#define LLVM_SUPPORT_BRANCHPROBABILITY_H
#include "llvm/Support/DataTypes.h"
-#include <algorithm>
#include <cassert>
-#include <climits>
-#include <numeric>
namespace llvm {
template <class ProbabilityList>
static void normalizeProbabilities(ProbabilityList &Probs);
- // Normalize a list of weights by scaling them down so that the sum of them
- // doesn't exceed UINT32_MAX.
- template <class WeightListIter>
- static void normalizeEdgeWeights(WeightListIter Begin, WeightListIter End);
-
uint32_t getNumerator() const { return N; }
static uint32_t getDenominator() { return D; }
Prob.N = (Prob.N * uint64_t(D) + Sum / 2) / Sum;
}
-template <class WeightListIter>
-void BranchProbability::normalizeEdgeWeights(WeightListIter Begin,
- WeightListIter End) {
- // First we compute the sum with 64-bits of precision.
- uint64_t Sum = std::accumulate(Begin, End, uint64_t(0));
-
- if (Sum > UINT32_MAX) {
- // Compute the scale necessary to cause the weights to fit, and re-sum with
- // that scale applied.
- assert(Sum / UINT32_MAX < UINT32_MAX &&
- "The sum of weights exceeds UINT32_MAX^2!");
- uint32_t Scale = Sum / UINT32_MAX + 1;
- for (auto I = Begin; I != End; ++I)
- *I /= Scale;
- Sum = std::accumulate(Begin, End, uint64_t(0));
- }
-
- // Eliminate zero weights.
- auto ZeroWeightNum = std::count(Begin, End, 0u);
- if (ZeroWeightNum > 0) {
- // If all weights are zeros, replace them by 1.
- if (Sum == 0)
- std::fill(Begin, End, 1u);
- else {
- // We are converting zeros into ones, and here we need to make sure that
- // after this the sum won't exceed UINT32_MAX.
- if (Sum + ZeroWeightNum > UINT32_MAX) {
- for (auto I = Begin; I != End; ++I)
- *I /= 2;
- ZeroWeightNum = std::count(Begin, End, 0u);
- Sum = std::accumulate(Begin, End, uint64_t(0));
- }
- // Scale up non-zero weights and turn zero weights into ones.
- uint64_t ScalingFactor = (UINT32_MAX - ZeroWeightNum) / Sum;
- assert(ScalingFactor >= 1);
- if (ScalingFactor > 1)
- for (auto I = Begin; I != End; ++I)
- *I *= ScalingFactor;
- std::replace(Begin, End, 0u, 1u);
- }
- }
-}
-
}
#endif
#include "llvm/ADT/Statistic.h"
#include "llvm/Analysis/GlobalsModRef.h"
#include "llvm/Analysis/CFG.h"
-#include "llvm/Analysis/BlockFrequencyInfo.h"
-#include "llvm/Analysis/BlockFrequencyInfoImpl.h"
-#include "llvm/Analysis/BranchProbabilityInfo.h"
#include "llvm/Analysis/ConstantFolding.h"
#include "llvm/Analysis/InstructionSimplify.h"
#include "llvm/Analysis/LazyValueInfo.h"
#include "llvm/Analysis/Loads.h"
-#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/TargetLibraryInfo.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/LLVMContext.h"
-#include "llvm/IR/MDBuilder.h"
#include "llvm/IR/Metadata.h"
#include "llvm/IR/ValueHandle.h"
#include "llvm/Pass.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/Local.h"
#include "llvm/Transforms/Utils/SSAUpdater.h"
-#include <algorithm>
-#include <memory>
using namespace llvm;
#define DEBUG_TYPE "jump-threading"
class JumpThreading : public FunctionPass {
TargetLibraryInfo *TLI;
LazyValueInfo *LVI;
- std::unique_ptr<BlockFrequencyInfo> BFI;
- std::unique_ptr<BranchProbabilityInfo> BPI;
- bool HasProfileData;
#ifdef NDEBUG
SmallPtrSet<BasicBlock*, 16> LoopHeaders;
#else
AU.addRequired<TargetLibraryInfoWrapperPass>();
}
- void releaseMemory() override {
- BFI.reset();
- BPI.reset();
- }
-
void FindLoopHeaders(Function &F);
bool ProcessBlock(BasicBlock *BB);
bool ThreadEdge(BasicBlock *BB, const SmallVectorImpl<BasicBlock*> &PredBBs,
bool SimplifyPartiallyRedundantLoad(LoadInst *LI);
bool TryToUnfoldSelect(CmpInst *CondCmp, BasicBlock *BB);
-
- private:
- BasicBlock *SplitBlockPreds(BasicBlock *BB, ArrayRef<BasicBlock *> Preds,
- const char *Suffix);
- void UpdateBlockFreqAndEdgeWeight(BasicBlock *PredBB, BasicBlock *BB,
- BasicBlock *NewBB, BasicBlock *SuccBB);
};
}
DEBUG(dbgs() << "Jump threading on function '" << F.getName() << "'\n");
TLI = &getAnalysis<TargetLibraryInfoWrapperPass>().getTLI();
LVI = &getAnalysis<LazyValueInfo>();
- BFI.reset();
- BPI.reset();
- // When profile data is available, we need to update edge weights after
- // successful jump threading, which requires both BPI and BFI being available.
- HasProfileData = F.getEntryCount().hasValue();
- if (HasProfileData) {
- LoopInfo LI{DominatorTree(F)};
- BPI.reset(new BranchProbabilityInfo(F, LI));
- BFI.reset(new BlockFrequencyInfo(F, *BPI, LI));
- }
// Remove unreachable blocks from function as they may result in infinite
// loop. We do threading if we found something profitable. Jump threading a
}
// Split them out to their own block.
- UnavailablePred = SplitBlockPreds(LoadBB, PredsToSplit, "thread-pre-split");
+ UnavailablePred =
+ SplitBlockPredecessors(LoadBB, PredsToSplit, "thread-pre-split");
}
// If the value isn't available in all predecessors, then there will be
else {
DEBUG(dbgs() << " Factoring out " << PredBBs.size()
<< " common predecessors.\n");
- PredBB = SplitBlockPreds(BB, PredBBs, ".thr_comm");
+ PredBB = SplitBlockPredecessors(BB, PredBBs, ".thr_comm");
}
// And finally, do it!
BB->getParent(), BB);
NewBB->moveAfter(PredBB);
- // Set the block frequency of NewBB.
- if (HasProfileData) {
- auto NewBBFreq =
- BFI->getBlockFreq(PredBB) * BPI->getEdgeProbability(PredBB, BB);
- BFI->setBlockFreq(NewBB, NewBBFreq.getFrequency());
- }
-
BasicBlock::iterator BI = BB->begin();
for (; PHINode *PN = dyn_cast<PHINode>(BI); ++BI)
ValueMapping[PN] = PN->getIncomingValueForBlock(PredBB);
// We didn't copy the terminator from BB over to NewBB, because there is now
// an unconditional jump to SuccBB. Insert the unconditional jump.
- BranchInst *NewBI = BranchInst::Create(SuccBB, NewBB);
+ BranchInst *NewBI =BranchInst::Create(SuccBB, NewBB);
NewBI->setDebugLoc(BB->getTerminator()->getDebugLoc());
// Check to see if SuccBB has PHI nodes. If so, we need to add entries to the
// frequently happens because of phi translation.
SimplifyInstructionsInBlock(NewBB, TLI);
- // Update the edge weight from BB to SuccBB, which should be less than before.
- UpdateBlockFreqAndEdgeWeight(PredBB, BB, NewBB, SuccBB);
-
// Threaded an edge!
++NumThreads;
return true;
}
-/// Create a new basic block that will be the predecessor of BB and successor of
-/// all blocks in Preds. When profile data is availble, update the frequency of
-/// this new block.
-BasicBlock *JumpThreading::SplitBlockPreds(BasicBlock *BB,
- ArrayRef<BasicBlock *> Preds,
- const char *Suffix) {
- // Collect the frequencies of all predecessors of BB, which will be used to
- // update the edge weight on BB->SuccBB.
- BlockFrequency PredBBFreq(0);
- if (HasProfileData)
- for (auto Pred : Preds)
- PredBBFreq += BFI->getBlockFreq(Pred) * BPI->getEdgeProbability(Pred, BB);
-
- BasicBlock *PredBB = SplitBlockPredecessors(BB, Preds, Suffix);
-
- // Set the block frequency of the newly created PredBB, which is the sum of
- // frequencies of Preds.
- if (HasProfileData)
- BFI->setBlockFreq(PredBB, PredBBFreq.getFrequency());
- return PredBB;
-}
-
-/// Update the block frequency of BB and branch weight and the metadata on the
-/// edge BB->SuccBB. This is done by scaling the weight of BB->SuccBB by 1 -
-/// Freq(PredBB->BB) / Freq(BB->SuccBB).
-void JumpThreading::UpdateBlockFreqAndEdgeWeight(BasicBlock *PredBB,
- BasicBlock *BB,
- BasicBlock *NewBB,
- BasicBlock *SuccBB) {
- if (!HasProfileData)
- return;
-
- assert(BFI && BPI && "BFI & BPI should have been created here");
-
- // As the edge from PredBB to BB is deleted, we have to update the block
- // frequency of BB.
- auto BBOrigFreq = BFI->getBlockFreq(BB);
- auto NewBBFreq = BFI->getBlockFreq(NewBB);
- auto BB2SuccBBFreq = BBOrigFreq * BPI->getEdgeProbability(BB, SuccBB);
- auto BBNewFreq = BBOrigFreq - NewBBFreq;
- BFI->setBlockFreq(BB, BBNewFreq.getFrequency());
-
- // Collect updated outgoing edges' frequencies from BB and use them to update
- // edge weights.
- SmallVector<uint64_t, 4> BBSuccFreq;
- for (auto I = succ_begin(BB), E = succ_end(BB); I != E; ++I) {
- auto SuccFreq = (*I == SuccBB)
- ? BB2SuccBBFreq - NewBBFreq
- : BBOrigFreq * BPI->getEdgeProbability(BB, *I);
- BBSuccFreq.push_back(SuccFreq.getFrequency());
- }
-
- // Normalize edge weights in Weights64 so that the sum of them can fit in
- BranchProbability::normalizeEdgeWeights(BBSuccFreq.begin(), BBSuccFreq.end());
-
- SmallVector<uint32_t, 4> Weights;
- for (auto Freq : BBSuccFreq)
- Weights.push_back(static_cast<uint32_t>(Freq));
-
- // Update edge weights in BPI.
- for (int I = 0, E = Weights.size(); I < E; I++)
- BPI->setEdgeWeight(BB, I, Weights[I]);
-
- if (Weights.size() >= 2) {
- auto TI = BB->getTerminator();
- TI->setMetadata(
- LLVMContext::MD_prof,
- MDBuilder(TI->getParent()->getContext()).createBranchWeights(Weights));
- }
-}
-
/// DuplicateCondBranchOnPHIIntoPred - PredBB contains an unconditional branch
/// to BB which contains an i1 PHI node and a conditional branch on that PHI.
/// If we can duplicate the contents of BB up into PredBB do so now, this
else {
DEBUG(dbgs() << " Factoring out " << PredBBs.size()
<< " common predecessors.\n");
- PredBB = SplitBlockPreds(BB, PredBBs, ".thr_comm");
+ PredBB = SplitBlockPredecessors(BB, PredBBs, ".thr_comm");
}
// Okay, we decided to do this! Clone all the instructions in BB onto the end
}
}
-TEST(BranchProbabilityTest, NormalizeEdgeWeights) {
- {
- SmallVector<uint32_t, 2> Weights{0, 0};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(1u, Weights[0]);
- EXPECT_EQ(1u, Weights[1]);
- }
- {
- SmallVector<uint32_t, 2> Weights{0, UINT32_MAX};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(1u, Weights[0]);
- EXPECT_EQ(UINT32_MAX - 1u, Weights[1]);
- }
- {
- SmallVector<uint32_t, 2> Weights{1, UINT32_MAX};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(1u, Weights[0]);
- EXPECT_EQ(UINT32_MAX - 1u, Weights[1]);
- }
- {
- SmallVector<uint32_t, 3> Weights{0, 0, UINT32_MAX};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(1u, Weights[0]);
- EXPECT_EQ(1u, Weights[1]);
- EXPECT_EQ(UINT32_MAX / 2u, Weights[2]);
- }
- {
- SmallVector<uint32_t, 2> Weights{UINT32_MAX, UINT32_MAX};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(UINT32_MAX / 3u, Weights[0]);
- EXPECT_EQ(UINT32_MAX / 3u, Weights[1]);
- }
- {
- SmallVector<uint32_t, 3> Weights{UINT32_MAX, UINT32_MAX, UINT32_MAX};
- BranchProbability::normalizeEdgeWeights(Weights.begin(), Weights.end());
- EXPECT_EQ(UINT32_MAX / 4u, Weights[0]);
- EXPECT_EQ(UINT32_MAX / 4u, Weights[1]);
- EXPECT_EQ(UINT32_MAX / 4u, Weights[2]);
- }
-}
-
}
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