mirror of
https://github.com/c64scene-ar/llvm-6502.git
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git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@207196 91177308-0d34-0410-b5e6-96231b3b80d8
1101 lines
42 KiB
C++
1101 lines
42 KiB
C++
//===- SampleProfile.cpp - Incorporate sample profiles into the IR --------===//
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//
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// The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements the SampleProfileLoader transformation. This pass
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// reads a profile file generated by a sampling profiler (e.g. Linux Perf -
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// http://perf.wiki.kernel.org/) and generates IR metadata to reflect the
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// profile information in the given profile.
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//
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// This pass generates branch weight annotations on the IR:
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//
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// - prof: Represents branch weights. This annotation is added to branches
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// to indicate the weights of each edge coming out of the branch.
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// The weight of each edge is the weight of the target block for
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// that edge. The weight of a block B is computed as the maximum
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// number of samples found in B.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Transforms/Scalar.h"
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#include "llvm/ADT/DenseMap.h"
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#include "llvm/ADT/SmallPtrSet.h"
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#include "llvm/ADT/SmallSet.h"
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#include "llvm/ADT/StringMap.h"
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#include "llvm/ADT/StringRef.h"
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#include "llvm/Analysis/LoopInfo.h"
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#include "llvm/Analysis/PostDominators.h"
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#include "llvm/IR/Constants.h"
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#include "llvm/IR/DebugInfo.h"
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#include "llvm/IR/DiagnosticInfo.h"
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#include "llvm/IR/Dominators.h"
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#include "llvm/IR/Function.h"
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#include "llvm/IR/InstIterator.h"
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#include "llvm/IR/Instructions.h"
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#include "llvm/IR/LLVMContext.h"
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#include "llvm/IR/MDBuilder.h"
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#include "llvm/IR/Metadata.h"
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#include "llvm/IR/Module.h"
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#include "llvm/Pass.h"
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#include "llvm/Support/CommandLine.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/LineIterator.h"
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#include "llvm/Support/MemoryBuffer.h"
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#include "llvm/Support/Regex.h"
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#include "llvm/Support/raw_ostream.h"
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#include <cctype>
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using namespace llvm;
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#define DEBUG_TYPE "sample-profile"
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// Command line option to specify the file to read samples from. This is
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// mainly used for debugging.
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static cl::opt<std::string> SampleProfileFile(
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"sample-profile-file", cl::init(""), cl::value_desc("filename"),
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cl::desc("Profile file loaded by -sample-profile"), cl::Hidden);
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static cl::opt<unsigned> SampleProfileMaxPropagateIterations(
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"sample-profile-max-propagate-iterations", cl::init(100),
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cl::desc("Maximum number of iterations to go through when propagating "
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"sample block/edge weights through the CFG."));
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namespace {
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/// \brief Represents the relative location of an instruction.
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///
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/// Instruction locations are specified by the line offset from the
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/// beginning of the function (marked by the line where the function
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/// header is) and the discriminator value within that line.
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///
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/// The discriminator value is useful to distinguish instructions
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/// that are on the same line but belong to different basic blocks
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/// (e.g., the two post-increment instructions in "if (p) x++; else y++;").
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struct InstructionLocation {
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InstructionLocation(int L, unsigned D) : LineOffset(L), Discriminator(D) {}
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int LineOffset;
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unsigned Discriminator;
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};
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}
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namespace llvm {
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template <> struct DenseMapInfo<InstructionLocation> {
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typedef DenseMapInfo<int> OffsetInfo;
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typedef DenseMapInfo<unsigned> DiscriminatorInfo;
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static inline InstructionLocation getEmptyKey() {
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return InstructionLocation(OffsetInfo::getEmptyKey(),
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DiscriminatorInfo::getEmptyKey());
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}
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static inline InstructionLocation getTombstoneKey() {
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return InstructionLocation(OffsetInfo::getTombstoneKey(),
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DiscriminatorInfo::getTombstoneKey());
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}
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static inline unsigned getHashValue(InstructionLocation Val) {
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return DenseMapInfo<std::pair<int, unsigned>>::getHashValue(
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std::pair<int, unsigned>(Val.LineOffset, Val.Discriminator));
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}
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static inline bool isEqual(InstructionLocation LHS, InstructionLocation RHS) {
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return LHS.LineOffset == RHS.LineOffset &&
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LHS.Discriminator == RHS.Discriminator;
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}
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};
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}
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namespace {
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typedef DenseMap<InstructionLocation, unsigned> BodySampleMap;
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typedef DenseMap<BasicBlock *, unsigned> BlockWeightMap;
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typedef DenseMap<BasicBlock *, BasicBlock *> EquivalenceClassMap;
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typedef std::pair<BasicBlock *, BasicBlock *> Edge;
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typedef DenseMap<Edge, unsigned> EdgeWeightMap;
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typedef DenseMap<BasicBlock *, SmallVector<BasicBlock *, 8>> BlockEdgeMap;
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/// \brief Representation of the runtime profile for a function.
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///
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/// This data structure contains the runtime profile for a given
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/// function. It contains the total number of samples collected
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/// in the function and a map of samples collected in every statement.
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class SampleFunctionProfile {
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public:
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SampleFunctionProfile()
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: TotalSamples(0), TotalHeadSamples(0), HeaderLineno(0), DT(nullptr),
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PDT(nullptr), LI(nullptr), Ctx(nullptr) {}
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unsigned getFunctionLoc(Function &F);
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bool emitAnnotations(Function &F, DominatorTree *DomTree,
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PostDominatorTree *PostDomTree, LoopInfo *Loops);
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unsigned getInstWeight(Instruction &I);
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unsigned getBlockWeight(BasicBlock *B);
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void addTotalSamples(unsigned Num) { TotalSamples += Num; }
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void addHeadSamples(unsigned Num) { TotalHeadSamples += Num; }
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void addBodySamples(int LineOffset, unsigned Discriminator, unsigned Num) {
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assert(LineOffset >= 0);
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BodySamples[InstructionLocation(LineOffset, Discriminator)] += Num;
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}
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void print(raw_ostream &OS);
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void printEdgeWeight(raw_ostream &OS, Edge E);
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void printBlockWeight(raw_ostream &OS, BasicBlock *BB);
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void printBlockEquivalence(raw_ostream &OS, BasicBlock *BB);
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bool computeBlockWeights(Function &F);
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void findEquivalenceClasses(Function &F);
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void findEquivalencesFor(BasicBlock *BB1,
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SmallVector<BasicBlock *, 8> Descendants,
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DominatorTreeBase<BasicBlock> *DomTree);
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void propagateWeights(Function &F);
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unsigned visitEdge(Edge E, unsigned *NumUnknownEdges, Edge *UnknownEdge);
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void buildEdges(Function &F);
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bool propagateThroughEdges(Function &F);
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bool empty() { return BodySamples.empty(); }
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protected:
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/// \brief Total number of samples collected inside this function.
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///
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/// Samples are cumulative, they include all the samples collected
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/// inside this function and all its inlined callees.
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unsigned TotalSamples;
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/// \brief Total number of samples collected at the head of the function.
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/// FIXME: Use head samples to estimate a cold/hot attribute for the function.
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unsigned TotalHeadSamples;
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/// \brief Line number for the function header. Used to compute relative
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/// line numbers from the absolute line LOCs found in instruction locations.
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/// The relative line numbers are needed to address the samples from the
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/// profile file.
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unsigned HeaderLineno;
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/// \brief Map line offsets to collected samples.
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///
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/// Each entry in this map contains the number of samples
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/// collected at the corresponding line offset. All line locations
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/// are an offset from the start of the function.
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BodySampleMap BodySamples;
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/// \brief Map basic blocks to their computed weights.
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///
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/// The weight of a basic block is defined to be the maximum
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/// of all the instruction weights in that block.
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BlockWeightMap BlockWeights;
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/// \brief Map edges to their computed weights.
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///
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/// Edge weights are computed by propagating basic block weights in
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/// SampleProfile::propagateWeights.
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EdgeWeightMap EdgeWeights;
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/// \brief Set of visited blocks during propagation.
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SmallPtrSet<BasicBlock *, 128> VisitedBlocks;
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/// \brief Set of visited edges during propagation.
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SmallSet<Edge, 128> VisitedEdges;
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/// \brief Equivalence classes for block weights.
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///
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/// Two blocks BB1 and BB2 are in the same equivalence class if they
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/// dominate and post-dominate each other, and they are in the same loop
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/// nest. When this happens, the two blocks are guaranteed to execute
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/// the same number of times.
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EquivalenceClassMap EquivalenceClass;
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/// \brief Dominance, post-dominance and loop information.
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DominatorTree *DT;
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PostDominatorTree *PDT;
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LoopInfo *LI;
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/// \brief Predecessors for each basic block in the CFG.
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BlockEdgeMap Predecessors;
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/// \brief Successors for each basic block in the CFG.
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BlockEdgeMap Successors;
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/// \brief LLVM context holding the debug data we need.
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LLVMContext *Ctx;
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};
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/// \brief Sample-based profile reader.
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///
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/// Each profile contains sample counts for all the functions
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/// executed. Inside each function, statements are annotated with the
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/// collected samples on all the instructions associated with that
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/// statement.
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///
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/// For this to produce meaningful data, the program needs to be
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/// compiled with some debug information (at minimum, line numbers:
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/// -gline-tables-only). Otherwise, it will be impossible to match IR
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/// instructions to the line numbers collected by the profiler.
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///
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/// From the profile file, we are interested in collecting the
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/// following information:
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///
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/// * A list of functions included in the profile (mangled names).
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///
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/// * For each function F:
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/// 1. The total number of samples collected in F.
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///
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/// 2. The samples collected at each line in F. To provide some
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/// protection against source code shuffling, line numbers should
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/// be relative to the start of the function.
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class SampleModuleProfile {
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public:
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SampleModuleProfile(const Module &M, StringRef F)
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: Profiles(0), Filename(F), M(M) {}
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void dump();
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bool loadText();
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void loadNative() { llvm_unreachable("not implemented"); }
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void printFunctionProfile(raw_ostream &OS, StringRef FName);
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void dumpFunctionProfile(StringRef FName);
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SampleFunctionProfile &getProfile(const Function &F) {
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return Profiles[F.getName()];
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}
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/// \brief Report a parse error message.
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void reportParseError(int64_t LineNumber, Twine Msg) const {
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DiagnosticInfoSampleProfile Diag(Filename.data(), LineNumber, Msg);
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M.getContext().diagnose(Diag);
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}
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protected:
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/// \brief Map every function to its associated profile.
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///
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/// The profile of every function executed at runtime is collected
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/// in the structure SampleFunctionProfile. This maps function objects
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/// to their corresponding profiles.
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StringMap<SampleFunctionProfile> Profiles;
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/// \brief Path name to the file holding the profile data.
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///
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/// The format of this file is defined by each profiler
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/// independently. If possible, the profiler should have a text
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/// version of the profile format to be used in constructing test
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/// cases and debugging.
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StringRef Filename;
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/// \brief Module being compiled. Used mainly to access the current
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/// LLVM context for diagnostics.
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const Module &M;
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};
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/// \brief Sample profile pass.
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///
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/// This pass reads profile data from the file specified by
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/// -sample-profile-file and annotates every affected function with the
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/// profile information found in that file.
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class SampleProfileLoader : public FunctionPass {
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public:
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// Class identification, replacement for typeinfo
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static char ID;
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SampleProfileLoader(StringRef Name = SampleProfileFile)
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: FunctionPass(ID), Profiler(), Filename(Name), ProfileIsValid(false) {
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initializeSampleProfileLoaderPass(*PassRegistry::getPassRegistry());
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}
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bool doInitialization(Module &M) override;
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void dump() { Profiler->dump(); }
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const char *getPassName() const override { return "Sample profile pass"; }
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bool runOnFunction(Function &F) override;
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void getAnalysisUsage(AnalysisUsage &AU) const override {
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AU.setPreservesCFG();
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AU.addRequired<LoopInfo>();
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AU.addRequired<DominatorTreeWrapperPass>();
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AU.addRequired<PostDominatorTree>();
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}
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protected:
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/// \brief Profile reader object.
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std::unique_ptr<SampleModuleProfile> Profiler;
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/// \brief Name of the profile file to load.
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StringRef Filename;
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/// \brief Flag indicating whether the profile input loaded successfully.
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bool ProfileIsValid;
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};
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}
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/// \brief Print this function profile on stream \p OS.
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///
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/// \param OS Stream to emit the output to.
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void SampleFunctionProfile::print(raw_ostream &OS) {
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OS << TotalSamples << ", " << TotalHeadSamples << ", " << BodySamples.size()
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<< " sampled lines\n";
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for (BodySampleMap::const_iterator SI = BodySamples.begin(),
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SE = BodySamples.end();
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SI != SE; ++SI)
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OS << "\tline offset: " << SI->first.LineOffset
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<< ", discriminator: " << SI->first.Discriminator
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<< ", number of samples: " << SI->second << "\n";
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OS << "\n";
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}
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/// \brief Print the weight of edge \p E on stream \p OS.
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///
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/// \param OS Stream to emit the output to.
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/// \param E Edge to print.
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void SampleFunctionProfile::printEdgeWeight(raw_ostream &OS, Edge E) {
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OS << "weight[" << E.first->getName() << "->" << E.second->getName()
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<< "]: " << EdgeWeights[E] << "\n";
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}
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/// \brief Print the equivalence class of block \p BB on stream \p OS.
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///
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/// \param OS Stream to emit the output to.
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/// \param BB Block to print.
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void SampleFunctionProfile::printBlockEquivalence(raw_ostream &OS,
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BasicBlock *BB) {
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BasicBlock *Equiv = EquivalenceClass[BB];
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OS << "equivalence[" << BB->getName()
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<< "]: " << ((Equiv) ? EquivalenceClass[BB]->getName() : "NONE") << "\n";
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}
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/// \brief Print the weight of block \p BB on stream \p OS.
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///
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/// \param OS Stream to emit the output to.
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/// \param BB Block to print.
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void SampleFunctionProfile::printBlockWeight(raw_ostream &OS, BasicBlock *BB) {
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OS << "weight[" << BB->getName() << "]: " << BlockWeights[BB] << "\n";
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}
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/// \brief Print the function profile for \p FName on stream \p OS.
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///
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/// \param OS Stream to emit the output to.
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/// \param FName Name of the function to print.
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void SampleModuleProfile::printFunctionProfile(raw_ostream &OS,
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StringRef FName) {
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OS << "Function: " << FName << ":\n";
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Profiles[FName].print(OS);
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}
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/// \brief Dump the function profile for \p FName.
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///
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/// \param FName Name of the function to print.
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void SampleModuleProfile::dumpFunctionProfile(StringRef FName) {
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printFunctionProfile(dbgs(), FName);
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}
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/// \brief Dump all the function profiles found.
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void SampleModuleProfile::dump() {
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for (StringMap<SampleFunctionProfile>::const_iterator I = Profiles.begin(),
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E = Profiles.end();
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I != E; ++I)
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dumpFunctionProfile(I->getKey());
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}
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/// \brief Load samples from a text file.
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///
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/// The file contains a list of samples for every function executed at
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/// runtime. Each function profile has the following format:
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///
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/// function1:total_samples:total_head_samples
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/// offset1[.discriminator]: number_of_samples [fn1:num fn2:num ... ]
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/// offset2[.discriminator]: number_of_samples [fn3:num fn4:num ... ]
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/// ...
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/// offsetN[.discriminator]: number_of_samples [fn5:num fn6:num ... ]
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///
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/// Function names must be mangled in order for the profile loader to
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/// match them in the current translation unit. The two numbers in the
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/// function header specify how many total samples were accumulated in
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/// the function (first number), and the total number of samples accumulated
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/// at the prologue of the function (second number). This head sample
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/// count provides an indicator of how frequent is the function invoked.
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///
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/// Each sampled line may contain several items. Some are optional
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/// (marked below):
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///
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/// a- Source line offset. This number represents the line number
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/// in the function where the sample was collected. The line number
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/// is always relative to the line where symbol of the function
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/// is defined. So, if the function has its header at line 280,
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/// the offset 13 is at line 293 in the file.
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///
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/// b- [OPTIONAL] Discriminator. This is used if the sampled program
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/// was compiled with DWARF discriminator support
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/// (http://wiki.dwarfstd.org/index.php?title=Path_Discriminators)
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///
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/// c- Number of samples. This is the number of samples collected by
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/// the profiler at this source location.
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///
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/// d- [OPTIONAL] Potential call targets and samples. If present, this
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/// line contains a call instruction. This models both direct and
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/// indirect calls. Each called target is listed together with the
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/// number of samples. For example,
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///
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/// 130: 7 foo:3 bar:2 baz:7
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///
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/// The above means that at relative line offset 130 there is a
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/// call instruction that calls one of foo(), bar() and baz(). With
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/// baz() being the relatively more frequent call target.
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///
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/// FIXME: This is currently unhandled, but it has a lot of
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/// potential for aiding the inliner.
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///
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///
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/// Since this is a flat profile, a function that shows up more than
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/// once gets all its samples aggregated across all its instances.
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///
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/// FIXME: flat profiles are too imprecise to provide good optimization
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/// opportunities. Convert them to context-sensitive profile.
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///
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/// This textual representation is useful to generate unit tests and
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/// for debugging purposes, but it should not be used to generate
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/// profiles for large programs, as the representation is extremely
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/// inefficient.
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///
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/// \returns true if the file was loaded successfully, false otherwise.
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bool SampleModuleProfile::loadText() {
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std::unique_ptr<MemoryBuffer> Buffer;
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error_code EC = MemoryBuffer::getFile(Filename, Buffer);
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if (EC) {
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std::string Msg(EC.message());
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M.getContext().diagnose(DiagnosticInfoSampleProfile(Filename.data(), Msg));
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return false;
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}
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line_iterator LineIt(*Buffer, '#');
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// Read the profile of each function. Since each function may be
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// mentioned more than once, and we are collecting flat profiles,
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// accumulate samples as we parse them.
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Regex HeadRE("^([^0-9].*):([0-9]+):([0-9]+)$");
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Regex LineSample("^([0-9]+)\\.?([0-9]+)?: ([0-9]+)(.*)$");
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while (!LineIt.is_at_eof()) {
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// Read the header of each function.
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//
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// Note that for function identifiers we are actually expecting
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// mangled names, but we may not always get them. This happens when
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// the compiler decides not to emit the function (e.g., it was inlined
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// and removed). In this case, the binary will not have the linkage
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// name for the function, so the profiler will emit the function's
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// unmangled name, which may contain characters like ':' and '>' in its
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// name (member functions, templates, etc).
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//
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// The only requirement we place on the identifier, then, is that it
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|
// should not begin with a number.
|
|
SmallVector<StringRef, 3> Matches;
|
|
if (!HeadRE.match(*LineIt, &Matches)) {
|
|
reportParseError(LineIt.line_number(),
|
|
"Expected 'mangled_name:NUM:NUM', found " + *LineIt);
|
|
return false;
|
|
}
|
|
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);
|
|
return false;
|
|
}
|
|
assert(Matches.size() == 5);
|
|
unsigned LineOffset, NumSamples, Discriminator = 0;
|
|
Matches[1].getAsInteger(10, LineOffset);
|
|
if (Matches[2] != "")
|
|
Matches[2].getAsInteger(10, Discriminator);
|
|
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, Discriminator, NumSamples);
|
|
++LineIt;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/// \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.
|
|
unsigned SampleFunctionProfile::getInstWeight(Instruction &Inst) {
|
|
DebugLoc DLoc = Inst.getDebugLoc();
|
|
unsigned Lineno = DLoc.getLine();
|
|
if (Lineno < HeaderLineno)
|
|
return 0;
|
|
|
|
DILocation DIL(DLoc.getAsMDNode(*Ctx));
|
|
int LOffset = Lineno - HeaderLineno;
|
|
unsigned Discriminator = DIL.getDiscriminator();
|
|
unsigned Weight =
|
|
BodySamples.lookup(InstructionLocation(LOffset, Discriminator));
|
|
DEBUG(dbgs() << " " << Lineno << "." << Discriminator << ":" << Inst
|
|
<< " (line offset: " << LOffset << "." << Discriminator
|
|
<< " - 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.
|
|
unsigned 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.
|
|
unsigned Weight = 0;
|
|
for (BasicBlock::iterator I = B->begin(), E = B->end(); I != E; ++I) {
|
|
unsigned 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) {
|
|
unsigned 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.
|
|
unsigned &BB1Weight = BlockWeights[BB1];
|
|
unsigned &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.
|
|
unsigned 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++) {
|
|
unsigned 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) {
|
|
unsigned &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)) {
|
|
unsigned &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() << ".\n");
|
|
SmallVector<unsigned, 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);
|
|
unsigned 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. If it returns 0,
|
|
/// it means that there is no debug information available for \p F.
|
|
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();
|
|
}
|
|
}
|
|
}
|
|
|
|
F.getContext().diagnose(DiagnosticInfoSampleProfile(
|
|
"No debug information found in function " + F.getName()));
|
|
return 0;
|
|
}
|
|
|
|
/// \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.
|
|
///
|
|
/// \returns true if \p F was modified. Returns false, otherwise.
|
|
bool SampleFunctionProfile::emitAnnotations(Function &F, DominatorTree *DomTree,
|
|
PostDominatorTree *PostDomTree,
|
|
LoopInfo *Loops) {
|
|
bool Changed = false;
|
|
|
|
// Initialize invariants used during computation and propagation.
|
|
HeaderLineno = getFunctionLoc(F);
|
|
if (HeaderLineno == 0)
|
|
return false;
|
|
|
|
DEBUG(dbgs() << "Line number for the first instruction in " << F.getName()
|
|
<< ": " << HeaderLineno << "\n");
|
|
DT = DomTree;
|
|
PDT = PostDomTree;
|
|
LI = Loops;
|
|
Ctx = &F.getParent()->getContext();
|
|
|
|
// 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_DEPENDENCY(AddDiscriminators)
|
|
INITIALIZE_PASS_END(SampleProfileLoader, "sample-profile",
|
|
"Sample Profile loader", false, false)
|
|
|
|
bool SampleProfileLoader::doInitialization(Module &M) {
|
|
Profiler.reset(new SampleModuleProfile(M, Filename));
|
|
ProfileIsValid = 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) {
|
|
if (!ProfileIsValid)
|
|
return false;
|
|
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;
|
|
}
|