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llvm-mirror/lib/Transforms/Vectorize/LoopVectorize.cpp
Florian Hahn 268685e51a [VPlan] Use isa<> instead of directly checking VPRecipeID (NFC). 
getVPRecipeID is intended to be only used in `classof` helpers. Instead
of checking it directly, use isa<> with the correct recipe type.
2020-10-02 17:47:35 +01:00

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//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// 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.
//
// There is a development effort going on to migrate loop vectorizer to the
// VPlan infrastructure and to introduce outer loop vectorization support (see
// docs/Proposal/VectorizationPlan.rst and
// http://lists.llvm.org/pipermail/llvm-dev/2017-December/119523.html). For this
// purpose, we temporarily introduced the VPlan-native vectorization path: an
// alternative vectorization path that is natively implemented on top of the
// VPlan infrastructure. See EnableVPlanNativePath for enabling.
//
//===----------------------------------------------------------------------===//
//
// 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.
//
// The interleaved access vectorization is based on the paper:
// Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved
// Data for SIMD
//
// 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/LoopVectorize.h"
#include "LoopVectorizationPlanner.h"
#include "VPRecipeBuilder.h"
#include "VPlan.h"
#include "VPlanHCFGBuilder.h"
#include "VPlanPredicator.h"
#include "VPlanTransforms.h"
#include "llvm/ADT/APInt.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DenseMapInfo.h"
#include "llvm/ADT/Hashing.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/None.h"
#include "llvm/ADT/Optional.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/Statistic.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/ADT/Twine.h"
#include "llvm/ADT/iterator_range.h"
#include "llvm/Analysis/AssumptionCache.h"
#include "llvm/Analysis/BasicAliasAnalysis.h"
#include "llvm/Analysis/BlockFrequencyInfo.h"
#include "llvm/Analysis/CFG.h"
#include "llvm/Analysis/CodeMetrics.h"
#include "llvm/Analysis/DemandedBits.h"
#include "llvm/Analysis/GlobalsModRef.h"
#include "llvm/Analysis/LoopAccessAnalysis.h"
#include "llvm/Analysis/LoopAnalysisManager.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/LoopIterator.h"
#include "llvm/Analysis/MemorySSA.h"
#include "llvm/Analysis/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/ProfileSummaryInfo.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/TargetLibraryInfo.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Analysis/VectorUtils.h"
#include "llvm/IR/Attributes.h"
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/CFG.h"
#include "llvm/IR/Constant.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/DebugInfoMetadata.h"
#include "llvm/IR/DebugLoc.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/InstrTypes.h"
#include "llvm/IR/Instruction.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/Intrinsics.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/IR/Metadata.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/Operator.h"
#include "llvm/IR/Type.h"
#include "llvm/IR/Use.h"
#include "llvm/IR/User.h"
#include "llvm/IR/Value.h"
#include "llvm/IR/ValueHandle.h"
#include "llvm/IR/Verifier.h"
#include "llvm/InitializePasses.h"
#include "llvm/Pass.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Compiler.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/InjectTLIMappings.h"
#include "llvm/Transforms/Utils/LoopSimplify.h"
#include "llvm/Transforms/Utils/LoopUtils.h"
#include "llvm/Transforms/Utils/LoopVersioning.h"
#include "llvm/Transforms/Utils/ScalarEvolutionExpander.h"
#include "llvm/Transforms/Utils/SizeOpts.h"
#include "llvm/Transforms/Vectorize/LoopVectorizationLegality.h"
#include <algorithm>
#include <cassert>
#include <cstdint>
#include <cstdlib>
#include <functional>
#include <iterator>
#include <limits>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
using namespace llvm;
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
/// @{
/// Metadata attribute names
static const char *const LLVMLoopVectorizeFollowupAll =
"llvm.loop.vectorize.followup_all";
static const char *const LLVMLoopVectorizeFollowupVectorized =
"llvm.loop.vectorize.followup_vectorized";
static const char *const LLVMLoopVectorizeFollowupEpilogue =
"llvm.loop.vectorize.followup_epilogue";
/// @}
STATISTIC(LoopsVectorized, "Number of loops vectorized");
STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
/// Loops with a known constant trip count below this number are vectorized only
/// if no scalar iteration overheads are incurred.
static cl::opt<unsigned> TinyTripCountVectorThreshold(
"vectorizer-min-trip-count", cl::init(16), cl::Hidden,
cl::desc("Loops with a constant trip count that is smaller than this "
"value are vectorized only if no scalar iteration overheads "
"are incurred."));
// Option prefer-predicate-over-epilogue indicates that an epilogue is undesired,
// that predication is preferred, and this lists all options. I.e., the
// vectorizer will try to fold the tail-loop (epilogue) into the vector body
// and predicate the instructions accordingly. If tail-folding fails, there are
// different fallback strategies depending on these values:
namespace PreferPredicateTy {
enum Option {
ScalarEpilogue = 0,
PredicateElseScalarEpilogue,
PredicateOrDontVectorize
};
} // namespace PreferPredicateTy
static cl::opt<PreferPredicateTy::Option> PreferPredicateOverEpilogue(
"prefer-predicate-over-epilogue",
cl::init(PreferPredicateTy::ScalarEpilogue),
cl::Hidden,
cl::desc("Tail-folding and predication preferences over creating a scalar "
"epilogue loop."),
cl::values(clEnumValN(PreferPredicateTy::ScalarEpilogue,
"scalar-epilogue",
"Don't tail-predicate loops, create scalar epilogue"),
clEnumValN(PreferPredicateTy::PredicateElseScalarEpilogue,
"predicate-else-scalar-epilogue",
"prefer tail-folding, create scalar epilogue if tail "
"folding fails."),
clEnumValN(PreferPredicateTy::PredicateOrDontVectorize,
"predicate-dont-vectorize",
"prefers tail-folding, don't attempt vectorization if "
"tail-folding fails.")));
static cl::opt<bool> MaximizeBandwidth(
"vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden,
cl::desc("Maximize bandwidth when selecting vectorization factor which "
"will be determined by the smallest type in loop."));
static cl::opt<bool> EnableInterleavedMemAccesses(
"enable-interleaved-mem-accesses", cl::init(false), cl::Hidden,
cl::desc("Enable vectorization on interleaved memory accesses in a loop"));
/// An interleave-group may need masking if it resides in a block that needs
/// predication, or in order to mask away gaps.
static cl::opt<bool> EnableMaskedInterleavedMemAccesses(
"enable-masked-interleaved-mem-accesses", cl::init(false), cl::Hidden,
cl::desc("Enable vectorization on masked interleaved memory accesses in a loop"));
static cl::opt<unsigned> TinyTripCountInterleaveThreshold(
"tiny-trip-count-interleave-threshold", cl::init(128), cl::Hidden,
cl::desc("We don't interleave loops with a estimated constant trip count "
"below this number"));
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."));
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 interleaver."));
static cl::opt<bool> LoopVectorizeWithBlockFrequency(
"loop-vectorize-with-block-frequency", cl::init(true), 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 interleave loops for load/store throughput.
static cl::opt<bool> EnableLoadStoreRuntimeInterleave(
"enable-loadstore-runtime-interleave", cl::init(true), cl::Hidden,
cl::desc(
"Enable runtime interleaving until load/store ports are saturated"));
/// Interleave small loops with scalar reductions.
static cl::opt<bool> InterleaveSmallLoopScalarReduction(
"interleave-small-loop-scalar-reduction", cl::init(false), cl::Hidden,
cl::desc("Enable interleaving for loops with small iteration counts that "
"contain scalar reductions to expose ILP."));
/// 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 interleaving"));
static cl::opt<bool> EnableCondStoresVectorization(
"enable-cond-stores-vec", cl::init(true), cl::Hidden,
cl::desc("Enable if predication of stores during vectorization."));
static cl::opt<unsigned> MaxNestedScalarReductionIC(
"max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden,
cl::desc("The maximum interleave count to use when interleaving a scalar "
"reduction in a nested loop."));
static cl::opt<bool>
PreferInLoopReductions("prefer-inloop-reductions", cl::init(false),
cl::Hidden,
cl::desc("Prefer in-loop vector reductions, "
"overriding the targets preference."));
static cl::opt<bool> PreferPredicatedReductionSelect(
"prefer-predicated-reduction-select", cl::init(false), cl::Hidden,
cl::desc(
"Prefer predicating a reduction operation over an after loop select."));
cl::opt<bool> EnableVPlanNativePath(
"enable-vplan-native-path", cl::init(false), cl::Hidden,
cl::desc("Enable VPlan-native vectorization path with "
"support for outer loop vectorization."));
// FIXME: Remove this switch once we have divergence analysis. Currently we
// assume divergent non-backedge branches when this switch is true.
cl::opt<bool> EnableVPlanPredication(
"enable-vplan-predication", cl::init(false), cl::Hidden,
cl::desc("Enable VPlan-native vectorization path predicator with "
"support for outer loop vectorization."));
// This flag enables the stress testing of the VPlan H-CFG construction in the
// VPlan-native vectorization path. It must be used in conjuction with
// -enable-vplan-native-path. -vplan-verify-hcfg can also be used to enable the
// verification of the H-CFGs built.
static cl::opt<bool> VPlanBuildStressTest(
"vplan-build-stress-test", cl::init(false), cl::Hidden,
cl::desc(
"Build VPlan for every supported loop nest in the function and bail "
"out right after the build (stress test the VPlan H-CFG construction "
"in the VPlan-native vectorization path)."));
cl::opt<bool> llvm::EnableLoopInterleaving(
"interleave-loops", cl::init(true), cl::Hidden,
cl::desc("Enable loop interleaving in Loop vectorization passes"));
cl::opt<bool> llvm::EnableLoopVectorization(
"vectorize-loops", cl::init(true), cl::Hidden,
cl::desc("Run the Loop vectorization passes"));
/// A helper function that returns the type of loaded or stored value.
static Type *getMemInstValueType(Value *I) {
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
"Expected Load or Store instruction");
if (auto *LI = dyn_cast<LoadInst>(I))
return LI->getType();
return cast<StoreInst>(I)->getValueOperand()->getType();
}
/// A helper function that returns true if the given type is irregular. The
/// type is irregular if its allocated size doesn't equal the store size of an
/// element of the corresponding vector type at the given vectorization factor.
static bool hasIrregularType(Type *Ty, const DataLayout &DL, ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
// Determine if an array of VF elements of type Ty is "bitcast compatible"
// with a <VF x Ty> vector.
if (VF.isVector()) {
auto *VectorTy = VectorType::get(Ty, VF);
return VF * DL.getTypeAllocSize(Ty) != DL.getTypeStoreSize(VectorTy);
}
// If the vectorization factor is one, we just check if an array of type Ty
// requires padding between elements.
return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty);
}
/// A helper function that returns the reciprocal of the block probability of
/// predicated blocks. If we return X, we are assuming the predicated block
/// will execute once for every X iterations of the loop header.
///
/// TODO: We should use actual block probability here, if available. Currently,
/// we always assume predicated blocks have a 50% chance of executing.
static unsigned getReciprocalPredBlockProb() { return 2; }
/// A helper function that adds a 'fast' flag to floating-point operations.
static Value *addFastMathFlag(Value *V) {
if (isa<FPMathOperator>(V))
cast<Instruction>(V)->setFastMathFlags(FastMathFlags::getFast());
return V;
}
static Value *addFastMathFlag(Value *V, FastMathFlags FMF) {
if (isa<FPMathOperator>(V))
cast<Instruction>(V)->setFastMathFlags(FMF);
return V;
}
/// A helper function that returns an integer or floating-point constant with
/// value C.
static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) {
return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C)
: ConstantFP::get(Ty, C);
}
/// Returns "best known" trip count for the specified loop \p L as defined by
/// the following procedure:
/// 1) Returns exact trip count if it is known.
/// 2) Returns expected trip count according to profile data if any.
/// 3) Returns upper bound estimate if it is known.
/// 4) Returns None if all of the above failed.
static Optional<unsigned> getSmallBestKnownTC(ScalarEvolution &SE, Loop *L) {
// Check if exact trip count is known.
if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L))
return ExpectedTC;
// Check if there is an expected trip count available from profile data.
if (LoopVectorizeWithBlockFrequency)
if (auto EstimatedTC = getLoopEstimatedTripCount(L))
return EstimatedTC;
// Check if upper bound estimate is known.
if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L))
return ExpectedTC;
return None;
}
namespace llvm {
/// 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, PredicatedScalarEvolution &PSE,
LoopInfo *LI, DominatorTree *DT,
const TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, ElementCount VecWidth,
unsigned UnrollFactor, LoopVectorizationLegality *LVL,
LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI)
: OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor),
Builder(PSE.getSE()->getContext()),
VectorLoopValueMap(UnrollFactor, VecWidth), Legal(LVL), Cost(CM),
BFI(BFI), PSI(PSI) {
// Query this against the original loop and save it here because the profile
// of the original loop header may change as the transformation happens.
OptForSizeBasedOnProfile = llvm::shouldOptimizeForSize(
OrigLoop->getHeader(), PSI, BFI, PGSOQueryType::IRPass);
}
virtual ~InnerLoopVectorizer() = default;
/// Create a new empty loop that will contain vectorized instructions later
/// on, while the old loop will be used as the scalar remainder. Control flow
/// is generated around the vectorized (and scalar epilogue) loops consisting
/// of various checks and bypasses. Return the pre-header block of the new
/// loop.
BasicBlock *createVectorizedLoopSkeleton();
/// Widen a single instruction within the innermost loop.
void widenInstruction(Instruction &I, VPUser &Operands,
VPTransformState &State);
/// Widen a single call instruction within the innermost loop.
void widenCallInstruction(CallInst &I, VPUser &ArgOperands,
VPTransformState &State);
/// Widen a single select instruction within the innermost loop.
void widenSelectInstruction(SelectInst &I, VPUser &Operands,
bool InvariantCond, VPTransformState &State);
/// Fix the vectorized code, taking care of header phi's, live-outs, and more.
void fixVectorizedLoop();
// Return true if any runtime check is added.
bool areSafetyChecksAdded() { return AddedSafetyChecks; }
/// A type for vectorized values in the new loop. Each value from the
/// original loop, when vectorized, is represented by UF vector values in the
/// new unrolled loop, where UF is the unroll factor.
using VectorParts = SmallVector<Value *, 2>;
/// Vectorize a single GetElementPtrInst based on information gathered and
/// decisions taken during planning.
void widenGEP(GetElementPtrInst *GEP, VPUser &Indices, unsigned UF,
ElementCount VF, bool IsPtrLoopInvariant,
SmallBitVector &IsIndexLoopInvariant, VPTransformState &State);
/// 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, unsigned UF, ElementCount VF);
/// A helper function to scalarize a single Instruction in the innermost loop.
/// Generates a sequence of scalar instances for each lane between \p MinLane
/// and \p MaxLane, times each part between \p MinPart and \p MaxPart,
/// inclusive. Uses the VPValue operands from \p Operands instead of \p
/// Instr's operands.
void scalarizeInstruction(Instruction *Instr, VPUser &Operands,
const VPIteration &Instance, bool IfPredicateInstr,
VPTransformState &State);
/// Widen an integer or floating-point induction variable \p IV. If \p Trunc
/// is provided, the integer induction variable will first be truncated to
/// the corresponding type.
void widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc = nullptr);
/// getOrCreateVectorValue and getOrCreateScalarValue coordinate to generate a
/// vector or scalar value on-demand if one is not yet available. When
/// vectorizing a loop, we visit the definition of an instruction before its
/// uses. When visiting the definition, we either vectorize or scalarize the
/// instruction, creating an entry for it in the corresponding map. (In some
/// cases, such as induction variables, we will create both vector and scalar
/// entries.) Then, as we encounter uses of the definition, we derive values
/// for each scalar or vector use unless such a value is already available.
/// For example, if we scalarize a definition and one of its uses is vector,
/// we build the required vector on-demand with an insertelement sequence
/// when visiting the use. Otherwise, if the use is scalar, we can use the
/// existing scalar definition.
///
/// Return a value in the new loop corresponding to \p V from the original
/// loop at unroll index \p Part. If the value has already been vectorized,
/// the corresponding vector entry in VectorLoopValueMap is returned. If,
/// however, the value has a scalar entry in VectorLoopValueMap, we construct
/// a new vector value on-demand by inserting the scalar values into a vector
/// with an insertelement sequence. If the value has been neither vectorized
/// nor scalarized, it must be loop invariant, so we simply broadcast the
/// value into a vector.
Value *getOrCreateVectorValue(Value *V, unsigned Part);
/// Return a value in the new loop corresponding to \p V from the original
/// loop at unroll and vector indices \p Instance. If the value has been
/// vectorized but not scalarized, the necessary extractelement instruction
/// will be generated.
Value *getOrCreateScalarValue(Value *V, const VPIteration &Instance);
/// Construct the vector value of a scalarized value \p V one lane at a time.
void packScalarIntoVectorValue(Value *V, const VPIteration &Instance);
/// Try to vectorize interleaved access group \p Group with the base address
/// given in \p Addr, optionally masking the vector operations if \p
/// BlockInMask is non-null. Use \p State to translate given VPValues to IR
/// values in the vectorized loop.
void vectorizeInterleaveGroup(const InterleaveGroup<Instruction> *Group,
VPTransformState &State, VPValue *Addr,
VPValue *BlockInMask = nullptr);
/// Vectorize Load and Store instructions with the base address given in \p
/// Addr, optionally masking the vector operations if \p BlockInMask is
/// non-null. Use \p State to translate given VPValues to IR values in the
/// vectorized loop.
void vectorizeMemoryInstruction(Instruction *Instr, VPTransformState &State,
VPValue *Addr, VPValue *StoredValue,
VPValue *BlockInMask);
/// Set the debug location in the builder using the debug location in
/// the instruction.
void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr);
/// Fix the non-induction PHIs in the OrigPHIsToFix vector.
void fixNonInductionPHIs(void);
protected:
friend class LoopVectorizationPlanner;
/// A small list of PHINodes.
using PhiVector = SmallVector<PHINode *, 4>;
/// A type for scalarized values in the new loop. Each value from the
/// original loop, when scalarized, is represented by UF x VF scalar values
/// in the new unrolled loop, where UF is the unroll factor and VF is the
/// vectorization factor.
using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>;
/// Set up the values of the IVs correctly when exiting the vector loop.
void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II,
Value *CountRoundDown, Value *EndValue,
BasicBlock *MiddleBlock);
/// Create a new induction variable inside L.
PHINode *createInductionVariable(Loop *L, Value *Start, Value *End,
Value *Step, Instruction *DL);
/// Handle all cross-iteration phis in the header.
void fixCrossIterationPHIs();
/// Fix a first-order recurrence. This is the second phase of vectorizing
/// this phi node.
void fixFirstOrderRecurrence(PHINode *Phi);
/// Fix a reduction cross-iteration phi. This is the second phase of
/// vectorizing this phi node.
void fixReduction(PHINode *Phi);
/// Clear NSW/NUW flags from reduction instructions if necessary.
void clearReductionWrapFlags(RecurrenceDescriptor &RdxDesc);
/// The Loop exit block may have single value PHI nodes with some
/// incoming value. While vectorizing we only handled real values
/// that were defined inside the loop and we should have one value for
/// each predecessor of its parent basic block. See PR14725.
void fixLCSSAPHIs();
/// Iteratively sink the scalarized operands of a predicated instruction into
/// the block that was created for it.
void sinkScalarOperands(Instruction *PredInst);
/// Shrinks vector element sizes to the smallest bitwidth they can be legally
/// represented as.
void truncateToMinimalBitwidths();
/// 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.
/// \p Opcode is relevant for FP induction variable.
virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step,
Instruction::BinaryOps Opcode =
Instruction::BinaryOpsEnd);
/// Compute scalar induction steps. \p ScalarIV is the scalar induction
/// variable on which to base the steps, \p Step is the size of the step, and
/// \p EntryVal is the value from the original loop that maps to the steps.
/// Note that \p EntryVal doesn't have to be an induction variable - it
/// can also be a truncate instruction.
void buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal,
const InductionDescriptor &ID);
/// Create a vector induction phi node based on an existing scalar one. \p
/// EntryVal is the value from the original loop that maps to the vector phi
/// node, and \p Step is the loop-invariant step. If \p EntryVal is a
/// truncate instruction, instead of widening the original IV, we widen a
/// version of the IV truncated to \p EntryVal's type.
void createVectorIntOrFpInductionPHI(const InductionDescriptor &II,
Value *Step, Instruction *EntryVal);
/// Returns true if an instruction \p I should be scalarized instead of
/// vectorized for the chosen vectorization factor.
bool shouldScalarizeInstruction(Instruction *I) const;
/// Returns true if we should generate a scalar version of \p IV.
bool needsScalarInduction(Instruction *IV) const;
/// If there is a cast involved in the induction variable \p ID, which should
/// be ignored in the vectorized loop body, this function records the
/// VectorLoopValue of the respective Phi also as the VectorLoopValue of the
/// cast. We had already proved that the casted Phi is equal to the uncasted
/// Phi in the vectorized loop (under a runtime guard), and therefore
/// there is no need to vectorize the cast - the same value can be used in the
/// vector loop for both the Phi and the cast.
/// If \p VectorLoopValue is a scalarized value, \p Lane is also specified,
/// Otherwise, \p VectorLoopValue is a widened/vectorized value.
///
/// \p EntryVal is the value from the original loop that maps to the vector
/// phi node and is used to distinguish what is the IV currently being
/// processed - original one (if \p EntryVal is a phi corresponding to the
/// original IV) or the "newly-created" one based on the proof mentioned above
/// (see also buildScalarSteps() and createVectorIntOrFPInductionPHI()). In the
/// latter case \p EntryVal is a TruncInst and we must not record anything for
/// that IV, but it's error-prone to expect callers of this routine to care
/// about that, hence this explicit parameter.
void recordVectorLoopValueForInductionCast(const InductionDescriptor &ID,
const Instruction *EntryVal,
Value *VectorLoopValue,
unsigned Part,
unsigned Lane = UINT_MAX);
/// Generate a shuffle sequence that will reverse the vector Vec.
virtual Value *reverseVector(Value *Vec);
/// Returns (and creates if needed) the original loop trip count.
Value *getOrCreateTripCount(Loop *NewLoop);
/// Returns (and creates if needed) the trip count of the widened loop.
Value *getOrCreateVectorTripCount(Loop *NewLoop);
/// Returns a bitcasted value to the requested vector type.
/// Also handles bitcasts of vector<float> <-> vector<pointer> types.
Value *createBitOrPointerCast(Value *V, VectorType *DstVTy,
const DataLayout &DL);
/// Emit a bypass check to see if the vector trip count is zero, including if
/// it overflows.
void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass);
/// Emit a bypass check to see if all of the SCEV assumptions we've
/// had to make are correct.
void emitSCEVChecks(Loop *L, BasicBlock *Bypass);
/// Emit bypass checks to check any memory assumptions we may have made.
void emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass);
/// 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 *emitTransformedIndex(IRBuilder<> &B, Value *Index, ScalarEvolution *SE,
const DataLayout &DL,
const InductionDescriptor &ID) const;
/// Emit basic blocks (prefixed with \p Prefix) for the iteration check,
/// vector loop preheader, middle block and scalar preheader. Also
/// allocate a loop object for the new vector loop and return it.
Loop *createVectorLoopSkeleton(StringRef Prefix);
/// Create new phi nodes for the induction variables to resume iteration count
/// in the scalar epilogue, from where the vectorized loop left off (given by
/// \p VectorTripCount).
void createInductionResumeValues(Loop *L, Value *VectorTripCount);
/// Complete the loop skeleton by adding debug MDs, creating appropriate
/// conditional branches in the middle block, preparing the builder and
/// running the verifier. Take in the vector loop \p L as argument, and return
/// the preheader of the completed vector loop.
BasicBlock *completeLoopSkeleton(Loop *L, MDNode *OrigLoopID);
/// Add additional metadata to \p To that was not present on \p Orig.
///
/// Currently this is used to add the noalias annotations based on the
/// inserted memchecks. Use this for instructions that are *cloned* into the
/// vector loop.
void addNewMetadata(Instruction *To, const Instruction *Orig);
/// Add metadata from one instruction to another.
///
/// This includes both the original MDs from \p From and additional ones (\see
/// addNewMetadata). Use this for *newly created* instructions in the vector
/// loop.
void addMetadata(Instruction *To, Instruction *From);
/// Similar to the previous function but it adds the metadata to a
/// vector of instructions.
void addMetadata(ArrayRef<Value *> To, Instruction *From);
/// The original loop.
Loop *OrigLoop;
/// A wrapper around ScalarEvolution used to add runtime SCEV checks. Applies
/// dynamic knowledge to simplify SCEV expressions and converts them to a
/// more usable form.
PredicatedScalarEvolution &PSE;
/// Loop Info.
LoopInfo *LI;
/// Dominator Tree.
DominatorTree *DT;
/// Alias Analysis.
AAResults *AA;
/// Target Library Info.
const TargetLibraryInfo *TLI;
/// Target Transform Info.
const TargetTransformInfo *TTI;
/// Assumption Cache.
AssumptionCache *AC;
/// Interface to emit optimization remarks.
OptimizationRemarkEmitter *ORE;
/// LoopVersioning. It's only set up (non-null) if memchecks were
/// used.
///
/// This is currently only used to add no-alias metadata based on the
/// memchecks. The actually versioning is performed manually.
std::unique_ptr<LoopVersioning> LVer;
/// The vectorization SIMD factor to use. Each vector will have this many
/// vector elements.
ElementCount VF;
/// 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.
BasicBlock *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 = nullptr;
/// The induction variable of the old basic block.
PHINode *OldInduction = nullptr;
/// Maps values from the original loop to their corresponding values in the
/// vectorized loop. A key value can map to either vector values, scalar
/// values or both kinds of values, depending on whether the key was
/// vectorized and scalarized.
VectorizerValueMap VectorLoopValueMap;
/// Store instructions that were predicated.
SmallVector<Instruction *, 4> PredicatedInstructions;
/// Trip count of the original loop.
Value *TripCount = nullptr;
/// Trip count of the widened loop (TripCount - TripCount % (VF*UF))
Value *VectorTripCount = nullptr;
/// The legality analysis.
LoopVectorizationLegality *Legal;
/// The profitablity analysis.
LoopVectorizationCostModel *Cost;
// Record whether runtime checks are added.
bool AddedSafetyChecks = false;
// Holds the end values for each induction variable. We save the end values
// so we can later fix-up the external users of the induction variables.
DenseMap<PHINode *, Value *> IVEndValues;
// Vector of original scalar PHIs whose corresponding widened PHIs need to be
// fixed up at the end of vector code generation.
SmallVector<PHINode *, 8> OrigPHIsToFix;
/// BFI and PSI are used to check for profile guided size optimizations.
BlockFrequencyInfo *BFI;
ProfileSummaryInfo *PSI;
// Whether this loop should be optimized for size based on profile guided size
// optimizatios.
bool OptForSizeBasedOnProfile;
};
class InnerLoopUnroller : public InnerLoopVectorizer {
public:
InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
LoopInfo *LI, DominatorTree *DT,
const TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, unsigned UnrollFactor,
LoopVectorizationLegality *LVL,
LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI)
: InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
ElementCount::getFixed(1), UnrollFactor, LVL, CM,
BFI, PSI) {}
private:
Value *getBroadcastInstrs(Value *V) override;
Value *getStepVector(Value *Val, int StartIdx, Value *Step,
Instruction::BinaryOps Opcode =
Instruction::BinaryOpsEnd) override;
Value *reverseVector(Value *Vec) override;
};
} // end namespace llvm
/// 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;
}
void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) {
if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr)) {
const DILocation *DIL = Inst->getDebugLoc();
if (DIL && Inst->getFunction()->isDebugInfoForProfiling() &&
!isa<DbgInfoIntrinsic>(Inst)) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto NewDIL =
DIL->cloneByMultiplyingDuplicationFactor(UF * VF.getKnownMinValue());
if (NewDIL)
B.SetCurrentDebugLocation(NewDIL.getValue());
else
LLVM_DEBUG(dbgs()
<< "Failed to create new discriminator: "
<< DIL->getFilename() << " Line: " << DIL->getLine());
}
else
B.SetCurrentDebugLocation(DIL);
} else
B.SetCurrentDebugLocation(DebugLoc());
}
/// Write a record \p DebugMsg about vectorization failure to the debug
/// output stream. If \p I is passed, it is an instruction that prevents
/// vectorization.
#ifndef NDEBUG
static void debugVectorizationFailure(const StringRef DebugMsg,
Instruction *I) {
dbgs() << "LV: Not vectorizing: " << DebugMsg;
if (I != nullptr)
dbgs() << " " << *I;
else
dbgs() << '.';
dbgs() << '\n';
}
#endif
/// Create an analysis remark that explains why vectorization failed
///
/// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p
/// RemarkName is the identifier for the remark. If \p I is passed it is an
/// instruction that prevents vectorization. Otherwise \p TheLoop is used for
/// the location of the remark. \return the remark object that can be
/// streamed to.
static OptimizationRemarkAnalysis createLVAnalysis(const char *PassName,
StringRef RemarkName, Loop *TheLoop, Instruction *I) {
Value *CodeRegion = TheLoop->getHeader();
DebugLoc DL = TheLoop->getStartLoc();
if (I) {
CodeRegion = I->getParent();
// If there is no debug location attached to the instruction, revert back to
// using the loop's.
if (I->getDebugLoc())
DL = I->getDebugLoc();
}
OptimizationRemarkAnalysis R(PassName, RemarkName, DL, CodeRegion);
R << "loop not vectorized: ";
return R;
}
namespace llvm {
void reportVectorizationFailure(const StringRef DebugMsg,
const StringRef OREMsg, const StringRef ORETag,
OptimizationRemarkEmitter *ORE, Loop *TheLoop, Instruction *I) {
LLVM_DEBUG(debugVectorizationFailure(DebugMsg, I));
LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE);
ORE->emit(createLVAnalysis(Hints.vectorizeAnalysisPassName(),
ORETag, TheLoop, I) << OREMsg);
}
} // end namespace llvm
#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
void InnerLoopVectorizer::addNewMetadata(Instruction *To,
const Instruction *Orig) {
// If the loop was versioned with memchecks, add the corresponding no-alias
// metadata.
if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig)))
LVer->annotateInstWithNoAlias(To, Orig);
}
void InnerLoopVectorizer::addMetadata(Instruction *To,
Instruction *From) {
propagateMetadata(To, From);
addNewMetadata(To, From);
}
void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To,
Instruction *From) {
for (Value *V : To) {
if (Instruction *I = dyn_cast<Instruction>(V))
addMetadata(I, From);
}
}
namespace llvm {
// Loop vectorization cost-model hints how the scalar epilogue loop should be
// lowered.
enum ScalarEpilogueLowering {
// The default: allowing scalar epilogues.
CM_ScalarEpilogueAllowed,
// Vectorization with OptForSize: don't allow epilogues.
CM_ScalarEpilogueNotAllowedOptSize,
// A special case of vectorisation with OptForSize: loops with a very small
// trip count are considered for vectorization under OptForSize, thereby
// making sure the cost of their loop body is dominant, free of runtime
// guards and scalar iteration overheads.
CM_ScalarEpilogueNotAllowedLowTripLoop,
// Loop hint predicate indicating an epilogue is undesired.
CM_ScalarEpilogueNotNeededUsePredicate
};
/// 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(ScalarEpilogueLowering SEL, Loop *L,
PredicatedScalarEvolution &PSE, LoopInfo *LI,
LoopVectorizationLegality *Legal,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI, DemandedBits *DB,
AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, const Function *F,
const LoopVectorizeHints *Hints,
InterleavedAccessInfo &IAI)
: ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal),
TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F),
Hints(Hints), InterleaveInfo(IAI) {}
/// \return An upper bound for the vectorization factor, or None if
/// vectorization and interleaving should be avoided up front.
Optional<unsigned> computeMaxVF(unsigned UserVF, unsigned UserIC);
/// \return True if runtime checks are required for vectorization, and false
/// otherwise.
bool runtimeChecksRequired();
/// \return The most profitable vectorization factor and the cost of that VF.
/// This method checks every power of two up to MaxVF. If UserVF is not ZERO
/// then this vectorization factor will be selected if vectorization is
/// possible.
VectorizationFactor selectVectorizationFactor(unsigned MaxVF);
/// Setup cost-based decisions for user vectorization factor.
void selectUserVectorizationFactor(ElementCount UserVF) {
collectUniformsAndScalars(UserVF);
collectInstsToScalarize(UserVF);
}
/// \return The size (in bits) of the smallest and widest types in the code
/// that needs to be vectorized. We ignore values that remain scalar such as
/// 64 bit loop indices.
std::pair<unsigned, unsigned> getSmallestAndWidestTypes();
/// \return The desired interleave count.
/// If interleave count has been specified by metadata it will be returned.
/// Otherwise, the interleave count is computed and returned. VF and LoopCost
/// are the selected vectorization factor and the cost of the selected VF.
unsigned selectInterleaveCount(ElementCount VF, unsigned LoopCost);
/// Memory access instruction may be vectorized in more than one way.
/// Form of instruction after vectorization depends on cost.
/// This function takes cost-based decisions for Load/Store instructions
/// and collects them in a map. This decisions map is used for building
/// the lists of loop-uniform and loop-scalar instructions.
/// The calculated cost is saved with widening decision in order to
/// avoid redundant calculations.
void setCostBasedWideningDecision(ElementCount VF);
/// 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.
/// The key is ClassID of target-provided register class.
SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs;
/// Holds the maximum number of concurrent live intervals in the loop.
/// The key is ClassID of target-provided register class.
SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers;
};
/// \return Returns information about the register usages of the loop for the
/// given vectorization factors.
SmallVector<RegisterUsage, 8>
calculateRegisterUsage(ArrayRef<ElementCount> VFs);
/// Collect values we want to ignore in the cost model.
void collectValuesToIgnore();
/// Split reductions into those that happen in the loop, and those that happen
/// outside. In loop reductions are collected into InLoopReductionChains.
void collectInLoopReductions();
/// \returns The smallest bitwidth each instruction can be represented with.
/// The vector equivalents of these instructions should be truncated to this
/// type.
const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const {
return MinBWs;
}
/// \returns True if it is more profitable to scalarize instruction \p I for
/// vectorization factor \p VF.
bool isProfitableToScalarize(Instruction *I, ElementCount VF) const {
assert(VF.isVector() &&
"Profitable to scalarize relevant only for VF > 1.");
// Cost model is not run in the VPlan-native path - return conservative
// result until this changes.
if (EnableVPlanNativePath)
return false;
auto Scalars = InstsToScalarize.find(VF);
assert(Scalars != InstsToScalarize.end() &&
"VF not yet analyzed for scalarization profitability");
return Scalars->second.find(I) != Scalars->second.end();
}
/// Returns true if \p I is known to be uniform after vectorization.
bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const {
if (VF.isScalar())
return true;
// Cost model is not run in the VPlan-native path - return conservative
// result until this changes.
if (EnableVPlanNativePath)
return false;
auto UniformsPerVF = Uniforms.find(VF);
assert(UniformsPerVF != Uniforms.end() &&
"VF not yet analyzed for uniformity");
return UniformsPerVF->second.count(I);
}
/// Returns true if \p I is known to be scalar after vectorization.
bool isScalarAfterVectorization(Instruction *I, ElementCount VF) const {
if (VF.isScalar())
return true;
// Cost model is not run in the VPlan-native path - return conservative
// result until this changes.
if (EnableVPlanNativePath)
return false;
auto ScalarsPerVF = Scalars.find(VF);
assert(ScalarsPerVF != Scalars.end() &&
"Scalar values are not calculated for VF");
return ScalarsPerVF->second.count(I);
}
/// \returns True if instruction \p I can be truncated to a smaller bitwidth
/// for vectorization factor \p VF.
bool canTruncateToMinimalBitwidth(Instruction *I, ElementCount VF) const {
return VF.isVector() && MinBWs.find(I) != MinBWs.end() &&
!isProfitableToScalarize(I, VF) &&
!isScalarAfterVectorization(I, VF);
}
/// Decision that was taken during cost calculation for memory instruction.
enum InstWidening {
CM_Unknown,
CM_Widen, // For consecutive accesses with stride +1.
CM_Widen_Reverse, // For consecutive accesses with stride -1.
CM_Interleave,
CM_GatherScatter,
CM_Scalarize
};
/// Save vectorization decision \p W and \p Cost taken by the cost model for
/// instruction \p I and vector width \p VF.
void setWideningDecision(Instruction *I, ElementCount VF, InstWidening W,
unsigned Cost) {
assert(VF.isVector() && "Expected VF >=2");
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
}
/// Save vectorization decision \p W and \p Cost taken by the cost model for
/// interleaving group \p Grp and vector width \p VF.
void setWideningDecision(const InterleaveGroup<Instruction> *Grp,
ElementCount VF, InstWidening W, unsigned Cost) {
assert(VF.isVector() && "Expected VF >=2");
/// Broadcast this decicion to all instructions inside the group.
/// But the cost will be assigned to one instruction only.
for (unsigned i = 0; i < Grp->getFactor(); ++i) {
if (auto *I = Grp->getMember(i)) {
if (Grp->getInsertPos() == I)
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
else
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0);
}
}
}
/// Return the cost model decision for the given instruction \p I and vector
/// width \p VF. Return CM_Unknown if this instruction did not pass
/// through the cost modeling.
InstWidening getWideningDecision(Instruction *I, ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
assert(VF.isVector() && "Expected VF >=2");
// Cost model is not run in the VPlan-native path - return conservative
// result until this changes.
if (EnableVPlanNativePath)
return CM_GatherScatter;
std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF);
auto Itr = WideningDecisions.find(InstOnVF);
if (Itr == WideningDecisions.end())
return CM_Unknown;
return Itr->second.first;
}
/// Return the vectorization cost for the given instruction \p I and vector
/// width \p VF.
unsigned getWideningCost(Instruction *I, ElementCount VF) {
assert(VF.isVector() && "Expected VF >=2");
std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF);
assert(WideningDecisions.find(InstOnVF) != WideningDecisions.end() &&
"The cost is not calculated");
return WideningDecisions[InstOnVF].second;
}
/// Return True if instruction \p I is an optimizable truncate whose operand
/// is an induction variable. Such a truncate will be removed by adding a new
/// induction variable with the destination type.
bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) {
// If the instruction is not a truncate, return false.
auto *Trunc = dyn_cast<TruncInst>(I);
if (!Trunc)
return false;
// Get the source and destination types of the truncate.
Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF);
Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF);
// If the truncate is free for the given types, return false. Replacing a
// free truncate with an induction variable would add an induction variable
// update instruction to each iteration of the loop. We exclude from this
// check the primary induction variable since it will need an update
// instruction regardless.
Value *Op = Trunc->getOperand(0);
if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy))
return false;
// If the truncated value is not an induction variable, return false.
return Legal->isInductionPhi(Op);
}
/// Collects the instructions to scalarize for each predicated instruction in
/// the loop.
void collectInstsToScalarize(ElementCount VF);
/// Collect Uniform and Scalar values for the given \p VF.
/// The sets depend on CM decision for Load/Store instructions
/// that may be vectorized as interleave, gather-scatter or scalarized.
void collectUniformsAndScalars(ElementCount VF) {
// Do the analysis once.
if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end())
return;
setCostBasedWideningDecision(VF);
collectLoopUniforms(VF);
collectLoopScalars(VF);
}
/// 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, Align Alignment) {
return Legal->isConsecutivePtr(Ptr) &&
TTI.isLegalMaskedStore(DataType, Alignment);
}
/// 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, Align Alignment) {
return Legal->isConsecutivePtr(Ptr) &&
TTI.isLegalMaskedLoad(DataType, Alignment);
}
/// Returns true if the target machine supports masked scatter operation
/// for the given \p DataType.
bool isLegalMaskedScatter(Type *DataType, Align Alignment) {
return TTI.isLegalMaskedScatter(DataType, Alignment);
}
/// Returns true if the target machine supports masked gather operation
/// for the given \p DataType.
bool isLegalMaskedGather(Type *DataType, Align Alignment) {
return TTI.isLegalMaskedGather(DataType, Alignment);
}
/// Returns true if the target machine can represent \p V as a masked gather
/// or scatter operation.
bool isLegalGatherOrScatter(Value *V) {
bool LI = isa<LoadInst>(V);
bool SI = isa<StoreInst>(V);
if (!LI && !SI)
return false;
auto *Ty = getMemInstValueType(V);
Align Align = getLoadStoreAlignment(V);
return (LI && isLegalMaskedGather(Ty, Align)) ||
(SI && isLegalMaskedScatter(Ty, Align));
}
/// Returns true if \p I is an instruction that will be scalarized with
/// predication. Such instructions include conditional stores and
/// instructions that may divide by zero.
/// If a non-zero VF has been calculated, we check if I will be scalarized
/// predication for that VF.
bool isScalarWithPredication(Instruction *I,
ElementCount VF = ElementCount::getFixed(1));
// Returns true if \p I is an instruction that will be predicated either
// through scalar predication or masked load/store or masked gather/scatter.
// Superset of instructions that return true for isScalarWithPredication.
bool isPredicatedInst(Instruction *I) {
if (!blockNeedsPredication(I->getParent()))
return false;
// Loads and stores that need some form of masked operation are predicated
// instructions.
if (isa<LoadInst>(I) || isa<StoreInst>(I))
return Legal->isMaskRequired(I);
return isScalarWithPredication(I);
}
/// Returns true if \p I is a memory instruction with consecutive memory
/// access that can be widened.
bool
memoryInstructionCanBeWidened(Instruction *I,
ElementCount VF = ElementCount::getFixed(1));
/// Returns true if \p I is a memory instruction in an interleaved-group
/// of memory accesses that can be vectorized with wide vector loads/stores
/// and shuffles.
bool
interleavedAccessCanBeWidened(Instruction *I,
ElementCount VF = ElementCount::getFixed(1));
/// Check if \p Instr belongs to any interleaved access group.
bool isAccessInterleaved(Instruction *Instr) {
return InterleaveInfo.isInterleaved(Instr);
}
/// Get the interleaved access group that \p Instr belongs to.
const InterleaveGroup<Instruction> *
getInterleavedAccessGroup(Instruction *Instr) {
return InterleaveInfo.getInterleaveGroup(Instr);
}
/// Returns true if an interleaved group requires a scalar iteration
/// to handle accesses with gaps, and there is nothing preventing us from
/// creating a scalar epilogue.
bool requiresScalarEpilogue() const {
return isScalarEpilogueAllowed() && InterleaveInfo.requiresScalarEpilogue();
}
/// Returns true if a scalar epilogue is not allowed due to optsize or a
/// loop hint annotation.
bool isScalarEpilogueAllowed() const {
return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed;
}
/// Returns true if all loop blocks should be masked to fold tail loop.
bool foldTailByMasking() const { return FoldTailByMasking; }
bool blockNeedsPredication(BasicBlock *BB) {
return foldTailByMasking() || Legal->blockNeedsPredication(BB);
}
/// A SmallMapVector to store the InLoop reduction op chains, mapping phi
/// nodes to the chain of instructions representing the reductions. Uses a
/// MapVector to ensure deterministic iteration order.
using ReductionChainMap =
SmallMapVector<PHINode *, SmallVector<Instruction *, 4>, 4>;
/// Return the chain of instructions representing an inloop reduction.
const ReductionChainMap &getInLoopReductionChains() const {
return InLoopReductionChains;
}
/// Returns true if the Phi is part of an inloop reduction.
bool isInLoopReduction(PHINode *Phi) const {
return InLoopReductionChains.count(Phi);
}
/// 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.
unsigned getVectorIntrinsicCost(CallInst *CI, ElementCount VF);
/// 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.
unsigned getVectorCallCost(CallInst *CI, ElementCount VF,
bool &NeedToScalarize);
/// Invalidates decisions already taken by the cost model.
void invalidateCostModelingDecisions() {
WideningDecisions.clear();
Uniforms.clear();
Scalars.clear();
}
private:
unsigned NumPredStores = 0;
/// \return An upper bound for the vectorization factor, a power-of-2 larger
/// than zero. One is returned if vectorization should best be avoided due
/// to cost.
unsigned computeFeasibleMaxVF(unsigned ConstTripCount);
/// The vectorization cost is a combination of the cost itself and a boolean
/// indicating whether any of the contributing operations will actually
/// operate on
/// vector values after type legalization in the backend. If this latter value
/// is
/// false, then all operations will be scalarized (i.e. no vectorization has
/// actually taken place).
using VectorizationCostTy = std::pair<unsigned, bool>;
/// 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.
VectorizationCostTy expectedCost(ElementCount VF);
/// Returns the execution time cost of an instruction for a given vector
/// width. Vector width of one means scalar.
VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF);
/// The cost-computation logic from getInstructionCost which provides
/// the vector type as an output parameter.
unsigned getInstructionCost(Instruction *I, ElementCount VF, Type *&VectorTy);
/// Calculate vectorization cost of memory instruction \p I.
unsigned getMemoryInstructionCost(Instruction *I, ElementCount VF);
/// The cost computation for scalarized memory instruction.
unsigned getMemInstScalarizationCost(Instruction *I, ElementCount VF);
/// The cost computation for interleaving group of memory instructions.
unsigned getInterleaveGroupCost(Instruction *I, ElementCount VF);
/// The cost computation for Gather/Scatter instruction.
unsigned getGatherScatterCost(Instruction *I, ElementCount VF);
/// The cost computation for widening instruction \p I with consecutive
/// memory access.
unsigned getConsecutiveMemOpCost(Instruction *I, ElementCount VF);
/// The cost calculation for Load/Store instruction \p I with uniform pointer -
/// Load: scalar load + broadcast.
/// Store: scalar store + (loop invariant value stored? 0 : extract of last
/// element)
unsigned getUniformMemOpCost(Instruction *I, ElementCount VF);
/// Estimate the overhead of scalarizing an instruction. This is a
/// convenience wrapper for the type-based getScalarizationOverhead API.
unsigned getScalarizationOverhead(Instruction *I, ElementCount VF);
/// Returns whether the instruction is a load or store and will be a emitted
/// as a vector operation.
bool isConsecutiveLoadOrStore(Instruction *I);
/// Returns true if an artificially high cost for emulated masked memrefs
/// should be used.
bool useEmulatedMaskMemRefHack(Instruction *I);
/// Map of scalar integer values to the smallest bitwidth they can be legally
/// represented as. The vector equivalents of these values should be truncated
/// to this type.
MapVector<Instruction *, uint64_t> MinBWs;
/// A type representing the costs for instructions if they were to be
/// scalarized rather than vectorized. The entries are Instruction-Cost
/// pairs.
using ScalarCostsTy = DenseMap<Instruction *, unsigned>;
/// A set containing all BasicBlocks that are known to present after
/// vectorization as a predicated block.
SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization;
/// Records whether it is allowed to have the original scalar loop execute at
/// least once. This may be needed as a fallback loop in case runtime
/// aliasing/dependence checks fail, or to handle the tail/remainder
/// iterations when the trip count is unknown or doesn't divide by the VF,
/// or as a peel-loop to handle gaps in interleave-groups.
/// Under optsize and when the trip count is very small we don't allow any
/// iterations to execute in the scalar loop.
ScalarEpilogueLowering ScalarEpilogueStatus = CM_ScalarEpilogueAllowed;
/// All blocks of loop are to be masked to fold tail of scalar iterations.
bool FoldTailByMasking = false;
/// A map holding scalar costs for different vectorization factors. The
/// presence of a cost for an instruction in the mapping indicates that the
/// instruction will be scalarized when vectorizing with the associated
/// vectorization factor. The entries are VF-ScalarCostTy pairs.
DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize;
/// Holds the instructions known to be uniform after vectorization.
/// The data is collected per VF.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms;
/// Holds the instructions known to be scalar after vectorization.
/// The data is collected per VF.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars;
/// Holds the instructions (address computations) that are forced to be
/// scalarized.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars;
/// PHINodes of the reductions that should be expanded in-loop along with
/// their associated chains of reduction operations, in program order from top
/// (PHI) to bottom
ReductionChainMap InLoopReductionChains;
/// Returns the expected difference in cost from scalarizing the expression
/// feeding a predicated instruction \p PredInst. The instructions to
/// scalarize and their scalar costs are collected in \p ScalarCosts. A
/// non-negative return value implies the expression will be scalarized.
/// Currently, only single-use chains are considered for scalarization.
int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts,
ElementCount VF);
/// Collect the instructions that are uniform after vectorization. An
/// instruction is uniform if we represent it with a single scalar value in
/// the vectorized loop corresponding to each vector iteration. Examples of
/// uniform instructions include pointer operands of consecutive or
/// interleaved memory accesses. Note that although uniformity implies an
/// instruction will be scalar, the reverse is not true. In general, a
/// scalarized instruction will be represented by VF scalar values in the
/// vectorized loop, each corresponding to an iteration of the original
/// scalar loop.
void collectLoopUniforms(ElementCount VF);
/// Collect the instructions that are scalar after vectorization. An
/// instruction is scalar if it is known to be uniform or will be scalarized
/// during vectorization. Non-uniform scalarized instructions will be
/// represented by VF values in the vectorized loop, each corresponding to an
/// iteration of the original scalar loop.
void collectLoopScalars(ElementCount VF);
/// Keeps cost model vectorization decision and cost for instructions.
/// Right now it is used for memory instructions only.
using DecisionList = DenseMap<std::pair<Instruction *, ElementCount>,
std::pair<InstWidening, unsigned>>;
DecisionList WideningDecisions;
/// Returns true if \p V is expected to be vectorized and it needs to be
/// extracted.
bool needsExtract(Value *V, ElementCount VF) const {
Instruction *I = dyn_cast<Instruction>(V);
if (VF.isScalar() || !I || !TheLoop->contains(I) ||
TheLoop->isLoopInvariant(I))
return false;
// Assume we can vectorize V (and hence we need extraction) if the
// scalars are not computed yet. This can happen, because it is called
// via getScalarizationOverhead from setCostBasedWideningDecision, before
// the scalars are collected. That should be a safe assumption in most
// cases, because we check if the operands have vectorizable types
// beforehand in LoopVectorizationLegality.
return Scalars.find(VF) == Scalars.end() ||
!isScalarAfterVectorization(I, VF);
};
/// Returns a range containing only operands needing to be extracted.
SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops,
ElementCount VF) {
return SmallVector<Value *, 4>(make_filter_range(
Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); }));
}
public:
/// The loop that we evaluate.
Loop *TheLoop;
/// Predicated scalar evolution analysis.
PredicatedScalarEvolution &PSE;
/// Loop Info analysis.
LoopInfo *LI;
/// Vectorization legality.
LoopVectorizationLegality *Legal;
/// Vector target information.
const TargetTransformInfo &TTI;
/// Target Library Info.
const TargetLibraryInfo *TLI;
/// Demanded bits analysis.
DemandedBits *DB;
/// Assumption cache.
AssumptionCache *AC;
/// Interface to emit optimization remarks.
OptimizationRemarkEmitter *ORE;
const Function *TheFunction;
/// Loop Vectorize Hint.
const LoopVectorizeHints *Hints;
/// The interleave access information contains groups of interleaved accesses
/// with the same stride and close to each other.
InterleavedAccessInfo &InterleaveInfo;
/// Values to ignore in the cost model.
SmallPtrSet<const Value *, 16> ValuesToIgnore;
/// Values to ignore in the cost model when VF > 1.
SmallPtrSet<const Value *, 16> VecValuesToIgnore;
};
} // end namespace llvm
// Return true if \p OuterLp is an outer loop annotated with hints for explicit
// vectorization. The loop needs to be annotated with #pragma omp simd
// simdlen(#) or #pragma clang vectorize(enable) vectorize_width(#). If the
// vector length information is not provided, vectorization is not considered
// explicit. Interleave hints are not allowed either. These limitations will be
// relaxed in the future.
// Please, note that we are currently forced to abuse the pragma 'clang
// vectorize' semantics. This pragma provides *auto-vectorization hints*
// (i.e., LV must check that vectorization is legal) whereas pragma 'omp simd'
// provides *explicit vectorization hints* (LV can bypass legal checks and
// assume that vectorization is legal). However, both hints are implemented
// using the same metadata (llvm.loop.vectorize, processed by
// LoopVectorizeHints). This will be fixed in the future when the native IR
// representation for pragma 'omp simd' is introduced.
static bool isExplicitVecOuterLoop(Loop *OuterLp,
OptimizationRemarkEmitter *ORE) {
assert(!OuterLp->isInnermost() && "This is not an outer loop");
LoopVectorizeHints Hints(OuterLp, true /*DisableInterleaving*/, *ORE);
// Only outer loops with an explicit vectorization hint are supported.
// Unannotated outer loops are ignored.
if (Hints.getForce() == LoopVectorizeHints::FK_Undefined)
return false;
Function *Fn = OuterLp->getHeader()->getParent();
if (!Hints.allowVectorization(Fn, OuterLp,
true /*VectorizeOnlyWhenForced*/)) {
LLVM_DEBUG(dbgs() << "LV: Loop hints prevent outer loop vectorization.\n");
return false;
}
if (Hints.getInterleave() > 1) {
// TODO: Interleave support is future work.
LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Interleave is not supported for "
"outer loops.\n");
Hints.emitRemarkWithHints();
return false;
}
return true;
}
static void collectSupportedLoops(Loop &L, LoopInfo *LI,
OptimizationRemarkEmitter *ORE,
SmallVectorImpl<Loop *> &V) {
// Collect inner loops and outer loops without irreducible control flow. For
// now, only collect outer loops that have explicit vectorization hints. If we
// are stress testing the VPlan H-CFG construction, we collect the outermost
// loop of every loop nest.
if (L.isInnermost() || VPlanBuildStressTest ||
(EnableVPlanNativePath && isExplicitVecOuterLoop(&L, ORE))) {
LoopBlocksRPO RPOT(&L);
RPOT.perform(LI);
if (!containsIrreducibleCFG<const BasicBlock *>(RPOT, *LI)) {
V.push_back(&L);
// TODO: Collect inner loops inside marked outer loops in case
// vectorization fails for the outer loop. Do not invoke
// 'containsIrreducibleCFG' again for inner loops when the outer loop is
// already known to be reducible. We can use an inherited attribute for
// that.
return;
}
}
for (Loop *InnerL : L)
collectSupportedLoops(*InnerL, LI, ORE, V);
}
namespace {
/// The LoopVectorize Pass.
struct LoopVectorize : public FunctionPass {
/// Pass identification, replacement for typeid
static char ID;
LoopVectorizePass Impl;
explicit LoopVectorize(bool InterleaveOnlyWhenForced = false,
bool VectorizeOnlyWhenForced = false)
: FunctionPass(ID),
Impl({InterleaveOnlyWhenForced, VectorizeOnlyWhenForced}) {
initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
}
bool runOnFunction(Function &F) override {
if (skipFunction(F))
return false;
auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE();
auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI();
auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>();
auto *TLI = TLIP ? &TLIP->getTLI(F) : nullptr;
auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults();
auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F);
auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>();
auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits();
auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
auto *PSI = &getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI();
std::function<const LoopAccessInfo &(Loop &)> GetLAA =
[&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); };
return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC,
GetLAA, *ORE, PSI).MadeAnyChange;
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<AssumptionCacheTracker>();
AU.addRequired<BlockFrequencyInfoWrapperPass>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<LoopInfoWrapperPass>();
AU.addRequired<ScalarEvolutionWrapperPass>();
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.addRequired<AAResultsWrapperPass>();
AU.addRequired<LoopAccessLegacyAnalysis>();
AU.addRequired<DemandedBitsWrapperPass>();
AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
AU.addRequired<InjectTLIMappingsLegacy>();
// We currently do not preserve loopinfo/dominator analyses with outer loop
// vectorization. Until this is addressed, mark these analyses as preserved
// only for non-VPlan-native path.
// TODO: Preserve Loop and Dominator analyses for VPlan-native path.
if (!EnableVPlanNativePath) {
AU.addPreserved<LoopInfoWrapperPass>();
AU.addPreserved<DominatorTreeWrapperPass>();
}
AU.addPreserved<BasicAAWrapperPass>();
AU.addPreserved<GlobalsAAWrapperPass>();
AU.addRequired<ProfileSummaryInfoWrapperPass>();
}
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
// LoopVectorizationCostModel and LoopVectorizationPlanner.
//===----------------------------------------------------------------------===//
Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
// We need to place the broadcast of invariant variables outside the loop,
// but only if it's proven safe to do so. Else, broadcast will be inside
// vector loop body.
Instruction *Instr = dyn_cast<Instruction>(V);
bool SafeToHoist = OrigLoop->isLoopInvariant(V) &&
(!Instr ||
DT->dominates(Instr->getParent(), LoopVectorPreHeader));
// Place the code for broadcasting invariant variables in the new preheader.
IRBuilder<>::InsertPointGuard Guard(Builder);
if (SafeToHoist)
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
// Broadcast the scalar into all locations in the vector.
Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
return Shuf;
}
void InnerLoopVectorizer::createVectorIntOrFpInductionPHI(
const InductionDescriptor &II, Value *Step, Instruction *EntryVal) {
assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) &&
"Expected either an induction phi-node or a truncate of it!");
Value *Start = II.getStartValue();
// Construct the initial value of the vector IV in the vector loop preheader
auto CurrIP = Builder.saveIP();
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
if (isa<TruncInst>(EntryVal)) {
assert(Start->getType()->isIntegerTy() &&
"Truncation requires an integer type");
auto *TruncType = cast<IntegerType>(EntryVal->getType());
Step = Builder.CreateTrunc(Step, TruncType);
Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType);
}
Value *SplatStart = Builder.CreateVectorSplat(VF, Start);
Value *SteppedStart =
getStepVector(SplatStart, 0, Step, II.getInductionOpcode());
// We create vector phi nodes for both integer and floating-point induction
// variables. Here, we determine the kind of arithmetic we will perform.
Instruction::BinaryOps AddOp;
Instruction::BinaryOps MulOp;
if (Step->getType()->isIntegerTy()) {
AddOp = Instruction::Add;
MulOp = Instruction::Mul;
} else {
AddOp = II.getInductionOpcode();
MulOp = Instruction::FMul;
}
// Multiply the vectorization factor by the step using integer or
// floating-point arithmetic as appropriate.
Value *ConstVF =
getSignedIntOrFpConstant(Step->getType(), VF.getKnownMinValue());
Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF));
// Create a vector splat to use in the induction update.
//
// FIXME: If the step is non-constant, we create the vector splat with
// IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't
// handle a constant vector splat.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Value *SplatVF = isa<Constant>(Mul)
? ConstantVector::getSplat(VF, cast<Constant>(Mul))
: Builder.CreateVectorSplat(VF, Mul);
Builder.restoreIP(CurrIP);
// We may need to add the step a number of times, depending on the unroll
// factor. The last of those goes into the PHI.
PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind",
&*LoopVectorBody->getFirstInsertionPt());
VecInd->setDebugLoc(EntryVal->getDebugLoc());
Instruction *LastInduction = VecInd;
for (unsigned Part = 0; Part < UF; ++Part) {
VectorLoopValueMap.setVectorValue(EntryVal, Part, LastInduction);
if (isa<TruncInst>(EntryVal))
addMetadata(LastInduction, EntryVal);
recordVectorLoopValueForInductionCast(II, EntryVal, LastInduction, Part);
LastInduction = cast<Instruction>(addFastMathFlag(
Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add")));
LastInduction->setDebugLoc(EntryVal->getDebugLoc());
}
// Move the last step to the end of the latch block. This ensures consistent
// placement of all induction updates.
auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch();
auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator());
auto *ICmp = cast<Instruction>(Br->getCondition());
LastInduction->moveBefore(ICmp);
LastInduction->setName("vec.ind.next");
VecInd->addIncoming(SteppedStart, LoopVectorPreHeader);
VecInd->addIncoming(LastInduction, LoopVectorLatch);
}
bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const {
return Cost->isScalarAfterVectorization(I, VF) ||
Cost->isProfitableToScalarize(I, VF);
}
bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const {
if (shouldScalarizeInstruction(IV))
return true;
auto isScalarInst = [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return (OrigLoop->contains(I) && shouldScalarizeInstruction(I));
};
return llvm::any_of(IV->users(), isScalarInst);
}
void InnerLoopVectorizer::recordVectorLoopValueForInductionCast(
const InductionDescriptor &ID, const Instruction *EntryVal,
Value *VectorLoopVal, unsigned Part, unsigned Lane) {
assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) &&
"Expected either an induction phi-node or a truncate of it!");
// This induction variable is not the phi from the original loop but the
// newly-created IV based on the proof that casted Phi is equal to the
// uncasted Phi in the vectorized loop (under a runtime guard possibly). It
// re-uses the same InductionDescriptor that original IV uses but we don't
// have to do any recording in this case - that is done when original IV is
// processed.
if (isa<TruncInst>(EntryVal))
return;
const SmallVectorImpl<Instruction *> &Casts = ID.getCastInsts();
if (Casts.empty())
return;
// Only the first Cast instruction in the Casts vector is of interest.
// The rest of the Casts (if exist) have no uses outside the
// induction update chain itself.
Instruction *CastInst = *Casts.begin();
if (Lane < UINT_MAX)
VectorLoopValueMap.setScalarValue(CastInst, {Part, Lane}, VectorLoopVal);
else
VectorLoopValueMap.setVectorValue(CastInst, Part, VectorLoopVal);
}
void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc) {
assert((IV->getType()->isIntegerTy() || IV != OldInduction) &&
"Primary induction variable must have an integer type");
auto II = Legal->getInductionVars().find(IV);
assert(II != Legal->getInductionVars().end() && "IV is not an induction");
auto ID = II->second;
assert(IV->getType() == ID.getStartValue()->getType() && "Types must match");
// The value from the original loop to which we are mapping the new induction
// variable.
Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV;
auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
// Generate code for the induction step. Note that induction steps are
// required to be loop-invariant
auto CreateStepValue = [&](const SCEV *Step) -> Value * {
assert(PSE.getSE()->isLoopInvariant(Step, OrigLoop) &&
"Induction step should be loop invariant");
if (PSE.getSE()->isSCEVable(IV->getType())) {
SCEVExpander Exp(*PSE.getSE(), DL, "induction");
return Exp.expandCodeFor(Step, Step->getType(),
LoopVectorPreHeader->getTerminator());
}
return cast<SCEVUnknown>(Step)->getValue();
};
// The scalar value to broadcast. This is derived from the canonical
// induction variable. If a truncation type is given, truncate the canonical
// induction variable and step. Otherwise, derive these values from the
// induction descriptor.
auto CreateScalarIV = [&](Value *&Step) -> Value * {
Value *ScalarIV = Induction;
if (IV != OldInduction) {
ScalarIV = IV->getType()->isIntegerTy()
? Builder.CreateSExtOrTrunc(Induction, IV->getType())
: Builder.CreateCast(Instruction::SIToFP, Induction,
IV->getType());
ScalarIV = emitTransformedIndex(Builder, ScalarIV, PSE.getSE(), DL, ID);
ScalarIV->setName("offset.idx");
}
if (Trunc) {
auto *TruncType = cast<IntegerType>(Trunc->getType());
assert(Step->getType()->isIntegerTy() &&
"Truncation requires an integer step");
ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType);
Step = Builder.CreateTrunc(Step, TruncType);
}
return ScalarIV;
};
// Create the vector values from the scalar IV, in the absence of creating a
// vector IV.
auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) {
Value *Broadcasted = getBroadcastInstrs(ScalarIV);
for (unsigned Part = 0; Part < UF; ++Part) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Value *EntryPart =
getStepVector(Broadcasted, VF.getKnownMinValue() * Part, Step,
ID.getInductionOpcode());
VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart);
if (Trunc)
addMetadata(EntryPart, Trunc);
recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, Part);
}
};
// Now do the actual transformations, and start with creating the step value.
Value *Step = CreateStepValue(ID.getStep());
if (VF.isZero() || VF.isScalar()) {
Value *ScalarIV = CreateScalarIV(Step);
CreateSplatIV(ScalarIV, Step);
return;
}
// Determine if we want a scalar version of the induction variable. This is
// true if the induction variable itself is not widened, or if it has at
// least one user in the loop that is not widened.
auto NeedsScalarIV = needsScalarInduction(EntryVal);
if (!NeedsScalarIV) {
createVectorIntOrFpInductionPHI(ID, Step, EntryVal);
return;
}
// Try to create a new independent vector induction variable. If we can't
// create the phi node, we will splat the scalar induction variable in each
// loop iteration.
if (!shouldScalarizeInstruction(EntryVal)) {
createVectorIntOrFpInductionPHI(ID, Step, EntryVal);
Value *ScalarIV = CreateScalarIV(Step);
// Create scalar steps that can be used by instructions we will later
// scalarize. Note that the addition of the scalar steps will not increase
// the number of instructions in the loop in the common case prior to
// InstCombine. We will be trading one vector extract for each scalar step.
buildScalarSteps(ScalarIV, Step, EntryVal, ID);
return;
}
// All IV users are scalar instructions, so only emit a scalar IV, not a
// vectorised IV. Except when we tail-fold, then the splat IV feeds the
// predicate used by the masked loads/stores.
Value *ScalarIV = CreateScalarIV(Step);
if (!Cost->isScalarEpilogueAllowed())
CreateSplatIV(ScalarIV, Step);
buildScalarSteps(ScalarIV, Step, EntryVal, ID);
}
Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step,
Instruction::BinaryOps BinOp) {
// Create and check the types.
auto *ValVTy = cast<FixedVectorType>(Val->getType());
int VLen = ValVTy->getNumElements();
Type *STy = Val->getType()->getScalarType();
assert((STy->isIntegerTy() || STy->isFloatingPointTy()) &&
"Induction Step must be an integer or FP");
assert(Step->getType() == STy && "Step has wrong type");
SmallVector<Constant *, 8> Indices;
if (STy->isIntegerTy()) {
// Create a vector of consecutive numbers from zero to VF.
for (int i = 0; i < VLen; ++i)
Indices.push_back(ConstantInt::get(STy, 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");
}
// Floating point induction.
assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) &&
"Binary Opcode should be specified for FP induction");
// Create a vector of consecutive numbers from zero to VF.
for (int i = 0; i < VLen; ++i)
Indices.push_back(ConstantFP::get(STy, (double)(StartIdx + i)));
// Add the consecutive indices to the vector value.
Constant *Cv = ConstantVector::get(Indices);
Step = Builder.CreateVectorSplat(VLen, Step);
// Floating point operations had to be 'fast' to enable the induction.
FastMathFlags Flags;
Flags.setFast();
Value *MulOp = Builder.CreateFMul(Cv, Step);
if (isa<Instruction>(MulOp))
// Have to check, MulOp may be a constant
cast<Instruction>(MulOp)->setFastMathFlags(Flags);
Value *BOp = Builder.CreateBinOp(BinOp, Val, MulOp, "induction");
if (isa<Instruction>(BOp))
cast<Instruction>(BOp)->setFastMathFlags(Flags);
return BOp;
}
void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step,
Instruction *EntryVal,
const InductionDescriptor &ID) {
// We shouldn't have to build scalar steps if we aren't vectorizing.
assert(VF.isVector() && "VF should be greater than one");
assert(!VF.isScalable() &&
"the code below assumes a fixed number of elements at compile time");
// Get the value type and ensure it and the step have the same integer type.
Type *ScalarIVTy = ScalarIV->getType()->getScalarType();
assert(ScalarIVTy == Step->getType() &&
"Val and Step should have the same type");
// We build scalar steps for both integer and floating-point induction
// variables. Here, we determine the kind of arithmetic we will perform.
Instruction::BinaryOps AddOp;
Instruction::BinaryOps MulOp;
if (ScalarIVTy->isIntegerTy()) {
AddOp = Instruction::Add;
MulOp = Instruction::Mul;
} else {
AddOp = ID.getInductionOpcode();
MulOp = Instruction::FMul;
}
// Determine the number of scalars we need to generate for each unroll
// iteration. If EntryVal is uniform, we only need to generate the first
// lane. Otherwise, we generate all VF values.
unsigned Lanes =
Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF)
? 1
: VF.getKnownMinValue();
// Compute the scalar steps and save the results in VectorLoopValueMap.
for (unsigned Part = 0; Part < UF; ++Part) {
for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
auto *StartIdx = getSignedIntOrFpConstant(
ScalarIVTy, VF.getKnownMinValue() * Part + Lane);
auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step));
auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul));
VectorLoopValueMap.setScalarValue(EntryVal, {Part, Lane}, Add);
recordVectorLoopValueForInductionCast(ID, EntryVal, Add, Part, Lane);
}
}
}
Value *InnerLoopVectorizer::getOrCreateVectorValue(Value *V, unsigned Part) {
assert(V != Induction && "The new induction variable should not be used.");
assert(!V->getType()->isVectorTy() && "Can't widen a vector");
assert(!V->getType()->isVoidTy() && "Type does not produce a value");
// If we have a stride that is replaced by one, do it here. Defer this for
// the VPlan-native path until we start running Legal checks in that path.
if (!EnableVPlanNativePath && Legal->hasStride(V))
V = ConstantInt::get(V->getType(), 1);
// If we have a vector mapped to this value, return it.
if (VectorLoopValueMap.hasVectorValue(V, Part))
return VectorLoopValueMap.getVectorValue(V, Part);
// If the value has not been vectorized, check if it has been scalarized
// instead. If it has been scalarized, and we actually need the value in
// vector form, we will construct the vector values on demand.
if (VectorLoopValueMap.hasAnyScalarValue(V)) {
Value *ScalarValue = VectorLoopValueMap.getScalarValue(V, {Part, 0});
// If we've scalarized a value, that value should be an instruction.
auto *I = cast<Instruction>(V);
// If we aren't vectorizing, we can just copy the scalar map values over to
// the vector map.
if (VF == 1) {
VectorLoopValueMap.setVectorValue(V, Part, ScalarValue);
return ScalarValue;
}
// Get the last scalar instruction we generated for V and Part. If the value
// is known to be uniform after vectorization, this corresponds to lane zero
// of the Part unroll iteration. Otherwise, the last instruction is the one
// we created for the last vector lane of the Part unroll iteration.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
unsigned LastLane = Cost->isUniformAfterVectorization(I, VF)
? 0
: VF.getKnownMinValue() - 1;
auto *LastInst = cast<Instruction>(
VectorLoopValueMap.getScalarValue(V, {Part, LastLane}));
// Set the insert point after the last scalarized instruction. This ensures
// the insertelement sequence will directly follow the scalar definitions.
auto OldIP = Builder.saveIP();
auto NewIP = std::next(BasicBlock::iterator(LastInst));
Builder.SetInsertPoint(&*NewIP);
// However, if we are vectorizing, we need to construct the vector values.
// If the value is known to be uniform after vectorization, we can just
// broadcast the scalar value corresponding to lane zero for each unroll
// iteration. Otherwise, we construct the vector values using insertelement
// instructions. Since the resulting vectors are stored in
// VectorLoopValueMap, we will only generate the insertelements once.
Value *VectorValue = nullptr;
if (Cost->isUniformAfterVectorization(I, VF)) {
VectorValue = getBroadcastInstrs(ScalarValue);
VectorLoopValueMap.setVectorValue(V, Part, VectorValue);
} else {
// Initialize packing with insertelements to start from undef.
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
Value *Undef = UndefValue::get(VectorType::get(V->getType(), VF));
VectorLoopValueMap.setVectorValue(V, Part, Undef);
for (unsigned Lane = 0; Lane < VF.getKnownMinValue(); ++Lane)
packScalarIntoVectorValue(V, {Part, Lane});
VectorValue = VectorLoopValueMap.getVectorValue(V, Part);
}
Builder.restoreIP(OldIP);
return VectorValue;
}
// 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);
VectorLoopValueMap.setVectorValue(V, Part, B);
return B;
}
Value *
InnerLoopVectorizer::getOrCreateScalarValue(Value *V,
const VPIteration &Instance) {
// If the value is not an instruction contained in the loop, it should
// already be scalar.
if (OrigLoop->isLoopInvariant(V))
return V;
assert(Instance.Lane > 0
? !Cost->isUniformAfterVectorization(cast<Instruction>(V), VF)
: true && "Uniform values only have lane zero");
// If the value from the original loop has not been vectorized, it is
// represented by UF x VF scalar values in the new loop. Return the requested
// scalar value.
if (VectorLoopValueMap.hasScalarValue(V, Instance))
return VectorLoopValueMap.getScalarValue(V, Instance);
// If the value has not been scalarized, get its entry in VectorLoopValueMap
// for the given unroll part. If this entry is not a vector type (i.e., the
// vectorization factor is one), there is no need to generate an
// extractelement instruction.
auto *U = getOrCreateVectorValue(V, Instance.Part);
if (!U->getType()->isVectorTy()) {
assert(VF == 1 && "Value not scalarized has non-vector type");
return U;
}
// Otherwise, the value from the original loop has been vectorized and is
// represented by UF vector values. Extract and return the requested scalar
// value from the appropriate vector lane.
return Builder.CreateExtractElement(U, Builder.getInt32(Instance.Lane));
}
void InnerLoopVectorizer::packScalarIntoVectorValue(
Value *V, const VPIteration &Instance) {
assert(V != Induction && "The new induction variable should not be used.");
assert(!V->getType()->isVectorTy() && "Can't pack a vector");
assert(!V->getType()->isVoidTy() && "Type does not produce a value");
Value *ScalarInst = VectorLoopValueMap.getScalarValue(V, Instance);
Value *VectorValue = VectorLoopValueMap.getVectorValue(V, Instance.Part);
VectorValue = Builder.CreateInsertElement(VectorValue, ScalarInst,
Builder.getInt32(Instance.Lane));
VectorLoopValueMap.resetVectorValue(V, Instance.Part, VectorValue);
}
Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
assert(Vec->getType()->isVectorTy() && "Invalid type");
assert(!VF.isScalable() && "Cannot reverse scalable vectors");
SmallVector<int, 8> ShuffleMask;
for (unsigned i = 0; i < VF.getKnownMinValue(); ++i)
ShuffleMask.push_back(VF.getKnownMinValue() - i - 1);
return Builder.CreateShuffleVector(Vec, ShuffleMask, "reverse");
}
// Return whether we allow using masked interleave-groups (for dealing with
// strided loads/stores that reside in predicated blocks, or for dealing
// with gaps).
static bool useMaskedInterleavedAccesses(const TargetTransformInfo &TTI) {
// If an override option has been passed in for interleaved accesses, use it.
if (EnableMaskedInterleavedMemAccesses.getNumOccurrences() > 0)
return EnableMaskedInterleavedMemAccesses;
return TTI.enableMaskedInterleavedAccessVectorization();
}
// Try to vectorize the interleave group that \p Instr belongs to.
//
// E.g. Translate following interleaved load group (factor = 3):
// for (i = 0; i < N; i+=3) {
// R = Pic[i]; // Member of index 0
// G = Pic[i+1]; // Member of index 1
// B = Pic[i+2]; // Member of index 2
// ... // do something to R, G, B
// }
// To:
// %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B
// %R.vec = shuffle %wide.vec, undef, <0, 3, 6, 9> ; R elements
// %G.vec = shuffle %wide.vec, undef, <1, 4, 7, 10> ; G elements
// %B.vec = shuffle %wide.vec, undef, <2, 5, 8, 11> ; B elements
//
// Or translate following interleaved store group (factor = 3):
// for (i = 0; i < N; i+=3) {
// ... do something to R, G, B
// Pic[i] = R; // Member of index 0
// Pic[i+1] = G; // Member of index 1
// Pic[i+2] = B; // Member of index 2
// }
// To:
// %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7>
// %B_U.vec = shuffle %B.vec, undef, <0, 1, 2, 3, u, u, u, u>
// %interleaved.vec = shuffle %R_G.vec, %B_U.vec,
// <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements
// store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B
void InnerLoopVectorizer::vectorizeInterleaveGroup(
const InterleaveGroup<Instruction> *Group, VPTransformState &State,
VPValue *Addr, VPValue *BlockInMask) {
Instruction *Instr = Group->getInsertPos();
const DataLayout &DL = Instr->getModule()->getDataLayout();
// Prepare for the vector type of the interleaved load/store.
Type *ScalarTy = getMemInstValueType(Instr);
unsigned InterleaveFactor = Group->getFactor();
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor);
// Prepare for the new pointers.
SmallVector<Value *, 2> AddrParts;
unsigned Index = Group->getIndex(Instr);
// TODO: extend the masked interleaved-group support to reversed access.
assert((!BlockInMask || !Group->isReverse()) &&
"Reversed masked interleave-group not supported.");
// If the group is reverse, adjust the index to refer to the last vector lane
// instead of the first. We adjust the index from the first vector lane,
// rather than directly getting the pointer for lane VF - 1, because the
// pointer operand of the interleaved access is supposed to be uniform. For
// uniform instructions, we're only required to generate a value for the
// first vector lane in each unroll iteration.
assert(!VF.isScalable() &&
"scalable vector reverse operation is not implemented");
if (Group->isReverse())
Index += (VF.getKnownMinValue() - 1) * Group->getFactor();
for (unsigned Part = 0; Part < UF; Part++) {
Value *AddrPart = State.get(Addr, {Part, 0});
setDebugLocFromInst(Builder, AddrPart);
// Notice current instruction could be any index. Need to adjust the address
// to the member of index 0.
//
// E.g. a = A[i+1]; // Member of index 1 (Current instruction)
// b = A[i]; // Member of index 0
// Current pointer is pointed to A[i+1], adjust it to A[i].
//
// E.g. A[i+1] = a; // Member of index 1
// A[i] = b; // Member of index 0
// A[i+2] = c; // Member of index 2 (Current instruction)
// Current pointer is pointed to A[i+2], adjust it to A[i].
bool InBounds = false;
if (auto *gep = dyn_cast<GetElementPtrInst>(AddrPart->stripPointerCasts()))
InBounds = gep->isInBounds();
AddrPart = Builder.CreateGEP(ScalarTy, AddrPart, Builder.getInt32(-Index));
cast<GetElementPtrInst>(AddrPart)->setIsInBounds(InBounds);
// Cast to the vector pointer type.
unsigned AddressSpace = AddrPart->getType()->getPointerAddressSpace();
Type *PtrTy = VecTy->getPointerTo(AddressSpace);
AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy));
}
setDebugLocFromInst(Builder, Instr);
Value *UndefVec = UndefValue::get(VecTy);
Value *MaskForGaps = nullptr;
if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group);
assert(MaskForGaps && "Mask for Gaps is required but it is null");
}
// Vectorize the interleaved load group.
if (isa<LoadInst>(Instr)) {
// For each unroll part, create a wide load for the group.
SmallVector<Value *, 2> NewLoads;
for (unsigned Part = 0; Part < UF; Part++) {
Instruction *NewLoad;
if (BlockInMask || MaskForGaps) {
assert(useMaskedInterleavedAccesses(*TTI) &&
"masked interleaved groups are not allowed.");
Value *GroupMask = MaskForGaps;
if (BlockInMask) {
Value *BlockInMaskPart = State.get(BlockInMask, Part);
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Value *ShuffledMask = Builder.CreateShuffleVector(
BlockInMaskPart,
createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()),
"interleaved.mask");
GroupMask = MaskForGaps
? Builder.CreateBinOp(Instruction::And, ShuffledMask,
MaskForGaps)
: ShuffledMask;
}
NewLoad =
Builder.CreateMaskedLoad(AddrParts[Part], Group->getAlign(),
GroupMask, UndefVec, "wide.masked.vec");
}
else
NewLoad = Builder.CreateAlignedLoad(VecTy, AddrParts[Part],
Group->getAlign(), "wide.vec");
Group->addMetadata(NewLoad);
NewLoads.push_back(NewLoad);
}
// For each member in the group, shuffle out the appropriate data from the
// wide loads.
for (unsigned I = 0; I < InterleaveFactor; ++I) {
Instruction *Member = Group->getMember(I);
// Skip the gaps in the group.
if (!Member)
continue;
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto StrideMask =
createStrideMask(I, InterleaveFactor, VF.getKnownMinValue());
for (unsigned Part = 0; Part < UF; Part++) {
Value *StridedVec = Builder.CreateShuffleVector(
NewLoads[Part], StrideMask, "strided.vec");
// If this member has different type, cast the result type.
if (Member->getType() != ScalarTy) {
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
VectorType *OtherVTy = VectorType::get(Member->getType(), VF);
StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL);
}
if (Group->isReverse())
StridedVec = reverseVector(StridedVec);
VectorLoopValueMap.setVectorValue(Member, Part, StridedVec);
}
}
return;
}
// The sub vector type for current instruction.
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
auto *SubVT = VectorType::get(ScalarTy, VF);
// Vectorize the interleaved store group.
for (unsigned Part = 0; Part < UF; Part++) {
// Collect the stored vector from each member.
SmallVector<Value *, 4> StoredVecs;
for (unsigned i = 0; i < InterleaveFactor; i++) {
// Interleaved store group doesn't allow a gap, so each index has a member
Instruction *Member = Group->getMember(i);
assert(Member && "Fail to get a member from an interleaved store group");
Value *StoredVec = getOrCreateVectorValue(
cast<StoreInst>(Member)->getValueOperand(), Part);
if (Group->isReverse())
StoredVec = reverseVector(StoredVec);
// If this member has different type, cast it to a unified type.
if (StoredVec->getType() != SubVT)
StoredVec = createBitOrPointerCast(StoredVec, SubVT, DL);
StoredVecs.push_back(StoredVec);
}
// Concatenate all vectors into a wide vector.
Value *WideVec = concatenateVectors(Builder, StoredVecs);
// Interleave the elements in the wide vector.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Value *IVec = Builder.CreateShuffleVector(
WideVec, createInterleaveMask(VF.getKnownMinValue(), InterleaveFactor),
"interleaved.vec");
Instruction *NewStoreInstr;
if (BlockInMask) {
Value *BlockInMaskPart = State.get(BlockInMask, Part);
Value *ShuffledMask = Builder.CreateShuffleVector(
BlockInMaskPart,
createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()),
"interleaved.mask");
NewStoreInstr = Builder.CreateMaskedStore(
IVec, AddrParts[Part], Group->getAlign(), ShuffledMask);
}
else
NewStoreInstr =
Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign());
Group->addMetadata(NewStoreInstr);
}
}
void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr,
VPTransformState &State,
VPValue *Addr,
VPValue *StoredValue,
VPValue *BlockInMask) {
// 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");
assert((!SI || StoredValue) && "No stored value provided for widened store");
assert((!LI || !StoredValue) && "Stored value provided for widened load");
LoopVectorizationCostModel::InstWidening Decision =
Cost->getWideningDecision(Instr, VF);
assert((Decision == LoopVectorizationCostModel::CM_Widen ||
Decision == LoopVectorizationCostModel::CM_Widen_Reverse ||
Decision == LoopVectorizationCostModel::CM_GatherScatter) &&
"CM decision is not to widen the memory instruction");
Type *ScalarDataTy = getMemInstValueType(Instr);
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto *DataTy = VectorType::get(ScalarDataTy, VF);
const Align Alignment = getLoadStoreAlignment(Instr);
// Determine if the pointer operand of the access is either consecutive or
// reverse consecutive.
bool Reverse = (Decision == LoopVectorizationCostModel::CM_Widen_Reverse);
bool ConsecutiveStride =
Reverse || (Decision == LoopVectorizationCostModel::CM_Widen);
bool CreateGatherScatter =
(Decision == LoopVectorizationCostModel::CM_GatherScatter);
// Either Ptr feeds a vector load/store, or a vector GEP should feed a vector
// gather/scatter. Otherwise Decision should have been to Scalarize.
assert((ConsecutiveStride || CreateGatherScatter) &&
"The instruction should be scalarized");
(void)ConsecutiveStride;
VectorParts BlockInMaskParts(UF);
bool isMaskRequired = BlockInMask;
if (isMaskRequired)
for (unsigned Part = 0; Part < UF; ++Part)
BlockInMaskParts[Part] = State.get(BlockInMask, Part);
const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * {
// Calculate the pointer for the specific unroll-part.
GetElementPtrInst *PartPtr = nullptr;
bool InBounds = false;
if (auto *gep = dyn_cast<GetElementPtrInst>(Ptr->stripPointerCasts()))
InBounds = gep->isInBounds();
if (Reverse) {
// If the address is consecutive but reversed, then the
// wide store needs to start at the last vector element.
PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
ScalarDataTy, Ptr, Builder.getInt32(-Part * VF.getKnownMinValue())));
PartPtr->setIsInBounds(InBounds);
PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
ScalarDataTy, PartPtr, Builder.getInt32(1 - VF.getKnownMinValue())));
PartPtr->setIsInBounds(InBounds);
if (isMaskRequired) // Reverse of a null all-one mask is a null mask.
BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]);
} else {
PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
ScalarDataTy, Ptr, Builder.getInt32(Part * VF.getKnownMinValue())));
PartPtr->setIsInBounds(InBounds);
}
unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace();
return Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
};
// Handle Stores:
if (SI) {
setDebugLocFromInst(Builder, SI);
for (unsigned Part = 0; Part < UF; ++Part) {
Instruction *NewSI = nullptr;
Value *StoredVal = State.get(StoredValue, Part);
if (CreateGatherScatter) {
Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr;
Value *VectorGep = State.get(Addr, Part);
NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment,
MaskPart);
} else {
if (Reverse) {
// If we store to reverse consecutive memory locations, then we need
// to reverse the order of elements in the stored value.
StoredVal = reverseVector(StoredVal);
// We don't want to update the value in the map as it might be used in
// another expression. So don't call resetVectorValue(StoredVal).
}
auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0}));
if (isMaskRequired)
NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment,
BlockInMaskParts[Part]);
else
NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment);
}
addMetadata(NewSI, SI);
}
return;
}
// Handle loads.
assert(LI && "Must have a load instruction");
setDebugLocFromInst(Builder, LI);
for (unsigned Part = 0; Part < UF; ++Part) {
Value *NewLI;
if (CreateGatherScatter) {
Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr;
Value *VectorGep = State.get(Addr, Part);
NewLI = Builder.CreateMaskedGather(VectorGep, Alignment, MaskPart,
nullptr, "wide.masked.gather");
addMetadata(NewLI, LI);
} else {
auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0}));
if (isMaskRequired)
NewLI = Builder.CreateMaskedLoad(
VecPtr, Alignment, BlockInMaskParts[Part], UndefValue::get(DataTy),
"wide.masked.load");
else
NewLI =
Builder.CreateAlignedLoad(DataTy, VecPtr, Alignment, "wide.load");
// Add metadata to the load, but setVectorValue to the reverse shuffle.
addMetadata(NewLI, LI);
if (Reverse)
NewLI = reverseVector(NewLI);
}
VectorLoopValueMap.setVectorValue(Instr, Part, NewLI);
}
}
void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, VPUser &User,
const VPIteration &Instance,
bool IfPredicateInstr,
VPTransformState &State) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
setDebugLocFromInst(Builder, Instr);
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy)
Cloned->setName(Instr->getName() + ".cloned");
// Replace the operands of the cloned instructions with their scalar
// equivalents in the new loop.
for (unsigned op = 0, e = User.getNumOperands(); op != e; ++op) {
auto *NewOp = State.get(User.getOperand(op), Instance);
Cloned->setOperand(op, NewOp);
}
addNewMetadata(Cloned, Instr);
// Place the cloned scalar in the new loop.
Builder.Insert(Cloned);
// Add the cloned scalar to the scalar map entry.
VectorLoopValueMap.setScalarValue(Instr, Instance, Cloned);
// If we just cloned a new assumption, add it the assumption cache.
if (auto *II = dyn_cast<IntrinsicInst>(Cloned))
if (II->getIntrinsicID() == Intrinsic::assume)
AC->registerAssumption(II);
// End if-block.
if (IfPredicateInstr)
PredicatedInstructions.push_back(Cloned);
}
PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start,
Value *End, Value *Step,
Instruction *DL) {
BasicBlock *Header = L->getHeader();
BasicBlock *Latch = L->getLoopLatch();
// As we're just creating this loop, it's possible no latch exists
// yet. If so, use the header as this will be a single block loop.
if (!Latch)
Latch = Header;
IRBuilder<> Builder(&*Header->getFirstInsertionPt());
Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction);
setDebugLocFromInst(Builder, OldInst);
auto *Induction = Builder.CreatePHI(Start->getType(), 2, "index");
Builder.SetInsertPoint(Latch->getTerminator());
setDebugLocFromInst(Builder, OldInst);
// Create i+1 and fill the PHINode.
Value *Next = Builder.CreateAdd(Induction, Step, "index.next");
Induction->addIncoming(Start, L->getLoopPreheader());
Induction->addIncoming(Next, Latch);
// Create the compare.
Value *ICmp = Builder.CreateICmpEQ(Next, End);
Builder.CreateCondBr(ICmp, L->getExitBlock(), Header);
// Now we have two terminators. Remove the old one from the block.
Latch->getTerminator()->eraseFromParent();
return Induction;
}
Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) {
if (TripCount)
return TripCount;
assert(L && "Create Trip Count for null loop.");
IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
// Find the loop boundaries.
ScalarEvolution *SE = PSE.getSE();
const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount();
assert(BackedgeTakenCount != SE->getCouldNotCompute() &&
"Invalid loop count");
Type *IdxTy = Legal->getWidestInductionType();
assert(IdxTy && "No type for induction");
// 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 (SE->getTypeSizeInBits(BackedgeTakenCount->getType()) >
IdxTy->getPrimitiveSizeInBits())
BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy);
BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy);
// Get the total trip count from the count by adding 1.
const SCEV *ExitCount = SE->getAddExpr(
BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType()));
const DataLayout &DL = L->getHeader()->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");
// Count holds the overall loop count (N).
TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
L->getLoopPreheader()->getTerminator());
if (TripCount->getType()->isPointerTy())
TripCount =
CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int",
L->getLoopPreheader()->getTerminator());
return TripCount;
}
Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) {
if (VectorTripCount)
return VectorTripCount;
Value *TC = getOrCreateTripCount(L);
IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
Type *Ty = TC->getType();
// This is where we can make the step a runtime constant.
assert(!VF.isScalable() && "scalable vectorization is not supported yet");
Constant *Step = ConstantInt::get(Ty, VF.getKnownMinValue() * UF);
// If the tail is to be folded by masking, round the number of iterations N
// up to a multiple of Step instead of rounding down. This is done by first
// adding Step-1 and then rounding down. Note that it's ok if this addition
// overflows: the vector induction variable will eventually wrap to zero given
// that it starts at zero and its Step is a power of two; the loop will then
// exit, with the last early-exit vector comparison also producing all-true.
if (Cost->foldTailByMasking()) {
assert(isPowerOf2_32(VF.getKnownMinValue() * UF) &&
"VF*UF must be a power of 2 when folding tail by masking");
TC = Builder.CreateAdd(
TC, ConstantInt::get(Ty, VF.getKnownMinValue() * UF - 1), "n.rnd.up");
}
// Now we need to generate the expression for the part of the loop that the
// vectorized body will execute. This is equal to N - (N % Step) if scalar
// iterations are not required for correctness, or N - Step, otherwise. Step
// is equal to the vectorization factor (number of SIMD elements) times the
// unroll factor (number of SIMD instructions).
Value *R = Builder.CreateURem(TC, Step, "n.mod.vf");
// If there is a non-reversed interleaved group that may speculatively access
// memory out-of-bounds, we need to ensure that there will be at least one
// iteration of the scalar epilogue loop. Thus, if the step evenly divides
// the trip count, we set the remainder to be equal to the step. If the step
// does not evenly divide the trip count, no adjustment is necessary since
// there will already be scalar iterations. Note that the minimum iterations
// check ensures that N >= Step.
if (VF.isVector() && Cost->requiresScalarEpilogue()) {
auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0));
R = Builder.CreateSelect(IsZero, Step, R);
}
VectorTripCount = Builder.CreateSub(TC, R, "n.vec");
return VectorTripCount;
}
Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy,
const DataLayout &DL) {
// Verify that V is a vector type with same number of elements as DstVTy.
auto *DstFVTy = cast<FixedVectorType>(DstVTy);
unsigned VF = DstFVTy->getNumElements();
auto *SrcVecTy = cast<FixedVectorType>(V->getType());
assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match");
Type *SrcElemTy = SrcVecTy->getElementType();
Type *DstElemTy = DstFVTy->getElementType();
assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) &&
"Vector elements must have same size");
// Do a direct cast if element types are castable.
if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) {
return Builder.CreateBitOrPointerCast(V, DstFVTy);
}
// V cannot be directly casted to desired vector type.
// May happen when V is a floating point vector but DstVTy is a vector of
// pointers or vice-versa. Handle this using a two-step bitcast using an
// intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float.
assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) &&
"Only one type should be a pointer type");
assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) &&
"Only one type should be a floating point type");
Type *IntTy =
IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy));
auto *VecIntTy = FixedVectorType::get(IntTy, VF);
Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy);
return Builder.CreateBitOrPointerCast(CastVal, DstFVTy);
}
void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L,
BasicBlock *Bypass) {
Value *Count = getOrCreateTripCount(L);
// Reuse existing vector loop preheader for TC checks.
// Note that new preheader block is generated for vector loop.
BasicBlock *const TCCheckBlock = LoopVectorPreHeader;
IRBuilder<> Builder(TCCheckBlock->getTerminator());
// Generate code to check if the loop's trip count is less than VF * UF, or
// equal to it in case a scalar epilogue is required; this implies that the
// vector trip count is zero. This check also covers the case where adding one
// to the backedge-taken count overflowed leading to an incorrect trip count
// of zero. In this case we will also jump to the scalar loop.
auto P = Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE
: ICmpInst::ICMP_ULT;
// If tail is to be folded, vector loop takes care of all iterations.
Value *CheckMinIters = Builder.getFalse();
if (!Cost->foldTailByMasking()) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
CheckMinIters = Builder.CreateICmp(
P, Count,
ConstantInt::get(Count->getType(), VF.getKnownMinValue() * UF),
"min.iters.check");
}
// Create new preheader for vector loop.
LoopVectorPreHeader =
SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr,
"vector.ph");
assert(DT->properlyDominates(DT->getNode(TCCheckBlock),
DT->getNode(Bypass)->getIDom()) &&
"TC check is expected to dominate Bypass");
// Update dominator for Bypass & LoopExit.
DT->changeImmediateDominator(Bypass, TCCheckBlock);
DT->changeImmediateDominator(LoopExitBlock, TCCheckBlock);
ReplaceInstWithInst(
TCCheckBlock->getTerminator(),
BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters));
LoopBypassBlocks.push_back(TCCheckBlock);
}
void InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) {
// Reuse existing vector loop preheader for SCEV checks.
// Note that new preheader block is generated for vector loop.
BasicBlock *const SCEVCheckBlock = LoopVectorPreHeader;
// Generate the code to check that the SCEV assumptions that we made.
// We want the new basic block to start at the first instruction in a
// sequence of instructions that form a check.
SCEVExpander Exp(*PSE.getSE(), Bypass->getModule()->getDataLayout(),
"scev.check");
Value *SCEVCheck = Exp.expandCodeForPredicate(
&PSE.getUnionPredicate(), SCEVCheckBlock->getTerminator());
if (auto *C = dyn_cast<ConstantInt>(SCEVCheck))
if (C->isZero())
return;
assert(!(SCEVCheckBlock->getParent()->hasOptSize() ||
(OptForSizeBasedOnProfile &&
Cost->Hints->getForce() != LoopVectorizeHints::FK_Enabled)) &&
"Cannot SCEV check stride or overflow when optimizing for size");
SCEVCheckBlock->setName("vector.scevcheck");
// Create new preheader for vector loop.
LoopVectorPreHeader =
SplitBlock(SCEVCheckBlock, SCEVCheckBlock->getTerminator(), DT, LI,
nullptr, "vector.ph");
// Update dominator only if this is first RT check.
if (LoopBypassBlocks.empty()) {
DT->changeImmediateDominator(Bypass, SCEVCheckBlock);
DT->changeImmediateDominator(LoopExitBlock, SCEVCheckBlock);
}
ReplaceInstWithInst(
SCEVCheckBlock->getTerminator(),
BranchInst::Create(Bypass, LoopVectorPreHeader, SCEVCheck));
LoopBypassBlocks.push_back(SCEVCheckBlock);
AddedSafetyChecks = true;
}
void InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass) {
// VPlan-native path does not do any analysis for runtime checks currently.
if (EnableVPlanNativePath)
return;
// Reuse existing vector loop preheader for runtime memory checks.
// Note that new preheader block is generated for vector loop.
BasicBlock *const MemCheckBlock = L->getLoopPreheader();
// 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.
auto *LAI = Legal->getLAI();
const auto &RtPtrChecking = *LAI->getRuntimePointerChecking();
if (!RtPtrChecking.Need)
return;
if (MemCheckBlock->getParent()->hasOptSize() || OptForSizeBasedOnProfile) {
assert(Cost->Hints->getForce() == LoopVectorizeHints::FK_Enabled &&
"Cannot emit memory checks when optimizing for size, unless forced "
"to vectorize.");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationCodeSize",
L->getStartLoc(), L->getHeader())
<< "Code-size may be reduced by not forcing "
"vectorization, or by source-code modifications "
"eliminating the need for runtime checks "
"(e.g., adding 'restrict').";
});
}
MemCheckBlock->setName("vector.memcheck");
// Create new preheader for vector loop.
LoopVectorPreHeader =
SplitBlock(MemCheckBlock, MemCheckBlock->getTerminator(), DT, LI, nullptr,
"vector.ph");
auto *CondBranch = cast<BranchInst>(
Builder.CreateCondBr(Builder.getTrue(), Bypass, LoopVectorPreHeader));
ReplaceInstWithInst(MemCheckBlock->getTerminator(), CondBranch);
LoopBypassBlocks.push_back(MemCheckBlock);
AddedSafetyChecks = true;
// Update dominator only if this is first RT check.
if (LoopBypassBlocks.empty()) {
DT->changeImmediateDominator(Bypass, MemCheckBlock);
DT->changeImmediateDominator(LoopExitBlock, MemCheckBlock);
}
Instruction *FirstCheckInst;
Instruction *MemRuntimeCheck;
std::tie(FirstCheckInst, MemRuntimeCheck) =
addRuntimeChecks(MemCheckBlock->getTerminator(), OrigLoop,
RtPtrChecking.getChecks(), RtPtrChecking.getSE());
assert(MemRuntimeCheck && "no RT checks generated although RtPtrChecking "
"claimed checks are required");
CondBranch->setCondition(MemRuntimeCheck);
// We currently don't use LoopVersioning for the actual loop cloning but we
// still use it to add the noalias metadata.
LVer = std::make_unique<LoopVersioning>(*Legal->getLAI(), OrigLoop, LI, DT,
PSE.getSE());
LVer->prepareNoAliasMetadata();
}
Value *InnerLoopVectorizer::emitTransformedIndex(
IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL,
const InductionDescriptor &ID) const {
SCEVExpander Exp(*SE, DL, "induction");
auto Step = ID.getStep();
auto StartValue = ID.getStartValue();
assert(Index->getType() == Step->getType() &&
"Index type does not match StepValue type");
// Note: the IR at this point is broken. We cannot use SE to create any new
// SCEV and then expand it, hoping that SCEV's simplification will give us
// a more optimal code. Unfortunately, attempt of doing so on invalid IR may
// lead to various SCEV crashes. So all we can do is to use builder and rely
// on InstCombine for future simplifications. Here we handle some trivial
// cases only.
auto CreateAdd = [&B](Value *X, Value *Y) {
assert(X->getType() == Y->getType() && "Types don't match!");
if (auto *CX = dyn_cast<ConstantInt>(X))
if (CX->isZero())
return Y;
if (auto *CY = dyn_cast<ConstantInt>(Y))
if (CY->isZero())
return X;
return B.CreateAdd(X, Y);
};
auto CreateMul = [&B](Value *X, Value *Y) {
assert(X->getType() == Y->getType() && "Types don't match!");
if (auto *CX = dyn_cast<ConstantInt>(X))
if (CX->isOne())
return Y;
if (auto *CY = dyn_cast<ConstantInt>(Y))
if (CY->isOne())
return X;
return B.CreateMul(X, Y);
};
// Get a suitable insert point for SCEV expansion. For blocks in the vector
// loop, choose the end of the vector loop header (=LoopVectorBody), because
// the DomTree is not kept up-to-date for additional blocks generated in the
// vector loop. By using the header as insertion point, we guarantee that the
// expanded instructions dominate all their uses.
auto GetInsertPoint = [this, &B]() {
BasicBlock *InsertBB = B.GetInsertPoint()->getParent();
if (InsertBB != LoopVectorBody &&
LI->getLoopFor(LoopVectorBody) == LI->getLoopFor(InsertBB))
return LoopVectorBody->getTerminator();
return &*B.GetInsertPoint();
};
switch (ID.getKind()) {
case InductionDescriptor::IK_IntInduction: {
assert(Index->getType() == StartValue->getType() &&
"Index type does not match StartValue type");
if (ID.getConstIntStepValue() && ID.getConstIntStepValue()->isMinusOne())
return B.CreateSub(StartValue, Index);
auto *Offset = CreateMul(
Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint()));
return CreateAdd(StartValue, Offset);
}
case InductionDescriptor::IK_PtrInduction: {
assert(isa<SCEVConstant>(Step) &&
"Expected constant step for pointer induction");
return B.CreateGEP(
StartValue->getType()->getPointerElementType(), StartValue,
CreateMul(Index,
Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint())));
}
case InductionDescriptor::IK_FpInduction: {
assert(Step->getType()->isFloatingPointTy() && "Expected FP Step value");
auto InductionBinOp = ID.getInductionBinOp();
assert(InductionBinOp &&
(InductionBinOp->getOpcode() == Instruction::FAdd ||
InductionBinOp->getOpcode() == Instruction::FSub) &&
"Original bin op should be defined for FP induction");
Value *StepValue = cast<SCEVUnknown>(Step)->getValue();
// Floating point operations had to be 'fast' to enable the induction.
FastMathFlags Flags;
Flags.setFast();
Value *MulExp = B.CreateFMul(StepValue, Index);
if (isa<Instruction>(MulExp))
// We have to check, the MulExp may be a constant.
cast<Instruction>(MulExp)->setFastMathFlags(Flags);
Value *BOp = B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp,
"induction");
if (isa<Instruction>(BOp))
cast<Instruction>(BOp)->setFastMathFlags(Flags);
return BOp;
}
case InductionDescriptor::IK_NoInduction:
return nullptr;
}
llvm_unreachable("invalid enum");
}
Loop *InnerLoopVectorizer::createVectorLoopSkeleton(StringRef Prefix) {
LoopScalarBody = OrigLoop->getHeader();
LoopVectorPreHeader = OrigLoop->getLoopPreheader();
LoopExitBlock = OrigLoop->getExitBlock();
assert(LoopExitBlock && "Must have an exit block");
assert(LoopVectorPreHeader && "Invalid loop structure");
LoopMiddleBlock =
SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT,
LI, nullptr, Twine(Prefix) + "middle.block");
LoopScalarPreHeader =
SplitBlock(LoopMiddleBlock, LoopMiddleBlock->getTerminator(), DT, LI,
nullptr, Twine(Prefix) + "scalar.ph");
// We intentionally don't let SplitBlock to update LoopInfo since
// LoopVectorBody should belong to another loop than LoopVectorPreHeader.
// LoopVectorBody is explicitly added to the correct place few lines later.
LoopVectorBody =
SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT,
nullptr, nullptr, Twine(Prefix) + "vector.body");
// Update dominator for loop exit.
DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock);
// Create and register the new vector loop.
Loop *Lp = LI->AllocateLoop();
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);
} else {
LI->addTopLevelLoop(Lp);
}
Lp->addBasicBlockToLoop(LoopVectorBody, *LI);
return Lp;
}
void InnerLoopVectorizer::createInductionResumeValues(Loop *L,
Value *VectorTripCount) {
assert(VectorTripCount && L && "Expected valid arguments");
// 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.
for (auto &InductionEntry : Legal->getInductionVars()) {
PHINode *OrigPhi = InductionEntry.first;
InductionDescriptor II = InductionEntry.second;
// Create phi nodes to merge from the backedge-taken check block.
PHINode *BCResumeVal =
PHINode::Create(OrigPhi->getType(), 3, "bc.resume.val",
LoopScalarPreHeader->getTerminator());
// Copy original phi DL over to the new one.
BCResumeVal->setDebugLoc(OrigPhi->getDebugLoc());
Value *&EndValue = IVEndValues[OrigPhi];
if (OrigPhi == OldInduction) {
// We know what the end value is.
EndValue = VectorTripCount;
} else {
IRBuilder<> B(L->getLoopPreheader()->getTerminator());
Type *StepType = II.getStep()->getType();
Instruction::CastOps CastOp =
CastInst::getCastOpcode(VectorTripCount, true, StepType, true);
Value *CRD = B.CreateCast(CastOp, VectorTripCount, StepType, "cast.crd");
const DataLayout &DL = LoopScalarBody->getModule()->getDataLayout();
EndValue = emitTransformedIndex(B, CRD, PSE.getSE(), DL, II);
EndValue->setName("ind.end");
}
// The new PHI merges the original incoming value, in case of a bypass,
// or the value at the end of the vectorized loop.
BCResumeVal->addIncoming(EndValue, LoopMiddleBlock);
// Fix the scalar body counter (PHI node).
// The old induction's phi node in the scalar body needs the truncated
// value.
for (BasicBlock *BB : LoopBypassBlocks)
BCResumeVal->addIncoming(II.getStartValue(), BB);
OrigPhi->setIncomingValueForBlock(LoopScalarPreHeader, BCResumeVal);
}
}
BasicBlock *InnerLoopVectorizer::completeLoopSkeleton(Loop *L,
MDNode *OrigLoopID) {
assert(L && "Expected valid loop.");
// The trip counts should be cached by now.
Value *Count = getOrCreateTripCount(L);
Value *VectorTripCount = getOrCreateVectorTripCount(L);
// We need the OrigLoop (scalar loop part) latch terminator to help
// produce correct debug info for the middle block BB instructions.
// The legality check stage guarantees that the loop will have a single
// latch.
assert(isa<BranchInst>(OrigLoop->getLoopLatch()->getTerminator()) &&
"Scalar loop latch terminator isn't a branch");
BranchInst *ScalarLatchBr =
cast<BranchInst>(OrigLoop->getLoopLatch()->getTerminator());
// 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.
// If tail is to be folded, we know we don't need to run the remainder.
Value *CmpN = Builder.getTrue();
if (!Cost->foldTailByMasking()) {
CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, Count,
VectorTripCount, "cmp.n",
LoopMiddleBlock->getTerminator());
// Here we use the same DebugLoc as the scalar loop latch branch instead
// of the corresponding compare because they may have ended up with
// different line numbers and we want to avoid awkward line stepping while
// debugging. Eg. if the compare has got a line number inside the loop.
cast<Instruction>(CmpN)->setDebugLoc(ScalarLatchBr->getDebugLoc());
}
BranchInst *BrInst =
BranchInst::Create(LoopExitBlock, LoopScalarPreHeader, CmpN);
BrInst->setDebugLoc(ScalarLatchBr->getDebugLoc());
ReplaceInstWithInst(LoopMiddleBlock->getTerminator(), BrInst);
// Get ready to start creating new instructions into the vectorized body.
assert(LoopVectorPreHeader == L->getLoopPreheader() &&
"Inconsistent vector loop preheader");
Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt());
Optional<MDNode *> VectorizedLoopID =
makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll,
LLVMLoopVectorizeFollowupVectorized});
if (VectorizedLoopID.hasValue()) {
L->setLoopID(VectorizedLoopID.getValue());
// Do not setAlreadyVectorized if loop attributes have been defined
// explicitly.
return LoopVectorPreHeader;
}
// Keep all loop hints from the original loop on the vector loop (we'll
// replace the vectorizer-specific hints below).
if (MDNode *LID = OrigLoop->getLoopID())
L->setLoopID(LID);
LoopVectorizeHints Hints(L, true, *ORE);
Hints.setAlreadyVectorized();
#ifdef EXPENSIVE_CHECKS
assert(DT->verify(DominatorTree::VerificationLevel::Fast));
LI->verify(*DT);
#endif
return LoopVectorPreHeader;
}
BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() {
/*
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.
[ ] <-- loop iteration number 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.
...
*/
// Get the metadata of the original loop before it gets modified.
MDNode *OrigLoopID = OrigLoop->getLoopID();
// Create an empty vector loop, and prepare basic blocks for the runtime
// checks.
Loop *Lp = createVectorLoopSkeleton("");
// Now, compare the new count to zero. If it is zero skip the vector loop and
// jump to the scalar loop. This check also covers the case where the
// backedge-taken count is uint##_max: adding one to it will overflow leading
// to an incorrect trip count of zero. In this (rare) case we will also jump
// to the scalar loop.
emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader);
// Generate the code to check any assumptions that we've made for SCEV
// expressions.
emitSCEVChecks(Lp, LoopScalarPreHeader);
// 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.
emitMemRuntimeChecks(Lp, LoopScalarPreHeader);
// 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.
//
// We try to obtain an induction variable from the original loop as hard
// as possible. However if we don't find one that:
// - is an integer
// - counts from zero, stepping by one
// - is the size of the widest induction variable type
// then we create a new one.
OldInduction = Legal->getPrimaryInduction();
Type *IdxTy = Legal->getWidestInductionType();
Value *StartIdx = ConstantInt::get(IdxTy, 0);
// The loop step is equal to the vectorization factor (num of SIMD elements)
// times the unroll factor (num of SIMD instructions).
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Constant *Step = ConstantInt::get(IdxTy, VF.getKnownMinValue() * UF);
Value *CountRoundDown = getOrCreateVectorTripCount(Lp);
Induction =
createInductionVariable(Lp, StartIdx, CountRoundDown, Step,
getDebugLocFromInstOrOperands(OldInduction));
// Emit phis for the new starting index of the scalar loop.
createInductionResumeValues(Lp, CountRoundDown);
return completeLoopSkeleton(Lp, OrigLoopID);
}
// Fix up external users of the induction variable. At this point, we are
// in LCSSA form, with all external PHIs that use the IV having one input value,
// coming from the remainder loop. We need those PHIs to also have a correct
// value for the IV when arriving directly from the middle block.
void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi,
const InductionDescriptor &II,
Value *CountRoundDown, Value *EndValue,
BasicBlock *MiddleBlock) {
// There are two kinds of external IV usages - those that use the value
// computed in the last iteration (the PHI) and those that use the penultimate
// value (the value that feeds into the phi from the loop latch).
// We allow both, but they, obviously, have different values.
assert(OrigLoop->getExitBlock() && "Expected a single exit block");
DenseMap<Value *, Value *> MissingVals;
// An external user of the last iteration's value should see the value that
// the remainder loop uses to initialize its own IV.
Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch());
for (User *U : PostInc->users()) {
Instruction *UI = cast<Instruction>(U);
if (!OrigLoop->contains(UI)) {
assert(isa<PHINode>(UI) && "Expected LCSSA form");
MissingVals[UI] = EndValue;
}
}
// An external user of the penultimate value need to see EndValue - Step.
// The simplest way to get this is to recompute it from the constituent SCEVs,
// that is Start + (Step * (CRD - 1)).
for (User *U : OrigPhi->users()) {
auto *UI = cast<Instruction>(U);
if (!OrigLoop->contains(UI)) {
const DataLayout &DL =
OrigLoop->getHeader()->getModule()->getDataLayout();
assert(isa<PHINode>(UI) && "Expected LCSSA form");
IRBuilder<> B(MiddleBlock->getTerminator());
Value *CountMinusOne = B.CreateSub(
CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1));
Value *CMO =
!II.getStep()->getType()->isIntegerTy()
? B.CreateCast(Instruction::SIToFP, CountMinusOne,
II.getStep()->getType())
: B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType());
CMO->setName("cast.cmo");
Value *Escape = emitTransformedIndex(B, CMO, PSE.getSE(), DL, II);
Escape->setName("ind.escape");
MissingVals[UI] = Escape;
}
}
for (auto &I : MissingVals) {
PHINode *PHI = cast<PHINode>(I.first);
// One corner case we have to handle is two IVs "chasing" each-other,
// that is %IV2 = phi [...], [ %IV1, %latch ]
// In this case, if IV1 has an external use, we need to avoid adding both
// "last value of IV1" and "penultimate value of IV2". So, verify that we
// don't already have an incoming value for the middle block.
if (PHI->getBasicBlockIndex(MiddleBlock) == -1)
PHI->addIncoming(I.second, MiddleBlock);
}
}
namespace {
struct CSEDenseMapInfo {
static bool canHandle(const 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(const 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(const Instruction *LHS, const Instruction *RHS) {
if (LHS == getEmptyKey() || RHS == getEmptyKey() ||
LHS == getTombstoneKey() || RHS == getTombstoneKey())
return LHS == RHS;
return LHS->isIdenticalTo(RHS);
}
};
} // end anonymous namespace
///Perform cse of induction variable instructions.
static void cse(BasicBlock *BB) {
// Perform simple cse.
SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
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;
}
CSEMap[In] = In;
}
}
unsigned LoopVectorizationCostModel::getVectorCallCost(CallInst *CI,
ElementCount VF,
bool &NeedToScalarize) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Function *F = CI->getCalledFunction();
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,
TTI::TCK_RecipThroughput);
if (VF.isScalar())
return ScalarCallCost;
// Compute corresponding vector type for return value and arguments.
Type *RetTy = ToVectorTy(ScalarRetTy, VF);
for (Type *ScalarTy : ScalarTys)
Tys.push_back(ToVectorTy(ScalarTy, VF));
// Compute costs of unpacking argument values for the scalar calls and
// packing the return values to a vector.
unsigned ScalarizationCost = getScalarizationOverhead(CI, VF);
unsigned Cost = ScalarCallCost * VF.getKnownMinValue() + 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;
VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/);
Function *VecFunc = VFDatabase(*CI).getVectorizedFunction(Shape);
if (!TLI || CI->isNoBuiltin() || !VecFunc)
return Cost;
// If the corresponding vector cost is cheaper, return its cost.
unsigned VectorCallCost = TTI.getCallInstrCost(nullptr, RetTy, Tys,
TTI::TCK_RecipThroughput);
if (VectorCallCost < Cost) {
NeedToScalarize = false;
return VectorCallCost;
}
return Cost;
}
unsigned LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI,
ElementCount VF) {
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
assert(ID && "Expected intrinsic call!");
IntrinsicCostAttributes CostAttrs(ID, *CI, VF);
return TTI.getIntrinsicInstrCost(CostAttrs,
TargetTransformInfo::TCK_RecipThroughput);
}
static Type *smallestIntegerVectorType(Type *T1, Type *T2) {
auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType());
auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType());
return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2;
}
static Type *largestIntegerVectorType(Type *T1, Type *T2) {
auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType());
auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType());
return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2;
}
void InnerLoopVectorizer::truncateToMinimalBitwidths() {
// For every instruction `I` in MinBWs, truncate the operands, create a
// truncated version of `I` and reextend its result. InstCombine runs
// later and will remove any ext/trunc pairs.
SmallPtrSet<Value *, 4> Erased;
for (const auto &KV : Cost->getMinimalBitwidths()) {
// If the value wasn't vectorized, we must maintain the original scalar
// type. The absence of the value from VectorLoopValueMap indicates that it
// wasn't vectorized.
if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
continue;
for (unsigned Part = 0; Part < UF; ++Part) {
Value *I = getOrCreateVectorValue(KV.first, Part);
if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I))
continue;
Type *OriginalTy = I->getType();
Type *ScalarTruncatedTy =
IntegerType::get(OriginalTy->getContext(), KV.second);
auto *TruncatedTy = FixedVectorType::get(
ScalarTruncatedTy,
cast<FixedVectorType>(OriginalTy)->getNumElements());
if (TruncatedTy == OriginalTy)
continue;
IRBuilder<> B(cast<Instruction>(I));
auto ShrinkOperand = [&](Value *V) -> Value * {
if (auto *ZI = dyn_cast<ZExtInst>(V))
if (ZI->getSrcTy() == TruncatedTy)
return ZI->getOperand(0);
return B.CreateZExtOrTrunc(V, TruncatedTy);
};
// The actual instruction modification depends on the instruction type,
// unfortunately.
Value *NewI = nullptr;
if (auto *BO = dyn_cast<BinaryOperator>(I)) {
NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)),
ShrinkOperand(BO->getOperand(1)));
// Any wrapping introduced by shrinking this operation shouldn't be
// considered undefined behavior. So, we can't unconditionally copy
// arithmetic wrapping flags to NewI.
cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false);
} else if (auto *CI = dyn_cast<ICmpInst>(I)) {
NewI =
B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)),
ShrinkOperand(CI->getOperand(1)));
} else if (auto *SI = dyn_cast<SelectInst>(I)) {
NewI = B.CreateSelect(SI->getCondition(),
ShrinkOperand(SI->getTrueValue()),
ShrinkOperand(SI->getFalseValue()));
} else if (auto *CI = dyn_cast<CastInst>(I)) {
switch (CI->getOpcode()) {
default:
llvm_unreachable("Unhandled cast!");
case Instruction::Trunc:
NewI = ShrinkOperand(CI->getOperand(0));
break;
case Instruction::SExt:
NewI = B.CreateSExtOrTrunc(
CI->getOperand(0),
smallestIntegerVectorType(OriginalTy, TruncatedTy));
break;
case Instruction::ZExt:
NewI = B.CreateZExtOrTrunc(
CI->getOperand(0),
smallestIntegerVectorType(OriginalTy, TruncatedTy));
break;
}
} else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) {
auto Elements0 = cast<FixedVectorType>(SI->getOperand(0)->getType())
->getNumElements();
auto *O0 = B.CreateZExtOrTrunc(
SI->getOperand(0),
FixedVectorType::get(ScalarTruncatedTy, Elements0));
auto Elements1 = cast<FixedVectorType>(SI->getOperand(1)->getType())
->getNumElements();
auto *O1 = B.CreateZExtOrTrunc(
SI->getOperand(1),
FixedVectorType::get(ScalarTruncatedTy, Elements1));
NewI = B.CreateShuffleVector(O0, O1, SI->getShuffleMask());
} else if (isa<LoadInst>(I) || isa<PHINode>(I)) {
// Don't do anything with the operands, just extend the result.
continue;
} else if (auto *IE = dyn_cast<InsertElementInst>(I)) {
auto Elements = cast<FixedVectorType>(IE->getOperand(0)->getType())
->getNumElements();
auto *O0 = B.CreateZExtOrTrunc(
IE->getOperand(0),
FixedVectorType::get(ScalarTruncatedTy, Elements));
auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy);
NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2));
} else if (auto *EE = dyn_cast<ExtractElementInst>(I)) {
auto Elements = cast<FixedVectorType>(EE->getOperand(0)->getType())
->getNumElements();
auto *O0 = B.CreateZExtOrTrunc(
EE->getOperand(0),
FixedVectorType::get(ScalarTruncatedTy, Elements));
NewI = B.CreateExtractElement(O0, EE->getOperand(2));
} else {
// If we don't know what to do, be conservative and don't do anything.
continue;
}
// Lastly, extend the result.
NewI->takeName(cast<Instruction>(I));
Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy);
I->replaceAllUsesWith(Res);
cast<Instruction>(I)->eraseFromParent();
Erased.insert(I);
VectorLoopValueMap.resetVectorValue(KV.first, Part, Res);
}
}
// We'll have created a bunch of ZExts that are now parentless. Clean up.
for (const auto &KV : Cost->getMinimalBitwidths()) {
// If the value wasn't vectorized, we must maintain the original scalar
// type. The absence of the value from VectorLoopValueMap indicates that it
// wasn't vectorized.
if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
continue;
for (unsigned Part = 0; Part < UF; ++Part) {
Value *I = getOrCreateVectorValue(KV.first, Part);
ZExtInst *Inst = dyn_cast<ZExtInst>(I);
if (Inst && Inst->use_empty()) {
Value *NewI = Inst->getOperand(0);
Inst->eraseFromParent();
VectorLoopValueMap.resetVectorValue(KV.first, Part, NewI);
}
}
}
}
void InnerLoopVectorizer::fixVectorizedLoop() {
// Insert truncates and extends for any truncated instructions as hints to
// InstCombine.
if (VF.isVector())
truncateToMinimalBitwidths();
// Fix widened non-induction PHIs by setting up the PHI operands.
if (OrigPHIsToFix.size()) {
assert(EnableVPlanNativePath &&
"Unexpected non-induction PHIs for fixup in non VPlan-native path");
fixNonInductionPHIs();
}
// At this point every instruction in the original loop is widened to a
// vector form. Now we need to fix the recurrences in the loop. These PHI
// nodes are currently empty because we did not want to introduce cycles.
// This is the second stage of vectorizing recurrences.
fixCrossIterationPHIs();
// Forget the original basic block.
PSE.getSE()->forgetLoop(OrigLoop);
// Fix-up external users of the induction variables.
for (auto &Entry : Legal->getInductionVars())
fixupIVUsers(Entry.first, Entry.second,
getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)),
IVEndValues[Entry.first], LoopMiddleBlock);
fixLCSSAPHIs();
for (Instruction *PI : PredicatedInstructions)
sinkScalarOperands(&*PI);
// Remove redundant induction instructions.
cse(LoopVectorBody);
// Set/update profile weights for the vector and remainder loops as original
// loop iterations are now distributed among them. Note that original loop
// represented by LoopScalarBody becomes remainder loop after vectorization.
//
// For cases like foldTailByMasking() and requiresScalarEpiloque() we may
// end up getting slightly roughened result but that should be OK since
// profile is not inherently precise anyway. Note also possible bypass of
// vector code caused by legality checks is ignored, assigning all the weight
// to the vector loop, optimistically.
assert(!VF.isScalable() &&
"cannot use scalable ElementCount to determine unroll factor");
setProfileInfoAfterUnrolling(
LI->getLoopFor(LoopScalarBody), LI->getLoopFor(LoopVectorBody),
LI->getLoopFor(LoopScalarBody), VF.getKnownMinValue() * UF);
}
void InnerLoopVectorizer::fixCrossIterationPHIs() {
// In order to support recurrences we need to be able to vectorize Phi nodes.
// Phi nodes have cycles, so we need to vectorize them in two stages. This is
// stage #2: We now need to fix the recurrences by adding incoming edges to
// the currently empty PHI nodes. At this point every instruction in the
// original loop is widened to a vector form so we can use them to construct
// the incoming edges.
for (PHINode &Phi : OrigLoop->getHeader()->phis()) {
// Handle first-order recurrences and reductions that need to be fixed.
if (Legal->isFirstOrderRecurrence(&Phi))
fixFirstOrderRecurrence(&Phi);
else if (Legal->isReductionVariable(&Phi))
fixReduction(&Phi);
}
}
void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) {
// This is the second phase of vectorizing first-order recurrences. An
// overview of the transformation is described below. Suppose we have the
// following loop.
//
// for (int i = 0; i < n; ++i)
// b[i] = a[i] - a[i - 1];
//
// There is a first-order recurrence on "a". For this loop, the shorthand
// scalar IR looks like:
//
// scalar.ph:
// s_init = a[-1]
// br scalar.body
//
// scalar.body:
// i = phi [0, scalar.ph], [i+1, scalar.body]
// s1 = phi [s_init, scalar.ph], [s2, scalar.body]
// s2 = a[i]
// b[i] = s2 - s1
// br cond, scalar.body, ...
//
// In this example, s1 is a recurrence because it's value depends on the
// previous iteration. In the first phase of vectorization, we created a
// temporary value for s1. We now complete the vectorization and produce the
// shorthand vector IR shown below (for VF = 4, UF = 1).
//
// vector.ph:
// v_init = vector(..., ..., ..., a[-1])
// br vector.body
//
// vector.body
// i = phi [0, vector.ph], [i+4, vector.body]
// v1 = phi [v_init, vector.ph], [v2, vector.body]
// v2 = a[i, i+1, i+2, i+3];
// v3 = vector(v1(3), v2(0, 1, 2))
// b[i, i+1, i+2, i+3] = v2 - v3
// br cond, vector.body, middle.block
//
// middle.block:
// x = v2(3)
// br scalar.ph
//
// scalar.ph:
// s_init = phi [x, middle.block], [a[-1], otherwise]
// br scalar.body
//
// After execution completes the vector loop, we extract the next value of
// the recurrence (x) to use as the initial value in the scalar loop.
// Get the original loop preheader and single loop latch.
auto *Preheader = OrigLoop->getLoopPreheader();
auto *Latch = OrigLoop->getLoopLatch();
// Get the initial and previous values of the scalar recurrence.
auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader);
auto *Previous = Phi->getIncomingValueForBlock(Latch);
// Create a vector from the initial value.
auto *VectorInit = ScalarInit;
if (VF.isVector()) {
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
VectorInit = Builder.CreateInsertElement(
UndefValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit,
Builder.getInt32(VF.getKnownMinValue() - 1), "vector.recur.init");
}
// We constructed a temporary phi node in the first phase of vectorization.
// This phi node will eventually be deleted.
Builder.SetInsertPoint(
cast<Instruction>(VectorLoopValueMap.getVectorValue(Phi, 0)));
// Create a phi node for the new recurrence. The current value will either be
// the initial value inserted into a vector or loop-varying vector value.
auto *VecPhi = Builder.CreatePHI(VectorInit->getType(), 2, "vector.recur");
VecPhi->addIncoming(VectorInit, LoopVectorPreHeader);
// Get the vectorized previous value of the last part UF - 1. It appears last
// among all unrolled iterations, due to the order of their construction.
Value *PreviousLastPart = getOrCreateVectorValue(Previous, UF - 1);
// Find and set the insertion point after the previous value if it is an
// instruction.
BasicBlock::iterator InsertPt;
// Note that the previous value may have been constant-folded so it is not
// guaranteed to be an instruction in the vector loop.
// FIXME: Loop invariant values do not form recurrences. We should deal with
// them earlier.
if (LI->getLoopFor(LoopVectorBody)->isLoopInvariant(PreviousLastPart))
InsertPt = LoopVectorBody->getFirstInsertionPt();
else {
Instruction *PreviousInst = cast<Instruction>(PreviousLastPart);
if (isa<PHINode>(PreviousLastPart))
// If the previous value is a phi node, we should insert after all the phi
// nodes in the block containing the PHI to avoid breaking basic block
// verification. Note that the basic block may be different to
// LoopVectorBody, in case we predicate the loop.
InsertPt = PreviousInst->getParent()->getFirstInsertionPt();
else
InsertPt = ++PreviousInst->getIterator();
}
Builder.SetInsertPoint(&*InsertPt);
// We will construct a vector for the recurrence by combining the values for
// the current and previous iterations. This is the required shuffle mask.
assert(!VF.isScalable());
SmallVector<int, 8> ShuffleMask(VF.getKnownMinValue());
ShuffleMask[0] = VF.getKnownMinValue() - 1;
for (unsigned I = 1; I < VF.getKnownMinValue(); ++I)
ShuffleMask[I] = I + VF.getKnownMinValue() - 1;
// The vector from which to take the initial value for the current iteration
// (actual or unrolled). Initially, this is the vector phi node.
Value *Incoming = VecPhi;
// Shuffle the current and previous vector and update the vector parts.
for (unsigned Part = 0; Part < UF; ++Part) {
Value *PreviousPart = getOrCreateVectorValue(Previous, Part);
Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part);
auto *Shuffle =
VF.isVector()
? Builder.CreateShuffleVector(Incoming, PreviousPart, ShuffleMask)
: Incoming;
PhiPart->replaceAllUsesWith(Shuffle);
cast<Instruction>(PhiPart)->eraseFromParent();
VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle);
Incoming = PreviousPart;
}
// Fix the latch value of the new recurrence in the vector loop.
VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
// Extract the last vector element in the middle block. This will be the
// initial value for the recurrence when jumping to the scalar loop.
auto *ExtractForScalar = Incoming;
if (VF.isVector()) {
Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
ExtractForScalar = Builder.CreateExtractElement(
ExtractForScalar, Builder.getInt32(VF.getKnownMinValue() - 1),
"vector.recur.extract");
}
// Extract the second last element in the middle block if the
// Phi is used outside the loop. We need to extract the phi itself
// and not the last element (the phi update in the current iteration). This
// will be the value when jumping to the exit block from the LoopMiddleBlock,
// when the scalar loop is not run at all.
Value *ExtractForPhiUsedOutsideLoop = nullptr;
if (VF.isVector())
ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement(
Incoming, Builder.getInt32(VF.getKnownMinValue() - 2),
"vector.recur.extract.for.phi");
// When loop is unrolled without vectorizing, initialize
// ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of
// `Incoming`. This is analogous to the vectorized case above: extracting the
// second last element when VF > 1.
else if (UF > 1)
ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2);
// Fix the initial value of the original recurrence in the scalar loop.
Builder.SetInsertPoint(&*LoopScalarPreHeader->begin());
auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init");
for (auto *BB : predecessors(LoopScalarPreHeader)) {
auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit;
Start->addIncoming(Incoming, BB);
}
Phi->setIncomingValueForBlock(LoopScalarPreHeader, Start);
Phi->setName("scalar.recur");
// Finally, fix users of the recurrence outside the loop. The users will need
// either the last value of the scalar recurrence or the last value of the
// vector recurrence we extracted in the middle block. Since the loop is in
// LCSSA form, we just need to find all the phi nodes for the original scalar
// recurrence in the exit block, and then add an edge for the middle block.
for (PHINode &LCSSAPhi : LoopExitBlock->phis()) {
if (LCSSAPhi.getIncomingValue(0) == Phi) {
LCSSAPhi.addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock);
}
}
}
void InnerLoopVectorizer::fixReduction(PHINode *Phi) {
Constant *Zero = Builder.getInt32(0);
// Get it's reduction variable descriptor.
assert(Legal->isReductionVariable(Phi) &&
"Unable to find the reduction variable");
RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi];
RecurrenceDescriptor::RecurrenceKind RK = RdxDesc.getRecurrenceKind();
TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue();
Instruction *LoopExitInst = RdxDesc.getLoopExitInstr();
RecurrenceDescriptor::MinMaxRecurrenceKind MinMaxKind =
RdxDesc.getMinMaxRecurrenceKind();
setDebugLocFromInst(Builder, ReductionStartValue);
bool IsInLoopReductionPhi = Cost->isInLoopReduction(Phi);
// 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(LoopVectorPreHeader->getTerminator());
// This is the vector-clone of the value that leaves the loop.
Type *VecTy = getOrCreateVectorValue(LoopExitInst, 0)->getType();
// Find the reduction identity variable. Zero for addition, or, xor,
// one for multiplication, -1 for And.
Value *Identity;
Value *VectorStart;
if (RK == RecurrenceDescriptor::RK_IntegerMinMax ||
RK == RecurrenceDescriptor::RK_FloatMinMax) {
// MinMax reduction have the start value as their identify.
if (VF == 1 || IsInLoopReductionPhi) {
VectorStart = Identity = ReductionStartValue;
} else {
VectorStart = Identity =
Builder.CreateVectorSplat(VF, ReductionStartValue, "minmax.ident");
}
} else {
// Handle other reduction kinds:
Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity(
RK, VecTy->getScalarType());
if (VF == 1 || IsInLoopReductionPhi) {
Identity = Iden;
// This vector is the Identity vector where the first element is the
// incoming scalar reduction.
VectorStart = ReductionStartValue;
} 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, ReductionStartValue, Zero);
}
}
// Wrap flags are in general invalid after vectorization, clear them.
clearReductionWrapFlags(RdxDesc);
// Fix the vector-loop phi.
// Reductions do not have to start at zero. They can start with
// any loop invariant values.
BasicBlock *Latch = OrigLoop->getLoopLatch();
Value *LoopVal = Phi->getIncomingValueForBlock(Latch);
for (unsigned Part = 0; Part < UF; ++Part) {
Value *VecRdxPhi = getOrCreateVectorValue(Phi, Part);
Value *Val = getOrCreateVectorValue(LoopVal, Part);
// Make sure to add the reduction start value only to the
// first unroll part.
Value *StartVal = (Part == 0) ? VectorStart : Identity;
cast<PHINode>(VecRdxPhi)->addIncoming(StartVal, LoopVectorPreHeader);
cast<PHINode>(VecRdxPhi)
->addIncoming(Val, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
}
// 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());
setDebugLocFromInst(Builder, LoopExitInst);
// If tail is folded by masking, the vector value to leave the loop should be
// a Select choosing between the vectorized LoopExitInst and vectorized Phi,
// instead of the former.
if (Cost->foldTailByMasking()) {
for (unsigned Part = 0; Part < UF; ++Part) {
Value *VecLoopExitInst =
VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
Value *Sel = nullptr;
for (User *U : VecLoopExitInst->users()) {
if (isa<SelectInst>(U)) {
assert(!Sel && "Reduction exit feeding two selects");
Sel = U;
} else
assert(isa<PHINode>(U) && "Reduction exit must feed Phi's or select");
}
assert(Sel && "Reduction exit feeds no select");
VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, Sel);
// If the target can create a predicated operator for the reduction at no
// extra cost in the loop (for example a predicated vadd), it can be
// cheaper for the select to remain in the loop than be sunk out of it,
// and so use the select value for the phi instead of the old
// LoopExitValue.
RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi];
if (PreferPredicatedReductionSelect ||
TTI->preferPredicatedReductionSelect(
RdxDesc.getRecurrenceBinOp(RdxDesc.getRecurrenceKind()),
Phi->getType(), TargetTransformInfo::ReductionFlags())) {
auto *VecRdxPhi = cast<PHINode>(getOrCreateVectorValue(Phi, Part));
VecRdxPhi->setIncomingValueForBlock(
LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel);
}
}
}
// If the vector reduction can be performed in a smaller type, we truncate
// then extend the loop exit value to enable InstCombine to evaluate the
// entire expression in the smaller type.
if (VF.isVector() && Phi->getType() != RdxDesc.getRecurrenceType()) {
assert(!IsInLoopReductionPhi && "Unexpected truncated inloop reduction!");
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF);
Builder.SetInsertPoint(
LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator());
VectorParts RdxParts(UF);
for (unsigned Part = 0; Part < UF; ++Part) {
RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy)
: Builder.CreateZExt(Trunc, VecTy);
for (Value::user_iterator UI = RdxParts[Part]->user_begin();
UI != RdxParts[Part]->user_end();)
if (*UI != Trunc) {
(*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd);
RdxParts[Part] = Extnd;
} else {
++UI;
}
}
Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt());
for (unsigned Part = 0; Part < UF; ++Part) {
RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, RdxParts[Part]);
}
}
// Reduce all of the unrolled parts into a single vector.
Value *ReducedPartRdx = VectorLoopValueMap.getVectorValue(LoopExitInst, 0);
unsigned Op = RecurrenceDescriptor::getRecurrenceBinOp(RK);
// The middle block terminator has already been assigned a DebugLoc here (the
// OrigLoop's single latch terminator). We want the whole middle block to
// appear to execute on this line because: (a) it is all compiler generated,
// (b) these instructions are always executed after evaluating the latch
// conditional branch, and (c) other passes may add new predecessors which
// terminate on this line. This is the easiest way to ensure we don't
// accidentally cause an extra step back into the loop while debugging.
setDebugLocFromInst(Builder, LoopMiddleBlock->getTerminator());
for (unsigned Part = 1; Part < UF; ++Part) {
Value *RdxPart = VectorLoopValueMap.getVectorValue(LoopExitInst, 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, RdxPart,
ReducedPartRdx, "bin.rdx"),
RdxDesc.getFastMathFlags());
else
ReducedPartRdx = createMinMaxOp(Builder, MinMaxKind, ReducedPartRdx,
RdxPart);
}
// Create the reduction after the loop. Note that inloop reductions create the
// target reduction in the loop using a Reduction recipe.
if (VF.isVector() && !IsInLoopReductionPhi) {
bool NoNaN = Legal->hasFunNoNaNAttr();
ReducedPartRdx =
createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx, NoNaN);
// If the reduction can be performed in a smaller type, we need to extend
// the reduction to the wider type before we branch to the original loop.
if (Phi->getType() != RdxDesc.getRecurrenceType())
ReducedPartRdx =
RdxDesc.isSigned()
? Builder.CreateSExt(ReducedPartRdx, Phi->getType())
: Builder.CreateZExt(ReducedPartRdx, Phi->getType());
}
// Create a phi node that merges control-flow from the backedge-taken check
// block and the middle block.
PHINode *BCBlockPhi = PHINode::Create(Phi->getType(), 2, "bc.merge.rdx",
LoopScalarPreHeader->getTerminator());
for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]);
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 (PHINode &LCSSAPhi : LoopExitBlock->phis()) {
// 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 a reduction value exit-PHI. Update it with the
// incoming bypass edge.
if (LCSSAPhi.getIncomingValue(0) == LoopExitInst)
LCSSAPhi.addIncoming(ReducedPartRdx, LoopMiddleBlock);
} // 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 =
Phi->getBasicBlockIndex(OrigLoop->getLoopLatch());
assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
// Pick the other block.
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
Phi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi);
Phi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst);
}
void InnerLoopVectorizer::clearReductionWrapFlags(
RecurrenceDescriptor &RdxDesc) {
RecurrenceDescriptor::RecurrenceKind RK = RdxDesc.getRecurrenceKind();
if (RK != RecurrenceDescriptor::RK_IntegerAdd &&
RK != RecurrenceDescriptor::RK_IntegerMult)
return;
Instruction *LoopExitInstr = RdxDesc.getLoopExitInstr();
assert(LoopExitInstr && "null loop exit instruction");
SmallVector<Instruction *, 8> Worklist;
SmallPtrSet<Instruction *, 8> Visited;
Worklist.push_back(LoopExitInstr);
Visited.insert(LoopExitInstr);
while (!Worklist.empty()) {
Instruction *Cur = Worklist.pop_back_val();
if (isa<OverflowingBinaryOperator>(Cur))
for (unsigned Part = 0; Part < UF; ++Part) {
Value *V = getOrCreateVectorValue(Cur, Part);
cast<Instruction>(V)->dropPoisonGeneratingFlags();
}
for (User *U : Cur->users()) {
Instruction *UI = cast<Instruction>(U);
if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) &&
Visited.insert(UI).second)
Worklist.push_back(UI);
}
}
}
void InnerLoopVectorizer::fixLCSSAPHIs() {
assert(!VF.isScalable() && "the code below assumes fixed width vectors");
for (PHINode &LCSSAPhi : LoopExitBlock->phis()) {
if (LCSSAPhi.getNumIncomingValues() == 1) {
auto *IncomingValue = LCSSAPhi.getIncomingValue(0);
// Non-instruction incoming values will have only one value.
unsigned LastLane = 0;
if (isa<Instruction>(IncomingValue))
LastLane = Cost->isUniformAfterVectorization(
cast<Instruction>(IncomingValue), VF)
? 0
: VF.getKnownMinValue() - 1;
// Can be a loop invariant incoming value or the last scalar value to be
// extracted from the vectorized loop.
Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
Value *lastIncomingValue =
getOrCreateScalarValue(IncomingValue, { UF - 1, LastLane });
LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock);
}
}
}
void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) {
// The basic block and loop containing the predicated instruction.
auto *PredBB = PredInst->getParent();
auto *VectorLoop = LI->getLoopFor(PredBB);
// Initialize a worklist with the operands of the predicated instruction.
SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end());
// Holds instructions that we need to analyze again. An instruction may be
// reanalyzed if we don't yet know if we can sink it or not.
SmallVector<Instruction *, 8> InstsToReanalyze;
// Returns true if a given use occurs in the predicated block. Phi nodes use
// their operands in their corresponding predecessor blocks.
auto isBlockOfUsePredicated = [&](Use &U) -> bool {
auto *I = cast<Instruction>(U.getUser());
BasicBlock *BB = I->getParent();
if (auto *Phi = dyn_cast<PHINode>(I))
BB = Phi->getIncomingBlock(
PHINode::getIncomingValueNumForOperand(U.getOperandNo()));
return BB == PredBB;
};
// Iteratively sink the scalarized operands of the predicated instruction
// into the block we created for it. When an instruction is sunk, it's
// operands are then added to the worklist. The algorithm ends after one pass
// through the worklist doesn't sink a single instruction.
bool Changed;
do {
// Add the instructions that need to be reanalyzed to the worklist, and
// reset the changed indicator.
Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end());
InstsToReanalyze.clear();
Changed = false;
while (!Worklist.empty()) {
auto *I = dyn_cast<Instruction>(Worklist.pop_back_val());
// We can't sink an instruction if it is a phi node, is already in the
// predicated block, is not in the loop, or may have side effects.
if (!I || isa<PHINode>(I) || I->getParent() == PredBB ||
!VectorLoop->contains(I) || I->mayHaveSideEffects())
continue;
// It's legal to sink the instruction if all its uses occur in the
// predicated block. Otherwise, there's nothing to do yet, and we may
// need to reanalyze the instruction.
if (!llvm::all_of(I->uses(), isBlockOfUsePredicated)) {
InstsToReanalyze.push_back(I);
continue;
}
// Move the instruction to the beginning of the predicated block, and add
// it's operands to the worklist.
I->moveBefore(&*PredBB->getFirstInsertionPt());
Worklist.insert(I->op_begin(), I->op_end());
// The sinking may have enabled other instructions to be sunk, so we will
// need to iterate.
Changed = true;
}
} while (Changed);
}
void InnerLoopVectorizer::fixNonInductionPHIs() {
for (PHINode *OrigPhi : OrigPHIsToFix) {
PHINode *NewPhi =
cast<PHINode>(VectorLoopValueMap.getVectorValue(OrigPhi, 0));
unsigned NumIncomingValues = OrigPhi->getNumIncomingValues();
SmallVector<BasicBlock *, 2> ScalarBBPredecessors(
predecessors(OrigPhi->getParent()));
SmallVector<BasicBlock *, 2> VectorBBPredecessors(
predecessors(NewPhi->getParent()));
assert(ScalarBBPredecessors.size() == VectorBBPredecessors.size() &&
"Scalar and Vector BB should have the same number of predecessors");
// The insertion point in Builder may be invalidated by the time we get
// here. Force the Builder insertion point to something valid so that we do
// not run into issues during insertion point restore in
// getOrCreateVectorValue calls below.
Builder.SetInsertPoint(NewPhi);
// The predecessor order is preserved and we can rely on mapping between
// scalar and vector block predecessors.
for (unsigned i = 0; i < NumIncomingValues; ++i) {
BasicBlock *NewPredBB = VectorBBPredecessors[i];
// When looking up the new scalar/vector values to fix up, use incoming
// values from original phi.
Value *ScIncV =
OrigPhi->getIncomingValueForBlock(ScalarBBPredecessors[i]);
// Scalar incoming value may need a broadcast
Value *NewIncV = getOrCreateVectorValue(ScIncV, 0);
NewPhi->addIncoming(NewIncV, NewPredBB);
}
}
}
void InnerLoopVectorizer::widenGEP(GetElementPtrInst *GEP, VPUser &Operands,
unsigned UF, ElementCount VF,
bool IsPtrLoopInvariant,
SmallBitVector &IsIndexLoopInvariant,
VPTransformState &State) {
// Construct a vector GEP by widening the operands of the scalar GEP as
// necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP
// results in a vector of pointers when at least one operand of the GEP
// is vector-typed. Thus, to keep the representation compact, we only use
// vector-typed operands for loop-varying values.
if (VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) {
// If we are vectorizing, but the GEP has only loop-invariant operands,
// the GEP we build (by only using vector-typed operands for
// loop-varying values) would be a scalar pointer. Thus, to ensure we
// produce a vector of pointers, we need to either arbitrarily pick an
// operand to broadcast, or broadcast a clone of the original GEP.
// Here, we broadcast a clone of the original.
//
// TODO: If at some point we decide to scalarize instructions having
// loop-invariant operands, this special case will no longer be
// required. We would add the scalarization decision to
// collectLoopScalars() and teach getVectorValue() to broadcast
// the lane-zero scalar value.
auto *Clone = Builder.Insert(GEP->clone());
for (unsigned Part = 0; Part < UF; ++Part) {
Value *EntryPart = Builder.CreateVectorSplat(VF, Clone);
VectorLoopValueMap.setVectorValue(GEP, Part, EntryPart);
addMetadata(EntryPart, GEP);
}
} else {
// If the GEP has at least one loop-varying operand, we are sure to
// produce a vector of pointers. But if we are only unrolling, we want
// to produce a scalar GEP for each unroll part. Thus, the GEP we
// produce with the code below will be scalar (if VF == 1) or vector
// (otherwise). Note that for the unroll-only case, we still maintain
// values in the vector mapping with initVector, as we do for other
// instructions.
for (unsigned Part = 0; Part < UF; ++Part) {
// The pointer operand of the new GEP. If it's loop-invariant, we
// won't broadcast it.
auto *Ptr = IsPtrLoopInvariant ? State.get(Operands.getOperand(0), {0, 0})
: State.get(Operands.getOperand(0), Part);
// Collect all the indices for the new GEP. If any index is
// loop-invariant, we won't broadcast it.
SmallVector<Value *, 4> Indices;
for (unsigned I = 1, E = Operands.getNumOperands(); I < E; I++) {
VPValue *Operand = Operands.getOperand(I);
if (IsIndexLoopInvariant[I - 1])
Indices.push_back(State.get(Operand, {0, 0}));
else
Indices.push_back(State.get(Operand, Part));
}
// Create the new GEP. Note that this GEP may be a scalar if VF == 1,
// but it should be a vector, otherwise.
auto *NewGEP =
GEP->isInBounds()
? Builder.CreateInBoundsGEP(GEP->getSourceElementType(), Ptr,
Indices)
: Builder.CreateGEP(GEP->getSourceElementType(), Ptr, Indices);
assert((VF == 1 || NewGEP->getType()->isVectorTy()) &&
"NewGEP is not a pointer vector");
VectorLoopValueMap.setVectorValue(GEP, Part, NewGEP);
addMetadata(NewGEP, GEP);
}
}
}
void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, unsigned UF,
ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
PHINode *P = cast<PHINode>(PN);
if (EnableVPlanNativePath) {
// Currently we enter here in the VPlan-native path for non-induction
// PHIs where all control flow is uniform. We simply widen these PHIs.
// Create a vector phi with no operands - the vector phi operands will be
// set at the end of vector code generation.
Type *VecTy =
(VF.isScalar()) ? PN->getType() : VectorType::get(PN->getType(), VF);
Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi");
VectorLoopValueMap.setVectorValue(P, 0, VecPhi);
OrigPHIsToFix.push_back(P);
return;
}
assert(PN->getParent() == OrigLoop->getHeader() &&
"Non-header phis should have been handled elsewhere");
// In order to support recurrences we need to be able to vectorize Phi nodes.
// Phi nodes have cycles, so we need to vectorize them in two stages. This is
// stage #1: We create a new vector PHI node with no incoming edges. We'll use
// this value when we vectorize all of the instructions that use the PHI.
if (Legal->isReductionVariable(P) || Legal->isFirstOrderRecurrence(P)) {
for (unsigned Part = 0; Part < UF; ++Part) {
// This is phase one of vectorizing PHIs.
bool ScalarPHI =
(VF.isScalar()) || Cost->isInLoopReduction(cast<PHINode>(PN));
Type *VecTy =
ScalarPHI ? PN->getType() : VectorType::get(PN->getType(), VF);
Value *EntryPart = PHINode::Create(
VecTy, 2, "vec.phi", &*LoopVectorBody->getFirstInsertionPt());
VectorLoopValueMap.setVectorValue(P, Part, EntryPart);
}
return;
}
setDebugLocFromInst(Builder, P);
// This PHINode must be an induction variable.
// Make sure that we know about it.
assert(Legal->getInductionVars().count(P) && "Not an induction variable");
InductionDescriptor II = Legal->getInductionVars().lookup(P);
const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
// FIXME: The newly created binary instructions should contain nsw/nuw flags,
// which can be found from the original scalar operations.
switch (II.getKind()) {
case InductionDescriptor::IK_NoInduction:
llvm_unreachable("Unknown induction");
case InductionDescriptor::IK_IntInduction:
case InductionDescriptor::IK_FpInduction:
llvm_unreachable("Integer/fp induction is handled elsewhere.");
case InductionDescriptor::IK_PtrInduction: {
// Handle the pointer induction variable case.
assert(P->getType()->isPointerTy() && "Unexpected type.");
if (Cost->isScalarAfterVectorization(P, VF)) {
// This is the normalized GEP that starts counting at zero.
Value *PtrInd =
Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType());
// Determine the number of scalars we need to generate for each unroll
// iteration. If the instruction is uniform, we only need to generate the
// first lane. Otherwise, we generate all VF values.
unsigned Lanes =
Cost->isUniformAfterVectorization(P, VF) ? 1 : VF.getKnownMinValue();
for (unsigned Part = 0; Part < UF; ++Part) {
for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
Constant *Idx = ConstantInt::get(PtrInd->getType(),
Lane + Part * VF.getKnownMinValue());
Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx);
Value *SclrGep =
emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II);
SclrGep->setName("next.gep");
VectorLoopValueMap.setScalarValue(P, {Part, Lane}, SclrGep);
}
}
return;
}
assert(isa<SCEVConstant>(II.getStep()) &&
"Induction step not a SCEV constant!");
Type *PhiType = II.getStep()->getType();
// Build a pointer phi
Value *ScalarStartValue = II.getStartValue();
Type *ScStValueType = ScalarStartValue->getType();
PHINode *NewPointerPhi =
PHINode::Create(ScStValueType, 2, "pointer.phi", Induction);
NewPointerPhi->addIncoming(ScalarStartValue, LoopVectorPreHeader);
// A pointer induction, performed by using a gep
BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch();
Instruction *InductionLoc = LoopLatch->getTerminator();
const SCEV *ScalarStep = II.getStep();
SCEVExpander Exp(*PSE.getSE(), DL, "induction");
Value *ScalarStepValue =
Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc);
Value *InductionGEP = GetElementPtrInst::Create(
ScStValueType->getPointerElementType(), NewPointerPhi,
Builder.CreateMul(
ScalarStepValue,
ConstantInt::get(PhiType, VF.getKnownMinValue() * UF)),
"ptr.ind", InductionLoc);
NewPointerPhi->addIncoming(InductionGEP, LoopLatch);
// Create UF many actual address geps that use the pointer
// phi as base and a vectorized version of the step value
// (<step*0, ..., step*N>) as offset.
for (unsigned Part = 0; Part < UF; ++Part) {
SmallVector<Constant *, 8> Indices;
// Create a vector of consecutive numbers from zero to VF.
for (unsigned i = 0; i < VF.getKnownMinValue(); ++i)
Indices.push_back(
ConstantInt::get(PhiType, i + Part * VF.getKnownMinValue()));
Constant *StartOffset = ConstantVector::get(Indices);
Value *GEP = Builder.CreateGEP(
ScStValueType->getPointerElementType(), NewPointerPhi,
Builder.CreateMul(
StartOffset,
Builder.CreateVectorSplat(VF.getKnownMinValue(), ScalarStepValue),
"vector.gep"));
VectorLoopValueMap.setVectorValue(P, Part, GEP);
}
}
}
}
/// A helper function for checking whether an integer division-related
/// instruction may divide by zero (in which case it must be predicated if
/// executed conditionally in the scalar code).
/// TODO: It may be worthwhile to generalize and check isKnownNonZero().
/// Non-zero divisors that are non compile-time constants will not be
/// converted into multiplication, so we will still end up scalarizing
/// the division, but can do so w/o predication.
static bool mayDivideByZero(Instruction &I) {
assert((I.getOpcode() == Instruction::UDiv ||
I.getOpcode() == Instruction::SDiv ||
I.getOpcode() == Instruction::URem ||
I.getOpcode() == Instruction::SRem) &&
"Unexpected instruction");
Value *Divisor = I.getOperand(1);
auto *CInt = dyn_cast<ConstantInt>(Divisor);
return !CInt || CInt->isZero();
}
void InnerLoopVectorizer::widenInstruction(Instruction &I, VPUser &User,
VPTransformState &State) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
switch (I.getOpcode()) {
case Instruction::Call:
case Instruction::Br:
case Instruction::PHI:
case Instruction::GetElementPtr:
case Instruction::Select:
llvm_unreachable("This instruction is handled by a different recipe.");
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::SRem:
case Instruction::URem:
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::FNeg:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::FDiv:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
// Just widen unops and binops.
setDebugLocFromInst(Builder, &I);
for (unsigned Part = 0; Part < UF; ++Part) {
SmallVector<Value *, 2> Ops;
for (VPValue *VPOp : User.operands())
Ops.push_back(State.get(VPOp, Part));
Value *V = Builder.CreateNAryOp(I.getOpcode(), Ops);
if (auto *VecOp = dyn_cast<Instruction>(V))
VecOp->copyIRFlags(&I);
// Use this vector value for all users of the original instruction.
VectorLoopValueMap.setVectorValue(&I, Part, V);
addMetadata(V, &I);
}
break;
}
case Instruction::ICmp:
case Instruction::FCmp: {
// Widen compares. Generate vector compares.
bool FCmp = (I.getOpcode() == Instruction::FCmp);
auto *Cmp = cast<CmpInst>(&I);
setDebugLocFromInst(Builder, Cmp);
for (unsigned Part = 0; Part < UF; ++Part) {
Value *A = State.get(User.getOperand(0), Part);
Value *B = State.get(User.getOperand(1), Part);
Value *C = nullptr;
if (FCmp) {
// Propagate fast math flags.
IRBuilder<>::FastMathFlagGuard FMFG(Builder);
Builder.setFastMathFlags(Cmp->getFastMathFlags());
C = Builder.CreateFCmp(Cmp->getPredicate(), A, B);
} else {
C = Builder.CreateICmp(Cmp->getPredicate(), A, B);
}
VectorLoopValueMap.setVectorValue(&I, Part, C);
addMetadata(C, &I);
}
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: {
auto *CI = cast<CastInst>(&I);
setDebugLocFromInst(Builder, CI);
/// Vectorize casts.
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
Type *DestTy =
(VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF);
for (unsigned Part = 0; Part < UF; ++Part) {
Value *A = State.get(User.getOperand(0), Part);
Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy);
VectorLoopValueMap.setVectorValue(&I, Part, Cast);
addMetadata(Cast, &I);
}
break;
}
default:
// This instruction is not vectorized by simple widening.
LLVM_DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I);
llvm_unreachable("Unhandled instruction!");
} // end of switch.
}
void InnerLoopVectorizer::widenCallInstruction(CallInst &I, VPUser &ArgOperands,
VPTransformState &State) {
assert(!isa<DbgInfoIntrinsic>(I) &&
"DbgInfoIntrinsic should have been dropped during VPlan construction");
setDebugLocFromInst(Builder, &I);
Module *M = I.getParent()->getParent()->getParent();
auto *CI = cast<CallInst>(&I);
SmallVector<Type *, 4> Tys;
for (Value *ArgOperand : CI->arg_operands())
Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.getKnownMinValue()));
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
// 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 = false;
unsigned CallCost = Cost->getVectorCallCost(CI, VF, NeedToScalarize);
bool UseVectorIntrinsic =
ID && Cost->getVectorIntrinsicCost(CI, VF) <= CallCost;
assert((UseVectorIntrinsic || !NeedToScalarize) &&
"Instruction should be scalarized elsewhere.");
for (unsigned Part = 0; Part < UF; ++Part) {
SmallVector<Value *, 4> Args;
for (auto &I : enumerate(ArgOperands.operands())) {
// Some intrinsics have a scalar argument - don't replace it with a
// vector.
Value *Arg;
if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, I.index()))
Arg = State.get(I.value(), Part);
else
Arg = State.get(I.value(), {0, 0});
Args.push_back(Arg);
}
Function *VectorF;
if (UseVectorIntrinsic) {
// Use vector version of the intrinsic.
Type *TysForDecl[] = {CI->getType()};
if (VF.isVector()) {
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF);
}
VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl);
assert(VectorF && "Can't retrieve vector intrinsic.");
} else {
// Use vector version of the function call.
const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/);
#ifndef NDEBUG
assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr &&
"Can't create vector function.");
#endif
VectorF = VFDatabase(*CI).getVectorizedFunction(Shape);
}
SmallVector<OperandBundleDef, 1> OpBundles;
CI->getOperandBundlesAsDefs(OpBundles);
CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles);
if (isa<FPMathOperator>(V))
V->copyFastMathFlags(CI);
VectorLoopValueMap.setVectorValue(&I, Part, V);
addMetadata(V, &I);
}
}
void InnerLoopVectorizer::widenSelectInstruction(SelectInst &I,
VPUser &Operands,
bool InvariantCond,
VPTransformState &State) {
setDebugLocFromInst(Builder, &I);
// 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.
auto *InvarCond =
InvariantCond ? State.get(Operands.getOperand(0), {0, 0}) : nullptr;
for (unsigned Part = 0; Part < UF; ++Part) {
Value *Cond =
InvarCond ? InvarCond : State.get(Operands.getOperand(0), Part);
Value *Op0 = State.get(Operands.getOperand(1), Part);
Value *Op1 = State.get(Operands.getOperand(2), Part);
Value *Sel = Builder.CreateSelect(Cond, Op0, Op1);
VectorLoopValueMap.setVectorValue(&I, Part, Sel);
addMetadata(Sel, &I);
}
}
void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) {
// We should not collect Scalars more than once per VF. Right now, this
// function is called from collectUniformsAndScalars(), which already does
// this check. Collecting Scalars for VF=1 does not make any sense.
assert(VF.isVector() && Scalars.find(VF) == Scalars.end() &&
"This function should not be visited twice for the same VF");
SmallSetVector<Instruction *, 8> Worklist;
// These sets are used to seed the analysis with pointers used by memory
// accesses that will remain scalar.
SmallSetVector<Instruction *, 8> ScalarPtrs;
SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs;
auto *Latch = TheLoop->getLoopLatch();
// A helper that returns true if the use of Ptr by MemAccess will be scalar.
// The pointer operands of loads and stores will be scalar as long as the
// memory access is not a gather or scatter operation. The value operand of a
// store will remain scalar if the store is scalarized.
auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) {
InstWidening WideningDecision = getWideningDecision(MemAccess, VF);
assert(WideningDecision != CM_Unknown &&
"Widening decision should be ready at this moment");
if (auto *Store = dyn_cast<StoreInst>(MemAccess))
if (Ptr == Store->getValueOperand())
return WideningDecision == CM_Scalarize;
assert(Ptr == getLoadStorePointerOperand(MemAccess) &&
"Ptr is neither a value or pointer operand");
return WideningDecision != CM_GatherScatter;
};
// A helper that returns true if the given value is a bitcast or
// getelementptr instruction contained in the loop.
auto isLoopVaryingBitCastOrGEP = [&](Value *V) {
return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) ||
isa<GetElementPtrInst>(V)) &&
!TheLoop->isLoopInvariant(V);
};
auto isScalarPtrInduction = [&](Instruction *MemAccess, Value *Ptr) {
if (!isa<PHINode>(Ptr) ||
!Legal->getInductionVars().count(cast<PHINode>(Ptr)))
return false;
auto &Induction = Legal->getInductionVars()[cast<PHINode>(Ptr)];
if (Induction.getKind() != InductionDescriptor::IK_PtrInduction)
return false;
return isScalarUse(MemAccess, Ptr);
};
// A helper that evaluates a memory access's use of a pointer. If the
// pointer is actually the pointer induction of a loop, it is being
// inserted into Worklist. If the use will be a scalar use, and the
// pointer is only used by memory accesses, we place the pointer in
// ScalarPtrs. Otherwise, the pointer is placed in PossibleNonScalarPtrs.
auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) {
if (isScalarPtrInduction(MemAccess, Ptr)) {
Worklist.insert(cast<Instruction>(Ptr));
Instruction *Update = cast<Instruction>(
cast<PHINode>(Ptr)->getIncomingValueForBlock(Latch));
Worklist.insert(Update);
LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Ptr
<< "\n");
LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Update
<< "\n");
return;
}
// We only care about bitcast and getelementptr instructions contained in
// the loop.
if (!isLoopVaryingBitCastOrGEP(Ptr))
return;
// If the pointer has already been identified as scalar (e.g., if it was
// also identified as uniform), there's nothing to do.
auto *I = cast<Instruction>(Ptr);
if (Worklist.count(I))
return;
// If the use of the pointer will be a scalar use, and all users of the
// pointer are memory accesses, place the pointer in ScalarPtrs. Otherwise,
// place the pointer in PossibleNonScalarPtrs.
if (isScalarUse(MemAccess, Ptr) && llvm::all_of(I->users(), [&](User *U) {
return isa<LoadInst>(U) || isa<StoreInst>(U);
}))
ScalarPtrs.insert(I);
else
PossibleNonScalarPtrs.insert(I);
};
// We seed the scalars analysis with three classes of instructions: (1)
// instructions marked uniform-after-vectorization and (2) bitcast,
// getelementptr and (pointer) phi instructions used by memory accesses
// requiring a scalar use.
//
// (1) Add to the worklist all instructions that have been identified as
// uniform-after-vectorization.
Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end());
// (2) Add to the worklist all bitcast and getelementptr instructions used by
// memory accesses requiring a scalar use. The pointer operands of loads and
// stores will be scalar as long as the memory accesses is not a gather or
// scatter operation. The value operand of a store will remain scalar if the
// store is scalarized.
for (auto *BB : TheLoop->blocks())
for (auto &I : *BB) {
if (auto *Load = dyn_cast<LoadInst>(&I)) {
evaluatePtrUse(Load, Load->getPointerOperand());
} else if (auto *Store = dyn_cast<StoreInst>(&I)) {
evaluatePtrUse(Store, Store->getPointerOperand());
evaluatePtrUse(Store, Store->getValueOperand());
}
}
for (auto *I : ScalarPtrs)
if (!PossibleNonScalarPtrs.count(I)) {
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n");
Worklist.insert(I);
}
// Insert the forced scalars.
// FIXME: Currently widenPHIInstruction() often creates a dead vector
// induction variable when the PHI user is scalarized.
auto ForcedScalar = ForcedScalars.find(VF);
if (ForcedScalar != ForcedScalars.end())
for (auto *I : ForcedScalar->second)
Worklist.insert(I);
// Expand the worklist by looking through any bitcasts and getelementptr
// instructions we've already identified as scalar. This is similar to the
// expansion step in collectLoopUniforms(); however, here we're only
// expanding to include additional bitcasts and getelementptr instructions.
unsigned Idx = 0;
while (Idx != Worklist.size()) {
Instruction *Dst = Worklist[Idx++];
if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0)))
continue;
auto *Src = cast<Instruction>(Dst->getOperand(0));
if (llvm::all_of(Src->users(), [&](User *U) -> bool {
auto *J = cast<Instruction>(U);
return !TheLoop->contains(J) || Worklist.count(J) ||
((isa<LoadInst>(J) || isa<StoreInst>(J)) &&
isScalarUse(J, Src));
})) {
Worklist.insert(Src);
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n");
}
}
// An induction variable will remain scalar if all users of the induction
// variable and induction variable update remain scalar.
for (auto &Induction : Legal->getInductionVars()) {
auto *Ind = Induction.first;
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
// If tail-folding is applied, the primary induction variable will be used
// to feed a vector compare.
if (Ind == Legal->getPrimaryInduction() && foldTailByMasking())
continue;
// Determine if all users of the induction variable are scalar after
// vectorization.
auto ScalarInd = llvm::all_of(Ind->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I);
});
if (!ScalarInd)
continue;
// Determine if all users of the induction variable update instruction are
// scalar after vectorization.
auto ScalarIndUpdate =
llvm::all_of(IndUpdate->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == Ind || !TheLoop->contains(I) || Worklist.count(I);
});
if (!ScalarIndUpdate)
continue;
// The induction variable and its update instruction will remain scalar.
Worklist.insert(Ind);
Worklist.insert(IndUpdate);
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate
<< "\n");
}
Scalars[VF].insert(Worklist.begin(), Worklist.end());
}
bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I,
ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
if (!blockNeedsPredication(I->getParent()))
return false;
switch(I->getOpcode()) {
default:
break;
case Instruction::Load:
case Instruction::Store: {
if (!Legal->isMaskRequired(I))
return false;
auto *Ptr = getLoadStorePointerOperand(I);
auto *Ty = getMemInstValueType(I);
// We have already decided how to vectorize this instruction, get that
// result.
if (VF.isVector()) {
InstWidening WideningDecision = getWideningDecision(I, VF);
assert(WideningDecision != CM_Unknown &&
"Widening decision should be ready at this moment");
return WideningDecision == CM_Scalarize;
}
const Align Alignment = getLoadStoreAlignment(I);
return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) ||
isLegalMaskedGather(Ty, Alignment))
: !(isLegalMaskedStore(Ty, Ptr, Alignment) ||
isLegalMaskedScatter(Ty, Alignment));
}
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::SRem:
case Instruction::URem:
return mayDivideByZero(*I);
}
return false;
}
bool LoopVectorizationCostModel::interleavedAccessCanBeWidened(
Instruction *I, ElementCount VF) {
assert(isAccessInterleaved(I) && "Expecting interleaved access.");
assert(getWideningDecision(I, VF) == CM_Unknown &&
"Decision should not be set yet.");
auto *Group = getInterleavedAccessGroup(I);
assert(Group && "Must have a group.");
// If the instruction's allocated size doesn't equal it's type size, it
// requires padding and will be scalarized.
auto &DL = I->getModule()->getDataLayout();
auto *ScalarTy = getMemInstValueType(I);
if (hasIrregularType(ScalarTy, DL, VF))
return false;
// Check if masking is required.
// A Group may need masking for one of two reasons: it resides in a block that
// needs predication, or it was decided to use masking to deal with gaps.
bool PredicatedAccessRequiresMasking =
Legal->blockNeedsPredication(I->getParent()) && Legal->isMaskRequired(I);
bool AccessWithGapsRequiresMasking =
Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed();
if (!PredicatedAccessRequiresMasking && !AccessWithGapsRequiresMasking)
return true;
// If masked interleaving is required, we expect that the user/target had
// enabled it, because otherwise it either wouldn't have been created or
// it should have been invalidated by the CostModel.
assert(useMaskedInterleavedAccesses(TTI) &&
"Masked interleave-groups for predicated accesses are not enabled.");
auto *Ty = getMemInstValueType(I);
const Align Alignment = getLoadStoreAlignment(I);
return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment)
: TTI.isLegalMaskedStore(Ty, Alignment);
}
bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(
Instruction *I, ElementCount VF) {
// Get and ensure we have a valid memory instruction.
LoadInst *LI = dyn_cast<LoadInst>(I);
StoreInst *SI = dyn_cast<StoreInst>(I);
assert((LI || SI) && "Invalid memory instruction");
auto *Ptr = getLoadStorePointerOperand(I);
// In order to be widened, the pointer should be consecutive, first of all.
if (!Legal->isConsecutivePtr(Ptr))
return false;
// If the instruction is a store located in a predicated block, it will be
// scalarized.
if (isScalarWithPredication(I))
return false;
// If the instruction's allocated size doesn't equal it's type size, it
// requires padding and will be scalarized.
auto &DL = I->getModule()->getDataLayout();
auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType();
if (hasIrregularType(ScalarTy, DL, VF))
return false;
return true;
}
void LoopVectorizationCostModel::collectLoopUniforms(ElementCount VF) {
// We should not collect Uniforms more than once per VF. Right now,
// this function is called from collectUniformsAndScalars(), which
// already does this check. Collecting Uniforms for VF=1 does not make any
// sense.
assert(VF.isVector() && Uniforms.find(VF) == Uniforms.end() &&
"This function should not be visited twice for the same VF");
// Visit the list of Uniforms. If we'll not find any uniform value, we'll
// not analyze again. Uniforms.count(VF) will return 1.
Uniforms[VF].clear();
// We now know that the loop is vectorizable!
// Collect instructions inside the loop that will remain uniform after
// vectorization.
// Global values, params and instructions outside of current loop are out of
// scope.
auto isOutOfScope = [&](Value *V) -> bool {
Instruction *I = dyn_cast<Instruction>(V);
return (!I || !TheLoop->contains(I));
};
SetVector<Instruction *> Worklist;
BasicBlock *Latch = TheLoop->getLoopLatch();
// Instructions that are scalar with predication must not be considered
// uniform after vectorization, because that would create an erroneous
// replicating region where only a single instance out of VF should be formed.
// TODO: optimize such seldom cases if found important, see PR40816.
auto addToWorklistIfAllowed = [&](Instruction *I) -> void {
if (isScalarWithPredication(I, VF)) {
LLVM_DEBUG(dbgs() << "LV: Found not uniform being ScalarWithPredication: "
<< *I << "\n");
return;
}
LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n");
Worklist.insert(I);
};
// Start with the conditional branch. If the branch condition is an
// instruction contained in the loop that is only used by the branch, it is
// uniform.
auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse())
addToWorklistIfAllowed(Cmp);
// Holds consecutive and consecutive-like pointers. Consecutive-like pointers
// are pointers that are treated like consecutive pointers during
// vectorization. The pointer operands of interleaved accesses are an
// example.
SmallSetVector<Instruction *, 8> ConsecutiveLikePtrs;
// Holds pointer operands of instructions that are possibly non-uniform.
SmallPtrSet<Instruction *, 8> PossibleNonUniformPtrs;
auto isUniformDecision = [&](Instruction *I, ElementCount VF) {
InstWidening WideningDecision = getWideningDecision(I, VF);
assert(WideningDecision != CM_Unknown &&
"Widening decision should be ready at this moment");
return (WideningDecision == CM_Widen ||
WideningDecision == CM_Widen_Reverse ||
WideningDecision == CM_Interleave);
};
// Iterate over the instructions in the loop, and collect all
// consecutive-like pointer operands in ConsecutiveLikePtrs. If it's possible
// that a consecutive-like pointer operand will be scalarized, we collect it
// in PossibleNonUniformPtrs instead. We use two sets here because a single
// getelementptr instruction can be used by both vectorized and scalarized
// memory instructions. For example, if a loop loads and stores from the same
// location, but the store is conditional, the store will be scalarized, and
// the getelementptr won't remain uniform.
for (auto *BB : TheLoop->blocks())
for (auto &I : *BB) {
// If there's no pointer operand, there's nothing to do.
auto *Ptr = dyn_cast_or_null<Instruction>(getLoadStorePointerOperand(&I));
if (!Ptr)
continue;
// True if all users of Ptr are memory accesses that have Ptr as their
// pointer operand.
auto UsersAreMemAccesses =
llvm::all_of(Ptr->users(), [&](User *U) -> bool {
return getLoadStorePointerOperand(U) == Ptr;
});
// Ensure the memory instruction will not be scalarized or used by
// gather/scatter, making its pointer operand non-uniform. If the pointer
// operand is used by any instruction other than a memory access, we
// conservatively assume the pointer operand may be non-uniform.
if (!UsersAreMemAccesses || !isUniformDecision(&I, VF))
PossibleNonUniformPtrs.insert(Ptr);
// If the memory instruction will be vectorized and its pointer operand
// is consecutive-like, or interleaving - the pointer operand should
// remain uniform.
else
ConsecutiveLikePtrs.insert(Ptr);
}
// Add to the Worklist all consecutive and consecutive-like pointers that
// aren't also identified as possibly non-uniform.
for (auto *V : ConsecutiveLikePtrs)
if (!PossibleNonUniformPtrs.count(V))
addToWorklistIfAllowed(V);
// Expand Worklist in topological order: whenever a new instruction
// is added , its users should be already inside Worklist. It ensures
// a uniform instruction will only be used by uniform instructions.
unsigned idx = 0;
while (idx != Worklist.size()) {
Instruction *I = Worklist[idx++];
for (auto OV : I->operand_values()) {
// isOutOfScope operands cannot be uniform instructions.
if (isOutOfScope(OV))
continue;
// First order recurrence Phi's should typically be considered
// non-uniform.
auto *OP = dyn_cast<PHINode>(OV);
if (OP && Legal->isFirstOrderRecurrence(OP))
continue;
// If all the users of the operand are uniform, then add the
// operand into the uniform worklist.
auto *OI = cast<Instruction>(OV);
if (llvm::all_of(OI->users(), [&](User *U) -> bool {
auto *J = cast<Instruction>(U);
return Worklist.count(J) ||
(OI == getLoadStorePointerOperand(J) &&
isUniformDecision(J, VF));
}))
addToWorklistIfAllowed(OI);
}
}
// Returns true if Ptr is the pointer operand of a memory access instruction
// I, and I is known to not require scalarization.
auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool {
return getLoadStorePointerOperand(I) == Ptr && isUniformDecision(I, VF);
};
// For an instruction to be added into Worklist above, all its users inside
// the loop should also be in Worklist. However, this condition cannot be
// true for phi nodes that form a cyclic dependence. We must process phi
// nodes separately. An induction variable will remain uniform if all users
// of the induction variable and induction variable update remain uniform.
// The code below handles both pointer and non-pointer induction variables.
for (auto &Induction : Legal->getInductionVars()) {
auto *Ind = Induction.first;
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
// Determine if all users of the induction variable are uniform after
// vectorization.
auto UniformInd = llvm::all_of(Ind->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) ||
isVectorizedMemAccessUse(I, Ind);
});
if (!UniformInd)
continue;
// Determine if all users of the induction variable update instruction are
// uniform after vectorization.
auto UniformIndUpdate =
llvm::all_of(IndUpdate->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == Ind || !TheLoop->contains(I) || Worklist.count(I) ||
isVectorizedMemAccessUse(I, IndUpdate);
});
if (!UniformIndUpdate)
continue;
// The induction variable and its update instruction will remain uniform.
addToWorklistIfAllowed(Ind);
addToWorklistIfAllowed(IndUpdate);
}
Uniforms[VF].insert(Worklist.begin(), Worklist.end());
}
bool LoopVectorizationCostModel::runtimeChecksRequired() {
LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n");
if (Legal->getRuntimePointerChecking()->Need) {
reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz",
"runtime pointer checks needed. Enable vectorization of this "
"loop with '#pragma clang loop vectorize(enable)' when "
"compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
if (!PSE.getUnionPredicate().getPredicates().empty()) {
reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz",
"runtime SCEV checks needed. Enable vectorization of this "
"loop with '#pragma clang loop vectorize(enable)' when "
"compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
// FIXME: Avoid specializing for stride==1 instead of bailing out.
if (!Legal->getLAI()->getSymbolicStrides().empty()) {
reportVectorizationFailure("Runtime stride check for small trip count",
"runtime stride == 1 checks needed. Enable vectorization of "
"this loop without such check by compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
return false;
}
Optional<unsigned> LoopVectorizationCostModel::computeMaxVF(unsigned UserVF,
unsigned UserIC) {
if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) {
// TODO: It may by useful to do since it's still likely to be dynamically
// uniform if the target can skip.
reportVectorizationFailure(
"Not inserting runtime ptr check for divergent target",
"runtime pointer checks needed. Not enabled for divergent target",
"CantVersionLoopWithDivergentTarget", ORE, TheLoop);
return None;
}
unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
if (TC == 1) {
reportVectorizationFailure("Single iteration (non) loop",
"loop trip count is one, irrelevant for vectorization",
"SingleIterationLoop", ORE, TheLoop);
return None;
}
switch (ScalarEpilogueStatus) {
case CM_ScalarEpilogueAllowed:
return UserVF ? UserVF : computeFeasibleMaxVF(TC);
case CM_ScalarEpilogueNotNeededUsePredicate:
LLVM_DEBUG(
dbgs() << "LV: vector predicate hint/switch found.\n"
<< "LV: Not allowing scalar epilogue, creating predicated "
<< "vector loop.\n");
break;
case CM_ScalarEpilogueNotAllowedLowTripLoop:
// fallthrough as a special case of OptForSize
case CM_ScalarEpilogueNotAllowedOptSize:
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedOptSize)
LLVM_DEBUG(
dbgs() << "LV: Not allowing scalar epilogue due to -Os/-Oz.\n");
else
LLVM_DEBUG(dbgs() << "LV: Not allowing scalar epilogue due to low trip "
<< "count.\n");
// Bail if runtime checks are required, which are not good when optimising
// for size.
if (runtimeChecksRequired())
return None;
break;
}
// Now try the tail folding
// Invalidate interleave groups that require an epilogue if we can't mask
// the interleave-group.
if (!useMaskedInterleavedAccesses(TTI)) {
assert(WideningDecisions.empty() && Uniforms.empty() && Scalars.empty() &&
"No decisions should have been taken at this point");
// Note: There is no need to invalidate any cost modeling decisions here, as
// non where taken so far.
InterleaveInfo.invalidateGroupsRequiringScalarEpilogue();
}
unsigned MaxVF = UserVF ? UserVF : computeFeasibleMaxVF(TC);
assert((UserVF || isPowerOf2_32(MaxVF)) && "MaxVF must be a power of 2");
unsigned MaxVFtimesIC = UserIC ? MaxVF * UserIC : MaxVF;
if (TC > 0 && TC % MaxVFtimesIC == 0) {
// Accept MaxVF if we do not have a tail.
LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n");
return MaxVF;
}
// If we don't know the precise trip count, or if the trip count that we
// found modulo the vectorization factor is not zero, try to fold the tail
// by masking.
// FIXME: look for a smaller MaxVF that does divide TC rather than masking.
if (Legal->prepareToFoldTailByMasking()) {
FoldTailByMasking = true;
return MaxVF;
}
// If there was a tail-folding hint/switch, but we can't fold the tail by
// masking, fallback to a vectorization with a scalar epilogue.
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) {
if (PreferPredicateOverEpilogue == PreferPredicateTy::PredicateOrDontVectorize) {
LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n");
return None;
}
LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a "
"scalar epilogue instead.\n");
ScalarEpilogueStatus = CM_ScalarEpilogueAllowed;
return MaxVF;
}
if (TC == 0) {
reportVectorizationFailure(
"Unable to calculate the loop count due to complex control flow",
"unable to calculate the loop count due to complex control flow",
"UnknownLoopCountComplexCFG", ORE, TheLoop);
return None;
}
reportVectorizationFailure(
"Cannot optimize for size and vectorize at the same time.",
"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/-Oz",
"NoTailLoopWithOptForSize", ORE, TheLoop);
return None;
}
unsigned
LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount) {
MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI);
unsigned SmallestType, WidestType;
std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes();
unsigned WidestRegister = TTI.getRegisterBitWidth(true);
// Get the maximum safe dependence distance in bits computed by LAA.
// It is computed by MaxVF * sizeOf(type) * 8, where type is taken from
// the memory accesses that is most restrictive (involved in the smallest
// dependence distance).
unsigned MaxSafeRegisterWidth = Legal->getMaxSafeRegisterWidth();
WidestRegister = std::min(WidestRegister, MaxSafeRegisterWidth);
// Ensure MaxVF is a power of 2; the dependence distance bound may not be.
// Note that both WidestRegister and WidestType may not be a powers of 2.
unsigned MaxVectorSize = PowerOf2Floor(WidestRegister / WidestType);
LLVM_DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType
<< " / " << WidestType << " bits.\n");
LLVM_DEBUG(dbgs() << "LV: The Widest register safe to use is: "
<< WidestRegister << " bits.\n");
assert(MaxVectorSize <= 256 && "Did not expect to pack so many elements"
" into one vector!");
if (MaxVectorSize == 0) {
LLVM_DEBUG(dbgs() << "LV: The target has no vector registers.\n");
MaxVectorSize = 1;
return MaxVectorSize;
} else if (ConstTripCount && ConstTripCount < MaxVectorSize &&
isPowerOf2_32(ConstTripCount)) {
// We need to clamp the VF to be the ConstTripCount. There is no point in
// choosing a higher viable VF as done in the loop below.
LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to the constant trip count: "
<< ConstTripCount << "\n");
MaxVectorSize = ConstTripCount;
return MaxVectorSize;
}
unsigned MaxVF = MaxVectorSize;
if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) ||
(MaximizeBandwidth && isScalarEpilogueAllowed())) {
// Collect all viable vectorization factors larger than the default MaxVF
// (i.e. MaxVectorSize).
SmallVector<ElementCount, 8> VFs;
unsigned NewMaxVectorSize = WidestRegister / SmallestType;
for (unsigned VS = MaxVectorSize * 2; VS <= NewMaxVectorSize; VS *= 2)
VFs.push_back(ElementCount::getFixed(VS));
// For each VF calculate its register usage.
auto RUs = calculateRegisterUsage(VFs);
// Select the largest VF which doesn't require more registers than existing
// ones.
for (int i = RUs.size() - 1; i >= 0; --i) {
bool Selected = true;
for (auto& pair : RUs[i].MaxLocalUsers) {
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first);
if (pair.second > TargetNumRegisters)
Selected = false;
}
if (Selected) {
MaxVF = VFs[i].getKnownMinValue();
break;
}
}
if (unsigned MinVF = TTI.getMinimumVF(SmallestType)) {
if (MaxVF < MinVF) {
LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF
<< ") with target's minimum: " << MinVF << '\n');
MaxVF = MinVF;
}
}
}
return MaxVF;
}
VectorizationFactor
LoopVectorizationCostModel::selectVectorizationFactor(unsigned MaxVF) {
float Cost = expectedCost(ElementCount::getFixed(1)).first;
const float ScalarCost = Cost;
unsigned Width = 1;
LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << (int)ScalarCost << ".\n");
bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled;
if (ForceVectorization && MaxVF > 1) {
// Ignore scalar width, because the user explicitly wants vectorization.
// Initialize cost to max so that VF = 2 is, at least, chosen during cost
// evaluation.
Cost = std::numeric_limits<float>::max();
}
for (unsigned i = 2; i <= MaxVF; 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.
VectorizationCostTy C = expectedCost(ElementCount::getFixed(i));
float VectorCost = C.first / (float)i;
LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << i
<< " costs: " << (int)VectorCost << ".\n");
if (!C.second && !ForceVectorization) {
LLVM_DEBUG(
dbgs() << "LV: Not considering vector loop of width " << i
<< " because it will not generate any vector instructions.\n");
continue;
}
if (VectorCost < Cost) {
Cost = VectorCost;
Width = i;
}
}
if (!EnableCondStoresVectorization && NumPredStores) {
reportVectorizationFailure("There are conditional stores.",
"store that is conditionally executed prevents vectorization",
"ConditionalStore", ORE, TheLoop);
Width = 1;
Cost = ScalarCost;
}
LLVM_DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs()
<< "LV: Vectorization seems to be not beneficial, "
<< "but was forced by a user.\n");
LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n");
VectorizationFactor Factor = {ElementCount::getFixed(Width),
(unsigned)(Width * Cost)};
return Factor;
}
std::pair<unsigned, unsigned>
LoopVectorizationCostModel::getSmallestAndWidestTypes() {
unsigned MinWidth = -1U;
unsigned MaxWidth = 8;
const DataLayout &DL = TheFunction->getParent()->getDataLayout();
// For each block.
for (BasicBlock *BB : TheLoop->blocks()) {
// For each instruction in the loop.
for (Instruction &I : BB->instructionsWithoutDebug()) {
Type *T = I.getType();
// Skip ignored values.
if (ValuesToIgnore.count(&I))
continue;
// Only examine Loads, Stores and PHINodes.
if (!isa<LoadInst>(I) && !isa<StoreInst>(I) && !isa<PHINode>(I))
continue;
// Examine PHI nodes that are reduction variables. Update the type to
// account for the recurrence type.
if (auto *PN = dyn_cast<PHINode>(&I)) {
if (!Legal->isReductionVariable(PN))
continue;
RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[PN];
T = RdxDesc.getRecurrenceType();
}
// Examine the stored values.
if (auto *ST = dyn_cast<StoreInst>(&I))
T = ST->getValueOperand()->getType();
// Ignore loaded pointer types and stored pointer types that are not
// vectorizable.
//
// FIXME: The check here attempts to predict whether a load or store will
// be vectorized. We only know this for certain after a VF has
// been selected. Here, we assume that if an access can be
// vectorized, it will be. We should also look at extending this
// optimization to non-pointer types.
//
if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) &&
!isAccessInterleaved(&I) && !isLegalGatherOrScatter(&I))
continue;
MinWidth = std::min(MinWidth,
(unsigned)DL.getTypeSizeInBits(T->getScalarType()));
MaxWidth = std::max(MaxWidth,
(unsigned)DL.getTypeSizeInBits(T->getScalarType()));
}
}
return {MinWidth, MaxWidth};
}
unsigned LoopVectorizationCostModel::selectInterleaveCount(ElementCount VF,
unsigned LoopCost) {
// -- The interleave heuristics --
// We interleave 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 interleave count:
// 1. If the code has reductions, then we interleave to break the cross
// iteration dependency.
// 2. If the loop is really small, then we interleave to reduce the loop
// overhead.
// 3. We don't interleave if we think that we will spill registers to memory
// due to the increased register pressure.
if (!isScalarEpilogueAllowed())
return 1;
// We used the distance for the interleave count.
if (Legal->getMaxSafeDepDistBytes() != -1U)
return 1;
auto BestKnownTC = getSmallBestKnownTC(*PSE.getSE(), TheLoop);
const bool HasReductions = !Legal->getReductionVars().empty();
// Do not interleave loops with a relatively small known or estimated trip
// count. But we will interleave when InterleaveSmallLoopScalarReduction is
// enabled, and the code has scalar reductions(HasReductions && VF = 1),
// because with the above conditions interleaving can expose ILP and break
// cross iteration dependences for reductions.
if (BestKnownTC && (*BestKnownTC < TinyTripCountInterleaveThreshold) &&
!(InterleaveSmallLoopScalarReduction && HasReductions && VF.isScalar()))
return 1;
RegisterUsage R = calculateRegisterUsage({VF})[0];
// We divide by these constants so assume that we have at least one
// instruction that uses at least one register.
for (auto& pair : R.MaxLocalUsers) {
pair.second = std::max(pair.second, 1U);
}
// We calculate the interleave count using the following formula.
// Subtract the number of loop invariants from the number of available
// registers. These registers are used by all of the interleaved 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 interleave count to simplify any
// addressing operations or alignment considerations.
// We also want power of two interleave counts to ensure that the induction
// variable of the vector loop wraps to zero, when tail is folded by masking;
// this currently happens when OptForSize, in which case IC is set to 1 above.
unsigned IC = UINT_MAX;
for (auto& pair : R.MaxLocalUsers) {
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first);
LLVM_DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters
<< " registers of "
<< TTI.getRegisterClassName(pair.first) << " register class\n");
if (VF.isScalar()) {
if (ForceTargetNumScalarRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumScalarRegs;
} else {
if (ForceTargetNumVectorRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumVectorRegs;
}
unsigned MaxLocalUsers = pair.second;
unsigned LoopInvariantRegs = 0;
if (R.LoopInvariantRegs.find(pair.first) != R.LoopInvariantRegs.end())
LoopInvariantRegs = R.LoopInvariantRegs[pair.first];
unsigned TmpIC = PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs) / MaxLocalUsers);
// Don't count the induction variable as interleaved.
if (EnableIndVarRegisterHeur) {
TmpIC =
PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs - 1) /
std::max(1U, (MaxLocalUsers - 1)));
}
IC = std::min(IC, TmpIC);
}
// Clamp the interleave ranges to reasonable counts.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
unsigned MaxInterleaveCount =
TTI.getMaxInterleaveFactor(VF.getKnownMinValue());
// Check if the user has overridden the max.
if (VF.isScalar()) {
if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
} else {
if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
}
// If trip count is known or estimated compile time constant, limit the
// interleave count to be less than the trip count divided by VF.
if (BestKnownTC) {
MaxInterleaveCount =
std::min(*BestKnownTC / VF.getKnownMinValue(), MaxInterleaveCount);
}
// 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).first;
assert(LoopCost && "Non-zero loop cost expected");
// Clamp the calculated IC to be between the 1 and the max interleave count
// that the target and trip count allows.
if (IC > MaxInterleaveCount)
IC = MaxInterleaveCount;
else if (IC < 1)
IC = 1;
// Interleave if we vectorized this loop and there is a reduction that could
// benefit from interleaving.
if (VF.isVector() && HasReductions) {
LLVM_DEBUG(dbgs() << "LV: Interleaving because of reductions.\n");
return IC;
}
// Note that if we've already vectorized the loop we will have done the
// runtime check and so interleaving won't require further checks.
bool InterleavingRequiresRuntimePointerCheck =
(VF.isScalar() && Legal->getRuntimePointerChecking()->Need);
// We want to interleave small loops in order to reduce the loop overhead and
// potentially expose ILP opportunities.
LLVM_DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n'
<< "LV: IC is " << IC << '\n'
<< "LV: VF is " << VF.getKnownMinValue() << '\n');
const bool AggressivelyInterleaveReductions =
TTI.enableAggressiveInterleaving(HasReductions);
if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) {
// We assume that the cost overhead is 1 and we use the cost model
// to estimate the cost of the loop and interleave until the cost of the
// loop overhead is about 5% of the cost of the loop.
unsigned SmallIC =
std::min(IC, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost));
// Interleave until store/load ports (estimated by max interleave count) are
// saturated.
unsigned NumStores = Legal->getNumStores();
unsigned NumLoads = Legal->getNumLoads();
unsigned StoresIC = IC / (NumStores ? NumStores : 1);
unsigned LoadsIC = IC / (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 interleaving is inside another loop. Limit, by default to 2, so the
// critical path only gets increased by one reduction operation.
if (HasReductions && TheLoop->getLoopDepth() > 1) {
unsigned F = static_cast<unsigned>(MaxNestedScalarReductionIC);
SmallIC = std::min(SmallIC, F);
StoresIC = std::min(StoresIC, F);
LoadsIC = std::min(LoadsIC, F);
}
if (EnableLoadStoreRuntimeInterleave &&
std::max(StoresIC, LoadsIC) > SmallIC) {
LLVM_DEBUG(
dbgs() << "LV: Interleaving to saturate store or load ports.\n");
return std::max(StoresIC, LoadsIC);
}
// If there are scalar reductions and TTI has enabled aggressive
// interleaving for reductions, we will interleave to expose ILP.
if (InterleaveSmallLoopScalarReduction && VF.isScalar() &&
AggressivelyInterleaveReductions) {
LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
// Interleave no less than SmallIC but not as aggressive as the normal IC
// to satisfy the rare situation when resources are too limited.
return std::max(IC / 2, SmallIC);
} else {
LLVM_DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n");
return SmallIC;
}
}
// Interleave if this is a large loop (small loops are already dealt with by
// this point) that could benefit from interleaving.
if (AggressivelyInterleaveReductions) {
LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
return IC;
}
LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n");
return 1;
}
SmallVector<LoopVectorizationCostModel::RegisterUsage, 8>
LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) {
// 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 RU;
// 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.
using IntervalMap = DenseMap<Instruction *, unsigned>;
// Maps instruction to its index.
SmallVector<Instruction *, 64> IdxToInstr;
// Marks the end of each interval.
IntervalMap EndPoint;
// Saves the list of instruction indices that are used in the loop.
SmallPtrSet<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;
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
for (Instruction &I : BB->instructionsWithoutDebug()) {
IdxToInstr.push_back(&I);
// Save the end location of each USE.
for (Value *U : I.operands()) {
auto *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] = IdxToInstr.size();
Ends.insert(Instr);
}
}
}
// Saves the list of intervals that end with the index in 'key'.
using InstrList = SmallVector<Instruction *, 2>;
DenseMap<unsigned, InstrList> TransposeEnds;
// Transpose the EndPoints to a list of values that end at each index.
for (auto &Interval : EndPoint)
TransposeEnds[Interval.second].push_back(Interval.first);
SmallPtrSet<Instruction *, 8> OpenIntervals;
// Get the size of the widest register.
unsigned MaxSafeDepDist = -1U;
if (Legal->getMaxSafeDepDistBytes() != -1U)
MaxSafeDepDist = Legal->getMaxSafeDepDistBytes() * 8;
unsigned WidestRegister =
std::min(TTI.getRegisterBitWidth(true), MaxSafeDepDist);
const DataLayout &DL = TheFunction->getParent()->getDataLayout();
SmallVector<RegisterUsage, 8> RUs(VFs.size());
SmallVector<SmallMapVector<unsigned, unsigned, 4>, 8> MaxUsages(VFs.size());
LLVM_DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
// A lambda that gets the register usage for the given type and VF.
auto GetRegUsage = [&DL, WidestRegister](Type *Ty, ElementCount VF) {
if (Ty->isTokenTy())
return 0U;
unsigned TypeSize = DL.getTypeSizeInBits(Ty->getScalarType());
assert(!VF.isScalable() && "scalable vectors not yet supported.");
return std::max<unsigned>(1, VF.getKnownMinValue() * TypeSize /
WidestRegister);
};
for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) {
Instruction *I = IdxToInstr[i];
// Remove all of the instructions that end at this location.
InstrList &List = TransposeEnds[i];
for (Instruction *ToRemove : List)
OpenIntervals.erase(ToRemove);
// Ignore instructions that are never used within the loop.
if (!Ends.count(I))
continue;
// Skip ignored values.
if (ValuesToIgnore.count(I))
continue;
// For each VF find the maximum usage of registers.
for (unsigned j = 0, e = VFs.size(); j < e; ++j) {
// Count the number of live intervals.
SmallMapVector<unsigned, unsigned, 4> RegUsage;
if (VFs[j].isScalar()) {
for (auto Inst : OpenIntervals) {
unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType());
if (RegUsage.find(ClassID) == RegUsage.end())
RegUsage[ClassID] = 1;
else
RegUsage[ClassID] += 1;
}
} else {
collectUniformsAndScalars(VFs[j]);
for (auto Inst : OpenIntervals) {
// Skip ignored values for VF > 1.
if (VecValuesToIgnore.count(Inst))
continue;
if (isScalarAfterVectorization(Inst, VFs[j])) {
unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType());
if (RegUsage.find(ClassID) == RegUsage.end())
RegUsage[ClassID] = 1;
else
RegUsage[ClassID] += 1;
} else {
unsigned ClassID = TTI.getRegisterClassForType(true, Inst->getType());
if (RegUsage.find(ClassID) == RegUsage.end())
RegUsage[ClassID] = GetRegUsage(Inst->getType(), VFs[j]);
else
RegUsage[ClassID] += GetRegUsage(Inst->getType(), VFs[j]);
}
}
}
for (auto& pair : RegUsage) {
if (MaxUsages[j].find(pair.first) != MaxUsages[j].end())
MaxUsages[j][pair.first] = std::max(MaxUsages[j][pair.first], pair.second);
else
MaxUsages[j][pair.first] = pair.second;
}
}
LLVM_DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # "
<< OpenIntervals.size() << '\n');
// Add the current instruction to the list of open intervals.
OpenIntervals.insert(I);
}
for (unsigned i = 0, e = VFs.size(); i < e; ++i) {
SmallMapVector<unsigned, unsigned, 4> Invariant;
for (auto Inst : LoopInvariants) {
unsigned Usage =
VFs[i].isScalar() ? 1 : GetRegUsage(Inst->getType(), VFs[i]);
unsigned ClassID =
TTI.getRegisterClassForType(VFs[i].isVector(), Inst->getType());
if (Invariant.find(ClassID) == Invariant.end())
Invariant[ClassID] = Usage;
else
Invariant[ClassID] += Usage;
}
LLVM_DEBUG({
dbgs() << "LV(REG): VF = " << VFs[i] << '\n';
dbgs() << "LV(REG): Found max usage: " << MaxUsages[i].size()
<< " item\n";
for (const auto &pair : MaxUsages[i]) {
dbgs() << "LV(REG): RegisterClass: "
<< TTI.getRegisterClassName(pair.first) << ", " << pair.second
<< " registers\n";
}
dbgs() << "LV(REG): Found invariant usage: " << Invariant.size()
<< " item\n";
for (const auto &pair : Invariant) {
dbgs() << "LV(REG): RegisterClass: "
<< TTI.getRegisterClassName(pair.first) << ", " << pair.second
<< " registers\n";
}
});
RU.LoopInvariantRegs = Invariant;
RU.MaxLocalUsers = MaxUsages[i];
RUs[i] = RU;
}
return RUs;
}
bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I){
// TODO: Cost model for emulated masked load/store is completely
// broken. This hack guides the cost model to use an artificially
// high enough value to practically disable vectorization with such
// operations, except where previously deployed legality hack allowed
// using very low cost values. This is to avoid regressions coming simply
// from moving "masked load/store" check from legality to cost model.
// Masked Load/Gather emulation was previously never allowed.
// Limited number of Masked Store/Scatter emulation was allowed.
assert(isPredicatedInst(I) && "Expecting a scalar emulated instruction");
return isa<LoadInst>(I) ||
(isa<StoreInst>(I) &&
NumPredStores > NumberOfStoresToPredicate);
}
void LoopVectorizationCostModel::collectInstsToScalarize(ElementCount VF) {
// If we aren't vectorizing the loop, or if we've already collected the
// instructions to scalarize, there's nothing to do. Collection may already
// have occurred if we have a user-selected VF and are now computing the
// expected cost for interleaving.
if (VF.isScalar() || VF.isZero() ||
InstsToScalarize.find(VF) != InstsToScalarize.end())
return;
// Initialize a mapping for VF in InstsToScalalarize. If we find that it's
// not profitable to scalarize any instructions, the presence of VF in the
// map will indicate that we've analyzed it already.
ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF];
// Find all the instructions that are scalar with predication in the loop and
// determine if it would be better to not if-convert the blocks they are in.
// If so, we also record the instructions to scalarize.
for (BasicBlock *BB : TheLoop->blocks()) {
if (!blockNeedsPredication(BB))
continue;
for (Instruction &I : *BB)
if (isScalarWithPredication(&I)) {
ScalarCostsTy ScalarCosts;
// Do not apply discount logic if hacked cost is needed
// for emulated masked memrefs.
if (!useEmulatedMaskMemRefHack(&I) &&
computePredInstDiscount(&I, ScalarCosts, VF) >= 0)
ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end());
// Remember that BB will remain after vectorization.
PredicatedBBsAfterVectorization.insert(BB);
}
}
}
int LoopVectorizationCostModel::computePredInstDiscount(
Instruction *PredInst, DenseMap<Instruction *, unsigned> &ScalarCosts,
ElementCount VF) {
assert(!isUniformAfterVectorization(PredInst, VF) &&
"Instruction marked uniform-after-vectorization will be predicated");
// Initialize the discount to zero, meaning that the scalar version and the
// vector version cost the same.
int Discount = 0;
// Holds instructions to analyze. The instructions we visit are mapped in
// ScalarCosts. Those instructions are the ones that would be scalarized if
// we find that the scalar version costs less.
SmallVector<Instruction *, 8> Worklist;
// Returns true if the given instruction can be scalarized.
auto canBeScalarized = [&](Instruction *I) -> bool {
// We only attempt to scalarize instructions forming a single-use chain
// from the original predicated block that would otherwise be vectorized.
// Although not strictly necessary, we give up on instructions we know will
// already be scalar to avoid traversing chains that are unlikely to be
// beneficial.
if (!I->hasOneUse() || PredInst->getParent() != I->getParent() ||
isScalarAfterVectorization(I, VF))
return false;
// If the instruction is scalar with predication, it will be analyzed
// separately. We ignore it within the context of PredInst.
if (isScalarWithPredication(I))
return false;
// If any of the instruction's operands are uniform after vectorization,
// the instruction cannot be scalarized. This prevents, for example, a
// masked load from being scalarized.
//
// We assume we will only emit a value for lane zero of an instruction
// marked uniform after vectorization, rather than VF identical values.
// Thus, if we scalarize an instruction that uses a uniform, we would
// create uses of values corresponding to the lanes we aren't emitting code
// for. This behavior can be changed by allowing getScalarValue to clone
// the lane zero values for uniforms rather than asserting.
for (Use &U : I->operands())
if (auto *J = dyn_cast<Instruction>(U.get()))
if (isUniformAfterVectorization(J, VF))
return false;
// Otherwise, we can scalarize the instruction.
return true;
};
// Compute the expected cost discount from scalarizing the entire expression
// feeding the predicated instruction. We currently only consider expressions
// that are single-use instruction chains.
Worklist.push_back(PredInst);
while (!Worklist.empty()) {
Instruction *I = Worklist.pop_back_val();
// If we've already analyzed the instruction, there's nothing to do.
if (ScalarCosts.find(I) != ScalarCosts.end())
continue;
// Compute the cost of the vector instruction. Note that this cost already
// includes the scalarization overhead of the predicated instruction.
unsigned VectorCost = getInstructionCost(I, VF).first;
// Compute the cost of the scalarized instruction. This cost is the cost of
// the instruction as if it wasn't if-converted and instead remained in the
// predicated block. We will scale this cost by block probability after
// computing the scalarization overhead.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
unsigned ScalarCost =
VF.getKnownMinValue() *
getInstructionCost(I, ElementCount::getFixed(1)).first;
// Compute the scalarization overhead of needed insertelement instructions
// and phi nodes.
if (isScalarWithPredication(I) && !I->getType()->isVoidTy()) {
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(ToVectorTy(I->getType(), VF)),
APInt::getAllOnesValue(VF.getKnownMinValue()), true, false);
assert(!VF.isScalable() && "scalable vectors not yet supported.");
ScalarCost +=
VF.getKnownMinValue() *
TTI.getCFInstrCost(Instruction::PHI, TTI::TCK_RecipThroughput);
}
// Compute the scalarization overhead of needed extractelement
// instructions. For each of the instruction's operands, if the operand can
// be scalarized, add it to the worklist; otherwise, account for the
// overhead.
for (Use &U : I->operands())
if (auto *J = dyn_cast<Instruction>(U.get())) {
assert(VectorType::isValidElementType(J->getType()) &&
"Instruction has non-scalar type");
if (canBeScalarized(J))
Worklist.push_back(J);
else if (needsExtract(J, VF)) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(ToVectorTy(J->getType(), VF)),
APInt::getAllOnesValue(VF.getKnownMinValue()), false, true);
}
}
// Scale the total scalar cost by block probability.
ScalarCost /= getReciprocalPredBlockProb();
// Compute the discount. A non-negative discount means the vector version
// of the instruction costs more, and scalarizing would be beneficial.
Discount += VectorCost - ScalarCost;
ScalarCosts[I] = ScalarCost;
}
return Discount;
}
LoopVectorizationCostModel::VectorizationCostTy
LoopVectorizationCostModel::expectedCost(ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
VectorizationCostTy Cost;
// For each block.
for (BasicBlock *BB : TheLoop->blocks()) {
VectorizationCostTy BlockCost;
// For each instruction in the old loop.
for (Instruction &I : BB->instructionsWithoutDebug()) {
// Skip ignored values.
if (ValuesToIgnore.count(&I) ||
(VF.isVector() && VecValuesToIgnore.count(&I)))
continue;
VectorizationCostTy C = getInstructionCost(&I, VF);
// Check if we should override the cost.
if (ForceTargetInstructionCost.getNumOccurrences() > 0)
C.first = ForceTargetInstructionCost;
BlockCost.first += C.first;
BlockCost.second |= C.second;
LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first
<< " for VF " << VF << " For instruction: " << I
<< '\n');
}
// If we are vectorizing a predicated block, it will have been
// if-converted. This means that the block's instructions (aside from
// stores and instructions that may divide by zero) will now be
// unconditionally executed. For the scalar case, we may not always execute
// the predicated block. Thus, scale the block's cost by the probability of
// executing it.
if (VF.isScalar() && blockNeedsPredication(BB))
BlockCost.first /= getReciprocalPredBlockProb();
Cost.first += BlockCost.first;
Cost.second |= BlockCost.second;
}
return Cost;
}
/// Gets Address Access SCEV after verifying that the access pattern
/// is loop invariant except the induction variable dependence.
///
/// This SCEV can be sent to the Target in order to estimate the address
/// calculation cost.
static const SCEV *getAddressAccessSCEV(
Value *Ptr,
LoopVectorizationLegality *Legal,
PredicatedScalarEvolution &PSE,
const Loop *TheLoop) {
auto *Gep = dyn_cast<GetElementPtrInst>(Ptr);
if (!Gep)
return nullptr;
// We are looking for a gep with all loop invariant indices except for one
// which should be an induction variable.
auto SE = PSE.getSE();
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 nullptr;
}
// Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV.
return PSE.getSCEV(Ptr);
}
static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) {
return Legal->hasStride(I->getOperand(0)) ||
Legal->hasStride(I->getOperand(1));
}
unsigned
LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
ElementCount VF) {
assert(VF.isVector() &&
"Scalarization cost of instruction implies vectorization.");
assert(!VF.isScalable() && "scalable vectors not yet supported.");
Type *ValTy = getMemInstValueType(I);
auto SE = PSE.getSE();
unsigned AS = getLoadStoreAddressSpace(I);
Value *Ptr = getLoadStorePointerOperand(I);
Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
// Figure out whether the access is strided and get the stride value
// if it's known in compile time
const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, PSE, TheLoop);
// Get the cost of the scalar memory instruction and address computation.
unsigned Cost =
VF.getKnownMinValue() * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
// Don't pass *I here, since it is scalar but will actually be part of a
// vectorized loop where the user of it is a vectorized instruction.
const Align Alignment = getLoadStoreAlignment(I);
Cost += VF.getKnownMinValue() *
TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment,
AS, TTI::TCK_RecipThroughput);
// Get the overhead of the extractelement and insertelement instructions
// we might create due to scalarization.
Cost += getScalarizationOverhead(I, VF);
// If we have a predicated store, it may not be executed for each vector
// lane. Scale the cost by the probability of executing the predicated
// block.
if (isPredicatedInst(I)) {
Cost /= getReciprocalPredBlockProb();
if (useEmulatedMaskMemRefHack(I))
// Artificially setting to a high enough value to practically disable
// vectorization with such operations.
Cost = 3000000;
}
return Cost;
}
unsigned LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getMemInstValueType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
Value *Ptr = getLoadStorePointerOperand(I);
unsigned AS = getLoadStoreAddressSpace(I);
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
"Stride should be 1 or -1 for consecutive memory access");
const Align Alignment = getLoadStoreAlignment(I);
unsigned Cost = 0;
if (Legal->isMaskRequired(I))
Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS,
CostKind);
else
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS,
CostKind, I);
bool Reverse = ConsecutiveStride < 0;
if (Reverse)
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
return Cost;
}
unsigned LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getMemInstValueType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
if (isa<LoadInst>(I)) {
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS,
CostKind) +
TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy);
}
StoreInst *SI = cast<StoreInst>(I);
bool isLoopInvariantStoreValue = Legal->isUniform(SI->getValueOperand());
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS,
CostKind) +
(isLoopInvariantStoreValue
? 0
: TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy,
VF.getKnownMinValue() - 1));
}
unsigned LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getMemInstValueType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
const Value *Ptr = getLoadStorePointerOperand(I);
return TTI.getAddressComputationCost(VectorTy) +
TTI.getGatherScatterOpCost(
I->getOpcode(), VectorTy, Ptr, Legal->isMaskRequired(I), Alignment,
TargetTransformInfo::TCK_RecipThroughput, I);
}
unsigned LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getMemInstValueType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
unsigned AS = getLoadStoreAddressSpace(I);
auto Group = getInterleavedAccessGroup(I);
assert(Group && "Fail to get an interleaved access group.");
unsigned InterleaveFactor = Group->getFactor();
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor);
// Holds the indices of existing members in an interleaved load group.
// An interleaved store group doesn't need this as it doesn't allow gaps.
SmallVector<unsigned, 4> Indices;
if (isa<LoadInst>(I)) {
for (unsigned i = 0; i < InterleaveFactor; i++)
if (Group->getMember(i))
Indices.push_back(i);
}
// Calculate the cost of the whole interleaved group.
bool UseMaskForGaps =
Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed();
unsigned Cost = TTI.getInterleavedMemoryOpCost(
I->getOpcode(), WideVecTy, Group->getFactor(), Indices, Group->getAlign(),
AS, TTI::TCK_RecipThroughput, Legal->isMaskRequired(I), UseMaskForGaps);
if (Group->isReverse()) {
// TODO: Add support for reversed masked interleaved access.
assert(!Legal->isMaskRequired(I) &&
"Reverse masked interleaved access not supported.");
Cost += Group->getNumMembers() *
TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
}
return Cost;
}
unsigned LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I,
ElementCount VF) {
// Calculate scalar cost only. Vectorization cost should be ready at this
// moment.
if (VF.isScalar()) {
Type *ValTy = getMemInstValueType(I);
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS,
TTI::TCK_RecipThroughput, I);
}
return getWideningCost(I, VF);
}
LoopVectorizationCostModel::VectorizationCostTy
LoopVectorizationCostModel::getInstructionCost(Instruction *I,
ElementCount VF) {
assert(!VF.isScalable() &&
"the cost model is not yet implemented for scalable vectorization");
// If we know that this instruction will remain uniform, check the cost of
// the scalar version.
if (isUniformAfterVectorization(I, VF))
VF = ElementCount::getFixed(1);
if (VF.isVector() && isProfitableToScalarize(I, VF))
return VectorizationCostTy(InstsToScalarize[VF][I], false);
// Forced scalars do not have any scalarization overhead.
auto ForcedScalar = ForcedScalars.find(VF);
if (VF.isVector() && ForcedScalar != ForcedScalars.end()) {
auto InstSet = ForcedScalar->second;
if (InstSet.count(I))
return VectorizationCostTy(
(getInstructionCost(I, ElementCount::getFixed(1)).first *
VF.getKnownMinValue()),
false);
}
Type *VectorTy;
unsigned C = getInstructionCost(I, VF, VectorTy);
bool TypeNotScalarized =
VF.isVector() && VectorTy->isVectorTy() &&
TTI.getNumberOfParts(VectorTy) < VF.getKnownMinValue();
return VectorizationCostTy(C, TypeNotScalarized);
}
unsigned LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I,
ElementCount VF) {
assert(!VF.isScalable() &&
"cannot compute scalarization overhead for scalable vectorization");
if (VF.isScalar())
return 0;
unsigned Cost = 0;
Type *RetTy = ToVectorTy(I->getType(), VF);
if (!RetTy->isVoidTy() &&
(!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore()))
Cost += TTI.getScalarizationOverhead(
cast<VectorType>(RetTy), APInt::getAllOnesValue(VF.getKnownMinValue()),
true, false);
// Some targets keep addresses scalar.
if (isa<LoadInst>(I) && !TTI.prefersVectorizedAddressing())
return Cost;
// Some targets support efficient element stores.
if (isa<StoreInst>(I) && TTI.supportsEfficientVectorElementLoadStore())
return Cost;
// Collect operands to consider.
CallInst *CI = dyn_cast<CallInst>(I);
Instruction::op_range Ops = CI ? CI->arg_operands() : I->operands();
// Skip operands that do not require extraction/scalarization and do not incur
// any overhead.
return Cost + TTI.getOperandsScalarizationOverhead(
filterExtractingOperands(Ops, VF), VF.getKnownMinValue());
}
void LoopVectorizationCostModel::setCostBasedWideningDecision(ElementCount VF) {
assert(!VF.isScalable() && "scalable vectors not yet supported.");
if (VF.isScalar())
return;
NumPredStores = 0;
for (BasicBlock *BB : TheLoop->blocks()) {
// For each instruction in the old loop.
for (Instruction &I : *BB) {
Value *Ptr = getLoadStorePointerOperand(&I);
if (!Ptr)
continue;
// TODO: We should generate better code and update the cost model for
// predicated uniform stores. Today they are treated as any other
// predicated store (see added test cases in
// invariant-store-vectorization.ll).
if (isa<StoreInst>(&I) && isScalarWithPredication(&I))
NumPredStores++;
if (Legal->isUniform(Ptr) &&
// Conditional loads and stores should be scalarized and predicated.
// isScalarWithPredication cannot be used here since masked
// gather/scatters are not considered scalar with predication.
!Legal->blockNeedsPredication(I.getParent())) {
// TODO: Avoid replicating loads and stores instead of
// relying on instcombine to remove them.
// Load: Scalar load + broadcast
// Store: Scalar store + isLoopInvariantStoreValue ? 0 : extract
unsigned Cost = getUniformMemOpCost(&I, VF);
setWideningDecision(&I, VF, CM_Scalarize, Cost);
continue;
}
// We assume that widening is the best solution when possible.
if (memoryInstructionCanBeWidened(&I, VF)) {
unsigned Cost = getConsecutiveMemOpCost(&I, VF);
int ConsecutiveStride =
Legal->isConsecutivePtr(getLoadStorePointerOperand(&I));
assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
"Expected consecutive stride.");
InstWidening Decision =
ConsecutiveStride == 1 ? CM_Widen : CM_Widen_Reverse;
setWideningDecision(&I, VF, Decision, Cost);
continue;
}
// Choose between Interleaving, Gather/Scatter or Scalarization.
unsigned InterleaveCost = std::numeric_limits<unsigned>::max();
unsigned NumAccesses = 1;
if (isAccessInterleaved(&I)) {
auto Group = getInterleavedAccessGroup(&I);
assert(Group && "Fail to get an interleaved access group.");
// Make one decision for the whole group.
if (getWideningDecision(&I, VF) != CM_Unknown)
continue;
NumAccesses = Group->getNumMembers();
if (interleavedAccessCanBeWidened(&I, VF))
InterleaveCost = getInterleaveGroupCost(&I, VF);
}
unsigned GatherScatterCost =
isLegalGatherOrScatter(&I)
? getGatherScatterCost(&I, VF) * NumAccesses
: std::numeric_limits<unsigned>::max();
unsigned ScalarizationCost =
getMemInstScalarizationCost(&I, VF) * NumAccesses;
// Choose better solution for the current VF,
// write down this decision and use it during vectorization.
unsigned Cost;
InstWidening Decision;
if (InterleaveCost <= GatherScatterCost &&
InterleaveCost < ScalarizationCost) {
Decision = CM_Interleave;
Cost = InterleaveCost;
} else if (GatherScatterCost < ScalarizationCost) {
Decision = CM_GatherScatter;
Cost = GatherScatterCost;
} else {
Decision = CM_Scalarize;
Cost = ScalarizationCost;
}
// If the instructions belongs to an interleave group, the whole group
// receives the same decision. The whole group receives the cost, but
// the cost will actually be assigned to one instruction.
if (auto Group = getInterleavedAccessGroup(&I))
setWideningDecision(Group, VF, Decision, Cost);
else
setWideningDecision(&I, VF, Decision, Cost);
}
}
// Make sure that any load of address and any other address computation
// remains scalar unless there is gather/scatter support. This avoids
// inevitable extracts into address registers, and also has the benefit of
// activating LSR more, since that pass can't optimize vectorized
// addresses.
if (TTI.prefersVectorizedAddressing())
return;
// Start with all scalar pointer uses.
SmallPtrSet<Instruction *, 8> AddrDefs;
for (BasicBlock *BB : TheLoop->blocks())
for (Instruction &I : *BB) {
Instruction *PtrDef =
dyn_cast_or_null<Instruction>(getLoadStorePointerOperand(&I));
if (PtrDef && TheLoop->contains(PtrDef) &&
getWideningDecision(&I, VF) != CM_GatherScatter)
AddrDefs.insert(PtrDef);
}
// Add all instructions used to generate the addresses.
SmallVector<Instruction *, 4> Worklist;
for (auto *I : AddrDefs)
Worklist.push_back(I);
while (!Worklist.empty()) {
Instruction *I = Worklist.pop_back_val();
for (auto &Op : I->operands())
if (auto *InstOp = dyn_cast<Instruction>(Op))
if ((InstOp->getParent() == I->getParent()) && !isa<PHINode>(InstOp) &&
AddrDefs.insert(InstOp).second)
Worklist.push_back(InstOp);
}
for (auto *I : AddrDefs) {
if (isa<LoadInst>(I)) {
// Setting the desired widening decision should ideally be handled in
// by cost functions, but since this involves the task of finding out
// if the loaded register is involved in an address computation, it is
// instead changed here when we know this is the case.
InstWidening Decision = getWideningDecision(I, VF);
if (Decision == CM_Widen || Decision == CM_Widen_Reverse)
// Scalarize a widened load of address.
setWideningDecision(
I, VF, CM_Scalarize,
(VF.getKnownMinValue() *
getMemoryInstructionCost(I, ElementCount::getFixed(1))));
else if (auto Group = getInterleavedAccessGroup(I)) {
// Scalarize an interleave group of address loads.
for (unsigned I = 0; I < Group->getFactor(); ++I) {
if (Instruction *Member = Group->getMember(I))
setWideningDecision(
Member, VF, CM_Scalarize,
(VF.getKnownMinValue() *
getMemoryInstructionCost(Member, ElementCount::getFixed(1))));
}
}
} else
// Make sure I gets scalarized and a cost estimate without
// scalarization overhead.
ForcedScalars[VF].insert(I);
}
}
unsigned LoopVectorizationCostModel::getInstructionCost(Instruction *I,
ElementCount VF,
Type *&VectorTy) {
Type *RetTy = I->getType();
if (canTruncateToMinimalBitwidth(I, VF))
RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]);
VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF);
auto SE = PSE.getSE();
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
// 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: {
// In cases of scalarized and predicated instructions, there will be VF
// predicated blocks in the vectorized loop. Each branch around these
// blocks requires also an extract of its vector compare i1 element.
bool ScalarPredicatedBB = false;
BranchInst *BI = cast<BranchInst>(I);
if (VF.isVector() && BI->isConditional() &&
(PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) ||
PredicatedBBsAfterVectorization.count(BI->getSuccessor(1))))
ScalarPredicatedBB = true;
if (ScalarPredicatedBB) {
// Return cost for branches around scalarized and predicated blocks.
assert(!VF.isScalable() && "scalable vectors not yet supported.");
auto *Vec_i1Ty =
VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
return (TTI.getScalarizationOverhead(
Vec_i1Ty, APInt::getAllOnesValue(VF.getKnownMinValue()),
false, true) +
(TTI.getCFInstrCost(Instruction::Br, CostKind) *
VF.getKnownMinValue()));
} else if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar())
// The back-edge branch will remain, as will all scalar branches.
return TTI.getCFInstrCost(Instruction::Br, CostKind);
else
// This branch will be eliminated by if-conversion.
return 0;
// Note: We currently assume zero cost for an unconditional branch inside
// a predicated block since it will become a fall-through, although we
// may decide in the future to call TTI for all branches.
}
case Instruction::PHI: {
auto *Phi = cast<PHINode>(I);
// First-order recurrences are replaced by vector shuffles inside the loop.
// NOTE: Don't use ToVectorTy as SK_ExtractSubvector expects a vector type.
if (VF.isVector() && Legal->isFirstOrderRecurrence(Phi))
return TTI.getShuffleCost(
TargetTransformInfo::SK_ExtractSubvector, cast<VectorType>(VectorTy),
VF.getKnownMinValue() - 1, FixedVectorType::get(RetTy, 1));
// Phi nodes in non-header blocks (not inductions, reductions, etc.) are
// converted into select instructions. We require N - 1 selects per phi
// node, where N is the number of incoming values.
if (VF.isVector() && Phi->getParent() != TheLoop->getHeader())
return (Phi->getNumIncomingValues() - 1) *
TTI.getCmpSelInstrCost(
Instruction::Select, ToVectorTy(Phi->getType(), VF),
ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF),
CostKind);
return TTI.getCFInstrCost(Instruction::PHI, CostKind);
}
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::URem:
case Instruction::SRem:
// If we have a predicated instruction, it may not be executed for each
// vector lane. Get the scalarization cost and scale this amount by the
// probability of executing the predicated block. If the instruction is not
// predicated, we fall through to the next case.
if (VF.isVector() && isScalarWithPredication(I)) {
unsigned Cost = 0;
// These instructions have a non-void type, so account for the phi nodes
// that we will create. This cost is likely to be zero. The phi node
// cost, if any, should be scaled by the block probability because it
// models a copy at the end of each predicated block.
Cost += VF.getKnownMinValue() *
TTI.getCFInstrCost(Instruction::PHI, CostKind);
// The cost of the non-predicated instruction.
Cost += VF.getKnownMinValue() *
TTI.getArithmeticInstrCost(I->getOpcode(), RetTy, CostKind);
// The cost of insertelement and extractelement instructions needed for
// scalarization.
Cost += getScalarizationOverhead(I, VF);
// Scale the cost by the probability of executing the predicated blocks.
// This assumes the predicated block for each vector lane is equally
// likely.
return Cost / getReciprocalPredBlockProb();
}
LLVM_FALLTHROUGH;
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::FDiv:
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.
Value *Op2 = I->getOperand(1);
TargetTransformInfo::OperandValueProperties Op2VP;
TargetTransformInfo::OperandValueKind Op2VK =
TTI.getOperandInfo(Op2, Op2VP);
if (Op2VK == TargetTransformInfo::OK_AnyValue && Legal->isUniform(Op2))
Op2VK = TargetTransformInfo::OK_UniformValue;
SmallVector<const Value *, 4> Operands(I->operand_values());
unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1;
return N * TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, CostKind,
TargetTransformInfo::OK_AnyValue,
Op2VK, TargetTransformInfo::OP_None, Op2VP, Operands, I);
}
case Instruction::FNeg: {
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1;
return N * TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, CostKind,
TargetTransformInfo::OK_AnyValue,
TargetTransformInfo::OK_AnyValue,
TargetTransformInfo::OP_None, TargetTransformInfo::OP_None,
I->getOperand(0), I);
}
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) {
assert(!VF.isScalable() && "VF is assumed to be non scalable.");
CondTy = VectorType::get(CondTy, VF);
}
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy,
CostKind, I);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]);
VectorTy = ToVectorTy(ValTy, VF);
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr, CostKind,
I);
}
case Instruction::Store:
case Instruction::Load: {
ElementCount Width = VF;
if (Width.isVector()) {
InstWidening Decision = getWideningDecision(I, Width);
assert(Decision != CM_Unknown &&
"CM decision should be taken at this point");
if (Decision == CM_Scalarize)
Width = ElementCount::getFixed(1);
}
VectorTy = ToVectorTy(getMemInstValueType(I), Width);
return getMemoryInstructionCost(I, VF);
}
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: {
// Computes the CastContextHint from a Load/Store instruction.
auto ComputeCCH = [&](Instruction *I) -> TTI::CastContextHint {
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
"Expected a load or a store!");
if (VF.isScalar() || !TheLoop->contains(I))
return TTI::CastContextHint::Normal;
switch (getWideningDecision(I, VF)) {
case LoopVectorizationCostModel::CM_GatherScatter:
return TTI::CastContextHint::GatherScatter;
case LoopVectorizationCostModel::CM_Interleave:
return TTI::CastContextHint::Interleave;
case LoopVectorizationCostModel::CM_Scalarize:
case LoopVectorizationCostModel::CM_Widen:
return Legal->isMaskRequired(I) ? TTI::CastContextHint::Masked
: TTI::CastContextHint::Normal;
case LoopVectorizationCostModel::CM_Widen_Reverse:
return TTI::CastContextHint::Reversed;
case LoopVectorizationCostModel::CM_Unknown:
llvm_unreachable("Instr did not go through cost modelling?");
}
llvm_unreachable("Unhandled case!");
};
unsigned Opcode = I->getOpcode();
TTI::CastContextHint CCH = TTI::CastContextHint::None;
// For Trunc, the context is the only user, which must be a StoreInst.
if (Opcode == Instruction::Trunc || Opcode == Instruction::FPTrunc) {
if (I->hasOneUse())
if (StoreInst *Store = dyn_cast<StoreInst>(*I->user_begin()))
CCH = ComputeCCH(Store);
}
// For Z/Sext, the context is the operand, which must be a LoadInst.
else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt ||
Opcode == Instruction::FPExt) {
if (LoadInst *Load = dyn_cast<LoadInst>(I->getOperand(0)))
CCH = ComputeCCH(Load);
}
// We optimize the truncation of induction variables having constant
// integer steps. The cost of these truncations is the same as the scalar
// operation.
if (isOptimizableIVTruncate(I, VF)) {
auto *Trunc = cast<TruncInst>(I);
return TTI.getCastInstrCost(Instruction::Trunc, Trunc->getDestTy(),
Trunc->getSrcTy(), CCH, CostKind, Trunc);
}
Type *SrcScalarTy = I->getOperand(0)->getType();
Type *SrcVecTy =
VectorTy->isVectorTy() ? ToVectorTy(SrcScalarTy, VF) : SrcScalarTy;
if (canTruncateToMinimalBitwidth(I, VF)) {
// This cast is going to be shrunk. This may remove the cast or it might
// turn it into slightly different cast. For example, if MinBW == 16,
// "zext i8 %1 to i32" becomes "zext i8 %1 to i16".
//
// Calculate the modified src and dest types.
Type *MinVecTy = VectorTy;
if (Opcode == Instruction::Trunc) {
SrcVecTy = smallestIntegerVectorType(SrcVecTy, MinVecTy);
VectorTy =
largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
} else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt) {
SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy);
VectorTy =
smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
}
}
assert(!VF.isScalable() && "VF is assumed to be non scalable");
unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1;
return N *
TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH, CostKind, I);
}
case Instruction::Call: {
bool NeedToScalarize;
CallInst *CI = cast<CallInst>(I);
unsigned CallCost = getVectorCallCost(CI, VF, NeedToScalarize);
if (getVectorIntrinsicIDForCall(CI, TLI))
return std::min(CallCost, getVectorIntrinsicCost(CI, VF));
return CallCost;
}
default:
// The cost of executing VF copies of the scalar instruction. This opcode
// is unknown. Assume that it is the same as 'mul'.
return VF.getKnownMinValue() * TTI.getArithmeticInstrCost(
Instruction::Mul, VectorTy, CostKind) +
getScalarizationOverhead(I, VF);
} // 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_PASS_DEPENDENCY(BasicAAWrapperPass)
INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass)
INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass)
INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker)
INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis)
INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass)
INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(InjectTLIMappingsLegacy)
INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
namespace llvm {
Pass *createLoopVectorizePass() { return new LoopVectorize(); }
Pass *createLoopVectorizePass(bool InterleaveOnlyWhenForced,
bool VectorizeOnlyWhenForced) {
return new LoopVectorize(InterleaveOnlyWhenForced, VectorizeOnlyWhenForced);
}
} // end namespace llvm
bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
// Check if the pointer operand of a load or store instruction is
// consecutive.
if (auto *Ptr = getLoadStorePointerOperand(Inst))
return Legal->isConsecutivePtr(Ptr);
return false;
}
void LoopVectorizationCostModel::collectValuesToIgnore() {
// Ignore ephemeral values.
CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore);
// Ignore type-promoting instructions we identified during reduction
// detection.
for (auto &Reduction : Legal->getReductionVars()) {
RecurrenceDescriptor &RedDes = Reduction.second;
const SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts();
VecValuesToIgnore.insert(Casts.begin(), Casts.end());
}
// Ignore type-casting instructions we identified during induction
// detection.
for (auto &Induction : Legal->getInductionVars()) {
InductionDescriptor &IndDes = Induction.second;
const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts();
VecValuesToIgnore.insert(Casts.begin(), Casts.end());
}
}
void LoopVectorizationCostModel::collectInLoopReductions() {
// For the moment, without predicated reduction instructions, we do not
// support inloop reductions whilst folding the tail, and hence in those cases
// all reductions are currently out of the loop.
if (foldTailByMasking())
return;
for (auto &Reduction : Legal->getReductionVars()) {
PHINode *Phi = Reduction.first;
RecurrenceDescriptor &RdxDesc = Reduction.second;
// We don't collect reductions that are type promoted (yet).
if (RdxDesc.getRecurrenceType() != Phi->getType())
continue;
// If the target would prefer this reduction to happen "in-loop", then we
// want to record it as such.
unsigned Opcode = RdxDesc.getRecurrenceBinOp(RdxDesc.getRecurrenceKind());
if (!PreferInLoopReductions &&
!TTI.preferInLoopReduction(Opcode, Phi->getType(),
TargetTransformInfo::ReductionFlags()))
continue;
// Check that we can correctly put the reductions into the loop, by
// finding the chain of operations that leads from the phi to the loop
// exit value.
SmallVector<Instruction *, 4> ReductionOperations =
RdxDesc.getReductionOpChain(Phi, TheLoop);
bool InLoop = !ReductionOperations.empty();
if (InLoop)
InLoopReductionChains[Phi] = ReductionOperations;
LLVM_DEBUG(dbgs() << "LV: Using " << (InLoop ? "inloop" : "out of loop")
<< " reduction for phi: " << *Phi << "\n");
}
}
// TODO: we could return a pair of values that specify the max VF and
// min VF, to be used in `buildVPlans(MinVF, MaxVF)` instead of
// `buildVPlans(VF, VF)`. We cannot do it because VPLAN at the moment
// doesn't have a cost model that can choose which plan to execute if
// more than one is generated.
static unsigned determineVPlanVF(const unsigned WidestVectorRegBits,
LoopVectorizationCostModel &CM) {
unsigned WidestType;
std::tie(std::ignore, WidestType) = CM.getSmallestAndWidestTypes();
return WidestVectorRegBits / WidestType;
}
VectorizationFactor
LoopVectorizationPlanner::planInVPlanNativePath(ElementCount UserVF) {
assert(!UserVF.isScalable() && "scalable vectors not yet supported");
ElementCount VF = UserVF;
// Outer loop handling: They may require CFG and instruction level
// transformations before even evaluating whether vectorization is profitable.
// Since we cannot modify the incoming IR, we need to build VPlan upfront in
// the vectorization pipeline.
if (!OrigLoop->isInnermost()) {
// If the user doesn't provide a vectorization factor, determine a
// reasonable one.
if (UserVF.isZero()) {
VF = ElementCount::getFixed(
determineVPlanVF(TTI->getRegisterBitWidth(true /* Vector*/), CM));
LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n");
// Make sure we have a VF > 1 for stress testing.
if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) {
LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: "
<< "overriding computed VF.\n");
VF = ElementCount::getFixed(4);
}
}
assert(EnableVPlanNativePath && "VPlan-native path is not enabled.");
assert(isPowerOf2_32(VF.getKnownMinValue()) &&
"VF needs to be a power of two");
LLVM_DEBUG(dbgs() << "LV: Using " << (!UserVF.isZero() ? "user " : "")
<< "VF " << VF << " to build VPlans.\n");
buildVPlans(VF.getKnownMinValue(), VF.getKnownMinValue());
// For VPlan build stress testing, we bail out after VPlan construction.
if (VPlanBuildStressTest)
return VectorizationFactor::Disabled();
return {VF, 0 /*Cost*/};
}
LLVM_DEBUG(
dbgs() << "LV: Not vectorizing. Inner loops aren't supported in the "
"VPlan-native path.\n");
return VectorizationFactor::Disabled();
}
Optional<VectorizationFactor>
LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) {
assert(!UserVF.isScalable() && "scalable vectorization not yet handled");
assert(OrigLoop->isInnermost() && "Inner loop expected.");
Optional<unsigned> MaybeMaxVF =
CM.computeMaxVF(UserVF.getKnownMinValue(), UserIC);
if (!MaybeMaxVF) // Cases that should not to be vectorized nor interleaved.
return None;
// Invalidate interleave groups if all blocks of loop will be predicated.
if (CM.blockNeedsPredication(OrigLoop->getHeader()) &&
!useMaskedInterleavedAccesses(*TTI)) {
LLVM_DEBUG(
dbgs()
<< "LV: Invalidate all interleaved groups due to fold-tail by masking "
"which requires masked-interleaved support.\n");
if (CM.InterleaveInfo.invalidateGroups())
// Invalidating interleave groups also requires invalidating all decisions
// based on them, which includes widening decisions and uniform and scalar
// values.
CM.invalidateCostModelingDecisions();
}
if (!UserVF.isZero()) {
LLVM_DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
assert(isPowerOf2_32(UserVF.getKnownMinValue()) &&
"VF needs to be a power of two");
// Collect the instructions (and their associated costs) that will be more
// profitable to scalarize.
CM.selectUserVectorizationFactor(UserVF);
CM.collectInLoopReductions();
buildVPlansWithVPRecipes(UserVF.getKnownMinValue(),
UserVF.getKnownMinValue());
LLVM_DEBUG(printPlans(dbgs()));
return {{UserVF, 0}};
}
unsigned MaxVF = MaybeMaxVF.getValue();
assert(MaxVF != 0 && "MaxVF is zero.");
for (unsigned VF = 1; VF <= MaxVF; VF *= 2) {
// Collect Uniform and Scalar instructions after vectorization with VF.
CM.collectUniformsAndScalars(ElementCount::getFixed(VF));
// Collect the instructions (and their associated costs) that will be more
// profitable to scalarize.
if (VF > 1)
CM.collectInstsToScalarize(ElementCount::getFixed(VF));
}
CM.collectInLoopReductions();
buildVPlansWithVPRecipes(1, MaxVF);
LLVM_DEBUG(printPlans(dbgs()));
if (MaxVF == 1)
return VectorizationFactor::Disabled();
// Select the optimal vectorization factor.
return CM.selectVectorizationFactor(MaxVF);
}
void LoopVectorizationPlanner::setBestPlan(ElementCount VF, unsigned UF) {
LLVM_DEBUG(dbgs() << "Setting best plan to VF=" << VF << ", UF=" << UF
<< '\n');
BestVF = VF;
BestUF = UF;
erase_if(VPlans, [VF](const VPlanPtr &Plan) {
return !Plan->hasVF(VF);
});
assert(VPlans.size() == 1 && "Best VF has not a single VPlan.");
}
void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV,
DominatorTree *DT) {
// Perform the actual loop transformation.
// 1. Create a new empty loop. Unlink the old loop and connect the new one.
VPCallbackILV CallbackILV(ILV);
assert(BestVF.hasValue() && "Vectorization Factor is missing");
VPTransformState State{*BestVF, BestUF, LI,
DT, ILV.Builder, ILV.VectorLoopValueMap,
&ILV, CallbackILV};
State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton();
State.TripCount = ILV.getOrCreateTripCount(nullptr);
State.CanonicalIV = ILV.Induction;
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
// 2. Copy and widen instructions from the old loop into the new loop.
assert(VPlans.size() == 1 && "Not a single VPlan to execute.");
VPlans.front()->execute(&State);
// 3. Fix the vectorized code: take care of header phi's, live-outs,
// predication, updating analyses.
ILV.fixVectorizedLoop();
}
void LoopVectorizationPlanner::collectTriviallyDeadInstructions(
SmallPtrSetImpl<Instruction *> &DeadInstructions) {
BasicBlock *Latch = OrigLoop->getLoopLatch();
// We create new control-flow for the vectorized loop, so the original
// condition will be dead after vectorization if it's only used by the
// branch.
auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
if (Cmp && Cmp->hasOneUse())
DeadInstructions.insert(Cmp);
// We create new "steps" for induction variable updates to which the original
// induction variables map. An original update instruction will be dead if
// all its users except the induction variable are dead.
for (auto &Induction : Legal->getInductionVars()) {
PHINode *Ind = Induction.first;
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
if (llvm::all_of(IndUpdate->users(), [&](User *U) -> bool {
return U == Ind || DeadInstructions.count(cast<Instruction>(U));
}))
DeadInstructions.insert(IndUpdate);
// We record as "Dead" also the type-casting instructions we had identified
// during induction analysis. We don't need any handling for them in the
// vectorized loop because we have proven that, under a proper runtime
// test guarding the vectorized loop, the value of the phi, and the casted
// value of the phi, are the same. The last instruction in this casting chain
// will get its scalar/vector/widened def from the scalar/vector/widened def
// of the respective phi node. Any other casts in the induction def-use chain
// have no other uses outside the phi update chain, and will be ignored.
InductionDescriptor &IndDes = Induction.second;
const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts();
DeadInstructions.insert(Casts.begin(), Casts.end());
}
}
Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; }
Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; }
Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step,
Instruction::BinaryOps BinOp) {
// When unrolling and the VF is 1, we only need to add a simple scalar.
Type *Ty = Val->getType();
assert(!Ty->isVectorTy() && "Val must be a scalar");
if (Ty->isFloatingPointTy()) {
Constant *C = ConstantFP::get(Ty, (double)StartIdx);
// Floating point operations had to be 'fast' to enable the unrolling.
Value *MulOp = addFastMathFlag(Builder.CreateFMul(C, Step));
return addFastMathFlag(Builder.CreateBinOp(BinOp, Val, MulOp));
}
Constant *C = ConstantInt::get(Ty, StartIdx);
return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction");
}
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) {
auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i));
if (MD) {
const auto *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 LoopVectorizationPlanner::getDecisionAndClampRange(
const std::function<bool(ElementCount)> &Predicate, VFRange &Range) {
assert(Range.End > Range.Start && "Trying to test an empty VF range.");
bool PredicateAtRangeStart = Predicate(ElementCount::getFixed(Range.Start));
for (unsigned TmpVF = Range.Start * 2; TmpVF < Range.End; TmpVF *= 2)
if (Predicate(ElementCount::getFixed(TmpVF)) != PredicateAtRangeStart) {
Range.End = TmpVF;
break;
}
return PredicateAtRangeStart;
}
/// Build VPlans for the full range of feasible VF's = {\p MinVF, 2 * \p MinVF,
/// 4 * \p MinVF, ..., \p MaxVF} by repeatedly building a VPlan for a sub-range
/// of VF's starting at a given VF and extending it as much as possible. Each
/// vectorization decision can potentially shorten this sub-range during
/// buildVPlan().
void LoopVectorizationPlanner::buildVPlans(unsigned MinVF, unsigned MaxVF) {
for (unsigned VF = MinVF; VF < MaxVF + 1;) {
VFRange SubRange = {VF, MaxVF + 1};
VPlans.push_back(buildVPlan(SubRange));
VF = SubRange.End;
}
}
VPValue *VPRecipeBuilder::createEdgeMask(BasicBlock *Src, BasicBlock *Dst,
VPlanPtr &Plan) {
assert(is_contained(predecessors(Dst), Src) && "Invalid edge");
// Look for cached value.
std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst);
EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge);
if (ECEntryIt != EdgeMaskCache.end())
return ECEntryIt->second;
VPValue *SrcMask = createBlockInMask(Src, Plan);
// The terminator has to be a branch inst!
BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
assert(BI && "Unexpected terminator found");
if (!BI->isConditional() || BI->getSuccessor(0) == BI->getSuccessor(1))
return EdgeMaskCache[Edge] = SrcMask;
VPValue *EdgeMask = Plan->getVPValue(BI->getCondition());
assert(EdgeMask && "No Edge Mask found for condition");
if (BI->getSuccessor(0) != Dst)
EdgeMask = Builder.createNot(EdgeMask);
if (SrcMask) // Otherwise block in-mask is all-one, no need to AND.
EdgeMask = Builder.createAnd(EdgeMask, SrcMask);
return EdgeMaskCache[Edge] = EdgeMask;
}
VPValue *VPRecipeBuilder::createBlockInMask(BasicBlock *BB, VPlanPtr &Plan) {
assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
// Look for cached value.
BlockMaskCacheTy::iterator BCEntryIt = BlockMaskCache.find(BB);
if (BCEntryIt != BlockMaskCache.end())
return BCEntryIt->second;
// All-one mask is modelled as no-mask following the convention for masked
// load/store/gather/scatter. Initialize BlockMask to no-mask.
VPValue *BlockMask = nullptr;
if (OrigLoop->getHeader() == BB) {
if (!CM.blockNeedsPredication(BB))
return BlockMaskCache[BB] = BlockMask; // Loop incoming mask is all-one.
// Introduce the early-exit compare IV <= BTC to form header block mask.
// This is used instead of IV < TC because TC may wrap, unlike BTC.
// Start by constructing the desired canonical IV.
VPValue *IV = nullptr;
if (Legal->getPrimaryInduction())
IV = Plan->getVPValue(Legal->getPrimaryInduction());
else {
auto IVRecipe = new VPWidenCanonicalIVRecipe();
Builder.getInsertBlock()->appendRecipe(IVRecipe);
IV = IVRecipe->getVPValue();
}
VPValue *BTC = Plan->getOrCreateBackedgeTakenCount();
bool TailFolded = !CM.isScalarEpilogueAllowed();
if (TailFolded && CM.TTI.emitGetActiveLaneMask()) {
// While ActiveLaneMask is a binary op that consumes the loop tripcount
// as a second argument, we only pass the IV here and extract the
// tripcount from the transform state where codegen of the VP instructions
// happen.
BlockMask = Builder.createNaryOp(VPInstruction::ActiveLaneMask, {IV});
} else {
BlockMask = Builder.createNaryOp(VPInstruction::ICmpULE, {IV, BTC});
}
return BlockMaskCache[BB] = BlockMask;
}
// This is the block mask. We OR all incoming edges.
for (auto *Predecessor : predecessors(BB)) {
VPValue *EdgeMask = createEdgeMask(Predecessor, BB, Plan);
if (!EdgeMask) // Mask of predecessor is all-one so mask of block is too.
return BlockMaskCache[BB] = EdgeMask;
if (!BlockMask) { // BlockMask has its initialized nullptr value.
BlockMask = EdgeMask;
continue;
}
BlockMask = Builder.createOr(BlockMask, EdgeMask);
}
return BlockMaskCache[BB] = BlockMask;
}
VPWidenMemoryInstructionRecipe *
VPRecipeBuilder::tryToWidenMemory(Instruction *I, VFRange &Range,
VPlanPtr &Plan) {
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
"Must be called with either a load or store");
auto willWiden = [&](ElementCount VF) -> bool {
assert(!VF.isScalable() && "unexpected scalable ElementCount");
if (VF.isScalar())
return false;
LoopVectorizationCostModel::InstWidening Decision =
CM.getWideningDecision(I, VF);
assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
"CM decision should be taken at this point.");
if (Decision == LoopVectorizationCostModel::CM_Interleave)
return true;
if (CM.isScalarAfterVectorization(I, VF) ||
CM.isProfitableToScalarize(I, VF))
return false;
return Decision != LoopVectorizationCostModel::CM_Scalarize;
};
if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range))
return nullptr;
VPValue *Mask = nullptr;
if (Legal->isMaskRequired(I))
Mask = createBlockInMask(I->getParent(), Plan);
VPValue *Addr = Plan->getOrAddVPValue(getLoadStorePointerOperand(I));
if (LoadInst *Load = dyn_cast<LoadInst>(I))
return new VPWidenMemoryInstructionRecipe(*Load, Addr, Mask);
StoreInst *Store = cast<StoreInst>(I);
VPValue *StoredValue = Plan->getOrAddVPValue(Store->getValueOperand());
return new VPWidenMemoryInstructionRecipe(*Store, Addr, StoredValue, Mask);
}
VPWidenIntOrFpInductionRecipe *
VPRecipeBuilder::tryToOptimizeInductionPHI(PHINode *Phi) const {
// Check if this is an integer or fp induction. If so, build the recipe that
// produces its scalar and vector values.
InductionDescriptor II = Legal->getInductionVars().lookup(Phi);
if (II.getKind() == InductionDescriptor::IK_IntInduction ||
II.getKind() == InductionDescriptor::IK_FpInduction)
return new VPWidenIntOrFpInductionRecipe(Phi);
return nullptr;
}
VPWidenIntOrFpInductionRecipe *
VPRecipeBuilder::tryToOptimizeInductionTruncate(TruncInst *I,
VFRange &Range) const {
// Optimize the special case where the source is a constant integer
// induction variable. Notice that we can only optimize the 'trunc' case
// because (a) FP conversions lose precision, (b) sext/zext may wrap, and
// (c) other casts depend on pointer size.
// Determine whether \p K is a truncation based on an induction variable that
// can be optimized.
auto isOptimizableIVTruncate =
[&](Instruction *K) -> std::function<bool(ElementCount)> {
return [=](ElementCount VF) -> bool {
return CM.isOptimizableIVTruncate(K, VF);
};
};
if (LoopVectorizationPlanner::getDecisionAndClampRange(
isOptimizableIVTruncate(I), Range))
return new VPWidenIntOrFpInductionRecipe(cast<PHINode>(I->getOperand(0)),
I);
return nullptr;
}
VPBlendRecipe *VPRecipeBuilder::tryToBlend(PHINode *Phi, VPlanPtr &Plan) {
// 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.
SmallVector<VPValue *, 2> Operands;
unsigned NumIncoming = Phi->getNumIncomingValues();
for (unsigned In = 0; In < NumIncoming; In++) {
VPValue *EdgeMask =
createEdgeMask(Phi->getIncomingBlock(In), Phi->getParent(), Plan);
assert((EdgeMask || NumIncoming == 1) &&
"Multiple predecessors with one having a full mask");
Operands.push_back(Plan->getOrAddVPValue(Phi->getIncomingValue(In)));
if (EdgeMask)
Operands.push_back(EdgeMask);
}
return new VPBlendRecipe(Phi, Operands);
}
VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI, VFRange &Range,
VPlan &Plan) const {
bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange(
[this, CI](ElementCount VF) {
return CM.isScalarWithPredication(CI, VF);
},
Range);
if (IsPredicated)
return nullptr;
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
ID == Intrinsic::lifetime_start || ID == Intrinsic::sideeffect))
return nullptr;
auto willWiden = [&](ElementCount VF) -> bool {
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
// The following case may be scalarized depending on the VF.
// 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 = false;
unsigned CallCost = CM.getVectorCallCost(CI, VF, NeedToScalarize);
bool UseVectorIntrinsic =
ID && CM.getVectorIntrinsicCost(CI, VF) <= CallCost;
return UseVectorIntrinsic || !NeedToScalarize;
};
if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range))
return nullptr;
return new VPWidenCallRecipe(*CI, Plan.mapToVPValues(CI->arg_operands()));
}
bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const {
assert(!isa<BranchInst>(I) && !isa<PHINode>(I) && !isa<LoadInst>(I) &&
!isa<StoreInst>(I) && "Instruction should have been handled earlier");
// Instruction should be widened, unless it is scalar after vectorization,
// scalarization is profitable or it is predicated.
auto WillScalarize = [this, I](ElementCount VF) -> bool {
return CM.isScalarAfterVectorization(I, VF) ||
CM.isProfitableToScalarize(I, VF) ||
CM.isScalarWithPredication(I, VF);
};
return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize,
Range);
}
VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I, VPlan &Plan) const {
auto IsVectorizableOpcode = [](unsigned Opcode) {
switch (Opcode) {
case Instruction::Add:
case Instruction::And:
case Instruction::AShr:
case Instruction::BitCast:
case Instruction::FAdd:
case Instruction::FCmp:
case Instruction::FDiv:
case Instruction::FMul:
case Instruction::FNeg:
case Instruction::FPExt:
case Instruction::FPToSI:
case Instruction::FPToUI:
case Instruction::FPTrunc:
case Instruction::FRem:
case Instruction::FSub:
case Instruction::ICmp:
case Instruction::IntToPtr:
case Instruction::LShr:
case Instruction::Mul:
case Instruction::Or:
case Instruction::PtrToInt:
case Instruction::SDiv:
case Instruction::Select:
case Instruction::SExt:
case Instruction::Shl:
case Instruction::SIToFP:
case Instruction::SRem:
case Instruction::Sub:
case Instruction::Trunc:
case Instruction::UDiv:
case Instruction::UIToFP:
case Instruction::URem:
case Instruction::Xor:
case Instruction::ZExt:
return true;
}
return false;
};
if (!IsVectorizableOpcode(I->getOpcode()))
return nullptr;
// Success: widen this instruction.
return new VPWidenRecipe(*I, Plan.mapToVPValues(I->operands()));
}
VPBasicBlock *VPRecipeBuilder::handleReplication(
Instruction *I, VFRange &Range, VPBasicBlock *VPBB,
DenseMap<Instruction *, VPReplicateRecipe *> &PredInst2Recipe,
VPlanPtr &Plan) {
bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); },
Range);
bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) { return CM.isScalarWithPredication(I, VF); },
Range);
auto *Recipe = new VPReplicateRecipe(I, Plan->mapToVPValues(I->operands()),
IsUniform, IsPredicated);
setRecipe(I, Recipe);
// Find if I uses a predicated instruction. If so, it will use its scalar
// value. Avoid hoisting the insert-element which packs the scalar value into
// a vector value, as that happens iff all users use the vector value.
for (auto &Op : I->operands())
if (auto *PredInst = dyn_cast<Instruction>(Op))
if (PredInst2Recipe.find(PredInst) != PredInst2Recipe.end())
PredInst2Recipe[PredInst]->setAlsoPack(false);
// Finalize the recipe for Instr, first if it is not predicated.
if (!IsPredicated) {
LLVM_DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n");
VPBB->appendRecipe(Recipe);
return VPBB;
}
LLVM_DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n");
assert(VPBB->getSuccessors().empty() &&
"VPBB has successors when handling predicated replication.");
// Record predicated instructions for above packing optimizations.
PredInst2Recipe[I] = Recipe;
VPBlockBase *Region = createReplicateRegion(I, Recipe, Plan);
VPBlockUtils::insertBlockAfter(Region, VPBB);
auto *RegSucc = new VPBasicBlock();
VPBlockUtils::insertBlockAfter(RegSucc, Region);
return RegSucc;
}
VPRegionBlock *VPRecipeBuilder::createReplicateRegion(Instruction *Instr,
VPRecipeBase *PredRecipe,
VPlanPtr &Plan) {
// Instructions marked for predication are replicated and placed under an
// if-then construct to prevent side-effects.
// Generate recipes to compute the block mask for this region.
VPValue *BlockInMask = createBlockInMask(Instr->getParent(), Plan);
// Build the triangular if-then region.
std::string RegionName = (Twine("pred.") + Instr->getOpcodeName()).str();
assert(Instr->getParent() && "Predicated instruction not in any basic block");
auto *BOMRecipe = new VPBranchOnMaskRecipe(BlockInMask);
auto *Entry = new VPBasicBlock(Twine(RegionName) + ".entry", BOMRecipe);
auto *PHIRecipe =
Instr->getType()->isVoidTy() ? nullptr : new VPPredInstPHIRecipe(Instr);
auto *Exit = new VPBasicBlock(Twine(RegionName) + ".continue", PHIRecipe);
auto *Pred = new VPBasicBlock(Twine(RegionName) + ".if", PredRecipe);
VPRegionBlock *Region = new VPRegionBlock(Entry, Exit, RegionName, true);
// Note: first set Entry as region entry and then connect successors starting
// from it in order, to propagate the "parent" of each VPBasicBlock.
VPBlockUtils::insertTwoBlocksAfter(Pred, Exit, BlockInMask, Entry);
VPBlockUtils::connectBlocks(Pred, Exit);
return Region;
}
VPRecipeBase *VPRecipeBuilder::tryToCreateWidenRecipe(Instruction *Instr,
VFRange &Range,
VPlanPtr &Plan) {
// First, check for specific widening recipes that deal with calls, memory
// operations, inductions and Phi nodes.
if (auto *CI = dyn_cast<CallInst>(Instr))
return tryToWidenCall(CI, Range, *Plan);
if (isa<LoadInst>(Instr) || isa<StoreInst>(Instr))
return tryToWidenMemory(Instr, Range, Plan);
VPRecipeBase *Recipe;
if (auto Phi = dyn_cast<PHINode>(Instr)) {
if (Phi->getParent() != OrigLoop->getHeader())
return tryToBlend(Phi, Plan);
if ((Recipe = tryToOptimizeInductionPHI(Phi)))
return Recipe;
return new VPWidenPHIRecipe(Phi);
}
if (isa<TruncInst>(Instr) &&
(Recipe = tryToOptimizeInductionTruncate(cast<TruncInst>(Instr), Range)))
return Recipe;
if (!shouldWiden(Instr, Range))
return nullptr;
if (auto GEP = dyn_cast<GetElementPtrInst>(Instr))
return new VPWidenGEPRecipe(GEP, Plan->mapToVPValues(GEP->operands()),
OrigLoop);
if (auto *SI = dyn_cast<SelectInst>(Instr)) {
bool InvariantCond =
PSE.getSE()->isLoopInvariant(PSE.getSCEV(SI->getOperand(0)), OrigLoop);
return new VPWidenSelectRecipe(*SI, Plan->mapToVPValues(SI->operands()),
InvariantCond);
}
return tryToWiden(Instr, *Plan);
}
void LoopVectorizationPlanner::buildVPlansWithVPRecipes(unsigned MinVF,
unsigned MaxVF) {
assert(OrigLoop->isInnermost() && "Inner loop expected.");
// Collect conditions feeding internal conditional branches; they need to be
// represented in VPlan for it to model masking.
SmallPtrSet<Value *, 1> NeedDef;
auto *Latch = OrigLoop->getLoopLatch();
for (BasicBlock *BB : OrigLoop->blocks()) {
if (BB == Latch)
continue;
BranchInst *Branch = dyn_cast<BranchInst>(BB->getTerminator());
if (Branch && Branch->isConditional())
NeedDef.insert(Branch->getCondition());
}
// If the tail is to be folded by masking, the primary induction variable, if
// exists needs to be represented in VPlan for it to model early-exit masking.
// Also, both the Phi and the live-out instruction of each reduction are
// required in order to introduce a select between them in VPlan.
if (CM.foldTailByMasking()) {
if (Legal->getPrimaryInduction())
NeedDef.insert(Legal->getPrimaryInduction());
for (auto &Reduction : Legal->getReductionVars()) {
NeedDef.insert(Reduction.first);
NeedDef.insert(Reduction.second.getLoopExitInstr());
}
}
// Collect instructions from the original loop that will become trivially dead
// in the vectorized loop. We don't need to vectorize these instructions. For
// example, original induction update instructions can become dead because we
// separately emit induction "steps" when generating code for the new loop.
// Similarly, we create a new latch condition when setting up the structure
// of the new loop, so the old one can become dead.
SmallPtrSet<Instruction *, 4> DeadInstructions;
collectTriviallyDeadInstructions(DeadInstructions);
// Add assume instructions we need to drop to DeadInstructions, to prevent
// them from being added to the VPlan.
// TODO: We only need to drop assumes in blocks that get flattend. If the
// control flow is preserved, we should keep them.
auto &ConditionalAssumes = Legal->getConditionalAssumes();
DeadInstructions.insert(ConditionalAssumes.begin(), ConditionalAssumes.end());
DenseMap<Instruction *, Instruction *> &SinkAfter = Legal->getSinkAfter();
// Dead instructions do not need sinking. Remove them from SinkAfter.
for (Instruction *I : DeadInstructions)
SinkAfter.erase(I);
for (unsigned VF = MinVF; VF < MaxVF + 1;) {
VFRange SubRange = {VF, MaxVF + 1};
VPlans.push_back(buildVPlanWithVPRecipes(SubRange, NeedDef,
DeadInstructions, SinkAfter));
VF = SubRange.End;
}
}
VPlanPtr LoopVectorizationPlanner::buildVPlanWithVPRecipes(
VFRange &Range, SmallPtrSetImpl<Value *> &NeedDef,
SmallPtrSetImpl<Instruction *> &DeadInstructions,
const DenseMap<Instruction *, Instruction *> &SinkAfter) {
// Hold a mapping from predicated instructions to their recipes, in order to
// fix their AlsoPack behavior if a user is determined to replicate and use a
// scalar instead of vector value.
DenseMap<Instruction *, VPReplicateRecipe *> PredInst2Recipe;
SmallPtrSet<const InterleaveGroup<Instruction> *, 1> InterleaveGroups;
VPRecipeBuilder RecipeBuilder(OrigLoop, TLI, Legal, CM, PSE, Builder);
// ---------------------------------------------------------------------------
// Pre-construction: record ingredients whose recipes we'll need to further
// process after constructing the initial VPlan.
// ---------------------------------------------------------------------------
// Mark instructions we'll need to sink later and their targets as
// ingredients whose recipe we'll need to record.
for (auto &Entry : SinkAfter) {
RecipeBuilder.recordRecipeOf(Entry.first);
RecipeBuilder.recordRecipeOf(Entry.second);
}
for (auto &Reduction : CM.getInLoopReductionChains()) {
PHINode *Phi = Reduction.first;
RecurrenceDescriptor::RecurrenceKind Kind =
Legal->getReductionVars()[Phi].getRecurrenceKind();
const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second;
RecipeBuilder.recordRecipeOf(Phi);
for (auto &R : ReductionOperations) {
RecipeBuilder.recordRecipeOf(R);
// For min/max reducitons, where we have a pair of icmp/select, we also
// need to record the ICmp recipe, so it can be removed later.
if (Kind == RecurrenceDescriptor::RK_IntegerMinMax ||
Kind == RecurrenceDescriptor::RK_FloatMinMax) {
RecipeBuilder.recordRecipeOf(cast<Instruction>(R->getOperand(0)));
}
}
}
// For each interleave group which is relevant for this (possibly trimmed)
// Range, add it to the set of groups to be later applied to the VPlan and add
// placeholders for its members' Recipes which we'll be replacing with a
// single VPInterleaveRecipe.
for (InterleaveGroup<Instruction> *IG : IAI.getInterleaveGroups()) {
auto applyIG = [IG, this](ElementCount VF) -> bool {
return (VF.isVector() && // Query is illegal for VF == 1
CM.getWideningDecision(IG->getInsertPos(), VF) ==
LoopVectorizationCostModel::CM_Interleave);
};
if (!getDecisionAndClampRange(applyIG, Range))
continue;
InterleaveGroups.insert(IG);
for (unsigned i = 0; i < IG->getFactor(); i++)
if (Instruction *Member = IG->getMember(i))
RecipeBuilder.recordRecipeOf(Member);
};
// ---------------------------------------------------------------------------
// Build initial VPlan: Scan the body of the loop in a topological order to
// visit each basic block after having visited its predecessor basic blocks.
// ---------------------------------------------------------------------------
// Create a dummy pre-entry VPBasicBlock to start building the VPlan.
auto Plan = std::make_unique<VPlan>();
VPBasicBlock *VPBB = new VPBasicBlock("Pre-Entry");
Plan->setEntry(VPBB);
// Represent values that will have defs inside VPlan.
for (Value *V : NeedDef)
Plan->addVPValue(V);
// Scan the body of the loop in a topological order to visit each basic block
// after having visited its predecessor basic blocks.
LoopBlocksDFS DFS(OrigLoop);
DFS.perform(LI);
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
// Relevant instructions from basic block BB will be grouped into VPRecipe
// ingredients and fill a new VPBasicBlock.
unsigned VPBBsForBB = 0;
auto *FirstVPBBForBB = new VPBasicBlock(BB->getName());
VPBlockUtils::insertBlockAfter(FirstVPBBForBB, VPBB);
VPBB = FirstVPBBForBB;
Builder.setInsertPoint(VPBB);
// Introduce each ingredient into VPlan.
// TODO: Model and preserve debug instrinsics in VPlan.
for (Instruction &I : BB->instructionsWithoutDebug()) {
Instruction *Instr = &I;
// First filter out irrelevant instructions, to ensure no recipes are
// built for them.
if (isa<BranchInst>(Instr) || DeadInstructions.count(Instr))
continue;
if (auto Recipe =
RecipeBuilder.tryToCreateWidenRecipe(Instr, Range, Plan)) {
RecipeBuilder.setRecipe(Instr, Recipe);
VPBB->appendRecipe(Recipe);
continue;
}
// Otherwise, if all widening options failed, Instruction is to be
// replicated. This may create a successor for VPBB.
VPBasicBlock *NextVPBB = RecipeBuilder.handleReplication(
Instr, Range, VPBB, PredInst2Recipe, Plan);
if (NextVPBB != VPBB) {
VPBB = NextVPBB;
VPBB->setName(BB->hasName() ? BB->getName() + "." + Twine(VPBBsForBB++)
: "");
}
}
}
// Discard empty dummy pre-entry VPBasicBlock. Note that other VPBasicBlocks
// may also be empty, such as the last one VPBB, reflecting original
// basic-blocks with no recipes.
VPBasicBlock *PreEntry = cast<VPBasicBlock>(Plan->getEntry());
assert(PreEntry->empty() && "Expecting empty pre-entry block.");
VPBlockBase *Entry = Plan->setEntry(PreEntry->getSingleSuccessor());
VPBlockUtils::disconnectBlocks(PreEntry, Entry);
delete PreEntry;
// ---------------------------------------------------------------------------
// Transform initial VPlan: Apply previously taken decisions, in order, to
// bring the VPlan to its final state.
// ---------------------------------------------------------------------------
// Apply Sink-After legal constraints.
for (auto &Entry : SinkAfter) {
VPRecipeBase *Sink = RecipeBuilder.getRecipe(Entry.first);
VPRecipeBase *Target = RecipeBuilder.getRecipe(Entry.second);
Sink->moveAfter(Target);
}
// Interleave memory: for each Interleave Group we marked earlier as relevant
// for this VPlan, replace the Recipes widening its memory instructions with a
// single VPInterleaveRecipe at its insertion point.
for (auto IG : InterleaveGroups) {
auto *Recipe = cast<VPWidenMemoryInstructionRecipe>(
RecipeBuilder.getRecipe(IG->getInsertPos()));
(new VPInterleaveRecipe(IG, Recipe->getAddr(), Recipe->getMask()))
->insertBefore(Recipe);
for (unsigned i = 0; i < IG->getFactor(); ++i)
if (Instruction *Member = IG->getMember(i)) {
RecipeBuilder.getRecipe(Member)->eraseFromParent();
}
}
// Adjust the recipes for any inloop reductions.
if (Range.Start > 1)
adjustRecipesForInLoopReductions(Plan, RecipeBuilder);
// Finally, if tail is folded by masking, introduce selects between the phi
// and the live-out instruction of each reduction, at the end of the latch.
if (CM.foldTailByMasking() && !Legal->getReductionVars().empty()) {
Builder.setInsertPoint(VPBB);
auto *Cond = RecipeBuilder.createBlockInMask(OrigLoop->getHeader(), Plan);
for (auto &Reduction : Legal->getReductionVars()) {
assert(!CM.isInLoopReduction(Reduction.first) &&
"Didn't expect inloop tail folded reduction yet!");
VPValue *Phi = Plan->getVPValue(Reduction.first);
VPValue *Red = Plan->getVPValue(Reduction.second.getLoopExitInstr());
Builder.createNaryOp(Instruction::Select, {Cond, Red, Phi});
}
}
std::string PlanName;
raw_string_ostream RSO(PlanName);
ElementCount VF = ElementCount::getFixed(Range.Start);
Plan->addVF(VF);
RSO << "Initial VPlan for VF={" << VF;
for (VF *= 2; VF.getKnownMinValue() < Range.End; VF *= 2) {
Plan->addVF(VF);
RSO << "," << VF;
}
RSO << "},UF>=1";
RSO.flush();
Plan->setName(PlanName);
return Plan;
}
VPlanPtr LoopVectorizationPlanner::buildVPlan(VFRange &Range) {
// Outer loop handling: They may require CFG and instruction level
// transformations before even evaluating whether vectorization is profitable.
// Since we cannot modify the incoming IR, we need to build VPlan upfront in
// the vectorization pipeline.
assert(!OrigLoop->isInnermost());
assert(EnableVPlanNativePath && "VPlan-native path is not enabled.");
// Create new empty VPlan
auto Plan = std::make_unique<VPlan>();
// Build hierarchical CFG
VPlanHCFGBuilder HCFGBuilder(OrigLoop, LI, *Plan);
HCFGBuilder.buildHierarchicalCFG();
for (unsigned VF = Range.Start; VF < Range.End; VF *= 2)
Plan->addVF(ElementCount::getFixed(VF));
if (EnableVPlanPredication) {
VPlanPredicator VPP(*Plan);
VPP.predicate();
// Avoid running transformation to recipes until masked code generation in
// VPlan-native path is in place.
return Plan;
}
SmallPtrSet<Instruction *, 1> DeadInstructions;
VPlanTransforms::VPInstructionsToVPRecipes(
OrigLoop, Plan, Legal->getInductionVars(), DeadInstructions);
return Plan;
}
// Adjust the recipes for any inloop reductions. The chain of instructions
// leading from the loop exit instr to the phi need to be converted to
// reductions, with one operand being vector and the other being the scalar
// reduction chain.
void LoopVectorizationPlanner::adjustRecipesForInLoopReductions(
VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder) {
for (auto &Reduction : CM.getInLoopReductionChains()) {
PHINode *Phi = Reduction.first;
RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi];
const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second;
// ReductionOperations are orders top-down from the phi's use to the
// LoopExitValue. We keep a track of the previous item (the Chain) to tell
// which of the two operands will remain scalar and which will be reduced.
// For minmax the chain will be the select instructions.
Instruction *Chain = Phi;
for (Instruction *R : ReductionOperations) {
VPRecipeBase *WidenRecipe = RecipeBuilder.getRecipe(R);
RecurrenceDescriptor::RecurrenceKind Kind = RdxDesc.getRecurrenceKind();
VPValue *ChainOp = Plan->getVPValue(Chain);
unsigned FirstOpId;
if (Kind == RecurrenceDescriptor::RK_IntegerMinMax ||
Kind == RecurrenceDescriptor::RK_FloatMinMax) {
assert(isa<VPWidenSelectRecipe>(WidenRecipe) &&
"Expected to replace a VPWidenSelectSC");
FirstOpId = 1;
} else {
assert(isa<VPWidenRecipe>(WidenRecipe) &&
"Expected to replace a VPWidenSC");
FirstOpId = 0;
}
unsigned VecOpId =
R->getOperand(FirstOpId) == Chain ? FirstOpId + 1 : FirstOpId;
VPValue *VecOp = Plan->getVPValue(R->getOperand(VecOpId));
VPReductionRecipe *RedRecipe = new VPReductionRecipe(
&RdxDesc, R, ChainOp, VecOp, Legal->hasFunNoNaNAttr(), TTI);
WidenRecipe->getParent()->insert(RedRecipe, WidenRecipe->getIterator());
WidenRecipe->eraseFromParent();
if (Kind == RecurrenceDescriptor::RK_IntegerMinMax ||
Kind == RecurrenceDescriptor::RK_FloatMinMax) {
VPRecipeBase *CompareRecipe =
RecipeBuilder.getRecipe(cast<Instruction>(R->getOperand(0)));
assert(isa<VPWidenRecipe>(CompareRecipe) &&
"Expected to replace a VPWidenSC");
CompareRecipe->eraseFromParent();
}
Chain = R;
}
}
}
Value* LoopVectorizationPlanner::VPCallbackILV::
getOrCreateVectorValues(Value *V, unsigned Part) {
return ILV.getOrCreateVectorValue(V, Part);
}
Value *LoopVectorizationPlanner::VPCallbackILV::getOrCreateScalarValue(
Value *V, const VPIteration &Instance) {
return ILV.getOrCreateScalarValue(V, Instance);
}
void VPInterleaveRecipe::print(raw_ostream &O, const Twine &Indent,
VPSlotTracker &SlotTracker) const {
O << "\"INTERLEAVE-GROUP with factor " << IG->getFactor() << " at ";
IG->getInsertPos()->printAsOperand(O, false);
O << ", ";
getAddr()->printAsOperand(O, SlotTracker);
VPValue *Mask = getMask();
if (Mask) {
O << ", ";
Mask->printAsOperand(O, SlotTracker);
}
for (unsigned i = 0; i < IG->getFactor(); ++i)
if (Instruction *I = IG->getMember(i))
O << "\\l\" +\n" << Indent << "\" " << VPlanIngredient(I) << " " << i;
}
void VPWidenCallRecipe::execute(VPTransformState &State) {
State.ILV->widenCallInstruction(Ingredient, *this, State);
}
void VPWidenSelectRecipe::execute(VPTransformState &State) {
State.ILV->widenSelectInstruction(Ingredient, *this, InvariantCond, State);
}
void VPWidenRecipe::execute(VPTransformState &State) {
State.ILV->widenInstruction(Ingredient, *this, State);
}
void VPWidenGEPRecipe::execute(VPTransformState &State) {
State.ILV->widenGEP(GEP, *this, State.UF, State.VF, IsPtrLoopInvariant,
IsIndexLoopInvariant, State);
}
void VPWidenIntOrFpInductionRecipe::execute(VPTransformState &State) {
assert(!State.Instance && "Int or FP induction being replicated.");
State.ILV->widenIntOrFpInduction(IV, Trunc);
}
void VPWidenPHIRecipe::execute(VPTransformState &State) {
State.ILV->widenPHIInstruction(Phi, State.UF, State.VF);
}
void VPBlendRecipe::execute(VPTransformState &State) {
State.ILV->setDebugLocFromInst(State.Builder, Phi);
// 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 = getNumIncomingValues();
// Generate a sequence of selects of the form:
// SELECT(Mask3, In3,
// SELECT(Mask2, In2,
// SELECT(Mask1, In1,
// In0)))
// Note that Mask0 is never used: lanes for which no path reaches this phi and
// are essentially undef are taken from In0.
InnerLoopVectorizer::VectorParts Entry(State.UF);
for (unsigned In = 0; In < NumIncoming; ++In) {
for (unsigned Part = 0; Part < State.UF; ++Part) {
// We might have single edge PHIs (blocks) - use an identity
// 'select' for the first PHI operand.
Value *In0 = State.get(getIncomingValue(In), Part);
if (In == 0)
Entry[Part] = In0; // Initialize with the first incoming value.
else {
// Select between the current value and the previous incoming edge
// based on the incoming mask.
Value *Cond = State.get(getMask(In), Part);
Entry[Part] =
State.Builder.CreateSelect(Cond, In0, Entry[Part], "predphi");
}
}
}
for (unsigned Part = 0; Part < State.UF; ++Part)
State.ValueMap.setVectorValue(Phi, Part, Entry[Part]);
}
void VPInterleaveRecipe::execute(VPTransformState &State) {
assert(!State.Instance && "Interleave group being replicated.");
State.ILV->vectorizeInterleaveGroup(IG, State, getAddr(), getMask());
}
void VPReductionRecipe::execute(VPTransformState &State) {
assert(!State.Instance && "Reduction being replicated.");
for (unsigned Part = 0; Part < State.UF; ++Part) {
unsigned Kind = RdxDesc->getRecurrenceKind();
Value *NewVecOp = State.get(VecOp, Part);
Value *NewRed =
createTargetReduction(State.Builder, TTI, *RdxDesc, NewVecOp, NoNaN);
Value *PrevInChain = State.get(ChainOp, Part);
Value *NextInChain;
if (Kind == RecurrenceDescriptor::RK_IntegerMinMax ||
Kind == RecurrenceDescriptor::RK_FloatMinMax) {
NextInChain =
createMinMaxOp(State.Builder, RdxDesc->getMinMaxRecurrenceKind(),
NewRed, PrevInChain);
} else {
NextInChain = State.Builder.CreateBinOp(
(Instruction::BinaryOps)I->getOpcode(), NewRed, PrevInChain);
}
State.ValueMap.setVectorValue(I, Part, NextInChain);
}
}
void VPReplicateRecipe::execute(VPTransformState &State) {
if (State.Instance) { // Generate a single instance.
State.ILV->scalarizeInstruction(Ingredient, *this, *State.Instance,
IsPredicated, State);
// Insert scalar instance packing it into a vector.
if (AlsoPack && State.VF.isVector()) {
// If we're constructing lane 0, initialize to start from undef.
if (State.Instance->Lane == 0) {
assert(!State.VF.isScalable() && "VF is assumed to be non scalable.");
Value *Undef =
UndefValue::get(VectorType::get(Ingredient->getType(), State.VF));
State.ValueMap.setVectorValue(Ingredient, State.Instance->Part, Undef);
}
State.ILV->packScalarIntoVectorValue(Ingredient, *State.Instance);
}
return;
}
// Generate scalar instances for all VF lanes of all UF parts, unless the
// instruction is uniform inwhich case generate only the first lane for each
// of the UF parts.
unsigned EndLane = IsUniform ? 1 : State.VF.getKnownMinValue();
for (unsigned Part = 0; Part < State.UF; ++Part)
for (unsigned Lane = 0; Lane < EndLane; ++Lane)
State.ILV->scalarizeInstruction(Ingredient, *this, {Part, Lane},
IsPredicated, State);
}
void VPBranchOnMaskRecipe::execute(VPTransformState &State) {
assert(State.Instance && "Branch on Mask works only on single instance.");
unsigned Part = State.Instance->Part;
unsigned Lane = State.Instance->Lane;
Value *ConditionBit = nullptr;
VPValue *BlockInMask = getMask();
if (BlockInMask) {
ConditionBit = State.get(BlockInMask, Part);
if (ConditionBit->getType()->isVectorTy())
ConditionBit = State.Builder.CreateExtractElement(
ConditionBit, State.Builder.getInt32(Lane));
} else // Block in mask is all-one.
ConditionBit = State.Builder.getTrue();
// Replace the temporary unreachable terminator with a new conditional branch,
// whose two destinations will be set later when they are created.
auto *CurrentTerminator = State.CFG.PrevBB->getTerminator();
assert(isa<UnreachableInst>(CurrentTerminator) &&
"Expected to replace unreachable terminator with conditional branch.");
auto *CondBr = BranchInst::Create(State.CFG.PrevBB, nullptr, ConditionBit);
CondBr->setSuccessor(0, nullptr);
ReplaceInstWithInst(CurrentTerminator, CondBr);
}
void VPPredInstPHIRecipe::execute(VPTransformState &State) {
assert(State.Instance && "Predicated instruction PHI works per instance.");
Instruction *ScalarPredInst = cast<Instruction>(
State.ValueMap.getScalarValue(PredInst, *State.Instance));
BasicBlock *PredicatedBB = ScalarPredInst->getParent();
BasicBlock *PredicatingBB = PredicatedBB->getSinglePredecessor();
assert(PredicatingBB && "Predicated block has no single predecessor.");
// By current pack/unpack logic we need to generate only a single phi node: if
// a vector value for the predicated instruction exists at this point it means
// the instruction has vector users only, and a phi for the vector value is
// needed. In this case the recipe of the predicated instruction is marked to
// also do that packing, thereby "hoisting" the insert-element sequence.
// Otherwise, a phi node for the scalar value is needed.
unsigned Part = State.Instance->Part;
if (State.ValueMap.hasVectorValue(PredInst, Part)) {
Value *VectorValue = State.ValueMap.getVectorValue(PredInst, Part);
InsertElementInst *IEI = cast<InsertElementInst>(VectorValue);
PHINode *VPhi = State.Builder.CreatePHI(IEI->getType(), 2);
VPhi->addIncoming(IEI->getOperand(0), PredicatingBB); // Unmodified vector.
VPhi->addIncoming(IEI, PredicatedBB); // New vector with inserted element.
State.ValueMap.resetVectorValue(PredInst, Part, VPhi); // Update cache.
} else {
Type *PredInstType = PredInst->getType();
PHINode *Phi = State.Builder.CreatePHI(PredInstType, 2);
Phi->addIncoming(UndefValue::get(ScalarPredInst->getType()), PredicatingBB);
Phi->addIncoming(ScalarPredInst, PredicatedBB);
State.ValueMap.resetScalarValue(PredInst, *State.Instance, Phi);
}
}
void VPWidenMemoryInstructionRecipe::execute(VPTransformState &State) {
VPValue *StoredValue = isa<StoreInst>(Instr) ? getStoredValue() : nullptr;
State.ILV->vectorizeMemoryInstruction(&Instr, State, getAddr(), StoredValue,
getMask());
}
// Determine how to lower the scalar epilogue, which depends on 1) optimising
// for minimum code-size, 2) predicate compiler options, 3) loop hints forcing
// predication, and 4) a TTI hook that analyses whether the loop is suitable
// for predication.
static ScalarEpilogueLowering getScalarEpilogueLowering(
Function *F, Loop *L, LoopVectorizeHints &Hints, ProfileSummaryInfo *PSI,
BlockFrequencyInfo *BFI, TargetTransformInfo *TTI, TargetLibraryInfo *TLI,
AssumptionCache *AC, LoopInfo *LI, ScalarEvolution *SE, DominatorTree *DT,
LoopVectorizationLegality &LVL) {
// 1) OptSize takes precedence over all other options, i.e. if this is set,
// don't look at hints or options, and don't request a scalar epilogue.
// (For PGSO, as shouldOptimizeForSize isn't currently accessible from
// LoopAccessInfo (due to code dependency and not being able to reliably get
// PSI/BFI from a loop analysis under NPM), we cannot suppress the collection
// of strides in LoopAccessInfo::analyzeLoop() and vectorize without
// versioning when the vectorization is forced, unlike hasOptSize. So revert
// back to the old way and vectorize with versioning when forced. See D81345.)
if (F->hasOptSize() || (llvm::shouldOptimizeForSize(L->getHeader(), PSI, BFI,
PGSOQueryType::IRPass) &&
Hints.getForce() != LoopVectorizeHints::FK_Enabled))
return CM_ScalarEpilogueNotAllowedOptSize;
bool PredicateOptDisabled = PreferPredicateOverEpilogue.getNumOccurrences() &&
!PreferPredicateOverEpilogue;
// 2) Next, if disabling predication is requested on the command line, honour
// this and request a scalar epilogue.
if (PredicateOptDisabled)
return CM_ScalarEpilogueAllowed;
// 3) and 4) look if enabling predication is requested on the command line,
// with a loop hint, or if the TTI hook indicates this is profitable, request
// predication.
if (PreferPredicateOverEpilogue ||
Hints.getPredicate() == LoopVectorizeHints::FK_Enabled ||
(TTI->preferPredicateOverEpilogue(L, LI, *SE, *AC, TLI, DT,
LVL.getLAI()) &&
Hints.getPredicate() != LoopVectorizeHints::FK_Disabled))
return CM_ScalarEpilogueNotNeededUsePredicate;
return CM_ScalarEpilogueAllowed;
}
// Process the loop in the VPlan-native vectorization path. This path builds
// VPlan upfront in the vectorization pipeline, which allows to apply
// VPlan-to-VPlan transformations from the very beginning without modifying the
// input LLVM IR.
static bool processLoopInVPlanNativePath(
Loop *L, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT,
LoopVectorizationLegality *LVL, TargetTransformInfo *TTI,
TargetLibraryInfo *TLI, DemandedBits *DB, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI, LoopVectorizeHints &Hints) {
if (PSE.getBackedgeTakenCount() == PSE.getSE()->getCouldNotCompute()) {
LLVM_DEBUG(dbgs() << "LV: cannot compute the outer-loop trip count\n");
return false;
}
assert(EnableVPlanNativePath && "VPlan-native path is disabled.");
Function *F = L->getHeader()->getParent();
InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL->getLAI());
ScalarEpilogueLowering SEL = getScalarEpilogueLowering(
F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, *LVL);
LoopVectorizationCostModel CM(SEL, L, PSE, LI, LVL, *TTI, TLI, DB, AC, ORE, F,
&Hints, IAI);
// Use the planner for outer loop vectorization.
// TODO: CM is not used at this point inside the planner. Turn CM into an
// optional argument if we don't need it in the future.
LoopVectorizationPlanner LVP(L, LI, TLI, TTI, LVL, CM, IAI, PSE);
// Get user vectorization factor.
const unsigned UserVF = Hints.getWidth();
// Plan how to best vectorize, return the best VF and its cost.
const VectorizationFactor VF =
LVP.planInVPlanNativePath(ElementCount::getFixed(UserVF));
// If we are stress testing VPlan builds, do not attempt to generate vector
// code. Masked vector code generation support will follow soon.
// Also, do not attempt to vectorize if no vector code will be produced.
if (VPlanBuildStressTest || EnableVPlanPredication ||
VectorizationFactor::Disabled() == VF)
return false;
LVP.setBestPlan(VF.Width, 1);
InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, 1, LVL,
&CM, BFI, PSI);
LLVM_DEBUG(dbgs() << "Vectorizing outer loop in \""
<< L->getHeader()->getParent()->getName() << "\"\n");
LVP.executePlan(LB, DT);
// Mark the loop as already vectorized to avoid vectorizing again.
Hints.setAlreadyVectorized();
assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs()));
return true;
}
LoopVectorizePass::LoopVectorizePass(LoopVectorizeOptions Opts)
: InterleaveOnlyWhenForced(Opts.InterleaveOnlyWhenForced ||
!EnableLoopInterleaving),
VectorizeOnlyWhenForced(Opts.VectorizeOnlyWhenForced ||
!EnableLoopVectorization) {}
bool LoopVectorizePass::processLoop(Loop *L) {
assert((EnableVPlanNativePath || L->isInnermost()) &&
"VPlan-native path is not enabled. Only process inner loops.");
#ifndef NDEBUG
const std::string DebugLocStr = getDebugLocString(L);
#endif /* NDEBUG */
LLVM_DEBUG(dbgs() << "\nLV: Checking a loop in \""
<< L->getHeader()->getParent()->getName() << "\" from "
<< DebugLocStr << "\n");
LoopVectorizeHints Hints(L, InterleaveOnlyWhenForced, *ORE);
LLVM_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 as OptimizationRemarkAnalysis. Remarks
// generated as OptimizationRemark and OptimizationRemarkMissed are
// less verbose reporting vectorized loops and unvectorized loops that may
// benefit from vectorization, respectively.
if (!Hints.allowVectorization(F, L, VectorizeOnlyWhenForced)) {
LLVM_DEBUG(dbgs() << "LV: Loop hints prevent vectorization.\n");
return false;
}
PredicatedScalarEvolution PSE(*SE, *L);
// Check if it is legal to vectorize the loop.
LoopVectorizationRequirements Requirements(*ORE);
LoopVectorizationLegality LVL(L, PSE, DT, TTI, TLI, AA, F, GetLAA, LI, ORE,
&Requirements, &Hints, DB, AC, BFI, PSI);
if (!LVL.canVectorize(EnableVPlanNativePath)) {
LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
Hints.emitRemarkWithHints();
return false;
}
// Check the function attributes and profiles to find out if this function
// should be optimized for size.
ScalarEpilogueLowering SEL = getScalarEpilogueLowering(
F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, LVL);
// Entrance to the VPlan-native vectorization path. Outer loops are processed
// here. They may require CFG and instruction level transformations before
// even evaluating whether vectorization is profitable. Since we cannot modify
// the incoming IR, we need to build VPlan upfront in the vectorization
// pipeline.
if (!L->isInnermost())
return processLoopInVPlanNativePath(L, PSE, LI, DT, &LVL, TTI, TLI, DB, AC,
ORE, BFI, PSI, Hints);
assert(L->isInnermost() && "Inner loop expected.");
// Check the loop for a trip count threshold: vectorize loops with a tiny trip
// count by optimizing for size, to minimize overheads.
auto ExpectedTC = getSmallBestKnownTC(*SE, L);
if (ExpectedTC && *ExpectedTC < TinyTripCountVectorThreshold) {
LLVM_DEBUG(dbgs() << "LV: Found a loop with a very small trip count. "
<< "This loop is worth vectorizing only if no scalar "
<< "iteration overheads are incurred.");
if (Hints.getForce() == LoopVectorizeHints::FK_Enabled)
LLVM_DEBUG(dbgs() << " But vectorizing was explicitly forced.\n");
else {
LLVM_DEBUG(dbgs() << "\n");
SEL = CM_ScalarEpilogueNotAllowedLowTripLoop;
}
}
// Check the function attributes to see if implicit floats are allowed.
// 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)) {
reportVectorizationFailure(
"Can't vectorize when the NoImplicitFloat attribute is used",
"loop not vectorized due to NoImplicitFloat attribute",
"NoImplicitFloat", ORE, L);
Hints.emitRemarkWithHints();
return false;
}
// Check if the target supports potentially unsafe FP vectorization.
// FIXME: Add a check for the type of safety issue (denormal, signaling)
// for the target we're vectorizing for, to make sure none of the
// additional fp-math flags can help.
if (Hints.isPotentiallyUnsafe() &&
TTI->isFPVectorizationPotentiallyUnsafe()) {
reportVectorizationFailure(
"Potentially unsafe FP op prevents vectorization",
"loop not vectorized due to unsafe FP support.",
"UnsafeFP", ORE, L);
Hints.emitRemarkWithHints();
return false;
}
bool UseInterleaved = TTI->enableInterleavedAccessVectorization();
InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL.getLAI());
// If an override option has been passed in for interleaved accesses, use it.
if (EnableInterleavedMemAccesses.getNumOccurrences() > 0)
UseInterleaved = EnableInterleavedMemAccesses;
// Analyze interleaved memory accesses.
if (UseInterleaved) {
IAI.analyzeInterleaving(useMaskedInterleavedAccesses(*TTI));
}
// Use the cost model.
LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE,
F, &Hints, IAI);
CM.collectValuesToIgnore();
// Use the planner for vectorization.
LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM, IAI, PSE);
// Get user vectorization factor and interleave count.
unsigned UserVF = Hints.getWidth();
unsigned UserIC = Hints.getInterleave();
// Plan how to best vectorize, return the best VF and its cost.
Optional<VectorizationFactor> MaybeVF =
LVP.plan(ElementCount::getFixed(UserVF), UserIC);
VectorizationFactor VF = VectorizationFactor::Disabled();
unsigned IC = 1;
if (MaybeVF) {
VF = *MaybeVF;
// Select the interleave count.
IC = CM.selectInterleaveCount(VF.Width, VF.Cost);
}
// Identify the diagnostic messages that should be produced.
std::pair<StringRef, std::string> VecDiagMsg, IntDiagMsg;
bool VectorizeLoop = true, InterleaveLoop = true;
if (Requirements.doesNotMeet(F, L, Hints)) {
LLVM_DEBUG(dbgs() << "LV: Not vectorizing: loop did not meet vectorization "
"requirements.\n");
Hints.emitRemarkWithHints();
return false;
}
if (VF.Width == 1) {
LLVM_DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
VecDiagMsg = std::make_pair(
"VectorizationNotBeneficial",
"the cost-model indicates that vectorization is not beneficial");
VectorizeLoop = false;
}
if (!MaybeVF && UserIC > 1) {
// Tell the user interleaving was avoided up-front, despite being explicitly
// requested.
LLVM_DEBUG(dbgs() << "LV: Ignoring UserIC, because vectorization and "
"interleaving should be avoided up front\n");
IntDiagMsg = std::make_pair(
"InterleavingAvoided",
"Ignoring UserIC, because interleaving was avoided up front");
InterleaveLoop = false;
} else if (IC == 1 && UserIC <= 1) {
// Tell the user interleaving is not beneficial.
LLVM_DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n");
IntDiagMsg = std::make_pair(
"InterleavingNotBeneficial",
"the cost-model indicates that interleaving is not beneficial");
InterleaveLoop = false;
if (UserIC == 1) {
IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled";
IntDiagMsg.second +=
" and is explicitly disabled or interleave count is set to 1";
}
} else if (IC > 1 && UserIC == 1) {
// Tell the user interleaving is beneficial, but it explicitly disabled.
LLVM_DEBUG(
dbgs() << "LV: Interleaving is beneficial but is explicitly disabled.");
IntDiagMsg = std::make_pair(
"InterleavingBeneficialButDisabled",
"the cost-model indicates that interleaving is beneficial "
"but is explicitly disabled or interleave count is set to 1");
InterleaveLoop = false;
}
// Override IC if user provided an interleave count.
IC = UserIC > 0 ? UserIC : IC;
// Emit diagnostic messages, if any.
const char *VAPassName = Hints.vectorizeAnalysisPassName();
if (!VectorizeLoop && !InterleaveLoop) {
// Do not vectorize or interleaving the loop.
ORE->emit([&]() {
return OptimizationRemarkMissed(VAPassName, VecDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< VecDiagMsg.second;
});
ORE->emit([&]() {
return OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< IntDiagMsg.second;
});
return false;
} else if (!VectorizeLoop && InterleaveLoop) {
LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
ORE->emit([&]() {
return OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< VecDiagMsg.second;
});
} else if (VectorizeLoop && !InterleaveLoop) {
LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width
<< ") in " << DebugLocStr << '\n');
ORE->emit([&]() {
return OptimizationRemarkAnalysis(LV_NAME, IntDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< IntDiagMsg.second;
});
} else if (VectorizeLoop && InterleaveLoop) {
LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width
<< ") in " << DebugLocStr << '\n');
LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
}
LVP.setBestPlan(VF.Width, IC);
using namespace ore;
bool DisableRuntimeUnroll = false;
MDNode *OrigLoopID = L->getLoopID();
if (!VectorizeLoop) {
assert(IC > 1 && "interleave count should not be 1 or 0");
// If we decided that it is not legal to vectorize the loop, then
// interleave it.
InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL, &CM,
BFI, PSI);
LVP.executePlan(Unroller, DT);
ORE->emit([&]() {
return OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(),
L->getHeader())
<< "interleaved loop (interleaved count: "
<< NV("InterleaveCount", IC) << ")";
});
} else {
// If we decided that it is *legal* to vectorize the loop, then do it.
InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, IC,
&LVL, &CM, BFI, PSI);
LVP.executePlan(LB, DT);
++LoopsVectorized;
// Add metadata to disable runtime unrolling a scalar loop when there are
// no runtime checks about strides and memory. A scalar loop that is
// rarely used is not worth unrolling.
if (!LB.areSafetyChecksAdded())
DisableRuntimeUnroll = true;
// Report the vectorization decision.
ORE->emit([&]() {
return OptimizationRemark(LV_NAME, "Vectorized", L->getStartLoc(),
L->getHeader())
<< "vectorized loop (vectorization width: "
<< NV("VectorizationFactor", VF.Width)
<< ", interleaved count: " << NV("InterleaveCount", IC) << ")";
});
}
Optional<MDNode *> RemainderLoopID =
makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll,
LLVMLoopVectorizeFollowupEpilogue});
if (RemainderLoopID.hasValue()) {
L->setLoopID(RemainderLoopID.getValue());
} else {
if (DisableRuntimeUnroll)
AddRuntimeUnrollDisableMetaData(L);
// Mark the loop as already vectorized to avoid vectorizing again.
Hints.setAlreadyVectorized();
}
assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs()));
return true;
}
LoopVectorizeResult LoopVectorizePass::runImpl(
Function &F, ScalarEvolution &SE_, LoopInfo &LI_, TargetTransformInfo &TTI_,
DominatorTree &DT_, BlockFrequencyInfo &BFI_, TargetLibraryInfo *TLI_,
DemandedBits &DB_, AAResults &AA_, AssumptionCache &AC_,
std::function<const LoopAccessInfo &(Loop &)> &GetLAA_,
OptimizationRemarkEmitter &ORE_, ProfileSummaryInfo *PSI_) {
SE = &SE_;
LI = &LI_;
TTI = &TTI_;
DT = &DT_;
BFI = &BFI_;
TLI = TLI_;
AA = &AA_;
AC = &AC_;
GetLAA = &GetLAA_;
DB = &DB_;
ORE = &ORE_;
PSI = PSI_;
// Don't attempt if
// 1. the target claims to have no vector registers, and
// 2. interleaving won't help ILP.
//
// The second condition is necessary because, even if the target has no
// vector registers, loop vectorization may still enable scalar
// interleaving.
if (!TTI->getNumberOfRegisters(TTI->getRegisterClassForType(true)) &&
TTI->getMaxInterleaveFactor(1) < 2)
return LoopVectorizeResult(false, false);
bool Changed = false, CFGChanged = false;
// The vectorizer requires loops to be in simplified form.
// Since simplification may add new inner loops, it has to run before the
// legality and profitability checks. This means running the loop vectorizer
// will simplify all loops, regardless of whether anything end up being
// vectorized.
for (auto &L : *LI)
Changed |= CFGChanged |=
simplifyLoop(L, DT, LI, SE, AC, nullptr, false /* PreserveLCSSA */);
// 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)
collectSupportedLoops(*L, LI, ORE, Worklist);
LoopsAnalyzed += Worklist.size();
// Now walk the identified inner loops.
while (!Worklist.empty()) {
Loop *L = Worklist.pop_back_val();
// For the inner loops we actually process, form LCSSA to simplify the
// transform.
Changed |= formLCSSARecursively(*L, *DT, LI, SE);
Changed |= CFGChanged |= processLoop(L);
}
// Process each loop nest in the function.
return LoopVectorizeResult(Changed, CFGChanged);
}
PreservedAnalyses LoopVectorizePass::run(Function &F,
FunctionAnalysisManager &AM) {
auto &SE = AM.getResult<ScalarEvolutionAnalysis>(F);
auto &LI = AM.getResult<LoopAnalysis>(F);
auto &TTI = AM.getResult<TargetIRAnalysis>(F);
auto &DT = AM.getResult<DominatorTreeAnalysis>(F);
auto &BFI = AM.getResult<BlockFrequencyAnalysis>(F);
auto &TLI = AM.getResult<TargetLibraryAnalysis>(F);
auto &AA = AM.getResult<AAManager>(F);
auto &AC = AM.getResult<AssumptionAnalysis>(F);
auto &DB = AM.getResult<DemandedBitsAnalysis>(F);
auto &ORE = AM.getResult<OptimizationRemarkEmitterAnalysis>(F);
MemorySSA *MSSA = EnableMSSALoopDependency
? &AM.getResult<MemorySSAAnalysis>(F).getMSSA()
: nullptr;
auto &LAM = AM.getResult<LoopAnalysisManagerFunctionProxy>(F).getManager();
std::function<const LoopAccessInfo &(Loop &)> GetLAA =
[&](Loop &L) -> const LoopAccessInfo & {
LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE,
TLI, TTI, nullptr, MSSA};
return LAM.getResult<LoopAccessAnalysis>(L, AR);
};
auto &MAMProxy = AM.getResult<ModuleAnalysisManagerFunctionProxy>(F);
ProfileSummaryInfo *PSI =
MAMProxy.getCachedResult<ProfileSummaryAnalysis>(*F.getParent());
LoopVectorizeResult Result =
runImpl(F, SE, LI, TTI, DT, BFI, &TLI, DB, AA, AC, GetLAA, ORE, PSI);
if (!Result.MadeAnyChange)
return PreservedAnalyses::all();
PreservedAnalyses PA;
// We currently do not preserve loopinfo/dominator analyses with outer loop
// vectorization. Until this is addressed, mark these analyses as preserved
// only for non-VPlan-native path.
// TODO: Preserve Loop and Dominator analyses for VPlan-native path.
if (!EnableVPlanNativePath) {
PA.preserve<LoopAnalysis>();
PA.preserve<DominatorTreeAnalysis>();
}
PA.preserve<BasicAA>();
PA.preserve<GlobalsAA>();
if (!Result.MadeCFGChange)
PA.preserveSet<CFGAnalyses>();
return PA;
}
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