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llvm-6502/lib/Transforms/Scalar/SampleProfile.cpp
Chandler Carruth 7f2eff792a [PM] Split DominatorTree into a concrete analysis result object which 
can be used by both the new pass manager and the old.

This removes it from any of the virtual mess of the pass interfaces and
lets it derive cleanly from the DominatorTreeBase<> template. In turn,
tons of boilerplate interface can be nuked and it turns into a very
straightforward extension of the base DominatorTree interface.

The old analysis pass is now a simple wrapper. The names and style of
this split should match the split between CallGraph and
CallGraphWrapperPass. All of the users of DominatorTree have been
updated to match using many of the same tricks as with CallGraph. The
goal is that the common type remains the resulting DominatorTree rather
than the pass. This will make subsequent work toward the new pass
manager significantly easier.

Also in numerous places things became cleaner because I switched from
re-running the pass (!!! mid way through some other passes run!!!) to
directly recomputing the domtree.

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@199104 91177308-0d34-0410-b5e6-96231b3b80d8
2014-01-13 13:07:17 +00:00

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//===- SampleProfile.cpp - Incorporate sample profiles into the IR --------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file implements the SampleProfileLoader transformation. This pass
// reads a profile file generated by a sampling profiler (e.g. Linux Perf -
// http://perf.wiki.kernel.org/) and generates IR metadata to reflect the
// profile information in the given profile.
//
// This pass generates branch weight annotations on the IR:
//
// - prof: Represents branch weights. This annotation is added to branches
// to indicate the weights of each edge coming out of the branch.
// The weight of each edge is the weight of the target block for
// that edge. The weight of a block B is computed as the maximum
// number of samples found in B.
//
//===----------------------------------------------------------------------===//
#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/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/InstIterator.h"
#include "llvm/Support/LineIterator.h"
#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/Regex.h"
#include "llvm/Support/raw_ostream.h"
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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