//=-- ProfilesummaryBuilder.cpp - Profile summary computation ---------------=// // // 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 // //===----------------------------------------------------------------------===// // // This file contains support for computing profile summary data. // //===----------------------------------------------------------------------===// #include "llvm/IR/Attributes.h" #include "llvm/IR/Function.h" #include "llvm/IR/Metadata.h" #include "llvm/IR/Type.h" #include "llvm/ProfileData/InstrProf.h" #include "llvm/ProfileData/ProfileCommon.h" #include "llvm/ProfileData/SampleProf.h" #include "llvm/Support/Casting.h" #include "llvm/Support/CommandLine.h" using namespace llvm; cl::opt UseContextLessSummary( "profile-summary-contextless", cl::Hidden, cl::init(false), cl::ZeroOrMore, cl::desc("Merge context profiles before calculating thresholds.")); // A set of cutoff values. Each value, when divided by ProfileSummary::Scale // (which is 1000000) is a desired percentile of total counts. static const uint32_t DefaultCutoffsData[] = { 10000, /* 1% */ 100000, /* 10% */ 200000, 300000, 400000, 500000, 600000, 700000, 800000, 900000, 950000, 990000, 999000, 999900, 999990, 999999}; const ArrayRef ProfileSummaryBuilder::DefaultCutoffs = DefaultCutoffsData; const ProfileSummaryEntry & ProfileSummaryBuilder::getEntryForPercentile(SummaryEntryVector &DS, uint64_t Percentile) { auto It = partition_point(DS, [=](const ProfileSummaryEntry &Entry) { return Entry.Cutoff < Percentile; }); // The required percentile has to be <= one of the percentiles in the // detailed summary. if (It == DS.end()) report_fatal_error("Desired percentile exceeds the maximum cutoff"); return *It; } void InstrProfSummaryBuilder::addRecord(const InstrProfRecord &R) { // The first counter is not necessarily an entry count for IR // instrumentation profiles. // Eventually MaxFunctionCount will become obsolete and this can be // removed. addEntryCount(R.Counts[0]); for (size_t I = 1, E = R.Counts.size(); I < E; ++I) addInternalCount(R.Counts[I]); } // To compute the detailed summary, we consider each line containing samples as // equivalent to a block with a count in the instrumented profile. void SampleProfileSummaryBuilder::addRecord( const sampleprof::FunctionSamples &FS, bool isCallsiteSample) { if (!isCallsiteSample) { NumFunctions++; if (FS.getHeadSamples() > MaxFunctionCount) MaxFunctionCount = FS.getHeadSamples(); } for (const auto &I : FS.getBodySamples()) addCount(I.second.getSamples()); for (const auto &I : FS.getCallsiteSamples()) for (const auto &CS : I.second) addRecord(CS.second, true); } // The argument to this method is a vector of cutoff percentages and the return // value is a vector of (Cutoff, MinCount, NumCounts) triplets. void ProfileSummaryBuilder::computeDetailedSummary() { if (DetailedSummaryCutoffs.empty()) return; llvm::sort(DetailedSummaryCutoffs); auto Iter = CountFrequencies.begin(); const auto End = CountFrequencies.end(); uint32_t CountsSeen = 0; uint64_t CurrSum = 0, Count = 0; for (const uint32_t Cutoff : DetailedSummaryCutoffs) { assert(Cutoff <= 999999); APInt Temp(128, TotalCount); APInt N(128, Cutoff); APInt D(128, ProfileSummary::Scale); Temp *= N; Temp = Temp.sdiv(D); uint64_t DesiredCount = Temp.getZExtValue(); assert(DesiredCount <= TotalCount); while (CurrSum < DesiredCount && Iter != End) { Count = Iter->first; uint32_t Freq = Iter->second; CurrSum += (Count * Freq); CountsSeen += Freq; Iter++; } assert(CurrSum >= DesiredCount); ProfileSummaryEntry PSE = {Cutoff, Count, CountsSeen}; DetailedSummary.push_back(PSE); } } std::unique_ptr SampleProfileSummaryBuilder::getSummary() { computeDetailedSummary(); return std::make_unique( ProfileSummary::PSK_Sample, DetailedSummary, TotalCount, MaxCount, 0, MaxFunctionCount, NumCounts, NumFunctions); } std::unique_ptr SampleProfileSummaryBuilder::computeSummaryForProfiles( const StringMap &Profiles) { assert(NumFunctions == 0 && "This can only be called on an empty summary builder"); StringMap ContextLessProfiles; const StringMap *ProfilesToUse = &Profiles; // For CSSPGO, context-sensitive profile effectively split a function profile // into many copies each representing the CFG profile of a particular calling // context. That makes the count distribution looks more flat as we now have // more function profiles each with lower counts, which in turn leads to lower // hot thresholds. To compensate for that, by defauly we merge context // profiles before coumputing profile summary. if (UseContextLessSummary || (sampleprof::FunctionSamples::ProfileIsCS && !UseContextLessSummary.getNumOccurrences())) { for (const auto &I : Profiles) { ContextLessProfiles[I.second.getName()].merge(I.second); } ProfilesToUse = &ContextLessProfiles; } for (const auto &I : *ProfilesToUse) { const sampleprof::FunctionSamples &Profile = I.second; addRecord(Profile); } return getSummary(); } std::unique_ptr InstrProfSummaryBuilder::getSummary() { computeDetailedSummary(); return std::make_unique( ProfileSummary::PSK_Instr, DetailedSummary, TotalCount, MaxCount, MaxInternalBlockCount, MaxFunctionCount, NumCounts, NumFunctions); } void InstrProfSummaryBuilder::addEntryCount(uint64_t Count) { NumFunctions++; // Skip invalid count. if (Count == (uint64_t)-1) return; addCount(Count); if (Count > MaxFunctionCount) MaxFunctionCount = Count; } void InstrProfSummaryBuilder::addInternalCount(uint64_t Count) { // Skip invalid count. if (Count == (uint64_t)-1) return; addCount(Count); if (Count > MaxInternalBlockCount) MaxInternalBlockCount = Count; }