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llvm-project/llvm/lib/Transforms/Scalar/SampleProfile.cpp

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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.
//
//===----------------------------------------------------------------------===//
#define DEBUG_TYPE "sample-profile"
#include "llvm/Transforms/Scalar.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/OwningPtr.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/StringMap.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/PostDominators.h"
#include "llvm/DebugInfo.h"
#include "llvm/IR/Constants.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/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/LineIterator.h"
#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/Regex.h"
#include "llvm/Support/raw_ostream.h"
#include <cctype>
using namespace llvm;
// 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."));
namespace {
typedef DenseMap<uint32_t, uint32_t> BodySampleMap;
typedef DenseMap<BasicBlock *, uint32_t> BlockWeightMap;
typedef DenseMap<BasicBlock *, BasicBlock *> EquivalenceClassMap;
typedef std::pair<BasicBlock *, BasicBlock *> Edge;
typedef DenseMap<Edge, uint32_t> EdgeWeightMap;
typedef DenseMap<BasicBlock *, SmallVector<BasicBlock *, 8> > BlockEdgeMap;
/// \brief Representation of the runtime profile for a function.
///
/// This data structure contains the runtime profile for a given
/// function. It contains the total number of samples collected
/// in the function and a map of samples collected in every statement.
class SampleFunctionProfile {
public:
SampleFunctionProfile()
: TotalSamples(0), TotalHeadSamples(0), HeaderLineno(0), DT(0), PDT(0),
LI(0) {}
unsigned getFunctionLoc(Function &F);
bool emitAnnotations(Function &F, DominatorTree *DomTree,
PostDominatorTree *PostDomTree, LoopInfo *Loops);
uint32_t getInstWeight(Instruction &I);
uint32_t getBlockWeight(BasicBlock *B);
void addTotalSamples(unsigned Num) { TotalSamples += Num; }
void addHeadSamples(unsigned Num) { TotalHeadSamples += Num; }
void addBodySamples(unsigned LineOffset, unsigned Num) {
BodySamples[LineOffset] += Num;
}
void print(raw_ostream &OS);
void printEdgeWeight(raw_ostream &OS, Edge E);
void printBlockWeight(raw_ostream &OS, BasicBlock *BB);
void printBlockEquivalence(raw_ostream &OS, 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);
uint32_t visitEdge(Edge E, unsigned *NumUnknownEdges, Edge *UnknownEdge);
void buildEdges(Function &F);
bool propagateThroughEdges(Function &F);
bool empty() { return BodySamples.empty(); }
protected:
/// \brief Total number of samples collected inside this function.
///
/// Samples are cumulative, they include all the samples collected
/// inside this function and all its inlined callees.
unsigned TotalSamples;
/// \brief Total number of samples collected at the head of the function.
/// FIXME: Use head samples to estimate a cold/hot attribute for the function.
unsigned TotalHeadSamples;
/// \brief Line number for the function header. Used to compute relative
/// line numbers from the absolute line LOCs found in instruction locations.
/// The relative line numbers are needed to address the samples from the
/// profile file.
unsigned HeaderLineno;
/// \brief Map line offsets to collected samples.
///
/// Each entry in this map contains the number of samples
/// collected at the corresponding line offset. All line locations
/// are an offset from the start of the function.
BodySampleMap BodySamples;
/// \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<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.
DominatorTree *DT;
PostDominatorTree *PDT;
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 Sample-based profile reader.
///
/// Each profile contains sample counts for all the functions
/// executed. Inside each function, statements are annotated with the
/// collected samples on all the instructions associated with that
/// statement.
///
/// For this to produce meaningful data, the program needs to be
/// compiled with some debug information (at minimum, line numbers:
/// -gline-tables-only). Otherwise, it will be impossible to match IR
/// instructions to the line numbers collected by the profiler.
///
/// From the profile file, we are interested in collecting the
/// following information:
///
/// * A list of functions included in the profile (mangled names).
///
/// * For each function F:
/// 1. The total number of samples collected in F.
///
/// 2. The samples collected at each line in F. To provide some
/// protection against source code shuffling, line numbers should
/// be relative to the start of the function.
class SampleModuleProfile {
public:
SampleModuleProfile(StringRef F) : Profiles(0), Filename(F) {}
void dump();
void loadText();
void loadNative() { llvm_unreachable("not implemented"); }
void printFunctionProfile(raw_ostream &OS, StringRef FName);
void dumpFunctionProfile(StringRef FName);
SampleFunctionProfile &getProfile(const Function &F) {
return Profiles[F.getName()];
}
/// \brief Report a parse error message and stop compilation.
void reportParseError(int64_t LineNumber, Twine Msg) const {
report_fatal_error(Filename + ":" + Twine(LineNumber) + ": " + Msg + "\n");
}
protected:
/// \brief Map every function to its associated profile.
///
/// The profile of every function executed at runtime is collected
/// in the structure SampleFunctionProfile. This maps function objects
/// to their corresponding profiles.
StringMap<SampleFunctionProfile> Profiles;
/// \brief Path name to the file holding the profile data.
///
/// The format of this file is defined by each profiler
/// independently. If possible, the profiler should have a text
/// version of the profile format to be used in constructing test
/// cases and debugging.
StringRef Filename;
};
/// \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 FunctionPass {
public:
// Class identification, replacement for typeinfo
static char ID;
SampleProfileLoader(StringRef Name = SampleProfileFile)
: FunctionPass(ID), Profiler(0), Filename(Name) {
initializeSampleProfileLoaderPass(*PassRegistry::getPassRegistry());
}
virtual bool doInitialization(Module &M);
void dump() { Profiler->dump(); }
virtual const char *getPassName() const { return "Sample profile pass"; }
virtual bool runOnFunction(Function &F);
virtual void getAnalysisUsage(AnalysisUsage &AU) const {
AU.setPreservesCFG();
AU.addRequired<LoopInfo>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<PostDominatorTree>();
}
protected:
/// \brief Profile reader object.
OwningPtr<SampleModuleProfile> Profiler;
/// \brief Name of the profile file to load.
StringRef Filename;
};
}
/// \brief Print this function profile on stream \p OS.
///
/// \param OS Stream to emit the output to.
void SampleFunctionProfile::print(raw_ostream &OS) {
OS << TotalSamples << ", " << TotalHeadSamples << ", " << BodySamples.size()
<< " sampled lines\n";
for (BodySampleMap::const_iterator SI = BodySamples.begin(),
SE = BodySamples.end();
SI != SE; ++SI)
OS << "\tline offset: " << SI->first
<< ", number of samples: " << SI->second << "\n";
OS << "\n";
}
/// \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 SampleFunctionProfile::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 SampleFunctionProfile::printBlockEquivalence(raw_ostream &OS,
BasicBlock *BB) {
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 SampleFunctionProfile::printBlockWeight(raw_ostream &OS, BasicBlock *BB) {
OS << "weight[" << BB->getName() << "]: " << BlockWeights[BB] << "\n";
}
/// \brief Print the function profile for \p FName on stream \p OS.
///
/// \param OS Stream to emit the output to.
/// \param FName Name of the function to print.
void SampleModuleProfile::printFunctionProfile(raw_ostream &OS,
StringRef FName) {
OS << "Function: " << FName << ":\n";
Profiles[FName].print(OS);
}
/// \brief Dump the function profile for \p FName.
///
/// \param FName Name of the function to print.
void SampleModuleProfile::dumpFunctionProfile(StringRef FName) {
printFunctionProfile(dbgs(), FName);
}
/// \brief Dump all the function profiles found.
void SampleModuleProfile::dump() {
for (StringMap<SampleFunctionProfile>::const_iterator I = Profiles.begin(),
E = Profiles.end();
I != E; ++I)
dumpFunctionProfile(I->getKey());
}
/// \brief Load samples from a text file.
///
/// The file contains a list of samples for every function executed at
/// runtime. Each function profile has the following format:
///
/// function1:total_samples:total_head_samples
/// offset1[.discriminator]: number_of_samples [fn1:num fn2:num ... ]
/// offset2[.discriminator]: number_of_samples [fn3:num fn4:num ... ]
/// ...
/// offsetN[.discriminator]: number_of_samples [fn5:num fn6:num ... ]
///
/// Function names must be mangled in order for the profile loader to
/// match them in the current translation unit. The two numbers in the
/// function header specify how many total samples were accumulated in
/// the function (first number), and the total number of samples accumulated
/// at the prologue of the function (second number). This head sample
/// count provides an indicator of how frequent is the function invoked.
///
/// Each sampled line may contain several items. Some are optional
/// (marked below):
///
/// a- Source line offset. This number represents the line number
/// in the function where the sample was collected. The line number
/// is always relative to the line where symbol of the function
/// is defined. So, if the function has its header at line 280,
/// the offset 13 is at line 293 in the file.
///
/// b- [OPTIONAL] Discriminator. This is used if the sampled program
/// was compiled with DWARF discriminator support
/// (http://wiki.dwarfstd.org/index.php?title=Path_Discriminators)
/// This is currently only emitted by GCC and we just ignore it.
///
/// FIXME: Handle discriminators, since they are needed to distinguish
/// multiple control flow within a single source LOC.
///
/// c- Number of samples. This is the number of samples collected by
/// the profiler at this source location.
///
/// d- [OPTIONAL] Potential call targets and samples. If present, this
/// line contains a call instruction. This models both direct and
/// indirect calls. Each called target is listed together with the
/// number of samples. For example,
///
/// 130: 7 foo:3 bar:2 baz:7
///
/// The above means that at relative line offset 130 there is a
/// call instruction that calls one of foo(), bar() and baz(). With
/// baz() being the relatively more frequent call target.
///
/// FIXME: This is currently unhandled, but it has a lot of
/// potential for aiding the inliner.
///
///
/// Since this is a flat profile, a function that shows up more than
/// once gets all its samples aggregated across all its instances.
///
/// FIXME: flat profiles are too imprecise to provide good optimization
/// opportunities. Convert them to context-sensitive profile.
///
/// This textual representation is useful to generate unit tests and
/// for debugging purposes, but it should not be used to generate
/// profiles for large programs, as the representation is extremely
/// inefficient.
void SampleModuleProfile::loadText() {
OwningPtr<MemoryBuffer> Buffer;
error_code EC = MemoryBuffer::getFile(Filename, Buffer);
if (EC)
report_fatal_error("Could not open file " + Filename + ": " + EC.message());
line_iterator LineIt(*Buffer, '#');
// Read the profile of each function. Since each function may be
// mentioned more than once, and we are collecting flat profiles,
// accumulate samples as we parse them.
Regex HeadRE("^([^:]+):([0-9]+):([0-9]+)$");
Regex LineSample("^([0-9]+)(\\.[0-9]+)?: ([0-9]+)(.*)$");
while (!LineIt.is_at_eof()) {
// Read the header of each function. The function header should
// have this format:
//
// function_name:total_samples:total_head_samples
//
// See above for an explanation of each field.
SmallVector<StringRef, 3> Matches;
if (!HeadRE.match(*LineIt, &Matches))
reportParseError(LineIt.line_number(),
"Expected 'mangled_name:NUM:NUM', found " + *LineIt);
assert(Matches.size() == 4);
StringRef FName = Matches[1];
unsigned NumSamples, NumHeadSamples;
Matches[2].getAsInteger(10, NumSamples);
Matches[3].getAsInteger(10, NumHeadSamples);
Profiles[FName] = SampleFunctionProfile();
SampleFunctionProfile &FProfile = Profiles[FName];
FProfile.addTotalSamples(NumSamples);
FProfile.addHeadSamples(NumHeadSamples);
++LineIt;
// Now read the body. The body of the function ends when we reach
// EOF or when we see the start of the next function.
while (!LineIt.is_at_eof() && isdigit((*LineIt)[0])) {
if (!LineSample.match(*LineIt, &Matches))
reportParseError(
LineIt.line_number(),
"Expected 'NUM[.NUM]: NUM[ mangled_name:NUM]*', found " + *LineIt);
assert(Matches.size() == 5);
unsigned LineOffset, NumSamples;
Matches[1].getAsInteger(10, LineOffset);
// FIXME: Handle discriminator information (in Matches[2]).
Matches[3].getAsInteger(10, NumSamples);
// FIXME: Handle called targets (in Matches[4]).
// When dealing with instruction weights, we use the value
// zero to indicate the absence of a sample. If we read an
// actual zero from the profile file, return it as 1 to
// avoid the confusion later on.
if (NumSamples == 0)
NumSamples = 1;
FProfile.addBodySamples(LineOffset, NumSamples);
++LineIt;
}
}
}
/// \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 profiled weight of I.
uint32_t SampleFunctionProfile::getInstWeight(Instruction &Inst) {
unsigned Lineno = Inst.getDebugLoc().getLine();
if (Lineno < HeaderLineno)
return 0;
unsigned LOffset = Lineno - HeaderLineno;
uint32_t Weight = BodySamples.lookup(LOffset);
DEBUG(dbgs() << " " << Lineno << ":" << Inst.getDebugLoc().getCol() << ":"
<< Inst << " (line offset: " << LOffset
<< " - weight: " << Weight << ")\n");
return Weight;
}
/// \brief Compute the weight of a basic block.
///
/// The weight of basic block \p B is the maximum weight of all the
/// instructions in B. The weight of \p B is computed and cached in
/// the BlockWeights map.
///
/// \param B The basic block to query.
///
/// \returns The computed weight of B.
uint32_t SampleFunctionProfile::getBlockWeight(BasicBlock *B) {
// If we've computed B's weight before, return it.
std::pair<BlockWeightMap::iterator, bool> Entry =
BlockWeights.insert(std::make_pair(B, 0));
if (!Entry.second)
return Entry.first->second;
// Otherwise, compute and cache B's weight.
uint32_t Weight = 0;
for (BasicBlock::iterator I = B->begin(), E = B->end(); I != E; ++I) {
uint32_t InstWeight = getInstWeight(*I);
if (InstWeight > Weight)
Weight = InstWeight;
}
Entry.first->second = Weight;
return Weight;
}
/// \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 SampleFunctionProfile::computeBlockWeights(Function &F) {
bool Changed = false;
DEBUG(dbgs() << "Block weights\n");
for (Function::iterator B = F.begin(), E = F.end(); B != E; ++B) {
uint32_t Weight = getBlockWeight(B);
Changed |= (Weight > 0);
DEBUG(printBlockWeight(dbgs(), B));
}
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 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 SampleFunctionProfile::findEquivalencesFor(
BasicBlock *BB1, SmallVector<BasicBlock *, 8> Descendants,
DominatorTreeBase<BasicBlock> *DomTree) {
for (SmallVectorImpl<BasicBlock *>::iterator I = Descendants.begin(),
E = Descendants.end();
I != E; ++I) {
BasicBlock *BB2 = *I;
bool IsDomParent = DomTree->dominates(BB2, BB1);
bool IsInSameLoop = LI->getLoopFor(BB1) == LI->getLoopFor(BB2);
if (BB1 != BB2 && VisitedBlocks.insert(BB2) && IsDomParent &&
IsInSameLoop) {
EquivalenceClass[BB2] = BB1;
// 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.
uint32_t &BB1Weight = BlockWeights[BB1];
uint32_t &BB2Weight = BlockWeights[BB2];
BB1Weight = std::max(BB1Weight, BB2Weight);
}
}
}
/// \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 SampleFunctionProfile::findEquivalenceClasses(Function &F) {
SmallVector<BasicBlock *, 8> DominatedBBs;
DEBUG(dbgs() << "\nBlock equivalence classes\n");
// Find equivalence sets based on dominance and post-dominance information.
for (Function::iterator B = F.begin(), E = F.end(); B != E; ++B) {
BasicBlock *BB1 = B;
// 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->DT);
// Repeat the same logic for all the blocks post-dominated by BB1.
// We are looking for every basic block BB2 such that:
//
// 1- BB1 post-dominates BB2.
// 2- BB2 dominates BB1.
// 3- BB1 and BB2 are in the same loop nest.
//
// If all those conditions hold, BB2's equivalence class is BB1.
DominatedBBs.clear();
PDT->getDescendants(BB1, DominatedBBs);
findEquivalencesFor(BB1, DominatedBBs, DT);
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 (Function::iterator B = F.begin(), E = F.end(); B != E; ++B) {
BasicBlock *BB = B;
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.
uint32_t SampleFunctionProfile::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 SampleFunctionProfile::propagateThroughEdges(Function &F) {
bool Changed = false;
DEBUG(dbgs() << "\nPropagation through edges\n");
for (Function::iterator BI = F.begin(), EI = F.end(); BI != EI; ++BI) {
BasicBlock *BB = BI;
// 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++) {
uint32_t TotalWeight = 0;
unsigned NumUnknownEdges = 0;
Edge UnknownEdge, SelfReferentialEdge;
if (i == 0) {
// First, visit all predecessor edges.
for (size_t I = 0; I < Predecessors[BB].size(); I++) {
Edge E = std::make_pair(Predecessors[BB][I], BB);
TotalWeight += visitEdge(E, &NumUnknownEdges, &UnknownEdge);
if (E.first == E.second)
SelfReferentialEdge = E;
}
} else {
// On the second round, visit all successor edges.
for (size_t I = 0; I < Successors[BB].size(); I++) {
Edge E = std::make_pair(BB, Successors[BB][I]);
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) {
uint32_t &BBWeight = BlockWeights[BB];
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(BB))
Changed = true;
} else if (NumUnknownEdges == 1 && VisitedBlocks.count(BB)) {
// 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(BB)) {
uint32_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 SampleFunctionProfile::buildEdges(Function &F) {
for (Function::iterator I = F.begin(), E = F.end(); I != E; ++I) {
BasicBlock *B1 = I;
// 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))
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))
Successors[B1].push_back(B2);
}
}
}
/// \brief Propagate weights into edges
///
/// The following rules are applied to every block B in the CFG:
///
/// - If B 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 B'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 SampleFunctionProfile::propagateWeights(Function &F) {
bool Changed = true;
unsigned i = 0;
// 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");
MDBuilder MDB(F.getContext());
for (Function::iterator I = F.begin(), E = F.end(); I != E; ++I) {
BasicBlock *B = I;
TerminatorInst *TI = B->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() << ":"
<< TI->getDebugLoc().getCol() << ".\n");
SmallVector<uint32_t, 4> Weights;
bool AllWeightsZero = true;
for (unsigned I = 0; I < TI->getNumSuccessors(); ++I) {
BasicBlock *Succ = TI->getSuccessor(I);
Edge E = std::make_pair(B, Succ);
uint32_t Weight = EdgeWeights[E];
DEBUG(dbgs() << "\t"; printEdgeWeight(dbgs(), E));
Weights.push_back(Weight);
if (Weight != 0)
AllWeightsZero = false;
}
// Only set weights if there is at least one non-zero weight.
// In any other case, let the analyzer set weights.
if (!AllWeightsZero) {
DEBUG(dbgs() << "SUCCESS. Found non-zero weights.\n");
TI->setMetadata(llvm::LLVMContext::MD_prof,
MDB.createBranchWeights(Weights));
} 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.
unsigned SampleFunctionProfile::getFunctionLoc(Function &F) {
NamedMDNode *CUNodes = F.getParent()->getNamedMetadata("llvm.dbg.cu");
if (CUNodes) {
for (unsigned I = 0, E1 = CUNodes->getNumOperands(); I != E1; ++I) {
DICompileUnit CU(CUNodes->getOperand(I));
DIArray Subprograms = CU.getSubprograms();
for (unsigned J = 0, E2 = Subprograms.getNumElements(); J != E2; ++J) {
DISubprogram Subprogram(Subprograms.getElement(J));
if (Subprogram.describes(&F))
return Subprogram.getLineNumber();
}
}
}
report_fatal_error("No debug information found in function " + F.getName() +
"\n");
}
/// \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 B in the CFG:
///
/// - If B 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 B'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 B using the computed values for each of its branches.
///
/// \param F The function to query.
bool SampleFunctionProfile::emitAnnotations(Function &F, DominatorTree *DomTree,
PostDominatorTree *PostDomTree,
LoopInfo *Loops) {
bool Changed = false;
// Initialize invariants used during computation and propagation.
HeaderLineno = getFunctionLoc(F);
DEBUG(dbgs() << "Line number for the first instruction in " << F.getName()
<< ": " << HeaderLineno << "\n");
DT = DomTree;
PDT = PostDomTree;
LI = Loops;
// Compute basic block weights.
Changed |= computeBlockWeights(F);
if (Changed) {
// 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(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(PostDominatorTree)
INITIALIZE_PASS_DEPENDENCY(LoopInfo)
INITIALIZE_PASS_END(SampleProfileLoader, "sample-profile",
"Sample Profile loader", false, false)
bool SampleProfileLoader::doInitialization(Module &M) {
Profiler.reset(new SampleModuleProfile(Filename));
Profiler->loadText();
return true;
}
FunctionPass *llvm::createSampleProfileLoaderPass() {
return new SampleProfileLoader(SampleProfileFile);
}
FunctionPass *llvm::createSampleProfileLoaderPass(StringRef Name) {
return new SampleProfileLoader(Name);
}
bool SampleProfileLoader::runOnFunction(Function &F) {
DominatorTree *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
PostDominatorTree *PDT = &getAnalysis<PostDominatorTree>();
LoopInfo *LI = &getAnalysis<LoopInfo>();
SampleFunctionProfile &FunctionProfile = Profiler->getProfile(F);
if (!FunctionProfile.empty())
return FunctionProfile.emitAnnotations(F, DT, PDT, LI);
return false;
}
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