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llvm-project/llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

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//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops
// and generates target-independent LLVM-IR. Legalization of the IR is done
// in the codegen. However, the vectorizes uses (will use) the codegen
// interfaces to generate IR that is likely to result in an optimal binary.
//
// The loop vectorizer combines consecutive loop iteration into a single
// 'wide' iteration. After this transformation the index is incremented
// by the SIMD vector width, and not by one.
//
// This pass has three parts:
// 1. The main loop pass that drives the different parts.
// 2. LoopVectorizationLegality - A unit that checks for the legality
// of the vectorization.
// 3. SingleBlockLoopVectorizer - A unit that performs the actual
// widening of instructions.
// 4. LoopVectorizationCostModel - A unit that checks for the profitability
// of vectorization. It decides on the optimal vector width, which
// can be one, if vectorization is not profitable.
//===----------------------------------------------------------------------===//
//
// The reduction-variable vectorization is based on the paper:
// D. Nuzman and R. Henderson. Multi-platform Auto-vectorization.
//
// Variable uniformity checks are inspired by:
// Karrenberg, R. and Hack, S. Whole Function Vectorization.
//
// Other ideas/concepts are from:
// A. Zaks and D. Nuzman. Autovectorization in GCC-two years later.
//
//===----------------------------------------------------------------------===//
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
#include "llvm/Constants.h"
#include "llvm/DerivedTypes.h"
#include "llvm/Instructions.h"
#include "llvm/LLVMContext.h"
#include "llvm/Pass.h"
#include "llvm/Analysis/LoopPass.h"
#include "llvm/Value.h"
#include "llvm/Function.h"
#include "llvm/Analysis/Verifier.h"
#include "llvm/Module.h"
#include "llvm/Type.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringExtras.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/AliasSetTracker.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/Dominators.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/ScalarEvolutionExpander.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/ValueTracking.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/TargetTransformInfo.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/DataLayout.h"
#include "llvm/Transforms/Utils/Local.h"
#include <algorithm>
using namespace llvm;
static cl::opt<unsigned>
VectorizationFactor("force-vector-width", cl::init(0), cl::Hidden,
cl::desc("Set the default vectorization width. Zero is autoselect."));
namespace {
// Forward declarations.
class LoopVectorizationLegality;
class LoopVectorizationCostModel;
/// SingleBlockLoopVectorizer vectorizes loops which contain only one basic
/// block to a specified vectorization factor (VF).
/// This class performs the widening of scalars into vectors, or multiple
/// scalars. This class also implements the following features:
/// * It inserts an epilogue loop for handling loops that don't have iteration
/// counts that are known to be a multiple of the vectorization factor.
/// * It handles the code generation for reduction variables.
/// * Scalarization (implementation using scalars) of un-vectorizable
/// instructions.
/// SingleBlockLoopVectorizer does not perform any vectorization-legality
/// checks, and relies on the caller to check for the different legality
/// aspects. The SingleBlockLoopVectorizer relies on the
/// LoopVectorizationLegality class to provide information about the induction
/// and reduction variables that were found to a given vectorization factor.
class SingleBlockLoopVectorizer {
public:
/// Ctor.
SingleBlockLoopVectorizer(Loop *Orig, ScalarEvolution *Se, LoopInfo *Li,
DominatorTree *dt, LPPassManager *Lpm,
unsigned VecWidth):
OrigLoop(Orig), SE(Se), LI(Li), DT(dt), LPM(Lpm), VF(VecWidth),
Builder(Se->getContext()), Induction(0), OldInduction(0) { }
// Perform the actual loop widening (vectorization).
void vectorize(LoopVectorizationLegality *Legal) {
///Create a new empty loop. Unlink the old loop and connect the new one.
createEmptyLoop(Legal);
/// Widen each instruction in the old loop to a new one in the new loop.
/// Use the Legality module to find the induction and reduction variables.
vectorizeLoop(Legal);
// register the new loop.
updateAnalysis();
}
private:
/// Create an empty loop, based on the loop ranges of the old loop.
void createEmptyLoop(LoopVectorizationLegality *Legal);
/// Copy and widen the instructions from the old loop.
void vectorizeLoop(LoopVectorizationLegality *Legal);
/// Insert the new loop to the loop hierarchy and pass manager.
void updateAnalysis();
/// This instruction is un-vectorizable. Implement it as a sequence
/// of scalars.
void scalarizeInstruction(Instruction *Instr);
/// Create a broadcast instruction. This method generates a broadcast
/// instruction (shuffle) for loop invariant values and for the induction
/// value. If this is the induction variable then we extend it to N, N+1, ...
/// this is needed because each iteration in the loop corresponds to a SIMD
/// element.
Value *getBroadcastInstrs(Value *V);
/// This is a helper function used by getBroadcastInstrs. It adds 0, 1, 2 ..
/// for each element in the vector. Starting from zero.
Value *getConsecutiveVector(Value* Val);
/// When we go over instructions in the basic block we rely on previous
/// values within the current basic block or on loop invariant values.
/// When we widen (vectorize) values we place them in the map. If the values
/// are not within the map, they have to be loop invariant, so we simply
/// broadcast them into a vector.
Value *getVectorValue(Value *V);
/// Get a uniform vector of constant integers. We use this to get
/// vectors of ones and zeros for the reduction code.
Constant* getUniformVector(unsigned Val, Type* ScalarTy);
typedef DenseMap<Value*, Value*> ValueMap;
/// The original loop.
Loop *OrigLoop;
// Scev analysis to use.
ScalarEvolution *SE;
// Loop Info.
LoopInfo *LI;
// Dominator Tree.
DominatorTree *DT;
// Loop Pass Manager;
LPPassManager *LPM;
// The vectorization factor to use.
unsigned VF;
// The builder that we use
IRBuilder<> Builder;
// --- Vectorization state ---
/// The vector-loop preheader.
BasicBlock *LoopVectorPreHeader;
/// The scalar-loop preheader.
BasicBlock *LoopScalarPreHeader;
/// Middle Block between the vector and the scalar.
BasicBlock *LoopMiddleBlock;
///The ExitBlock of the scalar loop.
BasicBlock *LoopExitBlock;
///The vector loop body.
BasicBlock *LoopVectorBody;
///The scalar loop body.
BasicBlock *LoopScalarBody;
///The first bypass block.
BasicBlock *LoopBypassBlock;
/// The new Induction variable which was added to the new block.
PHINode *Induction;
/// The induction variable of the old basic block.
PHINode *OldInduction;
// Maps scalars to widened vectors.
ValueMap WidenMap;
};
/// LoopVectorizationLegality checks if it is legal to vectorize a loop, and
/// to what vectorization factor.
/// This class does not look at the profitability of vectorization, only the
/// legality. This class has two main kinds of checks:
/// * Memory checks - The code in canVectorizeMemory checks if vectorization
/// will change the order of memory accesses in a way that will change the
/// correctness of the program.
/// * Scalars checks - The code in canVectorizeBlock checks for a number
/// of different conditions, such as the availability of a single induction
/// variable, that all types are supported and vectorize-able, etc.
/// This code reflects the capabilities of SingleBlockLoopVectorizer.
/// This class is also used by SingleBlockLoopVectorizer for identifying
/// induction variable and the different reduction variables.
class LoopVectorizationLegality {
public:
LoopVectorizationLegality(Loop *Lp, ScalarEvolution *Se, DataLayout *Dl):
TheLoop(Lp), SE(Se), DL(Dl), Induction(0) { }
/// This represents the kinds of reductions that we support.
enum ReductionKind {
NoReduction = -1, /// Not a reduction.
IntegerAdd = 0, /// Sum of numbers.
IntegerMult = 1, /// Product of numbers.
IntegerOr = 2, /// Bitwise or logical OR of numbers.
IntegerAnd = 3, /// Bitwise or logical AND of numbers.
IntegerXor = 4 /// Bitwise or logical XOR of numbers.
};
/// This POD struct holds information about reduction variables.
struct ReductionDescriptor {
// Default C'tor
ReductionDescriptor():
StartValue(0), LoopExitInstr(0), Kind(NoReduction) {}
// C'tor.
ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K):
StartValue(Start), LoopExitInstr(Exit), Kind(K) {}
// The starting value of the reduction.
// It does not have to be zero!
Value *StartValue;
// The instruction who's value is used outside the loop.
Instruction *LoopExitInstr;
// The kind of the reduction.
ReductionKind Kind;
};
/// ReductionList contains the reduction descriptors for all
/// of the reductions that were found in the loop.
typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList;
/// Returns true if it is legal to vectorize this loop.
/// This does not mean that it is profitable to vectorize this
/// loop, only that it is legal to do so.
bool canVectorize();
/// Returns the Induction variable.
PHINode *getInduction() {return Induction;}
/// Returns the reduction variables found in the loop.
ReductionList *getReductionVars() { return &Reductions; }
/// Check if the pointer returned by this GEP is consecutive
/// when the index is vectorized. This happens when the last
/// index of the GEP is consecutive, like the induction variable.
/// This check allows us to vectorize A[idx] into a wide load/store.
bool isConsecutiveGep(Value *Ptr);
/// Returns true if this instruction will remain scalar after vectorization.
bool isUniformAfterVectorization(Instruction* I) {return Uniforms.count(I);}
private:
/// Check if a single basic block loop is vectorizable.
/// At this point we know that this is a loop with a constant trip count
/// and we only need to check individual instructions.
bool canVectorizeBlock(BasicBlock &BB);
/// When we vectorize loops we may change the order in which
/// we read and write from memory. This method checks if it is
/// legal to vectorize the code, considering only memory constrains.
/// Returns true if BB is vectorizable
bool canVectorizeMemory(BasicBlock &BB);
/// Returns True, if 'Phi' is the kind of reduction variable for type
/// 'Kind'. If this is a reduction variable, it adds it to ReductionList.
bool AddReductionVar(PHINode *Phi, ReductionKind Kind);
/// Returns true if the instruction I can be a reduction variable of type
/// 'Kind'.
bool isReductionInstr(Instruction *I, ReductionKind Kind);
/// Returns True, if 'Phi' is an induction variable.
bool isInductionVariable(PHINode *Phi);
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// DataLayout analysis.
DataLayout *DL;
// --- vectorization state --- //
/// Holds the induction variable.
PHINode *Induction;
/// Holds the reduction variables.
ReductionList Reductions;
/// Allowed outside users. This holds the reduction
/// vars which can be accessed from outside the loop.
SmallPtrSet<Value*, 4> AllowedExit;
/// This set holds the variables which are known to be uniform after
/// vectorization.
SmallPtrSet<Instruction*, 4> Uniforms;
};
/// LoopVectorizationCostModel - estimates the expected speedups due to
/// vectorization.
/// In many cases vectorization is not profitable. This can happen because
/// of a number of reasons. In this class we mainly attempt to predict
/// the expected speedup/slowdowns due to the supported instruction set.
/// We use the VectorTargetTransformInfo to query the different backends
/// for the cost of different operations.
class LoopVectorizationCostModel {
public:
/// C'tor.
LoopVectorizationCostModel(Loop *Lp, ScalarEvolution *Se,
LoopVectorizationLegality *Leg,
const VectorTargetTransformInfo *Vtti):
TheLoop(Lp), SE(Se), Legal(Leg), VTTI(Vtti) { }
/// Returns the most profitable vectorization factor for the loop that is
/// smaller or equal to the VF argument. This method checks every power
/// of two up to VF.
unsigned findBestVectorizationFactor(unsigned VF = 8);
private:
/// Returns the expected execution cost. The unit of the cost does
/// not matter because we use the 'cost' units to compare different
/// vector widths. The cost that is returned is *not* normalized by
/// the factor width.
unsigned expectedCost(unsigned VF);
/// Returns the execution time cost of an instruction for a given vector
/// width. Vector width of one means scalar.
unsigned getInstructionCost(Instruction *I, unsigned VF);
/// A helper function for converting Scalar types to vector types.
/// If the incoming type is void, we return void. If the VF is 1, we return
/// the scalar type.
static Type* ToVectorTy(Type *Scalar, unsigned VF);
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// Vectorization legality.
LoopVectorizationLegality *Legal;
/// Vector target information.
const VectorTargetTransformInfo *VTTI;
};
struct LoopVectorize : public LoopPass {
static char ID; // Pass identification, replacement for typeid
LoopVectorize() : LoopPass(ID) {
initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
}
ScalarEvolution *SE;
DataLayout *DL;
LoopInfo *LI;
TargetTransformInfo *TTI;
DominatorTree *DT;
virtual bool runOnLoop(Loop *L, LPPassManager &LPM) {
// We only vectorize innermost loops.
if (!L->empty())
return false;
SE = &getAnalysis<ScalarEvolution>();
DL = getAnalysisIfAvailable<DataLayout>();
LI = &getAnalysis<LoopInfo>();
TTI = getAnalysisIfAvailable<TargetTransformInfo>();
DT = &getAnalysis<DominatorTree>();
DEBUG(dbgs() << "LV: Checking a loop in \"" <<
L->getHeader()->getParent()->getName() << "\"\n");
// Check if it is legal to vectorize the loop.
LoopVectorizationLegality LVL(L, SE, DL);
if (!LVL.canVectorize()) {
DEBUG(dbgs() << "LV: Not vectorizing.\n");
return false;
}
// Select the preffered vectorization factor.
unsigned VF = 1;
if (VectorizationFactor == 0) {
const VectorTargetTransformInfo *VTTI = 0;
if (TTI)
VTTI = TTI->getVectorTargetTransformInfo();
// Use the cost model.
LoopVectorizationCostModel CM(L, SE, &LVL, VTTI);
VF = CM.findBestVectorizationFactor();
if (VF == 1) {
DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
return false;
}
} else {
// Use the user command flag.
VF = VectorizationFactor;
}
DEBUG(dbgs() << "LV: Found a vectorizable loop ("<< VF << ") in "<<
L->getHeader()->getParent()->getParent()->getModuleIdentifier()<<
"\n");
// If we decided that it is *legal* to vectorizer the loop then do it.
SingleBlockLoopVectorizer LB(L, SE, LI, DT, &LPM, VF);
LB.vectorize(&LVL);
DEBUG(verifyFunction(*L->getHeader()->getParent()));
return true;
}
virtual void getAnalysisUsage(AnalysisUsage &AU) const {
LoopPass::getAnalysisUsage(AU);
AU.addRequiredID(LoopSimplifyID);
AU.addRequiredID(LCSSAID);
AU.addRequired<LoopInfo>();
AU.addRequired<ScalarEvolution>();
AU.addRequired<DominatorTree>();
AU.addPreserved<LoopInfo>();
AU.addPreserved<DominatorTree>();
}
};
Value *SingleBlockLoopVectorizer::getBroadcastInstrs(Value *V) {
// Instructions that access the old induction variable
// actually want to get the new one.
if (V == OldInduction)
V = Induction;
// Create the types.
LLVMContext &C = V->getContext();
Type *VTy = VectorType::get(V->getType(), VF);
Type *I32 = IntegerType::getInt32Ty(C);
Constant *Zero = ConstantInt::get(I32, 0);
Value *Zeros = ConstantAggregateZero::get(VectorType::get(I32, VF));
Value *UndefVal = UndefValue::get(VTy);
// Insert the value into a new vector.
Value *SingleElem = Builder.CreateInsertElement(UndefVal, V, Zero);
// Broadcast the scalar into all locations in the vector.
Value *Shuf = Builder.CreateShuffleVector(SingleElem, UndefVal, Zeros,
"broadcast");
// We are accessing the induction variable. Make sure to promote the
// index for each consecutive SIMD lane. This adds 0,1,2 ... to all lanes.
if (V == Induction)
return getConsecutiveVector(Shuf);
return Shuf;
}
Value *SingleBlockLoopVectorizer::getConsecutiveVector(Value* Val) {
assert(Val->getType()->isVectorTy() && "Must be a vector");
assert(Val->getType()->getScalarType()->isIntegerTy() &&
"Elem must be an integer");
// Create the types.
Type *ITy = Val->getType()->getScalarType();
VectorType *Ty = cast<VectorType>(Val->getType());
unsigned VLen = Ty->getNumElements();
SmallVector<Constant*, 8> Indices;
// Create a vector of consecutive numbers from zero to VF.
for (unsigned i = 0; i < VLen; ++i)
Indices.push_back(ConstantInt::get(ITy, i));
// Add the consecutive indices to the vector value.
Constant *Cv = ConstantVector::get(Indices);
assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
return Builder.CreateAdd(Val, Cv, "induction");
}
bool LoopVectorizationLegality::isConsecutiveGep(Value *Ptr) {
GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr);
if (!Gep)
return false;
unsigned NumOperands = Gep->getNumOperands();
Value *LastIndex = Gep->getOperand(NumOperands - 1);
// Check that all of the gep indices are uniform except for the last.
for (unsigned i = 0; i < NumOperands - 1; ++i)
if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
return false;
// We can emit wide load/stores only of the last index is the induction
// variable.
const SCEV *Last = SE->getSCEV(LastIndex);
if (const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Last)) {
const SCEV *Step = AR->getStepRecurrence(*SE);
// The memory is consecutive because the last index is consecutive
// and all other indices are loop invariant.
if (Step->isOne())
return true;
}
return false;
}
Value *SingleBlockLoopVectorizer::getVectorValue(Value *V) {
assert(!V->getType()->isVectorTy() && "Can't widen a vector");
// If we saved a vectorized copy of V, use it.
Value *&MapEntry = WidenMap[V];
if (MapEntry)
return MapEntry;
// Broadcast V and save the value for future uses.
Value *B = getBroadcastInstrs(V);
MapEntry = B;
return B;
}
Constant*
SingleBlockLoopVectorizer::getUniformVector(unsigned Val, Type* ScalarTy) {
SmallVector<Constant*, 8> Indices;
// Create a vector of consecutive numbers from zero to VF.
for (unsigned i = 0; i < VF; ++i)
Indices.push_back(ConstantInt::get(ScalarTy, Val, true));
// Add the consecutive indices to the vector value.
return ConstantVector::get(Indices);
}
void SingleBlockLoopVectorizer::scalarizeInstruction(Instruction *Instr) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
// Holds vector parameters or scalars, in case of uniform vals.
SmallVector<Value*, 8> Params;
// Find all of the vectorized parameters.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *SrcOp = Instr->getOperand(op);
// If we are accessing the old induction variable, use the new one.
if (SrcOp == OldInduction) {
Params.push_back(getBroadcastInstrs(Induction));
continue;
}
// Try using previously calculated values.
Instruction *SrcInst = dyn_cast<Instruction>(SrcOp);
// If the src is an instruction that appeared earlier in the basic block
// then it should already be vectorized.
if (SrcInst && SrcInst->getParent() == Instr->getParent()) {
assert(WidenMap.count(SrcInst) && "Source operand is unavailable");
// The parameter is a vector value from earlier.
Params.push_back(WidenMap[SrcInst]);
} else {
// The parameter is a scalar from outside the loop. Maybe even a constant.
Params.push_back(SrcOp);
}
}
assert(Params.size() == Instr->getNumOperands() &&
"Invalid number of operands");
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Value *VecResults = 0;
// If we have a return value, create an empty vector. We place the scalarized
// instructions in this vector.
if (!IsVoidRetTy)
VecResults = UndefValue::get(VectorType::get(Instr->getType(), VF));
// For each scalar that we create:
for (unsigned i = 0; i < VF; ++i) {
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy)
Cloned->setName(Instr->getName() + ".cloned");
// Replace the operands of the cloned instrucions with extracted scalars.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *Op = Params[op];
// Param is a vector. Need to extract the right lane.
if (Op->getType()->isVectorTy())
Op = Builder.CreateExtractElement(Op, Builder.getInt32(i));
Cloned->setOperand(op, Op);
}
// Place the cloned scalar in the new loop.
Builder.Insert(Cloned);
// If the original scalar returns a value we need to place it in a vector
// so that future users will be able to use it.
if (!IsVoidRetTy)
VecResults = Builder.CreateInsertElement(VecResults, Cloned,
Builder.getInt32(i));
}
if (!IsVoidRetTy)
WidenMap[Instr] = VecResults;
}
void
SingleBlockLoopVectorizer::createEmptyLoop(LoopVectorizationLegality *Legal) {
/*
In this function we generate a new loop. The new loop will contain
the vectorized instructions while the old loop will continue to run the
scalar remainder.
[ ] <-- vector loop bypass.
/ |
/ v
| [ ] <-- vector pre header.
| |
| v
| [ ] \
| [ ]_| <-- vector loop.
| |
\ v
>[ ] <--- middle-block.
/ |
/ v
| [ ] <--- new preheader.
| |
| v
| [ ] \
| [ ]_| <-- old scalar loop to handle remainder.
\ |
\ v
>[ ] <-- exit block.
...
*/
// This is the original scalar-loop preheader.
BasicBlock *BypassBlock = OrigLoop->getLoopPreheader();
BasicBlock *ExitBlock = OrigLoop->getExitBlock();
assert(ExitBlock && "Must have an exit block");
assert(OrigLoop->getNumBlocks() == 1 && "Invalid loop");
assert(BypassBlock && "Invalid loop structure");
BasicBlock *VectorPH =
BypassBlock->splitBasicBlock(BypassBlock->getTerminator(), "vector.ph");
BasicBlock *VecBody = VectorPH->splitBasicBlock(VectorPH->getTerminator(),
"vector.body");
BasicBlock *MiddleBlock = VecBody->splitBasicBlock(VecBody->getTerminator(),
"middle.block");
BasicBlock *ScalarPH =
MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(),
"scalar.preheader");
// Find the induction variable.
BasicBlock *OldBasicBlock = OrigLoop->getHeader();
OldInduction = Legal->getInduction();
assert(OldInduction && "We must have a single phi node.");
Type *IdxTy = OldInduction->getType();
// Use this IR builder to create the loop instructions (Phi, Br, Cmp)
// inside the loop.
Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
// Generate the induction variable.
Induction = Builder.CreatePHI(IdxTy, 2, "index");
Constant *Zero = ConstantInt::get(IdxTy, 0);
Constant *Step = ConstantInt::get(IdxTy, VF);
// Find the loop boundaries.
const SCEV *ExitCount = SE->getExitCount(OrigLoop, OrigLoop->getHeader());
assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count");
// Get the total trip count from the count by adding 1.
ExitCount = SE->getAddExpr(ExitCount,
SE->getConstant(ExitCount->getType(), 1));
// Expand the trip count and place the new instructions in the preheader.
// Notice that the pre-header does not change, only the loop body.
SCEVExpander Exp(*SE, "induction");
Instruction *Loc = BypassBlock->getTerminator();
// We may need to extend the index in case there is a type mismatch.
// We know that the count starts at zero and does not overflow.
// We are using Zext because it should be less expensive.
if (ExitCount->getType() != Induction->getType())
ExitCount = SE->getZeroExtendExpr(ExitCount, IdxTy);
// Count holds the overall loop count (N).
Value *Count = Exp.expandCodeFor(ExitCount, Induction->getType(), Loc);
// Now we need to generate the expression for N - (N % VF), which is
// the part that the vectorized body will execute.
Constant *CIVF = ConstantInt::get(IdxTy, VF);
Value *R = BinaryOperator::CreateURem(Count, CIVF, "n.mod.vf", Loc);
Value *CountRoundDown = BinaryOperator::CreateSub(Count, R, "n.vec", Loc);
// Now, compare the new count to zero. If it is zero, jump to the scalar part.
Value *Cmp = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ,
CountRoundDown, ConstantInt::getNullValue(IdxTy),
"cmp.zero", Loc);
BranchInst::Create(MiddleBlock, VectorPH, Cmp, Loc);
// Remove the old terminator.
Loc->eraseFromParent();
// Add a check in the middle block to see if we have completed
// all of the iterations in the first vector loop.
// If (N - N%VF) == N, then we *don't* need to run the remainder.
Value *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, Count,
CountRoundDown, "cmp.n",
MiddleBlock->getTerminator());
BranchInst::Create(ExitBlock, ScalarPH, CmpN, MiddleBlock->getTerminator());
// Remove the old terminator.
MiddleBlock->getTerminator()->eraseFromParent();
// Create i+1 and fill the PHINode.
Value *NextIdx = Builder.CreateAdd(Induction, Step, "index.next");
Induction->addIncoming(Zero, VectorPH);
Induction->addIncoming(NextIdx, VecBody);
// Create the compare.
Value *ICmp = Builder.CreateICmpEQ(NextIdx, CountRoundDown);
Builder.CreateCondBr(ICmp, MiddleBlock, VecBody);
// Now we have two terminators. Remove the old one from the block.
VecBody->getTerminator()->eraseFromParent();
// Fix the scalar body iteration count.
unsigned BlockIdx = OldInduction->getBasicBlockIndex(ScalarPH);
OldInduction->setIncomingValue(BlockIdx, CountRoundDown);
// Get ready to start creating new instructions into the vectorized body.
Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
// Register the new loop.
Loop* Lp = new Loop();
LPM->insertLoop(Lp, OrigLoop->getParentLoop());
Lp->addBasicBlockToLoop(VecBody, LI->getBase());
Loop *ParentLoop = OrigLoop->getParentLoop();
if (ParentLoop) {
ParentLoop->addBasicBlockToLoop(ScalarPH, LI->getBase());
ParentLoop->addBasicBlockToLoop(VectorPH, LI->getBase());
ParentLoop->addBasicBlockToLoop(MiddleBlock, LI->getBase());
}
// Save the state.
LoopVectorPreHeader = VectorPH;
LoopScalarPreHeader = ScalarPH;
LoopMiddleBlock = MiddleBlock;
LoopExitBlock = ExitBlock;
LoopVectorBody = VecBody;
LoopScalarBody = OldBasicBlock;
LoopBypassBlock = BypassBlock;
}
/// This function returns the identity element (or neutral element) for
/// the operation K.
static unsigned
getReductionIdentity(LoopVectorizationLegality::ReductionKind K) {
switch (K) {
case LoopVectorizationLegality::IntegerXor:
case LoopVectorizationLegality::IntegerAdd:
case LoopVectorizationLegality::IntegerOr:
// Adding, Xoring, Oring zero to a number does not change it.
return 0;
case LoopVectorizationLegality::IntegerMult:
// Multiplying a number by 1 does not change it.
return 1;
case LoopVectorizationLegality::IntegerAnd:
// AND-ing a number with an all-1 value does not change it.
return -1;
default:
llvm_unreachable("Unknown reduction kind");
}
}
void
SingleBlockLoopVectorizer::vectorizeLoop(LoopVectorizationLegality *Legal) {
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should be also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
typedef SmallVector<PHINode*, 4> PhiVector;
BasicBlock &BB = *OrigLoop->getHeader();
Constant *Zero = ConstantInt::get(
IntegerType::getInt32Ty(BB.getContext()), 0);
// In order to support reduction variables we need to be able to vectorize
// Phi nodes. Phi nodes have cycles, so we need to vectorize them in two
// steages. First, we create a new vector PHI node with no incoming edges.
// We use this value when we vectorize all of the instructions that use the
// PHI. Next, after all of the instructions in the block are complete we
// add the new incoming edges to the PHI. At this point all of the
// instructions in the basic block are vectorized, so we can use them to
// construct the PHI.
PhiVector PHIsToFix;
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB.begin(), e = BB.end(); it != e; ++it) {
Instruction *Inst = it;
switch (Inst->getOpcode()) {
case Instruction::Br:
// Nothing to do for PHIs and BR, since we already took care of the
// loop control flow instructions.
continue;
case Instruction::PHI:{
PHINode* P = cast<PHINode>(Inst);
// Special handling for the induction var.
if (OldInduction == Inst)
continue;
// This is phase one of vectorizing PHIs.
// This has to be a reduction variable.
assert(Legal->getReductionVars()->count(P) && "Not a Reduction");
Type *VecTy = VectorType::get(Inst->getType(), VF);
WidenMap[Inst] = Builder.CreatePHI(VecTy, 2, "vec.phi");
PHIsToFix.push_back(P);
continue;
}
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::FDiv:
case Instruction::URem:
case Instruction::SRem:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
// Just widen binops.
BinaryOperator *BinOp = dyn_cast<BinaryOperator>(Inst);
Value *A = getVectorValue(Inst->getOperand(0));
Value *B = getVectorValue(Inst->getOperand(1));
// Use this vector value for all users of the original instruction.
WidenMap[Inst] = Builder.CreateBinOp(BinOp->getOpcode(), A, B);
break;
}
case Instruction::Select: {
// Widen selects.
// If the selector is loop invariant we can create a select
// instruction with a scalar condition. Otherwise, use vector-select.
Value *Cond = Inst->getOperand(0);
bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(Cond), OrigLoop);
// The condition can be loop invariant but still defined inside the
// loop. This means that we can't just use the original 'cond' value.
// We have to take the 'vectorized' value and pick the first lane.
// Instcombine will make this a no-op.
Cond = getVectorValue(Cond);
if (InvariantCond)
Cond = Builder.CreateExtractElement(Cond, Builder.getInt32(0));
Value *Op0 = getVectorValue(Inst->getOperand(1));
Value *Op1 = getVectorValue(Inst->getOperand(2));
WidenMap[Inst] = Builder.CreateSelect(Cond, Op0, Op1);
break;
}
case Instruction::ICmp:
case Instruction::FCmp: {
// Widen compares. Generate vector compares.
bool FCmp = (Inst->getOpcode() == Instruction::FCmp);
CmpInst *Cmp = dyn_cast<CmpInst>(Inst);
Value *A = getVectorValue(Inst->getOperand(0));
Value *B = getVectorValue(Inst->getOperand(1));
if (FCmp)
WidenMap[Inst] = Builder.CreateFCmp(Cmp->getPredicate(), A, B);
else
WidenMap[Inst] = Builder.CreateICmp(Cmp->getPredicate(), A, B);
break;
}
case Instruction::Store: {
// Attempt to issue a wide store.
StoreInst *SI = dyn_cast<StoreInst>(Inst);
Type *StTy = VectorType::get(SI->getValueOperand()->getType(), VF);
Value *Ptr = SI->getPointerOperand();
unsigned Alignment = SI->getAlignment();
GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
// This store does not use GEPs.
if (!Legal->isConsecutiveGep(Gep)) {
scalarizeInstruction(Inst);
break;
}
// The last index does not have to be the induction. It can be
// consecutive and be a function of the index. For example A[I+1];
unsigned NumOperands = Gep->getNumOperands();
Value *LastIndex = getVectorValue(Gep->getOperand(NumOperands - 1));
LastIndex = Builder.CreateExtractElement(LastIndex, Zero);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
Gep2->setOperand(NumOperands - 1, LastIndex);
Ptr = Builder.Insert(Gep2);
Ptr = Builder.CreateBitCast(Ptr, StTy->getPointerTo());
Value *Val = getVectorValue(SI->getValueOperand());
Builder.CreateStore(Val, Ptr)->setAlignment(Alignment);
break;
}
case Instruction::Load: {
// Attempt to issue a wide load.
LoadInst *LI = dyn_cast<LoadInst>(Inst);
Type *RetTy = VectorType::get(LI->getType(), VF);
Value *Ptr = LI->getPointerOperand();
unsigned Alignment = LI->getAlignment();
GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
// We don't have a gep. Scalarize the load.
if (!Legal->isConsecutiveGep(Gep)) {
scalarizeInstruction(Inst);
break;
}
// The last index does not have to be the induction. It can be
// consecutive and be a function of the index. For example A[I+1];
unsigned NumOperands = Gep->getNumOperands();
Value *LastIndex = getVectorValue(Gep->getOperand(NumOperands -1));
LastIndex = Builder.CreateExtractElement(LastIndex, Zero);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
Gep2->setOperand(NumOperands - 1, LastIndex);
Ptr = Builder.Insert(Gep2);
Ptr = Builder.CreateBitCast(Ptr, RetTy->getPointerTo());
LI = Builder.CreateLoad(Ptr);
LI->setAlignment(Alignment);
// Use this vector value for all users of the load.
WidenMap[Inst] = LI;
break;
}
case Instruction::ZExt:
case Instruction::SExt:
case Instruction::FPToUI:
case Instruction::FPToSI:
case Instruction::FPExt:
case Instruction::PtrToInt:
case Instruction::IntToPtr:
case Instruction::SIToFP:
case Instruction::UIToFP:
case Instruction::Trunc:
case Instruction::FPTrunc:
case Instruction::BitCast: {
/// Vectorize bitcasts.
CastInst *CI = dyn_cast<CastInst>(Inst);
Value *A = getVectorValue(Inst->getOperand(0));
Type *DestTy = VectorType::get(CI->getType()->getScalarType(), VF);
WidenMap[Inst] = Builder.CreateCast(CI->getOpcode(), A, DestTy);
break;
}
default:
/// All other instructions are unsupported. Scalarize them.
scalarizeInstruction(Inst);
break;
}// end of switch.
}// end of for_each instr.
// At this point every instruction in the original loop is widended to
// a vector form. We are almost done. Now, we need to fix the PHI nodes
// that we vectorized. The PHI nodes are currently empty because we did
// not want to introduce cycles. Notice that the remaining PHI nodes
// that we need to fix are reduction variables.
// Create the 'reduced' values for each of the induction vars.
// The reduced values are the vector values that we scalarize and combine
// after the loop is finished.
for (PhiVector::iterator it = PHIsToFix.begin(), e = PHIsToFix.end();
it != e; ++it) {
PHINode *RdxPhi = *it;
PHINode *VecRdxPhi = dyn_cast<PHINode>(WidenMap[RdxPhi]);
assert(RdxPhi && "Unable to recover vectorized PHI");
// Find the reduction variable descriptor.
assert(Legal->getReductionVars()->count(RdxPhi) &&
"Unable to find the reduction variable");
LoopVectorizationLegality::ReductionDescriptor RdxDesc =
(*Legal->getReductionVars())[RdxPhi];
// We need to generate a reduction vector from the incoming scalar.
// To do so, we need to generate the 'identity' vector and overide
// one of the elements with the incoming scalar reduction. We need
// to do it in the vector-loop preheader.
Builder.SetInsertPoint(LoopBypassBlock->getTerminator());
// This is the vector-clone of the value that leaves the loop.
Value *VectorExit = getVectorValue(RdxDesc.LoopExitInstr);
Type *VecTy = VectorExit->getType();
// Find the reduction identity variable. Zero for addition, or, xor,
// one for multiplication, -1 for And.
Constant *Identity = getUniformVector(getReductionIdentity(RdxDesc.Kind),
VecTy->getScalarType());
// This vector is the Identity vector where the first element is the
// incoming scalar reduction.
Value *VectorStart = Builder.CreateInsertElement(Identity,
RdxDesc.StartValue, Zero);
// Fix the vector-loop phi.
// We created the induction variable so we know that the
// preheader is the first entry.
BasicBlock *VecPreheader = Induction->getIncomingBlock(0);
// Reductions do not have to start at zero. They can start with
// any loop invariant values.
VecRdxPhi->addIncoming(VectorStart, VecPreheader);
unsigned SelfEdgeIdx = (RdxPhi)->getBasicBlockIndex(LoopScalarBody);
Value *Val = getVectorValue(RdxPhi->getIncomingValue(SelfEdgeIdx));
VecRdxPhi->addIncoming(Val, LoopVectorBody);
// Before each round, move the insertion point right between
// the PHIs and the values we are going to write.
// This allows us to write both PHINodes and the extractelement
// instructions.
Builder.SetInsertPoint(LoopMiddleBlock->getFirstInsertionPt());
// This PHINode contains the vectorized reduction variable, or
// the initial value vector, if we bypass the vector loop.
PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi");
NewPhi->addIncoming(VectorStart, LoopBypassBlock);
NewPhi->addIncoming(getVectorValue(RdxDesc.LoopExitInstr), LoopVectorBody);
// Extract the first scalar.
Value *Scalar0 =
Builder.CreateExtractElement(NewPhi, Builder.getInt32(0));
// Extract and reduce the remaining vector elements.
for (unsigned i=1; i < VF; ++i) {
Value *Scalar1 =
Builder.CreateExtractElement(NewPhi, Builder.getInt32(i));
switch (RdxDesc.Kind) {
case LoopVectorizationLegality::IntegerAdd:
Scalar0 = Builder.CreateAdd(Scalar0, Scalar1);
break;
case LoopVectorizationLegality::IntegerMult:
Scalar0 = Builder.CreateMul(Scalar0, Scalar1);
break;
case LoopVectorizationLegality::IntegerOr:
Scalar0 = Builder.CreateOr(Scalar0, Scalar1);
break;
case LoopVectorizationLegality::IntegerAnd:
Scalar0 = Builder.CreateAnd(Scalar0, Scalar1);
break;
case LoopVectorizationLegality::IntegerXor:
Scalar0 = Builder.CreateXor(Scalar0, Scalar1);
break;
default:
llvm_unreachable("Unknown reduction operation");
}
}
// Now, we need to fix the users of the reduction variable
// inside and outside of the scalar remainder loop.
// We know that the loop is in LCSSA form. We need to update the
// PHI nodes in the exit blocks.
for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) {
PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
if (!LCSSAPhi) continue;
// All PHINodes need to have a single entry edge, or two if
// we already fixed them.
assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI");
// We found our reduction value exit-PHI. Update it with the
// incoming bypass edge.
if (LCSSAPhi->getIncomingValue(0) == RdxDesc.LoopExitInstr) {
// Add an edge coming from the bypass.
LCSSAPhi->addIncoming(Scalar0, LoopMiddleBlock);
break;
}
}// end of the LCSSA phi scan.
// Fix the scalar loop reduction variable with the incoming reduction sum
// from the vector body and from the backedge value.
int IncomingEdgeBlockIdx = (RdxPhi)->getBasicBlockIndex(LoopScalarBody);
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); // The other block.
(RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, Scalar0);
(RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr);
}// end of for each redux variable.
}
void SingleBlockLoopVectorizer::updateAnalysis() {
// The original basic block.
SE->forgetLoop(OrigLoop);
// Update the dominator tree information.
assert(DT->properlyDominates(LoopBypassBlock, LoopExitBlock) &&
"Entry does not dominate exit.");
DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlock);
DT->addNewBlock(LoopVectorBody, LoopVectorPreHeader);
DT->addNewBlock(LoopMiddleBlock, LoopBypassBlock);
DT->addNewBlock(LoopScalarPreHeader, LoopMiddleBlock);
DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock);
DEBUG(DT->verifyAnalysis());
}
bool LoopVectorizationLegality::canVectorize() {
if (!TheLoop->getLoopPreheader()) {
assert(false && "No preheader!!");
DEBUG(dbgs() << "LV: Loop not normalized." << "\n");
return false;
}
// We can only vectorize single basic block loops.
unsigned NumBlocks = TheLoop->getNumBlocks();
if (NumBlocks != 1) {
DEBUG(dbgs() << "LV: Too many blocks:" << NumBlocks << "\n");
return false;
}
// We need to have a loop header.
BasicBlock *BB = TheLoop->getHeader();
DEBUG(dbgs() << "LV: Found a loop: " << BB->getName() << "\n");
// Go over each instruction and look at memory deps.
if (!canVectorizeBlock(*BB)) {
DEBUG(dbgs() << "LV: Can't vectorize this loop header\n");
return false;
}
// ScalarEvolution needs to be able to find the exit count.
const SCEV *ExitCount = SE->getExitCount(TheLoop, BB);
if (ExitCount == SE->getCouldNotCompute()) {
DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n");
return false;
}
DEBUG(dbgs() << "LV: We can vectorize this loop!\n");
// Okay! We can vectorize. At this point we don't have any other mem analysis
// which may limit our maximum vectorization factor, so just return true with
// no restrictions.
return true;
}
bool LoopVectorizationLegality::canVectorizeBlock(BasicBlock &BB) {
// Scan the instructions in the block and look for hazards.
for (BasicBlock::iterator it = BB.begin(), e = BB.end(); it != e; ++it) {
Instruction *I = it;
PHINode *Phi = dyn_cast<PHINode>(I);
if (Phi) {
// This should not happen because the loop should be normalized.
if (Phi->getNumIncomingValues() != 2) {
DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
return false;
}
// We only look at integer phi nodes.
if (!Phi->getType()->isIntegerTy()) {
DEBUG(dbgs() << "LV: Found an non-int PHI.\n");
return false;
}
if (isInductionVariable(Phi)) {
if (Induction) {
DEBUG(dbgs() << "LV: Found too many inductions."<< *Phi <<"\n");
return false;
}
DEBUG(dbgs() << "LV: Found the induction PHI."<< *Phi <<"\n");
Induction = Phi;
continue;
}
if (AddReductionVar(Phi, IntegerAdd)) {
DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, IntegerMult)) {
DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, IntegerOr)) {
DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, IntegerAnd)) {
DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, IntegerXor)) {
DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n");
continue;
}
DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n");
return false;
}// end of PHI handling
// We still don't handle functions.
CallInst *CI = dyn_cast<CallInst>(I);
if (CI) {
DEBUG(dbgs() << "LV: Found a call site.\n");
return false;
}
// We do not re-vectorize vectors.
if (!VectorType::isValidElementType(I->getType()) &&
!I->getType()->isVoidTy()) {
DEBUG(dbgs() << "LV: Found unvectorizable type." << "\n");
return false;
}
// Reduction instructions are allowed to have exit users.
// All other instructions must not have external users.
if (!AllowedExit.count(I))
//Check that all of the users of the loop are inside the BB.
for (Value::use_iterator it = I->use_begin(), e = I->use_end();
it != e; ++it) {
Instruction *U = cast<Instruction>(*it);
// This user may be a reduction exit value.
BasicBlock *Parent = U->getParent();
if (Parent != &BB) {
DEBUG(dbgs() << "LV: Found an outside user for : "<< *U << "\n");
return false;
}
}
} // next instr.
if (!Induction) {
DEBUG(dbgs() << "LV: Did not find an induction var.\n");
return false;
}
// Don't vectorize if the memory dependencies do not allow vectorization.
if (!canVectorizeMemory(BB))
return false;
// We now know that the loop is vectorizable!
// Collect variables that will remain uniform after vectorization.
std::vector<Value*> Worklist;
// Start with the conditional branch and walk up the block.
Worklist.push_back(BB.getTerminator()->getOperand(0));
while (Worklist.size()) {
Instruction *I = dyn_cast<Instruction>(Worklist.back());
Worklist.pop_back();
// Look at instructions inside this block.
if (!I) continue;
if (I->getParent() != &BB) continue;
// Stop when reaching PHI nodes.
if (isa<PHINode>(I)) {
assert(I == Induction && "Found a uniform PHI that is not the induction");
break;
}
// This is a known uniform.
Uniforms.insert(I);
// Insert all operands.
for (int i=0, Op = I->getNumOperands(); i < Op; ++i) {
Worklist.push_back(I->getOperand(i));
}
}
return true;
}
bool LoopVectorizationLegality::canVectorizeMemory(BasicBlock &BB) {
typedef SmallVector<Value*, 16> ValueVector;
typedef SmallPtrSet<Value*, 16> ValueSet;
// Holds the Load and Store *instructions*.
ValueVector Loads;
ValueVector Stores;
// Scan the BB and collect legal loads and stores.
for (BasicBlock::iterator it = BB.begin(), e = BB.end(); it != e; ++it) {
Instruction *I = it;
// If this is a load, save it. If this instruction can read from memory
// but is not a load, then we quit. Notice that we don't handle function
// calls that read or write.
if (I->mayReadFromMemory()) {
LoadInst *Ld = dyn_cast<LoadInst>(I);
if (!Ld) return false;
if (!Ld->isSimple()) {
DEBUG(dbgs() << "LV: Found a non-simple load.\n");
return false;
}
Loads.push_back(Ld);
continue;
}
// Save store instructions. Abort if other instructions write to memory.
if (I->mayWriteToMemory()) {
StoreInst *St = dyn_cast<StoreInst>(I);
if (!St) return false;
if (!St->isSimple()) {
DEBUG(dbgs() << "LV: Found a non-simple store.\n");
return false;
}
Stores.push_back(St);
}
} // next instr.
// Now we have two lists that hold the loads and the stores.
// Next, we find the pointers that they use.
// Check if we see any stores. If there are no stores, then we don't
// care if the pointers are *restrict*.
if (!Stores.size()) {
DEBUG(dbgs() << "LV: Found a read-only loop!\n");
return true;
}
// Holds the read and read-write *pointers* that we find.
ValueVector Reads;
ValueVector ReadWrites;
// Holds the analyzed pointers. We don't want to call GetUnderlyingObjects
// multiple times on the same object. If the ptr is accessed twice, once
// for read and once for write, it will only appear once (on the write
// list). This is okay, since we are going to check for conflicts between
// writes and between reads and writes, but not between reads and reads.
ValueSet Seen;
ValueVector::iterator I, IE;
for (I = Stores.begin(), IE = Stores.end(); I != IE; ++I) {
StoreInst *ST = dyn_cast<StoreInst>(*I);
assert(ST && "Bad StoreInst");
Value* Ptr = ST->getPointerOperand();
// If we did *not* see this pointer before, insert it to
// the read-write list. At this phase it is only a 'write' list.
if (Seen.insert(Ptr))
ReadWrites.push_back(Ptr);
}
for (I = Loads.begin(), IE = Loads.end(); I != IE; ++I) {
LoadInst *LD = dyn_cast<LoadInst>(*I);
assert(LD && "Bad LoadInst");
Value* Ptr = LD->getPointerOperand();
// If we did *not* see this pointer before, insert it to the
// read list. If we *did* see it before, then it is already in
// the read-write list. This allows us to vectorize expressions
// such as A[i] += x; Because the address of A[i] is a read-write
// pointer. This only works if the index of A[i] is consecutive.
// If the address of i is unknown (for example A[B[i]]) then we may
// read a few words, modify, and write a few words, and some of the
// words may be written to the same address.
if (Seen.insert(Ptr) || !isConsecutiveGep(Ptr))
Reads.push_back(Ptr);
}
// If we write (or read-write) to a single destination and there are no
// other reads in this loop then is it safe to vectorize.
if (ReadWrites.size() == 1 && Reads.size() == 0) {
DEBUG(dbgs() << "LV: Found a write-only loop!\n");
return true;
}
// Now that the pointers are in two lists (Reads and ReadWrites), we
// can check that there are no conflicts between each of the writes and
// between the writes to the reads.
ValueSet WriteObjects;
ValueVector TempObjects;
// Check that the read-writes do not conflict with other read-write
// pointers.
for (I = ReadWrites.begin(), IE = ReadWrites.end(); I != IE; ++I) {
GetUnderlyingObjects(*I, TempObjects, DL);
for (ValueVector::iterator it=TempObjects.begin(), e=TempObjects.end();
it != e; ++it) {
if (!isIdentifiedObject(*it)) {
DEBUG(dbgs() << "LV: Found an unidentified write ptr:"<< **it <<"\n");
return false;
}
if (!WriteObjects.insert(*it)) {
DEBUG(dbgs() << "LV: Found a possible write-write reorder:"
<< **it <<"\n");
return false;
}
}
TempObjects.clear();
}
/// Check that the reads don't conflict with the read-writes.
for (I = Reads.begin(), IE = Reads.end(); I != IE; ++I) {
GetUnderlyingObjects(*I, TempObjects, DL);
for (ValueVector::iterator it=TempObjects.begin(), e=TempObjects.end();
it != e; ++it) {
if (!isIdentifiedObject(*it)) {
DEBUG(dbgs() << "LV: Found an unidentified read ptr:"<< **it <<"\n");
return false;
}
if (WriteObjects.count(*it)) {
DEBUG(dbgs() << "LV: Found a possible read/write reorder:"
<< **it <<"\n");
return false;
}
}
TempObjects.clear();
}
// All is okay.
return true;
}
bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi,
ReductionKind Kind) {
if (Phi->getNumIncomingValues() != 2)
return false;
// Find the possible incoming reduction variable.
BasicBlock *BB = Phi->getParent();
int SelfEdgeIdx = Phi->getBasicBlockIndex(BB);
int InEdgeBlockIdx = (SelfEdgeIdx ? 0 : 1); // The other entry.
Value *RdxStart = Phi->getIncomingValue(InEdgeBlockIdx);
// ExitInstruction is the single value which is used outside the loop.
// We only allow for a single reduction value to be used outside the loop.
// This includes users of the reduction, variables (which form a cycle
// which ends in the phi node).
Instruction *ExitInstruction = 0;
// Iter is our iterator. We start with the PHI node and scan for all of the
// users of this instruction. All users must be instructions which can be
// used as reduction variables (such as ADD). We may have a single
// out-of-block user. They cycle must end with the original PHI.
// Also, we can't have multiple block-local users.
Instruction *Iter = Phi;
while (true) {
// Any reduction instr must be of one of the allowed kinds.
if (!isReductionInstr(Iter, Kind))
return false;
// Did we found a user inside this block ?
bool FoundInBlockUser = false;
// Did we reach the initial PHI node ?
bool FoundStartPHI = false;
// If the instruction has no users then this is a broken
// chain and can't be a reduction variable.
if (Iter->use_empty())
return false;
// For each of the *users* of iter.
for (Value::use_iterator it = Iter->use_begin(), e = Iter->use_end();
it != e; ++it) {
Instruction *U = cast<Instruction>(*it);
// We already know that the PHI is a user.
if (U == Phi) {
FoundStartPHI = true;
continue;
}
// Check if we found the exit user.
BasicBlock *Parent = U->getParent();
if (Parent != BB) {
// We must have a single exit instruction.
if (ExitInstruction != 0)
return false;
ExitInstruction = Iter;
}
// We can't have multiple inside users.
if (FoundInBlockUser)
return false;
FoundInBlockUser = true;
Iter = U;
}
// We found a reduction var if we have reached the original
// phi node and we only have a single instruction with out-of-loop
// users.
if (FoundStartPHI && ExitInstruction) {
// This instruction is allowed to have out-of-loop users.
AllowedExit.insert(ExitInstruction);
// Save the description of this reduction variable.
ReductionDescriptor RD(RdxStart, ExitInstruction, Kind);
Reductions[Phi] = RD;
return true;
}
}
}
bool
LoopVectorizationLegality::isReductionInstr(Instruction *I,
ReductionKind Kind) {
switch (I->getOpcode()) {
default:
return false;
case Instruction::PHI:
// possibly.
return true;
case Instruction::Add:
case Instruction::Sub:
return Kind == IntegerAdd;
case Instruction::Mul:
case Instruction::UDiv:
case Instruction::SDiv:
return Kind == IntegerMult;
case Instruction::And:
return Kind == IntegerAnd;
case Instruction::Or:
return Kind == IntegerOr;
case Instruction::Xor:
return Kind == IntegerXor;
}
}
bool LoopVectorizationLegality::isInductionVariable(PHINode *Phi) {
// Check that the PHI is consecutive and starts at zero.
const SCEV *PhiScev = SE->getSCEV(Phi);
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev);
if (!AR) {
DEBUG(dbgs() << "LV: PHI is not a poly recurrence.\n");
return false;
}
const SCEV *Step = AR->getStepRecurrence(*SE);
const SCEV *Start = AR->getStart();
if (!Step->isOne() || !Start->isZero()) {
DEBUG(dbgs() << "LV: PHI does not start at zero or steps by one.\n");
return false;
}
return true;
}
unsigned
LoopVectorizationCostModel::findBestVectorizationFactor(unsigned VF) {
if (!VTTI) {
DEBUG(dbgs() << "LV: No vector target information. Not vectorizing. \n");
return 1;
}
float Cost = expectedCost(1);
unsigned Width = 1;
DEBUG(dbgs() << "LV: Scalar loop costs: "<< (int)Cost << ".\n");
for (unsigned i=2; i <= VF; i*=2) {
// Notice that the vector loop needs to be executed less times, so
// we need to divide the cost of the vector loops by the width of
// the vector elements.
float VectorCost = expectedCost(i) / (float)i;
DEBUG(dbgs() << "LV: Vector loop of width "<< i << " costs: " <<
(int)VectorCost << ".\n");
if (VectorCost < Cost) {
Cost = VectorCost;
Width = i;
}
}
DEBUG(dbgs() << "LV: Selecting VF = : "<< Width << ".\n");
return Width;
}
unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) {
// We can only estimate the cost of single basic block loops.
assert(1 == TheLoop->getNumBlocks() && "Too many blocks in loop");
BasicBlock *BB = TheLoop->getHeader();
unsigned Cost = 0;
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
Instruction *Inst = it;
unsigned C = getInstructionCost(Inst, VF);
Cost += C;
DEBUG(dbgs() << "LV: Found an estimated cost of "<< C <<" for VF "<< VF <<
" For instruction: "<< *Inst << "\n");
}
return Cost;
}
unsigned
LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
assert(VTTI && "Invalid vector target transformation info");
// If we know that this instruction will remain uniform, check the cost of
// the scalar version.
if (Legal->isUniformAfterVectorization(I))
VF = 1;
Type *RetTy = I->getType();
Type *VectorTy = ToVectorTy(RetTy, VF);
// TODO: We need to estimate the cost of intrinsic calls.
switch (I->getOpcode()) {
case Instruction::GetElementPtr:
// We mark this instruction as zero-cost because scalar GEPs are usually
// lowered to the intruction addressing mode. At the moment we don't
// generate vector geps.
return 0;
case Instruction::Br: {
return VTTI->getCFInstrCost(I->getOpcode());
}
case Instruction::PHI:
return 0;
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::FDiv:
case Instruction::URem:
case Instruction::SRem:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
return VTTI->getArithmeticInstrCost(I->getOpcode(), VectorTy);
}
case Instruction::Select: {
SelectInst *SI = cast<SelectInst>(I);
const SCEV *CondSCEV = SE->getSCEV(SI->getCondition());
bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop));
Type *CondTy = SI->getCondition()->getType();
if (ScalarCond)
CondTy = VectorType::get(CondTy, VF);
return VTTI->getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
VectorTy = ToVectorTy(ValTy, VF);
return VTTI->getCmpSelInstrCost(I->getOpcode(), VectorTy);
}
case Instruction::Store: {
StoreInst *SI = cast<StoreInst>(I);
Type *ValTy = SI->getValueOperand()->getType();
VectorTy = ToVectorTy(ValTy, VF);
if (VF == 1)
return VTTI->getMemoryOpCost(I->getOpcode(), ValTy,
SI->getAlignment(), SI->getPointerAddressSpace());
// Scalarized stores.
if (!Legal->isConsecutiveGep(SI->getPointerOperand())) {
unsigned Cost = 0;
unsigned ExtCost = VTTI->getInstrCost(Instruction::ExtractElement,
ValTy);
// The cost of extracting from the value vector.
Cost += VF * (ExtCost);
// The cost of the scalar stores.
Cost += VF * VTTI->getMemoryOpCost(I->getOpcode(),
ValTy->getScalarType(),
SI->getAlignment(),
SI->getPointerAddressSpace());
return Cost;
}
// Wide stores.
return VTTI->getMemoryOpCost(I->getOpcode(), VectorTy, SI->getAlignment(),
SI->getPointerAddressSpace());
}
case Instruction::Load: {
LoadInst *LI = cast<LoadInst>(I);
if (VF == 1)
return VTTI->getMemoryOpCost(I->getOpcode(), RetTy,
LI->getAlignment(),
LI->getPointerAddressSpace());
// Scalarized loads.
if (!Legal->isConsecutiveGep(LI->getPointerOperand())) {
unsigned Cost = 0;
unsigned InCost = VTTI->getInstrCost(Instruction::InsertElement, RetTy);
// The cost of inserting the loaded value into the result vector.
Cost += VF * (InCost);
// The cost of the scalar stores.
Cost += VF * VTTI->getMemoryOpCost(I->getOpcode(),
RetTy->getScalarType(),
LI->getAlignment(),
LI->getPointerAddressSpace());
return Cost;
}
// Wide loads.
return VTTI->getMemoryOpCost(I->getOpcode(), VectorTy, LI->getAlignment(),
LI->getPointerAddressSpace());
}
case Instruction::ZExt:
case Instruction::SExt:
case Instruction::FPToUI:
case Instruction::FPToSI:
case Instruction::FPExt:
case Instruction::PtrToInt:
case Instruction::IntToPtr:
case Instruction::SIToFP:
case Instruction::UIToFP:
case Instruction::Trunc:
case Instruction::FPTrunc:
case Instruction::BitCast: {
Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF);
return VTTI->getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy);
}
default: {
// We are scalarizing the instruction. Return the cost of the scalar
// instruction, plus the cost of insert and extract into vector
// elements, times the vector width.
unsigned Cost = 0;
bool IsVoid = RetTy->isVoidTy();
unsigned InsCost = (IsVoid ? 0 :
VTTI->getInstrCost(Instruction::InsertElement,
VectorTy));
unsigned ExtCost = VTTI->getInstrCost(Instruction::ExtractElement,
VectorTy);
// The cost of inserting the results plus extracting each one of the
// operands.
Cost += VF * (InsCost + ExtCost * I->getNumOperands());
// The cost of executing VF copies of the scalar instruction.
Cost += VF * VTTI->getInstrCost(I->getOpcode(), RetTy);
return Cost;
}
}// end of switch.
}
Type* LoopVectorizationCostModel::ToVectorTy(Type *Scalar, unsigned VF) {
if (Scalar->isVoidTy() || VF == 1)
return Scalar;
return VectorType::get(Scalar, VF);
}
} // namespace
char LoopVectorize::ID = 0;
static const char lv_name[] = "Loop Vectorization";
INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
INITIALIZE_PASS_DEPENDENCY(LoopSimplify)
INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
namespace llvm {
Pass *createLoopVectorizePass() {
return new LoopVectorize();
}
}
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