamples / (double)ParentTotalSamples * 100.0;
return PercentSamples >= SampleProfileHotThreshold;
}
}
/// Mark as used the sample record for the given function samples at
/// (LineOffset, Discriminator).
///
/// \returns true if this is the first time we mark the given record.
bool SampleCoverageTracker::markSamplesUsed(const FunctionSamples *FS,
uint32_t LineOffset,
uint32_t Discriminator,
uint64_t Samples) {
LineLocation Loc(LineOffset, Discriminator);
unsigned &Count = SampleCoverage[FS][Loc];
bool FirstTime = (++Count == 1);
if (FirstTime)
TotalUsedSamples += Samples;
return FirstTime;
}
/// Return the number of sample records that were applied from this profile.
///
/// This count does not include records from cold inlined callsites.
unsigned
SampleCoverageTracker::countUsedRecords(const FunctionSamples *FS) const {
auto I = SampleCoverage.find(FS);
// The size of the coverage map for FS represents the number of records
// that were marked used at least once.
unsigned Count = (I != SampleCoverage.end()) ? I->second.size() : 0;
// If there are inlined callsites in this function, count the samples found
// in the respective bodies. However, do not bother counting callees with 0
// total samples, these are callees that were never invoked at runtime.
for (const auto &I : FS->getCallsiteSamples()) {
const FunctionSamples *CalleeSamples = &I.second;
if (callsiteIsHot(FS, CalleeSamples))
Count += countUsedRecords(CalleeSamples);
}
return Count;
}
/// Return the number of sample records in the body of this profile.
///
/// This count does not include records from cold inlined callsites.
unsigned
SampleCoverageTracker::countBodyRecords(const FunctionSamples *FS) const {
unsigned Count = FS->getBodySamples().size();
// Only count records in hot callsites.
for (const auto &I : FS->getCallsiteSamples()) {
const FunctionSamples *CalleeSamples = &I.second;
if (callsiteIsHot(FS, CalleeSamples))
Count += countBodyRecords(CalleeSamples);
}
return Count;
}
/// Return the number of samples collected in the body of this profile.
///
/// This count does not include samples from cold inlined callsites.
uint64_t
SampleCoverageTracker::countBodySamples(const FunctionSamples *FS) const {
uint64_t Total = 0;
for (const auto &I : FS->getBodySamples())
Total += I.second.getSamples();
// Only count samples in hot callsites.
for (const auto &I : FS->getCallsiteSamples()) {
const FunctionSamples *CalleeSamples = &I.second;
if (callsiteIsHot(FS, CalleeSamples))
Total += countBodySamples(CalleeSamples);
}
return Total;
}
/// Return the fraction of sample records used in this profile.
///
/// The returned value is an unsigned integer in the range 0-100 indicating
/// the percentage of sample records that were used while applying this
/// profile to the associated function.
unsigned SampleCoverageTracker::computeCoverage(unsigned Used,
unsigned Total) const {
assert(Used <= Total &&
"number of used records cannot exceed the total number of records");
return Total > 0 ? Used * 100 / Total : 100;
}
/// Clear all the per-function data used to load samples and propagate weights.
void SampleProfileLoader::clearFunctionData() {
BlockWeights.clear();
EdgeWeights.clear();
VisitedBlocks.clear();
VisitedEdges.clear();
EquivalenceClass.clear();
DT = nullptr;
PDT = nullptr;
LI = nullptr;
Predecessors.clear();
Successors.clear();
CoverageTracker.clear();
}
/// \brief Returns the offset of lineno \p L to head_lineno \p H
///
/// \param L Lineno
/// \param H Header lineno of the function
///
/// \returns offset to the header lineno. 16 bits are used to represent offset.
/// We assume that a single function will not exceed 65535 LOC.
unsigned SampleProfileLoader::getOffset(unsigned L, unsigned H) const {
return (L - H) & 0xffff;
}
/// \brief Print the weight of edge \p E on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param E Edge to print.
void SampleProfileLoader::printEdgeWeight(raw_ostream &OS, Edge E) {
OS << "weight[" << E.first->getName() << "->" << E.second->getName()
<< "]: " << EdgeWeights[E] << "\n";
}
/// \brief 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.
void SampleProfileLoader::printBlockEquivalence(raw_ostream &OS,
const BasicBlock *BB) {
const BasicBlock *Equiv = EquivalenceClass[BB];
OS << "equivalence[" << BB->getName()
<< "]: " << ((Equiv) ? EquivalenceClass[BB]->getName() : "NONE") << "\n";
}
/// \brief Print the weight of block \p BB on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param BB Block to print.
void SampleProfileLoader::printBlockWeight(raw_ostream &OS,
const BasicBlock *BB) const {
const auto &I = BlockWeights.find(BB);
uint64_t W = (I == BlockWeights.end() ? 0 : I->second);
OS << "weight[" << BB->getName() << "]: " << W << "\n";
}
/// \brief 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.
ErrorOr<uint64_t>
SampleProfileLoader::getInstWeight(const Instruction &Inst) const {
DebugLoc DLoc = Inst.getDebugLoc();
if (!DLoc)
return std::error_code();
const FunctionSamples *FS = findFunctionSamples(Inst);
if (!FS)
return std::error_code();
const DILocation *DIL = DLoc;
unsigned Lineno = DLoc.getLine();
unsigned HeaderLineno = DIL->getScope()->getSubprogram()->getLine();
uint32_t LineOffset = getOffset(Lineno, HeaderLineno);
uint32_t Discriminator = DIL->getDiscriminator();
ErrorOr<uint64_t> R = FS->findSamplesAt(LineOffset, Discriminator);
if (R) {
bool FirstMark =
CoverageTracker.markSamplesUsed(FS, LineOffset, Discriminator, R.get());
if (FirstMark) {
const Function *F = Inst.getParent()->getParent();
LLVMContext &Ctx = F->getContext();
emitOptimizationRemark(
Ctx, DEBUG_TYPE, *F, DLoc,
Twine("Applied ") + Twine(*R) + " samples from profile (offset: " +
Twine(LineOffset) +
((Discriminator) ? Twine(".") + Twine(Discriminator) : "") + ")");
}
DEBUG(dbgs() << " " << Lineno << "." << DIL->getDiscriminator() << ":"
<< Inst << " (line offset: " << Lineno - HeaderLineno << "."
<< DIL->getDiscriminator() << " - weight: " << R.get()
<< ")\n");
}
return R;
}
/// \brief 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.
ErrorOr<uint64_t>
SampleProfileLoader::getBlockWeight(const BasicBlock *BB) const {
bool Found = false;
uint64_t Weight = 0;
for (auto &I : BB->getInstList()) {
const ErrorOr<uint64_t> &R = getInstWeight(I);
if (R && R.get() >= Weight) {
Weight = R.get();
Found = true;
}
}
if (Found)
return Weight;
else
return std::error_code();
}
/// \brief 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.
bool SampleProfileLoader::computeBlockWeights(Function &F) {
bool Changed = false;
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;
}
DEBUG(printBlockWeight(dbgs(), &BB));
}
return Changed;
}
/// \brief Get the FunctionSamples for a call instruction.
///
/// The FunctionSamples of a call instruction \p Inst is the inlined
/// instance in which that call instruction is calling to. It contains
/// all samples that resides in the inlined instance. We first find the
/// inlined instance in which the call instruction is from, then we
/// traverse its children to find the callsite with the matching
/// location and callee function name.
///
/// \param Inst Call instruction to query.
///
/// \returns The FunctionSamples pointer to the inlined instance.
const FunctionSamples *
SampleProfileLoader::findCalleeFunctionSamples(const CallInst &Inst) const {
const DILocation *DIL = Inst.getDebugLoc();
if (!DIL) {
return nullptr;
}
DISubprogram *SP = DIL->getScope()->getSubprogram();
if (!SP)
return nullptr;
Function *CalleeFunc = Inst.getCalledFunction();
if (!CalleeFunc) {
return nullptr;
}
StringRef CalleeName = CalleeFunc->getName();
const FunctionSamples *FS = findFunctionSamples(Inst);
if (FS == nullptr)
return nullptr;
return FS->findFunctionSamplesAt(
CallsiteLocation(getOffset(DIL->getLine(), SP->getLine()),
DIL->getDiscriminator(), CalleeName));
}
/// \brief 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.
const FunctionSamples *
SampleProfileLoader::findFunctionSamples(const Instruction &Inst) const {
SmallVector<CallsiteLocation, 10> S;
const DILocation *DIL = Inst.getDebugLoc();
if (!DIL) {
return Samples;
}
StringRef CalleeName;
for (const DILocation *DIL = Inst.getDebugLoc(); DIL;
DIL = DIL->getInlinedAt()) {
DISubprogram *SP = DIL->getScope()->getSubprogram();
if (!SP)
return nullptr;
if (!CalleeName.empty()) {
S.push_back(CallsiteLocation(getOffset(DIL->getLine(), SP->getLine()),
DIL->getDiscriminator(), CalleeName));
}
CalleeName = SP->getLinkageName();
}
if (S.size() == 0)
return Samples;
const FunctionSamples *FS = Samples;
for (int i = S.size() - 1; i >= 0 && FS != nullptr; i--) {
FS = FS->findFunctionSamplesAt(S[i]);
}
return FS;
}
/// \brief Emit an inline hint if \p F is globally hot or cold.
///
/// If \p F consumes a significant fraction of samples (indicated by
/// SampleProfileGlobalHotThreshold), apply the InlineHint attribute for the
/// inliner to consider the function hot.
///
/// If \p F consumes a small fraction of samples (indicated by
/// SampleProfileGlobalColdThreshold), apply the Cold attribute for the inliner
/// to consider the function cold.
///
/// FIXME - This setting of inline hints is sub-optimal. Instead of marking a
/// function globally hot or cold, we should be annotating individual callsites.
/// This is not currently possible, but work on the inliner will eventually
/// provide this ability. See http://reviews.llvm.org/D15003 for details and
/// discussion.
///
/// \returns True if either attribute was applied to \p F.
bool SampleProfileLoader::emitInlineHints(Function &F) {
if (TotalCollectedSamples == 0)
return false;
uint64_t FunctionSamples = Samples->getTotalSamples();
double SamplesPercent =
(double)FunctionSamples / (double)TotalCollectedSamples * 100.0;
// If the function collected more samples than the hot threshold, mark
// it globally hot.
if (SamplesPercent >= SampleProfileGlobalHotThreshold) {
F.addFnAttr(llvm::Attribute::InlineHint);
std::string Msg;
raw_string_ostream S(Msg);
S << "Applied inline hint to globally hot function '" << F.getName()
<< "' with " << format("%.2f", SamplesPercent)
<< "% of samples (threshold: "
<< format("%.2f", SampleProfileGlobalHotThreshold.getValue()) << "%)";
S.flush();
emitOptimizationRemark(F.getContext(), DEBUG_TYPE, F, DebugLoc(), Msg);
return true;
}
// If the function collected fewer samples than the cold threshold, mark
// it globally cold.
if (SamplesPercent <= SampleProfileGlobalColdThreshold) {
F.addFnAttr(llvm::Attribute::Cold);
std::string Msg;
raw_string_ostream S(Msg);
S << "Applied cold hint to globally cold function '" << F.getName()
<< "' with " << format("%.2f", SamplesPercent)
<< "% of samples (threshold: "
<< format("%.2f", SampleProfileGlobalColdThreshold.getValue()) << "%)";
S.flush();
emitOptimizationRemark(F.getContext(), DEBUG_TYPE, F, DebugLoc(), Msg);
return true;
}
return false;
}
/// \brief Iteratively inline hot callsites of a function.
///
/// Iteratively traverse all callsites of the function \p F, and find if
/// the corresponding inlined instance exists and is hot in profile. If
/// it is hot enough, inline the callsites and adds new callsites of the
/// callee into the caller.
///
/// TODO: investigate the possibility of not invoking InlineFunction directly.
///
/// \param F function to perform iterative inlining.
///
/// \returns True if there is any inline happened.
bool SampleProfileLoader::inlineHotFunctions(Function &F) {
bool Changed = false;
LLVMContext &Ctx = F.getContext();
while (true) {
bool LocalChanged = false;
SmallVector<CallInst *, 10> CIS;
for (auto &BB : F) {
for (auto &I : BB.getInstList()) {
CallInst *CI = dyn_cast<CallInst>(&I);
if (CI && callsiteIsHot(Samples, findCalleeFunctionSamples(*CI)))
CIS.push_back(CI);
}
}
for (auto CI : CIS) {
InlineFunctionInfo IFI;
Function *CalledFunction = CI->getCalledFunction();
DebugLoc DLoc = CI->getDebugLoc();
uint64_t NumSamples = findCalleeFunctionSamples(*CI)->getTotalSamples();
if (InlineFunction(CI, IFI)) {
LocalChanged = true;
emitOptimizationRemark(Ctx, DEBUG_TYPE, F, DLoc,
Twine("inlined hot callee '") +
CalledFunction->getName() + "' with " +
Twine(NumSamples) + " samples into '" +
F.getName() + "'");
}
}
if (LocalChanged) {
Changed = true;
} else {
break;
}
}
return Changed;
}
/// \brief 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.
void SampleProfileLoader::findEquivalencesFor(
BasicBlock *BB1, SmallVector<BasicBlock *, 8> Descendants,
DominatorTreeBase<BasicBlock> *DomTree) {
const BasicBlock *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 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]);
}
}
BlockWeights[EC] = Weight;
}
/// \brief 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.
void SampleProfileLoader::findEquivalenceClasses(Function &F) {
SmallVector<BasicBlock *, 8> DominatedBBs;
DEBUG(dbgs() << "\nBlock equivalence classes\n");
// Find equivalence sets based on dominance and post-dominance information.
for (auto &BB : F) {
BasicBlock *BB1 = &BB;
// Compute BB1's equivalence class once.
if (EquivalenceClass.count(BB1)) {
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.get());
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.
DEBUG(dbgs() << "\nAssign the same weight to all blocks in the same class\n");
for (auto &BI : F) {
const BasicBlock *BB = &BI;
const BasicBlock *EquivBB = EquivalenceClass[BB];
if (BB != EquivBB)
BlockWeights[BB] = BlockWeights[EquivBB];
DEBUG(printBlockWeight(dbgs(), BB));
}
}
/// \brief 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.
uint64_t SampleProfileLoader::visitEdge(Edge E, unsigned *NumUnknownEdges,
Edge *UnknownEdge) {
if (!VisitedEdges.count(E)) {
(*NumUnknownEdges)++;
*UnknownEdge = E;
return 0;
}
return EdgeWeights[E];
}
/// \brief 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.
///
/// \returns True if new weights were assigned to edges or blocks.
bool SampleProfileLoader::propagateThroughEdges(Function &F) {
bool Changed = false;
DEBUG(dbgs() << "\nPropagation through edges\n");
for (const auto &BI : F) {
const BasicBlock *BB = &BI;
const BasicBlock *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;
Edge UnknownEdge, SelfReferentialEdge;
if (i == 0) {
// First, visit all predecessor edges.
for (auto *Pred : Predecessors[BB]) {
Edge E = std::make_pair(Pred, BB);
TotalWeight += visitEdge(E, &NumUnknownEdges, &UnknownEdge);
if (E.first == E.second)
SelfReferentialEdge = E;
}
} else {
// On the second round, visit all successor edges.
for (auto *Succ : Successors[BB]) {
Edge E = std::make_pair(BB, Succ);
TotalWeight += visitEdge(E, &NumUnknownEdges, &UnknownEdge);
}
}
// 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 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;
DEBUG(dbgs() << "All edge weights for " << BB->getName()
<< " known. Set weight for block: ";
printBlockWeight(dbgs(), BB););
}
if (VisitedBlocks.insert(EC).second)
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;
VisitedEdges.insert(UnknownEdge);
Changed = true;
DEBUG(dbgs() << "Set weight for edge: ";
printEdgeWeight(dbgs(), UnknownEdge));
}
} 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;
DEBUG(dbgs() << "Set self-referential edge weight to: ";
printEdgeWeight(dbgs(), SelfReferentialEdge));
}
}
}
return Changed;
}
/// \brief 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.
void SampleProfileLoader::buildEdges(Function &F) {
for (auto &BI : F) {
BasicBlock *B1 = &BI;
// Add predecessors for B1.
SmallPtrSet<BasicBlock *, 16> Visited;
if (!Predecessors[B1].empty())
llvm_unreachable("Found a stale predecessors list in a basic block.");
for (pred_iterator PI = pred_begin(B1), PE = pred_end(B1); PI != PE; ++PI) {
BasicBlock *B2 = *PI;
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 (succ_iterator SI = succ_begin(B1), SE = succ_end(B1); SI != SE; ++SI) {
BasicBlock *B2 = *SI;
if (Visited.insert(B2).second)
Successors[B1].push_back(B2);
}
}
}
/// \brief 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).
void SampleProfileLoader::propagateWeights(Function &F) {
bool Changed = true;
unsigned I = 0;
// Add an entry count to the function using the samples gathered
// at the function entry.
F.setEntryCount(Samples->getHeadSamples());
// 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);
// Propagate until we converge or we go past the iteration limit.
while (Changed && I++ < SampleProfileMaxPropagateIterations) {
Changed = propagateThroughEdges(F);
}
// Generate MD_prof metadata for every branch instruction using the
// edge weights computed during propagation.
DEBUG(dbgs() << "\nPropagation complete. Setting branch weights\n");
LLVMContext &Ctx = F.getContext();
MDBuilder MDB(Ctx);
for (auto &BI : F) {
BasicBlock *BB = &BI;
TerminatorInst *TI = BB->getTerminator();
if (TI->getNumSuccessors() == 1)
continue;
if (!isa<BranchInst>(TI) && !isa<SwitchInst>(TI))
continue;
DEBUG(dbgs() << "\nGetting weights for branch at line "
<< TI->getDebugLoc().getLine() << ".\n");
SmallVector<uint32_t, 4> Weights;
uint32_t MaxWeight = 0;
DebugLoc MaxDestLoc;
for (unsigned I = 0; I < TI->getNumSuccessors(); ++I) {
BasicBlock *Succ = TI->getSuccessor(I);
Edge E = std::make_pair(BB, Succ);
uint64_t Weight = EdgeWeights[E];
DEBUG(dbgs() << "\t"; printEdgeWeight(dbgs(), E));
// Use uint32_t saturated arithmetic to adjust the incoming weights,
// if needed. Sample counts in profiles are 64-bit unsigned values,
// but internally branch weights are expressed as 32-bit values.
if (Weight > std::numeric_limits<uint32_t>::max()) {
DEBUG(dbgs() << " (saturated due to uint32_t overflow)");
Weight = std::numeric_limits<uint32_t>::max();
}
Weights.push_back(static_cast<uint32_t>(Weight));
if (Weight != 0) {
if (Weight > MaxWeight) {
MaxWeight = Weight;
MaxDestLoc = Succ->getFirstNonPHIOrDbgOrLifetime()->getDebugLoc();
}
}
}
// Only set weights if there is at least one non-zero weight.
// In any other case, let the analyzer set weights.
if (MaxWeight > 0) {
DEBUG(dbgs() << "SUCCESS. Found non-zero weights.\n");
TI->setMetadata(llvm::LLVMContext::MD_prof,
MDB.createBranchWeights(Weights));
DebugLoc BranchLoc = TI->getDebugLoc();
emitOptimizationRemark(
Ctx, DEBUG_TYPE, F, MaxDestLoc,
Twine("most popular destination for conditional branches at ") +
((BranchLoc) ? Twine(BranchLoc->getFilename() + ":" +
Twine(BranchLoc.getLine()) + ":" +
Twine(BranchLoc.getCol()))
: Twine("<UNKNOWN LOCATION>")));
} else {
DEBUG(dbgs() << "SKIPPED. All branch weights are zero.\n");
}
}
}
/// \brief 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.
unsigned SampleProfileLoader::getFunctionLoc(Function &F) {
if (DISubprogram *S = getDISubprogram(&F))
return S->getLine();
// If the start of \p F is missing, emit a diagnostic to inform the user
// about the missed opportunity.
F.getContext().diagnose(DiagnosticInfoSampleProfile(
"No debug information found in function " + F.getName() +
": Function profile not used",
DS_Warning));
return 0;
}
void SampleProfileLoader::computeDominanceAndLoopInfo(Function &F) {
DT.reset(new DominatorTree);
DT->recalculate(F);
PDT.reset(new DominatorTreeBase<BasicBlock>(true));
PDT->recalculate(F);
LI.reset(new LoopInfo);
LI->analyze(*DT);
}
/// \brief 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.
///
/// Once all the branch weights are computed, we emit the MD_prof
/// metadata on BB using the computed values for each of its branches.
///
/// \param F The function to query.
///
/// \returns true if \p F was modified. Returns false, otherwise.
bool SampleProfileLoader::emitAnnotations(Function &F) {
bool Changed = false;
if (getFunctionLoc(F) == 0)
return false;
DEBUG(dbgs() << "Line number for the first instruction in " << F.getName()
<< ": " << getFunctionLoc(F) << "\n");
Changed |= emitInlineHints(F);
Changed |= inlineHotFunctions(F);
// Compute basic block weights.
Changed |= computeBlockWeights(F);
if (Changed) {
// Compute dominance and loop info needed for propagation.
computeDominanceAndLoopInfo(F);
// Find equivalence classes.
findEquivalenceClasses(F);
// Propagate weights to all edges.
propagateWeights(F);
}
// If coverage checking was requested, compute it now.
if (SampleProfileRecordCoverage) {
unsigned Used = CoverageTracker.countUsedRecords(Samples);
unsigned Total = CoverageTracker.countBodyRecords(Samples);
unsigned Coverage = CoverageTracker.computeCoverage(Used, Total);
if (Coverage < SampleProfileRecordCoverage) {
F.getContext().diagnose(DiagnosticInfoSampleProfile(
getDISubprogram(&F)->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);
unsigned Coverage = CoverageTracker.computeCoverage(Used, Total);
if (Coverage < SampleProfileSampleCoverage) {
F.getContext().diagnose(DiagnosticInfoSampleProfile(
getDISubprogram(&F)->getFilename(), getFunctionLoc(F),
Twine(Used) + " of " + Twine(Total) + " available profile samples (" +
Twine(Coverage) + "%) were applied",
DS_Warning));
}
}
return Changed;
}
char SampleProfileLoader::ID = 0;
INITIALIZE_PASS_BEGIN(SampleProfileLoader, "sample-profile",
"Sample Profile loader", false, false)
INITIALIZE_PASS_DEPENDENCY(AddDiscriminators)
INITIALIZE_PASS_END(SampleProfileLoader, "sample-profile",
"Sample Profile loader", false, false)
bool SampleProfileLoader::doInitialization(Module &M) {
auto &Ctx = M.getContext();
auto ReaderOrErr = SampleProfileReader::create(Filename, Ctx);
if (std::error_code EC = ReaderOrErr.getError()) {
std::string Msg = "Could not open profile: " + EC.message();
Ctx.diagnose(DiagnosticInfoSampleProfile(Filename, Msg));
return false;
}
Reader = std::move(ReaderOrErr.get());
ProfileIsValid = (Reader->read() == sampleprof_error::success);
return true;
}
ModulePass *llvm::createSampleProfileLoaderPass() {
return new SampleProfileLoader(SampleProfileFile);
}
ModulePass *llvm::createSampleProfileLoaderPass(StringRef Name) {
return new SampleProfileLoader(Name);
}
bool SampleProfileLoader::runOnModule(Module &M) {
if (!ProfileIsValid)
return false;
// Compute the total number of samples collected in this profile.
for (const auto &I : Reader->getProfiles())
TotalCollectedSamples += I.second.getTotalSamples();
bool retval = false;
for (auto &F : M)
if (!F.isDeclaration()) {
clearFunctionData();
retval |= runOnFunction(F);
}
return retval;
}
bool SampleProfileLoader::runOnFunction(Function &F) {
Samples = Reader->getSamplesFor(F);
if (!Samples->empty())
return emitAnnotations(F);
return false;
}
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