xref: /llvm-project-15.0.7/llvm/lib/Transforms/IPO/SampleProfile.cpp (revision a26a1eff)

//===- 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/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> SampleProfileCoverage(
    "sample-profile-check-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."));

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) {
    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);
  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 *, 128> VisitedBlocks;

  /// \brief Set of visited edges during propagation.
  SmallSet<Edge, 128> 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;
};

class SampleCoverageTracker {
public:
  SampleCoverageTracker() : SampleCoverage() {}

  void markSamplesUsed(const FunctionSamples *Samples, uint32_t LineOffset,
                       uint32_t Discriminator);
  unsigned computeCoverage(const FunctionSamples *Samples) const;
  unsigned getNumUsedSamples(const FunctionSamples *Samples) const;

private:
  typedef DenseMap<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;
};

SampleCoverageTracker CoverageTracker;
}

/// Mark as used the sample record for the given function samples at
/// (LineOffset, Discriminator).
void SampleCoverageTracker::markSamplesUsed(const FunctionSamples *Samples,
                                            uint32_t LineOffset,
                                            uint32_t Discriminator) {
  BodySampleCoverageMap &Coverage = SampleCoverage[Samples];
  Coverage[LineLocation(LineOffset, Discriminator)]++;
}

/// Return the number of sample records that were applied from this profile.
unsigned
SampleCoverageTracker::getNumUsedSamples(const FunctionSamples *Samples) const {
  auto I = SampleCoverage.find(Samples);
  return (I != SampleCoverage.end()) ? I->second.size() : 0;
}

/// 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(const FunctionSamples *Samples) const {
  uint32_t NumTotalRecords = Samples->getBodySamples().size();
  uint32_t NumUsedRecords = getNumUsedSamples(Samples);
  assert(NumUsedRecords <= NumTotalRecords &&
         "number of used records cannot exceed the total number of records");
  return NumTotalRecords > 0 ? NumUsedRecords * 100 / NumTotalRecords : 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();
}

/// \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) {
    if (SampleProfileCoverage)
      CoverageTracker.markSamplesUsed(FS, LineOffset, 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));
  }

  if (SampleProfileCoverage) {
    unsigned Coverage = CoverageTracker.computeCoverage(Samples);
    if (Coverage < SampleProfileCoverage) {
      const char *Filename = getDISubprogram(&F)->getFilename().str().c_str();
      F.getContext().diagnose(DiagnosticInfoSampleProfile(
          Filename, getFunctionLoc(F),
          Twine(CoverageTracker.getNumUsedSamples(Samples)) + " of " +
              Twine(Samples->getBodySamples().size()) +
              " available profile records (" + Twine(Coverage) +
              "%) were applied",
          DS_Warning));
    }
  }

  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 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) {
          const FunctionSamples *FS = findCalleeFunctionSamples(*CI);
          if (FS && FS->getTotalSamples() > 0) {
            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 |= 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);
  }

  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.data(), 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;

  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;
}