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android / platform / external / llvm / 2c3e005 / . / 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.
// The vectorizer uses the TargetTransformInfo analysis to estimate the costs
// of instructions in order to estimate the profitability of vectorization.
//
// The loop vectorizer combines consecutive loop iterations 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. InnerLoopVectorizer - 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.
//
// S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of
// Vectorizing Compilers.
//
//===----------------------------------------------------------------------===//
#include "llvm/Transforms/Vectorize.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/EquivalenceClasses.h"
#include "llvm/ADT/Hashing.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/Statistic.h"
#include "llvm/ADT/StringExtras.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/AliasSetTracker.h"
#include "llvm/Analysis/AssumptionCache.h"
#include "llvm/Analysis/BlockFrequencyInfo.h"
#include "llvm/Analysis/CodeMetrics.h"
#include "llvm/Analysis/LoopAccessAnalysis.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/LoopIterator.h"
#include "llvm/Analysis/LoopPass.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/ScalarEvolutionExpander.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Analysis/ValueTracking.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/DebugInfo.h"
#include "llvm/IR/DerivedTypes.h"
#include "llvm/IR/DiagnosticInfo.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/IRBuilder.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/PatternMatch.h"
#include "llvm/IR/Type.h"
#include "llvm/IR/Value.h"
#include "llvm/IR/ValueHandle.h"
#include "llvm/IR/Verifier.h"
#include "llvm/Pass.h"
#include "llvm/Support/BranchProbability.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/Local.h"
#include "llvm/Transforms/Utils/VectorUtils.h"
#include "llvm/Transforms/Utils/LoopUtils.h"
#include <algorithm>
#include <map>
#include <tuple>
using namespace llvm;
using namespace llvm::PatternMatch;
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
STATISTIC(LoopsVectorized, "Number of loops vectorized");
STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
static cl::opt<bool>
EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden,
cl::desc("Enable if-conversion during vectorization."));
/// We don't vectorize loops with a known constant trip count below this number.
static cl::opt<unsigned>
TinyTripCountVectorThreshold("vectorizer-min-trip-count", cl::init(16),
cl::Hidden,
cl::desc("Don't vectorize loops with a constant "
"trip count that is smaller than this "
"value."));
/// This enables versioning on the strides of symbolically striding memory
/// accesses in code like the following.
/// for (i = 0; i < N; ++i)
/// A[i * Stride1] += B[i * Stride2] ...
///
/// Will be roughly translated to
/// if (Stride1 == 1 && Stride2 == 1) {
/// for (i = 0; i < N; i+=4)
/// A[i:i+3] += ...
/// } else
/// ...
static cl::opt<bool> EnableMemAccessVersioning(
"enable-mem-access-versioning", cl::init(true), cl::Hidden,
cl::desc("Enable symblic stride memory access versioning"));
/// We don't unroll loops with a known constant trip count below this number.
static const unsigned TinyTripCountUnrollThreshold = 128;
static cl::opt<unsigned> ForceTargetNumScalarRegs(
"force-target-num-scalar-regs", cl::init(0), cl::Hidden,
cl::desc("A flag that overrides the target's number of scalar registers."));
static cl::opt<unsigned> ForceTargetNumVectorRegs(
"force-target-num-vector-regs", cl::init(0), cl::Hidden,
cl::desc("A flag that overrides the target's number of vector registers."));
/// Maximum vectorization interleave count.
static const unsigned MaxInterleaveFactor = 16;
static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor(
"force-target-max-scalar-interleave", cl::init(0), cl::Hidden,
cl::desc("A flag that overrides the target's max interleave factor for "
"scalar loops."));
static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor(
"force-target-max-vector-interleave", cl::init(0), cl::Hidden,
cl::desc("A flag that overrides the target's max interleave factor for "
"vectorized loops."));
static cl::opt<unsigned> ForceTargetInstructionCost(
"force-target-instruction-cost", cl::init(0), cl::Hidden,
cl::desc("A flag that overrides the target's expected cost for "
"an instruction to a single constant value. Mostly "
"useful for getting consistent testing."));
static cl::opt<unsigned> SmallLoopCost(
"small-loop-cost", cl::init(20), cl::Hidden,
cl::desc("The cost of a loop that is considered 'small' by the unroller."));
static cl::opt<bool> LoopVectorizeWithBlockFrequency(
"loop-vectorize-with-block-frequency", cl::init(false), cl::Hidden,
cl::desc("Enable the use of the block frequency analysis to access PGO "
"heuristics minimizing code growth in cold regions and being more "
"aggressive in hot regions."));
// Runtime unroll loops for load/store throughput.
static cl::opt<bool> EnableLoadStoreRuntimeUnroll(
"enable-loadstore-runtime-unroll", cl::init(true), cl::Hidden,
cl::desc("Enable runtime unrolling until load/store ports are saturated"));
/// The number of stores in a loop that are allowed to need predication.
static cl::opt<unsigned> NumberOfStoresToPredicate(
"vectorize-num-stores-pred", cl::init(1), cl::Hidden,
cl::desc("Max number of stores to be predicated behind an if."));
static cl::opt<bool> EnableIndVarRegisterHeur(
"enable-ind-var-reg-heur", cl::init(true), cl::Hidden,
cl::desc("Count the induction variable only once when unrolling"));
static cl::opt<bool> EnableCondStoresVectorization(
"enable-cond-stores-vec", cl::init(false), cl::Hidden,
cl::desc("Enable if predication of stores during vectorization."));
static cl::opt<unsigned> MaxNestedScalarReductionUF(
"max-nested-scalar-reduction-unroll", cl::init(2), cl::Hidden,
cl::desc("The maximum unroll factor to use when unrolling a scalar "
"reduction in a nested loop."));
namespace {
// Forward declarations.
class LoopVectorizationLegality;
class LoopVectorizationCostModel;
class LoopVectorizeHints;
/// \brief This modifies LoopAccessReport to initialize message with
/// loop-vectorizer-specific part.
class VectorizationReport : public LoopAccessReport {
public:
VectorizationReport(Instruction *I = nullptr)
: LoopAccessReport("loop not vectorized: ", I) {}
/// \brief This allows promotion of the loop-access analysis report into the
/// loop-vectorizer report. It modifies the message to add the
/// loop-vectorizer-specific part of the message.
explicit VectorizationReport(const LoopAccessReport &R)
: LoopAccessReport(Twine("loop not vectorized: ") + R.str(),
R.getInstr()) {}
};
/// 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) {
if (Scalar->isVoidTy() || VF == 1)
return Scalar;
return VectorType::get(Scalar, VF);
}
/// InnerLoopVectorizer 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.
/// InnerLoopVectorizer does not perform any vectorization-legality
/// checks, and relies on the caller to check for the different legality
/// aspects. The InnerLoopVectorizer relies on the
/// LoopVectorizationLegality class to provide information about the induction
/// and reduction variables that were found to a given vectorization factor.
class InnerLoopVectorizer {
public:
InnerLoopVectorizer(Loop *OrigLoop, ScalarEvolution *SE, LoopInfo *LI,
DominatorTree *DT, const TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, unsigned VecWidth,
unsigned UnrollFactor)
: OrigLoop(OrigLoop), SE(SE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
VF(VecWidth), UF(UnrollFactor), Builder(SE->getContext()),
Induction(nullptr), OldInduction(nullptr), WidenMap(UnrollFactor),
Legal(nullptr), AddedSafetyChecks(false) {}
// Perform the actual loop widening (vectorization).
void vectorize(LoopVectorizationLegality *L) {
Legal = L;
// Create a new empty loop. Unlink the old loop and connect the new one.
createEmptyLoop();
// 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();
// Register the new loop and update the analysis passes.
updateAnalysis();
}
// Return true if any runtime check is added.
bool IsSafetyChecksAdded() {
return AddedSafetyChecks;
}
virtual ~InnerLoopVectorizer() {}
protected:
/// A small list of PHINodes.
typedef SmallVector<PHINode*, 4> PhiVector;
/// When we unroll loops we have multiple vector values for each scalar.
/// This data structure holds the unrolled and vectorized values that
/// originated from one scalar instruction.
typedef SmallVector<Value*, 2> VectorParts;
// When we if-convert we need create edge masks. We have to cache values so
// that we don't end up with exponential recursion/IR.
typedef DenseMap<std::pair<BasicBlock*, BasicBlock*>,
VectorParts> EdgeMaskCache;
/// \brief Add checks for strides that where assumed to be 1.
///
/// Returns the last check instruction and the first check instruction in the
/// pair as (first, last).
std::pair<Instruction *, Instruction *> addStrideCheck(Instruction *Loc);
/// Create an empty loop, based on the loop ranges of the old loop.
void createEmptyLoop();
/// Copy and widen the instructions from the old loop.
virtual void vectorizeLoop();
/// \brief The Loop exit block may have single value PHI nodes where the
/// incoming value is 'Undef'. While vectorizing we only handled real values
/// that were defined inside the loop. Here we fix the 'undef case'.
/// See PR14725.
void fixLCSSAPHIs();
/// A helper function that computes the predicate of the block BB, assuming
/// that the header block of the loop is set to True. It returns the *entry*
/// mask for the block BB.
VectorParts createBlockInMask(BasicBlock *BB);
/// A helper function that computes the predicate of the edge between SRC
/// and DST.
VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst);
/// A helper function to vectorize a single BB within the innermost loop.
void vectorizeBlockInLoop(BasicBlock *BB, PhiVector *PV);
/// Vectorize a single PHINode in a block. This method handles the induction
/// variable canonicalization. It supports both VF = 1 for unrolled loops and
/// arbitrary length vectors.
void widenPHIInstruction(Instruction *PN, VectorParts &Entry,
unsigned UF, unsigned VF, PhiVector *PV);
/// Insert the new loop to the loop hierarchy and pass manager
/// and update the analysis passes.
void updateAnalysis();
/// This instruction is un-vectorizable. Implement it as a sequence
/// of scalars. If \p IfPredicateStore is true we need to 'hide' each
/// scalarized instruction behind an if block predicated on the control
/// dependence of the instruction.
virtual void scalarizeInstruction(Instruction *Instr,
bool IfPredicateStore=false);
/// Vectorize Load and Store instructions,
virtual void vectorizeMemoryInstruction(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.
virtual Value *getBroadcastInstrs(Value *V);
/// This function adds (StartIdx, StartIdx + Step, StartIdx + 2*Step, ...)
/// to each vector element of Val. The sequence starts at StartIndex.
virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step);
/// 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.
VectorParts &getVectorValue(Value *V);
/// Generate a shuffle sequence that will reverse the vector Vec.
virtual Value *reverseVector(Value *Vec);
/// This is a helper class that holds the vectorizer state. It maps scalar
/// instructions to vector instructions. When the code is 'unrolled' then
/// then a single scalar value is mapped to multiple vector parts. The parts
/// are stored in the VectorPart type.
struct ValueMap {
/// C'tor. UnrollFactor controls the number of vectors ('parts') that
/// are mapped.
ValueMap(unsigned UnrollFactor) : UF(UnrollFactor) {}
/// \return True if 'Key' is saved in the Value Map.
bool has(Value *Key) const { return MapStorage.count(Key); }
/// Initializes a new entry in the map. Sets all of the vector parts to the
/// save value in 'Val'.
/// \return A reference to a vector with splat values.
VectorParts &splat(Value *Key, Value *Val) {
VectorParts &Entry = MapStorage[Key];
Entry.assign(UF, Val);
return Entry;
}
///\return A reference to the value that is stored at 'Key'.
VectorParts &get(Value *Key) {
VectorParts &Entry = MapStorage[Key];
if (Entry.empty())
Entry.resize(UF);
assert(Entry.size() == UF);
return Entry;
}
private:
/// The unroll factor. Each entry in the map stores this number of vector
/// elements.
unsigned UF;
/// Map storage. We use std::map and not DenseMap because insertions to a
/// dense map invalidates its iterators.
std::map<Value *, VectorParts> MapStorage;
};
/// The original loop.
Loop *OrigLoop;
/// Scev analysis to use.
ScalarEvolution *SE;
/// Loop Info.
LoopInfo *LI;
/// Dominator Tree.
DominatorTree *DT;
/// Alias Analysis.
AliasAnalysis *AA;
/// Target Library Info.
const TargetLibraryInfo *TLI;
/// Target Transform Info.
const TargetTransformInfo *TTI;
/// The vectorization SIMD factor to use. Each vector will have this many
/// vector elements.
unsigned VF;
protected:
/// The vectorization unroll factor to use. Each scalar is vectorized to this
/// many different vector instructions.
unsigned UF;
/// 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.
SmallVector<BasicBlock *, 4> LoopVectorBody;
///The scalar loop body.
BasicBlock *LoopScalarBody;
/// A list of all bypass blocks. The first block is the entry of the loop.
SmallVector<BasicBlock *, 4> LoopBypassBlocks;
/// The new Induction variable which was added to the new block.
PHINode *Induction;
/// The induction variable of the old basic block.
PHINode *OldInduction;
/// Holds the extended (to the widest induction type) start index.
Value *ExtendedIdx;
/// Maps scalars to widened vectors.
ValueMap WidenMap;
EdgeMaskCache MaskCache;
LoopVectorizationLegality *Legal;
// Record whether runtime check is added.
bool AddedSafetyChecks;
};
class InnerLoopUnroller : public InnerLoopVectorizer {
public:
InnerLoopUnroller(Loop *OrigLoop, ScalarEvolution *SE, LoopInfo *LI,
DominatorTree *DT, const TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, unsigned UnrollFactor)
: InnerLoopVectorizer(OrigLoop, SE, LI, DT, TLI, TTI, 1, UnrollFactor) {}
private:
void scalarizeInstruction(Instruction *Instr,
bool IfPredicateStore = false) override;
void vectorizeMemoryInstruction(Instruction *Instr) override;
Value *getBroadcastInstrs(Value *V) override;
Value *getStepVector(Value *Val, int StartIdx, Value *Step) override;
Value *reverseVector(Value *Vec) override;
};
/// \brief Look for a meaningful debug location on the instruction or it's
/// operands.
static Instruction *getDebugLocFromInstOrOperands(Instruction *I) {
if (!I)
return I;
DebugLoc Empty;
if (I->getDebugLoc() != Empty)
return I;
for (User::op_iterator OI = I->op_begin(), OE = I->op_end(); OI != OE; ++OI) {
if (Instruction *OpInst = dyn_cast<Instruction>(*OI))
if (OpInst->getDebugLoc() != Empty)
return OpInst;
}
return I;
}
/// \brief Set the debug location in the builder using the debug location in the
/// instruction.
static void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) {
if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr))
B.SetCurrentDebugLocation(Inst->getDebugLoc());
else
B.SetCurrentDebugLocation(DebugLoc());
}
#ifndef NDEBUG
/// \return string containing a file name and a line # for the given loop.
static std::string getDebugLocString(const Loop *L) {
std::string Result;
if (L) {
raw_string_ostream OS(Result);
if (const DebugLoc LoopDbgLoc = L->getStartLoc())
LoopDbgLoc.print(OS);
else
// Just print the module name.
OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier();
OS.flush();
}
return Result;
}
#endif
/// \brief Propagate known metadata from one instruction to another.
static void propagateMetadata(Instruction *To, const Instruction *From) {
SmallVector<std::pair<unsigned, MDNode *>, 4> Metadata;
From->getAllMetadataOtherThanDebugLoc(Metadata);
for (auto M : Metadata) {
unsigned Kind = M.first;
// These are safe to transfer (this is safe for TBAA, even when we
// if-convert, because should that metadata have had a control dependency
// on the condition, and thus actually aliased with some other
// non-speculated memory access when the condition was false, this would be
// caught by the runtime overlap checks).
if (Kind != LLVMContext::MD_tbaa &&
Kind != LLVMContext::MD_alias_scope &&
Kind != LLVMContext::MD_noalias &&
Kind != LLVMContext::MD_fpmath)
continue;
To->setMetadata(Kind, M.second);
}
}
/// \brief Propagate known metadata from one instruction to a vector of others.
static void propagateMetadata(SmallVectorImpl<Value *> &To, const Instruction *From) {
for (Value *V : To)
if (Instruction *I = dyn_cast<Instruction>(V))
propagateMetadata(I, From);
}
/// 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 canVectorizeInstrs and canVectorizeMemory
/// 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 InnerLoopVectorizer.
/// This class is also used by InnerLoopVectorizer for identifying
/// induction variable and the different reduction variables.
class LoopVectorizationLegality {
public:
LoopVectorizationLegality(Loop *L, ScalarEvolution *SE, DominatorTree *DT,
TargetLibraryInfo *TLI, AliasAnalysis *AA,
Function *F, const TargetTransformInfo *TTI,
LoopAccessAnalysis *LAA)
: NumPredStores(0), TheLoop(L), SE(SE), TLI(TLI), TheFunction(F),
TTI(TTI), DT(DT), LAA(LAA), LAI(nullptr), Induction(nullptr),
WidestIndTy(nullptr), HasFunNoNaNAttr(false) {}
/// This enum represents the kinds of reductions that we support.
enum ReductionKind {
RK_NoReduction, ///< Not a reduction.
RK_IntegerAdd, ///< Sum of integers.
RK_IntegerMult, ///< Product of integers.
RK_IntegerOr, ///< Bitwise or logical OR of numbers.
RK_IntegerAnd, ///< Bitwise or logical AND of numbers.
RK_IntegerXor, ///< Bitwise or logical XOR of numbers.
RK_IntegerMinMax, ///< Min/max implemented in terms of select(cmp()).
RK_FloatAdd, ///< Sum of floats.
RK_FloatMult, ///< Product of floats.
RK_FloatMinMax ///< Min/max implemented in terms of select(cmp()).
};
/// This enum represents the kinds of inductions that we support.
enum InductionKind {
IK_NoInduction, ///< Not an induction variable.
IK_IntInduction, ///< Integer induction variable. Step = C.
IK_PtrInduction ///< Pointer induction var. Step = C / sizeof(elem).
};
// This enum represents the kind of minmax reduction.
enum MinMaxReductionKind {
MRK_Invalid,
MRK_UIntMin,
MRK_UIntMax,
MRK_SIntMin,
MRK_SIntMax,
MRK_FloatMin,
MRK_FloatMax
};
/// This struct holds information about reduction variables.
struct ReductionDescriptor {
ReductionDescriptor() : StartValue(nullptr), LoopExitInstr(nullptr),
Kind(RK_NoReduction), MinMaxKind(MRK_Invalid) {}
ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K,
MinMaxReductionKind MK)
: StartValue(Start), LoopExitInstr(Exit), Kind(K), MinMaxKind(MK) {}
// The starting value of the reduction.
// It does not have to be zero!
TrackingVH<Value> StartValue;
// The instruction who's value is used outside the loop.
Instruction *LoopExitInstr;
// The kind of the reduction.
ReductionKind Kind;
// If this a min/max reduction the kind of reduction.
MinMaxReductionKind MinMaxKind;
};
/// This POD struct holds information about a potential reduction operation.
struct ReductionInstDesc {
ReductionInstDesc(bool IsRedux, Instruction *I) :
IsReduction(IsRedux), PatternLastInst(I), MinMaxKind(MRK_Invalid) {}
ReductionInstDesc(Instruction *I, MinMaxReductionKind K) :
IsReduction(true), PatternLastInst(I), MinMaxKind(K) {}
// Is this instruction a reduction candidate.
bool IsReduction;
// The last instruction in a min/max pattern (select of the select(icmp())
// pattern), or the current reduction instruction otherwise.
Instruction *PatternLastInst;
// If this is a min/max pattern the comparison predicate.
MinMaxReductionKind MinMaxKind;
};
/// A struct for saving information about induction variables.
struct InductionInfo {
InductionInfo(Value *Start, InductionKind K, ConstantInt *Step)
: StartValue(Start), IK(K), StepValue(Step) {
assert(IK != IK_NoInduction && "Not an induction");
assert(StartValue && "StartValue is null");
assert(StepValue && !StepValue->isZero() && "StepValue is zero");
assert((IK != IK_PtrInduction || StartValue->getType()->isPointerTy()) &&
"StartValue is not a pointer for pointer induction");
assert((IK != IK_IntInduction || StartValue->getType()->isIntegerTy()) &&
"StartValue is not an integer for integer induction");
assert(StepValue->getType()->isIntegerTy() &&
"StepValue is not an integer");
}
InductionInfo()
: StartValue(nullptr), IK(IK_NoInduction), StepValue(nullptr) {}
/// Get the consecutive direction. Returns:
/// 0 - unknown or non-consecutive.
/// 1 - consecutive and increasing.
/// -1 - consecutive and decreasing.
int getConsecutiveDirection() const {
if (StepValue && (StepValue->isOne() || StepValue->isMinusOne()))
return StepValue->getSExtValue();
return 0;
}
/// Compute the transformed value of Index at offset StartValue using step
/// StepValue.
/// For integer induction, returns StartValue + Index * StepValue.
/// For pointer induction, returns StartValue[Index * StepValue].
/// FIXME: The newly created binary instructions should contain nsw/nuw
/// flags, which can be found from the original scalar operations.
Value *transform(IRBuilder<> &B, Value *Index) const {
switch (IK) {
case IK_IntInduction:
assert(Index->getType() == StartValue->getType() &&
"Index type does not match StartValue type");
if (StepValue->isMinusOne())
return B.CreateSub(StartValue, Index);
if (!StepValue->isOne())
Index = B.CreateMul(Index, StepValue);
return B.CreateAdd(StartValue, Index);
case IK_PtrInduction:
if (StepValue->isMinusOne())
Index = B.CreateNeg(Index);
else if (!StepValue->isOne())
Index = B.CreateMul(Index, StepValue);
return B.CreateGEP(nullptr, StartValue, Index);
case IK_NoInduction:
return nullptr;
}
llvm_unreachable("invalid enum");
}
/// Start value.
TrackingVH<Value> StartValue;
/// Induction kind.
InductionKind IK;
/// Step value.
ConstantInt *StepValue;
};
/// ReductionList contains the reduction descriptors for all
/// of the reductions that were found in the loop.
typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList;
/// InductionList saves induction variables and maps them to the
/// induction descriptor.
typedef MapVector<PHINode*, InductionInfo> InductionList;
/// 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; }
/// Returns the induction variables found in the loop.
InductionList *getInductionVars() { return &Inductions; }
/// Returns the widest induction type.
Type *getWidestInductionType() { return WidestIndTy; }
/// Returns True if V is an induction variable in this loop.
bool isInductionVariable(const Value *V);
/// Return true if the block BB needs to be predicated in order for the loop
/// to be vectorized.
bool blockNeedsPredication(BasicBlock *BB);
/// Check if this pointer is consecutive when vectorizing. This happens
/// when the last index of the GEP is the induction variable, or that the
/// pointer itself is an induction variable.
/// This check allows us to vectorize A[idx] into a wide load/store.
/// Returns:
/// 0 - Stride is unknown or non-consecutive.
/// 1 - Address is consecutive.
/// -1 - Address is consecutive, and decreasing.
int isConsecutivePtr(Value *Ptr);
/// Returns true if the value V is uniform within the loop.
bool isUniform(Value *V);
/// Returns true if this instruction will remain scalar after vectorization.
bool isUniformAfterVectorization(Instruction* I) { return Uniforms.count(I); }
/// Returns the information that we collected about runtime memory check.
const LoopAccessInfo::RuntimePointerCheck *getRuntimePointerCheck() const {
return LAI->getRuntimePointerCheck();
}
const LoopAccessInfo *getLAI() const {
return LAI;
}
/// This function returns the identity element (or neutral element) for
/// the operation K.
static Constant *getReductionIdentity(ReductionKind K, Type *Tp);
unsigned getMaxSafeDepDistBytes() { return LAI->getMaxSafeDepDistBytes(); }
bool hasStride(Value *V) { return StrideSet.count(V); }
bool mustCheckStrides() { return !StrideSet.empty(); }
SmallPtrSet<Value *, 8>::iterator strides_begin() {
return StrideSet.begin();
}
SmallPtrSet<Value *, 8>::iterator strides_end() { return StrideSet.end(); }
/// Returns true if the target machine supports masked store operation
/// for the given \p DataType and kind of access to \p Ptr.
bool isLegalMaskedStore(Type *DataType, Value *Ptr) {
return TTI->isLegalMaskedStore(DataType, isConsecutivePtr(Ptr));
}
/// Returns true if the target machine supports masked load operation
/// for the given \p DataType and kind of access to \p Ptr.
bool isLegalMaskedLoad(Type *DataType, Value *Ptr) {
return TTI->isLegalMaskedLoad(DataType, isConsecutivePtr(Ptr));
}
/// Returns true if vector representation of the instruction \p I
/// requires mask.
bool isMaskRequired(const Instruction* I) {
return (MaskedOp.count(I) != 0);
}
unsigned getNumStores() const {
return LAI->getNumStores();
}
unsigned getNumLoads() const {
return LAI->getNumLoads();
}
unsigned getNumPredStores() const {
return NumPredStores;
}
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 canVectorizeInstrs();
/// 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 the loop is vectorizable
bool canVectorizeMemory();
/// Return true if we can vectorize this loop using the IF-conversion
/// transformation.
bool canVectorizeWithIfConvert();
/// Collect the variables that need to stay uniform after vectorization.
void collectLoopUniforms();
/// Return true if all of the instructions in the block can be speculatively
/// executed. \p SafePtrs is a list of addresses that are known to be legal
/// and we know that we can read from them without segfault.
bool blockCanBePredicated(BasicBlock *BB, SmallPtrSetImpl<Value *> &SafePtrs);
/// 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 a struct describing if the instruction 'I' can be a reduction
/// variable of type 'Kind'. If the reduction is a min/max pattern of
/// select(icmp()) this function advances the instruction pointer 'I' from the
/// compare instruction to the select instruction and stores this pointer in
/// 'PatternLastInst' member of the returned struct.
ReductionInstDesc isReductionInstr(Instruction *I, ReductionKind Kind,
ReductionInstDesc &Desc);
/// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
/// pattern corresponding to a min(X, Y) or max(X, Y).
static ReductionInstDesc isMinMaxSelectCmpPattern(Instruction *I,
ReductionInstDesc &Prev);
/// Returns the induction kind of Phi and record the step. This function may
/// return NoInduction if the PHI is not an induction variable.
InductionKind isInductionVariable(PHINode *Phi, ConstantInt *&StepValue);
/// \brief Collect memory access with loop invariant strides.
///
/// Looks for accesses like "a[i * StrideA]" where "StrideA" is loop
/// invariant.
void collectStridedAccess(Value *LoadOrStoreInst);
/// Report an analysis message to assist the user in diagnosing loops that are
/// not vectorized. These are handled as LoopAccessReport rather than
/// VectorizationReport because the << operator of VectorizationReport returns
/// LoopAccessReport.
void emitAnalysis(const LoopAccessReport &Message) {
LoopAccessReport::emitAnalysis(Message, TheFunction, TheLoop, LV_NAME);
}
unsigned NumPredStores;
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// Target Library Info.
TargetLibraryInfo *TLI;
/// Parent function
Function *TheFunction;
/// Target Transform Info
const TargetTransformInfo *TTI;
/// Dominator Tree.
DominatorTree *DT;
// LoopAccess analysis.
LoopAccessAnalysis *LAA;
// And the loop-accesses info corresponding to this loop. This pointer is
// null until canVectorizeMemory sets it up.
const LoopAccessInfo *LAI;
// --- vectorization state --- //
/// Holds the integer induction variable. This is the counter of the
/// loop.
PHINode *Induction;
/// Holds the reduction variables.
ReductionList Reductions;
/// Holds all of the induction variables that we found in the loop.
/// Notice that inductions don't need to start at zero and that induction
/// variables can be pointers.
InductionList Inductions;
/// Holds the widest induction type encountered.
Type *WidestIndTy;
/// 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;
/// Can we assume the absence of NaNs.
bool HasFunNoNaNAttr;
ValueToValueMap Strides;
SmallPtrSet<Value *, 8> StrideSet;
/// While vectorizing these instructions we have to generate a
/// call to the appropriate masked intrinsic
SmallPtrSet<const Instruction*, 8> MaskedOp;
};
/// 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
/// TargetTransformInfo to query the different backends for the cost of
/// different operations.
class LoopVectorizationCostModel {
public:
LoopVectorizationCostModel(Loop *L, ScalarEvolution *SE, LoopInfo *LI,
LoopVectorizationLegality *Legal,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI, AssumptionCache *AC,
const Function *F, const LoopVectorizeHints *Hints)
: TheLoop(L), SE(SE), LI(LI), Legal(Legal), TTI(TTI), TLI(TLI),
TheFunction(F), Hints(Hints) {
CodeMetrics::collectEphemeralValues(L, AC, EphValues);
}
/// Information about vectorization costs
struct VectorizationFactor {
unsigned Width; // Vector width with best cost
unsigned Cost; // Cost of the loop with that width
};
/// \return The most profitable vectorization factor and the cost of that VF.
/// This method checks every power of two up to VF. If UserVF is not ZERO
/// then this vectorization factor will be selected if vectorization is
/// possible.
VectorizationFactor selectVectorizationFactor(bool OptForSize);
/// \return The size (in bits) of the widest type in the code that
/// needs to be vectorized. We ignore values that remain scalar such as
/// 64 bit loop indices.
unsigned getWidestType();
/// \return The most profitable unroll factor.
/// If UserUF is non-zero then this method finds the best unroll-factor
/// based on register pressure and other parameters.
/// VF and LoopCost are the selected vectorization factor and the cost of the
/// selected VF.
unsigned selectUnrollFactor(bool OptForSize, unsigned VF, unsigned LoopCost);
/// \brief A struct that represents some properties of the register usage
/// of a loop.
struct RegisterUsage {
/// Holds the number of loop invariant values that are used in the loop.
unsigned LoopInvariantRegs;
/// Holds the maximum number of concurrent live intervals in the loop.
unsigned MaxLocalUsers;
/// Holds the number of instructions in the loop.
unsigned NumInstructions;
};
/// \return information about the register usage of the loop.
RegisterUsage calculateRegisterUsage();
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);
/// Returns whether the instruction is a load or store and will be a emitted
/// as a vector operation.
bool isConsecutiveLoadOrStore(Instruction *I);
/// Report an analysis message to assist the user in diagnosing loops that are
/// not vectorized. These are handled as LoopAccessReport rather than
/// VectorizationReport because the << operator of VectorizationReport returns
/// LoopAccessReport.
void emitAnalysis(const LoopAccessReport &Message) {
LoopAccessReport::emitAnalysis(Message, TheFunction, TheLoop, LV_NAME);
}
/// Values used only by @llvm.assume calls.
SmallPtrSet<const Value *, 32> EphValues;
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// Loop Info analysis.
LoopInfo *LI;
/// Vectorization legality.
LoopVectorizationLegality *Legal;
/// Vector target information.
const TargetTransformInfo &TTI;
/// Target Library Info.
const TargetLibraryInfo *TLI;
const Function *TheFunction;
// Loop Vectorize Hint.
const LoopVectorizeHints *Hints;
};
/// Utility class for getting and setting loop vectorizer hints in the form
/// of loop metadata.
/// This class keeps a number of loop annotations locally (as member variables)
/// and can, upon request, write them back as metadata on the loop. It will
/// initially scan the loop for existing metadata, and will update the local
/// values based on information in the loop.
/// We cannot write all values to metadata, as the mere presence of some info,
/// for example 'force', means a decision has been made. So, we need to be
/// careful NOT to add them if the user hasn't specifically asked so.
class LoopVectorizeHints {
enum HintKind {
HK_WIDTH,
HK_UNROLL,
HK_FORCE
};
/// Hint - associates name and validation with the hint value.
struct Hint {
const char * Name;
unsigned Value; // This may have to change for non-numeric values.
HintKind Kind;
Hint(const char * Name, unsigned Value, HintKind Kind)
: Name(Name), Value(Value), Kind(Kind) { }
bool validate(unsigned Val) {
switch (Kind) {
case HK_WIDTH:
return isPowerOf2_32(Val) && Val <= VectorizerParams::MaxVectorWidth;
case HK_UNROLL:
return isPowerOf2_32(Val) && Val <= MaxInterleaveFactor;
case HK_FORCE:
return (Val <= 1);
}
return false;
}
};
/// Vectorization width.
Hint Width;
/// Vectorization interleave factor.
Hint Interleave;
/// Vectorization forced
Hint Force;
/// Return the loop metadata prefix.
static StringRef Prefix() { return "llvm.loop."; }
public:
enum ForceKind {
FK_Undefined = -1, ///< Not selected.
FK_Disabled = 0, ///< Forcing disabled.
FK_Enabled = 1, ///< Forcing enabled.
};
LoopVectorizeHints(const Loop *L, bool DisableInterleaving)
: Width("vectorize.width", VectorizerParams::VectorizationFactor,
HK_WIDTH),
Interleave("interleave.count", DisableInterleaving, HK_UNROLL),
Force("vectorize.enable", FK_Undefined, HK_FORCE),
TheLoop(L) {
// Populate values with existing loop metadata.
getHintsFromMetadata();
// force-vector-interleave overrides DisableInterleaving.
if (VectorizerParams::isInterleaveForced())
Interleave.Value = VectorizerParams::VectorizationInterleave;
DEBUG(if (DisableInterleaving && Interleave.Value == 1) dbgs()
<< "LV: Interleaving disabled by the pass manager\n");
}
/// Mark the loop L as already vectorized by setting the width to 1.
void setAlreadyVectorized() {
Width.Value = Interleave.Value = 1;
Hint Hints[] = {Width, Interleave};
writeHintsToMetadata(Hints);
}
/// Dumps all the hint information.
std::string emitRemark() const {
VectorizationReport R;
if (Force.Value == LoopVectorizeHints::FK_Disabled)
R << "vectorization is explicitly disabled";
else {
R << "use -Rpass-analysis=loop-vectorize for more info";
if (Force.Value == LoopVectorizeHints::FK_Enabled) {
R << " (Force=true";
if (Width.Value != 0)
R << ", Vector Width=" << Width.Value;
if (Interleave.Value != 0)
R << ", Interleave Count=" << Interleave.Value;
R << ")";
}
}
return R.str();
}
unsigned getWidth() const { return Width.Value; }
unsigned getInterleave() const { return Interleave.Value; }
enum ForceKind getForce() const { return (ForceKind)Force.Value; }
private:
/// Find hints specified in the loop metadata and update local values.
void getHintsFromMetadata() {
MDNode *LoopID = TheLoop->getLoopID();
if (!LoopID)
return;
// First operand should refer to the loop id itself.
assert(LoopID->getNumOperands() > 0 && "requires at least one operand");
assert(LoopID->getOperand(0) == LoopID && "invalid loop id");
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
const MDString *S = nullptr;
SmallVector<Metadata *, 4> Args;
// The expected hint is either a MDString or a MDNode with the first
// operand a MDString.
if (const MDNode *MD = dyn_cast<MDNode>(LoopID->getOperand(i))) {
if (!MD || MD->getNumOperands() == 0)
continue;
S = dyn_cast<MDString>(MD->getOperand(0));
for (unsigned i = 1, ie = MD->getNumOperands(); i < ie; ++i)
Args.push_back(MD->getOperand(i));
} else {
S = dyn_cast<MDString>(LoopID->getOperand(i));
assert(Args.size() == 0 && "too many arguments for MDString");
}
if (!S)
continue;
// Check if the hint starts with the loop metadata prefix.
StringRef Name = S->getString();
if (Args.size() == 1)
setHint(Name, Args[0]);
}
}
/// Checks string hint with one operand and set value if valid.
void setHint(StringRef Name, Metadata *Arg) {
if (!Name.startswith(Prefix()))
return;
Name = Name.substr(Prefix().size(), StringRef::npos);
const ConstantInt *C = mdconst::dyn_extract<ConstantInt>(Arg);
if (!C) return;
unsigned Val = C->getZExtValue();
Hint *Hints[] = {&Width, &Interleave, &Force};
for (auto H : Hints) {
if (Name == H->Name) {
if (H->validate(Val))
H->Value = Val;
else
DEBUG(dbgs() << "LV: ignoring invalid hint '" << Name << "'\n");
break;
}
}
}
/// Create a new hint from name / value pair.
MDNode *createHintMetadata(StringRef Name, unsigned V) const {
LLVMContext &Context = TheLoop->getHeader()->getContext();
Metadata *MDs[] = {MDString::get(Context, Name),
ConstantAsMetadata::get(
ConstantInt::get(Type::getInt32Ty(Context), V))};
return MDNode::get(Context, MDs);
}
/// Matches metadata with hint name.
bool matchesHintMetadataName(MDNode *Node, ArrayRef<Hint> HintTypes) {
MDString* Name = dyn_cast<MDString>(Node->getOperand(0));
if (!Name)
return false;
for (auto H : HintTypes)
if (Name->getString().endswith(H.Name))
return true;
return false;
}
/// Sets current hints into loop metadata, keeping other values intact.
void writeHintsToMetadata(ArrayRef<Hint> HintTypes) {
if (HintTypes.size() == 0)
return;
// Reserve the first element to LoopID (see below).
SmallVector<Metadata *, 4> MDs(1);
// If the loop already has metadata, then ignore the existing operands.
MDNode *LoopID = TheLoop->getLoopID();
if (LoopID) {
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
MDNode *Node = cast<MDNode>(LoopID->getOperand(i));
// If node in update list, ignore old value.
if (!matchesHintMetadataName(Node, HintTypes))
MDs.push_back(Node);
}
}
// Now, add the missing hints.
for (auto H : HintTypes)
MDs.push_back(createHintMetadata(Twine(Prefix(), H.Name).str(), H.Value));
// Replace current metadata node with new one.
LLVMContext &Context = TheLoop->getHeader()->getContext();
MDNode *NewLoopID = MDNode::get(Context, MDs);
// Set operand 0 to refer to the loop id itself.
NewLoopID->replaceOperandWith(0, NewLoopID);
TheLoop->setLoopID(NewLoopID);
}
/// The loop these hints belong to.
const Loop *TheLoop;
};
static void emitMissedWarning(Function *F, Loop *L,
const LoopVectorizeHints &LH) {
emitOptimizationRemarkMissed(F->getContext(), DEBUG_TYPE, *F,
L->getStartLoc(), LH.emitRemark());
if (LH.getForce() == LoopVectorizeHints::FK_Enabled) {
if (LH.getWidth() != 1)
emitLoopVectorizeWarning(
F->getContext(), *F, L->getStartLoc(),
"failed explicitly specified loop vectorization");
else if (LH.getInterleave() != 1)
emitLoopInterleaveWarning(
F->getContext(), *F, L->getStartLoc(),
"failed explicitly specified loop interleaving");
}
}
static void addInnerLoop(Loop &L, SmallVectorImpl<Loop *> &V) {
if (L.empty())
return V.push_back(&L);
for (Loop *InnerL : L)
addInnerLoop(*InnerL, V);
}
/// The LoopVectorize Pass.
struct LoopVectorize : public FunctionPass {
/// Pass identification, replacement for typeid
static char ID;
explicit LoopVectorize(bool NoUnrolling = false, bool AlwaysVectorize = true)
: FunctionPass(ID),
DisableUnrolling(NoUnrolling),
AlwaysVectorize(AlwaysVectorize) {
initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
}
ScalarEvolution *SE;
LoopInfo *LI;
TargetTransformInfo *TTI;
DominatorTree *DT;
BlockFrequencyInfo *BFI;
TargetLibraryInfo *TLI;
AliasAnalysis *AA;
AssumptionCache *AC;
LoopAccessAnalysis *LAA;
bool DisableUnrolling;
bool AlwaysVectorize;
BlockFrequency ColdEntryFreq;
bool runOnFunction(Function &F) override {
SE = &getAnalysis<ScalarEvolution>();
LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
BFI = &getAnalysis<BlockFrequencyInfo>();
auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>();
TLI = TLIP ? &TLIP->getTLI() : nullptr;
AA = &getAnalysis<AliasAnalysis>();
AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F);
LAA = &getAnalysis<LoopAccessAnalysis>();
// Compute some weights outside of the loop over the loops. Compute this
// using a BranchProbability to re-use its scaling math.
const BranchProbability ColdProb(1, 5); // 20%
ColdEntryFreq = BlockFrequency(BFI->getEntryFreq()) * ColdProb;
// If the target claims to have no vector registers don't attempt
// vectorization.
if (!TTI->getNumberOfRegisters(true))
return false;
// Build up a worklist of inner-loops to vectorize. This is necessary as
// the act of vectorizing or partially unrolling a loop creates new loops
// and can invalidate iterators across the loops.
SmallVector<Loop *, 8> Worklist;
for (Loop *L : *LI)
addInnerLoop(*L, Worklist);
LoopsAnalyzed += Worklist.size();
// Now walk the identified inner loops.
bool Changed = false;
while (!Worklist.empty())
Changed |= processLoop(Worklist.pop_back_val());
// Process each loop nest in the function.
return Changed;
}
static void AddRuntimeUnrollDisableMetaData(Loop *L) {
SmallVector<Metadata *, 4> MDs;
// Reserve first location for self reference to the LoopID metadata node.
MDs.push_back(nullptr);
bool IsUnrollMetadata = false;
MDNode *LoopID = L->getLoopID();
if (LoopID) {
// First find existing loop unrolling disable metadata.
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
MDNode *MD = dyn_cast<MDNode>(LoopID->getOperand(i));
if (MD) {
const MDString *S = dyn_cast<MDString>(MD->getOperand(0));
IsUnrollMetadata =
S && S->getString().startswith("llvm.loop.unroll.disable");
}
MDs.push_back(LoopID->getOperand(i));
}
}
if (!IsUnrollMetadata) {
// Add runtime unroll disable metadata.
LLVMContext &Context = L->getHeader()->getContext();
SmallVector<Metadata *, 1> DisableOperands;
DisableOperands.push_back(
MDString::get(Context, "llvm.loop.unroll.runtime.disable"));
MDNode *DisableNode = MDNode::get(Context, DisableOperands);
MDs.push_back(DisableNode);
MDNode *NewLoopID = MDNode::get(Context, MDs);
// Set operand 0 to refer to the loop id itself.
NewLoopID->replaceOperandWith(0, NewLoopID);
L->setLoopID(NewLoopID);
}
}
bool processLoop(Loop *L) {
assert(L->empty() && "Only process inner loops.");
#ifndef NDEBUG
const std::string DebugLocStr = getDebugLocString(L);
#endif /* NDEBUG */
DEBUG(dbgs() << "\nLV: Checking a loop in \""
<< L->getHeader()->getParent()->getName() << "\" from "
<< DebugLocStr << "\n");
LoopVectorizeHints Hints(L, DisableUnrolling);
DEBUG(dbgs() << "LV: Loop hints:"
<< " force="
<< (Hints.getForce() == LoopVectorizeHints::FK_Disabled
? "disabled"
: (Hints.getForce() == LoopVectorizeHints::FK_Enabled
? "enabled"
: "?")) << " width=" << Hints.getWidth()
<< " unroll=" << Hints.getInterleave() << "\n");
// Function containing loop
Function *F = L->getHeader()->getParent();
// Looking at the diagnostic output is the only way to determine if a loop
// was vectorized (other than looking at the IR or machine code), so it
// is important to generate an optimization remark for each loop. Most of
// these messages are generated by emitOptimizationRemarkAnalysis. Remarks
// generated by emitOptimizationRemark and emitOptimizationRemarkMissed are
// less verbose reporting vectorized loops and unvectorized loops that may
// benefit from vectorization, respectively.
if (Hints.getForce() == LoopVectorizeHints::FK_Disabled) {
DEBUG(dbgs() << "LV: Not vectorizing: #pragma vectorize disable.\n");
emitOptimizationRemarkAnalysis(F->getContext(), DEBUG_TYPE, *F,
L->getStartLoc(), Hints.emitRemark());
return false;
}
if (!AlwaysVectorize && Hints.getForce() != LoopVectorizeHints::FK_Enabled) {
DEBUG(dbgs() << "LV: Not vectorizing: No #pragma vectorize enable.\n");
emitOptimizationRemarkAnalysis(F->getContext(), DEBUG_TYPE, *F,
L->getStartLoc(), Hints.emitRemark());
return false;
}
if (Hints.getWidth() == 1 && Hints.getInterleave() == 1) {
DEBUG(dbgs() << "LV: Not vectorizing: Disabled/already vectorized.\n");
emitOptimizationRemarkAnalysis(
F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
"loop not vectorized: vector width and interleave count are "
"explicitly set to 1");
return false;
}
// Check the loop for a trip count threshold:
// do not vectorize loops with a tiny trip count.
const unsigned TC = SE->getSmallConstantTripCount(L);
if (TC > 0u && TC < TinyTripCountVectorThreshold) {
DEBUG(dbgs() << "LV: Found a loop with a very small trip count. "
<< "This loop is not worth vectorizing.");
if (Hints.getForce() == LoopVectorizeHints::FK_Enabled)
DEBUG(dbgs() << " But vectorizing was explicitly forced.\n");
else {
DEBUG(dbgs() << "\n");
emitOptimizationRemarkAnalysis(
F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
"vectorization is not beneficial and is not explicitly forced");
return false;
}
}
// Check if it is legal to vectorize the loop.
LoopVectorizationLegality LVL(L, SE, DT, TLI, AA, F, TTI, LAA);
if (!LVL.canVectorize()) {
DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
emitMissedWarning(F, L, Hints);
return false;
}
// Use the cost model.
LoopVectorizationCostModel CM(L, SE, LI, &LVL, *TTI, TLI, AC, F, &Hints);
// Check the function attributes to find out if this function should be
// optimized for size.
bool OptForSize = Hints.getForce() != LoopVectorizeHints::FK_Enabled &&
F->hasFnAttribute(Attribute::OptimizeForSize);
// Compute the weighted frequency of this loop being executed and see if it
// is less than 20% of the function entry baseline frequency. Note that we
// always have a canonical loop here because we think we *can* vectoriez.
// FIXME: This is hidden behind a flag due to pervasive problems with
// exactly what block frequency models.
if (LoopVectorizeWithBlockFrequency) {
BlockFrequency LoopEntryFreq = BFI->getBlockFreq(L->getLoopPreheader());
if (Hints.getForce() != LoopVectorizeHints::FK_Enabled &&
LoopEntryFreq < ColdEntryFreq)
OptForSize = true;
}
// Check the function attributes to see if implicit floats are allowed.a
// FIXME: This check doesn't seem possibly correct -- what if the loop is
// an integer loop and the vector instructions selected are purely integer
// vector instructions?
if (F->hasFnAttribute(Attribute::NoImplicitFloat)) {
DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat"
"attribute is used.\n");
emitOptimizationRemarkAnalysis(
F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
"loop not vectorized due to NoImplicitFloat attribute");
emitMissedWarning(F, L, Hints);
return false;
}
// Select the optimal vectorization factor.
const LoopVectorizationCostModel::VectorizationFactor VF =
CM.selectVectorizationFactor(OptForSize);
// Select the unroll factor.
const unsigned UF =
CM.selectUnrollFactor(OptForSize, VF.Width, VF.Cost);
DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width << ") in "
<< DebugLocStr << '\n');
DEBUG(dbgs() << "LV: Unroll Factor is " << UF << '\n');
if (VF.Width == 1) {
DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial\n");
if (UF == 1) {
emitOptimizationRemarkAnalysis(
F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
"not beneficial to vectorize and user disabled interleaving");
return false;
}
DEBUG(dbgs() << "LV: Trying to at least unroll the loops.\n");
// Report the unrolling decision.
emitOptimizationRemark(F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
Twine("unrolled with interleaving factor " +
Twine(UF) +
" (vectorization not beneficial)"));
// We decided not to vectorize, but we may want to unroll.
InnerLoopUnroller Unroller(L, SE, LI, DT, TLI, TTI, UF);
Unroller.vectorize(&LVL);
} else {
// If we decided that it is *legal* to vectorize the loop then do it.
InnerLoopVectorizer LB(L, SE, LI, DT, TLI, TTI, VF.Width, UF);
LB.vectorize(&LVL);
++LoopsVectorized;
// Add metadata to disable runtime unrolling scalar loop when there's no
// runtime check about strides and memory. Because at this situation,
// scalar loop is rarely used not worthy to be unrolled.
if (!LB.IsSafetyChecksAdded())
AddRuntimeUnrollDisableMetaData(L);
// Report the vectorization decision.
emitOptimizationRemark(
F->getContext(), DEBUG_TYPE, *F, L->getStartLoc(),
Twine("vectorized loop (vectorization factor: ") + Twine(VF.Width) +
", unrolling interleave factor: " + Twine(UF) + ")");
}
// Mark the loop as already vectorized to avoid vectorizing again.
Hints.setAlreadyVectorized();
DEBUG(verifyFunction(*L->getHeader()->getParent()));
return true;
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<AssumptionCacheTracker>();
AU.addRequiredID(LoopSimplifyID);
AU.addRequiredID(LCSSAID);
AU.addRequired<BlockFrequencyInfo>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<LoopInfoWrapperPass>();
AU.addRequired<ScalarEvolution>();
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.addRequired<AliasAnalysis>();
AU.addRequired<LoopAccessAnalysis>();
AU.addPreserved<LoopInfoWrapperPass>();
AU.addPreserved<DominatorTreeWrapperPass>();
AU.addPreserved<AliasAnalysis>();
}
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
// LoopVectorizationCostModel.
//===----------------------------------------------------------------------===//
Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
// We need to place the broadcast of invariant variables outside the loop.
Instruction *Instr = dyn_cast<Instruction>(V);
bool NewInstr =
(Instr && std::find(LoopVectorBody.begin(), LoopVectorBody.end(),
Instr->getParent()) != LoopVectorBody.end());
bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr;
// Place the code for broadcasting invariant variables in the new preheader.
IRBuilder<>::InsertPointGuard Guard(Builder);
if (Invariant)
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
// Broadcast the scalar into all locations in the vector.
Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
return Shuf;
}
Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx,
Value *Step) {
assert(Val->getType()->isVectorTy() && "Must be a vector");
assert(Val->getType()->getScalarType()->isIntegerTy() &&
"Elem must be an integer");
assert(Step->getType() == Val->getType()->getScalarType() &&
"Step has wrong type");
// Create the types.
Type *ITy = Val->getType()->getScalarType();
VectorType *Ty = cast<VectorType>(Val->getType());
int VLen = Ty->getNumElements();
SmallVector<Constant*, 8> Indices;
// Create a vector of consecutive numbers from zero to VF.
for (int i = 0; i < VLen; ++i)
Indices.push_back(ConstantInt::get(ITy, StartIdx + i));
// Add the consecutive indices to the vector value.
Constant *Cv = ConstantVector::get(Indices);
assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
Step = Builder.CreateVectorSplat(VLen, Step);
assert(Step->getType() == Val->getType() && "Invalid step vec");
// FIXME: The newly created binary instructions should contain nsw/nuw flags,
// which can be found from the original scalar operations.
Step = Builder.CreateMul(Cv, Step);
return Builder.CreateAdd(Val, Step, "induction");
}
/// \brief Find the operand of the GEP that should be checked for consecutive
/// stores. This ignores trailing indices that have no effect on the final
/// pointer.
static unsigned getGEPInductionOperand(const GetElementPtrInst *Gep) {
const DataLayout &DL = Gep->getModule()->getDataLayout();
unsigned LastOperand = Gep->getNumOperands() - 1;
unsigned GEPAllocSize = DL.getTypeAllocSize(
cast<PointerType>(Gep->getType()->getScalarType())->getElementType());
// Walk backwards and try to peel off zeros.
while (LastOperand > 1 && match(Gep->getOperand(LastOperand), m_Zero())) {
// Find the type we're currently indexing into.
gep_type_iterator GEPTI = gep_type_begin(Gep);
std::advance(GEPTI, LastOperand - 1);
// If it's a type with the same allocation size as the result of the GEP we
// can peel off the zero index.
if (DL.getTypeAllocSize(*GEPTI) != GEPAllocSize)
break;
--LastOperand;
}
return LastOperand;
}
int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) {
assert(Ptr->getType()->isPointerTy() && "Unexpected non-ptr");
// Make sure that the pointer does not point to structs.
if (Ptr->getType()->getPointerElementType()->isAggregateType())
return 0;
// If this value is a pointer induction variable we know it is consecutive.
PHINode *Phi = dyn_cast_or_null<PHINode>(Ptr);
if (Phi && Inductions.count(Phi)) {
InductionInfo II = Inductions[Phi];
return II.getConsecutiveDirection();
}
GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr);
if (!Gep)
return 0;
unsigned NumOperands = Gep->getNumOperands();
Value *GpPtr = Gep->getPointerOperand();
// If this GEP value is a consecutive pointer induction variable and all of
// the indices are constant then we know it is consecutive. We can
Phi = dyn_cast<PHINode>(GpPtr);
if (Phi && Inductions.count(Phi)) {
// Make sure that the pointer does not point to structs.
PointerType *GepPtrType = cast<PointerType>(GpPtr->getType());
if (GepPtrType->getElementType()->isAggregateType())
return 0;
// Make sure that all of the index operands are loop invariant.
for (unsigned i = 1; i < NumOperands; ++i)
if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
return 0;
InductionInfo II = Inductions[Phi];
return II.getConsecutiveDirection();
}
unsigned InductionOperand = getGEPInductionOperand(Gep);
// Check that all of the gep indices are uniform except for our induction
// operand.
for (unsigned i = 0; i != NumOperands; ++i)
if (i != InductionOperand &&
!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
return 0;
// We can emit wide load/stores only if the last non-zero index is the
// induction variable.
const SCEV *Last = nullptr;
if (!Strides.count(Gep))
Last = SE->getSCEV(Gep->getOperand(InductionOperand));
else {
// Because of the multiplication by a stride we can have a s/zext cast.
// We are going to replace this stride by 1 so the cast is safe to ignore.
//
// %indvars.iv = phi i64 [ 0, %entry ], [ %indvars.iv.next, %for.body ]
// %0 = trunc i64 %indvars.iv to i32
// %mul = mul i32 %0, %Stride1
// %idxprom = zext i32 %mul to i64 << Safe cast.
// %arrayidx = getelementptr inbounds i32* %B, i64 %idxprom
//
Last = replaceSymbolicStrideSCEV(SE, Strides,
Gep->getOperand(InductionOperand), Gep);
if (const SCEVCastExpr *C = dyn_cast<SCEVCastExpr>(Last))
Last =
(C->getSCEVType() == scSignExtend || C->getSCEVType() == scZeroExtend)
? C->getOperand()
: Last;
}
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 1;
if (Step->isAllOnesValue())
return -1;
}
return 0;
}
bool LoopVectorizationLegality::isUniform(Value *V) {
return LAI->isUniform(V);
}
InnerLoopVectorizer::VectorParts&
InnerLoopVectorizer::getVectorValue(Value *V) {
assert(V != Induction && "The new induction variable should not be used.");
assert(!V->getType()->isVectorTy() && "Can't widen a vector");
// If we have a stride that is replaced by one, do it here.
if (Legal->hasStride(V))
V = ConstantInt::get(V->getType(), 1);
// If we have this scalar in the map, return it.
if (WidenMap.has(V))
return WidenMap.get(V);
// If this scalar is unknown, assume that it is a constant or that it is
// loop invariant. Broadcast V and save the value for future uses.
Value *B = getBroadcastInstrs(V);
return WidenMap.splat(V, B);
}
Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
assert(Vec->getType()->isVectorTy() && "Invalid type");
SmallVector<Constant*, 8> ShuffleMask;
for (unsigned i = 0; i < VF; ++i)
ShuffleMask.push_back(Builder.getInt32(VF - i - 1));
return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()),
ConstantVector::get(ShuffleMask),
"reverse");
}
void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr) {
// Attempt to issue a wide load.
LoadInst *LI = dyn_cast<LoadInst>(Instr);
StoreInst *SI = dyn_cast<StoreInst>(Instr);
assert((LI || SI) && "Invalid Load/Store instruction");
Type *ScalarDataTy = LI ? LI->getType() : SI->getValueOperand()->getType();
Type *DataTy = VectorType::get(ScalarDataTy, VF);
Value *Ptr = LI ? LI->getPointerOperand() : SI->getPointerOperand();
unsigned Alignment = LI ? LI->getAlignment() : SI->getAlignment();
// An alignment of 0 means target abi alignment. We need to use the scalar's
// target abi alignment in such a case.
const DataLayout &DL = Instr->getModule()->getDataLayout();
if (!Alignment)
Alignment = DL.getABITypeAlignment(ScalarDataTy);
unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace();
unsigned ScalarAllocatedSize = DL.getTypeAllocSize(ScalarDataTy);
unsigned VectorElementSize = DL.getTypeStoreSize(DataTy) / VF;
if (SI && Legal->blockNeedsPredication(SI->getParent()) &&
!Legal->isMaskRequired(SI))
return scalarizeInstruction(Instr, true);
if (ScalarAllocatedSize != VectorElementSize)
return scalarizeInstruction(Instr);
// If the pointer is loop invariant or if it is non-consecutive,
// scalarize the load.
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
bool Reverse = ConsecutiveStride < 0;
bool UniformLoad = LI && Legal->isUniform(Ptr);
if (!ConsecutiveStride || UniformLoad)
return scalarizeInstruction(Instr);
Constant *Zero = Builder.getInt32(0);
VectorParts &Entry = WidenMap.get(Instr);
// Handle consecutive loads/stores.
GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
if (Gep && Legal->isInductionVariable(Gep->getPointerOperand())) {
setDebugLocFromInst(Builder, Gep);
Value *PtrOperand = Gep->getPointerOperand();
Value *FirstBasePtr = getVectorValue(PtrOperand)[0];
FirstBasePtr = Builder.CreateExtractElement(FirstBasePtr, Zero);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
Gep2->setOperand(0, FirstBasePtr);
Gep2->setName("gep.indvar.base");
Ptr = Builder.Insert(Gep2);
} else if (Gep) {
setDebugLocFromInst(Builder, Gep);
assert(SE->isLoopInvariant(SE->getSCEV(Gep->getPointerOperand()),
OrigLoop) && "Base ptr must be invariant");
// 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();
unsigned InductionOperand = getGEPInductionOperand(Gep);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
for (unsigned i = 0; i < NumOperands; ++i) {
Value *GepOperand = Gep->getOperand(i);
Instruction *GepOperandInst = dyn_cast<Instruction>(GepOperand);
// Update last index or loop invariant instruction anchored in loop.
if (i == InductionOperand ||
(GepOperandInst && OrigLoop->contains(GepOperandInst))) {
assert((i == InductionOperand ||
SE->isLoopInvariant(SE->getSCEV(GepOperandInst), OrigLoop)) &&
"Must be last index or loop invariant");
VectorParts &GEPParts = getVectorValue(GepOperand);
Value *Index = GEPParts[0];
Index = Builder.CreateExtractElement(Index, Zero);
Gep2->setOperand(i, Index);
Gep2->setName("gep.indvar.idx");
}
}
Ptr = Builder.Insert(Gep2);
} else {
// Use the induction element ptr.
assert(isa<PHINode>(Ptr) && "Invalid induction ptr");
setDebugLocFromInst(Builder, Ptr);
VectorParts &PtrVal = getVectorValue(Ptr);
Ptr = Builder.CreateExtractElement(PtrVal[0], Zero);
}
VectorParts Mask = createBlockInMask(Instr->getParent());
// Handle Stores:
if (SI) {
assert(!Legal->isUniform(SI->getPointerOperand()) &&
"We do not allow storing to uniform addresses");
setDebugLocFromInst(Builder, SI);
// We don't want to update the value in the map as it might be used in
// another expression. So don't use a reference type for "StoredVal".
VectorParts StoredVal = getVectorValue(SI->getValueOperand());
for (unsigned Part = 0; Part < UF; ++Part) {
// Calculate the pointer for the specific unroll-part.
Value *PartPtr =
Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
if (Reverse) {
// If we store to reverse consecutive memory locations then we need
// to reverse the order of elements in the stored value.
StoredVal[Part] = reverseVector(StoredVal[Part]);
// If the address is consecutive but reversed, then the
// wide store needs to start at the last vector element.
PartPtr = Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
PartPtr = Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
Mask[Part] = reverseVector(Mask[Part]);
}
Value *VecPtr = Builder.CreateBitCast(PartPtr,
DataTy->getPointerTo(AddressSpace));
Instruction *NewSI;
if (Legal->isMaskRequired(SI))
NewSI = Builder.CreateMaskedStore(StoredVal[Part], VecPtr, Alignment,
Mask[Part]);
else
NewSI = Builder.CreateAlignedStore(StoredVal[Part], VecPtr, Alignment);
propagateMetadata(NewSI, SI);
}
return;
}
// Handle loads.
assert(LI && "Must have a load instruction");
setDebugLocFromInst(Builder, LI);
for (unsigned Part = 0; Part < UF; ++Part) {
// Calculate the pointer for the specific unroll-part.
Value *PartPtr =
Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
if (Reverse) {
// If the address is consecutive but reversed, then the
// wide load needs to start at the last vector element.
PartPtr = Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
PartPtr = Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
Mask[Part] = reverseVector(Mask[Part]);
}
Instruction* NewLI;
Value *VecPtr = Builder.CreateBitCast(PartPtr,
DataTy->getPointerTo(AddressSpace));
if (Legal->isMaskRequired(LI))
NewLI = Builder.CreateMaskedLoad(VecPtr, Alignment, Mask[Part],
UndefValue::get(DataTy),
"wide.masked.load");
else
NewLI = Builder.CreateAlignedLoad(VecPtr, Alignment, "wide.load");
propagateMetadata(NewLI, LI);
Entry[Part] = Reverse ? reverseVector(NewLI) : NewLI;
}
}
void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, bool IfPredicateStore) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
// Holds vector parameters or scalars, in case of uniform vals.
SmallVector<VectorParts, 4> Params;
setDebugLocFromInst(Builder, Instr);
// 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(getVectorValue(SrcOp));
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 && OrigLoop->contains(SrcInst)) {
assert(WidenMap.has(SrcInst) && "Source operand is unavailable");
// The parameter is a vector value from earlier.
Params.push_back(WidenMap.get(SrcInst));
} else {
// The parameter is a scalar from outside the loop. Maybe even a constant.
VectorParts Scalars;
Scalars.append(UF, SrcOp);
Params.push_back(Scalars);
}
}
assert(Params.size() == Instr->getNumOperands() &&
"Invalid number of operands");
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Value *UndefVec = IsVoidRetTy ? nullptr :
UndefValue::get(VectorType::get(Instr->getType(), VF));
// Create a new entry in the WidenMap and initialize it to Undef or Null.
VectorParts &VecResults = WidenMap.splat(Instr, UndefVec);
Instruction *InsertPt = Builder.GetInsertPoint();
BasicBlock *IfBlock = Builder.GetInsertBlock();
BasicBlock *CondBlock = nullptr;
VectorParts Cond;
Loop *VectorLp = nullptr;
if (IfPredicateStore) {
assert(Instr->getParent()->getSinglePredecessor() &&
"Only support single predecessor blocks");
Cond = createEdgeMask(Instr->getParent()->getSinglePredecessor(),
Instr->getParent());
VectorLp = LI->getLoopFor(IfBlock);
assert(VectorLp && "Must have a loop for this block");
}
// For each vector unroll 'part':
for (unsigned Part = 0; Part < UF; ++Part) {
// For each scalar that we create:
for (unsigned Width = 0; Width < VF; ++Width) {
// Start if-block.
Value *Cmp = nullptr;
if (IfPredicateStore) {
Cmp = Builder.CreateExtractElement(Cond[Part], Builder.getInt32(Width));
Cmp = Builder.CreateICmp(ICmpInst::ICMP_EQ, Cmp, ConstantInt::get(Cmp->getType(), 1));
CondBlock = IfBlock->splitBasicBlock(InsertPt, "cond.store");
LoopVectorBody.push_back(CondBlock);
VectorLp->addBasicBlockToLoop(CondBlock, *LI);
// Update Builder with newly created basic block.
Builder.SetInsertPoint(InsertPt);
}
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy)
Cloned->setName(Instr->getName() + ".cloned");
// Replace the operands of the cloned instructions with extracted scalars.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *Op = Params[op][Part];
// Param is a vector. Need to extract the right lane.
if (Op->getType()->isVectorTy())
Op = Builder.CreateExtractElement(Op, Builder.getInt32(Width));
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[Part] = Builder.CreateInsertElement(VecResults[Part], Cloned,
Builder.getInt32(Width));
// End if-block.
if (IfPredicateStore) {
BasicBlock *NewIfBlock = CondBlock->splitBasicBlock(InsertPt, "else");
LoopVectorBody.push_back(NewIfBlock);
VectorLp->addBasicBlockToLoop(NewIfBlock, *LI);
Builder.SetInsertPoint(InsertPt);
Instruction *OldBr = IfBlock->getTerminator();
BranchInst::Create(CondBlock, NewIfBlock, Cmp, OldBr);
OldBr->eraseFromParent();
IfBlock = NewIfBlock;
}
}
}
}
static Instruction *getFirstInst(Instruction *FirstInst, Value *V,
Instruction *Loc) {
if (FirstInst)
return FirstInst;
if (Instruction *I = dyn_cast<Instruction>(V))
return I->getParent() == Loc->getParent() ? I : nullptr;
return nullptr;
}
std::pair<Instruction *, Instruction *>
InnerLoopVectorizer::addStrideCheck(Instruction *Loc) {
Instruction *tnullptr = nullptr;
if (!Legal->mustCheckStrides())
return std::pair<Instruction *, Instruction *>(tnullptr, tnullptr);
IRBuilder<> ChkBuilder(Loc);
// Emit checks.
Value *Check = nullptr;
Instruction *FirstInst = nullptr;
for (SmallPtrSet<Value *, 8>::iterator SI = Legal->strides_begin(),
SE = Legal->strides_end();
SI != SE; ++SI) {
Value *Ptr = stripIntegerCast(*SI);
Value *C = ChkBuilder.CreateICmpNE(Ptr, ConstantInt::get(Ptr->getType(), 1),
"stride.chk");
// Store the first instruction we create.
FirstInst = getFirstInst(FirstInst, C, Loc);
if (Check)
Check = ChkBuilder.CreateOr(Check, C);
else
Check = C;
}
// We have to do this trickery because the IRBuilder might fold the check to a
// constant expression in which case there is no Instruction anchored in a
// the block.
LLVMContext &Ctx = Loc->getContext();
Instruction *TheCheck =
BinaryOperator::CreateAnd(Check, ConstantInt::getTrue(Ctx));
ChkBuilder.Insert(TheCheck, "stride.not.one");
FirstInst = getFirstInst(FirstInst, TheCheck, Loc);
return std::make_pair(FirstInst, TheCheck);
}
void InnerLoopVectorizer::createEmptyLoop() {
/*
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.
[ ] <-- Back-edge taken count overflow check.
/ |
/ v
| [ ] <-- vector loop bypass (may consist of multiple blocks).
| / |
| / v
|| [ ] <-- vector pre header.
|| |
|| v
|| [ ] \
|| [ ]_| <-- vector loop.
|| |
| \ v
| >[ ] <--- middle-block.
| / |
| / v
-|- >[ ] <--- new preheader.
| |
| v
| [ ] \
| [ ]_| <-- old scalar loop to handle remainder.
\ |
\ v
>[ ] <-- exit block.
...
*/
BasicBlock *OldBasicBlock = OrigLoop->getHeader();
BasicBlock *BypassBlock = OrigLoop->getLoopPreheader();
BasicBlock *ExitBlock = OrigLoop->getExitBlock();
assert(BypassBlock && "Invalid loop structure");
assert(ExitBlock && "Must have an exit block");
// Some loops have a single integer induction variable, while other loops
// don't. One example is c++ iterators that often have multiple pointer
// induction variables. In the code below we also support a case where we
// don't have a single induction variable.
OldInduction = Legal->getInduction();
Type *IdxTy = Legal->getWidestInductionType();
// Find the loop boundaries.
const SCEV *ExitCount = SE->getBackedgeTakenCount(OrigLoop);
assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count");
// The exit count might have the type of i64 while the phi is i32. This can
// happen if we have an induction variable that is sign extended before the
// compare. The only way that we get a backedge taken count is that the
// induction variable was signed and as such will not overflow. In such a case
// truncation is legal.
if (ExitCount->getType()->getPrimitiveSizeInBits() >
IdxTy->getPrimitiveSizeInBits())
ExitCount = SE->getTruncateOrNoop(ExitCount, IdxTy);
const SCEV *BackedgeTakeCount = SE->getNoopOrZeroExtend(ExitCount, IdxTy);
// Get the total trip count from the count by adding 1.
ExitCount = SE->getAddExpr(BackedgeTakeCount,
SE->getConstant(BackedgeTakeCount->getType(), 1));
const DataLayout &DL = OldBasicBlock->getModule()->getDataLayout();
// 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, DL, "induction");
// We need to test whether the backedge-taken count is uint##_max. Adding one
// to it will cause overflow and an incorrect loop trip count in the vector
// body. In case of overflow we want to directly jump to the scalar remainder
// loop.
Value *BackedgeCount =
Exp.expandCodeFor(BackedgeTakeCount, BackedgeTakeCount->getType(),
BypassBlock->getTerminator());
if (BackedgeCount->getType()->isPointerTy())
BackedgeCount = CastInst::CreatePointerCast(BackedgeCount, IdxTy,
"backedge.ptrcnt.to.int",
BypassBlock->getTerminator());
Instruction *CheckBCOverflow =
CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, BackedgeCount,
Constant::getAllOnesValue(BackedgeCount->getType()),
"backedge.overflow", BypassBlock->getTerminator());
// The loop index does not have to start at Zero. Find the original start
// value from the induction PHI node. If we don't have an induction variable
// then we know that it starts at zero.
Builder.SetInsertPoint(BypassBlock->getTerminator());
Value *StartIdx = ExtendedIdx = OldInduction ?
Builder.CreateZExt(OldInduction->getIncomingValueForBlock(BypassBlock),
IdxTy):
ConstantInt::get(IdxTy, 0);
// We need an instruction to anchor the overflow check on. StartIdx needs to
// be defined before the overflow check branch. Because the scalar preheader
// is going to merge the start index and so the overflow branch block needs to
// contain a definition of the start index.
Instruction *OverflowCheckAnchor = BinaryOperator::CreateAdd(
StartIdx, ConstantInt::get(IdxTy, 0), "overflow.check.anchor",
BypassBlock->getTerminator());
// Count holds the overall loop count (N).
Value *Count = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
BypassBlock->getTerminator());
LoopBypassBlocks.push_back(BypassBlock);
// Split the single block loop into the two loop structure described above.
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.ph");
// Create and register the new vector loop.
Loop* Lp = new Loop();
Loop *ParentLoop = OrigLoop->getParentLoop();
// Insert the new loop into the loop nest and register the new basic blocks
// before calling any utilities such as SCEV that require valid LoopInfo.
if (ParentLoop) {
ParentLoop->addChildLoop(Lp);
ParentLoop->addBasicBlockToLoop(ScalarPH, *LI);
ParentLoop->addBasicBlockToLoop(VectorPH, *LI);
ParentLoop->addBasicBlockToLoop(MiddleBlock, *LI);
} else {
LI->addTopLevelLoop(Lp);
}
Lp->addBasicBlockToLoop(VecBody, *LI);
// Use this IR builder to create the loop instructions (Phi, Br, Cmp)
// inside the loop.
Builder.SetInsertPoint(VecBody->getFirstNonPHI());
// Generate the induction variable.
setDebugLocFromInst(Builder, getDebugLocFromInstOrOperands(OldInduction));
Induction = Builder.CreatePHI(IdxTy, 2, "index");
// The loop step is equal to the vectorization factor (num of SIMD elements)
// times the unroll factor (num of SIMD instructions).
Constant *Step = ConstantInt::get(IdxTy, VF * UF);
// This is the IR builder that we use to add all of the logic for bypassing
// the new vector loop.
IRBuilder<> BypassBuilder(BypassBlock->getTerminator());
setDebugLocFromInst(BypassBuilder,
getDebugLocFromInstOrOperands(OldInduction));
// 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.
if (Count->getType() != IdxTy) {
// The exit count can be of pointer type. Convert it to the correct
// integer type.
if (ExitCount->getType()->isPointerTy())
Count = BypassBuilder.CreatePointerCast(Count, IdxTy, "ptrcnt.to.int");
else
Count = BypassBuilder.CreateZExtOrTrunc(Count, IdxTy, "cnt.cast");
}
// Add the start index to the loop count to get the new end index.
Value *IdxEnd = BypassBuilder.CreateAdd(Count, StartIdx, "end.idx");
// Now we need to generate the expression for N - (N % VF), which is
// the part that the vectorized body will execute.
Value *R = BypassBuilder.CreateURem(Count, Step, "n.mod.vf");
Value *CountRoundDown = BypassBuilder.CreateSub(Count, R, "n.vec");
Value *IdxEndRoundDown = BypassBuilder.CreateAdd(CountRoundDown, StartIdx,
"end.idx.rnd.down");
// Now, compare the new count to zero. If it is zero skip the vector loop and
// jump to the scalar loop.
Value *Cmp =
BypassBuilder.CreateICmpEQ(IdxEndRoundDown, StartIdx, "cmp.zero");
BasicBlock *LastBypassBlock = BypassBlock;
// Generate code to check that the loops trip count that we computed by adding
// one to the backedge-taken count will not overflow.
{
auto PastOverflowCheck =
std::next(BasicBlock::iterator(OverflowCheckAnchor));
BasicBlock *CheckBlock =
LastBypassBlock->splitBasicBlock(PastOverflowCheck, "overflow.checked");
if (ParentLoop)
ParentLoop->addBasicBlockToLoop(CheckBlock, *LI);
LoopBypassBlocks.push_back(CheckBlock);
Instruction *OldTerm = LastBypassBlock->getTerminator();
BranchInst::Create(ScalarPH, CheckBlock, CheckBCOverflow, OldTerm);
OldTerm->eraseFromParent();
LastBypassBlock = CheckBlock;
}
// Generate the code to check that the strides we assumed to be one are really
// one. We want the new basic block to start at the first instruction in a
// sequence of instructions that form a check.
Instruction *StrideCheck;
Instruction *FirstCheckInst;
std::tie(FirstCheckInst, StrideCheck) =
addStrideCheck(LastBypassBlock->getTerminator());
if (StrideCheck) {
AddedSafetyChecks = true;
// Create a new block containing the stride check.
BasicBlock *CheckBlock =
LastBypassBlock->splitBasicBlock(FirstCheckInst, "vector.stridecheck");
if (ParentLoop)
ParentLoop->addBasicBlockToLoop(CheckBlock, *LI);
LoopBypassBlocks.push_back(CheckBlock);
// Replace the branch into the memory check block with a conditional branch
// for the "few elements case".
Instruction *OldTerm = LastBypassBlock->getTerminator();
BranchInst::Create(MiddleBlock, CheckBlock, Cmp, OldTerm);
OldTerm->eraseFromParent();
Cmp = StrideCheck;
LastBypassBlock = CheckBlock;
}
// Generate the code that checks in runtime if arrays overlap. We put the
// checks into a separate block to make the more common case of few elements
// faster.
Instruction *MemRuntimeCheck;
std::tie(FirstCheckInst, MemRuntimeCheck) =
Legal->getLAI()->addRuntimeCheck(LastBypassBlock->getTerminator());
if (MemRuntimeCheck) {
AddedSafetyChecks = true;
// Create a new block containing the memory check.
BasicBlock *CheckBlock =
LastBypassBlock->splitBasicBlock(FirstCheckInst, "vector.memcheck");
if (ParentLoop)
ParentLoop->addBasicBlockToLoop(CheckBlock, *LI);
LoopBypassBlocks.push_back(CheckBlock);
// Replace the branch into the memory check block with a conditional branch
// for the "few elements case".
Instruction *OldTerm = LastBypassBlock->getTerminator();
BranchInst::Create(MiddleBlock, CheckBlock, Cmp, OldTerm);
OldTerm->eraseFromParent();
Cmp = MemRuntimeCheck;
LastBypassBlock = CheckBlock;
}
LastBypassBlock->getTerminator()->eraseFromParent();
BranchInst::Create(MiddleBlock, VectorPH, Cmp,
LastBypassBlock);
// We are going to resume the execution of the scalar loop.
// Go over all of the induction variables that we found and fix the
// PHIs that are left in the scalar version of the loop.
// The starting values of PHI nodes depend on the counter of the last
// iteration in the vectorized loop.
// If we come from a bypass edge then we need to start from the original
// start value.
// This variable saves the new starting index for the scalar loop.
PHINode *ResumeIndex = nullptr;
LoopVectorizationLegality::InductionList::iterator I, E;
LoopVectorizationLegality::InductionList *List = Legal->getInductionVars();
// Set builder to point to last bypass block.
BypassBuilder.SetInsertPoint(LoopBypassBlocks.back()->getTerminator());
for (I = List->begin(), E = List->end(); I != E; ++I) {
PHINode *OrigPhi = I->first;
LoopVectorizationLegality::InductionInfo II = I->second;
Type *ResumeValTy = (OrigPhi == OldInduction) ? IdxTy : OrigPhi->getType();
PHINode *ResumeVal = PHINode::Create(ResumeValTy, 2, "resume.val",
MiddleBlock->getTerminator());
// We might have extended the type of the induction variable but we need a
// truncated version for the scalar loop.
PHINode *TruncResumeVal = (OrigPhi == OldInduction) ?
PHINode::Create(OrigPhi->getType(), 2, "trunc.resume.val",
MiddleBlock->getTerminator()) : nullptr;
// Create phi nodes to merge from the backedge-taken check block.
PHINode *BCResumeVal = PHINode::Create(ResumeValTy, 3, "bc.resume.val",
ScalarPH->getTerminator());
BCResumeVal->addIncoming(ResumeVal, MiddleBlock);
PHINode *BCTruncResumeVal = nullptr;
if (OrigPhi == OldInduction) {
BCTruncResumeVal =
PHINode::Create(OrigPhi->getType(), 2, "bc.trunc.resume.val",
ScalarPH->getTerminator());
BCTruncResumeVal->addIncoming(TruncResumeVal, MiddleBlock);
}
Value *EndValue = nullptr;
switch (II.IK) {
case LoopVectorizationLegality::IK_NoInduction:
llvm_unreachable("Unknown induction");
case LoopVectorizationLegality::IK_IntInduction: {
// Handle the integer induction counter.
assert(OrigPhi->getType()->isIntegerTy() && "Invalid type");
// We have the canonical induction variable.
if (OrigPhi == OldInduction) {
// Create a truncated version of the resume value for the scalar loop,
// we might have promoted the type to a larger width.
EndValue =
BypassBuilder.CreateTrunc(IdxEndRoundDown, OrigPhi->getType());
// The new PHI merges the original incoming value, in case of a bypass,
// or the value at the end of the vectorized loop.
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
TruncResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[I]);
TruncResumeVal->addIncoming(EndValue, VecBody);
BCTruncResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[0]);
// We know what the end value is.
EndValue = IdxEndRoundDown;
// We also know which PHI node holds it.
ResumeIndex = ResumeVal;
break;
}
// Not the canonical induction variable - add the vector loop count to the
// start value.
Value *CRD = BypassBuilder.CreateSExtOrTrunc(CountRoundDown,
II.StartValue->getType(),
"cast.crd");
EndValue = II.transform(BypassBuilder, CRD);
EndValue->setName("ind.end");
break;
}
case LoopVectorizationLegality::IK_PtrInduction: {
EndValue = II.transform(BypassBuilder, CountRoundDown);
EndValue->setName("ptr.ind.end");
break;
}
}// end of case
// The new PHI merges the original incoming value, in case of a bypass,
// or the value at the end of the vectorized loop.
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I) {
if (OrigPhi == OldInduction)
ResumeVal->addIncoming(StartIdx, LoopBypassBlocks[I]);
else
ResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[I]);
}
ResumeVal->addIncoming(EndValue, VecBody);
// Fix the scalar body counter (PHI node).
unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH);
// The old induction's phi node in the scalar body needs the truncated
// value.
if (OrigPhi == OldInduction) {
BCResumeVal->addIncoming(StartIdx, LoopBypassBlocks[0]);
OrigPhi->setIncomingValue(BlockIdx, BCTruncResumeVal);
} else {
BCResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[0]);
OrigPhi->setIncomingValue(BlockIdx, BCResumeVal);
}
}
// If we are generating a new induction variable then we also need to
// generate the code that calculates the exit value. This value is not
// simply the end of the counter because we may skip the vectorized body
// in case of a runtime check.
if (!OldInduction){
assert(!ResumeIndex && "Unexpected resume value found");
ResumeIndex = PHINode::Create(IdxTy, 2, "new.indc.resume.val",
MiddleBlock->getTerminator());
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
ResumeIndex->addIncoming(StartIdx, LoopBypassBlocks[I]);
ResumeIndex->addIncoming(IdxEndRoundDown, VecBody);
}
// Make sure that we found the index where scalar loop needs to continue.
assert(ResumeIndex && ResumeIndex->getType()->isIntegerTy() &&
"Invalid resume Index");
// 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, IdxEnd,
ResumeIndex, "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(StartIdx, VectorPH);
Induction->addIncoming(NextIdx, VecBody);
// Create the compare.
Value *ICmp = Builder.CreateICmpEQ(NextIdx, IdxEndRoundDown);
Builder.CreateCondBr(ICmp, MiddleBlock, VecBody);
// Now we have two terminators. Remove the old one from the block.
VecBody->getTerminator()->eraseFromParent();
// Get ready to start creating new instructions into the vectorized body.
Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
// Save the state.
LoopVectorPreHeader = VectorPH;
LoopScalarPreHeader = ScalarPH;
LoopMiddleBlock = MiddleBlock;
LoopExitBlock = ExitBlock;
LoopVectorBody.push_back(VecBody);
LoopScalarBody = OldBasicBlock;
LoopVectorizeHints Hints(Lp, true);
Hints.setAlreadyVectorized();
}
/// This function returns the identity element (or neutral element) for
/// the operation K.
Constant*
LoopVectorizationLegality::getReductionIdentity(ReductionKind K, Type *Tp) {
switch (K) {
case RK_IntegerXor:
case RK_IntegerAdd:
case RK_IntegerOr:
// Adding, Xoring, Oring zero to a number does not change it.
return ConstantInt::get(Tp, 0);
case RK_IntegerMult:
// Multiplying a number by 1 does not change it.
return ConstantInt::get(Tp, 1);
case RK_IntegerAnd:
// AND-ing a number with an all-1 value does not change it.
return ConstantInt::get(Tp, -1, true);
case RK_FloatMult:
// Multiplying a number by 1 does not change it.
return ConstantFP::get(Tp, 1.0L);
case RK_FloatAdd:
// Adding zero to a number does not change it.
return ConstantFP::get(Tp, 0.0L);
default:
llvm_unreachable("Unknown reduction kind");
}
}
/// This function translates the reduction kind to an LLVM binary operator.
static unsigned
getReductionBinOp(LoopVectorizationLegality::ReductionKind Kind) {
switch (Kind) {
case LoopVectorizationLegality::RK_IntegerAdd:
return Instruction::Add;
case LoopVectorizationLegality::RK_IntegerMult:
return Instruction::Mul;
case LoopVectorizationLegality::RK_IntegerOr:
return Instruction::Or;
case LoopVectorizationLegality::RK_IntegerAnd:
return Instruction::And;
case LoopVectorizationLegality::RK_IntegerXor:
return Instruction::Xor;
case LoopVectorizationLegality::RK_FloatMult:
return Instruction::FMul;
case LoopVectorizationLegality::RK_FloatAdd:
return Instruction::FAdd;
case LoopVectorizationLegality::RK_IntegerMinMax:
return Instruction::ICmp;
case LoopVectorizationLegality::RK_FloatMinMax:
return Instruction::FCmp;
default:
llvm_unreachable("Unknown reduction operation");
}
}
static Value *createMinMaxOp(IRBuilder<> &Builder,
LoopVectorizationLegality::MinMaxReductionKind RK,
Value *Left, Value *Right) {
CmpInst::Predicate P = CmpInst::ICMP_NE;
switch (RK) {
default:
llvm_unreachable("Unknown min/max reduction kind");
case LoopVectorizationLegality::MRK_UIntMin:
P = CmpInst::ICMP_ULT;
break;
case LoopVectorizationLegality::MRK_UIntMax:
P = CmpInst::ICMP_UGT;
break;
case LoopVectorizationLegality::MRK_SIntMin:
P = CmpInst::ICMP_SLT;
break;
case LoopVectorizationLegality::MRK_SIntMax:
P = CmpInst::ICMP_SGT;
break;
case LoopVectorizationLegality::MRK_FloatMin:
P = CmpInst::FCMP_OLT;
break;
case LoopVectorizationLegality::MRK_FloatMax:
P = CmpInst::FCMP_OGT;
break;
}
Value *Cmp;
if (RK == LoopVectorizationLegality::MRK_FloatMin ||
RK == LoopVectorizationLegality::MRK_FloatMax)
Cmp = Builder.CreateFCmp(P, Left, Right, "rdx.minmax.cmp");
else
Cmp = Builder.CreateICmp(P, Left, Right, "rdx.minmax.cmp");
Value *Select = Builder.CreateSelect(Cmp, Left, Right, "rdx.minmax.select");
return Select;
}
namespace {
struct CSEDenseMapInfo {
static bool canHandle(Instruction *I) {
return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) ||
isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I);
}
static inline Instruction *getEmptyKey() {
return DenseMapInfo<Instruction *>::getEmptyKey();
}
static inline Instruction *getTombstoneKey() {
return DenseMapInfo<Instruction *>::getTombstoneKey();
}
static unsigned getHashValue(Instruction *I) {
assert(canHandle(I) && "Unknown instruction!");
return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(),
I->value_op_end()));
}
static bool isEqual(Instruction *LHS, Instruction *RHS) {
if (LHS == getEmptyKey() || RHS == getEmptyKey() ||
LHS == getTombstoneKey() || RHS == getTombstoneKey())
return LHS == RHS;
return LHS->isIdenticalTo(RHS);
}
};
}
/// \brief Check whether this block is a predicated block.
/// Due to if predication of stores we might create a sequence of "if(pred) a[i]
/// = ...; " blocks. We start with one vectorized basic block. For every
/// conditional block we split this vectorized block. Therefore, every second
/// block will be a predicated one.
static bool isPredicatedBlock(unsigned BlockNum) {
return BlockNum % 2;
}
///\brief Perform cse of induction variable instructions.
static void cse(SmallVector<BasicBlock *, 4> &BBs) {
// Perform simple cse.
SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
for (unsigned i = 0, e = BBs.size(); i != e; ++i) {
BasicBlock *BB = BBs[i];
for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) {
Instruction *In = I++;
if (!CSEDenseMapInfo::canHandle(In))
continue;
// Check if we can replace this instruction with any of the
// visited instructions.
if (Instruction *V = CSEMap.lookup(In)) {
In->replaceAllUsesWith(V);
In->eraseFromParent();
continue;
}
// Ignore instructions in conditional blocks. We create "if (pred) a[i] =
// ...;" blocks for predicated stores. Every second block is a predicated
// block.
if (isPredicatedBlock(i))
continue;
CSEMap[In] = In;
}
}
}
/// \brief Adds a 'fast' flag to floating point operations.
static Value *addFastMathFlag(Value *V) {
if (isa<FPMathOperator>(V)){
FastMathFlags Flags;
Flags.setUnsafeAlgebra();
cast<Instruction>(V)->setFastMathFlags(Flags);
}
return V;
}
/// Estimate the overhead of scalarizing a value. Insert and Extract are set if
/// the result needs to be inserted and/or extracted from vectors.
static unsigned getScalarizationOverhead(Type *Ty, bool Insert, bool Extract,
const TargetTransformInfo &TTI) {
if (Ty->isVoidTy())
return 0;
assert(Ty->isVectorTy() && "Can only scalarize vectors");
unsigned Cost = 0;
for (int i = 0, e = Ty->getVectorNumElements(); i < e; ++i) {
if (Insert)
Cost += TTI.getVectorInstrCost(Instruction::InsertElement, Ty, i);
if (Extract)
Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, Ty, i);
}
return Cost;
}
// Estimate cost of a call instruction CI if it were vectorized with factor VF.
// Return the cost of the instruction, including scalarization overhead if it's
// needed. The flag NeedToScalarize shows if the call needs to be scalarized -
// i.e. either vector version isn't available, or is too expensive.
static unsigned getVectorCallCost(CallInst *CI, unsigned VF,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI,
bool &NeedToScalarize) {
Function *F = CI->getCalledFunction();
StringRef FnName = CI->getCalledFunction()->getName();
Type *ScalarRetTy = CI->getType();
SmallVector<Type *, 4> Tys, ScalarTys;
for (auto &ArgOp : CI->arg_operands())
ScalarTys.push_back(ArgOp->getType());
// Estimate cost of scalarized vector call. The source operands are assumed
// to be vectors, so we need to extract individual elements from there,
// execute VF scalar calls, and then gather the result into the vector return
// value.
unsigned ScalarCallCost = TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys);
if (VF == 1)
return ScalarCallCost;
// Compute corresponding vector type for return value and arguments.
Type *RetTy = ToVectorTy(ScalarRetTy, VF);
for (unsigned i = 0, ie = ScalarTys.size(); i != ie; ++i)
Tys.push_back(ToVectorTy(ScalarTys[i], VF));
// Compute costs of unpacking argument values for the scalar calls and
// packing the return values to a vector.
unsigned ScalarizationCost =
getScalarizationOverhead(RetTy, true, false, TTI);
for (unsigned i = 0, ie = Tys.size(); i != ie; ++i)
ScalarizationCost += getScalarizationOverhead(Tys[i], false, true, TTI);
unsigned Cost = ScalarCallCost * VF + ScalarizationCost;
// If we can't emit a vector call for this function, then the currently found
// cost is the cost we need to return.
NeedToScalarize = true;
if (!TLI || !TLI->isFunctionVectorizable(FnName, VF) || CI->isNoBuiltin())
return Cost;
// If the corresponding vector cost is cheaper, return its cost.
unsigned VectorCallCost = TTI.getCallInstrCost(nullptr, RetTy, Tys);
if (VectorCallCost < Cost) {
NeedToScalarize = false;
return VectorCallCost;
}
return Cost;
}
// Estimate cost of an intrinsic call instruction CI if it were vectorized with
// factor VF. Return the cost of the instruction, including scalarization
// overhead if it's needed.
static unsigned getVectorIntrinsicCost(CallInst *CI, unsigned VF,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI) {
Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
assert(ID && "Expected intrinsic call!");
Type *RetTy = ToVectorTy(CI->getType(), VF);
SmallVector<Type *, 4> Tys;
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i)
Tys.push_back(ToVectorTy(CI->getArgOperand(i)->getType(), VF));
return TTI.getIntrinsicInstrCost(ID, RetTy, Tys);
}
void InnerLoopVectorizer::vectorizeLoop() {
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should be also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
Constant *Zero = Builder.getInt32(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
// stages. 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 RdxPHIsToFix;
// Scan the loop in a topological order to ensure that defs are vectorized
// before users.
LoopBlocksDFS DFS(OrigLoop);
DFS.perform(LI);
// Vectorize all of the blocks in the original loop.
for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
be = DFS.endRPO(); bb != be; ++bb)
vectorizeBlockInLoop(*bb, &RdxPHIsToFix);
// At this point every instruction in the original loop is widened 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 = RdxPHIsToFix.begin(), e = RdxPHIsToFix.end();
it != e; ++it) {
PHINode *RdxPhi = *it;
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];
setDebugLocFromInst(Builder, RdxDesc.StartValue);
// We need to generate a reduction vector from the incoming scalar.
// To do so, we need to generate the 'identity' vector and override
// one of the elements with the incoming scalar reduction. We need
// to do it in the vector-loop preheader.
Builder.SetInsertPoint(LoopBypassBlocks[1]->getTerminator());
// This is the vector-clone of the value that leaves the loop.
VectorParts &VectorExit = getVectorValue(RdxDesc.LoopExitInstr);
Type *VecTy = VectorExit[0]->getType();
// Find the reduction identity variable. Zero for addition, or, xor,
// one for multiplication, -1 for And.
Value *Identity;
Value *VectorStart;
if (RdxDesc.Kind == LoopVectorizationLegality::RK_IntegerMinMax ||
RdxDesc.Kind == LoopVectorizationLegality::RK_FloatMinMax) {
// MinMax reduction have the start value as their identify.
if (VF == 1) {
VectorStart = Identity = RdxDesc.StartValue;
} else {
VectorStart = Identity = Builder.CreateVectorSplat(VF,
RdxDesc.StartValue,
"minmax.ident");
}
} else {
// Handle other reduction kinds:
Constant *Iden =
LoopVectorizationLegality::getReductionIdentity(RdxDesc.Kind,
VecTy->getScalarType());
if (VF == 1) {
Identity = Iden;
// This vector is the Identity vector where the first element is the
// incoming scalar reduction.
VectorStart = RdxDesc.StartValue;
} else {
Identity = ConstantVector::getSplat(VF, Iden);
// This vector is the Identity vector where the first element is the
// incoming scalar reduction.
VectorStart = Builder.CreateInsertElement(Identity,
RdxDesc.StartValue, Zero);
}
}
// Fix the vector-loop phi.
// Reductions do not have to start at zero. They can start with
// any loop invariant values.
VectorParts &VecRdxPhi = WidenMap.get(RdxPhi);
BasicBlock *Latch = OrigLoop->getLoopLatch();
Value *LoopVal = RdxPhi->getIncomingValueForBlock(Latch);
VectorParts &Val = getVectorValue(LoopVal);
for (unsigned part = 0; part < UF; ++part) {
// Make sure to add the reduction stat value only to the
// first unroll part.
Value *StartVal = (part == 0) ? VectorStart : Identity;
cast<PHINode>(VecRdxPhi[part])->addIncoming(StartVal,
LoopVectorPreHeader);
cast<PHINode>(VecRdxPhi[part])->addIncoming(Val[part],
LoopVectorBody.back());
}
// 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());
VectorParts RdxParts;
setDebugLocFromInst(Builder, RdxDesc.LoopExitInstr);
for (unsigned part = 0; part < UF; ++part) {
// This PHINode contains the vectorized reduction variable, or
// the initial value vector, if we bypass the vector loop.
VectorParts &RdxExitVal = getVectorValue(RdxDesc.LoopExitInstr);
PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi");
Value *StartVal = (part == 0) ? VectorStart : Identity;
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
NewPhi->addIncoming(StartVal, LoopBypassBlocks[I]);
NewPhi->addIncoming(RdxExitVal[part],
LoopVectorBody.back());
RdxParts.push_back(NewPhi);
}
// Reduce all of the unrolled parts into a single vector.
Value *ReducedPartRdx = RdxParts[0];
unsigned Op = getReductionBinOp(RdxDesc.Kind);
setDebugLocFromInst(Builder, ReducedPartRdx);
for (unsigned part = 1; part < UF; ++part) {
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
// Floating point operations had to be 'fast' to enable the reduction.
ReducedPartRdx = addFastMathFlag(
Builder.CreateBinOp((Instruction::BinaryOps)Op, RdxParts[part],
ReducedPartRdx, "bin.rdx"));
else
ReducedPartRdx = createMinMaxOp(Builder, RdxDesc.MinMaxKind,
ReducedPartRdx, RdxParts[part]);
}
if (VF > 1) {
// VF is a power of 2 so we can emit the reduction using log2(VF) shuffles
// and vector ops, reducing the set of values being computed by half each
// round.
assert(isPowerOf2_32(VF) &&
"Reduction emission only supported for pow2 vectors!");
Value *TmpVec = ReducedPartRdx;
SmallVector<Constant*, 32> ShuffleMask(VF, nullptr);
for (unsigned i = VF; i != 1; i >>= 1) {
// Move the upper half of the vector to the lower half.
for (unsigned j = 0; j != i/2; ++j)
ShuffleMask[j] = Builder.getInt32(i/2 + j);
// Fill the rest of the mask with undef.
std::fill(&ShuffleMask[i/2], ShuffleMask.end(),
UndefValue::get(Builder.getInt32Ty()));
Value *Shuf =
Builder.CreateShuffleVector(TmpVec,
UndefValue::get(TmpVec->getType()),
ConstantVector::get(ShuffleMask),
"rdx.shuf");
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
// Floating point operations had to be 'fast' to enable the reduction.
TmpVec = addFastMathFlag(Builder.CreateBinOp(
(Instruction::BinaryOps)Op, TmpVec, Shuf, "bin.rdx"));
else
TmpVec = createMinMaxOp(Builder, RdxDesc.MinMaxKind, TmpVec, Shuf);
}
// The result is in the first element of the vector.
ReducedPartRdx = Builder.CreateExtractElement(TmpVec,
Builder.getInt32(0));
}
// Create a phi node that merges control-flow from the backedge-taken check
// block and the middle block.
PHINode *BCBlockPhi = PHINode::Create(RdxPhi->getType(), 2, "bc.merge.rdx",
LoopScalarPreHeader->getTerminator());
BCBlockPhi->addIncoming(RdxDesc.StartValue, LoopBypassBlocks[0]);
BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock);
// 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) break;
// 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(ReducedPartRdx, 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(OrigLoop->getLoopLatch());
assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
// Pick the other block.
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
(RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi);
(RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr);
}// end of for each redux variable.
fixLCSSAPHIs();
// Remove redundant induction instructions.
cse(LoopVectorBody);
}
void InnerLoopVectorizer::fixLCSSAPHIs() {
for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) {
PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
if (!LCSSAPhi) break;
if (LCSSAPhi->getNumIncomingValues() == 1)
LCSSAPhi->addIncoming(UndefValue::get(LCSSAPhi->getType()),
LoopMiddleBlock);
}
}
InnerLoopVectorizer::VectorParts
InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
assert(std::find(pred_begin(Dst), pred_end(Dst), Src) != pred_end(Dst) &&
"Invalid edge");
// Look for cached value.
std::pair<BasicBlock*, BasicBlock*> Edge(Src, Dst);
EdgeMaskCache::iterator ECEntryIt = MaskCache.find(Edge);
if (ECEntryIt != MaskCache.end())
return ECEntryIt->second;
VectorParts SrcMask = createBlockInMask(Src);
// The terminator has to be a branch inst!
BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
assert(BI && "Unexpected terminator found");
if (BI->isConditional()) {
VectorParts EdgeMask = getVectorValue(BI->getCondition());
if (BI->getSuccessor(0) != Dst)
for (unsigned part = 0; part < UF; ++part)
EdgeMask[part] = Builder.CreateNot(EdgeMask[part]);
for (unsigned part = 0; part < UF; ++part)
EdgeMask[part] = Builder.CreateAnd(EdgeMask[part], SrcMask[part]);
MaskCache[Edge] = EdgeMask;
return EdgeMask;
}
MaskCache[Edge] = SrcMask;
return SrcMask;
}
InnerLoopVectorizer::VectorParts
InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) {
assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
// Loop incoming mask is all-one.
if (OrigLoop->getHeader() == BB) {
Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1);
return getVectorValue(C);
}
// This is the block mask. We OR all incoming edges, and with zero.
Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0);
VectorParts BlockMask = getVectorValue(Zero);
// For each pred:
for (pred_iterator it = pred_begin(BB), e = pred_end(BB); it != e; ++it) {
VectorParts EM = createEdgeMask(*it, BB);
for (unsigned part = 0; part < UF; ++part)
BlockMask[part] = Builder.CreateOr(BlockMask[part], EM[part]);
}
return BlockMask;
}
void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN,
InnerLoopVectorizer::VectorParts &Entry,
unsigned UF, unsigned VF, PhiVector *PV) {
PHINode* P = cast<PHINode>(PN);
// Handle reduction variables:
if (Legal->getReductionVars()->count(P)) {
for (unsigned part = 0; part < UF; ++part) {
// This is phase one of vectorizing PHIs.
Type *VecTy = (VF == 1) ? PN->getType() :
VectorType::get(PN->getType(), VF);
Entry[part] = PHINode::Create(VecTy, 2, "vec.phi",
LoopVectorBody.back()-> getFirstInsertionPt());
}
PV->push_back(P);
return;
}
setDebugLocFromInst(Builder, P);
// Check for PHI nodes that are lowered to vector selects.
if (P->getParent() != OrigLoop->getHeader()) {
// We know that all PHIs in non-header blocks are converted into
// selects, so we don't have to worry about the insertion order and we
// can just use the builder.
// At this point we generate the predication tree. There may be
// duplications since this is a simple recursive scan, but future
// optimizations will clean it up.
unsigned NumIncoming = P->getNumIncomingValues();
// Generate a sequence of selects of the form:
// SELECT(Mask3, In3,
// SELECT(Mask2, In2,
// ( ...)))
for (unsigned In = 0; In < NumIncoming; In++) {
VectorParts Cond = createEdgeMask(P->getIncomingBlock(In),
P->getParent());
VectorParts &In0 = getVectorValue(P->getIncomingValue(In));
for (unsigned part = 0; part < UF; ++part) {
// We might have single edge PHIs (blocks) - use an identity
// 'select' for the first PHI operand.
if (In == 0)
Entry[part] = Builder.CreateSelect(Cond[part], In0[part],
In0[part]);
else
// Select between the current value and the previous incoming edge
// based on the incoming mask.
Entry[part] = Builder.CreateSelect(Cond[part], In0[part],
Entry[part], "predphi");
}
}
return;
}
// This PHINode must be an induction variable.
// Make sure that we know about it.
assert(Legal->getInductionVars()->count(P) &&
"Not an induction variable");
LoopVectorizationLegality::InductionInfo II =
Legal->getInductionVars()->lookup(P);
// FIXME: The newly created binary instructions should contain nsw/nuw flags,
// which can be found from the original scalar operations.
switch (II.IK) {
case LoopVectorizationLegality::IK_NoInduction:
llvm_unreachable("Unknown induction");
case LoopVectorizationLegality::IK_IntInduction: {
assert(P->getType() == II.StartValue->getType() && "Types must match");
Type *PhiTy = P->getType();
Value *Broadcasted;
if (P == OldInduction) {
// Handle the canonical induction variable. We might have had to
// extend the type.
Broadcasted = Builder.CreateTrunc(Induction, PhiTy);
} else {
// Handle other induction variables that are now based on the
// canonical one.
Value *NormalizedIdx = Builder.CreateSub(Induction, ExtendedIdx,
"normalized.idx");
NormalizedIdx = Builder.CreateSExtOrTrunc(NormalizedIdx, PhiTy);
Broadcasted = II.transform(Builder, NormalizedIdx);
Broadcasted->setName("offset.idx");
}
Broadcasted = getBroadcastInstrs(Broadcasted);
// After broadcasting the induction variable we need to make the vector
// consecutive by adding 0, 1, 2, etc.
for (unsigned part = 0; part < UF; ++part)
Entry[part] = getStepVector(Broadcasted, VF * part, II.StepValue);
return;
}
case LoopVectorizationLegality::IK_PtrInduction:
// Handle the pointer induction variable case.
assert(P->getType()->isPointerTy() && "Unexpected type.");
// This is the normalized GEP that starts counting at zero.
Value *NormalizedIdx =
Builder.CreateSub(Induction, ExtendedIdx, "normalized.idx");
// This is the vector of results. Notice that we don't generate
// vector geps because scalar geps result in better code.
for (unsigned part = 0; part < UF; ++part) {
if (VF == 1) {
int EltIndex = part;
Constant *Idx = ConstantInt::get(Induction->getType(), EltIndex);
Value *GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx);
Value *SclrGep = II.transform(Builder, GlobalIdx);
SclrGep->setName("next.gep");
Entry[part] = SclrGep;
continue;
}
Value *VecVal = UndefValue::get(VectorType::get(P->getType(), VF));
for (unsigned int i = 0; i < VF; ++i) {
int EltIndex = i + part * VF;
Constant *Idx = ConstantInt::get(Induction->getType(), EltIndex);
Value *GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx);
Value *SclrGep = II.transform(Builder, GlobalIdx);
SclrGep->setName("next.gep");
VecVal = Builder.CreateInsertElement(VecVal, SclrGep,
Builder.getInt32(i),
"insert.gep");
}
Entry[part] = VecVal;
}
return;
}
}
void InnerLoopVectorizer::vectorizeBlockInLoop(BasicBlock *BB, PhiVector *PV) {
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
VectorParts &Entry = WidenMap.get(it);
switch (it->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: {
// Vectorize PHINodes.
widenPHIInstruction(it, Entry, UF, VF, PV);
continue;
}// End of PHI.
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>(it);
setDebugLocFromInst(Builder, BinOp);
VectorParts &A = getVectorValue(it->getOperand(0));
VectorParts &B = getVectorValue(it->getOperand(1));
// Use this vector value for all users of the original instruction.
for (unsigned Part = 0; Part < UF; ++Part) {
Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A[Part], B[Part]);
if (BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V))
VecOp->copyIRFlags(BinOp);
Entry[Part] = V;
}
propagateMetadata(Entry, it);
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.
bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(it->getOperand(0)),
OrigLoop);
setDebugLocFromInst(Builder, it);
// 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.
VectorParts &Cond = getVectorValue(it->getOperand(0));
VectorParts &Op0 = getVectorValue(it->getOperand(1));
VectorParts &Op1 = getVectorValue(it->getOperand(2));
Value *ScalarCond = (VF == 1) ? Cond[0] :
Builder.CreateExtractElement(Cond[0], Builder.getInt32(0));
for (unsigned Part = 0; Part < UF; ++Part) {
Entry[Part] = Builder.CreateSelect(
InvariantCond ? ScalarCond : Cond[Part],
Op0[Part],
Op1[Part]);
}
propagateMetadata(Entry, it);
break;
}
case Instruction::ICmp:
case Instruction::FCmp: {
// Widen compares. Generate vector compares.
bool FCmp = (it->getOpcode() == Instruction::FCmp);
CmpInst *Cmp = dyn_cast<CmpInst>(it);
setDebugLocFromInst(Builder, it);
VectorParts &A = getVectorValue(it->getOperand(0));
VectorParts &B = getVectorValue(it->getOperand(1));
for (unsigned Part = 0; Part < UF; ++Part) {
Value *C = nullptr;
if (FCmp)
C = Builder.CreateFCmp(Cmp->getPredicate(), A[Part], B[Part]);
else
C = Builder.CreateICmp(Cmp->getPredicate(), A[Part], B[Part]);
Entry[Part] = C;
}
propagateMetadata(Entry, it);
break;
}
case Instruction::Store:
case Instruction::Load:
vectorizeMemoryInstruction(it);
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: {
CastInst *CI = dyn_cast<CastInst>(it);
setDebugLocFromInst(Builder, it);
/// Optimize the special case where the source is the induction
/// variable. Notice that we can only optimize the 'trunc' case
/// because: a. FP conversions lose precision, b. sext/zext may wrap,
/// c. other casts depend on pointer size.
if (CI->getOperand(0) == OldInduction &&
it->getOpcode() == Instruction::Trunc) {
Value *ScalarCast = Builder.CreateCast(CI->getOpcode(), Induction,
CI->getType());
Value *Broadcasted = getBroadcastInstrs(ScalarCast);
LoopVectorizationLegality::InductionInfo II =
Legal->getInductionVars()->lookup(OldInduction);
Constant *Step =
ConstantInt::getSigned(CI->getType(), II.StepValue->getSExtValue());
for (unsigned Part = 0; Part < UF; ++Part)
Entry[Part] = getStepVector(Broadcasted, VF * Part, Step);
propagateMetadata(Entry, it);
break;
}
/// Vectorize casts.
Type *DestTy = (VF == 1) ? CI->getType() :
VectorType::get(CI->getType(), VF);
VectorParts &A = getVectorValue(it->getOperand(0));
for (unsigned Part = 0; Part < UF; ++Part)
Entry[Part] = Builder.CreateCast(CI->getOpcode(), A[Part], DestTy);
propagateMetadata(Entry, it);
break;
}
case Instruction::Call: {
// Ignore dbg intrinsics.
if (isa<DbgInfoIntrinsic>(it))
break;
setDebugLocFromInst(Builder, it);
Module *M = BB->getParent()->getParent();
CallInst *CI = cast<CallInst>(it);
StringRef FnName = CI->getCalledFunction()->getName();
Function *F = CI->getCalledFunction();
Type *RetTy = ToVectorTy(CI->getType(), VF);
SmallVector<Type *, 4> Tys;
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i)
Tys.push_back(ToVectorTy(CI->getArgOperand(i)->getType(), VF));
Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
if (ID &&
(ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
ID == Intrinsic::lifetime_start)) {
scalarizeInstruction(it);
break;
}
// The flag shows whether we use Intrinsic or a usual Call for vectorized
// version of the instruction.
// Is it beneficial to perform intrinsic call compared to lib call?
bool NeedToScalarize;
unsigned CallCost = getVectorCallCost(CI, VF, *TTI, TLI, NeedToScalarize);
bool UseVectorIntrinsic =
ID && getVectorIntrinsicCost(CI, VF, *TTI, TLI) <= CallCost;
if (!UseVectorIntrinsic && NeedToScalarize) {
scalarizeInstruction(it);
break;
}
for (unsigned Part = 0; Part < UF; ++Part) {
SmallVector<Value *, 4> Args;
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) {
Value *Arg = CI->getArgOperand(i);
// Some intrinsics have a scalar argument - don't replace it with a
// vector.
if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, i)) {
VectorParts &VectorArg = getVectorValue(CI->getArgOperand(i));
Arg = VectorArg[Part];
}
Args.push_back(Arg);
}
Function *VectorF;
if (UseVectorIntrinsic) {
// Use vector version of the intrinsic.
Type *TysForDecl[] = {CI->getType()};
if (VF > 1)
TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF);
VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl);
} else {
// Use vector version of the library call.
StringRef VFnName = TLI->getVectorizedFunction(FnName, VF);
assert(!VFnName.empty() && "Vector function name is empty.");
VectorF = M->getFunction(VFnName);
if (!VectorF) {
// Generate a declaration
FunctionType *FTy = FunctionType::get(RetTy, Tys, false);
VectorF =
Function::Create(FTy, Function::ExternalLinkage, VFnName, M);
VectorF->copyAttributesFrom(F);
}
}
assert(VectorF && "Can't create vector function.");
Entry[Part] = Builder.CreateCall(VectorF, Args);
}
propagateMetadata(Entry, it);
break;
}
default:
// All other instructions are unsupported. Scalarize them.
scalarizeInstruction(it);
break;
}// end of switch.
}// end of for_each instr.
}
void InnerLoopVectorizer::updateAnalysis() {
// Forget the original basic block.
SE->forgetLoop(OrigLoop);
// Update the dominator tree information.
assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) &&
"Entry does not dominate exit.");
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
DT->addNewBlock(LoopBypassBlocks[I], LoopBypassBlocks[I-1]);
DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlocks.back());
// Due to if predication of stores we might create a sequence of "if(pred)
// a[i] = ...; " blocks.
for (unsigned i = 0, e = LoopVectorBody.size(); i != e; ++i) {
if (i == 0)
DT->addNewBlock(LoopVectorBody[0], LoopVectorPreHeader);
else if (isPredicatedBlock(i)) {
DT->addNewBlock(LoopVectorBody[i], LoopVectorBody[i-1]);
} else {
DT->addNewBlock(LoopVectorBody[i], LoopVectorBody[i-2]);
}
}
DT->addNewBlock(LoopMiddleBlock, LoopBypassBlocks[1]);
DT->addNewBlock(LoopScalarPreHeader, LoopBypassBlocks[0]);
DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
DT->changeImmediateDominator(LoopExitBlock, LoopBypassBlocks[0]);
DEBUG(DT->verifyDomTree());
}
/// \brief Check whether it is safe to if-convert this phi node.
///
/// Phi nodes with constant expressions that can trap are not safe to if
/// convert.
static bool canIfConvertPHINodes(BasicBlock *BB) {
for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E; ++I) {
PHINode *Phi = dyn_cast<PHINode>(I);
if (!Phi)
return true;
for (unsigned p = 0, e = Phi->getNumIncomingValues(); p != e; ++p)
if (Constant *C = dyn_cast<Constant>(Phi->getIncomingValue(p)))
if (C->canTrap())
return false;
}
return true;
}
bool LoopVectorizationLegality::canVectorizeWithIfConvert() {
if (!EnableIfConversion) {
emitAnalysis(VectorizationReport() << "if-conversion is disabled");
return false;
}
assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable");
// A list of pointers that we can safely read and write to.
SmallPtrSet<Value *, 8> SafePointes;
// Collect safe addresses.
for (Loop::block_iterator BI = TheLoop->block_begin(),
BE = TheLoop->block_end(); BI != BE; ++BI) {
BasicBlock *BB = *BI;
if (blockNeedsPredication(BB))
continue;
for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E; ++I) {
if (LoadInst *LI = dyn_cast<LoadInst>(I))
SafePointes.insert(LI->getPointerOperand());
else if (StoreInst *SI = dyn_cast<StoreInst>(I))
SafePointes.insert(SI->getPointerOperand());
}
}
// Collect the blocks that need predication.
BasicBlock *Header = TheLoop->getHeader();
for (Loop::block_iterator BI = TheLoop->block_begin(),
BE = TheLoop->block_end(); BI != BE; ++BI) {
BasicBlock *BB = *BI;
// We don't support switch statements inside loops.
if (!isa<BranchInst>(BB->getTerminator())) {
emitAnalysis(VectorizationReport(BB->getTerminator())
<< "loop contains a switch statement");
return false;
}
// We must be able to predicate all blocks that need to be predicated.
if (blockNeedsPredication(BB)) {
if (!blockCanBePredicated(BB, SafePointes)) {
emitAnalysis(VectorizationReport(BB->getTerminator())
<< "control flow cannot be substituted for a select");
return false;
}
} else if (BB != Header && !canIfConvertPHINodes(BB)) {
emitAnalysis(VectorizationReport(BB->getTerminator())
<< "control flow cannot be substituted for a select");
return false;
}
}
// We can if-convert this loop.
return true;
}
bool LoopVectorizationLegality::canVectorize() {
// We must have a loop in canonical form. Loops with indirectbr in them cannot
// be canonicalized.
if (!TheLoop->getLoopPreheader()) {
emitAnalysis(
VectorizationReport() <<
"loop control flow is not understood by vectorizer");
return false;
}
// We can only vectorize innermost loops.
if (!TheLoop->getSubLoopsVector().empty()) {
emitAnalysis(VectorizationReport() << "loop is not the innermost loop");
return false;
}
// We must have a single backedge.
if (TheLoop->getNumBackEdges() != 1) {
emitAnalysis(
VectorizationReport() <<
"loop control flow is not understood by vectorizer");
return false;
}
// We must have a single exiting block.
if (!TheLoop->getExitingBlock()) {
emitAnalysis(
VectorizationReport() <<
"loop control flow is not understood by vectorizer");
return false;
}
// We only handle bottom-tested loops, i.e. loop in which the condition is
// checked at the end of each iteration. With that we can assume that all
// instructions in the loop are executed the same number of times.
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) {
emitAnalysis(
VectorizationReport() <<
"loop control flow is not understood by vectorizer");
return false;
}
// We need to have a loop header.
DEBUG(dbgs() << "LV: Found a loop: " <<
TheLoop->getHeader()->getName() << '\n');
// Check if we can if-convert non-single-bb loops.
unsigned NumBlocks = TheLoop->getNumBlocks();
if (NumBlocks != 1 && !canVectorizeWithIfConvert()) {
DEBUG(dbgs() << "LV: Can't if-convert the loop.\n");
return false;
}
// ScalarEvolution needs to be able to find the exit count.
const SCEV *ExitCount = SE->getBackedgeTakenCount(TheLoop);
if (ExitCount == SE->getCouldNotCompute()) {
emitAnalysis(VectorizationReport() <<
"could not determine number of loop iterations");
DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n");
return false;
}
// Check if we can vectorize the instructions and CFG in this loop.
if (!canVectorizeInstrs()) {
DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n");
return false;
}
// Go over each instruction and look at memory deps.
if (!canVectorizeMemory()) {
DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n");
return false;
}
// Collect all of the variables that remain uniform after vectorization.
collectLoopUniforms();
DEBUG(dbgs() << "LV: We can vectorize this loop" <<
(LAI->getRuntimePointerCheck()->Need ? " (with a runtime bound check)" :
"")
<<"!\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;
}
static Type *convertPointerToIntegerType(const DataLayout &DL, Type *Ty) {
if (Ty->isPointerTy())
return DL.getIntPtrType(Ty);
// It is possible that char's or short's overflow when we ask for the loop's
// trip count, work around this by changing the type size.
if (Ty->getScalarSizeInBits() < 32)
return Type::getInt32Ty(Ty->getContext());
return Ty;
}
static Type* getWiderType(const DataLayout &DL, Type *Ty0, Type *Ty1) {
Ty0 = convertPointerToIntegerType(DL, Ty0);
Ty1 = convertPointerToIntegerType(DL, Ty1);
if (Ty0->getScalarSizeInBits() > Ty1->getScalarSizeInBits())
return Ty0;
return Ty1;
}
/// \brief Check that the instruction has outside loop users and is not an
/// identified reduction variable.
static bool hasOutsideLoopUser(const Loop *TheLoop, Instruction *Inst,
SmallPtrSetImpl<Value *> &Reductions) {
// Reduction instructions are allowed to have exit users. All other
// instructions must not have external users.
if (!Reductions.count(Inst))
//Check that all of the users of the loop are inside the BB.
for (User *U : Inst->users()) {
Instruction *UI = cast<Instruction>(U);
// This user may be a reduction exit value.
if (!TheLoop->contains(UI)) {
DEBUG(dbgs() << "LV: Found an outside user for : " << *UI << '\n');
return true;
}
}
return false;
}
bool LoopVectorizationLegality::canVectorizeInstrs() {
BasicBlock *PreHeader = TheLoop->getLoopPreheader();
BasicBlock *Header = TheLoop->getHeader();
// Look for the attribute signaling the absence of NaNs.
Function &F = *Header->getParent();
const DataLayout &DL = F.getParent()->getDataLayout();
if (F.hasFnAttribute("no-nans-fp-math"))
HasFunNoNaNAttr =
F.getFnAttribute("no-nans-fp-math").getValueAsString() == "true";
// For each block in the loop.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
// Scan the instructions in the block and look for hazards.
for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
++it) {
if (PHINode *Phi = dyn_cast<PHINode>(it)) {
Type *PhiTy = Phi->getType();
// Check that this PHI type is allowed.
if (!PhiTy->isIntegerTy() &&
!PhiTy->isFloatingPointTy() &&
!PhiTy->isPointerTy()) {
emitAnalysis(VectorizationReport(it)
<< "loop control flow is not understood by vectorizer");
DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n");
return false;
}
// If this PHINode is not in the header block, then we know that we
// can convert it to select during if-conversion. No need to check if
// the PHIs in this block are induction or reduction variables.
if (*bb != Header) {
// Check that this instruction has no outside users or is an
// identified reduction value with an outside user.
if (!hasOutsideLoopUser(TheLoop, it, AllowedExit))
continue;
emitAnalysis(VectorizationReport(it) <<
"value could not be identified as "
"an induction or reduction variable");
return false;
}
// We only allow if-converted PHIs with exactly two incoming values.
if (Phi->getNumIncomingValues() != 2) {
emitAnalysis(VectorizationReport(it)
<< "control flow not understood by vectorizer");
DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
return false;
}
// This is the value coming from the preheader.
Value *StartValue = Phi->getIncomingValueForBlock(PreHeader);
ConstantInt *StepValue = nullptr;
// Check if this is an induction variable.
InductionKind IK = isInductionVariable(Phi, StepValue);
if (IK_NoInduction != IK) {
// Get the widest type.
if (!WidestIndTy)
WidestIndTy = convertPointerToIntegerType(DL, PhiTy);
else
WidestIndTy = getWiderType(DL, PhiTy, WidestIndTy);
// Int inductions are special because we only allow one IV.
if (IK == IK_IntInduction && StepValue->isOne()) {
// Use the phi node with the widest type as induction. Use the last
// one if there are multiple (no good reason for doing this other
// than it is expedient).
if (!Induction || PhiTy == WidestIndTy)
Induction = Phi;
}
DEBUG(dbgs() << "LV: Found an induction variable.\n");
Inductions[Phi] = InductionInfo(StartValue, IK, StepValue);
// Until we explicitly handle the case of an induction variable with
// an outside loop user we have to give up vectorizing this loop.
if (hasOutsideLoopUser(TheLoop, it, AllowedExit)) {
emitAnalysis(VectorizationReport(it) <<
"use of induction value outside of the "
"loop is not handled by vectorizer");
return false;
}
continue;
}
if (AddReductionVar(Phi, RK_IntegerAdd)) {
DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerMult)) {
DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerOr)) {
DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerAnd)) {
DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerXor)) {
DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerMinMax)) {
DEBUG(dbgs() << "LV: Found a MINMAX reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatMult)) {
DEBUG(dbgs() << "LV: Found an FMult reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatAdd)) {
DEBUG(dbgs() << "LV: Found an FAdd reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatMinMax)) {
DEBUG(dbgs() << "LV: Found an float MINMAX reduction PHI."<< *Phi <<
"\n");
continue;
}
emitAnalysis(VectorizationReport(it) <<
"value that could not be identified as "
"reduction is used outside the loop");
DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n");
return false;
}// end of PHI handling
// We handle calls that:
// * Are debug info intrinsics.
// * Have a mapping to an IR intrinsic.
// * Have a vector version available.
CallInst *CI = dyn_cast<CallInst>(it);
if (CI && !getIntrinsicIDForCall(CI, TLI) && !isa<DbgInfoIntrinsic>(CI) &&
!(CI->getCalledFunction() && TLI &&
TLI->isFunctionVectorizable(CI->getCalledFunction()->getName()))) {
emitAnalysis(VectorizationReport(it) <<
"call instruction cannot be vectorized");
DEBUG(dbgs() << "LV: Found a non-intrinsic, non-libfunc callsite.\n");
return false;
}
// Intrinsics such as powi,cttz and ctlz are legal to vectorize if the
// second argument is the same (i.e. loop invariant)
if (CI &&
hasVectorInstrinsicScalarOpd(getIntrinsicIDForCall(CI, TLI), 1)) {
if (!SE->isLoopInvariant(SE->getSCEV(CI->getOperand(1)), TheLoop)) {
emitAnalysis(VectorizationReport(it)
<< "intrinsic instruction cannot be vectorized");
DEBUG(dbgs() << "LV: Found unvectorizable intrinsic " << *CI << "\n");
return false;
}
}
// Check that the instruction return type is vectorizable.
// Also, we can't vectorize extractelement instructions.
if ((!VectorType::isValidElementType(it->getType()) &&
!it->getType()->isVoidTy()) || isa<ExtractElementInst>(it)) {
emitAnalysis(VectorizationReport(it)
<< "instruction return type cannot be vectorized");
DEBUG(dbgs() << "LV: Found unvectorizable type.\n");
return false;
}
// Check that the stored type is vectorizable.
if (StoreInst *ST = dyn_cast<StoreInst>(it)) {
Type *T = ST->getValueOperand()->getType();
if (!VectorType::isValidElementType(T)) {
emitAnalysis(VectorizationReport(ST) <<
"store instruction cannot be vectorized");
return false;
}
if (EnableMemAccessVersioning)
collectStridedAccess(ST);
}
if (EnableMemAccessVersioning)
if (LoadInst *LI = dyn_cast<LoadInst>(it))
collectStridedAccess(LI);
// Reduction instructions are allowed to have exit users.
// All other instructions must not have external users.
if (hasOutsideLoopUser(TheLoop, it, AllowedExit)) {
emitAnalysis(VectorizationReport(it) <<
"value cannot be used outside the loop");
return false;
}
} // next instr.
}
if (!Induction) {
DEBUG(dbgs() << "LV: Did not find one integer induction var.\n");
if (Inductions.empty()) {
emitAnalysis(VectorizationReport()
<< "loop induction variable could not be identified");
return false;
}
}
return true;
}
///\brief Remove GEPs whose indices but the last one are loop invariant and
/// return the induction operand of the gep pointer.
static Value *stripGetElementPtr(Value *Ptr, ScalarEvolution *SE, Loop *Lp) {
GetElementPtrInst *GEP = dyn_cast<GetElementPtrInst>(Ptr);
if (!GEP)
return Ptr;
unsigned InductionOperand = getGEPInductionOperand(GEP);
// Check that all of the gep indices are uniform except for our induction
// operand.
for (unsigned i = 0, e = GEP->getNumOperands(); i != e; ++i)
if (i != InductionOperand &&
!SE->isLoopInvariant(SE->getSCEV(GEP->getOperand(i)), Lp))
return Ptr;
return GEP->getOperand(InductionOperand);
}
///\brief Look for a cast use of the passed value.
static Value *getUniqueCastUse(Value *Ptr, Loop *Lp, Type *Ty) {
Value *UniqueCast = nullptr;
for (User *U : Ptr->users()) {
CastInst *CI = dyn_cast<CastInst>(U);
if (CI && CI->getType() == Ty) {
if (!UniqueCast)
UniqueCast = CI;
else
return nullptr;
}
}
return UniqueCast;
}
///\brief Get the stride of a pointer access in a loop.
/// Looks for symbolic strides "a[i*stride]". Returns the symbolic stride as a
/// pointer to the Value, or null otherwise.
static Value *getStrideFromPointer(Value *Ptr, ScalarEvolution *SE, Loop *Lp) {
const PointerType *PtrTy = dyn_cast<PointerType>(Ptr->getType());
if (!PtrTy || PtrTy->isAggregateType())
return nullptr;
// Try to remove a gep instruction to make the pointer (actually index at this
// point) easier analyzable. If OrigPtr is equal to Ptr we are analzying the
// pointer, otherwise, we are analyzing the index.
Value *OrigPtr = Ptr;
// The size of the pointer access.
int64_t PtrAccessSize = 1;
Ptr = stripGetElementPtr(Ptr, SE, Lp);
const SCEV *V = SE->getSCEV(Ptr);
if (Ptr != OrigPtr)
// Strip off casts.
while (const SCEVCastExpr *C = dyn_cast<SCEVCastExpr>(V))
V = C->getOperand();
const SCEVAddRecExpr *S = dyn_cast<SCEVAddRecExpr>(V);
if (!S)
return nullptr;
V = S->getStepRecurrence(*SE);
if (!V)
return nullptr;
// Strip off the size of access multiplication if we are still analyzing the
// pointer.
if (OrigPtr == Ptr) {
const DataLayout &DL = Lp->getHeader()->getModule()->getDataLayout();
DL.getTypeAllocSize(PtrTy->getElementType());
if (const SCEVMulExpr *M = dyn_cast<SCEVMulExpr>(V)) {
if (M->getOperand(0)->getSCEVType() != scConstant)
return nullptr;
const APInt &APStepVal =
cast<SCEVConstant>(M->getOperand(0))->getValue()->getValue();
// Huge step value - give up.
if (APStepVal.getBitWidth() > 64)
return nullptr;
int64_t StepVal = APStepVal.getSExtValue();
if (PtrAccessSize != StepVal)
return nullptr;
V = M->getOperand(1);
}
}
// Strip off casts.
Type *StripedOffRecurrenceCast = nullptr;
if (const SCEVCastExpr *C = dyn_cast<SCEVCastExpr>(V)) {
StripedOffRecurrenceCast = C->getType();
V = C->getOperand();
}
// Look for the loop invariant symbolic value.
const SCEVUnknown *U = dyn_cast<SCEVUnknown>(V);
if (!U)
return nullptr;
Value *Stride = U->getValue();
if (!Lp->isLoopInvariant(Stride))
return nullptr;
// If we have stripped off the recurrence cast we have to make sure that we
// return the value that is used in this loop so that we can replace it later.
if (StripedOffRecurrenceCast)
Stride = getUniqueCastUse(Stride, Lp, StripedOffRecurrenceCast);
return Stride;
}
void LoopVectorizationLegality::collectStridedAccess(Value *MemAccess) {
Value *Ptr = nullptr;
if (LoadInst *LI = dyn_cast<LoadInst>(MemAccess))
Ptr = LI->getPointerOperand();
else if (StoreInst *SI = dyn_cast<StoreInst>(MemAccess))
Ptr = SI->getPointerOperand();
else
return;
Value *Stride = getStrideFromPointer(Ptr, SE, TheLoop);
if (!Stride)
return;
DEBUG(dbgs() << "LV: Found a strided access that we can version");
DEBUG(dbgs() << " Ptr: " << *Ptr << " Stride: " << *Stride << "\n");
Strides[Ptr] = Stride;
StrideSet.insert(Stride);
}
void LoopVectorizationLegality::collectLoopUniforms() {
// We now know that the loop is vectorizable!
// Collect variables that will remain uniform after vectorization.
std::vector<Value*> Worklist;
BasicBlock *Latch = TheLoop->getLoopLatch();
// Start with the conditional branch and walk up the block.
Worklist.push_back(Latch->getTerminator()->getOperand(0));
// Also add all consecutive pointer values; these values will be uniform
// after vectorization (and subsequent cleanup) and, until revectorization is
// supported, all dependencies must also be uniform.
for (Loop::block_iterator B = TheLoop->block_begin(),
BE = TheLoop->block_end(); B != BE; ++B)
for (BasicBlock::iterator I = (*B)->begin(), IE = (*B)->end();
I != IE; ++I)
if (I->getType()->isPointerTy() && isConsecutivePtr(I))
Worklist.insert(Worklist.end(), I->op_begin(), I->op_end());
while (!Worklist.empty()) {
Instruction *I = dyn_cast<Instruction>(Worklist.back());
Worklist.pop_back();
// Look at instructions inside this loop.
// Stop when reaching PHI nodes.
// TODO: we need to follow values all over the loop, not only in this block.
if (!I || !TheLoop->contains(I) || isa<PHINode>(I))
continue;
// This is a known uniform.
Uniforms.insert(I);
// Insert all operands.
Worklist.insert(Worklist.end(), I->op_begin(), I->op_end());
}
}
bool LoopVectorizationLegality::canVectorizeMemory() {
LAI = &LAA->getInfo(TheLoop, Strides);
auto &OptionalReport = LAI->getReport();
if (OptionalReport)
emitAnalysis(VectorizationReport(*OptionalReport));
if (!LAI->canVectorizeMemory())
return false;
if (LAI->hasStoreToLoopInvariantAddress()) {
emitAnalysis(
VectorizationReport()
<< "write to a loop invariant address could not be vectorized");
DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n");
return false;
}
if (LAI->getNumRuntimePointerChecks() >
VectorizerParams::RuntimeMemoryCheckThreshold) {
emitAnalysis(VectorizationReport()
<< LAI->getNumRuntimePointerChecks() << " exceeds limit of "
<< VectorizerParams::RuntimeMemoryCheckThreshold
<< " dependent memory operations checked at runtime");
DEBUG(dbgs() << "LV: Too many memory checks needed.\n");
return false;
}
return true;
}
static bool hasMultipleUsesOf(Instruction *I,
SmallPtrSetImpl<Instruction *> &Insts) {
unsigned NumUses = 0;
for(User::op_iterator Use = I->op_begin(), E = I->op_end(); Use != E; ++Use) {
if (Insts.count(dyn_cast<Instruction>(*Use)))
++NumUses;
if (NumUses > 1)
return true;
}
return false;
}
static bool areAllUsesIn(Instruction *I, SmallPtrSetImpl<Instruction *> &Set) {
for(User::op_iterator Use = I->op_begin(), E = I->op_end(); Use != E; ++Use)
if (!Set.count(dyn_cast<Instruction>(*Use)))
return false;
return true;
}
bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi,
ReductionKind Kind) {
if (Phi->getNumIncomingValues() != 2)
return false;
// Reduction variables are only found in the loop header block.
if (Phi->getParent() != TheLoop->getHeader())
return false;
// Obtain the reduction start value from the value that comes from the loop
// preheader.
Value *RdxStart = Phi->getIncomingValueForBlock(TheLoop->getLoopPreheader());
// 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 = nullptr;
// Indicates that we found a reduction operation in our scan.
bool FoundReduxOp = false;
// We start with the PHI node and scan for all of the users of this
// instruction. All users must be instructions that can be used as reduction
// variables (such as ADD). We must have a single out-of-block user. The cycle
// must include the original PHI.
bool FoundStartPHI = false;
// To recognize min/max patterns formed by a icmp select sequence, we store
// the number of instruction we saw from the recognized min/max pattern,
// to make sure we only see exactly the two instructions.
unsigned NumCmpSelectPatternInst = 0;
ReductionInstDesc ReduxDesc(false, nullptr);
SmallPtrSet<Instruction *, 8> VisitedInsts;
SmallVector<Instruction *, 8> Worklist;
Worklist.push_back(Phi);
VisitedInsts.insert(Phi);
// A value in the reduction can be used:
// - By the reduction:
// - Reduction operation:
// - One use of reduction value (safe).
// - Multiple use of reduction value (not safe).
// - PHI:
// - All uses of the PHI must be the reduction (safe).
// - Otherwise, not safe.
// - By one instruction outside of the loop (safe).
// - By further instructions outside of the loop (not safe).
// - By an instruction that is not part of the reduction (not safe).
// This is either:
// * An instruction type other than PHI or the reduction operation.
// * A PHI in the header other than the initial PHI.
while (!Worklist.empty()) {
Instruction *Cur = Worklist.back();
Worklist.pop_back();
// No Users.
// If the instruction has no users then this is a broken chain and can't be
// a reduction variable.
if (Cur->use_empty())
return false;
bool IsAPhi = isa<PHINode>(Cur);
// A header PHI use other than the original PHI.
if (Cur != Phi && IsAPhi && Cur->getParent() == Phi->getParent())
return false;
// Reductions of instructions such as Div, and Sub is only possible if the
// LHS is the reduction variable.
if (!Cur->isCommutative() && !IsAPhi && !isa<SelectInst>(Cur) &&
!isa<ICmpInst>(Cur) && !isa<FCmpInst>(Cur) &&
!VisitedInsts.count(dyn_cast<Instruction>(Cur->getOperand(0))))
return false;
// Any reduction instruction must be of one of the allowed kinds.
ReduxDesc = isReductionInstr(Cur, Kind, ReduxDesc);
if (!ReduxDesc.IsReduction)
return false;
// A reduction operation must only have one use of the reduction value.
if (!IsAPhi && Kind != RK_IntegerMinMax && Kind != RK_FloatMinMax &&
hasMultipleUsesOf(Cur, VisitedInsts))
return false;
// All inputs to a PHI node must be a reduction value.
if(IsAPhi && Cur != Phi && !areAllUsesIn(Cur, VisitedInsts))
return false;
if (Kind == RK_IntegerMinMax && (isa<ICmpInst>(Cur) ||
isa<SelectInst>(Cur)))
++NumCmpSelectPatternInst;
if (Kind == RK_FloatMinMax && (isa<FCmpInst>(Cur) ||
isa<SelectInst>(Cur)))
++NumCmpSelectPatternInst;
// Check whether we found a reduction operator.
FoundReduxOp |= !IsAPhi;
// Process users of current instruction. Push non-PHI nodes after PHI nodes
// onto the stack. This way we are going to have seen all inputs to PHI
// nodes once we get to them.
SmallVector<Instruction *, 8> NonPHIs;
SmallVector<Instruction *, 8> PHIs;
for (User *U : Cur->users()) {
Instruction *UI = cast<Instruction>(U);
// Check if we found the exit user.
BasicBlock *Parent = UI->getParent();
if (!TheLoop->contains(Parent)) {
// Exit if you find multiple outside users or if the header phi node is
// being used. In this case the user uses the value of the previous
// iteration, in which case we would loose "VF-1" iterations of the
// reduction operation if we vectorize.
if (ExitInstruction != nullptr || Cur == Phi)
return false;
// The instruction used by an outside user must be the last instruction
// before we feed back to the reduction phi. Otherwise, we loose VF-1
// operations on the value.
if (std::find(Phi->op_begin(), Phi->op_end(), Cur) == Phi->op_end())
return false;
ExitInstruction = Cur;
continue;
}
// Process instructions only once (termination). Each reduction cycle
// value must only be used once, except by phi nodes and min/max
// reductions which are represented as a cmp followed by a select.
ReductionInstDesc IgnoredVal(false, nullptr);
if (VisitedInsts.insert(UI).second) {
if (isa<PHINode>(UI))
PHIs.push_back(UI);
else
NonPHIs.push_back(UI);
} else if (!isa<PHINode>(UI) &&
((!isa<FCmpInst>(UI) &&
!isa<ICmpInst>(UI) &&
!isa<SelectInst>(UI)) ||
!isMinMaxSelectCmpPattern(UI, IgnoredVal).IsReduction))
return false;
// Remember that we completed the cycle.
if (UI == Phi)
FoundStartPHI = true;
}
Worklist.append(PHIs.begin(), PHIs.end());
Worklist.append(NonPHIs.begin(), NonPHIs.end());
}
// This means we have seen one but not the other instruction of the
// pattern or more than just a select and cmp.
if ((Kind == RK_IntegerMinMax || Kind == RK_FloatMinMax) &&
NumCmpSelectPatternInst != 2)
return false;
if (!FoundStartPHI || !FoundReduxOp || !ExitInstruction)
return false;
// 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.
// 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,
ReduxDesc.MinMaxKind);
Reductions[Phi] = RD;
// We've ended the cycle. This is a reduction variable if we have an
// outside user and it has a binary op.
return true;
}
/// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
/// pattern corresponding to a min(X, Y) or max(X, Y).
LoopVectorizationLegality::ReductionInstDesc
LoopVectorizationLegality::isMinMaxSelectCmpPattern(Instruction *I,
ReductionInstDesc &Prev) {
assert((isa<ICmpInst>(I) || isa<FCmpInst>(I) || isa<SelectInst>(I)) &&
"Expect a select instruction");
Instruction *Cmp = nullptr;
SelectInst *Select = nullptr;
// We must handle the select(cmp()) as a single instruction. Advance to the
// select.
if ((Cmp = dyn_cast<ICmpInst>(I)) || (Cmp = dyn_cast<FCmpInst>(I))) {
if (!Cmp->hasOneUse() || !(Select = dyn_cast<SelectInst>(*I->user_begin())))
return ReductionInstDesc(false, I);
return ReductionInstDesc(Select, Prev.MinMaxKind);
}
// Only handle single use cases for now.
if (!(Select = dyn_cast<SelectInst>(I)))
return ReductionInstDesc(false, I);
if (!(Cmp = dyn_cast<ICmpInst>(I->getOperand(0))) &&
!(Cmp = dyn_cast<FCmpInst>(I->getOperand(0))))
return ReductionInstDesc(false, I);
if (!Cmp->hasOneUse())
return ReductionInstDesc(false, I);
Value *CmpLeft;
Value *CmpRight;
// Look for a min/max pattern.
if (m_UMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_UIntMin);
else if (m_UMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_UIntMax);
else if (m_SMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_SIntMax);
else if (m_SMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_SIntMin);
else if (m_OrdFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMin);
else if (m_OrdFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMax);
else if (m_UnordFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMin);
else if (m_UnordFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMax);
return ReductionInstDesc(false, I);
}
LoopVectorizationLegality::ReductionInstDesc
LoopVectorizationLegality::isReductionInstr(Instruction *I,
ReductionKind Kind,
ReductionInstDesc &Prev) {
bool FP = I->getType()->isFloatingPointTy();
bool FastMath = FP && I->hasUnsafeAlgebra();
switch (I->getOpcode()) {
default:
return ReductionInstDesc(false, I);
case Instruction::PHI:
if (FP && (Kind != RK_FloatMult && Kind != RK_FloatAdd &&
Kind != RK_FloatMinMax))
return ReductionInstDesc(false, I);
return ReductionInstDesc(I, Prev.MinMaxKind);
case Instruction::Sub:
case Instruction::Add:
return ReductionInstDesc(Kind == RK_IntegerAdd, I);
case Instruction::Mul:
return ReductionInstDesc(Kind == RK_IntegerMult, I);
case Instruction::And:
return ReductionInstDesc(Kind == RK_IntegerAnd, I);
case Instruction::Or:
return ReductionInstDesc(Kind == RK_IntegerOr, I);
case Instruction::Xor:
return ReductionInstDesc(Kind == RK_IntegerXor, I);
case Instruction::FMul:
return ReductionInstDesc(Kind == RK_FloatMult && FastMath, I);
case Instruction::FSub:
case Instruction::FAdd:
return ReductionInstDesc(Kind == RK_FloatAdd && FastMath, I);
case Instruction::FCmp:
case Instruction::ICmp:
case Instruction::Select:
if (Kind != RK_IntegerMinMax &&
(!HasFunNoNaNAttr || Kind != RK_FloatMinMax))
return ReductionInstDesc(false, I);
return isMinMaxSelectCmpPattern(I, Prev);
}
}
bool llvm::isInductionPHI(PHINode *Phi, ScalarEvolution *SE,
ConstantInt *&StepValue) {
Type *PhiTy = Phi->getType();
// We only handle integer and pointer inductions variables.
if (!PhiTy->isIntegerTy() && !PhiTy->isPointerTy())
return false;
// Check that the PHI is consecutive.
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);
// Calculate the pointer stride and check if it is consecutive.
const SCEVConstant *C = dyn_cast<SCEVConstant>(Step);
if (!C)
return false;
ConstantInt *CV = C->getValue();
if (PhiTy->isIntegerTy()) {
StepValue = CV;
return true;
}
assert(PhiTy->isPointerTy() && "The PHI must be a pointer");
Type *PointerElementType = PhiTy->getPointerElementType();
// The pointer stride cannot be determined if the pointer element type is not
// sized.
if (!PointerElementType->isSized())
return false;
const DataLayout &DL = Phi->getModule()->getDataLayout();
int64_t Size = static_cast<int64_t>(DL.getTypeAllocSize(PointerElementType));
int64_t CVSize = CV->getSExtValue();
if (CVSize % Size)
return false;
StepValue = ConstantInt::getSigned(CV->getType(), CVSize / Size);
return true;
}
LoopVectorizationLegality::InductionKind
LoopVectorizationLegality::isInductionVariable(PHINode *Phi,
ConstantInt *&StepValue) {
if (!isInductionPHI(Phi, SE, StepValue))
return IK_NoInduction;
Type *PhiTy = Phi->getType();
// Found an Integer induction variable.
if (PhiTy->isIntegerTy())
return IK_IntInduction;
// Found an Pointer induction variable.
return IK_PtrInduction;
}
bool LoopVectorizationLegality::isInductionVariable(const Value *V) {
Value *In0 = const_cast<Value*>(V);
PHINode *PN = dyn_cast_or_null<PHINode>(In0);
if (!PN)
return false;
return Inductions.count(PN);
}
bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) {
return LoopAccessInfo::blockNeedsPredication(BB, TheLoop, DT);
}
bool LoopVectorizationLegality::blockCanBePredicated(BasicBlock *BB,
SmallPtrSetImpl<Value *> &SafePtrs) {
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
// Check that we don't have a constant expression that can trap as operand.
for (Instruction::op_iterator OI = it->op_begin(), OE = it->op_end();
OI != OE; ++OI) {
if (Constant *C = dyn_cast<Constant>(*OI))
if (C->canTrap())
return false;
}
// We might be able to hoist the load.
if (it->mayReadFromMemory()) {
LoadInst *LI = dyn_cast<LoadInst>(it);
if (!LI)
return false;
if (!SafePtrs.count(LI->getPointerOperand())) {
if (isLegalMaskedLoad(LI->getType(), LI->getPointerOperand())) {
MaskedOp.insert(LI);
continue;
}
return false;
}
}
// We don't predicate stores at the moment.
if (it->mayWriteToMemory()) {
StoreInst *SI = dyn_cast<StoreInst>(it);
// We only support predication of stores in basic blocks with one
// predecessor.
if (!SI)
return false;
bool isSafePtr = (SafePtrs.count(SI->getPointerOperand()) != 0);
bool isSinglePredecessor = SI->getParent()->getSinglePredecessor();
if (++NumPredStores > NumberOfStoresToPredicate || !isSafePtr ||
!isSinglePredecessor) {
// Build a masked store if it is legal for the target, otherwise scalarize
// the block.
bool isLegalMaskedOp =
isLegalMaskedStore(SI->getValueOperand()->getType(),
SI->getPointerOperand());
if (isLegalMaskedOp) {
--NumPredStores;
MaskedOp.insert(SI);
continue;
}
return false;
}
}
if (it->mayThrow())
return false;
// The instructions below can trap.
switch (it->getOpcode()) {
default: continue;
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::URem:
case Instruction::SRem:
return false;
}
}
return true;
}
LoopVectorizationCostModel::VectorizationFactor
LoopVectorizationCostModel::selectVectorizationFactor(bool OptForSize) {
// Width 1 means no vectorize
VectorizationFactor Factor = { 1U, 0U };
if (OptForSize && Legal->getRuntimePointerCheck()->Need) {
emitAnalysis(VectorizationReport() <<
"runtime pointer checks needed. Enable vectorization of this "
"loop with '#pragma clang loop vectorize(enable)' when "
"compiling with -Os");
DEBUG(dbgs() << "LV: Aborting. Runtime ptr check is required in Os.\n");
return Factor;
}
if (!EnableCondStoresVectorization && Legal->getNumPredStores()) {
emitAnalysis(VectorizationReport() <<
"store that is conditionally executed prevents vectorization");
DEBUG(dbgs() << "LV: No vectorization. There are conditional stores.\n");
return Factor;
}
// Find the trip count.
unsigned TC = SE->getSmallConstantTripCount(TheLoop);
DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
unsigned WidestType = getWidestType();
unsigned WidestRegister = TTI.getRegisterBitWidth(true);
unsigned MaxSafeDepDist = -1U;
if (Legal->getMaxSafeDepDistBytes() != -1U)
MaxSafeDepDist = Legal->getMaxSafeDepDistBytes() * 8;
WidestRegister = ((WidestRegister < MaxSafeDepDist) ?
WidestRegister : MaxSafeDepDist);
unsigned MaxVectorSize = WidestRegister / WidestType;
DEBUG(dbgs() << "LV: The Widest type: " << WidestType << " bits.\n");
DEBUG(dbgs() << "LV: The Widest register is: "
<< WidestRegister << " bits.\n");
if (MaxVectorSize == 0) {
DEBUG(dbgs() << "LV: The target has no vector registers.\n");
MaxVectorSize = 1;
}
assert(MaxVectorSize <= 64 && "Did not expect to pack so many elements"
" into one vector!");
unsigned VF = MaxVectorSize;
// If we optimize the program for size, avoid creating the tail loop.
if (OptForSize) {
// If we are unable to calculate the trip count then don't try to vectorize.
if (TC < 2) {
emitAnalysis
(VectorizationReport() <<
"unable to calculate the loop count due to complex control flow");
DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
return Factor;
}
// Find the maximum SIMD width that can fit within the trip count.
VF = TC % MaxVectorSize;
if (VF == 0)
VF = MaxVectorSize;
// If the trip count that we found modulo the vectorization factor is not
// zero then we require a tail.
if (VF < 2) {
emitAnalysis(VectorizationReport() <<
"cannot optimize for size and vectorize at the "
"same time. Enable vectorization of this loop "
"with '#pragma clang loop vectorize(enable)' "
"when compiling with -Os");
DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
return Factor;
}
}
int UserVF = Hints->getWidth();
if (UserVF != 0) {
assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
Factor.Width = UserVF;
return Factor;
}
float Cost = expectedCost(1);
#ifndef NDEBUG
const float ScalarCost = Cost;
#endif /* NDEBUG */
unsigned Width = 1;
DEBUG(dbgs() << "LV: Scalar loop costs: " << (int)ScalarCost << ".\n");
bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled;
// Ignore scalar width, because the user explicitly wants vectorization.
if (ForceVectorization && VF > 1) {
Width = 2;
Cost = expectedCost(Width) / (float)Width;
}
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(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs()
<< "LV: Vectorization seems to be not beneficial, "
<< "but was forced by a user.\n");
DEBUG(dbgs() << "LV: Selecting VF: "<< Width << ".\n");
Factor.Width = Width;
Factor.Cost = Width * Cost;
return Factor;
}
unsigned LoopVectorizationCostModel::getWidestType() {
unsigned MaxWidth = 8;
const DataLayout &DL = TheFunction->getParent()->getDataLayout();
// For each block.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
BasicBlock *BB = *bb;
// For each instruction in the loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
Type *T = it->getType();
// Ignore ephemeral values.
if (EphValues.count(it))
continue;
// Only examine Loads, Stores and PHINodes.
if (!isa<LoadInst>(it) && !isa<StoreInst>(it) && !isa<PHINode>(it))
continue;
// Examine PHI nodes that are reduction variables.
if (PHINode *PN = dyn_cast<PHINode>(it))
if (!Legal->getReductionVars()->count(PN))
continue;
// Examine the stored values.
if (StoreInst *ST = dyn_cast<StoreInst>(it))
T = ST->getValueOperand()->getType();
// Ignore loaded pointer types and stored pointer types that are not
// consecutive. However, we do want to take consecutive stores/loads of
// pointer vectors into account.
if (T->isPointerTy() && !isConsecutiveLoadOrStore(it))
continue;
MaxWidth = std::max(MaxWidth,
(unsigned)DL.getTypeSizeInBits(T->getScalarType()));
}
}
return MaxWidth;
}
unsigned
LoopVectorizationCostModel::selectUnrollFactor(bool OptForSize,
unsigned VF,
unsigned LoopCost) {
// -- The unroll heuristics --
// We unroll the loop in order to expose ILP and reduce the loop overhead.
// There are many micro-architectural considerations that we can't predict
// at this level. For example, frontend pressure (on decode or fetch) due to
// code size, or the number and capabilities of the execution ports.
//
// We use the following heuristics to select the unroll factor:
// 1. If the code has reductions, then we unroll in order to break the cross
// iteration dependency.
// 2. If the loop is really small, then we unroll in order to reduce the loop
// overhead.
// 3. We don't unroll if we think that we will spill registers to memory due
// to the increased register pressure.
// Use the user preference, unless 'auto' is selected.
int UserUF = Hints->getInterleave();
if (UserUF != 0)
return UserUF;
// When we optimize for size, we don't unroll.
if (OptForSize)
return 1;
// We used the distance for the unroll factor.
if (Legal->getMaxSafeDepDistBytes() != -1U)
return 1;
// Do not unroll loops with a relatively small trip count.
unsigned TC = SE->getSmallConstantTripCount(TheLoop);
if (TC > 1 && TC < TinyTripCountUnrollThreshold)
return 1;
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(VF > 1);
DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters <<
" registers\n");
if (VF == 1) {
if (ForceTargetNumScalarRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumScalarRegs;
} else {
if (ForceTargetNumVectorRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumVectorRegs;
}
LoopVectorizationCostModel::RegisterUsage R = calculateRegisterUsage();
// We divide by these constants so assume that we have at least one
// instruction that uses at least one register.
R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U);
R.NumInstructions = std::max(R.NumInstructions, 1U);
// We calculate the unroll factor using the following formula.
// Subtract the number of loop invariants from the number of available
// registers. These registers are used by all of the unrolled instances.
// Next, divide the remaining registers by the number of registers that is
// required by the loop, in order to estimate how many parallel instances
// fit without causing spills. All of this is rounded down if necessary to be
// a power of two. We want power of two unroll factors to simplify any
// addressing operations or alignment considerations.
unsigned UF = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs) /
R.MaxLocalUsers);
// Don't count the induction variable as unrolled.
if (EnableIndVarRegisterHeur)
UF = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs - 1) /
std::max(1U, (R.MaxLocalUsers - 1)));
// Clamp the unroll factor ranges to reasonable factors.
unsigned MaxInterleaveSize = TTI.getMaxInterleaveFactor();
// Check if the user has overridden the unroll max.
if (VF == 1) {
if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveSize = ForceTargetMaxScalarInterleaveFactor;
} else {
if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveSize = ForceTargetMaxVectorInterleaveFactor;
}
// If we did not calculate the cost for VF (because the user selected the VF)
// then we calculate the cost of VF here.
if (LoopCost == 0)
LoopCost = expectedCost(VF);
// Clamp the calculated UF to be between the 1 and the max unroll factor
// that the target allows.
if (UF > MaxInterleaveSize)
UF = MaxInterleaveSize;
else if (UF < 1)
UF = 1;
// Unroll if we vectorized this loop and there is a reduction that could
// benefit from unrolling.
if (VF > 1 && Legal->getReductionVars()->size()) {
DEBUG(dbgs() << "LV: Unrolling because of reductions.\n");
return UF;
}
// Note that if we've already vectorized the loop we will have done the
// runtime check and so unrolling won't require further checks.
bool UnrollingRequiresRuntimePointerCheck =
(VF == 1 && Legal->getRuntimePointerCheck()->Need);
// We want to unroll small loops in order to reduce the loop overhead and
// potentially expose ILP opportunities.
DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n');
if (!UnrollingRequiresRuntimePointerCheck &&
LoopCost < SmallLoopCost) {
// We assume that the cost overhead is 1 and we use the cost model
// to estimate the cost of the loop and unroll until the cost of the
// loop overhead is about 5% of the cost of the loop.
unsigned SmallUF = std::min(UF, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost));
// Unroll until store/load ports (estimated by max unroll factor) are
// saturated.
unsigned NumStores = Legal->getNumStores();
unsigned NumLoads = Legal->getNumLoads();
unsigned StoresUF = UF / (NumStores ? NumStores : 1);
unsigned LoadsUF = UF / (NumLoads ? NumLoads : 1);
// If we have a scalar reduction (vector reductions are already dealt with
// by this point), we can increase the critical path length if the loop
// we're unrolling is inside another loop. Limit, by default to 2, so the
// critical path only gets increased by one reduction operation.
if (Legal->getReductionVars()->size() &&
TheLoop->getLoopDepth() > 1) {
unsigned F = static_cast<unsigned>(MaxNestedScalarReductionUF);
SmallUF = std::min(SmallUF, F);
StoresUF = std::min(StoresUF, F);
LoadsUF = std::min(LoadsUF, F);
}
if (EnableLoadStoreRuntimeUnroll && std::max(StoresUF, LoadsUF) > SmallUF) {
DEBUG(dbgs() << "LV: Unrolling to saturate store or load ports.\n");
return std::max(StoresUF, LoadsUF);
}
DEBUG(dbgs() << "LV: Unrolling to reduce branch cost.\n");
return SmallUF;
}
// Unroll if this is a large loop (small loops are already dealt with by this
// point) that could benefit from interleaved unrolling.
bool HasReductions = (Legal->getReductionVars()->size() > 0);
if (TTI.enableAggressiveInterleaving(HasReductions)) {
DEBUG(dbgs() << "LV: Unrolling to expose ILP.\n");
return UF;
}
DEBUG(dbgs() << "LV: Not Unrolling.\n");
return 1;
}
LoopVectorizationCostModel::RegisterUsage
LoopVectorizationCostModel::calculateRegisterUsage() {
// This function calculates the register usage by measuring the highest number
// of values that are alive at a single location. Obviously, this is a very
// rough estimation. We scan the loop in a topological order in order and
// assign a number to each instruction. We use RPO to ensure that defs are
// met before their users. We assume that each instruction that has in-loop
// users starts an interval. We record every time that an in-loop value is
// used, so we have a list of the first and last occurrences of each
// instruction. Next, we transpose this data structure into a multi map that
// holds the list of intervals that *end* at a specific location. This multi
// map allows us to perform a linear search. We scan the instructions linearly
// and record each time that a new interval starts, by placing it in a set.
// If we find this value in the multi-map then we remove it from the set.
// The max register usage is the maximum size of the set.
// We also search for instructions that are defined outside the loop, but are
// used inside the loop. We need this number separately from the max-interval
// usage number because when we unroll, loop-invariant values do not take
// more register.
LoopBlocksDFS DFS(TheLoop);
DFS.perform(LI);
RegisterUsage R;
R.NumInstructions = 0;
// Each 'key' in the map opens a new interval. The values
// of the map are the index of the 'last seen' usage of the
// instruction that is the key.
typedef DenseMap<Instruction*, unsigned> IntervalMap;
// Maps instruction to its index.
DenseMap<unsigned, Instruction*> IdxToInstr;
// Marks the end of each interval.
IntervalMap EndPoint;
// Saves the list of instruction indices that are used in the loop.
SmallSet<Instruction*, 8> Ends;
// Saves the list of values that are used in the loop but are
// defined outside the loop, such as arguments and constants.
SmallPtrSet<Value*, 8> LoopInvariants;
unsigned Index = 0;
for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
be = DFS.endRPO(); bb != be; ++bb) {
R.NumInstructions += (*bb)->size();
for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
++it) {
Instruction *I = it;
IdxToInstr[Index++] = I;
// Save the end location of each USE.
for (unsigned i = 0; i < I->getNumOperands(); ++i) {
Value *U = I->getOperand(i);
Instruction *Instr = dyn_cast<Instruction>(U);
// Ignore non-instruction values such as arguments, constants, etc.
if (!Instr) continue;
// If this instruction is outside the loop then record it and continue.
if (!TheLoop->contains(Instr)) {
LoopInvariants.insert(Instr);
continue;
}
// Overwrite previous end points.
EndPoint[Instr] = Index;
Ends.insert(Instr);
}
}
}
// Saves the list of intervals that end with the index in 'key'.
typedef SmallVector<Instruction*, 2> InstrList;
DenseMap<unsigned, InstrList> TransposeEnds;
// Transpose the EndPoints to a list of values that end at each index.
for (IntervalMap::iterator it = EndPoint.begin(), e = EndPoint.end();
it != e; ++it)
TransposeEnds[it->second].push_back(it->first);
SmallSet<Instruction*, 8> OpenIntervals;
unsigned MaxUsage = 0;
DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
for (unsigned int i = 0; i < Index; ++i) {
Instruction *I = IdxToInstr[i];
// Ignore instructions that are never used within the loop.
if (!Ends.count(I)) continue;
// Ignore ephemeral values.
if (EphValues.count(I))
continue;
// Remove all of the instructions that end at this location.
InstrList &List = TransposeEnds[i];
for (unsigned int j=0, e = List.size(); j < e; ++j)
OpenIntervals.erase(List[j]);
// Count the number of live interals.
MaxUsage = std::max(MaxUsage, OpenIntervals.size());
DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " <<
OpenIntervals.size() << '\n');
// Add the current instruction to the list of open intervals.
OpenIntervals.insert(I);
}
unsigned Invariant = LoopInvariants.size();
DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsage << '\n');
DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << '\n');
DEBUG(dbgs() << "LV(REG): LoopSize: " << R.NumInstructions << '\n');
R.LoopInvariantRegs = Invariant;
R.MaxLocalUsers = MaxUsage;
return R;
}
unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) {
unsigned Cost = 0;
// For each block.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
unsigned BlockCost = 0;
BasicBlock *BB = *bb;
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
// Skip dbg intrinsics.
if (isa<DbgInfoIntrinsic>(it))
continue;
// Ignore ephemeral values.
if (EphValues.count(it))
continue;
unsigned C = getInstructionCost(it, VF);
// Check if we should override the cost.
if (ForceTargetInstructionCost.getNumOccurrences() > 0)
C = ForceTargetInstructionCost;
BlockCost += C;
DEBUG(dbgs() << "LV: Found an estimated cost of " << C << " for VF " <<
VF << " For instruction: " << *it << '\n');
}
// We assume that if-converted blocks have a 50% chance of being executed.
// When the code is scalar then some of the blocks are avoided due to CF.
// When the code is vectorized we execute all code paths.
if (VF == 1 && Legal->blockNeedsPredication(*bb))
BlockCost /= 2;
Cost += BlockCost;
}
return Cost;
}
/// \brief Check whether the address computation for a non-consecutive memory
/// access looks like an unlikely candidate for being merged into the indexing
/// mode.
///
/// We look for a GEP which has one index that is an induction variable and all
/// other indices are loop invariant. If the stride of this access is also
/// within a small bound we decide that this address computation can likely be
/// merged into the addressing mode.
/// In all other cases, we identify the address computation as complex.
static bool isLikelyComplexAddressComputation(Value *Ptr,
LoopVectorizationLegality *Legal,
ScalarEvolution *SE,
const Loop *TheLoop) {
GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
if (!Gep)
return true;
// We are looking for a gep with all loop invariant indices except for one
// which should be an induction variable.
unsigned NumOperands = Gep->getNumOperands();
for (unsigned i = 1; i < NumOperands; ++i) {
Value *Opd = Gep->getOperand(i);
if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) &&
!Legal->isInductionVariable(Opd))
return true;
}
// Now we know we have a GEP ptr, %inv, %ind, %inv. Make sure that the step
// can likely be merged into the address computation.
unsigned MaxMergeDistance = 64;
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(SE->getSCEV(Ptr));
if (!AddRec)
return true;
// Check the step is constant.
const SCEV *Step = AddRec->getStepRecurrence(*SE);
// Calculate the pointer stride and check if it is consecutive.
const SCEVConstant *C = dyn_cast<SCEVConstant>(Step);
if (!C)
return true;
const APInt &APStepVal = C->getValue()->getValue();
// Huge step value - give up.
if (APStepVal.getBitWidth() > 64)
return true;
int64_t StepVal = APStepVal.getSExtValue();
return StepVal > MaxMergeDistance;
}
static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) {
if (Legal->hasStride(I->getOperand(0)) || Legal->hasStride(I->getOperand(1)))
return true;
return false;
}
unsigned
LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
// 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 the cost of GEPs in
// vectorized code depends on whether the corresponding memory instruction
// is scalarized or not. Therefore, we handle GEPs with the memory
// instruction cost.
return 0;
case Instruction::Br: {
return TTI.getCFInstrCost(I->getOpcode());
}
case Instruction::PHI:
//TODO: IF-converted IFs become selects.
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: {
// Since we will replace the stride by 1 the multiplication should go away.
if (I->getOpcode() == Instruction::Mul && isStrideMul(I, Legal))
return 0;
// Certain instructions can be cheaper to vectorize if they have a constant
// second vector operand. One example of this are shifts on x86.
TargetTransformInfo::OperandValueKind Op1VK =
TargetTransformInfo::OK_AnyValue;
TargetTransformInfo::OperandValueKind Op2VK =
TargetTransformInfo::OK_AnyValue;
TargetTransformInfo::OperandValueProperties Op1VP =
TargetTransformInfo::OP_None;
TargetTransformInfo::OperandValueProperties Op2VP =
TargetTransformInfo::OP_None;
Value *Op2 = I->getOperand(1);
// Check for a splat of a constant or for a non uniform vector of constants.
if (isa<ConstantInt>(Op2)) {
ConstantInt *CInt = cast<ConstantInt>(Op2);
if (CInt && CInt->getValue().isPowerOf2())
Op2VP = TargetTransformInfo::OP_PowerOf2;
Op2VK = TargetTransformInfo::OK_UniformConstantValue;
} else if (isa<ConstantVector>(Op2) || isa<ConstantDataVector>(Op2)) {
Op2VK = TargetTransformInfo::OK_NonUniformConstantValue;
Constant *SplatValue = cast<Constant>(Op2)->getSplatValue();
if (SplatValue) {
ConstantInt *CInt = dyn_cast<ConstantInt>(SplatValue);
if (CInt && CInt->getValue().isPowerOf2())
Op2VP = TargetTransformInfo::OP_PowerOf2;
Op2VK = TargetTransformInfo::OK_UniformConstantValue;
}
}
return TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy, Op1VK, Op2VK,
Op1VP, Op2VP);
}
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 TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
VectorTy = ToVectorTy(ValTy, VF);
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy);
}
case Instruction::Store:
case Instruction::Load: {
StoreInst *SI = dyn_cast<StoreInst>(I);
LoadInst *LI = dyn_cast<LoadInst>(I);
Type *ValTy = (SI ? SI->getValueOperand()->getType() :
LI->getType());
VectorTy = ToVectorTy(ValTy, VF);
unsigned Alignment = SI ? SI->getAlignment() : LI->getAlignment();
unsigned AS = SI ? SI->getPointerAddressSpace() :
LI->getPointerAddressSpace();
Value *Ptr = SI ? SI->getPointerOperand() : LI->getPointerOperand();
// We add the cost of address computation here instead of with the gep
// instruction because only here we know whether the operation is
// scalarized.
if (VF == 1)
return TTI.getAddressComputationCost(VectorTy) +
TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
// Scalarized loads/stores.
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
bool Reverse = ConsecutiveStride < 0;
const DataLayout &DL = I->getModule()->getDataLayout();
unsigned ScalarAllocatedSize = DL.getTypeAllocSize(ValTy);
unsigned VectorElementSize = DL.getTypeStoreSize(VectorTy) / VF;
if (!ConsecutiveStride || ScalarAllocatedSize != VectorElementSize) {
bool IsComplexComputation =
isLikelyComplexAddressComputation(Ptr, Legal, SE, TheLoop);
unsigned Cost = 0;
// The cost of extracting from the value vector and pointer vector.
Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
for (unsigned i = 0; i < VF; ++i) {
// The cost of extracting the pointer operand.
Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i);
// In case of STORE, the cost of ExtractElement from the vector.
// In case of LOAD, the cost of InsertElement into the returned
// vector.
Cost += TTI.getVectorInstrCost(SI ? Instruction::ExtractElement :
Instruction::InsertElement,
VectorTy, i);
}
// The cost of the scalar loads/stores.
Cost += VF * TTI.getAddressComputationCost(PtrTy, IsComplexComputation);
Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(),
Alignment, AS);
return Cost;
}
// Wide load/stores.
unsigned Cost = TTI.getAddressComputationCost(VectorTy);
if (Legal->isMaskRequired(I))
Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment,
AS);
else
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
if (Reverse)
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse,
VectorTy, 0);
return Cost;
}
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: {
// We optimize the truncation of induction variable.
// The cost of these is the same as the scalar operation.
if (I->getOpcode() == Instruction::Trunc &&
Legal->isInductionVariable(I->getOperand(0)))
return TTI.getCastInstrCost(I->getOpcode(), I->getType(),
I->getOperand(0)->getType());
Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF);
return TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy);
}
case Instruction::Call: {
bool NeedToScalarize;
CallInst *CI = cast<CallInst>(I);
unsigned CallCost = getVectorCallCost(CI, VF, TTI, TLI, NeedToScalarize);
if (getIntrinsicIDForCall(CI, TLI))
return std::min(CallCost, getVectorIntrinsicCost(CI, VF, TTI, TLI));
return CallCost;
}
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;
if (!RetTy->isVoidTy() && VF != 1) {
unsigned InsCost = TTI.getVectorInstrCost(Instruction::InsertElement,
VectorTy);
unsigned ExtCost = TTI.getVectorInstrCost(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. This opcode
// is unknown. Assume that it is the same as 'mul'.
Cost += VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy);
return Cost;
}
}// end of switch.
}
char LoopVectorize::ID = 0;
static const char lv_name[] = "Loop Vectorization";
INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass)
INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker)
INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfo)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
INITIALIZE_PASS_DEPENDENCY(LCSSA)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(LoopSimplify)
INITIALIZE_PASS_DEPENDENCY(LoopAccessAnalysis)
INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
namespace llvm {
Pass *createLoopVectorizePass(bool NoUnrolling, bool AlwaysVectorize) {
return new LoopVectorize(NoUnrolling, AlwaysVectorize);
}
}
bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
// Check for a store.
if (StoreInst *ST = dyn_cast<StoreInst>(Inst))
return Legal->isConsecutivePtr(ST->getPointerOperand()) != 0;
// Check for a load.
if (LoadInst *LI = dyn_cast<LoadInst>(Inst))
return Legal->isConsecutivePtr(LI->getPointerOperand()) != 0;
return false;
}
void InnerLoopUnroller::scalarizeInstruction(Instruction *Instr,
bool IfPredicateStore) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
// Holds vector parameters or scalars, in case of uniform vals.
SmallVector<VectorParts, 4> Params;
setDebugLocFromInst(Builder, Instr);
// 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(getVectorValue(SrcOp));
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 && OrigLoop->contains(SrcInst)) {
assert(WidenMap.has(SrcInst) && "Source operand is unavailable");
// The parameter is a vector value from earlier.
Params.push_back(WidenMap.get(SrcInst));
} else {
// The parameter is a scalar from outside the loop. Maybe even a constant.
VectorParts Scalars;
Scalars.append(UF, SrcOp);
Params.push_back(Scalars);
}
}
assert(Params.size() == Instr->getNumOperands() &&
"Invalid number of operands");
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Value *UndefVec = IsVoidRetTy ? nullptr :
UndefValue::get(Instr->getType());
// Create a new entry in the WidenMap and initialize it to Undef or Null.
VectorParts &VecResults = WidenMap.splat(Instr, UndefVec);
Instruction *InsertPt = Builder.GetInsertPoint();
BasicBlock *IfBlock = Builder.GetInsertBlock();
BasicBlock *CondBlock = nullptr;
VectorParts Cond;
Loop *VectorLp = nullptr;
if (IfPredicateStore) {
assert(Instr->getParent()->getSinglePredecessor() &&
"Only support single predecessor blocks");
Cond = createEdgeMask(Instr->getParent()->getSinglePredecessor(),
Instr->getParent());
VectorLp = LI->getLoopFor(IfBlock);
assert(VectorLp && "Must have a loop for this block");
}
// For each vector unroll 'part':
for (unsigned Part = 0; Part < UF; ++Part) {
// For each scalar that we create:
// Start an "if (pred) a[i] = ..." block.
Value *Cmp = nullptr;
if (IfPredicateStore) {
if (Cond[Part]->getType()->isVectorTy())
Cond[Part] =
Builder.CreateExtractElement(Cond[Part], Builder.getInt32(0));
Cmp = Builder.CreateICmp(ICmpInst::ICMP_EQ, Cond[Part],
ConstantInt::get(Cond[Part]->getType(), 1));
CondBlock = IfBlock->splitBasicBlock(InsertPt, "cond.store");
LoopVectorBody.push_back(CondBlock);
VectorLp->addBasicBlockToLoop(CondBlock, *LI);
// Update Builder with newly created basic block.
Builder.SetInsertPoint(InsertPt);
}
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy)
Cloned->setName(Instr->getName() + ".cloned");
// Replace the operands of the cloned instructions with extracted scalars.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *Op = Params[op][Part];
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[Part] = Cloned;
// End if-block.
if (IfPredicateStore) {
BasicBlock *NewIfBlock = CondBlock->splitBasicBlock(InsertPt, "else");
LoopVectorBody.push_back(NewIfBlock);
VectorLp->addBasicBlockToLoop(NewIfBlock, *LI);
Builder.SetInsertPoint(InsertPt);
Instruction *OldBr = IfBlock->getTerminator();
BranchInst::Create(CondBlock, NewIfBlock, Cmp, OldBr);
OldBr->eraseFromParent();
IfBlock = NewIfBlock;
}
}
}
void InnerLoopUnroller::vectorizeMemoryInstruction(Instruction *Instr) {
StoreInst *SI = dyn_cast<StoreInst>(Instr);
bool IfPredicateStore = (SI && Legal->blockNeedsPredication(SI->getParent()));
return scalarizeInstruction(Instr, IfPredicateStore);
}
Value *InnerLoopUnroller::reverseVector(Value *Vec) {
return Vec;
}
Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) {
return V;
}
Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step) {
// When unrolling and the VF is 1, we only need to add a simple scalar.
Type *ITy = Val->getType();
assert(!ITy->isVectorTy() && "Val must be a scalar");
Constant *C = ConstantInt::get(ITy, StartIdx);
return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction");
}