176 lines
6.2 KiB
C++
176 lines
6.2 KiB
C++
//=-- ProfilesummaryBuilder.cpp - Profile summary computation ---------------=//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file contains support for computing profile summary data.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/IR/Attributes.h"
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#include "llvm/IR/Function.h"
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#include "llvm/IR/Metadata.h"
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#include "llvm/IR/Type.h"
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#include "llvm/ProfileData/InstrProf.h"
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#include "llvm/ProfileData/ProfileCommon.h"
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#include "llvm/ProfileData/SampleProf.h"
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#include "llvm/Support/Casting.h"
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#include "llvm/Support/CommandLine.h"
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using namespace llvm;
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cl::opt<bool> UseContextLessSummary(
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"profile-summary-contextless", cl::Hidden, cl::init(false), cl::ZeroOrMore,
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cl::desc("Merge context profiles before calculating thresholds."));
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// A set of cutoff values. Each value, when divided by ProfileSummary::Scale
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// (which is 1000000) is a desired percentile of total counts.
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static const uint32_t DefaultCutoffsData[] = {
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10000, /* 1% */
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100000, /* 10% */
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200000, 300000, 400000, 500000, 600000, 700000, 800000,
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900000, 950000, 990000, 999000, 999900, 999990, 999999};
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const ArrayRef<uint32_t> ProfileSummaryBuilder::DefaultCutoffs =
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DefaultCutoffsData;
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const ProfileSummaryEntry &
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ProfileSummaryBuilder::getEntryForPercentile(SummaryEntryVector &DS,
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uint64_t Percentile) {
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auto It = partition_point(DS, [=](const ProfileSummaryEntry &Entry) {
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return Entry.Cutoff < Percentile;
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});
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// The required percentile has to be <= one of the percentiles in the
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// detailed summary.
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if (It == DS.end())
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report_fatal_error("Desired percentile exceeds the maximum cutoff");
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return *It;
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}
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void InstrProfSummaryBuilder::addRecord(const InstrProfRecord &R) {
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// The first counter is not necessarily an entry count for IR
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// instrumentation profiles.
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// Eventually MaxFunctionCount will become obsolete and this can be
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// removed.
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addEntryCount(R.Counts[0]);
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for (size_t I = 1, E = R.Counts.size(); I < E; ++I)
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addInternalCount(R.Counts[I]);
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}
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// To compute the detailed summary, we consider each line containing samples as
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// equivalent to a block with a count in the instrumented profile.
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void SampleProfileSummaryBuilder::addRecord(
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const sampleprof::FunctionSamples &FS, bool isCallsiteSample) {
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if (!isCallsiteSample) {
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NumFunctions++;
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if (FS.getHeadSamples() > MaxFunctionCount)
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MaxFunctionCount = FS.getHeadSamples();
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}
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for (const auto &I : FS.getBodySamples())
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addCount(I.second.getSamples());
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for (const auto &I : FS.getCallsiteSamples())
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for (const auto &CS : I.second)
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addRecord(CS.second, true);
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}
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// The argument to this method is a vector of cutoff percentages and the return
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// value is a vector of (Cutoff, MinCount, NumCounts) triplets.
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void ProfileSummaryBuilder::computeDetailedSummary() {
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if (DetailedSummaryCutoffs.empty())
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return;
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llvm::sort(DetailedSummaryCutoffs);
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auto Iter = CountFrequencies.begin();
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const auto End = CountFrequencies.end();
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uint32_t CountsSeen = 0;
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uint64_t CurrSum = 0, Count = 0;
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for (const uint32_t Cutoff : DetailedSummaryCutoffs) {
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assert(Cutoff <= 999999);
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APInt Temp(128, TotalCount);
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APInt N(128, Cutoff);
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APInt D(128, ProfileSummary::Scale);
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Temp *= N;
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Temp = Temp.sdiv(D);
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uint64_t DesiredCount = Temp.getZExtValue();
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assert(DesiredCount <= TotalCount);
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while (CurrSum < DesiredCount && Iter != End) {
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Count = Iter->first;
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uint32_t Freq = Iter->second;
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CurrSum += (Count * Freq);
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CountsSeen += Freq;
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Iter++;
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}
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assert(CurrSum >= DesiredCount);
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ProfileSummaryEntry PSE = {Cutoff, Count, CountsSeen};
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DetailedSummary.push_back(PSE);
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}
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}
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std::unique_ptr<ProfileSummary> SampleProfileSummaryBuilder::getSummary() {
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computeDetailedSummary();
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return std::make_unique<ProfileSummary>(
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ProfileSummary::PSK_Sample, DetailedSummary, TotalCount, MaxCount, 0,
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MaxFunctionCount, NumCounts, NumFunctions);
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}
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std::unique_ptr<ProfileSummary>
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SampleProfileSummaryBuilder::computeSummaryForProfiles(
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const StringMap<sampleprof::FunctionSamples> &Profiles) {
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assert(NumFunctions == 0 &&
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"This can only be called on an empty summary builder");
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StringMap<sampleprof::FunctionSamples> ContextLessProfiles;
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const StringMap<sampleprof::FunctionSamples> *ProfilesToUse = &Profiles;
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// For CSSPGO, context-sensitive profile effectively split a function profile
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// into many copies each representing the CFG profile of a particular calling
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// context. That makes the count distribution looks more flat as we now have
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// more function profiles each with lower counts, which in turn leads to lower
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// hot thresholds. To compensate for that, by defauly we merge context
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// profiles before coumputing profile summary.
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if (UseContextLessSummary || (sampleprof::FunctionSamples::ProfileIsCS &&
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!UseContextLessSummary.getNumOccurrences())) {
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for (const auto &I : Profiles) {
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ContextLessProfiles[I.second.getName()].merge(I.second);
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}
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ProfilesToUse = &ContextLessProfiles;
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}
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for (const auto &I : *ProfilesToUse) {
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const sampleprof::FunctionSamples &Profile = I.second;
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addRecord(Profile);
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}
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return getSummary();
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}
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std::unique_ptr<ProfileSummary> InstrProfSummaryBuilder::getSummary() {
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computeDetailedSummary();
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return std::make_unique<ProfileSummary>(
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ProfileSummary::PSK_Instr, DetailedSummary, TotalCount, MaxCount,
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MaxInternalBlockCount, MaxFunctionCount, NumCounts, NumFunctions);
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}
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void InstrProfSummaryBuilder::addEntryCount(uint64_t Count) {
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NumFunctions++;
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// Skip invalid count.
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if (Count == (uint64_t)-1)
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return;
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addCount(Count);
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if (Count > MaxFunctionCount)
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MaxFunctionCount = Count;
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}
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void InstrProfSummaryBuilder::addInternalCount(uint64_t Count) {
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// Skip invalid count.
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if (Count == (uint64_t)-1)
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return;
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addCount(Count);
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if (Count > MaxInternalBlockCount)
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MaxInternalBlockCount = Count;
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}
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